Split (1)

train · ~171k rows (showing the first 171k)

identifier stringlengths 7 18	space stringclasses 4 values	uid stringlengths 1 6	arch_str stringlengths 1 32	input stringlengths 8.51k 461k	target_metric stringclasses 1 value	flops float64 31.1M 14.7B	params float64 227k 50M	metadata stringlengths 0 1.46k
FBNet_4924	FBNet	4924	4924	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_703[FLOAT, 16x3x3x3] %onnx::Conv_704[FLOAT, 16] %onnx::Conv_706[FLOAT, 16x8x1x1] %onnx::Conv_709[FLOAT, 16x1x5x5] %onnx::Conv_712[FLOAT, 16x8x1x1] %onnx::Conv_715[FLOAT, 96x16x1x1] %onnx::Conv_716[FLOAT, 96] %onnx::Conv_718[FLOAT, 96x1x3x3] %onnx::Conv_721[FLOAT, 24x96x1x1] %onnx::Conv_722[FLOAT, 24] %onnx::Conv_724[FLOAT, 24x12x1x1] %onnx::Conv_727[FLOAT, 24x1x5x5] %onnx::Conv_730[FLOAT, 24x12x1x1] %onnx::Conv_733[FLOAT, 24x24x1x1] %onnx::Conv_736[FLOAT, 24x1x3x3] %onnx::Conv_739[FLOAT, 24x24x1x1] %onnx::Conv_742[FLOAT, 24x12x1x1] %onnx::Conv_745[FLOAT, 24x1x3x3] %onnx::Conv_748[FLOAT, 24x12x1x1] %onnx::Conv_751[FLOAT, 24x12x1x1] %onnx::Conv_754[FLOAT, 24x1x3x3] %onnx::Conv_757[FLOAT, 32x12x1x1] %onnx::Conv_758[FLOAT, 32] %onnx::Conv_760[FLOAT, 96x32x1x1] %onnx::Conv_763[FLOAT, 96x1x5x5] %onnx::Conv_766[FLOAT, 32x96x1x1] %onnx::Conv_769[FLOAT, 192x32x1x1] %onnx::Conv_770[FLOAT, 192] %onnx::Conv_772[FLOAT, 192x1x5x5] %onnx::Conv_775[FLOAT, 32x192x1x1] %onnx::Conv_778[FLOAT, 192x32x1x1] %onnx::Conv_781[FLOAT, 192x1x5x5] %onnx::Conv_784[FLOAT, 32x192x1x1] %onnx::Conv_787[FLOAT, 32x32x1x1] %onnx::Conv_790[FLOAT, 32x1x5x5] %onnx::Conv_793[FLOAT, 64x32x1x1] %onnx::Conv_794[FLOAT, 64] %onnx::Conv_796[FLOAT, 64x32x1x1] %onnx::Conv_799[FLOAT, 64x1x3x3] %onnx::Conv_802[FLOAT, 64x32x1x1] %onnx::Conv_805[FLOAT, 384x64x1x1] %onnx::Conv_806[FLOAT, 384] %onnx::Conv_808[FLOAT, 384x1x3x3] %onnx::Conv_811[FLOAT, 64x384x1x1] %onnx::Conv_814[FLOAT, 192x64x1x1] %onnx::Conv_817[FLOAT, 192x1x3x3] %onnx::Conv_820[FLOAT, 64x192x1x1] %onnx::Conv_823[FLOAT, 64x32x1x1] %onnx::Conv_826[FLOAT, 64x1x3x3] %onnx::Conv_829[FLOAT, 112x32x1x1] %onnx::Conv_830[FLOAT, 112] %onnx::Conv_832[FLOAT, 672x112x1x1] %onnx::Conv_833[FLOAT, 672] %onnx::Conv_835[FLOAT, 672x1x5x5] %onnx::Conv_838[FLOAT, 112x672x1x1] %onnx::Conv_841[FLOAT, 336x112x1x1] %onnx::Conv_842[FLOAT, 336] %onnx::Conv_844[FLOAT, 336x1x5x5] %onnx::Conv_847[FLOAT, 112x336x1x1] %onnx::Conv_850[FLOAT, 672x112x1x1] %onnx::Conv_853[FLOAT, 672x1x3x3] %onnx::Conv_856[FLOAT, 184x672x1x1] %onnx::Conv_857[FLOAT, 184] %onnx::Conv_859[FLOAT, 184x184x1x1] %onnx::Conv_862[FLOAT, 184x1x3x3] %onnx::Conv_865[FLOAT, 184x184x1x1] %onnx::Conv_868[FLOAT, 1104x184x1x1] %onnx::Conv_869[FLOAT, 1104] %onnx::Conv_871[FLOAT, 1104x1x5x5] %onnx::Conv_874[FLOAT, 184x1104x1x1] %onnx::Conv_877[FLOAT, 552x184x1x1] %onnx::Conv_878[FLOAT, 552] %onnx::Conv_880[FLOAT, 552x1x5x5] %onnx::Conv_883[FLOAT, 184x552x1x1] %onnx::Conv_886[FLOAT, 184x184x1x1] %onnx::Conv_889[FLOAT, 184x1x5x5] %onnx::Conv_892[FLOAT, 352x184x1x1] %onnx::Conv_893[FLOAT, 352] %onnx::Conv_895[FLOAT, 1504x352x1x1] %onnx::Conv_896[FLOAT, 1504] ) { %onnx::Conv_890 = Identity(%onnx::Conv_857) %onnx::Conv_887 = Identity(%onnx::Conv_857) %onnx::Conv_884 = Identity(%onnx::Conv_857) %onnx::Conv_881 = Identity(%onnx::Conv_878) %onnx::Conv_875 = Identity(%onnx::Conv_857) %onnx::Conv_872 = Identity(%onnx::Conv_869) %onnx::Conv_866 = Identity(%onnx::Conv_857) %onnx::Conv_863 = Identity(%onnx::Conv_857) %onnx::Conv_860 = Identity(%onnx::Conv_857) %onnx::Conv_854 = Identity(%onnx::Conv_833) %onnx::Conv_851 = Identity(%onnx::Conv_833) %onnx::Conv_848 = Identity(%onnx::Conv_830) %onnx::Conv_845 = Identity(%onnx::Conv_842) %onnx::Conv_839 = Identity(%onnx::Conv_830) %onnx::Conv_836 = Identity(%onnx::Conv_833) %onnx::Conv_827 = Identity(%onnx::Conv_794) %onnx::Conv_824 = Identity(%onnx::Conv_794) %onnx::Conv_821 = Identity(%onnx::Conv_794) %onnx::Conv_818 = Identity(%onnx::Conv_770) %onnx::Conv_815 = Identity(%onnx::Conv_770) %onnx::Conv_812 = Identity(%onnx::Conv_794) %onnx::Conv_809 = Identity(%onnx::Conv_806) %onnx::Conv_803 = Identity(%onnx::Conv_794) %onnx::Conv_800 = Identity(%onnx::Conv_794) %onnx::Conv_797 = Identity(%onnx::Conv_794) %onnx::Conv_791 = Identity(%onnx::Conv_758) %onnx::Conv_788 = Identity(%onnx::Conv_758) %onnx::Conv_785 = Identity(%onnx::Conv_758) %onnx::Conv_782 = Identity(%onnx::Conv_770) %onnx::Conv_779 = Identity(%onnx::Conv_770) %onnx::Conv_776 = Identity(%onnx::Conv_758) %onnx::Conv_773 = Identity(%onnx::Conv_770) %onnx::Conv_767 = Identity(%onnx::Conv_758) %onnx::Conv_764 = Identity(%onnx::Conv_716) %onnx::Conv_761 = Identity(%onnx::Conv_716) %onnx::Conv_755 = Identity(%onnx::Conv_722) %onnx::Conv_752 = Identity(%onnx::Conv_722) %onnx::Conv_749 = Identity(%onnx::Conv_722) %onnx::Conv_746 = Identity(%onnx::Conv_722) %onnx::Conv_743 = Identity(%onnx::Conv_722) %onnx::Conv_740 = Identity(%onnx::Conv_722) %onnx::Conv_737 = Identity(%onnx::Conv_722) %onnx::Conv_734 = Identity(%onnx::Conv_722) %onnx::Conv_731 = Identity(%onnx::Conv_722) %onnx::Conv_728 = Identity(%onnx::Conv_722) %onnx::Conv_725 = Identity(%onnx::Conv_722) %onnx::Conv_719 = Identity(%onnx::Conv_716) %onnx::Conv_713 = Identity(%onnx::Conv_704) %onnx::Conv_710 = Identity(%onnx::Conv_704) %onnx::Conv_707 = Identity(%onnx::Conv_704) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_703, %onnx::Conv_704) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_706, %onnx::Conv_707) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.0/shuffle/Reshape_output_0 = Reshape(%/cells.0/nl/Relu_output_0, %/cells.0/shuffle/Constant_output_0) %/cells.0/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.0/shuffle/Reshape_output_0) %/cells.0/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.0/shuffle/Reshape_1_output_0 = Reshape(%/cells.0/shuffle/Transpose_output_0, %/cells.0/shuffle/Constant_1_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.0/shuffle/Reshape_1_output_0, %onnx::Conv_709, %onnx::Conv_710) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_712, %onnx::Conv_713) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_715, %onnx::Conv_716) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.1/nl/Relu_output_0, %onnx::Conv_718, %onnx::Conv_719) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_721, %onnx::Conv_722) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_724, %onnx::Conv_725) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.2/shuffle/Reshape_output_0 = Reshape(%/cells.2/nl/Relu_output_0, %/cells.2/shuffle/Constant_output_0) %/cells.2/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.2/shuffle/Reshape_output_0) %/cells.2/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.2/shuffle/Reshape_1_output_0 = Reshape(%/cells.2/shuffle/Transpose_output_0, %/cells.2/shuffle/Constant_1_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.2/shuffle/Reshape_1_output_0, %onnx::Conv_727, %onnx::Conv_728) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_730, %onnx::Conv_731) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_733, %onnx::Conv_734) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.3/nl/Relu_output_0, %onnx::Conv_736, %onnx::Conv_737) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_739, %onnx::Conv_740) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_742, %onnx::Conv_743) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.4/shuffle/Reshape_output_0 = Reshape(%/cells.4/nl/Relu_output_0, %/cells.4/shuffle/Constant_output_0) %/cells.4/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.4/shuffle/Reshape_output_0) %/cells.4/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.4/shuffle/Reshape_1_output_0 = Reshape(%/cells.4/shuffle/Transpose_output_0, %/cells.4/shuffle/Constant_1_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.4/shuffle/Reshape_1_output_0, %onnx::Conv_745, %onnx::Conv_746) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_748, %onnx::Conv_749) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/Add_output_0, %onnx::Conv_751, %onnx::Conv_752) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.5/shuffle/Reshape_output_0 = Reshape(%/cells.5/nl/Relu_output_0, %/cells.5/shuffle/Constant_output_0) %/cells.5/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.5/shuffle/Reshape_output_0) %/cells.5/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.5/shuffle/Reshape_1_output_0 = Reshape(%/cells.5/shuffle/Transpose_output_0, %/cells.5/shuffle/Constant_1_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.5/shuffle/Reshape_1_output_0, %onnx::Conv_754, %onnx::Conv_755) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_757, %onnx::Conv_758) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_760, %onnx::Conv_761) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.6/nl/Relu_output_0, %onnx::Conv_763, %onnx::Conv_764) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_766, %onnx::Conv_767) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_769, %onnx::Conv_770) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.7/nl/Relu_output_0, %onnx::Conv_772, %onnx::Conv_773) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_775, %onnx::Conv_776) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_778, %onnx::Conv_779) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.8/nl/Relu_output_0, %onnx::Conv_781, %onnx::Conv_782) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_784, %onnx::Conv_785) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_787, %onnx::Conv_788) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.9/nl/Relu_output_0, %onnx::Conv_790, %onnx::Conv_791) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_793, %onnx::Conv_794) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_796, %onnx::Conv_797) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.10/shuffle/Reshape_output_0 = Reshape(%/cells.10/nl/Relu_output_0, %/cells.10/shuffle/Constant_output_0) %/cells.10/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.10/shuffle/Reshape_output_0) %/cells.10/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.10/shuffle/Reshape_1_output_0 = Reshape(%/cells.10/shuffle/Transpose_output_0, %/cells.10/shuffle/Constant_1_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.10/shuffle/Reshape_1_output_0, %onnx::Conv_799, %onnx::Conv_800) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_802, %onnx::Conv_803) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_805, %onnx::Conv_806) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.11/nl/Relu_output_0, %onnx::Conv_808, %onnx::Conv_809) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_811, %onnx::Conv_812) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_814, %onnx::Conv_815) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.12/nl/Relu_output_0, %onnx::Conv_817, %onnx::Conv_818) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_820, %onnx::Conv_821) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_823, %onnx::Conv_824) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.13/shuffle/Reshape_output_0 = Reshape(%/cells.13/nl/Relu_output_0, %/cells.13/shuffle/Constant_output_0) %/cells.13/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.13/shuffle/Reshape_output_0) %/cells.13/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.13/shuffle/Reshape_1_output_0 = Reshape(%/cells.13/shuffle/Transpose_output_0, %/cells.13/shuffle/Constant_1_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.13/shuffle/Reshape_1_output_0, %onnx::Conv_826, %onnx::Conv_827) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_829, %onnx::Conv_830) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_832, %onnx::Conv_833) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.14/nl/Relu_output_0, %onnx::Conv_835, %onnx::Conv_836) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_838, %onnx::Conv_839) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_841, %onnx::Conv_842) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.15/nl/Relu_output_0, %onnx::Conv_844, %onnx::Conv_845) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_847, %onnx::Conv_848) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_850, %onnx::Conv_851) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.17/nl/Relu_output_0, %onnx::Conv_853, %onnx::Conv_854) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_856, %onnx::Conv_857) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_859, %onnx::Conv_860) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.18/nl/Relu_output_0, %onnx::Conv_862, %onnx::Conv_863) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_865, %onnx::Conv_866) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_868, %onnx::Conv_869) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.19/nl/Relu_output_0, %onnx::Conv_871, %onnx::Conv_872) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_874, %onnx::Conv_875) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_877, %onnx::Conv_878) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 552, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.20/nl/Relu_output_0, %onnx::Conv_880, %onnx::Conv_881) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_883, %onnx::Conv_884) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_886, %onnx::Conv_887) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.21/nl/Relu_output_0, %onnx::Conv_889, %onnx::Conv_890) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_892, %onnx::Conv_893) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_895, %onnx::Conv_896) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %701 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %701 }	val_accuracy	74,699,392	2,133,052	{'zcp_synflow': 77.89907159291953, 'zcp_zen': 70.61277770996094, 'zcp_epe_nas': 8.43931636281636, 'zcp_fisher': 0.1565728336572647, 'zcp_flops': 74699392.0, 'zcp_grad_norm': 25.163902282714844, 'zcp_grasp': -0.21435546875, 'zcp_jacov': -16.04736825096067, 'zcp_l2_norm': 663.7222290039062, 'zcp_nwot': 212.2607544191578, 'zcp_params': 2133052.0, 'zcp_plain': -0.004208901897072792, 'zcp_snip': 45.40211486816406, 'lat_1080ti_1': 0.7946235022758369, 'lat_1080ti_32': 0.574152896689911, 'lat_1080ti_64': 0.4586707154750304, 'lat_2080ti_1': 0.779352654819707, 'lat_2080ti_32': 0.6066092407108884, 'lat_2080ti_64': 0.47338807063787386, 'lat_essential_ph_1': 0.2830188679245283, 'lat_eyeriss': 0.5321548420222619, 'lat_fpga': 0.5397935859250536, 'lat_gold_6226': 0.48782888593706814, 'lat_gold_6240': 0.6990712125582299, 'lat_pixel2': 0.391304347826087, 'lat_pixel3': 0.5376971604779908, 'lat_raspi4': 0.5192462042277328, 'lat_samsung_a50': 0.24210526315789474, 'lat_samsung_s7': 0.2125984251968504, 'lat_silver_4114': 0.7296926792064687, 'lat_silver_4210r': 0.7917065008530597, 'lat_titan_rtx_1': 0.7186858811299227, 'lat_titan_rtx_32': 0.6096516822219448, 'lat_titan_rtx_64': 0.5086947516498742, 'lat_titanx_1': 0.3812906585203696, 'lat_titanx_32': 0.532479425782754, 'lat_titanx_64': 0.4939796470085282, 'lat_titanxp_1': 0.6759204755468297, 'lat_titanxp_32': 0.5659464902309068, 'lat_titanxp_64': 0.4655703641864076}
FBNet_4640	FBNet	4640	4640	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_696[FLOAT, 16x3x3x3] %onnx::Conv_697[FLOAT, 16] %onnx::Conv_699[FLOAT, 16x8x1x1] %onnx::Conv_702[FLOAT, 16x1x5x5] %onnx::Conv_705[FLOAT, 16x8x1x1] %onnx::Conv_708[FLOAT, 48x16x1x1] %onnx::Conv_709[FLOAT, 48] %onnx::Conv_711[FLOAT, 48x1x3x3] %onnx::Conv_714[FLOAT, 24x48x1x1] %onnx::Conv_715[FLOAT, 24] %onnx::Conv_717[FLOAT, 144x24x1x1] %onnx::Conv_718[FLOAT, 144] %onnx::Conv_720[FLOAT, 144x1x5x5] %onnx::Conv_723[FLOAT, 24x144x1x1] %onnx::Conv_726[FLOAT, 24x24x1x1] %onnx::Conv_729[FLOAT, 24x1x5x5] %onnx::Conv_732[FLOAT, 24x24x1x1] %onnx::Conv_735[FLOAT, 72x24x1x1] %onnx::Conv_736[FLOAT, 72] %onnx::Conv_738[FLOAT, 72x1x5x5] %onnx::Conv_741[FLOAT, 24x72x1x1] %onnx::Conv_744[FLOAT, 24x24x1x1] %onnx::Conv_747[FLOAT, 24x1x5x5] %onnx::Conv_750[FLOAT, 32x24x1x1] %onnx::Conv_751[FLOAT, 32] %onnx::Conv_753[FLOAT, 192x32x1x1] %onnx::Conv_754[FLOAT, 192] %onnx::Conv_756[FLOAT, 192x1x5x5] %onnx::Conv_759[FLOAT, 32x192x1x1] %onnx::Conv_762[FLOAT, 192x32x1x1] %onnx::Conv_765[FLOAT, 192x1x3x3] %onnx::Conv_768[FLOAT, 32x192x1x1] %onnx::Conv_771[FLOAT, 96x32x1x1] %onnx::Conv_772[FLOAT, 96] %onnx::Conv_774[FLOAT, 96x1x3x3] %onnx::Conv_777[FLOAT, 32x96x1x1] %onnx::Conv_780[FLOAT, 192x32x1x1] %onnx::Conv_783[FLOAT, 192x1x5x5] %onnx::Conv_786[FLOAT, 64x192x1x1] %onnx::Conv_787[FLOAT, 64] %onnx::Conv_789[FLOAT, 192x64x1x1] %onnx::Conv_792[FLOAT, 192x1x3x3] %onnx::Conv_795[FLOAT, 64x192x1x1] %onnx::Conv_798[FLOAT, 64x64x1x1] %onnx::Conv_801[FLOAT, 64x1x5x5] %onnx::Conv_804[FLOAT, 64x64x1x1] %onnx::Conv_807[FLOAT, 64x32x1x1] %onnx::Conv_810[FLOAT, 64x1x5x5] %onnx::Conv_813[FLOAT, 64x32x1x1] %onnx::Conv_816[FLOAT, 192x64x1x1] %onnx::Conv_819[FLOAT, 192x1x3x3] %onnx::Conv_822[FLOAT, 112x192x1x1] %onnx::Conv_823[FLOAT, 112] %onnx::Conv_825[FLOAT, 112x112x1x1] %onnx::Conv_828[FLOAT, 112x1x3x3] %onnx::Conv_831[FLOAT, 112x112x1x1] %onnx::Conv_834[FLOAT, 112x56x1x1] %onnx::Conv_837[FLOAT, 112x1x5x5] %onnx::Conv_840[FLOAT, 112x56x1x1] %onnx::Conv_843[FLOAT, 112x56x1x1] %onnx::Conv_846[FLOAT, 112x1x5x5] %onnx::Conv_849[FLOAT, 112x56x1x1] %onnx::Conv_852[FLOAT, 336x112x1x1] %onnx::Conv_853[FLOAT, 336] %onnx::Conv_855[FLOAT, 336x1x3x3] %onnx::Conv_858[FLOAT, 184x336x1x1] %onnx::Conv_859[FLOAT, 184] %onnx::Conv_861[FLOAT, 184x92x1x1] %onnx::Conv_864[FLOAT, 184x1x3x3] %onnx::Conv_867[FLOAT, 184x92x1x1] %onnx::Conv_870[FLOAT, 1104x184x1x1] %onnx::Conv_871[FLOAT, 1104] %onnx::Conv_873[FLOAT, 1104x1x3x3] %onnx::Conv_876[FLOAT, 184x1104x1x1] %onnx::Conv_879[FLOAT, 1104x184x1x1] %onnx::Conv_882[FLOAT, 1104x1x5x5] %onnx::Conv_885[FLOAT, 184x1104x1x1] %onnx::Conv_888[FLOAT, 352x184x1x1] %onnx::Conv_889[FLOAT, 352] %onnx::Conv_891[FLOAT, 1504x352x1x1] %onnx::Conv_892[FLOAT, 1504] ) { %onnx::Conv_886 = Identity(%onnx::Conv_859) %onnx::Conv_883 = Identity(%onnx::Conv_871) %onnx::Conv_880 = Identity(%onnx::Conv_871) %onnx::Conv_877 = Identity(%onnx::Conv_859) %onnx::Conv_874 = Identity(%onnx::Conv_871) %onnx::Conv_868 = Identity(%onnx::Conv_859) %onnx::Conv_865 = Identity(%onnx::Conv_859) %onnx::Conv_862 = Identity(%onnx::Conv_859) %onnx::Conv_856 = Identity(%onnx::Conv_853) %onnx::Conv_850 = Identity(%onnx::Conv_823) %onnx::Conv_847 = Identity(%onnx::Conv_823) %onnx::Conv_844 = Identity(%onnx::Conv_823) %onnx::Conv_841 = Identity(%onnx::Conv_823) %onnx::Conv_838 = Identity(%onnx::Conv_823) %onnx::Conv_835 = Identity(%onnx::Conv_823) %onnx::Conv_832 = Identity(%onnx::Conv_823) %onnx::Conv_829 = Identity(%onnx::Conv_823) %onnx::Conv_826 = Identity(%onnx::Conv_823) %onnx::Conv_820 = Identity(%onnx::Conv_754) %onnx::Conv_817 = Identity(%onnx::Conv_754) %onnx::Conv_814 = Identity(%onnx::Conv_787) %onnx::Conv_811 = Identity(%onnx::Conv_787) %onnx::Conv_808 = Identity(%onnx::Conv_787) %onnx::Conv_805 = Identity(%onnx::Conv_787) %onnx::Conv_802 = Identity(%onnx::Conv_787) %onnx::Conv_799 = Identity(%onnx::Conv_787) %onnx::Conv_796 = Identity(%onnx::Conv_787) %onnx::Conv_793 = Identity(%onnx::Conv_754) %onnx::Conv_790 = Identity(%onnx::Conv_754) %onnx::Conv_784 = Identity(%onnx::Conv_754) %onnx::Conv_781 = Identity(%onnx::Conv_754) %onnx::Conv_778 = Identity(%onnx::Conv_751) %onnx::Conv_775 = Identity(%onnx::Conv_772) %onnx::Conv_769 = Identity(%onnx::Conv_751) %onnx::Conv_766 = Identity(%onnx::Conv_754) %onnx::Conv_763 = Identity(%onnx::Conv_754) %onnx::Conv_760 = Identity(%onnx::Conv_751) %onnx::Conv_757 = Identity(%onnx::Conv_754) %onnx::Conv_748 = Identity(%onnx::Conv_715) %onnx::Conv_745 = Identity(%onnx::Conv_715) %onnx::Conv_742 = Identity(%onnx::Conv_715) %onnx::Conv_739 = Identity(%onnx::Conv_736) %onnx::Conv_733 = Identity(%onnx::Conv_715) %onnx::Conv_730 = Identity(%onnx::Conv_715) %onnx::Conv_727 = Identity(%onnx::Conv_715) %onnx::Conv_724 = Identity(%onnx::Conv_715) %onnx::Conv_721 = Identity(%onnx::Conv_718) %onnx::Conv_712 = Identity(%onnx::Conv_709) %onnx::Conv_706 = Identity(%onnx::Conv_697) %onnx::Conv_703 = Identity(%onnx::Conv_697) %onnx::Conv_700 = Identity(%onnx::Conv_697) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_696, %onnx::Conv_697) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_699, %onnx::Conv_700) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.0/shuffle/Reshape_output_0 = Reshape(%/cells.0/nl/Relu_output_0, %/cells.0/shuffle/Constant_output_0) %/cells.0/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.0/shuffle/Reshape_output_0) %/cells.0/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.0/shuffle/Reshape_1_output_0 = Reshape(%/cells.0/shuffle/Transpose_output_0, %/cells.0/shuffle/Constant_1_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.0/shuffle/Reshape_1_output_0, %onnx::Conv_702, %onnx::Conv_703) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_705, %onnx::Conv_706) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_708, %onnx::Conv_709) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 48, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.1/nl/Relu_output_0, %onnx::Conv_711, %onnx::Conv_712) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_714, %onnx::Conv_715) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_717, %onnx::Conv_718) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.2/nl/Relu_output_0, %onnx::Conv_720, %onnx::Conv_721) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_723, %onnx::Conv_724) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_726, %onnx::Conv_727) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.3/nl/Relu_output_0, %onnx::Conv_729, %onnx::Conv_730) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_732, %onnx::Conv_733) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_735, %onnx::Conv_736) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.4/nl/Relu_output_0, %onnx::Conv_738, %onnx::Conv_739) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_741, %onnx::Conv_742) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/Add_output_0, %onnx::Conv_744, %onnx::Conv_745) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.5/nl/Relu_output_0, %onnx::Conv_747, %onnx::Conv_748) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_750, %onnx::Conv_751) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_753, %onnx::Conv_754) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.6/nl/Relu_output_0, %onnx::Conv_756, %onnx::Conv_757) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_759, %onnx::Conv_760) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_762, %onnx::Conv_763) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.7/nl/Relu_output_0, %onnx::Conv_765, %onnx::Conv_766) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_768, %onnx::Conv_769) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_771, %onnx::Conv_772) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.8/nl/Relu_output_0, %onnx::Conv_774, %onnx::Conv_775) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_777, %onnx::Conv_778) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_780, %onnx::Conv_781) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.9/nl/Relu_output_0, %onnx::Conv_783, %onnx::Conv_784) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_786, %onnx::Conv_787) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_789, %onnx::Conv_790) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.10/nl/Relu_output_0, %onnx::Conv_792, %onnx::Conv_793) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_795, %onnx::Conv_796) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_798, %onnx::Conv_799) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.11/nl/Relu_output_0, %onnx::Conv_801, %onnx::Conv_802) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_804, %onnx::Conv_805) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_807, %onnx::Conv_808) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.12/shuffle/Reshape_output_0 = Reshape(%/cells.12/nl/Relu_output_0, %/cells.12/shuffle/Constant_output_0) %/cells.12/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.12/shuffle/Reshape_output_0) %/cells.12/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.12/shuffle/Reshape_1_output_0 = Reshape(%/cells.12/shuffle/Transpose_output_0, %/cells.12/shuffle/Constant_1_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.12/shuffle/Reshape_1_output_0, %onnx::Conv_810, %onnx::Conv_811) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_813, %onnx::Conv_814) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_816, %onnx::Conv_817) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.13/nl/Relu_output_0, %onnx::Conv_819, %onnx::Conv_820) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_822, %onnx::Conv_823) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_825, %onnx::Conv_826) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.14/nl/Relu_output_0, %onnx::Conv_828, %onnx::Conv_829) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_831, %onnx::Conv_832) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_834, %onnx::Conv_835) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.15/shuffle/Reshape_output_0 = Reshape(%/cells.15/nl/Relu_output_0, %/cells.15/shuffle/Constant_output_0) %/cells.15/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.15/shuffle/Reshape_output_0) %/cells.15/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.15/shuffle/Reshape_1_output_0 = Reshape(%/cells.15/shuffle/Transpose_output_0, %/cells.15/shuffle/Constant_1_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.15/shuffle/Reshape_1_output_0, %onnx::Conv_837, %onnx::Conv_838) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_840, %onnx::Conv_841) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_843, %onnx::Conv_844) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.16/shuffle/Reshape_output_0 = Reshape(%/cells.16/nl/Relu_output_0, %/cells.16/shuffle/Constant_output_0) %/cells.16/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.16/shuffle/Reshape_output_0) %/cells.16/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.16/shuffle/Reshape_1_output_0 = Reshape(%/cells.16/shuffle/Transpose_output_0, %/cells.16/shuffle/Constant_1_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.16/shuffle/Reshape_1_output_0, %onnx::Conv_846, %onnx::Conv_847) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_849, %onnx::Conv_850) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.15/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_852, %onnx::Conv_853) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.17/nl/Relu_output_0, %onnx::Conv_855, %onnx::Conv_856) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_858, %onnx::Conv_859) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_861, %onnx::Conv_862) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.18/shuffle/Reshape_output_0 = Reshape(%/cells.18/nl/Relu_output_0, %/cells.18/shuffle/Constant_output_0) %/cells.18/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.18/shuffle/Reshape_output_0) %/cells.18/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.18/shuffle/Reshape_1_output_0 = Reshape(%/cells.18/shuffle/Transpose_output_0, %/cells.18/shuffle/Constant_1_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.18/shuffle/Reshape_1_output_0, %onnx::Conv_864, %onnx::Conv_865) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_867, %onnx::Conv_868) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_870, %onnx::Conv_871) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.19/nl/Relu_output_0, %onnx::Conv_873, %onnx::Conv_874) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_876, %onnx::Conv_877) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_879, %onnx::Conv_880) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.20/nl/Relu_output_0, %onnx::Conv_882, %onnx::Conv_883) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_885, %onnx::Conv_886) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_888, %onnx::Conv_889) %/cells.21/relu/Relu_output_0 = Relu(%/cells.21/conv/Conv_output_0) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/relu/Relu_output_0, %onnx::Conv_891, %onnx::Conv_892) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %694 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %694 }	val_accuracy	74,147,456	1,976,500	{'zcp_synflow': 82.59762280741629, 'zcp_zen': 72.37373352050781, 'zcp_epe_nas': 0.00015999920000638146, 'zcp_fisher': 0.11599140614271164, 'zcp_flops': 74147456.0, 'zcp_grad_norm': 24.32544708251953, 'zcp_grasp': -0.08461380004882812, 'zcp_jacov': -16.04951288826483, 'zcp_l2_norm': 650.794921875, 'zcp_nwot': 214.76886286948437, 'zcp_params': 1976500.0, 'zcp_plain': -0.001490537659265101, 'zcp_snip': 47.130584716796875, 'lat_1080ti_1': 0.7197781611956146, 'lat_1080ti_32': 0.7480503063374878, 'lat_1080ti_64': 0.6316644611504854, 'lat_2080ti_1': 0.780764410934731, 'lat_2080ti_32': 0.745531791128425, 'lat_2080ti_64': 0.6411566451915212, 'lat_essential_ph_1': 0.2641509433962264, 'lat_eyeriss': 0.5737626758109768, 'lat_fpga': 0.49072324087909036, 'lat_gold_6226': 0.40205645528429423, 'lat_gold_6240': 0.5983768624378739, 'lat_pixel2': 0.3695652173913043, 'lat_pixel3': 0.5674410375133463, 'lat_raspi4': 0.5779333224345372, 'lat_samsung_a50': 0.23157894736842105, 'lat_samsung_s7': 0.14960629921259844, 'lat_silver_4114': 0.6548423230971923, 'lat_silver_4210r': 0.7349723521402465, 'lat_titan_rtx_1': 0.7309087171264346, 'lat_titan_rtx_32': 0.7363421158819502, 'lat_titan_rtx_64': 0.6656129822248834, 'lat_titanx_1': 0.38801358319458057, 'lat_titanx_32': 0.6921988186917651, 'lat_titanx_64': 0.665626935149572, 'lat_titanxp_1': 0.6785635395320547, 'lat_titanxp_32': 0.7195684729452241, 'lat_titanxp_64': 0.641702988507132}
FBNet_4889	FBNet	4889	4889	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_630[FLOAT, 16x3x3x3] %onnx::Conv_631[FLOAT, 16] %onnx::Conv_633[FLOAT, 48x16x1x1] %onnx::Conv_634[FLOAT, 48] %onnx::Conv_636[FLOAT, 48x1x3x3] %onnx::Conv_639[FLOAT, 24x48x1x1] %onnx::Conv_640[FLOAT, 24] %onnx::Conv_642[FLOAT, 72x24x1x1] %onnx::Conv_643[FLOAT, 72] %onnx::Conv_645[FLOAT, 72x1x3x3] %onnx::Conv_648[FLOAT, 24x72x1x1] %onnx::Conv_651[FLOAT, 24x24x1x1] %onnx::Conv_654[FLOAT, 24x1x5x5] %onnx::Conv_657[FLOAT, 24x24x1x1] %onnx::Conv_660[FLOAT, 144x24x1x1] %onnx::Conv_661[FLOAT, 144] %onnx::Conv_663[FLOAT, 144x1x3x3] %onnx::Conv_666[FLOAT, 24x144x1x1] %onnx::Conv_669[FLOAT, 24x24x1x1] %onnx::Conv_672[FLOAT, 24x1x3x3] %onnx::Conv_675[FLOAT, 32x24x1x1] %onnx::Conv_676[FLOAT, 32] %onnx::Conv_678[FLOAT, 32x32x1x1] %onnx::Conv_681[FLOAT, 32x1x3x3] %onnx::Conv_684[FLOAT, 32x32x1x1] %onnx::Conv_687[FLOAT, 32x16x1x1] %onnx::Conv_690[FLOAT, 32x1x5x5] %onnx::Conv_693[FLOAT, 32x16x1x1] %onnx::Conv_696[FLOAT, 32x32x1x1] %onnx::Conv_699[FLOAT, 32x1x5x5] %onnx::Conv_702[FLOAT, 32x32x1x1] %onnx::Conv_705[FLOAT, 192x32x1x1] %onnx::Conv_706[FLOAT, 192] %onnx::Conv_708[FLOAT, 192x1x3x3] %onnx::Conv_711[FLOAT, 64x192x1x1] %onnx::Conv_712[FLOAT, 64] %onnx::Conv_714[FLOAT, 192x64x1x1] %onnx::Conv_717[FLOAT, 192x1x5x5] %onnx::Conv_720[FLOAT, 64x192x1x1] %onnx::Conv_723[FLOAT, 192x64x1x1] %onnx::Conv_726[FLOAT, 192x1x3x3] %onnx::Conv_729[FLOAT, 64x192x1x1] %onnx::Conv_732[FLOAT, 192x64x1x1] %onnx::Conv_735[FLOAT, 192x1x3x3] %onnx::Conv_738[FLOAT, 64x192x1x1] %onnx::Conv_741[FLOAT, 64x32x1x1] %onnx::Conv_744[FLOAT, 64x1x3x3] %onnx::Conv_747[FLOAT, 112x32x1x1] %onnx::Conv_748[FLOAT, 112] %onnx::Conv_750[FLOAT, 112x112x1x1] %onnx::Conv_753[FLOAT, 112x1x3x3] %onnx::Conv_756[FLOAT, 112x112x1x1] %onnx::Conv_759[FLOAT, 672x112x1x1] %onnx::Conv_760[FLOAT, 672] %onnx::Conv_762[FLOAT, 672x1x5x5] %onnx::Conv_765[FLOAT, 112x672x1x1] %onnx::Conv_768[FLOAT, 336x112x1x1] %onnx::Conv_769[FLOAT, 336] %onnx::Conv_771[FLOAT, 336x1x5x5] %onnx::Conv_774[FLOAT, 112x336x1x1] %onnx::Conv_777[FLOAT, 112x112x1x1] %onnx::Conv_780[FLOAT, 112x1x3x3] %onnx::Conv_783[FLOAT, 184x112x1x1] %onnx::Conv_784[FLOAT, 184] %onnx::Conv_786[FLOAT, 184x184x1x1] %onnx::Conv_789[FLOAT, 184x1x3x3] %onnx::Conv_792[FLOAT, 184x184x1x1] %onnx::Conv_795[FLOAT, 1104x184x1x1] %onnx::Conv_796[FLOAT, 1104] %onnx::Conv_798[FLOAT, 1104x1x5x5] %onnx::Conv_801[FLOAT, 184x1104x1x1] %onnx::Conv_804[FLOAT, 184x184x1x1] %onnx::Conv_807[FLOAT, 184x1x3x3] %onnx::Conv_810[FLOAT, 184x184x1x1] %onnx::Conv_813[FLOAT, 1104x184x1x1] %onnx::Conv_816[FLOAT, 1104x1x3x3] %onnx::Conv_819[FLOAT, 352x1104x1x1] %onnx::Conv_820[FLOAT, 352] %onnx::Conv_822[FLOAT, 1504x352x1x1] %onnx::Conv_823[FLOAT, 1504] ) { %onnx::Conv_817 = Identity(%onnx::Conv_796) %onnx::Conv_814 = Identity(%onnx::Conv_796) %onnx::Conv_811 = Identity(%onnx::Conv_784) %onnx::Conv_808 = Identity(%onnx::Conv_784) %onnx::Conv_805 = Identity(%onnx::Conv_784) %onnx::Conv_802 = Identity(%onnx::Conv_784) %onnx::Conv_799 = Identity(%onnx::Conv_796) %onnx::Conv_793 = Identity(%onnx::Conv_784) %onnx::Conv_790 = Identity(%onnx::Conv_784) %onnx::Conv_787 = Identity(%onnx::Conv_784) %onnx::Conv_781 = Identity(%onnx::Conv_748) %onnx::Conv_778 = Identity(%onnx::Conv_748) %onnx::Conv_775 = Identity(%onnx::Conv_748) %onnx::Conv_772 = Identity(%onnx::Conv_769) %onnx::Conv_766 = Identity(%onnx::Conv_748) %onnx::Conv_763 = Identity(%onnx::Conv_760) %onnx::Conv_757 = Identity(%onnx::Conv_748) %onnx::Conv_754 = Identity(%onnx::Conv_748) %onnx::Conv_751 = Identity(%onnx::Conv_748) %onnx::Conv_745 = Identity(%onnx::Conv_712) %onnx::Conv_742 = Identity(%onnx::Conv_712) %onnx::Conv_739 = Identity(%onnx::Conv_712) %onnx::Conv_736 = Identity(%onnx::Conv_706) %onnx::Conv_733 = Identity(%onnx::Conv_706) %onnx::Conv_730 = Identity(%onnx::Conv_712) %onnx::Conv_727 = Identity(%onnx::Conv_706) %onnx::Conv_724 = Identity(%onnx::Conv_706) %onnx::Conv_721 = Identity(%onnx::Conv_712) %onnx::Conv_718 = Identity(%onnx::Conv_706) %onnx::Conv_715 = Identity(%onnx::Conv_706) %onnx::Conv_709 = Identity(%onnx::Conv_706) %onnx::Conv_703 = Identity(%onnx::Conv_676) %onnx::Conv_700 = Identity(%onnx::Conv_676) %onnx::Conv_697 = Identity(%onnx::Conv_676) %onnx::Conv_694 = Identity(%onnx::Conv_676) %onnx::Conv_691 = Identity(%onnx::Conv_676) %onnx::Conv_688 = Identity(%onnx::Conv_676) %onnx::Conv_685 = Identity(%onnx::Conv_676) %onnx::Conv_682 = Identity(%onnx::Conv_676) %onnx::Conv_679 = Identity(%onnx::Conv_676) %onnx::Conv_673 = Identity(%onnx::Conv_640) %onnx::Conv_670 = Identity(%onnx::Conv_640) %onnx::Conv_667 = Identity(%onnx::Conv_640) %onnx::Conv_664 = Identity(%onnx::Conv_661) %onnx::Conv_658 = Identity(%onnx::Conv_640) %onnx::Conv_655 = Identity(%onnx::Conv_640) %onnx::Conv_652 = Identity(%onnx::Conv_640) %onnx::Conv_649 = Identity(%onnx::Conv_640) %onnx::Conv_646 = Identity(%onnx::Conv_643) %onnx::Conv_637 = Identity(%onnx::Conv_634) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_630, %onnx::Conv_631) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_633, %onnx::Conv_634) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 48, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.1/nl/Relu_output_0, %onnx::Conv_636, %onnx::Conv_637) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_639, %onnx::Conv_640) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_642, %onnx::Conv_643) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.2/nl/Relu_output_0, %onnx::Conv_645, %onnx::Conv_646) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_648, %onnx::Conv_649) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_651, %onnx::Conv_652) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.3/nl/Relu_output_0, %onnx::Conv_654, %onnx::Conv_655) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_657, %onnx::Conv_658) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_660, %onnx::Conv_661) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.4/nl/Relu_output_0, %onnx::Conv_663, %onnx::Conv_664) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_666, %onnx::Conv_667) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/Add_output_0, %onnx::Conv_669, %onnx::Conv_670) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.5/nl/Relu_output_0, %onnx::Conv_672, %onnx::Conv_673) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_675, %onnx::Conv_676) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_678, %onnx::Conv_679) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.6/nl/Relu_output_0, %onnx::Conv_681, %onnx::Conv_682) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_684, %onnx::Conv_685) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_687, %onnx::Conv_688) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.7/shuffle/Reshape_output_0 = Reshape(%/cells.7/nl/Relu_output_0, %/cells.7/shuffle/Constant_output_0) %/cells.7/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.7/shuffle/Reshape_output_0) %/cells.7/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.7/shuffle/Reshape_1_output_0 = Reshape(%/cells.7/shuffle/Transpose_output_0, %/cells.7/shuffle/Constant_1_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.7/shuffle/Reshape_1_output_0, %onnx::Conv_690, %onnx::Conv_691) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_693, %onnx::Conv_694) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_696, %onnx::Conv_697) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.8/nl/Relu_output_0, %onnx::Conv_699, %onnx::Conv_700) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_702, %onnx::Conv_703) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_705, %onnx::Conv_706) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.9/nl/Relu_output_0, %onnx::Conv_708, %onnx::Conv_709) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_711, %onnx::Conv_712) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_714, %onnx::Conv_715) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.10/nl/Relu_output_0, %onnx::Conv_717, %onnx::Conv_718) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_720, %onnx::Conv_721) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_723, %onnx::Conv_724) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.11/nl/Relu_output_0, %onnx::Conv_726, %onnx::Conv_727) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_729, %onnx::Conv_730) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_732, %onnx::Conv_733) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.12/nl/Relu_output_0, %onnx::Conv_735, %onnx::Conv_736) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_738, %onnx::Conv_739) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_741, %onnx::Conv_742) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.13/shuffle/Reshape_output_0 = Reshape(%/cells.13/nl/Relu_output_0, %/cells.13/shuffle/Constant_output_0) %/cells.13/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.13/shuffle/Reshape_output_0) %/cells.13/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.13/shuffle/Reshape_1_output_0 = Reshape(%/cells.13/shuffle/Transpose_output_0, %/cells.13/shuffle/Constant_1_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.13/shuffle/Reshape_1_output_0, %onnx::Conv_744, %onnx::Conv_745) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_747, %onnx::Conv_748) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_750, %onnx::Conv_751) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.14/nl/Relu_output_0, %onnx::Conv_753, %onnx::Conv_754) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_756, %onnx::Conv_757) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_759, %onnx::Conv_760) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.15/nl/Relu_output_0, %onnx::Conv_762, %onnx::Conv_763) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_765, %onnx::Conv_766) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_768, %onnx::Conv_769) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.16/nl/Relu_output_0, %onnx::Conv_771, %onnx::Conv_772) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_774, %onnx::Conv_775) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.15/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_777, %onnx::Conv_778) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.17/nl/Relu_output_0, %onnx::Conv_780, %onnx::Conv_781) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_783, %onnx::Conv_784) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_786, %onnx::Conv_787) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.18/nl/Relu_output_0, %onnx::Conv_789, %onnx::Conv_790) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_792, %onnx::Conv_793) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_795, %onnx::Conv_796) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.19/nl/Relu_output_0, %onnx::Conv_798, %onnx::Conv_799) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_801, %onnx::Conv_802) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_804, %onnx::Conv_805) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.20/nl/Relu_output_0, %onnx::Conv_807, %onnx::Conv_808) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_810, %onnx::Conv_811) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_813, %onnx::Conv_814) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.21/nl/Relu_output_0, %onnx::Conv_816, %onnx::Conv_817) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_819, %onnx::Conv_820) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_822, %onnx::Conv_823) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %628 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %628 }	val_accuracy	77,415,808	2,326,476	{'zcp_synflow': 82.5309040969133, 'zcp_zen': 72.55218505859375, 'zcp_epe_nas': 6.570449287434718, 'zcp_fisher': 0.11501649767160416, 'zcp_flops': 77415808.0, 'zcp_grad_norm': 21.668869018554688, 'zcp_grasp': -0.013709068298339844, 'zcp_jacov': -16.06820386174849, 'zcp_l2_norm': 686.1187133789062, 'zcp_nwot': 212.9461177339475, 'zcp_params': 2326476.0, 'zcp_plain': 0.009781252592802048, 'zcp_snip': 43.24672317504883, 'lat_1080ti_1': 0.6087284230779342, 'lat_1080ti_32': 0.5913488454198659, 'lat_1080ti_64': 0.4550693152131566, 'lat_2080ti_1': 0.6694848726715908, 'lat_2080ti_32': 0.6108998020070059, 'lat_2080ti_64': 0.515208054665073, 'lat_essential_ph_1': 0.24528301886792453, 'lat_eyeriss': 0.5111950562045003, 'lat_fpga': 0.6269604884275521, 'lat_gold_6226': 0.4414121016306257, 'lat_gold_6240': 0.5721433418702853, 'lat_pixel2': 0.5217391304347826, 'lat_pixel3': 0.47760805494618513, 'lat_raspi4': 0.5526392867815173, 'lat_samsung_a50': 0.23157894736842105, 'lat_samsung_s7': 0.2047244094488189, 'lat_silver_4114': 0.6038793078574286, 'lat_silver_4210r': 0.6457988830065116, 'lat_titan_rtx_1': 0.6277712768495664, 'lat_titan_rtx_32': 0.5808068419536232, 'lat_titan_rtx_64': 0.5185219328887671, 'lat_titanx_1': 0.35067955936365103, 'lat_titanx_32': 0.5229746243371428, 'lat_titanx_64': 0.4658335726600207, 'lat_titanxp_1': 0.6065597320533347, 'lat_titanxp_32': 0.5665480511292275, 'lat_titanxp_64': 0.4879097440899573}
FBNet_1744	FBNet	1744	1744	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_632[FLOAT, 16x3x3x3] %onnx::Conv_633[FLOAT, 16] %onnx::Conv_635[FLOAT, 48x16x1x1] %onnx::Conv_636[FLOAT, 48] %onnx::Conv_638[FLOAT, 48x1x3x3] %onnx::Conv_641[FLOAT, 16x48x1x1] %onnx::Conv_644[FLOAT, 48x16x1x1] %onnx::Conv_647[FLOAT, 48x1x3x3] %onnx::Conv_650[FLOAT, 24x48x1x1] %onnx::Conv_651[FLOAT, 24] %onnx::Conv_653[FLOAT, 72x24x1x1] %onnx::Conv_654[FLOAT, 72] %onnx::Conv_656[FLOAT, 72x1x5x5] %onnx::Conv_659[FLOAT, 24x72x1x1] %onnx::Conv_662[FLOAT, 24x24x1x1] %onnx::Conv_665[FLOAT, 24x1x3x3] %onnx::Conv_668[FLOAT, 24x24x1x1] %onnx::Conv_671[FLOAT, 24x12x1x1] %onnx::Conv_674[FLOAT, 24x1x3x3] %onnx::Conv_677[FLOAT, 24x12x1x1] %onnx::Conv_680[FLOAT, 144x24x1x1] %onnx::Conv_681[FLOAT, 144] %onnx::Conv_683[FLOAT, 144x1x5x5] %onnx::Conv_686[FLOAT, 32x144x1x1] %onnx::Conv_687[FLOAT, 32] %onnx::Conv_689[FLOAT, 192x32x1x1] %onnx::Conv_690[FLOAT, 192] %onnx::Conv_692[FLOAT, 192x1x5x5] %onnx::Conv_695[FLOAT, 32x192x1x1] %onnx::Conv_698[FLOAT, 192x32x1x1] %onnx::Conv_701[FLOAT, 192x1x5x5] %onnx::Conv_704[FLOAT, 32x192x1x1] %onnx::Conv_707[FLOAT, 32x32x1x1] %onnx::Conv_710[FLOAT, 32x1x5x5] %onnx::Conv_713[FLOAT, 32x32x1x1] %onnx::Conv_716[FLOAT, 64x32x1x1] %onnx::Conv_717[FLOAT, 64] %onnx::Conv_719[FLOAT, 192x64x1x1] %onnx::Conv_722[FLOAT, 192x1x5x5] %onnx::Conv_725[FLOAT, 64x192x1x1] %onnx::Conv_728[FLOAT, 192x64x1x1] %onnx::Conv_731[FLOAT, 192x1x5x5] %onnx::Conv_734[FLOAT, 64x192x1x1] %onnx::Conv_737[FLOAT, 192x64x1x1] %onnx::Conv_740[FLOAT, 192x1x5x5] %onnx::Conv_743[FLOAT, 64x192x1x1] %onnx::Conv_746[FLOAT, 384x64x1x1] %onnx::Conv_747[FLOAT, 384] %onnx::Conv_749[FLOAT, 384x1x3x3] %onnx::Conv_752[FLOAT, 112x384x1x1] %onnx::Conv_753[FLOAT, 112] %onnx::Conv_755[FLOAT, 112x56x1x1] %onnx::Conv_758[FLOAT, 112x1x3x3] %onnx::Conv_761[FLOAT, 112x56x1x1] %onnx::Conv_764[FLOAT, 336x112x1x1] %onnx::Conv_765[FLOAT, 336] %onnx::Conv_767[FLOAT, 336x1x5x5] %onnx::Conv_770[FLOAT, 112x336x1x1] %onnx::Conv_773[FLOAT, 112x112x1x1] %onnx::Conv_776[FLOAT, 112x1x5x5] %onnx::Conv_779[FLOAT, 184x112x1x1] %onnx::Conv_780[FLOAT, 184] %onnx::Conv_782[FLOAT, 184x184x1x1] %onnx::Conv_785[FLOAT, 184x1x3x3] %onnx::Conv_788[FLOAT, 184x184x1x1] %onnx::Conv_791[FLOAT, 552x184x1x1] %onnx::Conv_792[FLOAT, 552] %onnx::Conv_794[FLOAT, 552x1x5x5] %onnx::Conv_797[FLOAT, 184x552x1x1] %onnx::Conv_800[FLOAT, 1104x184x1x1] %onnx::Conv_801[FLOAT, 1104] %onnx::Conv_803[FLOAT, 1104x1x5x5] %onnx::Conv_806[FLOAT, 184x1104x1x1] %onnx::Conv_809[FLOAT, 184x92x1x1] %onnx::Conv_812[FLOAT, 184x1x5x5] %onnx::Conv_815[FLOAT, 352x92x1x1] %onnx::Conv_816[FLOAT, 352] %onnx::Conv_818[FLOAT, 1504x352x1x1] %onnx::Conv_819[FLOAT, 1504] ) { %onnx::Conv_813 = Identity(%onnx::Conv_780) %onnx::Conv_810 = Identity(%onnx::Conv_780) %onnx::Conv_807 = Identity(%onnx::Conv_780) %onnx::Conv_804 = Identity(%onnx::Conv_801) %onnx::Conv_798 = Identity(%onnx::Conv_780) %onnx::Conv_795 = Identity(%onnx::Conv_792) %onnx::Conv_789 = Identity(%onnx::Conv_780) %onnx::Conv_786 = Identity(%onnx::Conv_780) %onnx::Conv_783 = Identity(%onnx::Conv_780) %onnx::Conv_777 = Identity(%onnx::Conv_753) %onnx::Conv_774 = Identity(%onnx::Conv_753) %onnx::Conv_771 = Identity(%onnx::Conv_753) %onnx::Conv_768 = Identity(%onnx::Conv_765) %onnx::Conv_762 = Identity(%onnx::Conv_753) %onnx::Conv_759 = Identity(%onnx::Conv_753) %onnx::Conv_756 = Identity(%onnx::Conv_753) %onnx::Conv_750 = Identity(%onnx::Conv_747) %onnx::Conv_744 = Identity(%onnx::Conv_717) %onnx::Conv_741 = Identity(%onnx::Conv_690) %onnx::Conv_738 = Identity(%onnx::Conv_690) %onnx::Conv_735 = Identity(%onnx::Conv_717) %onnx::Conv_732 = Identity(%onnx::Conv_690) %onnx::Conv_729 = Identity(%onnx::Conv_690) %onnx::Conv_726 = Identity(%onnx::Conv_717) %onnx::Conv_723 = Identity(%onnx::Conv_690) %onnx::Conv_720 = Identity(%onnx::Conv_690) %onnx::Conv_714 = Identity(%onnx::Conv_687) %onnx::Conv_711 = Identity(%onnx::Conv_687) %onnx::Conv_708 = Identity(%onnx::Conv_687) %onnx::Conv_705 = Identity(%onnx::Conv_687) %onnx::Conv_702 = Identity(%onnx::Conv_690) %onnx::Conv_699 = Identity(%onnx::Conv_690) %onnx::Conv_696 = Identity(%onnx::Conv_687) %onnx::Conv_693 = Identity(%onnx::Conv_690) %onnx::Conv_684 = Identity(%onnx::Conv_681) %onnx::Conv_678 = Identity(%onnx::Conv_651) %onnx::Conv_675 = Identity(%onnx::Conv_651) %onnx::Conv_672 = Identity(%onnx::Conv_651) %onnx::Conv_669 = Identity(%onnx::Conv_651) %onnx::Conv_666 = Identity(%onnx::Conv_651) %onnx::Conv_663 = Identity(%onnx::Conv_651) %onnx::Conv_660 = Identity(%onnx::Conv_651) %onnx::Conv_657 = Identity(%onnx::Conv_654) %onnx::Conv_648 = Identity(%onnx::Conv_636) %onnx::Conv_645 = Identity(%onnx::Conv_636) %onnx::Conv_642 = Identity(%onnx::Conv_633) %onnx::Conv_639 = Identity(%onnx::Conv_636) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_632, %onnx::Conv_633) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_635, %onnx::Conv_636) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 48, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.0/nl/Relu_output_0, %onnx::Conv_638, %onnx::Conv_639) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_641, %onnx::Conv_642) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_644, %onnx::Conv_645) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 48, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.1/nl/Relu_output_0, %onnx::Conv_647, %onnx::Conv_648) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_650, %onnx::Conv_651) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_653, %onnx::Conv_654) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.2/nl/Relu_output_0, %onnx::Conv_656, %onnx::Conv_657) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_659, %onnx::Conv_660) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_662, %onnx::Conv_663) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.3/nl/Relu_output_0, %onnx::Conv_665, %onnx::Conv_666) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_668, %onnx::Conv_669) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_671, %onnx::Conv_672) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.4/shuffle/Reshape_output_0 = Reshape(%/cells.4/nl/Relu_output_0, %/cells.4/shuffle/Constant_output_0) %/cells.4/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.4/shuffle/Reshape_output_0) %/cells.4/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.4/shuffle/Reshape_1_output_0 = Reshape(%/cells.4/shuffle/Transpose_output_0, %/cells.4/shuffle/Constant_1_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.4/shuffle/Reshape_1_output_0, %onnx::Conv_674, %onnx::Conv_675) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_677, %onnx::Conv_678) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/Add_output_0, %onnx::Conv_680, %onnx::Conv_681) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.5/nl/Relu_output_0, %onnx::Conv_683, %onnx::Conv_684) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_686, %onnx::Conv_687) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_689, %onnx::Conv_690) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.6/nl/Relu_output_0, %onnx::Conv_692, %onnx::Conv_693) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_695, %onnx::Conv_696) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_698, %onnx::Conv_699) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.7/nl/Relu_output_0, %onnx::Conv_701, %onnx::Conv_702) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_704, %onnx::Conv_705) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_707, %onnx::Conv_708) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.8/nl/Relu_output_0, %onnx::Conv_710, %onnx::Conv_711) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_713, %onnx::Conv_714) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [2, 2]](%/cells.8/Add_output_0, %onnx::Conv_716, %onnx::Conv_717) %/cells.9/relu/Relu_output_0 = Relu(%/cells.9/conv/Conv_output_0) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/relu/Relu_output_0, %onnx::Conv_719, %onnx::Conv_720) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.10/nl/Relu_output_0, %onnx::Conv_722, %onnx::Conv_723) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_725, %onnx::Conv_726) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/relu/Relu_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_728, %onnx::Conv_729) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.11/nl/Relu_output_0, %onnx::Conv_731, %onnx::Conv_732) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_734, %onnx::Conv_735) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_737, %onnx::Conv_738) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.12/nl/Relu_output_0, %onnx::Conv_740, %onnx::Conv_741) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_743, %onnx::Conv_744) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_746, %onnx::Conv_747) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.13/nl/Relu_output_0, %onnx::Conv_749, %onnx::Conv_750) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_752, %onnx::Conv_753) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_755, %onnx::Conv_756) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.14/shuffle/Reshape_output_0 = Reshape(%/cells.14/nl/Relu_output_0, %/cells.14/shuffle/Constant_output_0) %/cells.14/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.14/shuffle/Reshape_output_0) %/cells.14/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.14/shuffle/Reshape_1_output_0 = Reshape(%/cells.14/shuffle/Transpose_output_0, %/cells.14/shuffle/Constant_1_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.14/shuffle/Reshape_1_output_0, %onnx::Conv_758, %onnx::Conv_759) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_761, %onnx::Conv_762) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_764, %onnx::Conv_765) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.16/nl/Relu_output_0, %onnx::Conv_767, %onnx::Conv_768) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_770, %onnx::Conv_771) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_773, %onnx::Conv_774) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.17/nl/Relu_output_0, %onnx::Conv_776, %onnx::Conv_777) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_779, %onnx::Conv_780) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_782, %onnx::Conv_783) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.18/nl/Relu_output_0, %onnx::Conv_785, %onnx::Conv_786) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_788, %onnx::Conv_789) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_791, %onnx::Conv_792) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 552, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.19/nl/Relu_output_0, %onnx::Conv_794, %onnx::Conv_795) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_797, %onnx::Conv_798) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_800, %onnx::Conv_801) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.20/nl/Relu_output_0, %onnx::Conv_803, %onnx::Conv_804) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_806, %onnx::Conv_807) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_809, %onnx::Conv_810) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.21/shuffle/Reshape_output_0 = Reshape(%/cells.21/nl/Relu_output_0, %/cells.21/shuffle/Constant_output_0) %/cells.21/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.21/shuffle/Reshape_output_0) %/cells.21/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.21/shuffle/Reshape_1_output_0 = Reshape(%/cells.21/shuffle/Transpose_output_0, %/cells.21/shuffle/Constant_1_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.21/shuffle/Reshape_1_output_0, %onnx::Conv_812, %onnx::Conv_813) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_815, %onnx::Conv_816) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_818, %onnx::Conv_819) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %630 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %630 }	val_accuracy	69,532,800	1,833,396	{'zcp_synflow': 81.00151682552332, 'zcp_zen': 70.72832489013672, 'zcp_epe_nas': 0.00015999920000638146, 'zcp_fisher': 0.16060179471969604, 'zcp_flops': 69532800.0, 'zcp_grad_norm': 25.518957138061523, 'zcp_grasp': -0.7189178466796875, 'zcp_jacov': -16.054173817441924, 'zcp_l2_norm': 641.1897583007812, 'zcp_nwot': 213.6309817729959, 'zcp_params': 1833396.0, 'zcp_plain': 0.004338196944445372, 'zcp_snip': 48.080177307128906, 'lat_1080ti_1': 0.6896371377224065, 'lat_1080ti_32': 0.5158820399888641, 'lat_1080ti_64': 0.4699123657305433, 'lat_2080ti_1': 0.5993238351792396, 'lat_2080ti_32': 0.49608520749113666, 'lat_2080ti_64': 0.4860155876105916, 'lat_essential_ph_1': 0.4528301886792453, 'lat_eyeriss': 0.49997249371939956, 'lat_fpga': 0.45031120434597594, 'lat_gold_6226': 0.3789777949662443, 'lat_gold_6240': 0.5154425784017876, 'lat_pixel2': 0.32608695652173914, 'lat_pixel3': 0.4877994176860392, 'lat_raspi4': 0.4749247401413308, 'lat_samsung_a50': 0.2, 'lat_samsung_s7': 0.2283464566929134, 'lat_silver_4114': 0.5547105095996716, 'lat_silver_4210r': 0.5289520778792325, 'lat_titan_rtx_1': 0.5869892192857065, 'lat_titan_rtx_32': 0.4894397492913472, 'lat_titan_rtx_64': 0.4945278507261771, 'lat_titanx_1': 0.3037606608774482, 'lat_titanx_32': 0.47081678672697347, 'lat_titanx_64': 0.47308709168583607, 'lat_titanxp_1': 0.5375890266443537, 'lat_titanxp_32': 0.5007478207672101, 'lat_titanxp_64': 0.49433553605051983}
FBNet_2118	FBNet	2118	2118	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_622[FLOAT, 16x3x3x3] %onnx::Conv_623[FLOAT, 16] %onnx::Conv_625[FLOAT, 96x16x1x1] %onnx::Conv_626[FLOAT, 96] %onnx::Conv_628[FLOAT, 96x1x3x3] %onnx::Conv_631[FLOAT, 16x96x1x1] %onnx::Conv_634[FLOAT, 16x8x1x1] %onnx::Conv_637[FLOAT, 16x1x3x3] %onnx::Conv_640[FLOAT, 24x8x1x1] %onnx::Conv_641[FLOAT, 24] %onnx::Conv_643[FLOAT, 24x24x1x1] %onnx::Conv_646[FLOAT, 24x1x3x3] %onnx::Conv_649[FLOAT, 24x24x1x1] %onnx::Conv_652[FLOAT, 72x24x1x1] %onnx::Conv_653[FLOAT, 72] %onnx::Conv_655[FLOAT, 72x1x3x3] %onnx::Conv_658[FLOAT, 24x72x1x1] %onnx::Conv_661[FLOAT, 144x24x1x1] %onnx::Conv_662[FLOAT, 144] %onnx::Conv_664[FLOAT, 144x1x3x3] %onnx::Conv_667[FLOAT, 24x144x1x1] %onnx::Conv_670[FLOAT, 72x24x1x1] %onnx::Conv_673[FLOAT, 72x1x5x5] %onnx::Conv_676[FLOAT, 32x72x1x1] %onnx::Conv_677[FLOAT, 32] %onnx::Conv_679[FLOAT, 32x32x1x1] %onnx::Conv_682[FLOAT, 32x1x5x5] %onnx::Conv_685[FLOAT, 32x32x1x1] %onnx::Conv_688[FLOAT, 32x32x1x1] %onnx::Conv_691[FLOAT, 32x1x3x3] %onnx::Conv_694[FLOAT, 32x32x1x1] %onnx::Conv_697[FLOAT, 96x32x1x1] %onnx::Conv_700[FLOAT, 96x1x5x5] %onnx::Conv_703[FLOAT, 64x96x1x1] %onnx::Conv_704[FLOAT, 64] %onnx::Conv_706[FLOAT, 64x32x1x1] %onnx::Conv_709[FLOAT, 64x1x3x3] %onnx::Conv_712[FLOAT, 64x32x1x1] %onnx::Conv_715[FLOAT, 64x64x1x1] %onnx::Conv_718[FLOAT, 64x1x5x5] %onnx::Conv_721[FLOAT, 64x64x1x1] %onnx::Conv_724[FLOAT, 64x64x1x1] %onnx::Conv_727[FLOAT, 64x1x5x5] %onnx::Conv_730[FLOAT, 64x64x1x1] %onnx::Conv_733[FLOAT, 192x64x1x1] %onnx::Conv_734[FLOAT, 192] %onnx::Conv_736[FLOAT, 192x1x5x5] %onnx::Conv_739[FLOAT, 112x192x1x1] %onnx::Conv_740[FLOAT, 112] %onnx::Conv_742[FLOAT, 112x112x1x1] %onnx::Conv_745[FLOAT, 112x1x5x5] %onnx::Conv_748[FLOAT, 112x112x1x1] %onnx::Conv_751[FLOAT, 672x112x1x1] %onnx::Conv_752[FLOAT, 672] %onnx::Conv_754[FLOAT, 672x1x5x5] %onnx::Conv_757[FLOAT, 112x672x1x1] %onnx::Conv_760[FLOAT, 112x56x1x1] %onnx::Conv_763[FLOAT, 112x1x5x5] %onnx::Conv_766[FLOAT, 184x56x1x1] %onnx::Conv_767[FLOAT, 184] %onnx::Conv_769[FLOAT, 184x184x1x1] %onnx::Conv_772[FLOAT, 184x1x5x5] %onnx::Conv_775[FLOAT, 184x184x1x1] %onnx::Conv_778[FLOAT, 552x184x1x1] %onnx::Conv_779[FLOAT, 552] %onnx::Conv_781[FLOAT, 552x1x3x3] %onnx::Conv_784[FLOAT, 184x552x1x1] %onnx::Conv_787[FLOAT, 184x184x1x1] %onnx::Conv_790[FLOAT, 184x1x5x5] %onnx::Conv_793[FLOAT, 184x184x1x1] %onnx::Conv_796[FLOAT, 552x184x1x1] %onnx::Conv_799[FLOAT, 552x1x5x5] %onnx::Conv_802[FLOAT, 352x552x1x1] %onnx::Conv_803[FLOAT, 352] %onnx::Conv_805[FLOAT, 1504x352x1x1] %onnx::Conv_806[FLOAT, 1504] ) { %onnx::Conv_800 = Identity(%onnx::Conv_779) %onnx::Conv_797 = Identity(%onnx::Conv_779) %onnx::Conv_794 = Identity(%onnx::Conv_767) %onnx::Conv_791 = Identity(%onnx::Conv_767) %onnx::Conv_788 = Identity(%onnx::Conv_767) %onnx::Conv_785 = Identity(%onnx::Conv_767) %onnx::Conv_782 = Identity(%onnx::Conv_779) %onnx::Conv_776 = Identity(%onnx::Conv_767) %onnx::Conv_773 = Identity(%onnx::Conv_767) %onnx::Conv_770 = Identity(%onnx::Conv_767) %onnx::Conv_764 = Identity(%onnx::Conv_740) %onnx::Conv_761 = Identity(%onnx::Conv_740) %onnx::Conv_758 = Identity(%onnx::Conv_740) %onnx::Conv_755 = Identity(%onnx::Conv_752) %onnx::Conv_749 = Identity(%onnx::Conv_740) %onnx::Conv_746 = Identity(%onnx::Conv_740) %onnx::Conv_743 = Identity(%onnx::Conv_740) %onnx::Conv_737 = Identity(%onnx::Conv_734) %onnx::Conv_731 = Identity(%onnx::Conv_704) %onnx::Conv_728 = Identity(%onnx::Conv_704) %onnx::Conv_725 = Identity(%onnx::Conv_704) %onnx::Conv_722 = Identity(%onnx::Conv_704) %onnx::Conv_719 = Identity(%onnx::Conv_704) %onnx::Conv_716 = Identity(%onnx::Conv_704) %onnx::Conv_713 = Identity(%onnx::Conv_704) %onnx::Conv_710 = Identity(%onnx::Conv_704) %onnx::Conv_707 = Identity(%onnx::Conv_704) %onnx::Conv_701 = Identity(%onnx::Conv_626) %onnx::Conv_698 = Identity(%onnx::Conv_626) %onnx::Conv_695 = Identity(%onnx::Conv_677) %onnx::Conv_692 = Identity(%onnx::Conv_677) %onnx::Conv_689 = Identity(%onnx::Conv_677) %onnx::Conv_686 = Identity(%onnx::Conv_677) %onnx::Conv_683 = Identity(%onnx::Conv_677) %onnx::Conv_680 = Identity(%onnx::Conv_677) %onnx::Conv_674 = Identity(%onnx::Conv_653) %onnx::Conv_671 = Identity(%onnx::Conv_653) %onnx::Conv_668 = Identity(%onnx::Conv_641) %onnx::Conv_665 = Identity(%onnx::Conv_662) %onnx::Conv_659 = Identity(%onnx::Conv_641) %onnx::Conv_656 = Identity(%onnx::Conv_653) %onnx::Conv_650 = Identity(%onnx::Conv_641) %onnx::Conv_647 = Identity(%onnx::Conv_641) %onnx::Conv_644 = Identity(%onnx::Conv_641) %onnx::Conv_638 = Identity(%onnx::Conv_623) %onnx::Conv_635 = Identity(%onnx::Conv_623) %onnx::Conv_632 = Identity(%onnx::Conv_623) %onnx::Conv_629 = Identity(%onnx::Conv_626) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_622, %onnx::Conv_623) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_625, %onnx::Conv_626) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.0/nl/Relu_output_0, %onnx::Conv_628, %onnx::Conv_629) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_631, %onnx::Conv_632) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_634, %onnx::Conv_635) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.1/shuffle/Reshape_output_0 = Reshape(%/cells.1/nl/Relu_output_0, %/cells.1/shuffle/Constant_output_0) %/cells.1/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.1/shuffle/Reshape_output_0) %/cells.1/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.1/shuffle/Reshape_1_output_0 = Reshape(%/cells.1/shuffle/Transpose_output_0, %/cells.1/shuffle/Constant_1_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.1/shuffle/Reshape_1_output_0, %onnx::Conv_637, %onnx::Conv_638) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_640, %onnx::Conv_641) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_643, %onnx::Conv_644) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.2/nl/Relu_output_0, %onnx::Conv_646, %onnx::Conv_647) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_649, %onnx::Conv_650) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_652, %onnx::Conv_653) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.3/nl/Relu_output_0, %onnx::Conv_655, %onnx::Conv_656) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_658, %onnx::Conv_659) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_661, %onnx::Conv_662) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.4/nl/Relu_output_0, %onnx::Conv_664, %onnx::Conv_665) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_667, %onnx::Conv_668) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/Add_output_0, %onnx::Conv_670, %onnx::Conv_671) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.5/nl/Relu_output_0, %onnx::Conv_673, %onnx::Conv_674) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_676, %onnx::Conv_677) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_679, %onnx::Conv_680) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.6/nl/Relu_output_0, %onnx::Conv_682, %onnx::Conv_683) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_685, %onnx::Conv_686) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_688, %onnx::Conv_689) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.7/nl/Relu_output_0, %onnx::Conv_691, %onnx::Conv_692) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_694, %onnx::Conv_695) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_697, %onnx::Conv_698) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.9/nl/Relu_output_0, %onnx::Conv_700, %onnx::Conv_701) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_703, %onnx::Conv_704) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_706, %onnx::Conv_707) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.10/shuffle/Reshape_output_0 = Reshape(%/cells.10/nl/Relu_output_0, %/cells.10/shuffle/Constant_output_0) %/cells.10/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.10/shuffle/Reshape_output_0) %/cells.10/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.10/shuffle/Reshape_1_output_0 = Reshape(%/cells.10/shuffle/Transpose_output_0, %/cells.10/shuffle/Constant_1_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.10/shuffle/Reshape_1_output_0, %onnx::Conv_709, %onnx::Conv_710) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_712, %onnx::Conv_713) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_715, %onnx::Conv_716) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.11/nl/Relu_output_0, %onnx::Conv_718, %onnx::Conv_719) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_721, %onnx::Conv_722) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_724, %onnx::Conv_725) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.12/nl/Relu_output_0, %onnx::Conv_727, %onnx::Conv_728) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_730, %onnx::Conv_731) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_733, %onnx::Conv_734) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.13/nl/Relu_output_0, %onnx::Conv_736, %onnx::Conv_737) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_739, %onnx::Conv_740) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_742, %onnx::Conv_743) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.14/nl/Relu_output_0, %onnx::Conv_745, %onnx::Conv_746) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_748, %onnx::Conv_749) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_751, %onnx::Conv_752) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.15/nl/Relu_output_0, %onnx::Conv_754, %onnx::Conv_755) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_757, %onnx::Conv_758) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_760, %onnx::Conv_761) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.17/shuffle/Reshape_output_0 = Reshape(%/cells.17/nl/Relu_output_0, %/cells.17/shuffle/Constant_output_0) %/cells.17/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.17/shuffle/Reshape_output_0) %/cells.17/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.17/shuffle/Reshape_1_output_0 = Reshape(%/cells.17/shuffle/Transpose_output_0, %/cells.17/shuffle/Constant_1_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.17/shuffle/Reshape_1_output_0, %onnx::Conv_763, %onnx::Conv_764) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_766, %onnx::Conv_767) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_769, %onnx::Conv_770) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.18/nl/Relu_output_0, %onnx::Conv_772, %onnx::Conv_773) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_775, %onnx::Conv_776) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_778, %onnx::Conv_779) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 552, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.19/nl/Relu_output_0, %onnx::Conv_781, %onnx::Conv_782) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_784, %onnx::Conv_785) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_787, %onnx::Conv_788) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.20/nl/Relu_output_0, %onnx::Conv_790, %onnx::Conv_791) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_793, %onnx::Conv_794) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_796, %onnx::Conv_797) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 552, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.21/nl/Relu_output_0, %onnx::Conv_799, %onnx::Conv_800) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_802, %onnx::Conv_803) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_805, %onnx::Conv_806) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %620 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %620 }	val_accuracy	63,983,488	1,681,052	{'zcp_synflow': 78.4055229454222, 'zcp_zen': 66.96976470947266, 'zcp_epe_nas': 12.399530951803351, 'zcp_fisher': 0.14892186224460602, 'zcp_flops': 63983488.0, 'zcp_grad_norm': 22.675832748413086, 'zcp_grasp': -0.4149608612060547, 'zcp_jacov': -16.07879879224658, 'zcp_l2_norm': 591.436279296875, 'zcp_nwot': 213.24578005421318, 'zcp_params': 1681052.0, 'zcp_plain': 0.004269117955118418, 'zcp_snip': 40.18830490112305, 'lat_1080ti_1': 0.5425106121551576, 'lat_1080ti_32': 0.5641282489078705, 'lat_1080ti_64': 0.48063495225197206, 'lat_2080ti_1': 0.6429827022139064, 'lat_2080ti_32': 0.5538373305544197, 'lat_2080ti_64': 0.5111604739332586, 'lat_essential_ph_1': 0.2641509433962264, 'lat_eyeriss': 0.38138374928942087, 'lat_fpga': 0.4261767878170177, 'lat_gold_6226': 0.23985798199173278, 'lat_gold_6240': 0.36763476741001083, 'lat_pixel2': 0.2391304347826087, 'lat_pixel3': 0.3655350145328767, 'lat_raspi4': 0.4351767242952357, 'lat_samsung_a50': 0.15789473684210525, 'lat_samsung_s7': 0.13385826771653545, 'lat_silver_4114': 0.36841742246032433, 'lat_silver_4210r': 0.37541725798633657, 'lat_titan_rtx_1': 0.5598743300729182, 'lat_titan_rtx_32': 0.5231880303223926, 'lat_titan_rtx_64': 0.5203332997138033, 'lat_titanx_1': 0.2947744190200042, 'lat_titanx_32': 0.5204159847293879, 'lat_titanx_64': 0.46782239863699643, 'lat_titanxp_1': 0.534296094801705, 'lat_titanxp_32': 0.5187910842182968, 'lat_titanxp_64': 0.49476667684052783}
FBNet_3060	FBNet	3060	3060	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_614[FLOAT, 16x3x3x3] %onnx::Conv_615[FLOAT, 16] %onnx::Conv_617[FLOAT, 96x16x1x1] %onnx::Conv_618[FLOAT, 96] %onnx::Conv_620[FLOAT, 96x1x5x5] %onnx::Conv_623[FLOAT, 16x96x1x1] %onnx::Conv_626[FLOAT, 16x16x1x1] %onnx::Conv_629[FLOAT, 16x1x5x5] %onnx::Conv_632[FLOAT, 24x16x1x1] %onnx::Conv_633[FLOAT, 24] %onnx::Conv_635[FLOAT, 72x24x1x1] %onnx::Conv_636[FLOAT, 72] %onnx::Conv_638[FLOAT, 72x1x5x5] %onnx::Conv_641[FLOAT, 24x72x1x1] %onnx::Conv_644[FLOAT, 144x24x1x1] %onnx::Conv_645[FLOAT, 144] %onnx::Conv_647[FLOAT, 144x1x3x3] %onnx::Conv_650[FLOAT, 24x144x1x1] %onnx::Conv_653[FLOAT, 24x24x1x1] %onnx::Conv_656[FLOAT, 24x1x5x5] %onnx::Conv_659[FLOAT, 24x24x1x1] %onnx::Conv_662[FLOAT, 32x24x1x1] %onnx::Conv_663[FLOAT, 32] %onnx::Conv_665[FLOAT, 32x32x1x1] %onnx::Conv_668[FLOAT, 32x1x3x3] %onnx::Conv_671[FLOAT, 32x32x1x1] %onnx::Conv_674[FLOAT, 32x32x1x1] %onnx::Conv_677[FLOAT, 32x1x3x3] %onnx::Conv_680[FLOAT, 32x32x1x1] %onnx::Conv_683[FLOAT, 32x16x1x1] %onnx::Conv_686[FLOAT, 32x1x5x5] %onnx::Conv_689[FLOAT, 32x16x1x1] %onnx::Conv_692[FLOAT, 32x16x1x1] %onnx::Conv_695[FLOAT, 32x1x3x3] %onnx::Conv_698[FLOAT, 64x16x1x1] %onnx::Conv_699[FLOAT, 64] %onnx::Conv_701[FLOAT, 384x64x1x1] %onnx::Conv_702[FLOAT, 384] %onnx::Conv_704[FLOAT, 384x1x5x5] %onnx::Conv_707[FLOAT, 64x384x1x1] %onnx::Conv_710[FLOAT, 64x64x1x1] %onnx::Conv_713[FLOAT, 64x1x5x5] %onnx::Conv_716[FLOAT, 64x64x1x1] %onnx::Conv_719[FLOAT, 384x64x1x1] %onnx::Conv_722[FLOAT, 384x1x5x5] %onnx::Conv_725[FLOAT, 112x384x1x1] %onnx::Conv_726[FLOAT, 112] %onnx::Conv_728[FLOAT, 336x112x1x1] %onnx::Conv_729[FLOAT, 336] %onnx::Conv_731[FLOAT, 336x1x5x5] %onnx::Conv_734[FLOAT, 112x336x1x1] %onnx::Conv_737[FLOAT, 672x112x1x1] %onnx::Conv_738[FLOAT, 672] %onnx::Conv_740[FLOAT, 672x1x5x5] %onnx::Conv_743[FLOAT, 112x672x1x1] %onnx::Conv_746[FLOAT, 672x112x1x1] %onnx::Conv_749[FLOAT, 672x1x5x5] %onnx::Conv_752[FLOAT, 112x672x1x1] %onnx::Conv_755[FLOAT, 112x56x1x1] %onnx::Conv_758[FLOAT, 112x1x3x3] %onnx::Conv_761[FLOAT, 184x56x1x1] %onnx::Conv_762[FLOAT, 184] %onnx::Conv_764[FLOAT, 1104x184x1x1] %onnx::Conv_765[FLOAT, 1104] %onnx::Conv_767[FLOAT, 1104x1x3x3] %onnx::Conv_770[FLOAT, 184x1104x1x1] %onnx::Conv_773[FLOAT, 1104x184x1x1] %onnx::Conv_776[FLOAT, 1104x1x5x5] %onnx::Conv_779[FLOAT, 184x1104x1x1] %onnx::Conv_782[FLOAT, 184x184x1x1] %onnx::Conv_785[FLOAT, 184x1x3x3] %onnx::Conv_788[FLOAT, 184x184x1x1] %onnx::Conv_791[FLOAT, 352x184x1x1] %onnx::Conv_792[FLOAT, 352] %onnx::Conv_794[FLOAT, 1504x352x1x1] %onnx::Conv_795[FLOAT, 1504] ) { %onnx::Conv_789 = Identity(%onnx::Conv_762) %onnx::Conv_786 = Identity(%onnx::Conv_762) %onnx::Conv_783 = Identity(%onnx::Conv_762) %onnx::Conv_780 = Identity(%onnx::Conv_762) %onnx::Conv_777 = Identity(%onnx::Conv_765) %onnx::Conv_774 = Identity(%onnx::Conv_765) %onnx::Conv_771 = Identity(%onnx::Conv_762) %onnx::Conv_768 = Identity(%onnx::Conv_765) %onnx::Conv_759 = Identity(%onnx::Conv_726) %onnx::Conv_756 = Identity(%onnx::Conv_726) %onnx::Conv_753 = Identity(%onnx::Conv_726) %onnx::Conv_750 = Identity(%onnx::Conv_738) %onnx::Conv_747 = Identity(%onnx::Conv_738) %onnx::Conv_744 = Identity(%onnx::Conv_726) %onnx::Conv_741 = Identity(%onnx::Conv_738) %onnx::Conv_735 = Identity(%onnx::Conv_726) %onnx::Conv_732 = Identity(%onnx::Conv_729) %onnx::Conv_723 = Identity(%onnx::Conv_702) %onnx::Conv_720 = Identity(%onnx::Conv_702) %onnx::Conv_717 = Identity(%onnx::Conv_699) %onnx::Conv_714 = Identity(%onnx::Conv_699) %onnx::Conv_711 = Identity(%onnx::Conv_699) %onnx::Conv_708 = Identity(%onnx::Conv_699) %onnx::Conv_705 = Identity(%onnx::Conv_702) %onnx::Conv_696 = Identity(%onnx::Conv_663) %onnx::Conv_693 = Identity(%onnx::Conv_663) %onnx::Conv_690 = Identity(%onnx::Conv_663) %onnx::Conv_687 = Identity(%onnx::Conv_663) %onnx::Conv_684 = Identity(%onnx::Conv_663) %onnx::Conv_681 = Identity(%onnx::Conv_663) %onnx::Conv_678 = Identity(%onnx::Conv_663) %onnx::Conv_675 = Identity(%onnx::Conv_663) %onnx::Conv_672 = Identity(%onnx::Conv_663) %onnx::Conv_669 = Identity(%onnx::Conv_663) %onnx::Conv_666 = Identity(%onnx::Conv_663) %onnx::Conv_660 = Identity(%onnx::Conv_633) %onnx::Conv_657 = Identity(%onnx::Conv_633) %onnx::Conv_654 = Identity(%onnx::Conv_633) %onnx::Conv_651 = Identity(%onnx::Conv_633) %onnx::Conv_648 = Identity(%onnx::Conv_645) %onnx::Conv_642 = Identity(%onnx::Conv_633) %onnx::Conv_639 = Identity(%onnx::Conv_636) %onnx::Conv_630 = Identity(%onnx::Conv_615) %onnx::Conv_627 = Identity(%onnx::Conv_615) %onnx::Conv_624 = Identity(%onnx::Conv_615) %onnx::Conv_621 = Identity(%onnx::Conv_618) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_614, %onnx::Conv_615) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_617, %onnx::Conv_618) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.0/nl/Relu_output_0, %onnx::Conv_620, %onnx::Conv_621) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_623, %onnx::Conv_624) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_626, %onnx::Conv_627) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.1/nl/Relu_output_0, %onnx::Conv_629, %onnx::Conv_630) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_632, %onnx::Conv_633) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_635, %onnx::Conv_636) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.2/nl/Relu_output_0, %onnx::Conv_638, %onnx::Conv_639) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_641, %onnx::Conv_642) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_644, %onnx::Conv_645) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.3/nl/Relu_output_0, %onnx::Conv_647, %onnx::Conv_648) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_650, %onnx::Conv_651) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_653, %onnx::Conv_654) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.4/nl/Relu_output_0, %onnx::Conv_656, %onnx::Conv_657) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_659, %onnx::Conv_660) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [2, 2]](%/cells.4/Add_output_0, %onnx::Conv_662, %onnx::Conv_663) %/cells.5/relu/Relu_output_0 = Relu(%/cells.5/conv/Conv_output_0) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/relu/Relu_output_0, %onnx::Conv_665, %onnx::Conv_666) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.6/nl/Relu_output_0, %onnx::Conv_668, %onnx::Conv_669) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_671, %onnx::Conv_672) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/relu/Relu_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_674, %onnx::Conv_675) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.7/nl/Relu_output_0, %onnx::Conv_677, %onnx::Conv_678) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_680, %onnx::Conv_681) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_683, %onnx::Conv_684) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.8/shuffle/Reshape_output_0 = Reshape(%/cells.8/nl/Relu_output_0, %/cells.8/shuffle/Constant_output_0) %/cells.8/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.8/shuffle/Reshape_output_0) %/cells.8/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.8/shuffle/Reshape_1_output_0 = Reshape(%/cells.8/shuffle/Transpose_output_0, %/cells.8/shuffle/Constant_1_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.8/shuffle/Reshape_1_output_0, %onnx::Conv_686, %onnx::Conv_687) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_689, %onnx::Conv_690) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_692, %onnx::Conv_693) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.9/shuffle/Reshape_output_0 = Reshape(%/cells.9/nl/Relu_output_0, %/cells.9/shuffle/Constant_output_0) %/cells.9/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.9/shuffle/Reshape_output_0) %/cells.9/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.9/shuffle/Reshape_1_output_0 = Reshape(%/cells.9/shuffle/Transpose_output_0, %/cells.9/shuffle/Constant_1_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.9/shuffle/Reshape_1_output_0, %onnx::Conv_695, %onnx::Conv_696) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_698, %onnx::Conv_699) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_701, %onnx::Conv_702) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.10/nl/Relu_output_0, %onnx::Conv_704, %onnx::Conv_705) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_707, %onnx::Conv_708) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_710, %onnx::Conv_711) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.11/nl/Relu_output_0, %onnx::Conv_713, %onnx::Conv_714) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_716, %onnx::Conv_717) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_719, %onnx::Conv_720) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.13/nl/Relu_output_0, %onnx::Conv_722, %onnx::Conv_723) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_725, %onnx::Conv_726) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_728, %onnx::Conv_729) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.14/nl/Relu_output_0, %onnx::Conv_731, %onnx::Conv_732) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_734, %onnx::Conv_735) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_737, %onnx::Conv_738) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.15/nl/Relu_output_0, %onnx::Conv_740, %onnx::Conv_741) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_743, %onnx::Conv_744) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_746, %onnx::Conv_747) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.16/nl/Relu_output_0, %onnx::Conv_749, %onnx::Conv_750) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_752, %onnx::Conv_753) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.15/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_755, %onnx::Conv_756) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.17/shuffle/Reshape_output_0 = Reshape(%/cells.17/nl/Relu_output_0, %/cells.17/shuffle/Constant_output_0) %/cells.17/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.17/shuffle/Reshape_output_0) %/cells.17/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.17/shuffle/Reshape_1_output_0 = Reshape(%/cells.17/shuffle/Transpose_output_0, %/cells.17/shuffle/Constant_1_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.17/shuffle/Reshape_1_output_0, %onnx::Conv_758, %onnx::Conv_759) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_761, %onnx::Conv_762) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_764, %onnx::Conv_765) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.18/nl/Relu_output_0, %onnx::Conv_767, %onnx::Conv_768) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_770, %onnx::Conv_771) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_773, %onnx::Conv_774) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.19/nl/Relu_output_0, %onnx::Conv_776, %onnx::Conv_777) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_779, %onnx::Conv_780) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_782, %onnx::Conv_783) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.20/nl/Relu_output_0, %onnx::Conv_785, %onnx::Conv_786) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_788, %onnx::Conv_789) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_791, %onnx::Conv_792) %/cells.21/relu/Relu_output_0 = Relu(%/cells.21/conv/Conv_output_0) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/relu/Relu_output_0, %onnx::Conv_794, %onnx::Conv_795) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %612 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %612 }	val_accuracy	89,349,760	2,305,964	{'zcp_synflow': 80.09361736096291, 'zcp_zen': 69.13285827636719, 'zcp_epe_nas': 0.00015999920000638146, 'zcp_fisher': 0.1393628865480423, 'zcp_flops': 89349760.0, 'zcp_grad_norm': 24.19607925415039, 'zcp_grasp': -0.6269760131835938, 'zcp_jacov': -16.05194956558284, 'zcp_l2_norm': 662.1394653320312, 'zcp_nwot': 214.88378987383192, 'zcp_params': 2305964.0, 'zcp_plain': -0.0013865637592971325, 'zcp_snip': 38.00193786621094, 'lat_1080ti_1': 0.5204719997787972, 'lat_1080ti_32': 0.5659851206828316, 'lat_1080ti_64': 0.5990878778939719, 'lat_2080ti_1': 0.5958451295592189, 'lat_2080ti_32': 0.5836745915726919, 'lat_2080ti_64': 0.5704755487446825, 'lat_essential_ph_1': 0.32075471698113206, 'lat_eyeriss': 0.6683384372765114, 'lat_fpga': 0.7503636489575396, 'lat_gold_6226': 0.5450132258464436, 'lat_gold_6240': 0.6889197876907966, 'lat_pixel2': 0.43478260869565216, 'lat_pixel3': 0.6975302617914462, 'lat_raspi4': 0.6969157412732605, 'lat_samsung_a50': 0.2736842105263158, 'lat_samsung_s7': 0.2440944881889764, 'lat_silver_4114': 0.6517922776309893, 'lat_silver_4210r': 0.6255324665522993, 'lat_titan_rtx_1': 0.5341944988561914, 'lat_titan_rtx_32': 0.5561598051969695, 'lat_titan_rtx_64': 0.5718109531650867, 'lat_titanx_1': 0.28387214790005866, 'lat_titanx_32': 0.5696258353219211, 'lat_titanx_64': 0.6652357835215776, 'lat_titanxp_1': 0.518992713940281, 'lat_titanxp_32': 0.5788742219975535, 'lat_titanxp_64': 0.5935235743727539}
FBNet_4771	FBNet	4771	4771	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_678[FLOAT, 16x3x3x3] %onnx::Conv_679[FLOAT, 16] %onnx::Conv_681[FLOAT, 96x16x1x1] %onnx::Conv_682[FLOAT, 96] %onnx::Conv_684[FLOAT, 96x1x5x5] %onnx::Conv_687[FLOAT, 16x96x1x1] %onnx::Conv_690[FLOAT, 16x8x1x1] %onnx::Conv_693[FLOAT, 16x1x3x3] %onnx::Conv_696[FLOAT, 24x8x1x1] %onnx::Conv_697[FLOAT, 24] %onnx::Conv_699[FLOAT, 72x24x1x1] %onnx::Conv_700[FLOAT, 72] %onnx::Conv_702[FLOAT, 72x1x5x5] %onnx::Conv_705[FLOAT, 24x72x1x1] %onnx::Conv_708[FLOAT, 24x24x1x1] %onnx::Conv_711[FLOAT, 24x1x3x3] %onnx::Conv_714[FLOAT, 24x24x1x1] %onnx::Conv_717[FLOAT, 24x24x1x1] %onnx::Conv_720[FLOAT, 24x1x3x3] %onnx::Conv_723[FLOAT, 32x24x1x1] %onnx::Conv_724[FLOAT, 32] %onnx::Conv_726[FLOAT, 32x16x1x1] %onnx::Conv_729[FLOAT, 32x1x5x5] %onnx::Conv_732[FLOAT, 32x16x1x1] %onnx::Conv_735[FLOAT, 96x32x1x1] %onnx::Conv_738[FLOAT, 96x1x5x5] %onnx::Conv_741[FLOAT, 32x96x1x1] %onnx::Conv_744[FLOAT, 192x32x1x1] %onnx::Conv_745[FLOAT, 192] %onnx::Conv_747[FLOAT, 192x1x5x5] %onnx::Conv_750[FLOAT, 32x192x1x1] %onnx::Conv_753[FLOAT, 32x32x1x1] %onnx::Conv_756[FLOAT, 32x1x5x5] %onnx::Conv_759[FLOAT, 64x32x1x1] %onnx::Conv_760[FLOAT, 64] %onnx::Conv_762[FLOAT, 64x64x1x1] %onnx::Conv_765[FLOAT, 64x1x3x3] %onnx::Conv_768[FLOAT, 64x64x1x1] %onnx::Conv_771[FLOAT, 384x64x1x1] %onnx::Conv_772[FLOAT, 384] %onnx::Conv_774[FLOAT, 384x1x3x3] %onnx::Conv_777[FLOAT, 64x384x1x1] %onnx::Conv_780[FLOAT, 64x32x1x1] %onnx::Conv_783[FLOAT, 64x1x3x3] %onnx::Conv_786[FLOAT, 64x32x1x1] %onnx::Conv_789[FLOAT, 64x32x1x1] %onnx::Conv_792[FLOAT, 64x1x5x5] %onnx::Conv_795[FLOAT, 112x32x1x1] %onnx::Conv_796[FLOAT, 112] %onnx::Conv_798[FLOAT, 112x56x1x1] %onnx::Conv_801[FLOAT, 112x1x3x3] %onnx::Conv_804[FLOAT, 112x56x1x1] %onnx::Conv_807[FLOAT, 672x112x1x1] %onnx::Conv_808[FLOAT, 672] %onnx::Conv_810[FLOAT, 672x1x3x3] %onnx::Conv_813[FLOAT, 112x672x1x1] %onnx::Conv_816[FLOAT, 672x112x1x1] %onnx::Conv_819[FLOAT, 672x1x5x5] %onnx::Conv_822[FLOAT, 184x672x1x1] %onnx::Conv_823[FLOAT, 184] %onnx::Conv_825[FLOAT, 184x92x1x1] %onnx::Conv_828[FLOAT, 184x1x5x5] %onnx::Conv_831[FLOAT, 184x92x1x1] %onnx::Conv_834[FLOAT, 1104x184x1x1] %onnx::Conv_835[FLOAT, 1104] %onnx::Conv_837[FLOAT, 1104x1x5x5] %onnx::Conv_840[FLOAT, 184x1104x1x1] %onnx::Conv_843[FLOAT, 184x184x1x1] %onnx::Conv_846[FLOAT, 184x1x3x3] %onnx::Conv_849[FLOAT, 184x184x1x1] %onnx::Conv_852[FLOAT, 184x184x1x1] %onnx::Conv_855[FLOAT, 184x1x3x3] %onnx::Conv_858[FLOAT, 352x184x1x1] %onnx::Conv_859[FLOAT, 352] %onnx::Conv_861[FLOAT, 1504x352x1x1] %onnx::Conv_862[FLOAT, 1504] ) { %onnx::Conv_856 = Identity(%onnx::Conv_823) %onnx::Conv_853 = Identity(%onnx::Conv_823) %onnx::Conv_850 = Identity(%onnx::Conv_823) %onnx::Conv_847 = Identity(%onnx::Conv_823) %onnx::Conv_844 = Identity(%onnx::Conv_823) %onnx::Conv_841 = Identity(%onnx::Conv_823) %onnx::Conv_838 = Identity(%onnx::Conv_835) %onnx::Conv_832 = Identity(%onnx::Conv_823) %onnx::Conv_829 = Identity(%onnx::Conv_823) %onnx::Conv_826 = Identity(%onnx::Conv_823) %onnx::Conv_820 = Identity(%onnx::Conv_808) %onnx::Conv_817 = Identity(%onnx::Conv_808) %onnx::Conv_814 = Identity(%onnx::Conv_796) %onnx::Conv_811 = Identity(%onnx::Conv_808) %onnx::Conv_805 = Identity(%onnx::Conv_796) %onnx::Conv_802 = Identity(%onnx::Conv_796) %onnx::Conv_799 = Identity(%onnx::Conv_796) %onnx::Conv_793 = Identity(%onnx::Conv_760) %onnx::Conv_790 = Identity(%onnx::Conv_760) %onnx::Conv_787 = Identity(%onnx::Conv_760) %onnx::Conv_784 = Identity(%onnx::Conv_760) %onnx::Conv_781 = Identity(%onnx::Conv_760) %onnx::Conv_778 = Identity(%onnx::Conv_760) %onnx::Conv_775 = Identity(%onnx::Conv_772) %onnx::Conv_769 = Identity(%onnx::Conv_760) %onnx::Conv_766 = Identity(%onnx::Conv_760) %onnx::Conv_763 = Identity(%onnx::Conv_760) %onnx::Conv_757 = Identity(%onnx::Conv_724) %onnx::Conv_754 = Identity(%onnx::Conv_724) %onnx::Conv_751 = Identity(%onnx::Conv_724) %onnx::Conv_748 = Identity(%onnx::Conv_745) %onnx::Conv_742 = Identity(%onnx::Conv_724) %onnx::Conv_739 = Identity(%onnx::Conv_682) %onnx::Conv_736 = Identity(%onnx::Conv_682) %onnx::Conv_733 = Identity(%onnx::Conv_724) %onnx::Conv_730 = Identity(%onnx::Conv_724) %onnx::Conv_727 = Identity(%onnx::Conv_724) %onnx::Conv_721 = Identity(%onnx::Conv_697) %onnx::Conv_718 = Identity(%onnx::Conv_697) %onnx::Conv_715 = Identity(%onnx::Conv_697) %onnx::Conv_712 = Identity(%onnx::Conv_697) %onnx::Conv_709 = Identity(%onnx::Conv_697) %onnx::Conv_706 = Identity(%onnx::Conv_697) %onnx::Conv_703 = Identity(%onnx::Conv_700) %onnx::Conv_694 = Identity(%onnx::Conv_679) %onnx::Conv_691 = Identity(%onnx::Conv_679) %onnx::Conv_688 = Identity(%onnx::Conv_679) %onnx::Conv_685 = Identity(%onnx::Conv_682) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_678, %onnx::Conv_679) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_681, %onnx::Conv_682) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.0/nl/Relu_output_0, %onnx::Conv_684, %onnx::Conv_685) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_687, %onnx::Conv_688) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_690, %onnx::Conv_691) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.1/shuffle/Reshape_output_0 = Reshape(%/cells.1/nl/Relu_output_0, %/cells.1/shuffle/Constant_output_0) %/cells.1/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.1/shuffle/Reshape_output_0) %/cells.1/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.1/shuffle/Reshape_1_output_0 = Reshape(%/cells.1/shuffle/Transpose_output_0, %/cells.1/shuffle/Constant_1_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.1/shuffle/Reshape_1_output_0, %onnx::Conv_693, %onnx::Conv_694) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_696, %onnx::Conv_697) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_699, %onnx::Conv_700) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.2/nl/Relu_output_0, %onnx::Conv_702, %onnx::Conv_703) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_705, %onnx::Conv_706) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_708, %onnx::Conv_709) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.3/nl/Relu_output_0, %onnx::Conv_711, %onnx::Conv_712) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_714, %onnx::Conv_715) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_717, %onnx::Conv_718) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.5/nl/Relu_output_0, %onnx::Conv_720, %onnx::Conv_721) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_723, %onnx::Conv_724) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_726, %onnx::Conv_727) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.6/shuffle/Reshape_output_0 = Reshape(%/cells.6/nl/Relu_output_0, %/cells.6/shuffle/Constant_output_0) %/cells.6/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.6/shuffle/Reshape_output_0) %/cells.6/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.6/shuffle/Reshape_1_output_0 = Reshape(%/cells.6/shuffle/Transpose_output_0, %/cells.6/shuffle/Constant_1_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.6/shuffle/Reshape_1_output_0, %onnx::Conv_729, %onnx::Conv_730) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_732, %onnx::Conv_733) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_735, %onnx::Conv_736) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.7/nl/Relu_output_0, %onnx::Conv_738, %onnx::Conv_739) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_741, %onnx::Conv_742) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_744, %onnx::Conv_745) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.8/nl/Relu_output_0, %onnx::Conv_747, %onnx::Conv_748) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_750, %onnx::Conv_751) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_753, %onnx::Conv_754) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.9/nl/Relu_output_0, %onnx::Conv_756, %onnx::Conv_757) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_759, %onnx::Conv_760) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_762, %onnx::Conv_763) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.10/nl/Relu_output_0, %onnx::Conv_765, %onnx::Conv_766) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_768, %onnx::Conv_769) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_771, %onnx::Conv_772) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.11/nl/Relu_output_0, %onnx::Conv_774, %onnx::Conv_775) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_777, %onnx::Conv_778) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_780, %onnx::Conv_781) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.12/shuffle/Reshape_output_0 = Reshape(%/cells.12/nl/Relu_output_0, %/cells.12/shuffle/Constant_output_0) %/cells.12/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.12/shuffle/Reshape_output_0) %/cells.12/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.12/shuffle/Reshape_1_output_0 = Reshape(%/cells.12/shuffle/Transpose_output_0, %/cells.12/shuffle/Constant_1_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.12/shuffle/Reshape_1_output_0, %onnx::Conv_783, %onnx::Conv_784) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_786, %onnx::Conv_787) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_789, %onnx::Conv_790) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.13/shuffle/Reshape_output_0 = Reshape(%/cells.13/nl/Relu_output_0, %/cells.13/shuffle/Constant_output_0) %/cells.13/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.13/shuffle/Reshape_output_0) %/cells.13/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.13/shuffle/Reshape_1_output_0 = Reshape(%/cells.13/shuffle/Transpose_output_0, %/cells.13/shuffle/Constant_1_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.13/shuffle/Reshape_1_output_0, %onnx::Conv_792, %onnx::Conv_793) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_795, %onnx::Conv_796) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_798, %onnx::Conv_799) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.15/shuffle/Reshape_output_0 = Reshape(%/cells.15/nl/Relu_output_0, %/cells.15/shuffle/Constant_output_0) %/cells.15/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.15/shuffle/Reshape_output_0) %/cells.15/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.15/shuffle/Reshape_1_output_0 = Reshape(%/cells.15/shuffle/Transpose_output_0, %/cells.15/shuffle/Constant_1_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.15/shuffle/Reshape_1_output_0, %onnx::Conv_801, %onnx::Conv_802) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_804, %onnx::Conv_805) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_807, %onnx::Conv_808) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.16/nl/Relu_output_0, %onnx::Conv_810, %onnx::Conv_811) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_813, %onnx::Conv_814) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.15/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_816, %onnx::Conv_817) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.17/nl/Relu_output_0, %onnx::Conv_819, %onnx::Conv_820) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_822, %onnx::Conv_823) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_825, %onnx::Conv_826) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.18/shuffle/Reshape_output_0 = Reshape(%/cells.18/nl/Relu_output_0, %/cells.18/shuffle/Constant_output_0) %/cells.18/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.18/shuffle/Reshape_output_0) %/cells.18/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.18/shuffle/Reshape_1_output_0 = Reshape(%/cells.18/shuffle/Transpose_output_0, %/cells.18/shuffle/Constant_1_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.18/shuffle/Reshape_1_output_0, %onnx::Conv_828, %onnx::Conv_829) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_831, %onnx::Conv_832) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_834, %onnx::Conv_835) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.19/nl/Relu_output_0, %onnx::Conv_837, %onnx::Conv_838) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_840, %onnx::Conv_841) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_843, %onnx::Conv_844) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.20/nl/Relu_output_0, %onnx::Conv_846, %onnx::Conv_847) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_849, %onnx::Conv_850) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_852, %onnx::Conv_853) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.21/nl/Relu_output_0, %onnx::Conv_855, %onnx::Conv_856) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_858, %onnx::Conv_859) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_861, %onnx::Conv_862) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %676 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %676 }	val_accuracy	65,905,792	1,850,804	{'zcp_synflow': 74.45914513395928, 'zcp_zen': 65.81018829345703, 'zcp_epe_nas': 0.00015999920000638146, 'zcp_fisher': 0.1806827336549759, 'zcp_flops': 65905792.0, 'zcp_grad_norm': 26.138343811035156, 'zcp_grasp': -0.3742866516113281, 'zcp_jacov': -16.069036510659764, 'zcp_l2_norm': 595.3342895507812, 'zcp_nwot': 210.59283885337132, 'zcp_params': 1850804.0, 'zcp_plain': -0.005425572860985994, 'zcp_snip': 44.5767936706543, 'lat_1080ti_1': 0.6014201526658447, 'lat_1080ti_32': 0.5453269999146533, 'lat_1080ti_64': 0.4547705564046725, 'lat_2080ti_1': 0.6513435290634121, 'lat_2080ti_32': 0.5805432788169245, 'lat_2080ti_64': 0.4534496435581907, 'lat_essential_ph_1': 0.3018867924528302, 'lat_eyeriss': 0.4329305191352025, 'lat_fpga': 0.404466697671657, 'lat_gold_6226': 0.3340004640499575, 'lat_gold_6240': 0.4948968582963916, 'lat_pixel2': 0.43478260869565216, 'lat_pixel3': 0.4352346146182577, 'lat_raspi4': 0.43137202568855676, 'lat_samsung_a50': 0.17894736842105263, 'lat_samsung_s7': 0.3228346456692913, 'lat_silver_4114': 0.49559436419752245, 'lat_silver_4210r': 0.5396103748660447, 'lat_titan_rtx_1': 0.6148024991938857, 'lat_titan_rtx_32': 0.5775268822041426, 'lat_titan_rtx_64': 0.4913464369880751, 'lat_titanx_1': 0.3377277900145658, 'lat_titanx_32': 0.5138761004809946, 'lat_titanx_64': 0.4112458308271014, 'lat_titanxp_1': 0.5897930729637024, 'lat_titanxp_32': 0.5437168642685023, 'lat_titanxp_64': 0.45080964110827554}
FBNet_2978	FBNet	2978	2978	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_759[FLOAT, 16x3x3x3] %onnx::Conv_760[FLOAT, 16] %onnx::Conv_762[FLOAT, 16x16x1x1] %onnx::Conv_765[FLOAT, 16x1x5x5] %onnx::Conv_768[FLOAT, 16x16x1x1] %onnx::Conv_771[FLOAT, 48x16x1x1] %onnx::Conv_772[FLOAT, 48] %onnx::Conv_774[FLOAT, 48x1x3x3] %onnx::Conv_777[FLOAT, 24x48x1x1] %onnx::Conv_778[FLOAT, 24] %onnx::Conv_780[FLOAT, 24x12x1x1] %onnx::Conv_783[FLOAT, 24x1x3x3] %onnx::Conv_786[FLOAT, 24x12x1x1] %onnx::Conv_789[FLOAT, 24x12x1x1] %onnx::Conv_792[FLOAT, 24x1x5x5] %onnx::Conv_795[FLOAT, 24x12x1x1] %onnx::Conv_798[FLOAT, 24x12x1x1] %onnx::Conv_801[FLOAT, 24x1x3x3] %onnx::Conv_804[FLOAT, 24x12x1x1] %onnx::Conv_807[FLOAT, 72x24x1x1] %onnx::Conv_808[FLOAT, 72] %onnx::Conv_810[FLOAT, 72x1x3x3] %onnx::Conv_813[FLOAT, 32x72x1x1] %onnx::Conv_814[FLOAT, 32] %onnx::Conv_816[FLOAT, 32x16x1x1] %onnx::Conv_819[FLOAT, 32x1x5x5] %onnx::Conv_822[FLOAT, 32x16x1x1] %onnx::Conv_825[FLOAT, 96x32x1x1] %onnx::Conv_826[FLOAT, 96] %onnx::Conv_828[FLOAT, 96x1x3x3] %onnx::Conv_831[FLOAT, 32x96x1x1] %onnx::Conv_834[FLOAT, 192x32x1x1] %onnx::Conv_835[FLOAT, 192] %onnx::Conv_837[FLOAT, 192x1x3x3] %onnx::Conv_840[FLOAT, 32x192x1x1] %onnx::Conv_843[FLOAT, 96x32x1x1] %onnx::Conv_846[FLOAT, 96x1x5x5] %onnx::Conv_849[FLOAT, 64x96x1x1] %onnx::Conv_850[FLOAT, 64] %onnx::Conv_852[FLOAT, 64x32x1x1] %onnx::Conv_855[FLOAT, 64x1x3x3] %onnx::Conv_858[FLOAT, 64x32x1x1] %onnx::Conv_861[FLOAT, 64x32x1x1] %onnx::Conv_864[FLOAT, 64x1x5x5] %onnx::Conv_867[FLOAT, 64x32x1x1] %onnx::Conv_870[FLOAT, 384x64x1x1] %onnx::Conv_871[FLOAT, 384] %onnx::Conv_873[FLOAT, 384x1x5x5] %onnx::Conv_876[FLOAT, 64x384x1x1] %onnx::Conv_879[FLOAT, 64x32x1x1] %onnx::Conv_882[FLOAT, 64x1x3x3] %onnx::Conv_885[FLOAT, 112x32x1x1] %onnx::Conv_886[FLOAT, 112] %onnx::Conv_888[FLOAT, 112x56x1x1] %onnx::Conv_891[FLOAT, 112x1x5x5] %onnx::Conv_894[FLOAT, 112x56x1x1] %onnx::Conv_897[FLOAT, 336x112x1x1] %onnx::Conv_898[FLOAT, 336] %onnx::Conv_900[FLOAT, 336x1x3x3] %onnx::Conv_903[FLOAT, 112x336x1x1] %onnx::Conv_906[FLOAT, 112x112x1x1] %onnx::Conv_909[FLOAT, 112x1x5x5] %onnx::Conv_912[FLOAT, 112x112x1x1] %onnx::Conv_915[FLOAT, 112x112x1x1] %onnx::Conv_918[FLOAT, 112x1x5x5] %onnx::Conv_921[FLOAT, 184x112x1x1] %onnx::Conv_922[FLOAT, 184] %onnx::Conv_924[FLOAT, 184x184x1x1] %onnx::Conv_927[FLOAT, 184x1x3x3] %onnx::Conv_930[FLOAT, 184x184x1x1] %onnx::Conv_933[FLOAT, 184x92x1x1] %onnx::Conv_936[FLOAT, 184x1x3x3] %onnx::Conv_939[FLOAT, 184x92x1x1] %onnx::Conv_942[FLOAT, 552x184x1x1] %onnx::Conv_943[FLOAT, 552] %onnx::Conv_945[FLOAT, 552x1x3x3] %onnx::Conv_948[FLOAT, 352x552x1x1] %onnx::Conv_949[FLOAT, 352] %onnx::Conv_951[FLOAT, 1504x352x1x1] %onnx::Conv_952[FLOAT, 1504] ) { %onnx::Conv_946 = Identity(%onnx::Conv_943) %onnx::Conv_940 = Identity(%onnx::Conv_922) %onnx::Conv_937 = Identity(%onnx::Conv_922) %onnx::Conv_934 = Identity(%onnx::Conv_922) %onnx::Conv_931 = Identity(%onnx::Conv_922) %onnx::Conv_928 = Identity(%onnx::Conv_922) %onnx::Conv_925 = Identity(%onnx::Conv_922) %onnx::Conv_919 = Identity(%onnx::Conv_886) %onnx::Conv_916 = Identity(%onnx::Conv_886) %onnx::Conv_913 = Identity(%onnx::Conv_886) %onnx::Conv_910 = Identity(%onnx::Conv_886) %onnx::Conv_907 = Identity(%onnx::Conv_886) %onnx::Conv_904 = Identity(%onnx::Conv_886) %onnx::Conv_901 = Identity(%onnx::Conv_898) %onnx::Conv_895 = Identity(%onnx::Conv_886) %onnx::Conv_892 = Identity(%onnx::Conv_886) %onnx::Conv_889 = Identity(%onnx::Conv_886) %onnx::Conv_883 = Identity(%onnx::Conv_850) %onnx::Conv_880 = Identity(%onnx::Conv_850) %onnx::Conv_877 = Identity(%onnx::Conv_850) %onnx::Conv_874 = Identity(%onnx::Conv_871) %onnx::Conv_868 = Identity(%onnx::Conv_850) %onnx::Conv_865 = Identity(%onnx::Conv_850) %onnx::Conv_862 = Identity(%onnx::Conv_850) %onnx::Conv_859 = Identity(%onnx::Conv_850) %onnx::Conv_856 = Identity(%onnx::Conv_850) %onnx::Conv_853 = Identity(%onnx::Conv_850) %onnx::Conv_847 = Identity(%onnx::Conv_826) %onnx::Conv_844 = Identity(%onnx::Conv_826) %onnx::Conv_841 = Identity(%onnx::Conv_814) %onnx::Conv_838 = Identity(%onnx::Conv_835) %onnx::Conv_832 = Identity(%onnx::Conv_814) %onnx::Conv_829 = Identity(%onnx::Conv_826) %onnx::Conv_823 = Identity(%onnx::Conv_814) %onnx::Conv_820 = Identity(%onnx::Conv_814) %onnx::Conv_817 = Identity(%onnx::Conv_814) %onnx::Conv_811 = Identity(%onnx::Conv_808) %onnx::Conv_805 = Identity(%onnx::Conv_778) %onnx::Conv_802 = Identity(%onnx::Conv_778) %onnx::Conv_799 = Identity(%onnx::Conv_778) %onnx::Conv_796 = Identity(%onnx::Conv_778) %onnx::Conv_793 = Identity(%onnx::Conv_778) %onnx::Conv_790 = Identity(%onnx::Conv_778) %onnx::Conv_787 = Identity(%onnx::Conv_778) %onnx::Conv_784 = Identity(%onnx::Conv_778) %onnx::Conv_781 = Identity(%onnx::Conv_778) %onnx::Conv_775 = Identity(%onnx::Conv_772) %onnx::Conv_769 = Identity(%onnx::Conv_760) %onnx::Conv_766 = Identity(%onnx::Conv_760) %onnx::Conv_763 = Identity(%onnx::Conv_760) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_759, %onnx::Conv_760) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_762, %onnx::Conv_763) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.0/nl/Relu_output_0, %onnx::Conv_765, %onnx::Conv_766) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_768, %onnx::Conv_769) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_771, %onnx::Conv_772) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 48, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.1/nl/Relu_output_0, %onnx::Conv_774, %onnx::Conv_775) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_777, %onnx::Conv_778) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_780, %onnx::Conv_781) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.2/shuffle/Reshape_output_0 = Reshape(%/cells.2/nl/Relu_output_0, %/cells.2/shuffle/Constant_output_0) %/cells.2/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.2/shuffle/Reshape_output_0) %/cells.2/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.2/shuffle/Reshape_1_output_0 = Reshape(%/cells.2/shuffle/Transpose_output_0, %/cells.2/shuffle/Constant_1_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.2/shuffle/Reshape_1_output_0, %onnx::Conv_783, %onnx::Conv_784) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_786, %onnx::Conv_787) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_789, %onnx::Conv_790) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.3/shuffle/Reshape_output_0 = Reshape(%/cells.3/nl/Relu_output_0, %/cells.3/shuffle/Constant_output_0) %/cells.3/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.3/shuffle/Reshape_output_0) %/cells.3/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.3/shuffle/Reshape_1_output_0 = Reshape(%/cells.3/shuffle/Transpose_output_0, %/cells.3/shuffle/Constant_1_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.3/shuffle/Reshape_1_output_0, %onnx::Conv_792, %onnx::Conv_793) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_795, %onnx::Conv_796) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_798, %onnx::Conv_799) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.4/shuffle/Reshape_output_0 = Reshape(%/cells.4/nl/Relu_output_0, %/cells.4/shuffle/Constant_output_0) %/cells.4/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.4/shuffle/Reshape_output_0) %/cells.4/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.4/shuffle/Reshape_1_output_0 = Reshape(%/cells.4/shuffle/Transpose_output_0, %/cells.4/shuffle/Constant_1_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.4/shuffle/Reshape_1_output_0, %onnx::Conv_801, %onnx::Conv_802) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_804, %onnx::Conv_805) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/Add_output_0, %onnx::Conv_807, %onnx::Conv_808) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.5/nl/Relu_output_0, %onnx::Conv_810, %onnx::Conv_811) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_813, %onnx::Conv_814) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_816, %onnx::Conv_817) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.6/shuffle/Reshape_output_0 = Reshape(%/cells.6/nl/Relu_output_0, %/cells.6/shuffle/Constant_output_0) %/cells.6/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.6/shuffle/Reshape_output_0) %/cells.6/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.6/shuffle/Reshape_1_output_0 = Reshape(%/cells.6/shuffle/Transpose_output_0, %/cells.6/shuffle/Constant_1_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.6/shuffle/Reshape_1_output_0, %onnx::Conv_819, %onnx::Conv_820) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_822, %onnx::Conv_823) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_825, %onnx::Conv_826) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.7/nl/Relu_output_0, %onnx::Conv_828, %onnx::Conv_829) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_831, %onnx::Conv_832) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_834, %onnx::Conv_835) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.8/nl/Relu_output_0, %onnx::Conv_837, %onnx::Conv_838) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_840, %onnx::Conv_841) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_843, %onnx::Conv_844) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.9/nl/Relu_output_0, %onnx::Conv_846, %onnx::Conv_847) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_849, %onnx::Conv_850) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_852, %onnx::Conv_853) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.10/shuffle/Reshape_output_0 = Reshape(%/cells.10/nl/Relu_output_0, %/cells.10/shuffle/Constant_output_0) %/cells.10/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.10/shuffle/Reshape_output_0) %/cells.10/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.10/shuffle/Reshape_1_output_0 = Reshape(%/cells.10/shuffle/Transpose_output_0, %/cells.10/shuffle/Constant_1_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.10/shuffle/Reshape_1_output_0, %onnx::Conv_855, %onnx::Conv_856) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_858, %onnx::Conv_859) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_861, %onnx::Conv_862) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.11/shuffle/Reshape_output_0 = Reshape(%/cells.11/nl/Relu_output_0, %/cells.11/shuffle/Constant_output_0) %/cells.11/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.11/shuffle/Reshape_output_0) %/cells.11/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.11/shuffle/Reshape_1_output_0 = Reshape(%/cells.11/shuffle/Transpose_output_0, %/cells.11/shuffle/Constant_1_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.11/shuffle/Reshape_1_output_0, %onnx::Conv_864, %onnx::Conv_865) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_867, %onnx::Conv_868) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_870, %onnx::Conv_871) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.12/nl/Relu_output_0, %onnx::Conv_873, %onnx::Conv_874) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_876, %onnx::Conv_877) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_879, %onnx::Conv_880) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.13/shuffle/Reshape_output_0 = Reshape(%/cells.13/nl/Relu_output_0, %/cells.13/shuffle/Constant_output_0) %/cells.13/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.13/shuffle/Reshape_output_0) %/cells.13/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.13/shuffle/Reshape_1_output_0 = Reshape(%/cells.13/shuffle/Transpose_output_0, %/cells.13/shuffle/Constant_1_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.13/shuffle/Reshape_1_output_0, %onnx::Conv_882, %onnx::Conv_883) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_885, %onnx::Conv_886) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_888, %onnx::Conv_889) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.14/shuffle/Reshape_output_0 = Reshape(%/cells.14/nl/Relu_output_0, %/cells.14/shuffle/Constant_output_0) %/cells.14/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.14/shuffle/Reshape_output_0) %/cells.14/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.14/shuffle/Reshape_1_output_0 = Reshape(%/cells.14/shuffle/Transpose_output_0, %/cells.14/shuffle/Constant_1_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.14/shuffle/Reshape_1_output_0, %onnx::Conv_891, %onnx::Conv_892) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_894, %onnx::Conv_895) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_897, %onnx::Conv_898) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.15/nl/Relu_output_0, %onnx::Conv_900, %onnx::Conv_901) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_903, %onnx::Conv_904) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_906, %onnx::Conv_907) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.16/nl/Relu_output_0, %onnx::Conv_909, %onnx::Conv_910) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_912, %onnx::Conv_913) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.15/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_915, %onnx::Conv_916) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.17/nl/Relu_output_0, %onnx::Conv_918, %onnx::Conv_919) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_921, %onnx::Conv_922) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_924, %onnx::Conv_925) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.18/nl/Relu_output_0, %onnx::Conv_927, %onnx::Conv_928) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_930, %onnx::Conv_931) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_933, %onnx::Conv_934) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.20/shuffle/Reshape_output_0 = Reshape(%/cells.20/nl/Relu_output_0, %/cells.20/shuffle/Constant_output_0) %/cells.20/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.20/shuffle/Reshape_output_0) %/cells.20/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.20/shuffle/Reshape_1_output_0 = Reshape(%/cells.20/shuffle/Transpose_output_0, %/cells.20/shuffle/Constant_1_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.20/shuffle/Reshape_1_output_0, %onnx::Conv_936, %onnx::Conv_937) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_939, %onnx::Conv_940) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_942, %onnx::Conv_943) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 552, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.21/nl/Relu_output_0, %onnx::Conv_945, %onnx::Conv_946) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_948, %onnx::Conv_949) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_951, %onnx::Conv_952) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %757 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %757 }	val_accuracy	47,598,976	1,381,932	{'zcp_synflow': 73.81326985261533, 'zcp_zen': 64.21788024902344, 'zcp_epe_nas': 18.820997031323483, 'zcp_fisher': 0.1098727211356163, 'zcp_flops': 47598976.0, 'zcp_grad_norm': 25.36748504638672, 'zcp_grasp': -0.03985023498535156, 'zcp_jacov': -16.063570404960046, 'zcp_l2_norm': 543.2626953125, 'zcp_nwot': 207.66369724508857, 'zcp_params': 1381932.0, 'zcp_plain': 0.0037906013894826174, 'zcp_snip': 37.84451675415039, 'lat_1080ti_1': 0.7049381509007071, 'lat_1080ti_32': 0.5976877154634771, 'lat_1080ti_64': 0.3224248299101997, 'lat_2080ti_1': 0.8046651558512763, 'lat_2080ti_32': 0.6226126974464182, 'lat_2080ti_64': 0.38861172479949074, 'lat_essential_ph_1': 0.20754716981132076, 'lat_eyeriss': 0.19986063484495623, 'lat_fpga': 0.20368299706112256, 'lat_gold_6226': 0.15896434911288365, 'lat_gold_6240': 0.39633417902400797, 'lat_pixel2': 0.15217391304347827, 'lat_pixel3': 0.22190332324888393, 'lat_raspi4': 0.275410392507205, 'lat_samsung_a50': 0.17894736842105263, 'lat_samsung_s7': 0.10236220472440945, 'lat_silver_4114': 0.4410496628863664, 'lat_silver_4210r': 0.4883173332477323, 'lat_titan_rtx_1': 0.7562008569593196, 'lat_titan_rtx_32': 0.6277968272752354, 'lat_titan_rtx_64': 0.43228960885539097, 'lat_titanx_1': 0.40237414480895933, 'lat_titanx_32': 0.4846930332205807, 'lat_titanx_64': 0.31881814043542017, 'lat_titanxp_1': 0.7145627285405464, 'lat_titanxp_32': 0.558570588447686, 'lat_titanxp_64': 0.373763729337685}
FBNet_2426	FBNet	2426	2426	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_734[FLOAT, 16x3x3x3] %onnx::Conv_735[FLOAT, 16] %onnx::Conv_737[FLOAT, 16x8x1x1] %onnx::Conv_740[FLOAT, 16x1x5x5] %onnx::Conv_743[FLOAT, 16x8x1x1] %onnx::Conv_746[FLOAT, 48x16x1x1] %onnx::Conv_747[FLOAT, 48] %onnx::Conv_749[FLOAT, 48x1x5x5] %onnx::Conv_752[FLOAT, 24x48x1x1] %onnx::Conv_753[FLOAT, 24] %onnx::Conv_755[FLOAT, 24x12x1x1] %onnx::Conv_758[FLOAT, 24x1x5x5] %onnx::Conv_761[FLOAT, 24x12x1x1] %onnx::Conv_764[FLOAT, 144x24x1x1] %onnx::Conv_765[FLOAT, 144] %onnx::Conv_767[FLOAT, 144x1x5x5] %onnx::Conv_770[FLOAT, 24x144x1x1] %onnx::Conv_773[FLOAT, 144x24x1x1] %onnx::Conv_776[FLOAT, 144x1x5x5] %onnx::Conv_779[FLOAT, 24x144x1x1] %onnx::Conv_782[FLOAT, 144x24x1x1] %onnx::Conv_785[FLOAT, 144x1x3x3] %onnx::Conv_788[FLOAT, 32x144x1x1] %onnx::Conv_789[FLOAT, 32] %onnx::Conv_791[FLOAT, 96x32x1x1] %onnx::Conv_792[FLOAT, 96] %onnx::Conv_794[FLOAT, 96x1x5x5] %onnx::Conv_797[FLOAT, 32x96x1x1] %onnx::Conv_800[FLOAT, 32x16x1x1] %onnx::Conv_803[FLOAT, 32x1x5x5] %onnx::Conv_806[FLOAT, 32x16x1x1] %onnx::Conv_809[FLOAT, 96x32x1x1] %onnx::Conv_812[FLOAT, 96x1x5x5] %onnx::Conv_815[FLOAT, 32x96x1x1] %onnx::Conv_818[FLOAT, 32x16x1x1] %onnx::Conv_821[FLOAT, 32x1x3x3] %onnx::Conv_824[FLOAT, 64x16x1x1] %onnx::Conv_825[FLOAT, 64] %onnx::Conv_827[FLOAT, 64x32x1x1] %onnx::Conv_830[FLOAT, 64x1x5x5] %onnx::Conv_833[FLOAT, 64x32x1x1] %onnx::Conv_836[FLOAT, 64x32x1x1] %onnx::Conv_839[FLOAT, 64x1x5x5] %onnx::Conv_842[FLOAT, 64x32x1x1] %onnx::Conv_845[FLOAT, 112x64x1x1] %onnx::Conv_846[FLOAT, 112] %onnx::Conv_848[FLOAT, 672x112x1x1] %onnx::Conv_849[FLOAT, 672] %onnx::Conv_851[FLOAT, 672x1x5x5] %onnx::Conv_854[FLOAT, 112x672x1x1] %onnx::Conv_857[FLOAT, 112x56x1x1] %onnx::Conv_860[FLOAT, 112x1x3x3] %onnx::Conv_863[FLOAT, 112x56x1x1] %onnx::Conv_866[FLOAT, 112x56x1x1] %onnx::Conv_869[FLOAT, 112x1x3x3] %onnx::Conv_872[FLOAT, 184x56x1x1] %onnx::Conv_873[FLOAT, 184] %onnx::Conv_875[FLOAT, 1104x184x1x1] %onnx::Conv_876[FLOAT, 1104] %onnx::Conv_878[FLOAT, 1104x1x5x5] %onnx::Conv_881[FLOAT, 184x1104x1x1] %onnx::Conv_884[FLOAT, 184x92x1x1] %onnx::Conv_887[FLOAT, 184x1x3x3] %onnx::Conv_890[FLOAT, 184x92x1x1] %onnx::Conv_893[FLOAT, 552x184x1x1] %onnx::Conv_894[FLOAT, 552] %onnx::Conv_896[FLOAT, 552x1x5x5] %onnx::Conv_899[FLOAT, 184x552x1x1] %onnx::Conv_902[FLOAT, 184x92x1x1] %onnx::Conv_905[FLOAT, 184x1x5x5] %onnx::Conv_908[FLOAT, 352x92x1x1] %onnx::Conv_909[FLOAT, 352] %onnx::Conv_911[FLOAT, 1504x352x1x1] %onnx::Conv_912[FLOAT, 1504] ) { %onnx::Conv_906 = Identity(%onnx::Conv_873) %onnx::Conv_903 = Identity(%onnx::Conv_873) %onnx::Conv_900 = Identity(%onnx::Conv_873) %onnx::Conv_897 = Identity(%onnx::Conv_894) %onnx::Conv_891 = Identity(%onnx::Conv_873) %onnx::Conv_888 = Identity(%onnx::Conv_873) %onnx::Conv_885 = Identity(%onnx::Conv_873) %onnx::Conv_882 = Identity(%onnx::Conv_873) %onnx::Conv_879 = Identity(%onnx::Conv_876) %onnx::Conv_870 = Identity(%onnx::Conv_846) %onnx::Conv_867 = Identity(%onnx::Conv_846) %onnx::Conv_864 = Identity(%onnx::Conv_846) %onnx::Conv_861 = Identity(%onnx::Conv_846) %onnx::Conv_858 = Identity(%onnx::Conv_846) %onnx::Conv_855 = Identity(%onnx::Conv_846) %onnx::Conv_852 = Identity(%onnx::Conv_849) %onnx::Conv_843 = Identity(%onnx::Conv_825) %onnx::Conv_840 = Identity(%onnx::Conv_825) %onnx::Conv_837 = Identity(%onnx::Conv_825) %onnx::Conv_834 = Identity(%onnx::Conv_825) %onnx::Conv_831 = Identity(%onnx::Conv_825) %onnx::Conv_828 = Identity(%onnx::Conv_825) %onnx::Conv_822 = Identity(%onnx::Conv_789) %onnx::Conv_819 = Identity(%onnx::Conv_789) %onnx::Conv_816 = Identity(%onnx::Conv_789) %onnx::Conv_813 = Identity(%onnx::Conv_792) %onnx::Conv_810 = Identity(%onnx::Conv_792) %onnx::Conv_807 = Identity(%onnx::Conv_789) %onnx::Conv_804 = Identity(%onnx::Conv_789) %onnx::Conv_801 = Identity(%onnx::Conv_789) %onnx::Conv_798 = Identity(%onnx::Conv_789) %onnx::Conv_795 = Identity(%onnx::Conv_792) %onnx::Conv_786 = Identity(%onnx::Conv_765) %onnx::Conv_783 = Identity(%onnx::Conv_765) %onnx::Conv_780 = Identity(%onnx::Conv_753) %onnx::Conv_777 = Identity(%onnx::Conv_765) %onnx::Conv_774 = Identity(%onnx::Conv_765) %onnx::Conv_771 = Identity(%onnx::Conv_753) %onnx::Conv_768 = Identity(%onnx::Conv_765) %onnx::Conv_762 = Identity(%onnx::Conv_753) %onnx::Conv_759 = Identity(%onnx::Conv_753) %onnx::Conv_756 = Identity(%onnx::Conv_753) %onnx::Conv_750 = Identity(%onnx::Conv_747) %onnx::Conv_744 = Identity(%onnx::Conv_735) %onnx::Conv_741 = Identity(%onnx::Conv_735) %onnx::Conv_738 = Identity(%onnx::Conv_735) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_734, %onnx::Conv_735) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_737, %onnx::Conv_738) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.0/shuffle/Reshape_output_0 = Reshape(%/cells.0/nl/Relu_output_0, %/cells.0/shuffle/Constant_output_0) %/cells.0/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.0/shuffle/Reshape_output_0) %/cells.0/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.0/shuffle/Reshape_1_output_0 = Reshape(%/cells.0/shuffle/Transpose_output_0, %/cells.0/shuffle/Constant_1_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.0/shuffle/Reshape_1_output_0, %onnx::Conv_740, %onnx::Conv_741) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_743, %onnx::Conv_744) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_746, %onnx::Conv_747) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 48, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.1/nl/Relu_output_0, %onnx::Conv_749, %onnx::Conv_750) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_752, %onnx::Conv_753) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_755, %onnx::Conv_756) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.2/shuffle/Reshape_output_0 = Reshape(%/cells.2/nl/Relu_output_0, %/cells.2/shuffle/Constant_output_0) %/cells.2/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.2/shuffle/Reshape_output_0) %/cells.2/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.2/shuffle/Reshape_1_output_0 = Reshape(%/cells.2/shuffle/Transpose_output_0, %/cells.2/shuffle/Constant_1_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.2/shuffle/Reshape_1_output_0, %onnx::Conv_758, %onnx::Conv_759) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_761, %onnx::Conv_762) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_764, %onnx::Conv_765) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.3/nl/Relu_output_0, %onnx::Conv_767, %onnx::Conv_768) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_770, %onnx::Conv_771) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_773, %onnx::Conv_774) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.4/nl/Relu_output_0, %onnx::Conv_776, %onnx::Conv_777) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_779, %onnx::Conv_780) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/Add_output_0, %onnx::Conv_782, %onnx::Conv_783) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.5/nl/Relu_output_0, %onnx::Conv_785, %onnx::Conv_786) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_788, %onnx::Conv_789) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_791, %onnx::Conv_792) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.6/nl/Relu_output_0, %onnx::Conv_794, %onnx::Conv_795) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_797, %onnx::Conv_798) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_800, %onnx::Conv_801) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.7/shuffle/Reshape_output_0 = Reshape(%/cells.7/nl/Relu_output_0, %/cells.7/shuffle/Constant_output_0) %/cells.7/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.7/shuffle/Reshape_output_0) %/cells.7/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.7/shuffle/Reshape_1_output_0 = Reshape(%/cells.7/shuffle/Transpose_output_0, %/cells.7/shuffle/Constant_1_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.7/shuffle/Reshape_1_output_0, %onnx::Conv_803, %onnx::Conv_804) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_806, %onnx::Conv_807) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_809, %onnx::Conv_810) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.8/nl/Relu_output_0, %onnx::Conv_812, %onnx::Conv_813) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_815, %onnx::Conv_816) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_818, %onnx::Conv_819) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.9/shuffle/Reshape_output_0 = Reshape(%/cells.9/nl/Relu_output_0, %/cells.9/shuffle/Constant_output_0) %/cells.9/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.9/shuffle/Reshape_output_0) %/cells.9/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.9/shuffle/Reshape_1_output_0 = Reshape(%/cells.9/shuffle/Transpose_output_0, %/cells.9/shuffle/Constant_1_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.9/shuffle/Reshape_1_output_0, %onnx::Conv_821, %onnx::Conv_822) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_824, %onnx::Conv_825) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_827, %onnx::Conv_828) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.10/shuffle/Reshape_output_0 = Reshape(%/cells.10/nl/Relu_output_0, %/cells.10/shuffle/Constant_output_0) %/cells.10/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.10/shuffle/Reshape_output_0) %/cells.10/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.10/shuffle/Reshape_1_output_0 = Reshape(%/cells.10/shuffle/Transpose_output_0, %/cells.10/shuffle/Constant_1_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.10/shuffle/Reshape_1_output_0, %onnx::Conv_830, %onnx::Conv_831) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_833, %onnx::Conv_834) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_836, %onnx::Conv_837) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.11/shuffle/Reshape_output_0 = Reshape(%/cells.11/nl/Relu_output_0, %/cells.11/shuffle/Constant_output_0) %/cells.11/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.11/shuffle/Reshape_output_0) %/cells.11/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.11/shuffle/Reshape_1_output_0 = Reshape(%/cells.11/shuffle/Transpose_output_0, %/cells.11/shuffle/Constant_1_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.11/shuffle/Reshape_1_output_0, %onnx::Conv_839, %onnx::Conv_840) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_842, %onnx::Conv_843) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.13/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_845, %onnx::Conv_846) %/cells.13/relu/Relu_output_0 = Relu(%/cells.13/conv/Conv_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/relu/Relu_output_0, %onnx::Conv_848, %onnx::Conv_849) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.15/nl/Relu_output_0, %onnx::Conv_851, %onnx::Conv_852) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_854, %onnx::Conv_855) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.13/relu/Relu_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_857, %onnx::Conv_858) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.16/shuffle/Reshape_output_0 = Reshape(%/cells.16/nl/Relu_output_0, %/cells.16/shuffle/Constant_output_0) %/cells.16/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.16/shuffle/Reshape_output_0) %/cells.16/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.16/shuffle/Reshape_1_output_0 = Reshape(%/cells.16/shuffle/Transpose_output_0, %/cells.16/shuffle/Constant_1_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.16/shuffle/Reshape_1_output_0, %onnx::Conv_860, %onnx::Conv_861) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_863, %onnx::Conv_864) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.15/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_866, %onnx::Conv_867) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.17/shuffle/Reshape_output_0 = Reshape(%/cells.17/nl/Relu_output_0, %/cells.17/shuffle/Constant_output_0) %/cells.17/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.17/shuffle/Reshape_output_0) %/cells.17/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.17/shuffle/Reshape_1_output_0 = Reshape(%/cells.17/shuffle/Transpose_output_0, %/cells.17/shuffle/Constant_1_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.17/shuffle/Reshape_1_output_0, %onnx::Conv_869, %onnx::Conv_870) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_872, %onnx::Conv_873) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_875, %onnx::Conv_876) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.18/nl/Relu_output_0, %onnx::Conv_878, %onnx::Conv_879) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_881, %onnx::Conv_882) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_884, %onnx::Conv_885) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.19/shuffle/Reshape_output_0 = Reshape(%/cells.19/nl/Relu_output_0, %/cells.19/shuffle/Constant_output_0) %/cells.19/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.19/shuffle/Reshape_output_0) %/cells.19/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.19/shuffle/Reshape_1_output_0 = Reshape(%/cells.19/shuffle/Transpose_output_0, %/cells.19/shuffle/Constant_1_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.19/shuffle/Reshape_1_output_0, %onnx::Conv_887, %onnx::Conv_888) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_890, %onnx::Conv_891) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_893, %onnx::Conv_894) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 552, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.20/nl/Relu_output_0, %onnx::Conv_896, %onnx::Conv_897) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_899, %onnx::Conv_900) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_902, %onnx::Conv_903) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.21/shuffle/Reshape_output_0 = Reshape(%/cells.21/nl/Relu_output_0, %/cells.21/shuffle/Constant_output_0) %/cells.21/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.21/shuffle/Reshape_output_0) %/cells.21/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.21/shuffle/Reshape_1_output_0 = Reshape(%/cells.21/shuffle/Transpose_output_0, %/cells.21/shuffle/Constant_1_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.21/shuffle/Reshape_1_output_0, %onnx::Conv_905, %onnx::Conv_906) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_908, %onnx::Conv_909) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_911, %onnx::Conv_912) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %732 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %732 }	val_accuracy	74,623,104	1,715,716	{'zcp_synflow': 68.94106884809352, 'zcp_zen': 61.849754333496094, 'zcp_epe_nas': 7.152478175373565, 'zcp_fisher': 0.09083037823438644, 'zcp_flops': 74623104.0, 'zcp_grad_norm': 23.354724884033203, 'zcp_grasp': 0.0077342987060546875, 'zcp_jacov': -16.059575926471858, 'zcp_l2_norm': 538.6926879882812, 'zcp_nwot': 216.06289674156378, 'zcp_params': 1715716.0, 'zcp_plain': -0.001260754419490695, 'zcp_snip': 37.6983528137207, 'lat_1080ti_1': 0.6207302445687358, 'lat_1080ti_32': 0.7583868393514103, 'lat_1080ti_64': 0.7397552948560099, 'lat_2080ti_1': 0.6727056442483956, 'lat_2080ti_32': 0.7557724768056095, 'lat_2080ti_64': 0.7539631782617657, 'lat_essential_ph_1': 0.20754716981132076, 'lat_eyeriss': 0.5538481286560428, 'lat_fpga': 0.4708239741141314, 'lat_gold_6226': 0.3257243095426939, 'lat_gold_6240': 0.4637998127247336, 'lat_pixel2': 0.5, 'lat_pixel3': 0.662222789322503, 'lat_raspi4': 0.6726086995278147, 'lat_samsung_a50': 0.23157894736842105, 'lat_samsung_s7': 0.1732283464566929, 'lat_silver_4114': 0.4958487108256576, 'lat_silver_4210r': 0.5193614823782534, 'lat_titan_rtx_1': 0.6335721524609523, 'lat_titan_rtx_32': 0.6949547083450681, 'lat_titan_rtx_64': 0.7701995342460426, 'lat_titanx_1': 0.3316614596892496, 'lat_titanx_32': 0.729858854379494, 'lat_titanx_64': 0.7335081784272449, 'lat_titanxp_1': 0.6090553948140734, 'lat_titanxp_32': 0.7439904220705319, 'lat_titanxp_64': 0.7576275538325127}
FBNet_873	FBNet	873	873	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_696[FLOAT, 16x3x3x3] %onnx::Conv_697[FLOAT, 16] %onnx::Conv_699[FLOAT, 16x8x1x1] %onnx::Conv_702[FLOAT, 16x1x5x5] %onnx::Conv_705[FLOAT, 16x8x1x1] %onnx::Conv_708[FLOAT, 16x8x1x1] %onnx::Conv_711[FLOAT, 16x1x5x5] %onnx::Conv_714[FLOAT, 24x8x1x1] %onnx::Conv_715[FLOAT, 24] %onnx::Conv_717[FLOAT, 24x24x1x1] %onnx::Conv_720[FLOAT, 24x1x3x3] %onnx::Conv_723[FLOAT, 24x24x1x1] %onnx::Conv_726[FLOAT, 72x24x1x1] %onnx::Conv_727[FLOAT, 72] %onnx::Conv_729[FLOAT, 72x1x5x5] %onnx::Conv_732[FLOAT, 24x72x1x1] %onnx::Conv_735[FLOAT, 144x24x1x1] %onnx::Conv_736[FLOAT, 144] %onnx::Conv_738[FLOAT, 144x1x3x3] %onnx::Conv_741[FLOAT, 24x144x1x1] %onnx::Conv_744[FLOAT, 32x24x1x1] %onnx::Conv_745[FLOAT, 32] %onnx::Conv_747[FLOAT, 192x32x1x1] %onnx::Conv_748[FLOAT, 192] %onnx::Conv_750[FLOAT, 192x1x3x3] %onnx::Conv_753[FLOAT, 32x192x1x1] %onnx::Conv_756[FLOAT, 32x32x1x1] %onnx::Conv_759[FLOAT, 32x1x3x3] %onnx::Conv_762[FLOAT, 32x32x1x1] %onnx::Conv_765[FLOAT, 32x16x1x1] %onnx::Conv_768[FLOAT, 32x1x3x3] %onnx::Conv_771[FLOAT, 32x16x1x1] %onnx::Conv_774[FLOAT, 192x32x1x1] %onnx::Conv_777[FLOAT, 192x1x3x3] %onnx::Conv_780[FLOAT, 64x192x1x1] %onnx::Conv_781[FLOAT, 64] %onnx::Conv_783[FLOAT, 64x64x1x1] %onnx::Conv_786[FLOAT, 64x1x5x5] %onnx::Conv_789[FLOAT, 64x64x1x1] %onnx::Conv_792[FLOAT, 192x64x1x1] %onnx::Conv_795[FLOAT, 192x1x3x3] %onnx::Conv_798[FLOAT, 64x192x1x1] %onnx::Conv_801[FLOAT, 64x64x1x1] %onnx::Conv_804[FLOAT, 64x1x3x3] %onnx::Conv_807[FLOAT, 64x64x1x1] %onnx::Conv_810[FLOAT, 64x32x1x1] %onnx::Conv_813[FLOAT, 64x1x5x5] %onnx::Conv_816[FLOAT, 112x32x1x1] %onnx::Conv_817[FLOAT, 112] %onnx::Conv_819[FLOAT, 672x112x1x1] %onnx::Conv_820[FLOAT, 672] %onnx::Conv_822[FLOAT, 672x1x5x5] %onnx::Conv_825[FLOAT, 112x672x1x1] %onnx::Conv_828[FLOAT, 672x112x1x1] %onnx::Conv_831[FLOAT, 672x1x3x3] %onnx::Conv_834[FLOAT, 112x672x1x1] %onnx::Conv_837[FLOAT, 112x112x1x1] %onnx::Conv_840[FLOAT, 112x1x5x5] %onnx::Conv_843[FLOAT, 112x112x1x1] %onnx::Conv_846[FLOAT, 336x112x1x1] %onnx::Conv_847[FLOAT, 336] %onnx::Conv_849[FLOAT, 336x1x5x5] %onnx::Conv_852[FLOAT, 184x336x1x1] %onnx::Conv_853[FLOAT, 184] %onnx::Conv_855[FLOAT, 1104x184x1x1] %onnx::Conv_856[FLOAT, 1104] %onnx::Conv_858[FLOAT, 1104x1x5x5] %onnx::Conv_861[FLOAT, 184x1104x1x1] %onnx::Conv_864[FLOAT, 552x184x1x1] %onnx::Conv_865[FLOAT, 552] %onnx::Conv_867[FLOAT, 552x1x5x5] %onnx::Conv_870[FLOAT, 184x552x1x1] %onnx::Conv_873[FLOAT, 184x184x1x1] %onnx::Conv_876[FLOAT, 184x1x3x3] %onnx::Conv_879[FLOAT, 184x184x1x1] %onnx::Conv_882[FLOAT, 184x92x1x1] %onnx::Conv_885[FLOAT, 184x1x3x3] %onnx::Conv_888[FLOAT, 352x92x1x1] %onnx::Conv_889[FLOAT, 352] %onnx::Conv_891[FLOAT, 1504x352x1x1] %onnx::Conv_892[FLOAT, 1504] ) { %onnx::Conv_886 = Identity(%onnx::Conv_853) %onnx::Conv_883 = Identity(%onnx::Conv_853) %onnx::Conv_880 = Identity(%onnx::Conv_853) %onnx::Conv_877 = Identity(%onnx::Conv_853) %onnx::Conv_874 = Identity(%onnx::Conv_853) %onnx::Conv_871 = Identity(%onnx::Conv_853) %onnx::Conv_868 = Identity(%onnx::Conv_865) %onnx::Conv_862 = Identity(%onnx::Conv_853) %onnx::Conv_859 = Identity(%onnx::Conv_856) %onnx::Conv_850 = Identity(%onnx::Conv_847) %onnx::Conv_844 = Identity(%onnx::Conv_817) %onnx::Conv_841 = Identity(%onnx::Conv_817) %onnx::Conv_838 = Identity(%onnx::Conv_817) %onnx::Conv_835 = Identity(%onnx::Conv_817) %onnx::Conv_832 = Identity(%onnx::Conv_820) %onnx::Conv_829 = Identity(%onnx::Conv_820) %onnx::Conv_826 = Identity(%onnx::Conv_817) %onnx::Conv_823 = Identity(%onnx::Conv_820) %onnx::Conv_814 = Identity(%onnx::Conv_781) %onnx::Conv_811 = Identity(%onnx::Conv_781) %onnx::Conv_808 = Identity(%onnx::Conv_781) %onnx::Conv_805 = Identity(%onnx::Conv_781) %onnx::Conv_802 = Identity(%onnx::Conv_781) %onnx::Conv_799 = Identity(%onnx::Conv_781) %onnx::Conv_796 = Identity(%onnx::Conv_748) %onnx::Conv_793 = Identity(%onnx::Conv_748) %onnx::Conv_790 = Identity(%onnx::Conv_781) %onnx::Conv_787 = Identity(%onnx::Conv_781) %onnx::Conv_784 = Identity(%onnx::Conv_781) %onnx::Conv_778 = Identity(%onnx::Conv_748) %onnx::Conv_775 = Identity(%onnx::Conv_748) %onnx::Conv_772 = Identity(%onnx::Conv_745) %onnx::Conv_769 = Identity(%onnx::Conv_745) %onnx::Conv_766 = Identity(%onnx::Conv_745) %onnx::Conv_763 = Identity(%onnx::Conv_745) %onnx::Conv_760 = Identity(%onnx::Conv_745) %onnx::Conv_757 = Identity(%onnx::Conv_745) %onnx::Conv_754 = Identity(%onnx::Conv_745) %onnx::Conv_751 = Identity(%onnx::Conv_748) %onnx::Conv_742 = Identity(%onnx::Conv_715) %onnx::Conv_739 = Identity(%onnx::Conv_736) %onnx::Conv_733 = Identity(%onnx::Conv_715) %onnx::Conv_730 = Identity(%onnx::Conv_727) %onnx::Conv_724 = Identity(%onnx::Conv_715) %onnx::Conv_721 = Identity(%onnx::Conv_715) %onnx::Conv_718 = Identity(%onnx::Conv_715) %onnx::Conv_712 = Identity(%onnx::Conv_697) %onnx::Conv_709 = Identity(%onnx::Conv_697) %onnx::Conv_706 = Identity(%onnx::Conv_697) %onnx::Conv_703 = Identity(%onnx::Conv_697) %onnx::Conv_700 = Identity(%onnx::Conv_697) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_696, %onnx::Conv_697) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_699, %onnx::Conv_700) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.0/shuffle/Reshape_output_0 = Reshape(%/cells.0/nl/Relu_output_0, %/cells.0/shuffle/Constant_output_0) %/cells.0/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.0/shuffle/Reshape_output_0) %/cells.0/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.0/shuffle/Reshape_1_output_0 = Reshape(%/cells.0/shuffle/Transpose_output_0, %/cells.0/shuffle/Constant_1_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.0/shuffle/Reshape_1_output_0, %onnx::Conv_702, %onnx::Conv_703) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_705, %onnx::Conv_706) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_708, %onnx::Conv_709) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.1/shuffle/Reshape_output_0 = Reshape(%/cells.1/nl/Relu_output_0, %/cells.1/shuffle/Constant_output_0) %/cells.1/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.1/shuffle/Reshape_output_0) %/cells.1/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.1/shuffle/Reshape_1_output_0 = Reshape(%/cells.1/shuffle/Transpose_output_0, %/cells.1/shuffle/Constant_1_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.1/shuffle/Reshape_1_output_0, %onnx::Conv_711, %onnx::Conv_712) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_714, %onnx::Conv_715) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_717, %onnx::Conv_718) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.2/nl/Relu_output_0, %onnx::Conv_720, %onnx::Conv_721) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_723, %onnx::Conv_724) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_726, %onnx::Conv_727) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.3/nl/Relu_output_0, %onnx::Conv_729, %onnx::Conv_730) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_732, %onnx::Conv_733) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_735, %onnx::Conv_736) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.4/nl/Relu_output_0, %onnx::Conv_738, %onnx::Conv_739) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_741, %onnx::Conv_742) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [2, 2]](%/cells.4/Add_output_0, %onnx::Conv_744, %onnx::Conv_745) %/cells.5/relu/Relu_output_0 = Relu(%/cells.5/conv/Conv_output_0) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/relu/Relu_output_0, %onnx::Conv_747, %onnx::Conv_748) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.6/nl/Relu_output_0, %onnx::Conv_750, %onnx::Conv_751) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_753, %onnx::Conv_754) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/relu/Relu_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_756, %onnx::Conv_757) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.7/nl/Relu_output_0, %onnx::Conv_759, %onnx::Conv_760) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_762, %onnx::Conv_763) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_765, %onnx::Conv_766) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.8/shuffle/Reshape_output_0 = Reshape(%/cells.8/nl/Relu_output_0, %/cells.8/shuffle/Constant_output_0) %/cells.8/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.8/shuffle/Reshape_output_0) %/cells.8/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.8/shuffle/Reshape_1_output_0 = Reshape(%/cells.8/shuffle/Transpose_output_0, %/cells.8/shuffle/Constant_1_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.8/shuffle/Reshape_1_output_0, %onnx::Conv_768, %onnx::Conv_769) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_771, %onnx::Conv_772) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_774, %onnx::Conv_775) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.9/nl/Relu_output_0, %onnx::Conv_777, %onnx::Conv_778) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_780, %onnx::Conv_781) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_783, %onnx::Conv_784) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.10/nl/Relu_output_0, %onnx::Conv_786, %onnx::Conv_787) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_789, %onnx::Conv_790) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_792, %onnx::Conv_793) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.11/nl/Relu_output_0, %onnx::Conv_795, %onnx::Conv_796) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_798, %onnx::Conv_799) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_801, %onnx::Conv_802) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.12/nl/Relu_output_0, %onnx::Conv_804, %onnx::Conv_805) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_807, %onnx::Conv_808) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_810, %onnx::Conv_811) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.13/shuffle/Reshape_output_0 = Reshape(%/cells.13/nl/Relu_output_0, %/cells.13/shuffle/Constant_output_0) %/cells.13/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.13/shuffle/Reshape_output_0) %/cells.13/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.13/shuffle/Reshape_1_output_0 = Reshape(%/cells.13/shuffle/Transpose_output_0, %/cells.13/shuffle/Constant_1_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.13/shuffle/Reshape_1_output_0, %onnx::Conv_813, %onnx::Conv_814) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_816, %onnx::Conv_817) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_819, %onnx::Conv_820) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.14/nl/Relu_output_0, %onnx::Conv_822, %onnx::Conv_823) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_825, %onnx::Conv_826) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_828, %onnx::Conv_829) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.15/nl/Relu_output_0, %onnx::Conv_831, %onnx::Conv_832) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_834, %onnx::Conv_835) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_837, %onnx::Conv_838) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.16/nl/Relu_output_0, %onnx::Conv_840, %onnx::Conv_841) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_843, %onnx::Conv_844) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.15/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_846, %onnx::Conv_847) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.17/nl/Relu_output_0, %onnx::Conv_849, %onnx::Conv_850) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_852, %onnx::Conv_853) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_855, %onnx::Conv_856) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.18/nl/Relu_output_0, %onnx::Conv_858, %onnx::Conv_859) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_861, %onnx::Conv_862) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_864, %onnx::Conv_865) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 552, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.19/nl/Relu_output_0, %onnx::Conv_867, %onnx::Conv_868) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_870, %onnx::Conv_871) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_873, %onnx::Conv_874) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.20/nl/Relu_output_0, %onnx::Conv_876, %onnx::Conv_877) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_879, %onnx::Conv_880) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_882, %onnx::Conv_883) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.21/shuffle/Reshape_output_0 = Reshape(%/cells.21/nl/Relu_output_0, %/cells.21/shuffle/Constant_output_0) %/cells.21/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.21/shuffle/Reshape_output_0) %/cells.21/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.21/shuffle/Reshape_1_output_0 = Reshape(%/cells.21/shuffle/Transpose_output_0, %/cells.21/shuffle/Constant_1_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.21/shuffle/Reshape_1_output_0, %onnx::Conv_885, %onnx::Conv_886) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_888, %onnx::Conv_889) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_891, %onnx::Conv_892) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %694 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %694 }	val_accuracy	77,806,720	2,044,812	{'zcp_synflow': 81.12326995173713, 'zcp_zen': 71.46871185302734, 'zcp_epe_nas': 8.297040847397502, 'zcp_fisher': 0.1875540018081665, 'zcp_flops': 77806720.0, 'zcp_grad_norm': 22.163131713867188, 'zcp_grasp': 0.0687255859375, 'zcp_jacov': -16.05231173872607, 'zcp_l2_norm': 670.9891967773438, 'zcp_nwot': 213.35018232385735, 'zcp_params': 2044812.0, 'zcp_plain': 0.00037597602931782603, 'zcp_snip': 44.591514587402344, 'lat_1080ti_1': 0.6544065171504097, 'lat_1080ti_32': 0.7115171182288258, 'lat_1080ti_64': 0.5396081311438325, 'lat_2080ti_1': 0.7496120855293524, 'lat_2080ti_32': 0.6626995101678338, 'lat_2080ti_64': 0.5619101259815783, 'lat_essential_ph_1': 0.49056603773584906, 'lat_eyeriss': 0.5240863330460455, 'lat_fpga': 0.6055274641546027, 'lat_gold_6226': 0.41560954922999077, 'lat_gold_6240': 0.7351416531071326, 'lat_pixel2': 0.5434782608695652, 'lat_pixel3': 0.5137202317568728, 'lat_raspi4': 0.5230496307401673, 'lat_samsung_a50': 0.24210526315789474, 'lat_samsung_s7': 0.2125984251968504, 'lat_silver_4114': 0.6216641000028352, 'lat_silver_4210r': 0.6330297583313867, 'lat_titan_rtx_1': 0.7179269730869406, 'lat_titan_rtx_32': 0.6562181819849924, 'lat_titan_rtx_64': 0.597382525281863, 'lat_titanx_1': 0.38735373611635604, 'lat_titanx_32': 0.6164347466090458, 'lat_titanx_64': 0.5065377060190932, 'lat_titanxp_1': 0.7113764598101152, 'lat_titanxp_32': 0.6584308329351508, 'lat_titanxp_64': 0.5619441318039554}
FBNet_127	FBNet	127	127	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_768[FLOAT, 16x3x3x3] %onnx::Conv_769[FLOAT, 16] %onnx::Conv_771[FLOAT, 96x16x1x1] %onnx::Conv_772[FLOAT, 96] %onnx::Conv_774[FLOAT, 96x1x5x5] %onnx::Conv_777[FLOAT, 16x96x1x1] %onnx::Conv_780[FLOAT, 16x8x1x1] %onnx::Conv_783[FLOAT, 16x1x3x3] %onnx::Conv_786[FLOAT, 24x8x1x1] %onnx::Conv_787[FLOAT, 24] %onnx::Conv_789[FLOAT, 24x12x1x1] %onnx::Conv_792[FLOAT, 24x1x3x3] %onnx::Conv_795[FLOAT, 24x12x1x1] %onnx::Conv_798[FLOAT, 72x24x1x1] %onnx::Conv_799[FLOAT, 72] %onnx::Conv_801[FLOAT, 72x1x3x3] %onnx::Conv_804[FLOAT, 24x72x1x1] %onnx::Conv_807[FLOAT, 24x12x1x1] %onnx::Conv_810[FLOAT, 24x1x5x5] %onnx::Conv_813[FLOAT, 24x12x1x1] %onnx::Conv_816[FLOAT, 24x24x1x1] %onnx::Conv_819[FLOAT, 24x1x5x5] %onnx::Conv_822[FLOAT, 32x24x1x1] %onnx::Conv_823[FLOAT, 32] %onnx::Conv_825[FLOAT, 96x32x1x1] %onnx::Conv_828[FLOAT, 96x1x3x3] %onnx::Conv_831[FLOAT, 32x96x1x1] %onnx::Conv_834[FLOAT, 32x32x1x1] %onnx::Conv_837[FLOAT, 32x1x3x3] %onnx::Conv_840[FLOAT, 32x32x1x1] %onnx::Conv_843[FLOAT, 96x32x1x1] %onnx::Conv_846[FLOAT, 96x1x5x5] %onnx::Conv_849[FLOAT, 32x96x1x1] %onnx::Conv_852[FLOAT, 32x32x1x1] %onnx::Conv_855[FLOAT, 32x1x3x3] %onnx::Conv_858[FLOAT, 64x32x1x1] %onnx::Conv_859[FLOAT, 64] %onnx::Conv_861[FLOAT, 384x64x1x1] %onnx::Conv_862[FLOAT, 384] %onnx::Conv_864[FLOAT, 384x1x3x3] %onnx::Conv_867[FLOAT, 64x384x1x1] %onnx::Conv_870[FLOAT, 64x32x1x1] %onnx::Conv_873[FLOAT, 64x1x5x5] %onnx::Conv_876[FLOAT, 64x32x1x1] %onnx::Conv_879[FLOAT, 192x64x1x1] %onnx::Conv_880[FLOAT, 192] %onnx::Conv_882[FLOAT, 192x1x5x5] %onnx::Conv_885[FLOAT, 64x192x1x1] %onnx::Conv_888[FLOAT, 192x64x1x1] %onnx::Conv_891[FLOAT, 192x1x3x3] %onnx::Conv_894[FLOAT, 112x192x1x1] %onnx::Conv_895[FLOAT, 112] %onnx::Conv_897[FLOAT, 112x56x1x1] %onnx::Conv_900[FLOAT, 112x1x3x3] %onnx::Conv_903[FLOAT, 112x56x1x1] %onnx::Conv_906[FLOAT, 672x112x1x1] %onnx::Conv_907[FLOAT, 672] %onnx::Conv_909[FLOAT, 672x1x5x5] %onnx::Conv_912[FLOAT, 112x672x1x1] %onnx::Conv_915[FLOAT, 672x112x1x1] %onnx::Conv_918[FLOAT, 672x1x3x3] %onnx::Conv_921[FLOAT, 112x672x1x1] %onnx::Conv_924[FLOAT, 112x56x1x1] %onnx::Conv_927[FLOAT, 112x1x5x5] %onnx::Conv_930[FLOAT, 184x56x1x1] %onnx::Conv_931[FLOAT, 184] %onnx::Conv_933[FLOAT, 1104x184x1x1] %onnx::Conv_934[FLOAT, 1104] %onnx::Conv_936[FLOAT, 1104x1x5x5] %onnx::Conv_939[FLOAT, 184x1104x1x1] %onnx::Conv_942[FLOAT, 184x92x1x1] %onnx::Conv_945[FLOAT, 184x1x3x3] %onnx::Conv_948[FLOAT, 184x92x1x1] %onnx::Conv_951[FLOAT, 1104x184x1x1] %onnx::Conv_954[FLOAT, 1104x1x5x5] %onnx::Conv_957[FLOAT, 184x1104x1x1] %onnx::Conv_960[FLOAT, 184x92x1x1] %onnx::Conv_963[FLOAT, 184x1x5x5] %onnx::Conv_966[FLOAT, 352x92x1x1] %onnx::Conv_967[FLOAT, 352] %onnx::Conv_969[FLOAT, 1504x352x1x1] %onnx::Conv_970[FLOAT, 1504] ) { %onnx::Conv_964 = Identity(%onnx::Conv_931) %onnx::Conv_961 = Identity(%onnx::Conv_931) %onnx::Conv_958 = Identity(%onnx::Conv_931) %onnx::Conv_955 = Identity(%onnx::Conv_934) %onnx::Conv_952 = Identity(%onnx::Conv_934) %onnx::Conv_949 = Identity(%onnx::Conv_931) %onnx::Conv_946 = Identity(%onnx::Conv_931) %onnx::Conv_943 = Identity(%onnx::Conv_931) %onnx::Conv_940 = Identity(%onnx::Conv_931) %onnx::Conv_937 = Identity(%onnx::Conv_934) %onnx::Conv_928 = Identity(%onnx::Conv_895) %onnx::Conv_925 = Identity(%onnx::Conv_895) %onnx::Conv_922 = Identity(%onnx::Conv_895) %onnx::Conv_919 = Identity(%onnx::Conv_907) %onnx::Conv_916 = Identity(%onnx::Conv_907) %onnx::Conv_913 = Identity(%onnx::Conv_895) %onnx::Conv_910 = Identity(%onnx::Conv_907) %onnx::Conv_904 = Identity(%onnx::Conv_895) %onnx::Conv_901 = Identity(%onnx::Conv_895) %onnx::Conv_898 = Identity(%onnx::Conv_895) %onnx::Conv_892 = Identity(%onnx::Conv_880) %onnx::Conv_889 = Identity(%onnx::Conv_880) %onnx::Conv_886 = Identity(%onnx::Conv_859) %onnx::Conv_883 = Identity(%onnx::Conv_880) %onnx::Conv_877 = Identity(%onnx::Conv_859) %onnx::Conv_874 = Identity(%onnx::Conv_859) %onnx::Conv_871 = Identity(%onnx::Conv_859) %onnx::Conv_868 = Identity(%onnx::Conv_859) %onnx::Conv_865 = Identity(%onnx::Conv_862) %onnx::Conv_856 = Identity(%onnx::Conv_823) %onnx::Conv_853 = Identity(%onnx::Conv_823) %onnx::Conv_850 = Identity(%onnx::Conv_823) %onnx::Conv_847 = Identity(%onnx::Conv_772) %onnx::Conv_844 = Identity(%onnx::Conv_772) %onnx::Conv_841 = Identity(%onnx::Conv_823) %onnx::Conv_838 = Identity(%onnx::Conv_823) %onnx::Conv_835 = Identity(%onnx::Conv_823) %onnx::Conv_832 = Identity(%onnx::Conv_823) %onnx::Conv_829 = Identity(%onnx::Conv_772) %onnx::Conv_826 = Identity(%onnx::Conv_772) %onnx::Conv_820 = Identity(%onnx::Conv_787) %onnx::Conv_817 = Identity(%onnx::Conv_787) %onnx::Conv_814 = Identity(%onnx::Conv_787) %onnx::Conv_811 = Identity(%onnx::Conv_787) %onnx::Conv_808 = Identity(%onnx::Conv_787) %onnx::Conv_805 = Identity(%onnx::Conv_787) %onnx::Conv_802 = Identity(%onnx::Conv_799) %onnx::Conv_796 = Identity(%onnx::Conv_787) %onnx::Conv_793 = Identity(%onnx::Conv_787) %onnx::Conv_790 = Identity(%onnx::Conv_787) %onnx::Conv_784 = Identity(%onnx::Conv_769) %onnx::Conv_781 = Identity(%onnx::Conv_769) %onnx::Conv_778 = Identity(%onnx::Conv_769) %onnx::Conv_775 = Identity(%onnx::Conv_772) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_768, %onnx::Conv_769) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_771, %onnx::Conv_772) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.0/nl/Relu_output_0, %onnx::Conv_774, %onnx::Conv_775) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_777, %onnx::Conv_778) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_780, %onnx::Conv_781) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.1/shuffle/Reshape_output_0 = Reshape(%/cells.1/nl/Relu_output_0, %/cells.1/shuffle/Constant_output_0) %/cells.1/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.1/shuffle/Reshape_output_0) %/cells.1/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.1/shuffle/Reshape_1_output_0 = Reshape(%/cells.1/shuffle/Transpose_output_0, %/cells.1/shuffle/Constant_1_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.1/shuffle/Reshape_1_output_0, %onnx::Conv_783, %onnx::Conv_784) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_786, %onnx::Conv_787) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_789, %onnx::Conv_790) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.2/shuffle/Reshape_output_0 = Reshape(%/cells.2/nl/Relu_output_0, %/cells.2/shuffle/Constant_output_0) %/cells.2/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.2/shuffle/Reshape_output_0) %/cells.2/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.2/shuffle/Reshape_1_output_0 = Reshape(%/cells.2/shuffle/Transpose_output_0, %/cells.2/shuffle/Constant_1_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.2/shuffle/Reshape_1_output_0, %onnx::Conv_792, %onnx::Conv_793) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_795, %onnx::Conv_796) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_798, %onnx::Conv_799) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.3/nl/Relu_output_0, %onnx::Conv_801, %onnx::Conv_802) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_804, %onnx::Conv_805) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_807, %onnx::Conv_808) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.4/shuffle/Reshape_output_0 = Reshape(%/cells.4/nl/Relu_output_0, %/cells.4/shuffle/Constant_output_0) %/cells.4/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.4/shuffle/Reshape_output_0) %/cells.4/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.4/shuffle/Reshape_1_output_0 = Reshape(%/cells.4/shuffle/Transpose_output_0, %/cells.4/shuffle/Constant_1_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.4/shuffle/Reshape_1_output_0, %onnx::Conv_810, %onnx::Conv_811) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_813, %onnx::Conv_814) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/Add_output_0, %onnx::Conv_816, %onnx::Conv_817) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.5/nl/Relu_output_0, %onnx::Conv_819, %onnx::Conv_820) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_822, %onnx::Conv_823) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_825, %onnx::Conv_826) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.6/nl/Relu_output_0, %onnx::Conv_828, %onnx::Conv_829) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_831, %onnx::Conv_832) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_834, %onnx::Conv_835) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.7/nl/Relu_output_0, %onnx::Conv_837, %onnx::Conv_838) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_840, %onnx::Conv_841) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_843, %onnx::Conv_844) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.8/nl/Relu_output_0, %onnx::Conv_846, %onnx::Conv_847) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_849, %onnx::Conv_850) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_852, %onnx::Conv_853) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.9/nl/Relu_output_0, %onnx::Conv_855, %onnx::Conv_856) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_858, %onnx::Conv_859) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_861, %onnx::Conv_862) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.10/nl/Relu_output_0, %onnx::Conv_864, %onnx::Conv_865) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_867, %onnx::Conv_868) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_870, %onnx::Conv_871) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.11/shuffle/Reshape_output_0 = Reshape(%/cells.11/nl/Relu_output_0, %/cells.11/shuffle/Constant_output_0) %/cells.11/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.11/shuffle/Reshape_output_0) %/cells.11/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.11/shuffle/Reshape_1_output_0 = Reshape(%/cells.11/shuffle/Transpose_output_0, %/cells.11/shuffle/Constant_1_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.11/shuffle/Reshape_1_output_0, %onnx::Conv_873, %onnx::Conv_874) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_876, %onnx::Conv_877) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_879, %onnx::Conv_880) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.12/nl/Relu_output_0, %onnx::Conv_882, %onnx::Conv_883) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_885, %onnx::Conv_886) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_888, %onnx::Conv_889) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.13/nl/Relu_output_0, %onnx::Conv_891, %onnx::Conv_892) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_894, %onnx::Conv_895) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_897, %onnx::Conv_898) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.14/shuffle/Reshape_output_0 = Reshape(%/cells.14/nl/Relu_output_0, %/cells.14/shuffle/Constant_output_0) %/cells.14/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.14/shuffle/Reshape_output_0) %/cells.14/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.14/shuffle/Reshape_1_output_0 = Reshape(%/cells.14/shuffle/Transpose_output_0, %/cells.14/shuffle/Constant_1_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.14/shuffle/Reshape_1_output_0, %onnx::Conv_900, %onnx::Conv_901) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_903, %onnx::Conv_904) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_906, %onnx::Conv_907) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.15/nl/Relu_output_0, %onnx::Conv_909, %onnx::Conv_910) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_912, %onnx::Conv_913) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_915, %onnx::Conv_916) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.16/nl/Relu_output_0, %onnx::Conv_918, %onnx::Conv_919) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_921, %onnx::Conv_922) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.15/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_924, %onnx::Conv_925) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.17/shuffle/Reshape_output_0 = Reshape(%/cells.17/nl/Relu_output_0, %/cells.17/shuffle/Constant_output_0) %/cells.17/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.17/shuffle/Reshape_output_0) %/cells.17/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.17/shuffle/Reshape_1_output_0 = Reshape(%/cells.17/shuffle/Transpose_output_0, %/cells.17/shuffle/Constant_1_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.17/shuffle/Reshape_1_output_0, %onnx::Conv_927, %onnx::Conv_928) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_930, %onnx::Conv_931) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_933, %onnx::Conv_934) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.18/nl/Relu_output_0, %onnx::Conv_936, %onnx::Conv_937) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_939, %onnx::Conv_940) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_942, %onnx::Conv_943) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.19/shuffle/Reshape_output_0 = Reshape(%/cells.19/nl/Relu_output_0, %/cells.19/shuffle/Constant_output_0) %/cells.19/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.19/shuffle/Reshape_output_0) %/cells.19/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.19/shuffle/Reshape_1_output_0 = Reshape(%/cells.19/shuffle/Transpose_output_0, %/cells.19/shuffle/Constant_1_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.19/shuffle/Reshape_1_output_0, %onnx::Conv_945, %onnx::Conv_946) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_948, %onnx::Conv_949) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_951, %onnx::Conv_952) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.20/nl/Relu_output_0, %onnx::Conv_954, %onnx::Conv_955) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_957, %onnx::Conv_958) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_960, %onnx::Conv_961) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.21/shuffle/Reshape_output_0 = Reshape(%/cells.21/nl/Relu_output_0, %/cells.21/shuffle/Constant_output_0) %/cells.21/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.21/shuffle/Reshape_output_0) %/cells.21/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.21/shuffle/Reshape_1_output_0 = Reshape(%/cells.21/shuffle/Transpose_output_0, %/cells.21/shuffle/Constant_1_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.21/shuffle/Reshape_1_output_0, %onnx::Conv_963, %onnx::Conv_964) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_966, %onnx::Conv_967) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_969, %onnx::Conv_970) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %766 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %766 }	val_accuracy	75,786,112	2,181,796	{'zcp_synflow': 78.60491461786431, 'zcp_zen': 72.14070129394531, 'zcp_epe_nas': 16.875473245505738, 'zcp_fisher': 0.18045279383659363, 'zcp_flops': 75786112.0, 'zcp_grad_norm': 28.619937896728516, 'zcp_grasp': -0.23681640625, 'zcp_jacov': -16.05690921694754, 'zcp_l2_norm': 664.7483520507812, 'zcp_nwot': 212.23080900600684, 'zcp_params': 2181796.0, 'zcp_plain': -0.001139551866799593, 'zcp_snip': 52.018733978271484, 'lat_1080ti_1': 0.7641769324490955, 'lat_1080ti_32': 0.7267845391712681, 'lat_1080ti_64': 0.5453436171954767, 'lat_2080ti_1': 0.8612462572312516, 'lat_2080ti_32': 0.7275516841624232, 'lat_2080ti_64': 0.5356013975382279, 'lat_essential_ph_1': 0.5660377358490566, 'lat_eyeriss': 0.5378486421066143, 'lat_fpga': 0.6109401438763494, 'lat_gold_6226': 0.49330263850972444, 'lat_gold_6240': 0.9634733569106123, 'lat_pixel2': 0.41304347826086957, 'lat_pixel3': 0.552638472258359, 'lat_raspi4': 0.5878477254954615, 'lat_samsung_a50': 0.2631578947368421, 'lat_samsung_s7': 0.2755905511811024, 'lat_silver_4114': 0.837962332089428, 'lat_silver_4210r': 0.8676531129045599, 'lat_titan_rtx_1': 0.8271271736888957, 'lat_titan_rtx_32': 0.7496622657817921, 'lat_titan_rtx_64': 0.6016448534948983, 'lat_titanx_1': 0.447913096902871, 'lat_titanx_32': 0.6526914847690455, 'lat_titanx_64': 0.5614023645434489, 'lat_titanxp_1': 0.7948639827130105, 'lat_titanxp_32': 0.7117029532582454, 'lat_titanxp_64': 0.5565493868948226}
FBNet_3485	FBNet	3485	3485	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_676[FLOAT, 16x3x3x3] %onnx::Conv_677[FLOAT, 16] %onnx::Conv_679[FLOAT, 48x16x1x1] %onnx::Conv_680[FLOAT, 48] %onnx::Conv_682[FLOAT, 48x1x3x3] %onnx::Conv_685[FLOAT, 16x48x1x1] %onnx::Conv_688[FLOAT, 16x16x1x1] %onnx::Conv_691[FLOAT, 16x1x5x5] %onnx::Conv_694[FLOAT, 24x16x1x1] %onnx::Conv_695[FLOAT, 24] %onnx::Conv_697[FLOAT, 72x24x1x1] %onnx::Conv_698[FLOAT, 72] %onnx::Conv_700[FLOAT, 72x1x3x3] %onnx::Conv_703[FLOAT, 24x72x1x1] %onnx::Conv_706[FLOAT, 24x24x1x1] %onnx::Conv_709[FLOAT, 24x1x3x3] %onnx::Conv_712[FLOAT, 24x24x1x1] %onnx::Conv_715[FLOAT, 24x12x1x1] %onnx::Conv_718[FLOAT, 24x1x3x3] %onnx::Conv_721[FLOAT, 24x12x1x1] %onnx::Conv_724[FLOAT, 24x12x1x1] %onnx::Conv_727[FLOAT, 24x1x5x5] %onnx::Conv_730[FLOAT, 32x12x1x1] %onnx::Conv_731[FLOAT, 32] %onnx::Conv_733[FLOAT, 96x32x1x1] %onnx::Conv_734[FLOAT, 96] %onnx::Conv_736[FLOAT, 96x1x3x3] %onnx::Conv_739[FLOAT, 32x96x1x1] %onnx::Conv_742[FLOAT, 32x32x1x1] %onnx::Conv_745[FLOAT, 32x1x5x5] %onnx::Conv_748[FLOAT, 32x32x1x1] %onnx::Conv_751[FLOAT, 32x32x1x1] %onnx::Conv_754[FLOAT, 32x1x3x3] %onnx::Conv_757[FLOAT, 64x32x1x1] %onnx::Conv_758[FLOAT, 64] %onnx::Conv_760[FLOAT, 64x64x1x1] %onnx::Conv_763[FLOAT, 64x1x5x5] %onnx::Conv_766[FLOAT, 64x64x1x1] %onnx::Conv_769[FLOAT, 64x32x1x1] %onnx::Conv_772[FLOAT, 64x1x3x3] %onnx::Conv_775[FLOAT, 64x32x1x1] %onnx::Conv_778[FLOAT, 384x64x1x1] %onnx::Conv_779[FLOAT, 384] %onnx::Conv_781[FLOAT, 384x1x3x3] %onnx::Conv_784[FLOAT, 112x384x1x1] %onnx::Conv_785[FLOAT, 112] %onnx::Conv_787[FLOAT, 112x56x1x1] %onnx::Conv_790[FLOAT, 112x1x5x5] %onnx::Conv_793[FLOAT, 112x56x1x1] %onnx::Conv_796[FLOAT, 112x56x1x1] %onnx::Conv_799[FLOAT, 112x1x5x5] %onnx::Conv_802[FLOAT, 112x56x1x1] %onnx::Conv_805[FLOAT, 112x56x1x1] %onnx::Conv_808[FLOAT, 112x1x3x3] %onnx::Conv_811[FLOAT, 112x56x1x1] %onnx::Conv_814[FLOAT, 672x112x1x1] %onnx::Conv_815[FLOAT, 672] %onnx::Conv_817[FLOAT, 672x1x3x3] %onnx::Conv_820[FLOAT, 184x672x1x1] %onnx::Conv_821[FLOAT, 184] %onnx::Conv_823[FLOAT, 184x184x1x1] %onnx::Conv_826[FLOAT, 184x1x3x3] %onnx::Conv_829[FLOAT, 184x184x1x1] %onnx::Conv_832[FLOAT, 184x184x1x1] %onnx::Conv_835[FLOAT, 184x1x5x5] %onnx::Conv_838[FLOAT, 184x184x1x1] %onnx::Conv_841[FLOAT, 184x184x1x1] %onnx::Conv_844[FLOAT, 184x1x5x5] %onnx::Conv_847[FLOAT, 184x184x1x1] %onnx::Conv_850[FLOAT, 552x184x1x1] %onnx::Conv_851[FLOAT, 552] %onnx::Conv_853[FLOAT, 552x1x3x3] %onnx::Conv_856[FLOAT, 352x552x1x1] %onnx::Conv_857[FLOAT, 352] %onnx::Conv_859[FLOAT, 1504x352x1x1] %onnx::Conv_860[FLOAT, 1504] ) { %onnx::Conv_854 = Identity(%onnx::Conv_851) %onnx::Conv_848 = Identity(%onnx::Conv_821) %onnx::Conv_845 = Identity(%onnx::Conv_821) %onnx::Conv_842 = Identity(%onnx::Conv_821) %onnx::Conv_839 = Identity(%onnx::Conv_821) %onnx::Conv_836 = Identity(%onnx::Conv_821) %onnx::Conv_833 = Identity(%onnx::Conv_821) %onnx::Conv_830 = Identity(%onnx::Conv_821) %onnx::Conv_827 = Identity(%onnx::Conv_821) %onnx::Conv_824 = Identity(%onnx::Conv_821) %onnx::Conv_818 = Identity(%onnx::Conv_815) %onnx::Conv_812 = Identity(%onnx::Conv_785) %onnx::Conv_809 = Identity(%onnx::Conv_785) %onnx::Conv_806 = Identity(%onnx::Conv_785) %onnx::Conv_803 = Identity(%onnx::Conv_785) %onnx::Conv_800 = Identity(%onnx::Conv_785) %onnx::Conv_797 = Identity(%onnx::Conv_785) %onnx::Conv_794 = Identity(%onnx::Conv_785) %onnx::Conv_791 = Identity(%onnx::Conv_785) %onnx::Conv_788 = Identity(%onnx::Conv_785) %onnx::Conv_782 = Identity(%onnx::Conv_779) %onnx::Conv_776 = Identity(%onnx::Conv_758) %onnx::Conv_773 = Identity(%onnx::Conv_758) %onnx::Conv_770 = Identity(%onnx::Conv_758) %onnx::Conv_767 = Identity(%onnx::Conv_758) %onnx::Conv_764 = Identity(%onnx::Conv_758) %onnx::Conv_761 = Identity(%onnx::Conv_758) %onnx::Conv_755 = Identity(%onnx::Conv_731) %onnx::Conv_752 = Identity(%onnx::Conv_731) %onnx::Conv_749 = Identity(%onnx::Conv_731) %onnx::Conv_746 = Identity(%onnx::Conv_731) %onnx::Conv_743 = Identity(%onnx::Conv_731) %onnx::Conv_740 = Identity(%onnx::Conv_731) %onnx::Conv_737 = Identity(%onnx::Conv_734) %onnx::Conv_728 = Identity(%onnx::Conv_695) %onnx::Conv_725 = Identity(%onnx::Conv_695) %onnx::Conv_722 = Identity(%onnx::Conv_695) %onnx::Conv_719 = Identity(%onnx::Conv_695) %onnx::Conv_716 = Identity(%onnx::Conv_695) %onnx::Conv_713 = Identity(%onnx::Conv_695) %onnx::Conv_710 = Identity(%onnx::Conv_695) %onnx::Conv_707 = Identity(%onnx::Conv_695) %onnx::Conv_704 = Identity(%onnx::Conv_695) %onnx::Conv_701 = Identity(%onnx::Conv_698) %onnx::Conv_692 = Identity(%onnx::Conv_677) %onnx::Conv_689 = Identity(%onnx::Conv_677) %onnx::Conv_686 = Identity(%onnx::Conv_677) %onnx::Conv_683 = Identity(%onnx::Conv_680) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_676, %onnx::Conv_677) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_679, %onnx::Conv_680) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 48, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.0/nl/Relu_output_0, %onnx::Conv_682, %onnx::Conv_683) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_685, %onnx::Conv_686) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_688, %onnx::Conv_689) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.1/nl/Relu_output_0, %onnx::Conv_691, %onnx::Conv_692) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_694, %onnx::Conv_695) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_697, %onnx::Conv_698) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.2/nl/Relu_output_0, %onnx::Conv_700, %onnx::Conv_701) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_703, %onnx::Conv_704) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_706, %onnx::Conv_707) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.3/nl/Relu_output_0, %onnx::Conv_709, %onnx::Conv_710) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_712, %onnx::Conv_713) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_715, %onnx::Conv_716) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.4/shuffle/Reshape_output_0 = Reshape(%/cells.4/nl/Relu_output_0, %/cells.4/shuffle/Constant_output_0) %/cells.4/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.4/shuffle/Reshape_output_0) %/cells.4/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.4/shuffle/Reshape_1_output_0 = Reshape(%/cells.4/shuffle/Transpose_output_0, %/cells.4/shuffle/Constant_1_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.4/shuffle/Reshape_1_output_0, %onnx::Conv_718, %onnx::Conv_719) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_721, %onnx::Conv_722) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/Add_output_0, %onnx::Conv_724, %onnx::Conv_725) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.5/shuffle/Reshape_output_0 = Reshape(%/cells.5/nl/Relu_output_0, %/cells.5/shuffle/Constant_output_0) %/cells.5/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.5/shuffle/Reshape_output_0) %/cells.5/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.5/shuffle/Reshape_1_output_0 = Reshape(%/cells.5/shuffle/Transpose_output_0, %/cells.5/shuffle/Constant_1_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.5/shuffle/Reshape_1_output_0, %onnx::Conv_727, %onnx::Conv_728) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_730, %onnx::Conv_731) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_733, %onnx::Conv_734) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.6/nl/Relu_output_0, %onnx::Conv_736, %onnx::Conv_737) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_739, %onnx::Conv_740) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_742, %onnx::Conv_743) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.7/nl/Relu_output_0, %onnx::Conv_745, %onnx::Conv_746) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_748, %onnx::Conv_749) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_751, %onnx::Conv_752) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.9/nl/Relu_output_0, %onnx::Conv_754, %onnx::Conv_755) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_757, %onnx::Conv_758) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_760, %onnx::Conv_761) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.11/nl/Relu_output_0, %onnx::Conv_763, %onnx::Conv_764) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_766, %onnx::Conv_767) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_769, %onnx::Conv_770) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.12/shuffle/Reshape_output_0 = Reshape(%/cells.12/nl/Relu_output_0, %/cells.12/shuffle/Constant_output_0) %/cells.12/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.12/shuffle/Reshape_output_0) %/cells.12/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.12/shuffle/Reshape_1_output_0 = Reshape(%/cells.12/shuffle/Transpose_output_0, %/cells.12/shuffle/Constant_1_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.12/shuffle/Reshape_1_output_0, %onnx::Conv_772, %onnx::Conv_773) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_775, %onnx::Conv_776) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_778, %onnx::Conv_779) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.13/nl/Relu_output_0, %onnx::Conv_781, %onnx::Conv_782) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_784, %onnx::Conv_785) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_787, %onnx::Conv_788) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.14/shuffle/Reshape_output_0 = Reshape(%/cells.14/nl/Relu_output_0, %/cells.14/shuffle/Constant_output_0) %/cells.14/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.14/shuffle/Reshape_output_0) %/cells.14/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.14/shuffle/Reshape_1_output_0 = Reshape(%/cells.14/shuffle/Transpose_output_0, %/cells.14/shuffle/Constant_1_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.14/shuffle/Reshape_1_output_0, %onnx::Conv_790, %onnx::Conv_791) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_793, %onnx::Conv_794) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_796, %onnx::Conv_797) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.15/shuffle/Reshape_output_0 = Reshape(%/cells.15/nl/Relu_output_0, %/cells.15/shuffle/Constant_output_0) %/cells.15/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.15/shuffle/Reshape_output_0) %/cells.15/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.15/shuffle/Reshape_1_output_0 = Reshape(%/cells.15/shuffle/Transpose_output_0, %/cells.15/shuffle/Constant_1_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.15/shuffle/Reshape_1_output_0, %onnx::Conv_799, %onnx::Conv_800) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_802, %onnx::Conv_803) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_805, %onnx::Conv_806) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.16/shuffle/Reshape_output_0 = Reshape(%/cells.16/nl/Relu_output_0, %/cells.16/shuffle/Constant_output_0) %/cells.16/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.16/shuffle/Reshape_output_0) %/cells.16/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.16/shuffle/Reshape_1_output_0 = Reshape(%/cells.16/shuffle/Transpose_output_0, %/cells.16/shuffle/Constant_1_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.16/shuffle/Reshape_1_output_0, %onnx::Conv_808, %onnx::Conv_809) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_811, %onnx::Conv_812) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.15/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_814, %onnx::Conv_815) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.17/nl/Relu_output_0, %onnx::Conv_817, %onnx::Conv_818) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_820, %onnx::Conv_821) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_823, %onnx::Conv_824) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.18/nl/Relu_output_0, %onnx::Conv_826, %onnx::Conv_827) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_829, %onnx::Conv_830) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_832, %onnx::Conv_833) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.19/nl/Relu_output_0, %onnx::Conv_835, %onnx::Conv_836) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_838, %onnx::Conv_839) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_841, %onnx::Conv_842) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.20/nl/Relu_output_0, %onnx::Conv_844, %onnx::Conv_845) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_847, %onnx::Conv_848) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_850, %onnx::Conv_851) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 552, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.21/nl/Relu_output_0, %onnx::Conv_853, %onnx::Conv_854) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_856, %onnx::Conv_857) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_859, %onnx::Conv_860) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %674 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %674 }	val_accuracy	47,687,040	1,572,516	{'zcp_synflow': 73.30521690852835, 'zcp_zen': 63.072471618652344, 'zcp_epe_nas': 7.754109169371624, 'zcp_fisher': 0.11267126351594925, 'zcp_flops': 47687040.0, 'zcp_grad_norm': 23.091533660888672, 'zcp_grasp': -0.00634002685546875, 'zcp_jacov': -16.059510407077898, 'zcp_l2_norm': 550.7718505859375, 'zcp_nwot': 206.26059512430226, 'zcp_params': 1572516.0, 'zcp_plain': 0.0001299503492191434, 'zcp_snip': 38.88660430908203, 'lat_1080ti_1': 0.6355155492486343, 'lat_1080ti_32': 0.5242840882442154, 'lat_1080ti_64': 0.2790982724389212, 'lat_2080ti_1': 0.6372027574331622, 'lat_2080ti_32': 0.5084825579232929, 'lat_2080ti_64': 0.3146841125521647, 'lat_essential_ph_1': 0.18867924528301888, 'lat_eyeriss': 0.1846955054737499, 'lat_fpga': 0.1947179370466757, 'lat_gold_6226': 0.1410545793105418, 'lat_gold_6240': 0.3804779346828379, 'lat_pixel2': 0.2608695652173913, 'lat_pixel3': 0.17682180870853823, 'lat_raspi4': 0.2546763070479019, 'lat_samsung_a50': 0.16842105263157894, 'lat_samsung_s7': 0.10236220472440945, 'lat_silver_4114': 0.41081279696465195, 'lat_silver_4210r': 0.4172393214408419, 'lat_titan_rtx_1': 0.59860681691951, 'lat_titan_rtx_32': 0.5083984577318583, 'lat_titan_rtx_64': 0.36208970993635803, 'lat_titanx_1': 0.32379615396120043, 'lat_titanx_32': 0.4018133192053236, 'lat_titanx_64': 0.2552146787964519, 'lat_titanxp_1': 0.5614299114496685, 'lat_titanxp_32': 0.44989296218289154, 'lat_titanxp_64': 0.2999170059540494}
FBNet_1568	FBNet	1568	1568	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_677[FLOAT, 16x3x3x3] %onnx::Conv_678[FLOAT, 16] %onnx::Conv_680[FLOAT, 16x16x1x1] %onnx::Conv_683[FLOAT, 16x1x5x5] %onnx::Conv_686[FLOAT, 16x16x1x1] %onnx::Conv_689[FLOAT, 48x16x1x1] %onnx::Conv_690[FLOAT, 48] %onnx::Conv_692[FLOAT, 48x1x3x3] %onnx::Conv_695[FLOAT, 24x48x1x1] %onnx::Conv_696[FLOAT, 24] %onnx::Conv_698[FLOAT, 144x24x1x1] %onnx::Conv_699[FLOAT, 144] %onnx::Conv_701[FLOAT, 144x1x3x3] %onnx::Conv_704[FLOAT, 24x144x1x1] %onnx::Conv_707[FLOAT, 24x12x1x1] %onnx::Conv_710[FLOAT, 24x1x3x3] %onnx::Conv_713[FLOAT, 24x12x1x1] %onnx::Conv_716[FLOAT, 72x24x1x1] %onnx::Conv_717[FLOAT, 72] %onnx::Conv_719[FLOAT, 72x1x5x5] %onnx::Conv_722[FLOAT, 24x72x1x1] %onnx::Conv_725[FLOAT, 24x12x1x1] %onnx::Conv_728[FLOAT, 24x1x3x3] %onnx::Conv_731[FLOAT, 32x12x1x1] %onnx::Conv_732[FLOAT, 32] %onnx::Conv_734[FLOAT, 192x32x1x1] %onnx::Conv_735[FLOAT, 192] %onnx::Conv_737[FLOAT, 192x1x3x3] %onnx::Conv_740[FLOAT, 32x192x1x1] %onnx::Conv_743[FLOAT, 32x32x1x1] %onnx::Conv_746[FLOAT, 32x1x5x5] %onnx::Conv_749[FLOAT, 32x32x1x1] %onnx::Conv_752[FLOAT, 32x32x1x1] %onnx::Conv_755[FLOAT, 32x1x3x3] %onnx::Conv_758[FLOAT, 32x32x1x1] %onnx::Conv_761[FLOAT, 192x32x1x1] %onnx::Conv_764[FLOAT, 192x1x3x3] %onnx::Conv_767[FLOAT, 64x192x1x1] %onnx::Conv_768[FLOAT, 64] %onnx::Conv_770[FLOAT, 64x32x1x1] %onnx::Conv_773[FLOAT, 64x1x3x3] %onnx::Conv_776[FLOAT, 64x32x1x1] %onnx::Conv_779[FLOAT, 384x64x1x1] %onnx::Conv_780[FLOAT, 384] %onnx::Conv_782[FLOAT, 384x1x5x5] %onnx::Conv_785[FLOAT, 64x384x1x1] %onnx::Conv_788[FLOAT, 384x64x1x1] %onnx::Conv_791[FLOAT, 384x1x3x3] %onnx::Conv_794[FLOAT, 64x384x1x1] %onnx::Conv_797[FLOAT, 192x64x1x1] %onnx::Conv_800[FLOAT, 192x1x3x3] %onnx::Conv_803[FLOAT, 112x192x1x1] %onnx::Conv_804[FLOAT, 112] %onnx::Conv_806[FLOAT, 672x112x1x1] %onnx::Conv_807[FLOAT, 672] %onnx::Conv_809[FLOAT, 672x1x3x3] %onnx::Conv_812[FLOAT, 112x672x1x1] %onnx::Conv_815[FLOAT, 112x56x1x1] %onnx::Conv_818[FLOAT, 112x1x3x3] %onnx::Conv_821[FLOAT, 112x56x1x1] %onnx::Conv_824[FLOAT, 112x56x1x1] %onnx::Conv_827[FLOAT, 112x1x3x3] %onnx::Conv_830[FLOAT, 112x56x1x1] %onnx::Conv_833[FLOAT, 336x112x1x1] %onnx::Conv_834[FLOAT, 336] %onnx::Conv_836[FLOAT, 336x1x5x5] %onnx::Conv_839[FLOAT, 184x336x1x1] %onnx::Conv_840[FLOAT, 184] %onnx::Conv_842[FLOAT, 184x184x1x1] %onnx::Conv_845[FLOAT, 184x1x5x5] %onnx::Conv_848[FLOAT, 184x184x1x1] %onnx::Conv_851[FLOAT, 184x92x1x1] %onnx::Conv_854[FLOAT, 184x1x5x5] %onnx::Conv_857[FLOAT, 352x92x1x1] %onnx::Conv_858[FLOAT, 352] %onnx::Conv_860[FLOAT, 1504x352x1x1] %onnx::Conv_861[FLOAT, 1504] ) { %onnx::Conv_855 = Identity(%onnx::Conv_840) %onnx::Conv_852 = Identity(%onnx::Conv_840) %onnx::Conv_849 = Identity(%onnx::Conv_840) %onnx::Conv_846 = Identity(%onnx::Conv_840) %onnx::Conv_843 = Identity(%onnx::Conv_840) %onnx::Conv_837 = Identity(%onnx::Conv_834) %onnx::Conv_831 = Identity(%onnx::Conv_804) %onnx::Conv_828 = Identity(%onnx::Conv_804) %onnx::Conv_825 = Identity(%onnx::Conv_804) %onnx::Conv_822 = Identity(%onnx::Conv_804) %onnx::Conv_819 = Identity(%onnx::Conv_804) %onnx::Conv_816 = Identity(%onnx::Conv_804) %onnx::Conv_813 = Identity(%onnx::Conv_804) %onnx::Conv_810 = Identity(%onnx::Conv_807) %onnx::Conv_801 = Identity(%onnx::Conv_735) %onnx::Conv_798 = Identity(%onnx::Conv_735) %onnx::Conv_795 = Identity(%onnx::Conv_768) %onnx::Conv_792 = Identity(%onnx::Conv_780) %onnx::Conv_789 = Identity(%onnx::Conv_780) %onnx::Conv_786 = Identity(%onnx::Conv_768) %onnx::Conv_783 = Identity(%onnx::Conv_780) %onnx::Conv_777 = Identity(%onnx::Conv_768) %onnx::Conv_774 = Identity(%onnx::Conv_768) %onnx::Conv_771 = Identity(%onnx::Conv_768) %onnx::Conv_765 = Identity(%onnx::Conv_735) %onnx::Conv_762 = Identity(%onnx::Conv_735) %onnx::Conv_759 = Identity(%onnx::Conv_732) %onnx::Conv_756 = Identity(%onnx::Conv_732) %onnx::Conv_753 = Identity(%onnx::Conv_732) %onnx::Conv_750 = Identity(%onnx::Conv_732) %onnx::Conv_747 = Identity(%onnx::Conv_732) %onnx::Conv_744 = Identity(%onnx::Conv_732) %onnx::Conv_741 = Identity(%onnx::Conv_732) %onnx::Conv_738 = Identity(%onnx::Conv_735) %onnx::Conv_729 = Identity(%onnx::Conv_696) %onnx::Conv_726 = Identity(%onnx::Conv_696) %onnx::Conv_723 = Identity(%onnx::Conv_696) %onnx::Conv_720 = Identity(%onnx::Conv_717) %onnx::Conv_714 = Identity(%onnx::Conv_696) %onnx::Conv_711 = Identity(%onnx::Conv_696) %onnx::Conv_708 = Identity(%onnx::Conv_696) %onnx::Conv_705 = Identity(%onnx::Conv_696) %onnx::Conv_702 = Identity(%onnx::Conv_699) %onnx::Conv_693 = Identity(%onnx::Conv_690) %onnx::Conv_687 = Identity(%onnx::Conv_678) %onnx::Conv_684 = Identity(%onnx::Conv_678) %onnx::Conv_681 = Identity(%onnx::Conv_678) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_677, %onnx::Conv_678) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_680, %onnx::Conv_681) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.0/nl/Relu_output_0, %onnx::Conv_683, %onnx::Conv_684) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_686, %onnx::Conv_687) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_689, %onnx::Conv_690) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 48, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.1/nl/Relu_output_0, %onnx::Conv_692, %onnx::Conv_693) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_695, %onnx::Conv_696) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_698, %onnx::Conv_699) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.2/nl/Relu_output_0, %onnx::Conv_701, %onnx::Conv_702) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_704, %onnx::Conv_705) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_707, %onnx::Conv_708) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.3/shuffle/Reshape_output_0 = Reshape(%/cells.3/nl/Relu_output_0, %/cells.3/shuffle/Constant_output_0) %/cells.3/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.3/shuffle/Reshape_output_0) %/cells.3/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.3/shuffle/Reshape_1_output_0 = Reshape(%/cells.3/shuffle/Transpose_output_0, %/cells.3/shuffle/Constant_1_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.3/shuffle/Reshape_1_output_0, %onnx::Conv_710, %onnx::Conv_711) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_713, %onnx::Conv_714) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_716, %onnx::Conv_717) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.4/nl/Relu_output_0, %onnx::Conv_719, %onnx::Conv_720) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_722, %onnx::Conv_723) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/Add_output_0, %onnx::Conv_725, %onnx::Conv_726) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.5/shuffle/Reshape_output_0 = Reshape(%/cells.5/nl/Relu_output_0, %/cells.5/shuffle/Constant_output_0) %/cells.5/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.5/shuffle/Reshape_output_0) %/cells.5/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.5/shuffle/Reshape_1_output_0 = Reshape(%/cells.5/shuffle/Transpose_output_0, %/cells.5/shuffle/Constant_1_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.5/shuffle/Reshape_1_output_0, %onnx::Conv_728, %onnx::Conv_729) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_731, %onnx::Conv_732) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_734, %onnx::Conv_735) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.6/nl/Relu_output_0, %onnx::Conv_737, %onnx::Conv_738) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_740, %onnx::Conv_741) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_743, %onnx::Conv_744) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.7/nl/Relu_output_0, %onnx::Conv_746, %onnx::Conv_747) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_749, %onnx::Conv_750) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_752, %onnx::Conv_753) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.8/nl/Relu_output_0, %onnx::Conv_755, %onnx::Conv_756) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_758, %onnx::Conv_759) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_761, %onnx::Conv_762) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.9/nl/Relu_output_0, %onnx::Conv_764, %onnx::Conv_765) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_767, %onnx::Conv_768) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_770, %onnx::Conv_771) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.10/shuffle/Reshape_output_0 = Reshape(%/cells.10/nl/Relu_output_0, %/cells.10/shuffle/Constant_output_0) %/cells.10/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.10/shuffle/Reshape_output_0) %/cells.10/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.10/shuffle/Reshape_1_output_0 = Reshape(%/cells.10/shuffle/Transpose_output_0, %/cells.10/shuffle/Constant_1_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.10/shuffle/Reshape_1_output_0, %onnx::Conv_773, %onnx::Conv_774) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_776, %onnx::Conv_777) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_779, %onnx::Conv_780) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.11/nl/Relu_output_0, %onnx::Conv_782, %onnx::Conv_783) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_785, %onnx::Conv_786) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_788, %onnx::Conv_789) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.12/nl/Relu_output_0, %onnx::Conv_791, %onnx::Conv_792) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_794, %onnx::Conv_795) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_797, %onnx::Conv_798) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.13/nl/Relu_output_0, %onnx::Conv_800, %onnx::Conv_801) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_803, %onnx::Conv_804) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_806, %onnx::Conv_807) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.14/nl/Relu_output_0, %onnx::Conv_809, %onnx::Conv_810) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_812, %onnx::Conv_813) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_815, %onnx::Conv_816) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.15/shuffle/Reshape_output_0 = Reshape(%/cells.15/nl/Relu_output_0, %/cells.15/shuffle/Constant_output_0) %/cells.15/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.15/shuffle/Reshape_output_0) %/cells.15/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.15/shuffle/Reshape_1_output_0 = Reshape(%/cells.15/shuffle/Transpose_output_0, %/cells.15/shuffle/Constant_1_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.15/shuffle/Reshape_1_output_0, %onnx::Conv_818, %onnx::Conv_819) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_821, %onnx::Conv_822) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_824, %onnx::Conv_825) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.16/shuffle/Reshape_output_0 = Reshape(%/cells.16/nl/Relu_output_0, %/cells.16/shuffle/Constant_output_0) %/cells.16/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.16/shuffle/Reshape_output_0) %/cells.16/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.16/shuffle/Reshape_1_output_0 = Reshape(%/cells.16/shuffle/Transpose_output_0, %/cells.16/shuffle/Constant_1_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.16/shuffle/Reshape_1_output_0, %onnx::Conv_827, %onnx::Conv_828) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_830, %onnx::Conv_831) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.15/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_833, %onnx::Conv_834) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.17/nl/Relu_output_0, %onnx::Conv_836, %onnx::Conv_837) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_839, %onnx::Conv_840) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_842, %onnx::Conv_843) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.20/nl/Relu_output_0, %onnx::Conv_845, %onnx::Conv_846) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_848, %onnx::Conv_849) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_851, %onnx::Conv_852) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.21/shuffle/Reshape_output_0 = Reshape(%/cells.21/nl/Relu_output_0, %/cells.21/shuffle/Constant_output_0) %/cells.21/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.21/shuffle/Reshape_output_0) %/cells.21/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.21/shuffle/Reshape_1_output_0 = Reshape(%/cells.21/shuffle/Transpose_output_0, %/cells.21/shuffle/Constant_1_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.21/shuffle/Reshape_1_output_0, %onnx::Conv_854, %onnx::Conv_855) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_857, %onnx::Conv_858) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_860, %onnx::Conv_861) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %675 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %675 }	val_accuracy	65,187,200	1,327,388	{'zcp_synflow': 72.2635907550742, 'zcp_zen': 63.58768844604492, 'zcp_epe_nas': 7.1558327648049325, 'zcp_fisher': 0.0917033702135086, 'zcp_flops': 65187200.0, 'zcp_grad_norm': 23.125324249267578, 'zcp_grasp': 0.08304214477539062, 'zcp_jacov': -16.066632381758453, 'zcp_l2_norm': 558.7664184570312, 'zcp_nwot': 214.08015428147056, 'zcp_params': 1327388.0, 'zcp_plain': -0.0001658505789237097, 'zcp_snip': 40.27062225341797, 'lat_1080ti_1': 0.6519406743225438, 'lat_1080ti_32': 0.5656786971343442, 'lat_1080ti_64': 0.45211245471881784, 'lat_2080ti_1': 0.62905917594482, 'lat_2080ti_32': 0.578476621755608, 'lat_2080ti_64': 0.5015552313610923, 'lat_essential_ph_1': 0.32075471698113206, 'lat_eyeriss': 0.3928905433407293, 'lat_fpga': 0.4164497966534401, 'lat_gold_6226': 0.25588686873007604, 'lat_gold_6240': 0.4008920478480938, 'lat_pixel2': 0.2608695652173913, 'lat_pixel3': 0.3877969065260909, 'lat_raspi4': 0.3661232475450078, 'lat_samsung_a50': 0.15789473684210525, 'lat_samsung_s7': 0.14173228346456693, 'lat_silver_4114': 0.4322491337057003, 'lat_silver_4210r': 0.44710524047677264, 'lat_titan_rtx_1': 0.6035809206297426, 'lat_titan_rtx_32': 0.5677557161480838, 'lat_titan_rtx_64': 0.5255459950327348, 'lat_titanx_1': 0.31050339746189715, 'lat_titanx_32': 0.5456741550941001, 'lat_titanx_64': 0.4686689430976613, 'lat_titanxp_1': 0.5654950427181795, 'lat_titanxp_32': 0.5504368319396883, 'lat_titanxp_64': 0.492839207442998}
FBNet_22	FBNet	22	22	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_606[FLOAT, 16x3x3x3] %onnx::Conv_607[FLOAT, 16] %onnx::Conv_609[FLOAT, 16x16x1x1] %onnx::Conv_612[FLOAT, 16x1x3x3] %onnx::Conv_615[FLOAT, 24x16x1x1] %onnx::Conv_616[FLOAT, 24] %onnx::Conv_618[FLOAT, 24x12x1x1] %onnx::Conv_621[FLOAT, 24x1x5x5] %onnx::Conv_624[FLOAT, 24x12x1x1] %onnx::Conv_627[FLOAT, 24x12x1x1] %onnx::Conv_630[FLOAT, 24x1x5x5] %onnx::Conv_633[FLOAT, 24x12x1x1] %onnx::Conv_636[FLOAT, 24x24x1x1] %onnx::Conv_639[FLOAT, 24x1x5x5] %onnx::Conv_642[FLOAT, 32x24x1x1] %onnx::Conv_643[FLOAT, 32] %onnx::Conv_645[FLOAT, 192x32x1x1] %onnx::Conv_646[FLOAT, 192] %onnx::Conv_648[FLOAT, 192x1x5x5] %onnx::Conv_651[FLOAT, 32x192x1x1] %onnx::Conv_654[FLOAT, 32x16x1x1] %onnx::Conv_657[FLOAT, 32x1x3x3] %onnx::Conv_660[FLOAT, 32x16x1x1] %onnx::Conv_663[FLOAT, 192x32x1x1] %onnx::Conv_666[FLOAT, 192x1x3x3] %onnx::Conv_669[FLOAT, 32x192x1x1] %onnx::Conv_672[FLOAT, 96x32x1x1] %onnx::Conv_673[FLOAT, 96] %onnx::Conv_675[FLOAT, 96x1x3x3] %onnx::Conv_678[FLOAT, 64x96x1x1] %onnx::Conv_679[FLOAT, 64] %onnx::Conv_681[FLOAT, 64x32x1x1] %onnx::Conv_684[FLOAT, 64x1x3x3] %onnx::Conv_687[FLOAT, 64x32x1x1] %onnx::Conv_690[FLOAT, 384x64x1x1] %onnx::Conv_691[FLOAT, 384] %onnx::Conv_693[FLOAT, 384x1x3x3] %onnx::Conv_696[FLOAT, 64x384x1x1] %onnx::Conv_699[FLOAT, 384x64x1x1] %onnx::Conv_702[FLOAT, 384x1x5x5] %onnx::Conv_705[FLOAT, 64x384x1x1] %onnx::Conv_708[FLOAT, 112x64x1x1] %onnx::Conv_709[FLOAT, 112] %onnx::Conv_711[FLOAT, 336x112x1x1] %onnx::Conv_712[FLOAT, 336] %onnx::Conv_714[FLOAT, 336x1x3x3] %onnx::Conv_717[FLOAT, 112x336x1x1] %onnx::Conv_720[FLOAT, 336x112x1x1] %onnx::Conv_723[FLOAT, 336x1x5x5] %onnx::Conv_726[FLOAT, 112x336x1x1] %onnx::Conv_729[FLOAT, 112x112x1x1] %onnx::Conv_732[FLOAT, 112x1x3x3] %onnx::Conv_735[FLOAT, 112x112x1x1] %onnx::Conv_738[FLOAT, 112x112x1x1] %onnx::Conv_741[FLOAT, 112x1x5x5] %onnx::Conv_744[FLOAT, 184x112x1x1] %onnx::Conv_745[FLOAT, 184] %onnx::Conv_747[FLOAT, 1104x184x1x1] %onnx::Conv_748[FLOAT, 1104] %onnx::Conv_750[FLOAT, 1104x1x3x3] %onnx::Conv_753[FLOAT, 184x1104x1x1] %onnx::Conv_756[FLOAT, 552x184x1x1] %onnx::Conv_757[FLOAT, 552] %onnx::Conv_759[FLOAT, 552x1x3x3] %onnx::Conv_762[FLOAT, 184x552x1x1] %onnx::Conv_765[FLOAT, 184x184x1x1] %onnx::Conv_768[FLOAT, 184x1x5x5] %onnx::Conv_771[FLOAT, 184x184x1x1] %onnx::Conv_774[FLOAT, 352x184x1x1] %onnx::Conv_775[FLOAT, 352] %onnx::Conv_777[FLOAT, 1504x352x1x1] %onnx::Conv_778[FLOAT, 1504] ) { %onnx::Conv_772 = Identity(%onnx::Conv_745) %onnx::Conv_769 = Identity(%onnx::Conv_745) %onnx::Conv_766 = Identity(%onnx::Conv_745) %onnx::Conv_763 = Identity(%onnx::Conv_745) %onnx::Conv_760 = Identity(%onnx::Conv_757) %onnx::Conv_754 = Identity(%onnx::Conv_745) %onnx::Conv_751 = Identity(%onnx::Conv_748) %onnx::Conv_742 = Identity(%onnx::Conv_709) %onnx::Conv_739 = Identity(%onnx::Conv_709) %onnx::Conv_736 = Identity(%onnx::Conv_709) %onnx::Conv_733 = Identity(%onnx::Conv_709) %onnx::Conv_730 = Identity(%onnx::Conv_709) %onnx::Conv_727 = Identity(%onnx::Conv_709) %onnx::Conv_724 = Identity(%onnx::Conv_712) %onnx::Conv_721 = Identity(%onnx::Conv_712) %onnx::Conv_718 = Identity(%onnx::Conv_709) %onnx::Conv_715 = Identity(%onnx::Conv_712) %onnx::Conv_706 = Identity(%onnx::Conv_679) %onnx::Conv_703 = Identity(%onnx::Conv_691) %onnx::Conv_700 = Identity(%onnx::Conv_691) %onnx::Conv_697 = Identity(%onnx::Conv_679) %onnx::Conv_694 = Identity(%onnx::Conv_691) %onnx::Conv_688 = Identity(%onnx::Conv_679) %onnx::Conv_685 = Identity(%onnx::Conv_679) %onnx::Conv_682 = Identity(%onnx::Conv_679) %onnx::Conv_676 = Identity(%onnx::Conv_673) %onnx::Conv_670 = Identity(%onnx::Conv_643) %onnx::Conv_667 = Identity(%onnx::Conv_646) %onnx::Conv_664 = Identity(%onnx::Conv_646) %onnx::Conv_661 = Identity(%onnx::Conv_643) %onnx::Conv_658 = Identity(%onnx::Conv_643) %onnx::Conv_655 = Identity(%onnx::Conv_643) %onnx::Conv_652 = Identity(%onnx::Conv_643) %onnx::Conv_649 = Identity(%onnx::Conv_646) %onnx::Conv_640 = Identity(%onnx::Conv_616) %onnx::Conv_637 = Identity(%onnx::Conv_616) %onnx::Conv_634 = Identity(%onnx::Conv_616) %onnx::Conv_631 = Identity(%onnx::Conv_616) %onnx::Conv_628 = Identity(%onnx::Conv_616) %onnx::Conv_625 = Identity(%onnx::Conv_616) %onnx::Conv_622 = Identity(%onnx::Conv_616) %onnx::Conv_619 = Identity(%onnx::Conv_616) %onnx::Conv_613 = Identity(%onnx::Conv_607) %onnx::Conv_610 = Identity(%onnx::Conv_607) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_606, %onnx::Conv_607) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_609, %onnx::Conv_610) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.1/nl/Relu_output_0, %onnx::Conv_612, %onnx::Conv_613) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_615, %onnx::Conv_616) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_618, %onnx::Conv_619) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.2/shuffle/Reshape_output_0 = Reshape(%/cells.2/nl/Relu_output_0, %/cells.2/shuffle/Constant_output_0) %/cells.2/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.2/shuffle/Reshape_output_0) %/cells.2/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.2/shuffle/Reshape_1_output_0 = Reshape(%/cells.2/shuffle/Transpose_output_0, %/cells.2/shuffle/Constant_1_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.2/shuffle/Reshape_1_output_0, %onnx::Conv_621, %onnx::Conv_622) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_624, %onnx::Conv_625) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_627, %onnx::Conv_628) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.4/shuffle/Reshape_output_0 = Reshape(%/cells.4/nl/Relu_output_0, %/cells.4/shuffle/Constant_output_0) %/cells.4/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.4/shuffle/Reshape_output_0) %/cells.4/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.4/shuffle/Reshape_1_output_0 = Reshape(%/cells.4/shuffle/Transpose_output_0, %/cells.4/shuffle/Constant_1_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.4/shuffle/Reshape_1_output_0, %onnx::Conv_630, %onnx::Conv_631) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_633, %onnx::Conv_634) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/Add_output_0, %onnx::Conv_636, %onnx::Conv_637) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.5/nl/Relu_output_0, %onnx::Conv_639, %onnx::Conv_640) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_642, %onnx::Conv_643) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_645, %onnx::Conv_646) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.6/nl/Relu_output_0, %onnx::Conv_648, %onnx::Conv_649) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_651, %onnx::Conv_652) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_654, %onnx::Conv_655) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.7/shuffle/Reshape_output_0 = Reshape(%/cells.7/nl/Relu_output_0, %/cells.7/shuffle/Constant_output_0) %/cells.7/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.7/shuffle/Reshape_output_0) %/cells.7/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.7/shuffle/Reshape_1_output_0 = Reshape(%/cells.7/shuffle/Transpose_output_0, %/cells.7/shuffle/Constant_1_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.7/shuffle/Reshape_1_output_0, %onnx::Conv_657, %onnx::Conv_658) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_660, %onnx::Conv_661) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_663, %onnx::Conv_664) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.8/nl/Relu_output_0, %onnx::Conv_666, %onnx::Conv_667) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_669, %onnx::Conv_670) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_672, %onnx::Conv_673) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.9/nl/Relu_output_0, %onnx::Conv_675, %onnx::Conv_676) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_678, %onnx::Conv_679) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_681, %onnx::Conv_682) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.10/shuffle/Reshape_output_0 = Reshape(%/cells.10/nl/Relu_output_0, %/cells.10/shuffle/Constant_output_0) %/cells.10/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.10/shuffle/Reshape_output_0) %/cells.10/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.10/shuffle/Reshape_1_output_0 = Reshape(%/cells.10/shuffle/Transpose_output_0, %/cells.10/shuffle/Constant_1_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.10/shuffle/Reshape_1_output_0, %onnx::Conv_684, %onnx::Conv_685) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_687, %onnx::Conv_688) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_690, %onnx::Conv_691) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.11/nl/Relu_output_0, %onnx::Conv_693, %onnx::Conv_694) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_696, %onnx::Conv_697) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_699, %onnx::Conv_700) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.12/nl/Relu_output_0, %onnx::Conv_702, %onnx::Conv_703) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_705, %onnx::Conv_706) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_708, %onnx::Conv_709) %/cells.13/relu/Relu_output_0 = Relu(%/cells.13/conv/Conv_output_0) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/relu/Relu_output_0, %onnx::Conv_711, %onnx::Conv_712) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.14/nl/Relu_output_0, %onnx::Conv_714, %onnx::Conv_715) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_717, %onnx::Conv_718) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/relu/Relu_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_720, %onnx::Conv_721) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.15/nl/Relu_output_0, %onnx::Conv_723, %onnx::Conv_724) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_726, %onnx::Conv_727) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_729, %onnx::Conv_730) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.16/nl/Relu_output_0, %onnx::Conv_732, %onnx::Conv_733) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_735, %onnx::Conv_736) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.15/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_738, %onnx::Conv_739) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.17/nl/Relu_output_0, %onnx::Conv_741, %onnx::Conv_742) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_744, %onnx::Conv_745) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_747, %onnx::Conv_748) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.18/nl/Relu_output_0, %onnx::Conv_750, %onnx::Conv_751) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_753, %onnx::Conv_754) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_756, %onnx::Conv_757) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 552, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.19/nl/Relu_output_0, %onnx::Conv_759, %onnx::Conv_760) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_762, %onnx::Conv_763) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_765, %onnx::Conv_766) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.20/nl/Relu_output_0, %onnx::Conv_768, %onnx::Conv_769) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_771, %onnx::Conv_772) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_774, %onnx::Conv_775) %/cells.21/relu/Relu_output_0 = Relu(%/cells.21/conv/Conv_output_0) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/relu/Relu_output_0, %onnx::Conv_777, %onnx::Conv_778) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %604 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %604 }	val_accuracy	58,203,008	1,860,204	{'zcp_synflow': 74.5525551245383, 'zcp_zen': 65.06083679199219, 'zcp_epe_nas': 15.373548652096, 'zcp_fisher': 0.05859427899122238, 'zcp_flops': 58203008.0, 'zcp_grad_norm': 17.82573890686035, 'zcp_grasp': 0.035561561584472656, 'zcp_jacov': -16.058027933181407, 'zcp_l2_norm': 611.1372680664062, 'zcp_nwot': 206.54772575241628, 'zcp_params': 1860204.0, 'zcp_plain': -0.0023013001773506403, 'zcp_snip': 30.11920738220215, 'lat_1080ti_1': 0.3960522064143405, 'lat_1080ti_32': 0.32203974043302586, 'lat_1080ti_64': 0.18309816465267068, 'lat_2080ti_1': 0.5769741667970217, 'lat_2080ti_32': 0.3227684775816391, 'lat_2080ti_64': 0.18808272998638056, 'lat_essential_ph_1': 0.20754716981132076, 'lat_eyeriss': 0.3226670089670473, 'lat_fpga': 0.3439772805984622, 'lat_gold_6226': 0.4706890293639818, 'lat_gold_6240': 0.47152821354484575, 'lat_pixel2': 0.21739130434782608, 'lat_pixel3': 0.30400556164931125, 'lat_raspi4': 0.29160461299041307, 'lat_samsung_a50': 0.1368421052631579, 'lat_samsung_s7': 0.13385826771653545, 'lat_silver_4114': 0.5255813827122373, 'lat_silver_4210r': 0.535983804663495, 'lat_titan_rtx_1': 0.4344579625534331, 'lat_titan_rtx_32': 0.35393873942005816, 'lat_titan_rtx_64': 0.23214282421936883, 'lat_titanx_1': 0.23541117846898507, 'lat_titanx_32': 0.30053446096820857, 'lat_titanx_64': 0.23337650301555454, 'lat_titanxp_1': 0.42394341925338636, 'lat_titanxp_32': 0.3188174254202372, 'lat_titanxp_64': 0.1964568502814763}
FBNet_2936	FBNet	2936	2936	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_695[FLOAT, 16x3x3x3] %onnx::Conv_696[FLOAT, 16] %onnx::Conv_698[FLOAT, 16x8x1x1] %onnx::Conv_701[FLOAT, 16x1x3x3] %onnx::Conv_704[FLOAT, 16x8x1x1] %onnx::Conv_707[FLOAT, 16x16x1x1] %onnx::Conv_710[FLOAT, 16x1x5x5] %onnx::Conv_713[FLOAT, 24x16x1x1] %onnx::Conv_714[FLOAT, 24] %onnx::Conv_716[FLOAT, 24x12x1x1] %onnx::Conv_719[FLOAT, 24x1x3x3] %onnx::Conv_722[FLOAT, 24x12x1x1] %onnx::Conv_725[FLOAT, 72x24x1x1] %onnx::Conv_726[FLOAT, 72] %onnx::Conv_728[FLOAT, 72x1x5x5] %onnx::Conv_731[FLOAT, 24x72x1x1] %onnx::Conv_734[FLOAT, 144x24x1x1] %onnx::Conv_735[FLOAT, 144] %onnx::Conv_737[FLOAT, 144x1x3x3] %onnx::Conv_740[FLOAT, 24x144x1x1] %onnx::Conv_743[FLOAT, 72x24x1x1] %onnx::Conv_746[FLOAT, 72x1x3x3] %onnx::Conv_749[FLOAT, 32x72x1x1] %onnx::Conv_750[FLOAT, 32] %onnx::Conv_752[FLOAT, 32x32x1x1] %onnx::Conv_755[FLOAT, 32x1x3x3] %onnx::Conv_758[FLOAT, 32x32x1x1] %onnx::Conv_761[FLOAT, 32x16x1x1] %onnx::Conv_764[FLOAT, 32x1x5x5] %onnx::Conv_767[FLOAT, 32x16x1x1] %onnx::Conv_770[FLOAT, 96x32x1x1] %onnx::Conv_771[FLOAT, 96] %onnx::Conv_773[FLOAT, 96x1x5x5] %onnx::Conv_776[FLOAT, 32x96x1x1] %onnx::Conv_779[FLOAT, 96x32x1x1] %onnx::Conv_782[FLOAT, 96x1x3x3] %onnx::Conv_785[FLOAT, 64x96x1x1] %onnx::Conv_786[FLOAT, 64] %onnx::Conv_788[FLOAT, 384x64x1x1] %onnx::Conv_789[FLOAT, 384] %onnx::Conv_791[FLOAT, 384x1x5x5] %onnx::Conv_794[FLOAT, 64x384x1x1] %onnx::Conv_797[FLOAT, 192x64x1x1] %onnx::Conv_798[FLOAT, 192] %onnx::Conv_800[FLOAT, 192x1x5x5] %onnx::Conv_803[FLOAT, 64x192x1x1] %onnx::Conv_806[FLOAT, 192x64x1x1] %onnx::Conv_809[FLOAT, 192x1x3x3] %onnx::Conv_812[FLOAT, 64x192x1x1] %onnx::Conv_815[FLOAT, 384x64x1x1] %onnx::Conv_818[FLOAT, 384x1x5x5] %onnx::Conv_821[FLOAT, 112x384x1x1] %onnx::Conv_822[FLOAT, 112] %onnx::Conv_824[FLOAT, 112x56x1x1] %onnx::Conv_827[FLOAT, 112x1x3x3] %onnx::Conv_830[FLOAT, 112x56x1x1] %onnx::Conv_833[FLOAT, 672x112x1x1] %onnx::Conv_834[FLOAT, 672] %onnx::Conv_836[FLOAT, 672x1x3x3] %onnx::Conv_839[FLOAT, 112x672x1x1] %onnx::Conv_842[FLOAT, 112x112x1x1] %onnx::Conv_845[FLOAT, 112x1x5x5] %onnx::Conv_848[FLOAT, 112x112x1x1] %onnx::Conv_851[FLOAT, 112x112x1x1] %onnx::Conv_854[FLOAT, 112x1x3x3] %onnx::Conv_857[FLOAT, 184x112x1x1] %onnx::Conv_858[FLOAT, 184] %onnx::Conv_860[FLOAT, 552x184x1x1] %onnx::Conv_861[FLOAT, 552] %onnx::Conv_863[FLOAT, 552x1x5x5] %onnx::Conv_866[FLOAT, 184x552x1x1] %onnx::Conv_869[FLOAT, 1104x184x1x1] %onnx::Conv_870[FLOAT, 1104] %onnx::Conv_872[FLOAT, 1104x1x3x3] %onnx::Conv_875[FLOAT, 184x1104x1x1] %onnx::Conv_878[FLOAT, 1104x184x1x1] %onnx::Conv_881[FLOAT, 1104x1x5x5] %onnx::Conv_884[FLOAT, 184x1104x1x1] %onnx::Conv_887[FLOAT, 184x184x1x1] %onnx::Conv_890[FLOAT, 184x1x3x3] %onnx::Conv_893[FLOAT, 352x184x1x1] %onnx::Conv_894[FLOAT, 352] %onnx::Conv_896[FLOAT, 1504x352x1x1] %onnx::Conv_897[FLOAT, 1504] ) { %onnx::Conv_891 = Identity(%onnx::Conv_858) %onnx::Conv_888 = Identity(%onnx::Conv_858) %onnx::Conv_885 = Identity(%onnx::Conv_858) %onnx::Conv_882 = Identity(%onnx::Conv_870) %onnx::Conv_879 = Identity(%onnx::Conv_870) %onnx::Conv_876 = Identity(%onnx::Conv_858) %onnx::Conv_873 = Identity(%onnx::Conv_870) %onnx::Conv_867 = Identity(%onnx::Conv_858) %onnx::Conv_864 = Identity(%onnx::Conv_861) %onnx::Conv_855 = Identity(%onnx::Conv_822) %onnx::Conv_852 = Identity(%onnx::Conv_822) %onnx::Conv_849 = Identity(%onnx::Conv_822) %onnx::Conv_846 = Identity(%onnx::Conv_822) %onnx::Conv_843 = Identity(%onnx::Conv_822) %onnx::Conv_840 = Identity(%onnx::Conv_822) %onnx::Conv_837 = Identity(%onnx::Conv_834) %onnx::Conv_831 = Identity(%onnx::Conv_822) %onnx::Conv_828 = Identity(%onnx::Conv_822) %onnx::Conv_825 = Identity(%onnx::Conv_822) %onnx::Conv_819 = Identity(%onnx::Conv_789) %onnx::Conv_816 = Identity(%onnx::Conv_789) %onnx::Conv_813 = Identity(%onnx::Conv_786) %onnx::Conv_810 = Identity(%onnx::Conv_798) %onnx::Conv_807 = Identity(%onnx::Conv_798) %onnx::Conv_804 = Identity(%onnx::Conv_786) %onnx::Conv_801 = Identity(%onnx::Conv_798) %onnx::Conv_795 = Identity(%onnx::Conv_786) %onnx::Conv_792 = Identity(%onnx::Conv_789) %onnx::Conv_783 = Identity(%onnx::Conv_771) %onnx::Conv_780 = Identity(%onnx::Conv_771) %onnx::Conv_777 = Identity(%onnx::Conv_750) %onnx::Conv_774 = Identity(%onnx::Conv_771) %onnx::Conv_768 = Identity(%onnx::Conv_750) %onnx::Conv_765 = Identity(%onnx::Conv_750) %onnx::Conv_762 = Identity(%onnx::Conv_750) %onnx::Conv_759 = Identity(%onnx::Conv_750) %onnx::Conv_756 = Identity(%onnx::Conv_750) %onnx::Conv_753 = Identity(%onnx::Conv_750) %onnx::Conv_747 = Identity(%onnx::Conv_726) %onnx::Conv_744 = Identity(%onnx::Conv_726) %onnx::Conv_741 = Identity(%onnx::Conv_714) %onnx::Conv_738 = Identity(%onnx::Conv_735) %onnx::Conv_732 = Identity(%onnx::Conv_714) %onnx::Conv_729 = Identity(%onnx::Conv_726) %onnx::Conv_723 = Identity(%onnx::Conv_714) %onnx::Conv_720 = Identity(%onnx::Conv_714) %onnx::Conv_717 = Identity(%onnx::Conv_714) %onnx::Conv_711 = Identity(%onnx::Conv_696) %onnx::Conv_708 = Identity(%onnx::Conv_696) %onnx::Conv_705 = Identity(%onnx::Conv_696) %onnx::Conv_702 = Identity(%onnx::Conv_696) %onnx::Conv_699 = Identity(%onnx::Conv_696) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_695, %onnx::Conv_696) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_698, %onnx::Conv_699) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.0/shuffle/Reshape_output_0 = Reshape(%/cells.0/nl/Relu_output_0, %/cells.0/shuffle/Constant_output_0) %/cells.0/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.0/shuffle/Reshape_output_0) %/cells.0/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.0/shuffle/Reshape_1_output_0 = Reshape(%/cells.0/shuffle/Transpose_output_0, %/cells.0/shuffle/Constant_1_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.0/shuffle/Reshape_1_output_0, %onnx::Conv_701, %onnx::Conv_702) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_704, %onnx::Conv_705) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_707, %onnx::Conv_708) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.1/nl/Relu_output_0, %onnx::Conv_710, %onnx::Conv_711) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_713, %onnx::Conv_714) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_716, %onnx::Conv_717) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.2/shuffle/Reshape_output_0 = Reshape(%/cells.2/nl/Relu_output_0, %/cells.2/shuffle/Constant_output_0) %/cells.2/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.2/shuffle/Reshape_output_0) %/cells.2/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.2/shuffle/Reshape_1_output_0 = Reshape(%/cells.2/shuffle/Transpose_output_0, %/cells.2/shuffle/Constant_1_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.2/shuffle/Reshape_1_output_0, %onnx::Conv_719, %onnx::Conv_720) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_722, %onnx::Conv_723) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_725, %onnx::Conv_726) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.3/nl/Relu_output_0, %onnx::Conv_728, %onnx::Conv_729) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_731, %onnx::Conv_732) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_734, %onnx::Conv_735) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.4/nl/Relu_output_0, %onnx::Conv_737, %onnx::Conv_738) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_740, %onnx::Conv_741) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/Add_output_0, %onnx::Conv_743, %onnx::Conv_744) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.5/nl/Relu_output_0, %onnx::Conv_746, %onnx::Conv_747) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_749, %onnx::Conv_750) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_752, %onnx::Conv_753) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.6/nl/Relu_output_0, %onnx::Conv_755, %onnx::Conv_756) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_758, %onnx::Conv_759) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_761, %onnx::Conv_762) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.7/shuffle/Reshape_output_0 = Reshape(%/cells.7/nl/Relu_output_0, %/cells.7/shuffle/Constant_output_0) %/cells.7/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.7/shuffle/Reshape_output_0) %/cells.7/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.7/shuffle/Reshape_1_output_0 = Reshape(%/cells.7/shuffle/Transpose_output_0, %/cells.7/shuffle/Constant_1_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.7/shuffle/Reshape_1_output_0, %onnx::Conv_764, %onnx::Conv_765) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_767, %onnx::Conv_768) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_770, %onnx::Conv_771) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.8/nl/Relu_output_0, %onnx::Conv_773, %onnx::Conv_774) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_776, %onnx::Conv_777) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_779, %onnx::Conv_780) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.9/nl/Relu_output_0, %onnx::Conv_782, %onnx::Conv_783) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_785, %onnx::Conv_786) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_788, %onnx::Conv_789) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.10/nl/Relu_output_0, %onnx::Conv_791, %onnx::Conv_792) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_794, %onnx::Conv_795) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_797, %onnx::Conv_798) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.11/nl/Relu_output_0, %onnx::Conv_800, %onnx::Conv_801) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_803, %onnx::Conv_804) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_806, %onnx::Conv_807) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.12/nl/Relu_output_0, %onnx::Conv_809, %onnx::Conv_810) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_812, %onnx::Conv_813) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_815, %onnx::Conv_816) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.13/nl/Relu_output_0, %onnx::Conv_818, %onnx::Conv_819) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_821, %onnx::Conv_822) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_824, %onnx::Conv_825) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.14/shuffle/Reshape_output_0 = Reshape(%/cells.14/nl/Relu_output_0, %/cells.14/shuffle/Constant_output_0) %/cells.14/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.14/shuffle/Reshape_output_0) %/cells.14/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.14/shuffle/Reshape_1_output_0 = Reshape(%/cells.14/shuffle/Transpose_output_0, %/cells.14/shuffle/Constant_1_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.14/shuffle/Reshape_1_output_0, %onnx::Conv_827, %onnx::Conv_828) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_830, %onnx::Conv_831) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_833, %onnx::Conv_834) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.15/nl/Relu_output_0, %onnx::Conv_836, %onnx::Conv_837) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_839, %onnx::Conv_840) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_842, %onnx::Conv_843) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.16/nl/Relu_output_0, %onnx::Conv_845, %onnx::Conv_846) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_848, %onnx::Conv_849) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.15/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_851, %onnx::Conv_852) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.17/nl/Relu_output_0, %onnx::Conv_854, %onnx::Conv_855) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_857, %onnx::Conv_858) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_860, %onnx::Conv_861) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 552, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.18/nl/Relu_output_0, %onnx::Conv_863, %onnx::Conv_864) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_866, %onnx::Conv_867) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_869, %onnx::Conv_870) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.19/nl/Relu_output_0, %onnx::Conv_872, %onnx::Conv_873) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_875, %onnx::Conv_876) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_878, %onnx::Conv_879) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.20/nl/Relu_output_0, %onnx::Conv_881, %onnx::Conv_882) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_884, %onnx::Conv_885) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_887, %onnx::Conv_888) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.21/nl/Relu_output_0, %onnx::Conv_890, %onnx::Conv_891) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_893, %onnx::Conv_894) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_896, %onnx::Conv_897) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %693 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %693 }	val_accuracy	80,253,824	2,344,508	{'zcp_synflow': 83.70716490331355, 'zcp_zen': 75.48120880126953, 'zcp_epe_nas': 24.28508515396585, 'zcp_fisher': 0.16492517292499542, 'zcp_flops': 80253824.0, 'zcp_grad_norm': 25.28360366821289, 'zcp_grasp': -0.05890846252441406, 'zcp_jacov': -16.060671803882233, 'zcp_l2_norm': 717.1670532226562, 'zcp_nwot': 213.9933519780947, 'zcp_params': 2344508.0, 'zcp_plain': 0.0008313206490129232, 'zcp_snip': 48.689430236816406, 'lat_1080ti_1': 0.7791659266348182, 'lat_1080ti_32': 0.7251044864906936, 'lat_1080ti_64': 0.5275953378656225, 'lat_2080ti_1': 0.799937485842817, 'lat_2080ti_32': 0.7453452020333245, 'lat_2080ti_64': 0.5543590826766187, 'lat_essential_ph_1': 0.4339622641509434, 'lat_eyeriss': 0.5944107237819303, 'lat_fpga': 0.6459592911071748, 'lat_gold_6226': 0.536794079035895, 'lat_gold_6240': 0.6780279943798055, 'lat_pixel2': 0.43478260869565216, 'lat_pixel3': 0.5576340939868083, 'lat_raspi4': 0.6079967311554016, 'lat_samsung_a50': 0.28421052631578947, 'lat_samsung_s7': 0.25196850393700787, 'lat_silver_4114': 0.7175931219181496, 'lat_silver_4210r': 0.718686116018606, 'lat_titan_rtx_1': 0.7596757105565228, 'lat_titan_rtx_32': 0.7214454174709863, 'lat_titan_rtx_64': 0.591143873459364, 'lat_titanx_1': 0.4064929009607104, 'lat_titanx_32': 0.6495545084979906, 'lat_titanx_64': 0.5258846086250295, 'lat_titanxp_1': 0.7151692842403304, 'lat_titanxp_32': 0.6929579734924984, 'lat_titanxp_64': 0.5589391722269493}
FBNet_1669	FBNet	1669	1669	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_690[FLOAT, 16x3x3x3] %onnx::Conv_691[FLOAT, 16] %onnx::Conv_693[FLOAT, 96x16x1x1] %onnx::Conv_694[FLOAT, 96] %onnx::Conv_696[FLOAT, 96x1x5x5] %onnx::Conv_699[FLOAT, 16x96x1x1] %onnx::Conv_702[FLOAT, 16x8x1x1] %onnx::Conv_705[FLOAT, 16x1x5x5] %onnx::Conv_708[FLOAT, 24x8x1x1] %onnx::Conv_709[FLOAT, 24] %onnx::Conv_711[FLOAT, 144x24x1x1] %onnx::Conv_712[FLOAT, 144] %onnx::Conv_714[FLOAT, 144x1x5x5] %onnx::Conv_717[FLOAT, 24x144x1x1] %onnx::Conv_720[FLOAT, 24x12x1x1] %onnx::Conv_723[FLOAT, 24x1x3x3] %onnx::Conv_726[FLOAT, 24x12x1x1] %onnx::Conv_729[FLOAT, 72x24x1x1] %onnx::Conv_730[FLOAT, 72] %onnx::Conv_732[FLOAT, 72x1x5x5] %onnx::Conv_735[FLOAT, 24x72x1x1] %onnx::Conv_738[FLOAT, 32x24x1x1] %onnx::Conv_739[FLOAT, 32] %onnx::Conv_741[FLOAT, 32x32x1x1] %onnx::Conv_744[FLOAT, 32x1x3x3] %onnx::Conv_747[FLOAT, 32x32x1x1] %onnx::Conv_750[FLOAT, 192x32x1x1] %onnx::Conv_751[FLOAT, 192] %onnx::Conv_753[FLOAT, 192x1x5x5] %onnx::Conv_756[FLOAT, 32x192x1x1] %onnx::Conv_759[FLOAT, 32x16x1x1] %onnx::Conv_762[FLOAT, 32x1x3x3] %onnx::Conv_765[FLOAT, 32x16x1x1] %onnx::Conv_768[FLOAT, 64x32x1x1] %onnx::Conv_769[FLOAT, 64] %onnx::Conv_771[FLOAT, 64x32x1x1] %onnx::Conv_774[FLOAT, 64x1x3x3] %onnx::Conv_777[FLOAT, 64x32x1x1] %onnx::Conv_780[FLOAT, 64x32x1x1] %onnx::Conv_783[FLOAT, 64x1x3x3] %onnx::Conv_786[FLOAT, 64x32x1x1] %onnx::Conv_789[FLOAT, 384x64x1x1] %onnx::Conv_790[FLOAT, 384] %onnx::Conv_792[FLOAT, 384x1x5x5] %onnx::Conv_795[FLOAT, 112x384x1x1] %onnx::Conv_796[FLOAT, 112] %onnx::Conv_798[FLOAT, 336x112x1x1] %onnx::Conv_799[FLOAT, 336] %onnx::Conv_801[FLOAT, 336x1x5x5] %onnx::Conv_804[FLOAT, 112x336x1x1] %onnx::Conv_807[FLOAT, 672x112x1x1] %onnx::Conv_808[FLOAT, 672] %onnx::Conv_810[FLOAT, 672x1x3x3] %onnx::Conv_813[FLOAT, 112x672x1x1] %onnx::Conv_816[FLOAT, 672x112x1x1] %onnx::Conv_819[FLOAT, 672x1x3x3] %onnx::Conv_822[FLOAT, 112x672x1x1] %onnx::Conv_825[FLOAT, 112x56x1x1] %onnx::Conv_828[FLOAT, 112x1x3x3] %onnx::Conv_831[FLOAT, 184x56x1x1] %onnx::Conv_832[FLOAT, 184] %onnx::Conv_834[FLOAT, 184x92x1x1] %onnx::Conv_837[FLOAT, 184x1x3x3] %onnx::Conv_840[FLOAT, 184x92x1x1] %onnx::Conv_843[FLOAT, 1104x184x1x1] %onnx::Conv_844[FLOAT, 1104] %onnx::Conv_846[FLOAT, 1104x1x5x5] %onnx::Conv_849[FLOAT, 184x1104x1x1] %onnx::Conv_852[FLOAT, 184x184x1x1] %onnx::Conv_855[FLOAT, 184x1x3x3] %onnx::Conv_858[FLOAT, 184x184x1x1] %onnx::Conv_861[FLOAT, 184x184x1x1] %onnx::Conv_864[FLOAT, 184x1x5x5] %onnx::Conv_867[FLOAT, 352x184x1x1] %onnx::Conv_868[FLOAT, 352] %onnx::Conv_870[FLOAT, 1504x352x1x1] %onnx::Conv_871[FLOAT, 1504] ) { %onnx::Conv_865 = Identity(%onnx::Conv_832) %onnx::Conv_862 = Identity(%onnx::Conv_832) %onnx::Conv_859 = Identity(%onnx::Conv_832) %onnx::Conv_856 = Identity(%onnx::Conv_832) %onnx::Conv_853 = Identity(%onnx::Conv_832) %onnx::Conv_850 = Identity(%onnx::Conv_832) %onnx::Conv_847 = Identity(%onnx::Conv_844) %onnx::Conv_841 = Identity(%onnx::Conv_832) %onnx::Conv_838 = Identity(%onnx::Conv_832) %onnx::Conv_835 = Identity(%onnx::Conv_832) %onnx::Conv_829 = Identity(%onnx::Conv_796) %onnx::Conv_826 = Identity(%onnx::Conv_796) %onnx::Conv_823 = Identity(%onnx::Conv_796) %onnx::Conv_820 = Identity(%onnx::Conv_808) %onnx::Conv_817 = Identity(%onnx::Conv_808) %onnx::Conv_814 = Identity(%onnx::Conv_796) %onnx::Conv_811 = Identity(%onnx::Conv_808) %onnx::Conv_805 = Identity(%onnx::Conv_796) %onnx::Conv_802 = Identity(%onnx::Conv_799) %onnx::Conv_793 = Identity(%onnx::Conv_790) %onnx::Conv_787 = Identity(%onnx::Conv_769) %onnx::Conv_784 = Identity(%onnx::Conv_769) %onnx::Conv_781 = Identity(%onnx::Conv_769) %onnx::Conv_778 = Identity(%onnx::Conv_769) %onnx::Conv_775 = Identity(%onnx::Conv_769) %onnx::Conv_772 = Identity(%onnx::Conv_769) %onnx::Conv_766 = Identity(%onnx::Conv_739) %onnx::Conv_763 = Identity(%onnx::Conv_739) %onnx::Conv_760 = Identity(%onnx::Conv_739) %onnx::Conv_757 = Identity(%onnx::Conv_739) %onnx::Conv_754 = Identity(%onnx::Conv_751) %onnx::Conv_748 = Identity(%onnx::Conv_739) %onnx::Conv_745 = Identity(%onnx::Conv_739) %onnx::Conv_742 = Identity(%onnx::Conv_739) %onnx::Conv_736 = Identity(%onnx::Conv_709) %onnx::Conv_733 = Identity(%onnx::Conv_730) %onnx::Conv_727 = Identity(%onnx::Conv_709) %onnx::Conv_724 = Identity(%onnx::Conv_709) %onnx::Conv_721 = Identity(%onnx::Conv_709) %onnx::Conv_718 = Identity(%onnx::Conv_709) %onnx::Conv_715 = Identity(%onnx::Conv_712) %onnx::Conv_706 = Identity(%onnx::Conv_691) %onnx::Conv_703 = Identity(%onnx::Conv_691) %onnx::Conv_700 = Identity(%onnx::Conv_691) %onnx::Conv_697 = Identity(%onnx::Conv_694) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_690, %onnx::Conv_691) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_693, %onnx::Conv_694) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.0/nl/Relu_output_0, %onnx::Conv_696, %onnx::Conv_697) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_699, %onnx::Conv_700) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_702, %onnx::Conv_703) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.1/shuffle/Reshape_output_0 = Reshape(%/cells.1/nl/Relu_output_0, %/cells.1/shuffle/Constant_output_0) %/cells.1/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.1/shuffle/Reshape_output_0) %/cells.1/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.1/shuffle/Reshape_1_output_0 = Reshape(%/cells.1/shuffle/Transpose_output_0, %/cells.1/shuffle/Constant_1_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.1/shuffle/Reshape_1_output_0, %onnx::Conv_705, %onnx::Conv_706) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_708, %onnx::Conv_709) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_711, %onnx::Conv_712) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.2/nl/Relu_output_0, %onnx::Conv_714, %onnx::Conv_715) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_717, %onnx::Conv_718) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_720, %onnx::Conv_721) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.3/shuffle/Reshape_output_0 = Reshape(%/cells.3/nl/Relu_output_0, %/cells.3/shuffle/Constant_output_0) %/cells.3/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.3/shuffle/Reshape_output_0) %/cells.3/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.3/shuffle/Reshape_1_output_0 = Reshape(%/cells.3/shuffle/Transpose_output_0, %/cells.3/shuffle/Constant_1_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.3/shuffle/Reshape_1_output_0, %onnx::Conv_723, %onnx::Conv_724) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_726, %onnx::Conv_727) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_729, %onnx::Conv_730) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.4/nl/Relu_output_0, %onnx::Conv_732, %onnx::Conv_733) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_735, %onnx::Conv_736) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [2, 2]](%/cells.4/Add_output_0, %onnx::Conv_738, %onnx::Conv_739) %/cells.5/relu/Relu_output_0 = Relu(%/cells.5/conv/Conv_output_0) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/relu/Relu_output_0, %onnx::Conv_741, %onnx::Conv_742) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.6/nl/Relu_output_0, %onnx::Conv_744, %onnx::Conv_745) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_747, %onnx::Conv_748) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/relu/Relu_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_750, %onnx::Conv_751) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.7/nl/Relu_output_0, %onnx::Conv_753, %onnx::Conv_754) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_756, %onnx::Conv_757) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_759, %onnx::Conv_760) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.8/shuffle/Reshape_output_0 = Reshape(%/cells.8/nl/Relu_output_0, %/cells.8/shuffle/Constant_output_0) %/cells.8/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.8/shuffle/Reshape_output_0) %/cells.8/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.8/shuffle/Reshape_1_output_0 = Reshape(%/cells.8/shuffle/Transpose_output_0, %/cells.8/shuffle/Constant_1_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.8/shuffle/Reshape_1_output_0, %onnx::Conv_762, %onnx::Conv_763) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_765, %onnx::Conv_766) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [2, 2]](%/cells.8/Add_output_0, %onnx::Conv_768, %onnx::Conv_769) %/cells.9/relu/Relu_output_0 = Relu(%/cells.9/conv/Conv_output_0) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/relu/Relu_output_0, %onnx::Conv_771, %onnx::Conv_772) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.10/shuffle/Reshape_output_0 = Reshape(%/cells.10/nl/Relu_output_0, %/cells.10/shuffle/Constant_output_0) %/cells.10/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.10/shuffle/Reshape_output_0) %/cells.10/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.10/shuffle/Reshape_1_output_0 = Reshape(%/cells.10/shuffle/Transpose_output_0, %/cells.10/shuffle/Constant_1_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.10/shuffle/Reshape_1_output_0, %onnx::Conv_774, %onnx::Conv_775) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_777, %onnx::Conv_778) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/relu/Relu_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_780, %onnx::Conv_781) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.11/shuffle/Reshape_output_0 = Reshape(%/cells.11/nl/Relu_output_0, %/cells.11/shuffle/Constant_output_0) %/cells.11/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.11/shuffle/Reshape_output_0) %/cells.11/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.11/shuffle/Reshape_1_output_0 = Reshape(%/cells.11/shuffle/Transpose_output_0, %/cells.11/shuffle/Constant_1_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.11/shuffle/Reshape_1_output_0, %onnx::Conv_783, %onnx::Conv_784) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_786, %onnx::Conv_787) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_789, %onnx::Conv_790) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.13/nl/Relu_output_0, %onnx::Conv_792, %onnx::Conv_793) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_795, %onnx::Conv_796) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_798, %onnx::Conv_799) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.14/nl/Relu_output_0, %onnx::Conv_801, %onnx::Conv_802) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_804, %onnx::Conv_805) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_807, %onnx::Conv_808) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.15/nl/Relu_output_0, %onnx::Conv_810, %onnx::Conv_811) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_813, %onnx::Conv_814) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_816, %onnx::Conv_817) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.16/nl/Relu_output_0, %onnx::Conv_819, %onnx::Conv_820) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_822, %onnx::Conv_823) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.15/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_825, %onnx::Conv_826) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.17/shuffle/Reshape_output_0 = Reshape(%/cells.17/nl/Relu_output_0, %/cells.17/shuffle/Constant_output_0) %/cells.17/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.17/shuffle/Reshape_output_0) %/cells.17/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.17/shuffle/Reshape_1_output_0 = Reshape(%/cells.17/shuffle/Transpose_output_0, %/cells.17/shuffle/Constant_1_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.17/shuffle/Reshape_1_output_0, %onnx::Conv_828, %onnx::Conv_829) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_831, %onnx::Conv_832) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_834, %onnx::Conv_835) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.18/shuffle/Reshape_output_0 = Reshape(%/cells.18/nl/Relu_output_0, %/cells.18/shuffle/Constant_output_0) %/cells.18/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.18/shuffle/Reshape_output_0) %/cells.18/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.18/shuffle/Reshape_1_output_0 = Reshape(%/cells.18/shuffle/Transpose_output_0, %/cells.18/shuffle/Constant_1_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.18/shuffle/Reshape_1_output_0, %onnx::Conv_837, %onnx::Conv_838) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_840, %onnx::Conv_841) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_843, %onnx::Conv_844) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.19/nl/Relu_output_0, %onnx::Conv_846, %onnx::Conv_847) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_849, %onnx::Conv_850) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_852, %onnx::Conv_853) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.20/nl/Relu_output_0, %onnx::Conv_855, %onnx::Conv_856) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_858, %onnx::Conv_859) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_861, %onnx::Conv_862) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.21/nl/Relu_output_0, %onnx::Conv_864, %onnx::Conv_865) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_867, %onnx::Conv_868) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_870, %onnx::Conv_871) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %688 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %688 }	val_accuracy	83,338,368	1,894,796	{'zcp_synflow': 73.49614481696216, 'zcp_zen': 65.64171600341797, 'zcp_epe_nas': 17.400065855354523, 'zcp_fisher': 0.13407568633556366, 'zcp_flops': 83338368.0, 'zcp_grad_norm': 26.60150718688965, 'zcp_grasp': -0.27791595458984375, 'zcp_jacov': -16.0439628468719, 'zcp_l2_norm': 612.0596923828125, 'zcp_nwot': 215.1164576125214, 'zcp_params': 1894796.0, 'zcp_plain': -0.00828857347369194, 'zcp_snip': 41.60294723510742, 'lat_1080ti_1': 0.5718692153454157, 'lat_1080ti_32': 0.6951824066034068, 'lat_1080ti_64': 0.6587805446630907, 'lat_2080ti_1': 0.6534587355164404, 'lat_2080ti_32': 0.6670537968594331, 'lat_2080ti_64': 0.648626205643394, 'lat_essential_ph_1': 0.37735849056603776, 'lat_eyeriss': 0.6118588744429978, 'lat_fpga': 0.6612078092995181, 'lat_gold_6226': 0.3996651871378513, 'lat_gold_6240': 0.5519330139816825, 'lat_pixel2': 0.41304347826086957, 'lat_pixel3': 0.6869994263416034, 'lat_raspi4': 0.6482228993524398, 'lat_samsung_a50': 0.24210526315789474, 'lat_samsung_s7': 0.1968503937007874, 'lat_silver_4114': 0.6124888768673953, 'lat_silver_4210r': 0.5884130837249962, 'lat_titan_rtx_1': 0.6141261945336831, 'lat_titan_rtx_32': 0.6459729418013737, 'lat_titan_rtx_64': 0.6701334220937513, 'lat_titanx_1': 0.3197930982991015, 'lat_titanx_32': 0.659040775964624, 'lat_titanx_64': 0.7047425346120777, 'lat_titanxp_1': 0.5744726167225551, 'lat_titanxp_32': 0.6687011902048435, 'lat_titanxp_64': 0.6698307816552075}
FBNet_629	FBNet	629	629	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_687[FLOAT, 16x3x3x3] %onnx::Conv_688[FLOAT, 16] %onnx::Conv_690[FLOAT, 16x16x1x1] %onnx::Conv_693[FLOAT, 16x1x3x3] %onnx::Conv_696[FLOAT, 16x16x1x1] %onnx::Conv_699[FLOAT, 48x16x1x1] %onnx::Conv_700[FLOAT, 48] %onnx::Conv_702[FLOAT, 48x1x3x3] %onnx::Conv_705[FLOAT, 24x48x1x1] %onnx::Conv_706[FLOAT, 24] %onnx::Conv_708[FLOAT, 72x24x1x1] %onnx::Conv_709[FLOAT, 72] %onnx::Conv_711[FLOAT, 72x1x5x5] %onnx::Conv_714[FLOAT, 24x72x1x1] %onnx::Conv_717[FLOAT, 24x12x1x1] %onnx::Conv_720[FLOAT, 24x1x5x5] %onnx::Conv_723[FLOAT, 24x12x1x1] %onnx::Conv_726[FLOAT, 24x12x1x1] %onnx::Conv_729[FLOAT, 24x1x3x3] %onnx::Conv_732[FLOAT, 24x12x1x1] %onnx::Conv_735[FLOAT, 72x24x1x1] %onnx::Conv_738[FLOAT, 72x1x3x3] %onnx::Conv_741[FLOAT, 32x72x1x1] %onnx::Conv_742[FLOAT, 32] %onnx::Conv_744[FLOAT, 32x16x1x1] %onnx::Conv_747[FLOAT, 32x1x3x3] %onnx::Conv_750[FLOAT, 32x16x1x1] %onnx::Conv_753[FLOAT, 32x32x1x1] %onnx::Conv_756[FLOAT, 32x1x3x3] %onnx::Conv_759[FLOAT, 32x32x1x1] %onnx::Conv_762[FLOAT, 192x32x1x1] %onnx::Conv_763[FLOAT, 192] %onnx::Conv_765[FLOAT, 192x1x5x5] %onnx::Conv_768[FLOAT, 32x192x1x1] %onnx::Conv_771[FLOAT, 64x32x1x1] %onnx::Conv_772[FLOAT, 64] %onnx::Conv_774[FLOAT, 384x64x1x1] %onnx::Conv_775[FLOAT, 384] %onnx::Conv_777[FLOAT, 384x1x5x5] %onnx::Conv_780[FLOAT, 64x384x1x1] %onnx::Conv_783[FLOAT, 64x32x1x1] %onnx::Conv_786[FLOAT, 64x1x5x5] %onnx::Conv_789[FLOAT, 64x32x1x1] %onnx::Conv_792[FLOAT, 192x64x1x1] %onnx::Conv_795[FLOAT, 192x1x3x3] %onnx::Conv_798[FLOAT, 64x192x1x1] %onnx::Conv_801[FLOAT, 64x32x1x1] %onnx::Conv_804[FLOAT, 64x1x3x3] %onnx::Conv_807[FLOAT, 112x32x1x1] %onnx::Conv_808[FLOAT, 112] %onnx::Conv_810[FLOAT, 336x112x1x1] %onnx::Conv_811[FLOAT, 336] %onnx::Conv_813[FLOAT, 336x1x5x5] %onnx::Conv_816[FLOAT, 112x336x1x1] %onnx::Conv_819[FLOAT, 672x112x1x1] %onnx::Conv_820[FLOAT, 672] %onnx::Conv_822[FLOAT, 672x1x5x5] %onnx::Conv_825[FLOAT, 112x672x1x1] %onnx::Conv_828[FLOAT, 336x112x1x1] %onnx::Conv_831[FLOAT, 336x1x5x5] %onnx::Conv_834[FLOAT, 112x336x1x1] %onnx::Conv_837[FLOAT, 672x112x1x1] %onnx::Conv_840[FLOAT, 672x1x3x3] %onnx::Conv_843[FLOAT, 184x672x1x1] %onnx::Conv_844[FLOAT, 184] %onnx::Conv_846[FLOAT, 184x92x1x1] %onnx::Conv_849[FLOAT, 184x1x5x5] %onnx::Conv_852[FLOAT, 184x92x1x1] %onnx::Conv_855[FLOAT, 184x184x1x1] %onnx::Conv_858[FLOAT, 184x1x5x5] %onnx::Conv_861[FLOAT, 184x184x1x1] %onnx::Conv_864[FLOAT, 184x184x1x1] %onnx::Conv_867[FLOAT, 184x1x5x5] %onnx::Conv_870[FLOAT, 352x184x1x1] %onnx::Conv_871[FLOAT, 352] %onnx::Conv_873[FLOAT, 1504x352x1x1] %onnx::Conv_874[FLOAT, 1504] ) { %onnx::Conv_868 = Identity(%onnx::Conv_844) %onnx::Conv_865 = Identity(%onnx::Conv_844) %onnx::Conv_862 = Identity(%onnx::Conv_844) %onnx::Conv_859 = Identity(%onnx::Conv_844) %onnx::Conv_856 = Identity(%onnx::Conv_844) %onnx::Conv_853 = Identity(%onnx::Conv_844) %onnx::Conv_850 = Identity(%onnx::Conv_844) %onnx::Conv_847 = Identity(%onnx::Conv_844) %onnx::Conv_841 = Identity(%onnx::Conv_820) %onnx::Conv_838 = Identity(%onnx::Conv_820) %onnx::Conv_835 = Identity(%onnx::Conv_808) %onnx::Conv_832 = Identity(%onnx::Conv_811) %onnx::Conv_829 = Identity(%onnx::Conv_811) %onnx::Conv_826 = Identity(%onnx::Conv_808) %onnx::Conv_823 = Identity(%onnx::Conv_820) %onnx::Conv_817 = Identity(%onnx::Conv_808) %onnx::Conv_814 = Identity(%onnx::Conv_811) %onnx::Conv_805 = Identity(%onnx::Conv_772) %onnx::Conv_802 = Identity(%onnx::Conv_772) %onnx::Conv_799 = Identity(%onnx::Conv_772) %onnx::Conv_796 = Identity(%onnx::Conv_763) %onnx::Conv_793 = Identity(%onnx::Conv_763) %onnx::Conv_790 = Identity(%onnx::Conv_772) %onnx::Conv_787 = Identity(%onnx::Conv_772) %onnx::Conv_784 = Identity(%onnx::Conv_772) %onnx::Conv_781 = Identity(%onnx::Conv_772) %onnx::Conv_778 = Identity(%onnx::Conv_775) %onnx::Conv_769 = Identity(%onnx::Conv_742) %onnx::Conv_766 = Identity(%onnx::Conv_763) %onnx::Conv_760 = Identity(%onnx::Conv_742) %onnx::Conv_757 = Identity(%onnx::Conv_742) %onnx::Conv_754 = Identity(%onnx::Conv_742) %onnx::Conv_751 = Identity(%onnx::Conv_742) %onnx::Conv_748 = Identity(%onnx::Conv_742) %onnx::Conv_745 = Identity(%onnx::Conv_742) %onnx::Conv_739 = Identity(%onnx::Conv_709) %onnx::Conv_736 = Identity(%onnx::Conv_709) %onnx::Conv_733 = Identity(%onnx::Conv_706) %onnx::Conv_730 = Identity(%onnx::Conv_706) %onnx::Conv_727 = Identity(%onnx::Conv_706) %onnx::Conv_724 = Identity(%onnx::Conv_706) %onnx::Conv_721 = Identity(%onnx::Conv_706) %onnx::Conv_718 = Identity(%onnx::Conv_706) %onnx::Conv_715 = Identity(%onnx::Conv_706) %onnx::Conv_712 = Identity(%onnx::Conv_709) %onnx::Conv_703 = Identity(%onnx::Conv_700) %onnx::Conv_697 = Identity(%onnx::Conv_688) %onnx::Conv_694 = Identity(%onnx::Conv_688) %onnx::Conv_691 = Identity(%onnx::Conv_688) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_687, %onnx::Conv_688) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_690, %onnx::Conv_691) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.0/nl/Relu_output_0, %onnx::Conv_693, %onnx::Conv_694) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_696, %onnx::Conv_697) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_699, %onnx::Conv_700) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 48, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.1/nl/Relu_output_0, %onnx::Conv_702, %onnx::Conv_703) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_705, %onnx::Conv_706) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_708, %onnx::Conv_709) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.2/nl/Relu_output_0, %onnx::Conv_711, %onnx::Conv_712) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_714, %onnx::Conv_715) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_717, %onnx::Conv_718) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.3/shuffle/Reshape_output_0 = Reshape(%/cells.3/nl/Relu_output_0, %/cells.3/shuffle/Constant_output_0) %/cells.3/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.3/shuffle/Reshape_output_0) %/cells.3/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.3/shuffle/Reshape_1_output_0 = Reshape(%/cells.3/shuffle/Transpose_output_0, %/cells.3/shuffle/Constant_1_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.3/shuffle/Reshape_1_output_0, %onnx::Conv_720, %onnx::Conv_721) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_723, %onnx::Conv_724) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_726, %onnx::Conv_727) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.4/shuffle/Reshape_output_0 = Reshape(%/cells.4/nl/Relu_output_0, %/cells.4/shuffle/Constant_output_0) %/cells.4/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.4/shuffle/Reshape_output_0) %/cells.4/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.4/shuffle/Reshape_1_output_0 = Reshape(%/cells.4/shuffle/Transpose_output_0, %/cells.4/shuffle/Constant_1_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.4/shuffle/Reshape_1_output_0, %onnx::Conv_729, %onnx::Conv_730) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_732, %onnx::Conv_733) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/Add_output_0, %onnx::Conv_735, %onnx::Conv_736) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.5/nl/Relu_output_0, %onnx::Conv_738, %onnx::Conv_739) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_741, %onnx::Conv_742) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_744, %onnx::Conv_745) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.6/shuffle/Reshape_output_0 = Reshape(%/cells.6/nl/Relu_output_0, %/cells.6/shuffle/Constant_output_0) %/cells.6/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.6/shuffle/Reshape_output_0) %/cells.6/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.6/shuffle/Reshape_1_output_0 = Reshape(%/cells.6/shuffle/Transpose_output_0, %/cells.6/shuffle/Constant_1_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.6/shuffle/Reshape_1_output_0, %onnx::Conv_747, %onnx::Conv_748) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_750, %onnx::Conv_751) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_753, %onnx::Conv_754) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.7/nl/Relu_output_0, %onnx::Conv_756, %onnx::Conv_757) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_759, %onnx::Conv_760) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_762, %onnx::Conv_763) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.8/nl/Relu_output_0, %onnx::Conv_765, %onnx::Conv_766) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_768, %onnx::Conv_769) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [2, 2]](%/cells.8/Add_output_0, %onnx::Conv_771, %onnx::Conv_772) %/cells.9/relu/Relu_output_0 = Relu(%/cells.9/conv/Conv_output_0) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/relu/Relu_output_0, %onnx::Conv_774, %onnx::Conv_775) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.10/nl/Relu_output_0, %onnx::Conv_777, %onnx::Conv_778) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_780, %onnx::Conv_781) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/relu/Relu_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_783, %onnx::Conv_784) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.11/shuffle/Reshape_output_0 = Reshape(%/cells.11/nl/Relu_output_0, %/cells.11/shuffle/Constant_output_0) %/cells.11/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.11/shuffle/Reshape_output_0) %/cells.11/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.11/shuffle/Reshape_1_output_0 = Reshape(%/cells.11/shuffle/Transpose_output_0, %/cells.11/shuffle/Constant_1_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.11/shuffle/Reshape_1_output_0, %onnx::Conv_786, %onnx::Conv_787) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_789, %onnx::Conv_790) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_792, %onnx::Conv_793) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.12/nl/Relu_output_0, %onnx::Conv_795, %onnx::Conv_796) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_798, %onnx::Conv_799) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_801, %onnx::Conv_802) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.13/shuffle/Reshape_output_0 = Reshape(%/cells.13/nl/Relu_output_0, %/cells.13/shuffle/Constant_output_0) %/cells.13/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.13/shuffle/Reshape_output_0) %/cells.13/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.13/shuffle/Reshape_1_output_0 = Reshape(%/cells.13/shuffle/Transpose_output_0, %/cells.13/shuffle/Constant_1_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.13/shuffle/Reshape_1_output_0, %onnx::Conv_804, %onnx::Conv_805) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_807, %onnx::Conv_808) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_810, %onnx::Conv_811) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.14/nl/Relu_output_0, %onnx::Conv_813, %onnx::Conv_814) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_816, %onnx::Conv_817) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_819, %onnx::Conv_820) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.15/nl/Relu_output_0, %onnx::Conv_822, %onnx::Conv_823) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_825, %onnx::Conv_826) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_828, %onnx::Conv_829) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.16/nl/Relu_output_0, %onnx::Conv_831, %onnx::Conv_832) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_834, %onnx::Conv_835) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.15/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_837, %onnx::Conv_838) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.17/nl/Relu_output_0, %onnx::Conv_840, %onnx::Conv_841) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_843, %onnx::Conv_844) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_846, %onnx::Conv_847) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.19/shuffle/Reshape_output_0 = Reshape(%/cells.19/nl/Relu_output_0, %/cells.19/shuffle/Constant_output_0) %/cells.19/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.19/shuffle/Reshape_output_0) %/cells.19/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.19/shuffle/Reshape_1_output_0 = Reshape(%/cells.19/shuffle/Transpose_output_0, %/cells.19/shuffle/Constant_1_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.19/shuffle/Reshape_1_output_0, %onnx::Conv_849, %onnx::Conv_850) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_852, %onnx::Conv_853) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_855, %onnx::Conv_856) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.20/nl/Relu_output_0, %onnx::Conv_858, %onnx::Conv_859) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_861, %onnx::Conv_862) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_864, %onnx::Conv_865) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.21/nl/Relu_output_0, %onnx::Conv_867, %onnx::Conv_868) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_870, %onnx::Conv_871) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_873, %onnx::Conv_874) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %685 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %685 }	val_accuracy	68,298,624	1,590,476	{'zcp_synflow': 76.06163544566019, 'zcp_zen': 66.19113159179688, 'zcp_epe_nas': 22.932172878920138, 'zcp_fisher': 0.18984933197498322, 'zcp_flops': 68298624.0, 'zcp_grad_norm': 21.79640769958496, 'zcp_grasp': -0.029645919799804688, 'zcp_jacov': -16.07802476089226, 'zcp_l2_norm': 597.1907958984375, 'zcp_nwot': 211.14650980162241, 'zcp_params': 1590476.0, 'zcp_plain': 0.013250884599983692, 'zcp_snip': 42.57234191894531, 'lat_1080ti_1': 0.6065959685910776, 'lat_1080ti_32': 0.4964969228596849, 'lat_1080ti_64': 0.4051558418840206, 'lat_2080ti_1': 0.6557985401825907, 'lat_2080ti_32': 0.5208158821504691, 'lat_2080ti_64': 0.4175120619704263, 'lat_essential_ph_1': 0.2641509433962264, 'lat_eyeriss': 0.39278968697852673, 'lat_fpga': 0.41638053018533716, 'lat_gold_6226': 0.30149062035493024, 'lat_gold_6240': 0.5005800951359777, 'lat_pixel2': 0.2608695652173913, 'lat_pixel3': 0.4511894693146837, 'lat_raspi4': 0.3832955363225701, 'lat_samsung_a50': 0.16842105263157894, 'lat_samsung_s7': 0.14960629921259844, 'lat_silver_4114': 0.5437398096741248, 'lat_silver_4210r': 0.5095141671959629, 'lat_titan_rtx_1': 0.6465570952873244, 'lat_titan_rtx_32': 0.5547440792249628, 'lat_titan_rtx_64': 0.4472133884178652, 'lat_titanx_1': 0.3369187924649559, 'lat_titanx_32': 0.46096168692117656, 'lat_titanx_64': 0.42615748647071977, 'lat_titanxp_1': 0.5981007585422862, 'lat_titanxp_32': 0.5040273614721481, 'lat_titanxp_64': 0.43413743137069943}
FBNet_2987	FBNet	2987	2987	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_750[FLOAT, 16x3x3x3] %onnx::Conv_751[FLOAT, 16] %onnx::Conv_753[FLOAT, 16x8x1x1] %onnx::Conv_756[FLOAT, 16x1x5x5] %onnx::Conv_759[FLOAT, 16x8x1x1] %onnx::Conv_762[FLOAT, 16x8x1x1] %onnx::Conv_765[FLOAT, 16x1x3x3] %onnx::Conv_768[FLOAT, 24x8x1x1] %onnx::Conv_769[FLOAT, 24] %onnx::Conv_771[FLOAT, 24x24x1x1] %onnx::Conv_774[FLOAT, 24x1x3x3] %onnx::Conv_777[FLOAT, 24x24x1x1] %onnx::Conv_780[FLOAT, 144x24x1x1] %onnx::Conv_781[FLOAT, 144] %onnx::Conv_783[FLOAT, 144x1x5x5] %onnx::Conv_786[FLOAT, 24x144x1x1] %onnx::Conv_789[FLOAT, 24x12x1x1] %onnx::Conv_792[FLOAT, 24x1x5x5] %onnx::Conv_795[FLOAT, 24x12x1x1] %onnx::Conv_798[FLOAT, 24x12x1x1] %onnx::Conv_801[FLOAT, 24x1x5x5] %onnx::Conv_804[FLOAT, 32x12x1x1] %onnx::Conv_805[FLOAT, 32] %onnx::Conv_807[FLOAT, 32x16x1x1] %onnx::Conv_810[FLOAT, 32x1x5x5] %onnx::Conv_813[FLOAT, 32x16x1x1] %onnx::Conv_816[FLOAT, 32x32x1x1] %onnx::Conv_819[FLOAT, 32x1x5x5] %onnx::Conv_822[FLOAT, 32x32x1x1] %onnx::Conv_825[FLOAT, 192x32x1x1] %onnx::Conv_826[FLOAT, 192] %onnx::Conv_828[FLOAT, 192x1x3x3] %onnx::Conv_831[FLOAT, 32x192x1x1] %onnx::Conv_834[FLOAT, 32x32x1x1] %onnx::Conv_837[FLOAT, 32x1x5x5] %onnx::Conv_840[FLOAT, 64x32x1x1] %onnx::Conv_841[FLOAT, 64] %onnx::Conv_843[FLOAT, 64x64x1x1] %onnx::Conv_846[FLOAT, 64x1x3x3] %onnx::Conv_849[FLOAT, 64x64x1x1] %onnx::Conv_852[FLOAT, 384x64x1x1] %onnx::Conv_853[FLOAT, 384] %onnx::Conv_855[FLOAT, 384x1x3x3] %onnx::Conv_858[FLOAT, 64x384x1x1] %onnx::Conv_861[FLOAT, 64x32x1x1] %onnx::Conv_864[FLOAT, 64x1x3x3] %onnx::Conv_867[FLOAT, 64x32x1x1] %onnx::Conv_870[FLOAT, 64x32x1x1] %onnx::Conv_873[FLOAT, 64x1x3x3] %onnx::Conv_876[FLOAT, 112x32x1x1] %onnx::Conv_877[FLOAT, 112] %onnx::Conv_879[FLOAT, 336x112x1x1] %onnx::Conv_880[FLOAT, 336] %onnx::Conv_882[FLOAT, 336x1x5x5] %onnx::Conv_885[FLOAT, 112x336x1x1] %onnx::Conv_888[FLOAT, 112x112x1x1] %onnx::Conv_891[FLOAT, 112x1x3x3] %onnx::Conv_894[FLOAT, 184x112x1x1] %onnx::Conv_895[FLOAT, 184] %onnx::Conv_897[FLOAT, 1104x184x1x1] %onnx::Conv_898[FLOAT, 1104] %onnx::Conv_900[FLOAT, 1104x1x5x5] %onnx::Conv_903[FLOAT, 184x1104x1x1] %onnx::Conv_906[FLOAT, 184x92x1x1] %onnx::Conv_909[FLOAT, 184x1x5x5] %onnx::Conv_912[FLOAT, 184x92x1x1] %onnx::Conv_915[FLOAT, 184x92x1x1] %onnx::Conv_918[FLOAT, 184x1x5x5] %onnx::Conv_921[FLOAT, 184x92x1x1] %onnx::Conv_924[FLOAT, 184x92x1x1] %onnx::Conv_927[FLOAT, 184x1x5x5] %onnx::Conv_930[FLOAT, 352x92x1x1] %onnx::Conv_931[FLOAT, 352] %onnx::Conv_933[FLOAT, 1504x352x1x1] %onnx::Conv_934[FLOAT, 1504] ) { %onnx::Conv_928 = Identity(%onnx::Conv_895) %onnx::Conv_925 = Identity(%onnx::Conv_895) %onnx::Conv_922 = Identity(%onnx::Conv_895) %onnx::Conv_919 = Identity(%onnx::Conv_895) %onnx::Conv_916 = Identity(%onnx::Conv_895) %onnx::Conv_913 = Identity(%onnx::Conv_895) %onnx::Conv_910 = Identity(%onnx::Conv_895) %onnx::Conv_907 = Identity(%onnx::Conv_895) %onnx::Conv_904 = Identity(%onnx::Conv_895) %onnx::Conv_901 = Identity(%onnx::Conv_898) %onnx::Conv_892 = Identity(%onnx::Conv_877) %onnx::Conv_889 = Identity(%onnx::Conv_877) %onnx::Conv_886 = Identity(%onnx::Conv_877) %onnx::Conv_883 = Identity(%onnx::Conv_880) %onnx::Conv_874 = Identity(%onnx::Conv_841) %onnx::Conv_871 = Identity(%onnx::Conv_841) %onnx::Conv_868 = Identity(%onnx::Conv_841) %onnx::Conv_865 = Identity(%onnx::Conv_841) %onnx::Conv_862 = Identity(%onnx::Conv_841) %onnx::Conv_859 = Identity(%onnx::Conv_841) %onnx::Conv_856 = Identity(%onnx::Conv_853) %onnx::Conv_850 = Identity(%onnx::Conv_841) %onnx::Conv_847 = Identity(%onnx::Conv_841) %onnx::Conv_844 = Identity(%onnx::Conv_841) %onnx::Conv_838 = Identity(%onnx::Conv_805) %onnx::Conv_835 = Identity(%onnx::Conv_805) %onnx::Conv_832 = Identity(%onnx::Conv_805) %onnx::Conv_829 = Identity(%onnx::Conv_826) %onnx::Conv_823 = Identity(%onnx::Conv_805) %onnx::Conv_820 = Identity(%onnx::Conv_805) %onnx::Conv_817 = Identity(%onnx::Conv_805) %onnx::Conv_814 = Identity(%onnx::Conv_805) %onnx::Conv_811 = Identity(%onnx::Conv_805) %onnx::Conv_808 = Identity(%onnx::Conv_805) %onnx::Conv_802 = Identity(%onnx::Conv_769) %onnx::Conv_799 = Identity(%onnx::Conv_769) %onnx::Conv_796 = Identity(%onnx::Conv_769) %onnx::Conv_793 = Identity(%onnx::Conv_769) %onnx::Conv_790 = Identity(%onnx::Conv_769) %onnx::Conv_787 = Identity(%onnx::Conv_769) %onnx::Conv_784 = Identity(%onnx::Conv_781) %onnx::Conv_778 = Identity(%onnx::Conv_769) %onnx::Conv_775 = Identity(%onnx::Conv_769) %onnx::Conv_772 = Identity(%onnx::Conv_769) %onnx::Conv_766 = Identity(%onnx::Conv_751) %onnx::Conv_763 = Identity(%onnx::Conv_751) %onnx::Conv_760 = Identity(%onnx::Conv_751) %onnx::Conv_757 = Identity(%onnx::Conv_751) %onnx::Conv_754 = Identity(%onnx::Conv_751) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_750, %onnx::Conv_751) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_753, %onnx::Conv_754) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.0/shuffle/Reshape_output_0 = Reshape(%/cells.0/nl/Relu_output_0, %/cells.0/shuffle/Constant_output_0) %/cells.0/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.0/shuffle/Reshape_output_0) %/cells.0/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.0/shuffle/Reshape_1_output_0 = Reshape(%/cells.0/shuffle/Transpose_output_0, %/cells.0/shuffle/Constant_1_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.0/shuffle/Reshape_1_output_0, %onnx::Conv_756, %onnx::Conv_757) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_759, %onnx::Conv_760) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_762, %onnx::Conv_763) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.1/shuffle/Reshape_output_0 = Reshape(%/cells.1/nl/Relu_output_0, %/cells.1/shuffle/Constant_output_0) %/cells.1/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.1/shuffle/Reshape_output_0) %/cells.1/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.1/shuffle/Reshape_1_output_0 = Reshape(%/cells.1/shuffle/Transpose_output_0, %/cells.1/shuffle/Constant_1_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.1/shuffle/Reshape_1_output_0, %onnx::Conv_765, %onnx::Conv_766) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_768, %onnx::Conv_769) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_771, %onnx::Conv_772) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.2/nl/Relu_output_0, %onnx::Conv_774, %onnx::Conv_775) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_777, %onnx::Conv_778) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_780, %onnx::Conv_781) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.3/nl/Relu_output_0, %onnx::Conv_783, %onnx::Conv_784) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_786, %onnx::Conv_787) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_789, %onnx::Conv_790) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.4/shuffle/Reshape_output_0 = Reshape(%/cells.4/nl/Relu_output_0, %/cells.4/shuffle/Constant_output_0) %/cells.4/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.4/shuffle/Reshape_output_0) %/cells.4/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.4/shuffle/Reshape_1_output_0 = Reshape(%/cells.4/shuffle/Transpose_output_0, %/cells.4/shuffle/Constant_1_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.4/shuffle/Reshape_1_output_0, %onnx::Conv_792, %onnx::Conv_793) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_795, %onnx::Conv_796) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/Add_output_0, %onnx::Conv_798, %onnx::Conv_799) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.5/shuffle/Reshape_output_0 = Reshape(%/cells.5/nl/Relu_output_0, %/cells.5/shuffle/Constant_output_0) %/cells.5/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.5/shuffle/Reshape_output_0) %/cells.5/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.5/shuffle/Reshape_1_output_0 = Reshape(%/cells.5/shuffle/Transpose_output_0, %/cells.5/shuffle/Constant_1_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.5/shuffle/Reshape_1_output_0, %onnx::Conv_801, %onnx::Conv_802) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_804, %onnx::Conv_805) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_807, %onnx::Conv_808) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.6/shuffle/Reshape_output_0 = Reshape(%/cells.6/nl/Relu_output_0, %/cells.6/shuffle/Constant_output_0) %/cells.6/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.6/shuffle/Reshape_output_0) %/cells.6/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.6/shuffle/Reshape_1_output_0 = Reshape(%/cells.6/shuffle/Transpose_output_0, %/cells.6/shuffle/Constant_1_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.6/shuffle/Reshape_1_output_0, %onnx::Conv_810, %onnx::Conv_811) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_813, %onnx::Conv_814) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_816, %onnx::Conv_817) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.7/nl/Relu_output_0, %onnx::Conv_819, %onnx::Conv_820) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_822, %onnx::Conv_823) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_825, %onnx::Conv_826) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.8/nl/Relu_output_0, %onnx::Conv_828, %onnx::Conv_829) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_831, %onnx::Conv_832) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_834, %onnx::Conv_835) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.9/nl/Relu_output_0, %onnx::Conv_837, %onnx::Conv_838) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_840, %onnx::Conv_841) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_843, %onnx::Conv_844) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.10/nl/Relu_output_0, %onnx::Conv_846, %onnx::Conv_847) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_849, %onnx::Conv_850) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_852, %onnx::Conv_853) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.11/nl/Relu_output_0, %onnx::Conv_855, %onnx::Conv_856) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_858, %onnx::Conv_859) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_861, %onnx::Conv_862) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.12/shuffle/Reshape_output_0 = Reshape(%/cells.12/nl/Relu_output_0, %/cells.12/shuffle/Constant_output_0) %/cells.12/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.12/shuffle/Reshape_output_0) %/cells.12/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.12/shuffle/Reshape_1_output_0 = Reshape(%/cells.12/shuffle/Transpose_output_0, %/cells.12/shuffle/Constant_1_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.12/shuffle/Reshape_1_output_0, %onnx::Conv_864, %onnx::Conv_865) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_867, %onnx::Conv_868) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_870, %onnx::Conv_871) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.13/shuffle/Reshape_output_0 = Reshape(%/cells.13/nl/Relu_output_0, %/cells.13/shuffle/Constant_output_0) %/cells.13/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.13/shuffle/Reshape_output_0) %/cells.13/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.13/shuffle/Reshape_1_output_0 = Reshape(%/cells.13/shuffle/Transpose_output_0, %/cells.13/shuffle/Constant_1_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.13/shuffle/Reshape_1_output_0, %onnx::Conv_873, %onnx::Conv_874) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_876, %onnx::Conv_877) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_879, %onnx::Conv_880) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.15/nl/Relu_output_0, %onnx::Conv_882, %onnx::Conv_883) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_885, %onnx::Conv_886) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_888, %onnx::Conv_889) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.17/nl/Relu_output_0, %onnx::Conv_891, %onnx::Conv_892) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_894, %onnx::Conv_895) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_897, %onnx::Conv_898) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.18/nl/Relu_output_0, %onnx::Conv_900, %onnx::Conv_901) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_903, %onnx::Conv_904) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_906, %onnx::Conv_907) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.19/shuffle/Reshape_output_0 = Reshape(%/cells.19/nl/Relu_output_0, %/cells.19/shuffle/Constant_output_0) %/cells.19/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.19/shuffle/Reshape_output_0) %/cells.19/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.19/shuffle/Reshape_1_output_0 = Reshape(%/cells.19/shuffle/Transpose_output_0, %/cells.19/shuffle/Constant_1_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.19/shuffle/Reshape_1_output_0, %onnx::Conv_909, %onnx::Conv_910) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_912, %onnx::Conv_913) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_915, %onnx::Conv_916) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.20/shuffle/Reshape_output_0 = Reshape(%/cells.20/nl/Relu_output_0, %/cells.20/shuffle/Constant_output_0) %/cells.20/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.20/shuffle/Reshape_output_0) %/cells.20/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.20/shuffle/Reshape_1_output_0 = Reshape(%/cells.20/shuffle/Transpose_output_0, %/cells.20/shuffle/Constant_1_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.20/shuffle/Reshape_1_output_0, %onnx::Conv_918, %onnx::Conv_919) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_921, %onnx::Conv_922) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_924, %onnx::Conv_925) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.21/shuffle/Reshape_output_0 = Reshape(%/cells.21/nl/Relu_output_0, %/cells.21/shuffle/Constant_output_0) %/cells.21/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.21/shuffle/Reshape_output_0) %/cells.21/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.21/shuffle/Reshape_1_output_0 = Reshape(%/cells.21/shuffle/Transpose_output_0, %/cells.21/shuffle/Constant_1_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.21/shuffle/Reshape_1_output_0, %onnx::Conv_927, %onnx::Conv_928) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_930, %onnx::Conv_931) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_933, %onnx::Conv_934) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %748 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %748 }	val_accuracy	52,062,848	1,492,644	{'zcp_synflow': 68.61825673715964, 'zcp_zen': 60.849212646484375, 'zcp_epe_nas': 15.995908451178105, 'zcp_fisher': 0.08713539689779282, 'zcp_flops': 52062848.0, 'zcp_grad_norm': 23.480735778808594, 'zcp_grasp': -0.04129219055175781, 'zcp_jacov': -16.051646732617844, 'zcp_l2_norm': 511.00604248046875, 'zcp_nwot': 208.95638694903974, 'zcp_params': 1492644.0, 'zcp_plain': 0.0038916505873203278, 'zcp_snip': 33.86688995361328, 'lat_1080ti_1': 0.6614928909200354, 'lat_1080ti_32': 0.6200080838685642, 'lat_1080ti_64': 0.44575055615058845, 'lat_2080ti_1': 0.72800172334456, 'lat_2080ti_32': 0.6719823586637164, 'lat_2080ti_64': 0.4841543044714956, 'lat_essential_ph_1': 0.22641509433962265, 'lat_eyeriss': 0.29641684851374406, 'lat_fpga': 0.2569093301932535, 'lat_gold_6226': 0.2123921725991923, 'lat_gold_6240': 0.4416193782825652, 'lat_pixel2': 0.32608695652173914, 'lat_pixel3': 0.3498202641673198, 'lat_raspi4': 0.3732539466367794, 'lat_samsung_a50': 0.21052631578947367, 'lat_samsung_s7': 0.12598425196850394, 'lat_silver_4114': 0.5109599405229638, 'lat_silver_4210r': 0.5357450696944248, 'lat_titan_rtx_1': 0.6867489638466169, 'lat_titan_rtx_32': 0.6663232959405143, 'lat_titan_rtx_64': 0.5383456452943771, 'lat_titanx_1': 0.36278732211197484, 'lat_titanx_32': 0.5845716182083251, 'lat_titanx_64': 0.4427574549290583, 'lat_titanxp_1': 0.6453950646414411, 'lat_titanxp_32': 0.6394828499427918, 'lat_titanxp_64': 0.48601302725100326}
FBNet_1665	FBNet	1665	1665	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_724[FLOAT, 16x3x3x3] %onnx::Conv_725[FLOAT, 16] %onnx::Conv_727[FLOAT, 16x16x1x1] %onnx::Conv_730[FLOAT, 16x1x5x5] %onnx::Conv_733[FLOAT, 16x16x1x1] %onnx::Conv_736[FLOAT, 48x16x1x1] %onnx::Conv_737[FLOAT, 48] %onnx::Conv_739[FLOAT, 48x1x3x3] %onnx::Conv_742[FLOAT, 24x48x1x1] %onnx::Conv_743[FLOAT, 24] %onnx::Conv_745[FLOAT, 24x12x1x1] %onnx::Conv_748[FLOAT, 24x1x5x5] %onnx::Conv_751[FLOAT, 24x12x1x1] %onnx::Conv_754[FLOAT, 24x12x1x1] %onnx::Conv_757[FLOAT, 24x1x5x5] %onnx::Conv_760[FLOAT, 24x12x1x1] %onnx::Conv_763[FLOAT, 24x24x1x1] %onnx::Conv_766[FLOAT, 24x1x3x3] %onnx::Conv_769[FLOAT, 24x24x1x1] %onnx::Conv_772[FLOAT, 32x24x1x1] %onnx::Conv_773[FLOAT, 32] %onnx::Conv_775[FLOAT, 192x32x1x1] %onnx::Conv_776[FLOAT, 192] %onnx::Conv_778[FLOAT, 192x1x5x5] %onnx::Conv_781[FLOAT, 32x192x1x1] %onnx::Conv_784[FLOAT, 32x32x1x1] %onnx::Conv_787[FLOAT, 32x1x3x3] %onnx::Conv_790[FLOAT, 32x32x1x1] %onnx::Conv_793[FLOAT, 32x16x1x1] %onnx::Conv_796[FLOAT, 32x1x3x3] %onnx::Conv_799[FLOAT, 32x16x1x1] %onnx::Conv_802[FLOAT, 96x32x1x1] %onnx::Conv_803[FLOAT, 96] %onnx::Conv_805[FLOAT, 96x1x3x3] %onnx::Conv_808[FLOAT, 64x96x1x1] %onnx::Conv_809[FLOAT, 64] %onnx::Conv_811[FLOAT, 64x64x1x1] %onnx::Conv_814[FLOAT, 64x1x3x3] %onnx::Conv_817[FLOAT, 64x64x1x1] %onnx::Conv_820[FLOAT, 64x32x1x1] %onnx::Conv_823[FLOAT, 64x1x5x5] %onnx::Conv_826[FLOAT, 64x32x1x1] %onnx::Conv_829[FLOAT, 192x64x1x1] %onnx::Conv_832[FLOAT, 192x1x5x5] %onnx::Conv_835[FLOAT, 64x192x1x1] %onnx::Conv_838[FLOAT, 192x64x1x1] %onnx::Conv_841[FLOAT, 192x1x5x5] %onnx::Conv_844[FLOAT, 112x192x1x1] %onnx::Conv_845[FLOAT, 112] %onnx::Conv_847[FLOAT, 112x56x1x1] %onnx::Conv_850[FLOAT, 112x1x5x5] %onnx::Conv_853[FLOAT, 112x56x1x1] %onnx::Conv_856[FLOAT, 112x56x1x1] %onnx::Conv_859[FLOAT, 112x1x5x5] %onnx::Conv_862[FLOAT, 112x56x1x1] %onnx::Conv_865[FLOAT, 336x112x1x1] %onnx::Conv_866[FLOAT, 336] %onnx::Conv_868[FLOAT, 336x1x3x3] %onnx::Conv_871[FLOAT, 184x336x1x1] %onnx::Conv_872[FLOAT, 184] %onnx::Conv_874[FLOAT, 184x92x1x1] %onnx::Conv_877[FLOAT, 184x1x5x5] %onnx::Conv_880[FLOAT, 184x92x1x1] %onnx::Conv_883[FLOAT, 184x92x1x1] %onnx::Conv_886[FLOAT, 184x1x5x5] %onnx::Conv_889[FLOAT, 184x92x1x1] %onnx::Conv_892[FLOAT, 184x184x1x1] %onnx::Conv_895[FLOAT, 184x1x5x5] %onnx::Conv_898[FLOAT, 184x184x1x1] %onnx::Conv_901[FLOAT, 184x184x1x1] %onnx::Conv_904[FLOAT, 184x1x3x3] %onnx::Conv_907[FLOAT, 352x184x1x1] %onnx::Conv_908[FLOAT, 352] %onnx::Conv_910[FLOAT, 1504x352x1x1] %onnx::Conv_911[FLOAT, 1504] ) { %onnx::Conv_905 = Identity(%onnx::Conv_872) %onnx::Conv_902 = Identity(%onnx::Conv_872) %onnx::Conv_899 = Identity(%onnx::Conv_872) %onnx::Conv_896 = Identity(%onnx::Conv_872) %onnx::Conv_893 = Identity(%onnx::Conv_872) %onnx::Conv_890 = Identity(%onnx::Conv_872) %onnx::Conv_887 = Identity(%onnx::Conv_872) %onnx::Conv_884 = Identity(%onnx::Conv_872) %onnx::Conv_881 = Identity(%onnx::Conv_872) %onnx::Conv_878 = Identity(%onnx::Conv_872) %onnx::Conv_875 = Identity(%onnx::Conv_872) %onnx::Conv_869 = Identity(%onnx::Conv_866) %onnx::Conv_863 = Identity(%onnx::Conv_845) %onnx::Conv_860 = Identity(%onnx::Conv_845) %onnx::Conv_857 = Identity(%onnx::Conv_845) %onnx::Conv_854 = Identity(%onnx::Conv_845) %onnx::Conv_851 = Identity(%onnx::Conv_845) %onnx::Conv_848 = Identity(%onnx::Conv_845) %onnx::Conv_842 = Identity(%onnx::Conv_776) %onnx::Conv_839 = Identity(%onnx::Conv_776) %onnx::Conv_836 = Identity(%onnx::Conv_809) %onnx::Conv_833 = Identity(%onnx::Conv_776) %onnx::Conv_830 = Identity(%onnx::Conv_776) %onnx::Conv_827 = Identity(%onnx::Conv_809) %onnx::Conv_824 = Identity(%onnx::Conv_809) %onnx::Conv_821 = Identity(%onnx::Conv_809) %onnx::Conv_818 = Identity(%onnx::Conv_809) %onnx::Conv_815 = Identity(%onnx::Conv_809) %onnx::Conv_812 = Identity(%onnx::Conv_809) %onnx::Conv_806 = Identity(%onnx::Conv_803) %onnx::Conv_800 = Identity(%onnx::Conv_773) %onnx::Conv_797 = Identity(%onnx::Conv_773) %onnx::Conv_794 = Identity(%onnx::Conv_773) %onnx::Conv_791 = Identity(%onnx::Conv_773) %onnx::Conv_788 = Identity(%onnx::Conv_773) %onnx::Conv_785 = Identity(%onnx::Conv_773) %onnx::Conv_782 = Identity(%onnx::Conv_773) %onnx::Conv_779 = Identity(%onnx::Conv_776) %onnx::Conv_770 = Identity(%onnx::Conv_743) %onnx::Conv_767 = Identity(%onnx::Conv_743) %onnx::Conv_764 = Identity(%onnx::Conv_743) %onnx::Conv_761 = Identity(%onnx::Conv_743) %onnx::Conv_758 = Identity(%onnx::Conv_743) %onnx::Conv_755 = Identity(%onnx::Conv_743) %onnx::Conv_752 = Identity(%onnx::Conv_743) %onnx::Conv_749 = Identity(%onnx::Conv_743) %onnx::Conv_746 = Identity(%onnx::Conv_743) %onnx::Conv_740 = Identity(%onnx::Conv_737) %onnx::Conv_734 = Identity(%onnx::Conv_725) %onnx::Conv_731 = Identity(%onnx::Conv_725) %onnx::Conv_728 = Identity(%onnx::Conv_725) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_724, %onnx::Conv_725) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_727, %onnx::Conv_728) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.0/nl/Relu_output_0, %onnx::Conv_730, %onnx::Conv_731) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_733, %onnx::Conv_734) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_736, %onnx::Conv_737) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 48, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.1/nl/Relu_output_0, %onnx::Conv_739, %onnx::Conv_740) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_742, %onnx::Conv_743) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_745, %onnx::Conv_746) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.2/shuffle/Reshape_output_0 = Reshape(%/cells.2/nl/Relu_output_0, %/cells.2/shuffle/Constant_output_0) %/cells.2/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.2/shuffle/Reshape_output_0) %/cells.2/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.2/shuffle/Reshape_1_output_0 = Reshape(%/cells.2/shuffle/Transpose_output_0, %/cells.2/shuffle/Constant_1_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.2/shuffle/Reshape_1_output_0, %onnx::Conv_748, %onnx::Conv_749) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_751, %onnx::Conv_752) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_754, %onnx::Conv_755) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.3/shuffle/Reshape_output_0 = Reshape(%/cells.3/nl/Relu_output_0, %/cells.3/shuffle/Constant_output_0) %/cells.3/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.3/shuffle/Reshape_output_0) %/cells.3/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.3/shuffle/Reshape_1_output_0 = Reshape(%/cells.3/shuffle/Transpose_output_0, %/cells.3/shuffle/Constant_1_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.3/shuffle/Reshape_1_output_0, %onnx::Conv_757, %onnx::Conv_758) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_760, %onnx::Conv_761) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_763, %onnx::Conv_764) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.4/nl/Relu_output_0, %onnx::Conv_766, %onnx::Conv_767) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_769, %onnx::Conv_770) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [2, 2]](%/cells.4/Add_output_0, %onnx::Conv_772, %onnx::Conv_773) %/cells.5/relu/Relu_output_0 = Relu(%/cells.5/conv/Conv_output_0) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/relu/Relu_output_0, %onnx::Conv_775, %onnx::Conv_776) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.6/nl/Relu_output_0, %onnx::Conv_778, %onnx::Conv_779) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_781, %onnx::Conv_782) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/relu/Relu_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_784, %onnx::Conv_785) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.7/nl/Relu_output_0, %onnx::Conv_787, %onnx::Conv_788) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_790, %onnx::Conv_791) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_793, %onnx::Conv_794) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.8/shuffle/Reshape_output_0 = Reshape(%/cells.8/nl/Relu_output_0, %/cells.8/shuffle/Constant_output_0) %/cells.8/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.8/shuffle/Reshape_output_0) %/cells.8/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.8/shuffle/Reshape_1_output_0 = Reshape(%/cells.8/shuffle/Transpose_output_0, %/cells.8/shuffle/Constant_1_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.8/shuffle/Reshape_1_output_0, %onnx::Conv_796, %onnx::Conv_797) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_799, %onnx::Conv_800) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_802, %onnx::Conv_803) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.9/nl/Relu_output_0, %onnx::Conv_805, %onnx::Conv_806) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_808, %onnx::Conv_809) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_811, %onnx::Conv_812) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.10/nl/Relu_output_0, %onnx::Conv_814, %onnx::Conv_815) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_817, %onnx::Conv_818) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_820, %onnx::Conv_821) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.11/shuffle/Reshape_output_0 = Reshape(%/cells.11/nl/Relu_output_0, %/cells.11/shuffle/Constant_output_0) %/cells.11/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.11/shuffle/Reshape_output_0) %/cells.11/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.11/shuffle/Reshape_1_output_0 = Reshape(%/cells.11/shuffle/Transpose_output_0, %/cells.11/shuffle/Constant_1_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.11/shuffle/Reshape_1_output_0, %onnx::Conv_823, %onnx::Conv_824) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_826, %onnx::Conv_827) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_829, %onnx::Conv_830) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.12/nl/Relu_output_0, %onnx::Conv_832, %onnx::Conv_833) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_835, %onnx::Conv_836) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_838, %onnx::Conv_839) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.13/nl/Relu_output_0, %onnx::Conv_841, %onnx::Conv_842) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_844, %onnx::Conv_845) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_847, %onnx::Conv_848) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.14/shuffle/Reshape_output_0 = Reshape(%/cells.14/nl/Relu_output_0, %/cells.14/shuffle/Constant_output_0) %/cells.14/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.14/shuffle/Reshape_output_0) %/cells.14/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.14/shuffle/Reshape_1_output_0 = Reshape(%/cells.14/shuffle/Transpose_output_0, %/cells.14/shuffle/Constant_1_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.14/shuffle/Reshape_1_output_0, %onnx::Conv_850, %onnx::Conv_851) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_853, %onnx::Conv_854) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_856, %onnx::Conv_857) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.15/shuffle/Reshape_output_0 = Reshape(%/cells.15/nl/Relu_output_0, %/cells.15/shuffle/Constant_output_0) %/cells.15/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.15/shuffle/Reshape_output_0) %/cells.15/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.15/shuffle/Reshape_1_output_0 = Reshape(%/cells.15/shuffle/Transpose_output_0, %/cells.15/shuffle/Constant_1_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.15/shuffle/Reshape_1_output_0, %onnx::Conv_859, %onnx::Conv_860) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_862, %onnx::Conv_863) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_865, %onnx::Conv_866) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.17/nl/Relu_output_0, %onnx::Conv_868, %onnx::Conv_869) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_871, %onnx::Conv_872) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_874, %onnx::Conv_875) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.18/shuffle/Reshape_output_0 = Reshape(%/cells.18/nl/Relu_output_0, %/cells.18/shuffle/Constant_output_0) %/cells.18/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.18/shuffle/Reshape_output_0) %/cells.18/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.18/shuffle/Reshape_1_output_0 = Reshape(%/cells.18/shuffle/Transpose_output_0, %/cells.18/shuffle/Constant_1_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.18/shuffle/Reshape_1_output_0, %onnx::Conv_877, %onnx::Conv_878) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_880, %onnx::Conv_881) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_883, %onnx::Conv_884) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.19/shuffle/Reshape_output_0 = Reshape(%/cells.19/nl/Relu_output_0, %/cells.19/shuffle/Constant_output_0) %/cells.19/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.19/shuffle/Reshape_output_0) %/cells.19/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.19/shuffle/Reshape_1_output_0 = Reshape(%/cells.19/shuffle/Transpose_output_0, %/cells.19/shuffle/Constant_1_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.19/shuffle/Reshape_1_output_0, %onnx::Conv_886, %onnx::Conv_887) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_889, %onnx::Conv_890) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_892, %onnx::Conv_893) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.20/nl/Relu_output_0, %onnx::Conv_895, %onnx::Conv_896) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_898, %onnx::Conv_899) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_901, %onnx::Conv_902) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.21/nl/Relu_output_0, %onnx::Conv_904, %onnx::Conv_905) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_907, %onnx::Conv_908) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_910, %onnx::Conv_911) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %722 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %722 }	val_accuracy	39,636,864	1,200,076	{'zcp_synflow': 73.96011891207232, 'zcp_zen': 62.77631759643555, 'zcp_epe_nas': 16.832492736692014, 'zcp_fisher': 0.11935795098543167, 'zcp_flops': 39636864.0, 'zcp_grad_norm': 20.24577522277832, 'zcp_grasp': -0.0057964324951171875, 'zcp_jacov': -16.055020991145703, 'zcp_l2_norm': 516.763427734375, 'zcp_nwot': 204.37368940683737, 'zcp_params': 1200076.0, 'zcp_plain': -0.0012294086627662182, 'zcp_snip': 35.60567092895508, 'lat_1080ti_1': 0.6660088860041733, 'lat_1080ti_32': 0.5347875315482816, 'lat_1080ti_64': 0.28798058653212044, 'lat_2080ti_1': 0.7233058396223176, 'lat_2080ti_32': 0.5421030393745381, 'lat_2080ti_64': 0.3342755645777057, 'lat_essential_ph_1': 0.1320754716981132, 'lat_eyeriss': 0.12591458382997459, 'lat_fpga': 0.07850859398964963, 'lat_gold_6226': 0.07998256552635159, 'lat_gold_6240': 0.334277501308366, 'lat_pixel2': 0.08695652173913043, 'lat_pixel3': 0.1563788246839722, 'lat_raspi4': 0.16345772952360033, 'lat_samsung_a50': 0.06315789473684211, 'lat_samsung_s7': 0.07874015748031496, 'lat_silver_4114': 0.5112582340178571, 'lat_silver_4210r': 0.4160372208868963, 'lat_titan_rtx_1': 0.675538911298615, 'lat_titan_rtx_32': 0.5612193278952305, 'lat_titan_rtx_64': 0.3894773360182126, 'lat_titanx_1': 0.35549589135625503, 'lat_titanx_32': 0.49173922818212434, 'lat_titanx_64': 0.29102410847890403, 'lat_titanxp_1': 0.6491462080763376, 'lat_titanxp_32': 0.49376599521415276, 'lat_titanxp_64': 0.3401346396288165}
FBNet_2381	FBNet	2381	2381	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_659[FLOAT, 16x3x3x3] %onnx::Conv_660[FLOAT, 16] %onnx::Conv_662[FLOAT, 48x16x1x1] %onnx::Conv_663[FLOAT, 48] %onnx::Conv_665[FLOAT, 48x1x3x3] %onnx::Conv_668[FLOAT, 16x48x1x1] %onnx::Conv_671[FLOAT, 24x16x1x1] %onnx::Conv_672[FLOAT, 24] %onnx::Conv_674[FLOAT, 144x24x1x1] %onnx::Conv_675[FLOAT, 144] %onnx::Conv_677[FLOAT, 144x1x5x5] %onnx::Conv_680[FLOAT, 24x144x1x1] %onnx::Conv_683[FLOAT, 24x24x1x1] %onnx::Conv_686[FLOAT, 24x1x5x5] %onnx::Conv_689[FLOAT, 24x24x1x1] %onnx::Conv_692[FLOAT, 144x24x1x1] %onnx::Conv_695[FLOAT, 144x1x5x5] %onnx::Conv_698[FLOAT, 24x144x1x1] %onnx::Conv_701[FLOAT, 24x24x1x1] %onnx::Conv_704[FLOAT, 24x1x5x5] %onnx::Conv_707[FLOAT, 32x24x1x1] %onnx::Conv_708[FLOAT, 32] %onnx::Conv_710[FLOAT, 96x32x1x1] %onnx::Conv_711[FLOAT, 96] %onnx::Conv_713[FLOAT, 96x1x5x5] %onnx::Conv_716[FLOAT, 32x96x1x1] %onnx::Conv_719[FLOAT, 96x32x1x1] %onnx::Conv_722[FLOAT, 96x1x3x3] %onnx::Conv_725[FLOAT, 32x96x1x1] %onnx::Conv_728[FLOAT, 32x32x1x1] %onnx::Conv_731[FLOAT, 32x1x3x3] %onnx::Conv_734[FLOAT, 32x32x1x1] %onnx::Conv_737[FLOAT, 32x32x1x1] %onnx::Conv_740[FLOAT, 32x1x5x5] %onnx::Conv_743[FLOAT, 64x32x1x1] %onnx::Conv_744[FLOAT, 64] %onnx::Conv_746[FLOAT, 384x64x1x1] %onnx::Conv_747[FLOAT, 384] %onnx::Conv_749[FLOAT, 384x1x5x5] %onnx::Conv_752[FLOAT, 64x384x1x1] %onnx::Conv_755[FLOAT, 384x64x1x1] %onnx::Conv_758[FLOAT, 384x1x3x3] %onnx::Conv_761[FLOAT, 64x384x1x1] %onnx::Conv_764[FLOAT, 384x64x1x1] %onnx::Conv_767[FLOAT, 384x1x5x5] %onnx::Conv_770[FLOAT, 64x384x1x1] %onnx::Conv_773[FLOAT, 112x64x1x1] %onnx::Conv_774[FLOAT, 112] %onnx::Conv_776[FLOAT, 112x56x1x1] %onnx::Conv_779[FLOAT, 112x1x3x3] %onnx::Conv_782[FLOAT, 112x56x1x1] %onnx::Conv_785[FLOAT, 112x56x1x1] %onnx::Conv_788[FLOAT, 112x1x5x5] %onnx::Conv_791[FLOAT, 112x56x1x1] %onnx::Conv_794[FLOAT, 112x112x1x1] %onnx::Conv_797[FLOAT, 112x1x5x5] %onnx::Conv_800[FLOAT, 112x112x1x1] %onnx::Conv_803[FLOAT, 112x56x1x1] %onnx::Conv_806[FLOAT, 112x1x5x5] %onnx::Conv_809[FLOAT, 184x56x1x1] %onnx::Conv_810[FLOAT, 184] %onnx::Conv_812[FLOAT, 184x92x1x1] %onnx::Conv_815[FLOAT, 184x1x5x5] %onnx::Conv_818[FLOAT, 184x92x1x1] %onnx::Conv_821[FLOAT, 1104x184x1x1] %onnx::Conv_822[FLOAT, 1104] %onnx::Conv_824[FLOAT, 1104x1x5x5] %onnx::Conv_827[FLOAT, 184x1104x1x1] %onnx::Conv_830[FLOAT, 1104x184x1x1] %onnx::Conv_833[FLOAT, 1104x1x5x5] %onnx::Conv_836[FLOAT, 184x1104x1x1] %onnx::Conv_839[FLOAT, 1104x184x1x1] %onnx::Conv_842[FLOAT, 1104x1x3x3] %onnx::Conv_845[FLOAT, 352x1104x1x1] %onnx::Conv_846[FLOAT, 352] %onnx::Conv_848[FLOAT, 1504x352x1x1] %onnx::Conv_849[FLOAT, 1504] ) { %onnx::Conv_843 = Identity(%onnx::Conv_822) %onnx::Conv_840 = Identity(%onnx::Conv_822) %onnx::Conv_837 = Identity(%onnx::Conv_810) %onnx::Conv_834 = Identity(%onnx::Conv_822) %onnx::Conv_831 = Identity(%onnx::Conv_822) %onnx::Conv_828 = Identity(%onnx::Conv_810) %onnx::Conv_825 = Identity(%onnx::Conv_822) %onnx::Conv_819 = Identity(%onnx::Conv_810) %onnx::Conv_816 = Identity(%onnx::Conv_810) %onnx::Conv_813 = Identity(%onnx::Conv_810) %onnx::Conv_807 = Identity(%onnx::Conv_774) %onnx::Conv_804 = Identity(%onnx::Conv_774) %onnx::Conv_801 = Identity(%onnx::Conv_774) %onnx::Conv_798 = Identity(%onnx::Conv_774) %onnx::Conv_795 = Identity(%onnx::Conv_774) %onnx::Conv_792 = Identity(%onnx::Conv_774) %onnx::Conv_789 = Identity(%onnx::Conv_774) %onnx::Conv_786 = Identity(%onnx::Conv_774) %onnx::Conv_783 = Identity(%onnx::Conv_774) %onnx::Conv_780 = Identity(%onnx::Conv_774) %onnx::Conv_777 = Identity(%onnx::Conv_774) %onnx::Conv_771 = Identity(%onnx::Conv_744) %onnx::Conv_768 = Identity(%onnx::Conv_747) %onnx::Conv_765 = Identity(%onnx::Conv_747) %onnx::Conv_762 = Identity(%onnx::Conv_744) %onnx::Conv_759 = Identity(%onnx::Conv_747) %onnx::Conv_756 = Identity(%onnx::Conv_747) %onnx::Conv_753 = Identity(%onnx::Conv_744) %onnx::Conv_750 = Identity(%onnx::Conv_747) %onnx::Conv_741 = Identity(%onnx::Conv_708) %onnx::Conv_738 = Identity(%onnx::Conv_708) %onnx::Conv_735 = Identity(%onnx::Conv_708) %onnx::Conv_732 = Identity(%onnx::Conv_708) %onnx::Conv_729 = Identity(%onnx::Conv_708) %onnx::Conv_726 = Identity(%onnx::Conv_708) %onnx::Conv_723 = Identity(%onnx::Conv_711) %onnx::Conv_720 = Identity(%onnx::Conv_711) %onnx::Conv_717 = Identity(%onnx::Conv_708) %onnx::Conv_714 = Identity(%onnx::Conv_711) %onnx::Conv_705 = Identity(%onnx::Conv_672) %onnx::Conv_702 = Identity(%onnx::Conv_672) %onnx::Conv_699 = Identity(%onnx::Conv_672) %onnx::Conv_696 = Identity(%onnx::Conv_675) %onnx::Conv_693 = Identity(%onnx::Conv_675) %onnx::Conv_690 = Identity(%onnx::Conv_672) %onnx::Conv_687 = Identity(%onnx::Conv_672) %onnx::Conv_684 = Identity(%onnx::Conv_672) %onnx::Conv_681 = Identity(%onnx::Conv_672) %onnx::Conv_678 = Identity(%onnx::Conv_675) %onnx::Conv_669 = Identity(%onnx::Conv_660) %onnx::Conv_666 = Identity(%onnx::Conv_663) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_659, %onnx::Conv_660) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_662, %onnx::Conv_663) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 48, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.0/nl/Relu_output_0, %onnx::Conv_665, %onnx::Conv_666) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_668, %onnx::Conv_669) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_671, %onnx::Conv_672) %/cells.1/relu/Relu_output_0 = Relu(%/cells.1/conv/Conv_output_0) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/relu/Relu_output_0, %onnx::Conv_674, %onnx::Conv_675) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.2/nl/Relu_output_0, %onnx::Conv_677, %onnx::Conv_678) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_680, %onnx::Conv_681) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/relu/Relu_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_683, %onnx::Conv_684) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.3/nl/Relu_output_0, %onnx::Conv_686, %onnx::Conv_687) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_689, %onnx::Conv_690) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_692, %onnx::Conv_693) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.4/nl/Relu_output_0, %onnx::Conv_695, %onnx::Conv_696) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_698, %onnx::Conv_699) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/Add_output_0, %onnx::Conv_701, %onnx::Conv_702) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.5/nl/Relu_output_0, %onnx::Conv_704, %onnx::Conv_705) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_707, %onnx::Conv_708) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_710, %onnx::Conv_711) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.6/nl/Relu_output_0, %onnx::Conv_713, %onnx::Conv_714) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_716, %onnx::Conv_717) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_719, %onnx::Conv_720) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.7/nl/Relu_output_0, %onnx::Conv_722, %onnx::Conv_723) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_725, %onnx::Conv_726) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_728, %onnx::Conv_729) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.8/nl/Relu_output_0, %onnx::Conv_731, %onnx::Conv_732) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_734, %onnx::Conv_735) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_737, %onnx::Conv_738) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.9/nl/Relu_output_0, %onnx::Conv_740, %onnx::Conv_741) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_743, %onnx::Conv_744) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_746, %onnx::Conv_747) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.10/nl/Relu_output_0, %onnx::Conv_749, %onnx::Conv_750) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_752, %onnx::Conv_753) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_755, %onnx::Conv_756) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.11/nl/Relu_output_0, %onnx::Conv_758, %onnx::Conv_759) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_761, %onnx::Conv_762) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_764, %onnx::Conv_765) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.12/nl/Relu_output_0, %onnx::Conv_767, %onnx::Conv_768) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_770, %onnx::Conv_771) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_773, %onnx::Conv_774) %/cells.13/relu/Relu_output_0 = Relu(%/cells.13/conv/Conv_output_0) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/relu/Relu_output_0, %onnx::Conv_776, %onnx::Conv_777) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.14/shuffle/Reshape_output_0 = Reshape(%/cells.14/nl/Relu_output_0, %/cells.14/shuffle/Constant_output_0) %/cells.14/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.14/shuffle/Reshape_output_0) %/cells.14/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.14/shuffle/Reshape_1_output_0 = Reshape(%/cells.14/shuffle/Transpose_output_0, %/cells.14/shuffle/Constant_1_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.14/shuffle/Reshape_1_output_0, %onnx::Conv_779, %onnx::Conv_780) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_782, %onnx::Conv_783) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/relu/Relu_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_785, %onnx::Conv_786) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.15/shuffle/Reshape_output_0 = Reshape(%/cells.15/nl/Relu_output_0, %/cells.15/shuffle/Constant_output_0) %/cells.15/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.15/shuffle/Reshape_output_0) %/cells.15/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.15/shuffle/Reshape_1_output_0 = Reshape(%/cells.15/shuffle/Transpose_output_0, %/cells.15/shuffle/Constant_1_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.15/shuffle/Reshape_1_output_0, %onnx::Conv_788, %onnx::Conv_789) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_791, %onnx::Conv_792) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_794, %onnx::Conv_795) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.16/nl/Relu_output_0, %onnx::Conv_797, %onnx::Conv_798) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_800, %onnx::Conv_801) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.15/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_803, %onnx::Conv_804) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.17/shuffle/Reshape_output_0 = Reshape(%/cells.17/nl/Relu_output_0, %/cells.17/shuffle/Constant_output_0) %/cells.17/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.17/shuffle/Reshape_output_0) %/cells.17/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.17/shuffle/Reshape_1_output_0 = Reshape(%/cells.17/shuffle/Transpose_output_0, %/cells.17/shuffle/Constant_1_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.17/shuffle/Reshape_1_output_0, %onnx::Conv_806, %onnx::Conv_807) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_809, %onnx::Conv_810) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_812, %onnx::Conv_813) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.18/shuffle/Reshape_output_0 = Reshape(%/cells.18/nl/Relu_output_0, %/cells.18/shuffle/Constant_output_0) %/cells.18/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.18/shuffle/Reshape_output_0) %/cells.18/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.18/shuffle/Reshape_1_output_0 = Reshape(%/cells.18/shuffle/Transpose_output_0, %/cells.18/shuffle/Constant_1_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.18/shuffle/Reshape_1_output_0, %onnx::Conv_815, %onnx::Conv_816) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_818, %onnx::Conv_819) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_821, %onnx::Conv_822) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.19/nl/Relu_output_0, %onnx::Conv_824, %onnx::Conv_825) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_827, %onnx::Conv_828) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_830, %onnx::Conv_831) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.20/nl/Relu_output_0, %onnx::Conv_833, %onnx::Conv_834) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_836, %onnx::Conv_837) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_839, %onnx::Conv_840) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.21/nl/Relu_output_0, %onnx::Conv_842, %onnx::Conv_843) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_845, %onnx::Conv_846) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_848, %onnx::Conv_849) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %657 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %657 }	val_accuracy	84,067,968	2,520,540	{'zcp_synflow': 82.69001236925207, 'zcp_zen': 72.16889953613281, 'zcp_epe_nas': 24.776724630670312, 'zcp_fisher': 0.20904220640659332, 'zcp_flops': 84067968.0, 'zcp_grad_norm': 27.758460998535156, 'zcp_grasp': -0.08462905883789062, 'zcp_jacov': -16.052335177020325, 'zcp_l2_norm': 679.1284790039062, 'zcp_nwot': 215.50520931317237, 'zcp_params': 2520540.0, 'zcp_plain': 0.0030653791036456823, 'zcp_snip': 48.10475540161133, 'lat_1080ti_1': 0.6138327857021703, 'lat_1080ti_32': 0.7438320625550067, 'lat_1080ti_64': 0.6765905707232527, 'lat_2080ti_1': 0.6776395026646188, 'lat_2080ti_32': 0.7359538609497878, 'lat_2080ti_64': 0.6516847693102877, 'lat_essential_ph_1': 0.5471698113207547, 'lat_eyeriss': 0.7066913611941389, 'lat_fpga': 0.6514313420872956, 'lat_gold_6226': 0.5194436953295272, 'lat_gold_6240': 0.7267541906585421, 'lat_pixel2': 0.5869565217391305, 'lat_pixel3': 0.708664515241503, 'lat_raspi4': 0.8001429619000813, 'lat_samsung_a50': 0.30526315789473685, 'lat_samsung_s7': 0.25196850393700787, 'lat_silver_4114': 0.7617016148089301, 'lat_silver_4210r': 0.7488472622972063, 'lat_titan_rtx_1': 0.6514213854884604, 'lat_titan_rtx_32': 0.7469917713820504, 'lat_titan_rtx_64': 0.6780257603317328, 'lat_titanx_1': 0.35404455225725345, 'lat_titanx_32': 0.7165923835610974, 'lat_titanx_64': 0.6366934974456504, 'lat_titanxp_1': 0.6159951615897974, 'lat_titanxp_32': 0.7632071507786781, 'lat_titanxp_64': 0.6833923529013147}
FBNet_3631	FBNet	3631	3631	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_662[FLOAT, 16x3x3x3] %onnx::Conv_663[FLOAT, 16] %onnx::Conv_665[FLOAT, 16x8x1x1] %onnx::Conv_668[FLOAT, 16x1x5x5] %onnx::Conv_671[FLOAT, 24x8x1x1] %onnx::Conv_672[FLOAT, 24] %onnx::Conv_674[FLOAT, 24x24x1x1] %onnx::Conv_677[FLOAT, 24x1x3x3] %onnx::Conv_680[FLOAT, 24x24x1x1] %onnx::Conv_683[FLOAT, 24x24x1x1] %onnx::Conv_686[FLOAT, 24x1x3x3] %onnx::Conv_689[FLOAT, 24x24x1x1] %onnx::Conv_692[FLOAT, 24x24x1x1] %onnx::Conv_695[FLOAT, 24x1x3x3] %onnx::Conv_698[FLOAT, 24x24x1x1] %onnx::Conv_701[FLOAT, 24x12x1x1] %onnx::Conv_704[FLOAT, 24x1x3x3] %onnx::Conv_707[FLOAT, 32x12x1x1] %onnx::Conv_708[FLOAT, 32] %onnx::Conv_710[FLOAT, 32x16x1x1] %onnx::Conv_713[FLOAT, 32x1x3x3] %onnx::Conv_716[FLOAT, 32x16x1x1] %onnx::Conv_719[FLOAT, 32x16x1x1] %onnx::Conv_722[FLOAT, 32x1x3x3] %onnx::Conv_725[FLOAT, 32x16x1x1] %onnx::Conv_728[FLOAT, 192x32x1x1] %onnx::Conv_729[FLOAT, 192] %onnx::Conv_731[FLOAT, 192x1x5x5] %onnx::Conv_734[FLOAT, 32x192x1x1] %onnx::Conv_737[FLOAT, 32x16x1x1] %onnx::Conv_740[FLOAT, 32x1x3x3] %onnx::Conv_743[FLOAT, 64x16x1x1] %onnx::Conv_744[FLOAT, 64] %onnx::Conv_746[FLOAT, 64x64x1x1] %onnx::Conv_749[FLOAT, 64x1x5x5] %onnx::Conv_752[FLOAT, 64x64x1x1] %onnx::Conv_755[FLOAT, 192x64x1x1] %onnx::Conv_758[FLOAT, 192x1x5x5] %onnx::Conv_761[FLOAT, 64x192x1x1] %onnx::Conv_764[FLOAT, 112x64x1x1] %onnx::Conv_765[FLOAT, 112] %onnx::Conv_767[FLOAT, 672x112x1x1] %onnx::Conv_768[FLOAT, 672] %onnx::Conv_770[FLOAT, 672x1x5x5] %onnx::Conv_773[FLOAT, 112x672x1x1] %onnx::Conv_776[FLOAT, 672x112x1x1] %onnx::Conv_779[FLOAT, 672x1x5x5] %onnx::Conv_782[FLOAT, 112x672x1x1] %onnx::Conv_785[FLOAT, 336x112x1x1] %onnx::Conv_786[FLOAT, 336] %onnx::Conv_788[FLOAT, 336x1x5x5] %onnx::Conv_791[FLOAT, 112x336x1x1] %onnx::Conv_794[FLOAT, 112x56x1x1] %onnx::Conv_797[FLOAT, 112x1x5x5] %onnx::Conv_800[FLOAT, 184x56x1x1] %onnx::Conv_801[FLOAT, 184] %onnx::Conv_803[FLOAT, 552x184x1x1] %onnx::Conv_804[FLOAT, 552] %onnx::Conv_806[FLOAT, 552x1x3x3] %onnx::Conv_809[FLOAT, 184x552x1x1] %onnx::Conv_812[FLOAT, 184x184x1x1] %onnx::Conv_815[FLOAT, 184x1x3x3] %onnx::Conv_818[FLOAT, 184x184x1x1] %onnx::Conv_821[FLOAT, 184x92x1x1] %onnx::Conv_824[FLOAT, 184x1x3x3] %onnx::Conv_827[FLOAT, 184x92x1x1] %onnx::Conv_830[FLOAT, 352x184x1x1] %onnx::Conv_831[FLOAT, 352] %onnx::Conv_833[FLOAT, 1504x352x1x1] %onnx::Conv_834[FLOAT, 1504] ) { %onnx::Conv_828 = Identity(%onnx::Conv_801) %onnx::Conv_825 = Identity(%onnx::Conv_801) %onnx::Conv_822 = Identity(%onnx::Conv_801) %onnx::Conv_819 = Identity(%onnx::Conv_801) %onnx::Conv_816 = Identity(%onnx::Conv_801) %onnx::Conv_813 = Identity(%onnx::Conv_801) %onnx::Conv_810 = Identity(%onnx::Conv_801) %onnx::Conv_807 = Identity(%onnx::Conv_804) %onnx::Conv_798 = Identity(%onnx::Conv_765) %onnx::Conv_795 = Identity(%onnx::Conv_765) %onnx::Conv_792 = Identity(%onnx::Conv_765) %onnx::Conv_789 = Identity(%onnx::Conv_786) %onnx::Conv_783 = Identity(%onnx::Conv_765) %onnx::Conv_780 = Identity(%onnx::Conv_768) %onnx::Conv_777 = Identity(%onnx::Conv_768) %onnx::Conv_774 = Identity(%onnx::Conv_765) %onnx::Conv_771 = Identity(%onnx::Conv_768) %onnx::Conv_762 = Identity(%onnx::Conv_744) %onnx::Conv_759 = Identity(%onnx::Conv_729) %onnx::Conv_756 = Identity(%onnx::Conv_729) %onnx::Conv_753 = Identity(%onnx::Conv_744) %onnx::Conv_750 = Identity(%onnx::Conv_744) %onnx::Conv_747 = Identity(%onnx::Conv_744) %onnx::Conv_741 = Identity(%onnx::Conv_708) %onnx::Conv_738 = Identity(%onnx::Conv_708) %onnx::Conv_735 = Identity(%onnx::Conv_708) %onnx::Conv_732 = Identity(%onnx::Conv_729) %onnx::Conv_726 = Identity(%onnx::Conv_708) %onnx::Conv_723 = Identity(%onnx::Conv_708) %onnx::Conv_720 = Identity(%onnx::Conv_708) %onnx::Conv_717 = Identity(%onnx::Conv_708) %onnx::Conv_714 = Identity(%onnx::Conv_708) %onnx::Conv_711 = Identity(%onnx::Conv_708) %onnx::Conv_705 = Identity(%onnx::Conv_672) %onnx::Conv_702 = Identity(%onnx::Conv_672) %onnx::Conv_699 = Identity(%onnx::Conv_672) %onnx::Conv_696 = Identity(%onnx::Conv_672) %onnx::Conv_693 = Identity(%onnx::Conv_672) %onnx::Conv_690 = Identity(%onnx::Conv_672) %onnx::Conv_687 = Identity(%onnx::Conv_672) %onnx::Conv_684 = Identity(%onnx::Conv_672) %onnx::Conv_681 = Identity(%onnx::Conv_672) %onnx::Conv_678 = Identity(%onnx::Conv_672) %onnx::Conv_675 = Identity(%onnx::Conv_672) %onnx::Conv_669 = Identity(%onnx::Conv_663) %onnx::Conv_666 = Identity(%onnx::Conv_663) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_662, %onnx::Conv_663) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_665, %onnx::Conv_666) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.1/shuffle/Reshape_output_0 = Reshape(%/cells.1/nl/Relu_output_0, %/cells.1/shuffle/Constant_output_0) %/cells.1/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.1/shuffle/Reshape_output_0) %/cells.1/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.1/shuffle/Reshape_1_output_0 = Reshape(%/cells.1/shuffle/Transpose_output_0, %/cells.1/shuffle/Constant_1_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.1/shuffle/Reshape_1_output_0, %onnx::Conv_668, %onnx::Conv_669) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_671, %onnx::Conv_672) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_674, %onnx::Conv_675) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.2/nl/Relu_output_0, %onnx::Conv_677, %onnx::Conv_678) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_680, %onnx::Conv_681) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_683, %onnx::Conv_684) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.3/nl/Relu_output_0, %onnx::Conv_686, %onnx::Conv_687) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_689, %onnx::Conv_690) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_692, %onnx::Conv_693) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.4/nl/Relu_output_0, %onnx::Conv_695, %onnx::Conv_696) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_698, %onnx::Conv_699) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/Add_output_0, %onnx::Conv_701, %onnx::Conv_702) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.5/shuffle/Reshape_output_0 = Reshape(%/cells.5/nl/Relu_output_0, %/cells.5/shuffle/Constant_output_0) %/cells.5/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.5/shuffle/Reshape_output_0) %/cells.5/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.5/shuffle/Reshape_1_output_0 = Reshape(%/cells.5/shuffle/Transpose_output_0, %/cells.5/shuffle/Constant_1_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.5/shuffle/Reshape_1_output_0, %onnx::Conv_704, %onnx::Conv_705) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_707, %onnx::Conv_708) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_710, %onnx::Conv_711) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.6/shuffle/Reshape_output_0 = Reshape(%/cells.6/nl/Relu_output_0, %/cells.6/shuffle/Constant_output_0) %/cells.6/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.6/shuffle/Reshape_output_0) %/cells.6/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.6/shuffle/Reshape_1_output_0 = Reshape(%/cells.6/shuffle/Transpose_output_0, %/cells.6/shuffle/Constant_1_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.6/shuffle/Reshape_1_output_0, %onnx::Conv_713, %onnx::Conv_714) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_716, %onnx::Conv_717) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_719, %onnx::Conv_720) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.7/shuffle/Reshape_output_0 = Reshape(%/cells.7/nl/Relu_output_0, %/cells.7/shuffle/Constant_output_0) %/cells.7/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.7/shuffle/Reshape_output_0) %/cells.7/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.7/shuffle/Reshape_1_output_0 = Reshape(%/cells.7/shuffle/Transpose_output_0, %/cells.7/shuffle/Constant_1_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.7/shuffle/Reshape_1_output_0, %onnx::Conv_722, %onnx::Conv_723) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_725, %onnx::Conv_726) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_728, %onnx::Conv_729) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.8/nl/Relu_output_0, %onnx::Conv_731, %onnx::Conv_732) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_734, %onnx::Conv_735) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_737, %onnx::Conv_738) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.9/shuffle/Reshape_output_0 = Reshape(%/cells.9/nl/Relu_output_0, %/cells.9/shuffle/Constant_output_0) %/cells.9/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.9/shuffle/Reshape_output_0) %/cells.9/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.9/shuffle/Reshape_1_output_0 = Reshape(%/cells.9/shuffle/Transpose_output_0, %/cells.9/shuffle/Constant_1_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.9/shuffle/Reshape_1_output_0, %onnx::Conv_740, %onnx::Conv_741) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_743, %onnx::Conv_744) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_746, %onnx::Conv_747) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.10/nl/Relu_output_0, %onnx::Conv_749, %onnx::Conv_750) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_752, %onnx::Conv_753) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_755, %onnx::Conv_756) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.11/nl/Relu_output_0, %onnx::Conv_758, %onnx::Conv_759) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_761, %onnx::Conv_762) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.13/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_764, %onnx::Conv_765) %/cells.13/relu/Relu_output_0 = Relu(%/cells.13/conv/Conv_output_0) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/relu/Relu_output_0, %onnx::Conv_767, %onnx::Conv_768) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.14/nl/Relu_output_0, %onnx::Conv_770, %onnx::Conv_771) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_773, %onnx::Conv_774) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/relu/Relu_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_776, %onnx::Conv_777) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.15/nl/Relu_output_0, %onnx::Conv_779, %onnx::Conv_780) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_782, %onnx::Conv_783) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_785, %onnx::Conv_786) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.16/nl/Relu_output_0, %onnx::Conv_788, %onnx::Conv_789) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_791, %onnx::Conv_792) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.15/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_794, %onnx::Conv_795) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.17/shuffle/Reshape_output_0 = Reshape(%/cells.17/nl/Relu_output_0, %/cells.17/shuffle/Constant_output_0) %/cells.17/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.17/shuffle/Reshape_output_0) %/cells.17/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.17/shuffle/Reshape_1_output_0 = Reshape(%/cells.17/shuffle/Transpose_output_0, %/cells.17/shuffle/Constant_1_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.17/shuffle/Reshape_1_output_0, %onnx::Conv_797, %onnx::Conv_798) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_800, %onnx::Conv_801) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_803, %onnx::Conv_804) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 552, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.18/nl/Relu_output_0, %onnx::Conv_806, %onnx::Conv_807) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_809, %onnx::Conv_810) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_812, %onnx::Conv_813) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.19/nl/Relu_output_0, %onnx::Conv_815, %onnx::Conv_816) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_818, %onnx::Conv_819) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_821, %onnx::Conv_822) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.20/shuffle/Reshape_output_0 = Reshape(%/cells.20/nl/Relu_output_0, %/cells.20/shuffle/Constant_output_0) %/cells.20/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.20/shuffle/Reshape_output_0) %/cells.20/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.20/shuffle/Reshape_1_output_0 = Reshape(%/cells.20/shuffle/Transpose_output_0, %/cells.20/shuffle/Constant_1_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.20/shuffle/Reshape_1_output_0, %onnx::Conv_824, %onnx::Conv_825) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_827, %onnx::Conv_828) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_830, %onnx::Conv_831) %/cells.21/relu/Relu_output_0 = Relu(%/cells.21/conv/Conv_output_0) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/relu/Relu_output_0, %onnx::Conv_833, %onnx::Conv_834) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %660 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %660 }	val_accuracy	58,224,256	1,589,788	{'zcp_synflow': 70.89687000731197, 'zcp_zen': 60.59416198730469, 'zcp_epe_nas': 0.00015999920000638146, 'zcp_fisher': 0.049957919865846634, 'zcp_flops': 58224256.0, 'zcp_grad_norm': 13.85365104675293, 'zcp_grasp': 0.04513740539550781, 'zcp_jacov': -16.05926205138813, 'zcp_l2_norm': 542.2340698242188, 'zcp_nwot': 205.61433635831037, 'zcp_params': 1589788.0, 'zcp_plain': 0.0031067118979990482, 'zcp_snip': 24.773866653442383, 'lat_1080ti_1': 0.5940641418128856, 'lat_1080ti_32': 0.4158652894157104, 'lat_1080ti_64': 0.24556892873733682, 'lat_2080ti_1': 0.5613989720756255, 'lat_2080ti_32': 0.410480573125704, 'lat_2080ti_64': 0.27913953035549677, 'lat_essential_ph_1': 0.22641509433962265, 'lat_eyeriss': 0.23976307923642556, 'lat_fpga': 0.34161232547323833, 'lat_gold_6226': 0.2638304669982432, 'lat_gold_6240': 0.3999565637690365, 'lat_pixel2': 0.1956521739130435, 'lat_pixel3': 0.3070758991737698, 'lat_raspi4': 0.2875752314046157, 'lat_samsung_a50': 0.11578947368421053, 'lat_samsung_s7': 0.07086614173228346, 'lat_silver_4114': 0.4260297957923765, 'lat_silver_4210r': 0.4730946301369864, 'lat_titan_rtx_1': 0.5069501175633445, 'lat_titan_rtx_32': 0.412975710029107, 'lat_titan_rtx_64': 0.30512659271258286, 'lat_titanx_1': 0.2622111219345301, 'lat_titanx_32': 0.3187602130335742, 'lat_titanx_64': 0.23635105164496753, 'lat_titanxp_1': 0.46641122381973105, 'lat_titanxp_32': 0.36868874293805437, 'lat_titanxp_64': 0.2761868790812362}
FBNet_977	FBNet	977	977	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_570[FLOAT, 16x3x3x3] %onnx::Conv_571[FLOAT, 16] %onnx::Conv_573[FLOAT, 48x16x1x1] %onnx::Conv_574[FLOAT, 48] %onnx::Conv_576[FLOAT, 48x1x3x3] %onnx::Conv_579[FLOAT, 16x48x1x1] %onnx::Conv_582[FLOAT, 16x8x1x1] %onnx::Conv_585[FLOAT, 16x1x5x5] %onnx::Conv_588[FLOAT, 24x8x1x1] %onnx::Conv_589[FLOAT, 24] %onnx::Conv_591[FLOAT, 24x12x1x1] %onnx::Conv_594[FLOAT, 24x1x3x3] %onnx::Conv_597[FLOAT, 24x12x1x1] %onnx::Conv_600[FLOAT, 24x24x1x1] %onnx::Conv_603[FLOAT, 24x1x5x5] %onnx::Conv_606[FLOAT, 24x24x1x1] %onnx::Conv_609[FLOAT, 72x24x1x1] %onnx::Conv_610[FLOAT, 72] %onnx::Conv_612[FLOAT, 72x1x3x3] %onnx::Conv_615[FLOAT, 24x72x1x1] %onnx::Conv_618[FLOAT, 32x24x1x1] %onnx::Conv_619[FLOAT, 32] %onnx::Conv_621[FLOAT, 192x32x1x1] %onnx::Conv_622[FLOAT, 192] %onnx::Conv_624[FLOAT, 192x1x5x5] %onnx::Conv_627[FLOAT, 32x192x1x1] %onnx::Conv_630[FLOAT, 192x32x1x1] %onnx::Conv_633[FLOAT, 192x1x3x3] %onnx::Conv_636[FLOAT, 64x192x1x1] %onnx::Conv_637[FLOAT, 64] %onnx::Conv_639[FLOAT, 384x64x1x1] %onnx::Conv_640[FLOAT, 384] %onnx::Conv_642[FLOAT, 384x1x3x3] %onnx::Conv_645[FLOAT, 64x384x1x1] %onnx::Conv_648[FLOAT, 192x64x1x1] %onnx::Conv_651[FLOAT, 192x1x3x3] %onnx::Conv_654[FLOAT, 64x192x1x1] %onnx::Conv_657[FLOAT, 64x64x1x1] %onnx::Conv_660[FLOAT, 64x1x3x3] %onnx::Conv_663[FLOAT, 64x64x1x1] %onnx::Conv_666[FLOAT, 64x32x1x1] %onnx::Conv_669[FLOAT, 64x1x3x3] %onnx::Conv_672[FLOAT, 112x32x1x1] %onnx::Conv_673[FLOAT, 112] %onnx::Conv_675[FLOAT, 112x56x1x1] %onnx::Conv_678[FLOAT, 112x1x3x3] %onnx::Conv_681[FLOAT, 112x56x1x1] %onnx::Conv_684[FLOAT, 672x112x1x1] %onnx::Conv_685[FLOAT, 672] %onnx::Conv_687[FLOAT, 672x1x5x5] %onnx::Conv_690[FLOAT, 112x672x1x1] %onnx::Conv_693[FLOAT, 336x112x1x1] %onnx::Conv_694[FLOAT, 336] %onnx::Conv_696[FLOAT, 336x1x3x3] %onnx::Conv_699[FLOAT, 184x336x1x1] %onnx::Conv_700[FLOAT, 184] %onnx::Conv_702[FLOAT, 184x184x1x1] %onnx::Conv_705[FLOAT, 184x1x5x5] %onnx::Conv_708[FLOAT, 184x184x1x1] %onnx::Conv_711[FLOAT, 184x184x1x1] %onnx::Conv_714[FLOAT, 184x1x5x5] %onnx::Conv_717[FLOAT, 184x184x1x1] %onnx::Conv_720[FLOAT, 552x184x1x1] %onnx::Conv_721[FLOAT, 552] %onnx::Conv_723[FLOAT, 552x1x3x3] %onnx::Conv_726[FLOAT, 352x552x1x1] %onnx::Conv_727[FLOAT, 352] %onnx::Conv_729[FLOAT, 1504x352x1x1] %onnx::Conv_730[FLOAT, 1504] ) { %onnx::Conv_724 = Identity(%onnx::Conv_721) %onnx::Conv_718 = Identity(%onnx::Conv_700) %onnx::Conv_715 = Identity(%onnx::Conv_700) %onnx::Conv_712 = Identity(%onnx::Conv_700) %onnx::Conv_709 = Identity(%onnx::Conv_700) %onnx::Conv_706 = Identity(%onnx::Conv_700) %onnx::Conv_703 = Identity(%onnx::Conv_700) %onnx::Conv_697 = Identity(%onnx::Conv_694) %onnx::Conv_691 = Identity(%onnx::Conv_673) %onnx::Conv_688 = Identity(%onnx::Conv_685) %onnx::Conv_682 = Identity(%onnx::Conv_673) %onnx::Conv_679 = Identity(%onnx::Conv_673) %onnx::Conv_676 = Identity(%onnx::Conv_673) %onnx::Conv_670 = Identity(%onnx::Conv_637) %onnx::Conv_667 = Identity(%onnx::Conv_637) %onnx::Conv_664 = Identity(%onnx::Conv_637) %onnx::Conv_661 = Identity(%onnx::Conv_637) %onnx::Conv_658 = Identity(%onnx::Conv_637) %onnx::Conv_655 = Identity(%onnx::Conv_637) %onnx::Conv_652 = Identity(%onnx::Conv_622) %onnx::Conv_649 = Identity(%onnx::Conv_622) %onnx::Conv_646 = Identity(%onnx::Conv_637) %onnx::Conv_643 = Identity(%onnx::Conv_640) %onnx::Conv_634 = Identity(%onnx::Conv_622) %onnx::Conv_631 = Identity(%onnx::Conv_622) %onnx::Conv_628 = Identity(%onnx::Conv_619) %onnx::Conv_625 = Identity(%onnx::Conv_622) %onnx::Conv_616 = Identity(%onnx::Conv_589) %onnx::Conv_613 = Identity(%onnx::Conv_610) %onnx::Conv_607 = Identity(%onnx::Conv_589) %onnx::Conv_604 = Identity(%onnx::Conv_589) %onnx::Conv_601 = Identity(%onnx::Conv_589) %onnx::Conv_598 = Identity(%onnx::Conv_589) %onnx::Conv_595 = Identity(%onnx::Conv_589) %onnx::Conv_592 = Identity(%onnx::Conv_589) %onnx::Conv_586 = Identity(%onnx::Conv_571) %onnx::Conv_583 = Identity(%onnx::Conv_571) %onnx::Conv_580 = Identity(%onnx::Conv_571) %onnx::Conv_577 = Identity(%onnx::Conv_574) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_570, %onnx::Conv_571) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_573, %onnx::Conv_574) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 48, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.0/nl/Relu_output_0, %onnx::Conv_576, %onnx::Conv_577) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_579, %onnx::Conv_580) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_582, %onnx::Conv_583) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.1/shuffle/Reshape_output_0 = Reshape(%/cells.1/nl/Relu_output_0, %/cells.1/shuffle/Constant_output_0) %/cells.1/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.1/shuffle/Reshape_output_0) %/cells.1/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.1/shuffle/Reshape_1_output_0 = Reshape(%/cells.1/shuffle/Transpose_output_0, %/cells.1/shuffle/Constant_1_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.1/shuffle/Reshape_1_output_0, %onnx::Conv_585, %onnx::Conv_586) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_588, %onnx::Conv_589) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_591, %onnx::Conv_592) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.2/shuffle/Reshape_output_0 = Reshape(%/cells.2/nl/Relu_output_0, %/cells.2/shuffle/Constant_output_0) %/cells.2/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.2/shuffle/Reshape_output_0) %/cells.2/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.2/shuffle/Reshape_1_output_0 = Reshape(%/cells.2/shuffle/Transpose_output_0, %/cells.2/shuffle/Constant_1_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.2/shuffle/Reshape_1_output_0, %onnx::Conv_594, %onnx::Conv_595) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_597, %onnx::Conv_598) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_600, %onnx::Conv_601) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.3/nl/Relu_output_0, %onnx::Conv_603, %onnx::Conv_604) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_606, %onnx::Conv_607) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_609, %onnx::Conv_610) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.4/nl/Relu_output_0, %onnx::Conv_612, %onnx::Conv_613) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_615, %onnx::Conv_616) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [2, 2]](%/cells.4/Add_output_0, %onnx::Conv_618, %onnx::Conv_619) %/cells.5/relu/Relu_output_0 = Relu(%/cells.5/conv/Conv_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/relu/Relu_output_0, %onnx::Conv_621, %onnx::Conv_622) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.7/nl/Relu_output_0, %onnx::Conv_624, %onnx::Conv_625) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_627, %onnx::Conv_628) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.5/relu/Relu_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_630, %onnx::Conv_631) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.9/nl/Relu_output_0, %onnx::Conv_633, %onnx::Conv_634) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_636, %onnx::Conv_637) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_639, %onnx::Conv_640) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.10/nl/Relu_output_0, %onnx::Conv_642, %onnx::Conv_643) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_645, %onnx::Conv_646) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_648, %onnx::Conv_649) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.11/nl/Relu_output_0, %onnx::Conv_651, %onnx::Conv_652) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_654, %onnx::Conv_655) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_657, %onnx::Conv_658) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.12/nl/Relu_output_0, %onnx::Conv_660, %onnx::Conv_661) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_663, %onnx::Conv_664) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_666, %onnx::Conv_667) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.13/shuffle/Reshape_output_0 = Reshape(%/cells.13/nl/Relu_output_0, %/cells.13/shuffle/Constant_output_0) %/cells.13/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.13/shuffle/Reshape_output_0) %/cells.13/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.13/shuffle/Reshape_1_output_0 = Reshape(%/cells.13/shuffle/Transpose_output_0, %/cells.13/shuffle/Constant_1_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.13/shuffle/Reshape_1_output_0, %onnx::Conv_669, %onnx::Conv_670) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_672, %onnx::Conv_673) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_675, %onnx::Conv_676) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.14/shuffle/Reshape_output_0 = Reshape(%/cells.14/nl/Relu_output_0, %/cells.14/shuffle/Constant_output_0) %/cells.14/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.14/shuffle/Reshape_output_0) %/cells.14/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.14/shuffle/Reshape_1_output_0 = Reshape(%/cells.14/shuffle/Transpose_output_0, %/cells.14/shuffle/Constant_1_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.14/shuffle/Reshape_1_output_0, %onnx::Conv_678, %onnx::Conv_679) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_681, %onnx::Conv_682) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_684, %onnx::Conv_685) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.15/nl/Relu_output_0, %onnx::Conv_687, %onnx::Conv_688) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_690, %onnx::Conv_691) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_693, %onnx::Conv_694) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.17/nl/Relu_output_0, %onnx::Conv_696, %onnx::Conv_697) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_699, %onnx::Conv_700) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_702, %onnx::Conv_703) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.19/nl/Relu_output_0, %onnx::Conv_705, %onnx::Conv_706) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_708, %onnx::Conv_709) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_711, %onnx::Conv_712) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.20/nl/Relu_output_0, %onnx::Conv_714, %onnx::Conv_715) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_717, %onnx::Conv_718) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_720, %onnx::Conv_721) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 552, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.21/nl/Relu_output_0, %onnx::Conv_723, %onnx::Conv_724) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_726, %onnx::Conv_727) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_729, %onnx::Conv_730) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %568 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %568 }	val_accuracy	56,275,328	1,570,036	{'zcp_synflow': 66.65868089354227, 'zcp_zen': 58.0468635559082, 'zcp_epe_nas': 28.26971929751794, 'zcp_fisher': 0.06337349116802216, 'zcp_flops': 56275328.0, 'zcp_grad_norm': 17.93061065673828, 'zcp_grasp': 0.02790355682373047, 'zcp_jacov': -16.04469733303794, 'zcp_l2_norm': 534.3963623046875, 'zcp_nwot': 208.80379491692992, 'zcp_params': 1570036.0, 'zcp_plain': 0.0038529573939740658, 'zcp_snip': 28.77839469909668, 'lat_1080ti_1': 0.34010895243780603, 'lat_1080ti_32': 0.2648406236920127, 'lat_1080ti_64': 0.23264312382186525, 'lat_2080ti_1': 0.3186329667625666, 'lat_2080ti_32': 0.23842471410539476, 'lat_2080ti_64': 0.2247770949258896, 'lat_essential_ph_1': 0.32075471698113206, 'lat_eyeriss': 0.27336658537032615, 'lat_fpga': 0.305188058460899, 'lat_gold_6226': 0.237450530012048, 'lat_gold_6240': 0.3223952384117406, 'lat_pixel2': 0.2391304347826087, 'lat_pixel3': 0.28638333425316775, 'lat_raspi4': 0.31801689691198837, 'lat_samsung_a50': 0.18947368421052632, 'lat_samsung_s7': 0.14960629921259844, 'lat_silver_4114': 0.34549985179583786, 'lat_silver_4210r': 0.29929934595674434, 'lat_titan_rtx_1': 0.31055700424268534, 'lat_titan_rtx_32': 0.2418348643223213, 'lat_titan_rtx_64': 0.20894621231300955, 'lat_titanx_1': 0.16738707362451688, 'lat_titanx_32': 0.18385529145218998, 'lat_titanx_64': 0.24655517361924006, 'lat_titanxp_1': 0.2966360637917958, 'lat_titanxp_32': 0.2048460504642703, 'lat_titanxp_64': 0.23007733004055736}
FBNet_1456	FBNet	1456	1456	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_687[FLOAT, 16x3x3x3] %onnx::Conv_688[FLOAT, 16] %onnx::Conv_690[FLOAT, 48x16x1x1] %onnx::Conv_691[FLOAT, 48] %onnx::Conv_693[FLOAT, 48x1x3x3] %onnx::Conv_696[FLOAT, 16x48x1x1] %onnx::Conv_699[FLOAT, 96x16x1x1] %onnx::Conv_700[FLOAT, 96] %onnx::Conv_702[FLOAT, 96x1x3x3] %onnx::Conv_705[FLOAT, 24x96x1x1] %onnx::Conv_706[FLOAT, 24] %onnx::Conv_708[FLOAT, 144x24x1x1] %onnx::Conv_709[FLOAT, 144] %onnx::Conv_711[FLOAT, 144x1x3x3] %onnx::Conv_714[FLOAT, 24x144x1x1] %onnx::Conv_717[FLOAT, 24x12x1x1] %onnx::Conv_720[FLOAT, 24x1x3x3] %onnx::Conv_723[FLOAT, 24x12x1x1] %onnx::Conv_726[FLOAT, 144x24x1x1] %onnx::Conv_729[FLOAT, 144x1x3x3] %onnx::Conv_732[FLOAT, 24x144x1x1] %onnx::Conv_735[FLOAT, 144x24x1x1] %onnx::Conv_738[FLOAT, 144x1x5x5] %onnx::Conv_741[FLOAT, 32x144x1x1] %onnx::Conv_742[FLOAT, 32] %onnx::Conv_744[FLOAT, 32x32x1x1] %onnx::Conv_747[FLOAT, 32x1x5x5] %onnx::Conv_750[FLOAT, 32x32x1x1] %onnx::Conv_753[FLOAT, 96x32x1x1] %onnx::Conv_756[FLOAT, 96x1x5x5] %onnx::Conv_759[FLOAT, 32x96x1x1] %onnx::Conv_762[FLOAT, 192x32x1x1] %onnx::Conv_763[FLOAT, 192] %onnx::Conv_765[FLOAT, 192x1x5x5] %onnx::Conv_768[FLOAT, 32x192x1x1] %onnx::Conv_771[FLOAT, 32x16x1x1] %onnx::Conv_774[FLOAT, 32x1x3x3] %onnx::Conv_777[FLOAT, 64x16x1x1] %onnx::Conv_778[FLOAT, 64] %onnx::Conv_780[FLOAT, 64x64x1x1] %onnx::Conv_783[FLOAT, 64x1x5x5] %onnx::Conv_786[FLOAT, 64x64x1x1] %onnx::Conv_789[FLOAT, 192x64x1x1] %onnx::Conv_792[FLOAT, 192x1x3x3] %onnx::Conv_795[FLOAT, 64x192x1x1] %onnx::Conv_798[FLOAT, 384x64x1x1] %onnx::Conv_799[FLOAT, 384] %onnx::Conv_801[FLOAT, 384x1x3x3] %onnx::Conv_804[FLOAT, 64x384x1x1] %onnx::Conv_807[FLOAT, 64x32x1x1] %onnx::Conv_810[FLOAT, 64x1x5x5] %onnx::Conv_813[FLOAT, 112x32x1x1] %onnx::Conv_814[FLOAT, 112] %onnx::Conv_816[FLOAT, 672x112x1x1] %onnx::Conv_817[FLOAT, 672] %onnx::Conv_819[FLOAT, 672x1x3x3] %onnx::Conv_822[FLOAT, 112x672x1x1] %onnx::Conv_825[FLOAT, 112x56x1x1] %onnx::Conv_828[FLOAT, 112x1x3x3] %onnx::Conv_831[FLOAT, 112x56x1x1] %onnx::Conv_834[FLOAT, 672x112x1x1] %onnx::Conv_837[FLOAT, 672x1x5x5] %onnx::Conv_840[FLOAT, 184x672x1x1] %onnx::Conv_841[FLOAT, 184] %onnx::Conv_843[FLOAT, 184x92x1x1] %onnx::Conv_846[FLOAT, 184x1x5x5] %onnx::Conv_849[FLOAT, 184x92x1x1] %onnx::Conv_852[FLOAT, 1104x184x1x1] %onnx::Conv_853[FLOAT, 1104] %onnx::Conv_855[FLOAT, 1104x1x3x3] %onnx::Conv_858[FLOAT, 184x1104x1x1] %onnx::Conv_861[FLOAT, 184x184x1x1] %onnx::Conv_864[FLOAT, 184x1x5x5] %onnx::Conv_867[FLOAT, 184x184x1x1] %onnx::Conv_870[FLOAT, 184x184x1x1] %onnx::Conv_873[FLOAT, 184x1x5x5] %onnx::Conv_876[FLOAT, 352x184x1x1] %onnx::Conv_877[FLOAT, 352] %onnx::Conv_879[FLOAT, 1504x352x1x1] %onnx::Conv_880[FLOAT, 1504] ) { %onnx::Conv_874 = Identity(%onnx::Conv_841) %onnx::Conv_871 = Identity(%onnx::Conv_841) %onnx::Conv_868 = Identity(%onnx::Conv_841) %onnx::Conv_865 = Identity(%onnx::Conv_841) %onnx::Conv_862 = Identity(%onnx::Conv_841) %onnx::Conv_859 = Identity(%onnx::Conv_841) %onnx::Conv_856 = Identity(%onnx::Conv_853) %onnx::Conv_850 = Identity(%onnx::Conv_841) %onnx::Conv_847 = Identity(%onnx::Conv_841) %onnx::Conv_844 = Identity(%onnx::Conv_841) %onnx::Conv_838 = Identity(%onnx::Conv_817) %onnx::Conv_835 = Identity(%onnx::Conv_817) %onnx::Conv_832 = Identity(%onnx::Conv_814) %onnx::Conv_829 = Identity(%onnx::Conv_814) %onnx::Conv_826 = Identity(%onnx::Conv_814) %onnx::Conv_823 = Identity(%onnx::Conv_814) %onnx::Conv_820 = Identity(%onnx::Conv_817) %onnx::Conv_811 = Identity(%onnx::Conv_778) %onnx::Conv_808 = Identity(%onnx::Conv_778) %onnx::Conv_805 = Identity(%onnx::Conv_778) %onnx::Conv_802 = Identity(%onnx::Conv_799) %onnx::Conv_796 = Identity(%onnx::Conv_778) %onnx::Conv_793 = Identity(%onnx::Conv_763) %onnx::Conv_790 = Identity(%onnx::Conv_763) %onnx::Conv_787 = Identity(%onnx::Conv_778) %onnx::Conv_784 = Identity(%onnx::Conv_778) %onnx::Conv_781 = Identity(%onnx::Conv_778) %onnx::Conv_775 = Identity(%onnx::Conv_742) %onnx::Conv_772 = Identity(%onnx::Conv_742) %onnx::Conv_769 = Identity(%onnx::Conv_742) %onnx::Conv_766 = Identity(%onnx::Conv_763) %onnx::Conv_760 = Identity(%onnx::Conv_742) %onnx::Conv_757 = Identity(%onnx::Conv_700) %onnx::Conv_754 = Identity(%onnx::Conv_700) %onnx::Conv_751 = Identity(%onnx::Conv_742) %onnx::Conv_748 = Identity(%onnx::Conv_742) %onnx::Conv_745 = Identity(%onnx::Conv_742) %onnx::Conv_739 = Identity(%onnx::Conv_709) %onnx::Conv_736 = Identity(%onnx::Conv_709) %onnx::Conv_733 = Identity(%onnx::Conv_706) %onnx::Conv_730 = Identity(%onnx::Conv_709) %onnx::Conv_727 = Identity(%onnx::Conv_709) %onnx::Conv_724 = Identity(%onnx::Conv_706) %onnx::Conv_721 = Identity(%onnx::Conv_706) %onnx::Conv_718 = Identity(%onnx::Conv_706) %onnx::Conv_715 = Identity(%onnx::Conv_706) %onnx::Conv_712 = Identity(%onnx::Conv_709) %onnx::Conv_703 = Identity(%onnx::Conv_700) %onnx::Conv_697 = Identity(%onnx::Conv_688) %onnx::Conv_694 = Identity(%onnx::Conv_691) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_687, %onnx::Conv_688) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_690, %onnx::Conv_691) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 48, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.0/nl/Relu_output_0, %onnx::Conv_693, %onnx::Conv_694) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_696, %onnx::Conv_697) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_699, %onnx::Conv_700) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.1/nl/Relu_output_0, %onnx::Conv_702, %onnx::Conv_703) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_705, %onnx::Conv_706) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_708, %onnx::Conv_709) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.2/nl/Relu_output_0, %onnx::Conv_711, %onnx::Conv_712) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_714, %onnx::Conv_715) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_717, %onnx::Conv_718) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.3/shuffle/Reshape_output_0 = Reshape(%/cells.3/nl/Relu_output_0, %/cells.3/shuffle/Constant_output_0) %/cells.3/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.3/shuffle/Reshape_output_0) %/cells.3/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.3/shuffle/Reshape_1_output_0 = Reshape(%/cells.3/shuffle/Transpose_output_0, %/cells.3/shuffle/Constant_1_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.3/shuffle/Reshape_1_output_0, %onnx::Conv_720, %onnx::Conv_721) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_723, %onnx::Conv_724) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_726, %onnx::Conv_727) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.4/nl/Relu_output_0, %onnx::Conv_729, %onnx::Conv_730) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_732, %onnx::Conv_733) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/Add_output_0, %onnx::Conv_735, %onnx::Conv_736) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.5/nl/Relu_output_0, %onnx::Conv_738, %onnx::Conv_739) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_741, %onnx::Conv_742) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_744, %onnx::Conv_745) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.6/nl/Relu_output_0, %onnx::Conv_747, %onnx::Conv_748) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_750, %onnx::Conv_751) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_753, %onnx::Conv_754) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.7/nl/Relu_output_0, %onnx::Conv_756, %onnx::Conv_757) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_759, %onnx::Conv_760) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_762, %onnx::Conv_763) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.8/nl/Relu_output_0, %onnx::Conv_765, %onnx::Conv_766) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_768, %onnx::Conv_769) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_771, %onnx::Conv_772) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.9/shuffle/Reshape_output_0 = Reshape(%/cells.9/nl/Relu_output_0, %/cells.9/shuffle/Constant_output_0) %/cells.9/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.9/shuffle/Reshape_output_0) %/cells.9/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.9/shuffle/Reshape_1_output_0 = Reshape(%/cells.9/shuffle/Transpose_output_0, %/cells.9/shuffle/Constant_1_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.9/shuffle/Reshape_1_output_0, %onnx::Conv_774, %onnx::Conv_775) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_777, %onnx::Conv_778) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_780, %onnx::Conv_781) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.10/nl/Relu_output_0, %onnx::Conv_783, %onnx::Conv_784) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_786, %onnx::Conv_787) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_789, %onnx::Conv_790) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.11/nl/Relu_output_0, %onnx::Conv_792, %onnx::Conv_793) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_795, %onnx::Conv_796) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_798, %onnx::Conv_799) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.12/nl/Relu_output_0, %onnx::Conv_801, %onnx::Conv_802) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_804, %onnx::Conv_805) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_807, %onnx::Conv_808) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.13/shuffle/Reshape_output_0 = Reshape(%/cells.13/nl/Relu_output_0, %/cells.13/shuffle/Constant_output_0) %/cells.13/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.13/shuffle/Reshape_output_0) %/cells.13/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.13/shuffle/Reshape_1_output_0 = Reshape(%/cells.13/shuffle/Transpose_output_0, %/cells.13/shuffle/Constant_1_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.13/shuffle/Reshape_1_output_0, %onnx::Conv_810, %onnx::Conv_811) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_813, %onnx::Conv_814) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_816, %onnx::Conv_817) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.14/nl/Relu_output_0, %onnx::Conv_819, %onnx::Conv_820) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_822, %onnx::Conv_823) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_825, %onnx::Conv_826) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.16/shuffle/Reshape_output_0 = Reshape(%/cells.16/nl/Relu_output_0, %/cells.16/shuffle/Constant_output_0) %/cells.16/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.16/shuffle/Reshape_output_0) %/cells.16/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.16/shuffle/Reshape_1_output_0 = Reshape(%/cells.16/shuffle/Transpose_output_0, %/cells.16/shuffle/Constant_1_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.16/shuffle/Reshape_1_output_0, %onnx::Conv_828, %onnx::Conv_829) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_831, %onnx::Conv_832) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_834, %onnx::Conv_835) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.17/nl/Relu_output_0, %onnx::Conv_837, %onnx::Conv_838) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_840, %onnx::Conv_841) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_843, %onnx::Conv_844) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.18/shuffle/Reshape_output_0 = Reshape(%/cells.18/nl/Relu_output_0, %/cells.18/shuffle/Constant_output_0) %/cells.18/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.18/shuffle/Reshape_output_0) %/cells.18/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.18/shuffle/Reshape_1_output_0 = Reshape(%/cells.18/shuffle/Transpose_output_0, %/cells.18/shuffle/Constant_1_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.18/shuffle/Reshape_1_output_0, %onnx::Conv_846, %onnx::Conv_847) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_849, %onnx::Conv_850) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_852, %onnx::Conv_853) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.19/nl/Relu_output_0, %onnx::Conv_855, %onnx::Conv_856) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_858, %onnx::Conv_859) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_861, %onnx::Conv_862) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.20/nl/Relu_output_0, %onnx::Conv_864, %onnx::Conv_865) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_867, %onnx::Conv_868) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_870, %onnx::Conv_871) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.21/nl/Relu_output_0, %onnx::Conv_873, %onnx::Conv_874) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_876, %onnx::Conv_877) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_879, %onnx::Conv_880) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %685 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %685 }	val_accuracy	85,288,064	1,884,116	{'zcp_synflow': 78.52342729411869, 'zcp_zen': 70.81256103515625, 'zcp_epe_nas': 23.349216918150884, 'zcp_fisher': 0.20525476336479187, 'zcp_flops': 85288064.0, 'zcp_grad_norm': 28.741716384887695, 'zcp_grasp': -0.18802261352539062, 'zcp_jacov': -16.04850339771908, 'zcp_l2_norm': 655.4381713867188, 'zcp_nwot': 219.4822626319919, 'zcp_params': 1884116.0, 'zcp_plain': -0.000379930657800287, 'zcp_snip': 50.6787109375, 'lat_1080ti_1': 0.6650022554290913, 'lat_1080ti_32': 0.7850690051231006, 'lat_1080ti_64': 0.7428087381496417, 'lat_2080ti_1': 0.7143942657194451, 'lat_2080ti_32': 0.8801099916368947, 'lat_2080ti_64': 0.7822787014756524, 'lat_essential_ph_1': 0.4339622641509434, 'lat_eyeriss': 0.6610034291163148, 'lat_fpga': 0.650303288178193, 'lat_gold_6226': 0.4112322334992395, 'lat_gold_6240': 0.5839269427819607, 'lat_pixel2': 0.45652173913043476, 'lat_pixel3': 0.6039997668715977, 'lat_raspi4': 0.6429769664711993, 'lat_samsung_a50': 0.2736842105263158, 'lat_samsung_s7': 0.29133858267716534, 'lat_silver_4114': 0.6261078039745842, 'lat_silver_4210r': 0.6788509531495619, 'lat_titan_rtx_1': 0.6881944219461404, 'lat_titan_rtx_32': 0.6863463318167379, 'lat_titan_rtx_64': 0.7802326643580476, 'lat_titanx_1': 0.36292853557162885, 'lat_titanx_32': 0.7368196380294407, 'lat_titanx_64': 0.7482971035724212, 'lat_titanxp_1': 0.6477635780090486, 'lat_titanxp_32': 0.7461412528291, 'lat_titanxp_64': 0.7472555121906769}
FBNet_2725	FBNet	2725	2725	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_651[FLOAT, 16x3x3x3] %onnx::Conv_652[FLOAT, 16] %onnx::Conv_654[FLOAT, 16x16x1x1] %onnx::Conv_657[FLOAT, 16x1x3x3] %onnx::Conv_660[FLOAT, 16x16x1x1] %onnx::Conv_663[FLOAT, 16x16x1x1] %onnx::Conv_666[FLOAT, 16x1x3x3] %onnx::Conv_669[FLOAT, 24x16x1x1] %onnx::Conv_670[FLOAT, 24] %onnx::Conv_672[FLOAT, 72x24x1x1] %onnx::Conv_673[FLOAT, 72] %onnx::Conv_675[FLOAT, 72x1x5x5] %onnx::Conv_678[FLOAT, 24x72x1x1] %onnx::Conv_681[FLOAT, 24x24x1x1] %onnx::Conv_684[FLOAT, 24x1x5x5] %onnx::Conv_687[FLOAT, 24x24x1x1] %onnx::Conv_690[FLOAT, 72x24x1x1] %onnx::Conv_693[FLOAT, 72x1x5x5] %onnx::Conv_696[FLOAT, 24x72x1x1] %onnx::Conv_699[FLOAT, 32x24x1x1] %onnx::Conv_700[FLOAT, 32] %onnx::Conv_702[FLOAT, 32x16x1x1] %onnx::Conv_705[FLOAT, 32x1x5x5] %onnx::Conv_708[FLOAT, 32x16x1x1] %onnx::Conv_711[FLOAT, 32x32x1x1] %onnx::Conv_714[FLOAT, 32x1x5x5] %onnx::Conv_717[FLOAT, 32x32x1x1] %onnx::Conv_720[FLOAT, 192x32x1x1] %onnx::Conv_721[FLOAT, 192] %onnx::Conv_723[FLOAT, 192x1x3x3] %onnx::Conv_726[FLOAT, 64x192x1x1] %onnx::Conv_727[FLOAT, 64] %onnx::Conv_729[FLOAT, 64x64x1x1] %onnx::Conv_732[FLOAT, 64x1x3x3] %onnx::Conv_735[FLOAT, 64x64x1x1] %onnx::Conv_738[FLOAT, 64x32x1x1] %onnx::Conv_741[FLOAT, 64x1x5x5] %onnx::Conv_744[FLOAT, 64x32x1x1] %onnx::Conv_747[FLOAT, 192x64x1x1] %onnx::Conv_750[FLOAT, 192x1x5x5] %onnx::Conv_753[FLOAT, 64x192x1x1] %onnx::Conv_756[FLOAT, 64x64x1x1] %onnx::Conv_759[FLOAT, 64x1x5x5] %onnx::Conv_762[FLOAT, 112x64x1x1] %onnx::Conv_763[FLOAT, 112] %onnx::Conv_765[FLOAT, 112x56x1x1] %onnx::Conv_768[FLOAT, 112x1x3x3] %onnx::Conv_771[FLOAT, 112x56x1x1] %onnx::Conv_774[FLOAT, 672x112x1x1] %onnx::Conv_775[FLOAT, 672] %onnx::Conv_777[FLOAT, 672x1x3x3] %onnx::Conv_780[FLOAT, 112x672x1x1] %onnx::Conv_783[FLOAT, 336x112x1x1] %onnx::Conv_784[FLOAT, 336] %onnx::Conv_786[FLOAT, 336x1x3x3] %onnx::Conv_789[FLOAT, 112x336x1x1] %onnx::Conv_792[FLOAT, 112x112x1x1] %onnx::Conv_795[FLOAT, 112x1x5x5] %onnx::Conv_798[FLOAT, 184x112x1x1] %onnx::Conv_799[FLOAT, 184] %onnx::Conv_801[FLOAT, 184x92x1x1] %onnx::Conv_804[FLOAT, 184x1x5x5] %onnx::Conv_807[FLOAT, 184x92x1x1] %onnx::Conv_810[FLOAT, 1104x184x1x1] %onnx::Conv_811[FLOAT, 1104] %onnx::Conv_813[FLOAT, 1104x1x5x5] %onnx::Conv_816[FLOAT, 184x1104x1x1] %onnx::Conv_819[FLOAT, 552x184x1x1] %onnx::Conv_820[FLOAT, 552] %onnx::Conv_822[FLOAT, 552x1x5x5] %onnx::Conv_825[FLOAT, 184x552x1x1] %onnx::Conv_828[FLOAT, 184x184x1x1] %onnx::Conv_831[FLOAT, 184x1x3x3] %onnx::Conv_834[FLOAT, 352x184x1x1] %onnx::Conv_835[FLOAT, 352] %onnx::Conv_837[FLOAT, 1504x352x1x1] %onnx::Conv_838[FLOAT, 1504] ) { %onnx::Conv_832 = Identity(%onnx::Conv_799) %onnx::Conv_829 = Identity(%onnx::Conv_799) %onnx::Conv_826 = Identity(%onnx::Conv_799) %onnx::Conv_823 = Identity(%onnx::Conv_820) %onnx::Conv_817 = Identity(%onnx::Conv_799) %onnx::Conv_814 = Identity(%onnx::Conv_811) %onnx::Conv_808 = Identity(%onnx::Conv_799) %onnx::Conv_805 = Identity(%onnx::Conv_799) %onnx::Conv_802 = Identity(%onnx::Conv_799) %onnx::Conv_796 = Identity(%onnx::Conv_763) %onnx::Conv_793 = Identity(%onnx::Conv_763) %onnx::Conv_790 = Identity(%onnx::Conv_763) %onnx::Conv_787 = Identity(%onnx::Conv_784) %onnx::Conv_781 = Identity(%onnx::Conv_763) %onnx::Conv_778 = Identity(%onnx::Conv_775) %onnx::Conv_772 = Identity(%onnx::Conv_763) %onnx::Conv_769 = Identity(%onnx::Conv_763) %onnx::Conv_766 = Identity(%onnx::Conv_763) %onnx::Conv_760 = Identity(%onnx::Conv_727) %onnx::Conv_757 = Identity(%onnx::Conv_727) %onnx::Conv_754 = Identity(%onnx::Conv_727) %onnx::Conv_751 = Identity(%onnx::Conv_721) %onnx::Conv_748 = Identity(%onnx::Conv_721) %onnx::Conv_745 = Identity(%onnx::Conv_727) %onnx::Conv_742 = Identity(%onnx::Conv_727) %onnx::Conv_739 = Identity(%onnx::Conv_727) %onnx::Conv_736 = Identity(%onnx::Conv_727) %onnx::Conv_733 = Identity(%onnx::Conv_727) %onnx::Conv_730 = Identity(%onnx::Conv_727) %onnx::Conv_724 = Identity(%onnx::Conv_721) %onnx::Conv_718 = Identity(%onnx::Conv_700) %onnx::Conv_715 = Identity(%onnx::Conv_700) %onnx::Conv_712 = Identity(%onnx::Conv_700) %onnx::Conv_709 = Identity(%onnx::Conv_700) %onnx::Conv_706 = Identity(%onnx::Conv_700) %onnx::Conv_703 = Identity(%onnx::Conv_700) %onnx::Conv_697 = Identity(%onnx::Conv_670) %onnx::Conv_694 = Identity(%onnx::Conv_673) %onnx::Conv_691 = Identity(%onnx::Conv_673) %onnx::Conv_688 = Identity(%onnx::Conv_670) %onnx::Conv_685 = Identity(%onnx::Conv_670) %onnx::Conv_682 = Identity(%onnx::Conv_670) %onnx::Conv_679 = Identity(%onnx::Conv_670) %onnx::Conv_676 = Identity(%onnx::Conv_673) %onnx::Conv_667 = Identity(%onnx::Conv_652) %onnx::Conv_664 = Identity(%onnx::Conv_652) %onnx::Conv_661 = Identity(%onnx::Conv_652) %onnx::Conv_658 = Identity(%onnx::Conv_652) %onnx::Conv_655 = Identity(%onnx::Conv_652) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_651, %onnx::Conv_652) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_654, %onnx::Conv_655) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.0/nl/Relu_output_0, %onnx::Conv_657, %onnx::Conv_658) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_660, %onnx::Conv_661) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_663, %onnx::Conv_664) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.1/nl/Relu_output_0, %onnx::Conv_666, %onnx::Conv_667) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_669, %onnx::Conv_670) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_672, %onnx::Conv_673) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.2/nl/Relu_output_0, %onnx::Conv_675, %onnx::Conv_676) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_678, %onnx::Conv_679) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_681, %onnx::Conv_682) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.3/nl/Relu_output_0, %onnx::Conv_684, %onnx::Conv_685) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_687, %onnx::Conv_688) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_690, %onnx::Conv_691) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.4/nl/Relu_output_0, %onnx::Conv_693, %onnx::Conv_694) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_696, %onnx::Conv_697) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [2, 2]](%/cells.4/Add_output_0, %onnx::Conv_699, %onnx::Conv_700) %/cells.5/relu/Relu_output_0 = Relu(%/cells.5/conv/Conv_output_0) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/relu/Relu_output_0, %onnx::Conv_702, %onnx::Conv_703) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.6/shuffle/Reshape_output_0 = Reshape(%/cells.6/nl/Relu_output_0, %/cells.6/shuffle/Constant_output_0) %/cells.6/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.6/shuffle/Reshape_output_0) %/cells.6/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.6/shuffle/Reshape_1_output_0 = Reshape(%/cells.6/shuffle/Transpose_output_0, %/cells.6/shuffle/Constant_1_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.6/shuffle/Reshape_1_output_0, %onnx::Conv_705, %onnx::Conv_706) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_708, %onnx::Conv_709) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/relu/Relu_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_711, %onnx::Conv_712) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.8/nl/Relu_output_0, %onnx::Conv_714, %onnx::Conv_715) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_717, %onnx::Conv_718) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_720, %onnx::Conv_721) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.9/nl/Relu_output_0, %onnx::Conv_723, %onnx::Conv_724) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_726, %onnx::Conv_727) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_729, %onnx::Conv_730) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.10/nl/Relu_output_0, %onnx::Conv_732, %onnx::Conv_733) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_735, %onnx::Conv_736) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_738, %onnx::Conv_739) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.11/shuffle/Reshape_output_0 = Reshape(%/cells.11/nl/Relu_output_0, %/cells.11/shuffle/Constant_output_0) %/cells.11/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.11/shuffle/Reshape_output_0) %/cells.11/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.11/shuffle/Reshape_1_output_0 = Reshape(%/cells.11/shuffle/Transpose_output_0, %/cells.11/shuffle/Constant_1_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.11/shuffle/Reshape_1_output_0, %onnx::Conv_741, %onnx::Conv_742) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_744, %onnx::Conv_745) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_747, %onnx::Conv_748) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.12/nl/Relu_output_0, %onnx::Conv_750, %onnx::Conv_751) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_753, %onnx::Conv_754) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_756, %onnx::Conv_757) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.13/nl/Relu_output_0, %onnx::Conv_759, %onnx::Conv_760) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_762, %onnx::Conv_763) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_765, %onnx::Conv_766) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.14/shuffle/Reshape_output_0 = Reshape(%/cells.14/nl/Relu_output_0, %/cells.14/shuffle/Constant_output_0) %/cells.14/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.14/shuffle/Reshape_output_0) %/cells.14/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.14/shuffle/Reshape_1_output_0 = Reshape(%/cells.14/shuffle/Transpose_output_0, %/cells.14/shuffle/Constant_1_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.14/shuffle/Reshape_1_output_0, %onnx::Conv_768, %onnx::Conv_769) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_771, %onnx::Conv_772) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_774, %onnx::Conv_775) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.15/nl/Relu_output_0, %onnx::Conv_777, %onnx::Conv_778) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_780, %onnx::Conv_781) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_783, %onnx::Conv_784) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.16/nl/Relu_output_0, %onnx::Conv_786, %onnx::Conv_787) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_789, %onnx::Conv_790) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.15/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_792, %onnx::Conv_793) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.17/nl/Relu_output_0, %onnx::Conv_795, %onnx::Conv_796) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_798, %onnx::Conv_799) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_801, %onnx::Conv_802) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.18/shuffle/Reshape_output_0 = Reshape(%/cells.18/nl/Relu_output_0, %/cells.18/shuffle/Constant_output_0) %/cells.18/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.18/shuffle/Reshape_output_0) %/cells.18/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.18/shuffle/Reshape_1_output_0 = Reshape(%/cells.18/shuffle/Transpose_output_0, %/cells.18/shuffle/Constant_1_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.18/shuffle/Reshape_1_output_0, %onnx::Conv_804, %onnx::Conv_805) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_807, %onnx::Conv_808) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_810, %onnx::Conv_811) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.19/nl/Relu_output_0, %onnx::Conv_813, %onnx::Conv_814) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_816, %onnx::Conv_817) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_819, %onnx::Conv_820) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 552, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.20/nl/Relu_output_0, %onnx::Conv_822, %onnx::Conv_823) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_825, %onnx::Conv_826) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_828, %onnx::Conv_829) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.21/nl/Relu_output_0, %onnx::Conv_831, %onnx::Conv_832) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_834, %onnx::Conv_835) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_837, %onnx::Conv_838) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %649 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %649 }	val_accuracy	62,757,504	1,873,684	{'zcp_synflow': 79.60263600381207, 'zcp_zen': 68.39093017578125, 'zcp_epe_nas': 8.749251062495862, 'zcp_fisher': 0.10762377083301544, 'zcp_flops': 62757504.0, 'zcp_grad_norm': 19.915775299072266, 'zcp_grasp': 0.0023345947265625, 'zcp_jacov': -16.05211592776722, 'zcp_l2_norm': 613.3028564453125, 'zcp_nwot': 208.3791765386923, 'zcp_params': 1873684.0, 'zcp_plain': 0.011018408462405205, 'zcp_snip': 36.03140640258789, 'lat_1080ti_1': 0.5603535520314823, 'lat_1080ti_32': 0.6267071446356319, 'lat_1080ti_64': 0.3724315701215734, 'lat_2080ti_1': 0.6281406128636312, 'lat_2080ti_32': 0.5527249985113672, 'lat_2080ti_64': 0.39235220095814843, 'lat_essential_ph_1': 0.2830188679245283, 'lat_eyeriss': 0.3557020519685326, 'lat_fpga': 0.40051850898979785, 'lat_gold_6226': 0.31049685432766166, 'lat_gold_6240': 0.5049446946230867, 'lat_pixel2': 0.2608695652173913, 'lat_pixel3': 0.3616541454474755, 'lat_raspi4': 0.3700467946396342, 'lat_samsung_a50': 0.16842105263157894, 'lat_samsung_s7': 0.15748031496062992, 'lat_silver_4114': 0.6445681538642618, 'lat_silver_4210r': 0.48151308184355546, 'lat_titan_rtx_1': 0.605567263130708, 'lat_titan_rtx_32': 0.5371904122099512, 'lat_titan_rtx_64': 0.43127680985548283, 'lat_titanx_1': 0.3243835292191182, 'lat_titanx_32': 0.7485345698112594, 'lat_titanx_64': 0.4079557569763853, 'lat_titanxp_1': 0.5764563873229284, 'lat_titanxp_32': 0.6333180322506252, 'lat_titanxp_64': 0.40513197904268283}
FBNet_4854	FBNet	4854	4854	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_648[FLOAT, 16x3x3x3] %onnx::Conv_649[FLOAT, 16] %onnx::Conv_651[FLOAT, 16x16x1x1] %onnx::Conv_654[FLOAT, 16x1x3x3] %onnx::Conv_657[FLOAT, 24x16x1x1] %onnx::Conv_658[FLOAT, 24] %onnx::Conv_660[FLOAT, 144x24x1x1] %onnx::Conv_661[FLOAT, 144] %onnx::Conv_663[FLOAT, 144x1x3x3] %onnx::Conv_666[FLOAT, 24x144x1x1] %onnx::Conv_669[FLOAT, 144x24x1x1] %onnx::Conv_672[FLOAT, 144x1x3x3] %onnx::Conv_675[FLOAT, 24x144x1x1] %onnx::Conv_678[FLOAT, 72x24x1x1] %onnx::Conv_679[FLOAT, 72] %onnx::Conv_681[FLOAT, 72x1x3x3] %onnx::Conv_684[FLOAT, 24x72x1x1] %onnx::Conv_687[FLOAT, 24x24x1x1] %onnx::Conv_690[FLOAT, 24x1x5x5] %onnx::Conv_693[FLOAT, 32x24x1x1] %onnx::Conv_694[FLOAT, 32] %onnx::Conv_696[FLOAT, 32x16x1x1] %onnx::Conv_699[FLOAT, 32x1x5x5] %onnx::Conv_702[FLOAT, 32x16x1x1] %onnx::Conv_705[FLOAT, 192x32x1x1] %onnx::Conv_706[FLOAT, 192] %onnx::Conv_708[FLOAT, 192x1x3x3] %onnx::Conv_711[FLOAT, 32x192x1x1] %onnx::Conv_714[FLOAT, 32x32x1x1] %onnx::Conv_717[FLOAT, 32x1x3x3] %onnx::Conv_720[FLOAT, 32x32x1x1] %onnx::Conv_723[FLOAT, 32x16x1x1] %onnx::Conv_726[FLOAT, 32x1x5x5] %onnx::Conv_729[FLOAT, 64x16x1x1] %onnx::Conv_730[FLOAT, 64] %onnx::Conv_732[FLOAT, 384x64x1x1] %onnx::Conv_733[FLOAT, 384] %onnx::Conv_735[FLOAT, 384x1x3x3] %onnx::Conv_738[FLOAT, 64x384x1x1] %onnx::Conv_741[FLOAT, 384x64x1x1] %onnx::Conv_744[FLOAT, 384x1x5x5] %onnx::Conv_747[FLOAT, 64x384x1x1] %onnx::Conv_750[FLOAT, 64x64x1x1] %onnx::Conv_753[FLOAT, 64x1x3x3] %onnx::Conv_756[FLOAT, 64x64x1x1] %onnx::Conv_759[FLOAT, 64x64x1x1] %onnx::Conv_762[FLOAT, 64x1x5x5] %onnx::Conv_765[FLOAT, 112x64x1x1] %onnx::Conv_766[FLOAT, 112] %onnx::Conv_768[FLOAT, 112x112x1x1] %onnx::Conv_771[FLOAT, 112x1x3x3] %onnx::Conv_774[FLOAT, 112x112x1x1] %onnx::Conv_777[FLOAT, 672x112x1x1] %onnx::Conv_778[FLOAT, 672] %onnx::Conv_780[FLOAT, 672x1x5x5] %onnx::Conv_783[FLOAT, 112x672x1x1] %onnx::Conv_786[FLOAT, 336x112x1x1] %onnx::Conv_787[FLOAT, 336] %onnx::Conv_789[FLOAT, 336x1x3x3] %onnx::Conv_792[FLOAT, 112x336x1x1] %onnx::Conv_795[FLOAT, 336x112x1x1] %onnx::Conv_798[FLOAT, 336x1x5x5] %onnx::Conv_801[FLOAT, 184x336x1x1] %onnx::Conv_802[FLOAT, 184] %onnx::Conv_804[FLOAT, 184x92x1x1] %onnx::Conv_807[FLOAT, 184x1x3x3] %onnx::Conv_810[FLOAT, 184x92x1x1] %onnx::Conv_813[FLOAT, 1104x184x1x1] %onnx::Conv_814[FLOAT, 1104] %onnx::Conv_816[FLOAT, 1104x1x3x3] %onnx::Conv_819[FLOAT, 184x1104x1x1] %onnx::Conv_822[FLOAT, 1104x184x1x1] %onnx::Conv_825[FLOAT, 1104x1x3x3] %onnx::Conv_828[FLOAT, 184x1104x1x1] %onnx::Conv_831[FLOAT, 552x184x1x1] %onnx::Conv_832[FLOAT, 552] %onnx::Conv_834[FLOAT, 552x1x5x5] %onnx::Conv_837[FLOAT, 352x552x1x1] %onnx::Conv_838[FLOAT, 352] %onnx::Conv_840[FLOAT, 1504x352x1x1] %onnx::Conv_841[FLOAT, 1504] ) { %onnx::Conv_835 = Identity(%onnx::Conv_832) %onnx::Conv_829 = Identity(%onnx::Conv_802) %onnx::Conv_826 = Identity(%onnx::Conv_814) %onnx::Conv_823 = Identity(%onnx::Conv_814) %onnx::Conv_820 = Identity(%onnx::Conv_802) %onnx::Conv_817 = Identity(%onnx::Conv_814) %onnx::Conv_811 = Identity(%onnx::Conv_802) %onnx::Conv_808 = Identity(%onnx::Conv_802) %onnx::Conv_805 = Identity(%onnx::Conv_802) %onnx::Conv_799 = Identity(%onnx::Conv_787) %onnx::Conv_796 = Identity(%onnx::Conv_787) %onnx::Conv_793 = Identity(%onnx::Conv_766) %onnx::Conv_790 = Identity(%onnx::Conv_787) %onnx::Conv_784 = Identity(%onnx::Conv_766) %onnx::Conv_781 = Identity(%onnx::Conv_778) %onnx::Conv_775 = Identity(%onnx::Conv_766) %onnx::Conv_772 = Identity(%onnx::Conv_766) %onnx::Conv_769 = Identity(%onnx::Conv_766) %onnx::Conv_763 = Identity(%onnx::Conv_730) %onnx::Conv_760 = Identity(%onnx::Conv_730) %onnx::Conv_757 = Identity(%onnx::Conv_730) %onnx::Conv_754 = Identity(%onnx::Conv_730) %onnx::Conv_751 = Identity(%onnx::Conv_730) %onnx::Conv_748 = Identity(%onnx::Conv_730) %onnx::Conv_745 = Identity(%onnx::Conv_733) %onnx::Conv_742 = Identity(%onnx::Conv_733) %onnx::Conv_739 = Identity(%onnx::Conv_730) %onnx::Conv_736 = Identity(%onnx::Conv_733) %onnx::Conv_727 = Identity(%onnx::Conv_694) %onnx::Conv_724 = Identity(%onnx::Conv_694) %onnx::Conv_721 = Identity(%onnx::Conv_694) %onnx::Conv_718 = Identity(%onnx::Conv_694) %onnx::Conv_715 = Identity(%onnx::Conv_694) %onnx::Conv_712 = Identity(%onnx::Conv_694) %onnx::Conv_709 = Identity(%onnx::Conv_706) %onnx::Conv_703 = Identity(%onnx::Conv_694) %onnx::Conv_700 = Identity(%onnx::Conv_694) %onnx::Conv_697 = Identity(%onnx::Conv_694) %onnx::Conv_691 = Identity(%onnx::Conv_658) %onnx::Conv_688 = Identity(%onnx::Conv_658) %onnx::Conv_685 = Identity(%onnx::Conv_658) %onnx::Conv_682 = Identity(%onnx::Conv_679) %onnx::Conv_676 = Identity(%onnx::Conv_658) %onnx::Conv_673 = Identity(%onnx::Conv_661) %onnx::Conv_670 = Identity(%onnx::Conv_661) %onnx::Conv_667 = Identity(%onnx::Conv_658) %onnx::Conv_664 = Identity(%onnx::Conv_661) %onnx::Conv_655 = Identity(%onnx::Conv_649) %onnx::Conv_652 = Identity(%onnx::Conv_649) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_648, %onnx::Conv_649) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_651, %onnx::Conv_652) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.1/nl/Relu_output_0, %onnx::Conv_654, %onnx::Conv_655) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_657, %onnx::Conv_658) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_660, %onnx::Conv_661) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.2/nl/Relu_output_0, %onnx::Conv_663, %onnx::Conv_664) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_666, %onnx::Conv_667) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_669, %onnx::Conv_670) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.3/nl/Relu_output_0, %onnx::Conv_672, %onnx::Conv_673) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_675, %onnx::Conv_676) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_678, %onnx::Conv_679) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.4/nl/Relu_output_0, %onnx::Conv_681, %onnx::Conv_682) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_684, %onnx::Conv_685) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/Add_output_0, %onnx::Conv_687, %onnx::Conv_688) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.5/nl/Relu_output_0, %onnx::Conv_690, %onnx::Conv_691) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_693, %onnx::Conv_694) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_696, %onnx::Conv_697) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.6/shuffle/Reshape_output_0 = Reshape(%/cells.6/nl/Relu_output_0, %/cells.6/shuffle/Constant_output_0) %/cells.6/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.6/shuffle/Reshape_output_0) %/cells.6/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.6/shuffle/Reshape_1_output_0 = Reshape(%/cells.6/shuffle/Transpose_output_0, %/cells.6/shuffle/Constant_1_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.6/shuffle/Reshape_1_output_0, %onnx::Conv_699, %onnx::Conv_700) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_702, %onnx::Conv_703) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_705, %onnx::Conv_706) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.7/nl/Relu_output_0, %onnx::Conv_708, %onnx::Conv_709) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_711, %onnx::Conv_712) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_714, %onnx::Conv_715) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.8/nl/Relu_output_0, %onnx::Conv_717, %onnx::Conv_718) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_720, %onnx::Conv_721) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_723, %onnx::Conv_724) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.9/shuffle/Reshape_output_0 = Reshape(%/cells.9/nl/Relu_output_0, %/cells.9/shuffle/Constant_output_0) %/cells.9/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.9/shuffle/Reshape_output_0) %/cells.9/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.9/shuffle/Reshape_1_output_0 = Reshape(%/cells.9/shuffle/Transpose_output_0, %/cells.9/shuffle/Constant_1_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.9/shuffle/Reshape_1_output_0, %onnx::Conv_726, %onnx::Conv_727) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_729, %onnx::Conv_730) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_732, %onnx::Conv_733) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.10/nl/Relu_output_0, %onnx::Conv_735, %onnx::Conv_736) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_738, %onnx::Conv_739) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_741, %onnx::Conv_742) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.11/nl/Relu_output_0, %onnx::Conv_744, %onnx::Conv_745) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_747, %onnx::Conv_748) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_750, %onnx::Conv_751) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.12/nl/Relu_output_0, %onnx::Conv_753, %onnx::Conv_754) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_756, %onnx::Conv_757) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_759, %onnx::Conv_760) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.13/nl/Relu_output_0, %onnx::Conv_762, %onnx::Conv_763) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_765, %onnx::Conv_766) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_768, %onnx::Conv_769) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.14/nl/Relu_output_0, %onnx::Conv_771, %onnx::Conv_772) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_774, %onnx::Conv_775) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_777, %onnx::Conv_778) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.15/nl/Relu_output_0, %onnx::Conv_780, %onnx::Conv_781) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_783, %onnx::Conv_784) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_786, %onnx::Conv_787) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.16/nl/Relu_output_0, %onnx::Conv_789, %onnx::Conv_790) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_792, %onnx::Conv_793) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.15/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_795, %onnx::Conv_796) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.17/nl/Relu_output_0, %onnx::Conv_798, %onnx::Conv_799) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_801, %onnx::Conv_802) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_804, %onnx::Conv_805) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.18/shuffle/Reshape_output_0 = Reshape(%/cells.18/nl/Relu_output_0, %/cells.18/shuffle/Constant_output_0) %/cells.18/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.18/shuffle/Reshape_output_0) %/cells.18/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.18/shuffle/Reshape_1_output_0 = Reshape(%/cells.18/shuffle/Transpose_output_0, %/cells.18/shuffle/Constant_1_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.18/shuffle/Reshape_1_output_0, %onnx::Conv_807, %onnx::Conv_808) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_810, %onnx::Conv_811) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_813, %onnx::Conv_814) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.19/nl/Relu_output_0, %onnx::Conv_816, %onnx::Conv_817) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_819, %onnx::Conv_820) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_822, %onnx::Conv_823) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.20/nl/Relu_output_0, %onnx::Conv_825, %onnx::Conv_826) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_828, %onnx::Conv_829) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_831, %onnx::Conv_832) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 552, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.21/nl/Relu_output_0, %onnx::Conv_834, %onnx::Conv_835) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_837, %onnx::Conv_838) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_840, %onnx::Conv_841) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %646 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %646 }	val_accuracy	88,809,344	2,445,268	{'zcp_synflow': 81.71111739743966, 'zcp_zen': 73.83270263671875, 'zcp_epe_nas': 7.299595394917167, 'zcp_fisher': 0.1175716370344162, 'zcp_flops': 88809344.0, 'zcp_grad_norm': 22.936813354492188, 'zcp_grasp': 0.052028656005859375, 'zcp_jacov': -16.092916159675955, 'zcp_l2_norm': 716.5233764648438, 'zcp_nwot': 216.48925619564739, 'zcp_params': 2445268.0, 'zcp_plain': -0.0010344982147216797, 'zcp_snip': 46.37977600097656, 'lat_1080ti_1': 0.7249729001997913, 'lat_1080ti_32': 0.6333708397640014, 'lat_1080ti_64': 0.5792544442206685, 'lat_2080ti_1': 0.6964052373190356, 'lat_2080ti_32': 0.7068661532467401, 'lat_2080ti_64': 0.6341343753038854, 'lat_essential_ph_1': 0.3018867924528302, 'lat_eyeriss': 0.6567216181027999, 'lat_fpga': 0.7885096824627198, 'lat_gold_6226': 0.5542493076154436, 'lat_gold_6240': 0.7062311679330102, 'lat_pixel2': 0.43478260869565216, 'lat_pixel3': 0.5884447651314612, 'lat_raspi4': 0.6855015308182404, 'lat_samsung_a50': 0.30526315789473685, 'lat_samsung_s7': 0.25984251968503935, 'lat_silver_4114': 0.7164635156563302, 'lat_silver_4210r': 0.7456379863595223, 'lat_titan_rtx_1': 0.6545871449863078, 'lat_titan_rtx_32': 0.6624139943000431, 'lat_titan_rtx_64': 0.6490201534868741, 'lat_titanx_1': 0.35300476949910237, 'lat_titanx_32': 0.6569805557469713, 'lat_titanx_64': 0.5743992773995161, 'lat_titanxp_1': 0.6200068445879571, 'lat_titanxp_32': 0.6689961716856208, 'lat_titanxp_64': 0.6095101455024958}
FBNet_4061	FBNet	4061	4061	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_597[FLOAT, 16x3x3x3] %onnx::Conv_598[FLOAT, 16] %onnx::Conv_600[FLOAT, 16x8x1x1] %onnx::Conv_603[FLOAT, 16x1x5x5] %onnx::Conv_606[FLOAT, 24x8x1x1] %onnx::Conv_607[FLOAT, 24] %onnx::Conv_609[FLOAT, 24x12x1x1] %onnx::Conv_612[FLOAT, 24x1x3x3] %onnx::Conv_615[FLOAT, 24x12x1x1] %onnx::Conv_618[FLOAT, 144x24x1x1] %onnx::Conv_619[FLOAT, 144] %onnx::Conv_621[FLOAT, 144x1x5x5] %onnx::Conv_624[FLOAT, 24x144x1x1] %onnx::Conv_627[FLOAT, 24x24x1x1] %onnx::Conv_630[FLOAT, 24x1x3x3] %onnx::Conv_633[FLOAT, 24x24x1x1] %onnx::Conv_636[FLOAT, 24x24x1x1] %onnx::Conv_639[FLOAT, 24x1x5x5] %onnx::Conv_642[FLOAT, 32x24x1x1] %onnx::Conv_643[FLOAT, 32] %onnx::Conv_645[FLOAT, 96x32x1x1] %onnx::Conv_646[FLOAT, 96] %onnx::Conv_648[FLOAT, 96x1x5x5] %onnx::Conv_651[FLOAT, 32x96x1x1] %onnx::Conv_654[FLOAT, 32x16x1x1] %onnx::Conv_657[FLOAT, 32x1x5x5] %onnx::Conv_660[FLOAT, 32x16x1x1] %onnx::Conv_663[FLOAT, 32x32x1x1] %onnx::Conv_666[FLOAT, 32x1x3x3] %onnx::Conv_669[FLOAT, 32x32x1x1] %onnx::Conv_672[FLOAT, 32x16x1x1] %onnx::Conv_675[FLOAT, 32x1x5x5] %onnx::Conv_678[FLOAT, 64x16x1x1] %onnx::Conv_679[FLOAT, 64] %onnx::Conv_681[FLOAT, 64x64x1x1] %onnx::Conv_684[FLOAT, 64x1x3x3] %onnx::Conv_687[FLOAT, 112x64x1x1] %onnx::Conv_688[FLOAT, 112] %onnx::Conv_690[FLOAT, 112x56x1x1] %onnx::Conv_693[FLOAT, 112x1x3x3] %onnx::Conv_696[FLOAT, 112x56x1x1] %onnx::Conv_699[FLOAT, 672x112x1x1] %onnx::Conv_700[FLOAT, 672] %onnx::Conv_702[FLOAT, 672x1x3x3] %onnx::Conv_705[FLOAT, 112x672x1x1] %onnx::Conv_708[FLOAT, 672x112x1x1] %onnx::Conv_711[FLOAT, 672x1x5x5] %onnx::Conv_714[FLOAT, 112x672x1x1] %onnx::Conv_717[FLOAT, 112x112x1x1] %onnx::Conv_720[FLOAT, 112x1x5x5] %onnx::Conv_723[FLOAT, 184x112x1x1] %onnx::Conv_724[FLOAT, 184] %onnx::Conv_726[FLOAT, 184x184x1x1] %onnx::Conv_729[FLOAT, 184x1x3x3] %onnx::Conv_732[FLOAT, 184x184x1x1] %onnx::Conv_735[FLOAT, 184x184x1x1] %onnx::Conv_738[FLOAT, 184x1x5x5] %onnx::Conv_741[FLOAT, 184x184x1x1] %onnx::Conv_744[FLOAT, 184x92x1x1] %onnx::Conv_747[FLOAT, 184x1x3x3] %onnx::Conv_750[FLOAT, 352x92x1x1] %onnx::Conv_751[FLOAT, 352] %onnx::Conv_753[FLOAT, 1504x352x1x1] %onnx::Conv_754[FLOAT, 1504] ) { %onnx::Conv_748 = Identity(%onnx::Conv_724) %onnx::Conv_745 = Identity(%onnx::Conv_724) %onnx::Conv_742 = Identity(%onnx::Conv_724) %onnx::Conv_739 = Identity(%onnx::Conv_724) %onnx::Conv_736 = Identity(%onnx::Conv_724) %onnx::Conv_733 = Identity(%onnx::Conv_724) %onnx::Conv_730 = Identity(%onnx::Conv_724) %onnx::Conv_727 = Identity(%onnx::Conv_724) %onnx::Conv_721 = Identity(%onnx::Conv_688) %onnx::Conv_718 = Identity(%onnx::Conv_688) %onnx::Conv_715 = Identity(%onnx::Conv_688) %onnx::Conv_712 = Identity(%onnx::Conv_700) %onnx::Conv_709 = Identity(%onnx::Conv_700) %onnx::Conv_706 = Identity(%onnx::Conv_688) %onnx::Conv_703 = Identity(%onnx::Conv_700) %onnx::Conv_697 = Identity(%onnx::Conv_688) %onnx::Conv_694 = Identity(%onnx::Conv_688) %onnx::Conv_691 = Identity(%onnx::Conv_688) %onnx::Conv_685 = Identity(%onnx::Conv_679) %onnx::Conv_682 = Identity(%onnx::Conv_679) %onnx::Conv_676 = Identity(%onnx::Conv_643) %onnx::Conv_673 = Identity(%onnx::Conv_643) %onnx::Conv_670 = Identity(%onnx::Conv_643) %onnx::Conv_667 = Identity(%onnx::Conv_643) %onnx::Conv_664 = Identity(%onnx::Conv_643) %onnx::Conv_661 = Identity(%onnx::Conv_643) %onnx::Conv_658 = Identity(%onnx::Conv_643) %onnx::Conv_655 = Identity(%onnx::Conv_643) %onnx::Conv_652 = Identity(%onnx::Conv_643) %onnx::Conv_649 = Identity(%onnx::Conv_646) %onnx::Conv_640 = Identity(%onnx::Conv_607) %onnx::Conv_637 = Identity(%onnx::Conv_607) %onnx::Conv_634 = Identity(%onnx::Conv_607) %onnx::Conv_631 = Identity(%onnx::Conv_607) %onnx::Conv_628 = Identity(%onnx::Conv_607) %onnx::Conv_625 = Identity(%onnx::Conv_607) %onnx::Conv_622 = Identity(%onnx::Conv_619) %onnx::Conv_616 = Identity(%onnx::Conv_607) %onnx::Conv_613 = Identity(%onnx::Conv_607) %onnx::Conv_610 = Identity(%onnx::Conv_607) %onnx::Conv_604 = Identity(%onnx::Conv_598) %onnx::Conv_601 = Identity(%onnx::Conv_598) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_597, %onnx::Conv_598) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_600, %onnx::Conv_601) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.1/shuffle/Reshape_output_0 = Reshape(%/cells.1/nl/Relu_output_0, %/cells.1/shuffle/Constant_output_0) %/cells.1/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.1/shuffle/Reshape_output_0) %/cells.1/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.1/shuffle/Reshape_1_output_0 = Reshape(%/cells.1/shuffle/Transpose_output_0, %/cells.1/shuffle/Constant_1_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.1/shuffle/Reshape_1_output_0, %onnx::Conv_603, %onnx::Conv_604) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_606, %onnx::Conv_607) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_609, %onnx::Conv_610) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.2/shuffle/Reshape_output_0 = Reshape(%/cells.2/nl/Relu_output_0, %/cells.2/shuffle/Constant_output_0) %/cells.2/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.2/shuffle/Reshape_output_0) %/cells.2/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.2/shuffle/Reshape_1_output_0 = Reshape(%/cells.2/shuffle/Transpose_output_0, %/cells.2/shuffle/Constant_1_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.2/shuffle/Reshape_1_output_0, %onnx::Conv_612, %onnx::Conv_613) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_615, %onnx::Conv_616) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_618, %onnx::Conv_619) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.3/nl/Relu_output_0, %onnx::Conv_621, %onnx::Conv_622) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_624, %onnx::Conv_625) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_627, %onnx::Conv_628) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.4/nl/Relu_output_0, %onnx::Conv_630, %onnx::Conv_631) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_633, %onnx::Conv_634) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/Add_output_0, %onnx::Conv_636, %onnx::Conv_637) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.5/nl/Relu_output_0, %onnx::Conv_639, %onnx::Conv_640) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_642, %onnx::Conv_643) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_645, %onnx::Conv_646) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.6/nl/Relu_output_0, %onnx::Conv_648, %onnx::Conv_649) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_651, %onnx::Conv_652) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_654, %onnx::Conv_655) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.7/shuffle/Reshape_output_0 = Reshape(%/cells.7/nl/Relu_output_0, %/cells.7/shuffle/Constant_output_0) %/cells.7/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.7/shuffle/Reshape_output_0) %/cells.7/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.7/shuffle/Reshape_1_output_0 = Reshape(%/cells.7/shuffle/Transpose_output_0, %/cells.7/shuffle/Constant_1_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.7/shuffle/Reshape_1_output_0, %onnx::Conv_657, %onnx::Conv_658) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_660, %onnx::Conv_661) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_663, %onnx::Conv_664) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.8/nl/Relu_output_0, %onnx::Conv_666, %onnx::Conv_667) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_669, %onnx::Conv_670) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_672, %onnx::Conv_673) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.9/shuffle/Reshape_output_0 = Reshape(%/cells.9/nl/Relu_output_0, %/cells.9/shuffle/Constant_output_0) %/cells.9/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.9/shuffle/Reshape_output_0) %/cells.9/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.9/shuffle/Reshape_1_output_0 = Reshape(%/cells.9/shuffle/Transpose_output_0, %/cells.9/shuffle/Constant_1_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.9/shuffle/Reshape_1_output_0, %onnx::Conv_675, %onnx::Conv_676) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_678, %onnx::Conv_679) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_681, %onnx::Conv_682) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.13/nl/Relu_output_0, %onnx::Conv_684, %onnx::Conv_685) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_687, %onnx::Conv_688) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_690, %onnx::Conv_691) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.14/shuffle/Reshape_output_0 = Reshape(%/cells.14/nl/Relu_output_0, %/cells.14/shuffle/Constant_output_0) %/cells.14/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.14/shuffle/Reshape_output_0) %/cells.14/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.14/shuffle/Reshape_1_output_0 = Reshape(%/cells.14/shuffle/Transpose_output_0, %/cells.14/shuffle/Constant_1_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.14/shuffle/Reshape_1_output_0, %onnx::Conv_693, %onnx::Conv_694) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_696, %onnx::Conv_697) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_699, %onnx::Conv_700) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.15/nl/Relu_output_0, %onnx::Conv_702, %onnx::Conv_703) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_705, %onnx::Conv_706) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_708, %onnx::Conv_709) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.16/nl/Relu_output_0, %onnx::Conv_711, %onnx::Conv_712) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_714, %onnx::Conv_715) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.15/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_717, %onnx::Conv_718) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.17/nl/Relu_output_0, %onnx::Conv_720, %onnx::Conv_721) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_723, %onnx::Conv_724) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_726, %onnx::Conv_727) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.18/nl/Relu_output_0, %onnx::Conv_729, %onnx::Conv_730) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_732, %onnx::Conv_733) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_735, %onnx::Conv_736) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.20/nl/Relu_output_0, %onnx::Conv_738, %onnx::Conv_739) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_741, %onnx::Conv_742) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_744, %onnx::Conv_745) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.21/shuffle/Reshape_output_0 = Reshape(%/cells.21/nl/Relu_output_0, %/cells.21/shuffle/Constant_output_0) %/cells.21/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.21/shuffle/Reshape_output_0) %/cells.21/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.21/shuffle/Reshape_1_output_0 = Reshape(%/cells.21/shuffle/Transpose_output_0, %/cells.21/shuffle/Constant_1_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.21/shuffle/Reshape_1_output_0, %onnx::Conv_747, %onnx::Conv_748) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_750, %onnx::Conv_751) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_753, %onnx::Conv_754) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %595 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %595 }	val_accuracy	56,243,584	1,305,364	{'zcp_synflow': 63.64541742116945, 'zcp_zen': 54.712650299072266, 'zcp_epe_nas': 0.00015999920000638146, 'zcp_fisher': 0.03171515092253685, 'zcp_flops': 56243584.0, 'zcp_grad_norm': 15.755922317504883, 'zcp_grasp': -0.002586841583251953, 'zcp_jacov': -16.06861601552469, 'zcp_l2_norm': 463.97320556640625, 'zcp_nwot': 207.5056626850865, 'zcp_params': 1305364.0, 'zcp_plain': 0.005633890628814697, 'zcp_snip': 20.59406280517578, 'lat_1080ti_1': 0.2848912139234038, 'lat_1080ti_32': 0.3690669597275344, 'lat_1080ti_64': 0.28725602701180614, 'lat_2080ti_1': 0.38941354624661634, 'lat_2080ti_32': 0.35041346787550903, 'lat_2080ti_64': 0.3256923204740558, 'lat_essential_ph_1': 0.16981132075471697, 'lat_eyeriss': 0.24474171602515887, 'lat_fpga': 0.30520784888035696, 'lat_gold_6226': 0.21499030059327054, 'lat_gold_6240': 0.2962866164831053, 'lat_pixel2': 0.13043478260869565, 'lat_pixel3': 0.3353210445514197, 'lat_raspi4': 0.33533714206057846, 'lat_samsung_a50': 0.09473684210526316, 'lat_samsung_s7': 0.07874015748031496, 'lat_silver_4114': 0.2853475928872266, 'lat_silver_4210r': 0.3050498169649493, 'lat_titan_rtx_1': 0.3139892835650068, 'lat_titan_rtx_32': 0.3411382415635155, 'lat_titan_rtx_64': 0.30587180562393523, 'lat_titanx_1': 0.1538784678947559, 'lat_titanx_32': 0.3109305569486499, 'lat_titanx_64': 0.2829105297300473, 'lat_titanxp_1': 0.2816446748315376, 'lat_titanxp_32': 0.3241553881152402, 'lat_titanxp_64': 0.3268610276007739}
FBNet_98	FBNet	98	98	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_616[FLOAT, 16x3x3x3] %onnx::Conv_617[FLOAT, 16] %onnx::Conv_619[FLOAT, 16x16x1x1] %onnx::Conv_622[FLOAT, 16x1x5x5] %onnx::Conv_625[FLOAT, 16x16x1x1] %onnx::Conv_628[FLOAT, 96x16x1x1] %onnx::Conv_629[FLOAT, 96] %onnx::Conv_631[FLOAT, 96x1x3x3] %onnx::Conv_634[FLOAT, 24x96x1x1] %onnx::Conv_635[FLOAT, 24] %onnx::Conv_637[FLOAT, 24x12x1x1] %onnx::Conv_640[FLOAT, 24x1x3x3] %onnx::Conv_643[FLOAT, 24x12x1x1] %onnx::Conv_646[FLOAT, 144x24x1x1] %onnx::Conv_647[FLOAT, 144] %onnx::Conv_649[FLOAT, 144x1x5x5] %onnx::Conv_652[FLOAT, 24x144x1x1] %onnx::Conv_655[FLOAT, 72x24x1x1] %onnx::Conv_656[FLOAT, 72] %onnx::Conv_658[FLOAT, 72x1x3x3] %onnx::Conv_661[FLOAT, 32x72x1x1] %onnx::Conv_662[FLOAT, 32] %onnx::Conv_664[FLOAT, 32x32x1x1] %onnx::Conv_667[FLOAT, 32x1x5x5] %onnx::Conv_670[FLOAT, 32x32x1x1] %onnx::Conv_673[FLOAT, 192x32x1x1] %onnx::Conv_674[FLOAT, 192] %onnx::Conv_676[FLOAT, 192x1x5x5] %onnx::Conv_679[FLOAT, 32x192x1x1] %onnx::Conv_682[FLOAT, 32x16x1x1] %onnx::Conv_685[FLOAT, 32x1x3x3] %onnx::Conv_688[FLOAT, 64x16x1x1] %onnx::Conv_689[FLOAT, 64] %onnx::Conv_691[FLOAT, 384x64x1x1] %onnx::Conv_692[FLOAT, 384] %onnx::Conv_694[FLOAT, 384x1x5x5] %onnx::Conv_697[FLOAT, 64x384x1x1] %onnx::Conv_700[FLOAT, 192x64x1x1] %onnx::Conv_703[FLOAT, 192x1x5x5] %onnx::Conv_706[FLOAT, 64x192x1x1] %onnx::Conv_709[FLOAT, 384x64x1x1] %onnx::Conv_712[FLOAT, 384x1x3x3] %onnx::Conv_715[FLOAT, 64x384x1x1] %onnx::Conv_718[FLOAT, 64x32x1x1] %onnx::Conv_721[FLOAT, 64x1x3x3] %onnx::Conv_724[FLOAT, 112x32x1x1] %onnx::Conv_725[FLOAT, 112] %onnx::Conv_727[FLOAT, 336x112x1x1] %onnx::Conv_728[FLOAT, 336] %onnx::Conv_730[FLOAT, 336x1x5x5] %onnx::Conv_733[FLOAT, 112x336x1x1] %onnx::Conv_736[FLOAT, 112x112x1x1] %onnx::Conv_739[FLOAT, 112x1x3x3] %onnx::Conv_742[FLOAT, 112x112x1x1] %onnx::Conv_745[FLOAT, 112x56x1x1] %onnx::Conv_748[FLOAT, 112x1x5x5] %onnx::Conv_751[FLOAT, 112x56x1x1] %onnx::Conv_754[FLOAT, 184x112x1x1] %onnx::Conv_755[FLOAT, 184] %onnx::Conv_757[FLOAT, 184x92x1x1] %onnx::Conv_760[FLOAT, 184x1x5x5] %onnx::Conv_763[FLOAT, 184x92x1x1] %onnx::Conv_766[FLOAT, 552x184x1x1] %onnx::Conv_767[FLOAT, 552] %onnx::Conv_769[FLOAT, 552x1x3x3] %onnx::Conv_772[FLOAT, 184x552x1x1] %onnx::Conv_775[FLOAT, 1104x184x1x1] %onnx::Conv_776[FLOAT, 1104] %onnx::Conv_778[FLOAT, 1104x1x5x5] %onnx::Conv_781[FLOAT, 352x1104x1x1] %onnx::Conv_782[FLOAT, 352] %onnx::Conv_784[FLOAT, 1504x352x1x1] %onnx::Conv_785[FLOAT, 1504] ) { %onnx::Conv_779 = Identity(%onnx::Conv_776) %onnx::Conv_773 = Identity(%onnx::Conv_755) %onnx::Conv_770 = Identity(%onnx::Conv_767) %onnx::Conv_764 = Identity(%onnx::Conv_755) %onnx::Conv_761 = Identity(%onnx::Conv_755) %onnx::Conv_758 = Identity(%onnx::Conv_755) %onnx::Conv_752 = Identity(%onnx::Conv_725) %onnx::Conv_749 = Identity(%onnx::Conv_725) %onnx::Conv_746 = Identity(%onnx::Conv_725) %onnx::Conv_743 = Identity(%onnx::Conv_725) %onnx::Conv_740 = Identity(%onnx::Conv_725) %onnx::Conv_737 = Identity(%onnx::Conv_725) %onnx::Conv_734 = Identity(%onnx::Conv_725) %onnx::Conv_731 = Identity(%onnx::Conv_728) %onnx::Conv_722 = Identity(%onnx::Conv_689) %onnx::Conv_719 = Identity(%onnx::Conv_689) %onnx::Conv_716 = Identity(%onnx::Conv_689) %onnx::Conv_713 = Identity(%onnx::Conv_692) %onnx::Conv_710 = Identity(%onnx::Conv_692) %onnx::Conv_707 = Identity(%onnx::Conv_689) %onnx::Conv_704 = Identity(%onnx::Conv_674) %onnx::Conv_701 = Identity(%onnx::Conv_674) %onnx::Conv_698 = Identity(%onnx::Conv_689) %onnx::Conv_695 = Identity(%onnx::Conv_692) %onnx::Conv_686 = Identity(%onnx::Conv_662) %onnx::Conv_683 = Identity(%onnx::Conv_662) %onnx::Conv_680 = Identity(%onnx::Conv_662) %onnx::Conv_677 = Identity(%onnx::Conv_674) %onnx::Conv_671 = Identity(%onnx::Conv_662) %onnx::Conv_668 = Identity(%onnx::Conv_662) %onnx::Conv_665 = Identity(%onnx::Conv_662) %onnx::Conv_659 = Identity(%onnx::Conv_656) %onnx::Conv_653 = Identity(%onnx::Conv_635) %onnx::Conv_650 = Identity(%onnx::Conv_647) %onnx::Conv_644 = Identity(%onnx::Conv_635) %onnx::Conv_641 = Identity(%onnx::Conv_635) %onnx::Conv_638 = Identity(%onnx::Conv_635) %onnx::Conv_632 = Identity(%onnx::Conv_629) %onnx::Conv_626 = Identity(%onnx::Conv_617) %onnx::Conv_623 = Identity(%onnx::Conv_617) %onnx::Conv_620 = Identity(%onnx::Conv_617) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_616, %onnx::Conv_617) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_619, %onnx::Conv_620) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.0/nl/Relu_output_0, %onnx::Conv_622, %onnx::Conv_623) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_625, %onnx::Conv_626) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_628, %onnx::Conv_629) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.1/nl/Relu_output_0, %onnx::Conv_631, %onnx::Conv_632) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_634, %onnx::Conv_635) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_637, %onnx::Conv_638) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.2/shuffle/Reshape_output_0 = Reshape(%/cells.2/nl/Relu_output_0, %/cells.2/shuffle/Constant_output_0) %/cells.2/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.2/shuffle/Reshape_output_0) %/cells.2/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.2/shuffle/Reshape_1_output_0 = Reshape(%/cells.2/shuffle/Transpose_output_0, %/cells.2/shuffle/Constant_1_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.2/shuffle/Reshape_1_output_0, %onnx::Conv_640, %onnx::Conv_641) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_643, %onnx::Conv_644) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_646, %onnx::Conv_647) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.4/nl/Relu_output_0, %onnx::Conv_649, %onnx::Conv_650) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_652, %onnx::Conv_653) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/Add_output_0, %onnx::Conv_655, %onnx::Conv_656) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.5/nl/Relu_output_0, %onnx::Conv_658, %onnx::Conv_659) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_661, %onnx::Conv_662) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_664, %onnx::Conv_665) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.6/nl/Relu_output_0, %onnx::Conv_667, %onnx::Conv_668) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_670, %onnx::Conv_671) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_673, %onnx::Conv_674) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.8/nl/Relu_output_0, %onnx::Conv_676, %onnx::Conv_677) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_679, %onnx::Conv_680) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_682, %onnx::Conv_683) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.9/shuffle/Reshape_output_0 = Reshape(%/cells.9/nl/Relu_output_0, %/cells.9/shuffle/Constant_output_0) %/cells.9/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.9/shuffle/Reshape_output_0) %/cells.9/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.9/shuffle/Reshape_1_output_0 = Reshape(%/cells.9/shuffle/Transpose_output_0, %/cells.9/shuffle/Constant_1_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.9/shuffle/Reshape_1_output_0, %onnx::Conv_685, %onnx::Conv_686) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_688, %onnx::Conv_689) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_691, %onnx::Conv_692) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.10/nl/Relu_output_0, %onnx::Conv_694, %onnx::Conv_695) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_697, %onnx::Conv_698) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_700, %onnx::Conv_701) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.11/nl/Relu_output_0, %onnx::Conv_703, %onnx::Conv_704) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_706, %onnx::Conv_707) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_709, %onnx::Conv_710) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.12/nl/Relu_output_0, %onnx::Conv_712, %onnx::Conv_713) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_715, %onnx::Conv_716) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_718, %onnx::Conv_719) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.13/shuffle/Reshape_output_0 = Reshape(%/cells.13/nl/Relu_output_0, %/cells.13/shuffle/Constant_output_0) %/cells.13/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.13/shuffle/Reshape_output_0) %/cells.13/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.13/shuffle/Reshape_1_output_0 = Reshape(%/cells.13/shuffle/Transpose_output_0, %/cells.13/shuffle/Constant_1_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.13/shuffle/Reshape_1_output_0, %onnx::Conv_721, %onnx::Conv_722) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_724, %onnx::Conv_725) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_727, %onnx::Conv_728) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.14/nl/Relu_output_0, %onnx::Conv_730, %onnx::Conv_731) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_733, %onnx::Conv_734) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_736, %onnx::Conv_737) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.15/nl/Relu_output_0, %onnx::Conv_739, %onnx::Conv_740) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_742, %onnx::Conv_743) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_745, %onnx::Conv_746) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.16/shuffle/Reshape_output_0 = Reshape(%/cells.16/nl/Relu_output_0, %/cells.16/shuffle/Constant_output_0) %/cells.16/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.16/shuffle/Reshape_output_0) %/cells.16/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.16/shuffle/Reshape_1_output_0 = Reshape(%/cells.16/shuffle/Transpose_output_0, %/cells.16/shuffle/Constant_1_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.16/shuffle/Reshape_1_output_0, %onnx::Conv_748, %onnx::Conv_749) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_751, %onnx::Conv_752) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.15/Add_output_0) %/cells.17/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [2, 2]](%/cells.16/Add_output_0, %onnx::Conv_754, %onnx::Conv_755) %/cells.17/relu/Relu_output_0 = Relu(%/cells.17/conv/Conv_output_0) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/relu/Relu_output_0, %onnx::Conv_757, %onnx::Conv_758) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.18/shuffle/Reshape_output_0 = Reshape(%/cells.18/nl/Relu_output_0, %/cells.18/shuffle/Constant_output_0) %/cells.18/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.18/shuffle/Reshape_output_0) %/cells.18/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.18/shuffle/Reshape_1_output_0 = Reshape(%/cells.18/shuffle/Transpose_output_0, %/cells.18/shuffle/Constant_1_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.18/shuffle/Reshape_1_output_0, %onnx::Conv_760, %onnx::Conv_761) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_763, %onnx::Conv_764) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/relu/Relu_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_766, %onnx::Conv_767) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 552, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.19/nl/Relu_output_0, %onnx::Conv_769, %onnx::Conv_770) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_772, %onnx::Conv_773) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_775, %onnx::Conv_776) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.21/nl/Relu_output_0, %onnx::Conv_778, %onnx::Conv_779) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_781, %onnx::Conv_782) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_784, %onnx::Conv_785) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %614 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %614 }	val_accuracy	68,924,928	1,905,012	{'zcp_synflow': 70.7334460635232, 'zcp_zen': 62.31986999511719, 'zcp_epe_nas': 15.066489614883968, 'zcp_fisher': 0.07275404036045074, 'zcp_flops': 68924928.0, 'zcp_grad_norm': 21.549598693847656, 'zcp_grasp': -0.02418994903564453, 'zcp_jacov': -16.055625680822715, 'zcp_l2_norm': 571.6337890625, 'zcp_nwot': 213.05902244708795, 'zcp_params': 1905012.0, 'zcp_plain': -0.00044885941315442324, 'zcp_snip': 38.17238235473633, 'lat_1080ti_1': 0.44851821897951016, 'lat_1080ti_32': 0.45606644698237736, 'lat_1080ti_64': 0.4476714088651095, 'lat_2080ti_1': 0.44420093841553904, 'lat_2080ti_32': 0.4541623359500494, 'lat_2080ti_64': 0.4371966667078141, 'lat_essential_ph_1': 0.3584905660377358, 'lat_eyeriss': 0.4722094878330552, 'lat_fpga': 0.46219535123046923, 'lat_gold_6226': 0.34146959402788163, 'lat_gold_6240': 0.40250669412816936, 'lat_pixel2': 0.2826086956521739, 'lat_pixel3': 0.49254342083353925, 'lat_raspi4': 0.524265677712079, 'lat_samsung_a50': 0.18947368421052632, 'lat_samsung_s7': 0.14960629921259844, 'lat_silver_4114': 0.3931360981591943, 'lat_silver_4210r': 0.37757069043940444, 'lat_titan_rtx_1': 0.4249467607635073, 'lat_titan_rtx_32': 0.44258028418884837, 'lat_titan_rtx_64': 0.4467276865011536, 'lat_titanx_1': 0.2426884267874921, 'lat_titanx_32': 0.43849255668395853, 'lat_titanx_64': 0.45527099431235685, 'lat_titanxp_1': 0.3969375754433557, 'lat_titanxp_32': 0.46613411813345834, 'lat_titanxp_64': 0.4400576218585115}
FBNet_867	FBNet	867	867	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_650[FLOAT, 16x3x3x3] %onnx::Conv_651[FLOAT, 16] %onnx::Conv_653[FLOAT, 48x16x1x1] %onnx::Conv_654[FLOAT, 48] %onnx::Conv_656[FLOAT, 48x1x5x5] %onnx::Conv_659[FLOAT, 16x48x1x1] %onnx::Conv_662[FLOAT, 96x16x1x1] %onnx::Conv_663[FLOAT, 96] %onnx::Conv_665[FLOAT, 96x1x5x5] %onnx::Conv_668[FLOAT, 24x96x1x1] %onnx::Conv_669[FLOAT, 24] %onnx::Conv_671[FLOAT, 24x24x1x1] %onnx::Conv_674[FLOAT, 24x1x5x5] %onnx::Conv_677[FLOAT, 24x24x1x1] %onnx::Conv_680[FLOAT, 144x24x1x1] %onnx::Conv_681[FLOAT, 144] %onnx::Conv_683[FLOAT, 144x1x5x5] %onnx::Conv_686[FLOAT, 24x144x1x1] %onnx::Conv_689[FLOAT, 144x24x1x1] %onnx::Conv_692[FLOAT, 144x1x3x3] %onnx::Conv_695[FLOAT, 24x144x1x1] %onnx::Conv_698[FLOAT, 24x24x1x1] %onnx::Conv_701[FLOAT, 24x1x5x5] %onnx::Conv_704[FLOAT, 32x24x1x1] %onnx::Conv_705[FLOAT, 32] %onnx::Conv_707[FLOAT, 96x32x1x1] %onnx::Conv_710[FLOAT, 96x1x3x3] %onnx::Conv_713[FLOAT, 32x96x1x1] %onnx::Conv_716[FLOAT, 32x16x1x1] %onnx::Conv_719[FLOAT, 32x1x3x3] %onnx::Conv_722[FLOAT, 32x16x1x1] %onnx::Conv_725[FLOAT, 32x32x1x1] %onnx::Conv_728[FLOAT, 32x1x3x3] %onnx::Conv_731[FLOAT, 32x32x1x1] %onnx::Conv_734[FLOAT, 32x32x1x1] %onnx::Conv_737[FLOAT, 32x1x5x5] %onnx::Conv_740[FLOAT, 64x32x1x1] %onnx::Conv_741[FLOAT, 64] %onnx::Conv_743[FLOAT, 192x64x1x1] %onnx::Conv_744[FLOAT, 192] %onnx::Conv_746[FLOAT, 192x1x5x5] %onnx::Conv_749[FLOAT, 64x192x1x1] %onnx::Conv_752[FLOAT, 64x32x1x1] %onnx::Conv_755[FLOAT, 64x1x3x3] %onnx::Conv_758[FLOAT, 64x32x1x1] %onnx::Conv_761[FLOAT, 64x32x1x1] %onnx::Conv_764[FLOAT, 64x1x3x3] %onnx::Conv_767[FLOAT, 64x32x1x1] %onnx::Conv_770[FLOAT, 112x64x1x1] %onnx::Conv_771[FLOAT, 112] %onnx::Conv_773[FLOAT, 336x112x1x1] %onnx::Conv_774[FLOAT, 336] %onnx::Conv_776[FLOAT, 336x1x3x3] %onnx::Conv_779[FLOAT, 112x336x1x1] %onnx::Conv_782[FLOAT, 112x56x1x1] %onnx::Conv_785[FLOAT, 112x1x5x5] %onnx::Conv_788[FLOAT, 112x56x1x1] %onnx::Conv_791[FLOAT, 336x112x1x1] %onnx::Conv_794[FLOAT, 336x1x5x5] %onnx::Conv_797[FLOAT, 112x336x1x1] %onnx::Conv_800[FLOAT, 672x112x1x1] %onnx::Conv_801[FLOAT, 672] %onnx::Conv_803[FLOAT, 672x1x5x5] %onnx::Conv_806[FLOAT, 184x672x1x1] %onnx::Conv_807[FLOAT, 184] %onnx::Conv_809[FLOAT, 1104x184x1x1] %onnx::Conv_810[FLOAT, 1104] %onnx::Conv_812[FLOAT, 1104x1x5x5] %onnx::Conv_815[FLOAT, 184x1104x1x1] %onnx::Conv_818[FLOAT, 1104x184x1x1] %onnx::Conv_821[FLOAT, 1104x1x5x5] %onnx::Conv_824[FLOAT, 184x1104x1x1] %onnx::Conv_827[FLOAT, 184x184x1x1] %onnx::Conv_830[FLOAT, 184x1x3x3] %onnx::Conv_833[FLOAT, 352x184x1x1] %onnx::Conv_834[FLOAT, 352] %onnx::Conv_836[FLOAT, 1504x352x1x1] %onnx::Conv_837[FLOAT, 1504] ) { %onnx::Conv_831 = Identity(%onnx::Conv_807) %onnx::Conv_828 = Identity(%onnx::Conv_807) %onnx::Conv_825 = Identity(%onnx::Conv_807) %onnx::Conv_822 = Identity(%onnx::Conv_810) %onnx::Conv_819 = Identity(%onnx::Conv_810) %onnx::Conv_816 = Identity(%onnx::Conv_807) %onnx::Conv_813 = Identity(%onnx::Conv_810) %onnx::Conv_804 = Identity(%onnx::Conv_801) %onnx::Conv_798 = Identity(%onnx::Conv_771) %onnx::Conv_795 = Identity(%onnx::Conv_774) %onnx::Conv_792 = Identity(%onnx::Conv_774) %onnx::Conv_789 = Identity(%onnx::Conv_771) %onnx::Conv_786 = Identity(%onnx::Conv_771) %onnx::Conv_783 = Identity(%onnx::Conv_771) %onnx::Conv_780 = Identity(%onnx::Conv_771) %onnx::Conv_777 = Identity(%onnx::Conv_774) %onnx::Conv_768 = Identity(%onnx::Conv_741) %onnx::Conv_765 = Identity(%onnx::Conv_741) %onnx::Conv_762 = Identity(%onnx::Conv_741) %onnx::Conv_759 = Identity(%onnx::Conv_741) %onnx::Conv_756 = Identity(%onnx::Conv_741) %onnx::Conv_753 = Identity(%onnx::Conv_741) %onnx::Conv_750 = Identity(%onnx::Conv_741) %onnx::Conv_747 = Identity(%onnx::Conv_744) %onnx::Conv_738 = Identity(%onnx::Conv_705) %onnx::Conv_735 = Identity(%onnx::Conv_705) %onnx::Conv_732 = Identity(%onnx::Conv_705) %onnx::Conv_729 = Identity(%onnx::Conv_705) %onnx::Conv_726 = Identity(%onnx::Conv_705) %onnx::Conv_723 = Identity(%onnx::Conv_705) %onnx::Conv_720 = Identity(%onnx::Conv_705) %onnx::Conv_717 = Identity(%onnx::Conv_705) %onnx::Conv_714 = Identity(%onnx::Conv_705) %onnx::Conv_711 = Identity(%onnx::Conv_663) %onnx::Conv_708 = Identity(%onnx::Conv_663) %onnx::Conv_702 = Identity(%onnx::Conv_669) %onnx::Conv_699 = Identity(%onnx::Conv_669) %onnx::Conv_696 = Identity(%onnx::Conv_669) %onnx::Conv_693 = Identity(%onnx::Conv_681) %onnx::Conv_690 = Identity(%onnx::Conv_681) %onnx::Conv_687 = Identity(%onnx::Conv_669) %onnx::Conv_684 = Identity(%onnx::Conv_681) %onnx::Conv_678 = Identity(%onnx::Conv_669) %onnx::Conv_675 = Identity(%onnx::Conv_669) %onnx::Conv_672 = Identity(%onnx::Conv_669) %onnx::Conv_666 = Identity(%onnx::Conv_663) %onnx::Conv_660 = Identity(%onnx::Conv_651) %onnx::Conv_657 = Identity(%onnx::Conv_654) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_650, %onnx::Conv_651) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_653, %onnx::Conv_654) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 48, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.0/nl/Relu_output_0, %onnx::Conv_656, %onnx::Conv_657) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_659, %onnx::Conv_660) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_662, %onnx::Conv_663) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.1/nl/Relu_output_0, %onnx::Conv_665, %onnx::Conv_666) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_668, %onnx::Conv_669) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_671, %onnx::Conv_672) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.2/nl/Relu_output_0, %onnx::Conv_674, %onnx::Conv_675) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_677, %onnx::Conv_678) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_680, %onnx::Conv_681) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.3/nl/Relu_output_0, %onnx::Conv_683, %onnx::Conv_684) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_686, %onnx::Conv_687) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_689, %onnx::Conv_690) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.4/nl/Relu_output_0, %onnx::Conv_692, %onnx::Conv_693) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_695, %onnx::Conv_696) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/Add_output_0, %onnx::Conv_698, %onnx::Conv_699) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.5/nl/Relu_output_0, %onnx::Conv_701, %onnx::Conv_702) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_704, %onnx::Conv_705) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_707, %onnx::Conv_708) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.6/nl/Relu_output_0, %onnx::Conv_710, %onnx::Conv_711) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_713, %onnx::Conv_714) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_716, %onnx::Conv_717) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.7/shuffle/Reshape_output_0 = Reshape(%/cells.7/nl/Relu_output_0, %/cells.7/shuffle/Constant_output_0) %/cells.7/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.7/shuffle/Reshape_output_0) %/cells.7/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.7/shuffle/Reshape_1_output_0 = Reshape(%/cells.7/shuffle/Transpose_output_0, %/cells.7/shuffle/Constant_1_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.7/shuffle/Reshape_1_output_0, %onnx::Conv_719, %onnx::Conv_720) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_722, %onnx::Conv_723) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_725, %onnx::Conv_726) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.8/nl/Relu_output_0, %onnx::Conv_728, %onnx::Conv_729) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_731, %onnx::Conv_732) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_734, %onnx::Conv_735) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.9/nl/Relu_output_0, %onnx::Conv_737, %onnx::Conv_738) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_740, %onnx::Conv_741) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_743, %onnx::Conv_744) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.10/nl/Relu_output_0, %onnx::Conv_746, %onnx::Conv_747) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_749, %onnx::Conv_750) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_752, %onnx::Conv_753) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.11/shuffle/Reshape_output_0 = Reshape(%/cells.11/nl/Relu_output_0, %/cells.11/shuffle/Constant_output_0) %/cells.11/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.11/shuffle/Reshape_output_0) %/cells.11/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.11/shuffle/Reshape_1_output_0 = Reshape(%/cells.11/shuffle/Transpose_output_0, %/cells.11/shuffle/Constant_1_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.11/shuffle/Reshape_1_output_0, %onnx::Conv_755, %onnx::Conv_756) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_758, %onnx::Conv_759) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_761, %onnx::Conv_762) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.12/shuffle/Reshape_output_0 = Reshape(%/cells.12/nl/Relu_output_0, %/cells.12/shuffle/Constant_output_0) %/cells.12/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.12/shuffle/Reshape_output_0) %/cells.12/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.12/shuffle/Reshape_1_output_0 = Reshape(%/cells.12/shuffle/Transpose_output_0, %/cells.12/shuffle/Constant_1_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.12/shuffle/Reshape_1_output_0, %onnx::Conv_764, %onnx::Conv_765) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_767, %onnx::Conv_768) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_770, %onnx::Conv_771) %/cells.13/relu/Relu_output_0 = Relu(%/cells.13/conv/Conv_output_0) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/relu/Relu_output_0, %onnx::Conv_773, %onnx::Conv_774) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.14/nl/Relu_output_0, %onnx::Conv_776, %onnx::Conv_777) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_779, %onnx::Conv_780) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/relu/Relu_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_782, %onnx::Conv_783) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.15/shuffle/Reshape_output_0 = Reshape(%/cells.15/nl/Relu_output_0, %/cells.15/shuffle/Constant_output_0) %/cells.15/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.15/shuffle/Reshape_output_0) %/cells.15/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.15/shuffle/Reshape_1_output_0 = Reshape(%/cells.15/shuffle/Transpose_output_0, %/cells.15/shuffle/Constant_1_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.15/shuffle/Reshape_1_output_0, %onnx::Conv_785, %onnx::Conv_786) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_788, %onnx::Conv_789) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_791, %onnx::Conv_792) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.16/nl/Relu_output_0, %onnx::Conv_794, %onnx::Conv_795) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_797, %onnx::Conv_798) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.15/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_800, %onnx::Conv_801) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.17/nl/Relu_output_0, %onnx::Conv_803, %onnx::Conv_804) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_806, %onnx::Conv_807) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_809, %onnx::Conv_810) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.18/nl/Relu_output_0, %onnx::Conv_812, %onnx::Conv_813) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_815, %onnx::Conv_816) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_818, %onnx::Conv_819) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.20/nl/Relu_output_0, %onnx::Conv_821, %onnx::Conv_822) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_824, %onnx::Conv_825) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_827, %onnx::Conv_828) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.21/nl/Relu_output_0, %onnx::Conv_830, %onnx::Conv_831) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_833, %onnx::Conv_834) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_836, %onnx::Conv_837) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %648 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %648 }	val_accuracy	84,025,728	2,159,276	{'zcp_synflow': 79.9136404169015, 'zcp_zen': 68.33043670654297, 'zcp_epe_nas': 7.420656371263341, 'zcp_fisher': 0.14776551723480225, 'zcp_flops': 84025728.0, 'zcp_grad_norm': 25.375350952148438, 'zcp_grasp': -0.041660308837890625, 'zcp_jacov': -16.059133444666955, 'zcp_l2_norm': 632.8645629882812, 'zcp_nwot': 216.86899266667615, 'zcp_params': 2159276.0, 'zcp_plain': 0.004822744056582451, 'zcp_snip': 49.007869720458984, 'lat_1080ti_1': 0.5969398248066976, 'lat_1080ti_32': 0.6859913613785864, 'lat_1080ti_64': 0.7326161262529913, 'lat_2080ti_1': 0.6214955993856983, 'lat_2080ti_32': 0.7149740089231446, 'lat_2080ti_64': 0.6992445844322749, 'lat_essential_ph_1': 0.4339622641509434, 'lat_eyeriss': 0.6592888709588687, 'lat_fpga': 0.6145617906371523, 'lat_gold_6226': 0.44215434075420473, 'lat_gold_6240': 0.6306558280207539, 'lat_pixel2': 0.43478260869565216, 'lat_pixel3': 0.6755426104891864, 'lat_raspi4': 0.7027186047789863, 'lat_samsung_a50': 0.2631578947368421, 'lat_samsung_s7': 0.2204724409448819, 'lat_silver_4114': 0.6883681389716697, 'lat_silver_4210r': 0.661588313582039, 'lat_titan_rtx_1': 0.6074514761395485, 'lat_titan_rtx_32': 0.671713928170547, 'lat_titan_rtx_64': 0.7244112010791929, 'lat_titanx_1': 0.3332221823012326, 'lat_titanx_32': 0.744517322885735, 'lat_titanx_64': 0.6891871138697985, 'lat_titanxp_1': 0.5683939373542527, 'lat_titanxp_32': 0.7327500227037979, 'lat_titanxp_64': 0.7262642774253648}
FBNet_2483	FBNet	2483	2483	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_713[FLOAT, 16x3x3x3] %onnx::Conv_714[FLOAT, 16] %onnx::Conv_716[FLOAT, 16x16x1x1] %onnx::Conv_719[FLOAT, 16x1x5x5] %onnx::Conv_722[FLOAT, 16x16x1x1] %onnx::Conv_725[FLOAT, 16x8x1x1] %onnx::Conv_728[FLOAT, 16x1x3x3] %onnx::Conv_731[FLOAT, 24x8x1x1] %onnx::Conv_732[FLOAT, 24] %onnx::Conv_734[FLOAT, 144x24x1x1] %onnx::Conv_735[FLOAT, 144] %onnx::Conv_737[FLOAT, 144x1x5x5] %onnx::Conv_740[FLOAT, 24x144x1x1] %onnx::Conv_743[FLOAT, 24x24x1x1] %onnx::Conv_746[FLOAT, 24x1x5x5] %onnx::Conv_749[FLOAT, 24x24x1x1] %onnx::Conv_752[FLOAT, 72x24x1x1] %onnx::Conv_753[FLOAT, 72] %onnx::Conv_755[FLOAT, 72x1x5x5] %onnx::Conv_758[FLOAT, 24x72x1x1] %onnx::Conv_761[FLOAT, 24x24x1x1] %onnx::Conv_764[FLOAT, 24x1x3x3] %onnx::Conv_767[FLOAT, 32x24x1x1] %onnx::Conv_768[FLOAT, 32] %onnx::Conv_770[FLOAT, 32x32x1x1] %onnx::Conv_773[FLOAT, 32x1x5x5] %onnx::Conv_776[FLOAT, 32x32x1x1] %onnx::Conv_779[FLOAT, 96x32x1x1] %onnx::Conv_780[FLOAT, 96] %onnx::Conv_782[FLOAT, 96x1x3x3] %onnx::Conv_785[FLOAT, 32x96x1x1] %onnx::Conv_788[FLOAT, 192x32x1x1] %onnx::Conv_789[FLOAT, 192] %onnx::Conv_791[FLOAT, 192x1x3x3] %onnx::Conv_794[FLOAT, 32x192x1x1] %onnx::Conv_797[FLOAT, 32x16x1x1] %onnx::Conv_800[FLOAT, 32x1x5x5] %onnx::Conv_803[FLOAT, 64x16x1x1] %onnx::Conv_804[FLOAT, 64] %onnx::Conv_806[FLOAT, 384x64x1x1] %onnx::Conv_807[FLOAT, 384] %onnx::Conv_809[FLOAT, 384x1x5x5] %onnx::Conv_812[FLOAT, 64x384x1x1] %onnx::Conv_815[FLOAT, 192x64x1x1] %onnx::Conv_818[FLOAT, 192x1x3x3] %onnx::Conv_821[FLOAT, 64x192x1x1] %onnx::Conv_824[FLOAT, 192x64x1x1] %onnx::Conv_827[FLOAT, 192x1x3x3] %onnx::Conv_830[FLOAT, 64x192x1x1] %onnx::Conv_833[FLOAT, 192x64x1x1] %onnx::Conv_836[FLOAT, 192x1x5x5] %onnx::Conv_839[FLOAT, 112x192x1x1] %onnx::Conv_840[FLOAT, 112] %onnx::Conv_842[FLOAT, 112x112x1x1] %onnx::Conv_845[FLOAT, 112x1x5x5] %onnx::Conv_848[FLOAT, 112x112x1x1] %onnx::Conv_851[FLOAT, 336x112x1x1] %onnx::Conv_852[FLOAT, 336] %onnx::Conv_854[FLOAT, 336x1x3x3] %onnx::Conv_857[FLOAT, 112x336x1x1] %onnx::Conv_860[FLOAT, 112x56x1x1] %onnx::Conv_863[FLOAT, 112x1x5x5] %onnx::Conv_866[FLOAT, 112x56x1x1] %onnx::Conv_869[FLOAT, 672x112x1x1] %onnx::Conv_870[FLOAT, 672] %onnx::Conv_872[FLOAT, 672x1x5x5] %onnx::Conv_875[FLOAT, 184x672x1x1] %onnx::Conv_876[FLOAT, 184] %onnx::Conv_878[FLOAT, 184x92x1x1] %onnx::Conv_881[FLOAT, 184x1x3x3] %onnx::Conv_884[FLOAT, 184x92x1x1] %onnx::Conv_887[FLOAT, 184x92x1x1] %onnx::Conv_890[FLOAT, 184x1x3x3] %onnx::Conv_893[FLOAT, 184x92x1x1] %onnx::Conv_896[FLOAT, 1104x184x1x1] %onnx::Conv_897[FLOAT, 1104] %onnx::Conv_899[FLOAT, 1104x1x5x5] %onnx::Conv_902[FLOAT, 184x1104x1x1] %onnx::Conv_905[FLOAT, 552x184x1x1] %onnx::Conv_906[FLOAT, 552] %onnx::Conv_908[FLOAT, 552x1x5x5] %onnx::Conv_911[FLOAT, 352x552x1x1] %onnx::Conv_912[FLOAT, 352] %onnx::Conv_914[FLOAT, 1504x352x1x1] %onnx::Conv_915[FLOAT, 1504] ) { %onnx::Conv_909 = Identity(%onnx::Conv_906) %onnx::Conv_903 = Identity(%onnx::Conv_876) %onnx::Conv_900 = Identity(%onnx::Conv_897) %onnx::Conv_894 = Identity(%onnx::Conv_876) %onnx::Conv_891 = Identity(%onnx::Conv_876) %onnx::Conv_888 = Identity(%onnx::Conv_876) %onnx::Conv_885 = Identity(%onnx::Conv_876) %onnx::Conv_882 = Identity(%onnx::Conv_876) %onnx::Conv_879 = Identity(%onnx::Conv_876) %onnx::Conv_873 = Identity(%onnx::Conv_870) %onnx::Conv_867 = Identity(%onnx::Conv_840) %onnx::Conv_864 = Identity(%onnx::Conv_840) %onnx::Conv_861 = Identity(%onnx::Conv_840) %onnx::Conv_858 = Identity(%onnx::Conv_840) %onnx::Conv_855 = Identity(%onnx::Conv_852) %onnx::Conv_849 = Identity(%onnx::Conv_840) %onnx::Conv_846 = Identity(%onnx::Conv_840) %onnx::Conv_843 = Identity(%onnx::Conv_840) %onnx::Conv_837 = Identity(%onnx::Conv_789) %onnx::Conv_834 = Identity(%onnx::Conv_789) %onnx::Conv_831 = Identity(%onnx::Conv_804) %onnx::Conv_828 = Identity(%onnx::Conv_789) %onnx::Conv_825 = Identity(%onnx::Conv_789) %onnx::Conv_822 = Identity(%onnx::Conv_804) %onnx::Conv_819 = Identity(%onnx::Conv_789) %onnx::Conv_816 = Identity(%onnx::Conv_789) %onnx::Conv_813 = Identity(%onnx::Conv_804) %onnx::Conv_810 = Identity(%onnx::Conv_807) %onnx::Conv_801 = Identity(%onnx::Conv_768) %onnx::Conv_798 = Identity(%onnx::Conv_768) %onnx::Conv_795 = Identity(%onnx::Conv_768) %onnx::Conv_792 = Identity(%onnx::Conv_789) %onnx::Conv_786 = Identity(%onnx::Conv_768) %onnx::Conv_783 = Identity(%onnx::Conv_780) %onnx::Conv_777 = Identity(%onnx::Conv_768) %onnx::Conv_774 = Identity(%onnx::Conv_768) %onnx::Conv_771 = Identity(%onnx::Conv_768) %onnx::Conv_765 = Identity(%onnx::Conv_732) %onnx::Conv_762 = Identity(%onnx::Conv_732) %onnx::Conv_759 = Identity(%onnx::Conv_732) %onnx::Conv_756 = Identity(%onnx::Conv_753) %onnx::Conv_750 = Identity(%onnx::Conv_732) %onnx::Conv_747 = Identity(%onnx::Conv_732) %onnx::Conv_744 = Identity(%onnx::Conv_732) %onnx::Conv_741 = Identity(%onnx::Conv_732) %onnx::Conv_738 = Identity(%onnx::Conv_735) %onnx::Conv_729 = Identity(%onnx::Conv_714) %onnx::Conv_726 = Identity(%onnx::Conv_714) %onnx::Conv_723 = Identity(%onnx::Conv_714) %onnx::Conv_720 = Identity(%onnx::Conv_714) %onnx::Conv_717 = Identity(%onnx::Conv_714) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_713, %onnx::Conv_714) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_716, %onnx::Conv_717) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.0/nl/Relu_output_0, %onnx::Conv_719, %onnx::Conv_720) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_722, %onnx::Conv_723) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_725, %onnx::Conv_726) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.1/shuffle/Reshape_output_0 = Reshape(%/cells.1/nl/Relu_output_0, %/cells.1/shuffle/Constant_output_0) %/cells.1/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.1/shuffle/Reshape_output_0) %/cells.1/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.1/shuffle/Reshape_1_output_0 = Reshape(%/cells.1/shuffle/Transpose_output_0, %/cells.1/shuffle/Constant_1_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.1/shuffle/Reshape_1_output_0, %onnx::Conv_728, %onnx::Conv_729) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_731, %onnx::Conv_732) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_734, %onnx::Conv_735) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.2/nl/Relu_output_0, %onnx::Conv_737, %onnx::Conv_738) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_740, %onnx::Conv_741) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_743, %onnx::Conv_744) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.3/nl/Relu_output_0, %onnx::Conv_746, %onnx::Conv_747) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_749, %onnx::Conv_750) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_752, %onnx::Conv_753) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.4/nl/Relu_output_0, %onnx::Conv_755, %onnx::Conv_756) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_758, %onnx::Conv_759) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/Add_output_0, %onnx::Conv_761, %onnx::Conv_762) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.5/nl/Relu_output_0, %onnx::Conv_764, %onnx::Conv_765) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_767, %onnx::Conv_768) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_770, %onnx::Conv_771) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.6/nl/Relu_output_0, %onnx::Conv_773, %onnx::Conv_774) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_776, %onnx::Conv_777) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_779, %onnx::Conv_780) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.7/nl/Relu_output_0, %onnx::Conv_782, %onnx::Conv_783) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_785, %onnx::Conv_786) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_788, %onnx::Conv_789) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.8/nl/Relu_output_0, %onnx::Conv_791, %onnx::Conv_792) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_794, %onnx::Conv_795) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_797, %onnx::Conv_798) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.9/shuffle/Reshape_output_0 = Reshape(%/cells.9/nl/Relu_output_0, %/cells.9/shuffle/Constant_output_0) %/cells.9/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.9/shuffle/Reshape_output_0) %/cells.9/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.9/shuffle/Reshape_1_output_0 = Reshape(%/cells.9/shuffle/Transpose_output_0, %/cells.9/shuffle/Constant_1_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.9/shuffle/Reshape_1_output_0, %onnx::Conv_800, %onnx::Conv_801) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_803, %onnx::Conv_804) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_806, %onnx::Conv_807) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.10/nl/Relu_output_0, %onnx::Conv_809, %onnx::Conv_810) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_812, %onnx::Conv_813) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_815, %onnx::Conv_816) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.11/nl/Relu_output_0, %onnx::Conv_818, %onnx::Conv_819) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_821, %onnx::Conv_822) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_824, %onnx::Conv_825) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.12/nl/Relu_output_0, %onnx::Conv_827, %onnx::Conv_828) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_830, %onnx::Conv_831) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_833, %onnx::Conv_834) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.13/nl/Relu_output_0, %onnx::Conv_836, %onnx::Conv_837) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_839, %onnx::Conv_840) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_842, %onnx::Conv_843) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.14/nl/Relu_output_0, %onnx::Conv_845, %onnx::Conv_846) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_848, %onnx::Conv_849) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_851, %onnx::Conv_852) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.15/nl/Relu_output_0, %onnx::Conv_854, %onnx::Conv_855) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_857, %onnx::Conv_858) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_860, %onnx::Conv_861) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.16/shuffle/Reshape_output_0 = Reshape(%/cells.16/nl/Relu_output_0, %/cells.16/shuffle/Constant_output_0) %/cells.16/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.16/shuffle/Reshape_output_0) %/cells.16/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.16/shuffle/Reshape_1_output_0 = Reshape(%/cells.16/shuffle/Transpose_output_0, %/cells.16/shuffle/Constant_1_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.16/shuffle/Reshape_1_output_0, %onnx::Conv_863, %onnx::Conv_864) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_866, %onnx::Conv_867) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.15/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_869, %onnx::Conv_870) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.17/nl/Relu_output_0, %onnx::Conv_872, %onnx::Conv_873) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_875, %onnx::Conv_876) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_878, %onnx::Conv_879) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.18/shuffle/Reshape_output_0 = Reshape(%/cells.18/nl/Relu_output_0, %/cells.18/shuffle/Constant_output_0) %/cells.18/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.18/shuffle/Reshape_output_0) %/cells.18/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.18/shuffle/Reshape_1_output_0 = Reshape(%/cells.18/shuffle/Transpose_output_0, %/cells.18/shuffle/Constant_1_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.18/shuffle/Reshape_1_output_0, %onnx::Conv_881, %onnx::Conv_882) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_884, %onnx::Conv_885) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_887, %onnx::Conv_888) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.19/shuffle/Reshape_output_0 = Reshape(%/cells.19/nl/Relu_output_0, %/cells.19/shuffle/Constant_output_0) %/cells.19/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.19/shuffle/Reshape_output_0) %/cells.19/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.19/shuffle/Reshape_1_output_0 = Reshape(%/cells.19/shuffle/Transpose_output_0, %/cells.19/shuffle/Constant_1_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.19/shuffle/Reshape_1_output_0, %onnx::Conv_890, %onnx::Conv_891) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_893, %onnx::Conv_894) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_896, %onnx::Conv_897) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.20/nl/Relu_output_0, %onnx::Conv_899, %onnx::Conv_900) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_902, %onnx::Conv_903) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_905, %onnx::Conv_906) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 552, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.21/nl/Relu_output_0, %onnx::Conv_908, %onnx::Conv_909) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_911, %onnx::Conv_912) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_914, %onnx::Conv_915) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %711 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %711 }	val_accuracy	76,309,120	2,055,316	{'zcp_synflow': 83.56861032675428, 'zcp_zen': 74.57333374023438, 'zcp_epe_nas': 17.877022372353984, 'zcp_fisher': 0.21897007524967194, 'zcp_flops': 76309120.0, 'zcp_grad_norm': 28.238840103149414, 'zcp_grasp': -0.179229736328125, 'zcp_jacov': -16.06523806403315, 'zcp_l2_norm': 674.9060668945312, 'zcp_nwot': 213.32531880158862, 'zcp_params': 2055316.0, 'zcp_plain': -0.005173602607101202, 'zcp_snip': 52.92670440673828, 'lat_1080ti_1': 0.7896031511900831, 'lat_1080ti_32': 0.784547037804591, 'lat_1080ti_64': 0.5872739264034975, 'lat_2080ti_1': 0.822737897555638, 'lat_2080ti_32': 0.7700670135486624, 'lat_2080ti_64': 0.6016238269812483, 'lat_essential_ph_1': 0.2830188679245283, 'lat_eyeriss': 0.556085306144903, 'lat_fpga': 0.5266131665660653, 'lat_gold_6226': 0.4355937359593458, 'lat_gold_6240': 0.6638070690247628, 'lat_pixel2': 0.6086956521739131, 'lat_pixel3': 0.5675214990022578, 'lat_raspi4': 0.6254960022728727, 'lat_samsung_a50': 0.3368421052631579, 'lat_samsung_s7': 0.2283464566929134, 'lat_silver_4114': 0.6639413989991274, 'lat_silver_4210r': 0.720813794029344, 'lat_titan_rtx_1': 0.7818004923879389, 'lat_titan_rtx_32': 0.7594127400773912, 'lat_titan_rtx_64': 0.6538037884314374, 'lat_titanx_1': 0.41165550198747447, 'lat_titanx_32': 0.7048934096548889, 'lat_titanx_64': 0.5593650378163864, 'lat_titanxp_1': 0.7215742023705763, 'lat_titanxp_32': 0.754829432067174, 'lat_titanxp_64': 0.6226593157398124}
FBNet_4615	FBNet	4615	4615	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_713[FLOAT, 16x3x3x3] %onnx::Conv_714[FLOAT, 16] %onnx::Conv_716[FLOAT, 96x16x1x1] %onnx::Conv_717[FLOAT, 96] %onnx::Conv_719[FLOAT, 96x1x3x3] %onnx::Conv_722[FLOAT, 16x96x1x1] %onnx::Conv_725[FLOAT, 96x16x1x1] %onnx::Conv_728[FLOAT, 96x1x5x5] %onnx::Conv_731[FLOAT, 24x96x1x1] %onnx::Conv_732[FLOAT, 24] %onnx::Conv_734[FLOAT, 72x24x1x1] %onnx::Conv_735[FLOAT, 72] %onnx::Conv_737[FLOAT, 72x1x5x5] %onnx::Conv_740[FLOAT, 24x72x1x1] %onnx::Conv_743[FLOAT, 24x12x1x1] %onnx::Conv_746[FLOAT, 24x1x5x5] %onnx::Conv_749[FLOAT, 24x12x1x1] %onnx::Conv_752[FLOAT, 72x24x1x1] %onnx::Conv_755[FLOAT, 72x1x3x3] %onnx::Conv_758[FLOAT, 24x72x1x1] %onnx::Conv_761[FLOAT, 144x24x1x1] %onnx::Conv_762[FLOAT, 144] %onnx::Conv_764[FLOAT, 144x1x3x3] %onnx::Conv_767[FLOAT, 32x144x1x1] %onnx::Conv_768[FLOAT, 32] %onnx::Conv_770[FLOAT, 32x32x1x1] %onnx::Conv_773[FLOAT, 32x1x3x3] %onnx::Conv_776[FLOAT, 32x32x1x1] %onnx::Conv_779[FLOAT, 32x16x1x1] %onnx::Conv_782[FLOAT, 32x1x3x3] %onnx::Conv_785[FLOAT, 32x16x1x1] %onnx::Conv_788[FLOAT, 96x32x1x1] %onnx::Conv_791[FLOAT, 96x1x5x5] %onnx::Conv_794[FLOAT, 32x96x1x1] %onnx::Conv_797[FLOAT, 32x16x1x1] %onnx::Conv_800[FLOAT, 32x1x3x3] %onnx::Conv_803[FLOAT, 64x16x1x1] %onnx::Conv_804[FLOAT, 64] %onnx::Conv_806[FLOAT, 192x64x1x1] %onnx::Conv_807[FLOAT, 192] %onnx::Conv_809[FLOAT, 192x1x5x5] %onnx::Conv_812[FLOAT, 64x192x1x1] %onnx::Conv_815[FLOAT, 384x64x1x1] %onnx::Conv_816[FLOAT, 384] %onnx::Conv_818[FLOAT, 384x1x5x5] %onnx::Conv_821[FLOAT, 64x384x1x1] %onnx::Conv_824[FLOAT, 192x64x1x1] %onnx::Conv_827[FLOAT, 192x1x5x5] %onnx::Conv_830[FLOAT, 64x192x1x1] %onnx::Conv_833[FLOAT, 64x32x1x1] %onnx::Conv_836[FLOAT, 64x1x5x5] %onnx::Conv_839[FLOAT, 112x32x1x1] %onnx::Conv_840[FLOAT, 112] %onnx::Conv_842[FLOAT, 672x112x1x1] %onnx::Conv_843[FLOAT, 672] %onnx::Conv_845[FLOAT, 672x1x5x5] %onnx::Conv_848[FLOAT, 112x672x1x1] %onnx::Conv_851[FLOAT, 336x112x1x1] %onnx::Conv_852[FLOAT, 336] %onnx::Conv_854[FLOAT, 336x1x3x3] %onnx::Conv_857[FLOAT, 112x336x1x1] %onnx::Conv_860[FLOAT, 672x112x1x1] %onnx::Conv_863[FLOAT, 672x1x5x5] %onnx::Conv_866[FLOAT, 112x672x1x1] %onnx::Conv_869[FLOAT, 672x112x1x1] %onnx::Conv_872[FLOAT, 672x1x5x5] %onnx::Conv_875[FLOAT, 184x672x1x1] %onnx::Conv_876[FLOAT, 184] %onnx::Conv_878[FLOAT, 1104x184x1x1] %onnx::Conv_879[FLOAT, 1104] %onnx::Conv_881[FLOAT, 1104x1x5x5] %onnx::Conv_884[FLOAT, 184x1104x1x1] %onnx::Conv_887[FLOAT, 184x92x1x1] %onnx::Conv_890[FLOAT, 184x1x3x3] %onnx::Conv_893[FLOAT, 184x92x1x1] %onnx::Conv_896[FLOAT, 1104x184x1x1] %onnx::Conv_899[FLOAT, 1104x1x5x5] %onnx::Conv_902[FLOAT, 184x1104x1x1] %onnx::Conv_905[FLOAT, 184x184x1x1] %onnx::Conv_908[FLOAT, 184x1x3x3] %onnx::Conv_911[FLOAT, 352x184x1x1] %onnx::Conv_912[FLOAT, 352] %onnx::Conv_914[FLOAT, 1504x352x1x1] %onnx::Conv_915[FLOAT, 1504] ) { %onnx::Conv_909 = Identity(%onnx::Conv_876) %onnx::Conv_906 = Identity(%onnx::Conv_876) %onnx::Conv_903 = Identity(%onnx::Conv_876) %onnx::Conv_900 = Identity(%onnx::Conv_879) %onnx::Conv_897 = Identity(%onnx::Conv_879) %onnx::Conv_894 = Identity(%onnx::Conv_876) %onnx::Conv_891 = Identity(%onnx::Conv_876) %onnx::Conv_888 = Identity(%onnx::Conv_876) %onnx::Conv_885 = Identity(%onnx::Conv_876) %onnx::Conv_882 = Identity(%onnx::Conv_879) %onnx::Conv_873 = Identity(%onnx::Conv_843) %onnx::Conv_870 = Identity(%onnx::Conv_843) %onnx::Conv_867 = Identity(%onnx::Conv_840) %onnx::Conv_864 = Identity(%onnx::Conv_843) %onnx::Conv_861 = Identity(%onnx::Conv_843) %onnx::Conv_858 = Identity(%onnx::Conv_840) %onnx::Conv_855 = Identity(%onnx::Conv_852) %onnx::Conv_849 = Identity(%onnx::Conv_840) %onnx::Conv_846 = Identity(%onnx::Conv_843) %onnx::Conv_837 = Identity(%onnx::Conv_804) %onnx::Conv_834 = Identity(%onnx::Conv_804) %onnx::Conv_831 = Identity(%onnx::Conv_804) %onnx::Conv_828 = Identity(%onnx::Conv_807) %onnx::Conv_825 = Identity(%onnx::Conv_807) %onnx::Conv_822 = Identity(%onnx::Conv_804) %onnx::Conv_819 = Identity(%onnx::Conv_816) %onnx::Conv_813 = Identity(%onnx::Conv_804) %onnx::Conv_810 = Identity(%onnx::Conv_807) %onnx::Conv_801 = Identity(%onnx::Conv_768) %onnx::Conv_798 = Identity(%onnx::Conv_768) %onnx::Conv_795 = Identity(%onnx::Conv_768) %onnx::Conv_792 = Identity(%onnx::Conv_717) %onnx::Conv_789 = Identity(%onnx::Conv_717) %onnx::Conv_786 = Identity(%onnx::Conv_768) %onnx::Conv_783 = Identity(%onnx::Conv_768) %onnx::Conv_780 = Identity(%onnx::Conv_768) %onnx::Conv_777 = Identity(%onnx::Conv_768) %onnx::Conv_774 = Identity(%onnx::Conv_768) %onnx::Conv_771 = Identity(%onnx::Conv_768) %onnx::Conv_765 = Identity(%onnx::Conv_762) %onnx::Conv_759 = Identity(%onnx::Conv_732) %onnx::Conv_756 = Identity(%onnx::Conv_735) %onnx::Conv_753 = Identity(%onnx::Conv_735) %onnx::Conv_750 = Identity(%onnx::Conv_732) %onnx::Conv_747 = Identity(%onnx::Conv_732) %onnx::Conv_744 = Identity(%onnx::Conv_732) %onnx::Conv_741 = Identity(%onnx::Conv_732) %onnx::Conv_738 = Identity(%onnx::Conv_735) %onnx::Conv_729 = Identity(%onnx::Conv_717) %onnx::Conv_726 = Identity(%onnx::Conv_717) %onnx::Conv_723 = Identity(%onnx::Conv_714) %onnx::Conv_720 = Identity(%onnx::Conv_717) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_713, %onnx::Conv_714) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_716, %onnx::Conv_717) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.0/nl/Relu_output_0, %onnx::Conv_719, %onnx::Conv_720) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_722, %onnx::Conv_723) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_725, %onnx::Conv_726) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.1/nl/Relu_output_0, %onnx::Conv_728, %onnx::Conv_729) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_731, %onnx::Conv_732) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_734, %onnx::Conv_735) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.2/nl/Relu_output_0, %onnx::Conv_737, %onnx::Conv_738) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_740, %onnx::Conv_741) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_743, %onnx::Conv_744) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.3/shuffle/Reshape_output_0 = Reshape(%/cells.3/nl/Relu_output_0, %/cells.3/shuffle/Constant_output_0) %/cells.3/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.3/shuffle/Reshape_output_0) %/cells.3/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.3/shuffle/Reshape_1_output_0 = Reshape(%/cells.3/shuffle/Transpose_output_0, %/cells.3/shuffle/Constant_1_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.3/shuffle/Reshape_1_output_0, %onnx::Conv_746, %onnx::Conv_747) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_749, %onnx::Conv_750) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_752, %onnx::Conv_753) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.4/nl/Relu_output_0, %onnx::Conv_755, %onnx::Conv_756) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_758, %onnx::Conv_759) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/Add_output_0, %onnx::Conv_761, %onnx::Conv_762) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.5/nl/Relu_output_0, %onnx::Conv_764, %onnx::Conv_765) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_767, %onnx::Conv_768) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_770, %onnx::Conv_771) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.6/nl/Relu_output_0, %onnx::Conv_773, %onnx::Conv_774) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_776, %onnx::Conv_777) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_779, %onnx::Conv_780) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.7/shuffle/Reshape_output_0 = Reshape(%/cells.7/nl/Relu_output_0, %/cells.7/shuffle/Constant_output_0) %/cells.7/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.7/shuffle/Reshape_output_0) %/cells.7/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.7/shuffle/Reshape_1_output_0 = Reshape(%/cells.7/shuffle/Transpose_output_0, %/cells.7/shuffle/Constant_1_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.7/shuffle/Reshape_1_output_0, %onnx::Conv_782, %onnx::Conv_783) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_785, %onnx::Conv_786) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_788, %onnx::Conv_789) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.8/nl/Relu_output_0, %onnx::Conv_791, %onnx::Conv_792) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_794, %onnx::Conv_795) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_797, %onnx::Conv_798) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.9/shuffle/Reshape_output_0 = Reshape(%/cells.9/nl/Relu_output_0, %/cells.9/shuffle/Constant_output_0) %/cells.9/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.9/shuffle/Reshape_output_0) %/cells.9/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.9/shuffle/Reshape_1_output_0 = Reshape(%/cells.9/shuffle/Transpose_output_0, %/cells.9/shuffle/Constant_1_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.9/shuffle/Reshape_1_output_0, %onnx::Conv_800, %onnx::Conv_801) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_803, %onnx::Conv_804) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_806, %onnx::Conv_807) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.10/nl/Relu_output_0, %onnx::Conv_809, %onnx::Conv_810) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_812, %onnx::Conv_813) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_815, %onnx::Conv_816) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.11/nl/Relu_output_0, %onnx::Conv_818, %onnx::Conv_819) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_821, %onnx::Conv_822) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_824, %onnx::Conv_825) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.12/nl/Relu_output_0, %onnx::Conv_827, %onnx::Conv_828) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_830, %onnx::Conv_831) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_833, %onnx::Conv_834) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.13/shuffle/Reshape_output_0 = Reshape(%/cells.13/nl/Relu_output_0, %/cells.13/shuffle/Constant_output_0) %/cells.13/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.13/shuffle/Reshape_output_0) %/cells.13/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.13/shuffle/Reshape_1_output_0 = Reshape(%/cells.13/shuffle/Transpose_output_0, %/cells.13/shuffle/Constant_1_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.13/shuffle/Reshape_1_output_0, %onnx::Conv_836, %onnx::Conv_837) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_839, %onnx::Conv_840) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_842, %onnx::Conv_843) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.14/nl/Relu_output_0, %onnx::Conv_845, %onnx::Conv_846) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_848, %onnx::Conv_849) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_851, %onnx::Conv_852) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.15/nl/Relu_output_0, %onnx::Conv_854, %onnx::Conv_855) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_857, %onnx::Conv_858) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_860, %onnx::Conv_861) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.16/nl/Relu_output_0, %onnx::Conv_863, %onnx::Conv_864) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_866, %onnx::Conv_867) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.15/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_869, %onnx::Conv_870) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.17/nl/Relu_output_0, %onnx::Conv_872, %onnx::Conv_873) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_875, %onnx::Conv_876) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_878, %onnx::Conv_879) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.18/nl/Relu_output_0, %onnx::Conv_881, %onnx::Conv_882) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_884, %onnx::Conv_885) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_887, %onnx::Conv_888) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.19/shuffle/Reshape_output_0 = Reshape(%/cells.19/nl/Relu_output_0, %/cells.19/shuffle/Constant_output_0) %/cells.19/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.19/shuffle/Reshape_output_0) %/cells.19/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.19/shuffle/Reshape_1_output_0 = Reshape(%/cells.19/shuffle/Transpose_output_0, %/cells.19/shuffle/Constant_1_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.19/shuffle/Reshape_1_output_0, %onnx::Conv_890, %onnx::Conv_891) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_893, %onnx::Conv_894) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_896, %onnx::Conv_897) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.20/nl/Relu_output_0, %onnx::Conv_899, %onnx::Conv_900) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_902, %onnx::Conv_903) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_905, %onnx::Conv_906) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.21/nl/Relu_output_0, %onnx::Conv_908, %onnx::Conv_909) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_911, %onnx::Conv_912) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_914, %onnx::Conv_915) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %711 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %711 }	val_accuracy	100,360,576	2,514,284	{'zcp_synflow': 83.36304425976688, 'zcp_zen': 77.1395492553711, 'zcp_epe_nas': 0.00015999920000638146, 'zcp_fisher': 0.24749159812927246, 'zcp_flops': 100360576.0, 'zcp_grad_norm': 34.91120910644531, 'zcp_grasp': -1.0549087524414062, 'zcp_jacov': -16.06844230622861, 'zcp_l2_norm': 742.0388793945312, 'zcp_nwot': 218.13760462745535, 'zcp_params': 2514284.0, 'zcp_plain': -0.0013986150734126568, 'zcp_snip': 63.894832611083984, 'lat_1080ti_1': 0.8793800887091583, 'lat_1080ti_32': 0.7477786566679977, 'lat_1080ti_64': 0.7663355386189671, 'lat_2080ti_1': 0.842205340883347, 'lat_2080ti_32': 0.7863296441562043, 'lat_2080ti_64': 0.7582025753187811, 'lat_essential_ph_1': 0.37735849056603776, 'lat_eyeriss': 0.8099407698091059, 'lat_fpga': 0.8247360452804798, 'lat_gold_6226': 0.6652760616313226, 'lat_gold_6240': 0.8005774818013645, 'lat_pixel2': 0.5652173913043478, 'lat_pixel3': 0.8206746282564966, 'lat_raspi4': 0.7958138848602136, 'lat_samsung_a50': 0.35789473684210527, 'lat_samsung_s7': 0.23622047244094488, 'lat_silver_4114': 0.8766028927564806, 'lat_silver_4210r': 0.8934047322392413, 'lat_titan_rtx_1': 0.794169951716153, 'lat_titan_rtx_32': 0.7410194036158645, 'lat_titan_rtx_64': 0.7704285208618178, 'lat_titanx_1': 0.42728778326028727, 'lat_titanx_32': 0.7756467828696982, 'lat_titanx_64': 0.7417558141349826, 'lat_titanxp_1': 0.7523491989901359, 'lat_titanxp_32': 0.7721679458773102, 'lat_titanxp_64': 0.7650341599994472}
FBNet_1083	FBNet	1083	1083	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_712[FLOAT, 16x3x3x3] %onnx::Conv_713[FLOAT, 16] %onnx::Conv_715[FLOAT, 16x16x1x1] %onnx::Conv_718[FLOAT, 16x1x5x5] %onnx::Conv_721[FLOAT, 16x16x1x1] %onnx::Conv_724[FLOAT, 96x16x1x1] %onnx::Conv_725[FLOAT, 96] %onnx::Conv_727[FLOAT, 96x1x5x5] %onnx::Conv_730[FLOAT, 24x96x1x1] %onnx::Conv_731[FLOAT, 24] %onnx::Conv_733[FLOAT, 24x12x1x1] %onnx::Conv_736[FLOAT, 24x1x5x5] %onnx::Conv_739[FLOAT, 24x12x1x1] %onnx::Conv_742[FLOAT, 24x12x1x1] %onnx::Conv_745[FLOAT, 24x1x3x3] %onnx::Conv_748[FLOAT, 24x12x1x1] %onnx::Conv_751[FLOAT, 24x12x1x1] %onnx::Conv_754[FLOAT, 24x1x3x3] %onnx::Conv_757[FLOAT, 24x12x1x1] %onnx::Conv_760[FLOAT, 72x24x1x1] %onnx::Conv_761[FLOAT, 72] %onnx::Conv_763[FLOAT, 72x1x3x3] %onnx::Conv_766[FLOAT, 32x72x1x1] %onnx::Conv_767[FLOAT, 32] %onnx::Conv_769[FLOAT, 192x32x1x1] %onnx::Conv_770[FLOAT, 192] %onnx::Conv_772[FLOAT, 192x1x3x3] %onnx::Conv_775[FLOAT, 32x192x1x1] %onnx::Conv_778[FLOAT, 96x32x1x1] %onnx::Conv_781[FLOAT, 96x1x5x5] %onnx::Conv_784[FLOAT, 32x96x1x1] %onnx::Conv_787[FLOAT, 32x32x1x1] %onnx::Conv_790[FLOAT, 32x1x5x5] %onnx::Conv_793[FLOAT, 32x32x1x1] %onnx::Conv_796[FLOAT, 192x32x1x1] %onnx::Conv_799[FLOAT, 192x1x5x5] %onnx::Conv_802[FLOAT, 64x192x1x1] %onnx::Conv_803[FLOAT, 64] %onnx::Conv_805[FLOAT, 192x64x1x1] %onnx::Conv_808[FLOAT, 192x1x5x5] %onnx::Conv_811[FLOAT, 64x192x1x1] %onnx::Conv_814[FLOAT, 64x32x1x1] %onnx::Conv_817[FLOAT, 64x1x3x3] %onnx::Conv_820[FLOAT, 64x32x1x1] %onnx::Conv_823[FLOAT, 384x64x1x1] %onnx::Conv_824[FLOAT, 384] %onnx::Conv_826[FLOAT, 384x1x5x5] %onnx::Conv_829[FLOAT, 64x384x1x1] %onnx::Conv_832[FLOAT, 192x64x1x1] %onnx::Conv_835[FLOAT, 192x1x5x5] %onnx::Conv_838[FLOAT, 112x192x1x1] %onnx::Conv_839[FLOAT, 112] %onnx::Conv_841[FLOAT, 672x112x1x1] %onnx::Conv_842[FLOAT, 672] %onnx::Conv_844[FLOAT, 672x1x5x5] %onnx::Conv_847[FLOAT, 112x672x1x1] %onnx::Conv_850[FLOAT, 672x112x1x1] %onnx::Conv_853[FLOAT, 672x1x3x3] %onnx::Conv_856[FLOAT, 112x672x1x1] %onnx::Conv_859[FLOAT, 112x56x1x1] %onnx::Conv_862[FLOAT, 112x1x3x3] %onnx::Conv_865[FLOAT, 112x56x1x1] %onnx::Conv_868[FLOAT, 336x112x1x1] %onnx::Conv_869[FLOAT, 336] %onnx::Conv_871[FLOAT, 336x1x3x3] %onnx::Conv_874[FLOAT, 184x336x1x1] %onnx::Conv_875[FLOAT, 184] %onnx::Conv_877[FLOAT, 552x184x1x1] %onnx::Conv_878[FLOAT, 552] %onnx::Conv_880[FLOAT, 552x1x3x3] %onnx::Conv_883[FLOAT, 184x552x1x1] %onnx::Conv_886[FLOAT, 552x184x1x1] %onnx::Conv_889[FLOAT, 552x1x3x3] %onnx::Conv_892[FLOAT, 184x552x1x1] %onnx::Conv_895[FLOAT, 184x184x1x1] %onnx::Conv_898[FLOAT, 184x1x5x5] %onnx::Conv_901[FLOAT, 184x184x1x1] %onnx::Conv_904[FLOAT, 184x184x1x1] %onnx::Conv_907[FLOAT, 184x1x5x5] %onnx::Conv_910[FLOAT, 352x184x1x1] %onnx::Conv_911[FLOAT, 352] %onnx::Conv_913[FLOAT, 1504x352x1x1] %onnx::Conv_914[FLOAT, 1504] ) { %onnx::Conv_908 = Identity(%onnx::Conv_875) %onnx::Conv_905 = Identity(%onnx::Conv_875) %onnx::Conv_902 = Identity(%onnx::Conv_875) %onnx::Conv_899 = Identity(%onnx::Conv_875) %onnx::Conv_896 = Identity(%onnx::Conv_875) %onnx::Conv_893 = Identity(%onnx::Conv_875) %onnx::Conv_890 = Identity(%onnx::Conv_878) %onnx::Conv_887 = Identity(%onnx::Conv_878) %onnx::Conv_884 = Identity(%onnx::Conv_875) %onnx::Conv_881 = Identity(%onnx::Conv_878) %onnx::Conv_872 = Identity(%onnx::Conv_869) %onnx::Conv_866 = Identity(%onnx::Conv_839) %onnx::Conv_863 = Identity(%onnx::Conv_839) %onnx::Conv_860 = Identity(%onnx::Conv_839) %onnx::Conv_857 = Identity(%onnx::Conv_839) %onnx::Conv_854 = Identity(%onnx::Conv_842) %onnx::Conv_851 = Identity(%onnx::Conv_842) %onnx::Conv_848 = Identity(%onnx::Conv_839) %onnx::Conv_845 = Identity(%onnx::Conv_842) %onnx::Conv_836 = Identity(%onnx::Conv_770) %onnx::Conv_833 = Identity(%onnx::Conv_770) %onnx::Conv_830 = Identity(%onnx::Conv_803) %onnx::Conv_827 = Identity(%onnx::Conv_824) %onnx::Conv_821 = Identity(%onnx::Conv_803) %onnx::Conv_818 = Identity(%onnx::Conv_803) %onnx::Conv_815 = Identity(%onnx::Conv_803) %onnx::Conv_812 = Identity(%onnx::Conv_803) %onnx::Conv_809 = Identity(%onnx::Conv_770) %onnx::Conv_806 = Identity(%onnx::Conv_770) %onnx::Conv_800 = Identity(%onnx::Conv_770) %onnx::Conv_797 = Identity(%onnx::Conv_770) %onnx::Conv_794 = Identity(%onnx::Conv_767) %onnx::Conv_791 = Identity(%onnx::Conv_767) %onnx::Conv_788 = Identity(%onnx::Conv_767) %onnx::Conv_785 = Identity(%onnx::Conv_767) %onnx::Conv_782 = Identity(%onnx::Conv_725) %onnx::Conv_779 = Identity(%onnx::Conv_725) %onnx::Conv_776 = Identity(%onnx::Conv_767) %onnx::Conv_773 = Identity(%onnx::Conv_770) %onnx::Conv_764 = Identity(%onnx::Conv_761) %onnx::Conv_758 = Identity(%onnx::Conv_731) %onnx::Conv_755 = Identity(%onnx::Conv_731) %onnx::Conv_752 = Identity(%onnx::Conv_731) %onnx::Conv_749 = Identity(%onnx::Conv_731) %onnx::Conv_746 = Identity(%onnx::Conv_731) %onnx::Conv_743 = Identity(%onnx::Conv_731) %onnx::Conv_740 = Identity(%onnx::Conv_731) %onnx::Conv_737 = Identity(%onnx::Conv_731) %onnx::Conv_734 = Identity(%onnx::Conv_731) %onnx::Conv_728 = Identity(%onnx::Conv_725) %onnx::Conv_722 = Identity(%onnx::Conv_713) %onnx::Conv_719 = Identity(%onnx::Conv_713) %onnx::Conv_716 = Identity(%onnx::Conv_713) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_712, %onnx::Conv_713) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_715, %onnx::Conv_716) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.0/nl/Relu_output_0, %onnx::Conv_718, %onnx::Conv_719) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_721, %onnx::Conv_722) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_724, %onnx::Conv_725) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.1/nl/Relu_output_0, %onnx::Conv_727, %onnx::Conv_728) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_730, %onnx::Conv_731) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_733, %onnx::Conv_734) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.2/shuffle/Reshape_output_0 = Reshape(%/cells.2/nl/Relu_output_0, %/cells.2/shuffle/Constant_output_0) %/cells.2/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.2/shuffle/Reshape_output_0) %/cells.2/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.2/shuffle/Reshape_1_output_0 = Reshape(%/cells.2/shuffle/Transpose_output_0, %/cells.2/shuffle/Constant_1_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.2/shuffle/Reshape_1_output_0, %onnx::Conv_736, %onnx::Conv_737) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_739, %onnx::Conv_740) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_742, %onnx::Conv_743) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.3/shuffle/Reshape_output_0 = Reshape(%/cells.3/nl/Relu_output_0, %/cells.3/shuffle/Constant_output_0) %/cells.3/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.3/shuffle/Reshape_output_0) %/cells.3/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.3/shuffle/Reshape_1_output_0 = Reshape(%/cells.3/shuffle/Transpose_output_0, %/cells.3/shuffle/Constant_1_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.3/shuffle/Reshape_1_output_0, %onnx::Conv_745, %onnx::Conv_746) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_748, %onnx::Conv_749) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_751, %onnx::Conv_752) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.4/shuffle/Reshape_output_0 = Reshape(%/cells.4/nl/Relu_output_0, %/cells.4/shuffle/Constant_output_0) %/cells.4/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.4/shuffle/Reshape_output_0) %/cells.4/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.4/shuffle/Reshape_1_output_0 = Reshape(%/cells.4/shuffle/Transpose_output_0, %/cells.4/shuffle/Constant_1_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.4/shuffle/Reshape_1_output_0, %onnx::Conv_754, %onnx::Conv_755) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_757, %onnx::Conv_758) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/Add_output_0, %onnx::Conv_760, %onnx::Conv_761) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.5/nl/Relu_output_0, %onnx::Conv_763, %onnx::Conv_764) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_766, %onnx::Conv_767) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_769, %onnx::Conv_770) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.6/nl/Relu_output_0, %onnx::Conv_772, %onnx::Conv_773) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_775, %onnx::Conv_776) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_778, %onnx::Conv_779) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.7/nl/Relu_output_0, %onnx::Conv_781, %onnx::Conv_782) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_784, %onnx::Conv_785) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_787, %onnx::Conv_788) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.8/nl/Relu_output_0, %onnx::Conv_790, %onnx::Conv_791) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_793, %onnx::Conv_794) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_796, %onnx::Conv_797) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.9/nl/Relu_output_0, %onnx::Conv_799, %onnx::Conv_800) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_802, %onnx::Conv_803) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_805, %onnx::Conv_806) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.10/nl/Relu_output_0, %onnx::Conv_808, %onnx::Conv_809) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_811, %onnx::Conv_812) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_814, %onnx::Conv_815) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.11/shuffle/Reshape_output_0 = Reshape(%/cells.11/nl/Relu_output_0, %/cells.11/shuffle/Constant_output_0) %/cells.11/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.11/shuffle/Reshape_output_0) %/cells.11/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.11/shuffle/Reshape_1_output_0 = Reshape(%/cells.11/shuffle/Transpose_output_0, %/cells.11/shuffle/Constant_1_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.11/shuffle/Reshape_1_output_0, %onnx::Conv_817, %onnx::Conv_818) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_820, %onnx::Conv_821) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_823, %onnx::Conv_824) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.12/nl/Relu_output_0, %onnx::Conv_826, %onnx::Conv_827) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_829, %onnx::Conv_830) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_832, %onnx::Conv_833) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.13/nl/Relu_output_0, %onnx::Conv_835, %onnx::Conv_836) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_838, %onnx::Conv_839) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_841, %onnx::Conv_842) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.14/nl/Relu_output_0, %onnx::Conv_844, %onnx::Conv_845) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_847, %onnx::Conv_848) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_850, %onnx::Conv_851) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.15/nl/Relu_output_0, %onnx::Conv_853, %onnx::Conv_854) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_856, %onnx::Conv_857) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_859, %onnx::Conv_860) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.16/shuffle/Reshape_output_0 = Reshape(%/cells.16/nl/Relu_output_0, %/cells.16/shuffle/Constant_output_0) %/cells.16/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.16/shuffle/Reshape_output_0) %/cells.16/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.16/shuffle/Reshape_1_output_0 = Reshape(%/cells.16/shuffle/Transpose_output_0, %/cells.16/shuffle/Constant_1_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.16/shuffle/Reshape_1_output_0, %onnx::Conv_862, %onnx::Conv_863) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_865, %onnx::Conv_866) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.15/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_868, %onnx::Conv_869) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.17/nl/Relu_output_0, %onnx::Conv_871, %onnx::Conv_872) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_874, %onnx::Conv_875) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_877, %onnx::Conv_878) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 552, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.18/nl/Relu_output_0, %onnx::Conv_880, %onnx::Conv_881) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_883, %onnx::Conv_884) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_886, %onnx::Conv_887) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 552, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.19/nl/Relu_output_0, %onnx::Conv_889, %onnx::Conv_890) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_892, %onnx::Conv_893) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_895, %onnx::Conv_896) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.20/nl/Relu_output_0, %onnx::Conv_898, %onnx::Conv_899) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_901, %onnx::Conv_902) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_904, %onnx::Conv_905) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.21/nl/Relu_output_0, %onnx::Conv_907, %onnx::Conv_908) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_910, %onnx::Conv_911) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_913, %onnx::Conv_914) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %710 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %710 }	val_accuracy	76,993,408	1,933,204	{'zcp_synflow': 83.56968799078639, 'zcp_zen': 74.916259765625, 'zcp_epe_nas': 23.13211973948068, 'zcp_fisher': 0.1970348358154297, 'zcp_flops': 76993408.0, 'zcp_grad_norm': 28.509090423583984, 'zcp_grasp': -0.20929718017578125, 'zcp_jacov': -16.053661828744183, 'zcp_l2_norm': 695.3572387695312, 'zcp_nwot': 213.11666813303137, 'zcp_params': 1933204.0, 'zcp_plain': 0.001173208700492978, 'zcp_snip': 56.73067092895508, 'lat_1080ti_1': 0.7289074829094644, 'lat_1080ti_32': 0.6664573949464554, 'lat_1080ti_64': 0.5103101859922673, 'lat_2080ti_1': 0.8001080219386271, 'lat_2080ti_32': 0.6583955002196794, 'lat_2080ti_64': 0.49839920205943594, 'lat_essential_ph_1': 0.39622641509433965, 'lat_eyeriss': 0.5269653237489225, 'lat_fpga': 0.5538348885304623, 'lat_gold_6226': 0.43710949947396877, 'lat_gold_6240': 0.6662104490303572, 'lat_pixel2': 0.34782608695652173, 'lat_pixel3': 0.5413812402117252, 'lat_raspi4': 0.5068504523635982, 'lat_samsung_a50': 0.23157894736842105, 'lat_samsung_s7': 0.2125984251968504, 'lat_silver_4114': 0.7151404685414706, 'lat_silver_4210r': 0.7582731448373737, 'lat_titan_rtx_1': 0.7744778014126772, 'lat_titan_rtx_32': 0.6755560128864255, 'lat_titan_rtx_64': 0.5413297786570901, 'lat_titanx_1': 0.4133218797365527, 'lat_titanx_32': 0.5942582624908618, 'lat_titanx_64': 0.4887258884278444, 'lat_titanxp_1': 0.7455195080095598, 'lat_titanxp_32': 0.6639937471599109, 'lat_titanxp_64': 0.5017422708060032}
FBNet_2064	FBNet	2064	2064	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_632[FLOAT, 16x3x3x3] %onnx::Conv_633[FLOAT, 16] %onnx::Conv_635[FLOAT, 16x8x1x1] %onnx::Conv_638[FLOAT, 16x1x3x3] %onnx::Conv_641[FLOAT, 16x8x1x1] %onnx::Conv_644[FLOAT, 16x8x1x1] %onnx::Conv_647[FLOAT, 16x1x5x5] %onnx::Conv_650[FLOAT, 24x8x1x1] %onnx::Conv_651[FLOAT, 24] %onnx::Conv_653[FLOAT, 72x24x1x1] %onnx::Conv_654[FLOAT, 72] %onnx::Conv_656[FLOAT, 72x1x5x5] %onnx::Conv_659[FLOAT, 24x72x1x1] %onnx::Conv_662[FLOAT, 72x24x1x1] %onnx::Conv_665[FLOAT, 72x1x5x5] %onnx::Conv_668[FLOAT, 24x72x1x1] %onnx::Conv_671[FLOAT, 144x24x1x1] %onnx::Conv_672[FLOAT, 144] %onnx::Conv_674[FLOAT, 144x1x5x5] %onnx::Conv_677[FLOAT, 24x144x1x1] %onnx::Conv_680[FLOAT, 72x24x1x1] %onnx::Conv_683[FLOAT, 72x1x3x3] %onnx::Conv_686[FLOAT, 32x72x1x1] %onnx::Conv_687[FLOAT, 32] %onnx::Conv_689[FLOAT, 32x16x1x1] %onnx::Conv_692[FLOAT, 32x1x3x3] %onnx::Conv_695[FLOAT, 32x16x1x1] %onnx::Conv_698[FLOAT, 96x32x1x1] %onnx::Conv_699[FLOAT, 96] %onnx::Conv_701[FLOAT, 96x1x5x5] %onnx::Conv_704[FLOAT, 32x96x1x1] %onnx::Conv_707[FLOAT, 32x32x1x1] %onnx::Conv_710[FLOAT, 32x1x5x5] %onnx::Conv_713[FLOAT, 32x32x1x1] %onnx::Conv_716[FLOAT, 96x32x1x1] %onnx::Conv_719[FLOAT, 96x1x3x3] %onnx::Conv_722[FLOAT, 64x96x1x1] %onnx::Conv_723[FLOAT, 64] %onnx::Conv_725[FLOAT, 384x64x1x1] %onnx::Conv_726[FLOAT, 384] %onnx::Conv_728[FLOAT, 384x1x5x5] %onnx::Conv_731[FLOAT, 64x384x1x1] %onnx::Conv_734[FLOAT, 192x64x1x1] %onnx::Conv_735[FLOAT, 192] %onnx::Conv_737[FLOAT, 192x1x3x3] %onnx::Conv_740[FLOAT, 64x192x1x1] %onnx::Conv_743[FLOAT, 384x64x1x1] %onnx::Conv_746[FLOAT, 384x1x5x5] %onnx::Conv_749[FLOAT, 112x384x1x1] %onnx::Conv_750[FLOAT, 112] %onnx::Conv_752[FLOAT, 672x112x1x1] %onnx::Conv_753[FLOAT, 672] %onnx::Conv_755[FLOAT, 672x1x5x5] %onnx::Conv_758[FLOAT, 112x672x1x1] %onnx::Conv_761[FLOAT, 112x56x1x1] %onnx::Conv_764[FLOAT, 112x1x3x3] %onnx::Conv_767[FLOAT, 112x56x1x1] %onnx::Conv_770[FLOAT, 336x112x1x1] %onnx::Conv_771[FLOAT, 336] %onnx::Conv_773[FLOAT, 336x1x3x3] %onnx::Conv_776[FLOAT, 184x336x1x1] %onnx::Conv_777[FLOAT, 184] %onnx::Conv_779[FLOAT, 184x92x1x1] %onnx::Conv_782[FLOAT, 184x1x5x5] %onnx::Conv_785[FLOAT, 184x92x1x1] %onnx::Conv_788[FLOAT, 552x184x1x1] %onnx::Conv_789[FLOAT, 552] %onnx::Conv_791[FLOAT, 552x1x3x3] %onnx::Conv_794[FLOAT, 184x552x1x1] %onnx::Conv_797[FLOAT, 184x184x1x1] %onnx::Conv_800[FLOAT, 184x1x5x5] %onnx::Conv_803[FLOAT, 352x184x1x1] %onnx::Conv_804[FLOAT, 352] %onnx::Conv_806[FLOAT, 1504x352x1x1] %onnx::Conv_807[FLOAT, 1504] ) { %onnx::Conv_801 = Identity(%onnx::Conv_777) %onnx::Conv_798 = Identity(%onnx::Conv_777) %onnx::Conv_795 = Identity(%onnx::Conv_777) %onnx::Conv_792 = Identity(%onnx::Conv_789) %onnx::Conv_786 = Identity(%onnx::Conv_777) %onnx::Conv_783 = Identity(%onnx::Conv_777) %onnx::Conv_780 = Identity(%onnx::Conv_777) %onnx::Conv_774 = Identity(%onnx::Conv_771) %onnx::Conv_768 = Identity(%onnx::Conv_750) %onnx::Conv_765 = Identity(%onnx::Conv_750) %onnx::Conv_762 = Identity(%onnx::Conv_750) %onnx::Conv_759 = Identity(%onnx::Conv_750) %onnx::Conv_756 = Identity(%onnx::Conv_753) %onnx::Conv_747 = Identity(%onnx::Conv_726) %onnx::Conv_744 = Identity(%onnx::Conv_726) %onnx::Conv_741 = Identity(%onnx::Conv_723) %onnx::Conv_738 = Identity(%onnx::Conv_735) %onnx::Conv_732 = Identity(%onnx::Conv_723) %onnx::Conv_729 = Identity(%onnx::Conv_726) %onnx::Conv_720 = Identity(%onnx::Conv_699) %onnx::Conv_717 = Identity(%onnx::Conv_699) %onnx::Conv_714 = Identity(%onnx::Conv_687) %onnx::Conv_711 = Identity(%onnx::Conv_687) %onnx::Conv_708 = Identity(%onnx::Conv_687) %onnx::Conv_705 = Identity(%onnx::Conv_687) %onnx::Conv_702 = Identity(%onnx::Conv_699) %onnx::Conv_696 = Identity(%onnx::Conv_687) %onnx::Conv_693 = Identity(%onnx::Conv_687) %onnx::Conv_690 = Identity(%onnx::Conv_687) %onnx::Conv_684 = Identity(%onnx::Conv_654) %onnx::Conv_681 = Identity(%onnx::Conv_654) %onnx::Conv_678 = Identity(%onnx::Conv_651) %onnx::Conv_675 = Identity(%onnx::Conv_672) %onnx::Conv_669 = Identity(%onnx::Conv_651) %onnx::Conv_666 = Identity(%onnx::Conv_654) %onnx::Conv_663 = Identity(%onnx::Conv_654) %onnx::Conv_660 = Identity(%onnx::Conv_651) %onnx::Conv_657 = Identity(%onnx::Conv_654) %onnx::Conv_648 = Identity(%onnx::Conv_633) %onnx::Conv_645 = Identity(%onnx::Conv_633) %onnx::Conv_642 = Identity(%onnx::Conv_633) %onnx::Conv_639 = Identity(%onnx::Conv_633) %onnx::Conv_636 = Identity(%onnx::Conv_633) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_632, %onnx::Conv_633) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_635, %onnx::Conv_636) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.0/shuffle/Reshape_output_0 = Reshape(%/cells.0/nl/Relu_output_0, %/cells.0/shuffle/Constant_output_0) %/cells.0/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.0/shuffle/Reshape_output_0) %/cells.0/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.0/shuffle/Reshape_1_output_0 = Reshape(%/cells.0/shuffle/Transpose_output_0, %/cells.0/shuffle/Constant_1_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.0/shuffle/Reshape_1_output_0, %onnx::Conv_638, %onnx::Conv_639) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_641, %onnx::Conv_642) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_644, %onnx::Conv_645) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.1/shuffle/Reshape_output_0 = Reshape(%/cells.1/nl/Relu_output_0, %/cells.1/shuffle/Constant_output_0) %/cells.1/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.1/shuffle/Reshape_output_0) %/cells.1/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.1/shuffle/Reshape_1_output_0 = Reshape(%/cells.1/shuffle/Transpose_output_0, %/cells.1/shuffle/Constant_1_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.1/shuffle/Reshape_1_output_0, %onnx::Conv_647, %onnx::Conv_648) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_650, %onnx::Conv_651) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_653, %onnx::Conv_654) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.2/nl/Relu_output_0, %onnx::Conv_656, %onnx::Conv_657) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_659, %onnx::Conv_660) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_662, %onnx::Conv_663) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.3/nl/Relu_output_0, %onnx::Conv_665, %onnx::Conv_666) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_668, %onnx::Conv_669) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_671, %onnx::Conv_672) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.4/nl/Relu_output_0, %onnx::Conv_674, %onnx::Conv_675) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_677, %onnx::Conv_678) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/Add_output_0, %onnx::Conv_680, %onnx::Conv_681) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.5/nl/Relu_output_0, %onnx::Conv_683, %onnx::Conv_684) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_686, %onnx::Conv_687) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_689, %onnx::Conv_690) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.6/shuffle/Reshape_output_0 = Reshape(%/cells.6/nl/Relu_output_0, %/cells.6/shuffle/Constant_output_0) %/cells.6/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.6/shuffle/Reshape_output_0) %/cells.6/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.6/shuffle/Reshape_1_output_0 = Reshape(%/cells.6/shuffle/Transpose_output_0, %/cells.6/shuffle/Constant_1_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.6/shuffle/Reshape_1_output_0, %onnx::Conv_692, %onnx::Conv_693) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_695, %onnx::Conv_696) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_698, %onnx::Conv_699) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.7/nl/Relu_output_0, %onnx::Conv_701, %onnx::Conv_702) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_704, %onnx::Conv_705) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_707, %onnx::Conv_708) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.8/nl/Relu_output_0, %onnx::Conv_710, %onnx::Conv_711) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_713, %onnx::Conv_714) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_716, %onnx::Conv_717) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.9/nl/Relu_output_0, %onnx::Conv_719, %onnx::Conv_720) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_722, %onnx::Conv_723) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_725, %onnx::Conv_726) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.11/nl/Relu_output_0, %onnx::Conv_728, %onnx::Conv_729) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_731, %onnx::Conv_732) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_734, %onnx::Conv_735) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.12/nl/Relu_output_0, %onnx::Conv_737, %onnx::Conv_738) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_740, %onnx::Conv_741) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_743, %onnx::Conv_744) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.13/nl/Relu_output_0, %onnx::Conv_746, %onnx::Conv_747) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_749, %onnx::Conv_750) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_752, %onnx::Conv_753) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.14/nl/Relu_output_0, %onnx::Conv_755, %onnx::Conv_756) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_758, %onnx::Conv_759) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_761, %onnx::Conv_762) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.16/shuffle/Reshape_output_0 = Reshape(%/cells.16/nl/Relu_output_0, %/cells.16/shuffle/Constant_output_0) %/cells.16/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.16/shuffle/Reshape_output_0) %/cells.16/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.16/shuffle/Reshape_1_output_0 = Reshape(%/cells.16/shuffle/Transpose_output_0, %/cells.16/shuffle/Constant_1_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.16/shuffle/Reshape_1_output_0, %onnx::Conv_764, %onnx::Conv_765) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_767, %onnx::Conv_768) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_770, %onnx::Conv_771) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.17/nl/Relu_output_0, %onnx::Conv_773, %onnx::Conv_774) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_776, %onnx::Conv_777) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_779, %onnx::Conv_780) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.19/shuffle/Reshape_output_0 = Reshape(%/cells.19/nl/Relu_output_0, %/cells.19/shuffle/Constant_output_0) %/cells.19/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.19/shuffle/Reshape_output_0) %/cells.19/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.19/shuffle/Reshape_1_output_0 = Reshape(%/cells.19/shuffle/Transpose_output_0, %/cells.19/shuffle/Constant_1_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.19/shuffle/Reshape_1_output_0, %onnx::Conv_782, %onnx::Conv_783) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_785, %onnx::Conv_786) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_788, %onnx::Conv_789) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 552, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.20/nl/Relu_output_0, %onnx::Conv_791, %onnx::Conv_792) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_794, %onnx::Conv_795) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_797, %onnx::Conv_798) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.21/nl/Relu_output_0, %onnx::Conv_800, %onnx::Conv_801) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_803, %onnx::Conv_804) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_806, %onnx::Conv_807) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %630 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %630 }	val_accuracy	73,094,528	1,546,308	{'zcp_synflow': 72.05931626082429, 'zcp_zen': 63.173866271972656, 'zcp_epe_nas': 6.509456140531739, 'zcp_fisher': 0.08784433454275131, 'zcp_flops': 73094528.0, 'zcp_grad_norm': 21.37310028076172, 'zcp_grasp': -0.0031604766845703125, 'zcp_jacov': -16.061071231659824, 'zcp_l2_norm': 562.6128540039062, 'zcp_nwot': 214.2816084474337, 'zcp_params': 1546308.0, 'zcp_plain': 0.0023509932216256857, 'zcp_snip': 35.99818420410156, 'lat_1080ti_1': 0.4649444421742405, 'lat_1080ti_32': 0.5782369924009593, 'lat_1080ti_64': 0.5593638455677479, 'lat_2080ti_1': 0.5133546634459308, 'lat_2080ti_32': 0.5387851417754577, 'lat_2080ti_64': 0.5372759168491291, 'lat_essential_ph_1': 0.3018867924528302, 'lat_eyeriss': 0.49056534575394717, 'lat_fpga': 0.45741596493137665, 'lat_gold_6226': 0.31768531446303955, 'lat_gold_6240': 0.37509998837878694, 'lat_pixel2': 0.2826086956521739, 'lat_pixel3': 0.5772022838226079, 'lat_raspi4': 0.5171388817986874, 'lat_samsung_a50': 0.17894736842105263, 'lat_samsung_s7': 0.14960629921259844, 'lat_silver_4114': 0.39452734788166643, 'lat_silver_4210r': 0.3548036797452912, 'lat_titan_rtx_1': 0.487629011695059, 'lat_titan_rtx_32': 0.49163819845138473, 'lat_titan_rtx_64': 0.5427141856717849, 'lat_titanx_1': 0.249243402784473, 'lat_titanx_32': 0.5544563921548483, 'lat_titanx_64': 0.5713486995194104, 'lat_titanxp_1': 0.46617341008849794, 'lat_titanxp_32': 0.5437632786924703, 'lat_titanxp_64': 0.561757944948107}
FBNet_3391	FBNet	3391	3391	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_597[FLOAT, 16x3x3x3] %onnx::Conv_598[FLOAT, 16] %onnx::Conv_600[FLOAT, 96x16x1x1] %onnx::Conv_601[FLOAT, 96] %onnx::Conv_603[FLOAT, 96x1x3x3] %onnx::Conv_606[FLOAT, 16x96x1x1] %onnx::Conv_609[FLOAT, 24x16x1x1] %onnx::Conv_610[FLOAT, 24] %onnx::Conv_612[FLOAT, 24x24x1x1] %onnx::Conv_615[FLOAT, 24x1x5x5] %onnx::Conv_618[FLOAT, 24x24x1x1] %onnx::Conv_621[FLOAT, 72x24x1x1] %onnx::Conv_622[FLOAT, 72] %onnx::Conv_624[FLOAT, 72x1x3x3] %onnx::Conv_627[FLOAT, 24x72x1x1] %onnx::Conv_630[FLOAT, 144x24x1x1] %onnx::Conv_631[FLOAT, 144] %onnx::Conv_633[FLOAT, 144x1x5x5] %onnx::Conv_636[FLOAT, 24x144x1x1] %onnx::Conv_639[FLOAT, 24x12x1x1] %onnx::Conv_642[FLOAT, 24x1x5x5] %onnx::Conv_645[FLOAT, 32x12x1x1] %onnx::Conv_646[FLOAT, 32] %onnx::Conv_648[FLOAT, 192x32x1x1] %onnx::Conv_649[FLOAT, 192] %onnx::Conv_651[FLOAT, 192x1x5x5] %onnx::Conv_654[FLOAT, 32x192x1x1] %onnx::Conv_657[FLOAT, 192x32x1x1] %onnx::Conv_660[FLOAT, 192x1x3x3] %onnx::Conv_663[FLOAT, 32x192x1x1] %onnx::Conv_666[FLOAT, 96x32x1x1] %onnx::Conv_669[FLOAT, 96x1x5x5] %onnx::Conv_672[FLOAT, 32x96x1x1] %onnx::Conv_675[FLOAT, 192x32x1x1] %onnx::Conv_678[FLOAT, 192x1x3x3] %onnx::Conv_681[FLOAT, 64x192x1x1] %onnx::Conv_682[FLOAT, 64] %onnx::Conv_684[FLOAT, 64x32x1x1] %onnx::Conv_687[FLOAT, 64x1x3x3] %onnx::Conv_690[FLOAT, 64x32x1x1] %onnx::Conv_693[FLOAT, 64x64x1x1] %onnx::Conv_696[FLOAT, 64x1x3x3] %onnx::Conv_699[FLOAT, 64x64x1x1] %onnx::Conv_702[FLOAT, 192x64x1x1] %onnx::Conv_705[FLOAT, 192x1x3x3] %onnx::Conv_708[FLOAT, 112x192x1x1] %onnx::Conv_709[FLOAT, 112] %onnx::Conv_711[FLOAT, 112x56x1x1] %onnx::Conv_714[FLOAT, 112x1x5x5] %onnx::Conv_717[FLOAT, 112x56x1x1] %onnx::Conv_720[FLOAT, 112x112x1x1] %onnx::Conv_723[FLOAT, 112x1x3x3] %onnx::Conv_726[FLOAT, 112x112x1x1] %onnx::Conv_729[FLOAT, 672x112x1x1] %onnx::Conv_730[FLOAT, 672] %onnx::Conv_732[FLOAT, 672x1x5x5] %onnx::Conv_735[FLOAT, 184x672x1x1] %onnx::Conv_736[FLOAT, 184] %onnx::Conv_738[FLOAT, 1104x184x1x1] %onnx::Conv_739[FLOAT, 1104] %onnx::Conv_741[FLOAT, 1104x1x5x5] %onnx::Conv_744[FLOAT, 184x1104x1x1] %onnx::Conv_747[FLOAT, 184x92x1x1] %onnx::Conv_750[FLOAT, 184x1x5x5] %onnx::Conv_753[FLOAT, 184x92x1x1] %onnx::Conv_756[FLOAT, 184x184x1x1] %onnx::Conv_759[FLOAT, 184x1x3x3] %onnx::Conv_762[FLOAT, 352x184x1x1] %onnx::Conv_763[FLOAT, 352] %onnx::Conv_765[FLOAT, 1504x352x1x1] %onnx::Conv_766[FLOAT, 1504] ) { %onnx::Conv_760 = Identity(%onnx::Conv_736) %onnx::Conv_757 = Identity(%onnx::Conv_736) %onnx::Conv_754 = Identity(%onnx::Conv_736) %onnx::Conv_751 = Identity(%onnx::Conv_736) %onnx::Conv_748 = Identity(%onnx::Conv_736) %onnx::Conv_745 = Identity(%onnx::Conv_736) %onnx::Conv_742 = Identity(%onnx::Conv_739) %onnx::Conv_733 = Identity(%onnx::Conv_730) %onnx::Conv_727 = Identity(%onnx::Conv_709) %onnx::Conv_724 = Identity(%onnx::Conv_709) %onnx::Conv_721 = Identity(%onnx::Conv_709) %onnx::Conv_718 = Identity(%onnx::Conv_709) %onnx::Conv_715 = Identity(%onnx::Conv_709) %onnx::Conv_712 = Identity(%onnx::Conv_709) %onnx::Conv_706 = Identity(%onnx::Conv_649) %onnx::Conv_703 = Identity(%onnx::Conv_649) %onnx::Conv_700 = Identity(%onnx::Conv_682) %onnx::Conv_697 = Identity(%onnx::Conv_682) %onnx::Conv_694 = Identity(%onnx::Conv_682) %onnx::Conv_691 = Identity(%onnx::Conv_682) %onnx::Conv_688 = Identity(%onnx::Conv_682) %onnx::Conv_685 = Identity(%onnx::Conv_682) %onnx::Conv_679 = Identity(%onnx::Conv_649) %onnx::Conv_676 = Identity(%onnx::Conv_649) %onnx::Conv_673 = Identity(%onnx::Conv_646) %onnx::Conv_670 = Identity(%onnx::Conv_601) %onnx::Conv_667 = Identity(%onnx::Conv_601) %onnx::Conv_664 = Identity(%onnx::Conv_646) %onnx::Conv_661 = Identity(%onnx::Conv_649) %onnx::Conv_658 = Identity(%onnx::Conv_649) %onnx::Conv_655 = Identity(%onnx::Conv_646) %onnx::Conv_652 = Identity(%onnx::Conv_649) %onnx::Conv_643 = Identity(%onnx::Conv_610) %onnx::Conv_640 = Identity(%onnx::Conv_610) %onnx::Conv_637 = Identity(%onnx::Conv_610) %onnx::Conv_634 = Identity(%onnx::Conv_631) %onnx::Conv_628 = Identity(%onnx::Conv_610) %onnx::Conv_625 = Identity(%onnx::Conv_622) %onnx::Conv_619 = Identity(%onnx::Conv_610) %onnx::Conv_616 = Identity(%onnx::Conv_610) %onnx::Conv_613 = Identity(%onnx::Conv_610) %onnx::Conv_607 = Identity(%onnx::Conv_598) %onnx::Conv_604 = Identity(%onnx::Conv_601) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_597, %onnx::Conv_598) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_600, %onnx::Conv_601) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.0/nl/Relu_output_0, %onnx::Conv_603, %onnx::Conv_604) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_606, %onnx::Conv_607) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_609, %onnx::Conv_610) %/cells.1/relu/Relu_output_0 = Relu(%/cells.1/conv/Conv_output_0) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/relu/Relu_output_0, %onnx::Conv_612, %onnx::Conv_613) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.2/nl/Relu_output_0, %onnx::Conv_615, %onnx::Conv_616) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_618, %onnx::Conv_619) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/relu/Relu_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_621, %onnx::Conv_622) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.3/nl/Relu_output_0, %onnx::Conv_624, %onnx::Conv_625) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_627, %onnx::Conv_628) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_630, %onnx::Conv_631) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.4/nl/Relu_output_0, %onnx::Conv_633, %onnx::Conv_634) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_636, %onnx::Conv_637) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/Add_output_0, %onnx::Conv_639, %onnx::Conv_640) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.5/shuffle/Reshape_output_0 = Reshape(%/cells.5/nl/Relu_output_0, %/cells.5/shuffle/Constant_output_0) %/cells.5/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.5/shuffle/Reshape_output_0) %/cells.5/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.5/shuffle/Reshape_1_output_0 = Reshape(%/cells.5/shuffle/Transpose_output_0, %/cells.5/shuffle/Constant_1_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.5/shuffle/Reshape_1_output_0, %onnx::Conv_642, %onnx::Conv_643) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_645, %onnx::Conv_646) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_648, %onnx::Conv_649) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.6/nl/Relu_output_0, %onnx::Conv_651, %onnx::Conv_652) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_654, %onnx::Conv_655) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_657, %onnx::Conv_658) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.7/nl/Relu_output_0, %onnx::Conv_660, %onnx::Conv_661) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_663, %onnx::Conv_664) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_666, %onnx::Conv_667) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.8/nl/Relu_output_0, %onnx::Conv_669, %onnx::Conv_670) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_672, %onnx::Conv_673) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_675, %onnx::Conv_676) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.9/nl/Relu_output_0, %onnx::Conv_678, %onnx::Conv_679) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_681, %onnx::Conv_682) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_684, %onnx::Conv_685) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.11/shuffle/Reshape_output_0 = Reshape(%/cells.11/nl/Relu_output_0, %/cells.11/shuffle/Constant_output_0) %/cells.11/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.11/shuffle/Reshape_output_0) %/cells.11/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.11/shuffle/Reshape_1_output_0 = Reshape(%/cells.11/shuffle/Transpose_output_0, %/cells.11/shuffle/Constant_1_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.11/shuffle/Reshape_1_output_0, %onnx::Conv_687, %onnx::Conv_688) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_690, %onnx::Conv_691) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_693, %onnx::Conv_694) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.12/nl/Relu_output_0, %onnx::Conv_696, %onnx::Conv_697) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_699, %onnx::Conv_700) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_702, %onnx::Conv_703) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.13/nl/Relu_output_0, %onnx::Conv_705, %onnx::Conv_706) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_708, %onnx::Conv_709) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_711, %onnx::Conv_712) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.15/shuffle/Reshape_output_0 = Reshape(%/cells.15/nl/Relu_output_0, %/cells.15/shuffle/Constant_output_0) %/cells.15/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.15/shuffle/Reshape_output_0) %/cells.15/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.15/shuffle/Reshape_1_output_0 = Reshape(%/cells.15/shuffle/Transpose_output_0, %/cells.15/shuffle/Constant_1_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.15/shuffle/Reshape_1_output_0, %onnx::Conv_714, %onnx::Conv_715) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_717, %onnx::Conv_718) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_720, %onnx::Conv_721) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.16/nl/Relu_output_0, %onnx::Conv_723, %onnx::Conv_724) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_726, %onnx::Conv_727) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.15/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_729, %onnx::Conv_730) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.17/nl/Relu_output_0, %onnx::Conv_732, %onnx::Conv_733) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_735, %onnx::Conv_736) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_738, %onnx::Conv_739) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.18/nl/Relu_output_0, %onnx::Conv_741, %onnx::Conv_742) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_744, %onnx::Conv_745) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_747, %onnx::Conv_748) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.20/shuffle/Reshape_output_0 = Reshape(%/cells.20/nl/Relu_output_0, %/cells.20/shuffle/Constant_output_0) %/cells.20/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.20/shuffle/Reshape_output_0) %/cells.20/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.20/shuffle/Reshape_1_output_0 = Reshape(%/cells.20/shuffle/Transpose_output_0, %/cells.20/shuffle/Constant_1_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.20/shuffle/Reshape_1_output_0, %onnx::Conv_750, %onnx::Conv_751) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_753, %onnx::Conv_754) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_756, %onnx::Conv_757) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.21/nl/Relu_output_0, %onnx::Conv_759, %onnx::Conv_760) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_762, %onnx::Conv_763) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_765, %onnx::Conv_766) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %595 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %595 }	val_accuracy	68,934,784	1,662,092	{'zcp_synflow': 70.82944233234227, 'zcp_zen': 61.49188232421875, 'zcp_epe_nas': 0.00015999920000638146, 'zcp_fisher': 0.1240704208612442, 'zcp_flops': 68934784.0, 'zcp_grad_norm': 21.72194480895996, 'zcp_grasp': 0.005954742431640625, 'zcp_jacov': -16.06550596713288, 'zcp_l2_norm': 554.9910278320312, 'zcp_nwot': 215.48503355225165, 'zcp_params': 1662092.0, 'zcp_plain': -0.0020721079781651497, 'zcp_snip': 37.56768035888672, 'lat_1080ti_1': 0.5210796630085648, 'lat_1080ti_32': 0.45208777012067347, 'lat_1080ti_64': 0.5145810197772828, 'lat_2080ti_1': 0.4454772316206013, 'lat_2080ti_32': 0.4920501534520401, 'lat_2080ti_64': 0.5127941129599862, 'lat_essential_ph_1': 0.3018867924528302, 'lat_eyeriss': 0.5005409568518145, 'lat_fpga': 0.40501093420675044, 'lat_gold_6226': 0.2968962319721096, 'lat_gold_6240': 0.4151286594979279, 'lat_pixel2': 0.34782608695652173, 'lat_pixel3': 0.4924561105305831, 'lat_raspi4': 0.4806853214203025, 'lat_samsung_a50': 0.18947368421052632, 'lat_samsung_s7': 0.14173228346456693, 'lat_silver_4114': 0.4423780791548692, 'lat_silver_4210r': 0.4337801782602023, 'lat_titan_rtx_1': 0.41784833391114945, 'lat_titan_rtx_32': 0.45149541777658253, 'lat_titan_rtx_64': 0.4972671427506655, 'lat_titanx_1': 0.23069738592353528, 'lat_titanx_32': 0.454127744597074, 'lat_titanx_64': 0.4931504971606041, 'lat_titanxp_1': 0.39646135150234035, 'lat_titanxp_32': 0.4647025744681946, 'lat_titanxp_64': 0.5102546593996505}
FBNet_1508	FBNet	1508	1508	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_712[FLOAT, 16x3x3x3] %onnx::Conv_713[FLOAT, 16] %onnx::Conv_715[FLOAT, 96x16x1x1] %onnx::Conv_716[FLOAT, 96] %onnx::Conv_718[FLOAT, 96x1x3x3] %onnx::Conv_721[FLOAT, 16x96x1x1] %onnx::Conv_724[FLOAT, 96x16x1x1] %onnx::Conv_727[FLOAT, 96x1x3x3] %onnx::Conv_730[FLOAT, 24x96x1x1] %onnx::Conv_731[FLOAT, 24] %onnx::Conv_733[FLOAT, 144x24x1x1] %onnx::Conv_734[FLOAT, 144] %onnx::Conv_736[FLOAT, 144x1x3x3] %onnx::Conv_739[FLOAT, 24x144x1x1] %onnx::Conv_742[FLOAT, 24x12x1x1] %onnx::Conv_745[FLOAT, 24x1x3x3] %onnx::Conv_748[FLOAT, 24x12x1x1] %onnx::Conv_751[FLOAT, 144x24x1x1] %onnx::Conv_754[FLOAT, 144x1x5x5] %onnx::Conv_757[FLOAT, 24x144x1x1] %onnx::Conv_760[FLOAT, 24x12x1x1] %onnx::Conv_763[FLOAT, 24x1x3x3] %onnx::Conv_766[FLOAT, 32x12x1x1] %onnx::Conv_767[FLOAT, 32] %onnx::Conv_769[FLOAT, 32x32x1x1] %onnx::Conv_772[FLOAT, 32x1x3x3] %onnx::Conv_775[FLOAT, 32x32x1x1] %onnx::Conv_778[FLOAT, 32x32x1x1] %onnx::Conv_781[FLOAT, 32x1x3x3] %onnx::Conv_784[FLOAT, 32x32x1x1] %onnx::Conv_787[FLOAT, 192x32x1x1] %onnx::Conv_788[FLOAT, 192] %onnx::Conv_790[FLOAT, 192x1x5x5] %onnx::Conv_793[FLOAT, 32x192x1x1] %onnx::Conv_796[FLOAT, 32x16x1x1] %onnx::Conv_799[FLOAT, 32x1x3x3] %onnx::Conv_802[FLOAT, 64x16x1x1] %onnx::Conv_803[FLOAT, 64] %onnx::Conv_805[FLOAT, 192x64x1x1] %onnx::Conv_808[FLOAT, 192x1x5x5] %onnx::Conv_811[FLOAT, 64x192x1x1] %onnx::Conv_814[FLOAT, 64x64x1x1] %onnx::Conv_817[FLOAT, 64x1x5x5] %onnx::Conv_820[FLOAT, 64x64x1x1] %onnx::Conv_823[FLOAT, 64x64x1x1] %onnx::Conv_826[FLOAT, 64x1x5x5] %onnx::Conv_829[FLOAT, 64x64x1x1] %onnx::Conv_832[FLOAT, 192x64x1x1] %onnx::Conv_835[FLOAT, 192x1x5x5] %onnx::Conv_838[FLOAT, 112x192x1x1] %onnx::Conv_839[FLOAT, 112] %onnx::Conv_841[FLOAT, 112x112x1x1] %onnx::Conv_844[FLOAT, 112x1x5x5] %onnx::Conv_847[FLOAT, 112x112x1x1] %onnx::Conv_850[FLOAT, 336x112x1x1] %onnx::Conv_851[FLOAT, 336] %onnx::Conv_853[FLOAT, 336x1x5x5] %onnx::Conv_856[FLOAT, 112x336x1x1] %onnx::Conv_859[FLOAT, 112x56x1x1] %onnx::Conv_862[FLOAT, 112x1x5x5] %onnx::Conv_865[FLOAT, 112x56x1x1] %onnx::Conv_868[FLOAT, 112x56x1x1] %onnx::Conv_871[FLOAT, 112x1x3x3] %onnx::Conv_874[FLOAT, 184x56x1x1] %onnx::Conv_875[FLOAT, 184] %onnx::Conv_877[FLOAT, 552x184x1x1] %onnx::Conv_878[FLOAT, 552] %onnx::Conv_880[FLOAT, 552x1x5x5] %onnx::Conv_883[FLOAT, 184x552x1x1] %onnx::Conv_886[FLOAT, 184x184x1x1] %onnx::Conv_889[FLOAT, 184x1x3x3] %onnx::Conv_892[FLOAT, 184x184x1x1] %onnx::Conv_895[FLOAT, 552x184x1x1] %onnx::Conv_898[FLOAT, 552x1x5x5] %onnx::Conv_901[FLOAT, 184x552x1x1] %onnx::Conv_904[FLOAT, 184x184x1x1] %onnx::Conv_907[FLOAT, 184x1x3x3] %onnx::Conv_910[FLOAT, 352x184x1x1] %onnx::Conv_911[FLOAT, 352] %onnx::Conv_913[FLOAT, 1504x352x1x1] %onnx::Conv_914[FLOAT, 1504] ) { %onnx::Conv_908 = Identity(%onnx::Conv_875) %onnx::Conv_905 = Identity(%onnx::Conv_875) %onnx::Conv_902 = Identity(%onnx::Conv_875) %onnx::Conv_899 = Identity(%onnx::Conv_878) %onnx::Conv_896 = Identity(%onnx::Conv_878) %onnx::Conv_893 = Identity(%onnx::Conv_875) %onnx::Conv_890 = Identity(%onnx::Conv_875) %onnx::Conv_887 = Identity(%onnx::Conv_875) %onnx::Conv_884 = Identity(%onnx::Conv_875) %onnx::Conv_881 = Identity(%onnx::Conv_878) %onnx::Conv_872 = Identity(%onnx::Conv_839) %onnx::Conv_869 = Identity(%onnx::Conv_839) %onnx::Conv_866 = Identity(%onnx::Conv_839) %onnx::Conv_863 = Identity(%onnx::Conv_839) %onnx::Conv_860 = Identity(%onnx::Conv_839) %onnx::Conv_857 = Identity(%onnx::Conv_839) %onnx::Conv_854 = Identity(%onnx::Conv_851) %onnx::Conv_848 = Identity(%onnx::Conv_839) %onnx::Conv_845 = Identity(%onnx::Conv_839) %onnx::Conv_842 = Identity(%onnx::Conv_839) %onnx::Conv_836 = Identity(%onnx::Conv_788) %onnx::Conv_833 = Identity(%onnx::Conv_788) %onnx::Conv_830 = Identity(%onnx::Conv_803) %onnx::Conv_827 = Identity(%onnx::Conv_803) %onnx::Conv_824 = Identity(%onnx::Conv_803) %onnx::Conv_821 = Identity(%onnx::Conv_803) %onnx::Conv_818 = Identity(%onnx::Conv_803) %onnx::Conv_815 = Identity(%onnx::Conv_803) %onnx::Conv_812 = Identity(%onnx::Conv_803) %onnx::Conv_809 = Identity(%onnx::Conv_788) %onnx::Conv_806 = Identity(%onnx::Conv_788) %onnx::Conv_800 = Identity(%onnx::Conv_767) %onnx::Conv_797 = Identity(%onnx::Conv_767) %onnx::Conv_794 = Identity(%onnx::Conv_767) %onnx::Conv_791 = Identity(%onnx::Conv_788) %onnx::Conv_785 = Identity(%onnx::Conv_767) %onnx::Conv_782 = Identity(%onnx::Conv_767) %onnx::Conv_779 = Identity(%onnx::Conv_767) %onnx::Conv_776 = Identity(%onnx::Conv_767) %onnx::Conv_773 = Identity(%onnx::Conv_767) %onnx::Conv_770 = Identity(%onnx::Conv_767) %onnx::Conv_764 = Identity(%onnx::Conv_731) %onnx::Conv_761 = Identity(%onnx::Conv_731) %onnx::Conv_758 = Identity(%onnx::Conv_731) %onnx::Conv_755 = Identity(%onnx::Conv_734) %onnx::Conv_752 = Identity(%onnx::Conv_734) %onnx::Conv_749 = Identity(%onnx::Conv_731) %onnx::Conv_746 = Identity(%onnx::Conv_731) %onnx::Conv_743 = Identity(%onnx::Conv_731) %onnx::Conv_740 = Identity(%onnx::Conv_731) %onnx::Conv_737 = Identity(%onnx::Conv_734) %onnx::Conv_728 = Identity(%onnx::Conv_716) %onnx::Conv_725 = Identity(%onnx::Conv_716) %onnx::Conv_722 = Identity(%onnx::Conv_713) %onnx::Conv_719 = Identity(%onnx::Conv_716) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_712, %onnx::Conv_713) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_715, %onnx::Conv_716) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.0/nl/Relu_output_0, %onnx::Conv_718, %onnx::Conv_719) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_721, %onnx::Conv_722) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_724, %onnx::Conv_725) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.1/nl/Relu_output_0, %onnx::Conv_727, %onnx::Conv_728) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_730, %onnx::Conv_731) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_733, %onnx::Conv_734) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.2/nl/Relu_output_0, %onnx::Conv_736, %onnx::Conv_737) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_739, %onnx::Conv_740) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_742, %onnx::Conv_743) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.3/shuffle/Reshape_output_0 = Reshape(%/cells.3/nl/Relu_output_0, %/cells.3/shuffle/Constant_output_0) %/cells.3/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.3/shuffle/Reshape_output_0) %/cells.3/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.3/shuffle/Reshape_1_output_0 = Reshape(%/cells.3/shuffle/Transpose_output_0, %/cells.3/shuffle/Constant_1_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.3/shuffle/Reshape_1_output_0, %onnx::Conv_745, %onnx::Conv_746) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_748, %onnx::Conv_749) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_751, %onnx::Conv_752) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.4/nl/Relu_output_0, %onnx::Conv_754, %onnx::Conv_755) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_757, %onnx::Conv_758) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/Add_output_0, %onnx::Conv_760, %onnx::Conv_761) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.5/shuffle/Reshape_output_0 = Reshape(%/cells.5/nl/Relu_output_0, %/cells.5/shuffle/Constant_output_0) %/cells.5/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.5/shuffle/Reshape_output_0) %/cells.5/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.5/shuffle/Reshape_1_output_0 = Reshape(%/cells.5/shuffle/Transpose_output_0, %/cells.5/shuffle/Constant_1_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.5/shuffle/Reshape_1_output_0, %onnx::Conv_763, %onnx::Conv_764) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_766, %onnx::Conv_767) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_769, %onnx::Conv_770) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.6/nl/Relu_output_0, %onnx::Conv_772, %onnx::Conv_773) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_775, %onnx::Conv_776) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_778, %onnx::Conv_779) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.7/nl/Relu_output_0, %onnx::Conv_781, %onnx::Conv_782) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_784, %onnx::Conv_785) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_787, %onnx::Conv_788) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.8/nl/Relu_output_0, %onnx::Conv_790, %onnx::Conv_791) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_793, %onnx::Conv_794) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_796, %onnx::Conv_797) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.9/shuffle/Reshape_output_0 = Reshape(%/cells.9/nl/Relu_output_0, %/cells.9/shuffle/Constant_output_0) %/cells.9/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.9/shuffle/Reshape_output_0) %/cells.9/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.9/shuffle/Reshape_1_output_0 = Reshape(%/cells.9/shuffle/Transpose_output_0, %/cells.9/shuffle/Constant_1_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.9/shuffle/Reshape_1_output_0, %onnx::Conv_799, %onnx::Conv_800) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_802, %onnx::Conv_803) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_805, %onnx::Conv_806) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.10/nl/Relu_output_0, %onnx::Conv_808, %onnx::Conv_809) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_811, %onnx::Conv_812) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_814, %onnx::Conv_815) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.11/nl/Relu_output_0, %onnx::Conv_817, %onnx::Conv_818) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_820, %onnx::Conv_821) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_823, %onnx::Conv_824) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.12/nl/Relu_output_0, %onnx::Conv_826, %onnx::Conv_827) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_829, %onnx::Conv_830) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_832, %onnx::Conv_833) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.13/nl/Relu_output_0, %onnx::Conv_835, %onnx::Conv_836) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_838, %onnx::Conv_839) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_841, %onnx::Conv_842) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.14/nl/Relu_output_0, %onnx::Conv_844, %onnx::Conv_845) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_847, %onnx::Conv_848) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_850, %onnx::Conv_851) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.15/nl/Relu_output_0, %onnx::Conv_853, %onnx::Conv_854) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_856, %onnx::Conv_857) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_859, %onnx::Conv_860) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.16/shuffle/Reshape_output_0 = Reshape(%/cells.16/nl/Relu_output_0, %/cells.16/shuffle/Constant_output_0) %/cells.16/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.16/shuffle/Reshape_output_0) %/cells.16/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.16/shuffle/Reshape_1_output_0 = Reshape(%/cells.16/shuffle/Transpose_output_0, %/cells.16/shuffle/Constant_1_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.16/shuffle/Reshape_1_output_0, %onnx::Conv_862, %onnx::Conv_863) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_865, %onnx::Conv_866) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.15/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_868, %onnx::Conv_869) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.17/shuffle/Reshape_output_0 = Reshape(%/cells.17/nl/Relu_output_0, %/cells.17/shuffle/Constant_output_0) %/cells.17/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.17/shuffle/Reshape_output_0) %/cells.17/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.17/shuffle/Reshape_1_output_0 = Reshape(%/cells.17/shuffle/Transpose_output_0, %/cells.17/shuffle/Constant_1_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.17/shuffle/Reshape_1_output_0, %onnx::Conv_871, %onnx::Conv_872) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_874, %onnx::Conv_875) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_877, %onnx::Conv_878) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 552, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.18/nl/Relu_output_0, %onnx::Conv_880, %onnx::Conv_881) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_883, %onnx::Conv_884) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_886, %onnx::Conv_887) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.19/nl/Relu_output_0, %onnx::Conv_889, %onnx::Conv_890) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_892, %onnx::Conv_893) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_895, %onnx::Conv_896) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 552, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.20/nl/Relu_output_0, %onnx::Conv_898, %onnx::Conv_899) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_901, %onnx::Conv_902) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_904, %onnx::Conv_905) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.21/nl/Relu_output_0, %onnx::Conv_907, %onnx::Conv_908) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_910, %onnx::Conv_911) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_913, %onnx::Conv_914) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %710 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %710 }	val_accuracy	72,132,480	1,589,508	{'zcp_synflow': 82.41409011535735, 'zcp_zen': 72.57198333740234, 'zcp_epe_nas': 20.13821753262958, 'zcp_fisher': 0.20046404004096985, 'zcp_flops': 72132480.0, 'zcp_grad_norm': 32.14208221435547, 'zcp_grasp': 0.21848297119140625, 'zcp_jacov': -16.05548384487505, 'zcp_l2_norm': 639.556640625, 'zcp_nwot': 217.4415932932129, 'zcp_params': 1589508.0, 'zcp_plain': -0.005313220899552107, 'zcp_snip': 54.81707763671875, 'lat_1080ti_1': 0.7638787165588634, 'lat_1080ti_32': 0.8507526571093872, 'lat_1080ti_64': 0.772555510528869, 'lat_2080ti_1': 0.8271010551528558, 'lat_2080ti_32': 0.8765037280173938, 'lat_2080ti_64': 0.8185571030798464, 'lat_essential_ph_1': 0.37735849056603776, 'lat_eyeriss': 0.5607797113674289, 'lat_fpga': 0.5166585855787212, 'lat_gold_6226': 0.27705787322895364, 'lat_gold_6240': 0.6127329892705821, 'lat_pixel2': 0.30434782608695654, 'lat_pixel3': 0.5599966066096433, 'lat_raspi4': 0.5517212613308284, 'lat_samsung_a50': 0.7894736842105263, 'lat_samsung_s7': 0.16535433070866143, 'lat_silver_4114': 0.5609719826421874, 'lat_silver_4210r': 0.5936116454790101, 'lat_titan_rtx_1': 0.7808109227135185, 'lat_titan_rtx_32': 0.8226261504980561, 'lat_titan_rtx_64': 0.8694174128317127, 'lat_titanx_1': 0.4108603458308636, 'lat_titanx_32': 0.9641060207690002, 'lat_titanx_64': 0.7528071748687393, 'lat_titanxp_1': 0.7274884145025095, 'lat_titanxp_32': 0.8910315768761846, 'lat_titanxp_64': 0.8161114048583924}
FBNet_2624	FBNet	2624	2624	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_705[FLOAT, 16x3x3x3] %onnx::Conv_706[FLOAT, 16] %onnx::Conv_708[FLOAT, 96x16x1x1] %onnx::Conv_709[FLOAT, 96] %onnx::Conv_711[FLOAT, 96x1x5x5] %onnx::Conv_714[FLOAT, 16x96x1x1] %onnx::Conv_717[FLOAT, 48x16x1x1] %onnx::Conv_718[FLOAT, 48] %onnx::Conv_720[FLOAT, 48x1x3x3] %onnx::Conv_723[FLOAT, 24x48x1x1] %onnx::Conv_724[FLOAT, 24] %onnx::Conv_726[FLOAT, 72x24x1x1] %onnx::Conv_727[FLOAT, 72] %onnx::Conv_729[FLOAT, 72x1x5x5] %onnx::Conv_732[FLOAT, 24x72x1x1] %onnx::Conv_735[FLOAT, 24x12x1x1] %onnx::Conv_738[FLOAT, 24x1x5x5] %onnx::Conv_741[FLOAT, 24x12x1x1] %onnx::Conv_744[FLOAT, 144x24x1x1] %onnx::Conv_745[FLOAT, 144] %onnx::Conv_747[FLOAT, 144x1x5x5] %onnx::Conv_750[FLOAT, 32x144x1x1] %onnx::Conv_751[FLOAT, 32] %onnx::Conv_753[FLOAT, 32x32x1x1] %onnx::Conv_756[FLOAT, 32x1x5x5] %onnx::Conv_759[FLOAT, 32x32x1x1] %onnx::Conv_762[FLOAT, 32x16x1x1] %onnx::Conv_765[FLOAT, 32x1x5x5] %onnx::Conv_768[FLOAT, 32x16x1x1] %onnx::Conv_771[FLOAT, 32x16x1x1] %onnx::Conv_774[FLOAT, 32x1x3x3] %onnx::Conv_777[FLOAT, 32x16x1x1] %onnx::Conv_780[FLOAT, 192x32x1x1] %onnx::Conv_781[FLOAT, 192] %onnx::Conv_783[FLOAT, 192x1x3x3] %onnx::Conv_786[FLOAT, 64x192x1x1] %onnx::Conv_787[FLOAT, 64] %onnx::Conv_789[FLOAT, 384x64x1x1] %onnx::Conv_790[FLOAT, 384] %onnx::Conv_792[FLOAT, 384x1x3x3] %onnx::Conv_795[FLOAT, 64x384x1x1] %onnx::Conv_798[FLOAT, 64x32x1x1] %onnx::Conv_801[FLOAT, 64x1x3x3] %onnx::Conv_804[FLOAT, 64x32x1x1] %onnx::Conv_807[FLOAT, 64x32x1x1] %onnx::Conv_810[FLOAT, 64x1x5x5] %onnx::Conv_813[FLOAT, 112x32x1x1] %onnx::Conv_814[FLOAT, 112] %onnx::Conv_816[FLOAT, 672x112x1x1] %onnx::Conv_817[FLOAT, 672] %onnx::Conv_819[FLOAT, 672x1x5x5] %onnx::Conv_822[FLOAT, 112x672x1x1] %onnx::Conv_825[FLOAT, 336x112x1x1] %onnx::Conv_826[FLOAT, 336] %onnx::Conv_828[FLOAT, 336x1x5x5] %onnx::Conv_831[FLOAT, 112x336x1x1] %onnx::Conv_834[FLOAT, 112x56x1x1] %onnx::Conv_837[FLOAT, 112x1x5x5] %onnx::Conv_840[FLOAT, 112x56x1x1] %onnx::Conv_843[FLOAT, 112x56x1x1] %onnx::Conv_846[FLOAT, 112x1x3x3] %onnx::Conv_849[FLOAT, 184x56x1x1] %onnx::Conv_850[FLOAT, 184] %onnx::Conv_852[FLOAT, 184x92x1x1] %onnx::Conv_855[FLOAT, 184x1x5x5] %onnx::Conv_858[FLOAT, 184x92x1x1] %onnx::Conv_861[FLOAT, 184x184x1x1] %onnx::Conv_864[FLOAT, 184x1x3x3] %onnx::Conv_867[FLOAT, 184x184x1x1] %onnx::Conv_870[FLOAT, 184x92x1x1] %onnx::Conv_873[FLOAT, 184x1x3x3] %onnx::Conv_876[FLOAT, 352x92x1x1] %onnx::Conv_877[FLOAT, 352] %onnx::Conv_879[FLOAT, 1504x352x1x1] %onnx::Conv_880[FLOAT, 1504] ) { %onnx::Conv_874 = Identity(%onnx::Conv_850) %onnx::Conv_871 = Identity(%onnx::Conv_850) %onnx::Conv_868 = Identity(%onnx::Conv_850) %onnx::Conv_865 = Identity(%onnx::Conv_850) %onnx::Conv_862 = Identity(%onnx::Conv_850) %onnx::Conv_859 = Identity(%onnx::Conv_850) %onnx::Conv_856 = Identity(%onnx::Conv_850) %onnx::Conv_853 = Identity(%onnx::Conv_850) %onnx::Conv_847 = Identity(%onnx::Conv_814) %onnx::Conv_844 = Identity(%onnx::Conv_814) %onnx::Conv_841 = Identity(%onnx::Conv_814) %onnx::Conv_838 = Identity(%onnx::Conv_814) %onnx::Conv_835 = Identity(%onnx::Conv_814) %onnx::Conv_832 = Identity(%onnx::Conv_814) %onnx::Conv_829 = Identity(%onnx::Conv_826) %onnx::Conv_823 = Identity(%onnx::Conv_814) %onnx::Conv_820 = Identity(%onnx::Conv_817) %onnx::Conv_811 = Identity(%onnx::Conv_787) %onnx::Conv_808 = Identity(%onnx::Conv_787) %onnx::Conv_805 = Identity(%onnx::Conv_787) %onnx::Conv_802 = Identity(%onnx::Conv_787) %onnx::Conv_799 = Identity(%onnx::Conv_787) %onnx::Conv_796 = Identity(%onnx::Conv_787) %onnx::Conv_793 = Identity(%onnx::Conv_790) %onnx::Conv_784 = Identity(%onnx::Conv_781) %onnx::Conv_778 = Identity(%onnx::Conv_751) %onnx::Conv_775 = Identity(%onnx::Conv_751) %onnx::Conv_772 = Identity(%onnx::Conv_751) %onnx::Conv_769 = Identity(%onnx::Conv_751) %onnx::Conv_766 = Identity(%onnx::Conv_751) %onnx::Conv_763 = Identity(%onnx::Conv_751) %onnx::Conv_760 = Identity(%onnx::Conv_751) %onnx::Conv_757 = Identity(%onnx::Conv_751) %onnx::Conv_754 = Identity(%onnx::Conv_751) %onnx::Conv_748 = Identity(%onnx::Conv_745) %onnx::Conv_742 = Identity(%onnx::Conv_724) %onnx::Conv_739 = Identity(%onnx::Conv_724) %onnx::Conv_736 = Identity(%onnx::Conv_724) %onnx::Conv_733 = Identity(%onnx::Conv_724) %onnx::Conv_730 = Identity(%onnx::Conv_727) %onnx::Conv_721 = Identity(%onnx::Conv_718) %onnx::Conv_715 = Identity(%onnx::Conv_706) %onnx::Conv_712 = Identity(%onnx::Conv_709) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_705, %onnx::Conv_706) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_708, %onnx::Conv_709) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.0/nl/Relu_output_0, %onnx::Conv_711, %onnx::Conv_712) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_714, %onnx::Conv_715) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_717, %onnx::Conv_718) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 48, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.1/nl/Relu_output_0, %onnx::Conv_720, %onnx::Conv_721) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_723, %onnx::Conv_724) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_726, %onnx::Conv_727) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.2/nl/Relu_output_0, %onnx::Conv_729, %onnx::Conv_730) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_732, %onnx::Conv_733) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_735, %onnx::Conv_736) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.3/shuffle/Reshape_output_0 = Reshape(%/cells.3/nl/Relu_output_0, %/cells.3/shuffle/Constant_output_0) %/cells.3/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.3/shuffle/Reshape_output_0) %/cells.3/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.3/shuffle/Reshape_1_output_0 = Reshape(%/cells.3/shuffle/Transpose_output_0, %/cells.3/shuffle/Constant_1_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.3/shuffle/Reshape_1_output_0, %onnx::Conv_738, %onnx::Conv_739) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_741, %onnx::Conv_742) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_744, %onnx::Conv_745) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.5/nl/Relu_output_0, %onnx::Conv_747, %onnx::Conv_748) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_750, %onnx::Conv_751) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_753, %onnx::Conv_754) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.6/nl/Relu_output_0, %onnx::Conv_756, %onnx::Conv_757) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_759, %onnx::Conv_760) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_762, %onnx::Conv_763) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.7/shuffle/Reshape_output_0 = Reshape(%/cells.7/nl/Relu_output_0, %/cells.7/shuffle/Constant_output_0) %/cells.7/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.7/shuffle/Reshape_output_0) %/cells.7/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.7/shuffle/Reshape_1_output_0 = Reshape(%/cells.7/shuffle/Transpose_output_0, %/cells.7/shuffle/Constant_1_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.7/shuffle/Reshape_1_output_0, %onnx::Conv_765, %onnx::Conv_766) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_768, %onnx::Conv_769) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_771, %onnx::Conv_772) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.8/shuffle/Reshape_output_0 = Reshape(%/cells.8/nl/Relu_output_0, %/cells.8/shuffle/Constant_output_0) %/cells.8/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.8/shuffle/Reshape_output_0) %/cells.8/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.8/shuffle/Reshape_1_output_0 = Reshape(%/cells.8/shuffle/Transpose_output_0, %/cells.8/shuffle/Constant_1_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.8/shuffle/Reshape_1_output_0, %onnx::Conv_774, %onnx::Conv_775) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_777, %onnx::Conv_778) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_780, %onnx::Conv_781) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.9/nl/Relu_output_0, %onnx::Conv_783, %onnx::Conv_784) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_786, %onnx::Conv_787) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_789, %onnx::Conv_790) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.10/nl/Relu_output_0, %onnx::Conv_792, %onnx::Conv_793) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_795, %onnx::Conv_796) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_798, %onnx::Conv_799) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.12/shuffle/Reshape_output_0 = Reshape(%/cells.12/nl/Relu_output_0, %/cells.12/shuffle/Constant_output_0) %/cells.12/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.12/shuffle/Reshape_output_0) %/cells.12/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.12/shuffle/Reshape_1_output_0 = Reshape(%/cells.12/shuffle/Transpose_output_0, %/cells.12/shuffle/Constant_1_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.12/shuffle/Reshape_1_output_0, %onnx::Conv_801, %onnx::Conv_802) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_804, %onnx::Conv_805) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_807, %onnx::Conv_808) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.13/shuffle/Reshape_output_0 = Reshape(%/cells.13/nl/Relu_output_0, %/cells.13/shuffle/Constant_output_0) %/cells.13/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.13/shuffle/Reshape_output_0) %/cells.13/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.13/shuffle/Reshape_1_output_0 = Reshape(%/cells.13/shuffle/Transpose_output_0, %/cells.13/shuffle/Constant_1_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.13/shuffle/Reshape_1_output_0, %onnx::Conv_810, %onnx::Conv_811) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_813, %onnx::Conv_814) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_816, %onnx::Conv_817) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.14/nl/Relu_output_0, %onnx::Conv_819, %onnx::Conv_820) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_822, %onnx::Conv_823) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_825, %onnx::Conv_826) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.15/nl/Relu_output_0, %onnx::Conv_828, %onnx::Conv_829) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_831, %onnx::Conv_832) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_834, %onnx::Conv_835) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.16/shuffle/Reshape_output_0 = Reshape(%/cells.16/nl/Relu_output_0, %/cells.16/shuffle/Constant_output_0) %/cells.16/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.16/shuffle/Reshape_output_0) %/cells.16/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.16/shuffle/Reshape_1_output_0 = Reshape(%/cells.16/shuffle/Transpose_output_0, %/cells.16/shuffle/Constant_1_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.16/shuffle/Reshape_1_output_0, %onnx::Conv_837, %onnx::Conv_838) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_840, %onnx::Conv_841) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.15/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_843, %onnx::Conv_844) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.17/shuffle/Reshape_output_0 = Reshape(%/cells.17/nl/Relu_output_0, %/cells.17/shuffle/Constant_output_0) %/cells.17/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.17/shuffle/Reshape_output_0) %/cells.17/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.17/shuffle/Reshape_1_output_0 = Reshape(%/cells.17/shuffle/Transpose_output_0, %/cells.17/shuffle/Constant_1_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.17/shuffle/Reshape_1_output_0, %onnx::Conv_846, %onnx::Conv_847) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_849, %onnx::Conv_850) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_852, %onnx::Conv_853) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.18/shuffle/Reshape_output_0 = Reshape(%/cells.18/nl/Relu_output_0, %/cells.18/shuffle/Constant_output_0) %/cells.18/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.18/shuffle/Reshape_output_0) %/cells.18/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.18/shuffle/Reshape_1_output_0 = Reshape(%/cells.18/shuffle/Transpose_output_0, %/cells.18/shuffle/Constant_1_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.18/shuffle/Reshape_1_output_0, %onnx::Conv_855, %onnx::Conv_856) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_858, %onnx::Conv_859) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_861, %onnx::Conv_862) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.19/nl/Relu_output_0, %onnx::Conv_864, %onnx::Conv_865) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_867, %onnx::Conv_868) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_870, %onnx::Conv_871) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.21/shuffle/Reshape_output_0 = Reshape(%/cells.21/nl/Relu_output_0, %/cells.21/shuffle/Constant_output_0) %/cells.21/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.21/shuffle/Reshape_output_0) %/cells.21/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.21/shuffle/Reshape_1_output_0 = Reshape(%/cells.21/shuffle/Transpose_output_0, %/cells.21/shuffle/Constant_1_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.21/shuffle/Reshape_1_output_0, %onnx::Conv_873, %onnx::Conv_874) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_876, %onnx::Conv_877) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_879, %onnx::Conv_880) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %703 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %703 }	val_accuracy	60,581,248	1,258,060	{'zcp_synflow': 67.15072935550815, 'zcp_zen': 60.004085540771484, 'zcp_epe_nas': 26.523252119186125, 'zcp_fisher': 0.10441358387470245, 'zcp_flops': 60581248.0, 'zcp_grad_norm': 26.43796157836914, 'zcp_grasp': 0.0594940185546875, 'zcp_jacov': -16.068248751288223, 'zcp_l2_norm': 506.2966613769531, 'zcp_nwot': 212.832594540579, 'zcp_params': 1258060.0, 'zcp_plain': -0.005504657980054617, 'zcp_snip': 41.32075500488281, 'lat_1080ti_1': 0.5423606493286267, 'lat_1080ti_32': 0.5682700537212207, 'lat_1080ti_64': 0.518860020264342, 'lat_2080ti_1': 0.6161009038389278, 'lat_2080ti_32': 0.5823894272966744, 'lat_2080ti_64': 0.511968428899135, 'lat_essential_ph_1': 0.1320754716981132, 'lat_eyeriss': 0.3386756642766766, 'lat_fpga': 0.3101257681156552, 'lat_gold_6226': 0.2082046110437166, 'lat_gold_6240': 0.3163655463028865, 'lat_pixel2': 0.1956521739130435, 'lat_pixel3': 0.4157613826425847, 'lat_raspi4': 0.37626892240481663, 'lat_samsung_a50': 0.1368421052631579, 'lat_samsung_s7': 0.48031496062992124, 'lat_silver_4114': 0.35654247652936744, 'lat_silver_4210r': 0.365825167445868, 'lat_titan_rtx_1': 0.5856706994899229, 'lat_titan_rtx_32': 0.5556198617006639, 'lat_titan_rtx_64': 0.5308142382668953, 'lat_titanx_1': 0.2939562574475924, 'lat_titanx_32': 0.5390290009560516, 'lat_titanx_64': 0.5279746515000555, 'lat_titanxp_1': 0.5425887079434754, 'lat_titanxp_32': 0.5329747654916599, 'lat_titanxp_64': 0.5288939215215337}
FBNet_3009	FBNet	3009	3009	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_586[FLOAT, 16x3x3x3] %onnx::Conv_587[FLOAT, 16] %onnx::Conv_589[FLOAT, 96x16x1x1] %onnx::Conv_590[FLOAT, 96] %onnx::Conv_592[FLOAT, 96x1x5x5] %onnx::Conv_595[FLOAT, 16x96x1x1] %onnx::Conv_598[FLOAT, 24x16x1x1] %onnx::Conv_599[FLOAT, 24] %onnx::Conv_601[FLOAT, 72x24x1x1] %onnx::Conv_602[FLOAT, 72] %onnx::Conv_604[FLOAT, 72x1x5x5] %onnx::Conv_607[FLOAT, 24x72x1x1] %onnx::Conv_610[FLOAT, 72x24x1x1] %onnx::Conv_613[FLOAT, 72x1x3x3] %onnx::Conv_616[FLOAT, 24x72x1x1] %onnx::Conv_619[FLOAT, 72x24x1x1] %onnx::Conv_622[FLOAT, 72x1x3x3] %onnx::Conv_625[FLOAT, 24x72x1x1] %onnx::Conv_628[FLOAT, 24x24x1x1] %onnx::Conv_631[FLOAT, 24x1x3x3] %onnx::Conv_634[FLOAT, 32x24x1x1] %onnx::Conv_635[FLOAT, 32] %onnx::Conv_637[FLOAT, 32x32x1x1] %onnx::Conv_640[FLOAT, 32x1x5x5] %onnx::Conv_643[FLOAT, 32x32x1x1] %onnx::Conv_646[FLOAT, 32x16x1x1] %onnx::Conv_649[FLOAT, 32x1x3x3] %onnx::Conv_652[FLOAT, 32x16x1x1] %onnx::Conv_655[FLOAT, 32x32x1x1] %onnx::Conv_658[FLOAT, 32x1x5x5] %onnx::Conv_661[FLOAT, 64x32x1x1] %onnx::Conv_662[FLOAT, 64] %onnx::Conv_664[FLOAT, 384x64x1x1] %onnx::Conv_665[FLOAT, 384] %onnx::Conv_667[FLOAT, 384x1x3x3] %onnx::Conv_670[FLOAT, 64x384x1x1] %onnx::Conv_673[FLOAT, 384x64x1x1] %onnx::Conv_676[FLOAT, 384x1x5x5] %onnx::Conv_679[FLOAT, 64x384x1x1] %onnx::Conv_682[FLOAT, 384x64x1x1] %onnx::Conv_685[FLOAT, 384x1x5x5] %onnx::Conv_688[FLOAT, 112x384x1x1] %onnx::Conv_689[FLOAT, 112] %onnx::Conv_691[FLOAT, 112x112x1x1] %onnx::Conv_694[FLOAT, 112x1x5x5] %onnx::Conv_697[FLOAT, 112x112x1x1] %onnx::Conv_700[FLOAT, 336x112x1x1] %onnx::Conv_701[FLOAT, 336] %onnx::Conv_703[FLOAT, 336x1x5x5] %onnx::Conv_706[FLOAT, 112x336x1x1] %onnx::Conv_709[FLOAT, 112x112x1x1] %onnx::Conv_712[FLOAT, 112x1x5x5] %onnx::Conv_715[FLOAT, 112x112x1x1] %onnx::Conv_718[FLOAT, 112x112x1x1] %onnx::Conv_721[FLOAT, 112x1x5x5] %onnx::Conv_724[FLOAT, 184x112x1x1] %onnx::Conv_725[FLOAT, 184] %onnx::Conv_727[FLOAT, 184x184x1x1] %onnx::Conv_730[FLOAT, 184x1x5x5] %onnx::Conv_733[FLOAT, 184x184x1x1] %onnx::Conv_736[FLOAT, 552x184x1x1] %onnx::Conv_737[FLOAT, 552] %onnx::Conv_739[FLOAT, 552x1x3x3] %onnx::Conv_742[FLOAT, 184x552x1x1] %onnx::Conv_745[FLOAT, 552x184x1x1] %onnx::Conv_748[FLOAT, 552x1x5x5] %onnx::Conv_751[FLOAT, 184x552x1x1] %onnx::Conv_754[FLOAT, 184x92x1x1] %onnx::Conv_757[FLOAT, 184x1x5x5] %onnx::Conv_760[FLOAT, 352x92x1x1] %onnx::Conv_761[FLOAT, 352] %onnx::Conv_763[FLOAT, 1504x352x1x1] %onnx::Conv_764[FLOAT, 1504] ) { %onnx::Conv_758 = Identity(%onnx::Conv_725) %onnx::Conv_755 = Identity(%onnx::Conv_725) %onnx::Conv_752 = Identity(%onnx::Conv_725) %onnx::Conv_749 = Identity(%onnx::Conv_737) %onnx::Conv_746 = Identity(%onnx::Conv_737) %onnx::Conv_743 = Identity(%onnx::Conv_725) %onnx::Conv_740 = Identity(%onnx::Conv_737) %onnx::Conv_734 = Identity(%onnx::Conv_725) %onnx::Conv_731 = Identity(%onnx::Conv_725) %onnx::Conv_728 = Identity(%onnx::Conv_725) %onnx::Conv_722 = Identity(%onnx::Conv_689) %onnx::Conv_719 = Identity(%onnx::Conv_689) %onnx::Conv_716 = Identity(%onnx::Conv_689) %onnx::Conv_713 = Identity(%onnx::Conv_689) %onnx::Conv_710 = Identity(%onnx::Conv_689) %onnx::Conv_707 = Identity(%onnx::Conv_689) %onnx::Conv_704 = Identity(%onnx::Conv_701) %onnx::Conv_698 = Identity(%onnx::Conv_689) %onnx::Conv_695 = Identity(%onnx::Conv_689) %onnx::Conv_692 = Identity(%onnx::Conv_689) %onnx::Conv_686 = Identity(%onnx::Conv_665) %onnx::Conv_683 = Identity(%onnx::Conv_665) %onnx::Conv_680 = Identity(%onnx::Conv_662) %onnx::Conv_677 = Identity(%onnx::Conv_665) %onnx::Conv_674 = Identity(%onnx::Conv_665) %onnx::Conv_671 = Identity(%onnx::Conv_662) %onnx::Conv_668 = Identity(%onnx::Conv_665) %onnx::Conv_659 = Identity(%onnx::Conv_635) %onnx::Conv_656 = Identity(%onnx::Conv_635) %onnx::Conv_653 = Identity(%onnx::Conv_635) %onnx::Conv_650 = Identity(%onnx::Conv_635) %onnx::Conv_647 = Identity(%onnx::Conv_635) %onnx::Conv_644 = Identity(%onnx::Conv_635) %onnx::Conv_641 = Identity(%onnx::Conv_635) %onnx::Conv_638 = Identity(%onnx::Conv_635) %onnx::Conv_632 = Identity(%onnx::Conv_599) %onnx::Conv_629 = Identity(%onnx::Conv_599) %onnx::Conv_626 = Identity(%onnx::Conv_599) %onnx::Conv_623 = Identity(%onnx::Conv_602) %onnx::Conv_620 = Identity(%onnx::Conv_602) %onnx::Conv_617 = Identity(%onnx::Conv_599) %onnx::Conv_614 = Identity(%onnx::Conv_602) %onnx::Conv_611 = Identity(%onnx::Conv_602) %onnx::Conv_608 = Identity(%onnx::Conv_599) %onnx::Conv_605 = Identity(%onnx::Conv_602) %onnx::Conv_596 = Identity(%onnx::Conv_587) %onnx::Conv_593 = Identity(%onnx::Conv_590) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_586, %onnx::Conv_587) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_589, %onnx::Conv_590) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.0/nl/Relu_output_0, %onnx::Conv_592, %onnx::Conv_593) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_595, %onnx::Conv_596) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_598, %onnx::Conv_599) %/cells.1/relu/Relu_output_0 = Relu(%/cells.1/conv/Conv_output_0) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/relu/Relu_output_0, %onnx::Conv_601, %onnx::Conv_602) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.2/nl/Relu_output_0, %onnx::Conv_604, %onnx::Conv_605) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_607, %onnx::Conv_608) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/relu/Relu_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_610, %onnx::Conv_611) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.3/nl/Relu_output_0, %onnx::Conv_613, %onnx::Conv_614) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_616, %onnx::Conv_617) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_619, %onnx::Conv_620) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.4/nl/Relu_output_0, %onnx::Conv_622, %onnx::Conv_623) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_625, %onnx::Conv_626) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/Add_output_0, %onnx::Conv_628, %onnx::Conv_629) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.5/nl/Relu_output_0, %onnx::Conv_631, %onnx::Conv_632) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_634, %onnx::Conv_635) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_637, %onnx::Conv_638) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.6/nl/Relu_output_0, %onnx::Conv_640, %onnx::Conv_641) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_643, %onnx::Conv_644) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_646, %onnx::Conv_647) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.8/shuffle/Reshape_output_0 = Reshape(%/cells.8/nl/Relu_output_0, %/cells.8/shuffle/Constant_output_0) %/cells.8/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.8/shuffle/Reshape_output_0) %/cells.8/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.8/shuffle/Reshape_1_output_0 = Reshape(%/cells.8/shuffle/Transpose_output_0, %/cells.8/shuffle/Constant_1_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.8/shuffle/Reshape_1_output_0, %onnx::Conv_649, %onnx::Conv_650) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_652, %onnx::Conv_653) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_655, %onnx::Conv_656) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.9/nl/Relu_output_0, %onnx::Conv_658, %onnx::Conv_659) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_661, %onnx::Conv_662) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_664, %onnx::Conv_665) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.10/nl/Relu_output_0, %onnx::Conv_667, %onnx::Conv_668) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_670, %onnx::Conv_671) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_673, %onnx::Conv_674) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.12/nl/Relu_output_0, %onnx::Conv_676, %onnx::Conv_677) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_679, %onnx::Conv_680) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_682, %onnx::Conv_683) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.13/nl/Relu_output_0, %onnx::Conv_685, %onnx::Conv_686) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_688, %onnx::Conv_689) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_691, %onnx::Conv_692) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.14/nl/Relu_output_0, %onnx::Conv_694, %onnx::Conv_695) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_697, %onnx::Conv_698) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_700, %onnx::Conv_701) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.15/nl/Relu_output_0, %onnx::Conv_703, %onnx::Conv_704) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_706, %onnx::Conv_707) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_709, %onnx::Conv_710) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.16/nl/Relu_output_0, %onnx::Conv_712, %onnx::Conv_713) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_715, %onnx::Conv_716) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.15/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_718, %onnx::Conv_719) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.17/nl/Relu_output_0, %onnx::Conv_721, %onnx::Conv_722) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_724, %onnx::Conv_725) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_727, %onnx::Conv_728) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.18/nl/Relu_output_0, %onnx::Conv_730, %onnx::Conv_731) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_733, %onnx::Conv_734) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_736, %onnx::Conv_737) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 552, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.19/nl/Relu_output_0, %onnx::Conv_739, %onnx::Conv_740) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_742, %onnx::Conv_743) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_745, %onnx::Conv_746) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 552, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.20/nl/Relu_output_0, %onnx::Conv_748, %onnx::Conv_749) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_751, %onnx::Conv_752) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_754, %onnx::Conv_755) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.21/shuffle/Reshape_output_0 = Reshape(%/cells.21/nl/Relu_output_0, %/cells.21/shuffle/Constant_output_0) %/cells.21/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.21/shuffle/Reshape_output_0) %/cells.21/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.21/shuffle/Reshape_1_output_0 = Reshape(%/cells.21/shuffle/Transpose_output_0, %/cells.21/shuffle/Constant_1_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.21/shuffle/Reshape_1_output_0, %onnx::Conv_757, %onnx::Conv_758) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_760, %onnx::Conv_761) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_763, %onnx::Conv_764) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %584 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %584 }	val_accuracy	65,206,656	1,646,276	{'zcp_synflow': 79.26606757300127, 'zcp_zen': 67.9158706665039, 'zcp_epe_nas': 0.00015999920000638146, 'zcp_fisher': 0.19832275807857513, 'zcp_flops': 65206656.0, 'zcp_grad_norm': 24.702083587646484, 'zcp_grasp': 0.0693817138671875, 'zcp_jacov': -16.04723842803506, 'zcp_l2_norm': 614.22509765625, 'zcp_nwot': 212.3687489879455, 'zcp_params': 1646276.0, 'zcp_plain': 0.006040152162313461, 'zcp_snip': 44.55903625488281, 'lat_1080ti_1': 0.4082218259186606, 'lat_1080ti_32': 0.5101511485706759, 'lat_1080ti_64': 0.471031958977185, 'lat_2080ti_1': 0.5023253697158979, 'lat_2080ti_32': 0.4918560781159881, 'lat_2080ti_64': 0.4349366903838045, 'lat_essential_ph_1': 0.1509433962264151, 'lat_eyeriss': 0.423294152164744, 'lat_fpga': 0.4262064734462046, 'lat_gold_6226': 0.3004172020530827, 'lat_gold_6240': 0.39690258871792283, 'lat_pixel2': 0.2391304347826087, 'lat_pixel3': 0.41690980415254053, 'lat_raspi4': 0.3558163109061589, 'lat_samsung_a50': 0.2736842105263158, 'lat_samsung_s7': 0.07874015748031496, 'lat_silver_4114': 0.3837972485515198, 'lat_silver_4210r': 0.3852950300647068, 'lat_titan_rtx_1': 0.46901334946749873, 'lat_titan_rtx_32': 0.4597066777381853, 'lat_titan_rtx_64': 0.4447950438627867, 'lat_titanx_1': 0.24341430157219004, 'lat_titanx_32': 0.4552007962260998, 'lat_titanx_64': 0.4205783165424812, 'lat_titanxp_1': 0.4576110732475289, 'lat_titanxp_32': 0.4668462819108253, 'lat_titanxp_64': 0.44347545455958554}
FBNet_979	FBNet	979	979	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_704[FLOAT, 16x3x3x3] %onnx::Conv_705[FLOAT, 16] %onnx::Conv_707[FLOAT, 48x16x1x1] %onnx::Conv_708[FLOAT, 48] %onnx::Conv_710[FLOAT, 48x1x5x5] %onnx::Conv_713[FLOAT, 16x48x1x1] %onnx::Conv_716[FLOAT, 48x16x1x1] %onnx::Conv_719[FLOAT, 48x1x3x3] %onnx::Conv_722[FLOAT, 24x48x1x1] %onnx::Conv_723[FLOAT, 24] %onnx::Conv_725[FLOAT, 72x24x1x1] %onnx::Conv_726[FLOAT, 72] %onnx::Conv_728[FLOAT, 72x1x5x5] %onnx::Conv_731[FLOAT, 24x72x1x1] %onnx::Conv_734[FLOAT, 24x12x1x1] %onnx::Conv_737[FLOAT, 24x1x5x5] %onnx::Conv_740[FLOAT, 24x12x1x1] %onnx::Conv_743[FLOAT, 144x24x1x1] %onnx::Conv_744[FLOAT, 144] %onnx::Conv_746[FLOAT, 144x1x3x3] %onnx::Conv_749[FLOAT, 24x144x1x1] %onnx::Conv_752[FLOAT, 24x12x1x1] %onnx::Conv_755[FLOAT, 24x1x5x5] %onnx::Conv_758[FLOAT, 32x12x1x1] %onnx::Conv_759[FLOAT, 32] %onnx::Conv_761[FLOAT, 96x32x1x1] %onnx::Conv_762[FLOAT, 96] %onnx::Conv_764[FLOAT, 96x1x5x5] %onnx::Conv_767[FLOAT, 32x96x1x1] %onnx::Conv_770[FLOAT, 32x32x1x1] %onnx::Conv_773[FLOAT, 32x1x5x5] %onnx::Conv_776[FLOAT, 32x32x1x1] %onnx::Conv_779[FLOAT, 96x32x1x1] %onnx::Conv_782[FLOAT, 96x1x5x5] %onnx::Conv_785[FLOAT, 32x96x1x1] %onnx::Conv_788[FLOAT, 32x16x1x1] %onnx::Conv_791[FLOAT, 32x1x3x3] %onnx::Conv_794[FLOAT, 64x16x1x1] %onnx::Conv_795[FLOAT, 64] %onnx::Conv_797[FLOAT, 192x64x1x1] %onnx::Conv_798[FLOAT, 192] %onnx::Conv_800[FLOAT, 192x1x3x3] %onnx::Conv_803[FLOAT, 64x192x1x1] %onnx::Conv_806[FLOAT, 64x64x1x1] %onnx::Conv_809[FLOAT, 64x1x5x5] %onnx::Conv_812[FLOAT, 64x64x1x1] %onnx::Conv_815[FLOAT, 384x64x1x1] %onnx::Conv_816[FLOAT, 384] %onnx::Conv_818[FLOAT, 384x1x5x5] %onnx::Conv_821[FLOAT, 64x384x1x1] %onnx::Conv_824[FLOAT, 64x64x1x1] %onnx::Conv_827[FLOAT, 64x1x3x3] %onnx::Conv_830[FLOAT, 112x64x1x1] %onnx::Conv_831[FLOAT, 112] %onnx::Conv_833[FLOAT, 112x56x1x1] %onnx::Conv_836[FLOAT, 112x1x3x3] %onnx::Conv_839[FLOAT, 112x56x1x1] %onnx::Conv_842[FLOAT, 336x112x1x1] %onnx::Conv_843[FLOAT, 336] %onnx::Conv_845[FLOAT, 336x1x5x5] %onnx::Conv_848[FLOAT, 112x336x1x1] %onnx::Conv_851[FLOAT, 112x112x1x1] %onnx::Conv_854[FLOAT, 112x1x3x3] %onnx::Conv_857[FLOAT, 184x112x1x1] %onnx::Conv_858[FLOAT, 184] %onnx::Conv_860[FLOAT, 184x92x1x1] %onnx::Conv_863[FLOAT, 184x1x3x3] %onnx::Conv_866[FLOAT, 184x92x1x1] %onnx::Conv_869[FLOAT, 1104x184x1x1] %onnx::Conv_870[FLOAT, 1104] %onnx::Conv_872[FLOAT, 1104x1x3x3] %onnx::Conv_875[FLOAT, 184x1104x1x1] %onnx::Conv_878[FLOAT, 1104x184x1x1] %onnx::Conv_881[FLOAT, 1104x1x5x5] %onnx::Conv_884[FLOAT, 184x1104x1x1] %onnx::Conv_887[FLOAT, 184x92x1x1] %onnx::Conv_890[FLOAT, 184x1x5x5] %onnx::Conv_893[FLOAT, 352x92x1x1] %onnx::Conv_894[FLOAT, 352] %onnx::Conv_896[FLOAT, 1504x352x1x1] %onnx::Conv_897[FLOAT, 1504] ) { %onnx::Conv_891 = Identity(%onnx::Conv_858) %onnx::Conv_888 = Identity(%onnx::Conv_858) %onnx::Conv_885 = Identity(%onnx::Conv_858) %onnx::Conv_882 = Identity(%onnx::Conv_870) %onnx::Conv_879 = Identity(%onnx::Conv_870) %onnx::Conv_876 = Identity(%onnx::Conv_858) %onnx::Conv_873 = Identity(%onnx::Conv_870) %onnx::Conv_867 = Identity(%onnx::Conv_858) %onnx::Conv_864 = Identity(%onnx::Conv_858) %onnx::Conv_861 = Identity(%onnx::Conv_858) %onnx::Conv_855 = Identity(%onnx::Conv_831) %onnx::Conv_852 = Identity(%onnx::Conv_831) %onnx::Conv_849 = Identity(%onnx::Conv_831) %onnx::Conv_846 = Identity(%onnx::Conv_843) %onnx::Conv_840 = Identity(%onnx::Conv_831) %onnx::Conv_837 = Identity(%onnx::Conv_831) %onnx::Conv_834 = Identity(%onnx::Conv_831) %onnx::Conv_828 = Identity(%onnx::Conv_795) %onnx::Conv_825 = Identity(%onnx::Conv_795) %onnx::Conv_822 = Identity(%onnx::Conv_795) %onnx::Conv_819 = Identity(%onnx::Conv_816) %onnx::Conv_813 = Identity(%onnx::Conv_795) %onnx::Conv_810 = Identity(%onnx::Conv_795) %onnx::Conv_807 = Identity(%onnx::Conv_795) %onnx::Conv_804 = Identity(%onnx::Conv_795) %onnx::Conv_801 = Identity(%onnx::Conv_798) %onnx::Conv_792 = Identity(%onnx::Conv_759) %onnx::Conv_789 = Identity(%onnx::Conv_759) %onnx::Conv_786 = Identity(%onnx::Conv_759) %onnx::Conv_783 = Identity(%onnx::Conv_762) %onnx::Conv_780 = Identity(%onnx::Conv_762) %onnx::Conv_777 = Identity(%onnx::Conv_759) %onnx::Conv_774 = Identity(%onnx::Conv_759) %onnx::Conv_771 = Identity(%onnx::Conv_759) %onnx::Conv_768 = Identity(%onnx::Conv_759) %onnx::Conv_765 = Identity(%onnx::Conv_762) %onnx::Conv_756 = Identity(%onnx::Conv_723) %onnx::Conv_753 = Identity(%onnx::Conv_723) %onnx::Conv_750 = Identity(%onnx::Conv_723) %onnx::Conv_747 = Identity(%onnx::Conv_744) %onnx::Conv_741 = Identity(%onnx::Conv_723) %onnx::Conv_738 = Identity(%onnx::Conv_723) %onnx::Conv_735 = Identity(%onnx::Conv_723) %onnx::Conv_732 = Identity(%onnx::Conv_723) %onnx::Conv_729 = Identity(%onnx::Conv_726) %onnx::Conv_720 = Identity(%onnx::Conv_708) %onnx::Conv_717 = Identity(%onnx::Conv_708) %onnx::Conv_714 = Identity(%onnx::Conv_705) %onnx::Conv_711 = Identity(%onnx::Conv_708) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_704, %onnx::Conv_705) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_707, %onnx::Conv_708) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 48, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.0/nl/Relu_output_0, %onnx::Conv_710, %onnx::Conv_711) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_713, %onnx::Conv_714) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_716, %onnx::Conv_717) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 48, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.1/nl/Relu_output_0, %onnx::Conv_719, %onnx::Conv_720) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_722, %onnx::Conv_723) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_725, %onnx::Conv_726) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.2/nl/Relu_output_0, %onnx::Conv_728, %onnx::Conv_729) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_731, %onnx::Conv_732) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_734, %onnx::Conv_735) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.3/shuffle/Reshape_output_0 = Reshape(%/cells.3/nl/Relu_output_0, %/cells.3/shuffle/Constant_output_0) %/cells.3/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.3/shuffle/Reshape_output_0) %/cells.3/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.3/shuffle/Reshape_1_output_0 = Reshape(%/cells.3/shuffle/Transpose_output_0, %/cells.3/shuffle/Constant_1_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.3/shuffle/Reshape_1_output_0, %onnx::Conv_737, %onnx::Conv_738) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_740, %onnx::Conv_741) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_743, %onnx::Conv_744) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.4/nl/Relu_output_0, %onnx::Conv_746, %onnx::Conv_747) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_749, %onnx::Conv_750) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/Add_output_0, %onnx::Conv_752, %onnx::Conv_753) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.5/shuffle/Reshape_output_0 = Reshape(%/cells.5/nl/Relu_output_0, %/cells.5/shuffle/Constant_output_0) %/cells.5/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.5/shuffle/Reshape_output_0) %/cells.5/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.5/shuffle/Reshape_1_output_0 = Reshape(%/cells.5/shuffle/Transpose_output_0, %/cells.5/shuffle/Constant_1_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.5/shuffle/Reshape_1_output_0, %onnx::Conv_755, %onnx::Conv_756) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_758, %onnx::Conv_759) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_761, %onnx::Conv_762) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.6/nl/Relu_output_0, %onnx::Conv_764, %onnx::Conv_765) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_767, %onnx::Conv_768) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_770, %onnx::Conv_771) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.7/nl/Relu_output_0, %onnx::Conv_773, %onnx::Conv_774) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_776, %onnx::Conv_777) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_779, %onnx::Conv_780) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.8/nl/Relu_output_0, %onnx::Conv_782, %onnx::Conv_783) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_785, %onnx::Conv_786) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_788, %onnx::Conv_789) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.9/shuffle/Reshape_output_0 = Reshape(%/cells.9/nl/Relu_output_0, %/cells.9/shuffle/Constant_output_0) %/cells.9/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.9/shuffle/Reshape_output_0) %/cells.9/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.9/shuffle/Reshape_1_output_0 = Reshape(%/cells.9/shuffle/Transpose_output_0, %/cells.9/shuffle/Constant_1_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.9/shuffle/Reshape_1_output_0, %onnx::Conv_791, %onnx::Conv_792) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_794, %onnx::Conv_795) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_797, %onnx::Conv_798) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.10/nl/Relu_output_0, %onnx::Conv_800, %onnx::Conv_801) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_803, %onnx::Conv_804) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_806, %onnx::Conv_807) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.11/nl/Relu_output_0, %onnx::Conv_809, %onnx::Conv_810) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_812, %onnx::Conv_813) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_815, %onnx::Conv_816) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.12/nl/Relu_output_0, %onnx::Conv_818, %onnx::Conv_819) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_821, %onnx::Conv_822) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_824, %onnx::Conv_825) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.13/nl/Relu_output_0, %onnx::Conv_827, %onnx::Conv_828) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_830, %onnx::Conv_831) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_833, %onnx::Conv_834) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.15/shuffle/Reshape_output_0 = Reshape(%/cells.15/nl/Relu_output_0, %/cells.15/shuffle/Constant_output_0) %/cells.15/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.15/shuffle/Reshape_output_0) %/cells.15/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.15/shuffle/Reshape_1_output_0 = Reshape(%/cells.15/shuffle/Transpose_output_0, %/cells.15/shuffle/Constant_1_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.15/shuffle/Reshape_1_output_0, %onnx::Conv_836, %onnx::Conv_837) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_839, %onnx::Conv_840) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_842, %onnx::Conv_843) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.16/nl/Relu_output_0, %onnx::Conv_845, %onnx::Conv_846) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_848, %onnx::Conv_849) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.15/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_851, %onnx::Conv_852) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.17/nl/Relu_output_0, %onnx::Conv_854, %onnx::Conv_855) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_857, %onnx::Conv_858) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_860, %onnx::Conv_861) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.18/shuffle/Reshape_output_0 = Reshape(%/cells.18/nl/Relu_output_0, %/cells.18/shuffle/Constant_output_0) %/cells.18/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.18/shuffle/Reshape_output_0) %/cells.18/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.18/shuffle/Reshape_1_output_0 = Reshape(%/cells.18/shuffle/Transpose_output_0, %/cells.18/shuffle/Constant_1_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.18/shuffle/Reshape_1_output_0, %onnx::Conv_863, %onnx::Conv_864) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_866, %onnx::Conv_867) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_869, %onnx::Conv_870) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.19/nl/Relu_output_0, %onnx::Conv_872, %onnx::Conv_873) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_875, %onnx::Conv_876) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_878, %onnx::Conv_879) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.20/nl/Relu_output_0, %onnx::Conv_881, %onnx::Conv_882) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_884, %onnx::Conv_885) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_887, %onnx::Conv_888) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.21/shuffle/Reshape_output_0 = Reshape(%/cells.21/nl/Relu_output_0, %/cells.21/shuffle/Constant_output_0) %/cells.21/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.21/shuffle/Reshape_output_0) %/cells.21/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.21/shuffle/Reshape_1_output_0 = Reshape(%/cells.21/shuffle/Transpose_output_0, %/cells.21/shuffle/Constant_1_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.21/shuffle/Reshape_1_output_0, %onnx::Conv_890, %onnx::Conv_891) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_893, %onnx::Conv_894) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_896, %onnx::Conv_897) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %702 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %702 }	val_accuracy	67,603,328	1,925,372	{'zcp_synflow': 78.04566251351734, 'zcp_zen': 69.16915893554688, 'zcp_epe_nas': 27.483571251860894, 'zcp_fisher': 0.20160841941833496, 'zcp_flops': 67603328.0, 'zcp_grad_norm': 28.640541076660156, 'zcp_grasp': -0.0546112060546875, 'zcp_jacov': -16.044237079764642, 'zcp_l2_norm': 617.7158813476562, 'zcp_nwot': 213.60562572618028, 'zcp_params': 1925372.0, 'zcp_plain': 0.00012948966468684375, 'zcp_snip': 41.15473556518555, 'lat_1080ti_1': 0.6985827910946548, 'lat_1080ti_32': 0.6222946967358968, 'lat_1080ti_64': 0.5487234831535173, 'lat_2080ti_1': 0.7265128469910751, 'lat_2080ti_32': 0.6818567450265148, 'lat_2080ti_64': 0.5494032279236001, 'lat_essential_ph_1': 0.4528301886792453, 'lat_eyeriss': 0.49209652870738796, 'lat_fpga': 0.475870531075906, 'lat_gold_6226': 0.3662625352116513, 'lat_gold_6240': 0.5836970076980404, 'lat_pixel2': 0.5652173913043478, 'lat_pixel3': 0.4905689305757351, 'lat_raspi4': 0.5258386034660864, 'lat_samsung_a50': 0.29473684210526313, 'lat_samsung_s7': 0.25196850393700787, 'lat_silver_4114': 0.706473917850199, 'lat_silver_4210r': 0.6402609968263787, 'lat_titan_rtx_1': 0.6978564134077572, 'lat_titan_rtx_32': 0.6623592114700674, 'lat_titan_rtx_64': 0.589179213771884, 'lat_titanx_1': 0.3706961197300617, 'lat_titanx_32': 0.6280637998908923, 'lat_titanx_64': 0.537688795565567, 'lat_titanxp_1': 0.6564651901824237, 'lat_titanxp_32': 0.6588898151098105, 'lat_titanxp_64': 0.5786393241077982}
FBNet_2741	FBNet	2741	2741	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_659[FLOAT, 16x3x3x3] %onnx::Conv_660[FLOAT, 16] %onnx::Conv_662[FLOAT, 16x16x1x1] %onnx::Conv_665[FLOAT, 16x1x3x3] %onnx::Conv_668[FLOAT, 16x16x1x1] %onnx::Conv_671[FLOAT, 96x16x1x1] %onnx::Conv_672[FLOAT, 96] %onnx::Conv_674[FLOAT, 96x1x5x5] %onnx::Conv_677[FLOAT, 24x96x1x1] %onnx::Conv_678[FLOAT, 24] %onnx::Conv_680[FLOAT, 144x24x1x1] %onnx::Conv_681[FLOAT, 144] %onnx::Conv_683[FLOAT, 144x1x3x3] %onnx::Conv_686[FLOAT, 24x144x1x1] %onnx::Conv_689[FLOAT, 72x24x1x1] %onnx::Conv_690[FLOAT, 72] %onnx::Conv_692[FLOAT, 72x1x3x3] %onnx::Conv_695[FLOAT, 24x72x1x1] %onnx::Conv_698[FLOAT, 72x24x1x1] %onnx::Conv_701[FLOAT, 72x1x3x3] %onnx::Conv_704[FLOAT, 24x72x1x1] %onnx::Conv_707[FLOAT, 32x24x1x1] %onnx::Conv_708[FLOAT, 32] %onnx::Conv_710[FLOAT, 192x32x1x1] %onnx::Conv_711[FLOAT, 192] %onnx::Conv_713[FLOAT, 192x1x3x3] %onnx::Conv_716[FLOAT, 32x192x1x1] %onnx::Conv_719[FLOAT, 192x32x1x1] %onnx::Conv_722[FLOAT, 192x1x5x5] %onnx::Conv_725[FLOAT, 32x192x1x1] %onnx::Conv_728[FLOAT, 32x16x1x1] %onnx::Conv_731[FLOAT, 32x1x3x3] %onnx::Conv_734[FLOAT, 32x16x1x1] %onnx::Conv_737[FLOAT, 32x16x1x1] %onnx::Conv_740[FLOAT, 32x1x3x3] %onnx::Conv_743[FLOAT, 64x16x1x1] %onnx::Conv_744[FLOAT, 64] %onnx::Conv_746[FLOAT, 192x64x1x1] %onnx::Conv_749[FLOAT, 192x1x3x3] %onnx::Conv_752[FLOAT, 64x192x1x1] %onnx::Conv_755[FLOAT, 64x32x1x1] %onnx::Conv_758[FLOAT, 64x1x5x5] %onnx::Conv_761[FLOAT, 64x32x1x1] %onnx::Conv_764[FLOAT, 64x32x1x1] %onnx::Conv_767[FLOAT, 64x1x3x3] %onnx::Conv_770[FLOAT, 112x32x1x1] %onnx::Conv_771[FLOAT, 112] %onnx::Conv_773[FLOAT, 112x112x1x1] %onnx::Conv_776[FLOAT, 112x1x5x5] %onnx::Conv_779[FLOAT, 112x112x1x1] %onnx::Conv_782[FLOAT, 672x112x1x1] %onnx::Conv_783[FLOAT, 672] %onnx::Conv_785[FLOAT, 672x1x3x3] %onnx::Conv_788[FLOAT, 112x672x1x1] %onnx::Conv_791[FLOAT, 336x112x1x1] %onnx::Conv_792[FLOAT, 336] %onnx::Conv_794[FLOAT, 336x1x5x5] %onnx::Conv_797[FLOAT, 184x336x1x1] %onnx::Conv_798[FLOAT, 184] %onnx::Conv_800[FLOAT, 184x92x1x1] %onnx::Conv_803[FLOAT, 184x1x3x3] %onnx::Conv_806[FLOAT, 184x92x1x1] %onnx::Conv_809[FLOAT, 1104x184x1x1] %onnx::Conv_810[FLOAT, 1104] %onnx::Conv_812[FLOAT, 1104x1x5x5] %onnx::Conv_815[FLOAT, 184x1104x1x1] %onnx::Conv_818[FLOAT, 184x92x1x1] %onnx::Conv_821[FLOAT, 184x1x5x5] %onnx::Conv_824[FLOAT, 184x92x1x1] %onnx::Conv_827[FLOAT, 184x184x1x1] %onnx::Conv_830[FLOAT, 184x1x3x3] %onnx::Conv_833[FLOAT, 352x184x1x1] %onnx::Conv_834[FLOAT, 352] %onnx::Conv_836[FLOAT, 1504x352x1x1] %onnx::Conv_837[FLOAT, 1504] ) { %onnx::Conv_831 = Identity(%onnx::Conv_798) %onnx::Conv_828 = Identity(%onnx::Conv_798) %onnx::Conv_825 = Identity(%onnx::Conv_798) %onnx::Conv_822 = Identity(%onnx::Conv_798) %onnx::Conv_819 = Identity(%onnx::Conv_798) %onnx::Conv_816 = Identity(%onnx::Conv_798) %onnx::Conv_813 = Identity(%onnx::Conv_810) %onnx::Conv_807 = Identity(%onnx::Conv_798) %onnx::Conv_804 = Identity(%onnx::Conv_798) %onnx::Conv_801 = Identity(%onnx::Conv_798) %onnx::Conv_795 = Identity(%onnx::Conv_792) %onnx::Conv_789 = Identity(%onnx::Conv_771) %onnx::Conv_786 = Identity(%onnx::Conv_783) %onnx::Conv_780 = Identity(%onnx::Conv_771) %onnx::Conv_777 = Identity(%onnx::Conv_771) %onnx::Conv_774 = Identity(%onnx::Conv_771) %onnx::Conv_768 = Identity(%onnx::Conv_744) %onnx::Conv_765 = Identity(%onnx::Conv_744) %onnx::Conv_762 = Identity(%onnx::Conv_744) %onnx::Conv_759 = Identity(%onnx::Conv_744) %onnx::Conv_756 = Identity(%onnx::Conv_744) %onnx::Conv_753 = Identity(%onnx::Conv_744) %onnx::Conv_750 = Identity(%onnx::Conv_711) %onnx::Conv_747 = Identity(%onnx::Conv_711) %onnx::Conv_741 = Identity(%onnx::Conv_708) %onnx::Conv_738 = Identity(%onnx::Conv_708) %onnx::Conv_735 = Identity(%onnx::Conv_708) %onnx::Conv_732 = Identity(%onnx::Conv_708) %onnx::Conv_729 = Identity(%onnx::Conv_708) %onnx::Conv_726 = Identity(%onnx::Conv_708) %onnx::Conv_723 = Identity(%onnx::Conv_711) %onnx::Conv_720 = Identity(%onnx::Conv_711) %onnx::Conv_717 = Identity(%onnx::Conv_708) %onnx::Conv_714 = Identity(%onnx::Conv_711) %onnx::Conv_705 = Identity(%onnx::Conv_678) %onnx::Conv_702 = Identity(%onnx::Conv_690) %onnx::Conv_699 = Identity(%onnx::Conv_690) %onnx::Conv_696 = Identity(%onnx::Conv_678) %onnx::Conv_693 = Identity(%onnx::Conv_690) %onnx::Conv_687 = Identity(%onnx::Conv_678) %onnx::Conv_684 = Identity(%onnx::Conv_681) %onnx::Conv_675 = Identity(%onnx::Conv_672) %onnx::Conv_669 = Identity(%onnx::Conv_660) %onnx::Conv_666 = Identity(%onnx::Conv_660) %onnx::Conv_663 = Identity(%onnx::Conv_660) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_659, %onnx::Conv_660) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_662, %onnx::Conv_663) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.0/nl/Relu_output_0, %onnx::Conv_665, %onnx::Conv_666) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_668, %onnx::Conv_669) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_671, %onnx::Conv_672) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.1/nl/Relu_output_0, %onnx::Conv_674, %onnx::Conv_675) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_677, %onnx::Conv_678) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_680, %onnx::Conv_681) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.2/nl/Relu_output_0, %onnx::Conv_683, %onnx::Conv_684) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_686, %onnx::Conv_687) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_689, %onnx::Conv_690) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.3/nl/Relu_output_0, %onnx::Conv_692, %onnx::Conv_693) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_695, %onnx::Conv_696) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_698, %onnx::Conv_699) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.4/nl/Relu_output_0, %onnx::Conv_701, %onnx::Conv_702) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_704, %onnx::Conv_705) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [2, 2]](%/cells.4/Add_output_0, %onnx::Conv_707, %onnx::Conv_708) %/cells.5/relu/Relu_output_0 = Relu(%/cells.5/conv/Conv_output_0) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/relu/Relu_output_0, %onnx::Conv_710, %onnx::Conv_711) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.6/nl/Relu_output_0, %onnx::Conv_713, %onnx::Conv_714) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_716, %onnx::Conv_717) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/relu/Relu_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_719, %onnx::Conv_720) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.7/nl/Relu_output_0, %onnx::Conv_722, %onnx::Conv_723) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_725, %onnx::Conv_726) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_728, %onnx::Conv_729) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.8/shuffle/Reshape_output_0 = Reshape(%/cells.8/nl/Relu_output_0, %/cells.8/shuffle/Constant_output_0) %/cells.8/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.8/shuffle/Reshape_output_0) %/cells.8/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.8/shuffle/Reshape_1_output_0 = Reshape(%/cells.8/shuffle/Transpose_output_0, %/cells.8/shuffle/Constant_1_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.8/shuffle/Reshape_1_output_0, %onnx::Conv_731, %onnx::Conv_732) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_734, %onnx::Conv_735) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_737, %onnx::Conv_738) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.9/shuffle/Reshape_output_0 = Reshape(%/cells.9/nl/Relu_output_0, %/cells.9/shuffle/Constant_output_0) %/cells.9/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.9/shuffle/Reshape_output_0) %/cells.9/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.9/shuffle/Reshape_1_output_0 = Reshape(%/cells.9/shuffle/Transpose_output_0, %/cells.9/shuffle/Constant_1_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.9/shuffle/Reshape_1_output_0, %onnx::Conv_740, %onnx::Conv_741) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_743, %onnx::Conv_744) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_746, %onnx::Conv_747) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.11/nl/Relu_output_0, %onnx::Conv_749, %onnx::Conv_750) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_752, %onnx::Conv_753) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_755, %onnx::Conv_756) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.12/shuffle/Reshape_output_0 = Reshape(%/cells.12/nl/Relu_output_0, %/cells.12/shuffle/Constant_output_0) %/cells.12/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.12/shuffle/Reshape_output_0) %/cells.12/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.12/shuffle/Reshape_1_output_0 = Reshape(%/cells.12/shuffle/Transpose_output_0, %/cells.12/shuffle/Constant_1_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.12/shuffle/Reshape_1_output_0, %onnx::Conv_758, %onnx::Conv_759) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_761, %onnx::Conv_762) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_764, %onnx::Conv_765) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.13/shuffle/Reshape_output_0 = Reshape(%/cells.13/nl/Relu_output_0, %/cells.13/shuffle/Constant_output_0) %/cells.13/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.13/shuffle/Reshape_output_0) %/cells.13/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.13/shuffle/Reshape_1_output_0 = Reshape(%/cells.13/shuffle/Transpose_output_0, %/cells.13/shuffle/Constant_1_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.13/shuffle/Reshape_1_output_0, %onnx::Conv_767, %onnx::Conv_768) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_770, %onnx::Conv_771) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_773, %onnx::Conv_774) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.14/nl/Relu_output_0, %onnx::Conv_776, %onnx::Conv_777) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_779, %onnx::Conv_780) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_782, %onnx::Conv_783) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.16/nl/Relu_output_0, %onnx::Conv_785, %onnx::Conv_786) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_788, %onnx::Conv_789) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_791, %onnx::Conv_792) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.17/nl/Relu_output_0, %onnx::Conv_794, %onnx::Conv_795) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_797, %onnx::Conv_798) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_800, %onnx::Conv_801) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.18/shuffle/Reshape_output_0 = Reshape(%/cells.18/nl/Relu_output_0, %/cells.18/shuffle/Constant_output_0) %/cells.18/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.18/shuffle/Reshape_output_0) %/cells.18/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.18/shuffle/Reshape_1_output_0 = Reshape(%/cells.18/shuffle/Transpose_output_0, %/cells.18/shuffle/Constant_1_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.18/shuffle/Reshape_1_output_0, %onnx::Conv_803, %onnx::Conv_804) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_806, %onnx::Conv_807) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_809, %onnx::Conv_810) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.19/nl/Relu_output_0, %onnx::Conv_812, %onnx::Conv_813) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_815, %onnx::Conv_816) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_818, %onnx::Conv_819) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.20/shuffle/Reshape_output_0 = Reshape(%/cells.20/nl/Relu_output_0, %/cells.20/shuffle/Constant_output_0) %/cells.20/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.20/shuffle/Reshape_output_0) %/cells.20/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.20/shuffle/Reshape_1_output_0 = Reshape(%/cells.20/shuffle/Transpose_output_0, %/cells.20/shuffle/Constant_1_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.20/shuffle/Reshape_1_output_0, %onnx::Conv_821, %onnx::Conv_822) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_824, %onnx::Conv_825) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_827, %onnx::Conv_828) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.21/nl/Relu_output_0, %onnx::Conv_830, %onnx::Conv_831) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_833, %onnx::Conv_834) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_836, %onnx::Conv_837) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %657 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %657 }	val_accuracy	72,856,704	1,699,836	{'zcp_synflow': 70.80075015495724, 'zcp_zen': 63.22147750854492, 'zcp_epe_nas': 7.158469976956584, 'zcp_fisher': 0.09444251656532288, 'zcp_flops': 72856704.0, 'zcp_grad_norm': 23.356863021850586, 'zcp_grasp': -0.07339286804199219, 'zcp_jacov': -16.05398042866809, 'zcp_l2_norm': 576.80908203125, 'zcp_nwot': 215.93345039567674, 'zcp_params': 1699836.0, 'zcp_plain': 0.0004599294625222683, 'zcp_snip': 40.994178771972656, 'lat_1080ti_1': 0.5708173975577565, 'lat_1080ti_32': 0.5941605295645357, 'lat_1080ti_64': 0.5835416700452104, 'lat_2080ti_1': 0.5844782503058678, 'lat_2080ti_32': 0.5992747746160417, 'lat_2080ti_64': 0.6244239450930225, 'lat_essential_ph_1': 0.22641509433962265, 'lat_eyeriss': 0.508435259384226, 'lat_fpga': 0.5326591397104661, 'lat_gold_6226': 0.3051733089367065, 'lat_gold_6240': 0.4376872010312354, 'lat_pixel2': 0.32608695652173914, 'lat_pixel3': 0.4774450993753669, 'lat_raspi4': 0.46071176770338346, 'lat_samsung_a50': 0.21052631578947367, 'lat_samsung_s7': 0.4251968503937008, 'lat_silver_4114': 0.5359832882973946, 'lat_silver_4210r': 0.4763044359066534, 'lat_titan_rtx_1': 0.5575206242233791, 'lat_titan_rtx_32': 0.5507298137518231, 'lat_titan_rtx_64': 0.6138700364951332, 'lat_titanx_1': 0.304243396724978, 'lat_titanx_32': 0.5982378841417486, 'lat_titanx_64': 0.60353107043808, 'lat_titanxp_1': 0.5110617661758738, 'lat_titanxp_32': 0.5736079608826504, 'lat_titanxp_64': 0.5967022768813578}
FBNet_4809	FBNet	4809	4809	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_630[FLOAT, 16x3x3x3] %onnx::Conv_631[FLOAT, 16] %onnx::Conv_633[FLOAT, 96x16x1x1] %onnx::Conv_634[FLOAT, 96] %onnx::Conv_636[FLOAT, 96x1x5x5] %onnx::Conv_639[FLOAT, 16x96x1x1] %onnx::Conv_642[FLOAT, 48x16x1x1] %onnx::Conv_643[FLOAT, 48] %onnx::Conv_645[FLOAT, 48x1x3x3] %onnx::Conv_648[FLOAT, 24x48x1x1] %onnx::Conv_649[FLOAT, 24] %onnx::Conv_651[FLOAT, 72x24x1x1] %onnx::Conv_652[FLOAT, 72] %onnx::Conv_654[FLOAT, 72x1x5x5] %onnx::Conv_657[FLOAT, 24x72x1x1] %onnx::Conv_660[FLOAT, 72x24x1x1] %onnx::Conv_663[FLOAT, 72x1x3x3] %onnx::Conv_666[FLOAT, 24x72x1x1] %onnx::Conv_669[FLOAT, 24x24x1x1] %onnx::Conv_672[FLOAT, 24x1x3x3] %onnx::Conv_675[FLOAT, 24x24x1x1] %onnx::Conv_678[FLOAT, 24x12x1x1] %onnx::Conv_681[FLOAT, 24x1x3x3] %onnx::Conv_684[FLOAT, 32x12x1x1] %onnx::Conv_685[FLOAT, 32] %onnx::Conv_687[FLOAT, 96x32x1x1] %onnx::Conv_690[FLOAT, 96x1x3x3] %onnx::Conv_693[FLOAT, 32x96x1x1] %onnx::Conv_696[FLOAT, 192x32x1x1] %onnx::Conv_697[FLOAT, 192] %onnx::Conv_699[FLOAT, 192x1x3x3] %onnx::Conv_702[FLOAT, 32x192x1x1] %onnx::Conv_705[FLOAT, 32x16x1x1] %onnx::Conv_708[FLOAT, 32x1x5x5] %onnx::Conv_711[FLOAT, 32x16x1x1] %onnx::Conv_714[FLOAT, 96x32x1x1] %onnx::Conv_717[FLOAT, 96x1x5x5] %onnx::Conv_720[FLOAT, 64x96x1x1] %onnx::Conv_721[FLOAT, 64] %onnx::Conv_723[FLOAT, 64x64x1x1] %onnx::Conv_726[FLOAT, 64x1x5x5] %onnx::Conv_729[FLOAT, 64x64x1x1] %onnx::Conv_732[FLOAT, 384x64x1x1] %onnx::Conv_733[FLOAT, 384] %onnx::Conv_735[FLOAT, 384x1x5x5] %onnx::Conv_738[FLOAT, 64x384x1x1] %onnx::Conv_741[FLOAT, 192x64x1x1] %onnx::Conv_744[FLOAT, 192x1x3x3] %onnx::Conv_747[FLOAT, 64x192x1x1] %onnx::Conv_750[FLOAT, 64x64x1x1] %onnx::Conv_753[FLOAT, 64x1x3x3] %onnx::Conv_756[FLOAT, 112x64x1x1] %onnx::Conv_757[FLOAT, 112] %onnx::Conv_759[FLOAT, 112x112x1x1] %onnx::Conv_762[FLOAT, 112x1x3x3] %onnx::Conv_765[FLOAT, 112x112x1x1] %onnx::Conv_768[FLOAT, 112x112x1x1] %onnx::Conv_771[FLOAT, 112x1x3x3] %onnx::Conv_774[FLOAT, 112x112x1x1] %onnx::Conv_777[FLOAT, 112x112x1x1] %onnx::Conv_780[FLOAT, 112x1x5x5] %onnx::Conv_783[FLOAT, 112x112x1x1] %onnx::Conv_786[FLOAT, 336x112x1x1] %onnx::Conv_787[FLOAT, 336] %onnx::Conv_789[FLOAT, 336x1x3x3] %onnx::Conv_792[FLOAT, 184x336x1x1] %onnx::Conv_793[FLOAT, 184] %onnx::Conv_795[FLOAT, 552x184x1x1] %onnx::Conv_796[FLOAT, 552] %onnx::Conv_798[FLOAT, 552x1x5x5] %onnx::Conv_801[FLOAT, 184x552x1x1] %onnx::Conv_804[FLOAT, 1104x184x1x1] %onnx::Conv_805[FLOAT, 1104] %onnx::Conv_807[FLOAT, 1104x1x5x5] %onnx::Conv_810[FLOAT, 184x1104x1x1] %onnx::Conv_813[FLOAT, 552x184x1x1] %onnx::Conv_816[FLOAT, 552x1x5x5] %onnx::Conv_819[FLOAT, 352x552x1x1] %onnx::Conv_820[FLOAT, 352] %onnx::Conv_822[FLOAT, 1504x352x1x1] %onnx::Conv_823[FLOAT, 1504] ) { %onnx::Conv_817 = Identity(%onnx::Conv_796) %onnx::Conv_814 = Identity(%onnx::Conv_796) %onnx::Conv_811 = Identity(%onnx::Conv_793) %onnx::Conv_808 = Identity(%onnx::Conv_805) %onnx::Conv_802 = Identity(%onnx::Conv_793) %onnx::Conv_799 = Identity(%onnx::Conv_796) %onnx::Conv_790 = Identity(%onnx::Conv_787) %onnx::Conv_784 = Identity(%onnx::Conv_757) %onnx::Conv_781 = Identity(%onnx::Conv_757) %onnx::Conv_778 = Identity(%onnx::Conv_757) %onnx::Conv_775 = Identity(%onnx::Conv_757) %onnx::Conv_772 = Identity(%onnx::Conv_757) %onnx::Conv_769 = Identity(%onnx::Conv_757) %onnx::Conv_766 = Identity(%onnx::Conv_757) %onnx::Conv_763 = Identity(%onnx::Conv_757) %onnx::Conv_760 = Identity(%onnx::Conv_757) %onnx::Conv_754 = Identity(%onnx::Conv_721) %onnx::Conv_751 = Identity(%onnx::Conv_721) %onnx::Conv_748 = Identity(%onnx::Conv_721) %onnx::Conv_745 = Identity(%onnx::Conv_697) %onnx::Conv_742 = Identity(%onnx::Conv_697) %onnx::Conv_739 = Identity(%onnx::Conv_721) %onnx::Conv_736 = Identity(%onnx::Conv_733) %onnx::Conv_730 = Identity(%onnx::Conv_721) %onnx::Conv_727 = Identity(%onnx::Conv_721) %onnx::Conv_724 = Identity(%onnx::Conv_721) %onnx::Conv_718 = Identity(%onnx::Conv_634) %onnx::Conv_715 = Identity(%onnx::Conv_634) %onnx::Conv_712 = Identity(%onnx::Conv_685) %onnx::Conv_709 = Identity(%onnx::Conv_685) %onnx::Conv_706 = Identity(%onnx::Conv_685) %onnx::Conv_703 = Identity(%onnx::Conv_685) %onnx::Conv_700 = Identity(%onnx::Conv_697) %onnx::Conv_694 = Identity(%onnx::Conv_685) %onnx::Conv_691 = Identity(%onnx::Conv_634) %onnx::Conv_688 = Identity(%onnx::Conv_634) %onnx::Conv_682 = Identity(%onnx::Conv_649) %onnx::Conv_679 = Identity(%onnx::Conv_649) %onnx::Conv_676 = Identity(%onnx::Conv_649) %onnx::Conv_673 = Identity(%onnx::Conv_649) %onnx::Conv_670 = Identity(%onnx::Conv_649) %onnx::Conv_667 = Identity(%onnx::Conv_649) %onnx::Conv_664 = Identity(%onnx::Conv_652) %onnx::Conv_661 = Identity(%onnx::Conv_652) %onnx::Conv_658 = Identity(%onnx::Conv_649) %onnx::Conv_655 = Identity(%onnx::Conv_652) %onnx::Conv_646 = Identity(%onnx::Conv_643) %onnx::Conv_640 = Identity(%onnx::Conv_631) %onnx::Conv_637 = Identity(%onnx::Conv_634) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_630, %onnx::Conv_631) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_633, %onnx::Conv_634) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.0/nl/Relu_output_0, %onnx::Conv_636, %onnx::Conv_637) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_639, %onnx::Conv_640) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_642, %onnx::Conv_643) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 48, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.1/nl/Relu_output_0, %onnx::Conv_645, %onnx::Conv_646) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_648, %onnx::Conv_649) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_651, %onnx::Conv_652) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.2/nl/Relu_output_0, %onnx::Conv_654, %onnx::Conv_655) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_657, %onnx::Conv_658) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_660, %onnx::Conv_661) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.3/nl/Relu_output_0, %onnx::Conv_663, %onnx::Conv_664) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_666, %onnx::Conv_667) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_669, %onnx::Conv_670) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.4/nl/Relu_output_0, %onnx::Conv_672, %onnx::Conv_673) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_675, %onnx::Conv_676) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/Add_output_0, %onnx::Conv_678, %onnx::Conv_679) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.5/shuffle/Reshape_output_0 = Reshape(%/cells.5/nl/Relu_output_0, %/cells.5/shuffle/Constant_output_0) %/cells.5/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.5/shuffle/Reshape_output_0) %/cells.5/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.5/shuffle/Reshape_1_output_0 = Reshape(%/cells.5/shuffle/Transpose_output_0, %/cells.5/shuffle/Constant_1_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.5/shuffle/Reshape_1_output_0, %onnx::Conv_681, %onnx::Conv_682) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_684, %onnx::Conv_685) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_687, %onnx::Conv_688) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.6/nl/Relu_output_0, %onnx::Conv_690, %onnx::Conv_691) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_693, %onnx::Conv_694) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_696, %onnx::Conv_697) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.7/nl/Relu_output_0, %onnx::Conv_699, %onnx::Conv_700) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_702, %onnx::Conv_703) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_705, %onnx::Conv_706) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.8/shuffle/Reshape_output_0 = Reshape(%/cells.8/nl/Relu_output_0, %/cells.8/shuffle/Constant_output_0) %/cells.8/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.8/shuffle/Reshape_output_0) %/cells.8/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.8/shuffle/Reshape_1_output_0 = Reshape(%/cells.8/shuffle/Transpose_output_0, %/cells.8/shuffle/Constant_1_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.8/shuffle/Reshape_1_output_0, %onnx::Conv_708, %onnx::Conv_709) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_711, %onnx::Conv_712) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_714, %onnx::Conv_715) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.9/nl/Relu_output_0, %onnx::Conv_717, %onnx::Conv_718) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_720, %onnx::Conv_721) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_723, %onnx::Conv_724) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.10/nl/Relu_output_0, %onnx::Conv_726, %onnx::Conv_727) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_729, %onnx::Conv_730) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_732, %onnx::Conv_733) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.11/nl/Relu_output_0, %onnx::Conv_735, %onnx::Conv_736) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_738, %onnx::Conv_739) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_741, %onnx::Conv_742) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.12/nl/Relu_output_0, %onnx::Conv_744, %onnx::Conv_745) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_747, %onnx::Conv_748) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_750, %onnx::Conv_751) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.13/nl/Relu_output_0, %onnx::Conv_753, %onnx::Conv_754) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_756, %onnx::Conv_757) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_759, %onnx::Conv_760) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.14/nl/Relu_output_0, %onnx::Conv_762, %onnx::Conv_763) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_765, %onnx::Conv_766) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_768, %onnx::Conv_769) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.15/nl/Relu_output_0, %onnx::Conv_771, %onnx::Conv_772) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_774, %onnx::Conv_775) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_777, %onnx::Conv_778) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.16/nl/Relu_output_0, %onnx::Conv_780, %onnx::Conv_781) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_783, %onnx::Conv_784) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.15/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_786, %onnx::Conv_787) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.17/nl/Relu_output_0, %onnx::Conv_789, %onnx::Conv_790) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_792, %onnx::Conv_793) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_795, %onnx::Conv_796) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 552, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.18/nl/Relu_output_0, %onnx::Conv_798, %onnx::Conv_799) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_801, %onnx::Conv_802) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_804, %onnx::Conv_805) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.19/nl/Relu_output_0, %onnx::Conv_807, %onnx::Conv_808) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_810, %onnx::Conv_811) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_813, %onnx::Conv_814) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 552, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.21/nl/Relu_output_0, %onnx::Conv_816, %onnx::Conv_817) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_819, %onnx::Conv_820) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_822, %onnx::Conv_823) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %628 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %628 }	val_accuracy	69,728,384	2,008,052	{'zcp_synflow': 81.96932481184571, 'zcp_zen': 71.54882049560547, 'zcp_epe_nas': 12.931266673476102, 'zcp_fisher': 0.1710318624973297, 'zcp_flops': 69728384.0, 'zcp_grad_norm': 29.785842895507812, 'zcp_grasp': -0.39174652099609375, 'zcp_jacov': -16.066411981054273, 'zcp_l2_norm': 656.1029663085938, 'zcp_nwot': 213.68795261835785, 'zcp_params': 2008052.0, 'zcp_plain': -0.003771368646994233, 'zcp_snip': 53.1436882019043, 'lat_1080ti_1': 0.5984367670532972, 'lat_1080ti_32': 0.5736473947391637, 'lat_1080ti_64': 0.4947695279862751, 'lat_2080ti_1': 0.670345874796053, 'lat_2080ti_32': 0.6038472768060736, 'lat_2080ti_64': 0.507735094393635, 'lat_essential_ph_1': 0.18867924528301888, 'lat_eyeriss': 0.5104615553884805, 'lat_fpga': 0.4901097378758941, 'lat_gold_6226': 0.38583377086502474, 'lat_gold_6240': 0.5037925772867117, 'lat_pixel2': 0.4782608695652174, 'lat_pixel3': 0.47487644629873815, 'lat_raspi4': 0.5063253994384515, 'lat_samsung_a50': 0.2, 'lat_samsung_s7': 0.12598425196850394, 'lat_silver_4114': 0.544268563135819, 'lat_silver_4210r': 0.5421605858176445, 'lat_titan_rtx_1': 0.6252104304604963, 'lat_titan_rtx_32': 0.5889926694645862, 'lat_titan_rtx_64': 0.5472837968525062, 'lat_titanx_1': 0.3356450369016482, 'lat_titanx_32': 0.5467646878262908, 'lat_titanx_64': 0.5323869906389195, 'lat_titanxp_1': 0.6406031749960691, 'lat_titanxp_32': 0.5707664022260868, 'lat_titanxp_64': 0.5374112268833166}
FBNet_3018	FBNet	3018	3018	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_662[FLOAT, 16x3x3x3] %onnx::Conv_663[FLOAT, 16] %onnx::Conv_665[FLOAT, 48x16x1x1] %onnx::Conv_666[FLOAT, 48] %onnx::Conv_668[FLOAT, 48x1x3x3] %onnx::Conv_671[FLOAT, 16x48x1x1] %onnx::Conv_674[FLOAT, 16x16x1x1] %onnx::Conv_677[FLOAT, 16x1x3x3] %onnx::Conv_680[FLOAT, 24x16x1x1] %onnx::Conv_681[FLOAT, 24] %onnx::Conv_683[FLOAT, 24x12x1x1] %onnx::Conv_686[FLOAT, 24x1x3x3] %onnx::Conv_689[FLOAT, 24x12x1x1] %onnx::Conv_692[FLOAT, 32x24x1x1] %onnx::Conv_693[FLOAT, 32] %onnx::Conv_695[FLOAT, 32x16x1x1] %onnx::Conv_698[FLOAT, 32x1x3x3] %onnx::Conv_701[FLOAT, 32x16x1x1] %onnx::Conv_704[FLOAT, 192x32x1x1] %onnx::Conv_705[FLOAT, 192] %onnx::Conv_707[FLOAT, 192x1x3x3] %onnx::Conv_710[FLOAT, 32x192x1x1] %onnx::Conv_713[FLOAT, 32x16x1x1] %onnx::Conv_716[FLOAT, 32x1x5x5] %onnx::Conv_719[FLOAT, 32x16x1x1] %onnx::Conv_722[FLOAT, 32x32x1x1] %onnx::Conv_725[FLOAT, 32x1x5x5] %onnx::Conv_728[FLOAT, 64x32x1x1] %onnx::Conv_729[FLOAT, 64] %onnx::Conv_731[FLOAT, 192x64x1x1] %onnx::Conv_734[FLOAT, 192x1x5x5] %onnx::Conv_737[FLOAT, 64x192x1x1] %onnx::Conv_740[FLOAT, 384x64x1x1] %onnx::Conv_741[FLOAT, 384] %onnx::Conv_743[FLOAT, 384x1x3x3] %onnx::Conv_746[FLOAT, 64x384x1x1] %onnx::Conv_749[FLOAT, 64x32x1x1] %onnx::Conv_752[FLOAT, 64x1x3x3] %onnx::Conv_755[FLOAT, 64x32x1x1] %onnx::Conv_758[FLOAT, 112x64x1x1] %onnx::Conv_759[FLOAT, 112] %onnx::Conv_761[FLOAT, 112x56x1x1] %onnx::Conv_764[FLOAT, 112x1x3x3] %onnx::Conv_767[FLOAT, 112x56x1x1] %onnx::Conv_770[FLOAT, 336x112x1x1] %onnx::Conv_771[FLOAT, 336] %onnx::Conv_773[FLOAT, 336x1x3x3] %onnx::Conv_776[FLOAT, 112x336x1x1] %onnx::Conv_779[FLOAT, 112x112x1x1] %onnx::Conv_782[FLOAT, 112x1x5x5] %onnx::Conv_785[FLOAT, 112x112x1x1] %onnx::Conv_788[FLOAT, 672x112x1x1] %onnx::Conv_789[FLOAT, 672] %onnx::Conv_791[FLOAT, 672x1x5x5] %onnx::Conv_794[FLOAT, 184x672x1x1] %onnx::Conv_795[FLOAT, 184] %onnx::Conv_797[FLOAT, 184x92x1x1] %onnx::Conv_800[FLOAT, 184x1x3x3] %onnx::Conv_803[FLOAT, 184x92x1x1] %onnx::Conv_806[FLOAT, 184x92x1x1] %onnx::Conv_809[FLOAT, 184x1x5x5] %onnx::Conv_812[FLOAT, 184x92x1x1] %onnx::Conv_815[FLOAT, 1104x184x1x1] %onnx::Conv_816[FLOAT, 1104] %onnx::Conv_818[FLOAT, 1104x1x3x3] %onnx::Conv_821[FLOAT, 184x1104x1x1] %onnx::Conv_824[FLOAT, 552x184x1x1] %onnx::Conv_825[FLOAT, 552] %onnx::Conv_827[FLOAT, 552x1x3x3] %onnx::Conv_830[FLOAT, 352x552x1x1] %onnx::Conv_831[FLOAT, 352] %onnx::Conv_833[FLOAT, 1504x352x1x1] %onnx::Conv_834[FLOAT, 1504] ) { %onnx::Conv_828 = Identity(%onnx::Conv_825) %onnx::Conv_822 = Identity(%onnx::Conv_795) %onnx::Conv_819 = Identity(%onnx::Conv_816) %onnx::Conv_813 = Identity(%onnx::Conv_795) %onnx::Conv_810 = Identity(%onnx::Conv_795) %onnx::Conv_807 = Identity(%onnx::Conv_795) %onnx::Conv_804 = Identity(%onnx::Conv_795) %onnx::Conv_801 = Identity(%onnx::Conv_795) %onnx::Conv_798 = Identity(%onnx::Conv_795) %onnx::Conv_792 = Identity(%onnx::Conv_789) %onnx::Conv_786 = Identity(%onnx::Conv_759) %onnx::Conv_783 = Identity(%onnx::Conv_759) %onnx::Conv_780 = Identity(%onnx::Conv_759) %onnx::Conv_777 = Identity(%onnx::Conv_759) %onnx::Conv_774 = Identity(%onnx::Conv_771) %onnx::Conv_768 = Identity(%onnx::Conv_759) %onnx::Conv_765 = Identity(%onnx::Conv_759) %onnx::Conv_762 = Identity(%onnx::Conv_759) %onnx::Conv_756 = Identity(%onnx::Conv_729) %onnx::Conv_753 = Identity(%onnx::Conv_729) %onnx::Conv_750 = Identity(%onnx::Conv_729) %onnx::Conv_747 = Identity(%onnx::Conv_729) %onnx::Conv_744 = Identity(%onnx::Conv_741) %onnx::Conv_738 = Identity(%onnx::Conv_729) %onnx::Conv_735 = Identity(%onnx::Conv_705) %onnx::Conv_732 = Identity(%onnx::Conv_705) %onnx::Conv_726 = Identity(%onnx::Conv_693) %onnx::Conv_723 = Identity(%onnx::Conv_693) %onnx::Conv_720 = Identity(%onnx::Conv_693) %onnx::Conv_717 = Identity(%onnx::Conv_693) %onnx::Conv_714 = Identity(%onnx::Conv_693) %onnx::Conv_711 = Identity(%onnx::Conv_693) %onnx::Conv_708 = Identity(%onnx::Conv_705) %onnx::Conv_702 = Identity(%onnx::Conv_693) %onnx::Conv_699 = Identity(%onnx::Conv_693) %onnx::Conv_696 = Identity(%onnx::Conv_693) %onnx::Conv_690 = Identity(%onnx::Conv_681) %onnx::Conv_687 = Identity(%onnx::Conv_681) %onnx::Conv_684 = Identity(%onnx::Conv_681) %onnx::Conv_678 = Identity(%onnx::Conv_663) %onnx::Conv_675 = Identity(%onnx::Conv_663) %onnx::Conv_672 = Identity(%onnx::Conv_663) %onnx::Conv_669 = Identity(%onnx::Conv_666) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_662, %onnx::Conv_663) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_665, %onnx::Conv_666) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 48, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.0/nl/Relu_output_0, %onnx::Conv_668, %onnx::Conv_669) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_671, %onnx::Conv_672) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_674, %onnx::Conv_675) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.1/nl/Relu_output_0, %onnx::Conv_677, %onnx::Conv_678) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_680, %onnx::Conv_681) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_683, %onnx::Conv_684) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.4/shuffle/Reshape_output_0 = Reshape(%/cells.4/nl/Relu_output_0, %/cells.4/shuffle/Constant_output_0) %/cells.4/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.4/shuffle/Reshape_output_0) %/cells.4/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.4/shuffle/Reshape_1_output_0 = Reshape(%/cells.4/shuffle/Transpose_output_0, %/cells.4/shuffle/Constant_1_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.4/shuffle/Reshape_1_output_0, %onnx::Conv_686, %onnx::Conv_687) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_689, %onnx::Conv_690) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.5/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [2, 2]](%/cells.4/Add_output_0, %onnx::Conv_692, %onnx::Conv_693) %/cells.5/relu/Relu_output_0 = Relu(%/cells.5/conv/Conv_output_0) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/relu/Relu_output_0, %onnx::Conv_695, %onnx::Conv_696) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.6/shuffle/Reshape_output_0 = Reshape(%/cells.6/nl/Relu_output_0, %/cells.6/shuffle/Constant_output_0) %/cells.6/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.6/shuffle/Reshape_output_0) %/cells.6/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.6/shuffle/Reshape_1_output_0 = Reshape(%/cells.6/shuffle/Transpose_output_0, %/cells.6/shuffle/Constant_1_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.6/shuffle/Reshape_1_output_0, %onnx::Conv_698, %onnx::Conv_699) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_701, %onnx::Conv_702) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/relu/Relu_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_704, %onnx::Conv_705) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.7/nl/Relu_output_0, %onnx::Conv_707, %onnx::Conv_708) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_710, %onnx::Conv_711) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_713, %onnx::Conv_714) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.8/shuffle/Reshape_output_0 = Reshape(%/cells.8/nl/Relu_output_0, %/cells.8/shuffle/Constant_output_0) %/cells.8/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.8/shuffle/Reshape_output_0) %/cells.8/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.8/shuffle/Reshape_1_output_0 = Reshape(%/cells.8/shuffle/Transpose_output_0, %/cells.8/shuffle/Constant_1_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.8/shuffle/Reshape_1_output_0, %onnx::Conv_716, %onnx::Conv_717) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_719, %onnx::Conv_720) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_722, %onnx::Conv_723) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.9/nl/Relu_output_0, %onnx::Conv_725, %onnx::Conv_726) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_728, %onnx::Conv_729) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_731, %onnx::Conv_732) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.10/nl/Relu_output_0, %onnx::Conv_734, %onnx::Conv_735) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_737, %onnx::Conv_738) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_740, %onnx::Conv_741) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.11/nl/Relu_output_0, %onnx::Conv_743, %onnx::Conv_744) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_746, %onnx::Conv_747) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_749, %onnx::Conv_750) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.12/shuffle/Reshape_output_0 = Reshape(%/cells.12/nl/Relu_output_0, %/cells.12/shuffle/Constant_output_0) %/cells.12/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.12/shuffle/Reshape_output_0) %/cells.12/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.12/shuffle/Reshape_1_output_0 = Reshape(%/cells.12/shuffle/Transpose_output_0, %/cells.12/shuffle/Constant_1_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.12/shuffle/Reshape_1_output_0, %onnx::Conv_752, %onnx::Conv_753) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_755, %onnx::Conv_756) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_758, %onnx::Conv_759) %/cells.13/relu/Relu_output_0 = Relu(%/cells.13/conv/Conv_output_0) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/relu/Relu_output_0, %onnx::Conv_761, %onnx::Conv_762) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.14/shuffle/Reshape_output_0 = Reshape(%/cells.14/nl/Relu_output_0, %/cells.14/shuffle/Constant_output_0) %/cells.14/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.14/shuffle/Reshape_output_0) %/cells.14/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.14/shuffle/Reshape_1_output_0 = Reshape(%/cells.14/shuffle/Transpose_output_0, %/cells.14/shuffle/Constant_1_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.14/shuffle/Reshape_1_output_0, %onnx::Conv_764, %onnx::Conv_765) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_767, %onnx::Conv_768) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/relu/Relu_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_770, %onnx::Conv_771) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.15/nl/Relu_output_0, %onnx::Conv_773, %onnx::Conv_774) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_776, %onnx::Conv_777) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_779, %onnx::Conv_780) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.16/nl/Relu_output_0, %onnx::Conv_782, %onnx::Conv_783) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_785, %onnx::Conv_786) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.15/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_788, %onnx::Conv_789) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.17/nl/Relu_output_0, %onnx::Conv_791, %onnx::Conv_792) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_794, %onnx::Conv_795) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_797, %onnx::Conv_798) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.18/shuffle/Reshape_output_0 = Reshape(%/cells.18/nl/Relu_output_0, %/cells.18/shuffle/Constant_output_0) %/cells.18/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.18/shuffle/Reshape_output_0) %/cells.18/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.18/shuffle/Reshape_1_output_0 = Reshape(%/cells.18/shuffle/Transpose_output_0, %/cells.18/shuffle/Constant_1_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.18/shuffle/Reshape_1_output_0, %onnx::Conv_800, %onnx::Conv_801) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_803, %onnx::Conv_804) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_806, %onnx::Conv_807) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.19/shuffle/Reshape_output_0 = Reshape(%/cells.19/nl/Relu_output_0, %/cells.19/shuffle/Constant_output_0) %/cells.19/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.19/shuffle/Reshape_output_0) %/cells.19/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.19/shuffle/Reshape_1_output_0 = Reshape(%/cells.19/shuffle/Transpose_output_0, %/cells.19/shuffle/Constant_1_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.19/shuffle/Reshape_1_output_0, %onnx::Conv_809, %onnx::Conv_810) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_812, %onnx::Conv_813) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_815, %onnx::Conv_816) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.20/nl/Relu_output_0, %onnx::Conv_818, %onnx::Conv_819) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_821, %onnx::Conv_822) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_824, %onnx::Conv_825) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 552, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.21/nl/Relu_output_0, %onnx::Conv_827, %onnx::Conv_828) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_830, %onnx::Conv_831) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_833, %onnx::Conv_834) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %660 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %660 }	val_accuracy	53,059,200	1,950,052	{'zcp_synflow': 69.26715946296319, 'zcp_zen': 62.08187484741211, 'zcp_epe_nas': 0.00015999920000638146, 'zcp_fisher': 0.07738900184631348, 'zcp_flops': 53059200.0, 'zcp_grad_norm': 19.711544036865234, 'zcp_grasp': 0.3363170623779297, 'zcp_jacov': -16.0559589127272, 'zcp_l2_norm': 579.224365234375, 'zcp_nwot': 204.60586821876552, 'zcp_params': 1950052.0, 'zcp_plain': 0.006845320109277964, 'zcp_snip': 34.83905792236328, 'lat_1080ti_1': 0.4856399791304593, 'lat_1080ti_32': 0.47218536104067826, 'lat_1080ti_64': 0.19799004705196221, 'lat_2080ti_1': 0.5463375225347561, 'lat_2080ti_32': 0.39997964592427304, 'lat_2080ti_64': 0.22122650613814396, 'lat_essential_ph_1': 0.11320754716981132, 'lat_eyeriss': 0.23614141895732843, 'lat_fpga': 0.2728999891152692, 'lat_gold_6226': 0.4289212959835446, 'lat_gold_6240': 0.3991155704522727, 'lat_pixel2': 0.21739130434782608, 'lat_pixel3': 0.20104948134743061, 'lat_raspi4': 0.28875271526831153, 'lat_samsung_a50': 0.5473684210526316, 'lat_samsung_s7': 0.07874015748031496, 'lat_silver_4114': 0.4439681398778762, 'lat_silver_4210r': 0.37516378080560153, 'lat_titan_rtx_1': 0.5120521140673475, 'lat_titan_rtx_32': 0.40831451416511166, 'lat_titan_rtx_64': 0.25932678946194965, 'lat_titanx_1': 0.28488946151097255, 'lat_titanx_32': 0.2891099757090886, 'lat_titanx_64': 0.16361957334494542, 'lat_titanxp_1': 0.49834304938911106, 'lat_titanxp_32': 0.3513477172791263, 'lat_titanxp_64': 0.2075082425013706}
FBNet_4894	FBNet	4894	4894	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_569[FLOAT, 16x3x3x3] %onnx::Conv_570[FLOAT, 16] %onnx::Conv_572[FLOAT, 16x8x1x1] %onnx::Conv_575[FLOAT, 16x1x3x3] %onnx::Conv_578[FLOAT, 16x8x1x1] %onnx::Conv_581[FLOAT, 48x16x1x1] %onnx::Conv_582[FLOAT, 48] %onnx::Conv_584[FLOAT, 48x1x3x3] %onnx::Conv_587[FLOAT, 24x48x1x1] %onnx::Conv_588[FLOAT, 24] %onnx::Conv_590[FLOAT, 72x24x1x1] %onnx::Conv_591[FLOAT, 72] %onnx::Conv_593[FLOAT, 72x1x5x5] %onnx::Conv_596[FLOAT, 24x72x1x1] %onnx::Conv_599[FLOAT, 72x24x1x1] %onnx::Conv_602[FLOAT, 72x1x3x3] %onnx::Conv_605[FLOAT, 24x72x1x1] %onnx::Conv_608[FLOAT, 24x24x1x1] %onnx::Conv_611[FLOAT, 24x1x3x3] %onnx::Conv_614[FLOAT, 32x24x1x1] %onnx::Conv_615[FLOAT, 32] %onnx::Conv_617[FLOAT, 32x32x1x1] %onnx::Conv_620[FLOAT, 32x1x5x5] %onnx::Conv_623[FLOAT, 32x32x1x1] %onnx::Conv_626[FLOAT, 32x32x1x1] %onnx::Conv_629[FLOAT, 32x1x3x3] %onnx::Conv_632[FLOAT, 32x32x1x1] %onnx::Conv_635[FLOAT, 96x32x1x1] %onnx::Conv_636[FLOAT, 96] %onnx::Conv_638[FLOAT, 96x1x3x3] %onnx::Conv_641[FLOAT, 32x96x1x1] %onnx::Conv_644[FLOAT, 96x32x1x1] %onnx::Conv_647[FLOAT, 96x1x3x3] %onnx::Conv_650[FLOAT, 64x96x1x1] %onnx::Conv_651[FLOAT, 64] %onnx::Conv_653[FLOAT, 384x64x1x1] %onnx::Conv_654[FLOAT, 384] %onnx::Conv_656[FLOAT, 384x1x5x5] %onnx::Conv_659[FLOAT, 64x384x1x1] %onnx::Conv_662[FLOAT, 192x64x1x1] %onnx::Conv_663[FLOAT, 192] %onnx::Conv_665[FLOAT, 192x1x3x3] %onnx::Conv_668[FLOAT, 64x192x1x1] %onnx::Conv_671[FLOAT, 112x64x1x1] %onnx::Conv_672[FLOAT, 112] %onnx::Conv_674[FLOAT, 112x112x1x1] %onnx::Conv_677[FLOAT, 112x1x3x3] %onnx::Conv_680[FLOAT, 112x112x1x1] %onnx::Conv_683[FLOAT, 112x112x1x1] %onnx::Conv_686[FLOAT, 112x1x3x3] %onnx::Conv_689[FLOAT, 112x112x1x1] %onnx::Conv_692[FLOAT, 336x112x1x1] %onnx::Conv_693[FLOAT, 336] %onnx::Conv_695[FLOAT, 336x1x5x5] %onnx::Conv_698[FLOAT, 112x336x1x1] %onnx::Conv_701[FLOAT, 184x112x1x1] %onnx::Conv_702[FLOAT, 184] %onnx::Conv_704[FLOAT, 184x92x1x1] %onnx::Conv_707[FLOAT, 184x1x3x3] %onnx::Conv_710[FLOAT, 184x92x1x1] %onnx::Conv_713[FLOAT, 1104x184x1x1] %onnx::Conv_714[FLOAT, 1104] %onnx::Conv_716[FLOAT, 1104x1x3x3] %onnx::Conv_719[FLOAT, 184x1104x1x1] %onnx::Conv_722[FLOAT, 184x184x1x1] %onnx::Conv_725[FLOAT, 184x1x3x3] %onnx::Conv_728[FLOAT, 184x184x1x1] %onnx::Conv_731[FLOAT, 1104x184x1x1] %onnx::Conv_734[FLOAT, 1104x1x3x3] %onnx::Conv_737[FLOAT, 352x1104x1x1] %onnx::Conv_738[FLOAT, 352] %onnx::Conv_740[FLOAT, 1504x352x1x1] %onnx::Conv_741[FLOAT, 1504] ) { %onnx::Conv_735 = Identity(%onnx::Conv_714) %onnx::Conv_732 = Identity(%onnx::Conv_714) %onnx::Conv_729 = Identity(%onnx::Conv_702) %onnx::Conv_726 = Identity(%onnx::Conv_702) %onnx::Conv_723 = Identity(%onnx::Conv_702) %onnx::Conv_720 = Identity(%onnx::Conv_702) %onnx::Conv_717 = Identity(%onnx::Conv_714) %onnx::Conv_711 = Identity(%onnx::Conv_702) %onnx::Conv_708 = Identity(%onnx::Conv_702) %onnx::Conv_705 = Identity(%onnx::Conv_702) %onnx::Conv_699 = Identity(%onnx::Conv_672) %onnx::Conv_696 = Identity(%onnx::Conv_693) %onnx::Conv_690 = Identity(%onnx::Conv_672) %onnx::Conv_687 = Identity(%onnx::Conv_672) %onnx::Conv_684 = Identity(%onnx::Conv_672) %onnx::Conv_681 = Identity(%onnx::Conv_672) %onnx::Conv_678 = Identity(%onnx::Conv_672) %onnx::Conv_675 = Identity(%onnx::Conv_672) %onnx::Conv_669 = Identity(%onnx::Conv_651) %onnx::Conv_666 = Identity(%onnx::Conv_663) %onnx::Conv_660 = Identity(%onnx::Conv_651) %onnx::Conv_657 = Identity(%onnx::Conv_654) %onnx::Conv_648 = Identity(%onnx::Conv_636) %onnx::Conv_645 = Identity(%onnx::Conv_636) %onnx::Conv_642 = Identity(%onnx::Conv_615) %onnx::Conv_639 = Identity(%onnx::Conv_636) %onnx::Conv_633 = Identity(%onnx::Conv_615) %onnx::Conv_630 = Identity(%onnx::Conv_615) %onnx::Conv_627 = Identity(%onnx::Conv_615) %onnx::Conv_624 = Identity(%onnx::Conv_615) %onnx::Conv_621 = Identity(%onnx::Conv_615) %onnx::Conv_618 = Identity(%onnx::Conv_615) %onnx::Conv_612 = Identity(%onnx::Conv_588) %onnx::Conv_609 = Identity(%onnx::Conv_588) %onnx::Conv_606 = Identity(%onnx::Conv_588) %onnx::Conv_603 = Identity(%onnx::Conv_591) %onnx::Conv_600 = Identity(%onnx::Conv_591) %onnx::Conv_597 = Identity(%onnx::Conv_588) %onnx::Conv_594 = Identity(%onnx::Conv_591) %onnx::Conv_585 = Identity(%onnx::Conv_582) %onnx::Conv_579 = Identity(%onnx::Conv_570) %onnx::Conv_576 = Identity(%onnx::Conv_570) %onnx::Conv_573 = Identity(%onnx::Conv_570) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_569, %onnx::Conv_570) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_572, %onnx::Conv_573) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.0/shuffle/Reshape_output_0 = Reshape(%/cells.0/nl/Relu_output_0, %/cells.0/shuffle/Constant_output_0) %/cells.0/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.0/shuffle/Reshape_output_0) %/cells.0/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.0/shuffle/Reshape_1_output_0 = Reshape(%/cells.0/shuffle/Transpose_output_0, %/cells.0/shuffle/Constant_1_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.0/shuffle/Reshape_1_output_0, %onnx::Conv_575, %onnx::Conv_576) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_578, %onnx::Conv_579) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_581, %onnx::Conv_582) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 48, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.1/nl/Relu_output_0, %onnx::Conv_584, %onnx::Conv_585) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_587, %onnx::Conv_588) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_590, %onnx::Conv_591) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.2/nl/Relu_output_0, %onnx::Conv_593, %onnx::Conv_594) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_596, %onnx::Conv_597) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_599, %onnx::Conv_600) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.3/nl/Relu_output_0, %onnx::Conv_602, %onnx::Conv_603) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_605, %onnx::Conv_606) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_608, %onnx::Conv_609) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.5/nl/Relu_output_0, %onnx::Conv_611, %onnx::Conv_612) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_614, %onnx::Conv_615) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_617, %onnx::Conv_618) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.6/nl/Relu_output_0, %onnx::Conv_620, %onnx::Conv_621) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_623, %onnx::Conv_624) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_626, %onnx::Conv_627) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.7/nl/Relu_output_0, %onnx::Conv_629, %onnx::Conv_630) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_632, %onnx::Conv_633) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_635, %onnx::Conv_636) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.8/nl/Relu_output_0, %onnx::Conv_638, %onnx::Conv_639) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_641, %onnx::Conv_642) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_644, %onnx::Conv_645) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.9/nl/Relu_output_0, %onnx::Conv_647, %onnx::Conv_648) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_650, %onnx::Conv_651) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_653, %onnx::Conv_654) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.10/nl/Relu_output_0, %onnx::Conv_656, %onnx::Conv_657) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_659, %onnx::Conv_660) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_662, %onnx::Conv_663) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.12/nl/Relu_output_0, %onnx::Conv_665, %onnx::Conv_666) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_668, %onnx::Conv_669) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.13/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_671, %onnx::Conv_672) %/cells.13/relu/Relu_output_0 = Relu(%/cells.13/conv/Conv_output_0) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/relu/Relu_output_0, %onnx::Conv_674, %onnx::Conv_675) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.14/nl/Relu_output_0, %onnx::Conv_677, %onnx::Conv_678) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_680, %onnx::Conv_681) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/relu/Relu_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_683, %onnx::Conv_684) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.15/nl/Relu_output_0, %onnx::Conv_686, %onnx::Conv_687) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_689, %onnx::Conv_690) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_692, %onnx::Conv_693) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.16/nl/Relu_output_0, %onnx::Conv_695, %onnx::Conv_696) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_698, %onnx::Conv_699) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.15/Add_output_0) %/cells.17/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [2, 2]](%/cells.16/Add_output_0, %onnx::Conv_701, %onnx::Conv_702) %/cells.17/relu/Relu_output_0 = Relu(%/cells.17/conv/Conv_output_0) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/relu/Relu_output_0, %onnx::Conv_704, %onnx::Conv_705) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.18/shuffle/Reshape_output_0 = Reshape(%/cells.18/nl/Relu_output_0, %/cells.18/shuffle/Constant_output_0) %/cells.18/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.18/shuffle/Reshape_output_0) %/cells.18/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.18/shuffle/Reshape_1_output_0 = Reshape(%/cells.18/shuffle/Transpose_output_0, %/cells.18/shuffle/Constant_1_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.18/shuffle/Reshape_1_output_0, %onnx::Conv_707, %onnx::Conv_708) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_710, %onnx::Conv_711) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/relu/Relu_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_713, %onnx::Conv_714) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.19/nl/Relu_output_0, %onnx::Conv_716, %onnx::Conv_717) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_719, %onnx::Conv_720) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_722, %onnx::Conv_723) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.20/nl/Relu_output_0, %onnx::Conv_725, %onnx::Conv_726) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_728, %onnx::Conv_729) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_731, %onnx::Conv_732) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.21/nl/Relu_output_0, %onnx::Conv_734, %onnx::Conv_735) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_737, %onnx::Conv_738) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_740, %onnx::Conv_741) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %567 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %567 }	val_accuracy	62,047,488	2,111,468	{'zcp_synflow': 75.52973882845787, 'zcp_zen': 63.28504943847656, 'zcp_epe_nas': 6.945313534760109, 'zcp_fisher': 0.08329888433218002, 'zcp_flops': 62047488.0, 'zcp_grad_norm': 22.083473205566406, 'zcp_grasp': -0.14723587036132812, 'zcp_jacov': -16.070440955720663, 'zcp_l2_norm': 600.6205444335938, 'zcp_nwot': 208.9532112386357, 'zcp_params': 2111468.0, 'zcp_plain': -0.003565885592252016, 'zcp_snip': 38.139930725097656, 'lat_1080ti_1': 0.38988490644511703, 'lat_1080ti_32': 0.34571929308313637, 'lat_1080ti_64': 0.25060050907467885, 'lat_2080ti_1': 0.44258958463456366, 'lat_2080ti_32': 0.3773756840201998, 'lat_2080ti_64': 0.29705242095382245, 'lat_essential_ph_1': 0.18867924528301888, 'lat_eyeriss': 0.3552711202391214, 'lat_fpga': 0.4188147517786638, 'lat_gold_6226': 0.3208357533624636, 'lat_gold_6240': 0.3859734649475495, 'lat_pixel2': 0.2391304347826087, 'lat_pixel3': 0.3241723467044164, 'lat_raspi4': 0.38389501409816695, 'lat_samsung_a50': 0.15789473684210525, 'lat_samsung_s7': 0.14173228346456693, 'lat_silver_4114': 0.3730967516339919, 'lat_silver_4210r': 0.38165003807833975, 'lat_titan_rtx_1': 0.4161853377961562, 'lat_titan_rtx_32': 0.36290175090029364, 'lat_titan_rtx_64': 0.2906606882956978, 'lat_titanx_1': 0.21959751357540916, 'lat_titanx_32': 0.3009884243015199, 'lat_titanx_64': 0.24925439756672282, 'lat_titanxp_1': 0.3871911021037922, 'lat_titanxp_32': 0.3436513207836004, 'lat_titanxp_64': 0.28242740706799313}
FBNet_1177	FBNet	1177	1177	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_614[FLOAT, 16x3x3x3] %onnx::Conv_615[FLOAT, 16] %onnx::Conv_617[FLOAT, 48x16x1x1] %onnx::Conv_618[FLOAT, 48] %onnx::Conv_620[FLOAT, 48x1x3x3] %onnx::Conv_623[FLOAT, 16x48x1x1] %onnx::Conv_626[FLOAT, 16x8x1x1] %onnx::Conv_629[FLOAT, 16x1x5x5] %onnx::Conv_632[FLOAT, 24x8x1x1] %onnx::Conv_633[FLOAT, 24] %onnx::Conv_635[FLOAT, 144x24x1x1] %onnx::Conv_636[FLOAT, 144] %onnx::Conv_638[FLOAT, 144x1x5x5] %onnx::Conv_641[FLOAT, 24x144x1x1] %onnx::Conv_644[FLOAT, 24x12x1x1] %onnx::Conv_647[FLOAT, 24x1x5x5] %onnx::Conv_650[FLOAT, 24x12x1x1] %onnx::Conv_653[FLOAT, 144x24x1x1] %onnx::Conv_656[FLOAT, 144x1x3x3] %onnx::Conv_659[FLOAT, 32x144x1x1] %onnx::Conv_660[FLOAT, 32] %onnx::Conv_662[FLOAT, 96x32x1x1] %onnx::Conv_663[FLOAT, 96] %onnx::Conv_665[FLOAT, 96x1x3x3] %onnx::Conv_668[FLOAT, 32x96x1x1] %onnx::Conv_671[FLOAT, 96x32x1x1] %onnx::Conv_674[FLOAT, 96x1x3x3] %onnx::Conv_677[FLOAT, 32x96x1x1] %onnx::Conv_680[FLOAT, 32x32x1x1] %onnx::Conv_683[FLOAT, 32x1x3x3] %onnx::Conv_686[FLOAT, 32x32x1x1] %onnx::Conv_689[FLOAT, 32x32x1x1] %onnx::Conv_692[FLOAT, 32x1x5x5] %onnx::Conv_695[FLOAT, 64x32x1x1] %onnx::Conv_696[FLOAT, 64] %onnx::Conv_698[FLOAT, 384x64x1x1] %onnx::Conv_699[FLOAT, 384] %onnx::Conv_701[FLOAT, 384x1x5x5] %onnx::Conv_704[FLOAT, 64x384x1x1] %onnx::Conv_707[FLOAT, 384x64x1x1] %onnx::Conv_710[FLOAT, 384x1x5x5] %onnx::Conv_713[FLOAT, 64x384x1x1] %onnx::Conv_716[FLOAT, 64x64x1x1] %onnx::Conv_719[FLOAT, 64x1x5x5] %onnx::Conv_722[FLOAT, 64x64x1x1] %onnx::Conv_725[FLOAT, 384x64x1x1] %onnx::Conv_728[FLOAT, 384x1x3x3] %onnx::Conv_731[FLOAT, 112x384x1x1] %onnx::Conv_732[FLOAT, 112] %onnx::Conv_734[FLOAT, 112x112x1x1] %onnx::Conv_737[FLOAT, 112x1x5x5] %onnx::Conv_740[FLOAT, 112x112x1x1] %onnx::Conv_743[FLOAT, 112x56x1x1] %onnx::Conv_746[FLOAT, 112x1x5x5] %onnx::Conv_749[FLOAT, 112x56x1x1] %onnx::Conv_752[FLOAT, 112x112x1x1] %onnx::Conv_755[FLOAT, 112x1x3x3] %onnx::Conv_758[FLOAT, 184x112x1x1] %onnx::Conv_759[FLOAT, 184] %onnx::Conv_761[FLOAT, 1104x184x1x1] %onnx::Conv_762[FLOAT, 1104] %onnx::Conv_764[FLOAT, 1104x1x5x5] %onnx::Conv_767[FLOAT, 184x1104x1x1] %onnx::Conv_770[FLOAT, 184x92x1x1] %onnx::Conv_773[FLOAT, 184x1x5x5] %onnx::Conv_776[FLOAT, 184x92x1x1] %onnx::Conv_779[FLOAT, 184x184x1x1] %onnx::Conv_782[FLOAT, 184x1x3x3] %onnx::Conv_785[FLOAT, 352x184x1x1] %onnx::Conv_786[FLOAT, 352] %onnx::Conv_788[FLOAT, 1504x352x1x1] %onnx::Conv_789[FLOAT, 1504] ) { %onnx::Conv_783 = Identity(%onnx::Conv_759) %onnx::Conv_780 = Identity(%onnx::Conv_759) %onnx::Conv_777 = Identity(%onnx::Conv_759) %onnx::Conv_774 = Identity(%onnx::Conv_759) %onnx::Conv_771 = Identity(%onnx::Conv_759) %onnx::Conv_768 = Identity(%onnx::Conv_759) %onnx::Conv_765 = Identity(%onnx::Conv_762) %onnx::Conv_756 = Identity(%onnx::Conv_732) %onnx::Conv_753 = Identity(%onnx::Conv_732) %onnx::Conv_750 = Identity(%onnx::Conv_732) %onnx::Conv_747 = Identity(%onnx::Conv_732) %onnx::Conv_744 = Identity(%onnx::Conv_732) %onnx::Conv_741 = Identity(%onnx::Conv_732) %onnx::Conv_738 = Identity(%onnx::Conv_732) %onnx::Conv_735 = Identity(%onnx::Conv_732) %onnx::Conv_729 = Identity(%onnx::Conv_699) %onnx::Conv_726 = Identity(%onnx::Conv_699) %onnx::Conv_723 = Identity(%onnx::Conv_696) %onnx::Conv_720 = Identity(%onnx::Conv_696) %onnx::Conv_717 = Identity(%onnx::Conv_696) %onnx::Conv_714 = Identity(%onnx::Conv_696) %onnx::Conv_711 = Identity(%onnx::Conv_699) %onnx::Conv_708 = Identity(%onnx::Conv_699) %onnx::Conv_705 = Identity(%onnx::Conv_696) %onnx::Conv_702 = Identity(%onnx::Conv_699) %onnx::Conv_693 = Identity(%onnx::Conv_660) %onnx::Conv_690 = Identity(%onnx::Conv_660) %onnx::Conv_687 = Identity(%onnx::Conv_660) %onnx::Conv_684 = Identity(%onnx::Conv_660) %onnx::Conv_681 = Identity(%onnx::Conv_660) %onnx::Conv_678 = Identity(%onnx::Conv_660) %onnx::Conv_675 = Identity(%onnx::Conv_663) %onnx::Conv_672 = Identity(%onnx::Conv_663) %onnx::Conv_669 = Identity(%onnx::Conv_660) %onnx::Conv_666 = Identity(%onnx::Conv_663) %onnx::Conv_657 = Identity(%onnx::Conv_636) %onnx::Conv_654 = Identity(%onnx::Conv_636) %onnx::Conv_651 = Identity(%onnx::Conv_633) %onnx::Conv_648 = Identity(%onnx::Conv_633) %onnx::Conv_645 = Identity(%onnx::Conv_633) %onnx::Conv_642 = Identity(%onnx::Conv_633) %onnx::Conv_639 = Identity(%onnx::Conv_636) %onnx::Conv_630 = Identity(%onnx::Conv_615) %onnx::Conv_627 = Identity(%onnx::Conv_615) %onnx::Conv_624 = Identity(%onnx::Conv_615) %onnx::Conv_621 = Identity(%onnx::Conv_618) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_614, %onnx::Conv_615) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_617, %onnx::Conv_618) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 48, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.0/nl/Relu_output_0, %onnx::Conv_620, %onnx::Conv_621) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_623, %onnx::Conv_624) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_626, %onnx::Conv_627) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.1/shuffle/Reshape_output_0 = Reshape(%/cells.1/nl/Relu_output_0, %/cells.1/shuffle/Constant_output_0) %/cells.1/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.1/shuffle/Reshape_output_0) %/cells.1/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.1/shuffle/Reshape_1_output_0 = Reshape(%/cells.1/shuffle/Transpose_output_0, %/cells.1/shuffle/Constant_1_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.1/shuffle/Reshape_1_output_0, %onnx::Conv_629, %onnx::Conv_630) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_632, %onnx::Conv_633) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_635, %onnx::Conv_636) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.2/nl/Relu_output_0, %onnx::Conv_638, %onnx::Conv_639) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_641, %onnx::Conv_642) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_644, %onnx::Conv_645) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.3/shuffle/Reshape_output_0 = Reshape(%/cells.3/nl/Relu_output_0, %/cells.3/shuffle/Constant_output_0) %/cells.3/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.3/shuffle/Reshape_output_0) %/cells.3/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.3/shuffle/Reshape_1_output_0 = Reshape(%/cells.3/shuffle/Transpose_output_0, %/cells.3/shuffle/Constant_1_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.3/shuffle/Reshape_1_output_0, %onnx::Conv_647, %onnx::Conv_648) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_650, %onnx::Conv_651) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_653, %onnx::Conv_654) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.5/nl/Relu_output_0, %onnx::Conv_656, %onnx::Conv_657) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_659, %onnx::Conv_660) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_662, %onnx::Conv_663) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.6/nl/Relu_output_0, %onnx::Conv_665, %onnx::Conv_666) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_668, %onnx::Conv_669) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_671, %onnx::Conv_672) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.7/nl/Relu_output_0, %onnx::Conv_674, %onnx::Conv_675) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_677, %onnx::Conv_678) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_680, %onnx::Conv_681) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.8/nl/Relu_output_0, %onnx::Conv_683, %onnx::Conv_684) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_686, %onnx::Conv_687) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_689, %onnx::Conv_690) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.9/nl/Relu_output_0, %onnx::Conv_692, %onnx::Conv_693) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_695, %onnx::Conv_696) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_698, %onnx::Conv_699) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.10/nl/Relu_output_0, %onnx::Conv_701, %onnx::Conv_702) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_704, %onnx::Conv_705) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_707, %onnx::Conv_708) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.11/nl/Relu_output_0, %onnx::Conv_710, %onnx::Conv_711) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_713, %onnx::Conv_714) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_716, %onnx::Conv_717) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.12/nl/Relu_output_0, %onnx::Conv_719, %onnx::Conv_720) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_722, %onnx::Conv_723) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_725, %onnx::Conv_726) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.13/nl/Relu_output_0, %onnx::Conv_728, %onnx::Conv_729) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_731, %onnx::Conv_732) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_734, %onnx::Conv_735) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.15/nl/Relu_output_0, %onnx::Conv_737, %onnx::Conv_738) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_740, %onnx::Conv_741) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_743, %onnx::Conv_744) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.16/shuffle/Reshape_output_0 = Reshape(%/cells.16/nl/Relu_output_0, %/cells.16/shuffle/Constant_output_0) %/cells.16/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.16/shuffle/Reshape_output_0) %/cells.16/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.16/shuffle/Reshape_1_output_0 = Reshape(%/cells.16/shuffle/Transpose_output_0, %/cells.16/shuffle/Constant_1_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.16/shuffle/Reshape_1_output_0, %onnx::Conv_746, %onnx::Conv_747) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_749, %onnx::Conv_750) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.15/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_752, %onnx::Conv_753) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.17/nl/Relu_output_0, %onnx::Conv_755, %onnx::Conv_756) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_758, %onnx::Conv_759) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_761, %onnx::Conv_762) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.18/nl/Relu_output_0, %onnx::Conv_764, %onnx::Conv_765) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_767, %onnx::Conv_768) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_770, %onnx::Conv_771) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.19/shuffle/Reshape_output_0 = Reshape(%/cells.19/nl/Relu_output_0, %/cells.19/shuffle/Constant_output_0) %/cells.19/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.19/shuffle/Reshape_output_0) %/cells.19/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.19/shuffle/Reshape_1_output_0 = Reshape(%/cells.19/shuffle/Transpose_output_0, %/cells.19/shuffle/Constant_1_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.19/shuffle/Reshape_1_output_0, %onnx::Conv_773, %onnx::Conv_774) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_776, %onnx::Conv_777) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_779, %onnx::Conv_780) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.21/nl/Relu_output_0, %onnx::Conv_782, %onnx::Conv_783) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_785, %onnx::Conv_786) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_788, %onnx::Conv_789) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %612 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %612 }	val_accuracy	62,232,704	1,593,724	{'zcp_synflow': 73.12583165903555, 'zcp_zen': 63.321815490722656, 'zcp_epe_nas': 13.99931457787665, 'zcp_fisher': 0.10641936212778091, 'zcp_flops': 62232704.0, 'zcp_grad_norm': 20.727046966552734, 'zcp_grasp': -0.055812835693359375, 'zcp_jacov': -16.063131880573376, 'zcp_l2_norm': 565.16162109375, 'zcp_nwot': 212.39798133832232, 'zcp_params': 1593724.0, 'zcp_plain': 0.0016415122663602233, 'zcp_snip': 36.4100227355957, 'lat_1080ti_1': 0.438950730787246, 'lat_1080ti_32': 0.4881708902474572, 'lat_1080ti_64': 0.42938347993150006, 'lat_2080ti_1': 0.47883835995160035, 'lat_2080ti_32': 0.47957700414089316, 'lat_2080ti_64': 0.421539341281429, 'lat_essential_ph_1': 0.2830188679245283, 'lat_eyeriss': 0.41663763225936606, 'lat_fpga': 0.3527444364183297, 'lat_gold_6226': 0.27178981546731223, 'lat_gold_6240': 0.38842704597886685, 'lat_pixel2': 0.2608695652173913, 'lat_pixel3': 0.42079872548796066, 'lat_raspi4': 0.4212840424019899, 'lat_samsung_a50': 0.15789473684210525, 'lat_samsung_s7': 0.12598425196850394, 'lat_silver_4114': 0.4124067596842864, 'lat_silver_4210r': 0.3614996404637908, 'lat_titan_rtx_1': 0.47190682478176965, 'lat_titan_rtx_32': 0.44846798103973845, 'lat_titan_rtx_64': 0.44005279398933167, 'lat_titanx_1': 0.24447796750524997, 'lat_titanx_32': 0.4479369275839617, 'lat_titanx_64': 0.41583401409992715, 'lat_titanxp_1': 0.4617261214395834, 'lat_titanxp_32': 0.4609259769835364, 'lat_titanxp_64': 0.44656549814676416}
FBNet_3121	FBNet	3121	3121	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_605[FLOAT, 16x3x3x3] %onnx::Conv_606[FLOAT, 16] %onnx::Conv_608[FLOAT, 16x16x1x1] %onnx::Conv_611[FLOAT, 16x1x3x3] %onnx::Conv_614[FLOAT, 24x16x1x1] %onnx::Conv_615[FLOAT, 24] %onnx::Conv_617[FLOAT, 24x24x1x1] %onnx::Conv_620[FLOAT, 24x1x5x5] %onnx::Conv_623[FLOAT, 24x24x1x1] %onnx::Conv_626[FLOAT, 72x24x1x1] %onnx::Conv_627[FLOAT, 72] %onnx::Conv_629[FLOAT, 72x1x5x5] %onnx::Conv_632[FLOAT, 24x72x1x1] %onnx::Conv_635[FLOAT, 144x24x1x1] %onnx::Conv_636[FLOAT, 144] %onnx::Conv_638[FLOAT, 144x1x3x3] %onnx::Conv_641[FLOAT, 24x144x1x1] %onnx::Conv_644[FLOAT, 72x24x1x1] %onnx::Conv_647[FLOAT, 72x1x5x5] %onnx::Conv_650[FLOAT, 32x72x1x1] %onnx::Conv_651[FLOAT, 32] %onnx::Conv_653[FLOAT, 96x32x1x1] %onnx::Conv_654[FLOAT, 96] %onnx::Conv_656[FLOAT, 96x1x5x5] %onnx::Conv_659[FLOAT, 32x96x1x1] %onnx::Conv_662[FLOAT, 32x32x1x1] %onnx::Conv_665[FLOAT, 32x1x3x3] %onnx::Conv_668[FLOAT, 32x32x1x1] %onnx::Conv_671[FLOAT, 192x32x1x1] %onnx::Conv_672[FLOAT, 192] %onnx::Conv_674[FLOAT, 192x1x5x5] %onnx::Conv_677[FLOAT, 32x192x1x1] %onnx::Conv_680[FLOAT, 64x32x1x1] %onnx::Conv_681[FLOAT, 64] %onnx::Conv_683[FLOAT, 64x64x1x1] %onnx::Conv_686[FLOAT, 64x1x3x3] %onnx::Conv_689[FLOAT, 64x64x1x1] %onnx::Conv_692[FLOAT, 64x64x1x1] %onnx::Conv_695[FLOAT, 64x1x5x5] %onnx::Conv_698[FLOAT, 64x64x1x1] %onnx::Conv_701[FLOAT, 64x32x1x1] %onnx::Conv_704[FLOAT, 64x1x5x5] %onnx::Conv_707[FLOAT, 64x32x1x1] %onnx::Conv_710[FLOAT, 192x64x1x1] %onnx::Conv_713[FLOAT, 192x1x3x3] %onnx::Conv_716[FLOAT, 112x192x1x1] %onnx::Conv_717[FLOAT, 112] %onnx::Conv_719[FLOAT, 112x56x1x1] %onnx::Conv_722[FLOAT, 112x1x3x3] %onnx::Conv_725[FLOAT, 112x56x1x1] %onnx::Conv_728[FLOAT, 112x112x1x1] %onnx::Conv_731[FLOAT, 112x1x3x3] %onnx::Conv_734[FLOAT, 112x112x1x1] %onnx::Conv_737[FLOAT, 112x112x1x1] %onnx::Conv_740[FLOAT, 112x1x3x3] %onnx::Conv_743[FLOAT, 184x112x1x1] %onnx::Conv_744[FLOAT, 184] %onnx::Conv_746[FLOAT, 184x184x1x1] %onnx::Conv_749[FLOAT, 184x1x3x3] %onnx::Conv_752[FLOAT, 184x184x1x1] %onnx::Conv_755[FLOAT, 552x184x1x1] %onnx::Conv_756[FLOAT, 552] %onnx::Conv_758[FLOAT, 552x1x3x3] %onnx::Conv_761[FLOAT, 184x552x1x1] %onnx::Conv_764[FLOAT, 184x92x1x1] %onnx::Conv_767[FLOAT, 184x1x5x5] %onnx::Conv_770[FLOAT, 184x92x1x1] %onnx::Conv_773[FLOAT, 1104x184x1x1] %onnx::Conv_774[FLOAT, 1104] %onnx::Conv_776[FLOAT, 1104x1x3x3] %onnx::Conv_779[FLOAT, 352x1104x1x1] %onnx::Conv_780[FLOAT, 352] %onnx::Conv_782[FLOAT, 1504x352x1x1] %onnx::Conv_783[FLOAT, 1504] ) { %onnx::Conv_777 = Identity(%onnx::Conv_774) %onnx::Conv_771 = Identity(%onnx::Conv_744) %onnx::Conv_768 = Identity(%onnx::Conv_744) %onnx::Conv_765 = Identity(%onnx::Conv_744) %onnx::Conv_762 = Identity(%onnx::Conv_744) %onnx::Conv_759 = Identity(%onnx::Conv_756) %onnx::Conv_753 = Identity(%onnx::Conv_744) %onnx::Conv_750 = Identity(%onnx::Conv_744) %onnx::Conv_747 = Identity(%onnx::Conv_744) %onnx::Conv_741 = Identity(%onnx::Conv_717) %onnx::Conv_738 = Identity(%onnx::Conv_717) %onnx::Conv_735 = Identity(%onnx::Conv_717) %onnx::Conv_732 = Identity(%onnx::Conv_717) %onnx::Conv_729 = Identity(%onnx::Conv_717) %onnx::Conv_726 = Identity(%onnx::Conv_717) %onnx::Conv_723 = Identity(%onnx::Conv_717) %onnx::Conv_720 = Identity(%onnx::Conv_717) %onnx::Conv_714 = Identity(%onnx::Conv_672) %onnx::Conv_711 = Identity(%onnx::Conv_672) %onnx::Conv_708 = Identity(%onnx::Conv_681) %onnx::Conv_705 = Identity(%onnx::Conv_681) %onnx::Conv_702 = Identity(%onnx::Conv_681) %onnx::Conv_699 = Identity(%onnx::Conv_681) %onnx::Conv_696 = Identity(%onnx::Conv_681) %onnx::Conv_693 = Identity(%onnx::Conv_681) %onnx::Conv_690 = Identity(%onnx::Conv_681) %onnx::Conv_687 = Identity(%onnx::Conv_681) %onnx::Conv_684 = Identity(%onnx::Conv_681) %onnx::Conv_678 = Identity(%onnx::Conv_651) %onnx::Conv_675 = Identity(%onnx::Conv_672) %onnx::Conv_669 = Identity(%onnx::Conv_651) %onnx::Conv_666 = Identity(%onnx::Conv_651) %onnx::Conv_663 = Identity(%onnx::Conv_651) %onnx::Conv_660 = Identity(%onnx::Conv_651) %onnx::Conv_657 = Identity(%onnx::Conv_654) %onnx::Conv_648 = Identity(%onnx::Conv_627) %onnx::Conv_645 = Identity(%onnx::Conv_627) %onnx::Conv_642 = Identity(%onnx::Conv_615) %onnx::Conv_639 = Identity(%onnx::Conv_636) %onnx::Conv_633 = Identity(%onnx::Conv_615) %onnx::Conv_630 = Identity(%onnx::Conv_627) %onnx::Conv_624 = Identity(%onnx::Conv_615) %onnx::Conv_621 = Identity(%onnx::Conv_615) %onnx::Conv_618 = Identity(%onnx::Conv_615) %onnx::Conv_612 = Identity(%onnx::Conv_606) %onnx::Conv_609 = Identity(%onnx::Conv_606) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_605, %onnx::Conv_606) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_608, %onnx::Conv_609) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.1/nl/Relu_output_0, %onnx::Conv_611, %onnx::Conv_612) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_614, %onnx::Conv_615) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_617, %onnx::Conv_618) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.2/nl/Relu_output_0, %onnx::Conv_620, %onnx::Conv_621) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_623, %onnx::Conv_624) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_626, %onnx::Conv_627) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.3/nl/Relu_output_0, %onnx::Conv_629, %onnx::Conv_630) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_632, %onnx::Conv_633) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_635, %onnx::Conv_636) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.4/nl/Relu_output_0, %onnx::Conv_638, %onnx::Conv_639) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_641, %onnx::Conv_642) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/Add_output_0, %onnx::Conv_644, %onnx::Conv_645) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.5/nl/Relu_output_0, %onnx::Conv_647, %onnx::Conv_648) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_650, %onnx::Conv_651) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_653, %onnx::Conv_654) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.6/nl/Relu_output_0, %onnx::Conv_656, %onnx::Conv_657) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_659, %onnx::Conv_660) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_662, %onnx::Conv_663) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.7/nl/Relu_output_0, %onnx::Conv_665, %onnx::Conv_666) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_668, %onnx::Conv_669) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_671, %onnx::Conv_672) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.8/nl/Relu_output_0, %onnx::Conv_674, %onnx::Conv_675) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_677, %onnx::Conv_678) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [2, 2]](%/cells.8/Add_output_0, %onnx::Conv_680, %onnx::Conv_681) %/cells.9/relu/Relu_output_0 = Relu(%/cells.9/conv/Conv_output_0) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/relu/Relu_output_0, %onnx::Conv_683, %onnx::Conv_684) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.10/nl/Relu_output_0, %onnx::Conv_686, %onnx::Conv_687) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_689, %onnx::Conv_690) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/relu/Relu_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_692, %onnx::Conv_693) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.11/nl/Relu_output_0, %onnx::Conv_695, %onnx::Conv_696) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_698, %onnx::Conv_699) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_701, %onnx::Conv_702) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.12/shuffle/Reshape_output_0 = Reshape(%/cells.12/nl/Relu_output_0, %/cells.12/shuffle/Constant_output_0) %/cells.12/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.12/shuffle/Reshape_output_0) %/cells.12/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.12/shuffle/Reshape_1_output_0 = Reshape(%/cells.12/shuffle/Transpose_output_0, %/cells.12/shuffle/Constant_1_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.12/shuffle/Reshape_1_output_0, %onnx::Conv_704, %onnx::Conv_705) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_707, %onnx::Conv_708) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_710, %onnx::Conv_711) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.13/nl/Relu_output_0, %onnx::Conv_713, %onnx::Conv_714) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_716, %onnx::Conv_717) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_719, %onnx::Conv_720) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.15/shuffle/Reshape_output_0 = Reshape(%/cells.15/nl/Relu_output_0, %/cells.15/shuffle/Constant_output_0) %/cells.15/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.15/shuffle/Reshape_output_0) %/cells.15/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.15/shuffle/Reshape_1_output_0 = Reshape(%/cells.15/shuffle/Transpose_output_0, %/cells.15/shuffle/Constant_1_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.15/shuffle/Reshape_1_output_0, %onnx::Conv_722, %onnx::Conv_723) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_725, %onnx::Conv_726) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_728, %onnx::Conv_729) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.16/nl/Relu_output_0, %onnx::Conv_731, %onnx::Conv_732) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_734, %onnx::Conv_735) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.15/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_737, %onnx::Conv_738) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.17/nl/Relu_output_0, %onnx::Conv_740, %onnx::Conv_741) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_743, %onnx::Conv_744) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_746, %onnx::Conv_747) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.18/nl/Relu_output_0, %onnx::Conv_749, %onnx::Conv_750) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_752, %onnx::Conv_753) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_755, %onnx::Conv_756) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 552, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.19/nl/Relu_output_0, %onnx::Conv_758, %onnx::Conv_759) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_761, %onnx::Conv_762) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_764, %onnx::Conv_765) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.20/shuffle/Reshape_output_0 = Reshape(%/cells.20/nl/Relu_output_0, %/cells.20/shuffle/Constant_output_0) %/cells.20/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.20/shuffle/Reshape_output_0) %/cells.20/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.20/shuffle/Reshape_1_output_0 = Reshape(%/cells.20/shuffle/Transpose_output_0, %/cells.20/shuffle/Constant_1_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.20/shuffle/Reshape_1_output_0, %onnx::Conv_767, %onnx::Conv_768) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_770, %onnx::Conv_771) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_773, %onnx::Conv_774) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.21/nl/Relu_output_0, %onnx::Conv_776, %onnx::Conv_777) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_779, %onnx::Conv_780) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_782, %onnx::Conv_783) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %603 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %603 }	val_accuracy	60,573,312	1,803,796	{'zcp_synflow': 76.45931147200541, 'zcp_zen': 64.93451690673828, 'zcp_epe_nas': 6.988575197729411, 'zcp_fisher': 0.07417842745780945, 'zcp_flops': 60573312.0, 'zcp_grad_norm': 19.273193359375, 'zcp_grasp': -0.004267692565917969, 'zcp_jacov': -16.047147518139397, 'zcp_l2_norm': 574.279296875, 'zcp_nwot': 211.37053014094516, 'zcp_params': 1803796.0, 'zcp_plain': 0.005169478245079517, 'zcp_snip': 33.20492935180664, 'lat_1080ti_1': 0.5147957447037077, 'lat_1080ti_32': 0.5063014914880604, 'lat_1080ti_64': 0.4106841540707805, 'lat_2080ti_1': 0.5120415513520703, 'lat_2080ti_32': 0.5021534554517187, 'lat_2080ti_64': 0.44755786736780384, 'lat_essential_ph_1': 0.22641509433962265, 'lat_eyeriss': 0.36055232611446264, 'lat_fpga': 0.36861635282359817, 'lat_gold_6226': 0.22266817395872077, 'lat_gold_6240': 0.3285287632266191, 'lat_pixel2': 0.1956521739130435, 'lat_pixel3': 0.35568103268779233, 'lat_raspi4': 0.421229073851387, 'lat_samsung_a50': 0.1368421052631579, 'lat_samsung_s7': 0.11811023622047244, 'lat_silver_4114': 0.3703043107368067, 'lat_silver_4210r': 0.36407806611715665, 'lat_titan_rtx_1': 0.4922407062439456, 'lat_titan_rtx_32': 0.4852864393397932, 'lat_titan_rtx_64': 0.4486486642538611, 'lat_titanx_1': 0.2628339161452178, 'lat_titanx_32': 0.44838195856997737, 'lat_titanx_64': 0.3958653108510549, 'lat_titanxp_1': 0.4616043898309391, 'lat_titanxp_32': 0.4796516222953598, 'lat_titanxp_64': 0.43593609164455394}
FBNet_2101	FBNet	2101	2101	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_714[FLOAT, 16x3x3x3] %onnx::Conv_715[FLOAT, 16] %onnx::Conv_717[FLOAT, 48x16x1x1] %onnx::Conv_718[FLOAT, 48] %onnx::Conv_720[FLOAT, 48x1x3x3] %onnx::Conv_723[FLOAT, 16x48x1x1] %onnx::Conv_726[FLOAT, 24x16x1x1] %onnx::Conv_727[FLOAT, 24] %onnx::Conv_729[FLOAT, 144x24x1x1] %onnx::Conv_730[FLOAT, 144] %onnx::Conv_732[FLOAT, 144x1x5x5] %onnx::Conv_735[FLOAT, 24x144x1x1] %onnx::Conv_738[FLOAT, 24x12x1x1] %onnx::Conv_741[FLOAT, 24x1x5x5] %onnx::Conv_744[FLOAT, 24x12x1x1] %onnx::Conv_747[FLOAT, 24x12x1x1] %onnx::Conv_750[FLOAT, 24x1x3x3] %onnx::Conv_753[FLOAT, 24x12x1x1] %onnx::Conv_756[FLOAT, 24x24x1x1] %onnx::Conv_759[FLOAT, 24x1x3x3] %onnx::Conv_762[FLOAT, 32x24x1x1] %onnx::Conv_763[FLOAT, 32] %onnx::Conv_765[FLOAT, 96x32x1x1] %onnx::Conv_766[FLOAT, 96] %onnx::Conv_768[FLOAT, 96x1x3x3] %onnx::Conv_771[FLOAT, 32x96x1x1] %onnx::Conv_774[FLOAT, 32x16x1x1] %onnx::Conv_777[FLOAT, 32x1x3x3] %onnx::Conv_780[FLOAT, 32x16x1x1] %onnx::Conv_783[FLOAT, 32x32x1x1] %onnx::Conv_786[FLOAT, 32x1x3x3] %onnx::Conv_789[FLOAT, 32x32x1x1] %onnx::Conv_792[FLOAT, 192x32x1x1] %onnx::Conv_793[FLOAT, 192] %onnx::Conv_795[FLOAT, 192x1x3x3] %onnx::Conv_798[FLOAT, 64x192x1x1] %onnx::Conv_799[FLOAT, 64] %onnx::Conv_801[FLOAT, 64x64x1x1] %onnx::Conv_804[FLOAT, 64x1x5x5] %onnx::Conv_807[FLOAT, 64x64x1x1] %onnx::Conv_810[FLOAT, 192x64x1x1] %onnx::Conv_813[FLOAT, 192x1x5x5] %onnx::Conv_816[FLOAT, 64x192x1x1] %onnx::Conv_819[FLOAT, 384x64x1x1] %onnx::Conv_820[FLOAT, 384] %onnx::Conv_822[FLOAT, 384x1x5x5] %onnx::Conv_825[FLOAT, 64x384x1x1] %onnx::Conv_828[FLOAT, 384x64x1x1] %onnx::Conv_831[FLOAT, 384x1x3x3] %onnx::Conv_834[FLOAT, 112x384x1x1] %onnx::Conv_835[FLOAT, 112] %onnx::Conv_837[FLOAT, 112x112x1x1] %onnx::Conv_840[FLOAT, 112x1x5x5] %onnx::Conv_843[FLOAT, 112x112x1x1] %onnx::Conv_846[FLOAT, 112x56x1x1] %onnx::Conv_849[FLOAT, 112x1x3x3] %onnx::Conv_852[FLOAT, 112x56x1x1] %onnx::Conv_855[FLOAT, 672x112x1x1] %onnx::Conv_856[FLOAT, 672] %onnx::Conv_858[FLOAT, 672x1x5x5] %onnx::Conv_861[FLOAT, 112x672x1x1] %onnx::Conv_864[FLOAT, 112x112x1x1] %onnx::Conv_867[FLOAT, 112x1x3x3] %onnx::Conv_870[FLOAT, 184x112x1x1] %onnx::Conv_871[FLOAT, 184] %onnx::Conv_873[FLOAT, 1104x184x1x1] %onnx::Conv_874[FLOAT, 1104] %onnx::Conv_876[FLOAT, 1104x1x3x3] %onnx::Conv_879[FLOAT, 184x1104x1x1] %onnx::Conv_882[FLOAT, 1104x184x1x1] %onnx::Conv_885[FLOAT, 1104x1x5x5] %onnx::Conv_888[FLOAT, 184x1104x1x1] %onnx::Conv_891[FLOAT, 184x92x1x1] %onnx::Conv_894[FLOAT, 184x1x5x5] %onnx::Conv_897[FLOAT, 184x92x1x1] %onnx::Conv_900[FLOAT, 184x92x1x1] %onnx::Conv_903[FLOAT, 184x1x3x3] %onnx::Conv_906[FLOAT, 352x92x1x1] %onnx::Conv_907[FLOAT, 352] %onnx::Conv_909[FLOAT, 1504x352x1x1] %onnx::Conv_910[FLOAT, 1504] ) { %onnx::Conv_904 = Identity(%onnx::Conv_871) %onnx::Conv_901 = Identity(%onnx::Conv_871) %onnx::Conv_898 = Identity(%onnx::Conv_871) %onnx::Conv_895 = Identity(%onnx::Conv_871) %onnx::Conv_892 = Identity(%onnx::Conv_871) %onnx::Conv_889 = Identity(%onnx::Conv_871) %onnx::Conv_886 = Identity(%onnx::Conv_874) %onnx::Conv_883 = Identity(%onnx::Conv_874) %onnx::Conv_880 = Identity(%onnx::Conv_871) %onnx::Conv_877 = Identity(%onnx::Conv_874) %onnx::Conv_868 = Identity(%onnx::Conv_835) %onnx::Conv_865 = Identity(%onnx::Conv_835) %onnx::Conv_862 = Identity(%onnx::Conv_835) %onnx::Conv_859 = Identity(%onnx::Conv_856) %onnx::Conv_853 = Identity(%onnx::Conv_835) %onnx::Conv_850 = Identity(%onnx::Conv_835) %onnx::Conv_847 = Identity(%onnx::Conv_835) %onnx::Conv_844 = Identity(%onnx::Conv_835) %onnx::Conv_841 = Identity(%onnx::Conv_835) %onnx::Conv_838 = Identity(%onnx::Conv_835) %onnx::Conv_832 = Identity(%onnx::Conv_820) %onnx::Conv_829 = Identity(%onnx::Conv_820) %onnx::Conv_826 = Identity(%onnx::Conv_799) %onnx::Conv_823 = Identity(%onnx::Conv_820) %onnx::Conv_817 = Identity(%onnx::Conv_799) %onnx::Conv_814 = Identity(%onnx::Conv_793) %onnx::Conv_811 = Identity(%onnx::Conv_793) %onnx::Conv_808 = Identity(%onnx::Conv_799) %onnx::Conv_805 = Identity(%onnx::Conv_799) %onnx::Conv_802 = Identity(%onnx::Conv_799) %onnx::Conv_796 = Identity(%onnx::Conv_793) %onnx::Conv_790 = Identity(%onnx::Conv_763) %onnx::Conv_787 = Identity(%onnx::Conv_763) %onnx::Conv_784 = Identity(%onnx::Conv_763) %onnx::Conv_781 = Identity(%onnx::Conv_763) %onnx::Conv_778 = Identity(%onnx::Conv_763) %onnx::Conv_775 = Identity(%onnx::Conv_763) %onnx::Conv_772 = Identity(%onnx::Conv_763) %onnx::Conv_769 = Identity(%onnx::Conv_766) %onnx::Conv_760 = Identity(%onnx::Conv_727) %onnx::Conv_757 = Identity(%onnx::Conv_727) %onnx::Conv_754 = Identity(%onnx::Conv_727) %onnx::Conv_751 = Identity(%onnx::Conv_727) %onnx::Conv_748 = Identity(%onnx::Conv_727) %onnx::Conv_745 = Identity(%onnx::Conv_727) %onnx::Conv_742 = Identity(%onnx::Conv_727) %onnx::Conv_739 = Identity(%onnx::Conv_727) %onnx::Conv_736 = Identity(%onnx::Conv_727) %onnx::Conv_733 = Identity(%onnx::Conv_730) %onnx::Conv_724 = Identity(%onnx::Conv_715) %onnx::Conv_721 = Identity(%onnx::Conv_718) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_714, %onnx::Conv_715) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_717, %onnx::Conv_718) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 48, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.0/nl/Relu_output_0, %onnx::Conv_720, %onnx::Conv_721) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_723, %onnx::Conv_724) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_726, %onnx::Conv_727) %/cells.1/relu/Relu_output_0 = Relu(%/cells.1/conv/Conv_output_0) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/relu/Relu_output_0, %onnx::Conv_729, %onnx::Conv_730) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.2/nl/Relu_output_0, %onnx::Conv_732, %onnx::Conv_733) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_735, %onnx::Conv_736) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/relu/Relu_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_738, %onnx::Conv_739) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.3/shuffle/Reshape_output_0 = Reshape(%/cells.3/nl/Relu_output_0, %/cells.3/shuffle/Constant_output_0) %/cells.3/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.3/shuffle/Reshape_output_0) %/cells.3/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.3/shuffle/Reshape_1_output_0 = Reshape(%/cells.3/shuffle/Transpose_output_0, %/cells.3/shuffle/Constant_1_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.3/shuffle/Reshape_1_output_0, %onnx::Conv_741, %onnx::Conv_742) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_744, %onnx::Conv_745) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_747, %onnx::Conv_748) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.4/shuffle/Reshape_output_0 = Reshape(%/cells.4/nl/Relu_output_0, %/cells.4/shuffle/Constant_output_0) %/cells.4/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.4/shuffle/Reshape_output_0) %/cells.4/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.4/shuffle/Reshape_1_output_0 = Reshape(%/cells.4/shuffle/Transpose_output_0, %/cells.4/shuffle/Constant_1_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.4/shuffle/Reshape_1_output_0, %onnx::Conv_750, %onnx::Conv_751) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_753, %onnx::Conv_754) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/Add_output_0, %onnx::Conv_756, %onnx::Conv_757) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.5/nl/Relu_output_0, %onnx::Conv_759, %onnx::Conv_760) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_762, %onnx::Conv_763) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_765, %onnx::Conv_766) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.6/nl/Relu_output_0, %onnx::Conv_768, %onnx::Conv_769) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_771, %onnx::Conv_772) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_774, %onnx::Conv_775) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.7/shuffle/Reshape_output_0 = Reshape(%/cells.7/nl/Relu_output_0, %/cells.7/shuffle/Constant_output_0) %/cells.7/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.7/shuffle/Reshape_output_0) %/cells.7/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.7/shuffle/Reshape_1_output_0 = Reshape(%/cells.7/shuffle/Transpose_output_0, %/cells.7/shuffle/Constant_1_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.7/shuffle/Reshape_1_output_0, %onnx::Conv_777, %onnx::Conv_778) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_780, %onnx::Conv_781) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_783, %onnx::Conv_784) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.8/nl/Relu_output_0, %onnx::Conv_786, %onnx::Conv_787) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_789, %onnx::Conv_790) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_792, %onnx::Conv_793) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.9/nl/Relu_output_0, %onnx::Conv_795, %onnx::Conv_796) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_798, %onnx::Conv_799) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_801, %onnx::Conv_802) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.10/nl/Relu_output_0, %onnx::Conv_804, %onnx::Conv_805) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_807, %onnx::Conv_808) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_810, %onnx::Conv_811) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.11/nl/Relu_output_0, %onnx::Conv_813, %onnx::Conv_814) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_816, %onnx::Conv_817) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_819, %onnx::Conv_820) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.12/nl/Relu_output_0, %onnx::Conv_822, %onnx::Conv_823) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_825, %onnx::Conv_826) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_828, %onnx::Conv_829) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.13/nl/Relu_output_0, %onnx::Conv_831, %onnx::Conv_832) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_834, %onnx::Conv_835) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_837, %onnx::Conv_838) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.14/nl/Relu_output_0, %onnx::Conv_840, %onnx::Conv_841) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_843, %onnx::Conv_844) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_846, %onnx::Conv_847) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.15/shuffle/Reshape_output_0 = Reshape(%/cells.15/nl/Relu_output_0, %/cells.15/shuffle/Constant_output_0) %/cells.15/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.15/shuffle/Reshape_output_0) %/cells.15/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.15/shuffle/Reshape_1_output_0 = Reshape(%/cells.15/shuffle/Transpose_output_0, %/cells.15/shuffle/Constant_1_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.15/shuffle/Reshape_1_output_0, %onnx::Conv_849, %onnx::Conv_850) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_852, %onnx::Conv_853) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_855, %onnx::Conv_856) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.16/nl/Relu_output_0, %onnx::Conv_858, %onnx::Conv_859) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_861, %onnx::Conv_862) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.15/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_864, %onnx::Conv_865) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.17/nl/Relu_output_0, %onnx::Conv_867, %onnx::Conv_868) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_870, %onnx::Conv_871) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_873, %onnx::Conv_874) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.18/nl/Relu_output_0, %onnx::Conv_876, %onnx::Conv_877) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_879, %onnx::Conv_880) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_882, %onnx::Conv_883) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.19/nl/Relu_output_0, %onnx::Conv_885, %onnx::Conv_886) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_888, %onnx::Conv_889) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_891, %onnx::Conv_892) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.20/shuffle/Reshape_output_0 = Reshape(%/cells.20/nl/Relu_output_0, %/cells.20/shuffle/Constant_output_0) %/cells.20/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.20/shuffle/Reshape_output_0) %/cells.20/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.20/shuffle/Reshape_1_output_0 = Reshape(%/cells.20/shuffle/Transpose_output_0, %/cells.20/shuffle/Constant_1_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.20/shuffle/Reshape_1_output_0, %onnx::Conv_894, %onnx::Conv_895) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_897, %onnx::Conv_898) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_900, %onnx::Conv_901) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.21/shuffle/Reshape_output_0 = Reshape(%/cells.21/nl/Relu_output_0, %/cells.21/shuffle/Constant_output_0) %/cells.21/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.21/shuffle/Reshape_output_0) %/cells.21/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.21/shuffle/Reshape_1_output_0 = Reshape(%/cells.21/shuffle/Transpose_output_0, %/cells.21/shuffle/Constant_1_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.21/shuffle/Reshape_1_output_0, %onnx::Conv_903, %onnx::Conv_904) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_906, %onnx::Conv_907) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_909, %onnx::Conv_910) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %712 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %712 }	val_accuracy	73,712,512	2,106,940	{'zcp_synflow': 78.84054143195394, 'zcp_zen': 70.91156768798828, 'zcp_epe_nas': 11.376384386774255, 'zcp_fisher': 0.27579694986343384, 'zcp_flops': 73712512.0, 'zcp_grad_norm': 28.324142456054688, 'zcp_grasp': -0.0270538330078125, 'zcp_jacov': -16.111084934339047, 'zcp_l2_norm': 664.3692016601562, 'zcp_nwot': 212.5358730152896, 'zcp_params': 2106940.0, 'zcp_plain': -0.005591399967670441, 'zcp_snip': 51.515403747558594, 'lat_1080ti_1': 0.7382658936642915, 'lat_1080ti_32': 0.6491078130231012, 'lat_1080ti_64': 0.5153888320169119, 'lat_2080ti_1': 0.8238694329224377, 'lat_2080ti_32': 0.6943684011467408, 'lat_2080ti_64': 0.5306463738936851, 'lat_essential_ph_1': 0.49056603773584906, 'lat_eyeriss': 0.5262593292135036, 'lat_fpga': 0.5354495888540355, 'lat_gold_6226': 0.4518167024388184, 'lat_gold_6240': 0.7152152447834327, 'lat_pixel2': 0.3695652173913043, 'lat_pixel3': 0.534364580933024, 'lat_raspi4': 0.5619474832109494, 'lat_samsung_a50': 0.24210526315789474, 'lat_samsung_s7': 0.2283464566929134, 'lat_silver_4114': 0.7277019439134089, 'lat_silver_4210r': 0.7514703541203591, 'lat_titan_rtx_1': 0.7462073522546082, 'lat_titan_rtx_32': 0.6849088342949504, 'lat_titan_rtx_64': 0.5890938406885765, 'lat_titanx_1': 0.3982497627390307, 'lat_titanx_32': 0.6426888322167212, 'lat_titanx_64': 0.5030637388882628, 'lat_titanxp_1': 0.7149720782528283, 'lat_titanxp_32': 0.7088701716035309, 'lat_titanxp_64': 0.5556164016981993}
FBNet_4430	FBNet	4430	4430	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_678[FLOAT, 16x3x3x3] %onnx::Conv_679[FLOAT, 16] %onnx::Conv_681[FLOAT, 16x16x1x1] %onnx::Conv_684[FLOAT, 16x1x5x5] %onnx::Conv_687[FLOAT, 16x16x1x1] %onnx::Conv_690[FLOAT, 16x16x1x1] %onnx::Conv_693[FLOAT, 16x1x5x5] %onnx::Conv_696[FLOAT, 24x16x1x1] %onnx::Conv_697[FLOAT, 24] %onnx::Conv_699[FLOAT, 144x24x1x1] %onnx::Conv_700[FLOAT, 144] %onnx::Conv_702[FLOAT, 144x1x3x3] %onnx::Conv_705[FLOAT, 24x144x1x1] %onnx::Conv_708[FLOAT, 24x12x1x1] %onnx::Conv_711[FLOAT, 24x1x5x5] %onnx::Conv_714[FLOAT, 24x12x1x1] %onnx::Conv_717[FLOAT, 72x24x1x1] %onnx::Conv_718[FLOAT, 72] %onnx::Conv_720[FLOAT, 72x1x3x3] %onnx::Conv_723[FLOAT, 24x72x1x1] %onnx::Conv_726[FLOAT, 32x24x1x1] %onnx::Conv_727[FLOAT, 32] %onnx::Conv_729[FLOAT, 96x32x1x1] %onnx::Conv_730[FLOAT, 96] %onnx::Conv_732[FLOAT, 96x1x5x5] %onnx::Conv_735[FLOAT, 32x96x1x1] %onnx::Conv_738[FLOAT, 32x16x1x1] %onnx::Conv_741[FLOAT, 32x1x5x5] %onnx::Conv_744[FLOAT, 32x16x1x1] %onnx::Conv_747[FLOAT, 32x32x1x1] %onnx::Conv_750[FLOAT, 32x1x3x3] %onnx::Conv_753[FLOAT, 64x32x1x1] %onnx::Conv_754[FLOAT, 64] %onnx::Conv_756[FLOAT, 64x32x1x1] %onnx::Conv_759[FLOAT, 64x1x3x3] %onnx::Conv_762[FLOAT, 64x32x1x1] %onnx::Conv_765[FLOAT, 384x64x1x1] %onnx::Conv_766[FLOAT, 384] %onnx::Conv_768[FLOAT, 384x1x3x3] %onnx::Conv_771[FLOAT, 64x384x1x1] %onnx::Conv_774[FLOAT, 64x32x1x1] %onnx::Conv_777[FLOAT, 64x1x3x3] %onnx::Conv_780[FLOAT, 112x32x1x1] %onnx::Conv_781[FLOAT, 112] %onnx::Conv_783[FLOAT, 336x112x1x1] %onnx::Conv_784[FLOAT, 336] %onnx::Conv_786[FLOAT, 336x1x3x3] %onnx::Conv_789[FLOAT, 112x336x1x1] %onnx::Conv_792[FLOAT, 112x56x1x1] %onnx::Conv_795[FLOAT, 112x1x3x3] %onnx::Conv_798[FLOAT, 112x56x1x1] %onnx::Conv_801[FLOAT, 112x56x1x1] %onnx::Conv_804[FLOAT, 112x1x5x5] %onnx::Conv_807[FLOAT, 112x56x1x1] %onnx::Conv_810[FLOAT, 112x56x1x1] %onnx::Conv_813[FLOAT, 112x1x5x5] %onnx::Conv_816[FLOAT, 184x56x1x1] %onnx::Conv_817[FLOAT, 184] %onnx::Conv_819[FLOAT, 1104x184x1x1] %onnx::Conv_820[FLOAT, 1104] %onnx::Conv_822[FLOAT, 1104x1x3x3] %onnx::Conv_825[FLOAT, 184x1104x1x1] %onnx::Conv_828[FLOAT, 1104x184x1x1] %onnx::Conv_831[FLOAT, 1104x1x3x3] %onnx::Conv_834[FLOAT, 184x1104x1x1] %onnx::Conv_837[FLOAT, 552x184x1x1] %onnx::Conv_838[FLOAT, 552] %onnx::Conv_840[FLOAT, 552x1x3x3] %onnx::Conv_843[FLOAT, 184x552x1x1] %onnx::Conv_846[FLOAT, 552x184x1x1] %onnx::Conv_849[FLOAT, 552x1x3x3] %onnx::Conv_852[FLOAT, 352x552x1x1] %onnx::Conv_853[FLOAT, 352] %onnx::Conv_855[FLOAT, 1504x352x1x1] %onnx::Conv_856[FLOAT, 1504] ) { %onnx::Conv_850 = Identity(%onnx::Conv_838) %onnx::Conv_847 = Identity(%onnx::Conv_838) %onnx::Conv_844 = Identity(%onnx::Conv_817) %onnx::Conv_841 = Identity(%onnx::Conv_838) %onnx::Conv_835 = Identity(%onnx::Conv_817) %onnx::Conv_832 = Identity(%onnx::Conv_820) %onnx::Conv_829 = Identity(%onnx::Conv_820) %onnx::Conv_826 = Identity(%onnx::Conv_817) %onnx::Conv_823 = Identity(%onnx::Conv_820) %onnx::Conv_814 = Identity(%onnx::Conv_781) %onnx::Conv_811 = Identity(%onnx::Conv_781) %onnx::Conv_808 = Identity(%onnx::Conv_781) %onnx::Conv_805 = Identity(%onnx::Conv_781) %onnx::Conv_802 = Identity(%onnx::Conv_781) %onnx::Conv_799 = Identity(%onnx::Conv_781) %onnx::Conv_796 = Identity(%onnx::Conv_781) %onnx::Conv_793 = Identity(%onnx::Conv_781) %onnx::Conv_790 = Identity(%onnx::Conv_781) %onnx::Conv_787 = Identity(%onnx::Conv_784) %onnx::Conv_778 = Identity(%onnx::Conv_754) %onnx::Conv_775 = Identity(%onnx::Conv_754) %onnx::Conv_772 = Identity(%onnx::Conv_754) %onnx::Conv_769 = Identity(%onnx::Conv_766) %onnx::Conv_763 = Identity(%onnx::Conv_754) %onnx::Conv_760 = Identity(%onnx::Conv_754) %onnx::Conv_757 = Identity(%onnx::Conv_754) %onnx::Conv_751 = Identity(%onnx::Conv_727) %onnx::Conv_748 = Identity(%onnx::Conv_727) %onnx::Conv_745 = Identity(%onnx::Conv_727) %onnx::Conv_742 = Identity(%onnx::Conv_727) %onnx::Conv_739 = Identity(%onnx::Conv_727) %onnx::Conv_736 = Identity(%onnx::Conv_727) %onnx::Conv_733 = Identity(%onnx::Conv_730) %onnx::Conv_724 = Identity(%onnx::Conv_697) %onnx::Conv_721 = Identity(%onnx::Conv_718) %onnx::Conv_715 = Identity(%onnx::Conv_697) %onnx::Conv_712 = Identity(%onnx::Conv_697) %onnx::Conv_709 = Identity(%onnx::Conv_697) %onnx::Conv_706 = Identity(%onnx::Conv_697) %onnx::Conv_703 = Identity(%onnx::Conv_700) %onnx::Conv_694 = Identity(%onnx::Conv_679) %onnx::Conv_691 = Identity(%onnx::Conv_679) %onnx::Conv_688 = Identity(%onnx::Conv_679) %onnx::Conv_685 = Identity(%onnx::Conv_679) %onnx::Conv_682 = Identity(%onnx::Conv_679) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_678, %onnx::Conv_679) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_681, %onnx::Conv_682) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.0/nl/Relu_output_0, %onnx::Conv_684, %onnx::Conv_685) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_687, %onnx::Conv_688) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_690, %onnx::Conv_691) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.1/nl/Relu_output_0, %onnx::Conv_693, %onnx::Conv_694) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_696, %onnx::Conv_697) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_699, %onnx::Conv_700) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.2/nl/Relu_output_0, %onnx::Conv_702, %onnx::Conv_703) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_705, %onnx::Conv_706) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_708, %onnx::Conv_709) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.3/shuffle/Reshape_output_0 = Reshape(%/cells.3/nl/Relu_output_0, %/cells.3/shuffle/Constant_output_0) %/cells.3/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.3/shuffle/Reshape_output_0) %/cells.3/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.3/shuffle/Reshape_1_output_0 = Reshape(%/cells.3/shuffle/Transpose_output_0, %/cells.3/shuffle/Constant_1_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.3/shuffle/Reshape_1_output_0, %onnx::Conv_711, %onnx::Conv_712) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_714, %onnx::Conv_715) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_717, %onnx::Conv_718) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.4/nl/Relu_output_0, %onnx::Conv_720, %onnx::Conv_721) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_723, %onnx::Conv_724) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [2, 2]](%/cells.4/Add_output_0, %onnx::Conv_726, %onnx::Conv_727) %/cells.5/relu/Relu_output_0 = Relu(%/cells.5/conv/Conv_output_0) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/relu/Relu_output_0, %onnx::Conv_729, %onnx::Conv_730) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.6/nl/Relu_output_0, %onnx::Conv_732, %onnx::Conv_733) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_735, %onnx::Conv_736) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/relu/Relu_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_738, %onnx::Conv_739) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.8/shuffle/Reshape_output_0 = Reshape(%/cells.8/nl/Relu_output_0, %/cells.8/shuffle/Constant_output_0) %/cells.8/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.8/shuffle/Reshape_output_0) %/cells.8/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.8/shuffle/Reshape_1_output_0 = Reshape(%/cells.8/shuffle/Transpose_output_0, %/cells.8/shuffle/Constant_1_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.8/shuffle/Reshape_1_output_0, %onnx::Conv_741, %onnx::Conv_742) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_744, %onnx::Conv_745) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_747, %onnx::Conv_748) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.9/nl/Relu_output_0, %onnx::Conv_750, %onnx::Conv_751) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_753, %onnx::Conv_754) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_756, %onnx::Conv_757) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.11/shuffle/Reshape_output_0 = Reshape(%/cells.11/nl/Relu_output_0, %/cells.11/shuffle/Constant_output_0) %/cells.11/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.11/shuffle/Reshape_output_0) %/cells.11/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.11/shuffle/Reshape_1_output_0 = Reshape(%/cells.11/shuffle/Transpose_output_0, %/cells.11/shuffle/Constant_1_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.11/shuffle/Reshape_1_output_0, %onnx::Conv_759, %onnx::Conv_760) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_762, %onnx::Conv_763) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_765, %onnx::Conv_766) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.12/nl/Relu_output_0, %onnx::Conv_768, %onnx::Conv_769) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_771, %onnx::Conv_772) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_774, %onnx::Conv_775) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.13/shuffle/Reshape_output_0 = Reshape(%/cells.13/nl/Relu_output_0, %/cells.13/shuffle/Constant_output_0) %/cells.13/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.13/shuffle/Reshape_output_0) %/cells.13/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.13/shuffle/Reshape_1_output_0 = Reshape(%/cells.13/shuffle/Transpose_output_0, %/cells.13/shuffle/Constant_1_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.13/shuffle/Reshape_1_output_0, %onnx::Conv_777, %onnx::Conv_778) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_780, %onnx::Conv_781) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_783, %onnx::Conv_784) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.14/nl/Relu_output_0, %onnx::Conv_786, %onnx::Conv_787) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_789, %onnx::Conv_790) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_792, %onnx::Conv_793) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.15/shuffle/Reshape_output_0 = Reshape(%/cells.15/nl/Relu_output_0, %/cells.15/shuffle/Constant_output_0) %/cells.15/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.15/shuffle/Reshape_output_0) %/cells.15/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.15/shuffle/Reshape_1_output_0 = Reshape(%/cells.15/shuffle/Transpose_output_0, %/cells.15/shuffle/Constant_1_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.15/shuffle/Reshape_1_output_0, %onnx::Conv_795, %onnx::Conv_796) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_798, %onnx::Conv_799) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_801, %onnx::Conv_802) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.16/shuffle/Reshape_output_0 = Reshape(%/cells.16/nl/Relu_output_0, %/cells.16/shuffle/Constant_output_0) %/cells.16/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.16/shuffle/Reshape_output_0) %/cells.16/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.16/shuffle/Reshape_1_output_0 = Reshape(%/cells.16/shuffle/Transpose_output_0, %/cells.16/shuffle/Constant_1_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.16/shuffle/Reshape_1_output_0, %onnx::Conv_804, %onnx::Conv_805) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_807, %onnx::Conv_808) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.15/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_810, %onnx::Conv_811) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.17/shuffle/Reshape_output_0 = Reshape(%/cells.17/nl/Relu_output_0, %/cells.17/shuffle/Constant_output_0) %/cells.17/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.17/shuffle/Reshape_output_0) %/cells.17/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.17/shuffle/Reshape_1_output_0 = Reshape(%/cells.17/shuffle/Transpose_output_0, %/cells.17/shuffle/Constant_1_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.17/shuffle/Reshape_1_output_0, %onnx::Conv_813, %onnx::Conv_814) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_816, %onnx::Conv_817) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_819, %onnx::Conv_820) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.18/nl/Relu_output_0, %onnx::Conv_822, %onnx::Conv_823) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_825, %onnx::Conv_826) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_828, %onnx::Conv_829) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.19/nl/Relu_output_0, %onnx::Conv_831, %onnx::Conv_832) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_834, %onnx::Conv_835) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_837, %onnx::Conv_838) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 552, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.20/nl/Relu_output_0, %onnx::Conv_840, %onnx::Conv_841) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_843, %onnx::Conv_844) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_846, %onnx::Conv_847) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 552, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.21/nl/Relu_output_0, %onnx::Conv_849, %onnx::Conv_850) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_852, %onnx::Conv_853) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_855, %onnx::Conv_856) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %676 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %676 }	val_accuracy	63,887,744	2,268,308	{'zcp_synflow': 71.02461901478333, 'zcp_zen': 63.527732849121094, 'zcp_epe_nas': 0.00015999920000638146, 'zcp_fisher': 0.12609528005123138, 'zcp_flops': 63887744.0, 'zcp_grad_norm': 21.002586364746094, 'zcp_grasp': -0.07602500915527344, 'zcp_jacov': -16.057762565084744, 'zcp_l2_norm': 600.5360717773438, 'zcp_nwot': 210.39223032263902, 'zcp_params': 2268308.0, 'zcp_plain': 0.00040505584911443293, 'zcp_snip': 36.94902420043945, 'lat_1080ti_1': 0.6264234790324253, 'lat_1080ti_32': 0.5210146954697361, 'lat_1080ti_64': 0.4045974106665779, 'lat_2080ti_1': 0.6202134915474411, 'lat_2080ti_32': 0.5647491757878653, 'lat_2080ti_64': 0.42520448883331846, 'lat_essential_ph_1': 0.32075471698113206, 'lat_eyeriss': 0.39411915720756246, 'lat_fpga': 0.4766423574347658, 'lat_gold_6226': 0.39250389433656424, 'lat_gold_6240': 0.5244135339157201, 'lat_pixel2': 0.4782608695652174, 'lat_pixel3': 0.3625597152382877, 'lat_raspi4': 0.5015448765136339, 'lat_samsung_a50': 0.21052631578947367, 'lat_samsung_s7': 0.13385826771653545, 'lat_silver_4114': 0.5336470323348024, 'lat_silver_4210r': 0.5660960718018999, 'lat_titan_rtx_1': 0.5765561546803116, 'lat_titan_rtx_32': 0.5277997234688756, 'lat_titan_rtx_64': 0.4511706325570238, 'lat_titanx_1': 0.307813928198178, 'lat_titanx_32': 0.48047055634885455, 'lat_titanx_64': 0.4019620886111541, 'lat_titanxp_1': 0.5399544792843363, 'lat_titanxp_32': 0.5090918748163292, 'lat_titanxp_64': 0.4274033569167305}
FBNet_2637	FBNet	2637	2637	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_550[FLOAT, 16x3x3x3] %onnx::Conv_551[FLOAT, 16] %onnx::Conv_553[FLOAT, 96x16x1x1] %onnx::Conv_554[FLOAT, 96] %onnx::Conv_556[FLOAT, 96x1x5x5] %onnx::Conv_559[FLOAT, 24x96x1x1] %onnx::Conv_560[FLOAT, 24] %onnx::Conv_562[FLOAT, 144x24x1x1] %onnx::Conv_563[FLOAT, 144] %onnx::Conv_565[FLOAT, 144x1x3x3] %onnx::Conv_568[FLOAT, 24x144x1x1] %onnx::Conv_571[FLOAT, 72x24x1x1] %onnx::Conv_572[FLOAT, 72] %onnx::Conv_574[FLOAT, 72x1x3x3] %onnx::Conv_577[FLOAT, 24x72x1x1] %onnx::Conv_580[FLOAT, 72x24x1x1] %onnx::Conv_583[FLOAT, 72x1x5x5] %onnx::Conv_586[FLOAT, 24x72x1x1] %onnx::Conv_589[FLOAT, 24x12x1x1] %onnx::Conv_592[FLOAT, 24x1x3x3] %onnx::Conv_595[FLOAT, 32x12x1x1] %onnx::Conv_596[FLOAT, 32] %onnx::Conv_598[FLOAT, 32x32x1x1] %onnx::Conv_601[FLOAT, 32x1x3x3] %onnx::Conv_604[FLOAT, 32x32x1x1] %onnx::Conv_607[FLOAT, 32x16x1x1] %onnx::Conv_610[FLOAT, 32x1x3x3] %onnx::Conv_613[FLOAT, 32x16x1x1] %onnx::Conv_616[FLOAT, 32x32x1x1] %onnx::Conv_619[FLOAT, 32x1x3x3] %onnx::Conv_622[FLOAT, 64x32x1x1] %onnx::Conv_623[FLOAT, 64] %onnx::Conv_625[FLOAT, 64x32x1x1] %onnx::Conv_628[FLOAT, 64x1x5x5] %onnx::Conv_631[FLOAT, 64x32x1x1] %onnx::Conv_634[FLOAT, 64x32x1x1] %onnx::Conv_637[FLOAT, 64x1x5x5] %onnx::Conv_640[FLOAT, 64x32x1x1] %onnx::Conv_643[FLOAT, 64x64x1x1] %onnx::Conv_646[FLOAT, 64x1x3x3] %onnx::Conv_649[FLOAT, 64x64x1x1] %onnx::Conv_652[FLOAT, 64x32x1x1] %onnx::Conv_655[FLOAT, 64x1x5x5] %onnx::Conv_658[FLOAT, 112x32x1x1] %onnx::Conv_659[FLOAT, 112] %onnx::Conv_661[FLOAT, 336x112x1x1] %onnx::Conv_662[FLOAT, 336] %onnx::Conv_664[FLOAT, 336x1x5x5] %onnx::Conv_667[FLOAT, 112x336x1x1] %onnx::Conv_670[FLOAT, 672x112x1x1] %onnx::Conv_671[FLOAT, 672] %onnx::Conv_673[FLOAT, 672x1x5x5] %onnx::Conv_676[FLOAT, 184x672x1x1] %onnx::Conv_677[FLOAT, 184] %onnx::Conv_679[FLOAT, 552x184x1x1] %onnx::Conv_680[FLOAT, 552] %onnx::Conv_682[FLOAT, 552x1x3x3] %onnx::Conv_685[FLOAT, 184x552x1x1] %onnx::Conv_688[FLOAT, 1104x184x1x1] %onnx::Conv_689[FLOAT, 1104] %onnx::Conv_691[FLOAT, 1104x1x5x5] %onnx::Conv_694[FLOAT, 352x1104x1x1] %onnx::Conv_695[FLOAT, 352] %onnx::Conv_697[FLOAT, 1504x352x1x1] %onnx::Conv_698[FLOAT, 1504] ) { %onnx::Conv_692 = Identity(%onnx::Conv_689) %onnx::Conv_686 = Identity(%onnx::Conv_677) %onnx::Conv_683 = Identity(%onnx::Conv_680) %onnx::Conv_674 = Identity(%onnx::Conv_671) %onnx::Conv_668 = Identity(%onnx::Conv_659) %onnx::Conv_665 = Identity(%onnx::Conv_662) %onnx::Conv_656 = Identity(%onnx::Conv_623) %onnx::Conv_653 = Identity(%onnx::Conv_623) %onnx::Conv_650 = Identity(%onnx::Conv_623) %onnx::Conv_647 = Identity(%onnx::Conv_623) %onnx::Conv_644 = Identity(%onnx::Conv_623) %onnx::Conv_641 = Identity(%onnx::Conv_623) %onnx::Conv_638 = Identity(%onnx::Conv_623) %onnx::Conv_635 = Identity(%onnx::Conv_623) %onnx::Conv_632 = Identity(%onnx::Conv_623) %onnx::Conv_629 = Identity(%onnx::Conv_623) %onnx::Conv_626 = Identity(%onnx::Conv_623) %onnx::Conv_620 = Identity(%onnx::Conv_596) %onnx::Conv_617 = Identity(%onnx::Conv_596) %onnx::Conv_614 = Identity(%onnx::Conv_596) %onnx::Conv_611 = Identity(%onnx::Conv_596) %onnx::Conv_608 = Identity(%onnx::Conv_596) %onnx::Conv_605 = Identity(%onnx::Conv_596) %onnx::Conv_602 = Identity(%onnx::Conv_596) %onnx::Conv_599 = Identity(%onnx::Conv_596) %onnx::Conv_593 = Identity(%onnx::Conv_560) %onnx::Conv_590 = Identity(%onnx::Conv_560) %onnx::Conv_587 = Identity(%onnx::Conv_560) %onnx::Conv_584 = Identity(%onnx::Conv_572) %onnx::Conv_581 = Identity(%onnx::Conv_572) %onnx::Conv_578 = Identity(%onnx::Conv_560) %onnx::Conv_575 = Identity(%onnx::Conv_572) %onnx::Conv_569 = Identity(%onnx::Conv_560) %onnx::Conv_566 = Identity(%onnx::Conv_563) %onnx::Conv_557 = Identity(%onnx::Conv_554) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_550, %onnx::Conv_551) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_553, %onnx::Conv_554) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.1/nl/Relu_output_0, %onnx::Conv_556, %onnx::Conv_557) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_559, %onnx::Conv_560) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_562, %onnx::Conv_563) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.2/nl/Relu_output_0, %onnx::Conv_565, %onnx::Conv_566) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_568, %onnx::Conv_569) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_571, %onnx::Conv_572) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.3/nl/Relu_output_0, %onnx::Conv_574, %onnx::Conv_575) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_577, %onnx::Conv_578) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_580, %onnx::Conv_581) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.4/nl/Relu_output_0, %onnx::Conv_583, %onnx::Conv_584) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_586, %onnx::Conv_587) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/Add_output_0, %onnx::Conv_589, %onnx::Conv_590) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.5/shuffle/Reshape_output_0 = Reshape(%/cells.5/nl/Relu_output_0, %/cells.5/shuffle/Constant_output_0) %/cells.5/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.5/shuffle/Reshape_output_0) %/cells.5/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.5/shuffle/Reshape_1_output_0 = Reshape(%/cells.5/shuffle/Transpose_output_0, %/cells.5/shuffle/Constant_1_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.5/shuffle/Reshape_1_output_0, %onnx::Conv_592, %onnx::Conv_593) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_595, %onnx::Conv_596) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_598, %onnx::Conv_599) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.6/nl/Relu_output_0, %onnx::Conv_601, %onnx::Conv_602) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_604, %onnx::Conv_605) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_607, %onnx::Conv_608) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.7/shuffle/Reshape_output_0 = Reshape(%/cells.7/nl/Relu_output_0, %/cells.7/shuffle/Constant_output_0) %/cells.7/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.7/shuffle/Reshape_output_0) %/cells.7/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.7/shuffle/Reshape_1_output_0 = Reshape(%/cells.7/shuffle/Transpose_output_0, %/cells.7/shuffle/Constant_1_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.7/shuffle/Reshape_1_output_0, %onnx::Conv_610, %onnx::Conv_611) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_613, %onnx::Conv_614) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_616, %onnx::Conv_617) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.9/nl/Relu_output_0, %onnx::Conv_619, %onnx::Conv_620) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_622, %onnx::Conv_623) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_625, %onnx::Conv_626) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.10/shuffle/Reshape_output_0 = Reshape(%/cells.10/nl/Relu_output_0, %/cells.10/shuffle/Constant_output_0) %/cells.10/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.10/shuffle/Reshape_output_0) %/cells.10/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.10/shuffle/Reshape_1_output_0 = Reshape(%/cells.10/shuffle/Transpose_output_0, %/cells.10/shuffle/Constant_1_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.10/shuffle/Reshape_1_output_0, %onnx::Conv_628, %onnx::Conv_629) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_631, %onnx::Conv_632) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_634, %onnx::Conv_635) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.11/shuffle/Reshape_output_0 = Reshape(%/cells.11/nl/Relu_output_0, %/cells.11/shuffle/Constant_output_0) %/cells.11/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.11/shuffle/Reshape_output_0) %/cells.11/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.11/shuffle/Reshape_1_output_0 = Reshape(%/cells.11/shuffle/Transpose_output_0, %/cells.11/shuffle/Constant_1_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.11/shuffle/Reshape_1_output_0, %onnx::Conv_637, %onnx::Conv_638) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_640, %onnx::Conv_641) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_643, %onnx::Conv_644) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.12/nl/Relu_output_0, %onnx::Conv_646, %onnx::Conv_647) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_649, %onnx::Conv_650) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_652, %onnx::Conv_653) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.13/shuffle/Reshape_output_0 = Reshape(%/cells.13/nl/Relu_output_0, %/cells.13/shuffle/Constant_output_0) %/cells.13/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.13/shuffle/Reshape_output_0) %/cells.13/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.13/shuffle/Reshape_1_output_0 = Reshape(%/cells.13/shuffle/Transpose_output_0, %/cells.13/shuffle/Constant_1_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.13/shuffle/Reshape_1_output_0, %onnx::Conv_655, %onnx::Conv_656) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_658, %onnx::Conv_659) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_661, %onnx::Conv_662) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.15/nl/Relu_output_0, %onnx::Conv_664, %onnx::Conv_665) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_667, %onnx::Conv_668) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_670, %onnx::Conv_671) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.17/nl/Relu_output_0, %onnx::Conv_673, %onnx::Conv_674) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_676, %onnx::Conv_677) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_679, %onnx::Conv_680) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 552, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.20/nl/Relu_output_0, %onnx::Conv_682, %onnx::Conv_683) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_685, %onnx::Conv_686) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_688, %onnx::Conv_689) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.21/nl/Relu_output_0, %onnx::Conv_691, %onnx::Conv_692) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_694, %onnx::Conv_695) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_697, %onnx::Conv_698) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %548 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %548 }	val_accuracy	65,701,504	1,885,780	{'zcp_synflow': 60.1785146267341, 'zcp_zen': 52.395713806152344, 'zcp_epe_nas': 26.49770074413774, 'zcp_fisher': 0.04929730296134949, 'zcp_flops': 65701504.0, 'zcp_grad_norm': 16.70769500732422, 'zcp_grasp': -0.010433197021484375, 'zcp_jacov': -16.06170114545526, 'zcp_l2_norm': 468.02020263671875, 'zcp_nwot': 212.8370491947139, 'zcp_params': 1885780.0, 'zcp_plain': 0.0005249266396276653, 'zcp_snip': 27.276098251342773, 'lat_1080ti_1': 0.18766911267580486, 'lat_1080ti_32': 0.2892460938934374, 'lat_1080ti_64': 0.3965756224330005, 'lat_2080ti_1': 0.22551531625678142, 'lat_2080ti_32': 0.31902774135347545, 'lat_2080ti_64': 0.4150874648084101, 'lat_essential_ph_1': 0.11320754716981132, 'lat_eyeriss': 0.38947059578603754, 'lat_fpga': 0.399519092807172, 'lat_gold_6226': 0.2767780058720389, 'lat_gold_6240': 0.2403618601436152, 'lat_pixel2': 0.21739130434782608, 'lat_pixel3': 0.4293475093184594, 'lat_raspi4': 0.46175914507740695, 'lat_samsung_a50': 0.14736842105263157, 'lat_samsung_s7': 0.11023622047244094, 'lat_silver_4114': 0.25508433625831534, 'lat_silver_4210r': 0.20303919010743288, 'lat_titan_rtx_1': 0.22799131289498423, 'lat_titan_rtx_32': 0.2411211182826978, 'lat_titan_rtx_64': 0.37213231495711874, 'lat_titanx_1': 0.11260965084362058, 'lat_titanx_32': 0.3723811314545948, 'lat_titanx_64': 0.4012471196465477, 'lat_titanxp_1': 0.20583945161264883, 'lat_titanxp_32': 0.3225270100102328, 'lat_titanxp_64': 0.3993860979789909}
FBNet_414	FBNet	414	414	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_632[FLOAT, 16x3x3x3] %onnx::Conv_633[FLOAT, 16] %onnx::Conv_635[FLOAT, 48x16x1x1] %onnx::Conv_636[FLOAT, 48] %onnx::Conv_638[FLOAT, 48x1x3x3] %onnx::Conv_641[FLOAT, 16x48x1x1] %onnx::Conv_644[FLOAT, 16x16x1x1] %onnx::Conv_647[FLOAT, 16x1x5x5] %onnx::Conv_650[FLOAT, 24x16x1x1] %onnx::Conv_651[FLOAT, 24] %onnx::Conv_653[FLOAT, 144x24x1x1] %onnx::Conv_654[FLOAT, 144] %onnx::Conv_656[FLOAT, 144x1x3x3] %onnx::Conv_659[FLOAT, 24x144x1x1] %onnx::Conv_662[FLOAT, 24x24x1x1] %onnx::Conv_665[FLOAT, 24x1x3x3] %onnx::Conv_668[FLOAT, 24x24x1x1] %onnx::Conv_671[FLOAT, 24x12x1x1] %onnx::Conv_674[FLOAT, 24x1x3x3] %onnx::Conv_677[FLOAT, 24x12x1x1] %onnx::Conv_680[FLOAT, 144x24x1x1] %onnx::Conv_683[FLOAT, 144x1x5x5] %onnx::Conv_686[FLOAT, 32x144x1x1] %onnx::Conv_687[FLOAT, 32] %onnx::Conv_689[FLOAT, 96x32x1x1] %onnx::Conv_690[FLOAT, 96] %onnx::Conv_692[FLOAT, 96x1x5x5] %onnx::Conv_695[FLOAT, 32x96x1x1] %onnx::Conv_698[FLOAT, 96x32x1x1] %onnx::Conv_701[FLOAT, 96x1x5x5] %onnx::Conv_704[FLOAT, 32x96x1x1] %onnx::Conv_707[FLOAT, 32x32x1x1] %onnx::Conv_710[FLOAT, 32x1x5x5] %onnx::Conv_713[FLOAT, 64x32x1x1] %onnx::Conv_714[FLOAT, 64] %onnx::Conv_716[FLOAT, 384x64x1x1] %onnx::Conv_717[FLOAT, 384] %onnx::Conv_719[FLOAT, 384x1x5x5] %onnx::Conv_722[FLOAT, 64x384x1x1] %onnx::Conv_725[FLOAT, 64x32x1x1] %onnx::Conv_728[FLOAT, 64x1x3x3] %onnx::Conv_731[FLOAT, 64x32x1x1] %onnx::Conv_734[FLOAT, 64x64x1x1] %onnx::Conv_737[FLOAT, 64x1x5x5] %onnx::Conv_740[FLOAT, 64x64x1x1] %onnx::Conv_743[FLOAT, 64x64x1x1] %onnx::Conv_746[FLOAT, 64x1x5x5] %onnx::Conv_749[FLOAT, 112x64x1x1] %onnx::Conv_750[FLOAT, 112] %onnx::Conv_752[FLOAT, 336x112x1x1] %onnx::Conv_753[FLOAT, 336] %onnx::Conv_755[FLOAT, 336x1x3x3] %onnx::Conv_758[FLOAT, 112x336x1x1] %onnx::Conv_761[FLOAT, 112x56x1x1] %onnx::Conv_764[FLOAT, 112x1x5x5] %onnx::Conv_767[FLOAT, 112x56x1x1] %onnx::Conv_770[FLOAT, 112x56x1x1] %onnx::Conv_773[FLOAT, 112x1x5x5] %onnx::Conv_776[FLOAT, 184x56x1x1] %onnx::Conv_777[FLOAT, 184] %onnx::Conv_779[FLOAT, 1104x184x1x1] %onnx::Conv_780[FLOAT, 1104] %onnx::Conv_782[FLOAT, 1104x1x3x3] %onnx::Conv_785[FLOAT, 184x1104x1x1] %onnx::Conv_788[FLOAT, 184x92x1x1] %onnx::Conv_791[FLOAT, 184x1x5x5] %onnx::Conv_794[FLOAT, 184x92x1x1] %onnx::Conv_797[FLOAT, 552x184x1x1] %onnx::Conv_798[FLOAT, 552] %onnx::Conv_800[FLOAT, 552x1x5x5] %onnx::Conv_803[FLOAT, 352x552x1x1] %onnx::Conv_804[FLOAT, 352] %onnx::Conv_806[FLOAT, 1504x352x1x1] %onnx::Conv_807[FLOAT, 1504] ) { %onnx::Conv_801 = Identity(%onnx::Conv_798) %onnx::Conv_795 = Identity(%onnx::Conv_777) %onnx::Conv_792 = Identity(%onnx::Conv_777) %onnx::Conv_789 = Identity(%onnx::Conv_777) %onnx::Conv_786 = Identity(%onnx::Conv_777) %onnx::Conv_783 = Identity(%onnx::Conv_780) %onnx::Conv_774 = Identity(%onnx::Conv_750) %onnx::Conv_771 = Identity(%onnx::Conv_750) %onnx::Conv_768 = Identity(%onnx::Conv_750) %onnx::Conv_765 = Identity(%onnx::Conv_750) %onnx::Conv_762 = Identity(%onnx::Conv_750) %onnx::Conv_759 = Identity(%onnx::Conv_750) %onnx::Conv_756 = Identity(%onnx::Conv_753) %onnx::Conv_747 = Identity(%onnx::Conv_714) %onnx::Conv_744 = Identity(%onnx::Conv_714) %onnx::Conv_741 = Identity(%onnx::Conv_714) %onnx::Conv_738 = Identity(%onnx::Conv_714) %onnx::Conv_735 = Identity(%onnx::Conv_714) %onnx::Conv_732 = Identity(%onnx::Conv_714) %onnx::Conv_729 = Identity(%onnx::Conv_714) %onnx::Conv_726 = Identity(%onnx::Conv_714) %onnx::Conv_723 = Identity(%onnx::Conv_714) %onnx::Conv_720 = Identity(%onnx::Conv_717) %onnx::Conv_711 = Identity(%onnx::Conv_687) %onnx::Conv_708 = Identity(%onnx::Conv_687) %onnx::Conv_705 = Identity(%onnx::Conv_687) %onnx::Conv_702 = Identity(%onnx::Conv_690) %onnx::Conv_699 = Identity(%onnx::Conv_690) %onnx::Conv_696 = Identity(%onnx::Conv_687) %onnx::Conv_693 = Identity(%onnx::Conv_690) %onnx::Conv_684 = Identity(%onnx::Conv_654) %onnx::Conv_681 = Identity(%onnx::Conv_654) %onnx::Conv_678 = Identity(%onnx::Conv_651) %onnx::Conv_675 = Identity(%onnx::Conv_651) %onnx::Conv_672 = Identity(%onnx::Conv_651) %onnx::Conv_669 = Identity(%onnx::Conv_651) %onnx::Conv_666 = Identity(%onnx::Conv_651) %onnx::Conv_663 = Identity(%onnx::Conv_651) %onnx::Conv_660 = Identity(%onnx::Conv_651) %onnx::Conv_657 = Identity(%onnx::Conv_654) %onnx::Conv_648 = Identity(%onnx::Conv_633) %onnx::Conv_645 = Identity(%onnx::Conv_633) %onnx::Conv_642 = Identity(%onnx::Conv_633) %onnx::Conv_639 = Identity(%onnx::Conv_636) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_632, %onnx::Conv_633) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_635, %onnx::Conv_636) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 48, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.0/nl/Relu_output_0, %onnx::Conv_638, %onnx::Conv_639) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_641, %onnx::Conv_642) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_644, %onnx::Conv_645) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.1/nl/Relu_output_0, %onnx::Conv_647, %onnx::Conv_648) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_650, %onnx::Conv_651) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_653, %onnx::Conv_654) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.2/nl/Relu_output_0, %onnx::Conv_656, %onnx::Conv_657) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_659, %onnx::Conv_660) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_662, %onnx::Conv_663) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.3/nl/Relu_output_0, %onnx::Conv_665, %onnx::Conv_666) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_668, %onnx::Conv_669) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_671, %onnx::Conv_672) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.4/shuffle/Reshape_output_0 = Reshape(%/cells.4/nl/Relu_output_0, %/cells.4/shuffle/Constant_output_0) %/cells.4/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.4/shuffle/Reshape_output_0) %/cells.4/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.4/shuffle/Reshape_1_output_0 = Reshape(%/cells.4/shuffle/Transpose_output_0, %/cells.4/shuffle/Constant_1_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.4/shuffle/Reshape_1_output_0, %onnx::Conv_674, %onnx::Conv_675) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_677, %onnx::Conv_678) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/Add_output_0, %onnx::Conv_680, %onnx::Conv_681) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.5/nl/Relu_output_0, %onnx::Conv_683, %onnx::Conv_684) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_686, %onnx::Conv_687) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_689, %onnx::Conv_690) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.6/nl/Relu_output_0, %onnx::Conv_692, %onnx::Conv_693) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_695, %onnx::Conv_696) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_698, %onnx::Conv_699) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.7/nl/Relu_output_0, %onnx::Conv_701, %onnx::Conv_702) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_704, %onnx::Conv_705) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_707, %onnx::Conv_708) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.9/nl/Relu_output_0, %onnx::Conv_710, %onnx::Conv_711) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_713, %onnx::Conv_714) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_716, %onnx::Conv_717) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.10/nl/Relu_output_0, %onnx::Conv_719, %onnx::Conv_720) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_722, %onnx::Conv_723) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_725, %onnx::Conv_726) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.11/shuffle/Reshape_output_0 = Reshape(%/cells.11/nl/Relu_output_0, %/cells.11/shuffle/Constant_output_0) %/cells.11/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.11/shuffle/Reshape_output_0) %/cells.11/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.11/shuffle/Reshape_1_output_0 = Reshape(%/cells.11/shuffle/Transpose_output_0, %/cells.11/shuffle/Constant_1_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.11/shuffle/Reshape_1_output_0, %onnx::Conv_728, %onnx::Conv_729) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_731, %onnx::Conv_732) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_734, %onnx::Conv_735) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.12/nl/Relu_output_0, %onnx::Conv_737, %onnx::Conv_738) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_740, %onnx::Conv_741) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_743, %onnx::Conv_744) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.13/nl/Relu_output_0, %onnx::Conv_746, %onnx::Conv_747) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_749, %onnx::Conv_750) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_752, %onnx::Conv_753) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.14/nl/Relu_output_0, %onnx::Conv_755, %onnx::Conv_756) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_758, %onnx::Conv_759) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_761, %onnx::Conv_762) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.15/shuffle/Reshape_output_0 = Reshape(%/cells.15/nl/Relu_output_0, %/cells.15/shuffle/Constant_output_0) %/cells.15/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.15/shuffle/Reshape_output_0) %/cells.15/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.15/shuffle/Reshape_1_output_0 = Reshape(%/cells.15/shuffle/Transpose_output_0, %/cells.15/shuffle/Constant_1_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.15/shuffle/Reshape_1_output_0, %onnx::Conv_764, %onnx::Conv_765) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_767, %onnx::Conv_768) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_770, %onnx::Conv_771) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.17/shuffle/Reshape_output_0 = Reshape(%/cells.17/nl/Relu_output_0, %/cells.17/shuffle/Constant_output_0) %/cells.17/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.17/shuffle/Reshape_output_0) %/cells.17/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.17/shuffle/Reshape_1_output_0 = Reshape(%/cells.17/shuffle/Transpose_output_0, %/cells.17/shuffle/Constant_1_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.17/shuffle/Reshape_1_output_0, %onnx::Conv_773, %onnx::Conv_774) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_776, %onnx::Conv_777) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_779, %onnx::Conv_780) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.18/nl/Relu_output_0, %onnx::Conv_782, %onnx::Conv_783) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_785, %onnx::Conv_786) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_788, %onnx::Conv_789) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.19/shuffle/Reshape_output_0 = Reshape(%/cells.19/nl/Relu_output_0, %/cells.19/shuffle/Constant_output_0) %/cells.19/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.19/shuffle/Reshape_output_0) %/cells.19/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.19/shuffle/Reshape_1_output_0 = Reshape(%/cells.19/shuffle/Transpose_output_0, %/cells.19/shuffle/Constant_1_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.19/shuffle/Reshape_1_output_0, %onnx::Conv_791, %onnx::Conv_792) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_794, %onnx::Conv_795) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_797, %onnx::Conv_798) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 552, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.21/nl/Relu_output_0, %onnx::Conv_800, %onnx::Conv_801) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_803, %onnx::Conv_804) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_806, %onnx::Conv_807) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %630 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %630 }	val_accuracy	60,569,216	1,710,612	{'zcp_synflow': 72.24877584189727, 'zcp_zen': 62.279640197753906, 'zcp_epe_nas': 24.60891096370069, 'zcp_fisher': 0.10068421065807343, 'zcp_flops': 60569216.0, 'zcp_grad_norm': 22.65353012084961, 'zcp_grasp': -0.12835693359375, 'zcp_jacov': -16.06841513415983, 'zcp_l2_norm': 543.0186157226562, 'zcp_nwot': 212.4230761534971, 'zcp_params': 1710612.0, 'zcp_plain': -0.0016415484715253115, 'zcp_snip': 37.51091766357422, 'lat_1080ti_1': 0.45781264287869383, 'lat_1080ti_32': 0.44333267447793584, 'lat_1080ti_64': 0.4008255845311011, 'lat_2080ti_1': 0.5018352782739743, 'lat_2080ti_32': 0.4927394943976316, 'lat_2080ti_64': 0.4007799163666782, 'lat_essential_ph_1': 0.20754716981132076, 'lat_eyeriss': 0.3562338400601469, 'lat_fpga': 0.359680978438338, 'lat_gold_6226': 0.26635288459950834, 'lat_gold_6240': 0.3747249945903418, 'lat_pixel2': 0.2391304347826087, 'lat_pixel3': 0.3519999362678951, 'lat_raspi4': 0.47451770745860405, 'lat_samsung_a50': 0.15789473684210525, 'lat_samsung_s7': 0.2204724409448819, 'lat_silver_4114': 0.3994591185051755, 'lat_silver_4210r': 0.38584711564204544, 'lat_titan_rtx_1': 0.5043212404310582, 'lat_titan_rtx_32': 0.4643597611920607, 'lat_titan_rtx_64': 0.42673832225110186, 'lat_titanx_1': 0.267961443110561, 'lat_titanx_32': 0.4230042621378616, 'lat_titanx_64': 0.3876988672203329, 'lat_titanxp_1': 0.4558017829602437, 'lat_titanxp_32': 0.44614992347828286, 'lat_titanxp_64': 0.4115839614080406}
FBNet_2834	FBNet	2834	2834	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_606[FLOAT, 16x3x3x3] %onnx::Conv_607[FLOAT, 16] %onnx::Conv_609[FLOAT, 48x16x1x1] %onnx::Conv_610[FLOAT, 48] %onnx::Conv_612[FLOAT, 48x1x5x5] %onnx::Conv_615[FLOAT, 16x48x1x1] %onnx::Conv_618[FLOAT, 24x16x1x1] %onnx::Conv_619[FLOAT, 24] %onnx::Conv_621[FLOAT, 72x24x1x1] %onnx::Conv_622[FLOAT, 72] %onnx::Conv_624[FLOAT, 72x1x3x3] %onnx::Conv_627[FLOAT, 24x72x1x1] %onnx::Conv_630[FLOAT, 72x24x1x1] %onnx::Conv_633[FLOAT, 72x1x3x3] %onnx::Conv_636[FLOAT, 24x72x1x1] %onnx::Conv_639[FLOAT, 24x24x1x1] %onnx::Conv_642[FLOAT, 24x1x3x3] %onnx::Conv_645[FLOAT, 32x24x1x1] %onnx::Conv_646[FLOAT, 32] %onnx::Conv_648[FLOAT, 192x32x1x1] %onnx::Conv_649[FLOAT, 192] %onnx::Conv_651[FLOAT, 192x1x5x5] %onnx::Conv_654[FLOAT, 32x192x1x1] %onnx::Conv_657[FLOAT, 32x16x1x1] %onnx::Conv_660[FLOAT, 32x1x3x3] %onnx::Conv_663[FLOAT, 32x16x1x1] %onnx::Conv_666[FLOAT, 192x32x1x1] %onnx::Conv_669[FLOAT, 192x1x3x3] %onnx::Conv_672[FLOAT, 32x192x1x1] %onnx::Conv_675[FLOAT, 32x32x1x1] %onnx::Conv_678[FLOAT, 32x1x5x5] %onnx::Conv_681[FLOAT, 64x32x1x1] %onnx::Conv_682[FLOAT, 64] %onnx::Conv_684[FLOAT, 64x32x1x1] %onnx::Conv_687[FLOAT, 64x1x5x5] %onnx::Conv_690[FLOAT, 64x32x1x1] %onnx::Conv_693[FLOAT, 64x32x1x1] %onnx::Conv_696[FLOAT, 64x1x3x3] %onnx::Conv_699[FLOAT, 64x32x1x1] %onnx::Conv_702[FLOAT, 384x64x1x1] %onnx::Conv_703[FLOAT, 384] %onnx::Conv_705[FLOAT, 384x1x3x3] %onnx::Conv_708[FLOAT, 64x384x1x1] %onnx::Conv_711[FLOAT, 112x64x1x1] %onnx::Conv_712[FLOAT, 112] %onnx::Conv_714[FLOAT, 112x112x1x1] %onnx::Conv_717[FLOAT, 112x1x3x3] %onnx::Conv_720[FLOAT, 112x112x1x1] %onnx::Conv_723[FLOAT, 112x112x1x1] %onnx::Conv_726[FLOAT, 112x1x5x5] %onnx::Conv_729[FLOAT, 112x112x1x1] %onnx::Conv_732[FLOAT, 336x112x1x1] %onnx::Conv_733[FLOAT, 336] %onnx::Conv_735[FLOAT, 336x1x5x5] %onnx::Conv_738[FLOAT, 184x336x1x1] %onnx::Conv_739[FLOAT, 184] %onnx::Conv_741[FLOAT, 184x184x1x1] %onnx::Conv_744[FLOAT, 184x1x3x3] %onnx::Conv_747[FLOAT, 184x184x1x1] %onnx::Conv_750[FLOAT, 184x184x1x1] %onnx::Conv_753[FLOAT, 184x1x3x3] %onnx::Conv_756[FLOAT, 184x184x1x1] %onnx::Conv_759[FLOAT, 184x184x1x1] %onnx::Conv_762[FLOAT, 184x1x5x5] %onnx::Conv_765[FLOAT, 184x184x1x1] %onnx::Conv_768[FLOAT, 184x92x1x1] %onnx::Conv_771[FLOAT, 184x1x3x3] %onnx::Conv_774[FLOAT, 352x92x1x1] %onnx::Conv_775[FLOAT, 352] %onnx::Conv_777[FLOAT, 1504x352x1x1] %onnx::Conv_778[FLOAT, 1504] ) { %onnx::Conv_772 = Identity(%onnx::Conv_739) %onnx::Conv_769 = Identity(%onnx::Conv_739) %onnx::Conv_766 = Identity(%onnx::Conv_739) %onnx::Conv_763 = Identity(%onnx::Conv_739) %onnx::Conv_760 = Identity(%onnx::Conv_739) %onnx::Conv_757 = Identity(%onnx::Conv_739) %onnx::Conv_754 = Identity(%onnx::Conv_739) %onnx::Conv_751 = Identity(%onnx::Conv_739) %onnx::Conv_748 = Identity(%onnx::Conv_739) %onnx::Conv_745 = Identity(%onnx::Conv_739) %onnx::Conv_742 = Identity(%onnx::Conv_739) %onnx::Conv_736 = Identity(%onnx::Conv_733) %onnx::Conv_730 = Identity(%onnx::Conv_712) %onnx::Conv_727 = Identity(%onnx::Conv_712) %onnx::Conv_724 = Identity(%onnx::Conv_712) %onnx::Conv_721 = Identity(%onnx::Conv_712) %onnx::Conv_718 = Identity(%onnx::Conv_712) %onnx::Conv_715 = Identity(%onnx::Conv_712) %onnx::Conv_709 = Identity(%onnx::Conv_682) %onnx::Conv_706 = Identity(%onnx::Conv_703) %onnx::Conv_700 = Identity(%onnx::Conv_682) %onnx::Conv_697 = Identity(%onnx::Conv_682) %onnx::Conv_694 = Identity(%onnx::Conv_682) %onnx::Conv_691 = Identity(%onnx::Conv_682) %onnx::Conv_688 = Identity(%onnx::Conv_682) %onnx::Conv_685 = Identity(%onnx::Conv_682) %onnx::Conv_679 = Identity(%onnx::Conv_646) %onnx::Conv_676 = Identity(%onnx::Conv_646) %onnx::Conv_673 = Identity(%onnx::Conv_646) %onnx::Conv_670 = Identity(%onnx::Conv_649) %onnx::Conv_667 = Identity(%onnx::Conv_649) %onnx::Conv_664 = Identity(%onnx::Conv_646) %onnx::Conv_661 = Identity(%onnx::Conv_646) %onnx::Conv_658 = Identity(%onnx::Conv_646) %onnx::Conv_655 = Identity(%onnx::Conv_646) %onnx::Conv_652 = Identity(%onnx::Conv_649) %onnx::Conv_643 = Identity(%onnx::Conv_619) %onnx::Conv_640 = Identity(%onnx::Conv_619) %onnx::Conv_637 = Identity(%onnx::Conv_619) %onnx::Conv_634 = Identity(%onnx::Conv_622) %onnx::Conv_631 = Identity(%onnx::Conv_622) %onnx::Conv_628 = Identity(%onnx::Conv_619) %onnx::Conv_625 = Identity(%onnx::Conv_622) %onnx::Conv_616 = Identity(%onnx::Conv_607) %onnx::Conv_613 = Identity(%onnx::Conv_610) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_606, %onnx::Conv_607) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_609, %onnx::Conv_610) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 48, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.0/nl/Relu_output_0, %onnx::Conv_612, %onnx::Conv_613) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_615, %onnx::Conv_616) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_618, %onnx::Conv_619) %/cells.1/relu/Relu_output_0 = Relu(%/cells.1/conv/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/relu/Relu_output_0, %onnx::Conv_621, %onnx::Conv_622) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.3/nl/Relu_output_0, %onnx::Conv_624, %onnx::Conv_625) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_627, %onnx::Conv_628) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.1/relu/Relu_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_630, %onnx::Conv_631) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.4/nl/Relu_output_0, %onnx::Conv_633, %onnx::Conv_634) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_636, %onnx::Conv_637) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/Add_output_0, %onnx::Conv_639, %onnx::Conv_640) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.5/nl/Relu_output_0, %onnx::Conv_642, %onnx::Conv_643) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_645, %onnx::Conv_646) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_648, %onnx::Conv_649) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.6/nl/Relu_output_0, %onnx::Conv_651, %onnx::Conv_652) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_654, %onnx::Conv_655) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_657, %onnx::Conv_658) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.7/shuffle/Reshape_output_0 = Reshape(%/cells.7/nl/Relu_output_0, %/cells.7/shuffle/Constant_output_0) %/cells.7/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.7/shuffle/Reshape_output_0) %/cells.7/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.7/shuffle/Reshape_1_output_0 = Reshape(%/cells.7/shuffle/Transpose_output_0, %/cells.7/shuffle/Constant_1_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.7/shuffle/Reshape_1_output_0, %onnx::Conv_660, %onnx::Conv_661) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_663, %onnx::Conv_664) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_666, %onnx::Conv_667) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.8/nl/Relu_output_0, %onnx::Conv_669, %onnx::Conv_670) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_672, %onnx::Conv_673) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_675, %onnx::Conv_676) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.9/nl/Relu_output_0, %onnx::Conv_678, %onnx::Conv_679) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_681, %onnx::Conv_682) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_684, %onnx::Conv_685) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.10/shuffle/Reshape_output_0 = Reshape(%/cells.10/nl/Relu_output_0, %/cells.10/shuffle/Constant_output_0) %/cells.10/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.10/shuffle/Reshape_output_0) %/cells.10/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.10/shuffle/Reshape_1_output_0 = Reshape(%/cells.10/shuffle/Transpose_output_0, %/cells.10/shuffle/Constant_1_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.10/shuffle/Reshape_1_output_0, %onnx::Conv_687, %onnx::Conv_688) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_690, %onnx::Conv_691) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_693, %onnx::Conv_694) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.11/shuffle/Reshape_output_0 = Reshape(%/cells.11/nl/Relu_output_0, %/cells.11/shuffle/Constant_output_0) %/cells.11/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.11/shuffle/Reshape_output_0) %/cells.11/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.11/shuffle/Reshape_1_output_0 = Reshape(%/cells.11/shuffle/Transpose_output_0, %/cells.11/shuffle/Constant_1_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.11/shuffle/Reshape_1_output_0, %onnx::Conv_696, %onnx::Conv_697) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_699, %onnx::Conv_700) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_702, %onnx::Conv_703) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.12/nl/Relu_output_0, %onnx::Conv_705, %onnx::Conv_706) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_708, %onnx::Conv_709) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_711, %onnx::Conv_712) %/cells.13/relu/Relu_output_0 = Relu(%/cells.13/conv/Conv_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/relu/Relu_output_0, %onnx::Conv_714, %onnx::Conv_715) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.15/nl/Relu_output_0, %onnx::Conv_717, %onnx::Conv_718) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_720, %onnx::Conv_721) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.13/relu/Relu_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_723, %onnx::Conv_724) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.16/nl/Relu_output_0, %onnx::Conv_726, %onnx::Conv_727) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_729, %onnx::Conv_730) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.15/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_732, %onnx::Conv_733) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.17/nl/Relu_output_0, %onnx::Conv_735, %onnx::Conv_736) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_738, %onnx::Conv_739) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_741, %onnx::Conv_742) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.18/nl/Relu_output_0, %onnx::Conv_744, %onnx::Conv_745) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_747, %onnx::Conv_748) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_750, %onnx::Conv_751) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.19/nl/Relu_output_0, %onnx::Conv_753, %onnx::Conv_754) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_756, %onnx::Conv_757) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_759, %onnx::Conv_760) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.20/nl/Relu_output_0, %onnx::Conv_762, %onnx::Conv_763) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_765, %onnx::Conv_766) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_768, %onnx::Conv_769) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.21/shuffle/Reshape_output_0 = Reshape(%/cells.21/nl/Relu_output_0, %/cells.21/shuffle/Constant_output_0) %/cells.21/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.21/shuffle/Reshape_output_0) %/cells.21/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.21/shuffle/Reshape_1_output_0 = Reshape(%/cells.21/shuffle/Transpose_output_0, %/cells.21/shuffle/Constant_1_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.21/shuffle/Reshape_1_output_0, %onnx::Conv_771, %onnx::Conv_772) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_774, %onnx::Conv_775) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_777, %onnx::Conv_778) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %604 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %604 }	val_accuracy	48,408,960	1,240,236	{'zcp_synflow': 73.1921602091619, 'zcp_zen': 61.76199722290039, 'zcp_epe_nas': 14.935259719959753, 'zcp_fisher': 0.11903674900531769, 'zcp_flops': 48408960.0, 'zcp_grad_norm': 20.033218383789062, 'zcp_grasp': -0.01355743408203125, 'zcp_jacov': -16.058667545223493, 'zcp_l2_norm': 535.8496704101562, 'zcp_nwot': 209.2825304163933, 'zcp_params': 1240236.0, 'zcp_plain': 0.004363566171377897, 'zcp_snip': 41.448421478271484, 'lat_1080ti_1': 0.5135426681764776, 'lat_1080ti_32': 0.3994803490136812, 'lat_1080ti_64': 0.2973799653198091, 'lat_2080ti_1': 0.4717193385416488, 'lat_2080ti_32': 0.3697744072030572, 'lat_2080ti_64': 0.3439068977382186, 'lat_essential_ph_1': 0.1509433962264151, 'lat_eyeriss': 0.22623915794106333, 'lat_fpga': 0.19056194896050824, 'lat_gold_6226': 0.15848676236539344, 'lat_gold_6240': 0.21883147827199925, 'lat_pixel2': 0.10869565217391304, 'lat_pixel3': 0.19050180770253647, 'lat_raspi4': 0.14771950915255794, 'lat_samsung_a50': 0.08421052631578947, 'lat_samsung_s7': 0.07086614173228346, 'lat_silver_4114': 0.2397347344421421, 'lat_silver_4210r': 0.21547931824636415, 'lat_titan_rtx_1': 0.44862184238768077, 'lat_titan_rtx_32': 0.3666992012721641, 'lat_titan_rtx_64': 0.3451914974967084, 'lat_titanx_1': 0.23230194603972384, 'lat_titanx_32': 0.3332548852800203, 'lat_titanx_64': 0.2888614666038115, 'lat_titanxp_1': 0.4206334136414335, 'lat_titanxp_32': 0.35240170523648345, 'lat_titanxp_64': 0.31755497224659573}
FBNet_3007	FBNet	3007	3007	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_623[FLOAT, 16x3x3x3] %onnx::Conv_624[FLOAT, 16] %onnx::Conv_626[FLOAT, 96x16x1x1] %onnx::Conv_627[FLOAT, 96] %onnx::Conv_629[FLOAT, 96x1x3x3] %onnx::Conv_632[FLOAT, 16x96x1x1] %onnx::Conv_635[FLOAT, 16x8x1x1] %onnx::Conv_638[FLOAT, 16x1x5x5] %onnx::Conv_641[FLOAT, 24x8x1x1] %onnx::Conv_642[FLOAT, 24] %onnx::Conv_644[FLOAT, 72x24x1x1] %onnx::Conv_645[FLOAT, 72] %onnx::Conv_647[FLOAT, 72x1x3x3] %onnx::Conv_650[FLOAT, 24x72x1x1] %onnx::Conv_653[FLOAT, 72x24x1x1] %onnx::Conv_656[FLOAT, 72x1x3x3] %onnx::Conv_659[FLOAT, 24x72x1x1] %onnx::Conv_662[FLOAT, 24x24x1x1] %onnx::Conv_665[FLOAT, 24x1x3x3] %onnx::Conv_668[FLOAT, 24x24x1x1] %onnx::Conv_671[FLOAT, 32x24x1x1] %onnx::Conv_672[FLOAT, 32] %onnx::Conv_674[FLOAT, 32x16x1x1] %onnx::Conv_677[FLOAT, 32x1x3x3] %onnx::Conv_680[FLOAT, 32x16x1x1] %onnx::Conv_683[FLOAT, 32x32x1x1] %onnx::Conv_686[FLOAT, 32x1x3x3] %onnx::Conv_689[FLOAT, 32x32x1x1] %onnx::Conv_692[FLOAT, 32x32x1x1] %onnx::Conv_695[FLOAT, 32x1x3x3] %onnx::Conv_698[FLOAT, 32x32x1x1] %onnx::Conv_701[FLOAT, 32x16x1x1] %onnx::Conv_704[FLOAT, 32x1x3x3] %onnx::Conv_707[FLOAT, 64x16x1x1] %onnx::Conv_708[FLOAT, 64] %onnx::Conv_710[FLOAT, 64x64x1x1] %onnx::Conv_713[FLOAT, 64x1x3x3] %onnx::Conv_716[FLOAT, 64x64x1x1] %onnx::Conv_719[FLOAT, 64x32x1x1] %onnx::Conv_722[FLOAT, 64x1x5x5] %onnx::Conv_725[FLOAT, 64x32x1x1] %onnx::Conv_728[FLOAT, 192x64x1x1] %onnx::Conv_729[FLOAT, 192] %onnx::Conv_731[FLOAT, 192x1x3x3] %onnx::Conv_734[FLOAT, 64x192x1x1] %onnx::Conv_737[FLOAT, 192x64x1x1] %onnx::Conv_740[FLOAT, 192x1x3x3] %onnx::Conv_743[FLOAT, 112x192x1x1] %onnx::Conv_744[FLOAT, 112] %onnx::Conv_746[FLOAT, 112x112x1x1] %onnx::Conv_749[FLOAT, 112x1x3x3] %onnx::Conv_752[FLOAT, 112x112x1x1] %onnx::Conv_755[FLOAT, 112x112x1x1] %onnx::Conv_758[FLOAT, 112x1x5x5] %onnx::Conv_761[FLOAT, 112x112x1x1] %onnx::Conv_764[FLOAT, 112x112x1x1] %onnx::Conv_767[FLOAT, 112x1x5x5] %onnx::Conv_770[FLOAT, 112x112x1x1] %onnx::Conv_773[FLOAT, 336x112x1x1] %onnx::Conv_774[FLOAT, 336] %onnx::Conv_776[FLOAT, 336x1x5x5] %onnx::Conv_779[FLOAT, 184x336x1x1] %onnx::Conv_780[FLOAT, 184] %onnx::Conv_782[FLOAT, 1104x184x1x1] %onnx::Conv_783[FLOAT, 1104] %onnx::Conv_785[FLOAT, 1104x1x3x3] %onnx::Conv_788[FLOAT, 184x1104x1x1] %onnx::Conv_791[FLOAT, 184x184x1x1] %onnx::Conv_794[FLOAT, 184x1x5x5] %onnx::Conv_797[FLOAT, 352x184x1x1] %onnx::Conv_798[FLOAT, 352] %onnx::Conv_800[FLOAT, 1504x352x1x1] %onnx::Conv_801[FLOAT, 1504] ) { %onnx::Conv_795 = Identity(%onnx::Conv_780) %onnx::Conv_792 = Identity(%onnx::Conv_780) %onnx::Conv_789 = Identity(%onnx::Conv_780) %onnx::Conv_786 = Identity(%onnx::Conv_783) %onnx::Conv_777 = Identity(%onnx::Conv_774) %onnx::Conv_771 = Identity(%onnx::Conv_744) %onnx::Conv_768 = Identity(%onnx::Conv_744) %onnx::Conv_765 = Identity(%onnx::Conv_744) %onnx::Conv_762 = Identity(%onnx::Conv_744) %onnx::Conv_759 = Identity(%onnx::Conv_744) %onnx::Conv_756 = Identity(%onnx::Conv_744) %onnx::Conv_753 = Identity(%onnx::Conv_744) %onnx::Conv_750 = Identity(%onnx::Conv_744) %onnx::Conv_747 = Identity(%onnx::Conv_744) %onnx::Conv_741 = Identity(%onnx::Conv_729) %onnx::Conv_738 = Identity(%onnx::Conv_729) %onnx::Conv_735 = Identity(%onnx::Conv_708) %onnx::Conv_732 = Identity(%onnx::Conv_729) %onnx::Conv_726 = Identity(%onnx::Conv_708) %onnx::Conv_723 = Identity(%onnx::Conv_708) %onnx::Conv_720 = Identity(%onnx::Conv_708) %onnx::Conv_717 = Identity(%onnx::Conv_708) %onnx::Conv_714 = Identity(%onnx::Conv_708) %onnx::Conv_711 = Identity(%onnx::Conv_708) %onnx::Conv_705 = Identity(%onnx::Conv_672) %onnx::Conv_702 = Identity(%onnx::Conv_672) %onnx::Conv_699 = Identity(%onnx::Conv_672) %onnx::Conv_696 = Identity(%onnx::Conv_672) %onnx::Conv_693 = Identity(%onnx::Conv_672) %onnx::Conv_690 = Identity(%onnx::Conv_672) %onnx::Conv_687 = Identity(%onnx::Conv_672) %onnx::Conv_684 = Identity(%onnx::Conv_672) %onnx::Conv_681 = Identity(%onnx::Conv_672) %onnx::Conv_678 = Identity(%onnx::Conv_672) %onnx::Conv_675 = Identity(%onnx::Conv_672) %onnx::Conv_669 = Identity(%onnx::Conv_642) %onnx::Conv_666 = Identity(%onnx::Conv_642) %onnx::Conv_663 = Identity(%onnx::Conv_642) %onnx::Conv_660 = Identity(%onnx::Conv_642) %onnx::Conv_657 = Identity(%onnx::Conv_645) %onnx::Conv_654 = Identity(%onnx::Conv_645) %onnx::Conv_651 = Identity(%onnx::Conv_642) %onnx::Conv_648 = Identity(%onnx::Conv_645) %onnx::Conv_639 = Identity(%onnx::Conv_624) %onnx::Conv_636 = Identity(%onnx::Conv_624) %onnx::Conv_633 = Identity(%onnx::Conv_624) %onnx::Conv_630 = Identity(%onnx::Conv_627) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_623, %onnx::Conv_624) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_626, %onnx::Conv_627) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.0/nl/Relu_output_0, %onnx::Conv_629, %onnx::Conv_630) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_632, %onnx::Conv_633) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_635, %onnx::Conv_636) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.1/shuffle/Reshape_output_0 = Reshape(%/cells.1/nl/Relu_output_0, %/cells.1/shuffle/Constant_output_0) %/cells.1/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.1/shuffle/Reshape_output_0) %/cells.1/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.1/shuffle/Reshape_1_output_0 = Reshape(%/cells.1/shuffle/Transpose_output_0, %/cells.1/shuffle/Constant_1_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.1/shuffle/Reshape_1_output_0, %onnx::Conv_638, %onnx::Conv_639) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_641, %onnx::Conv_642) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_644, %onnx::Conv_645) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.2/nl/Relu_output_0, %onnx::Conv_647, %onnx::Conv_648) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_650, %onnx::Conv_651) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_653, %onnx::Conv_654) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.3/nl/Relu_output_0, %onnx::Conv_656, %onnx::Conv_657) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_659, %onnx::Conv_660) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_662, %onnx::Conv_663) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.4/nl/Relu_output_0, %onnx::Conv_665, %onnx::Conv_666) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_668, %onnx::Conv_669) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [2, 2]](%/cells.4/Add_output_0, %onnx::Conv_671, %onnx::Conv_672) %/cells.5/relu/Relu_output_0 = Relu(%/cells.5/conv/Conv_output_0) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/relu/Relu_output_0, %onnx::Conv_674, %onnx::Conv_675) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.6/shuffle/Reshape_output_0 = Reshape(%/cells.6/nl/Relu_output_0, %/cells.6/shuffle/Constant_output_0) %/cells.6/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.6/shuffle/Reshape_output_0) %/cells.6/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.6/shuffle/Reshape_1_output_0 = Reshape(%/cells.6/shuffle/Transpose_output_0, %/cells.6/shuffle/Constant_1_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.6/shuffle/Reshape_1_output_0, %onnx::Conv_677, %onnx::Conv_678) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_680, %onnx::Conv_681) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/relu/Relu_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_683, %onnx::Conv_684) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.7/nl/Relu_output_0, %onnx::Conv_686, %onnx::Conv_687) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_689, %onnx::Conv_690) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_692, %onnx::Conv_693) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.8/nl/Relu_output_0, %onnx::Conv_695, %onnx::Conv_696) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_698, %onnx::Conv_699) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_701, %onnx::Conv_702) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.9/shuffle/Reshape_output_0 = Reshape(%/cells.9/nl/Relu_output_0, %/cells.9/shuffle/Constant_output_0) %/cells.9/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.9/shuffle/Reshape_output_0) %/cells.9/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.9/shuffle/Reshape_1_output_0 = Reshape(%/cells.9/shuffle/Transpose_output_0, %/cells.9/shuffle/Constant_1_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.9/shuffle/Reshape_1_output_0, %onnx::Conv_704, %onnx::Conv_705) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_707, %onnx::Conv_708) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_710, %onnx::Conv_711) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.10/nl/Relu_output_0, %onnx::Conv_713, %onnx::Conv_714) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_716, %onnx::Conv_717) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_719, %onnx::Conv_720) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.11/shuffle/Reshape_output_0 = Reshape(%/cells.11/nl/Relu_output_0, %/cells.11/shuffle/Constant_output_0) %/cells.11/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.11/shuffle/Reshape_output_0) %/cells.11/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.11/shuffle/Reshape_1_output_0 = Reshape(%/cells.11/shuffle/Transpose_output_0, %/cells.11/shuffle/Constant_1_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.11/shuffle/Reshape_1_output_0, %onnx::Conv_722, %onnx::Conv_723) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_725, %onnx::Conv_726) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_728, %onnx::Conv_729) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.12/nl/Relu_output_0, %onnx::Conv_731, %onnx::Conv_732) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_734, %onnx::Conv_735) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_737, %onnx::Conv_738) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.13/nl/Relu_output_0, %onnx::Conv_740, %onnx::Conv_741) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_743, %onnx::Conv_744) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_746, %onnx::Conv_747) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.14/nl/Relu_output_0, %onnx::Conv_749, %onnx::Conv_750) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_752, %onnx::Conv_753) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_755, %onnx::Conv_756) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.15/nl/Relu_output_0, %onnx::Conv_758, %onnx::Conv_759) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_761, %onnx::Conv_762) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_764, %onnx::Conv_765) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.16/nl/Relu_output_0, %onnx::Conv_767, %onnx::Conv_768) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_770, %onnx::Conv_771) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.15/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_773, %onnx::Conv_774) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.17/nl/Relu_output_0, %onnx::Conv_776, %onnx::Conv_777) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_779, %onnx::Conv_780) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_782, %onnx::Conv_783) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.18/nl/Relu_output_0, %onnx::Conv_785, %onnx::Conv_786) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_788, %onnx::Conv_789) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_791, %onnx::Conv_792) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.21/nl/Relu_output_0, %onnx::Conv_794, %onnx::Conv_795) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_797, %onnx::Conv_798) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_800, %onnx::Conv_801) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %621 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %621 }	val_accuracy	50,119,808	1,506,484	{'zcp_synflow': 73.06311106124562, 'zcp_zen': 60.72926330566406, 'zcp_epe_nas': 0.00015999920000638146, 'zcp_fisher': 0.11469637602567673, 'zcp_flops': 50119808.0, 'zcp_grad_norm': 21.276643753051758, 'zcp_grasp': -0.15116119384765625, 'zcp_jacov': -16.054217350002208, 'zcp_l2_norm': 531.1348266601562, 'zcp_nwot': 208.99173752197908, 'zcp_params': 1506484.0, 'zcp_plain': 0.0015455330722033978, 'zcp_snip': 36.321441650390625, 'lat_1080ti_1': 0.5524984338353185, 'lat_1080ti_32': 0.45487290817352916, 'lat_1080ti_64': 0.34168708525946795, 'lat_2080ti_1': 0.5486596919813663, 'lat_2080ti_32': 0.4803111232817133, 'lat_2080ti_64': 0.36098895647983076, 'lat_essential_ph_1': 0.07547169811320754, 'lat_eyeriss': 0.2306218253167806, 'lat_fpga': 0.2569785966613561, 'lat_gold_6226': 0.16102336759997432, 'lat_gold_6240': 0.2622072337276185, 'lat_pixel2': 0.13043478260869565, 'lat_pixel3': 0.19689862029549984, 'lat_raspi4': 0.22502988957418413, 'lat_samsung_a50': 0.09473684210526316, 'lat_samsung_s7': 0.023622047244094488, 'lat_silver_4114': 0.3765709002061873, 'lat_silver_4210r': 0.27280114453325655, 'lat_titan_rtx_1': 0.5108061059402071, 'lat_titan_rtx_32': 0.4461728218157702, 'lat_titan_rtx_64': 0.38283715437240134, 'lat_titanx_1': 0.274944190061838, 'lat_titanx_32': 0.4088741465308878, 'lat_titanx_64': 0.32241473989308667, 'lat_titanxp_1': 0.47342869989290537, 'lat_titanxp_32': 0.44043491212045577, 'lat_titanxp_64': 0.35821327238885453}
FBNet_2954	FBNet	2954	2954	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_625[FLOAT, 16x3x3x3] %onnx::Conv_626[FLOAT, 16] %onnx::Conv_628[FLOAT, 16x16x1x1] %onnx::Conv_631[FLOAT, 16x1x5x5] %onnx::Conv_634[FLOAT, 16x16x1x1] %onnx::Conv_637[FLOAT, 16x8x1x1] %onnx::Conv_640[FLOAT, 16x1x5x5] %onnx::Conv_643[FLOAT, 24x8x1x1] %onnx::Conv_644[FLOAT, 24] %onnx::Conv_646[FLOAT, 144x24x1x1] %onnx::Conv_647[FLOAT, 144] %onnx::Conv_649[FLOAT, 144x1x3x3] %onnx::Conv_652[FLOAT, 24x144x1x1] %onnx::Conv_655[FLOAT, 24x12x1x1] %onnx::Conv_658[FLOAT, 24x1x5x5] %onnx::Conv_661[FLOAT, 24x12x1x1] %onnx::Conv_664[FLOAT, 24x24x1x1] %onnx::Conv_667[FLOAT, 24x1x5x5] %onnx::Conv_670[FLOAT, 32x24x1x1] %onnx::Conv_671[FLOAT, 32] %onnx::Conv_673[FLOAT, 32x32x1x1] %onnx::Conv_676[FLOAT, 32x1x3x3] %onnx::Conv_679[FLOAT, 32x32x1x1] %onnx::Conv_682[FLOAT, 32x32x1x1] %onnx::Conv_685[FLOAT, 32x1x5x5] %onnx::Conv_688[FLOAT, 32x32x1x1] %onnx::Conv_691[FLOAT, 32x16x1x1] %onnx::Conv_694[FLOAT, 32x1x3x3] %onnx::Conv_697[FLOAT, 64x16x1x1] %onnx::Conv_698[FLOAT, 64] %onnx::Conv_700[FLOAT, 192x64x1x1] %onnx::Conv_701[FLOAT, 192] %onnx::Conv_703[FLOAT, 192x1x3x3] %onnx::Conv_706[FLOAT, 64x192x1x1] %onnx::Conv_709[FLOAT, 64x64x1x1] %onnx::Conv_712[FLOAT, 64x1x5x5] %onnx::Conv_715[FLOAT, 64x64x1x1] %onnx::Conv_718[FLOAT, 64x32x1x1] %onnx::Conv_721[FLOAT, 64x1x5x5] %onnx::Conv_724[FLOAT, 64x32x1x1] %onnx::Conv_727[FLOAT, 192x64x1x1] %onnx::Conv_730[FLOAT, 192x1x3x3] %onnx::Conv_733[FLOAT, 112x192x1x1] %onnx::Conv_734[FLOAT, 112] %onnx::Conv_736[FLOAT, 112x56x1x1] %onnx::Conv_739[FLOAT, 112x1x3x3] %onnx::Conv_742[FLOAT, 112x56x1x1] %onnx::Conv_745[FLOAT, 336x112x1x1] %onnx::Conv_746[FLOAT, 336] %onnx::Conv_748[FLOAT, 336x1x5x5] %onnx::Conv_751[FLOAT, 112x336x1x1] %onnx::Conv_754[FLOAT, 672x112x1x1] %onnx::Conv_755[FLOAT, 672] %onnx::Conv_757[FLOAT, 672x1x5x5] %onnx::Conv_760[FLOAT, 184x672x1x1] %onnx::Conv_761[FLOAT, 184] %onnx::Conv_763[FLOAT, 1104x184x1x1] %onnx::Conv_764[FLOAT, 1104] %onnx::Conv_766[FLOAT, 1104x1x5x5] %onnx::Conv_769[FLOAT, 184x1104x1x1] %onnx::Conv_772[FLOAT, 184x92x1x1] %onnx::Conv_775[FLOAT, 184x1x3x3] %onnx::Conv_778[FLOAT, 184x92x1x1] %onnx::Conv_781[FLOAT, 552x184x1x1] %onnx::Conv_782[FLOAT, 552] %onnx::Conv_784[FLOAT, 552x1x5x5] %onnx::Conv_787[FLOAT, 352x552x1x1] %onnx::Conv_788[FLOAT, 352] %onnx::Conv_790[FLOAT, 1504x352x1x1] %onnx::Conv_791[FLOAT, 1504] ) { %onnx::Conv_785 = Identity(%onnx::Conv_782) %onnx::Conv_779 = Identity(%onnx::Conv_761) %onnx::Conv_776 = Identity(%onnx::Conv_761) %onnx::Conv_773 = Identity(%onnx::Conv_761) %onnx::Conv_770 = Identity(%onnx::Conv_761) %onnx::Conv_767 = Identity(%onnx::Conv_764) %onnx::Conv_758 = Identity(%onnx::Conv_755) %onnx::Conv_752 = Identity(%onnx::Conv_734) %onnx::Conv_749 = Identity(%onnx::Conv_746) %onnx::Conv_743 = Identity(%onnx::Conv_734) %onnx::Conv_740 = Identity(%onnx::Conv_734) %onnx::Conv_737 = Identity(%onnx::Conv_734) %onnx::Conv_731 = Identity(%onnx::Conv_701) %onnx::Conv_728 = Identity(%onnx::Conv_701) %onnx::Conv_725 = Identity(%onnx::Conv_698) %onnx::Conv_722 = Identity(%onnx::Conv_698) %onnx::Conv_719 = Identity(%onnx::Conv_698) %onnx::Conv_716 = Identity(%onnx::Conv_698) %onnx::Conv_713 = Identity(%onnx::Conv_698) %onnx::Conv_710 = Identity(%onnx::Conv_698) %onnx::Conv_707 = Identity(%onnx::Conv_698) %onnx::Conv_704 = Identity(%onnx::Conv_701) %onnx::Conv_695 = Identity(%onnx::Conv_671) %onnx::Conv_692 = Identity(%onnx::Conv_671) %onnx::Conv_689 = Identity(%onnx::Conv_671) %onnx::Conv_686 = Identity(%onnx::Conv_671) %onnx::Conv_683 = Identity(%onnx::Conv_671) %onnx::Conv_680 = Identity(%onnx::Conv_671) %onnx::Conv_677 = Identity(%onnx::Conv_671) %onnx::Conv_674 = Identity(%onnx::Conv_671) %onnx::Conv_668 = Identity(%onnx::Conv_644) %onnx::Conv_665 = Identity(%onnx::Conv_644) %onnx::Conv_662 = Identity(%onnx::Conv_644) %onnx::Conv_659 = Identity(%onnx::Conv_644) %onnx::Conv_656 = Identity(%onnx::Conv_644) %onnx::Conv_653 = Identity(%onnx::Conv_644) %onnx::Conv_650 = Identity(%onnx::Conv_647) %onnx::Conv_641 = Identity(%onnx::Conv_626) %onnx::Conv_638 = Identity(%onnx::Conv_626) %onnx::Conv_635 = Identity(%onnx::Conv_626) %onnx::Conv_632 = Identity(%onnx::Conv_626) %onnx::Conv_629 = Identity(%onnx::Conv_626) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_625, %onnx::Conv_626) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_628, %onnx::Conv_629) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.0/nl/Relu_output_0, %onnx::Conv_631, %onnx::Conv_632) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_634, %onnx::Conv_635) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_637, %onnx::Conv_638) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.1/shuffle/Reshape_output_0 = Reshape(%/cells.1/nl/Relu_output_0, %/cells.1/shuffle/Constant_output_0) %/cells.1/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.1/shuffle/Reshape_output_0) %/cells.1/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.1/shuffle/Reshape_1_output_0 = Reshape(%/cells.1/shuffle/Transpose_output_0, %/cells.1/shuffle/Constant_1_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.1/shuffle/Reshape_1_output_0, %onnx::Conv_640, %onnx::Conv_641) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_643, %onnx::Conv_644) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_646, %onnx::Conv_647) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.2/nl/Relu_output_0, %onnx::Conv_649, %onnx::Conv_650) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_652, %onnx::Conv_653) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_655, %onnx::Conv_656) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.4/shuffle/Reshape_output_0 = Reshape(%/cells.4/nl/Relu_output_0, %/cells.4/shuffle/Constant_output_0) %/cells.4/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.4/shuffle/Reshape_output_0) %/cells.4/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.4/shuffle/Reshape_1_output_0 = Reshape(%/cells.4/shuffle/Transpose_output_0, %/cells.4/shuffle/Constant_1_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.4/shuffle/Reshape_1_output_0, %onnx::Conv_658, %onnx::Conv_659) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_661, %onnx::Conv_662) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/Add_output_0, %onnx::Conv_664, %onnx::Conv_665) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.5/nl/Relu_output_0, %onnx::Conv_667, %onnx::Conv_668) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_670, %onnx::Conv_671) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_673, %onnx::Conv_674) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.6/nl/Relu_output_0, %onnx::Conv_676, %onnx::Conv_677) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_679, %onnx::Conv_680) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_682, %onnx::Conv_683) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.7/nl/Relu_output_0, %onnx::Conv_685, %onnx::Conv_686) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_688, %onnx::Conv_689) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_691, %onnx::Conv_692) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.9/shuffle/Reshape_output_0 = Reshape(%/cells.9/nl/Relu_output_0, %/cells.9/shuffle/Constant_output_0) %/cells.9/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.9/shuffle/Reshape_output_0) %/cells.9/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.9/shuffle/Reshape_1_output_0 = Reshape(%/cells.9/shuffle/Transpose_output_0, %/cells.9/shuffle/Constant_1_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.9/shuffle/Reshape_1_output_0, %onnx::Conv_694, %onnx::Conv_695) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_697, %onnx::Conv_698) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_700, %onnx::Conv_701) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.10/nl/Relu_output_0, %onnx::Conv_703, %onnx::Conv_704) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_706, %onnx::Conv_707) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_709, %onnx::Conv_710) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.11/nl/Relu_output_0, %onnx::Conv_712, %onnx::Conv_713) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_715, %onnx::Conv_716) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_718, %onnx::Conv_719) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.12/shuffle/Reshape_output_0 = Reshape(%/cells.12/nl/Relu_output_0, %/cells.12/shuffle/Constant_output_0) %/cells.12/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.12/shuffle/Reshape_output_0) %/cells.12/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.12/shuffle/Reshape_1_output_0 = Reshape(%/cells.12/shuffle/Transpose_output_0, %/cells.12/shuffle/Constant_1_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.12/shuffle/Reshape_1_output_0, %onnx::Conv_721, %onnx::Conv_722) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_724, %onnx::Conv_725) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_727, %onnx::Conv_728) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.13/nl/Relu_output_0, %onnx::Conv_730, %onnx::Conv_731) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_733, %onnx::Conv_734) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_736, %onnx::Conv_737) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.14/shuffle/Reshape_output_0 = Reshape(%/cells.14/nl/Relu_output_0, %/cells.14/shuffle/Constant_output_0) %/cells.14/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.14/shuffle/Reshape_output_0) %/cells.14/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.14/shuffle/Reshape_1_output_0 = Reshape(%/cells.14/shuffle/Transpose_output_0, %/cells.14/shuffle/Constant_1_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.14/shuffle/Reshape_1_output_0, %onnx::Conv_739, %onnx::Conv_740) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_742, %onnx::Conv_743) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_745, %onnx::Conv_746) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.16/nl/Relu_output_0, %onnx::Conv_748, %onnx::Conv_749) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_751, %onnx::Conv_752) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_754, %onnx::Conv_755) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.17/nl/Relu_output_0, %onnx::Conv_757, %onnx::Conv_758) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_760, %onnx::Conv_761) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_763, %onnx::Conv_764) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.18/nl/Relu_output_0, %onnx::Conv_766, %onnx::Conv_767) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_769, %onnx::Conv_770) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_772, %onnx::Conv_773) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.19/shuffle/Reshape_output_0 = Reshape(%/cells.19/nl/Relu_output_0, %/cells.19/shuffle/Constant_output_0) %/cells.19/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.19/shuffle/Reshape_output_0) %/cells.19/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.19/shuffle/Reshape_1_output_0 = Reshape(%/cells.19/shuffle/Transpose_output_0, %/cells.19/shuffle/Constant_1_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.19/shuffle/Reshape_1_output_0, %onnx::Conv_775, %onnx::Conv_776) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_778, %onnx::Conv_779) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_781, %onnx::Conv_782) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 552, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.21/nl/Relu_output_0, %onnx::Conv_784, %onnx::Conv_785) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_787, %onnx::Conv_788) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_790, %onnx::Conv_791) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %623 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %623 }	val_accuracy	55,772,800	1,891,172	{'zcp_synflow': 67.60243232399223, 'zcp_zen': 58.49372863769531, 'zcp_epe_nas': 16.0138799880452, 'zcp_fisher': 0.08534539490938187, 'zcp_flops': 55772800.0, 'zcp_grad_norm': 19.220531463623047, 'zcp_grasp': -0.006394386291503906, 'zcp_jacov': -16.049468020290483, 'zcp_l2_norm': 525.6923217773438, 'zcp_nwot': 206.9672870556231, 'zcp_params': 1891172.0, 'zcp_plain': 0.006311564240604639, 'zcp_snip': 31.453907012939453, 'lat_1080ti_1': 0.4859955955184796, 'lat_1080ti_32': 0.40351220295303697, 'lat_1080ti_64': 0.28331838409888244, 'lat_2080ti_1': 0.4895446077869, 'lat_2080ti_32': 0.4243334783757398, 'lat_2080ti_64': 0.2811020478509944, 'lat_essential_ph_1': 0.22641509433962265, 'lat_eyeriss': 0.27123026424366903, 'lat_fpga': 0.297242205048536, 'lat_gold_6226': 0.29338578708837343, 'lat_gold_6240': 0.38349690825064764, 'lat_pixel2': 0.1956521739130435, 'lat_pixel3': 0.2860603465150462, 'lat_raspi4': 0.3978134050591111, 'lat_samsung_a50': 0.1368421052631579, 'lat_samsung_s7': 0.11811023622047244, 'lat_silver_4114': 0.5088296509619661, 'lat_silver_4210r': 0.40297846299097834, 'lat_titan_rtx_1': 0.41630923249533186, 'lat_titan_rtx_32': 0.39861597910416596, 'lat_titan_rtx_64': 0.3016473779109095, 'lat_titanx_1': 0.22005656836088563, 'lat_titanx_32': 0.3250800548368004, 'lat_titanx_64': 0.28181673565658605, 'lat_titanxp_1': 0.4378958390214596, 'lat_titanxp_32': 0.3566535853176335, 'lat_titanxp_64': 0.28182521754651385}
FBNet_707	FBNet	707	707	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_649[FLOAT, 16x3x3x3] %onnx::Conv_650[FLOAT, 16] %onnx::Conv_652[FLOAT, 16x16x1x1] %onnx::Conv_655[FLOAT, 16x1x3x3] %onnx::Conv_658[FLOAT, 16x16x1x1] %onnx::Conv_661[FLOAT, 96x16x1x1] %onnx::Conv_662[FLOAT, 96] %onnx::Conv_664[FLOAT, 96x1x5x5] %onnx::Conv_667[FLOAT, 24x96x1x1] %onnx::Conv_668[FLOAT, 24] %onnx::Conv_670[FLOAT, 24x24x1x1] %onnx::Conv_673[FLOAT, 24x1x3x3] %onnx::Conv_676[FLOAT, 24x24x1x1] %onnx::Conv_679[FLOAT, 72x24x1x1] %onnx::Conv_680[FLOAT, 72] %onnx::Conv_682[FLOAT, 72x1x3x3] %onnx::Conv_685[FLOAT, 24x72x1x1] %onnx::Conv_688[FLOAT, 144x24x1x1] %onnx::Conv_689[FLOAT, 144] %onnx::Conv_691[FLOAT, 144x1x3x3] %onnx::Conv_694[FLOAT, 24x144x1x1] %onnx::Conv_697[FLOAT, 24x24x1x1] %onnx::Conv_700[FLOAT, 24x1x5x5] %onnx::Conv_703[FLOAT, 32x24x1x1] %onnx::Conv_704[FLOAT, 32] %onnx::Conv_706[FLOAT, 192x32x1x1] %onnx::Conv_707[FLOAT, 192] %onnx::Conv_709[FLOAT, 192x1x5x5] %onnx::Conv_712[FLOAT, 32x192x1x1] %onnx::Conv_715[FLOAT, 192x32x1x1] %onnx::Conv_718[FLOAT, 192x1x5x5] %onnx::Conv_721[FLOAT, 32x192x1x1] %onnx::Conv_724[FLOAT, 64x32x1x1] %onnx::Conv_725[FLOAT, 64] %onnx::Conv_727[FLOAT, 384x64x1x1] %onnx::Conv_728[FLOAT, 384] %onnx::Conv_730[FLOAT, 384x1x3x3] %onnx::Conv_733[FLOAT, 64x384x1x1] %onnx::Conv_736[FLOAT, 64x64x1x1] %onnx::Conv_739[FLOAT, 64x1x5x5] %onnx::Conv_742[FLOAT, 64x64x1x1] %onnx::Conv_745[FLOAT, 384x64x1x1] %onnx::Conv_748[FLOAT, 384x1x3x3] %onnx::Conv_751[FLOAT, 64x384x1x1] %onnx::Conv_754[FLOAT, 192x64x1x1] %onnx::Conv_757[FLOAT, 192x1x3x3] %onnx::Conv_760[FLOAT, 112x192x1x1] %onnx::Conv_761[FLOAT, 112] %onnx::Conv_763[FLOAT, 112x56x1x1] %onnx::Conv_766[FLOAT, 112x1x5x5] %onnx::Conv_769[FLOAT, 112x56x1x1] %onnx::Conv_772[FLOAT, 112x56x1x1] %onnx::Conv_775[FLOAT, 112x1x5x5] %onnx::Conv_778[FLOAT, 112x56x1x1] %onnx::Conv_781[FLOAT, 112x56x1x1] %onnx::Conv_784[FLOAT, 112x1x3x3] %onnx::Conv_787[FLOAT, 112x56x1x1] %onnx::Conv_790[FLOAT, 112x112x1x1] %onnx::Conv_793[FLOAT, 112x1x5x5] %onnx::Conv_796[FLOAT, 184x112x1x1] %onnx::Conv_797[FLOAT, 184] %onnx::Conv_799[FLOAT, 1104x184x1x1] %onnx::Conv_800[FLOAT, 1104] %onnx::Conv_802[FLOAT, 1104x1x5x5] %onnx::Conv_805[FLOAT, 184x1104x1x1] %onnx::Conv_808[FLOAT, 552x184x1x1] %onnx::Conv_809[FLOAT, 552] %onnx::Conv_811[FLOAT, 552x1x3x3] %onnx::Conv_814[FLOAT, 184x552x1x1] %onnx::Conv_817[FLOAT, 184x92x1x1] %onnx::Conv_820[FLOAT, 184x1x3x3] %onnx::Conv_823[FLOAT, 184x92x1x1] %onnx::Conv_826[FLOAT, 552x184x1x1] %onnx::Conv_829[FLOAT, 552x1x5x5] %onnx::Conv_832[FLOAT, 352x552x1x1] %onnx::Conv_833[FLOAT, 352] %onnx::Conv_835[FLOAT, 1504x352x1x1] %onnx::Conv_836[FLOAT, 1504] ) { %onnx::Conv_830 = Identity(%onnx::Conv_809) %onnx::Conv_827 = Identity(%onnx::Conv_809) %onnx::Conv_824 = Identity(%onnx::Conv_797) %onnx::Conv_821 = Identity(%onnx::Conv_797) %onnx::Conv_818 = Identity(%onnx::Conv_797) %onnx::Conv_815 = Identity(%onnx::Conv_797) %onnx::Conv_812 = Identity(%onnx::Conv_809) %onnx::Conv_806 = Identity(%onnx::Conv_797) %onnx::Conv_803 = Identity(%onnx::Conv_800) %onnx::Conv_794 = Identity(%onnx::Conv_761) %onnx::Conv_791 = Identity(%onnx::Conv_761) %onnx::Conv_788 = Identity(%onnx::Conv_761) %onnx::Conv_785 = Identity(%onnx::Conv_761) %onnx::Conv_782 = Identity(%onnx::Conv_761) %onnx::Conv_779 = Identity(%onnx::Conv_761) %onnx::Conv_776 = Identity(%onnx::Conv_761) %onnx::Conv_773 = Identity(%onnx::Conv_761) %onnx::Conv_770 = Identity(%onnx::Conv_761) %onnx::Conv_767 = Identity(%onnx::Conv_761) %onnx::Conv_764 = Identity(%onnx::Conv_761) %onnx::Conv_758 = Identity(%onnx::Conv_707) %onnx::Conv_755 = Identity(%onnx::Conv_707) %onnx::Conv_752 = Identity(%onnx::Conv_725) %onnx::Conv_749 = Identity(%onnx::Conv_728) %onnx::Conv_746 = Identity(%onnx::Conv_728) %onnx::Conv_743 = Identity(%onnx::Conv_725) %onnx::Conv_740 = Identity(%onnx::Conv_725) %onnx::Conv_737 = Identity(%onnx::Conv_725) %onnx::Conv_734 = Identity(%onnx::Conv_725) %onnx::Conv_731 = Identity(%onnx::Conv_728) %onnx::Conv_722 = Identity(%onnx::Conv_704) %onnx::Conv_719 = Identity(%onnx::Conv_707) %onnx::Conv_716 = Identity(%onnx::Conv_707) %onnx::Conv_713 = Identity(%onnx::Conv_704) %onnx::Conv_710 = Identity(%onnx::Conv_707) %onnx::Conv_701 = Identity(%onnx::Conv_668) %onnx::Conv_698 = Identity(%onnx::Conv_668) %onnx::Conv_695 = Identity(%onnx::Conv_668) %onnx::Conv_692 = Identity(%onnx::Conv_689) %onnx::Conv_686 = Identity(%onnx::Conv_668) %onnx::Conv_683 = Identity(%onnx::Conv_680) %onnx::Conv_677 = Identity(%onnx::Conv_668) %onnx::Conv_674 = Identity(%onnx::Conv_668) %onnx::Conv_671 = Identity(%onnx::Conv_668) %onnx::Conv_665 = Identity(%onnx::Conv_662) %onnx::Conv_659 = Identity(%onnx::Conv_650) %onnx::Conv_656 = Identity(%onnx::Conv_650) %onnx::Conv_653 = Identity(%onnx::Conv_650) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_649, %onnx::Conv_650) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_652, %onnx::Conv_653) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.0/nl/Relu_output_0, %onnx::Conv_655, %onnx::Conv_656) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_658, %onnx::Conv_659) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_661, %onnx::Conv_662) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.1/nl/Relu_output_0, %onnx::Conv_664, %onnx::Conv_665) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_667, %onnx::Conv_668) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_670, %onnx::Conv_671) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.2/nl/Relu_output_0, %onnx::Conv_673, %onnx::Conv_674) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_676, %onnx::Conv_677) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_679, %onnx::Conv_680) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.3/nl/Relu_output_0, %onnx::Conv_682, %onnx::Conv_683) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_685, %onnx::Conv_686) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_688, %onnx::Conv_689) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.4/nl/Relu_output_0, %onnx::Conv_691, %onnx::Conv_692) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_694, %onnx::Conv_695) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/Add_output_0, %onnx::Conv_697, %onnx::Conv_698) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.5/nl/Relu_output_0, %onnx::Conv_700, %onnx::Conv_701) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_703, %onnx::Conv_704) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_706, %onnx::Conv_707) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.7/nl/Relu_output_0, %onnx::Conv_709, %onnx::Conv_710) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_712, %onnx::Conv_713) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_715, %onnx::Conv_716) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.8/nl/Relu_output_0, %onnx::Conv_718, %onnx::Conv_719) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_721, %onnx::Conv_722) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [2, 2]](%/cells.8/Add_output_0, %onnx::Conv_724, %onnx::Conv_725) %/cells.9/relu/Relu_output_0 = Relu(%/cells.9/conv/Conv_output_0) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/relu/Relu_output_0, %onnx::Conv_727, %onnx::Conv_728) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.10/nl/Relu_output_0, %onnx::Conv_730, %onnx::Conv_731) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_733, %onnx::Conv_734) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/relu/Relu_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_736, %onnx::Conv_737) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.11/nl/Relu_output_0, %onnx::Conv_739, %onnx::Conv_740) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_742, %onnx::Conv_743) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_745, %onnx::Conv_746) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.12/nl/Relu_output_0, %onnx::Conv_748, %onnx::Conv_749) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_751, %onnx::Conv_752) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_754, %onnx::Conv_755) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.13/nl/Relu_output_0, %onnx::Conv_757, %onnx::Conv_758) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_760, %onnx::Conv_761) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_763, %onnx::Conv_764) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.14/shuffle/Reshape_output_0 = Reshape(%/cells.14/nl/Relu_output_0, %/cells.14/shuffle/Constant_output_0) %/cells.14/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.14/shuffle/Reshape_output_0) %/cells.14/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.14/shuffle/Reshape_1_output_0 = Reshape(%/cells.14/shuffle/Transpose_output_0, %/cells.14/shuffle/Constant_1_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.14/shuffle/Reshape_1_output_0, %onnx::Conv_766, %onnx::Conv_767) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_769, %onnx::Conv_770) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_772, %onnx::Conv_773) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.15/shuffle/Reshape_output_0 = Reshape(%/cells.15/nl/Relu_output_0, %/cells.15/shuffle/Constant_output_0) %/cells.15/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.15/shuffle/Reshape_output_0) %/cells.15/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.15/shuffle/Reshape_1_output_0 = Reshape(%/cells.15/shuffle/Transpose_output_0, %/cells.15/shuffle/Constant_1_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.15/shuffle/Reshape_1_output_0, %onnx::Conv_775, %onnx::Conv_776) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_778, %onnx::Conv_779) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_781, %onnx::Conv_782) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.16/shuffle/Reshape_output_0 = Reshape(%/cells.16/nl/Relu_output_0, %/cells.16/shuffle/Constant_output_0) %/cells.16/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.16/shuffle/Reshape_output_0) %/cells.16/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.16/shuffle/Reshape_1_output_0 = Reshape(%/cells.16/shuffle/Transpose_output_0, %/cells.16/shuffle/Constant_1_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.16/shuffle/Reshape_1_output_0, %onnx::Conv_784, %onnx::Conv_785) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_787, %onnx::Conv_788) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.15/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_790, %onnx::Conv_791) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.17/nl/Relu_output_0, %onnx::Conv_793, %onnx::Conv_794) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_796, %onnx::Conv_797) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_799, %onnx::Conv_800) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.18/nl/Relu_output_0, %onnx::Conv_802, %onnx::Conv_803) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_805, %onnx::Conv_806) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_808, %onnx::Conv_809) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 552, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.19/nl/Relu_output_0, %onnx::Conv_811, %onnx::Conv_812) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_814, %onnx::Conv_815) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_817, %onnx::Conv_818) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.20/shuffle/Reshape_output_0 = Reshape(%/cells.20/nl/Relu_output_0, %/cells.20/shuffle/Constant_output_0) %/cells.20/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.20/shuffle/Reshape_output_0) %/cells.20/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.20/shuffle/Reshape_1_output_0 = Reshape(%/cells.20/shuffle/Transpose_output_0, %/cells.20/shuffle/Constant_1_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.20/shuffle/Reshape_1_output_0, %onnx::Conv_820, %onnx::Conv_821) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_823, %onnx::Conv_824) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_826, %onnx::Conv_827) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 552, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.21/nl/Relu_output_0, %onnx::Conv_829, %onnx::Conv_830) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_832, %onnx::Conv_833) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_835, %onnx::Conv_836) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %647 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %647 }	val_accuracy	72,950,912	1,982,500	{'zcp_synflow': 79.31254469514637, 'zcp_zen': 70.05613708496094, 'zcp_epe_nas': 27.669689063693287, 'zcp_fisher': 0.1569853127002716, 'zcp_flops': 72950912.0, 'zcp_grad_norm': 25.775901794433594, 'zcp_grasp': 0.01519012451171875, 'zcp_jacov': -16.049820398378934, 'zcp_l2_norm': 645.3258056640625, 'zcp_nwot': 215.1524952067877, 'zcp_params': 1982500.0, 'zcp_plain': -0.0026829401031136513, 'zcp_snip': 49.50503158569336, 'lat_1080ti_1': 0.5633344966986695, 'lat_1080ti_32': 0.6490254926040252, 'lat_1080ti_64': 0.5768573008881428, 'lat_2080ti_1': 0.6213488998207728, 'lat_2080ti_32': 0.6121990297630612, 'lat_2080ti_64': 0.5938562572807241, 'lat_essential_ph_1': 0.4716981132075472, 'lat_eyeriss': 0.5567454568793205, 'lat_fpga': 0.5415054572081655, 'lat_gold_6226': 0.3892811663407982, 'lat_gold_6240': 0.6755381992658022, 'lat_pixel2': 0.34782608695652173, 'lat_pixel3': 0.5062568772651392, 'lat_raspi4': 0.5738322553721562, 'lat_samsung_a50': 0.23157894736842105, 'lat_samsung_s7': 0.25196850393700787, 'lat_silver_4114': 0.6222983927641532, 'lat_silver_4210r': 0.6315563327215116, 'lat_titan_rtx_1': 0.5994307148870647, 'lat_titan_rtx_32': 0.5986763279145939, 'lat_titan_rtx_64': 0.6107347017255238, 'lat_titanx_1': 0.3209073868707144, 'lat_titanx_32': 0.5947696116567351, 'lat_titanx_64': 0.550785779390409, 'lat_titanxp_1': 0.5569481257190403, 'lat_titanxp_32': 0.6004482303084533, 'lat_titanxp_64': 0.5800990662257484}
FBNet_3780	FBNet	3780	3780	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_705[FLOAT, 16x3x3x3] %onnx::Conv_706[FLOAT, 16] %onnx::Conv_708[FLOAT, 48x16x1x1] %onnx::Conv_709[FLOAT, 48] %onnx::Conv_711[FLOAT, 48x1x3x3] %onnx::Conv_714[FLOAT, 16x48x1x1] %onnx::Conv_717[FLOAT, 16x8x1x1] %onnx::Conv_720[FLOAT, 16x1x3x3] %onnx::Conv_723[FLOAT, 24x8x1x1] %onnx::Conv_724[FLOAT, 24] %onnx::Conv_726[FLOAT, 144x24x1x1] %onnx::Conv_727[FLOAT, 144] %onnx::Conv_729[FLOAT, 144x1x3x3] %onnx::Conv_732[FLOAT, 24x144x1x1] %onnx::Conv_735[FLOAT, 24x12x1x1] %onnx::Conv_738[FLOAT, 24x1x3x3] %onnx::Conv_741[FLOAT, 24x12x1x1] %onnx::Conv_744[FLOAT, 144x24x1x1] %onnx::Conv_747[FLOAT, 144x1x3x3] %onnx::Conv_750[FLOAT, 24x144x1x1] %onnx::Conv_753[FLOAT, 72x24x1x1] %onnx::Conv_754[FLOAT, 72] %onnx::Conv_756[FLOAT, 72x1x3x3] %onnx::Conv_759[FLOAT, 32x72x1x1] %onnx::Conv_760[FLOAT, 32] %onnx::Conv_762[FLOAT, 192x32x1x1] %onnx::Conv_763[FLOAT, 192] %onnx::Conv_765[FLOAT, 192x1x3x3] %onnx::Conv_768[FLOAT, 32x192x1x1] %onnx::Conv_771[FLOAT, 96x32x1x1] %onnx::Conv_772[FLOAT, 96] %onnx::Conv_774[FLOAT, 96x1x3x3] %onnx::Conv_777[FLOAT, 32x96x1x1] %onnx::Conv_780[FLOAT, 32x16x1x1] %onnx::Conv_783[FLOAT, 32x1x5x5] %onnx::Conv_786[FLOAT, 32x16x1x1] %onnx::Conv_789[FLOAT, 32x32x1x1] %onnx::Conv_792[FLOAT, 32x1x5x5] %onnx::Conv_795[FLOAT, 64x32x1x1] %onnx::Conv_796[FLOAT, 64] %onnx::Conv_798[FLOAT, 384x64x1x1] %onnx::Conv_799[FLOAT, 384] %onnx::Conv_801[FLOAT, 384x1x3x3] %onnx::Conv_804[FLOAT, 64x384x1x1] %onnx::Conv_807[FLOAT, 64x32x1x1] %onnx::Conv_810[FLOAT, 64x1x5x5] %onnx::Conv_813[FLOAT, 64x32x1x1] %onnx::Conv_816[FLOAT, 192x64x1x1] %onnx::Conv_819[FLOAT, 192x1x3x3] %onnx::Conv_822[FLOAT, 64x192x1x1] %onnx::Conv_825[FLOAT, 64x64x1x1] %onnx::Conv_828[FLOAT, 64x1x3x3] %onnx::Conv_831[FLOAT, 112x64x1x1] %onnx::Conv_832[FLOAT, 112] %onnx::Conv_834[FLOAT, 672x112x1x1] %onnx::Conv_835[FLOAT, 672] %onnx::Conv_837[FLOAT, 672x1x5x5] %onnx::Conv_840[FLOAT, 112x672x1x1] %onnx::Conv_843[FLOAT, 112x56x1x1] %onnx::Conv_846[FLOAT, 112x1x5x5] %onnx::Conv_849[FLOAT, 112x56x1x1] %onnx::Conv_852[FLOAT, 112x56x1x1] %onnx::Conv_855[FLOAT, 112x1x5x5] %onnx::Conv_858[FLOAT, 112x56x1x1] %onnx::Conv_861[FLOAT, 672x112x1x1] %onnx::Conv_864[FLOAT, 672x1x3x3] %onnx::Conv_867[FLOAT, 184x672x1x1] %onnx::Conv_868[FLOAT, 184] %onnx::Conv_870[FLOAT, 552x184x1x1] %onnx::Conv_871[FLOAT, 552] %onnx::Conv_873[FLOAT, 552x1x3x3] %onnx::Conv_876[FLOAT, 184x552x1x1] %onnx::Conv_879[FLOAT, 184x184x1x1] %onnx::Conv_882[FLOAT, 184x1x5x5] %onnx::Conv_885[FLOAT, 184x184x1x1] %onnx::Conv_888[FLOAT, 552x184x1x1] %onnx::Conv_891[FLOAT, 552x1x3x3] %onnx::Conv_894[FLOAT, 352x552x1x1] %onnx::Conv_895[FLOAT, 352] %onnx::Conv_897[FLOAT, 1504x352x1x1] %onnx::Conv_898[FLOAT, 1504] ) { %onnx::Conv_892 = Identity(%onnx::Conv_871) %onnx::Conv_889 = Identity(%onnx::Conv_871) %onnx::Conv_886 = Identity(%onnx::Conv_868) %onnx::Conv_883 = Identity(%onnx::Conv_868) %onnx::Conv_880 = Identity(%onnx::Conv_868) %onnx::Conv_877 = Identity(%onnx::Conv_868) %onnx::Conv_874 = Identity(%onnx::Conv_871) %onnx::Conv_865 = Identity(%onnx::Conv_835) %onnx::Conv_862 = Identity(%onnx::Conv_835) %onnx::Conv_859 = Identity(%onnx::Conv_832) %onnx::Conv_856 = Identity(%onnx::Conv_832) %onnx::Conv_853 = Identity(%onnx::Conv_832) %onnx::Conv_850 = Identity(%onnx::Conv_832) %onnx::Conv_847 = Identity(%onnx::Conv_832) %onnx::Conv_844 = Identity(%onnx::Conv_832) %onnx::Conv_841 = Identity(%onnx::Conv_832) %onnx::Conv_838 = Identity(%onnx::Conv_835) %onnx::Conv_829 = Identity(%onnx::Conv_796) %onnx::Conv_826 = Identity(%onnx::Conv_796) %onnx::Conv_823 = Identity(%onnx::Conv_796) %onnx::Conv_820 = Identity(%onnx::Conv_763) %onnx::Conv_817 = Identity(%onnx::Conv_763) %onnx::Conv_814 = Identity(%onnx::Conv_796) %onnx::Conv_811 = Identity(%onnx::Conv_796) %onnx::Conv_808 = Identity(%onnx::Conv_796) %onnx::Conv_805 = Identity(%onnx::Conv_796) %onnx::Conv_802 = Identity(%onnx::Conv_799) %onnx::Conv_793 = Identity(%onnx::Conv_760) %onnx::Conv_790 = Identity(%onnx::Conv_760) %onnx::Conv_787 = Identity(%onnx::Conv_760) %onnx::Conv_784 = Identity(%onnx::Conv_760) %onnx::Conv_781 = Identity(%onnx::Conv_760) %onnx::Conv_778 = Identity(%onnx::Conv_760) %onnx::Conv_775 = Identity(%onnx::Conv_772) %onnx::Conv_769 = Identity(%onnx::Conv_760) %onnx::Conv_766 = Identity(%onnx::Conv_763) %onnx::Conv_757 = Identity(%onnx::Conv_754) %onnx::Conv_751 = Identity(%onnx::Conv_724) %onnx::Conv_748 = Identity(%onnx::Conv_727) %onnx::Conv_745 = Identity(%onnx::Conv_727) %onnx::Conv_742 = Identity(%onnx::Conv_724) %onnx::Conv_739 = Identity(%onnx::Conv_724) %onnx::Conv_736 = Identity(%onnx::Conv_724) %onnx::Conv_733 = Identity(%onnx::Conv_724) %onnx::Conv_730 = Identity(%onnx::Conv_727) %onnx::Conv_721 = Identity(%onnx::Conv_706) %onnx::Conv_718 = Identity(%onnx::Conv_706) %onnx::Conv_715 = Identity(%onnx::Conv_706) %onnx::Conv_712 = Identity(%onnx::Conv_709) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_705, %onnx::Conv_706) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_708, %onnx::Conv_709) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 48, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.0/nl/Relu_output_0, %onnx::Conv_711, %onnx::Conv_712) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_714, %onnx::Conv_715) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_717, %onnx::Conv_718) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.1/shuffle/Reshape_output_0 = Reshape(%/cells.1/nl/Relu_output_0, %/cells.1/shuffle/Constant_output_0) %/cells.1/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.1/shuffle/Reshape_output_0) %/cells.1/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.1/shuffle/Reshape_1_output_0 = Reshape(%/cells.1/shuffle/Transpose_output_0, %/cells.1/shuffle/Constant_1_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.1/shuffle/Reshape_1_output_0, %onnx::Conv_720, %onnx::Conv_721) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_723, %onnx::Conv_724) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_726, %onnx::Conv_727) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.2/nl/Relu_output_0, %onnx::Conv_729, %onnx::Conv_730) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_732, %onnx::Conv_733) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_735, %onnx::Conv_736) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.3/shuffle/Reshape_output_0 = Reshape(%/cells.3/nl/Relu_output_0, %/cells.3/shuffle/Constant_output_0) %/cells.3/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.3/shuffle/Reshape_output_0) %/cells.3/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.3/shuffle/Reshape_1_output_0 = Reshape(%/cells.3/shuffle/Transpose_output_0, %/cells.3/shuffle/Constant_1_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.3/shuffle/Reshape_1_output_0, %onnx::Conv_738, %onnx::Conv_739) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_741, %onnx::Conv_742) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_744, %onnx::Conv_745) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.4/nl/Relu_output_0, %onnx::Conv_747, %onnx::Conv_748) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_750, %onnx::Conv_751) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/Add_output_0, %onnx::Conv_753, %onnx::Conv_754) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.5/nl/Relu_output_0, %onnx::Conv_756, %onnx::Conv_757) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_759, %onnx::Conv_760) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_762, %onnx::Conv_763) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.6/nl/Relu_output_0, %onnx::Conv_765, %onnx::Conv_766) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_768, %onnx::Conv_769) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_771, %onnx::Conv_772) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.7/nl/Relu_output_0, %onnx::Conv_774, %onnx::Conv_775) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_777, %onnx::Conv_778) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_780, %onnx::Conv_781) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.8/shuffle/Reshape_output_0 = Reshape(%/cells.8/nl/Relu_output_0, %/cells.8/shuffle/Constant_output_0) %/cells.8/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.8/shuffle/Reshape_output_0) %/cells.8/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.8/shuffle/Reshape_1_output_0 = Reshape(%/cells.8/shuffle/Transpose_output_0, %/cells.8/shuffle/Constant_1_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.8/shuffle/Reshape_1_output_0, %onnx::Conv_783, %onnx::Conv_784) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_786, %onnx::Conv_787) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_789, %onnx::Conv_790) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.9/nl/Relu_output_0, %onnx::Conv_792, %onnx::Conv_793) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_795, %onnx::Conv_796) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_798, %onnx::Conv_799) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.10/nl/Relu_output_0, %onnx::Conv_801, %onnx::Conv_802) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_804, %onnx::Conv_805) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_807, %onnx::Conv_808) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.11/shuffle/Reshape_output_0 = Reshape(%/cells.11/nl/Relu_output_0, %/cells.11/shuffle/Constant_output_0) %/cells.11/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.11/shuffle/Reshape_output_0) %/cells.11/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.11/shuffle/Reshape_1_output_0 = Reshape(%/cells.11/shuffle/Transpose_output_0, %/cells.11/shuffle/Constant_1_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.11/shuffle/Reshape_1_output_0, %onnx::Conv_810, %onnx::Conv_811) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_813, %onnx::Conv_814) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_816, %onnx::Conv_817) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.12/nl/Relu_output_0, %onnx::Conv_819, %onnx::Conv_820) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_822, %onnx::Conv_823) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_825, %onnx::Conv_826) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.13/nl/Relu_output_0, %onnx::Conv_828, %onnx::Conv_829) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_831, %onnx::Conv_832) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_834, %onnx::Conv_835) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.14/nl/Relu_output_0, %onnx::Conv_837, %onnx::Conv_838) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_840, %onnx::Conv_841) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_843, %onnx::Conv_844) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.15/shuffle/Reshape_output_0 = Reshape(%/cells.15/nl/Relu_output_0, %/cells.15/shuffle/Constant_output_0) %/cells.15/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.15/shuffle/Reshape_output_0) %/cells.15/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.15/shuffle/Reshape_1_output_0 = Reshape(%/cells.15/shuffle/Transpose_output_0, %/cells.15/shuffle/Constant_1_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.15/shuffle/Reshape_1_output_0, %onnx::Conv_846, %onnx::Conv_847) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_849, %onnx::Conv_850) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_852, %onnx::Conv_853) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.16/shuffle/Reshape_output_0 = Reshape(%/cells.16/nl/Relu_output_0, %/cells.16/shuffle/Constant_output_0) %/cells.16/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.16/shuffle/Reshape_output_0) %/cells.16/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.16/shuffle/Reshape_1_output_0 = Reshape(%/cells.16/shuffle/Transpose_output_0, %/cells.16/shuffle/Constant_1_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.16/shuffle/Reshape_1_output_0, %onnx::Conv_855, %onnx::Conv_856) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_858, %onnx::Conv_859) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.15/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_861, %onnx::Conv_862) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.17/nl/Relu_output_0, %onnx::Conv_864, %onnx::Conv_865) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_867, %onnx::Conv_868) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_870, %onnx::Conv_871) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 552, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.19/nl/Relu_output_0, %onnx::Conv_873, %onnx::Conv_874) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_876, %onnx::Conv_877) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_879, %onnx::Conv_880) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.20/nl/Relu_output_0, %onnx::Conv_882, %onnx::Conv_883) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_885, %onnx::Conv_886) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_888, %onnx::Conv_889) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 552, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.21/nl/Relu_output_0, %onnx::Conv_891, %onnx::Conv_892) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_894, %onnx::Conv_895) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_897, %onnx::Conv_898) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %703 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %703 }	val_accuracy	76,883,840	1,836,236	{'zcp_synflow': 76.19508860025184, 'zcp_zen': 68.54346466064453, 'zcp_epe_nas': 11.348494854314993, 'zcp_fisher': 0.12957333028316498, 'zcp_flops': 76883840.0, 'zcp_grad_norm': 24.505197525024414, 'zcp_grasp': -0.13550567626953125, 'zcp_jacov': -16.059564116018464, 'zcp_l2_norm': 633.31494140625, 'zcp_nwot': 216.90903342296957, 'zcp_params': 1836236.0, 'zcp_plain': 0.005873228423297405, 'zcp_snip': 46.535797119140625, 'lat_1080ti_1': 0.6870110748060682, 'lat_1080ti_32': 0.7144761401051807, 'lat_1080ti_64': 0.5782872256067424, 'lat_2080ti_1': 0.7595158544818134, 'lat_2080ti_32': 0.7434445008909039, 'lat_2080ti_64': 0.6649925453968377, 'lat_essential_ph_1': 0.3584905660377358, 'lat_eyeriss': 0.5199053783947333, 'lat_fpga': 0.57153741873559, 'lat_gold_6226': 0.3601962271740842, 'lat_gold_6240': 0.5730001770268948, 'lat_pixel2': 0.3695652173913043, 'lat_pixel3': 0.4976466348025464, 'lat_raspi4': 0.5670823167659377, 'lat_samsung_a50': 0.24210526315789474, 'lat_samsung_s7': 0.1968503937007874, 'lat_silver_4114': 0.5461386258590446, 'lat_silver_4210r': 0.5925444787225648, 'lat_titan_rtx_1': 0.7071327908414307, 'lat_titan_rtx_32': 0.7017602504618767, 'lat_titan_rtx_64': 0.6708048285763631, 'lat_titanx_1': 0.3901026462495198, 'lat_titanx_32': 0.6874386726668197, 'lat_titanx_64': 0.5601899646424652, 'lat_titanxp_1': 0.6615436311969013, 'lat_titanxp_32': 0.6878933451940039, 'lat_titanxp_64': 0.6151898634398998}
FBNet_3102	FBNet	3102	3102	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_595[FLOAT, 16x3x3x3] %onnx::Conv_596[FLOAT, 16] %onnx::Conv_598[FLOAT, 48x16x1x1] %onnx::Conv_599[FLOAT, 48] %onnx::Conv_601[FLOAT, 48x1x5x5] %onnx::Conv_604[FLOAT, 16x48x1x1] %onnx::Conv_607[FLOAT, 96x16x1x1] %onnx::Conv_608[FLOAT, 96] %onnx::Conv_610[FLOAT, 96x1x3x3] %onnx::Conv_613[FLOAT, 24x96x1x1] %onnx::Conv_614[FLOAT, 24] %onnx::Conv_616[FLOAT, 24x24x1x1] %onnx::Conv_619[FLOAT, 24x1x5x5] %onnx::Conv_622[FLOAT, 32x24x1x1] %onnx::Conv_623[FLOAT, 32] %onnx::Conv_625[FLOAT, 96x32x1x1] %onnx::Conv_628[FLOAT, 96x1x3x3] %onnx::Conv_631[FLOAT, 32x96x1x1] %onnx::Conv_634[FLOAT, 32x16x1x1] %onnx::Conv_637[FLOAT, 32x1x3x3] %onnx::Conv_640[FLOAT, 32x16x1x1] %onnx::Conv_643[FLOAT, 32x32x1x1] %onnx::Conv_646[FLOAT, 32x1x3x3] %onnx::Conv_649[FLOAT, 32x32x1x1] %onnx::Conv_652[FLOAT, 192x32x1x1] %onnx::Conv_653[FLOAT, 192] %onnx::Conv_655[FLOAT, 192x1x5x5] %onnx::Conv_658[FLOAT, 64x192x1x1] %onnx::Conv_659[FLOAT, 64] %onnx::Conv_661[FLOAT, 384x64x1x1] %onnx::Conv_662[FLOAT, 384] %onnx::Conv_664[FLOAT, 384x1x5x5] %onnx::Conv_667[FLOAT, 64x384x1x1] %onnx::Conv_670[FLOAT, 64x64x1x1] %onnx::Conv_673[FLOAT, 64x1x5x5] %onnx::Conv_676[FLOAT, 64x64x1x1] %onnx::Conv_679[FLOAT, 192x64x1x1] %onnx::Conv_682[FLOAT, 192x1x5x5] %onnx::Conv_685[FLOAT, 64x192x1x1] %onnx::Conv_688[FLOAT, 64x64x1x1] %onnx::Conv_691[FLOAT, 64x1x3x3] %onnx::Conv_694[FLOAT, 112x64x1x1] %onnx::Conv_695[FLOAT, 112] %onnx::Conv_697[FLOAT, 672x112x1x1] %onnx::Conv_698[FLOAT, 672] %onnx::Conv_700[FLOAT, 672x1x5x5] %onnx::Conv_703[FLOAT, 112x672x1x1] %onnx::Conv_706[FLOAT, 336x112x1x1] %onnx::Conv_707[FLOAT, 336] %onnx::Conv_709[FLOAT, 336x1x3x3] %onnx::Conv_712[FLOAT, 112x336x1x1] %onnx::Conv_715[FLOAT, 112x56x1x1] %onnx::Conv_718[FLOAT, 112x1x5x5] %onnx::Conv_721[FLOAT, 112x56x1x1] %onnx::Conv_724[FLOAT, 112x112x1x1] %onnx::Conv_727[FLOAT, 112x1x3x3] %onnx::Conv_730[FLOAT, 184x112x1x1] %onnx::Conv_731[FLOAT, 184] %onnx::Conv_733[FLOAT, 184x92x1x1] %onnx::Conv_736[FLOAT, 184x1x5x5] %onnx::Conv_739[FLOAT, 184x92x1x1] %onnx::Conv_742[FLOAT, 184x184x1x1] %onnx::Conv_745[FLOAT, 184x1x5x5] %onnx::Conv_748[FLOAT, 184x184x1x1] %onnx::Conv_751[FLOAT, 184x184x1x1] %onnx::Conv_754[FLOAT, 184x1x3x3] %onnx::Conv_757[FLOAT, 184x184x1x1] %onnx::Conv_760[FLOAT, 552x184x1x1] %onnx::Conv_761[FLOAT, 552] %onnx::Conv_763[FLOAT, 552x1x5x5] %onnx::Conv_766[FLOAT, 352x552x1x1] %onnx::Conv_767[FLOAT, 352] %onnx::Conv_769[FLOAT, 1504x352x1x1] %onnx::Conv_770[FLOAT, 1504] ) { %onnx::Conv_764 = Identity(%onnx::Conv_761) %onnx::Conv_758 = Identity(%onnx::Conv_731) %onnx::Conv_755 = Identity(%onnx::Conv_731) %onnx::Conv_752 = Identity(%onnx::Conv_731) %onnx::Conv_749 = Identity(%onnx::Conv_731) %onnx::Conv_746 = Identity(%onnx::Conv_731) %onnx::Conv_743 = Identity(%onnx::Conv_731) %onnx::Conv_740 = Identity(%onnx::Conv_731) %onnx::Conv_737 = Identity(%onnx::Conv_731) %onnx::Conv_734 = Identity(%onnx::Conv_731) %onnx::Conv_728 = Identity(%onnx::Conv_695) %onnx::Conv_725 = Identity(%onnx::Conv_695) %onnx::Conv_722 = Identity(%onnx::Conv_695) %onnx::Conv_719 = Identity(%onnx::Conv_695) %onnx::Conv_716 = Identity(%onnx::Conv_695) %onnx::Conv_713 = Identity(%onnx::Conv_695) %onnx::Conv_710 = Identity(%onnx::Conv_707) %onnx::Conv_704 = Identity(%onnx::Conv_695) %onnx::Conv_701 = Identity(%onnx::Conv_698) %onnx::Conv_692 = Identity(%onnx::Conv_659) %onnx::Conv_689 = Identity(%onnx::Conv_659) %onnx::Conv_686 = Identity(%onnx::Conv_659) %onnx::Conv_683 = Identity(%onnx::Conv_653) %onnx::Conv_680 = Identity(%onnx::Conv_653) %onnx::Conv_677 = Identity(%onnx::Conv_659) %onnx::Conv_674 = Identity(%onnx::Conv_659) %onnx::Conv_671 = Identity(%onnx::Conv_659) %onnx::Conv_668 = Identity(%onnx::Conv_659) %onnx::Conv_665 = Identity(%onnx::Conv_662) %onnx::Conv_656 = Identity(%onnx::Conv_653) %onnx::Conv_650 = Identity(%onnx::Conv_623) %onnx::Conv_647 = Identity(%onnx::Conv_623) %onnx::Conv_644 = Identity(%onnx::Conv_623) %onnx::Conv_641 = Identity(%onnx::Conv_623) %onnx::Conv_638 = Identity(%onnx::Conv_623) %onnx::Conv_635 = Identity(%onnx::Conv_623) %onnx::Conv_632 = Identity(%onnx::Conv_623) %onnx::Conv_629 = Identity(%onnx::Conv_608) %onnx::Conv_626 = Identity(%onnx::Conv_608) %onnx::Conv_620 = Identity(%onnx::Conv_614) %onnx::Conv_617 = Identity(%onnx::Conv_614) %onnx::Conv_611 = Identity(%onnx::Conv_608) %onnx::Conv_605 = Identity(%onnx::Conv_596) %onnx::Conv_602 = Identity(%onnx::Conv_599) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_595, %onnx::Conv_596) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_598, %onnx::Conv_599) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 48, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.0/nl/Relu_output_0, %onnx::Conv_601, %onnx::Conv_602) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_604, %onnx::Conv_605) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_607, %onnx::Conv_608) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.1/nl/Relu_output_0, %onnx::Conv_610, %onnx::Conv_611) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_613, %onnx::Conv_614) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_616, %onnx::Conv_617) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.5/nl/Relu_output_0, %onnx::Conv_619, %onnx::Conv_620) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_622, %onnx::Conv_623) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_625, %onnx::Conv_626) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.6/nl/Relu_output_0, %onnx::Conv_628, %onnx::Conv_629) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_631, %onnx::Conv_632) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_634, %onnx::Conv_635) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.7/shuffle/Reshape_output_0 = Reshape(%/cells.7/nl/Relu_output_0, %/cells.7/shuffle/Constant_output_0) %/cells.7/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.7/shuffle/Reshape_output_0) %/cells.7/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.7/shuffle/Reshape_1_output_0 = Reshape(%/cells.7/shuffle/Transpose_output_0, %/cells.7/shuffle/Constant_1_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.7/shuffle/Reshape_1_output_0, %onnx::Conv_637, %onnx::Conv_638) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_640, %onnx::Conv_641) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_643, %onnx::Conv_644) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.8/nl/Relu_output_0, %onnx::Conv_646, %onnx::Conv_647) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_649, %onnx::Conv_650) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_652, %onnx::Conv_653) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.9/nl/Relu_output_0, %onnx::Conv_655, %onnx::Conv_656) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_658, %onnx::Conv_659) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_661, %onnx::Conv_662) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.10/nl/Relu_output_0, %onnx::Conv_664, %onnx::Conv_665) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_667, %onnx::Conv_668) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_670, %onnx::Conv_671) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.11/nl/Relu_output_0, %onnx::Conv_673, %onnx::Conv_674) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_676, %onnx::Conv_677) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_679, %onnx::Conv_680) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.12/nl/Relu_output_0, %onnx::Conv_682, %onnx::Conv_683) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_685, %onnx::Conv_686) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_688, %onnx::Conv_689) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.13/nl/Relu_output_0, %onnx::Conv_691, %onnx::Conv_692) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_694, %onnx::Conv_695) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_697, %onnx::Conv_698) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.14/nl/Relu_output_0, %onnx::Conv_700, %onnx::Conv_701) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_703, %onnx::Conv_704) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_706, %onnx::Conv_707) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.15/nl/Relu_output_0, %onnx::Conv_709, %onnx::Conv_710) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_712, %onnx::Conv_713) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_715, %onnx::Conv_716) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.16/shuffle/Reshape_output_0 = Reshape(%/cells.16/nl/Relu_output_0, %/cells.16/shuffle/Constant_output_0) %/cells.16/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.16/shuffle/Reshape_output_0) %/cells.16/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.16/shuffle/Reshape_1_output_0 = Reshape(%/cells.16/shuffle/Transpose_output_0, %/cells.16/shuffle/Constant_1_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.16/shuffle/Reshape_1_output_0, %onnx::Conv_718, %onnx::Conv_719) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_721, %onnx::Conv_722) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.15/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_724, %onnx::Conv_725) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.17/nl/Relu_output_0, %onnx::Conv_727, %onnx::Conv_728) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_730, %onnx::Conv_731) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_733, %onnx::Conv_734) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.18/shuffle/Reshape_output_0 = Reshape(%/cells.18/nl/Relu_output_0, %/cells.18/shuffle/Constant_output_0) %/cells.18/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.18/shuffle/Reshape_output_0) %/cells.18/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.18/shuffle/Reshape_1_output_0 = Reshape(%/cells.18/shuffle/Transpose_output_0, %/cells.18/shuffle/Constant_1_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.18/shuffle/Reshape_1_output_0, %onnx::Conv_736, %onnx::Conv_737) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_739, %onnx::Conv_740) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_742, %onnx::Conv_743) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.19/nl/Relu_output_0, %onnx::Conv_745, %onnx::Conv_746) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_748, %onnx::Conv_749) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_751, %onnx::Conv_752) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.20/nl/Relu_output_0, %onnx::Conv_754, %onnx::Conv_755) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_757, %onnx::Conv_758) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_760, %onnx::Conv_761) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 552, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.21/nl/Relu_output_0, %onnx::Conv_763, %onnx::Conv_764) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_766, %onnx::Conv_767) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_769, %onnx::Conv_770) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %593 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %593 }	val_accuracy	57,982,336	1,639,500	{'zcp_synflow': 75.78072315105754, 'zcp_zen': 66.9281997680664, 'zcp_epe_nas': 8.622507553970122, 'zcp_fisher': 0.0714372992515564, 'zcp_flops': 57982336.0, 'zcp_grad_norm': 19.6416015625, 'zcp_grasp': -0.017350196838378906, 'zcp_jacov': -16.060433803950104, 'zcp_l2_norm': 596.61376953125, 'zcp_nwot': 207.78491133519762, 'zcp_params': 1639500.0, 'zcp_plain': -0.005369692109525204, 'zcp_snip': 35.05158996582031, 'lat_1080ti_1': 0.3959050533693802, 'lat_1080ti_32': 0.43851820960140026, 'lat_1080ti_64': 0.23990792773510364, 'lat_2080ti_1': 0.47718882852774663, 'lat_2080ti_32': 0.38063719634736803, 'lat_2080ti_64': 0.2714713570727867, 'lat_essential_ph_1': 0.22641509433962265, 'lat_eyeriss': 0.29125483652100564, 'lat_fpga': 0.33750581343571573, 'lat_gold_6226': 0.26091874363662865, 'lat_gold_6240': 0.4373140830344725, 'lat_pixel2': 0.1956521739130435, 'lat_pixel3': 0.27917823343346465, 'lat_raspi4': 0.3111527474048053, 'lat_samsung_a50': 0.11578947368421053, 'lat_samsung_s7': 0.11811023622047244, 'lat_silver_4114': 0.35404410688856885, 'lat_silver_4210r': 0.33805885981044836, 'lat_titan_rtx_1': 0.4492389199729846, 'lat_titan_rtx_32': 0.3748018498684889, 'lat_titan_rtx_64': 0.29277235428499565, 'lat_titanx_1': 0.23831120821836338, 'lat_titanx_32': 0.3066066117579808, 'lat_titanx_64': 0.24888774877095113, 'lat_titanxp_1': 0.4308476328758937, 'lat_titanxp_32': 0.3709940658440607, 'lat_titanxp_64': 0.28570689024405593}
FBNet_3377	FBNet	3377	3377	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_622[FLOAT, 16x3x3x3] %onnx::Conv_623[FLOAT, 16] %onnx::Conv_625[FLOAT, 16x8x1x1] %onnx::Conv_628[FLOAT, 16x1x5x5] %onnx::Conv_631[FLOAT, 16x8x1x1] %onnx::Conv_634[FLOAT, 16x16x1x1] %onnx::Conv_637[FLOAT, 16x1x5x5] %onnx::Conv_640[FLOAT, 24x16x1x1] %onnx::Conv_641[FLOAT, 24] %onnx::Conv_643[FLOAT, 72x24x1x1] %onnx::Conv_644[FLOAT, 72] %onnx::Conv_646[FLOAT, 72x1x5x5] %onnx::Conv_649[FLOAT, 24x72x1x1] %onnx::Conv_652[FLOAT, 24x24x1x1] %onnx::Conv_655[FLOAT, 24x1x5x5] %onnx::Conv_658[FLOAT, 24x24x1x1] %onnx::Conv_661[FLOAT, 144x24x1x1] %onnx::Conv_662[FLOAT, 144] %onnx::Conv_664[FLOAT, 144x1x5x5] %onnx::Conv_667[FLOAT, 24x144x1x1] %onnx::Conv_670[FLOAT, 24x24x1x1] %onnx::Conv_673[FLOAT, 24x1x3x3] %onnx::Conv_676[FLOAT, 32x24x1x1] %onnx::Conv_677[FLOAT, 32] %onnx::Conv_679[FLOAT, 32x32x1x1] %onnx::Conv_682[FLOAT, 32x1x3x3] %onnx::Conv_685[FLOAT, 32x32x1x1] %onnx::Conv_688[FLOAT, 96x32x1x1] %onnx::Conv_689[FLOAT, 96] %onnx::Conv_691[FLOAT, 96x1x3x3] %onnx::Conv_694[FLOAT, 32x96x1x1] %onnx::Conv_697[FLOAT, 96x32x1x1] %onnx::Conv_700[FLOAT, 96x1x5x5] %onnx::Conv_703[FLOAT, 32x96x1x1] %onnx::Conv_706[FLOAT, 64x32x1x1] %onnx::Conv_707[FLOAT, 64] %onnx::Conv_709[FLOAT, 64x64x1x1] %onnx::Conv_712[FLOAT, 64x1x5x5] %onnx::Conv_715[FLOAT, 64x64x1x1] %onnx::Conv_718[FLOAT, 64x64x1x1] %onnx::Conv_721[FLOAT, 64x1x5x5] %onnx::Conv_724[FLOAT, 64x64x1x1] %onnx::Conv_727[FLOAT, 192x64x1x1] %onnx::Conv_728[FLOAT, 192] %onnx::Conv_730[FLOAT, 192x1x3x3] %onnx::Conv_733[FLOAT, 112x192x1x1] %onnx::Conv_734[FLOAT, 112] %onnx::Conv_736[FLOAT, 112x112x1x1] %onnx::Conv_739[FLOAT, 112x1x3x3] %onnx::Conv_742[FLOAT, 112x112x1x1] %onnx::Conv_745[FLOAT, 672x112x1x1] %onnx::Conv_746[FLOAT, 672] %onnx::Conv_748[FLOAT, 672x1x5x5] %onnx::Conv_751[FLOAT, 112x672x1x1] %onnx::Conv_754[FLOAT, 672x112x1x1] %onnx::Conv_757[FLOAT, 672x1x3x3] %onnx::Conv_760[FLOAT, 184x672x1x1] %onnx::Conv_761[FLOAT, 184] %onnx::Conv_763[FLOAT, 1104x184x1x1] %onnx::Conv_764[FLOAT, 1104] %onnx::Conv_766[FLOAT, 1104x1x5x5] %onnx::Conv_769[FLOAT, 184x1104x1x1] %onnx::Conv_772[FLOAT, 184x92x1x1] %onnx::Conv_775[FLOAT, 184x1x3x3] %onnx::Conv_778[FLOAT, 184x92x1x1] %onnx::Conv_781[FLOAT, 184x92x1x1] %onnx::Conv_784[FLOAT, 184x1x3x3] %onnx::Conv_787[FLOAT, 184x92x1x1] %onnx::Conv_790[FLOAT, 184x92x1x1] %onnx::Conv_793[FLOAT, 184x1x5x5] %onnx::Conv_796[FLOAT, 352x92x1x1] %onnx::Conv_797[FLOAT, 352] %onnx::Conv_799[FLOAT, 1504x352x1x1] %onnx::Conv_800[FLOAT, 1504] ) { %onnx::Conv_794 = Identity(%onnx::Conv_761) %onnx::Conv_791 = Identity(%onnx::Conv_761) %onnx::Conv_788 = Identity(%onnx::Conv_761) %onnx::Conv_785 = Identity(%onnx::Conv_761) %onnx::Conv_782 = Identity(%onnx::Conv_761) %onnx::Conv_779 = Identity(%onnx::Conv_761) %onnx::Conv_776 = Identity(%onnx::Conv_761) %onnx::Conv_773 = Identity(%onnx::Conv_761) %onnx::Conv_770 = Identity(%onnx::Conv_761) %onnx::Conv_767 = Identity(%onnx::Conv_764) %onnx::Conv_758 = Identity(%onnx::Conv_746) %onnx::Conv_755 = Identity(%onnx::Conv_746) %onnx::Conv_752 = Identity(%onnx::Conv_734) %onnx::Conv_749 = Identity(%onnx::Conv_746) %onnx::Conv_743 = Identity(%onnx::Conv_734) %onnx::Conv_740 = Identity(%onnx::Conv_734) %onnx::Conv_737 = Identity(%onnx::Conv_734) %onnx::Conv_731 = Identity(%onnx::Conv_728) %onnx::Conv_725 = Identity(%onnx::Conv_707) %onnx::Conv_722 = Identity(%onnx::Conv_707) %onnx::Conv_719 = Identity(%onnx::Conv_707) %onnx::Conv_716 = Identity(%onnx::Conv_707) %onnx::Conv_713 = Identity(%onnx::Conv_707) %onnx::Conv_710 = Identity(%onnx::Conv_707) %onnx::Conv_704 = Identity(%onnx::Conv_677) %onnx::Conv_701 = Identity(%onnx::Conv_689) %onnx::Conv_698 = Identity(%onnx::Conv_689) %onnx::Conv_695 = Identity(%onnx::Conv_677) %onnx::Conv_692 = Identity(%onnx::Conv_689) %onnx::Conv_686 = Identity(%onnx::Conv_677) %onnx::Conv_683 = Identity(%onnx::Conv_677) %onnx::Conv_680 = Identity(%onnx::Conv_677) %onnx::Conv_674 = Identity(%onnx::Conv_641) %onnx::Conv_671 = Identity(%onnx::Conv_641) %onnx::Conv_668 = Identity(%onnx::Conv_641) %onnx::Conv_665 = Identity(%onnx::Conv_662) %onnx::Conv_659 = Identity(%onnx::Conv_641) %onnx::Conv_656 = Identity(%onnx::Conv_641) %onnx::Conv_653 = Identity(%onnx::Conv_641) %onnx::Conv_650 = Identity(%onnx::Conv_641) %onnx::Conv_647 = Identity(%onnx::Conv_644) %onnx::Conv_638 = Identity(%onnx::Conv_623) %onnx::Conv_635 = Identity(%onnx::Conv_623) %onnx::Conv_632 = Identity(%onnx::Conv_623) %onnx::Conv_629 = Identity(%onnx::Conv_623) %onnx::Conv_626 = Identity(%onnx::Conv_623) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_622, %onnx::Conv_623) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_625, %onnx::Conv_626) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.0/shuffle/Reshape_output_0 = Reshape(%/cells.0/nl/Relu_output_0, %/cells.0/shuffle/Constant_output_0) %/cells.0/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.0/shuffle/Reshape_output_0) %/cells.0/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.0/shuffle/Reshape_1_output_0 = Reshape(%/cells.0/shuffle/Transpose_output_0, %/cells.0/shuffle/Constant_1_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.0/shuffle/Reshape_1_output_0, %onnx::Conv_628, %onnx::Conv_629) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_631, %onnx::Conv_632) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_634, %onnx::Conv_635) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.1/nl/Relu_output_0, %onnx::Conv_637, %onnx::Conv_638) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_640, %onnx::Conv_641) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_643, %onnx::Conv_644) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.2/nl/Relu_output_0, %onnx::Conv_646, %onnx::Conv_647) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_649, %onnx::Conv_650) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_652, %onnx::Conv_653) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.3/nl/Relu_output_0, %onnx::Conv_655, %onnx::Conv_656) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_658, %onnx::Conv_659) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_661, %onnx::Conv_662) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.4/nl/Relu_output_0, %onnx::Conv_664, %onnx::Conv_665) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_667, %onnx::Conv_668) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/Add_output_0, %onnx::Conv_670, %onnx::Conv_671) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.5/nl/Relu_output_0, %onnx::Conv_673, %onnx::Conv_674) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_676, %onnx::Conv_677) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_679, %onnx::Conv_680) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.6/nl/Relu_output_0, %onnx::Conv_682, %onnx::Conv_683) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_685, %onnx::Conv_686) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_688, %onnx::Conv_689) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.7/nl/Relu_output_0, %onnx::Conv_691, %onnx::Conv_692) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_694, %onnx::Conv_695) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_697, %onnx::Conv_698) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.8/nl/Relu_output_0, %onnx::Conv_700, %onnx::Conv_701) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_703, %onnx::Conv_704) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [2, 2]](%/cells.8/Add_output_0, %onnx::Conv_706, %onnx::Conv_707) %/cells.9/relu/Relu_output_0 = Relu(%/cells.9/conv/Conv_output_0) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/relu/Relu_output_0, %onnx::Conv_709, %onnx::Conv_710) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.10/nl/Relu_output_0, %onnx::Conv_712, %onnx::Conv_713) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_715, %onnx::Conv_716) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/relu/Relu_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_718, %onnx::Conv_719) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.12/nl/Relu_output_0, %onnx::Conv_721, %onnx::Conv_722) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_724, %onnx::Conv_725) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_727, %onnx::Conv_728) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.13/nl/Relu_output_0, %onnx::Conv_730, %onnx::Conv_731) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_733, %onnx::Conv_734) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_736, %onnx::Conv_737) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.14/nl/Relu_output_0, %onnx::Conv_739, %onnx::Conv_740) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_742, %onnx::Conv_743) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_745, %onnx::Conv_746) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.15/nl/Relu_output_0, %onnx::Conv_748, %onnx::Conv_749) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_751, %onnx::Conv_752) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_754, %onnx::Conv_755) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.17/nl/Relu_output_0, %onnx::Conv_757, %onnx::Conv_758) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_760, %onnx::Conv_761) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_763, %onnx::Conv_764) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.18/nl/Relu_output_0, %onnx::Conv_766, %onnx::Conv_767) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_769, %onnx::Conv_770) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_772, %onnx::Conv_773) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.19/shuffle/Reshape_output_0 = Reshape(%/cells.19/nl/Relu_output_0, %/cells.19/shuffle/Constant_output_0) %/cells.19/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.19/shuffle/Reshape_output_0) %/cells.19/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.19/shuffle/Reshape_1_output_0 = Reshape(%/cells.19/shuffle/Transpose_output_0, %/cells.19/shuffle/Constant_1_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.19/shuffle/Reshape_1_output_0, %onnx::Conv_775, %onnx::Conv_776) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_778, %onnx::Conv_779) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_781, %onnx::Conv_782) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.20/shuffle/Reshape_output_0 = Reshape(%/cells.20/nl/Relu_output_0, %/cells.20/shuffle/Constant_output_0) %/cells.20/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.20/shuffle/Reshape_output_0) %/cells.20/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.20/shuffle/Reshape_1_output_0 = Reshape(%/cells.20/shuffle/Transpose_output_0, %/cells.20/shuffle/Constant_1_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.20/shuffle/Reshape_1_output_0, %onnx::Conv_784, %onnx::Conv_785) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_787, %onnx::Conv_788) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_790, %onnx::Conv_791) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.21/shuffle/Reshape_output_0 = Reshape(%/cells.21/nl/Relu_output_0, %/cells.21/shuffle/Constant_output_0) %/cells.21/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.21/shuffle/Reshape_output_0) %/cells.21/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.21/shuffle/Reshape_1_output_0 = Reshape(%/cells.21/shuffle/Transpose_output_0, %/cells.21/shuffle/Constant_1_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.21/shuffle/Reshape_1_output_0, %onnx::Conv_793, %onnx::Conv_794) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_796, %onnx::Conv_797) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_799, %onnx::Conv_800) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %620 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %620 }	val_accuracy	68,929,152	1,755,908	{'zcp_synflow': 75.59524104597021, 'zcp_zen': 64.01332092285156, 'zcp_epe_nas': 0.00015999920000638146, 'zcp_fisher': 0.08100873976945877, 'zcp_flops': 68929152.0, 'zcp_grad_norm': 21.151615142822266, 'zcp_grasp': -0.051494598388671875, 'zcp_jacov': -16.0550999334471, 'zcp_l2_norm': 569.1984252929688, 'zcp_nwot': 211.4811878651135, 'zcp_params': 1755908.0, 'zcp_plain': 0.0015526837669312954, 'zcp_snip': 35.363277435302734, 'lat_1080ti_1': 0.5783103051249239, 'lat_1080ti_32': 0.5969851523104048, 'lat_1080ti_64': 0.5034406919053336, 'lat_2080ti_1': 0.5459534262965159, 'lat_2080ti_32': 0.5214408923545973, 'lat_2080ti_64': 0.46724109354965326, 'lat_essential_ph_1': 0.22641509433962265, 'lat_eyeriss': 0.4395045201987786, 'lat_fpga': 0.42010112904343005, 'lat_gold_6226': 0.3025747088648476, 'lat_gold_6240': 0.426433017685598, 'lat_pixel2': 0.43478260869565216, 'lat_pixel3': 0.4916174640097593, 'lat_raspi4': 0.5053165738554132, 'lat_samsung_a50': 0.17894736842105263, 'lat_samsung_s7': 0.14960629921259844, 'lat_silver_4114': 0.46003508802330456, 'lat_silver_4210r': 0.4818526576006559, 'lat_titan_rtx_1': 0.5115205117672147, 'lat_titan_rtx_32': 0.5090594515928994, 'lat_titan_rtx_64': 0.4912868999999521, 'lat_titanx_1': 0.26658467882857007, 'lat_titanx_32': 0.5191744821396502, 'lat_titanx_64': 0.4615709975927815, 'lat_titanxp_1': 0.5087972111707852, 'lat_titanxp_32': 0.5320575036517385, 'lat_titanxp_64': 0.5046025428229564}
FBNet_1313	FBNet	1313	1313	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_486[FLOAT, 16x3x3x3] %onnx::Conv_487[FLOAT, 16] %onnx::Conv_489[FLOAT, 16x16x1x1] %onnx::Conv_492[FLOAT, 16x1x3x3] %onnx::Conv_495[FLOAT, 16x16x1x1] %onnx::Conv_498[FLOAT, 96x16x1x1] %onnx::Conv_499[FLOAT, 96] %onnx::Conv_501[FLOAT, 96x1x5x5] %onnx::Conv_504[FLOAT, 24x96x1x1] %onnx::Conv_505[FLOAT, 24] %onnx::Conv_507[FLOAT, 72x24x1x1] %onnx::Conv_508[FLOAT, 72] %onnx::Conv_510[FLOAT, 72x1x3x3] %onnx::Conv_513[FLOAT, 24x72x1x1] %onnx::Conv_516[FLOAT, 24x24x1x1] %onnx::Conv_519[FLOAT, 24x1x5x5] %onnx::Conv_522[FLOAT, 24x24x1x1] %onnx::Conv_525[FLOAT, 72x24x1x1] %onnx::Conv_528[FLOAT, 72x1x5x5] %onnx::Conv_531[FLOAT, 32x72x1x1] %onnx::Conv_532[FLOAT, 32] %onnx::Conv_534[FLOAT, 192x32x1x1] %onnx::Conv_535[FLOAT, 192] %onnx::Conv_537[FLOAT, 192x1x5x5] %onnx::Conv_540[FLOAT, 32x192x1x1] %onnx::Conv_543[FLOAT, 32x16x1x1] %onnx::Conv_546[FLOAT, 32x1x5x5] %onnx::Conv_549[FLOAT, 32x16x1x1] %onnx::Conv_552[FLOAT, 96x32x1x1] %onnx::Conv_555[FLOAT, 96x1x3x3] %onnx::Conv_558[FLOAT, 64x96x1x1] %onnx::Conv_559[FLOAT, 64] %onnx::Conv_561[FLOAT, 384x64x1x1] %onnx::Conv_562[FLOAT, 384] %onnx::Conv_564[FLOAT, 384x1x3x3] %onnx::Conv_567[FLOAT, 64x384x1x1] %onnx::Conv_570[FLOAT, 64x64x1x1] %onnx::Conv_573[FLOAT, 64x1x5x5] %onnx::Conv_576[FLOAT, 64x64x1x1] %onnx::Conv_579[FLOAT, 112x64x1x1] %onnx::Conv_580[FLOAT, 112] %onnx::Conv_582[FLOAT, 336x112x1x1] %onnx::Conv_583[FLOAT, 336] %onnx::Conv_585[FLOAT, 336x1x3x3] %onnx::Conv_588[FLOAT, 112x336x1x1] %onnx::Conv_591[FLOAT, 672x112x1x1] %onnx::Conv_592[FLOAT, 672] %onnx::Conv_594[FLOAT, 672x1x3x3] %onnx::Conv_597[FLOAT, 112x672x1x1] %onnx::Conv_600[FLOAT, 112x112x1x1] %onnx::Conv_603[FLOAT, 112x1x5x5] %onnx::Conv_606[FLOAT, 184x112x1x1] %onnx::Conv_607[FLOAT, 184] %onnx::Conv_609[FLOAT, 1104x184x1x1] %onnx::Conv_610[FLOAT, 1104] %onnx::Conv_612[FLOAT, 1104x1x5x5] %onnx::Conv_615[FLOAT, 184x1104x1x1] %onnx::Conv_618[FLOAT, 552x184x1x1] %onnx::Conv_619[FLOAT, 552] %onnx::Conv_621[FLOAT, 552x1x3x3] %onnx::Conv_624[FLOAT, 184x552x1x1] %onnx::Conv_627[FLOAT, 184x184x1x1] %onnx::Conv_630[FLOAT, 184x1x5x5] %onnx::Conv_633[FLOAT, 352x184x1x1] %onnx::Conv_634[FLOAT, 352] %onnx::Conv_636[FLOAT, 1504x352x1x1] %onnx::Conv_637[FLOAT, 1504] ) { %onnx::Conv_631 = Identity(%onnx::Conv_607) %onnx::Conv_628 = Identity(%onnx::Conv_607) %onnx::Conv_625 = Identity(%onnx::Conv_607) %onnx::Conv_622 = Identity(%onnx::Conv_619) %onnx::Conv_616 = Identity(%onnx::Conv_607) %onnx::Conv_613 = Identity(%onnx::Conv_610) %onnx::Conv_604 = Identity(%onnx::Conv_580) %onnx::Conv_601 = Identity(%onnx::Conv_580) %onnx::Conv_598 = Identity(%onnx::Conv_580) %onnx::Conv_595 = Identity(%onnx::Conv_592) %onnx::Conv_589 = Identity(%onnx::Conv_580) %onnx::Conv_586 = Identity(%onnx::Conv_583) %onnx::Conv_577 = Identity(%onnx::Conv_559) %onnx::Conv_574 = Identity(%onnx::Conv_559) %onnx::Conv_571 = Identity(%onnx::Conv_559) %onnx::Conv_568 = Identity(%onnx::Conv_559) %onnx::Conv_565 = Identity(%onnx::Conv_562) %onnx::Conv_556 = Identity(%onnx::Conv_499) %onnx::Conv_553 = Identity(%onnx::Conv_499) %onnx::Conv_550 = Identity(%onnx::Conv_532) %onnx::Conv_547 = Identity(%onnx::Conv_532) %onnx::Conv_544 = Identity(%onnx::Conv_532) %onnx::Conv_541 = Identity(%onnx::Conv_532) %onnx::Conv_538 = Identity(%onnx::Conv_535) %onnx::Conv_529 = Identity(%onnx::Conv_508) %onnx::Conv_526 = Identity(%onnx::Conv_508) %onnx::Conv_523 = Identity(%onnx::Conv_505) %onnx::Conv_520 = Identity(%onnx::Conv_505) %onnx::Conv_517 = Identity(%onnx::Conv_505) %onnx::Conv_514 = Identity(%onnx::Conv_505) %onnx::Conv_511 = Identity(%onnx::Conv_508) %onnx::Conv_502 = Identity(%onnx::Conv_499) %onnx::Conv_496 = Identity(%onnx::Conv_487) %onnx::Conv_493 = Identity(%onnx::Conv_487) %onnx::Conv_490 = Identity(%onnx::Conv_487) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_486, %onnx::Conv_487) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_489, %onnx::Conv_490) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.0/nl/Relu_output_0, %onnx::Conv_492, %onnx::Conv_493) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_495, %onnx::Conv_496) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_498, %onnx::Conv_499) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.1/nl/Relu_output_0, %onnx::Conv_501, %onnx::Conv_502) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_504, %onnx::Conv_505) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_507, %onnx::Conv_508) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.2/nl/Relu_output_0, %onnx::Conv_510, %onnx::Conv_511) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_513, %onnx::Conv_514) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_516, %onnx::Conv_517) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.4/nl/Relu_output_0, %onnx::Conv_519, %onnx::Conv_520) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_522, %onnx::Conv_523) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/Add_output_0, %onnx::Conv_525, %onnx::Conv_526) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.5/nl/Relu_output_0, %onnx::Conv_528, %onnx::Conv_529) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_531, %onnx::Conv_532) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_534, %onnx::Conv_535) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.6/nl/Relu_output_0, %onnx::Conv_537, %onnx::Conv_538) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_540, %onnx::Conv_541) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_543, %onnx::Conv_544) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.7/shuffle/Reshape_output_0 = Reshape(%/cells.7/nl/Relu_output_0, %/cells.7/shuffle/Constant_output_0) %/cells.7/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.7/shuffle/Reshape_output_0) %/cells.7/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.7/shuffle/Reshape_1_output_0 = Reshape(%/cells.7/shuffle/Transpose_output_0, %/cells.7/shuffle/Constant_1_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.7/shuffle/Reshape_1_output_0, %onnx::Conv_546, %onnx::Conv_547) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_549, %onnx::Conv_550) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_552, %onnx::Conv_553) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.9/nl/Relu_output_0, %onnx::Conv_555, %onnx::Conv_556) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_558, %onnx::Conv_559) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_561, %onnx::Conv_562) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.10/nl/Relu_output_0, %onnx::Conv_564, %onnx::Conv_565) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_567, %onnx::Conv_568) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_570, %onnx::Conv_571) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.11/nl/Relu_output_0, %onnx::Conv_573, %onnx::Conv_574) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_576, %onnx::Conv_577) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.13/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_579, %onnx::Conv_580) %/cells.13/relu/Relu_output_0 = Relu(%/cells.13/conv/Conv_output_0) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/relu/Relu_output_0, %onnx::Conv_582, %onnx::Conv_583) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.14/nl/Relu_output_0, %onnx::Conv_585, %onnx::Conv_586) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_588, %onnx::Conv_589) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/relu/Relu_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_591, %onnx::Conv_592) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.15/nl/Relu_output_0, %onnx::Conv_594, %onnx::Conv_595) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_597, %onnx::Conv_598) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_600, %onnx::Conv_601) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.17/nl/Relu_output_0, %onnx::Conv_603, %onnx::Conv_604) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_606, %onnx::Conv_607) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_609, %onnx::Conv_610) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.19/nl/Relu_output_0, %onnx::Conv_612, %onnx::Conv_613) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_615, %onnx::Conv_616) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_618, %onnx::Conv_619) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 552, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.20/nl/Relu_output_0, %onnx::Conv_621, %onnx::Conv_622) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_624, %onnx::Conv_625) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_627, %onnx::Conv_628) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.21/nl/Relu_output_0, %onnx::Conv_630, %onnx::Conv_631) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_633, %onnx::Conv_634) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_636, %onnx::Conv_637) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %484 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %484 }	val_accuracy	66,335,360	1,835,804	{'zcp_synflow': 68.74327749975693, 'zcp_zen': 57.93806838989258, 'zcp_epe_nas': 7.37134371780781, 'zcp_fisher': 0.060048677027225494, 'zcp_flops': 66335360.0, 'zcp_grad_norm': 17.59305191040039, 'zcp_grasp': -0.024094581604003906, 'zcp_jacov': -16.07538944131789, 'zcp_l2_norm': 545.9761962890625, 'zcp_nwot': 211.11004266000754, 'zcp_params': 1835804.0, 'zcp_plain': 0.0016223592683672905, 'zcp_snip': 31.715898513793945, 'lat_1080ti_1': 0.12338897747513919, 'lat_1080ti_32': 0.14020110268747, 'lat_1080ti_64': 0.25757440006454196, 'lat_2080ti_1': 0.163668011238352, 'lat_2080ti_32': 0.1685023207378393, 'lat_2080ti_64': 0.21650949995753835, 'lat_essential_ph_1': 0.3584905660377358, 'lat_eyeriss': 0.39667724130343074, 'lat_fpga': 0.4315103058609324, 'lat_gold_6226': 0.33323218815405414, 'lat_gold_6240': 0.3128857388079196, 'lat_pixel2': 0.30434782608695654, 'lat_pixel3': 0.38170199814638517, 'lat_raspi4': 0.3727304117573015, 'lat_samsung_a50': 0.14736842105263157, 'lat_samsung_s7': 0.47244094488188976, 'lat_silver_4114': 0.31360625694845506, 'lat_silver_4210r': 0.25301258179994174, 'lat_titan_rtx_1': 0.15446957134589862, 'lat_titan_rtx_32': 0.1523581100324474, 'lat_titan_rtx_64': 0.17602753874415994, 'lat_titanx_1': 0.07965644828198135, 'lat_titanx_32': 0.14383394476990533, 'lat_titanx_64': 0.2532749399296517, 'lat_titanxp_1': 0.15034482746485034, 'lat_titanxp_32': 0.1586523225682748, 'lat_titanxp_64': 0.2302751958567607}
FBNet_2675	FBNet	2675	2675	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_499[FLOAT, 16x3x3x3] %onnx::Conv_500[FLOAT, 16] %onnx::Conv_502[FLOAT, 16x8x1x1] %onnx::Conv_505[FLOAT, 16x1x3x3] %onnx::Conv_508[FLOAT, 24x8x1x1] %onnx::Conv_509[FLOAT, 24] %onnx::Conv_511[FLOAT, 24x24x1x1] %onnx::Conv_514[FLOAT, 24x1x3x3] %onnx::Conv_517[FLOAT, 24x24x1x1] %onnx::Conv_520[FLOAT, 24x24x1x1] %onnx::Conv_523[FLOAT, 24x1x5x5] %onnx::Conv_526[FLOAT, 24x24x1x1] %onnx::Conv_529[FLOAT, 144x24x1x1] %onnx::Conv_530[FLOAT, 144] %onnx::Conv_532[FLOAT, 144x1x3x3] %onnx::Conv_535[FLOAT, 24x144x1x1] %onnx::Conv_538[FLOAT, 32x24x1x1] %onnx::Conv_539[FLOAT, 32] %onnx::Conv_541[FLOAT, 192x32x1x1] %onnx::Conv_542[FLOAT, 192] %onnx::Conv_544[FLOAT, 192x1x3x3] %onnx::Conv_547[FLOAT, 32x192x1x1] %onnx::Conv_550[FLOAT, 192x32x1x1] %onnx::Conv_553[FLOAT, 192x1x3x3] %onnx::Conv_556[FLOAT, 32x192x1x1] %onnx::Conv_559[FLOAT, 96x32x1x1] %onnx::Conv_560[FLOAT, 96] %onnx::Conv_562[FLOAT, 96x1x5x5] %onnx::Conv_565[FLOAT, 32x96x1x1] %onnx::Conv_568[FLOAT, 64x32x1x1] %onnx::Conv_569[FLOAT, 64] %onnx::Conv_571[FLOAT, 64x64x1x1] %onnx::Conv_574[FLOAT, 64x1x5x5] %onnx::Conv_577[FLOAT, 64x64x1x1] %onnx::Conv_580[FLOAT, 64x32x1x1] %onnx::Conv_583[FLOAT, 64x1x5x5] %onnx::Conv_586[FLOAT, 112x32x1x1] %onnx::Conv_587[FLOAT, 112] %onnx::Conv_589[FLOAT, 336x112x1x1] %onnx::Conv_590[FLOAT, 336] %onnx::Conv_592[FLOAT, 336x1x3x3] %onnx::Conv_595[FLOAT, 112x336x1x1] %onnx::Conv_598[FLOAT, 112x56x1x1] %onnx::Conv_601[FLOAT, 112x1x3x3] %onnx::Conv_604[FLOAT, 112x56x1x1] %onnx::Conv_607[FLOAT, 672x112x1x1] %onnx::Conv_608[FLOAT, 672] %onnx::Conv_610[FLOAT, 672x1x3x3] %onnx::Conv_613[FLOAT, 184x672x1x1] %onnx::Conv_614[FLOAT, 184] %onnx::Conv_616[FLOAT, 184x184x1x1] %onnx::Conv_619[FLOAT, 184x1x5x5] %onnx::Conv_622[FLOAT, 184x184x1x1] %onnx::Conv_625[FLOAT, 184x92x1x1] %onnx::Conv_628[FLOAT, 184x1x3x3] %onnx::Conv_631[FLOAT, 352x92x1x1] %onnx::Conv_632[FLOAT, 352] %onnx::Conv_634[FLOAT, 1504x352x1x1] %onnx::Conv_635[FLOAT, 1504] ) { %onnx::Conv_629 = Identity(%onnx::Conv_614) %onnx::Conv_626 = Identity(%onnx::Conv_614) %onnx::Conv_623 = Identity(%onnx::Conv_614) %onnx::Conv_620 = Identity(%onnx::Conv_614) %onnx::Conv_617 = Identity(%onnx::Conv_614) %onnx::Conv_611 = Identity(%onnx::Conv_608) %onnx::Conv_605 = Identity(%onnx::Conv_587) %onnx::Conv_602 = Identity(%onnx::Conv_587) %onnx::Conv_599 = Identity(%onnx::Conv_587) %onnx::Conv_596 = Identity(%onnx::Conv_587) %onnx::Conv_593 = Identity(%onnx::Conv_590) %onnx::Conv_584 = Identity(%onnx::Conv_569) %onnx::Conv_581 = Identity(%onnx::Conv_569) %onnx::Conv_578 = Identity(%onnx::Conv_569) %onnx::Conv_575 = Identity(%onnx::Conv_569) %onnx::Conv_572 = Identity(%onnx::Conv_569) %onnx::Conv_566 = Identity(%onnx::Conv_539) %onnx::Conv_563 = Identity(%onnx::Conv_560) %onnx::Conv_557 = Identity(%onnx::Conv_539) %onnx::Conv_554 = Identity(%onnx::Conv_542) %onnx::Conv_551 = Identity(%onnx::Conv_542) %onnx::Conv_548 = Identity(%onnx::Conv_539) %onnx::Conv_545 = Identity(%onnx::Conv_542) %onnx::Conv_536 = Identity(%onnx::Conv_509) %onnx::Conv_533 = Identity(%onnx::Conv_530) %onnx::Conv_527 = Identity(%onnx::Conv_509) %onnx::Conv_524 = Identity(%onnx::Conv_509) %onnx::Conv_521 = Identity(%onnx::Conv_509) %onnx::Conv_518 = Identity(%onnx::Conv_509) %onnx::Conv_515 = Identity(%onnx::Conv_509) %onnx::Conv_512 = Identity(%onnx::Conv_509) %onnx::Conv_506 = Identity(%onnx::Conv_500) %onnx::Conv_503 = Identity(%onnx::Conv_500) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_499, %onnx::Conv_500) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_502, %onnx::Conv_503) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.1/shuffle/Reshape_output_0 = Reshape(%/cells.1/nl/Relu_output_0, %/cells.1/shuffle/Constant_output_0) %/cells.1/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.1/shuffle/Reshape_output_0) %/cells.1/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.1/shuffle/Reshape_1_output_0 = Reshape(%/cells.1/shuffle/Transpose_output_0, %/cells.1/shuffle/Constant_1_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.1/shuffle/Reshape_1_output_0, %onnx::Conv_505, %onnx::Conv_506) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_508, %onnx::Conv_509) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_511, %onnx::Conv_512) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.2/nl/Relu_output_0, %onnx::Conv_514, %onnx::Conv_515) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_517, %onnx::Conv_518) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_520, %onnx::Conv_521) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.3/nl/Relu_output_0, %onnx::Conv_523, %onnx::Conv_524) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_526, %onnx::Conv_527) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_529, %onnx::Conv_530) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.4/nl/Relu_output_0, %onnx::Conv_532, %onnx::Conv_533) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_535, %onnx::Conv_536) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [2, 2]](%/cells.4/Add_output_0, %onnx::Conv_538, %onnx::Conv_539) %/cells.5/relu/Relu_output_0 = Relu(%/cells.5/conv/Conv_output_0) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/relu/Relu_output_0, %onnx::Conv_541, %onnx::Conv_542) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.6/nl/Relu_output_0, %onnx::Conv_544, %onnx::Conv_545) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_547, %onnx::Conv_548) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/relu/Relu_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_550, %onnx::Conv_551) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.7/nl/Relu_output_0, %onnx::Conv_553, %onnx::Conv_554) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_556, %onnx::Conv_557) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_559, %onnx::Conv_560) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.8/nl/Relu_output_0, %onnx::Conv_562, %onnx::Conv_563) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_565, %onnx::Conv_566) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [2, 2]](%/cells.8/Add_output_0, %onnx::Conv_568, %onnx::Conv_569) %/cells.9/relu/Relu_output_0 = Relu(%/cells.9/conv/Conv_output_0) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/relu/Relu_output_0, %onnx::Conv_571, %onnx::Conv_572) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.10/nl/Relu_output_0, %onnx::Conv_574, %onnx::Conv_575) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_577, %onnx::Conv_578) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/relu/Relu_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_580, %onnx::Conv_581) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.13/shuffle/Reshape_output_0 = Reshape(%/cells.13/nl/Relu_output_0, %/cells.13/shuffle/Constant_output_0) %/cells.13/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.13/shuffle/Reshape_output_0) %/cells.13/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.13/shuffle/Reshape_1_output_0 = Reshape(%/cells.13/shuffle/Transpose_output_0, %/cells.13/shuffle/Constant_1_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.13/shuffle/Reshape_1_output_0, %onnx::Conv_583, %onnx::Conv_584) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_586, %onnx::Conv_587) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_589, %onnx::Conv_590) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.14/nl/Relu_output_0, %onnx::Conv_592, %onnx::Conv_593) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_595, %onnx::Conv_596) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_598, %onnx::Conv_599) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.16/shuffle/Reshape_output_0 = Reshape(%/cells.16/nl/Relu_output_0, %/cells.16/shuffle/Constant_output_0) %/cells.16/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.16/shuffle/Reshape_output_0) %/cells.16/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.16/shuffle/Reshape_1_output_0 = Reshape(%/cells.16/shuffle/Transpose_output_0, %/cells.16/shuffle/Constant_1_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.16/shuffle/Reshape_1_output_0, %onnx::Conv_601, %onnx::Conv_602) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_604, %onnx::Conv_605) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_607, %onnx::Conv_608) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.17/nl/Relu_output_0, %onnx::Conv_610, %onnx::Conv_611) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_613, %onnx::Conv_614) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_616, %onnx::Conv_617) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.18/nl/Relu_output_0, %onnx::Conv_619, %onnx::Conv_620) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_622, %onnx::Conv_623) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_625, %onnx::Conv_626) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.21/shuffle/Reshape_output_0 = Reshape(%/cells.21/nl/Relu_output_0, %/cells.21/shuffle/Constant_output_0) %/cells.21/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.21/shuffle/Reshape_output_0) %/cells.21/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.21/shuffle/Reshape_1_output_0 = Reshape(%/cells.21/shuffle/Transpose_output_0, %/cells.21/shuffle/Constant_1_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.21/shuffle/Reshape_1_output_0, %onnx::Conv_628, %onnx::Conv_629) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_631, %onnx::Conv_632) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_634, %onnx::Conv_635) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %497 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %497 }	val_accuracy	49,174,912	1,183,700	{'zcp_synflow': 57.10559370447588, 'zcp_zen': 48.26923751831055, 'zcp_epe_nas': 17.713526910958418, 'zcp_fisher': 0.025261545553803444, 'zcp_flops': 49174912.0, 'zcp_grad_norm': 11.857154846191406, 'zcp_grasp': -0.018759727478027344, 'zcp_jacov': -16.054974549223758, 'zcp_l2_norm': 418.6827697753906, 'zcp_nwot': 209.016306891491, 'zcp_params': 1183700.0, 'zcp_plain': -0.003269054926931858, 'zcp_snip': 18.012813568115234, 'lat_1080ti_1': 0.026304379942028645, 'lat_1080ti_32': 0.11657565006065905, 'lat_1080ti_64': 0.14127456871040084, 'lat_2080ti_1': 0.07726940325720276, 'lat_2080ti_32': 0.10538367294024073, 'lat_2080ti_64': 0.14620587917698655, 'lat_essential_ph_1': 0.05660377358490566, 'lat_eyeriss': 0.18297177855610353, 'lat_fpga': 0.20003166467113262, 'lat_gold_6226': 0.10881947442993571, 'lat_gold_6240': 0.08600802747594366, 'lat_pixel2': 0.08695652173913043, 'lat_pixel3': 0.184830777373263, 'lat_raspi4': 0.19284077634232036, 'lat_samsung_a50': 0.06315789473684211, 'lat_samsung_s7': 0.03937007874015748, 'lat_silver_4114': 0.11644918005094086, 'lat_silver_4210r': 0.08465408922382509, 'lat_titan_rtx_1': 0.0643137090247807, 'lat_titan_rtx_32': 0.0785444168705912, 'lat_titan_rtx_64': 0.10137332686266208, 'lat_titanx_1': 0.019455104531910437, 'lat_titanx_32': 0.06948402978822658, 'lat_titanx_64': 0.15712926694759952, 'lat_titanxp_1': 0.0536676719183089, 'lat_titanxp_32': 0.0774734076195841, 'lat_titanxp_64': 0.11013686999570724}
FBNet_887	FBNet	887	887	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_650[FLOAT, 16x3x3x3] %onnx::Conv_651[FLOAT, 16] %onnx::Conv_653[FLOAT, 48x16x1x1] %onnx::Conv_654[FLOAT, 48] %onnx::Conv_656[FLOAT, 48x1x5x5] %onnx::Conv_659[FLOAT, 16x48x1x1] %onnx::Conv_662[FLOAT, 48x16x1x1] %onnx::Conv_665[FLOAT, 48x1x5x5] %onnx::Conv_668[FLOAT, 24x48x1x1] %onnx::Conv_669[FLOAT, 24] %onnx::Conv_671[FLOAT, 72x24x1x1] %onnx::Conv_672[FLOAT, 72] %onnx::Conv_674[FLOAT, 72x1x3x3] %onnx::Conv_677[FLOAT, 24x72x1x1] %onnx::Conv_680[FLOAT, 24x24x1x1] %onnx::Conv_683[FLOAT, 24x1x5x5] %onnx::Conv_686[FLOAT, 24x24x1x1] %onnx::Conv_689[FLOAT, 144x24x1x1] %onnx::Conv_690[FLOAT, 144] %onnx::Conv_692[FLOAT, 144x1x5x5] %onnx::Conv_695[FLOAT, 24x144x1x1] %onnx::Conv_698[FLOAT, 144x24x1x1] %onnx::Conv_701[FLOAT, 144x1x3x3] %onnx::Conv_704[FLOAT, 32x144x1x1] %onnx::Conv_705[FLOAT, 32] %onnx::Conv_707[FLOAT, 96x32x1x1] %onnx::Conv_708[FLOAT, 96] %onnx::Conv_710[FLOAT, 96x1x3x3] %onnx::Conv_713[FLOAT, 32x96x1x1] %onnx::Conv_716[FLOAT, 192x32x1x1] %onnx::Conv_717[FLOAT, 192] %onnx::Conv_719[FLOAT, 192x1x5x5] %onnx::Conv_722[FLOAT, 32x192x1x1] %onnx::Conv_725[FLOAT, 64x32x1x1] %onnx::Conv_726[FLOAT, 64] %onnx::Conv_728[FLOAT, 64x32x1x1] %onnx::Conv_731[FLOAT, 64x1x3x3] %onnx::Conv_734[FLOAT, 64x32x1x1] %onnx::Conv_737[FLOAT, 384x64x1x1] %onnx::Conv_738[FLOAT, 384] %onnx::Conv_740[FLOAT, 384x1x5x5] %onnx::Conv_743[FLOAT, 64x384x1x1] %onnx::Conv_746[FLOAT, 384x64x1x1] %onnx::Conv_749[FLOAT, 384x1x3x3] %onnx::Conv_752[FLOAT, 64x384x1x1] %onnx::Conv_755[FLOAT, 384x64x1x1] %onnx::Conv_758[FLOAT, 384x1x5x5] %onnx::Conv_761[FLOAT, 112x384x1x1] %onnx::Conv_762[FLOAT, 112] %onnx::Conv_764[FLOAT, 112x112x1x1] %onnx::Conv_767[FLOAT, 112x1x3x3] %onnx::Conv_770[FLOAT, 112x112x1x1] %onnx::Conv_773[FLOAT, 112x112x1x1] %onnx::Conv_776[FLOAT, 112x1x3x3] %onnx::Conv_779[FLOAT, 112x112x1x1] %onnx::Conv_782[FLOAT, 112x56x1x1] %onnx::Conv_785[FLOAT, 112x1x5x5] %onnx::Conv_788[FLOAT, 112x56x1x1] %onnx::Conv_791[FLOAT, 112x112x1x1] %onnx::Conv_794[FLOAT, 112x1x5x5] %onnx::Conv_797[FLOAT, 184x112x1x1] %onnx::Conv_798[FLOAT, 184] %onnx::Conv_800[FLOAT, 552x184x1x1] %onnx::Conv_801[FLOAT, 552] %onnx::Conv_803[FLOAT, 552x1x5x5] %onnx::Conv_806[FLOAT, 184x552x1x1] %onnx::Conv_809[FLOAT, 552x184x1x1] %onnx::Conv_812[FLOAT, 552x1x5x5] %onnx::Conv_815[FLOAT, 184x552x1x1] %onnx::Conv_818[FLOAT, 184x92x1x1] %onnx::Conv_821[FLOAT, 184x1x5x5] %onnx::Conv_824[FLOAT, 184x92x1x1] %onnx::Conv_827[FLOAT, 184x92x1x1] %onnx::Conv_830[FLOAT, 184x1x5x5] %onnx::Conv_833[FLOAT, 352x92x1x1] %onnx::Conv_834[FLOAT, 352] %onnx::Conv_836[FLOAT, 1504x352x1x1] %onnx::Conv_837[FLOAT, 1504] ) { %onnx::Conv_831 = Identity(%onnx::Conv_798) %onnx::Conv_828 = Identity(%onnx::Conv_798) %onnx::Conv_825 = Identity(%onnx::Conv_798) %onnx::Conv_822 = Identity(%onnx::Conv_798) %onnx::Conv_819 = Identity(%onnx::Conv_798) %onnx::Conv_816 = Identity(%onnx::Conv_798) %onnx::Conv_813 = Identity(%onnx::Conv_801) %onnx::Conv_810 = Identity(%onnx::Conv_801) %onnx::Conv_807 = Identity(%onnx::Conv_798) %onnx::Conv_804 = Identity(%onnx::Conv_801) %onnx::Conv_795 = Identity(%onnx::Conv_762) %onnx::Conv_792 = Identity(%onnx::Conv_762) %onnx::Conv_789 = Identity(%onnx::Conv_762) %onnx::Conv_786 = Identity(%onnx::Conv_762) %onnx::Conv_783 = Identity(%onnx::Conv_762) %onnx::Conv_780 = Identity(%onnx::Conv_762) %onnx::Conv_777 = Identity(%onnx::Conv_762) %onnx::Conv_774 = Identity(%onnx::Conv_762) %onnx::Conv_771 = Identity(%onnx::Conv_762) %onnx::Conv_768 = Identity(%onnx::Conv_762) %onnx::Conv_765 = Identity(%onnx::Conv_762) %onnx::Conv_759 = Identity(%onnx::Conv_738) %onnx::Conv_756 = Identity(%onnx::Conv_738) %onnx::Conv_753 = Identity(%onnx::Conv_726) %onnx::Conv_750 = Identity(%onnx::Conv_738) %onnx::Conv_747 = Identity(%onnx::Conv_738) %onnx::Conv_744 = Identity(%onnx::Conv_726) %onnx::Conv_741 = Identity(%onnx::Conv_738) %onnx::Conv_735 = Identity(%onnx::Conv_726) %onnx::Conv_732 = Identity(%onnx::Conv_726) %onnx::Conv_729 = Identity(%onnx::Conv_726) %onnx::Conv_723 = Identity(%onnx::Conv_705) %onnx::Conv_720 = Identity(%onnx::Conv_717) %onnx::Conv_714 = Identity(%onnx::Conv_705) %onnx::Conv_711 = Identity(%onnx::Conv_708) %onnx::Conv_702 = Identity(%onnx::Conv_690) %onnx::Conv_699 = Identity(%onnx::Conv_690) %onnx::Conv_696 = Identity(%onnx::Conv_669) %onnx::Conv_693 = Identity(%onnx::Conv_690) %onnx::Conv_687 = Identity(%onnx::Conv_669) %onnx::Conv_684 = Identity(%onnx::Conv_669) %onnx::Conv_681 = Identity(%onnx::Conv_669) %onnx::Conv_678 = Identity(%onnx::Conv_669) %onnx::Conv_675 = Identity(%onnx::Conv_672) %onnx::Conv_666 = Identity(%onnx::Conv_654) %onnx::Conv_663 = Identity(%onnx::Conv_654) %onnx::Conv_660 = Identity(%onnx::Conv_651) %onnx::Conv_657 = Identity(%onnx::Conv_654) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_650, %onnx::Conv_651) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_653, %onnx::Conv_654) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 48, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.0/nl/Relu_output_0, %onnx::Conv_656, %onnx::Conv_657) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_659, %onnx::Conv_660) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_662, %onnx::Conv_663) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 48, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.1/nl/Relu_output_0, %onnx::Conv_665, %onnx::Conv_666) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_668, %onnx::Conv_669) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_671, %onnx::Conv_672) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.2/nl/Relu_output_0, %onnx::Conv_674, %onnx::Conv_675) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_677, %onnx::Conv_678) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_680, %onnx::Conv_681) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.3/nl/Relu_output_0, %onnx::Conv_683, %onnx::Conv_684) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_686, %onnx::Conv_687) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_689, %onnx::Conv_690) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.4/nl/Relu_output_0, %onnx::Conv_692, %onnx::Conv_693) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_695, %onnx::Conv_696) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/Add_output_0, %onnx::Conv_698, %onnx::Conv_699) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.5/nl/Relu_output_0, %onnx::Conv_701, %onnx::Conv_702) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_704, %onnx::Conv_705) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_707, %onnx::Conv_708) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.7/nl/Relu_output_0, %onnx::Conv_710, %onnx::Conv_711) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_713, %onnx::Conv_714) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_716, %onnx::Conv_717) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.8/nl/Relu_output_0, %onnx::Conv_719, %onnx::Conv_720) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_722, %onnx::Conv_723) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [2, 2]](%/cells.8/Add_output_0, %onnx::Conv_725, %onnx::Conv_726) %/cells.9/relu/Relu_output_0 = Relu(%/cells.9/conv/Conv_output_0) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/relu/Relu_output_0, %onnx::Conv_728, %onnx::Conv_729) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.10/shuffle/Reshape_output_0 = Reshape(%/cells.10/nl/Relu_output_0, %/cells.10/shuffle/Constant_output_0) %/cells.10/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.10/shuffle/Reshape_output_0) %/cells.10/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.10/shuffle/Reshape_1_output_0 = Reshape(%/cells.10/shuffle/Transpose_output_0, %/cells.10/shuffle/Constant_1_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.10/shuffle/Reshape_1_output_0, %onnx::Conv_731, %onnx::Conv_732) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_734, %onnx::Conv_735) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/relu/Relu_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_737, %onnx::Conv_738) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.11/nl/Relu_output_0, %onnx::Conv_740, %onnx::Conv_741) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_743, %onnx::Conv_744) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_746, %onnx::Conv_747) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.12/nl/Relu_output_0, %onnx::Conv_749, %onnx::Conv_750) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_752, %onnx::Conv_753) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_755, %onnx::Conv_756) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.13/nl/Relu_output_0, %onnx::Conv_758, %onnx::Conv_759) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_761, %onnx::Conv_762) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_764, %onnx::Conv_765) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.14/nl/Relu_output_0, %onnx::Conv_767, %onnx::Conv_768) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_770, %onnx::Conv_771) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_773, %onnx::Conv_774) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.15/nl/Relu_output_0, %onnx::Conv_776, %onnx::Conv_777) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_779, %onnx::Conv_780) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_782, %onnx::Conv_783) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.16/shuffle/Reshape_output_0 = Reshape(%/cells.16/nl/Relu_output_0, %/cells.16/shuffle/Constant_output_0) %/cells.16/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.16/shuffle/Reshape_output_0) %/cells.16/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.16/shuffle/Reshape_1_output_0 = Reshape(%/cells.16/shuffle/Transpose_output_0, %/cells.16/shuffle/Constant_1_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.16/shuffle/Reshape_1_output_0, %onnx::Conv_785, %onnx::Conv_786) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_788, %onnx::Conv_789) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.15/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_791, %onnx::Conv_792) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.17/nl/Relu_output_0, %onnx::Conv_794, %onnx::Conv_795) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_797, %onnx::Conv_798) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_800, %onnx::Conv_801) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 552, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.18/nl/Relu_output_0, %onnx::Conv_803, %onnx::Conv_804) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_806, %onnx::Conv_807) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_809, %onnx::Conv_810) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 552, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.19/nl/Relu_output_0, %onnx::Conv_812, %onnx::Conv_813) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_815, %onnx::Conv_816) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_818, %onnx::Conv_819) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.20/shuffle/Reshape_output_0 = Reshape(%/cells.20/nl/Relu_output_0, %/cells.20/shuffle/Constant_output_0) %/cells.20/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.20/shuffle/Reshape_output_0) %/cells.20/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.20/shuffle/Reshape_1_output_0 = Reshape(%/cells.20/shuffle/Transpose_output_0, %/cells.20/shuffle/Constant_1_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.20/shuffle/Reshape_1_output_0, %onnx::Conv_821, %onnx::Conv_822) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_824, %onnx::Conv_825) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_827, %onnx::Conv_828) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.21/shuffle/Reshape_output_0 = Reshape(%/cells.21/nl/Relu_output_0, %/cells.21/shuffle/Constant_output_0) %/cells.21/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.21/shuffle/Reshape_output_0) %/cells.21/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.21/shuffle/Reshape_1_output_0 = Reshape(%/cells.21/shuffle/Transpose_output_0, %/cells.21/shuffle/Constant_1_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.21/shuffle/Reshape_1_output_0, %onnx::Conv_830, %onnx::Conv_831) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_833, %onnx::Conv_834) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_836, %onnx::Conv_837) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %648 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %648 }	val_accuracy	73,137,536	1,583,668	{'zcp_synflow': 79.83848370518197, 'zcp_zen': 70.56588745117188, 'zcp_epe_nas': 12.061186769233341, 'zcp_fisher': 0.1633748710155487, 'zcp_flops': 73137536.0, 'zcp_grad_norm': 26.723121643066406, 'zcp_grasp': -0.07468414306640625, 'zcp_jacov': -16.05645149397424, 'zcp_l2_norm': 629.2454223632812, 'zcp_nwot': 216.117848829333, 'zcp_params': 1583668.0, 'zcp_plain': 0.003819590201601386, 'zcp_snip': 50.20244598388672, 'lat_1080ti_1': 0.6595398324717665, 'lat_1080ti_32': 0.5698137782999446, 'lat_1080ti_64': 0.6343186899514832, 'lat_2080ti_1': 0.6302711265713068, 'lat_2080ti_32': 0.5953152073498489, 'lat_2080ti_64': 0.6444686605179989, 'lat_essential_ph_1': 0.4339622641509434, 'lat_eyeriss': 0.551372563401977, 'lat_fpga': 0.4727535400112806, 'lat_gold_6226': 0.31708247628314185, 'lat_gold_6240': 0.48173501419342646, 'lat_pixel2': 0.43478260869565216, 'lat_pixel3': 0.5542900007422393, 'lat_raspi4': 0.5003536457668392, 'lat_samsung_a50': 0.22105263157894736, 'lat_samsung_s7': 0.23622047244094488, 'lat_silver_4114': 0.4928804592715755, 'lat_silver_4210r': 0.5151515078544792, 'lat_titan_rtx_1': 0.6075027046586241, 'lat_titan_rtx_32': 0.5647884662107552, 'lat_titan_rtx_64': 0.644066667711963, 'lat_titanx_1': 0.31497921500821563, 'lat_titanx_32': 0.6095065594204341, 'lat_titanx_64': 0.6341479099498369, 'lat_titanxp_1': 0.5512673682985255, 'lat_titanxp_32': 0.6096332468586098, 'lat_titanxp_64': 0.6536251366356192}
FBNet_1724	FBNet	1724	1724	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_695[FLOAT, 16x3x3x3] %onnx::Conv_696[FLOAT, 16] %onnx::Conv_698[FLOAT, 48x16x1x1] %onnx::Conv_699[FLOAT, 48] %onnx::Conv_701[FLOAT, 48x1x5x5] %onnx::Conv_704[FLOAT, 16x48x1x1] %onnx::Conv_707[FLOAT, 96x16x1x1] %onnx::Conv_708[FLOAT, 96] %onnx::Conv_710[FLOAT, 96x1x5x5] %onnx::Conv_713[FLOAT, 24x96x1x1] %onnx::Conv_714[FLOAT, 24] %onnx::Conv_716[FLOAT, 24x12x1x1] %onnx::Conv_719[FLOAT, 24x1x5x5] %onnx::Conv_722[FLOAT, 24x12x1x1] %onnx::Conv_725[FLOAT, 72x24x1x1] %onnx::Conv_726[FLOAT, 72] %onnx::Conv_728[FLOAT, 72x1x5x5] %onnx::Conv_731[FLOAT, 24x72x1x1] %onnx::Conv_734[FLOAT, 24x24x1x1] %onnx::Conv_737[FLOAT, 24x1x3x3] %onnx::Conv_740[FLOAT, 24x24x1x1] %onnx::Conv_743[FLOAT, 144x24x1x1] %onnx::Conv_744[FLOAT, 144] %onnx::Conv_746[FLOAT, 144x1x3x3] %onnx::Conv_749[FLOAT, 32x144x1x1] %onnx::Conv_750[FLOAT, 32] %onnx::Conv_752[FLOAT, 32x32x1x1] %onnx::Conv_755[FLOAT, 32x1x3x3] %onnx::Conv_758[FLOAT, 32x32x1x1] %onnx::Conv_761[FLOAT, 32x16x1x1] %onnx::Conv_764[FLOAT, 32x1x5x5] %onnx::Conv_767[FLOAT, 32x16x1x1] %onnx::Conv_770[FLOAT, 32x16x1x1] %onnx::Conv_773[FLOAT, 32x1x3x3] %onnx::Conv_776[FLOAT, 32x16x1x1] %onnx::Conv_779[FLOAT, 32x32x1x1] %onnx::Conv_782[FLOAT, 32x1x3x3] %onnx::Conv_785[FLOAT, 64x32x1x1] %onnx::Conv_786[FLOAT, 64] %onnx::Conv_788[FLOAT, 64x32x1x1] %onnx::Conv_791[FLOAT, 64x1x5x5] %onnx::Conv_794[FLOAT, 64x32x1x1] %onnx::Conv_797[FLOAT, 192x64x1x1] %onnx::Conv_798[FLOAT, 192] %onnx::Conv_800[FLOAT, 192x1x3x3] %onnx::Conv_803[FLOAT, 64x192x1x1] %onnx::Conv_806[FLOAT, 64x32x1x1] %onnx::Conv_809[FLOAT, 64x1x3x3] %onnx::Conv_812[FLOAT, 112x32x1x1] %onnx::Conv_813[FLOAT, 112] %onnx::Conv_815[FLOAT, 112x56x1x1] %onnx::Conv_818[FLOAT, 112x1x5x5] %onnx::Conv_821[FLOAT, 112x56x1x1] %onnx::Conv_824[FLOAT, 112x56x1x1] %onnx::Conv_827[FLOAT, 112x1x3x3] %onnx::Conv_830[FLOAT, 112x56x1x1] %onnx::Conv_833[FLOAT, 112x112x1x1] %onnx::Conv_836[FLOAT, 112x1x3x3] %onnx::Conv_839[FLOAT, 112x112x1x1] %onnx::Conv_842[FLOAT, 672x112x1x1] %onnx::Conv_843[FLOAT, 672] %onnx::Conv_845[FLOAT, 672x1x5x5] %onnx::Conv_848[FLOAT, 184x672x1x1] %onnx::Conv_849[FLOAT, 184] %onnx::Conv_851[FLOAT, 184x184x1x1] %onnx::Conv_854[FLOAT, 184x1x5x5] %onnx::Conv_857[FLOAT, 184x184x1x1] %onnx::Conv_860[FLOAT, 184x184x1x1] %onnx::Conv_863[FLOAT, 184x1x5x5] %onnx::Conv_866[FLOAT, 184x184x1x1] %onnx::Conv_869[FLOAT, 184x184x1x1] %onnx::Conv_872[FLOAT, 184x1x5x5] %onnx::Conv_875[FLOAT, 352x184x1x1] %onnx::Conv_876[FLOAT, 352] %onnx::Conv_878[FLOAT, 1504x352x1x1] %onnx::Conv_879[FLOAT, 1504] ) { %onnx::Conv_873 = Identity(%onnx::Conv_849) %onnx::Conv_870 = Identity(%onnx::Conv_849) %onnx::Conv_867 = Identity(%onnx::Conv_849) %onnx::Conv_864 = Identity(%onnx::Conv_849) %onnx::Conv_861 = Identity(%onnx::Conv_849) %onnx::Conv_858 = Identity(%onnx::Conv_849) %onnx::Conv_855 = Identity(%onnx::Conv_849) %onnx::Conv_852 = Identity(%onnx::Conv_849) %onnx::Conv_846 = Identity(%onnx::Conv_843) %onnx::Conv_840 = Identity(%onnx::Conv_813) %onnx::Conv_837 = Identity(%onnx::Conv_813) %onnx::Conv_834 = Identity(%onnx::Conv_813) %onnx::Conv_831 = Identity(%onnx::Conv_813) %onnx::Conv_828 = Identity(%onnx::Conv_813) %onnx::Conv_825 = Identity(%onnx::Conv_813) %onnx::Conv_822 = Identity(%onnx::Conv_813) %onnx::Conv_819 = Identity(%onnx::Conv_813) %onnx::Conv_816 = Identity(%onnx::Conv_813) %onnx::Conv_810 = Identity(%onnx::Conv_786) %onnx::Conv_807 = Identity(%onnx::Conv_786) %onnx::Conv_804 = Identity(%onnx::Conv_786) %onnx::Conv_801 = Identity(%onnx::Conv_798) %onnx::Conv_795 = Identity(%onnx::Conv_786) %onnx::Conv_792 = Identity(%onnx::Conv_786) %onnx::Conv_789 = Identity(%onnx::Conv_786) %onnx::Conv_783 = Identity(%onnx::Conv_750) %onnx::Conv_780 = Identity(%onnx::Conv_750) %onnx::Conv_777 = Identity(%onnx::Conv_750) %onnx::Conv_774 = Identity(%onnx::Conv_750) %onnx::Conv_771 = Identity(%onnx::Conv_750) %onnx::Conv_768 = Identity(%onnx::Conv_750) %onnx::Conv_765 = Identity(%onnx::Conv_750) %onnx::Conv_762 = Identity(%onnx::Conv_750) %onnx::Conv_759 = Identity(%onnx::Conv_750) %onnx::Conv_756 = Identity(%onnx::Conv_750) %onnx::Conv_753 = Identity(%onnx::Conv_750) %onnx::Conv_747 = Identity(%onnx::Conv_744) %onnx::Conv_741 = Identity(%onnx::Conv_714) %onnx::Conv_738 = Identity(%onnx::Conv_714) %onnx::Conv_735 = Identity(%onnx::Conv_714) %onnx::Conv_732 = Identity(%onnx::Conv_714) %onnx::Conv_729 = Identity(%onnx::Conv_726) %onnx::Conv_723 = Identity(%onnx::Conv_714) %onnx::Conv_720 = Identity(%onnx::Conv_714) %onnx::Conv_717 = Identity(%onnx::Conv_714) %onnx::Conv_711 = Identity(%onnx::Conv_708) %onnx::Conv_705 = Identity(%onnx::Conv_696) %onnx::Conv_702 = Identity(%onnx::Conv_699) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_695, %onnx::Conv_696) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_698, %onnx::Conv_699) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 48, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.0/nl/Relu_output_0, %onnx::Conv_701, %onnx::Conv_702) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_704, %onnx::Conv_705) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_707, %onnx::Conv_708) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.1/nl/Relu_output_0, %onnx::Conv_710, %onnx::Conv_711) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_713, %onnx::Conv_714) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_716, %onnx::Conv_717) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.2/shuffle/Reshape_output_0 = Reshape(%/cells.2/nl/Relu_output_0, %/cells.2/shuffle/Constant_output_0) %/cells.2/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.2/shuffle/Reshape_output_0) %/cells.2/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.2/shuffle/Reshape_1_output_0 = Reshape(%/cells.2/shuffle/Transpose_output_0, %/cells.2/shuffle/Constant_1_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.2/shuffle/Reshape_1_output_0, %onnx::Conv_719, %onnx::Conv_720) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_722, %onnx::Conv_723) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_725, %onnx::Conv_726) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.3/nl/Relu_output_0, %onnx::Conv_728, %onnx::Conv_729) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_731, %onnx::Conv_732) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_734, %onnx::Conv_735) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.4/nl/Relu_output_0, %onnx::Conv_737, %onnx::Conv_738) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_740, %onnx::Conv_741) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/Add_output_0, %onnx::Conv_743, %onnx::Conv_744) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.5/nl/Relu_output_0, %onnx::Conv_746, %onnx::Conv_747) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_749, %onnx::Conv_750) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_752, %onnx::Conv_753) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.6/nl/Relu_output_0, %onnx::Conv_755, %onnx::Conv_756) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_758, %onnx::Conv_759) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_761, %onnx::Conv_762) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.7/shuffle/Reshape_output_0 = Reshape(%/cells.7/nl/Relu_output_0, %/cells.7/shuffle/Constant_output_0) %/cells.7/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.7/shuffle/Reshape_output_0) %/cells.7/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.7/shuffle/Reshape_1_output_0 = Reshape(%/cells.7/shuffle/Transpose_output_0, %/cells.7/shuffle/Constant_1_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.7/shuffle/Reshape_1_output_0, %onnx::Conv_764, %onnx::Conv_765) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_767, %onnx::Conv_768) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_770, %onnx::Conv_771) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.8/shuffle/Reshape_output_0 = Reshape(%/cells.8/nl/Relu_output_0, %/cells.8/shuffle/Constant_output_0) %/cells.8/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.8/shuffle/Reshape_output_0) %/cells.8/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.8/shuffle/Reshape_1_output_0 = Reshape(%/cells.8/shuffle/Transpose_output_0, %/cells.8/shuffle/Constant_1_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.8/shuffle/Reshape_1_output_0, %onnx::Conv_773, %onnx::Conv_774) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_776, %onnx::Conv_777) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_779, %onnx::Conv_780) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.9/nl/Relu_output_0, %onnx::Conv_782, %onnx::Conv_783) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_785, %onnx::Conv_786) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_788, %onnx::Conv_789) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.10/shuffle/Reshape_output_0 = Reshape(%/cells.10/nl/Relu_output_0, %/cells.10/shuffle/Constant_output_0) %/cells.10/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.10/shuffle/Reshape_output_0) %/cells.10/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.10/shuffle/Reshape_1_output_0 = Reshape(%/cells.10/shuffle/Transpose_output_0, %/cells.10/shuffle/Constant_1_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.10/shuffle/Reshape_1_output_0, %onnx::Conv_791, %onnx::Conv_792) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_794, %onnx::Conv_795) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_797, %onnx::Conv_798) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.11/nl/Relu_output_0, %onnx::Conv_800, %onnx::Conv_801) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_803, %onnx::Conv_804) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_806, %onnx::Conv_807) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.13/shuffle/Reshape_output_0 = Reshape(%/cells.13/nl/Relu_output_0, %/cells.13/shuffle/Constant_output_0) %/cells.13/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.13/shuffle/Reshape_output_0) %/cells.13/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.13/shuffle/Reshape_1_output_0 = Reshape(%/cells.13/shuffle/Transpose_output_0, %/cells.13/shuffle/Constant_1_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.13/shuffle/Reshape_1_output_0, %onnx::Conv_809, %onnx::Conv_810) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_812, %onnx::Conv_813) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_815, %onnx::Conv_816) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.14/shuffle/Reshape_output_0 = Reshape(%/cells.14/nl/Relu_output_0, %/cells.14/shuffle/Constant_output_0) %/cells.14/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.14/shuffle/Reshape_output_0) %/cells.14/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.14/shuffle/Reshape_1_output_0 = Reshape(%/cells.14/shuffle/Transpose_output_0, %/cells.14/shuffle/Constant_1_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.14/shuffle/Reshape_1_output_0, %onnx::Conv_818, %onnx::Conv_819) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_821, %onnx::Conv_822) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_824, %onnx::Conv_825) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.15/shuffle/Reshape_output_0 = Reshape(%/cells.15/nl/Relu_output_0, %/cells.15/shuffle/Constant_output_0) %/cells.15/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.15/shuffle/Reshape_output_0) %/cells.15/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.15/shuffle/Reshape_1_output_0 = Reshape(%/cells.15/shuffle/Transpose_output_0, %/cells.15/shuffle/Constant_1_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.15/shuffle/Reshape_1_output_0, %onnx::Conv_827, %onnx::Conv_828) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_830, %onnx::Conv_831) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_833, %onnx::Conv_834) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.16/nl/Relu_output_0, %onnx::Conv_836, %onnx::Conv_837) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_839, %onnx::Conv_840) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.15/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_842, %onnx::Conv_843) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.17/nl/Relu_output_0, %onnx::Conv_845, %onnx::Conv_846) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_848, %onnx::Conv_849) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_851, %onnx::Conv_852) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.18/nl/Relu_output_0, %onnx::Conv_854, %onnx::Conv_855) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_857, %onnx::Conv_858) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_860, %onnx::Conv_861) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.20/nl/Relu_output_0, %onnx::Conv_863, %onnx::Conv_864) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_866, %onnx::Conv_867) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_869, %onnx::Conv_870) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.21/nl/Relu_output_0, %onnx::Conv_872, %onnx::Conv_873) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_875, %onnx::Conv_876) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_878, %onnx::Conv_879) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %693 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %693 }	val_accuracy	52,925,312	1,288,340	{'zcp_synflow': 72.61156580370888, 'zcp_zen': 61.685707092285156, 'zcp_epe_nas': 0.00015999920000638146, 'zcp_fisher': 0.15394224226474762, 'zcp_flops': 52925312.0, 'zcp_grad_norm': 25.865331649780273, 'zcp_grasp': -0.05295753479003906, 'zcp_jacov': -16.054371968731736, 'zcp_l2_norm': 514.4423217773438, 'zcp_nwot': 211.20921127838866, 'zcp_params': 1288340.0, 'zcp_plain': 0.0011673462577164173, 'zcp_snip': 40.51675796508789, 'lat_1080ti_1': 0.6224952458031439, 'lat_1080ti_32': 0.6276482658283753, 'lat_1080ti_64': 0.510575768565501, 'lat_2080ti_1': 0.6564893963749099, 'lat_2080ti_32': 0.6242641640788955, 'lat_2080ti_64': 0.5236474384083943, 'lat_essential_ph_1': 0.2830188679245283, 'lat_eyeriss': 0.2831588212641887, 'lat_fpga': 0.17683729306642657, 'lat_gold_6226': 0.11391167888346188, 'lat_gold_6240': 0.3055265570550494, 'lat_pixel2': 0.15217391304347827, 'lat_pixel3': 0.32207896601846747, 'lat_raspi4': 0.30265277371485455, 'lat_samsung_a50': 0.10526315789473684, 'lat_samsung_s7': 0.13385826771653545, 'lat_silver_4114': 0.3718391563332418, 'lat_silver_4210r': 0.3489964947491546, 'lat_titan_rtx_1': 0.6252173158778799, 'lat_titan_rtx_32': 0.6009923204623362, 'lat_titan_rtx_64': 0.5555895314283416, 'lat_titanx_1': 0.3259112746401966, 'lat_titanx_32': 0.5469129046807867, 'lat_titanx_64': 0.48849508566673383, 'lat_titanxp_1': 0.572348169115554, 'lat_titanxp_32': 0.5970775259229275, 'lat_titanxp_64': 0.5264834844619269}
FBNet_4091	FBNet	4091	4091	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_677[FLOAT, 16x3x3x3] %onnx::Conv_678[FLOAT, 16] %onnx::Conv_680[FLOAT, 96x16x1x1] %onnx::Conv_681[FLOAT, 96] %onnx::Conv_683[FLOAT, 96x1x3x3] %onnx::Conv_686[FLOAT, 16x96x1x1] %onnx::Conv_689[FLOAT, 96x16x1x1] %onnx::Conv_692[FLOAT, 96x1x3x3] %onnx::Conv_695[FLOAT, 24x96x1x1] %onnx::Conv_696[FLOAT, 24] %onnx::Conv_698[FLOAT, 24x24x1x1] %onnx::Conv_701[FLOAT, 24x1x5x5] %onnx::Conv_704[FLOAT, 24x24x1x1] %onnx::Conv_707[FLOAT, 144x24x1x1] %onnx::Conv_708[FLOAT, 144] %onnx::Conv_710[FLOAT, 144x1x3x3] %onnx::Conv_713[FLOAT, 24x144x1x1] %onnx::Conv_716[FLOAT, 24x24x1x1] %onnx::Conv_719[FLOAT, 24x1x3x3] %onnx::Conv_722[FLOAT, 24x24x1x1] %onnx::Conv_725[FLOAT, 144x24x1x1] %onnx::Conv_728[FLOAT, 144x1x3x3] %onnx::Conv_731[FLOAT, 32x144x1x1] %onnx::Conv_732[FLOAT, 32] %onnx::Conv_734[FLOAT, 192x32x1x1] %onnx::Conv_735[FLOAT, 192] %onnx::Conv_737[FLOAT, 192x1x5x5] %onnx::Conv_740[FLOAT, 32x192x1x1] %onnx::Conv_743[FLOAT, 32x16x1x1] %onnx::Conv_746[FLOAT, 32x1x3x3] %onnx::Conv_749[FLOAT, 32x16x1x1] %onnx::Conv_752[FLOAT, 192x32x1x1] %onnx::Conv_755[FLOAT, 192x1x5x5] %onnx::Conv_758[FLOAT, 32x192x1x1] %onnx::Conv_761[FLOAT, 32x16x1x1] %onnx::Conv_764[FLOAT, 32x1x5x5] %onnx::Conv_767[FLOAT, 64x16x1x1] %onnx::Conv_768[FLOAT, 64] %onnx::Conv_770[FLOAT, 64x64x1x1] %onnx::Conv_773[FLOAT, 64x1x3x3] %onnx::Conv_776[FLOAT, 64x64x1x1] %onnx::Conv_779[FLOAT, 64x64x1x1] %onnx::Conv_782[FLOAT, 64x1x5x5] %onnx::Conv_785[FLOAT, 64x64x1x1] %onnx::Conv_788[FLOAT, 384x64x1x1] %onnx::Conv_789[FLOAT, 384] %onnx::Conv_791[FLOAT, 384x1x5x5] %onnx::Conv_794[FLOAT, 64x384x1x1] %onnx::Conv_797[FLOAT, 64x64x1x1] %onnx::Conv_800[FLOAT, 64x1x5x5] %onnx::Conv_803[FLOAT, 112x64x1x1] %onnx::Conv_804[FLOAT, 112] %onnx::Conv_806[FLOAT, 112x112x1x1] %onnx::Conv_809[FLOAT, 112x1x5x5] %onnx::Conv_812[FLOAT, 112x112x1x1] %onnx::Conv_815[FLOAT, 112x56x1x1] %onnx::Conv_818[FLOAT, 112x1x5x5] %onnx::Conv_821[FLOAT, 112x56x1x1] %onnx::Conv_824[FLOAT, 672x112x1x1] %onnx::Conv_825[FLOAT, 672] %onnx::Conv_827[FLOAT, 672x1x3x3] %onnx::Conv_830[FLOAT, 112x672x1x1] %onnx::Conv_833[FLOAT, 112x112x1x1] %onnx::Conv_836[FLOAT, 112x1x3x3] %onnx::Conv_839[FLOAT, 184x112x1x1] %onnx::Conv_840[FLOAT, 184] %onnx::Conv_842[FLOAT, 184x92x1x1] %onnx::Conv_845[FLOAT, 184x1x5x5] %onnx::Conv_848[FLOAT, 184x92x1x1] %onnx::Conv_851[FLOAT, 552x184x1x1] %onnx::Conv_852[FLOAT, 552] %onnx::Conv_854[FLOAT, 552x1x3x3] %onnx::Conv_857[FLOAT, 184x552x1x1] %onnx::Conv_860[FLOAT, 184x184x1x1] %onnx::Conv_863[FLOAT, 184x1x5x5] %onnx::Conv_866[FLOAT, 184x184x1x1] %onnx::Conv_869[FLOAT, 352x184x1x1] %onnx::Conv_870[FLOAT, 352] %onnx::Conv_872[FLOAT, 1504x352x1x1] %onnx::Conv_873[FLOAT, 1504] ) { %onnx::Conv_867 = Identity(%onnx::Conv_840) %onnx::Conv_864 = Identity(%onnx::Conv_840) %onnx::Conv_861 = Identity(%onnx::Conv_840) %onnx::Conv_858 = Identity(%onnx::Conv_840) %onnx::Conv_855 = Identity(%onnx::Conv_852) %onnx::Conv_849 = Identity(%onnx::Conv_840) %onnx::Conv_846 = Identity(%onnx::Conv_840) %onnx::Conv_843 = Identity(%onnx::Conv_840) %onnx::Conv_837 = Identity(%onnx::Conv_804) %onnx::Conv_834 = Identity(%onnx::Conv_804) %onnx::Conv_831 = Identity(%onnx::Conv_804) %onnx::Conv_828 = Identity(%onnx::Conv_825) %onnx::Conv_822 = Identity(%onnx::Conv_804) %onnx::Conv_819 = Identity(%onnx::Conv_804) %onnx::Conv_816 = Identity(%onnx::Conv_804) %onnx::Conv_813 = Identity(%onnx::Conv_804) %onnx::Conv_810 = Identity(%onnx::Conv_804) %onnx::Conv_807 = Identity(%onnx::Conv_804) %onnx::Conv_801 = Identity(%onnx::Conv_768) %onnx::Conv_798 = Identity(%onnx::Conv_768) %onnx::Conv_795 = Identity(%onnx::Conv_768) %onnx::Conv_792 = Identity(%onnx::Conv_789) %onnx::Conv_786 = Identity(%onnx::Conv_768) %onnx::Conv_783 = Identity(%onnx::Conv_768) %onnx::Conv_780 = Identity(%onnx::Conv_768) %onnx::Conv_777 = Identity(%onnx::Conv_768) %onnx::Conv_774 = Identity(%onnx::Conv_768) %onnx::Conv_771 = Identity(%onnx::Conv_768) %onnx::Conv_765 = Identity(%onnx::Conv_732) %onnx::Conv_762 = Identity(%onnx::Conv_732) %onnx::Conv_759 = Identity(%onnx::Conv_732) %onnx::Conv_756 = Identity(%onnx::Conv_735) %onnx::Conv_753 = Identity(%onnx::Conv_735) %onnx::Conv_750 = Identity(%onnx::Conv_732) %onnx::Conv_747 = Identity(%onnx::Conv_732) %onnx::Conv_744 = Identity(%onnx::Conv_732) %onnx::Conv_741 = Identity(%onnx::Conv_732) %onnx::Conv_738 = Identity(%onnx::Conv_735) %onnx::Conv_729 = Identity(%onnx::Conv_708) %onnx::Conv_726 = Identity(%onnx::Conv_708) %onnx::Conv_723 = Identity(%onnx::Conv_696) %onnx::Conv_720 = Identity(%onnx::Conv_696) %onnx::Conv_717 = Identity(%onnx::Conv_696) %onnx::Conv_714 = Identity(%onnx::Conv_696) %onnx::Conv_711 = Identity(%onnx::Conv_708) %onnx::Conv_705 = Identity(%onnx::Conv_696) %onnx::Conv_702 = Identity(%onnx::Conv_696) %onnx::Conv_699 = Identity(%onnx::Conv_696) %onnx::Conv_693 = Identity(%onnx::Conv_681) %onnx::Conv_690 = Identity(%onnx::Conv_681) %onnx::Conv_687 = Identity(%onnx::Conv_678) %onnx::Conv_684 = Identity(%onnx::Conv_681) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_677, %onnx::Conv_678) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_680, %onnx::Conv_681) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.0/nl/Relu_output_0, %onnx::Conv_683, %onnx::Conv_684) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_686, %onnx::Conv_687) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_689, %onnx::Conv_690) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.1/nl/Relu_output_0, %onnx::Conv_692, %onnx::Conv_693) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_695, %onnx::Conv_696) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_698, %onnx::Conv_699) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.2/nl/Relu_output_0, %onnx::Conv_701, %onnx::Conv_702) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_704, %onnx::Conv_705) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_707, %onnx::Conv_708) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.3/nl/Relu_output_0, %onnx::Conv_710, %onnx::Conv_711) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_713, %onnx::Conv_714) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_716, %onnx::Conv_717) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.4/nl/Relu_output_0, %onnx::Conv_719, %onnx::Conv_720) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_722, %onnx::Conv_723) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/Add_output_0, %onnx::Conv_725, %onnx::Conv_726) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.5/nl/Relu_output_0, %onnx::Conv_728, %onnx::Conv_729) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_731, %onnx::Conv_732) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_734, %onnx::Conv_735) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.6/nl/Relu_output_0, %onnx::Conv_737, %onnx::Conv_738) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_740, %onnx::Conv_741) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_743, %onnx::Conv_744) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.7/shuffle/Reshape_output_0 = Reshape(%/cells.7/nl/Relu_output_0, %/cells.7/shuffle/Constant_output_0) %/cells.7/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.7/shuffle/Reshape_output_0) %/cells.7/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.7/shuffle/Reshape_1_output_0 = Reshape(%/cells.7/shuffle/Transpose_output_0, %/cells.7/shuffle/Constant_1_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.7/shuffle/Reshape_1_output_0, %onnx::Conv_746, %onnx::Conv_747) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_749, %onnx::Conv_750) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_752, %onnx::Conv_753) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.8/nl/Relu_output_0, %onnx::Conv_755, %onnx::Conv_756) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_758, %onnx::Conv_759) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_761, %onnx::Conv_762) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.9/shuffle/Reshape_output_0 = Reshape(%/cells.9/nl/Relu_output_0, %/cells.9/shuffle/Constant_output_0) %/cells.9/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.9/shuffle/Reshape_output_0) %/cells.9/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.9/shuffle/Reshape_1_output_0 = Reshape(%/cells.9/shuffle/Transpose_output_0, %/cells.9/shuffle/Constant_1_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.9/shuffle/Reshape_1_output_0, %onnx::Conv_764, %onnx::Conv_765) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_767, %onnx::Conv_768) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_770, %onnx::Conv_771) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.10/nl/Relu_output_0, %onnx::Conv_773, %onnx::Conv_774) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_776, %onnx::Conv_777) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_779, %onnx::Conv_780) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.11/nl/Relu_output_0, %onnx::Conv_782, %onnx::Conv_783) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_785, %onnx::Conv_786) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_788, %onnx::Conv_789) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.12/nl/Relu_output_0, %onnx::Conv_791, %onnx::Conv_792) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_794, %onnx::Conv_795) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_797, %onnx::Conv_798) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.13/nl/Relu_output_0, %onnx::Conv_800, %onnx::Conv_801) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_803, %onnx::Conv_804) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_806, %onnx::Conv_807) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.14/nl/Relu_output_0, %onnx::Conv_809, %onnx::Conv_810) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_812, %onnx::Conv_813) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_815, %onnx::Conv_816) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.15/shuffle/Reshape_output_0 = Reshape(%/cells.15/nl/Relu_output_0, %/cells.15/shuffle/Constant_output_0) %/cells.15/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.15/shuffle/Reshape_output_0) %/cells.15/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.15/shuffle/Reshape_1_output_0 = Reshape(%/cells.15/shuffle/Transpose_output_0, %/cells.15/shuffle/Constant_1_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.15/shuffle/Reshape_1_output_0, %onnx::Conv_818, %onnx::Conv_819) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_821, %onnx::Conv_822) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_824, %onnx::Conv_825) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.16/nl/Relu_output_0, %onnx::Conv_827, %onnx::Conv_828) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_830, %onnx::Conv_831) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.15/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_833, %onnx::Conv_834) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.17/nl/Relu_output_0, %onnx::Conv_836, %onnx::Conv_837) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_839, %onnx::Conv_840) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_842, %onnx::Conv_843) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.18/shuffle/Reshape_output_0 = Reshape(%/cells.18/nl/Relu_output_0, %/cells.18/shuffle/Constant_output_0) %/cells.18/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.18/shuffle/Reshape_output_0) %/cells.18/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.18/shuffle/Reshape_1_output_0 = Reshape(%/cells.18/shuffle/Transpose_output_0, %/cells.18/shuffle/Constant_1_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.18/shuffle/Reshape_1_output_0, %onnx::Conv_845, %onnx::Conv_846) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_848, %onnx::Conv_849) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_851, %onnx::Conv_852) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 552, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.19/nl/Relu_output_0, %onnx::Conv_854, %onnx::Conv_855) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_857, %onnx::Conv_858) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_860, %onnx::Conv_861) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.20/nl/Relu_output_0, %onnx::Conv_863, %onnx::Conv_864) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_866, %onnx::Conv_867) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_869, %onnx::Conv_870) %/cells.21/relu/Relu_output_0 = Relu(%/cells.21/conv/Conv_output_0) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/relu/Relu_output_0, %onnx::Conv_872, %onnx::Conv_873) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %675 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %675 }	val_accuracy	73,801,344	1,476,300	{'zcp_synflow': 82.94016108760044, 'zcp_zen': 71.67914581298828, 'zcp_epe_nas': 0.00015999920000638146, 'zcp_fisher': 0.16872406005859375, 'zcp_flops': 73801344.0, 'zcp_grad_norm': 25.065242767333984, 'zcp_grasp': 0.1876220703125, 'zcp_jacov': -16.073176788754125, 'zcp_l2_norm': 627.9616088867188, 'zcp_nwot': 217.6517123286367, 'zcp_params': 1476300.0, 'zcp_plain': -0.0005653644911944866, 'zcp_snip': 46.56029510498047, 'lat_1080ti_1': 0.7311044197156856, 'lat_1080ti_32': 0.6859180336583245, 'lat_1080ti_64': 0.6903239160265495, 'lat_2080ti_1': 0.7464281363832634, 'lat_2080ti_32': 0.7362170047724549, 'lat_2080ti_64': 0.7413923428742631, 'lat_essential_ph_1': 0.32075471698113206, 'lat_eyeriss': 0.5331725744044892, 'lat_fpga': 0.5053483608585083, 'lat_gold_6226': 0.2654312869851368, 'lat_gold_6240': 0.45597967809559037, 'lat_pixel2': 0.30434782608695654, 'lat_pixel3': 0.4900797472009896, 'lat_raspi4': 0.47511131919812943, 'lat_samsung_a50': 0.22105263157894736, 'lat_samsung_s7': 0.16535433070866143, 'lat_silver_4114': 0.5375347927151654, 'lat_silver_4210r': 0.5738636560227136, 'lat_titan_rtx_1': 0.7233076623242458, 'lat_titan_rtx_32': 0.6838749332173928, 'lat_titan_rtx_64': 0.734958459517707, 'lat_titanx_1': 0.3823898162450338, 'lat_titanx_32': 0.6863244831483192, 'lat_titanx_64': 0.664792368573467, 'lat_titanxp_1': 0.6607610805886149, 'lat_titanxp_32': 0.6905008627160001, 'lat_titanxp_64': 0.6983926252433894}
FBNet_2469	FBNet	2469	2469	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_696[FLOAT, 16x3x3x3] %onnx::Conv_697[FLOAT, 16] %onnx::Conv_699[FLOAT, 16x8x1x1] %onnx::Conv_702[FLOAT, 16x1x5x5] %onnx::Conv_705[FLOAT, 16x8x1x1] %onnx::Conv_708[FLOAT, 96x16x1x1] %onnx::Conv_709[FLOAT, 96] %onnx::Conv_711[FLOAT, 96x1x3x3] %onnx::Conv_714[FLOAT, 24x96x1x1] %onnx::Conv_715[FLOAT, 24] %onnx::Conv_717[FLOAT, 24x24x1x1] %onnx::Conv_720[FLOAT, 24x1x3x3] %onnx::Conv_723[FLOAT, 24x24x1x1] %onnx::Conv_726[FLOAT, 144x24x1x1] %onnx::Conv_727[FLOAT, 144] %onnx::Conv_729[FLOAT, 144x1x5x5] %onnx::Conv_732[FLOAT, 24x144x1x1] %onnx::Conv_735[FLOAT, 24x12x1x1] %onnx::Conv_738[FLOAT, 24x1x3x3] %onnx::Conv_741[FLOAT, 24x12x1x1] %onnx::Conv_744[FLOAT, 72x24x1x1] %onnx::Conv_745[FLOAT, 72] %onnx::Conv_747[FLOAT, 72x1x3x3] %onnx::Conv_750[FLOAT, 32x72x1x1] %onnx::Conv_751[FLOAT, 32] %onnx::Conv_753[FLOAT, 32x16x1x1] %onnx::Conv_756[FLOAT, 32x1x3x3] %onnx::Conv_759[FLOAT, 32x16x1x1] %onnx::Conv_762[FLOAT, 32x32x1x1] %onnx::Conv_765[FLOAT, 32x1x3x3] %onnx::Conv_768[FLOAT, 32x32x1x1] %onnx::Conv_771[FLOAT, 192x32x1x1] %onnx::Conv_772[FLOAT, 192] %onnx::Conv_774[FLOAT, 192x1x3x3] %onnx::Conv_777[FLOAT, 32x192x1x1] %onnx::Conv_780[FLOAT, 32x16x1x1] %onnx::Conv_783[FLOAT, 32x1x5x5] %onnx::Conv_786[FLOAT, 64x16x1x1] %onnx::Conv_787[FLOAT, 64] %onnx::Conv_789[FLOAT, 384x64x1x1] %onnx::Conv_790[FLOAT, 384] %onnx::Conv_792[FLOAT, 384x1x3x3] %onnx::Conv_795[FLOAT, 64x384x1x1] %onnx::Conv_798[FLOAT, 192x64x1x1] %onnx::Conv_801[FLOAT, 192x1x3x3] %onnx::Conv_804[FLOAT, 64x192x1x1] %onnx::Conv_807[FLOAT, 384x64x1x1] %onnx::Conv_810[FLOAT, 384x1x3x3] %onnx::Conv_813[FLOAT, 112x384x1x1] %onnx::Conv_814[FLOAT, 112] %onnx::Conv_816[FLOAT, 336x112x1x1] %onnx::Conv_817[FLOAT, 336] %onnx::Conv_819[FLOAT, 336x1x5x5] %onnx::Conv_822[FLOAT, 112x336x1x1] %onnx::Conv_825[FLOAT, 112x112x1x1] %onnx::Conv_828[FLOAT, 112x1x3x3] %onnx::Conv_831[FLOAT, 112x112x1x1] %onnx::Conv_834[FLOAT, 112x56x1x1] %onnx::Conv_837[FLOAT, 112x1x5x5] %onnx::Conv_840[FLOAT, 112x56x1x1] %onnx::Conv_843[FLOAT, 112x56x1x1] %onnx::Conv_846[FLOAT, 112x1x5x5] %onnx::Conv_849[FLOAT, 184x56x1x1] %onnx::Conv_850[FLOAT, 184] %onnx::Conv_852[FLOAT, 184x184x1x1] %onnx::Conv_855[FLOAT, 184x1x5x5] %onnx::Conv_858[FLOAT, 184x184x1x1] %onnx::Conv_861[FLOAT, 184x92x1x1] %onnx::Conv_864[FLOAT, 184x1x3x3] %onnx::Conv_867[FLOAT, 184x92x1x1] %onnx::Conv_870[FLOAT, 1104x184x1x1] %onnx::Conv_871[FLOAT, 1104] %onnx::Conv_873[FLOAT, 1104x1x3x3] %onnx::Conv_876[FLOAT, 352x1104x1x1] %onnx::Conv_877[FLOAT, 352] %onnx::Conv_879[FLOAT, 1504x352x1x1] %onnx::Conv_880[FLOAT, 1504] ) { %onnx::Conv_874 = Identity(%onnx::Conv_871) %onnx::Conv_868 = Identity(%onnx::Conv_850) %onnx::Conv_865 = Identity(%onnx::Conv_850) %onnx::Conv_862 = Identity(%onnx::Conv_850) %onnx::Conv_859 = Identity(%onnx::Conv_850) %onnx::Conv_856 = Identity(%onnx::Conv_850) %onnx::Conv_853 = Identity(%onnx::Conv_850) %onnx::Conv_847 = Identity(%onnx::Conv_814) %onnx::Conv_844 = Identity(%onnx::Conv_814) %onnx::Conv_841 = Identity(%onnx::Conv_814) %onnx::Conv_838 = Identity(%onnx::Conv_814) %onnx::Conv_835 = Identity(%onnx::Conv_814) %onnx::Conv_832 = Identity(%onnx::Conv_814) %onnx::Conv_829 = Identity(%onnx::Conv_814) %onnx::Conv_826 = Identity(%onnx::Conv_814) %onnx::Conv_823 = Identity(%onnx::Conv_814) %onnx::Conv_820 = Identity(%onnx::Conv_817) %onnx::Conv_811 = Identity(%onnx::Conv_790) %onnx::Conv_808 = Identity(%onnx::Conv_790) %onnx::Conv_805 = Identity(%onnx::Conv_787) %onnx::Conv_802 = Identity(%onnx::Conv_772) %onnx::Conv_799 = Identity(%onnx::Conv_772) %onnx::Conv_796 = Identity(%onnx::Conv_787) %onnx::Conv_793 = Identity(%onnx::Conv_790) %onnx::Conv_784 = Identity(%onnx::Conv_751) %onnx::Conv_781 = Identity(%onnx::Conv_751) %onnx::Conv_778 = Identity(%onnx::Conv_751) %onnx::Conv_775 = Identity(%onnx::Conv_772) %onnx::Conv_769 = Identity(%onnx::Conv_751) %onnx::Conv_766 = Identity(%onnx::Conv_751) %onnx::Conv_763 = Identity(%onnx::Conv_751) %onnx::Conv_760 = Identity(%onnx::Conv_751) %onnx::Conv_757 = Identity(%onnx::Conv_751) %onnx::Conv_754 = Identity(%onnx::Conv_751) %onnx::Conv_748 = Identity(%onnx::Conv_745) %onnx::Conv_742 = Identity(%onnx::Conv_715) %onnx::Conv_739 = Identity(%onnx::Conv_715) %onnx::Conv_736 = Identity(%onnx::Conv_715) %onnx::Conv_733 = Identity(%onnx::Conv_715) %onnx::Conv_730 = Identity(%onnx::Conv_727) %onnx::Conv_724 = Identity(%onnx::Conv_715) %onnx::Conv_721 = Identity(%onnx::Conv_715) %onnx::Conv_718 = Identity(%onnx::Conv_715) %onnx::Conv_712 = Identity(%onnx::Conv_709) %onnx::Conv_706 = Identity(%onnx::Conv_697) %onnx::Conv_703 = Identity(%onnx::Conv_697) %onnx::Conv_700 = Identity(%onnx::Conv_697) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_696, %onnx::Conv_697) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_699, %onnx::Conv_700) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.0/shuffle/Reshape_output_0 = Reshape(%/cells.0/nl/Relu_output_0, %/cells.0/shuffle/Constant_output_0) %/cells.0/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.0/shuffle/Reshape_output_0) %/cells.0/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.0/shuffle/Reshape_1_output_0 = Reshape(%/cells.0/shuffle/Transpose_output_0, %/cells.0/shuffle/Constant_1_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.0/shuffle/Reshape_1_output_0, %onnx::Conv_702, %onnx::Conv_703) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_705, %onnx::Conv_706) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_708, %onnx::Conv_709) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.1/nl/Relu_output_0, %onnx::Conv_711, %onnx::Conv_712) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_714, %onnx::Conv_715) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_717, %onnx::Conv_718) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.2/nl/Relu_output_0, %onnx::Conv_720, %onnx::Conv_721) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_723, %onnx::Conv_724) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_726, %onnx::Conv_727) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.3/nl/Relu_output_0, %onnx::Conv_729, %onnx::Conv_730) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_732, %onnx::Conv_733) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_735, %onnx::Conv_736) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.4/shuffle/Reshape_output_0 = Reshape(%/cells.4/nl/Relu_output_0, %/cells.4/shuffle/Constant_output_0) %/cells.4/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.4/shuffle/Reshape_output_0) %/cells.4/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.4/shuffle/Reshape_1_output_0 = Reshape(%/cells.4/shuffle/Transpose_output_0, %/cells.4/shuffle/Constant_1_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.4/shuffle/Reshape_1_output_0, %onnx::Conv_738, %onnx::Conv_739) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_741, %onnx::Conv_742) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/Add_output_0, %onnx::Conv_744, %onnx::Conv_745) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.5/nl/Relu_output_0, %onnx::Conv_747, %onnx::Conv_748) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_750, %onnx::Conv_751) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_753, %onnx::Conv_754) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.6/shuffle/Reshape_output_0 = Reshape(%/cells.6/nl/Relu_output_0, %/cells.6/shuffle/Constant_output_0) %/cells.6/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.6/shuffle/Reshape_output_0) %/cells.6/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.6/shuffle/Reshape_1_output_0 = Reshape(%/cells.6/shuffle/Transpose_output_0, %/cells.6/shuffle/Constant_1_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.6/shuffle/Reshape_1_output_0, %onnx::Conv_756, %onnx::Conv_757) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_759, %onnx::Conv_760) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_762, %onnx::Conv_763) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.7/nl/Relu_output_0, %onnx::Conv_765, %onnx::Conv_766) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_768, %onnx::Conv_769) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_771, %onnx::Conv_772) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.8/nl/Relu_output_0, %onnx::Conv_774, %onnx::Conv_775) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_777, %onnx::Conv_778) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_780, %onnx::Conv_781) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.9/shuffle/Reshape_output_0 = Reshape(%/cells.9/nl/Relu_output_0, %/cells.9/shuffle/Constant_output_0) %/cells.9/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.9/shuffle/Reshape_output_0) %/cells.9/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.9/shuffle/Reshape_1_output_0 = Reshape(%/cells.9/shuffle/Transpose_output_0, %/cells.9/shuffle/Constant_1_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.9/shuffle/Reshape_1_output_0, %onnx::Conv_783, %onnx::Conv_784) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_786, %onnx::Conv_787) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_789, %onnx::Conv_790) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.10/nl/Relu_output_0, %onnx::Conv_792, %onnx::Conv_793) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_795, %onnx::Conv_796) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_798, %onnx::Conv_799) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.11/nl/Relu_output_0, %onnx::Conv_801, %onnx::Conv_802) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_804, %onnx::Conv_805) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_807, %onnx::Conv_808) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.13/nl/Relu_output_0, %onnx::Conv_810, %onnx::Conv_811) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_813, %onnx::Conv_814) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_816, %onnx::Conv_817) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.14/nl/Relu_output_0, %onnx::Conv_819, %onnx::Conv_820) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_822, %onnx::Conv_823) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_825, %onnx::Conv_826) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.15/nl/Relu_output_0, %onnx::Conv_828, %onnx::Conv_829) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_831, %onnx::Conv_832) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_834, %onnx::Conv_835) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.16/shuffle/Reshape_output_0 = Reshape(%/cells.16/nl/Relu_output_0, %/cells.16/shuffle/Constant_output_0) %/cells.16/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.16/shuffle/Reshape_output_0) %/cells.16/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.16/shuffle/Reshape_1_output_0 = Reshape(%/cells.16/shuffle/Transpose_output_0, %/cells.16/shuffle/Constant_1_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.16/shuffle/Reshape_1_output_0, %onnx::Conv_837, %onnx::Conv_838) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_840, %onnx::Conv_841) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.15/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_843, %onnx::Conv_844) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.17/shuffle/Reshape_output_0 = Reshape(%/cells.17/nl/Relu_output_0, %/cells.17/shuffle/Constant_output_0) %/cells.17/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.17/shuffle/Reshape_output_0) %/cells.17/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.17/shuffle/Reshape_1_output_0 = Reshape(%/cells.17/shuffle/Transpose_output_0, %/cells.17/shuffle/Constant_1_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.17/shuffle/Reshape_1_output_0, %onnx::Conv_846, %onnx::Conv_847) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_849, %onnx::Conv_850) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_852, %onnx::Conv_853) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.18/nl/Relu_output_0, %onnx::Conv_855, %onnx::Conv_856) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_858, %onnx::Conv_859) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_861, %onnx::Conv_862) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.20/shuffle/Reshape_output_0 = Reshape(%/cells.20/nl/Relu_output_0, %/cells.20/shuffle/Constant_output_0) %/cells.20/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.20/shuffle/Reshape_output_0) %/cells.20/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.20/shuffle/Reshape_1_output_0 = Reshape(%/cells.20/shuffle/Transpose_output_0, %/cells.20/shuffle/Constant_1_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.20/shuffle/Reshape_1_output_0, %onnx::Conv_864, %onnx::Conv_865) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_867, %onnx::Conv_868) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_870, %onnx::Conv_871) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.21/nl/Relu_output_0, %onnx::Conv_873, %onnx::Conv_874) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_876, %onnx::Conv_877) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_879, %onnx::Conv_880) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %694 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %694 }	val_accuracy	67,674,240	1,748,668	{'zcp_synflow': 71.25522517406321, 'zcp_zen': 63.72784423828125, 'zcp_epe_nas': 18.61555983517918, 'zcp_fisher': 0.11191883683204651, 'zcp_flops': 67674240.0, 'zcp_grad_norm': 24.021320343017578, 'zcp_grasp': 0.04236602783203125, 'zcp_jacov': -16.057524834638507, 'zcp_l2_norm': 570.289306640625, 'zcp_nwot': 213.88996573413803, 'zcp_params': 1748668.0, 'zcp_plain': 0.004698107019066811, 'zcp_snip': 40.04111099243164, 'lat_1080ti_1': 0.6789923124535597, 'lat_1080ti_32': 0.6365703465636342, 'lat_1080ti_64': 0.5033259745765325, 'lat_2080ti_1': 0.6572814127599639, 'lat_2080ti_32': 0.6799135591366928, 'lat_2080ti_64': 0.5518568574472937, 'lat_essential_ph_1': 0.20754716981132076, 'lat_eyeriss': 0.43566280967487586, 'lat_fpga': 0.47121978250329005, 'lat_gold_6226': 0.2831195741409995, 'lat_gold_6240': 0.44113892672961497, 'lat_pixel2': 0.2826086956521739, 'lat_pixel3': 0.45488385541282833, 'lat_raspi4': 0.511263002917351, 'lat_samsung_a50': 0.18947368421052632, 'lat_samsung_s7': 0.16535433070866143, 'lat_silver_4114': 0.47169442762744335, 'lat_silver_4210r': 0.5066684071082785, 'lat_titan_rtx_1': 0.6235685630208833, 'lat_titan_rtx_32': 0.6592400702759945, 'lat_titan_rtx_64': 0.5688166416481629, 'lat_titanx_1': 0.32647720564119237, 'lat_titanx_32': 0.594531134220536, 'lat_titanx_64': 0.47854596555627205, 'lat_titanxp_1': 0.5763744438782189, 'lat_titanxp_32': 0.6361770643400405, 'lat_titanxp_64': 0.5164158150534461}
FBNet_4262	FBNet	4262	4262	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_706[FLOAT, 16x3x3x3] %onnx::Conv_707[FLOAT, 16] %onnx::Conv_709[FLOAT, 16x8x1x1] %onnx::Conv_712[FLOAT, 16x1x5x5] %onnx::Conv_715[FLOAT, 16x8x1x1] %onnx::Conv_718[FLOAT, 24x16x1x1] %onnx::Conv_719[FLOAT, 24] %onnx::Conv_721[FLOAT, 24x12x1x1] %onnx::Conv_724[FLOAT, 24x1x3x3] %onnx::Conv_727[FLOAT, 24x12x1x1] %onnx::Conv_730[FLOAT, 144x24x1x1] %onnx::Conv_731[FLOAT, 144] %onnx::Conv_733[FLOAT, 144x1x3x3] %onnx::Conv_736[FLOAT, 24x144x1x1] %onnx::Conv_739[FLOAT, 24x24x1x1] %onnx::Conv_742[FLOAT, 24x1x5x5] %onnx::Conv_745[FLOAT, 24x24x1x1] %onnx::Conv_748[FLOAT, 144x24x1x1] %onnx::Conv_751[FLOAT, 144x1x5x5] %onnx::Conv_754[FLOAT, 32x144x1x1] %onnx::Conv_755[FLOAT, 32] %onnx::Conv_757[FLOAT, 96x32x1x1] %onnx::Conv_758[FLOAT, 96] %onnx::Conv_760[FLOAT, 96x1x5x5] %onnx::Conv_763[FLOAT, 32x96x1x1] %onnx::Conv_766[FLOAT, 192x32x1x1] %onnx::Conv_767[FLOAT, 192] %onnx::Conv_769[FLOAT, 192x1x3x3] %onnx::Conv_772[FLOAT, 32x192x1x1] %onnx::Conv_775[FLOAT, 32x16x1x1] %onnx::Conv_778[FLOAT, 32x1x3x3] %onnx::Conv_781[FLOAT, 64x16x1x1] %onnx::Conv_782[FLOAT, 64] %onnx::Conv_784[FLOAT, 64x32x1x1] %onnx::Conv_787[FLOAT, 64x1x3x3] %onnx::Conv_790[FLOAT, 64x32x1x1] %onnx::Conv_793[FLOAT, 64x64x1x1] %onnx::Conv_796[FLOAT, 64x1x3x3] %onnx::Conv_799[FLOAT, 64x64x1x1] %onnx::Conv_802[FLOAT, 384x64x1x1] %onnx::Conv_803[FLOAT, 384] %onnx::Conv_805[FLOAT, 384x1x5x5] %onnx::Conv_808[FLOAT, 64x384x1x1] %onnx::Conv_811[FLOAT, 64x32x1x1] %onnx::Conv_814[FLOAT, 64x1x5x5] %onnx::Conv_817[FLOAT, 112x32x1x1] %onnx::Conv_818[FLOAT, 112] %onnx::Conv_820[FLOAT, 112x112x1x1] %onnx::Conv_823[FLOAT, 112x1x3x3] %onnx::Conv_826[FLOAT, 112x112x1x1] %onnx::Conv_829[FLOAT, 112x112x1x1] %onnx::Conv_832[FLOAT, 112x1x5x5] %onnx::Conv_835[FLOAT, 112x112x1x1] %onnx::Conv_838[FLOAT, 336x112x1x1] %onnx::Conv_839[FLOAT, 336] %onnx::Conv_841[FLOAT, 336x1x3x3] %onnx::Conv_844[FLOAT, 112x336x1x1] %onnx::Conv_847[FLOAT, 112x112x1x1] %onnx::Conv_850[FLOAT, 112x1x3x3] %onnx::Conv_853[FLOAT, 184x112x1x1] %onnx::Conv_854[FLOAT, 184] %onnx::Conv_856[FLOAT, 1104x184x1x1] %onnx::Conv_857[FLOAT, 1104] %onnx::Conv_859[FLOAT, 1104x1x3x3] %onnx::Conv_862[FLOAT, 184x1104x1x1] %onnx::Conv_865[FLOAT, 184x184x1x1] %onnx::Conv_868[FLOAT, 184x1x3x3] %onnx::Conv_871[FLOAT, 184x184x1x1] %onnx::Conv_874[FLOAT, 184x92x1x1] %onnx::Conv_877[FLOAT, 184x1x5x5] %onnx::Conv_880[FLOAT, 184x92x1x1] %onnx::Conv_883[FLOAT, 184x92x1x1] %onnx::Conv_886[FLOAT, 184x1x5x5] %onnx::Conv_889[FLOAT, 352x92x1x1] %onnx::Conv_890[FLOAT, 352] %onnx::Conv_892[FLOAT, 1504x352x1x1] %onnx::Conv_893[FLOAT, 1504] ) { %onnx::Conv_887 = Identity(%onnx::Conv_854) %onnx::Conv_884 = Identity(%onnx::Conv_854) %onnx::Conv_881 = Identity(%onnx::Conv_854) %onnx::Conv_878 = Identity(%onnx::Conv_854) %onnx::Conv_875 = Identity(%onnx::Conv_854) %onnx::Conv_872 = Identity(%onnx::Conv_854) %onnx::Conv_869 = Identity(%onnx::Conv_854) %onnx::Conv_866 = Identity(%onnx::Conv_854) %onnx::Conv_863 = Identity(%onnx::Conv_854) %onnx::Conv_860 = Identity(%onnx::Conv_857) %onnx::Conv_851 = Identity(%onnx::Conv_818) %onnx::Conv_848 = Identity(%onnx::Conv_818) %onnx::Conv_845 = Identity(%onnx::Conv_818) %onnx::Conv_842 = Identity(%onnx::Conv_839) %onnx::Conv_836 = Identity(%onnx::Conv_818) %onnx::Conv_833 = Identity(%onnx::Conv_818) %onnx::Conv_830 = Identity(%onnx::Conv_818) %onnx::Conv_827 = Identity(%onnx::Conv_818) %onnx::Conv_824 = Identity(%onnx::Conv_818) %onnx::Conv_821 = Identity(%onnx::Conv_818) %onnx::Conv_815 = Identity(%onnx::Conv_782) %onnx::Conv_812 = Identity(%onnx::Conv_782) %onnx::Conv_809 = Identity(%onnx::Conv_782) %onnx::Conv_806 = Identity(%onnx::Conv_803) %onnx::Conv_800 = Identity(%onnx::Conv_782) %onnx::Conv_797 = Identity(%onnx::Conv_782) %onnx::Conv_794 = Identity(%onnx::Conv_782) %onnx::Conv_791 = Identity(%onnx::Conv_782) %onnx::Conv_788 = Identity(%onnx::Conv_782) %onnx::Conv_785 = Identity(%onnx::Conv_782) %onnx::Conv_779 = Identity(%onnx::Conv_755) %onnx::Conv_776 = Identity(%onnx::Conv_755) %onnx::Conv_773 = Identity(%onnx::Conv_755) %onnx::Conv_770 = Identity(%onnx::Conv_767) %onnx::Conv_764 = Identity(%onnx::Conv_755) %onnx::Conv_761 = Identity(%onnx::Conv_758) %onnx::Conv_752 = Identity(%onnx::Conv_731) %onnx::Conv_749 = Identity(%onnx::Conv_731) %onnx::Conv_746 = Identity(%onnx::Conv_719) %onnx::Conv_743 = Identity(%onnx::Conv_719) %onnx::Conv_740 = Identity(%onnx::Conv_719) %onnx::Conv_737 = Identity(%onnx::Conv_719) %onnx::Conv_734 = Identity(%onnx::Conv_731) %onnx::Conv_728 = Identity(%onnx::Conv_719) %onnx::Conv_725 = Identity(%onnx::Conv_719) %onnx::Conv_722 = Identity(%onnx::Conv_719) %onnx::Conv_716 = Identity(%onnx::Conv_707) %onnx::Conv_713 = Identity(%onnx::Conv_707) %onnx::Conv_710 = Identity(%onnx::Conv_707) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_706, %onnx::Conv_707) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_709, %onnx::Conv_710) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.0/shuffle/Reshape_output_0 = Reshape(%/cells.0/nl/Relu_output_0, %/cells.0/shuffle/Constant_output_0) %/cells.0/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.0/shuffle/Reshape_output_0) %/cells.0/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.0/shuffle/Reshape_1_output_0 = Reshape(%/cells.0/shuffle/Transpose_output_0, %/cells.0/shuffle/Constant_1_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.0/shuffle/Reshape_1_output_0, %onnx::Conv_712, %onnx::Conv_713) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_715, %onnx::Conv_716) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_718, %onnx::Conv_719) %/cells.1/relu/Relu_output_0 = Relu(%/cells.1/conv/Conv_output_0) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/relu/Relu_output_0, %onnx::Conv_721, %onnx::Conv_722) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.2/shuffle/Reshape_output_0 = Reshape(%/cells.2/nl/Relu_output_0, %/cells.2/shuffle/Constant_output_0) %/cells.2/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.2/shuffle/Reshape_output_0) %/cells.2/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.2/shuffle/Reshape_1_output_0 = Reshape(%/cells.2/shuffle/Transpose_output_0, %/cells.2/shuffle/Constant_1_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.2/shuffle/Reshape_1_output_0, %onnx::Conv_724, %onnx::Conv_725) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_727, %onnx::Conv_728) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/relu/Relu_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_730, %onnx::Conv_731) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.3/nl/Relu_output_0, %onnx::Conv_733, %onnx::Conv_734) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_736, %onnx::Conv_737) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_739, %onnx::Conv_740) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.4/nl/Relu_output_0, %onnx::Conv_742, %onnx::Conv_743) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_745, %onnx::Conv_746) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/Add_output_0, %onnx::Conv_748, %onnx::Conv_749) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.5/nl/Relu_output_0, %onnx::Conv_751, %onnx::Conv_752) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_754, %onnx::Conv_755) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_757, %onnx::Conv_758) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.6/nl/Relu_output_0, %onnx::Conv_760, %onnx::Conv_761) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_763, %onnx::Conv_764) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_766, %onnx::Conv_767) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.7/nl/Relu_output_0, %onnx::Conv_769, %onnx::Conv_770) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_772, %onnx::Conv_773) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_775, %onnx::Conv_776) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.9/shuffle/Reshape_output_0 = Reshape(%/cells.9/nl/Relu_output_0, %/cells.9/shuffle/Constant_output_0) %/cells.9/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.9/shuffle/Reshape_output_0) %/cells.9/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.9/shuffle/Reshape_1_output_0 = Reshape(%/cells.9/shuffle/Transpose_output_0, %/cells.9/shuffle/Constant_1_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.9/shuffle/Reshape_1_output_0, %onnx::Conv_778, %onnx::Conv_779) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_781, %onnx::Conv_782) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_784, %onnx::Conv_785) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.10/shuffle/Reshape_output_0 = Reshape(%/cells.10/nl/Relu_output_0, %/cells.10/shuffle/Constant_output_0) %/cells.10/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.10/shuffle/Reshape_output_0) %/cells.10/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.10/shuffle/Reshape_1_output_0 = Reshape(%/cells.10/shuffle/Transpose_output_0, %/cells.10/shuffle/Constant_1_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.10/shuffle/Reshape_1_output_0, %onnx::Conv_787, %onnx::Conv_788) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_790, %onnx::Conv_791) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_793, %onnx::Conv_794) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.11/nl/Relu_output_0, %onnx::Conv_796, %onnx::Conv_797) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_799, %onnx::Conv_800) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_802, %onnx::Conv_803) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.12/nl/Relu_output_0, %onnx::Conv_805, %onnx::Conv_806) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_808, %onnx::Conv_809) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_811, %onnx::Conv_812) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.13/shuffle/Reshape_output_0 = Reshape(%/cells.13/nl/Relu_output_0, %/cells.13/shuffle/Constant_output_0) %/cells.13/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.13/shuffle/Reshape_output_0) %/cells.13/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.13/shuffle/Reshape_1_output_0 = Reshape(%/cells.13/shuffle/Transpose_output_0, %/cells.13/shuffle/Constant_1_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.13/shuffle/Reshape_1_output_0, %onnx::Conv_814, %onnx::Conv_815) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_817, %onnx::Conv_818) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_820, %onnx::Conv_821) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.14/nl/Relu_output_0, %onnx::Conv_823, %onnx::Conv_824) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_826, %onnx::Conv_827) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_829, %onnx::Conv_830) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.15/nl/Relu_output_0, %onnx::Conv_832, %onnx::Conv_833) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_835, %onnx::Conv_836) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_838, %onnx::Conv_839) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.16/nl/Relu_output_0, %onnx::Conv_841, %onnx::Conv_842) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_844, %onnx::Conv_845) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.15/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_847, %onnx::Conv_848) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.17/nl/Relu_output_0, %onnx::Conv_850, %onnx::Conv_851) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_853, %onnx::Conv_854) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_856, %onnx::Conv_857) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.18/nl/Relu_output_0, %onnx::Conv_859, %onnx::Conv_860) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_862, %onnx::Conv_863) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_865, %onnx::Conv_866) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.19/nl/Relu_output_0, %onnx::Conv_868, %onnx::Conv_869) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_871, %onnx::Conv_872) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_874, %onnx::Conv_875) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.20/shuffle/Reshape_output_0 = Reshape(%/cells.20/nl/Relu_output_0, %/cells.20/shuffle/Constant_output_0) %/cells.20/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.20/shuffle/Reshape_output_0) %/cells.20/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.20/shuffle/Reshape_1_output_0 = Reshape(%/cells.20/shuffle/Transpose_output_0, %/cells.20/shuffle/Constant_1_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.20/shuffle/Reshape_1_output_0, %onnx::Conv_877, %onnx::Conv_878) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_880, %onnx::Conv_881) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_883, %onnx::Conv_884) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.21/shuffle/Reshape_output_0 = Reshape(%/cells.21/nl/Relu_output_0, %/cells.21/shuffle/Constant_output_0) %/cells.21/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.21/shuffle/Reshape_output_0) %/cells.21/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.21/shuffle/Reshape_1_output_0 = Reshape(%/cells.21/shuffle/Transpose_output_0, %/cells.21/shuffle/Constant_1_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.21/shuffle/Reshape_1_output_0, %onnx::Conv_886, %onnx::Conv_887) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_889, %onnx::Conv_890) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_892, %onnx::Conv_893) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %704 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %704 }	val_accuracy	59,664,000	1,573,324	{'zcp_synflow': 74.52961003696058, 'zcp_zen': 66.47081756591797, 'zcp_epe_nas': 0.00015999920000638146, 'zcp_fisher': 0.10407499969005585, 'zcp_flops': 59664000.0, 'zcp_grad_norm': 28.018022537231445, 'zcp_grasp': 0.004535675048828125, 'zcp_jacov': -16.060617001043294, 'zcp_l2_norm': 588.041748046875, 'zcp_nwot': 211.96696748431128, 'zcp_params': 1573324.0, 'zcp_plain': 0.0011235139099881053, 'zcp_snip': 35.79764938354492, 'lat_1080ti_1': 0.6357921509374421, 'lat_1080ti_32': 0.6661538224593424, 'lat_1080ti_64': 0.4732051043156445, 'lat_2080ti_1': 0.7344172956460268, 'lat_2080ti_32': 0.6449162236002317, 'lat_2080ti_64': 0.520171811667443, 'lat_essential_ph_1': 0.2641509433962264, 'lat_eyeriss': 0.3503199897309889, 'lat_fpga': 0.3428888075282756, 'lat_gold_6226': 0.23632680035263642, 'lat_gold_6240': 0.43658544082161427, 'lat_pixel2': 0.2608695652173913, 'lat_pixel3': 0.34095872111638953, 'lat_raspi4': 0.38267164008574095, 'lat_samsung_a50': 0.16842105263157894, 'lat_samsung_s7': 0.09448818897637795, 'lat_silver_4114': 0.48229275588950654, 'lat_silver_4210r': 0.49406937123875433, 'lat_titan_rtx_1': 0.6664155639574169, 'lat_titan_rtx_32': 0.6058891315963866, 'lat_titan_rtx_64': 0.5424461044453455, 'lat_titanx_1': 0.3536253965915286, 'lat_titanx_32': 0.5547552032225949, 'lat_titanx_64': 0.44317876554650015, 'lat_titanxp_1': 0.6216522545508498, 'lat_titanxp_32': 0.5959509916693183, 'lat_titanxp_64': 0.5086474708711398}
FBNet_1718	FBNet	1718	1718	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_759[FLOAT, 16x3x3x3] %onnx::Conv_760[FLOAT, 16] %onnx::Conv_762[FLOAT, 16x16x1x1] %onnx::Conv_765[FLOAT, 16x1x3x3] %onnx::Conv_768[FLOAT, 16x16x1x1] %onnx::Conv_771[FLOAT, 16x16x1x1] %onnx::Conv_774[FLOAT, 16x1x5x5] %onnx::Conv_777[FLOAT, 24x16x1x1] %onnx::Conv_778[FLOAT, 24] %onnx::Conv_780[FLOAT, 24x12x1x1] %onnx::Conv_783[FLOAT, 24x1x3x3] %onnx::Conv_786[FLOAT, 24x12x1x1] %onnx::Conv_789[FLOAT, 24x24x1x1] %onnx::Conv_792[FLOAT, 24x1x5x5] %onnx::Conv_795[FLOAT, 24x24x1x1] %onnx::Conv_798[FLOAT, 24x12x1x1] %onnx::Conv_801[FLOAT, 24x1x5x5] %onnx::Conv_804[FLOAT, 24x12x1x1] %onnx::Conv_807[FLOAT, 72x24x1x1] %onnx::Conv_808[FLOAT, 72] %onnx::Conv_810[FLOAT, 72x1x3x3] %onnx::Conv_813[FLOAT, 32x72x1x1] %onnx::Conv_814[FLOAT, 32] %onnx::Conv_816[FLOAT, 32x32x1x1] %onnx::Conv_819[FLOAT, 32x1x3x3] %onnx::Conv_822[FLOAT, 32x32x1x1] %onnx::Conv_825[FLOAT, 96x32x1x1] %onnx::Conv_826[FLOAT, 96] %onnx::Conv_828[FLOAT, 96x1x3x3] %onnx::Conv_831[FLOAT, 32x96x1x1] %onnx::Conv_834[FLOAT, 32x16x1x1] %onnx::Conv_837[FLOAT, 32x1x3x3] %onnx::Conv_840[FLOAT, 32x16x1x1] %onnx::Conv_843[FLOAT, 96x32x1x1] %onnx::Conv_846[FLOAT, 96x1x5x5] %onnx::Conv_849[FLOAT, 64x96x1x1] %onnx::Conv_850[FLOAT, 64] %onnx::Conv_852[FLOAT, 64x32x1x1] %onnx::Conv_855[FLOAT, 64x1x3x3] %onnx::Conv_858[FLOAT, 64x32x1x1] %onnx::Conv_861[FLOAT, 64x64x1x1] %onnx::Conv_864[FLOAT, 64x1x3x3] %onnx::Conv_867[FLOAT, 64x64x1x1] %onnx::Conv_870[FLOAT, 64x32x1x1] %onnx::Conv_873[FLOAT, 64x1x5x5] %onnx::Conv_876[FLOAT, 64x32x1x1] %onnx::Conv_879[FLOAT, 192x64x1x1] %onnx::Conv_880[FLOAT, 192] %onnx::Conv_882[FLOAT, 192x1x3x3] %onnx::Conv_885[FLOAT, 112x192x1x1] %onnx::Conv_886[FLOAT, 112] %onnx::Conv_888[FLOAT, 672x112x1x1] %onnx::Conv_889[FLOAT, 672] %onnx::Conv_891[FLOAT, 672x1x3x3] %onnx::Conv_894[FLOAT, 112x672x1x1] %onnx::Conv_897[FLOAT, 112x56x1x1] %onnx::Conv_900[FLOAT, 112x1x3x3] %onnx::Conv_903[FLOAT, 112x56x1x1] %onnx::Conv_906[FLOAT, 112x56x1x1] %onnx::Conv_909[FLOAT, 112x1x3x3] %onnx::Conv_912[FLOAT, 184x56x1x1] %onnx::Conv_913[FLOAT, 184] %onnx::Conv_915[FLOAT, 184x92x1x1] %onnx::Conv_918[FLOAT, 184x1x3x3] %onnx::Conv_921[FLOAT, 184x92x1x1] %onnx::Conv_924[FLOAT, 184x184x1x1] %onnx::Conv_927[FLOAT, 184x1x5x5] %onnx::Conv_930[FLOAT, 184x184x1x1] %onnx::Conv_933[FLOAT, 552x184x1x1] %onnx::Conv_934[FLOAT, 552] %onnx::Conv_936[FLOAT, 552x1x5x5] %onnx::Conv_939[FLOAT, 184x552x1x1] %onnx::Conv_942[FLOAT, 184x92x1x1] %onnx::Conv_945[FLOAT, 184x1x5x5] %onnx::Conv_948[FLOAT, 352x92x1x1] %onnx::Conv_949[FLOAT, 352] %onnx::Conv_951[FLOAT, 1504x352x1x1] %onnx::Conv_952[FLOAT, 1504] ) { %onnx::Conv_946 = Identity(%onnx::Conv_913) %onnx::Conv_943 = Identity(%onnx::Conv_913) %onnx::Conv_940 = Identity(%onnx::Conv_913) %onnx::Conv_937 = Identity(%onnx::Conv_934) %onnx::Conv_931 = Identity(%onnx::Conv_913) %onnx::Conv_928 = Identity(%onnx::Conv_913) %onnx::Conv_925 = Identity(%onnx::Conv_913) %onnx::Conv_922 = Identity(%onnx::Conv_913) %onnx::Conv_919 = Identity(%onnx::Conv_913) %onnx::Conv_916 = Identity(%onnx::Conv_913) %onnx::Conv_910 = Identity(%onnx::Conv_886) %onnx::Conv_907 = Identity(%onnx::Conv_886) %onnx::Conv_904 = Identity(%onnx::Conv_886) %onnx::Conv_901 = Identity(%onnx::Conv_886) %onnx::Conv_898 = Identity(%onnx::Conv_886) %onnx::Conv_895 = Identity(%onnx::Conv_886) %onnx::Conv_892 = Identity(%onnx::Conv_889) %onnx::Conv_883 = Identity(%onnx::Conv_880) %onnx::Conv_877 = Identity(%onnx::Conv_850) %onnx::Conv_874 = Identity(%onnx::Conv_850) %onnx::Conv_871 = Identity(%onnx::Conv_850) %onnx::Conv_868 = Identity(%onnx::Conv_850) %onnx::Conv_865 = Identity(%onnx::Conv_850) %onnx::Conv_862 = Identity(%onnx::Conv_850) %onnx::Conv_859 = Identity(%onnx::Conv_850) %onnx::Conv_856 = Identity(%onnx::Conv_850) %onnx::Conv_853 = Identity(%onnx::Conv_850) %onnx::Conv_847 = Identity(%onnx::Conv_826) %onnx::Conv_844 = Identity(%onnx::Conv_826) %onnx::Conv_841 = Identity(%onnx::Conv_814) %onnx::Conv_838 = Identity(%onnx::Conv_814) %onnx::Conv_835 = Identity(%onnx::Conv_814) %onnx::Conv_832 = Identity(%onnx::Conv_814) %onnx::Conv_829 = Identity(%onnx::Conv_826) %onnx::Conv_823 = Identity(%onnx::Conv_814) %onnx::Conv_820 = Identity(%onnx::Conv_814) %onnx::Conv_817 = Identity(%onnx::Conv_814) %onnx::Conv_811 = Identity(%onnx::Conv_808) %onnx::Conv_805 = Identity(%onnx::Conv_778) %onnx::Conv_802 = Identity(%onnx::Conv_778) %onnx::Conv_799 = Identity(%onnx::Conv_778) %onnx::Conv_796 = Identity(%onnx::Conv_778) %onnx::Conv_793 = Identity(%onnx::Conv_778) %onnx::Conv_790 = Identity(%onnx::Conv_778) %onnx::Conv_787 = Identity(%onnx::Conv_778) %onnx::Conv_784 = Identity(%onnx::Conv_778) %onnx::Conv_781 = Identity(%onnx::Conv_778) %onnx::Conv_775 = Identity(%onnx::Conv_760) %onnx::Conv_772 = Identity(%onnx::Conv_760) %onnx::Conv_769 = Identity(%onnx::Conv_760) %onnx::Conv_766 = Identity(%onnx::Conv_760) %onnx::Conv_763 = Identity(%onnx::Conv_760) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_759, %onnx::Conv_760) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_762, %onnx::Conv_763) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.0/nl/Relu_output_0, %onnx::Conv_765, %onnx::Conv_766) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_768, %onnx::Conv_769) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_771, %onnx::Conv_772) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.1/nl/Relu_output_0, %onnx::Conv_774, %onnx::Conv_775) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_777, %onnx::Conv_778) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_780, %onnx::Conv_781) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.2/shuffle/Reshape_output_0 = Reshape(%/cells.2/nl/Relu_output_0, %/cells.2/shuffle/Constant_output_0) %/cells.2/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.2/shuffle/Reshape_output_0) %/cells.2/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.2/shuffle/Reshape_1_output_0 = Reshape(%/cells.2/shuffle/Transpose_output_0, %/cells.2/shuffle/Constant_1_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.2/shuffle/Reshape_1_output_0, %onnx::Conv_783, %onnx::Conv_784) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_786, %onnx::Conv_787) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_789, %onnx::Conv_790) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.3/nl/Relu_output_0, %onnx::Conv_792, %onnx::Conv_793) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_795, %onnx::Conv_796) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_798, %onnx::Conv_799) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.4/shuffle/Reshape_output_0 = Reshape(%/cells.4/nl/Relu_output_0, %/cells.4/shuffle/Constant_output_0) %/cells.4/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.4/shuffle/Reshape_output_0) %/cells.4/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.4/shuffle/Reshape_1_output_0 = Reshape(%/cells.4/shuffle/Transpose_output_0, %/cells.4/shuffle/Constant_1_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.4/shuffle/Reshape_1_output_0, %onnx::Conv_801, %onnx::Conv_802) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_804, %onnx::Conv_805) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/Add_output_0, %onnx::Conv_807, %onnx::Conv_808) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.5/nl/Relu_output_0, %onnx::Conv_810, %onnx::Conv_811) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_813, %onnx::Conv_814) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_816, %onnx::Conv_817) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.6/nl/Relu_output_0, %onnx::Conv_819, %onnx::Conv_820) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_822, %onnx::Conv_823) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_825, %onnx::Conv_826) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.7/nl/Relu_output_0, %onnx::Conv_828, %onnx::Conv_829) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_831, %onnx::Conv_832) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_834, %onnx::Conv_835) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.8/shuffle/Reshape_output_0 = Reshape(%/cells.8/nl/Relu_output_0, %/cells.8/shuffle/Constant_output_0) %/cells.8/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.8/shuffle/Reshape_output_0) %/cells.8/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.8/shuffle/Reshape_1_output_0 = Reshape(%/cells.8/shuffle/Transpose_output_0, %/cells.8/shuffle/Constant_1_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.8/shuffle/Reshape_1_output_0, %onnx::Conv_837, %onnx::Conv_838) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_840, %onnx::Conv_841) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_843, %onnx::Conv_844) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.9/nl/Relu_output_0, %onnx::Conv_846, %onnx::Conv_847) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_849, %onnx::Conv_850) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_852, %onnx::Conv_853) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.10/shuffle/Reshape_output_0 = Reshape(%/cells.10/nl/Relu_output_0, %/cells.10/shuffle/Constant_output_0) %/cells.10/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.10/shuffle/Reshape_output_0) %/cells.10/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.10/shuffle/Reshape_1_output_0 = Reshape(%/cells.10/shuffle/Transpose_output_0, %/cells.10/shuffle/Constant_1_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.10/shuffle/Reshape_1_output_0, %onnx::Conv_855, %onnx::Conv_856) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_858, %onnx::Conv_859) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_861, %onnx::Conv_862) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.11/nl/Relu_output_0, %onnx::Conv_864, %onnx::Conv_865) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_867, %onnx::Conv_868) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_870, %onnx::Conv_871) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.12/shuffle/Reshape_output_0 = Reshape(%/cells.12/nl/Relu_output_0, %/cells.12/shuffle/Constant_output_0) %/cells.12/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.12/shuffle/Reshape_output_0) %/cells.12/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.12/shuffle/Reshape_1_output_0 = Reshape(%/cells.12/shuffle/Transpose_output_0, %/cells.12/shuffle/Constant_1_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.12/shuffle/Reshape_1_output_0, %onnx::Conv_873, %onnx::Conv_874) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_876, %onnx::Conv_877) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_879, %onnx::Conv_880) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.13/nl/Relu_output_0, %onnx::Conv_882, %onnx::Conv_883) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_885, %onnx::Conv_886) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_888, %onnx::Conv_889) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.15/nl/Relu_output_0, %onnx::Conv_891, %onnx::Conv_892) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_894, %onnx::Conv_895) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_897, %onnx::Conv_898) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.16/shuffle/Reshape_output_0 = Reshape(%/cells.16/nl/Relu_output_0, %/cells.16/shuffle/Constant_output_0) %/cells.16/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.16/shuffle/Reshape_output_0) %/cells.16/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.16/shuffle/Reshape_1_output_0 = Reshape(%/cells.16/shuffle/Transpose_output_0, %/cells.16/shuffle/Constant_1_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.16/shuffle/Reshape_1_output_0, %onnx::Conv_900, %onnx::Conv_901) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_903, %onnx::Conv_904) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.15/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_906, %onnx::Conv_907) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.17/shuffle/Reshape_output_0 = Reshape(%/cells.17/nl/Relu_output_0, %/cells.17/shuffle/Constant_output_0) %/cells.17/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.17/shuffle/Reshape_output_0) %/cells.17/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.17/shuffle/Reshape_1_output_0 = Reshape(%/cells.17/shuffle/Transpose_output_0, %/cells.17/shuffle/Constant_1_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.17/shuffle/Reshape_1_output_0, %onnx::Conv_909, %onnx::Conv_910) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_912, %onnx::Conv_913) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_915, %onnx::Conv_916) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.18/shuffle/Reshape_output_0 = Reshape(%/cells.18/nl/Relu_output_0, %/cells.18/shuffle/Constant_output_0) %/cells.18/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.18/shuffle/Reshape_output_0) %/cells.18/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.18/shuffle/Reshape_1_output_0 = Reshape(%/cells.18/shuffle/Transpose_output_0, %/cells.18/shuffle/Constant_1_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.18/shuffle/Reshape_1_output_0, %onnx::Conv_918, %onnx::Conv_919) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_921, %onnx::Conv_922) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_924, %onnx::Conv_925) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.19/nl/Relu_output_0, %onnx::Conv_927, %onnx::Conv_928) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_930, %onnx::Conv_931) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_933, %onnx::Conv_934) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 552, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.20/nl/Relu_output_0, %onnx::Conv_936, %onnx::Conv_937) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_939, %onnx::Conv_940) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_942, %onnx::Conv_943) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.21/shuffle/Reshape_output_0 = Reshape(%/cells.21/nl/Relu_output_0, %/cells.21/shuffle/Constant_output_0) %/cells.21/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.21/shuffle/Reshape_output_0) %/cells.21/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.21/shuffle/Reshape_1_output_0 = Reshape(%/cells.21/shuffle/Transpose_output_0, %/cells.21/shuffle/Constant_1_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.21/shuffle/Reshape_1_output_0, %onnx::Conv_945, %onnx::Conv_946) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_948, %onnx::Conv_949) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_951, %onnx::Conv_952) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %757 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %757 }	val_accuracy	44,263,296	1,351,892	{'zcp_synflow': 72.28403210215593, 'zcp_zen': 62.93086624145508, 'zcp_epe_nas': 0.00015999920000638146, 'zcp_fisher': 0.14014832675457, 'zcp_flops': 44263296.0, 'zcp_grad_norm': 22.095478057861328, 'zcp_grasp': -0.042446136474609375, 'zcp_jacov': -16.051716294736252, 'zcp_l2_norm': 529.6461791992188, 'zcp_nwot': 204.75237019284697, 'zcp_params': 1351892.0, 'zcp_plain': 0.000751342682633549, 'zcp_snip': 37.089107513427734, 'lat_1080ti_1': 0.7803252146917442, 'lat_1080ti_32': 0.563113207707113, 'lat_1080ti_64': 0.30173270511241634, 'lat_2080ti_1': 0.7905980313384565, 'lat_2080ti_32': 0.6069761449709833, 'lat_2080ti_64': 0.3524712155917802, 'lat_essential_ph_1': 0.18867924528301888, 'lat_eyeriss': 0.1342764931325984, 'lat_fpga': 0.1676743288574001, 'lat_gold_6226': 0.215060702604864, 'lat_gold_6240': 0.3958393067570667, 'lat_pixel2': 0.15217391304347827, 'lat_pixel3': 0.16307438573922003, 'lat_raspi4': 0.20090142973143366, 'lat_samsung_a50': 0.08421052631578947, 'lat_samsung_s7': 0.14173228346456693, 'lat_silver_4114': 0.43551919248671206, 'lat_silver_4210r': 0.5381500570327049, 'lat_titan_rtx_1': 0.7591124150988989, 'lat_titan_rtx_32': 0.6203815476254432, 'lat_titan_rtx_64': 0.4027675778420871, 'lat_titanx_1': 0.40026628724521734, 'lat_titanx_32': 0.47924292547717307, 'lat_titanx_64': 0.2944037691852134, 'lat_titanxp_1': 0.7315374184071287, 'lat_titanxp_32': 0.5539324476910845, 'lat_titanxp_64': 0.3532289693715222}
FBNet_1961	FBNet	1961	1961	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_714[FLOAT, 16x3x3x3] %onnx::Conv_715[FLOAT, 16] %onnx::Conv_717[FLOAT, 96x16x1x1] %onnx::Conv_718[FLOAT, 96] %onnx::Conv_720[FLOAT, 96x1x3x3] %onnx::Conv_723[FLOAT, 16x96x1x1] %onnx::Conv_726[FLOAT, 48x16x1x1] %onnx::Conv_727[FLOAT, 48] %onnx::Conv_729[FLOAT, 48x1x5x5] %onnx::Conv_732[FLOAT, 24x48x1x1] %onnx::Conv_733[FLOAT, 24] %onnx::Conv_735[FLOAT, 144x24x1x1] %onnx::Conv_736[FLOAT, 144] %onnx::Conv_738[FLOAT, 144x1x5x5] %onnx::Conv_741[FLOAT, 24x144x1x1] %onnx::Conv_744[FLOAT, 72x24x1x1] %onnx::Conv_745[FLOAT, 72] %onnx::Conv_747[FLOAT, 72x1x3x3] %onnx::Conv_750[FLOAT, 24x72x1x1] %onnx::Conv_753[FLOAT, 24x12x1x1] %onnx::Conv_756[FLOAT, 24x1x5x5] %onnx::Conv_759[FLOAT, 24x12x1x1] %onnx::Conv_762[FLOAT, 24x12x1x1] %onnx::Conv_765[FLOAT, 24x1x5x5] %onnx::Conv_768[FLOAT, 32x12x1x1] %onnx::Conv_769[FLOAT, 32] %onnx::Conv_771[FLOAT, 192x32x1x1] %onnx::Conv_772[FLOAT, 192] %onnx::Conv_774[FLOAT, 192x1x5x5] %onnx::Conv_777[FLOAT, 32x192x1x1] %onnx::Conv_780[FLOAT, 96x32x1x1] %onnx::Conv_783[FLOAT, 96x1x5x5] %onnx::Conv_786[FLOAT, 32x96x1x1] %onnx::Conv_789[FLOAT, 32x16x1x1] %onnx::Conv_792[FLOAT, 32x1x5x5] %onnx::Conv_795[FLOAT, 64x16x1x1] %onnx::Conv_796[FLOAT, 64] %onnx::Conv_798[FLOAT, 64x32x1x1] %onnx::Conv_801[FLOAT, 64x1x3x3] %onnx::Conv_804[FLOAT, 64x32x1x1] %onnx::Conv_807[FLOAT, 64x32x1x1] %onnx::Conv_810[FLOAT, 64x1x3x3] %onnx::Conv_813[FLOAT, 64x32x1x1] %onnx::Conv_816[FLOAT, 384x64x1x1] %onnx::Conv_817[FLOAT, 384] %onnx::Conv_819[FLOAT, 384x1x5x5] %onnx::Conv_822[FLOAT, 112x384x1x1] %onnx::Conv_823[FLOAT, 112] %onnx::Conv_825[FLOAT, 112x56x1x1] %onnx::Conv_828[FLOAT, 112x1x3x3] %onnx::Conv_831[FLOAT, 112x56x1x1] %onnx::Conv_834[FLOAT, 672x112x1x1] %onnx::Conv_835[FLOAT, 672] %onnx::Conv_837[FLOAT, 672x1x3x3] %onnx::Conv_840[FLOAT, 112x672x1x1] %onnx::Conv_843[FLOAT, 112x112x1x1] %onnx::Conv_846[FLOAT, 112x1x5x5] %onnx::Conv_849[FLOAT, 112x112x1x1] %onnx::Conv_852[FLOAT, 112x56x1x1] %onnx::Conv_855[FLOAT, 112x1x3x3] %onnx::Conv_858[FLOAT, 184x56x1x1] %onnx::Conv_859[FLOAT, 184] %onnx::Conv_861[FLOAT, 184x184x1x1] %onnx::Conv_864[FLOAT, 184x1x3x3] %onnx::Conv_867[FLOAT, 184x184x1x1] %onnx::Conv_870[FLOAT, 184x92x1x1] %onnx::Conv_873[FLOAT, 184x1x5x5] %onnx::Conv_876[FLOAT, 184x92x1x1] %onnx::Conv_879[FLOAT, 1104x184x1x1] %onnx::Conv_880[FLOAT, 1104] %onnx::Conv_882[FLOAT, 1104x1x3x3] %onnx::Conv_885[FLOAT, 184x1104x1x1] %onnx::Conv_888[FLOAT, 184x184x1x1] %onnx::Conv_891[FLOAT, 184x1x5x5] %onnx::Conv_894[FLOAT, 352x184x1x1] %onnx::Conv_895[FLOAT, 352] %onnx::Conv_897[FLOAT, 1504x352x1x1] %onnx::Conv_898[FLOAT, 1504] ) { %onnx::Conv_892 = Identity(%onnx::Conv_859) %onnx::Conv_889 = Identity(%onnx::Conv_859) %onnx::Conv_886 = Identity(%onnx::Conv_859) %onnx::Conv_883 = Identity(%onnx::Conv_880) %onnx::Conv_877 = Identity(%onnx::Conv_859) %onnx::Conv_874 = Identity(%onnx::Conv_859) %onnx::Conv_871 = Identity(%onnx::Conv_859) %onnx::Conv_868 = Identity(%onnx::Conv_859) %onnx::Conv_865 = Identity(%onnx::Conv_859) %onnx::Conv_862 = Identity(%onnx::Conv_859) %onnx::Conv_856 = Identity(%onnx::Conv_823) %onnx::Conv_853 = Identity(%onnx::Conv_823) %onnx::Conv_850 = Identity(%onnx::Conv_823) %onnx::Conv_847 = Identity(%onnx::Conv_823) %onnx::Conv_844 = Identity(%onnx::Conv_823) %onnx::Conv_841 = Identity(%onnx::Conv_823) %onnx::Conv_838 = Identity(%onnx::Conv_835) %onnx::Conv_832 = Identity(%onnx::Conv_823) %onnx::Conv_829 = Identity(%onnx::Conv_823) %onnx::Conv_826 = Identity(%onnx::Conv_823) %onnx::Conv_820 = Identity(%onnx::Conv_817) %onnx::Conv_814 = Identity(%onnx::Conv_796) %onnx::Conv_811 = Identity(%onnx::Conv_796) %onnx::Conv_808 = Identity(%onnx::Conv_796) %onnx::Conv_805 = Identity(%onnx::Conv_796) %onnx::Conv_802 = Identity(%onnx::Conv_796) %onnx::Conv_799 = Identity(%onnx::Conv_796) %onnx::Conv_793 = Identity(%onnx::Conv_769) %onnx::Conv_790 = Identity(%onnx::Conv_769) %onnx::Conv_787 = Identity(%onnx::Conv_769) %onnx::Conv_784 = Identity(%onnx::Conv_718) %onnx::Conv_781 = Identity(%onnx::Conv_718) %onnx::Conv_778 = Identity(%onnx::Conv_769) %onnx::Conv_775 = Identity(%onnx::Conv_772) %onnx::Conv_766 = Identity(%onnx::Conv_733) %onnx::Conv_763 = Identity(%onnx::Conv_733) %onnx::Conv_760 = Identity(%onnx::Conv_733) %onnx::Conv_757 = Identity(%onnx::Conv_733) %onnx::Conv_754 = Identity(%onnx::Conv_733) %onnx::Conv_751 = Identity(%onnx::Conv_733) %onnx::Conv_748 = Identity(%onnx::Conv_745) %onnx::Conv_742 = Identity(%onnx::Conv_733) %onnx::Conv_739 = Identity(%onnx::Conv_736) %onnx::Conv_730 = Identity(%onnx::Conv_727) %onnx::Conv_724 = Identity(%onnx::Conv_715) %onnx::Conv_721 = Identity(%onnx::Conv_718) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_714, %onnx::Conv_715) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_717, %onnx::Conv_718) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.0/nl/Relu_output_0, %onnx::Conv_720, %onnx::Conv_721) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_723, %onnx::Conv_724) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_726, %onnx::Conv_727) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 48, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.1/nl/Relu_output_0, %onnx::Conv_729, %onnx::Conv_730) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_732, %onnx::Conv_733) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_735, %onnx::Conv_736) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.2/nl/Relu_output_0, %onnx::Conv_738, %onnx::Conv_739) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_741, %onnx::Conv_742) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_744, %onnx::Conv_745) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.3/nl/Relu_output_0, %onnx::Conv_747, %onnx::Conv_748) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_750, %onnx::Conv_751) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_753, %onnx::Conv_754) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.4/shuffle/Reshape_output_0 = Reshape(%/cells.4/nl/Relu_output_0, %/cells.4/shuffle/Constant_output_0) %/cells.4/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.4/shuffle/Reshape_output_0) %/cells.4/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.4/shuffle/Reshape_1_output_0 = Reshape(%/cells.4/shuffle/Transpose_output_0, %/cells.4/shuffle/Constant_1_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.4/shuffle/Reshape_1_output_0, %onnx::Conv_756, %onnx::Conv_757) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_759, %onnx::Conv_760) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/Add_output_0, %onnx::Conv_762, %onnx::Conv_763) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.5/shuffle/Reshape_output_0 = Reshape(%/cells.5/nl/Relu_output_0, %/cells.5/shuffle/Constant_output_0) %/cells.5/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.5/shuffle/Reshape_output_0) %/cells.5/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.5/shuffle/Reshape_1_output_0 = Reshape(%/cells.5/shuffle/Transpose_output_0, %/cells.5/shuffle/Constant_1_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.5/shuffle/Reshape_1_output_0, %onnx::Conv_765, %onnx::Conv_766) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_768, %onnx::Conv_769) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_771, %onnx::Conv_772) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.7/nl/Relu_output_0, %onnx::Conv_774, %onnx::Conv_775) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_777, %onnx::Conv_778) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_780, %onnx::Conv_781) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.8/nl/Relu_output_0, %onnx::Conv_783, %onnx::Conv_784) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_786, %onnx::Conv_787) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_789, %onnx::Conv_790) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.9/shuffle/Reshape_output_0 = Reshape(%/cells.9/nl/Relu_output_0, %/cells.9/shuffle/Constant_output_0) %/cells.9/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.9/shuffle/Reshape_output_0) %/cells.9/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.9/shuffle/Reshape_1_output_0 = Reshape(%/cells.9/shuffle/Transpose_output_0, %/cells.9/shuffle/Constant_1_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.9/shuffle/Reshape_1_output_0, %onnx::Conv_792, %onnx::Conv_793) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_795, %onnx::Conv_796) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_798, %onnx::Conv_799) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.11/shuffle/Reshape_output_0 = Reshape(%/cells.11/nl/Relu_output_0, %/cells.11/shuffle/Constant_output_0) %/cells.11/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.11/shuffle/Reshape_output_0) %/cells.11/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.11/shuffle/Reshape_1_output_0 = Reshape(%/cells.11/shuffle/Transpose_output_0, %/cells.11/shuffle/Constant_1_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.11/shuffle/Reshape_1_output_0, %onnx::Conv_801, %onnx::Conv_802) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_804, %onnx::Conv_805) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_807, %onnx::Conv_808) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.12/shuffle/Reshape_output_0 = Reshape(%/cells.12/nl/Relu_output_0, %/cells.12/shuffle/Constant_output_0) %/cells.12/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.12/shuffle/Reshape_output_0) %/cells.12/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.12/shuffle/Reshape_1_output_0 = Reshape(%/cells.12/shuffle/Transpose_output_0, %/cells.12/shuffle/Constant_1_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.12/shuffle/Reshape_1_output_0, %onnx::Conv_810, %onnx::Conv_811) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_813, %onnx::Conv_814) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_816, %onnx::Conv_817) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.13/nl/Relu_output_0, %onnx::Conv_819, %onnx::Conv_820) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_822, %onnx::Conv_823) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_825, %onnx::Conv_826) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.14/shuffle/Reshape_output_0 = Reshape(%/cells.14/nl/Relu_output_0, %/cells.14/shuffle/Constant_output_0) %/cells.14/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.14/shuffle/Reshape_output_0) %/cells.14/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.14/shuffle/Reshape_1_output_0 = Reshape(%/cells.14/shuffle/Transpose_output_0, %/cells.14/shuffle/Constant_1_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.14/shuffle/Reshape_1_output_0, %onnx::Conv_828, %onnx::Conv_829) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_831, %onnx::Conv_832) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_834, %onnx::Conv_835) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.15/nl/Relu_output_0, %onnx::Conv_837, %onnx::Conv_838) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_840, %onnx::Conv_841) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_843, %onnx::Conv_844) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.16/nl/Relu_output_0, %onnx::Conv_846, %onnx::Conv_847) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_849, %onnx::Conv_850) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.15/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_852, %onnx::Conv_853) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.17/shuffle/Reshape_output_0 = Reshape(%/cells.17/nl/Relu_output_0, %/cells.17/shuffle/Constant_output_0) %/cells.17/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.17/shuffle/Reshape_output_0) %/cells.17/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.17/shuffle/Reshape_1_output_0 = Reshape(%/cells.17/shuffle/Transpose_output_0, %/cells.17/shuffle/Constant_1_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.17/shuffle/Reshape_1_output_0, %onnx::Conv_855, %onnx::Conv_856) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_858, %onnx::Conv_859) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_861, %onnx::Conv_862) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.18/nl/Relu_output_0, %onnx::Conv_864, %onnx::Conv_865) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_867, %onnx::Conv_868) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_870, %onnx::Conv_871) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.19/shuffle/Reshape_output_0 = Reshape(%/cells.19/nl/Relu_output_0, %/cells.19/shuffle/Constant_output_0) %/cells.19/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.19/shuffle/Reshape_output_0) %/cells.19/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.19/shuffle/Reshape_1_output_0 = Reshape(%/cells.19/shuffle/Transpose_output_0, %/cells.19/shuffle/Constant_1_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.19/shuffle/Reshape_1_output_0, %onnx::Conv_873, %onnx::Conv_874) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_876, %onnx::Conv_877) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_879, %onnx::Conv_880) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.20/nl/Relu_output_0, %onnx::Conv_882, %onnx::Conv_883) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_885, %onnx::Conv_886) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_888, %onnx::Conv_889) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.21/nl/Relu_output_0, %onnx::Conv_891, %onnx::Conv_892) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_894, %onnx::Conv_895) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_897, %onnx::Conv_898) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %712 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %712 }	val_accuracy	72,213,632	1,684,340	{'zcp_synflow': 72.16098340295207, 'zcp_zen': 65.30683135986328, 'zcp_epe_nas': 0.00015999920000638146, 'zcp_fisher': 0.1759364753961563, 'zcp_flops': 72213632.0, 'zcp_grad_norm': 26.0400333404541, 'zcp_grasp': -0.06778335571289062, 'zcp_jacov': -16.05187931263494, 'zcp_l2_norm': 580.2186889648438, 'zcp_nwot': 215.0088445524501, 'zcp_params': 1684340.0, 'zcp_plain': -0.0024275428149849176, 'zcp_snip': 49.111915588378906, 'lat_1080ti_1': 0.718151034452475, 'lat_1080ti_32': 0.6843022329240172, 'lat_1080ti_64': 0.6611436004629561, 'lat_2080ti_1': 0.6927862334509848, 'lat_2080ti_32': 0.6752094828916441, 'lat_2080ti_64': 0.6520457054008263, 'lat_essential_ph_1': 0.4339622641509434, 'lat_eyeriss': 0.5231511195056205, 'lat_fpga': 0.5156195885571794, 'lat_gold_6226': 0.28099797790099723, 'lat_gold_6240': 0.5344752446459036, 'lat_pixel2': 0.4782608695652174, 'lat_pixel3': 0.5587282198179816, 'lat_raspi4': 0.5461735113470381, 'lat_samsung_a50': 0.21052631578947367, 'lat_samsung_s7': 0.23622047244094488, 'lat_silver_4114': 0.565983982471086, 'lat_silver_4210r': 0.6087643846989625, 'lat_titan_rtx_1': 0.6450327230071463, 'lat_titan_rtx_32': 0.6313709687253601, 'lat_titan_rtx_64': 0.6674181637536437, 'lat_titanx_1': 0.3399724510357793, 'lat_titanx_32': 0.6544205868448596, 'lat_titanx_64': 0.6957404769281293, 'lat_titanxp_1': 0.6202347775846293, 'lat_titanxp_32': 0.6574628688625995, 'lat_titanxp_64': 0.6554421211301811}
FBNet_2216	FBNet	2216	2216	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_605[FLOAT, 16x3x3x3] %onnx::Conv_606[FLOAT, 16] %onnx::Conv_608[FLOAT, 96x16x1x1] %onnx::Conv_609[FLOAT, 96] %onnx::Conv_611[FLOAT, 96x1x5x5] %onnx::Conv_614[FLOAT, 16x96x1x1] %onnx::Conv_617[FLOAT, 16x8x1x1] %onnx::Conv_620[FLOAT, 16x1x5x5] %onnx::Conv_623[FLOAT, 24x8x1x1] %onnx::Conv_624[FLOAT, 24] %onnx::Conv_626[FLOAT, 144x24x1x1] %onnx::Conv_627[FLOAT, 144] %onnx::Conv_629[FLOAT, 144x1x3x3] %onnx::Conv_632[FLOAT, 24x144x1x1] %onnx::Conv_635[FLOAT, 144x24x1x1] %onnx::Conv_638[FLOAT, 144x1x5x5] %onnx::Conv_641[FLOAT, 24x144x1x1] %onnx::Conv_644[FLOAT, 144x24x1x1] %onnx::Conv_647[FLOAT, 144x1x5x5] %onnx::Conv_650[FLOAT, 32x144x1x1] %onnx::Conv_651[FLOAT, 32] %onnx::Conv_653[FLOAT, 96x32x1x1] %onnx::Conv_656[FLOAT, 96x1x5x5] %onnx::Conv_659[FLOAT, 32x96x1x1] %onnx::Conv_662[FLOAT, 192x32x1x1] %onnx::Conv_663[FLOAT, 192] %onnx::Conv_665[FLOAT, 192x1x5x5] %onnx::Conv_668[FLOAT, 32x192x1x1] %onnx::Conv_671[FLOAT, 32x32x1x1] %onnx::Conv_674[FLOAT, 32x1x5x5] %onnx::Conv_677[FLOAT, 64x32x1x1] %onnx::Conv_678[FLOAT, 64] %onnx::Conv_680[FLOAT, 192x64x1x1] %onnx::Conv_683[FLOAT, 192x1x3x3] %onnx::Conv_686[FLOAT, 64x192x1x1] %onnx::Conv_689[FLOAT, 64x32x1x1] %onnx::Conv_692[FLOAT, 64x1x5x5] %onnx::Conv_695[FLOAT, 64x32x1x1] %onnx::Conv_698[FLOAT, 192x64x1x1] %onnx::Conv_701[FLOAT, 192x1x5x5] %onnx::Conv_704[FLOAT, 64x192x1x1] %onnx::Conv_707[FLOAT, 64x64x1x1] %onnx::Conv_710[FLOAT, 64x1x5x5] %onnx::Conv_713[FLOAT, 112x64x1x1] %onnx::Conv_714[FLOAT, 112] %onnx::Conv_716[FLOAT, 672x112x1x1] %onnx::Conv_717[FLOAT, 672] %onnx::Conv_719[FLOAT, 672x1x3x3] %onnx::Conv_722[FLOAT, 112x672x1x1] %onnx::Conv_725[FLOAT, 112x56x1x1] %onnx::Conv_728[FLOAT, 112x1x5x5] %onnx::Conv_731[FLOAT, 112x56x1x1] %onnx::Conv_734[FLOAT, 336x112x1x1] %onnx::Conv_735[FLOAT, 336] %onnx::Conv_737[FLOAT, 336x1x3x3] %onnx::Conv_740[FLOAT, 184x336x1x1] %onnx::Conv_741[FLOAT, 184] %onnx::Conv_743[FLOAT, 184x92x1x1] %onnx::Conv_746[FLOAT, 184x1x3x3] %onnx::Conv_749[FLOAT, 184x92x1x1] %onnx::Conv_752[FLOAT, 184x184x1x1] %onnx::Conv_755[FLOAT, 184x1x3x3] %onnx::Conv_758[FLOAT, 184x184x1x1] %onnx::Conv_761[FLOAT, 184x92x1x1] %onnx::Conv_764[FLOAT, 184x1x3x3] %onnx::Conv_767[FLOAT, 352x92x1x1] %onnx::Conv_768[FLOAT, 352] %onnx::Conv_770[FLOAT, 1504x352x1x1] %onnx::Conv_771[FLOAT, 1504] ) { %onnx::Conv_765 = Identity(%onnx::Conv_741) %onnx::Conv_762 = Identity(%onnx::Conv_741) %onnx::Conv_759 = Identity(%onnx::Conv_741) %onnx::Conv_756 = Identity(%onnx::Conv_741) %onnx::Conv_753 = Identity(%onnx::Conv_741) %onnx::Conv_750 = Identity(%onnx::Conv_741) %onnx::Conv_747 = Identity(%onnx::Conv_741) %onnx::Conv_744 = Identity(%onnx::Conv_741) %onnx::Conv_738 = Identity(%onnx::Conv_735) %onnx::Conv_732 = Identity(%onnx::Conv_714) %onnx::Conv_729 = Identity(%onnx::Conv_714) %onnx::Conv_726 = Identity(%onnx::Conv_714) %onnx::Conv_723 = Identity(%onnx::Conv_714) %onnx::Conv_720 = Identity(%onnx::Conv_717) %onnx::Conv_711 = Identity(%onnx::Conv_678) %onnx::Conv_708 = Identity(%onnx::Conv_678) %onnx::Conv_705 = Identity(%onnx::Conv_678) %onnx::Conv_702 = Identity(%onnx::Conv_663) %onnx::Conv_699 = Identity(%onnx::Conv_663) %onnx::Conv_696 = Identity(%onnx::Conv_678) %onnx::Conv_693 = Identity(%onnx::Conv_678) %onnx::Conv_690 = Identity(%onnx::Conv_678) %onnx::Conv_687 = Identity(%onnx::Conv_678) %onnx::Conv_684 = Identity(%onnx::Conv_663) %onnx::Conv_681 = Identity(%onnx::Conv_663) %onnx::Conv_675 = Identity(%onnx::Conv_651) %onnx::Conv_672 = Identity(%onnx::Conv_651) %onnx::Conv_669 = Identity(%onnx::Conv_651) %onnx::Conv_666 = Identity(%onnx::Conv_663) %onnx::Conv_660 = Identity(%onnx::Conv_651) %onnx::Conv_657 = Identity(%onnx::Conv_609) %onnx::Conv_654 = Identity(%onnx::Conv_609) %onnx::Conv_648 = Identity(%onnx::Conv_627) %onnx::Conv_645 = Identity(%onnx::Conv_627) %onnx::Conv_642 = Identity(%onnx::Conv_624) %onnx::Conv_639 = Identity(%onnx::Conv_627) %onnx::Conv_636 = Identity(%onnx::Conv_627) %onnx::Conv_633 = Identity(%onnx::Conv_624) %onnx::Conv_630 = Identity(%onnx::Conv_627) %onnx::Conv_621 = Identity(%onnx::Conv_606) %onnx::Conv_618 = Identity(%onnx::Conv_606) %onnx::Conv_615 = Identity(%onnx::Conv_606) %onnx::Conv_612 = Identity(%onnx::Conv_609) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_605, %onnx::Conv_606) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_608, %onnx::Conv_609) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.0/nl/Relu_output_0, %onnx::Conv_611, %onnx::Conv_612) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_614, %onnx::Conv_615) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_617, %onnx::Conv_618) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.1/shuffle/Reshape_output_0 = Reshape(%/cells.1/nl/Relu_output_0, %/cells.1/shuffle/Constant_output_0) %/cells.1/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.1/shuffle/Reshape_output_0) %/cells.1/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.1/shuffle/Reshape_1_output_0 = Reshape(%/cells.1/shuffle/Transpose_output_0, %/cells.1/shuffle/Constant_1_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.1/shuffle/Reshape_1_output_0, %onnx::Conv_620, %onnx::Conv_621) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_623, %onnx::Conv_624) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_626, %onnx::Conv_627) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.2/nl/Relu_output_0, %onnx::Conv_629, %onnx::Conv_630) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_632, %onnx::Conv_633) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_635, %onnx::Conv_636) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.3/nl/Relu_output_0, %onnx::Conv_638, %onnx::Conv_639) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_641, %onnx::Conv_642) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_644, %onnx::Conv_645) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.5/nl/Relu_output_0, %onnx::Conv_647, %onnx::Conv_648) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_650, %onnx::Conv_651) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_653, %onnx::Conv_654) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.6/nl/Relu_output_0, %onnx::Conv_656, %onnx::Conv_657) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_659, %onnx::Conv_660) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_662, %onnx::Conv_663) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.7/nl/Relu_output_0, %onnx::Conv_665, %onnx::Conv_666) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_668, %onnx::Conv_669) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_671, %onnx::Conv_672) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.9/nl/Relu_output_0, %onnx::Conv_674, %onnx::Conv_675) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_677, %onnx::Conv_678) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_680, %onnx::Conv_681) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.10/nl/Relu_output_0, %onnx::Conv_683, %onnx::Conv_684) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_686, %onnx::Conv_687) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_689, %onnx::Conv_690) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.11/shuffle/Reshape_output_0 = Reshape(%/cells.11/nl/Relu_output_0, %/cells.11/shuffle/Constant_output_0) %/cells.11/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.11/shuffle/Reshape_output_0) %/cells.11/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.11/shuffle/Reshape_1_output_0 = Reshape(%/cells.11/shuffle/Transpose_output_0, %/cells.11/shuffle/Constant_1_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.11/shuffle/Reshape_1_output_0, %onnx::Conv_692, %onnx::Conv_693) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_695, %onnx::Conv_696) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_698, %onnx::Conv_699) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.12/nl/Relu_output_0, %onnx::Conv_701, %onnx::Conv_702) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_704, %onnx::Conv_705) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_707, %onnx::Conv_708) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.13/nl/Relu_output_0, %onnx::Conv_710, %onnx::Conv_711) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_713, %onnx::Conv_714) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_716, %onnx::Conv_717) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.14/nl/Relu_output_0, %onnx::Conv_719, %onnx::Conv_720) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_722, %onnx::Conv_723) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_725, %onnx::Conv_726) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.15/shuffle/Reshape_output_0 = Reshape(%/cells.15/nl/Relu_output_0, %/cells.15/shuffle/Constant_output_0) %/cells.15/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.15/shuffle/Reshape_output_0) %/cells.15/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.15/shuffle/Reshape_1_output_0 = Reshape(%/cells.15/shuffle/Transpose_output_0, %/cells.15/shuffle/Constant_1_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.15/shuffle/Reshape_1_output_0, %onnx::Conv_728, %onnx::Conv_729) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_731, %onnx::Conv_732) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_734, %onnx::Conv_735) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.17/nl/Relu_output_0, %onnx::Conv_737, %onnx::Conv_738) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_740, %onnx::Conv_741) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_743, %onnx::Conv_744) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.19/shuffle/Reshape_output_0 = Reshape(%/cells.19/nl/Relu_output_0, %/cells.19/shuffle/Constant_output_0) %/cells.19/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.19/shuffle/Reshape_output_0) %/cells.19/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.19/shuffle/Reshape_1_output_0 = Reshape(%/cells.19/shuffle/Transpose_output_0, %/cells.19/shuffle/Constant_1_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.19/shuffle/Reshape_1_output_0, %onnx::Conv_746, %onnx::Conv_747) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_749, %onnx::Conv_750) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_752, %onnx::Conv_753) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.20/nl/Relu_output_0, %onnx::Conv_755, %onnx::Conv_756) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_758, %onnx::Conv_759) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_761, %onnx::Conv_762) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.21/shuffle/Reshape_output_0 = Reshape(%/cells.21/nl/Relu_output_0, %/cells.21/shuffle/Constant_output_0) %/cells.21/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.21/shuffle/Reshape_output_0) %/cells.21/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.21/shuffle/Reshape_1_output_0 = Reshape(%/cells.21/shuffle/Transpose_output_0, %/cells.21/shuffle/Constant_1_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.21/shuffle/Reshape_1_output_0, %onnx::Conv_764, %onnx::Conv_765) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_767, %onnx::Conv_768) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_770, %onnx::Conv_771) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %603 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %603 }	val_accuracy	72,755,584	1,269,500	{'zcp_synflow': 69.72454176838542, 'zcp_zen': 61.03034210205078, 'zcp_epe_nas': 17.83729975521595, 'zcp_fisher': 0.08358211070299149, 'zcp_flops': 72755584.0, 'zcp_grad_norm': 22.653701782226562, 'zcp_grasp': -0.0006809234619140625, 'zcp_jacov': -16.066662506597673, 'zcp_l2_norm': 527.0225830078125, 'zcp_nwot': 217.13904630536965, 'zcp_params': 1269500.0, 'zcp_plain': 0.004553189035505056, 'zcp_snip': 43.02566146850586, 'lat_1080ti_1': 0.38781081986918636, 'lat_1080ti_32': 0.5948390707305211, 'lat_1080ti_64': 0.6725981161999156, 'lat_2080ti_1': 0.4279162825300311, 'lat_2080ti_32': 0.5583175336843874, 'lat_2080ti_64': 0.6622974918467476, 'lat_essential_ph_1': 0.22641509433962265, 'lat_eyeriss': 0.5124236700713329, 'lat_fpga': 0.4445917731226315, 'lat_gold_6226': 0.23439564410251654, 'lat_gold_6240': 0.2582081352167082, 'lat_pixel2': 0.2608695652173913, 'lat_pixel3': 0.5703715680630281, 'lat_raspi4': 0.5598115182286123, 'lat_samsung_a50': 0.17894736842105263, 'lat_samsung_s7': 0.12598425196850394, 'lat_silver_4114': 0.2686161717875743, 'lat_silver_4210r': 0.2723668165027129, 'lat_titan_rtx_1': 0.4052986287736654, 'lat_titan_rtx_32': 0.5046745853673067, 'lat_titan_rtx_64': 0.6462552314545361, 'lat_titanx_1': 0.2051960311582265, 'lat_titanx_32': 0.6155664450180144, 'lat_titanx_64': 0.6728628717081855, 'lat_titanxp_1': 0.3594370823410989, 'lat_titanxp_32': 0.5881910536523592, 'lat_titanxp_64': 0.6670197636799778}
FBNet_3666	FBNet	3666	3666	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_677[FLOAT, 16x3x3x3] %onnx::Conv_678[FLOAT, 16] %onnx::Conv_680[FLOAT, 48x16x1x1] %onnx::Conv_681[FLOAT, 48] %onnx::Conv_683[FLOAT, 48x1x3x3] %onnx::Conv_686[FLOAT, 16x48x1x1] %onnx::Conv_689[FLOAT, 16x16x1x1] %onnx::Conv_692[FLOAT, 16x1x3x3] %onnx::Conv_695[FLOAT, 24x16x1x1] %onnx::Conv_696[FLOAT, 24] %onnx::Conv_698[FLOAT, 24x24x1x1] %onnx::Conv_701[FLOAT, 24x1x5x5] %onnx::Conv_704[FLOAT, 24x24x1x1] %onnx::Conv_707[FLOAT, 144x24x1x1] %onnx::Conv_708[FLOAT, 144] %onnx::Conv_710[FLOAT, 144x1x3x3] %onnx::Conv_713[FLOAT, 24x144x1x1] %onnx::Conv_716[FLOAT, 144x24x1x1] %onnx::Conv_719[FLOAT, 144x1x5x5] %onnx::Conv_722[FLOAT, 24x144x1x1] %onnx::Conv_725[FLOAT, 24x12x1x1] %onnx::Conv_728[FLOAT, 24x1x5x5] %onnx::Conv_731[FLOAT, 32x12x1x1] %onnx::Conv_732[FLOAT, 32] %onnx::Conv_734[FLOAT, 96x32x1x1] %onnx::Conv_735[FLOAT, 96] %onnx::Conv_737[FLOAT, 96x1x5x5] %onnx::Conv_740[FLOAT, 32x96x1x1] %onnx::Conv_743[FLOAT, 32x16x1x1] %onnx::Conv_746[FLOAT, 32x1x5x5] %onnx::Conv_749[FLOAT, 32x16x1x1] %onnx::Conv_752[FLOAT, 192x32x1x1] %onnx::Conv_753[FLOAT, 192] %onnx::Conv_755[FLOAT, 192x1x3x3] %onnx::Conv_758[FLOAT, 32x192x1x1] %onnx::Conv_761[FLOAT, 192x32x1x1] %onnx::Conv_764[FLOAT, 192x1x3x3] %onnx::Conv_767[FLOAT, 64x192x1x1] %onnx::Conv_768[FLOAT, 64] %onnx::Conv_770[FLOAT, 384x64x1x1] %onnx::Conv_771[FLOAT, 384] %onnx::Conv_773[FLOAT, 384x1x3x3] %onnx::Conv_776[FLOAT, 64x384x1x1] %onnx::Conv_779[FLOAT, 64x32x1x1] %onnx::Conv_782[FLOAT, 64x1x5x5] %onnx::Conv_785[FLOAT, 64x32x1x1] %onnx::Conv_788[FLOAT, 64x32x1x1] %onnx::Conv_791[FLOAT, 64x1x3x3] %onnx::Conv_794[FLOAT, 64x32x1x1] %onnx::Conv_797[FLOAT, 112x64x1x1] %onnx::Conv_798[FLOAT, 112] %onnx::Conv_800[FLOAT, 336x112x1x1] %onnx::Conv_801[FLOAT, 336] %onnx::Conv_803[FLOAT, 336x1x5x5] %onnx::Conv_806[FLOAT, 112x336x1x1] %onnx::Conv_809[FLOAT, 336x112x1x1] %onnx::Conv_812[FLOAT, 336x1x5x5] %onnx::Conv_815[FLOAT, 112x336x1x1] %onnx::Conv_818[FLOAT, 336x112x1x1] %onnx::Conv_821[FLOAT, 336x1x5x5] %onnx::Conv_824[FLOAT, 112x336x1x1] %onnx::Conv_827[FLOAT, 112x112x1x1] %onnx::Conv_830[FLOAT, 112x1x5x5] %onnx::Conv_833[FLOAT, 184x112x1x1] %onnx::Conv_834[FLOAT, 184] %onnx::Conv_836[FLOAT, 184x184x1x1] %onnx::Conv_839[FLOAT, 184x1x5x5] %onnx::Conv_842[FLOAT, 184x184x1x1] %onnx::Conv_845[FLOAT, 184x184x1x1] %onnx::Conv_848[FLOAT, 184x1x5x5] %onnx::Conv_851[FLOAT, 184x184x1x1] %onnx::Conv_854[FLOAT, 552x184x1x1] %onnx::Conv_855[FLOAT, 552] %onnx::Conv_857[FLOAT, 552x1x5x5] %onnx::Conv_860[FLOAT, 184x552x1x1] %onnx::Conv_863[FLOAT, 552x184x1x1] %onnx::Conv_866[FLOAT, 552x1x3x3] %onnx::Conv_869[FLOAT, 352x552x1x1] %onnx::Conv_870[FLOAT, 352] %onnx::Conv_872[FLOAT, 1504x352x1x1] %onnx::Conv_873[FLOAT, 1504] ) { %onnx::Conv_867 = Identity(%onnx::Conv_855) %onnx::Conv_864 = Identity(%onnx::Conv_855) %onnx::Conv_861 = Identity(%onnx::Conv_834) %onnx::Conv_858 = Identity(%onnx::Conv_855) %onnx::Conv_852 = Identity(%onnx::Conv_834) %onnx::Conv_849 = Identity(%onnx::Conv_834) %onnx::Conv_846 = Identity(%onnx::Conv_834) %onnx::Conv_843 = Identity(%onnx::Conv_834) %onnx::Conv_840 = Identity(%onnx::Conv_834) %onnx::Conv_837 = Identity(%onnx::Conv_834) %onnx::Conv_831 = Identity(%onnx::Conv_798) %onnx::Conv_828 = Identity(%onnx::Conv_798) %onnx::Conv_825 = Identity(%onnx::Conv_798) %onnx::Conv_822 = Identity(%onnx::Conv_801) %onnx::Conv_819 = Identity(%onnx::Conv_801) %onnx::Conv_816 = Identity(%onnx::Conv_798) %onnx::Conv_813 = Identity(%onnx::Conv_801) %onnx::Conv_810 = Identity(%onnx::Conv_801) %onnx::Conv_807 = Identity(%onnx::Conv_798) %onnx::Conv_804 = Identity(%onnx::Conv_801) %onnx::Conv_795 = Identity(%onnx::Conv_768) %onnx::Conv_792 = Identity(%onnx::Conv_768) %onnx::Conv_789 = Identity(%onnx::Conv_768) %onnx::Conv_786 = Identity(%onnx::Conv_768) %onnx::Conv_783 = Identity(%onnx::Conv_768) %onnx::Conv_780 = Identity(%onnx::Conv_768) %onnx::Conv_777 = Identity(%onnx::Conv_768) %onnx::Conv_774 = Identity(%onnx::Conv_771) %onnx::Conv_765 = Identity(%onnx::Conv_753) %onnx::Conv_762 = Identity(%onnx::Conv_753) %onnx::Conv_759 = Identity(%onnx::Conv_732) %onnx::Conv_756 = Identity(%onnx::Conv_753) %onnx::Conv_750 = Identity(%onnx::Conv_732) %onnx::Conv_747 = Identity(%onnx::Conv_732) %onnx::Conv_744 = Identity(%onnx::Conv_732) %onnx::Conv_741 = Identity(%onnx::Conv_732) %onnx::Conv_738 = Identity(%onnx::Conv_735) %onnx::Conv_729 = Identity(%onnx::Conv_696) %onnx::Conv_726 = Identity(%onnx::Conv_696) %onnx::Conv_723 = Identity(%onnx::Conv_696) %onnx::Conv_720 = Identity(%onnx::Conv_708) %onnx::Conv_717 = Identity(%onnx::Conv_708) %onnx::Conv_714 = Identity(%onnx::Conv_696) %onnx::Conv_711 = Identity(%onnx::Conv_708) %onnx::Conv_705 = Identity(%onnx::Conv_696) %onnx::Conv_702 = Identity(%onnx::Conv_696) %onnx::Conv_699 = Identity(%onnx::Conv_696) %onnx::Conv_693 = Identity(%onnx::Conv_678) %onnx::Conv_690 = Identity(%onnx::Conv_678) %onnx::Conv_687 = Identity(%onnx::Conv_678) %onnx::Conv_684 = Identity(%onnx::Conv_681) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_677, %onnx::Conv_678) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_680, %onnx::Conv_681) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 48, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.0/nl/Relu_output_0, %onnx::Conv_683, %onnx::Conv_684) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_686, %onnx::Conv_687) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_689, %onnx::Conv_690) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.1/nl/Relu_output_0, %onnx::Conv_692, %onnx::Conv_693) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_695, %onnx::Conv_696) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_698, %onnx::Conv_699) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.2/nl/Relu_output_0, %onnx::Conv_701, %onnx::Conv_702) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_704, %onnx::Conv_705) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_707, %onnx::Conv_708) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.3/nl/Relu_output_0, %onnx::Conv_710, %onnx::Conv_711) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_713, %onnx::Conv_714) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_716, %onnx::Conv_717) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.4/nl/Relu_output_0, %onnx::Conv_719, %onnx::Conv_720) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_722, %onnx::Conv_723) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/Add_output_0, %onnx::Conv_725, %onnx::Conv_726) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.5/shuffle/Reshape_output_0 = Reshape(%/cells.5/nl/Relu_output_0, %/cells.5/shuffle/Constant_output_0) %/cells.5/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.5/shuffle/Reshape_output_0) %/cells.5/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.5/shuffle/Reshape_1_output_0 = Reshape(%/cells.5/shuffle/Transpose_output_0, %/cells.5/shuffle/Constant_1_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.5/shuffle/Reshape_1_output_0, %onnx::Conv_728, %onnx::Conv_729) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_731, %onnx::Conv_732) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_734, %onnx::Conv_735) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.6/nl/Relu_output_0, %onnx::Conv_737, %onnx::Conv_738) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_740, %onnx::Conv_741) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_743, %onnx::Conv_744) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.7/shuffle/Reshape_output_0 = Reshape(%/cells.7/nl/Relu_output_0, %/cells.7/shuffle/Constant_output_0) %/cells.7/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.7/shuffle/Reshape_output_0) %/cells.7/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.7/shuffle/Reshape_1_output_0 = Reshape(%/cells.7/shuffle/Transpose_output_0, %/cells.7/shuffle/Constant_1_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.7/shuffle/Reshape_1_output_0, %onnx::Conv_746, %onnx::Conv_747) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_749, %onnx::Conv_750) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_752, %onnx::Conv_753) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.8/nl/Relu_output_0, %onnx::Conv_755, %onnx::Conv_756) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_758, %onnx::Conv_759) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_761, %onnx::Conv_762) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.9/nl/Relu_output_0, %onnx::Conv_764, %onnx::Conv_765) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_767, %onnx::Conv_768) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_770, %onnx::Conv_771) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.10/nl/Relu_output_0, %onnx::Conv_773, %onnx::Conv_774) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_776, %onnx::Conv_777) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_779, %onnx::Conv_780) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.11/shuffle/Reshape_output_0 = Reshape(%/cells.11/nl/Relu_output_0, %/cells.11/shuffle/Constant_output_0) %/cells.11/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.11/shuffle/Reshape_output_0) %/cells.11/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.11/shuffle/Reshape_1_output_0 = Reshape(%/cells.11/shuffle/Transpose_output_0, %/cells.11/shuffle/Constant_1_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.11/shuffle/Reshape_1_output_0, %onnx::Conv_782, %onnx::Conv_783) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_785, %onnx::Conv_786) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_788, %onnx::Conv_789) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.12/shuffle/Reshape_output_0 = Reshape(%/cells.12/nl/Relu_output_0, %/cells.12/shuffle/Constant_output_0) %/cells.12/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.12/shuffle/Reshape_output_0) %/cells.12/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.12/shuffle/Reshape_1_output_0 = Reshape(%/cells.12/shuffle/Transpose_output_0, %/cells.12/shuffle/Constant_1_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.12/shuffle/Reshape_1_output_0, %onnx::Conv_791, %onnx::Conv_792) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_794, %onnx::Conv_795) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_797, %onnx::Conv_798) %/cells.13/relu/Relu_output_0 = Relu(%/cells.13/conv/Conv_output_0) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/relu/Relu_output_0, %onnx::Conv_800, %onnx::Conv_801) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.14/nl/Relu_output_0, %onnx::Conv_803, %onnx::Conv_804) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_806, %onnx::Conv_807) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/relu/Relu_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_809, %onnx::Conv_810) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.15/nl/Relu_output_0, %onnx::Conv_812, %onnx::Conv_813) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_815, %onnx::Conv_816) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_818, %onnx::Conv_819) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.16/nl/Relu_output_0, %onnx::Conv_821, %onnx::Conv_822) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_824, %onnx::Conv_825) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.15/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_827, %onnx::Conv_828) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.17/nl/Relu_output_0, %onnx::Conv_830, %onnx::Conv_831) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_833, %onnx::Conv_834) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_836, %onnx::Conv_837) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.18/nl/Relu_output_0, %onnx::Conv_839, %onnx::Conv_840) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_842, %onnx::Conv_843) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_845, %onnx::Conv_846) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.19/nl/Relu_output_0, %onnx::Conv_848, %onnx::Conv_849) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_851, %onnx::Conv_852) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_854, %onnx::Conv_855) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 552, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.20/nl/Relu_output_0, %onnx::Conv_857, %onnx::Conv_858) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_860, %onnx::Conv_861) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_863, %onnx::Conv_864) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 552, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.21/nl/Relu_output_0, %onnx::Conv_866, %onnx::Conv_867) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_869, %onnx::Conv_870) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_872, %onnx::Conv_873) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %675 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %675 }	val_accuracy	78,165,888	1,792,036	{'zcp_synflow': 83.08383025869732, 'zcp_zen': 73.02928924560547, 'zcp_epe_nas': 0.00015999920000638146, 'zcp_fisher': 0.12756547331809998, 'zcp_flops': 78165888.0, 'zcp_grad_norm': 26.60373878479004, 'zcp_grasp': 0.48154449462890625, 'zcp_jacov': -16.074595696477346, 'zcp_l2_norm': 661.9393920898438, 'zcp_nwot': 216.1848825950431, 'zcp_params': 1792036.0, 'zcp_plain': 0.0013559507206082344, 'zcp_snip': 52.48326110839844, 'lat_1080ti_1': 0.7427558804003764, 'lat_1080ti_32': 0.7695878065270721, 'lat_1080ti_64': 0.6157347299625864, 'lat_2080ti_1': 0.7437858238803605, 'lat_2080ti_32': 0.7609874111202787, 'lat_2080ti_64': 0.6517447536890476, 'lat_essential_ph_1': 0.3584905660377358, 'lat_eyeriss': 0.5621183503566649, 'lat_fpga': 0.5796119098744297, 'lat_gold_6226': 0.35855155931826554, 'lat_gold_6240': 0.555686342724389, 'lat_pixel2': 0.391304347826087, 'lat_pixel3': 0.5750582083159347, 'lat_raspi4': 0.6108891325077674, 'lat_samsung_a50': 0.22105263157894736, 'lat_samsung_s7': 0.1889763779527559, 'lat_silver_4114': 0.616190914719056, 'lat_silver_4210r': 0.6315801264147778, 'lat_titan_rtx_1': 0.7049410737020617, 'lat_titan_rtx_32': 0.7293941433640063, 'lat_titan_rtx_64': 0.68785673167926, 'lat_titanx_1': 0.3747811492223131, 'lat_titanx_32': 0.7051430045285797, 'lat_titanx_64': 0.634213049686599, 'lat_titanxp_1': 0.6585321945292785, 'lat_titanxp_32': 0.7299251763222666, 'lat_titanxp_64': 0.6512844890038776}
FBNet_715	FBNet	715	715	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_606[FLOAT, 16x3x3x3] %onnx::Conv_607[FLOAT, 16] %onnx::Conv_609[FLOAT, 16x8x1x1] %onnx::Conv_612[FLOAT, 16x1x5x5] %onnx::Conv_615[FLOAT, 24x8x1x1] %onnx::Conv_616[FLOAT, 24] %onnx::Conv_618[FLOAT, 144x24x1x1] %onnx::Conv_619[FLOAT, 144] %onnx::Conv_621[FLOAT, 144x1x5x5] %onnx::Conv_624[FLOAT, 24x144x1x1] %onnx::Conv_627[FLOAT, 24x12x1x1] %onnx::Conv_630[FLOAT, 24x1x5x5] %onnx::Conv_633[FLOAT, 24x12x1x1] %onnx::Conv_636[FLOAT, 24x12x1x1] %onnx::Conv_639[FLOAT, 24x1x3x3] %onnx::Conv_642[FLOAT, 32x12x1x1] %onnx::Conv_643[FLOAT, 32] %onnx::Conv_645[FLOAT, 192x32x1x1] %onnx::Conv_646[FLOAT, 192] %onnx::Conv_648[FLOAT, 192x1x5x5] %onnx::Conv_651[FLOAT, 32x192x1x1] %onnx::Conv_654[FLOAT, 96x32x1x1] %onnx::Conv_655[FLOAT, 96] %onnx::Conv_657[FLOAT, 96x1x3x3] %onnx::Conv_660[FLOAT, 32x96x1x1] %onnx::Conv_663[FLOAT, 32x32x1x1] %onnx::Conv_666[FLOAT, 32x1x5x5] %onnx::Conv_669[FLOAT, 64x32x1x1] %onnx::Conv_670[FLOAT, 64] %onnx::Conv_672[FLOAT, 64x32x1x1] %onnx::Conv_675[FLOAT, 64x1x5x5] %onnx::Conv_678[FLOAT, 64x32x1x1] %onnx::Conv_681[FLOAT, 384x64x1x1] %onnx::Conv_682[FLOAT, 384] %onnx::Conv_684[FLOAT, 384x1x5x5] %onnx::Conv_687[FLOAT, 64x384x1x1] %onnx::Conv_690[FLOAT, 192x64x1x1] %onnx::Conv_693[FLOAT, 192x1x3x3] %onnx::Conv_696[FLOAT, 64x192x1x1] %onnx::Conv_699[FLOAT, 64x32x1x1] %onnx::Conv_702[FLOAT, 64x1x5x5] %onnx::Conv_705[FLOAT, 112x32x1x1] %onnx::Conv_706[FLOAT, 112] %onnx::Conv_708[FLOAT, 112x56x1x1] %onnx::Conv_711[FLOAT, 112x1x5x5] %onnx::Conv_714[FLOAT, 112x56x1x1] %onnx::Conv_717[FLOAT, 672x112x1x1] %onnx::Conv_718[FLOAT, 672] %onnx::Conv_720[FLOAT, 672x1x5x5] %onnx::Conv_723[FLOAT, 184x672x1x1] %onnx::Conv_724[FLOAT, 184] %onnx::Conv_726[FLOAT, 184x184x1x1] %onnx::Conv_729[FLOAT, 184x1x5x5] %onnx::Conv_732[FLOAT, 184x184x1x1] %onnx::Conv_735[FLOAT, 184x92x1x1] %onnx::Conv_738[FLOAT, 184x1x5x5] %onnx::Conv_741[FLOAT, 184x92x1x1] %onnx::Conv_744[FLOAT, 184x92x1x1] %onnx::Conv_747[FLOAT, 184x1x5x5] %onnx::Conv_750[FLOAT, 352x92x1x1] %onnx::Conv_751[FLOAT, 352] %onnx::Conv_753[FLOAT, 1504x352x1x1] %onnx::Conv_754[FLOAT, 1504] ) { %onnx::Conv_748 = Identity(%onnx::Conv_724) %onnx::Conv_745 = Identity(%onnx::Conv_724) %onnx::Conv_742 = Identity(%onnx::Conv_724) %onnx::Conv_739 = Identity(%onnx::Conv_724) %onnx::Conv_736 = Identity(%onnx::Conv_724) %onnx::Conv_733 = Identity(%onnx::Conv_724) %onnx::Conv_730 = Identity(%onnx::Conv_724) %onnx::Conv_727 = Identity(%onnx::Conv_724) %onnx::Conv_721 = Identity(%onnx::Conv_718) %onnx::Conv_715 = Identity(%onnx::Conv_706) %onnx::Conv_712 = Identity(%onnx::Conv_706) %onnx::Conv_709 = Identity(%onnx::Conv_706) %onnx::Conv_703 = Identity(%onnx::Conv_670) %onnx::Conv_700 = Identity(%onnx::Conv_670) %onnx::Conv_697 = Identity(%onnx::Conv_670) %onnx::Conv_694 = Identity(%onnx::Conv_646) %onnx::Conv_691 = Identity(%onnx::Conv_646) %onnx::Conv_688 = Identity(%onnx::Conv_670) %onnx::Conv_685 = Identity(%onnx::Conv_682) %onnx::Conv_679 = Identity(%onnx::Conv_670) %onnx::Conv_676 = Identity(%onnx::Conv_670) %onnx::Conv_673 = Identity(%onnx::Conv_670) %onnx::Conv_667 = Identity(%onnx::Conv_643) %onnx::Conv_664 = Identity(%onnx::Conv_643) %onnx::Conv_661 = Identity(%onnx::Conv_643) %onnx::Conv_658 = Identity(%onnx::Conv_655) %onnx::Conv_652 = Identity(%onnx::Conv_643) %onnx::Conv_649 = Identity(%onnx::Conv_646) %onnx::Conv_640 = Identity(%onnx::Conv_616) %onnx::Conv_637 = Identity(%onnx::Conv_616) %onnx::Conv_634 = Identity(%onnx::Conv_616) %onnx::Conv_631 = Identity(%onnx::Conv_616) %onnx::Conv_628 = Identity(%onnx::Conv_616) %onnx::Conv_625 = Identity(%onnx::Conv_616) %onnx::Conv_622 = Identity(%onnx::Conv_619) %onnx::Conv_613 = Identity(%onnx::Conv_607) %onnx::Conv_610 = Identity(%onnx::Conv_607) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_606, %onnx::Conv_607) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_609, %onnx::Conv_610) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.1/shuffle/Reshape_output_0 = Reshape(%/cells.1/nl/Relu_output_0, %/cells.1/shuffle/Constant_output_0) %/cells.1/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.1/shuffle/Reshape_output_0) %/cells.1/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.1/shuffle/Reshape_1_output_0 = Reshape(%/cells.1/shuffle/Transpose_output_0, %/cells.1/shuffle/Constant_1_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.1/shuffle/Reshape_1_output_0, %onnx::Conv_612, %onnx::Conv_613) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_615, %onnx::Conv_616) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_618, %onnx::Conv_619) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.2/nl/Relu_output_0, %onnx::Conv_621, %onnx::Conv_622) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_624, %onnx::Conv_625) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_627, %onnx::Conv_628) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.4/shuffle/Reshape_output_0 = Reshape(%/cells.4/nl/Relu_output_0, %/cells.4/shuffle/Constant_output_0) %/cells.4/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.4/shuffle/Reshape_output_0) %/cells.4/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.4/shuffle/Reshape_1_output_0 = Reshape(%/cells.4/shuffle/Transpose_output_0, %/cells.4/shuffle/Constant_1_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.4/shuffle/Reshape_1_output_0, %onnx::Conv_630, %onnx::Conv_631) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_633, %onnx::Conv_634) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/Add_output_0, %onnx::Conv_636, %onnx::Conv_637) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.5/shuffle/Reshape_output_0 = Reshape(%/cells.5/nl/Relu_output_0, %/cells.5/shuffle/Constant_output_0) %/cells.5/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.5/shuffle/Reshape_output_0) %/cells.5/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.5/shuffle/Reshape_1_output_0 = Reshape(%/cells.5/shuffle/Transpose_output_0, %/cells.5/shuffle/Constant_1_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.5/shuffle/Reshape_1_output_0, %onnx::Conv_639, %onnx::Conv_640) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_642, %onnx::Conv_643) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_645, %onnx::Conv_646) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.6/nl/Relu_output_0, %onnx::Conv_648, %onnx::Conv_649) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_651, %onnx::Conv_652) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_654, %onnx::Conv_655) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.8/nl/Relu_output_0, %onnx::Conv_657, %onnx::Conv_658) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_660, %onnx::Conv_661) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_663, %onnx::Conv_664) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.9/nl/Relu_output_0, %onnx::Conv_666, %onnx::Conv_667) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_669, %onnx::Conv_670) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_672, %onnx::Conv_673) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.10/shuffle/Reshape_output_0 = Reshape(%/cells.10/nl/Relu_output_0, %/cells.10/shuffle/Constant_output_0) %/cells.10/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.10/shuffle/Reshape_output_0) %/cells.10/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.10/shuffle/Reshape_1_output_0 = Reshape(%/cells.10/shuffle/Transpose_output_0, %/cells.10/shuffle/Constant_1_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.10/shuffle/Reshape_1_output_0, %onnx::Conv_675, %onnx::Conv_676) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_678, %onnx::Conv_679) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_681, %onnx::Conv_682) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.11/nl/Relu_output_0, %onnx::Conv_684, %onnx::Conv_685) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_687, %onnx::Conv_688) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_690, %onnx::Conv_691) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.12/nl/Relu_output_0, %onnx::Conv_693, %onnx::Conv_694) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_696, %onnx::Conv_697) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_699, %onnx::Conv_700) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.13/shuffle/Reshape_output_0 = Reshape(%/cells.13/nl/Relu_output_0, %/cells.13/shuffle/Constant_output_0) %/cells.13/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.13/shuffle/Reshape_output_0) %/cells.13/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.13/shuffle/Reshape_1_output_0 = Reshape(%/cells.13/shuffle/Transpose_output_0, %/cells.13/shuffle/Constant_1_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.13/shuffle/Reshape_1_output_0, %onnx::Conv_702, %onnx::Conv_703) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_705, %onnx::Conv_706) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_708, %onnx::Conv_709) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.14/shuffle/Reshape_output_0 = Reshape(%/cells.14/nl/Relu_output_0, %/cells.14/shuffle/Constant_output_0) %/cells.14/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.14/shuffle/Reshape_output_0) %/cells.14/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.14/shuffle/Reshape_1_output_0 = Reshape(%/cells.14/shuffle/Transpose_output_0, %/cells.14/shuffle/Constant_1_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.14/shuffle/Reshape_1_output_0, %onnx::Conv_711, %onnx::Conv_712) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_714, %onnx::Conv_715) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_717, %onnx::Conv_718) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.17/nl/Relu_output_0, %onnx::Conv_720, %onnx::Conv_721) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_723, %onnx::Conv_724) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_726, %onnx::Conv_727) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.18/nl/Relu_output_0, %onnx::Conv_729, %onnx::Conv_730) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_732, %onnx::Conv_733) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_735, %onnx::Conv_736) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.20/shuffle/Reshape_output_0 = Reshape(%/cells.20/nl/Relu_output_0, %/cells.20/shuffle/Constant_output_0) %/cells.20/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.20/shuffle/Reshape_output_0) %/cells.20/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.20/shuffle/Reshape_1_output_0 = Reshape(%/cells.20/shuffle/Transpose_output_0, %/cells.20/shuffle/Constant_1_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.20/shuffle/Reshape_1_output_0, %onnx::Conv_738, %onnx::Conv_739) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_741, %onnx::Conv_742) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_744, %onnx::Conv_745) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.21/shuffle/Reshape_output_0 = Reshape(%/cells.21/nl/Relu_output_0, %/cells.21/shuffle/Constant_output_0) %/cells.21/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.21/shuffle/Reshape_output_0) %/cells.21/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.21/shuffle/Reshape_1_output_0 = Reshape(%/cells.21/shuffle/Transpose_output_0, %/cells.21/shuffle/Constant_1_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.21/shuffle/Reshape_1_output_0, %onnx::Conv_747, %onnx::Conv_748) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_750, %onnx::Conv_751) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_753, %onnx::Conv_754) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %604 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %604 }	val_accuracy	48,039,296	1,231,740	{'zcp_synflow': 58.7386504683667, 'zcp_zen': 52.44569396972656, 'zcp_epe_nas': 22.420466564738188, 'zcp_fisher': 0.03454165905714035, 'zcp_flops': 48039296.0, 'zcp_grad_norm': 14.529096603393555, 'zcp_grasp': -0.006956577301025391, 'zcp_jacov': -16.064135604268206, 'zcp_l2_norm': 436.2222900390625, 'zcp_nwot': 207.46073366975105, 'zcp_params': 1231740.0, 'zcp_plain': 0.006229395978152752, 'zcp_snip': 22.970956802368164, 'lat_1080ti_1': 0.2400837114081096, 'lat_1080ti_32': 0.27651979633149815, 'lat_1080ti_64': 0.270883130863782, 'lat_2080ti_1': 0.2645403106071307, 'lat_2080ti_32': 0.2856184488686383, 'lat_2080ti_64': 0.26262436987264953, 'lat_essential_ph_1': 0.22641509433962265, 'lat_eyeriss': 0.2271743714814883, 'lat_fpga': 0.13583154394957395, 'lat_gold_6226': 0.14035983764847337, 'lat_gold_6240': 0.27583750108151356, 'lat_pixel2': 0.15217391304347827, 'lat_pixel3': 0.2975623168137376, 'lat_raspi4': 0.2655659714352195, 'lat_samsung_a50': 0.07368421052631578, 'lat_samsung_s7': 0.10236220472440945, 'lat_silver_4114': 0.2627763493679061, 'lat_silver_4210r': 0.19413507984697487, 'lat_titan_rtx_1': 0.24933557364914138, 'lat_titan_rtx_32': 0.2724411464037471, 'lat_titan_rtx_64': 0.268282695937418, 'lat_titanx_1': 0.12758365213763342, 'lat_titanx_32': 0.2526220844190015, 'lat_titanx_64': 0.28743163310213854, 'lat_titanxp_1': 0.2317261443234968, 'lat_titanxp_32': 0.28205478754806046, 'lat_titanxp_64': 0.27500923287422935}
FBNet_474	FBNet	474	474	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_687[FLOAT, 16x3x3x3] %onnx::Conv_688[FLOAT, 16] %onnx::Conv_690[FLOAT, 16x8x1x1] %onnx::Conv_693[FLOAT, 16x1x3x3] %onnx::Conv_696[FLOAT, 16x8x1x1] %onnx::Conv_699[FLOAT, 16x8x1x1] %onnx::Conv_702[FLOAT, 16x1x5x5] %onnx::Conv_705[FLOAT, 24x8x1x1] %onnx::Conv_706[FLOAT, 24] %onnx::Conv_708[FLOAT, 24x12x1x1] %onnx::Conv_711[FLOAT, 24x1x5x5] %onnx::Conv_714[FLOAT, 24x12x1x1] %onnx::Conv_717[FLOAT, 24x24x1x1] %onnx::Conv_720[FLOAT, 24x1x3x3] %onnx::Conv_723[FLOAT, 24x24x1x1] %onnx::Conv_726[FLOAT, 72x24x1x1] %onnx::Conv_727[FLOAT, 72] %onnx::Conv_729[FLOAT, 72x1x5x5] %onnx::Conv_732[FLOAT, 32x72x1x1] %onnx::Conv_733[FLOAT, 32] %onnx::Conv_735[FLOAT, 192x32x1x1] %onnx::Conv_736[FLOAT, 192] %onnx::Conv_738[FLOAT, 192x1x3x3] %onnx::Conv_741[FLOAT, 32x192x1x1] %onnx::Conv_744[FLOAT, 32x16x1x1] %onnx::Conv_747[FLOAT, 32x1x3x3] %onnx::Conv_750[FLOAT, 32x16x1x1] %onnx::Conv_753[FLOAT, 96x32x1x1] %onnx::Conv_754[FLOAT, 96] %onnx::Conv_756[FLOAT, 96x1x3x3] %onnx::Conv_759[FLOAT, 64x96x1x1] %onnx::Conv_760[FLOAT, 64] %onnx::Conv_762[FLOAT, 64x32x1x1] %onnx::Conv_765[FLOAT, 64x1x5x5] %onnx::Conv_768[FLOAT, 64x32x1x1] %onnx::Conv_771[FLOAT, 64x64x1x1] %onnx::Conv_774[FLOAT, 64x1x5x5] %onnx::Conv_777[FLOAT, 64x64x1x1] %onnx::Conv_780[FLOAT, 64x32x1x1] %onnx::Conv_783[FLOAT, 64x1x5x5] %onnx::Conv_786[FLOAT, 112x32x1x1] %onnx::Conv_787[FLOAT, 112] %onnx::Conv_789[FLOAT, 672x112x1x1] %onnx::Conv_790[FLOAT, 672] %onnx::Conv_792[FLOAT, 672x1x3x3] %onnx::Conv_795[FLOAT, 112x672x1x1] %onnx::Conv_798[FLOAT, 672x112x1x1] %onnx::Conv_801[FLOAT, 672x1x5x5] %onnx::Conv_804[FLOAT, 112x672x1x1] %onnx::Conv_807[FLOAT, 336x112x1x1] %onnx::Conv_808[FLOAT, 336] %onnx::Conv_810[FLOAT, 336x1x3x3] %onnx::Conv_813[FLOAT, 112x336x1x1] %onnx::Conv_816[FLOAT, 336x112x1x1] %onnx::Conv_819[FLOAT, 336x1x5x5] %onnx::Conv_822[FLOAT, 184x336x1x1] %onnx::Conv_823[FLOAT, 184] %onnx::Conv_825[FLOAT, 184x92x1x1] %onnx::Conv_828[FLOAT, 184x1x3x3] %onnx::Conv_831[FLOAT, 184x92x1x1] %onnx::Conv_834[FLOAT, 184x92x1x1] %onnx::Conv_837[FLOAT, 184x1x5x5] %onnx::Conv_840[FLOAT, 184x92x1x1] %onnx::Conv_843[FLOAT, 1104x184x1x1] %onnx::Conv_844[FLOAT, 1104] %onnx::Conv_846[FLOAT, 1104x1x5x5] %onnx::Conv_849[FLOAT, 184x1104x1x1] %onnx::Conv_852[FLOAT, 1104x184x1x1] %onnx::Conv_855[FLOAT, 1104x1x5x5] %onnx::Conv_858[FLOAT, 352x1104x1x1] %onnx::Conv_859[FLOAT, 352] %onnx::Conv_861[FLOAT, 1504x352x1x1] %onnx::Conv_862[FLOAT, 1504] ) { %onnx::Conv_856 = Identity(%onnx::Conv_844) %onnx::Conv_853 = Identity(%onnx::Conv_844) %onnx::Conv_850 = Identity(%onnx::Conv_823) %onnx::Conv_847 = Identity(%onnx::Conv_844) %onnx::Conv_841 = Identity(%onnx::Conv_823) %onnx::Conv_838 = Identity(%onnx::Conv_823) %onnx::Conv_835 = Identity(%onnx::Conv_823) %onnx::Conv_832 = Identity(%onnx::Conv_823) %onnx::Conv_829 = Identity(%onnx::Conv_823) %onnx::Conv_826 = Identity(%onnx::Conv_823) %onnx::Conv_820 = Identity(%onnx::Conv_808) %onnx::Conv_817 = Identity(%onnx::Conv_808) %onnx::Conv_814 = Identity(%onnx::Conv_787) %onnx::Conv_811 = Identity(%onnx::Conv_808) %onnx::Conv_805 = Identity(%onnx::Conv_787) %onnx::Conv_802 = Identity(%onnx::Conv_790) %onnx::Conv_799 = Identity(%onnx::Conv_790) %onnx::Conv_796 = Identity(%onnx::Conv_787) %onnx::Conv_793 = Identity(%onnx::Conv_790) %onnx::Conv_784 = Identity(%onnx::Conv_760) %onnx::Conv_781 = Identity(%onnx::Conv_760) %onnx::Conv_778 = Identity(%onnx::Conv_760) %onnx::Conv_775 = Identity(%onnx::Conv_760) %onnx::Conv_772 = Identity(%onnx::Conv_760) %onnx::Conv_769 = Identity(%onnx::Conv_760) %onnx::Conv_766 = Identity(%onnx::Conv_760) %onnx::Conv_763 = Identity(%onnx::Conv_760) %onnx::Conv_757 = Identity(%onnx::Conv_754) %onnx::Conv_751 = Identity(%onnx::Conv_733) %onnx::Conv_748 = Identity(%onnx::Conv_733) %onnx::Conv_745 = Identity(%onnx::Conv_733) %onnx::Conv_742 = Identity(%onnx::Conv_733) %onnx::Conv_739 = Identity(%onnx::Conv_736) %onnx::Conv_730 = Identity(%onnx::Conv_727) %onnx::Conv_724 = Identity(%onnx::Conv_706) %onnx::Conv_721 = Identity(%onnx::Conv_706) %onnx::Conv_718 = Identity(%onnx::Conv_706) %onnx::Conv_715 = Identity(%onnx::Conv_706) %onnx::Conv_712 = Identity(%onnx::Conv_706) %onnx::Conv_709 = Identity(%onnx::Conv_706) %onnx::Conv_703 = Identity(%onnx::Conv_688) %onnx::Conv_700 = Identity(%onnx::Conv_688) %onnx::Conv_697 = Identity(%onnx::Conv_688) %onnx::Conv_694 = Identity(%onnx::Conv_688) %onnx::Conv_691 = Identity(%onnx::Conv_688) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_687, %onnx::Conv_688) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_690, %onnx::Conv_691) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.0/shuffle/Reshape_output_0 = Reshape(%/cells.0/nl/Relu_output_0, %/cells.0/shuffle/Constant_output_0) %/cells.0/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.0/shuffle/Reshape_output_0) %/cells.0/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.0/shuffle/Reshape_1_output_0 = Reshape(%/cells.0/shuffle/Transpose_output_0, %/cells.0/shuffle/Constant_1_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.0/shuffle/Reshape_1_output_0, %onnx::Conv_693, %onnx::Conv_694) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_696, %onnx::Conv_697) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_699, %onnx::Conv_700) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.1/shuffle/Reshape_output_0 = Reshape(%/cells.1/nl/Relu_output_0, %/cells.1/shuffle/Constant_output_0) %/cells.1/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.1/shuffle/Reshape_output_0) %/cells.1/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.1/shuffle/Reshape_1_output_0 = Reshape(%/cells.1/shuffle/Transpose_output_0, %/cells.1/shuffle/Constant_1_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.1/shuffle/Reshape_1_output_0, %onnx::Conv_702, %onnx::Conv_703) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_705, %onnx::Conv_706) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_708, %onnx::Conv_709) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.3/shuffle/Reshape_output_0 = Reshape(%/cells.3/nl/Relu_output_0, %/cells.3/shuffle/Constant_output_0) %/cells.3/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.3/shuffle/Reshape_output_0) %/cells.3/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.3/shuffle/Reshape_1_output_0 = Reshape(%/cells.3/shuffle/Transpose_output_0, %/cells.3/shuffle/Constant_1_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.3/shuffle/Reshape_1_output_0, %onnx::Conv_711, %onnx::Conv_712) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_714, %onnx::Conv_715) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_717, %onnx::Conv_718) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.4/nl/Relu_output_0, %onnx::Conv_720, %onnx::Conv_721) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_723, %onnx::Conv_724) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/Add_output_0, %onnx::Conv_726, %onnx::Conv_727) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.5/nl/Relu_output_0, %onnx::Conv_729, %onnx::Conv_730) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_732, %onnx::Conv_733) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_735, %onnx::Conv_736) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.7/nl/Relu_output_0, %onnx::Conv_738, %onnx::Conv_739) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_741, %onnx::Conv_742) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_744, %onnx::Conv_745) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.8/shuffle/Reshape_output_0 = Reshape(%/cells.8/nl/Relu_output_0, %/cells.8/shuffle/Constant_output_0) %/cells.8/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.8/shuffle/Reshape_output_0) %/cells.8/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.8/shuffle/Reshape_1_output_0 = Reshape(%/cells.8/shuffle/Transpose_output_0, %/cells.8/shuffle/Constant_1_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.8/shuffle/Reshape_1_output_0, %onnx::Conv_747, %onnx::Conv_748) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_750, %onnx::Conv_751) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_753, %onnx::Conv_754) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.9/nl/Relu_output_0, %onnx::Conv_756, %onnx::Conv_757) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_759, %onnx::Conv_760) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_762, %onnx::Conv_763) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.10/shuffle/Reshape_output_0 = Reshape(%/cells.10/nl/Relu_output_0, %/cells.10/shuffle/Constant_output_0) %/cells.10/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.10/shuffle/Reshape_output_0) %/cells.10/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.10/shuffle/Reshape_1_output_0 = Reshape(%/cells.10/shuffle/Transpose_output_0, %/cells.10/shuffle/Constant_1_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.10/shuffle/Reshape_1_output_0, %onnx::Conv_765, %onnx::Conv_766) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_768, %onnx::Conv_769) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_771, %onnx::Conv_772) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.12/nl/Relu_output_0, %onnx::Conv_774, %onnx::Conv_775) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_777, %onnx::Conv_778) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_780, %onnx::Conv_781) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.13/shuffle/Reshape_output_0 = Reshape(%/cells.13/nl/Relu_output_0, %/cells.13/shuffle/Constant_output_0) %/cells.13/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.13/shuffle/Reshape_output_0) %/cells.13/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.13/shuffle/Reshape_1_output_0 = Reshape(%/cells.13/shuffle/Transpose_output_0, %/cells.13/shuffle/Constant_1_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.13/shuffle/Reshape_1_output_0, %onnx::Conv_783, %onnx::Conv_784) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_786, %onnx::Conv_787) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_789, %onnx::Conv_790) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.14/nl/Relu_output_0, %onnx::Conv_792, %onnx::Conv_793) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_795, %onnx::Conv_796) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_798, %onnx::Conv_799) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.15/nl/Relu_output_0, %onnx::Conv_801, %onnx::Conv_802) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_804, %onnx::Conv_805) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_807, %onnx::Conv_808) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.16/nl/Relu_output_0, %onnx::Conv_810, %onnx::Conv_811) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_813, %onnx::Conv_814) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.15/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_816, %onnx::Conv_817) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.17/nl/Relu_output_0, %onnx::Conv_819, %onnx::Conv_820) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_822, %onnx::Conv_823) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_825, %onnx::Conv_826) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.18/shuffle/Reshape_output_0 = Reshape(%/cells.18/nl/Relu_output_0, %/cells.18/shuffle/Constant_output_0) %/cells.18/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.18/shuffle/Reshape_output_0) %/cells.18/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.18/shuffle/Reshape_1_output_0 = Reshape(%/cells.18/shuffle/Transpose_output_0, %/cells.18/shuffle/Constant_1_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.18/shuffle/Reshape_1_output_0, %onnx::Conv_828, %onnx::Conv_829) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_831, %onnx::Conv_832) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_834, %onnx::Conv_835) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.19/shuffle/Reshape_output_0 = Reshape(%/cells.19/nl/Relu_output_0, %/cells.19/shuffle/Constant_output_0) %/cells.19/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.19/shuffle/Reshape_output_0) %/cells.19/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.19/shuffle/Reshape_1_output_0 = Reshape(%/cells.19/shuffle/Transpose_output_0, %/cells.19/shuffle/Constant_1_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.19/shuffle/Reshape_1_output_0, %onnx::Conv_837, %onnx::Conv_838) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_840, %onnx::Conv_841) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_843, %onnx::Conv_844) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.20/nl/Relu_output_0, %onnx::Conv_846, %onnx::Conv_847) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_849, %onnx::Conv_850) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_852, %onnx::Conv_853) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.21/nl/Relu_output_0, %onnx::Conv_855, %onnx::Conv_856) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_858, %onnx::Conv_859) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_861, %onnx::Conv_862) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %685 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %685 }	val_accuracy	71,878,016	2,403,084	{'zcp_synflow': 68.6618540407017, 'zcp_zen': 63.444435119628906, 'zcp_epe_nas': 8.586031168621183, 'zcp_fisher': 0.07030002772808075, 'zcp_flops': 71878016.0, 'zcp_grad_norm': 19.991031646728516, 'zcp_grasp': 0.019578933715820312, 'zcp_jacov': -16.047277547912053, 'zcp_l2_norm': 601.6653442382812, 'zcp_nwot': 207.66988512814754, 'zcp_params': 2403084.0, 'zcp_plain': -0.0026216921396553516, 'zcp_snip': 30.554553985595703, 'lat_1080ti_1': 0.5671930021489168, 'lat_1080ti_32': 0.4658963836690308, 'lat_1080ti_64': 0.27727654575526317, 'lat_2080ti_1': 0.5551527753053608, 'lat_2080ti_32': 0.429723234065987, 'lat_2080ti_64': 0.2895811159158151, 'lat_essential_ph_1': 0.4716981132075472, 'lat_eyeriss': 0.39981295729191485, 'lat_fpga': 0.5357860259848207, 'lat_gold_6226': 0.4551136820238274, 'lat_gold_6240': 0.6805419699645449, 'lat_pixel2': 0.5217391304347826, 'lat_pixel3': 0.43740658356341505, 'lat_raspi4': 0.5130471792705643, 'lat_samsung_a50': 0.30526315789473685, 'lat_samsung_s7': 0.25196850393700787, 'lat_silver_4114': 0.6515126861367936, 'lat_silver_4210r': 0.6266003116075551, 'lat_titan_rtx_1': 0.5368451463387669, 'lat_titan_rtx_32': 0.4519487981990676, 'lat_titan_rtx_64': 0.30631362483617314, 'lat_titanx_1': 0.2964531425151006, 'lat_titanx_32': 0.3249085593400143, 'lat_titanx_64': 0.3113854427063962, 'lat_titanxp_1': 0.517236869730436, 'lat_titanxp_32': 0.39525545225702186, 'lat_titanxp_64': 0.2935171012780416}
FBNet_91	FBNet	91	91	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_667[FLOAT, 16x3x3x3] %onnx::Conv_668[FLOAT, 16] %onnx::Conv_670[FLOAT, 16x8x1x1] %onnx::Conv_673[FLOAT, 16x1x3x3] %onnx::Conv_676[FLOAT, 16x8x1x1] %onnx::Conv_679[FLOAT, 48x16x1x1] %onnx::Conv_680[FLOAT, 48] %onnx::Conv_682[FLOAT, 48x1x5x5] %onnx::Conv_685[FLOAT, 24x48x1x1] %onnx::Conv_686[FLOAT, 24] %onnx::Conv_688[FLOAT, 72x24x1x1] %onnx::Conv_689[FLOAT, 72] %onnx::Conv_691[FLOAT, 72x1x5x5] %onnx::Conv_694[FLOAT, 24x72x1x1] %onnx::Conv_697[FLOAT, 144x24x1x1] %onnx::Conv_698[FLOAT, 144] %onnx::Conv_700[FLOAT, 144x1x3x3] %onnx::Conv_703[FLOAT, 24x144x1x1] %onnx::Conv_706[FLOAT, 24x24x1x1] %onnx::Conv_709[FLOAT, 24x1x5x5] %onnx::Conv_712[FLOAT, 24x24x1x1] %onnx::Conv_715[FLOAT, 72x24x1x1] %onnx::Conv_718[FLOAT, 72x1x3x3] %onnx::Conv_721[FLOAT, 32x72x1x1] %onnx::Conv_722[FLOAT, 32] %onnx::Conv_724[FLOAT, 32x32x1x1] %onnx::Conv_727[FLOAT, 32x1x5x5] %onnx::Conv_730[FLOAT, 32x32x1x1] %onnx::Conv_733[FLOAT, 192x32x1x1] %onnx::Conv_734[FLOAT, 192] %onnx::Conv_736[FLOAT, 192x1x5x5] %onnx::Conv_739[FLOAT, 32x192x1x1] %onnx::Conv_742[FLOAT, 32x32x1x1] %onnx::Conv_745[FLOAT, 32x1x3x3] %onnx::Conv_748[FLOAT, 32x32x1x1] %onnx::Conv_751[FLOAT, 32x32x1x1] %onnx::Conv_754[FLOAT, 32x1x5x5] %onnx::Conv_757[FLOAT, 64x32x1x1] %onnx::Conv_758[FLOAT, 64] %onnx::Conv_760[FLOAT, 192x64x1x1] %onnx::Conv_763[FLOAT, 192x1x5x5] %onnx::Conv_766[FLOAT, 64x192x1x1] %onnx::Conv_769[FLOAT, 64x32x1x1] %onnx::Conv_772[FLOAT, 64x1x3x3] %onnx::Conv_775[FLOAT, 64x32x1x1] %onnx::Conv_778[FLOAT, 64x32x1x1] %onnx::Conv_781[FLOAT, 64x1x5x5] %onnx::Conv_784[FLOAT, 64x32x1x1] %onnx::Conv_787[FLOAT, 64x64x1x1] %onnx::Conv_790[FLOAT, 64x1x3x3] %onnx::Conv_793[FLOAT, 112x64x1x1] %onnx::Conv_794[FLOAT, 112] %onnx::Conv_796[FLOAT, 672x112x1x1] %onnx::Conv_797[FLOAT, 672] %onnx::Conv_799[FLOAT, 672x1x3x3] %onnx::Conv_802[FLOAT, 112x672x1x1] %onnx::Conv_805[FLOAT, 112x56x1x1] %onnx::Conv_808[FLOAT, 112x1x3x3] %onnx::Conv_811[FLOAT, 112x56x1x1] %onnx::Conv_814[FLOAT, 672x112x1x1] %onnx::Conv_817[FLOAT, 672x1x5x5] %onnx::Conv_820[FLOAT, 112x672x1x1] %onnx::Conv_823[FLOAT, 672x112x1x1] %onnx::Conv_826[FLOAT, 672x1x5x5] %onnx::Conv_829[FLOAT, 184x672x1x1] %onnx::Conv_830[FLOAT, 184] %onnx::Conv_832[FLOAT, 1104x184x1x1] %onnx::Conv_833[FLOAT, 1104] %onnx::Conv_835[FLOAT, 1104x1x5x5] %onnx::Conv_838[FLOAT, 184x1104x1x1] %onnx::Conv_841[FLOAT, 184x184x1x1] %onnx::Conv_844[FLOAT, 184x1x3x3] %onnx::Conv_847[FLOAT, 184x184x1x1] %onnx::Conv_850[FLOAT, 552x184x1x1] %onnx::Conv_851[FLOAT, 552] %onnx::Conv_853[FLOAT, 552x1x5x5] %onnx::Conv_856[FLOAT, 352x552x1x1] %onnx::Conv_857[FLOAT, 352] %onnx::Conv_859[FLOAT, 1504x352x1x1] %onnx::Conv_860[FLOAT, 1504] ) { %onnx::Conv_854 = Identity(%onnx::Conv_851) %onnx::Conv_848 = Identity(%onnx::Conv_830) %onnx::Conv_845 = Identity(%onnx::Conv_830) %onnx::Conv_842 = Identity(%onnx::Conv_830) %onnx::Conv_839 = Identity(%onnx::Conv_830) %onnx::Conv_836 = Identity(%onnx::Conv_833) %onnx::Conv_827 = Identity(%onnx::Conv_797) %onnx::Conv_824 = Identity(%onnx::Conv_797) %onnx::Conv_821 = Identity(%onnx::Conv_794) %onnx::Conv_818 = Identity(%onnx::Conv_797) %onnx::Conv_815 = Identity(%onnx::Conv_797) %onnx::Conv_812 = Identity(%onnx::Conv_794) %onnx::Conv_809 = Identity(%onnx::Conv_794) %onnx::Conv_806 = Identity(%onnx::Conv_794) %onnx::Conv_803 = Identity(%onnx::Conv_794) %onnx::Conv_800 = Identity(%onnx::Conv_797) %onnx::Conv_791 = Identity(%onnx::Conv_758) %onnx::Conv_788 = Identity(%onnx::Conv_758) %onnx::Conv_785 = Identity(%onnx::Conv_758) %onnx::Conv_782 = Identity(%onnx::Conv_758) %onnx::Conv_779 = Identity(%onnx::Conv_758) %onnx::Conv_776 = Identity(%onnx::Conv_758) %onnx::Conv_773 = Identity(%onnx::Conv_758) %onnx::Conv_770 = Identity(%onnx::Conv_758) %onnx::Conv_767 = Identity(%onnx::Conv_758) %onnx::Conv_764 = Identity(%onnx::Conv_734) %onnx::Conv_761 = Identity(%onnx::Conv_734) %onnx::Conv_755 = Identity(%onnx::Conv_722) %onnx::Conv_752 = Identity(%onnx::Conv_722) %onnx::Conv_749 = Identity(%onnx::Conv_722) %onnx::Conv_746 = Identity(%onnx::Conv_722) %onnx::Conv_743 = Identity(%onnx::Conv_722) %onnx::Conv_740 = Identity(%onnx::Conv_722) %onnx::Conv_737 = Identity(%onnx::Conv_734) %onnx::Conv_731 = Identity(%onnx::Conv_722) %onnx::Conv_728 = Identity(%onnx::Conv_722) %onnx::Conv_725 = Identity(%onnx::Conv_722) %onnx::Conv_719 = Identity(%onnx::Conv_689) %onnx::Conv_716 = Identity(%onnx::Conv_689) %onnx::Conv_713 = Identity(%onnx::Conv_686) %onnx::Conv_710 = Identity(%onnx::Conv_686) %onnx::Conv_707 = Identity(%onnx::Conv_686) %onnx::Conv_704 = Identity(%onnx::Conv_686) %onnx::Conv_701 = Identity(%onnx::Conv_698) %onnx::Conv_695 = Identity(%onnx::Conv_686) %onnx::Conv_692 = Identity(%onnx::Conv_689) %onnx::Conv_683 = Identity(%onnx::Conv_680) %onnx::Conv_677 = Identity(%onnx::Conv_668) %onnx::Conv_674 = Identity(%onnx::Conv_668) %onnx::Conv_671 = Identity(%onnx::Conv_668) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_667, %onnx::Conv_668) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_670, %onnx::Conv_671) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.0/shuffle/Reshape_output_0 = Reshape(%/cells.0/nl/Relu_output_0, %/cells.0/shuffle/Constant_output_0) %/cells.0/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.0/shuffle/Reshape_output_0) %/cells.0/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.0/shuffle/Reshape_1_output_0 = Reshape(%/cells.0/shuffle/Transpose_output_0, %/cells.0/shuffle/Constant_1_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.0/shuffle/Reshape_1_output_0, %onnx::Conv_673, %onnx::Conv_674) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_676, %onnx::Conv_677) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_679, %onnx::Conv_680) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 48, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.1/nl/Relu_output_0, %onnx::Conv_682, %onnx::Conv_683) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_685, %onnx::Conv_686) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_688, %onnx::Conv_689) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.2/nl/Relu_output_0, %onnx::Conv_691, %onnx::Conv_692) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_694, %onnx::Conv_695) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_697, %onnx::Conv_698) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.3/nl/Relu_output_0, %onnx::Conv_700, %onnx::Conv_701) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_703, %onnx::Conv_704) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_706, %onnx::Conv_707) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.4/nl/Relu_output_0, %onnx::Conv_709, %onnx::Conv_710) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_712, %onnx::Conv_713) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/Add_output_0, %onnx::Conv_715, %onnx::Conv_716) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.5/nl/Relu_output_0, %onnx::Conv_718, %onnx::Conv_719) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_721, %onnx::Conv_722) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_724, %onnx::Conv_725) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.6/nl/Relu_output_0, %onnx::Conv_727, %onnx::Conv_728) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_730, %onnx::Conv_731) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_733, %onnx::Conv_734) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.7/nl/Relu_output_0, %onnx::Conv_736, %onnx::Conv_737) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_739, %onnx::Conv_740) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_742, %onnx::Conv_743) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.8/nl/Relu_output_0, %onnx::Conv_745, %onnx::Conv_746) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_748, %onnx::Conv_749) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_751, %onnx::Conv_752) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.9/nl/Relu_output_0, %onnx::Conv_754, %onnx::Conv_755) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_757, %onnx::Conv_758) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_760, %onnx::Conv_761) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.10/nl/Relu_output_0, %onnx::Conv_763, %onnx::Conv_764) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_766, %onnx::Conv_767) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_769, %onnx::Conv_770) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.11/shuffle/Reshape_output_0 = Reshape(%/cells.11/nl/Relu_output_0, %/cells.11/shuffle/Constant_output_0) %/cells.11/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.11/shuffle/Reshape_output_0) %/cells.11/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.11/shuffle/Reshape_1_output_0 = Reshape(%/cells.11/shuffle/Transpose_output_0, %/cells.11/shuffle/Constant_1_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.11/shuffle/Reshape_1_output_0, %onnx::Conv_772, %onnx::Conv_773) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_775, %onnx::Conv_776) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_778, %onnx::Conv_779) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.12/shuffle/Reshape_output_0 = Reshape(%/cells.12/nl/Relu_output_0, %/cells.12/shuffle/Constant_output_0) %/cells.12/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.12/shuffle/Reshape_output_0) %/cells.12/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.12/shuffle/Reshape_1_output_0 = Reshape(%/cells.12/shuffle/Transpose_output_0, %/cells.12/shuffle/Constant_1_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.12/shuffle/Reshape_1_output_0, %onnx::Conv_781, %onnx::Conv_782) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_784, %onnx::Conv_785) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_787, %onnx::Conv_788) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.13/nl/Relu_output_0, %onnx::Conv_790, %onnx::Conv_791) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_793, %onnx::Conv_794) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_796, %onnx::Conv_797) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.14/nl/Relu_output_0, %onnx::Conv_799, %onnx::Conv_800) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_802, %onnx::Conv_803) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_805, %onnx::Conv_806) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.15/shuffle/Reshape_output_0 = Reshape(%/cells.15/nl/Relu_output_0, %/cells.15/shuffle/Constant_output_0) %/cells.15/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.15/shuffle/Reshape_output_0) %/cells.15/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.15/shuffle/Reshape_1_output_0 = Reshape(%/cells.15/shuffle/Transpose_output_0, %/cells.15/shuffle/Constant_1_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.15/shuffle/Reshape_1_output_0, %onnx::Conv_808, %onnx::Conv_809) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_811, %onnx::Conv_812) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_814, %onnx::Conv_815) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.16/nl/Relu_output_0, %onnx::Conv_817, %onnx::Conv_818) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_820, %onnx::Conv_821) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.15/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_823, %onnx::Conv_824) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.17/nl/Relu_output_0, %onnx::Conv_826, %onnx::Conv_827) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_829, %onnx::Conv_830) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_832, %onnx::Conv_833) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.19/nl/Relu_output_0, %onnx::Conv_835, %onnx::Conv_836) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_838, %onnx::Conv_839) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_841, %onnx::Conv_842) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.20/nl/Relu_output_0, %onnx::Conv_844, %onnx::Conv_845) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_847, %onnx::Conv_848) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_850, %onnx::Conv_851) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 552, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.21/nl/Relu_output_0, %onnx::Conv_853, %onnx::Conv_854) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_856, %onnx::Conv_857) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_859, %onnx::Conv_860) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %665 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %665 }	val_accuracy	85,412,480	2,174,364	{'zcp_synflow': 81.07535416428257, 'zcp_zen': 70.60218811035156, 'zcp_epe_nas': 17.200160390419253, 'zcp_fisher': 0.17068618535995483, 'zcp_flops': 85412480.0, 'zcp_grad_norm': 26.253141403198242, 'zcp_grasp': 0.015216827392578125, 'zcp_jacov': -16.055936450424785, 'zcp_l2_norm': 652.5189208984375, 'zcp_nwot': 215.19303531006616, 'zcp_params': 2174364.0, 'zcp_plain': -0.001044464879669249, 'zcp_snip': 47.10875701904297, 'lat_1080ti_1': 0.6741098364277394, 'lat_1080ti_32': 0.6091944360171978, 'lat_1080ti_64': 0.6046083375369165, 'lat_2080ti_1': 0.680365922662098, 'lat_2080ti_32': 0.625825922546343, 'lat_2080ti_64': 0.5877423521572832, 'lat_essential_ph_1': 0.41509433962264153, 'lat_eyeriss': 0.6001870427080848, 'lat_fpga': 0.6518469408959122, 'lat_gold_6226': 0.4573029613135279, 'lat_gold_6240': 0.6452225109681305, 'lat_pixel2': 0.391304347826087, 'lat_pixel3': 0.6179667911256446, 'lat_raspi4': 0.6821578206643125, 'lat_samsung_a50': 0.2631578947368421, 'lat_samsung_s7': 0.2204724409448819, 'lat_silver_4114': 0.6387289047204899, 'lat_silver_4210r': 0.6305282723533394, 'lat_titan_rtx_1': 0.650026193227934, 'lat_titan_rtx_32': 0.6140755034423476, 'lat_titan_rtx_64': 0.610134402960906, 'lat_titanx_1': 0.35731996686499845, 'lat_titanx_32': 0.5862762153228289, 'lat_titanx_64': 0.5791579930609748, 'lat_titanxp_1': 0.6291105172865918, 'lat_titanxp_32': 0.61044148180643, 'lat_titanxp_64': 0.5989767145310327}
FBNet_3802	FBNet	3802	3802	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_594[FLOAT, 16x3x3x3] %onnx::Conv_595[FLOAT, 16] %onnx::Conv_597[FLOAT, 16x8x1x1] %onnx::Conv_600[FLOAT, 16x1x3x3] %onnx::Conv_603[FLOAT, 24x8x1x1] %onnx::Conv_604[FLOAT, 24] %onnx::Conv_606[FLOAT, 72x24x1x1] %onnx::Conv_607[FLOAT, 72] %onnx::Conv_609[FLOAT, 72x1x5x5] %onnx::Conv_612[FLOAT, 24x72x1x1] %onnx::Conv_615[FLOAT, 72x24x1x1] %onnx::Conv_618[FLOAT, 72x1x3x3] %onnx::Conv_621[FLOAT, 24x72x1x1] %onnx::Conv_624[FLOAT, 24x24x1x1] %onnx::Conv_627[FLOAT, 24x1x3x3] %onnx::Conv_630[FLOAT, 32x24x1x1] %onnx::Conv_631[FLOAT, 32] %onnx::Conv_633[FLOAT, 192x32x1x1] %onnx::Conv_634[FLOAT, 192] %onnx::Conv_636[FLOAT, 192x1x3x3] %onnx::Conv_639[FLOAT, 32x192x1x1] %onnx::Conv_642[FLOAT, 192x32x1x1] %onnx::Conv_645[FLOAT, 192x1x5x5] %onnx::Conv_648[FLOAT, 32x192x1x1] %onnx::Conv_651[FLOAT, 192x32x1x1] %onnx::Conv_654[FLOAT, 192x1x5x5] %onnx::Conv_657[FLOAT, 64x192x1x1] %onnx::Conv_658[FLOAT, 64] %onnx::Conv_660[FLOAT, 192x64x1x1] %onnx::Conv_663[FLOAT, 192x1x5x5] %onnx::Conv_666[FLOAT, 64x192x1x1] %onnx::Conv_669[FLOAT, 64x64x1x1] %onnx::Conv_672[FLOAT, 64x1x5x5] %onnx::Conv_675[FLOAT, 64x64x1x1] %onnx::Conv_678[FLOAT, 192x64x1x1] %onnx::Conv_681[FLOAT, 192x1x5x5] %onnx::Conv_684[FLOAT, 64x192x1x1] %onnx::Conv_687[FLOAT, 384x64x1x1] %onnx::Conv_688[FLOAT, 384] %onnx::Conv_690[FLOAT, 384x1x5x5] %onnx::Conv_693[FLOAT, 112x384x1x1] %onnx::Conv_694[FLOAT, 112] %onnx::Conv_696[FLOAT, 336x112x1x1] %onnx::Conv_697[FLOAT, 336] %onnx::Conv_699[FLOAT, 336x1x3x3] %onnx::Conv_702[FLOAT, 112x336x1x1] %onnx::Conv_705[FLOAT, 112x56x1x1] %onnx::Conv_708[FLOAT, 112x1x3x3] %onnx::Conv_711[FLOAT, 112x56x1x1] %onnx::Conv_714[FLOAT, 112x56x1x1] %onnx::Conv_717[FLOAT, 112x1x5x5] %onnx::Conv_720[FLOAT, 112x56x1x1] %onnx::Conv_723[FLOAT, 336x112x1x1] %onnx::Conv_726[FLOAT, 336x1x5x5] %onnx::Conv_729[FLOAT, 184x336x1x1] %onnx::Conv_730[FLOAT, 184] %onnx::Conv_732[FLOAT, 1104x184x1x1] %onnx::Conv_733[FLOAT, 1104] %onnx::Conv_735[FLOAT, 1104x1x5x5] %onnx::Conv_738[FLOAT, 184x1104x1x1] %onnx::Conv_741[FLOAT, 184x184x1x1] %onnx::Conv_744[FLOAT, 184x1x5x5] %onnx::Conv_747[FLOAT, 184x184x1x1] %onnx::Conv_750[FLOAT, 184x184x1x1] %onnx::Conv_753[FLOAT, 184x1x3x3] %onnx::Conv_756[FLOAT, 184x184x1x1] %onnx::Conv_759[FLOAT, 184x184x1x1] %onnx::Conv_762[FLOAT, 184x1x3x3] %onnx::Conv_765[FLOAT, 352x184x1x1] %onnx::Conv_766[FLOAT, 352] %onnx::Conv_768[FLOAT, 1504x352x1x1] %onnx::Conv_769[FLOAT, 1504] ) { %onnx::Conv_763 = Identity(%onnx::Conv_730) %onnx::Conv_760 = Identity(%onnx::Conv_730) %onnx::Conv_757 = Identity(%onnx::Conv_730) %onnx::Conv_754 = Identity(%onnx::Conv_730) %onnx::Conv_751 = Identity(%onnx::Conv_730) %onnx::Conv_748 = Identity(%onnx::Conv_730) %onnx::Conv_745 = Identity(%onnx::Conv_730) %onnx::Conv_742 = Identity(%onnx::Conv_730) %onnx::Conv_739 = Identity(%onnx::Conv_730) %onnx::Conv_736 = Identity(%onnx::Conv_733) %onnx::Conv_727 = Identity(%onnx::Conv_697) %onnx::Conv_724 = Identity(%onnx::Conv_697) %onnx::Conv_721 = Identity(%onnx::Conv_694) %onnx::Conv_718 = Identity(%onnx::Conv_694) %onnx::Conv_715 = Identity(%onnx::Conv_694) %onnx::Conv_712 = Identity(%onnx::Conv_694) %onnx::Conv_709 = Identity(%onnx::Conv_694) %onnx::Conv_706 = Identity(%onnx::Conv_694) %onnx::Conv_703 = Identity(%onnx::Conv_694) %onnx::Conv_700 = Identity(%onnx::Conv_697) %onnx::Conv_691 = Identity(%onnx::Conv_688) %onnx::Conv_685 = Identity(%onnx::Conv_658) %onnx::Conv_682 = Identity(%onnx::Conv_634) %onnx::Conv_679 = Identity(%onnx::Conv_634) %onnx::Conv_676 = Identity(%onnx::Conv_658) %onnx::Conv_673 = Identity(%onnx::Conv_658) %onnx::Conv_670 = Identity(%onnx::Conv_658) %onnx::Conv_667 = Identity(%onnx::Conv_658) %onnx::Conv_664 = Identity(%onnx::Conv_634) %onnx::Conv_661 = Identity(%onnx::Conv_634) %onnx::Conv_655 = Identity(%onnx::Conv_634) %onnx::Conv_652 = Identity(%onnx::Conv_634) %onnx::Conv_649 = Identity(%onnx::Conv_631) %onnx::Conv_646 = Identity(%onnx::Conv_634) %onnx::Conv_643 = Identity(%onnx::Conv_634) %onnx::Conv_640 = Identity(%onnx::Conv_631) %onnx::Conv_637 = Identity(%onnx::Conv_634) %onnx::Conv_628 = Identity(%onnx::Conv_604) %onnx::Conv_625 = Identity(%onnx::Conv_604) %onnx::Conv_622 = Identity(%onnx::Conv_604) %onnx::Conv_619 = Identity(%onnx::Conv_607) %onnx::Conv_616 = Identity(%onnx::Conv_607) %onnx::Conv_613 = Identity(%onnx::Conv_604) %onnx::Conv_610 = Identity(%onnx::Conv_607) %onnx::Conv_601 = Identity(%onnx::Conv_595) %onnx::Conv_598 = Identity(%onnx::Conv_595) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_594, %onnx::Conv_595) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_597, %onnx::Conv_598) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.1/shuffle/Reshape_output_0 = Reshape(%/cells.1/nl/Relu_output_0, %/cells.1/shuffle/Constant_output_0) %/cells.1/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.1/shuffle/Reshape_output_0) %/cells.1/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.1/shuffle/Reshape_1_output_0 = Reshape(%/cells.1/shuffle/Transpose_output_0, %/cells.1/shuffle/Constant_1_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.1/shuffle/Reshape_1_output_0, %onnx::Conv_600, %onnx::Conv_601) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_603, %onnx::Conv_604) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_606, %onnx::Conv_607) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.3/nl/Relu_output_0, %onnx::Conv_609, %onnx::Conv_610) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_612, %onnx::Conv_613) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_615, %onnx::Conv_616) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.4/nl/Relu_output_0, %onnx::Conv_618, %onnx::Conv_619) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_621, %onnx::Conv_622) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/Add_output_0, %onnx::Conv_624, %onnx::Conv_625) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.5/nl/Relu_output_0, %onnx::Conv_627, %onnx::Conv_628) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_630, %onnx::Conv_631) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_633, %onnx::Conv_634) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.6/nl/Relu_output_0, %onnx::Conv_636, %onnx::Conv_637) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_639, %onnx::Conv_640) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_642, %onnx::Conv_643) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.8/nl/Relu_output_0, %onnx::Conv_645, %onnx::Conv_646) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_648, %onnx::Conv_649) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_651, %onnx::Conv_652) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.9/nl/Relu_output_0, %onnx::Conv_654, %onnx::Conv_655) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_657, %onnx::Conv_658) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_660, %onnx::Conv_661) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.10/nl/Relu_output_0, %onnx::Conv_663, %onnx::Conv_664) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_666, %onnx::Conv_667) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_669, %onnx::Conv_670) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.11/nl/Relu_output_0, %onnx::Conv_672, %onnx::Conv_673) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_675, %onnx::Conv_676) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_678, %onnx::Conv_679) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.12/nl/Relu_output_0, %onnx::Conv_681, %onnx::Conv_682) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_684, %onnx::Conv_685) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_687, %onnx::Conv_688) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.13/nl/Relu_output_0, %onnx::Conv_690, %onnx::Conv_691) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_693, %onnx::Conv_694) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_696, %onnx::Conv_697) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.14/nl/Relu_output_0, %onnx::Conv_699, %onnx::Conv_700) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_702, %onnx::Conv_703) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_705, %onnx::Conv_706) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.15/shuffle/Reshape_output_0 = Reshape(%/cells.15/nl/Relu_output_0, %/cells.15/shuffle/Constant_output_0) %/cells.15/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.15/shuffle/Reshape_output_0) %/cells.15/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.15/shuffle/Reshape_1_output_0 = Reshape(%/cells.15/shuffle/Transpose_output_0, %/cells.15/shuffle/Constant_1_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.15/shuffle/Reshape_1_output_0, %onnx::Conv_708, %onnx::Conv_709) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_711, %onnx::Conv_712) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_714, %onnx::Conv_715) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.16/shuffle/Reshape_output_0 = Reshape(%/cells.16/nl/Relu_output_0, %/cells.16/shuffle/Constant_output_0) %/cells.16/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.16/shuffle/Reshape_output_0) %/cells.16/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.16/shuffle/Reshape_1_output_0 = Reshape(%/cells.16/shuffle/Transpose_output_0, %/cells.16/shuffle/Constant_1_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.16/shuffle/Reshape_1_output_0, %onnx::Conv_717, %onnx::Conv_718) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_720, %onnx::Conv_721) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.15/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_723, %onnx::Conv_724) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.17/nl/Relu_output_0, %onnx::Conv_726, %onnx::Conv_727) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_729, %onnx::Conv_730) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_732, %onnx::Conv_733) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.18/nl/Relu_output_0, %onnx::Conv_735, %onnx::Conv_736) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_738, %onnx::Conv_739) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_741, %onnx::Conv_742) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.19/nl/Relu_output_0, %onnx::Conv_744, %onnx::Conv_745) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_747, %onnx::Conv_748) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_750, %onnx::Conv_751) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.20/nl/Relu_output_0, %onnx::Conv_753, %onnx::Conv_754) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_756, %onnx::Conv_757) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_759, %onnx::Conv_760) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.21/nl/Relu_output_0, %onnx::Conv_762, %onnx::Conv_763) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_765, %onnx::Conv_766) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_768, %onnx::Conv_769) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %592 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %592 }	val_accuracy	63,743,104	1,806,196	{'zcp_synflow': 75.97176072773357, 'zcp_zen': 68.74366760253906, 'zcp_epe_nas': 14.07355917774585, 'zcp_fisher': 0.08442208170890808, 'zcp_flops': 63743104.0, 'zcp_grad_norm': 21.058055877685547, 'zcp_grasp': -0.057463645935058594, 'zcp_jacov': -16.08449093105343, 'zcp_l2_norm': 627.096923828125, 'zcp_nwot': 210.01726578654873, 'zcp_params': 1806196.0, 'zcp_plain': -0.006243598647415638, 'zcp_snip': 40.38206100463867, 'lat_1080ti_1': 0.467726332148428, 'lat_1080ti_32': 0.40806861865890653, 'lat_1080ti_64': 0.29996610316118355, 'lat_2080ti_1': 0.4900786904445772, 'lat_2080ti_32': 0.3801964822708406, 'lat_2080ti_64': 0.3228560745840269, 'lat_essential_ph_1': 0.1509433962264151, 'lat_eyeriss': 0.39964791960831053, 'lat_fpga': 0.3895744070295568, 'lat_gold_6226': 0.3315489822618539, 'lat_gold_6240': 0.47055044397440327, 'lat_pixel2': 0.2826086956521739, 'lat_pixel3': 0.37392035584916805, 'lat_raspi4': 0.3395954576007082, 'lat_samsung_a50': 0.15789473684210525, 'lat_samsung_s7': 0.3858267716535433, 'lat_silver_4114': 0.4219127955233039, 'lat_silver_4210r': 0.41585851160446113, 'lat_titan_rtx_1': 0.4449270438046064, 'lat_titan_rtx_32': 0.37199312449323996, 'lat_titan_rtx_64': 0.3325281846771187, 'lat_titanx_1': 0.2376614700770762, 'lat_titanx_32': 0.33815687795690036, 'lat_titanx_64': 0.339112251115742, 'lat_titanxp_1': 0.42475764675750066, 'lat_titanxp_32': 0.3664482284482172, 'lat_titanxp_64': 0.3158278914939279}
FBNet_325	FBNet	325	325	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_640[FLOAT, 16x3x3x3] %onnx::Conv_641[FLOAT, 16] %onnx::Conv_643[FLOAT, 96x16x1x1] %onnx::Conv_644[FLOAT, 96] %onnx::Conv_646[FLOAT, 96x1x3x3] %onnx::Conv_649[FLOAT, 16x96x1x1] %onnx::Conv_652[FLOAT, 48x16x1x1] %onnx::Conv_653[FLOAT, 48] %onnx::Conv_655[FLOAT, 48x1x5x5] %onnx::Conv_658[FLOAT, 24x48x1x1] %onnx::Conv_659[FLOAT, 24] %onnx::Conv_661[FLOAT, 24x12x1x1] %onnx::Conv_664[FLOAT, 24x1x5x5] %onnx::Conv_667[FLOAT, 24x12x1x1] %onnx::Conv_670[FLOAT, 72x24x1x1] %onnx::Conv_671[FLOAT, 72] %onnx::Conv_673[FLOAT, 72x1x3x3] %onnx::Conv_676[FLOAT, 24x72x1x1] %onnx::Conv_679[FLOAT, 144x24x1x1] %onnx::Conv_680[FLOAT, 144] %onnx::Conv_682[FLOAT, 144x1x5x5] %onnx::Conv_685[FLOAT, 24x144x1x1] %onnx::Conv_688[FLOAT, 24x24x1x1] %onnx::Conv_691[FLOAT, 24x1x5x5] %onnx::Conv_694[FLOAT, 32x24x1x1] %onnx::Conv_695[FLOAT, 32] %onnx::Conv_697[FLOAT, 32x32x1x1] %onnx::Conv_700[FLOAT, 32x1x5x5] %onnx::Conv_703[FLOAT, 32x32x1x1] %onnx::Conv_706[FLOAT, 32x32x1x1] %onnx::Conv_709[FLOAT, 32x1x3x3] %onnx::Conv_712[FLOAT, 32x32x1x1] %onnx::Conv_715[FLOAT, 32x32x1x1] %onnx::Conv_718[FLOAT, 32x1x5x5] %onnx::Conv_721[FLOAT, 64x32x1x1] %onnx::Conv_722[FLOAT, 64] %onnx::Conv_724[FLOAT, 64x32x1x1] %onnx::Conv_727[FLOAT, 64x1x3x3] %onnx::Conv_730[FLOAT, 64x32x1x1] %onnx::Conv_733[FLOAT, 64x32x1x1] %onnx::Conv_736[FLOAT, 64x1x3x3] %onnx::Conv_739[FLOAT, 64x32x1x1] %onnx::Conv_742[FLOAT, 192x64x1x1] %onnx::Conv_743[FLOAT, 192] %onnx::Conv_745[FLOAT, 192x1x3x3] %onnx::Conv_748[FLOAT, 64x192x1x1] %onnx::Conv_751[FLOAT, 384x64x1x1] %onnx::Conv_752[FLOAT, 384] %onnx::Conv_754[FLOAT, 384x1x3x3] %onnx::Conv_757[FLOAT, 112x384x1x1] %onnx::Conv_758[FLOAT, 112] %onnx::Conv_760[FLOAT, 672x112x1x1] %onnx::Conv_761[FLOAT, 672] %onnx::Conv_763[FLOAT, 672x1x5x5] %onnx::Conv_766[FLOAT, 112x672x1x1] %onnx::Conv_769[FLOAT, 336x112x1x1] %onnx::Conv_770[FLOAT, 336] %onnx::Conv_772[FLOAT, 336x1x3x3] %onnx::Conv_775[FLOAT, 112x336x1x1] %onnx::Conv_778[FLOAT, 336x112x1x1] %onnx::Conv_781[FLOAT, 336x1x5x5] %onnx::Conv_784[FLOAT, 184x336x1x1] %onnx::Conv_785[FLOAT, 184] %onnx::Conv_787[FLOAT, 1104x184x1x1] %onnx::Conv_788[FLOAT, 1104] %onnx::Conv_790[FLOAT, 1104x1x5x5] %onnx::Conv_793[FLOAT, 184x1104x1x1] %onnx::Conv_796[FLOAT, 1104x184x1x1] %onnx::Conv_799[FLOAT, 1104x1x5x5] %onnx::Conv_802[FLOAT, 184x1104x1x1] %onnx::Conv_805[FLOAT, 184x92x1x1] %onnx::Conv_808[FLOAT, 184x1x5x5] %onnx::Conv_811[FLOAT, 184x92x1x1] %onnx::Conv_814[FLOAT, 184x184x1x1] %onnx::Conv_817[FLOAT, 184x1x3x3] %onnx::Conv_820[FLOAT, 352x184x1x1] %onnx::Conv_821[FLOAT, 352] %onnx::Conv_823[FLOAT, 1504x352x1x1] %onnx::Conv_824[FLOAT, 1504] ) { %onnx::Conv_818 = Identity(%onnx::Conv_785) %onnx::Conv_815 = Identity(%onnx::Conv_785) %onnx::Conv_812 = Identity(%onnx::Conv_785) %onnx::Conv_809 = Identity(%onnx::Conv_785) %onnx::Conv_806 = Identity(%onnx::Conv_785) %onnx::Conv_803 = Identity(%onnx::Conv_785) %onnx::Conv_800 = Identity(%onnx::Conv_788) %onnx::Conv_797 = Identity(%onnx::Conv_788) %onnx::Conv_794 = Identity(%onnx::Conv_785) %onnx::Conv_791 = Identity(%onnx::Conv_788) %onnx::Conv_782 = Identity(%onnx::Conv_770) %onnx::Conv_779 = Identity(%onnx::Conv_770) %onnx::Conv_776 = Identity(%onnx::Conv_758) %onnx::Conv_773 = Identity(%onnx::Conv_770) %onnx::Conv_767 = Identity(%onnx::Conv_758) %onnx::Conv_764 = Identity(%onnx::Conv_761) %onnx::Conv_755 = Identity(%onnx::Conv_752) %onnx::Conv_749 = Identity(%onnx::Conv_722) %onnx::Conv_746 = Identity(%onnx::Conv_743) %onnx::Conv_740 = Identity(%onnx::Conv_722) %onnx::Conv_737 = Identity(%onnx::Conv_722) %onnx::Conv_734 = Identity(%onnx::Conv_722) %onnx::Conv_731 = Identity(%onnx::Conv_722) %onnx::Conv_728 = Identity(%onnx::Conv_722) %onnx::Conv_725 = Identity(%onnx::Conv_722) %onnx::Conv_719 = Identity(%onnx::Conv_695) %onnx::Conv_716 = Identity(%onnx::Conv_695) %onnx::Conv_713 = Identity(%onnx::Conv_695) %onnx::Conv_710 = Identity(%onnx::Conv_695) %onnx::Conv_707 = Identity(%onnx::Conv_695) %onnx::Conv_704 = Identity(%onnx::Conv_695) %onnx::Conv_701 = Identity(%onnx::Conv_695) %onnx::Conv_698 = Identity(%onnx::Conv_695) %onnx::Conv_692 = Identity(%onnx::Conv_659) %onnx::Conv_689 = Identity(%onnx::Conv_659) %onnx::Conv_686 = Identity(%onnx::Conv_659) %onnx::Conv_683 = Identity(%onnx::Conv_680) %onnx::Conv_677 = Identity(%onnx::Conv_659) %onnx::Conv_674 = Identity(%onnx::Conv_671) %onnx::Conv_668 = Identity(%onnx::Conv_659) %onnx::Conv_665 = Identity(%onnx::Conv_659) %onnx::Conv_662 = Identity(%onnx::Conv_659) %onnx::Conv_656 = Identity(%onnx::Conv_653) %onnx::Conv_650 = Identity(%onnx::Conv_641) %onnx::Conv_647 = Identity(%onnx::Conv_644) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_640, %onnx::Conv_641) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_643, %onnx::Conv_644) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.0/nl/Relu_output_0, %onnx::Conv_646, %onnx::Conv_647) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_649, %onnx::Conv_650) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_652, %onnx::Conv_653) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 48, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.1/nl/Relu_output_0, %onnx::Conv_655, %onnx::Conv_656) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_658, %onnx::Conv_659) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_661, %onnx::Conv_662) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.2/shuffle/Reshape_output_0 = Reshape(%/cells.2/nl/Relu_output_0, %/cells.2/shuffle/Constant_output_0) %/cells.2/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.2/shuffle/Reshape_output_0) %/cells.2/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.2/shuffle/Reshape_1_output_0 = Reshape(%/cells.2/shuffle/Transpose_output_0, %/cells.2/shuffle/Constant_1_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.2/shuffle/Reshape_1_output_0, %onnx::Conv_664, %onnx::Conv_665) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_667, %onnx::Conv_668) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_670, %onnx::Conv_671) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.3/nl/Relu_output_0, %onnx::Conv_673, %onnx::Conv_674) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_676, %onnx::Conv_677) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_679, %onnx::Conv_680) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.4/nl/Relu_output_0, %onnx::Conv_682, %onnx::Conv_683) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_685, %onnx::Conv_686) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/Add_output_0, %onnx::Conv_688, %onnx::Conv_689) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.5/nl/Relu_output_0, %onnx::Conv_691, %onnx::Conv_692) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_694, %onnx::Conv_695) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_697, %onnx::Conv_698) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.7/nl/Relu_output_0, %onnx::Conv_700, %onnx::Conv_701) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_703, %onnx::Conv_704) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_706, %onnx::Conv_707) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.8/nl/Relu_output_0, %onnx::Conv_709, %onnx::Conv_710) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_712, %onnx::Conv_713) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_715, %onnx::Conv_716) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.9/nl/Relu_output_0, %onnx::Conv_718, %onnx::Conv_719) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_721, %onnx::Conv_722) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_724, %onnx::Conv_725) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.10/shuffle/Reshape_output_0 = Reshape(%/cells.10/nl/Relu_output_0, %/cells.10/shuffle/Constant_output_0) %/cells.10/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.10/shuffle/Reshape_output_0) %/cells.10/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.10/shuffle/Reshape_1_output_0 = Reshape(%/cells.10/shuffle/Transpose_output_0, %/cells.10/shuffle/Constant_1_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.10/shuffle/Reshape_1_output_0, %onnx::Conv_727, %onnx::Conv_728) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_730, %onnx::Conv_731) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_733, %onnx::Conv_734) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.11/shuffle/Reshape_output_0 = Reshape(%/cells.11/nl/Relu_output_0, %/cells.11/shuffle/Constant_output_0) %/cells.11/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.11/shuffle/Reshape_output_0) %/cells.11/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.11/shuffle/Reshape_1_output_0 = Reshape(%/cells.11/shuffle/Transpose_output_0, %/cells.11/shuffle/Constant_1_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.11/shuffle/Reshape_1_output_0, %onnx::Conv_736, %onnx::Conv_737) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_739, %onnx::Conv_740) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_742, %onnx::Conv_743) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.12/nl/Relu_output_0, %onnx::Conv_745, %onnx::Conv_746) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_748, %onnx::Conv_749) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_751, %onnx::Conv_752) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.13/nl/Relu_output_0, %onnx::Conv_754, %onnx::Conv_755) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_757, %onnx::Conv_758) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_760, %onnx::Conv_761) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.14/nl/Relu_output_0, %onnx::Conv_763, %onnx::Conv_764) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_766, %onnx::Conv_767) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_769, %onnx::Conv_770) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.16/nl/Relu_output_0, %onnx::Conv_772, %onnx::Conv_773) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_775, %onnx::Conv_776) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_778, %onnx::Conv_779) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.17/nl/Relu_output_0, %onnx::Conv_781, %onnx::Conv_782) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_784, %onnx::Conv_785) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_787, %onnx::Conv_788) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.18/nl/Relu_output_0, %onnx::Conv_790, %onnx::Conv_791) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_793, %onnx::Conv_794) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_796, %onnx::Conv_797) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.19/nl/Relu_output_0, %onnx::Conv_799, %onnx::Conv_800) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_802, %onnx::Conv_803) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_805, %onnx::Conv_806) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.20/shuffle/Reshape_output_0 = Reshape(%/cells.20/nl/Relu_output_0, %/cells.20/shuffle/Constant_output_0) %/cells.20/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.20/shuffle/Reshape_output_0) %/cells.20/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.20/shuffle/Reshape_1_output_0 = Reshape(%/cells.20/shuffle/Transpose_output_0, %/cells.20/shuffle/Constant_1_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.20/shuffle/Reshape_1_output_0, %onnx::Conv_808, %onnx::Conv_809) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_811, %onnx::Conv_812) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_814, %onnx::Conv_815) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.21/nl/Relu_output_0, %onnx::Conv_817, %onnx::Conv_818) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_820, %onnx::Conv_821) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_823, %onnx::Conv_824) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %638 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %638 }	val_accuracy	80,840,064	2,208,156	{'zcp_synflow': 77.06702871723586, 'zcp_zen': 68.25071716308594, 'zcp_epe_nas': 0.00015999920000638146, 'zcp_fisher': 0.11379509419202805, 'zcp_flops': 80840064.0, 'zcp_grad_norm': 26.32708740234375, 'zcp_grasp': 0.17194366455078125, 'zcp_jacov': -16.063187925289803, 'zcp_l2_norm': 640.7468872070312, 'zcp_nwot': 214.669222244019, 'zcp_params': 2208156.0, 'zcp_plain': -0.003868569852784276, 'zcp_snip': 45.33941650390625, 'lat_1080ti_1': 0.6384493566438508, 'lat_1080ti_32': 0.6015598035293082, 'lat_1080ti_64': 0.6057023323821158, 'lat_2080ti_1': 0.5876176335995351, 'lat_2080ti_32': 0.5816591252200684, 'lat_2080ti_64': 0.5752968181084026, 'lat_essential_ph_1': 0.41509433962264153, 'lat_eyeriss': 0.6017640694625274, 'lat_fpga': 0.6264657279411037, 'lat_gold_6226': 0.4623141485165055, 'lat_gold_6240': 0.6496230518929061, 'lat_pixel2': 0.45652173913043476, 'lat_pixel3': 0.6111059099685374, 'lat_raspi4': 0.6210113521757505, 'lat_samsung_a50': 0.2631578947368421, 'lat_samsung_s7': 0.2283464566929134, 'lat_silver_4114': 0.6886222115426252, 'lat_silver_4210r': 0.6858827512509809, 'lat_titan_rtx_1': 0.5580190910230469, 'lat_titan_rtx_32': 0.5393363874131952, 'lat_titan_rtx_64': 0.5952134138093343, 'lat_titanx_1': 0.30614008826236006, 'lat_titanx_32': 0.6325537325536632, 'lat_titanx_64': 0.5673741723222907, 'lat_titanxp_1': 0.5393234431224788, 'lat_titanxp_32': 0.6017546515124705, 'lat_titanxp_64': 0.632863925911294}
FBNet_3707	FBNet	3707	3707	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_748[FLOAT, 16x3x3x3] %onnx::Conv_749[FLOAT, 16] %onnx::Conv_751[FLOAT, 16x8x1x1] %onnx::Conv_754[FLOAT, 16x1x5x5] %onnx::Conv_757[FLOAT, 16x8x1x1] %onnx::Conv_760[FLOAT, 16x8x1x1] %onnx::Conv_763[FLOAT, 16x1x5x5] %onnx::Conv_766[FLOAT, 24x8x1x1] %onnx::Conv_767[FLOAT, 24] %onnx::Conv_769[FLOAT, 24x12x1x1] %onnx::Conv_772[FLOAT, 24x1x3x3] %onnx::Conv_775[FLOAT, 24x12x1x1] %onnx::Conv_778[FLOAT, 24x12x1x1] %onnx::Conv_781[FLOAT, 24x1x3x3] %onnx::Conv_784[FLOAT, 24x12x1x1] %onnx::Conv_787[FLOAT, 72x24x1x1] %onnx::Conv_788[FLOAT, 72] %onnx::Conv_790[FLOAT, 72x1x3x3] %onnx::Conv_793[FLOAT, 24x72x1x1] %onnx::Conv_796[FLOAT, 24x24x1x1] %onnx::Conv_799[FLOAT, 24x1x3x3] %onnx::Conv_802[FLOAT, 32x24x1x1] %onnx::Conv_803[FLOAT, 32] %onnx::Conv_805[FLOAT, 96x32x1x1] %onnx::Conv_806[FLOAT, 96] %onnx::Conv_808[FLOAT, 96x1x5x5] %onnx::Conv_811[FLOAT, 32x96x1x1] %onnx::Conv_814[FLOAT, 96x32x1x1] %onnx::Conv_817[FLOAT, 96x1x5x5] %onnx::Conv_820[FLOAT, 32x96x1x1] %onnx::Conv_823[FLOAT, 32x32x1x1] %onnx::Conv_826[FLOAT, 32x1x5x5] %onnx::Conv_829[FLOAT, 32x32x1x1] %onnx::Conv_832[FLOAT, 192x32x1x1] %onnx::Conv_833[FLOAT, 192] %onnx::Conv_835[FLOAT, 192x1x5x5] %onnx::Conv_838[FLOAT, 64x192x1x1] %onnx::Conv_839[FLOAT, 64] %onnx::Conv_841[FLOAT, 192x64x1x1] %onnx::Conv_844[FLOAT, 192x1x3x3] %onnx::Conv_847[FLOAT, 64x192x1x1] %onnx::Conv_850[FLOAT, 64x64x1x1] %onnx::Conv_853[FLOAT, 64x1x5x5] %onnx::Conv_856[FLOAT, 64x64x1x1] %onnx::Conv_859[FLOAT, 192x64x1x1] %onnx::Conv_862[FLOAT, 192x1x3x3] %onnx::Conv_865[FLOAT, 64x192x1x1] %onnx::Conv_868[FLOAT, 384x64x1x1] %onnx::Conv_869[FLOAT, 384] %onnx::Conv_871[FLOAT, 384x1x5x5] %onnx::Conv_874[FLOAT, 112x384x1x1] %onnx::Conv_875[FLOAT, 112] %onnx::Conv_877[FLOAT, 112x56x1x1] %onnx::Conv_880[FLOAT, 112x1x5x5] %onnx::Conv_883[FLOAT, 112x56x1x1] %onnx::Conv_886[FLOAT, 336x112x1x1] %onnx::Conv_887[FLOAT, 336] %onnx::Conv_889[FLOAT, 336x1x5x5] %onnx::Conv_892[FLOAT, 112x336x1x1] %onnx::Conv_895[FLOAT, 112x56x1x1] %onnx::Conv_898[FLOAT, 112x1x3x3] %onnx::Conv_901[FLOAT, 112x56x1x1] %onnx::Conv_904[FLOAT, 112x56x1x1] %onnx::Conv_907[FLOAT, 112x1x5x5] %onnx::Conv_910[FLOAT, 184x56x1x1] %onnx::Conv_911[FLOAT, 184] %onnx::Conv_913[FLOAT, 1104x184x1x1] %onnx::Conv_914[FLOAT, 1104] %onnx::Conv_916[FLOAT, 1104x1x5x5] %onnx::Conv_919[FLOAT, 184x1104x1x1] %onnx::Conv_922[FLOAT, 1104x184x1x1] %onnx::Conv_925[FLOAT, 1104x1x3x3] %onnx::Conv_928[FLOAT, 184x1104x1x1] %onnx::Conv_931[FLOAT, 552x184x1x1] %onnx::Conv_932[FLOAT, 552] %onnx::Conv_934[FLOAT, 552x1x5x5] %onnx::Conv_937[FLOAT, 184x552x1x1] %onnx::Conv_940[FLOAT, 184x184x1x1] %onnx::Conv_943[FLOAT, 184x1x3x3] %onnx::Conv_946[FLOAT, 352x184x1x1] %onnx::Conv_947[FLOAT, 352] %onnx::Conv_949[FLOAT, 1504x352x1x1] %onnx::Conv_950[FLOAT, 1504] ) { %onnx::Conv_944 = Identity(%onnx::Conv_911) %onnx::Conv_941 = Identity(%onnx::Conv_911) %onnx::Conv_938 = Identity(%onnx::Conv_911) %onnx::Conv_935 = Identity(%onnx::Conv_932) %onnx::Conv_929 = Identity(%onnx::Conv_911) %onnx::Conv_926 = Identity(%onnx::Conv_914) %onnx::Conv_923 = Identity(%onnx::Conv_914) %onnx::Conv_920 = Identity(%onnx::Conv_911) %onnx::Conv_917 = Identity(%onnx::Conv_914) %onnx::Conv_908 = Identity(%onnx::Conv_875) %onnx::Conv_905 = Identity(%onnx::Conv_875) %onnx::Conv_902 = Identity(%onnx::Conv_875) %onnx::Conv_899 = Identity(%onnx::Conv_875) %onnx::Conv_896 = Identity(%onnx::Conv_875) %onnx::Conv_893 = Identity(%onnx::Conv_875) %onnx::Conv_890 = Identity(%onnx::Conv_887) %onnx::Conv_884 = Identity(%onnx::Conv_875) %onnx::Conv_881 = Identity(%onnx::Conv_875) %onnx::Conv_878 = Identity(%onnx::Conv_875) %onnx::Conv_872 = Identity(%onnx::Conv_869) %onnx::Conv_866 = Identity(%onnx::Conv_839) %onnx::Conv_863 = Identity(%onnx::Conv_833) %onnx::Conv_860 = Identity(%onnx::Conv_833) %onnx::Conv_857 = Identity(%onnx::Conv_839) %onnx::Conv_854 = Identity(%onnx::Conv_839) %onnx::Conv_851 = Identity(%onnx::Conv_839) %onnx::Conv_848 = Identity(%onnx::Conv_839) %onnx::Conv_845 = Identity(%onnx::Conv_833) %onnx::Conv_842 = Identity(%onnx::Conv_833) %onnx::Conv_836 = Identity(%onnx::Conv_833) %onnx::Conv_830 = Identity(%onnx::Conv_803) %onnx::Conv_827 = Identity(%onnx::Conv_803) %onnx::Conv_824 = Identity(%onnx::Conv_803) %onnx::Conv_821 = Identity(%onnx::Conv_803) %onnx::Conv_818 = Identity(%onnx::Conv_806) %onnx::Conv_815 = Identity(%onnx::Conv_806) %onnx::Conv_812 = Identity(%onnx::Conv_803) %onnx::Conv_809 = Identity(%onnx::Conv_806) %onnx::Conv_800 = Identity(%onnx::Conv_767) %onnx::Conv_797 = Identity(%onnx::Conv_767) %onnx::Conv_794 = Identity(%onnx::Conv_767) %onnx::Conv_791 = Identity(%onnx::Conv_788) %onnx::Conv_785 = Identity(%onnx::Conv_767) %onnx::Conv_782 = Identity(%onnx::Conv_767) %onnx::Conv_779 = Identity(%onnx::Conv_767) %onnx::Conv_776 = Identity(%onnx::Conv_767) %onnx::Conv_773 = Identity(%onnx::Conv_767) %onnx::Conv_770 = Identity(%onnx::Conv_767) %onnx::Conv_764 = Identity(%onnx::Conv_749) %onnx::Conv_761 = Identity(%onnx::Conv_749) %onnx::Conv_758 = Identity(%onnx::Conv_749) %onnx::Conv_755 = Identity(%onnx::Conv_749) %onnx::Conv_752 = Identity(%onnx::Conv_749) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_748, %onnx::Conv_749) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_751, %onnx::Conv_752) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.0/shuffle/Reshape_output_0 = Reshape(%/cells.0/nl/Relu_output_0, %/cells.0/shuffle/Constant_output_0) %/cells.0/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.0/shuffle/Reshape_output_0) %/cells.0/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.0/shuffle/Reshape_1_output_0 = Reshape(%/cells.0/shuffle/Transpose_output_0, %/cells.0/shuffle/Constant_1_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.0/shuffle/Reshape_1_output_0, %onnx::Conv_754, %onnx::Conv_755) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_757, %onnx::Conv_758) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_760, %onnx::Conv_761) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.1/shuffle/Reshape_output_0 = Reshape(%/cells.1/nl/Relu_output_0, %/cells.1/shuffle/Constant_output_0) %/cells.1/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.1/shuffle/Reshape_output_0) %/cells.1/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.1/shuffle/Reshape_1_output_0 = Reshape(%/cells.1/shuffle/Transpose_output_0, %/cells.1/shuffle/Constant_1_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.1/shuffle/Reshape_1_output_0, %onnx::Conv_763, %onnx::Conv_764) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_766, %onnx::Conv_767) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_769, %onnx::Conv_770) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.2/shuffle/Reshape_output_0 = Reshape(%/cells.2/nl/Relu_output_0, %/cells.2/shuffle/Constant_output_0) %/cells.2/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.2/shuffle/Reshape_output_0) %/cells.2/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.2/shuffle/Reshape_1_output_0 = Reshape(%/cells.2/shuffle/Transpose_output_0, %/cells.2/shuffle/Constant_1_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.2/shuffle/Reshape_1_output_0, %onnx::Conv_772, %onnx::Conv_773) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_775, %onnx::Conv_776) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_778, %onnx::Conv_779) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.3/shuffle/Reshape_output_0 = Reshape(%/cells.3/nl/Relu_output_0, %/cells.3/shuffle/Constant_output_0) %/cells.3/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.3/shuffle/Reshape_output_0) %/cells.3/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.3/shuffle/Reshape_1_output_0 = Reshape(%/cells.3/shuffle/Transpose_output_0, %/cells.3/shuffle/Constant_1_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.3/shuffle/Reshape_1_output_0, %onnx::Conv_781, %onnx::Conv_782) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_784, %onnx::Conv_785) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_787, %onnx::Conv_788) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.4/nl/Relu_output_0, %onnx::Conv_790, %onnx::Conv_791) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_793, %onnx::Conv_794) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/Add_output_0, %onnx::Conv_796, %onnx::Conv_797) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.5/nl/Relu_output_0, %onnx::Conv_799, %onnx::Conv_800) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_802, %onnx::Conv_803) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_805, %onnx::Conv_806) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.6/nl/Relu_output_0, %onnx::Conv_808, %onnx::Conv_809) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_811, %onnx::Conv_812) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_814, %onnx::Conv_815) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.7/nl/Relu_output_0, %onnx::Conv_817, %onnx::Conv_818) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_820, %onnx::Conv_821) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_823, %onnx::Conv_824) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.8/nl/Relu_output_0, %onnx::Conv_826, %onnx::Conv_827) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_829, %onnx::Conv_830) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_832, %onnx::Conv_833) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.9/nl/Relu_output_0, %onnx::Conv_835, %onnx::Conv_836) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_838, %onnx::Conv_839) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_841, %onnx::Conv_842) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.10/nl/Relu_output_0, %onnx::Conv_844, %onnx::Conv_845) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_847, %onnx::Conv_848) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_850, %onnx::Conv_851) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.11/nl/Relu_output_0, %onnx::Conv_853, %onnx::Conv_854) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_856, %onnx::Conv_857) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_859, %onnx::Conv_860) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.12/nl/Relu_output_0, %onnx::Conv_862, %onnx::Conv_863) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_865, %onnx::Conv_866) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_868, %onnx::Conv_869) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.13/nl/Relu_output_0, %onnx::Conv_871, %onnx::Conv_872) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_874, %onnx::Conv_875) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_877, %onnx::Conv_878) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.14/shuffle/Reshape_output_0 = Reshape(%/cells.14/nl/Relu_output_0, %/cells.14/shuffle/Constant_output_0) %/cells.14/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.14/shuffle/Reshape_output_0) %/cells.14/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.14/shuffle/Reshape_1_output_0 = Reshape(%/cells.14/shuffle/Transpose_output_0, %/cells.14/shuffle/Constant_1_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.14/shuffle/Reshape_1_output_0, %onnx::Conv_880, %onnx::Conv_881) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_883, %onnx::Conv_884) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_886, %onnx::Conv_887) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.15/nl/Relu_output_0, %onnx::Conv_889, %onnx::Conv_890) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_892, %onnx::Conv_893) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_895, %onnx::Conv_896) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.16/shuffle/Reshape_output_0 = Reshape(%/cells.16/nl/Relu_output_0, %/cells.16/shuffle/Constant_output_0) %/cells.16/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.16/shuffle/Reshape_output_0) %/cells.16/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.16/shuffle/Reshape_1_output_0 = Reshape(%/cells.16/shuffle/Transpose_output_0, %/cells.16/shuffle/Constant_1_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.16/shuffle/Reshape_1_output_0, %onnx::Conv_898, %onnx::Conv_899) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_901, %onnx::Conv_902) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.15/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_904, %onnx::Conv_905) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.17/shuffle/Reshape_output_0 = Reshape(%/cells.17/nl/Relu_output_0, %/cells.17/shuffle/Constant_output_0) %/cells.17/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.17/shuffle/Reshape_output_0) %/cells.17/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.17/shuffle/Reshape_1_output_0 = Reshape(%/cells.17/shuffle/Transpose_output_0, %/cells.17/shuffle/Constant_1_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.17/shuffle/Reshape_1_output_0, %onnx::Conv_907, %onnx::Conv_908) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_910, %onnx::Conv_911) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_913, %onnx::Conv_914) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.18/nl/Relu_output_0, %onnx::Conv_916, %onnx::Conv_917) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_919, %onnx::Conv_920) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_922, %onnx::Conv_923) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.19/nl/Relu_output_0, %onnx::Conv_925, %onnx::Conv_926) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_928, %onnx::Conv_929) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_931, %onnx::Conv_932) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 552, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.20/nl/Relu_output_0, %onnx::Conv_934, %onnx::Conv_935) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_937, %onnx::Conv_938) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_940, %onnx::Conv_941) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.21/nl/Relu_output_0, %onnx::Conv_943, %onnx::Conv_944) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_946, %onnx::Conv_947) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_949, %onnx::Conv_950) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %746 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %746 }	val_accuracy	62,867,328	2,198,212	{'zcp_synflow': 80.9948283701751, 'zcp_zen': 72.8564453125, 'zcp_epe_nas': 16.230188454001254, 'zcp_fisher': 0.12021753191947937, 'zcp_flops': 62867328.0, 'zcp_grad_norm': 26.757488250732422, 'zcp_grasp': 0.111297607421875, 'zcp_jacov': -16.06243781366433, 'zcp_l2_norm': 666.4940795898438, 'zcp_nwot': 208.7577963431719, 'zcp_params': 2198212.0, 'zcp_plain': 0.009930354543030262, 'zcp_snip': 41.2845573425293, 'lat_1080ti_1': 0.8188178956623013, 'lat_1080ti_32': 0.6767206850265736, 'lat_1080ti_64': 0.37604845078433236, 'lat_2080ti_1': 0.8647639091466924, 'lat_2080ti_32': 0.6842699860694602, 'lat_2080ti_64': 0.40287912813034504, 'lat_essential_ph_1': 0.49056603773584906, 'lat_eyeriss': 0.4124658463682539, 'lat_fpga': 0.42969948248053097, 'lat_gold_6226': 0.43946417245159475, 'lat_gold_6240': 0.6745373623587088, 'lat_pixel2': 0.45652173913043476, 'lat_pixel3': 0.3968571762431691, 'lat_raspi4': 0.45784506501565925, 'lat_samsung_a50': 0.21052631578947367, 'lat_samsung_s7': 0.1968503937007874, 'lat_silver_4114': 0.6751375768910196, 'lat_silver_4210r': 0.7441914183354393, 'lat_titan_rtx_1': 0.8186751704102602, 'lat_titan_rtx_32': 0.6891025853136494, 'lat_titan_rtx_64': 0.4785543714485962, 'lat_titanx_1': 0.4416924569355788, 'lat_titanx_32': 0.5566753716395882, 'lat_titanx_64': 0.355923494897099, 'lat_titanxp_1': 0.7783090035338499, 'lat_titanxp_32': 0.6274385967365039, 'lat_titanxp_64': 0.4061352431082069}
FBNet_3784	FBNet	3784	3784	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_632[FLOAT, 16x3x3x3] %onnx::Conv_633[FLOAT, 16] %onnx::Conv_635[FLOAT, 16x16x1x1] %onnx::Conv_638[FLOAT, 16x1x3x3] %onnx::Conv_641[FLOAT, 16x16x1x1] %onnx::Conv_644[FLOAT, 96x16x1x1] %onnx::Conv_645[FLOAT, 96] %onnx::Conv_647[FLOAT, 96x1x5x5] %onnx::Conv_650[FLOAT, 24x96x1x1] %onnx::Conv_651[FLOAT, 24] %onnx::Conv_653[FLOAT, 24x12x1x1] %onnx::Conv_656[FLOAT, 24x1x3x3] %onnx::Conv_659[FLOAT, 24x12x1x1] %onnx::Conv_662[FLOAT, 144x24x1x1] %onnx::Conv_663[FLOAT, 144] %onnx::Conv_665[FLOAT, 144x1x5x5] %onnx::Conv_668[FLOAT, 24x144x1x1] %onnx::Conv_671[FLOAT, 144x24x1x1] %onnx::Conv_674[FLOAT, 144x1x5x5] %onnx::Conv_677[FLOAT, 24x144x1x1] %onnx::Conv_680[FLOAT, 72x24x1x1] %onnx::Conv_681[FLOAT, 72] %onnx::Conv_683[FLOAT, 72x1x3x3] %onnx::Conv_686[FLOAT, 32x72x1x1] %onnx::Conv_687[FLOAT, 32] %onnx::Conv_689[FLOAT, 192x32x1x1] %onnx::Conv_690[FLOAT, 192] %onnx::Conv_692[FLOAT, 192x1x5x5] %onnx::Conv_695[FLOAT, 32x192x1x1] %onnx::Conv_698[FLOAT, 32x16x1x1] %onnx::Conv_701[FLOAT, 32x1x3x3] %onnx::Conv_704[FLOAT, 32x16x1x1] %onnx::Conv_707[FLOAT, 32x16x1x1] %onnx::Conv_710[FLOAT, 32x1x3x3] %onnx::Conv_713[FLOAT, 64x16x1x1] %onnx::Conv_714[FLOAT, 64] %onnx::Conv_716[FLOAT, 192x64x1x1] %onnx::Conv_719[FLOAT, 192x1x3x3] %onnx::Conv_722[FLOAT, 64x192x1x1] %onnx::Conv_725[FLOAT, 64x32x1x1] %onnx::Conv_728[FLOAT, 64x1x3x3] %onnx::Conv_731[FLOAT, 64x32x1x1] %onnx::Conv_734[FLOAT, 64x64x1x1] %onnx::Conv_737[FLOAT, 64x1x3x3] %onnx::Conv_740[FLOAT, 112x64x1x1] %onnx::Conv_741[FLOAT, 112] %onnx::Conv_743[FLOAT, 112x112x1x1] %onnx::Conv_746[FLOAT, 112x1x3x3] %onnx::Conv_749[FLOAT, 112x112x1x1] %onnx::Conv_752[FLOAT, 112x112x1x1] %onnx::Conv_755[FLOAT, 112x1x5x5] %onnx::Conv_758[FLOAT, 112x112x1x1] %onnx::Conv_761[FLOAT, 672x112x1x1] %onnx::Conv_762[FLOAT, 672] %onnx::Conv_764[FLOAT, 672x1x3x3] %onnx::Conv_767[FLOAT, 112x672x1x1] %onnx::Conv_770[FLOAT, 112x56x1x1] %onnx::Conv_773[FLOAT, 112x1x5x5] %onnx::Conv_776[FLOAT, 184x56x1x1] %onnx::Conv_777[FLOAT, 184] %onnx::Conv_779[FLOAT, 552x184x1x1] %onnx::Conv_780[FLOAT, 552] %onnx::Conv_782[FLOAT, 552x1x3x3] %onnx::Conv_785[FLOAT, 184x552x1x1] %onnx::Conv_788[FLOAT, 552x184x1x1] %onnx::Conv_791[FLOAT, 552x1x5x5] %onnx::Conv_794[FLOAT, 184x552x1x1] %onnx::Conv_797[FLOAT, 1104x184x1x1] %onnx::Conv_798[FLOAT, 1104] %onnx::Conv_800[FLOAT, 1104x1x5x5] %onnx::Conv_803[FLOAT, 352x1104x1x1] %onnx::Conv_804[FLOAT, 352] %onnx::Conv_806[FLOAT, 1504x352x1x1] %onnx::Conv_807[FLOAT, 1504] ) { %onnx::Conv_801 = Identity(%onnx::Conv_798) %onnx::Conv_795 = Identity(%onnx::Conv_777) %onnx::Conv_792 = Identity(%onnx::Conv_780) %onnx::Conv_789 = Identity(%onnx::Conv_780) %onnx::Conv_786 = Identity(%onnx::Conv_777) %onnx::Conv_783 = Identity(%onnx::Conv_780) %onnx::Conv_774 = Identity(%onnx::Conv_741) %onnx::Conv_771 = Identity(%onnx::Conv_741) %onnx::Conv_768 = Identity(%onnx::Conv_741) %onnx::Conv_765 = Identity(%onnx::Conv_762) %onnx::Conv_759 = Identity(%onnx::Conv_741) %onnx::Conv_756 = Identity(%onnx::Conv_741) %onnx::Conv_753 = Identity(%onnx::Conv_741) %onnx::Conv_750 = Identity(%onnx::Conv_741) %onnx::Conv_747 = Identity(%onnx::Conv_741) %onnx::Conv_744 = Identity(%onnx::Conv_741) %onnx::Conv_738 = Identity(%onnx::Conv_714) %onnx::Conv_735 = Identity(%onnx::Conv_714) %onnx::Conv_732 = Identity(%onnx::Conv_714) %onnx::Conv_729 = Identity(%onnx::Conv_714) %onnx::Conv_726 = Identity(%onnx::Conv_714) %onnx::Conv_723 = Identity(%onnx::Conv_714) %onnx::Conv_720 = Identity(%onnx::Conv_690) %onnx::Conv_717 = Identity(%onnx::Conv_690) %onnx::Conv_711 = Identity(%onnx::Conv_687) %onnx::Conv_708 = Identity(%onnx::Conv_687) %onnx::Conv_705 = Identity(%onnx::Conv_687) %onnx::Conv_702 = Identity(%onnx::Conv_687) %onnx::Conv_699 = Identity(%onnx::Conv_687) %onnx::Conv_696 = Identity(%onnx::Conv_687) %onnx::Conv_693 = Identity(%onnx::Conv_690) %onnx::Conv_684 = Identity(%onnx::Conv_681) %onnx::Conv_678 = Identity(%onnx::Conv_651) %onnx::Conv_675 = Identity(%onnx::Conv_663) %onnx::Conv_672 = Identity(%onnx::Conv_663) %onnx::Conv_669 = Identity(%onnx::Conv_651) %onnx::Conv_666 = Identity(%onnx::Conv_663) %onnx::Conv_660 = Identity(%onnx::Conv_651) %onnx::Conv_657 = Identity(%onnx::Conv_651) %onnx::Conv_654 = Identity(%onnx::Conv_651) %onnx::Conv_648 = Identity(%onnx::Conv_645) %onnx::Conv_642 = Identity(%onnx::Conv_633) %onnx::Conv_639 = Identity(%onnx::Conv_633) %onnx::Conv_636 = Identity(%onnx::Conv_633) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_632, %onnx::Conv_633) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_635, %onnx::Conv_636) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.0/nl/Relu_output_0, %onnx::Conv_638, %onnx::Conv_639) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_641, %onnx::Conv_642) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_644, %onnx::Conv_645) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.1/nl/Relu_output_0, %onnx::Conv_647, %onnx::Conv_648) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_650, %onnx::Conv_651) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_653, %onnx::Conv_654) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.2/shuffle/Reshape_output_0 = Reshape(%/cells.2/nl/Relu_output_0, %/cells.2/shuffle/Constant_output_0) %/cells.2/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.2/shuffle/Reshape_output_0) %/cells.2/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.2/shuffle/Reshape_1_output_0 = Reshape(%/cells.2/shuffle/Transpose_output_0, %/cells.2/shuffle/Constant_1_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.2/shuffle/Reshape_1_output_0, %onnx::Conv_656, %onnx::Conv_657) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_659, %onnx::Conv_660) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_662, %onnx::Conv_663) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.3/nl/Relu_output_0, %onnx::Conv_665, %onnx::Conv_666) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_668, %onnx::Conv_669) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_671, %onnx::Conv_672) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.4/nl/Relu_output_0, %onnx::Conv_674, %onnx::Conv_675) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_677, %onnx::Conv_678) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/Add_output_0, %onnx::Conv_680, %onnx::Conv_681) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.5/nl/Relu_output_0, %onnx::Conv_683, %onnx::Conv_684) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_686, %onnx::Conv_687) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_689, %onnx::Conv_690) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.6/nl/Relu_output_0, %onnx::Conv_692, %onnx::Conv_693) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_695, %onnx::Conv_696) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_698, %onnx::Conv_699) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.7/shuffle/Reshape_output_0 = Reshape(%/cells.7/nl/Relu_output_0, %/cells.7/shuffle/Constant_output_0) %/cells.7/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.7/shuffle/Reshape_output_0) %/cells.7/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.7/shuffle/Reshape_1_output_0 = Reshape(%/cells.7/shuffle/Transpose_output_0, %/cells.7/shuffle/Constant_1_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.7/shuffle/Reshape_1_output_0, %onnx::Conv_701, %onnx::Conv_702) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_704, %onnx::Conv_705) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_707, %onnx::Conv_708) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.9/shuffle/Reshape_output_0 = Reshape(%/cells.9/nl/Relu_output_0, %/cells.9/shuffle/Constant_output_0) %/cells.9/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.9/shuffle/Reshape_output_0) %/cells.9/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.9/shuffle/Reshape_1_output_0 = Reshape(%/cells.9/shuffle/Transpose_output_0, %/cells.9/shuffle/Constant_1_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.9/shuffle/Reshape_1_output_0, %onnx::Conv_710, %onnx::Conv_711) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_713, %onnx::Conv_714) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_716, %onnx::Conv_717) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.10/nl/Relu_output_0, %onnx::Conv_719, %onnx::Conv_720) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_722, %onnx::Conv_723) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_725, %onnx::Conv_726) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.12/shuffle/Reshape_output_0 = Reshape(%/cells.12/nl/Relu_output_0, %/cells.12/shuffle/Constant_output_0) %/cells.12/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.12/shuffle/Reshape_output_0) %/cells.12/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.12/shuffle/Reshape_1_output_0 = Reshape(%/cells.12/shuffle/Transpose_output_0, %/cells.12/shuffle/Constant_1_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.12/shuffle/Reshape_1_output_0, %onnx::Conv_728, %onnx::Conv_729) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_731, %onnx::Conv_732) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_734, %onnx::Conv_735) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.13/nl/Relu_output_0, %onnx::Conv_737, %onnx::Conv_738) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_740, %onnx::Conv_741) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_743, %onnx::Conv_744) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.14/nl/Relu_output_0, %onnx::Conv_746, %onnx::Conv_747) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_749, %onnx::Conv_750) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_752, %onnx::Conv_753) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.15/nl/Relu_output_0, %onnx::Conv_755, %onnx::Conv_756) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_758, %onnx::Conv_759) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_761, %onnx::Conv_762) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.16/nl/Relu_output_0, %onnx::Conv_764, %onnx::Conv_765) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_767, %onnx::Conv_768) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.15/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_770, %onnx::Conv_771) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.17/shuffle/Reshape_output_0 = Reshape(%/cells.17/nl/Relu_output_0, %/cells.17/shuffle/Constant_output_0) %/cells.17/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.17/shuffle/Reshape_output_0) %/cells.17/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.17/shuffle/Reshape_1_output_0 = Reshape(%/cells.17/shuffle/Transpose_output_0, %/cells.17/shuffle/Constant_1_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.17/shuffle/Reshape_1_output_0, %onnx::Conv_773, %onnx::Conv_774) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_776, %onnx::Conv_777) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_779, %onnx::Conv_780) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 552, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.18/nl/Relu_output_0, %onnx::Conv_782, %onnx::Conv_783) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_785, %onnx::Conv_786) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_788, %onnx::Conv_789) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 552, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.20/nl/Relu_output_0, %onnx::Conv_791, %onnx::Conv_792) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_794, %onnx::Conv_795) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_797, %onnx::Conv_798) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.21/nl/Relu_output_0, %onnx::Conv_800, %onnx::Conv_801) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_803, %onnx::Conv_804) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_806, %onnx::Conv_807) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %630 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %630 }	val_accuracy	83,255,424	2,074,788	{'zcp_synflow': 70.90376968866734, 'zcp_zen': 63.12879943847656, 'zcp_epe_nas': 11.644047998231114, 'zcp_fisher': 0.10170716047286987, 'zcp_flops': 83255424.0, 'zcp_grad_norm': 24.038969039916992, 'zcp_grasp': 0.0030307769775390625, 'zcp_jacov': -16.055396594118886, 'zcp_l2_norm': 587.9946899414062, 'zcp_nwot': 216.74447276494573, 'zcp_params': 2074788.0, 'zcp_plain': 0.002443465869873762, 'zcp_snip': 41.31827163696289, 'lat_1080ti_1': 0.480210894870756, 'lat_1080ti_32': 0.6697886118438265, 'lat_1080ti_64': 0.7019415607650453, 'lat_2080ti_1': 0.5330069781384074, 'lat_2080ti_32': 0.6845823340377435, 'lat_2080ti_64': 0.7416988055035902, 'lat_essential_ph_1': 0.3584905660377358, 'lat_eyeriss': 0.6403645499055617, 'lat_fpga': 0.6245955333023283, 'lat_gold_6226': 0.3895166937623985, 'lat_gold_6240': 0.4704307772473966, 'lat_pixel2': 0.4782608695652174, 'lat_pixel3': 0.7086839143046377, 'lat_raspi4': 0.7372658864904106, 'lat_samsung_a50': 0.24210526315789474, 'lat_samsung_s7': 0.18110236220472442, 'lat_silver_4114': 0.46392305761471836, 'lat_silver_4210r': 0.49663340398440675, 'lat_titan_rtx_1': 0.4968211881181733, 'lat_titan_rtx_32': 0.6397709077152512, 'lat_titan_rtx_64': 0.7041435784411935, 'lat_titanx_1': 0.265443181091252, 'lat_titanx_32': 0.683066461354012, 'lat_titanx_64': 0.7385203950112501, 'lat_titanxp_1': 0.47175962360657603, 'lat_titanxp_32': 0.664163188393861, 'lat_titanxp_64': 0.7145504428923939}
FBNet_3928	FBNet	3928	3928	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_650[FLOAT, 16x3x3x3] %onnx::Conv_651[FLOAT, 16] %onnx::Conv_653[FLOAT, 48x16x1x1] %onnx::Conv_654[FLOAT, 48] %onnx::Conv_656[FLOAT, 48x1x5x5] %onnx::Conv_659[FLOAT, 16x48x1x1] %onnx::Conv_662[FLOAT, 96x16x1x1] %onnx::Conv_663[FLOAT, 96] %onnx::Conv_665[FLOAT, 96x1x3x3] %onnx::Conv_668[FLOAT, 24x96x1x1] %onnx::Conv_669[FLOAT, 24] %onnx::Conv_671[FLOAT, 144x24x1x1] %onnx::Conv_672[FLOAT, 144] %onnx::Conv_674[FLOAT, 144x1x3x3] %onnx::Conv_677[FLOAT, 24x144x1x1] %onnx::Conv_680[FLOAT, 24x12x1x1] %onnx::Conv_683[FLOAT, 24x1x3x3] %onnx::Conv_686[FLOAT, 24x12x1x1] %onnx::Conv_689[FLOAT, 72x24x1x1] %onnx::Conv_690[FLOAT, 72] %onnx::Conv_692[FLOAT, 72x1x3x3] %onnx::Conv_695[FLOAT, 24x72x1x1] %onnx::Conv_698[FLOAT, 24x24x1x1] %onnx::Conv_701[FLOAT, 24x1x3x3] %onnx::Conv_704[FLOAT, 32x24x1x1] %onnx::Conv_705[FLOAT, 32] %onnx::Conv_707[FLOAT, 192x32x1x1] %onnx::Conv_708[FLOAT, 192] %onnx::Conv_710[FLOAT, 192x1x3x3] %onnx::Conv_713[FLOAT, 32x192x1x1] %onnx::Conv_716[FLOAT, 32x32x1x1] %onnx::Conv_719[FLOAT, 32x1x3x3] %onnx::Conv_722[FLOAT, 32x32x1x1] %onnx::Conv_725[FLOAT, 192x32x1x1] %onnx::Conv_728[FLOAT, 192x1x5x5] %onnx::Conv_731[FLOAT, 32x192x1x1] %onnx::Conv_734[FLOAT, 32x32x1x1] %onnx::Conv_737[FLOAT, 32x1x5x5] %onnx::Conv_740[FLOAT, 64x32x1x1] %onnx::Conv_741[FLOAT, 64] %onnx::Conv_743[FLOAT, 64x64x1x1] %onnx::Conv_746[FLOAT, 64x1x5x5] %onnx::Conv_749[FLOAT, 64x64x1x1] %onnx::Conv_752[FLOAT, 384x64x1x1] %onnx::Conv_753[FLOAT, 384] %onnx::Conv_755[FLOAT, 384x1x3x3] %onnx::Conv_758[FLOAT, 64x384x1x1] %onnx::Conv_761[FLOAT, 64x64x1x1] %onnx::Conv_764[FLOAT, 64x1x3x3] %onnx::Conv_767[FLOAT, 64x64x1x1] %onnx::Conv_770[FLOAT, 112x64x1x1] %onnx::Conv_771[FLOAT, 112] %onnx::Conv_773[FLOAT, 672x112x1x1] %onnx::Conv_774[FLOAT, 672] %onnx::Conv_776[FLOAT, 672x1x3x3] %onnx::Conv_779[FLOAT, 112x672x1x1] %onnx::Conv_782[FLOAT, 336x112x1x1] %onnx::Conv_783[FLOAT, 336] %onnx::Conv_785[FLOAT, 336x1x5x5] %onnx::Conv_788[FLOAT, 112x336x1x1] %onnx::Conv_791[FLOAT, 112x56x1x1] %onnx::Conv_794[FLOAT, 112x1x3x3] %onnx::Conv_797[FLOAT, 184x56x1x1] %onnx::Conv_798[FLOAT, 184] %onnx::Conv_800[FLOAT, 1104x184x1x1] %onnx::Conv_801[FLOAT, 1104] %onnx::Conv_803[FLOAT, 1104x1x5x5] %onnx::Conv_806[FLOAT, 184x1104x1x1] %onnx::Conv_809[FLOAT, 184x184x1x1] %onnx::Conv_812[FLOAT, 184x1x3x3] %onnx::Conv_815[FLOAT, 184x184x1x1] %onnx::Conv_818[FLOAT, 184x92x1x1] %onnx::Conv_821[FLOAT, 184x1x5x5] %onnx::Conv_824[FLOAT, 184x92x1x1] %onnx::Conv_827[FLOAT, 184x92x1x1] %onnx::Conv_830[FLOAT, 184x1x3x3] %onnx::Conv_833[FLOAT, 352x92x1x1] %onnx::Conv_834[FLOAT, 352] %onnx::Conv_836[FLOAT, 1504x352x1x1] %onnx::Conv_837[FLOAT, 1504] ) { %onnx::Conv_831 = Identity(%onnx::Conv_798) %onnx::Conv_828 = Identity(%onnx::Conv_798) %onnx::Conv_825 = Identity(%onnx::Conv_798) %onnx::Conv_822 = Identity(%onnx::Conv_798) %onnx::Conv_819 = Identity(%onnx::Conv_798) %onnx::Conv_816 = Identity(%onnx::Conv_798) %onnx::Conv_813 = Identity(%onnx::Conv_798) %onnx::Conv_810 = Identity(%onnx::Conv_798) %onnx::Conv_807 = Identity(%onnx::Conv_798) %onnx::Conv_804 = Identity(%onnx::Conv_801) %onnx::Conv_795 = Identity(%onnx::Conv_771) %onnx::Conv_792 = Identity(%onnx::Conv_771) %onnx::Conv_789 = Identity(%onnx::Conv_771) %onnx::Conv_786 = Identity(%onnx::Conv_783) %onnx::Conv_780 = Identity(%onnx::Conv_771) %onnx::Conv_777 = Identity(%onnx::Conv_774) %onnx::Conv_768 = Identity(%onnx::Conv_741) %onnx::Conv_765 = Identity(%onnx::Conv_741) %onnx::Conv_762 = Identity(%onnx::Conv_741) %onnx::Conv_759 = Identity(%onnx::Conv_741) %onnx::Conv_756 = Identity(%onnx::Conv_753) %onnx::Conv_750 = Identity(%onnx::Conv_741) %onnx::Conv_747 = Identity(%onnx::Conv_741) %onnx::Conv_744 = Identity(%onnx::Conv_741) %onnx::Conv_738 = Identity(%onnx::Conv_705) %onnx::Conv_735 = Identity(%onnx::Conv_705) %onnx::Conv_732 = Identity(%onnx::Conv_705) %onnx::Conv_729 = Identity(%onnx::Conv_708) %onnx::Conv_726 = Identity(%onnx::Conv_708) %onnx::Conv_723 = Identity(%onnx::Conv_705) %onnx::Conv_720 = Identity(%onnx::Conv_705) %onnx::Conv_717 = Identity(%onnx::Conv_705) %onnx::Conv_714 = Identity(%onnx::Conv_705) %onnx::Conv_711 = Identity(%onnx::Conv_708) %onnx::Conv_702 = Identity(%onnx::Conv_669) %onnx::Conv_699 = Identity(%onnx::Conv_669) %onnx::Conv_696 = Identity(%onnx::Conv_669) %onnx::Conv_693 = Identity(%onnx::Conv_690) %onnx::Conv_687 = Identity(%onnx::Conv_669) %onnx::Conv_684 = Identity(%onnx::Conv_669) %onnx::Conv_681 = Identity(%onnx::Conv_669) %onnx::Conv_678 = Identity(%onnx::Conv_669) %onnx::Conv_675 = Identity(%onnx::Conv_672) %onnx::Conv_666 = Identity(%onnx::Conv_663) %onnx::Conv_660 = Identity(%onnx::Conv_651) %onnx::Conv_657 = Identity(%onnx::Conv_654) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_650, %onnx::Conv_651) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_653, %onnx::Conv_654) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 48, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.0/nl/Relu_output_0, %onnx::Conv_656, %onnx::Conv_657) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_659, %onnx::Conv_660) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_662, %onnx::Conv_663) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.1/nl/Relu_output_0, %onnx::Conv_665, %onnx::Conv_666) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_668, %onnx::Conv_669) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_671, %onnx::Conv_672) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.2/nl/Relu_output_0, %onnx::Conv_674, %onnx::Conv_675) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_677, %onnx::Conv_678) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_680, %onnx::Conv_681) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.3/shuffle/Reshape_output_0 = Reshape(%/cells.3/nl/Relu_output_0, %/cells.3/shuffle/Constant_output_0) %/cells.3/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.3/shuffle/Reshape_output_0) %/cells.3/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.3/shuffle/Reshape_1_output_0 = Reshape(%/cells.3/shuffle/Transpose_output_0, %/cells.3/shuffle/Constant_1_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.3/shuffle/Reshape_1_output_0, %onnx::Conv_683, %onnx::Conv_684) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_686, %onnx::Conv_687) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_689, %onnx::Conv_690) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.4/nl/Relu_output_0, %onnx::Conv_692, %onnx::Conv_693) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_695, %onnx::Conv_696) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/Add_output_0, %onnx::Conv_698, %onnx::Conv_699) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.5/nl/Relu_output_0, %onnx::Conv_701, %onnx::Conv_702) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_704, %onnx::Conv_705) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_707, %onnx::Conv_708) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.6/nl/Relu_output_0, %onnx::Conv_710, %onnx::Conv_711) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_713, %onnx::Conv_714) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_716, %onnx::Conv_717) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.7/nl/Relu_output_0, %onnx::Conv_719, %onnx::Conv_720) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_722, %onnx::Conv_723) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_725, %onnx::Conv_726) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.8/nl/Relu_output_0, %onnx::Conv_728, %onnx::Conv_729) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_731, %onnx::Conv_732) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_734, %onnx::Conv_735) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.9/nl/Relu_output_0, %onnx::Conv_737, %onnx::Conv_738) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_740, %onnx::Conv_741) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_743, %onnx::Conv_744) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.10/nl/Relu_output_0, %onnx::Conv_746, %onnx::Conv_747) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_749, %onnx::Conv_750) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_752, %onnx::Conv_753) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.11/nl/Relu_output_0, %onnx::Conv_755, %onnx::Conv_756) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_758, %onnx::Conv_759) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_761, %onnx::Conv_762) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.12/nl/Relu_output_0, %onnx::Conv_764, %onnx::Conv_765) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_767, %onnx::Conv_768) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_770, %onnx::Conv_771) %/cells.13/relu/Relu_output_0 = Relu(%/cells.13/conv/Conv_output_0) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/relu/Relu_output_0, %onnx::Conv_773, %onnx::Conv_774) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.14/nl/Relu_output_0, %onnx::Conv_776, %onnx::Conv_777) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_779, %onnx::Conv_780) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/relu/Relu_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_782, %onnx::Conv_783) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.15/nl/Relu_output_0, %onnx::Conv_785, %onnx::Conv_786) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_788, %onnx::Conv_789) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_791, %onnx::Conv_792) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.17/shuffle/Reshape_output_0 = Reshape(%/cells.17/nl/Relu_output_0, %/cells.17/shuffle/Constant_output_0) %/cells.17/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.17/shuffle/Reshape_output_0) %/cells.17/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.17/shuffle/Reshape_1_output_0 = Reshape(%/cells.17/shuffle/Transpose_output_0, %/cells.17/shuffle/Constant_1_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.17/shuffle/Reshape_1_output_0, %onnx::Conv_794, %onnx::Conv_795) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_797, %onnx::Conv_798) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_800, %onnx::Conv_801) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.18/nl/Relu_output_0, %onnx::Conv_803, %onnx::Conv_804) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_806, %onnx::Conv_807) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_809, %onnx::Conv_810) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.19/nl/Relu_output_0, %onnx::Conv_812, %onnx::Conv_813) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_815, %onnx::Conv_816) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_818, %onnx::Conv_819) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.20/shuffle/Reshape_output_0 = Reshape(%/cells.20/nl/Relu_output_0, %/cells.20/shuffle/Constant_output_0) %/cells.20/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.20/shuffle/Reshape_output_0) %/cells.20/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.20/shuffle/Reshape_1_output_0 = Reshape(%/cells.20/shuffle/Transpose_output_0, %/cells.20/shuffle/Constant_1_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.20/shuffle/Reshape_1_output_0, %onnx::Conv_821, %onnx::Conv_822) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_824, %onnx::Conv_825) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_827, %onnx::Conv_828) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.21/shuffle/Reshape_output_0 = Reshape(%/cells.21/nl/Relu_output_0, %/cells.21/shuffle/Constant_output_0) %/cells.21/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.21/shuffle/Reshape_output_0) %/cells.21/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.21/shuffle/Reshape_1_output_0 = Reshape(%/cells.21/shuffle/Transpose_output_0, %/cells.21/shuffle/Constant_1_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.21/shuffle/Reshape_1_output_0, %onnx::Conv_830, %onnx::Conv_831) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_833, %onnx::Conv_834) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_836, %onnx::Conv_837) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %648 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %648 }	val_accuracy	74,186,368	1,692,036	{'zcp_synflow': 77.06671787396081, 'zcp_zen': 67.56119537353516, 'zcp_epe_nas': 11.299887171018339, 'zcp_fisher': 0.19095204770565033, 'zcp_flops': 74186368.0, 'zcp_grad_norm': 23.953937530517578, 'zcp_grasp': 0.02962493896484375, 'zcp_jacov': -16.079538194364318, 'zcp_l2_norm': 613.7095947265625, 'zcp_nwot': 216.22988668333363, 'zcp_params': 1692036.0, 'zcp_plain': -0.0011065370636060834, 'zcp_snip': 50.19142532348633, 'lat_1080ti_1': 0.7005921447547099, 'lat_1080ti_32': 0.6429024171743567, 'lat_1080ti_64': 0.586082155332154, 'lat_2080ti_1': 0.6702154555092251, 'lat_2080ti_32': 0.6044395860966266, 'lat_2080ti_64': 0.6188075825744163, 'lat_essential_ph_1': 0.33962264150943394, 'lat_eyeriss': 0.5317422478132504, 'lat_fpga': 0.574604933751571, 'lat_gold_6226': 0.4648615944964237, 'lat_gold_6240': 0.47115566986679447, 'lat_pixel2': 0.34782608695652173, 'lat_pixel3': 0.486395419752295, 'lat_raspi4': 0.48328834797962494, 'lat_samsung_a50': 0.22105263157894736, 'lat_samsung_s7': 0.1889763779527559, 'lat_silver_4114': 0.5116094478419141, 'lat_silver_4210r': 0.5758354908319231, 'lat_titan_rtx_1': 0.6105514483524571, 'lat_titan_rtx_32': 0.641129561091722, 'lat_titan_rtx_64': 0.6054575823969338, 'lat_titanx_1': 0.3203210313090838, 'lat_titanx_32': 0.5893998770653106, 'lat_titanx_64': 0.6004657884135334, 'lat_titanxp_1': 0.5652635828564605, 'lat_titanxp_32': 0.5940491670914536, 'lat_titanxp_64': 0.5823911433081564}
FBNet_1110	FBNet	1110	1110	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_603[FLOAT, 16x3x3x3] %onnx::Conv_604[FLOAT, 16] %onnx::Conv_606[FLOAT, 48x16x1x1] %onnx::Conv_607[FLOAT, 48] %onnx::Conv_609[FLOAT, 48x1x3x3] %onnx::Conv_612[FLOAT, 16x48x1x1] %onnx::Conv_615[FLOAT, 16x16x1x1] %onnx::Conv_618[FLOAT, 16x1x3x3] %onnx::Conv_621[FLOAT, 24x16x1x1] %onnx::Conv_622[FLOAT, 24] %onnx::Conv_624[FLOAT, 144x24x1x1] %onnx::Conv_625[FLOAT, 144] %onnx::Conv_627[FLOAT, 144x1x3x3] %onnx::Conv_630[FLOAT, 24x144x1x1] %onnx::Conv_633[FLOAT, 24x12x1x1] %onnx::Conv_636[FLOAT, 24x1x5x5] %onnx::Conv_639[FLOAT, 24x12x1x1] %onnx::Conv_642[FLOAT, 24x24x1x1] %onnx::Conv_645[FLOAT, 24x1x3x3] %onnx::Conv_648[FLOAT, 24x24x1x1] %onnx::Conv_651[FLOAT, 24x24x1x1] %onnx::Conv_654[FLOAT, 24x1x5x5] %onnx::Conv_657[FLOAT, 32x24x1x1] %onnx::Conv_658[FLOAT, 32] %onnx::Conv_660[FLOAT, 192x32x1x1] %onnx::Conv_661[FLOAT, 192] %onnx::Conv_663[FLOAT, 192x1x3x3] %onnx::Conv_666[FLOAT, 32x192x1x1] %onnx::Conv_669[FLOAT, 96x32x1x1] %onnx::Conv_670[FLOAT, 96] %onnx::Conv_672[FLOAT, 96x1x5x5] %onnx::Conv_675[FLOAT, 64x96x1x1] %onnx::Conv_676[FLOAT, 64] %onnx::Conv_678[FLOAT, 64x64x1x1] %onnx::Conv_681[FLOAT, 64x1x3x3] %onnx::Conv_684[FLOAT, 64x64x1x1] %onnx::Conv_687[FLOAT, 192x64x1x1] %onnx::Conv_690[FLOAT, 192x1x5x5] %onnx::Conv_693[FLOAT, 64x192x1x1] %onnx::Conv_696[FLOAT, 192x64x1x1] %onnx::Conv_699[FLOAT, 192x1x3x3] %onnx::Conv_702[FLOAT, 64x192x1x1] %onnx::Conv_705[FLOAT, 64x32x1x1] %onnx::Conv_708[FLOAT, 64x1x5x5] %onnx::Conv_711[FLOAT, 112x32x1x1] %onnx::Conv_712[FLOAT, 112] %onnx::Conv_714[FLOAT, 672x112x1x1] %onnx::Conv_715[FLOAT, 672] %onnx::Conv_717[FLOAT, 672x1x3x3] %onnx::Conv_720[FLOAT, 112x672x1x1] %onnx::Conv_723[FLOAT, 672x112x1x1] %onnx::Conv_726[FLOAT, 672x1x5x5] %onnx::Conv_729[FLOAT, 112x672x1x1] %onnx::Conv_732[FLOAT, 112x112x1x1] %onnx::Conv_735[FLOAT, 112x1x3x3] %onnx::Conv_738[FLOAT, 112x112x1x1] %onnx::Conv_741[FLOAT, 336x112x1x1] %onnx::Conv_742[FLOAT, 336] %onnx::Conv_744[FLOAT, 336x1x3x3] %onnx::Conv_747[FLOAT, 184x336x1x1] %onnx::Conv_748[FLOAT, 184] %onnx::Conv_750[FLOAT, 1104x184x1x1] %onnx::Conv_751[FLOAT, 1104] %onnx::Conv_753[FLOAT, 1104x1x3x3] %onnx::Conv_756[FLOAT, 184x1104x1x1] %onnx::Conv_759[FLOAT, 1104x184x1x1] %onnx::Conv_762[FLOAT, 1104x1x3x3] %onnx::Conv_765[FLOAT, 184x1104x1x1] %onnx::Conv_768[FLOAT, 184x184x1x1] %onnx::Conv_771[FLOAT, 184x1x3x3] %onnx::Conv_774[FLOAT, 184x184x1x1] %onnx::Conv_777[FLOAT, 184x184x1x1] %onnx::Conv_780[FLOAT, 184x1x5x5] %onnx::Conv_783[FLOAT, 352x184x1x1] %onnx::Conv_784[FLOAT, 352] %onnx::Conv_786[FLOAT, 1504x352x1x1] %onnx::Conv_787[FLOAT, 1504] ) { %onnx::Conv_781 = Identity(%onnx::Conv_748) %onnx::Conv_778 = Identity(%onnx::Conv_748) %onnx::Conv_775 = Identity(%onnx::Conv_748) %onnx::Conv_772 = Identity(%onnx::Conv_748) %onnx::Conv_769 = Identity(%onnx::Conv_748) %onnx::Conv_766 = Identity(%onnx::Conv_748) %onnx::Conv_763 = Identity(%onnx::Conv_751) %onnx::Conv_760 = Identity(%onnx::Conv_751) %onnx::Conv_757 = Identity(%onnx::Conv_748) %onnx::Conv_754 = Identity(%onnx::Conv_751) %onnx::Conv_745 = Identity(%onnx::Conv_742) %onnx::Conv_739 = Identity(%onnx::Conv_712) %onnx::Conv_736 = Identity(%onnx::Conv_712) %onnx::Conv_733 = Identity(%onnx::Conv_712) %onnx::Conv_730 = Identity(%onnx::Conv_712) %onnx::Conv_727 = Identity(%onnx::Conv_715) %onnx::Conv_724 = Identity(%onnx::Conv_715) %onnx::Conv_721 = Identity(%onnx::Conv_712) %onnx::Conv_718 = Identity(%onnx::Conv_715) %onnx::Conv_709 = Identity(%onnx::Conv_676) %onnx::Conv_706 = Identity(%onnx::Conv_676) %onnx::Conv_703 = Identity(%onnx::Conv_676) %onnx::Conv_700 = Identity(%onnx::Conv_661) %onnx::Conv_697 = Identity(%onnx::Conv_661) %onnx::Conv_694 = Identity(%onnx::Conv_676) %onnx::Conv_691 = Identity(%onnx::Conv_661) %onnx::Conv_688 = Identity(%onnx::Conv_661) %onnx::Conv_685 = Identity(%onnx::Conv_676) %onnx::Conv_682 = Identity(%onnx::Conv_676) %onnx::Conv_679 = Identity(%onnx::Conv_676) %onnx::Conv_673 = Identity(%onnx::Conv_670) %onnx::Conv_667 = Identity(%onnx::Conv_658) %onnx::Conv_664 = Identity(%onnx::Conv_661) %onnx::Conv_655 = Identity(%onnx::Conv_622) %onnx::Conv_652 = Identity(%onnx::Conv_622) %onnx::Conv_649 = Identity(%onnx::Conv_622) %onnx::Conv_646 = Identity(%onnx::Conv_622) %onnx::Conv_643 = Identity(%onnx::Conv_622) %onnx::Conv_640 = Identity(%onnx::Conv_622) %onnx::Conv_637 = Identity(%onnx::Conv_622) %onnx::Conv_634 = Identity(%onnx::Conv_622) %onnx::Conv_631 = Identity(%onnx::Conv_622) %onnx::Conv_628 = Identity(%onnx::Conv_625) %onnx::Conv_619 = Identity(%onnx::Conv_604) %onnx::Conv_616 = Identity(%onnx::Conv_604) %onnx::Conv_613 = Identity(%onnx::Conv_604) %onnx::Conv_610 = Identity(%onnx::Conv_607) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_603, %onnx::Conv_604) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_606, %onnx::Conv_607) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 48, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.0/nl/Relu_output_0, %onnx::Conv_609, %onnx::Conv_610) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_612, %onnx::Conv_613) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_615, %onnx::Conv_616) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.1/nl/Relu_output_0, %onnx::Conv_618, %onnx::Conv_619) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_621, %onnx::Conv_622) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_624, %onnx::Conv_625) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.2/nl/Relu_output_0, %onnx::Conv_627, %onnx::Conv_628) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_630, %onnx::Conv_631) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_633, %onnx::Conv_634) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.3/shuffle/Reshape_output_0 = Reshape(%/cells.3/nl/Relu_output_0, %/cells.3/shuffle/Constant_output_0) %/cells.3/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.3/shuffle/Reshape_output_0) %/cells.3/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.3/shuffle/Reshape_1_output_0 = Reshape(%/cells.3/shuffle/Transpose_output_0, %/cells.3/shuffle/Constant_1_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.3/shuffle/Reshape_1_output_0, %onnx::Conv_636, %onnx::Conv_637) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_639, %onnx::Conv_640) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_642, %onnx::Conv_643) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.4/nl/Relu_output_0, %onnx::Conv_645, %onnx::Conv_646) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_648, %onnx::Conv_649) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/Add_output_0, %onnx::Conv_651, %onnx::Conv_652) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.5/nl/Relu_output_0, %onnx::Conv_654, %onnx::Conv_655) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_657, %onnx::Conv_658) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_660, %onnx::Conv_661) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.7/nl/Relu_output_0, %onnx::Conv_663, %onnx::Conv_664) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_666, %onnx::Conv_667) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_669, %onnx::Conv_670) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.9/nl/Relu_output_0, %onnx::Conv_672, %onnx::Conv_673) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_675, %onnx::Conv_676) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_678, %onnx::Conv_679) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.10/nl/Relu_output_0, %onnx::Conv_681, %onnx::Conv_682) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_684, %onnx::Conv_685) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_687, %onnx::Conv_688) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.11/nl/Relu_output_0, %onnx::Conv_690, %onnx::Conv_691) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_693, %onnx::Conv_694) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_696, %onnx::Conv_697) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.12/nl/Relu_output_0, %onnx::Conv_699, %onnx::Conv_700) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_702, %onnx::Conv_703) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_705, %onnx::Conv_706) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.13/shuffle/Reshape_output_0 = Reshape(%/cells.13/nl/Relu_output_0, %/cells.13/shuffle/Constant_output_0) %/cells.13/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.13/shuffle/Reshape_output_0) %/cells.13/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.13/shuffle/Reshape_1_output_0 = Reshape(%/cells.13/shuffle/Transpose_output_0, %/cells.13/shuffle/Constant_1_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.13/shuffle/Reshape_1_output_0, %onnx::Conv_708, %onnx::Conv_709) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_711, %onnx::Conv_712) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_714, %onnx::Conv_715) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.14/nl/Relu_output_0, %onnx::Conv_717, %onnx::Conv_718) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_720, %onnx::Conv_721) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_723, %onnx::Conv_724) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.15/nl/Relu_output_0, %onnx::Conv_726, %onnx::Conv_727) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_729, %onnx::Conv_730) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_732, %onnx::Conv_733) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.16/nl/Relu_output_0, %onnx::Conv_735, %onnx::Conv_736) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_738, %onnx::Conv_739) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.15/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_741, %onnx::Conv_742) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.17/nl/Relu_output_0, %onnx::Conv_744, %onnx::Conv_745) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_747, %onnx::Conv_748) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_750, %onnx::Conv_751) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.18/nl/Relu_output_0, %onnx::Conv_753, %onnx::Conv_754) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_756, %onnx::Conv_757) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_759, %onnx::Conv_760) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.19/nl/Relu_output_0, %onnx::Conv_762, %onnx::Conv_763) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_765, %onnx::Conv_766) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_768, %onnx::Conv_769) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.20/nl/Relu_output_0, %onnx::Conv_771, %onnx::Conv_772) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_774, %onnx::Conv_775) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_777, %onnx::Conv_778) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.21/nl/Relu_output_0, %onnx::Conv_780, %onnx::Conv_781) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_783, %onnx::Conv_784) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_786, %onnx::Conv_787) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %601 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %601 }	val_accuracy	78,385,536	2,279,356	{'zcp_synflow': 79.13185745813524, 'zcp_zen': 70.08782196044922, 'zcp_epe_nas': 8.510709621613676, 'zcp_fisher': 0.1042877584695816, 'zcp_flops': 78385536.0, 'zcp_grad_norm': 24.395854949951172, 'zcp_grasp': -0.49172401428222656, 'zcp_jacov': -16.064699173929746, 'zcp_l2_norm': 678.3722534179688, 'zcp_nwot': 212.75292345868428, 'zcp_params': 2279356.0, 'zcp_plain': -0.00240230280905962, 'zcp_snip': 43.343345642089844, 'lat_1080ti_1': 0.5606280770618194, 'lat_1080ti_32': 0.4816872898427541, 'lat_1080ti_64': 0.3892865130720201, 'lat_2080ti_1': 0.5340526665116767, 'lat_2080ti_32': 0.4821665935380954, 'lat_2080ti_64': 0.4114334342140137, 'lat_essential_ph_1': 0.5094339622641509, 'lat_eyeriss': 0.5177232134670752, 'lat_fpga': 0.6443958479699976, 'lat_gold_6226': 0.4650701207649354, 'lat_gold_6240': 0.6503838241749711, 'lat_pixel2': 0.391304347826087, 'lat_pixel3': 0.47358189232075226, 'lat_raspi4': 0.5230395917445582, 'lat_samsung_a50': 0.23157894736842105, 'lat_samsung_s7': 0.2047244094488189, 'lat_silver_4114': 0.812636719996599, 'lat_silver_4210r': 0.7183244899612877, 'lat_titan_rtx_1': 0.5143837338351113, 'lat_titan_rtx_32': 0.48228848974310773, 'lat_titan_rtx_64': 0.4224725014440752, 'lat_titanx_1': 0.2830060117901337, 'lat_titanx_32': 0.4223103076972613, 'lat_titanx_64': 0.38278154912156, 'lat_titanxp_1': 0.5102766117191905, 'lat_titanxp_32': 0.46309248008004883, 'lat_titanxp_64': 0.4141394481721926}
FBNet_4778	FBNet	4778	4778	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_650[FLOAT, 16x3x3x3] %onnx::Conv_651[FLOAT, 16] %onnx::Conv_653[FLOAT, 16x16x1x1] %onnx::Conv_656[FLOAT, 16x1x3x3] %onnx::Conv_659[FLOAT, 16x16x1x1] %onnx::Conv_662[FLOAT, 96x16x1x1] %onnx::Conv_663[FLOAT, 96] %onnx::Conv_665[FLOAT, 96x1x3x3] %onnx::Conv_668[FLOAT, 24x96x1x1] %onnx::Conv_669[FLOAT, 24] %onnx::Conv_671[FLOAT, 24x12x1x1] %onnx::Conv_674[FLOAT, 24x1x3x3] %onnx::Conv_677[FLOAT, 24x12x1x1] %onnx::Conv_680[FLOAT, 144x24x1x1] %onnx::Conv_681[FLOAT, 144] %onnx::Conv_683[FLOAT, 144x1x3x3] %onnx::Conv_686[FLOAT, 24x144x1x1] %onnx::Conv_689[FLOAT, 24x12x1x1] %onnx::Conv_692[FLOAT, 24x1x3x3] %onnx::Conv_695[FLOAT, 32x12x1x1] %onnx::Conv_696[FLOAT, 32] %onnx::Conv_698[FLOAT, 32x32x1x1] %onnx::Conv_701[FLOAT, 32x1x5x5] %onnx::Conv_704[FLOAT, 32x32x1x1] %onnx::Conv_707[FLOAT, 32x16x1x1] %onnx::Conv_710[FLOAT, 32x1x3x3] %onnx::Conv_713[FLOAT, 32x16x1x1] %onnx::Conv_716[FLOAT, 192x32x1x1] %onnx::Conv_717[FLOAT, 192] %onnx::Conv_719[FLOAT, 192x1x5x5] %onnx::Conv_722[FLOAT, 32x192x1x1] %onnx::Conv_725[FLOAT, 192x32x1x1] %onnx::Conv_728[FLOAT, 192x1x3x3] %onnx::Conv_731[FLOAT, 64x192x1x1] %onnx::Conv_732[FLOAT, 64] %onnx::Conv_734[FLOAT, 192x64x1x1] %onnx::Conv_737[FLOAT, 192x1x3x3] %onnx::Conv_740[FLOAT, 64x192x1x1] %onnx::Conv_743[FLOAT, 192x64x1x1] %onnx::Conv_746[FLOAT, 192x1x5x5] %onnx::Conv_749[FLOAT, 64x192x1x1] %onnx::Conv_752[FLOAT, 192x64x1x1] %onnx::Conv_755[FLOAT, 192x1x3x3] %onnx::Conv_758[FLOAT, 64x192x1x1] %onnx::Conv_761[FLOAT, 112x64x1x1] %onnx::Conv_762[FLOAT, 112] %onnx::Conv_764[FLOAT, 112x112x1x1] %onnx::Conv_767[FLOAT, 112x1x3x3] %onnx::Conv_770[FLOAT, 112x112x1x1] %onnx::Conv_773[FLOAT, 672x112x1x1] %onnx::Conv_774[FLOAT, 672] %onnx::Conv_776[FLOAT, 672x1x3x3] %onnx::Conv_779[FLOAT, 112x672x1x1] %onnx::Conv_782[FLOAT, 112x112x1x1] %onnx::Conv_785[FLOAT, 112x1x3x3] %onnx::Conv_788[FLOAT, 112x112x1x1] %onnx::Conv_791[FLOAT, 336x112x1x1] %onnx::Conv_792[FLOAT, 336] %onnx::Conv_794[FLOAT, 336x1x5x5] %onnx::Conv_797[FLOAT, 184x336x1x1] %onnx::Conv_798[FLOAT, 184] %onnx::Conv_800[FLOAT, 184x92x1x1] %onnx::Conv_803[FLOAT, 184x1x5x5] %onnx::Conv_806[FLOAT, 184x92x1x1] %onnx::Conv_809[FLOAT, 552x184x1x1] %onnx::Conv_810[FLOAT, 552] %onnx::Conv_812[FLOAT, 552x1x5x5] %onnx::Conv_815[FLOAT, 184x552x1x1] %onnx::Conv_818[FLOAT, 1104x184x1x1] %onnx::Conv_819[FLOAT, 1104] %onnx::Conv_821[FLOAT, 1104x1x3x3] %onnx::Conv_824[FLOAT, 184x1104x1x1] %onnx::Conv_827[FLOAT, 552x184x1x1] %onnx::Conv_830[FLOAT, 552x1x5x5] %onnx::Conv_833[FLOAT, 352x552x1x1] %onnx::Conv_834[FLOAT, 352] %onnx::Conv_836[FLOAT, 1504x352x1x1] %onnx::Conv_837[FLOAT, 1504] ) { %onnx::Conv_831 = Identity(%onnx::Conv_810) %onnx::Conv_828 = Identity(%onnx::Conv_810) %onnx::Conv_825 = Identity(%onnx::Conv_798) %onnx::Conv_822 = Identity(%onnx::Conv_819) %onnx::Conv_816 = Identity(%onnx::Conv_798) %onnx::Conv_813 = Identity(%onnx::Conv_810) %onnx::Conv_807 = Identity(%onnx::Conv_798) %onnx::Conv_804 = Identity(%onnx::Conv_798) %onnx::Conv_801 = Identity(%onnx::Conv_798) %onnx::Conv_795 = Identity(%onnx::Conv_792) %onnx::Conv_789 = Identity(%onnx::Conv_762) %onnx::Conv_786 = Identity(%onnx::Conv_762) %onnx::Conv_783 = Identity(%onnx::Conv_762) %onnx::Conv_780 = Identity(%onnx::Conv_762) %onnx::Conv_777 = Identity(%onnx::Conv_774) %onnx::Conv_771 = Identity(%onnx::Conv_762) %onnx::Conv_768 = Identity(%onnx::Conv_762) %onnx::Conv_765 = Identity(%onnx::Conv_762) %onnx::Conv_759 = Identity(%onnx::Conv_732) %onnx::Conv_756 = Identity(%onnx::Conv_717) %onnx::Conv_753 = Identity(%onnx::Conv_717) %onnx::Conv_750 = Identity(%onnx::Conv_732) %onnx::Conv_747 = Identity(%onnx::Conv_717) %onnx::Conv_744 = Identity(%onnx::Conv_717) %onnx::Conv_741 = Identity(%onnx::Conv_732) %onnx::Conv_738 = Identity(%onnx::Conv_717) %onnx::Conv_735 = Identity(%onnx::Conv_717) %onnx::Conv_729 = Identity(%onnx::Conv_717) %onnx::Conv_726 = Identity(%onnx::Conv_717) %onnx::Conv_723 = Identity(%onnx::Conv_696) %onnx::Conv_720 = Identity(%onnx::Conv_717) %onnx::Conv_714 = Identity(%onnx::Conv_696) %onnx::Conv_711 = Identity(%onnx::Conv_696) %onnx::Conv_708 = Identity(%onnx::Conv_696) %onnx::Conv_705 = Identity(%onnx::Conv_696) %onnx::Conv_702 = Identity(%onnx::Conv_696) %onnx::Conv_699 = Identity(%onnx::Conv_696) %onnx::Conv_693 = Identity(%onnx::Conv_669) %onnx::Conv_690 = Identity(%onnx::Conv_669) %onnx::Conv_687 = Identity(%onnx::Conv_669) %onnx::Conv_684 = Identity(%onnx::Conv_681) %onnx::Conv_678 = Identity(%onnx::Conv_669) %onnx::Conv_675 = Identity(%onnx::Conv_669) %onnx::Conv_672 = Identity(%onnx::Conv_669) %onnx::Conv_666 = Identity(%onnx::Conv_663) %onnx::Conv_660 = Identity(%onnx::Conv_651) %onnx::Conv_657 = Identity(%onnx::Conv_651) %onnx::Conv_654 = Identity(%onnx::Conv_651) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_650, %onnx::Conv_651) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_653, %onnx::Conv_654) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.0/nl/Relu_output_0, %onnx::Conv_656, %onnx::Conv_657) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_659, %onnx::Conv_660) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_662, %onnx::Conv_663) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.1/nl/Relu_output_0, %onnx::Conv_665, %onnx::Conv_666) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_668, %onnx::Conv_669) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_671, %onnx::Conv_672) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.2/shuffle/Reshape_output_0 = Reshape(%/cells.2/nl/Relu_output_0, %/cells.2/shuffle/Constant_output_0) %/cells.2/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.2/shuffle/Reshape_output_0) %/cells.2/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.2/shuffle/Reshape_1_output_0 = Reshape(%/cells.2/shuffle/Transpose_output_0, %/cells.2/shuffle/Constant_1_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.2/shuffle/Reshape_1_output_0, %onnx::Conv_674, %onnx::Conv_675) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_677, %onnx::Conv_678) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_680, %onnx::Conv_681) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.4/nl/Relu_output_0, %onnx::Conv_683, %onnx::Conv_684) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_686, %onnx::Conv_687) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/Add_output_0, %onnx::Conv_689, %onnx::Conv_690) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.5/shuffle/Reshape_output_0 = Reshape(%/cells.5/nl/Relu_output_0, %/cells.5/shuffle/Constant_output_0) %/cells.5/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.5/shuffle/Reshape_output_0) %/cells.5/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.5/shuffle/Reshape_1_output_0 = Reshape(%/cells.5/shuffle/Transpose_output_0, %/cells.5/shuffle/Constant_1_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.5/shuffle/Reshape_1_output_0, %onnx::Conv_692, %onnx::Conv_693) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_695, %onnx::Conv_696) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_698, %onnx::Conv_699) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.6/nl/Relu_output_0, %onnx::Conv_701, %onnx::Conv_702) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_704, %onnx::Conv_705) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_707, %onnx::Conv_708) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.7/shuffle/Reshape_output_0 = Reshape(%/cells.7/nl/Relu_output_0, %/cells.7/shuffle/Constant_output_0) %/cells.7/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.7/shuffle/Reshape_output_0) %/cells.7/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.7/shuffle/Reshape_1_output_0 = Reshape(%/cells.7/shuffle/Transpose_output_0, %/cells.7/shuffle/Constant_1_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.7/shuffle/Reshape_1_output_0, %onnx::Conv_710, %onnx::Conv_711) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_713, %onnx::Conv_714) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_716, %onnx::Conv_717) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.8/nl/Relu_output_0, %onnx::Conv_719, %onnx::Conv_720) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_722, %onnx::Conv_723) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_725, %onnx::Conv_726) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.9/nl/Relu_output_0, %onnx::Conv_728, %onnx::Conv_729) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_731, %onnx::Conv_732) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_734, %onnx::Conv_735) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.10/nl/Relu_output_0, %onnx::Conv_737, %onnx::Conv_738) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_740, %onnx::Conv_741) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_743, %onnx::Conv_744) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.11/nl/Relu_output_0, %onnx::Conv_746, %onnx::Conv_747) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_749, %onnx::Conv_750) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_752, %onnx::Conv_753) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.12/nl/Relu_output_0, %onnx::Conv_755, %onnx::Conv_756) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_758, %onnx::Conv_759) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_761, %onnx::Conv_762) %/cells.13/relu/Relu_output_0 = Relu(%/cells.13/conv/Conv_output_0) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/relu/Relu_output_0, %onnx::Conv_764, %onnx::Conv_765) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.14/nl/Relu_output_0, %onnx::Conv_767, %onnx::Conv_768) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_770, %onnx::Conv_771) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/relu/Relu_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_773, %onnx::Conv_774) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.15/nl/Relu_output_0, %onnx::Conv_776, %onnx::Conv_777) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_779, %onnx::Conv_780) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_782, %onnx::Conv_783) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.16/nl/Relu_output_0, %onnx::Conv_785, %onnx::Conv_786) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_788, %onnx::Conv_789) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.15/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_791, %onnx::Conv_792) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.17/nl/Relu_output_0, %onnx::Conv_794, %onnx::Conv_795) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_797, %onnx::Conv_798) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_800, %onnx::Conv_801) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.18/shuffle/Reshape_output_0 = Reshape(%/cells.18/nl/Relu_output_0, %/cells.18/shuffle/Constant_output_0) %/cells.18/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.18/shuffle/Reshape_output_0) %/cells.18/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.18/shuffle/Reshape_1_output_0 = Reshape(%/cells.18/shuffle/Transpose_output_0, %/cells.18/shuffle/Constant_1_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.18/shuffle/Reshape_1_output_0, %onnx::Conv_803, %onnx::Conv_804) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_806, %onnx::Conv_807) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_809, %onnx::Conv_810) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 552, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.19/nl/Relu_output_0, %onnx::Conv_812, %onnx::Conv_813) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_815, %onnx::Conv_816) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_818, %onnx::Conv_819) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.20/nl/Relu_output_0, %onnx::Conv_821, %onnx::Conv_822) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_824, %onnx::Conv_825) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_827, %onnx::Conv_828) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 552, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.21/nl/Relu_output_0, %onnx::Conv_830, %onnx::Conv_831) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_833, %onnx::Conv_834) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_836, %onnx::Conv_837) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %648 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %648 }	val_accuracy	73,633,920	2,150,908	{'zcp_synflow': 77.9478877374232, 'zcp_zen': 69.50531005859375, 'zcp_epe_nas': 0.00015999920000638146, 'zcp_fisher': 0.11020199209451675, 'zcp_flops': 73633920.0, 'zcp_grad_norm': 24.785978317260742, 'zcp_grasp': 0.25147438049316406, 'zcp_jacov': -16.05603509487478, 'zcp_l2_norm': 667.73681640625, 'zcp_nwot': 213.5381474894798, 'zcp_params': 2150908.0, 'zcp_plain': 0.0017778489273041487, 'zcp_snip': 43.935855865478516, 'lat_1080ti_1': 0.7228433729309652, 'lat_1080ti_32': 0.5910071396598946, 'lat_1080ti_64': 0.44589319345917094, 'lat_2080ti_1': 0.651598359811438, 'lat_2080ti_32': 0.6212184231663143, 'lat_2080ti_64': 0.4996403943129299, 'lat_essential_ph_1': 0.3584905660377358, 'lat_eyeriss': 0.4973135532613282, 'lat_fpga': 0.5554577029260134, 'lat_gold_6226': 0.44279121638671315, 'lat_gold_6240': 0.6836545859849604, 'lat_pixel2': 0.34782608695652173, 'lat_pixel3': 0.44280518610825736, 'lat_raspi4': 0.5242919659270282, 'lat_samsung_a50': 0.23157894736842105, 'lat_samsung_s7': 0.1968503937007874, 'lat_silver_4114': 0.8051192847835233, 'lat_silver_4210r': 0.612295068671597, 'lat_titan_rtx_1': 0.613738656161187, 'lat_titan_rtx_32': 0.5832977180385639, 'lat_titan_rtx_64': 0.5217540108585967, 'lat_titanx_1': 0.32768561013568653, 'lat_titanx_32': 0.5301280896639994, 'lat_titanx_64': 0.479417446793514, 'lat_titanxp_1': 0.5695545179150503, 'lat_titanxp_32': 0.5602725522566827, 'lat_titanxp_64': 0.4731377048437549}
FBNet_277	FBNet	277	277	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_588[FLOAT, 16x3x3x3] %onnx::Conv_589[FLOAT, 16] %onnx::Conv_591[FLOAT, 16x16x1x1] %onnx::Conv_594[FLOAT, 16x1x5x5] %onnx::Conv_597[FLOAT, 16x16x1x1] %onnx::Conv_600[FLOAT, 16x8x1x1] %onnx::Conv_603[FLOAT, 16x1x5x5] %onnx::Conv_606[FLOAT, 24x8x1x1] %onnx::Conv_607[FLOAT, 24] %onnx::Conv_609[FLOAT, 24x12x1x1] %onnx::Conv_612[FLOAT, 24x1x5x5] %onnx::Conv_615[FLOAT, 24x12x1x1] %onnx::Conv_618[FLOAT, 72x24x1x1] %onnx::Conv_619[FLOAT, 72] %onnx::Conv_621[FLOAT, 72x1x5x5] %onnx::Conv_624[FLOAT, 24x72x1x1] %onnx::Conv_627[FLOAT, 144x24x1x1] %onnx::Conv_628[FLOAT, 144] %onnx::Conv_630[FLOAT, 144x1x5x5] %onnx::Conv_633[FLOAT, 32x144x1x1] %onnx::Conv_634[FLOAT, 32] %onnx::Conv_636[FLOAT, 32x32x1x1] %onnx::Conv_639[FLOAT, 32x1x5x5] %onnx::Conv_642[FLOAT, 32x32x1x1] %onnx::Conv_645[FLOAT, 192x32x1x1] %onnx::Conv_646[FLOAT, 192] %onnx::Conv_648[FLOAT, 192x1x3x3] %onnx::Conv_651[FLOAT, 32x192x1x1] %onnx::Conv_654[FLOAT, 32x16x1x1] %onnx::Conv_657[FLOAT, 32x1x5x5] %onnx::Conv_660[FLOAT, 64x16x1x1] %onnx::Conv_661[FLOAT, 64] %onnx::Conv_663[FLOAT, 384x64x1x1] %onnx::Conv_664[FLOAT, 384] %onnx::Conv_666[FLOAT, 384x1x5x5] %onnx::Conv_669[FLOAT, 64x384x1x1] %onnx::Conv_672[FLOAT, 384x64x1x1] %onnx::Conv_675[FLOAT, 384x1x5x5] %onnx::Conv_678[FLOAT, 64x384x1x1] %onnx::Conv_681[FLOAT, 64x64x1x1] %onnx::Conv_684[FLOAT, 64x1x3x3] %onnx::Conv_687[FLOAT, 64x64x1x1] %onnx::Conv_690[FLOAT, 112x64x1x1] %onnx::Conv_691[FLOAT, 112] %onnx::Conv_693[FLOAT, 112x112x1x1] %onnx::Conv_696[FLOAT, 112x1x5x5] %onnx::Conv_699[FLOAT, 112x112x1x1] %onnx::Conv_702[FLOAT, 336x112x1x1] %onnx::Conv_703[FLOAT, 336] %onnx::Conv_705[FLOAT, 336x1x3x3] %onnx::Conv_708[FLOAT, 112x336x1x1] %onnx::Conv_711[FLOAT, 672x112x1x1] %onnx::Conv_712[FLOAT, 672] %onnx::Conv_714[FLOAT, 672x1x3x3] %onnx::Conv_717[FLOAT, 184x672x1x1] %onnx::Conv_718[FLOAT, 184] %onnx::Conv_720[FLOAT, 184x92x1x1] %onnx::Conv_723[FLOAT, 184x1x5x5] %onnx::Conv_726[FLOAT, 184x92x1x1] %onnx::Conv_729[FLOAT, 552x184x1x1] %onnx::Conv_730[FLOAT, 552] %onnx::Conv_732[FLOAT, 552x1x5x5] %onnx::Conv_735[FLOAT, 184x552x1x1] %onnx::Conv_738[FLOAT, 184x92x1x1] %onnx::Conv_741[FLOAT, 184x1x3x3] %onnx::Conv_744[FLOAT, 352x92x1x1] %onnx::Conv_745[FLOAT, 352] %onnx::Conv_747[FLOAT, 1504x352x1x1] %onnx::Conv_748[FLOAT, 1504] ) { %onnx::Conv_742 = Identity(%onnx::Conv_718) %onnx::Conv_739 = Identity(%onnx::Conv_718) %onnx::Conv_736 = Identity(%onnx::Conv_718) %onnx::Conv_733 = Identity(%onnx::Conv_730) %onnx::Conv_727 = Identity(%onnx::Conv_718) %onnx::Conv_724 = Identity(%onnx::Conv_718) %onnx::Conv_721 = Identity(%onnx::Conv_718) %onnx::Conv_715 = Identity(%onnx::Conv_712) %onnx::Conv_709 = Identity(%onnx::Conv_691) %onnx::Conv_706 = Identity(%onnx::Conv_703) %onnx::Conv_700 = Identity(%onnx::Conv_691) %onnx::Conv_697 = Identity(%onnx::Conv_691) %onnx::Conv_694 = Identity(%onnx::Conv_691) %onnx::Conv_688 = Identity(%onnx::Conv_661) %onnx::Conv_685 = Identity(%onnx::Conv_661) %onnx::Conv_682 = Identity(%onnx::Conv_661) %onnx::Conv_679 = Identity(%onnx::Conv_661) %onnx::Conv_676 = Identity(%onnx::Conv_664) %onnx::Conv_673 = Identity(%onnx::Conv_664) %onnx::Conv_670 = Identity(%onnx::Conv_661) %onnx::Conv_667 = Identity(%onnx::Conv_664) %onnx::Conv_658 = Identity(%onnx::Conv_634) %onnx::Conv_655 = Identity(%onnx::Conv_634) %onnx::Conv_652 = Identity(%onnx::Conv_634) %onnx::Conv_649 = Identity(%onnx::Conv_646) %onnx::Conv_643 = Identity(%onnx::Conv_634) %onnx::Conv_640 = Identity(%onnx::Conv_634) %onnx::Conv_637 = Identity(%onnx::Conv_634) %onnx::Conv_631 = Identity(%onnx::Conv_628) %onnx::Conv_625 = Identity(%onnx::Conv_607) %onnx::Conv_622 = Identity(%onnx::Conv_619) %onnx::Conv_616 = Identity(%onnx::Conv_607) %onnx::Conv_613 = Identity(%onnx::Conv_607) %onnx::Conv_610 = Identity(%onnx::Conv_607) %onnx::Conv_604 = Identity(%onnx::Conv_589) %onnx::Conv_601 = Identity(%onnx::Conv_589) %onnx::Conv_598 = Identity(%onnx::Conv_589) %onnx::Conv_595 = Identity(%onnx::Conv_589) %onnx::Conv_592 = Identity(%onnx::Conv_589) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_588, %onnx::Conv_589) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_591, %onnx::Conv_592) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.0/nl/Relu_output_0, %onnx::Conv_594, %onnx::Conv_595) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_597, %onnx::Conv_598) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_600, %onnx::Conv_601) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.1/shuffle/Reshape_output_0 = Reshape(%/cells.1/nl/Relu_output_0, %/cells.1/shuffle/Constant_output_0) %/cells.1/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.1/shuffle/Reshape_output_0) %/cells.1/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.1/shuffle/Reshape_1_output_0 = Reshape(%/cells.1/shuffle/Transpose_output_0, %/cells.1/shuffle/Constant_1_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.1/shuffle/Reshape_1_output_0, %onnx::Conv_603, %onnx::Conv_604) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_606, %onnx::Conv_607) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_609, %onnx::Conv_610) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.2/shuffle/Reshape_output_0 = Reshape(%/cells.2/nl/Relu_output_0, %/cells.2/shuffle/Constant_output_0) %/cells.2/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.2/shuffle/Reshape_output_0) %/cells.2/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.2/shuffle/Reshape_1_output_0 = Reshape(%/cells.2/shuffle/Transpose_output_0, %/cells.2/shuffle/Constant_1_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.2/shuffle/Reshape_1_output_0, %onnx::Conv_612, %onnx::Conv_613) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_615, %onnx::Conv_616) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_618, %onnx::Conv_619) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.3/nl/Relu_output_0, %onnx::Conv_621, %onnx::Conv_622) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_624, %onnx::Conv_625) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_627, %onnx::Conv_628) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.5/nl/Relu_output_0, %onnx::Conv_630, %onnx::Conv_631) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_633, %onnx::Conv_634) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_636, %onnx::Conv_637) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.6/nl/Relu_output_0, %onnx::Conv_639, %onnx::Conv_640) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_642, %onnx::Conv_643) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_645, %onnx::Conv_646) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.8/nl/Relu_output_0, %onnx::Conv_648, %onnx::Conv_649) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_651, %onnx::Conv_652) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_654, %onnx::Conv_655) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.9/shuffle/Reshape_output_0 = Reshape(%/cells.9/nl/Relu_output_0, %/cells.9/shuffle/Constant_output_0) %/cells.9/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.9/shuffle/Reshape_output_0) %/cells.9/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.9/shuffle/Reshape_1_output_0 = Reshape(%/cells.9/shuffle/Transpose_output_0, %/cells.9/shuffle/Constant_1_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.9/shuffle/Reshape_1_output_0, %onnx::Conv_657, %onnx::Conv_658) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_660, %onnx::Conv_661) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_663, %onnx::Conv_664) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.10/nl/Relu_output_0, %onnx::Conv_666, %onnx::Conv_667) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_669, %onnx::Conv_670) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_672, %onnx::Conv_673) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.11/nl/Relu_output_0, %onnx::Conv_675, %onnx::Conv_676) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_678, %onnx::Conv_679) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_681, %onnx::Conv_682) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.12/nl/Relu_output_0, %onnx::Conv_684, %onnx::Conv_685) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_687, %onnx::Conv_688) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_690, %onnx::Conv_691) %/cells.13/relu/Relu_output_0 = Relu(%/cells.13/conv/Conv_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/relu/Relu_output_0, %onnx::Conv_693, %onnx::Conv_694) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.15/nl/Relu_output_0, %onnx::Conv_696, %onnx::Conv_697) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_699, %onnx::Conv_700) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.13/relu/Relu_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_702, %onnx::Conv_703) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.16/nl/Relu_output_0, %onnx::Conv_705, %onnx::Conv_706) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_708, %onnx::Conv_709) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.15/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_711, %onnx::Conv_712) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.17/nl/Relu_output_0, %onnx::Conv_714, %onnx::Conv_715) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_717, %onnx::Conv_718) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_720, %onnx::Conv_721) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.19/shuffle/Reshape_output_0 = Reshape(%/cells.19/nl/Relu_output_0, %/cells.19/shuffle/Constant_output_0) %/cells.19/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.19/shuffle/Reshape_output_0) %/cells.19/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.19/shuffle/Reshape_1_output_0 = Reshape(%/cells.19/shuffle/Transpose_output_0, %/cells.19/shuffle/Constant_1_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.19/shuffle/Reshape_1_output_0, %onnx::Conv_723, %onnx::Conv_724) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_726, %onnx::Conv_727) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_729, %onnx::Conv_730) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 552, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.20/nl/Relu_output_0, %onnx::Conv_732, %onnx::Conv_733) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_735, %onnx::Conv_736) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_738, %onnx::Conv_739) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.21/shuffle/Reshape_output_0 = Reshape(%/cells.21/nl/Relu_output_0, %/cells.21/shuffle/Constant_output_0) %/cells.21/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.21/shuffle/Reshape_output_0) %/cells.21/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.21/shuffle/Reshape_1_output_0 = Reshape(%/cells.21/shuffle/Transpose_output_0, %/cells.21/shuffle/Constant_1_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.21/shuffle/Reshape_1_output_0, %onnx::Conv_741, %onnx::Conv_742) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_744, %onnx::Conv_745) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_747, %onnx::Conv_748) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %586 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %586 }	val_accuracy	57,113,472	1,490,204	{'zcp_synflow': 68.0209134467973, 'zcp_zen': 58.78862380981445, 'zcp_epe_nas': 6.648239002043625, 'zcp_fisher': 0.06493034958839417, 'zcp_flops': 57113472.0, 'zcp_grad_norm': 20.88568878173828, 'zcp_grasp': -0.024444580078125, 'zcp_jacov': -16.058677636143905, 'zcp_l2_norm': 520.86669921875, 'zcp_nwot': 209.33801378832936, 'zcp_params': 1490204.0, 'zcp_plain': 0.00838951300829649, 'zcp_snip': 31.93008041381836, 'lat_1080ti_1': 0.3319971412422008, 'lat_1080ti_32': 0.3144846522176697, 'lat_1080ti_64': 0.25602394885108015, 'lat_2080ti_1': 0.3334223436692151, 'lat_2080ti_32': 0.28031366261092955, 'lat_2080ti_64': 0.24391914155682315, 'lat_essential_ph_1': 0.22641509433962265, 'lat_eyeriss': 0.29908495773201543, 'lat_fpga': 0.241433222177144, 'lat_gold_6226': 0.2485004833304872, 'lat_gold_6240': 0.2860351724650671, 'lat_pixel2': 0.1956521739130435, 'lat_pixel3': 0.32538783047997694, 'lat_raspi4': 0.30145694223959946, 'lat_samsung_a50': 0.12631578947368421, 'lat_samsung_s7': 0.10236220472440945, 'lat_silver_4114': 0.3229413657718079, 'lat_silver_4210r': 0.27533326101435834, 'lat_titan_rtx_1': 0.32500740915187054, 'lat_titan_rtx_32': 0.29309366425416367, 'lat_titan_rtx_64': 0.25908643382065366, 'lat_titanx_1': 0.16927432987654925, 'lat_titanx_32': 0.27036356214492385, 'lat_titanx_64': 0.27409201942770123, 'lat_titanxp_1': 0.31579525382878765, 'lat_titanxp_32': 0.27923109077659125, 'lat_titanxp_64': 0.2659230603091384}
FBNet_3731	FBNet	3731	3731	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_622[FLOAT, 16x3x3x3] %onnx::Conv_623[FLOAT, 16] %onnx::Conv_625[FLOAT, 48x16x1x1] %onnx::Conv_626[FLOAT, 48] %onnx::Conv_628[FLOAT, 48x1x3x3] %onnx::Conv_631[FLOAT, 16x48x1x1] %onnx::Conv_634[FLOAT, 16x8x1x1] %onnx::Conv_637[FLOAT, 16x1x5x5] %onnx::Conv_640[FLOAT, 24x8x1x1] %onnx::Conv_641[FLOAT, 24] %onnx::Conv_643[FLOAT, 144x24x1x1] %onnx::Conv_644[FLOAT, 144] %onnx::Conv_646[FLOAT, 144x1x5x5] %onnx::Conv_649[FLOAT, 24x144x1x1] %onnx::Conv_652[FLOAT, 144x24x1x1] %onnx::Conv_655[FLOAT, 144x1x5x5] %onnx::Conv_658[FLOAT, 24x144x1x1] %onnx::Conv_661[FLOAT, 72x24x1x1] %onnx::Conv_662[FLOAT, 72] %onnx::Conv_664[FLOAT, 72x1x3x3] %onnx::Conv_667[FLOAT, 32x72x1x1] %onnx::Conv_668[FLOAT, 32] %onnx::Conv_670[FLOAT, 96x32x1x1] %onnx::Conv_671[FLOAT, 96] %onnx::Conv_673[FLOAT, 96x1x3x3] %onnx::Conv_676[FLOAT, 32x96x1x1] %onnx::Conv_679[FLOAT, 96x32x1x1] %onnx::Conv_682[FLOAT, 96x1x3x3] %onnx::Conv_685[FLOAT, 32x96x1x1] %onnx::Conv_688[FLOAT, 96x32x1x1] %onnx::Conv_691[FLOAT, 96x1x5x5] %onnx::Conv_694[FLOAT, 32x96x1x1] %onnx::Conv_697[FLOAT, 192x32x1x1] %onnx::Conv_698[FLOAT, 192] %onnx::Conv_700[FLOAT, 192x1x5x5] %onnx::Conv_703[FLOAT, 64x192x1x1] %onnx::Conv_704[FLOAT, 64] %onnx::Conv_706[FLOAT, 384x64x1x1] %onnx::Conv_707[FLOAT, 384] %onnx::Conv_709[FLOAT, 384x1x3x3] %onnx::Conv_712[FLOAT, 64x384x1x1] %onnx::Conv_715[FLOAT, 384x64x1x1] %onnx::Conv_718[FLOAT, 384x1x5x5] %onnx::Conv_721[FLOAT, 64x384x1x1] %onnx::Conv_724[FLOAT, 192x64x1x1] %onnx::Conv_727[FLOAT, 192x1x5x5] %onnx::Conv_730[FLOAT, 64x192x1x1] %onnx::Conv_733[FLOAT, 192x64x1x1] %onnx::Conv_736[FLOAT, 192x1x5x5] %onnx::Conv_739[FLOAT, 112x192x1x1] %onnx::Conv_740[FLOAT, 112] %onnx::Conv_742[FLOAT, 112x56x1x1] %onnx::Conv_745[FLOAT, 112x1x5x5] %onnx::Conv_748[FLOAT, 112x56x1x1] %onnx::Conv_751[FLOAT, 112x112x1x1] %onnx::Conv_754[FLOAT, 112x1x5x5] %onnx::Conv_757[FLOAT, 112x112x1x1] %onnx::Conv_760[FLOAT, 672x112x1x1] %onnx::Conv_761[FLOAT, 672] %onnx::Conv_763[FLOAT, 672x1x5x5] %onnx::Conv_766[FLOAT, 184x672x1x1] %onnx::Conv_767[FLOAT, 184] %onnx::Conv_769[FLOAT, 184x184x1x1] %onnx::Conv_772[FLOAT, 184x1x5x5] %onnx::Conv_775[FLOAT, 184x184x1x1] %onnx::Conv_778[FLOAT, 1104x184x1x1] %onnx::Conv_779[FLOAT, 1104] %onnx::Conv_781[FLOAT, 1104x1x5x5] %onnx::Conv_784[FLOAT, 184x1104x1x1] %onnx::Conv_787[FLOAT, 184x92x1x1] %onnx::Conv_790[FLOAT, 184x1x3x3] %onnx::Conv_793[FLOAT, 184x92x1x1] %onnx::Conv_796[FLOAT, 1104x184x1x1] %onnx::Conv_799[FLOAT, 1104x1x5x5] %onnx::Conv_802[FLOAT, 352x1104x1x1] %onnx::Conv_803[FLOAT, 352] %onnx::Conv_805[FLOAT, 1504x352x1x1] %onnx::Conv_806[FLOAT, 1504] ) { %onnx::Conv_800 = Identity(%onnx::Conv_779) %onnx::Conv_797 = Identity(%onnx::Conv_779) %onnx::Conv_794 = Identity(%onnx::Conv_767) %onnx::Conv_791 = Identity(%onnx::Conv_767) %onnx::Conv_788 = Identity(%onnx::Conv_767) %onnx::Conv_785 = Identity(%onnx::Conv_767) %onnx::Conv_782 = Identity(%onnx::Conv_779) %onnx::Conv_776 = Identity(%onnx::Conv_767) %onnx::Conv_773 = Identity(%onnx::Conv_767) %onnx::Conv_770 = Identity(%onnx::Conv_767) %onnx::Conv_764 = Identity(%onnx::Conv_761) %onnx::Conv_758 = Identity(%onnx::Conv_740) %onnx::Conv_755 = Identity(%onnx::Conv_740) %onnx::Conv_752 = Identity(%onnx::Conv_740) %onnx::Conv_749 = Identity(%onnx::Conv_740) %onnx::Conv_746 = Identity(%onnx::Conv_740) %onnx::Conv_743 = Identity(%onnx::Conv_740) %onnx::Conv_737 = Identity(%onnx::Conv_698) %onnx::Conv_734 = Identity(%onnx::Conv_698) %onnx::Conv_731 = Identity(%onnx::Conv_704) %onnx::Conv_728 = Identity(%onnx::Conv_698) %onnx::Conv_725 = Identity(%onnx::Conv_698) %onnx::Conv_722 = Identity(%onnx::Conv_704) %onnx::Conv_719 = Identity(%onnx::Conv_707) %onnx::Conv_716 = Identity(%onnx::Conv_707) %onnx::Conv_713 = Identity(%onnx::Conv_704) %onnx::Conv_710 = Identity(%onnx::Conv_707) %onnx::Conv_701 = Identity(%onnx::Conv_698) %onnx::Conv_695 = Identity(%onnx::Conv_668) %onnx::Conv_692 = Identity(%onnx::Conv_671) %onnx::Conv_689 = Identity(%onnx::Conv_671) %onnx::Conv_686 = Identity(%onnx::Conv_668) %onnx::Conv_683 = Identity(%onnx::Conv_671) %onnx::Conv_680 = Identity(%onnx::Conv_671) %onnx::Conv_677 = Identity(%onnx::Conv_668) %onnx::Conv_674 = Identity(%onnx::Conv_671) %onnx::Conv_665 = Identity(%onnx::Conv_662) %onnx::Conv_659 = Identity(%onnx::Conv_641) %onnx::Conv_656 = Identity(%onnx::Conv_644) %onnx::Conv_653 = Identity(%onnx::Conv_644) %onnx::Conv_650 = Identity(%onnx::Conv_641) %onnx::Conv_647 = Identity(%onnx::Conv_644) %onnx::Conv_638 = Identity(%onnx::Conv_623) %onnx::Conv_635 = Identity(%onnx::Conv_623) %onnx::Conv_632 = Identity(%onnx::Conv_623) %onnx::Conv_629 = Identity(%onnx::Conv_626) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_622, %onnx::Conv_623) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_625, %onnx::Conv_626) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 48, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.0/nl/Relu_output_0, %onnx::Conv_628, %onnx::Conv_629) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_631, %onnx::Conv_632) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_634, %onnx::Conv_635) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.1/shuffle/Reshape_output_0 = Reshape(%/cells.1/nl/Relu_output_0, %/cells.1/shuffle/Constant_output_0) %/cells.1/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.1/shuffle/Reshape_output_0) %/cells.1/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.1/shuffle/Reshape_1_output_0 = Reshape(%/cells.1/shuffle/Transpose_output_0, %/cells.1/shuffle/Constant_1_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.1/shuffle/Reshape_1_output_0, %onnx::Conv_637, %onnx::Conv_638) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_640, %onnx::Conv_641) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_643, %onnx::Conv_644) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.3/nl/Relu_output_0, %onnx::Conv_646, %onnx::Conv_647) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_649, %onnx::Conv_650) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_652, %onnx::Conv_653) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.4/nl/Relu_output_0, %onnx::Conv_655, %onnx::Conv_656) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_658, %onnx::Conv_659) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/Add_output_0, %onnx::Conv_661, %onnx::Conv_662) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.5/nl/Relu_output_0, %onnx::Conv_664, %onnx::Conv_665) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_667, %onnx::Conv_668) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_670, %onnx::Conv_671) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.6/nl/Relu_output_0, %onnx::Conv_673, %onnx::Conv_674) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_676, %onnx::Conv_677) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_679, %onnx::Conv_680) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.7/nl/Relu_output_0, %onnx::Conv_682, %onnx::Conv_683) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_685, %onnx::Conv_686) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_688, %onnx::Conv_689) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.8/nl/Relu_output_0, %onnx::Conv_691, %onnx::Conv_692) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_694, %onnx::Conv_695) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_697, %onnx::Conv_698) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.9/nl/Relu_output_0, %onnx::Conv_700, %onnx::Conv_701) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_703, %onnx::Conv_704) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_706, %onnx::Conv_707) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.10/nl/Relu_output_0, %onnx::Conv_709, %onnx::Conv_710) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_712, %onnx::Conv_713) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_715, %onnx::Conv_716) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.11/nl/Relu_output_0, %onnx::Conv_718, %onnx::Conv_719) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_721, %onnx::Conv_722) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_724, %onnx::Conv_725) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.12/nl/Relu_output_0, %onnx::Conv_727, %onnx::Conv_728) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_730, %onnx::Conv_731) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_733, %onnx::Conv_734) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.13/nl/Relu_output_0, %onnx::Conv_736, %onnx::Conv_737) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_739, %onnx::Conv_740) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_742, %onnx::Conv_743) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.15/shuffle/Reshape_output_0 = Reshape(%/cells.15/nl/Relu_output_0, %/cells.15/shuffle/Constant_output_0) %/cells.15/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.15/shuffle/Reshape_output_0) %/cells.15/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.15/shuffle/Reshape_1_output_0 = Reshape(%/cells.15/shuffle/Transpose_output_0, %/cells.15/shuffle/Constant_1_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.15/shuffle/Reshape_1_output_0, %onnx::Conv_745, %onnx::Conv_746) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_748, %onnx::Conv_749) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_751, %onnx::Conv_752) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.16/nl/Relu_output_0, %onnx::Conv_754, %onnx::Conv_755) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_757, %onnx::Conv_758) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.15/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_760, %onnx::Conv_761) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.17/nl/Relu_output_0, %onnx::Conv_763, %onnx::Conv_764) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_766, %onnx::Conv_767) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_769, %onnx::Conv_770) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.18/nl/Relu_output_0, %onnx::Conv_772, %onnx::Conv_773) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_775, %onnx::Conv_776) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_778, %onnx::Conv_779) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.19/nl/Relu_output_0, %onnx::Conv_781, %onnx::Conv_782) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_784, %onnx::Conv_785) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_787, %onnx::Conv_788) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.20/shuffle/Reshape_output_0 = Reshape(%/cells.20/nl/Relu_output_0, %/cells.20/shuffle/Constant_output_0) %/cells.20/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.20/shuffle/Reshape_output_0) %/cells.20/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.20/shuffle/Reshape_1_output_0 = Reshape(%/cells.20/shuffle/Transpose_output_0, %/cells.20/shuffle/Constant_1_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.20/shuffle/Reshape_1_output_0, %onnx::Conv_790, %onnx::Conv_791) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_793, %onnx::Conv_794) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_796, %onnx::Conv_797) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.21/nl/Relu_output_0, %onnx::Conv_799, %onnx::Conv_800) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_802, %onnx::Conv_803) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_805, %onnx::Conv_806) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %620 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %620 }	val_accuracy	87,971,200	2,382,780	{'zcp_synflow': 79.28399273219512, 'zcp_zen': 72.06456756591797, 'zcp_epe_nas': 12.348089368704997, 'zcp_fisher': 0.10779722779989243, 'zcp_flops': 87971200.0, 'zcp_grad_norm': 26.885242462158203, 'zcp_grasp': 0.000148773193359375, 'zcp_jacov': -16.068617228487774, 'zcp_l2_norm': 682.105224609375, 'zcp_nwot': 216.76652140445646, 'zcp_params': 2382780.0, 'zcp_plain': 0.0014858671929687262, 'zcp_snip': 46.96458435058594, 'lat_1080ti_1': 0.5179463151026099, 'lat_1080ti_32': 0.6530932147744529, 'lat_1080ti_64': 0.6351764725616234, 'lat_2080ti_1': 0.5953877861131296, 'lat_2080ti_32': 0.6547909204769217, 'lat_2080ti_64': 0.6099769475446296, 'lat_essential_ph_1': 0.4339622641509434, 'lat_eyeriss': 0.7325656024792325, 'lat_fpga': 0.637637419725111, 'lat_gold_6226': 0.5259803792481037, 'lat_gold_6240': 0.6528692679755935, 'lat_pixel2': 0.6304347826086957, 'lat_pixel3': 0.7347237905067819, 'lat_raspi4': 0.7946322669179615, 'lat_samsung_a50': 0.28421052631578947, 'lat_samsung_s7': 0.23622047244094488, 'lat_silver_4114': 0.5778354071093161, 'lat_silver_4210r': 0.5919501754164144, 'lat_titan_rtx_1': 0.5583986208793538, 'lat_titan_rtx_32': 0.6497904081048794, 'lat_titan_rtx_64': 0.6229471172090001, 'lat_titanx_1': 0.4274413469253191, 'lat_titanx_32': 0.637520812462468, 'lat_titanx_64': 0.6022832434955248, 'lat_titanxp_1': 0.5263833937739493, 'lat_titanxp_32': 0.655095034728328, 'lat_titanxp_64': 0.627519489216593}
FBNet_4195	FBNet	4195	4195	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_687[FLOAT, 16x3x3x3] %onnx::Conv_688[FLOAT, 16] %onnx::Conv_690[FLOAT, 16x8x1x1] %onnx::Conv_693[FLOAT, 16x1x3x3] %onnx::Conv_696[FLOAT, 16x8x1x1] %onnx::Conv_699[FLOAT, 48x16x1x1] %onnx::Conv_700[FLOAT, 48] %onnx::Conv_702[FLOAT, 48x1x3x3] %onnx::Conv_705[FLOAT, 24x48x1x1] %onnx::Conv_706[FLOAT, 24] %onnx::Conv_708[FLOAT, 72x24x1x1] %onnx::Conv_709[FLOAT, 72] %onnx::Conv_711[FLOAT, 72x1x5x5] %onnx::Conv_714[FLOAT, 24x72x1x1] %onnx::Conv_717[FLOAT, 144x24x1x1] %onnx::Conv_718[FLOAT, 144] %onnx::Conv_720[FLOAT, 144x1x5x5] %onnx::Conv_723[FLOAT, 24x144x1x1] %onnx::Conv_726[FLOAT, 24x12x1x1] %onnx::Conv_729[FLOAT, 24x1x5x5] %onnx::Conv_732[FLOAT, 24x12x1x1] %onnx::Conv_735[FLOAT, 72x24x1x1] %onnx::Conv_738[FLOAT, 72x1x3x3] %onnx::Conv_741[FLOAT, 32x72x1x1] %onnx::Conv_742[FLOAT, 32] %onnx::Conv_744[FLOAT, 32x16x1x1] %onnx::Conv_747[FLOAT, 32x1x3x3] %onnx::Conv_750[FLOAT, 32x16x1x1] %onnx::Conv_753[FLOAT, 32x16x1x1] %onnx::Conv_756[FLOAT, 32x1x3x3] %onnx::Conv_759[FLOAT, 32x16x1x1] %onnx::Conv_762[FLOAT, 192x32x1x1] %onnx::Conv_763[FLOAT, 192] %onnx::Conv_765[FLOAT, 192x1x3x3] %onnx::Conv_768[FLOAT, 64x192x1x1] %onnx::Conv_769[FLOAT, 64] %onnx::Conv_771[FLOAT, 384x64x1x1] %onnx::Conv_772[FLOAT, 384] %onnx::Conv_774[FLOAT, 384x1x3x3] %onnx::Conv_777[FLOAT, 64x384x1x1] %onnx::Conv_780[FLOAT, 64x32x1x1] %onnx::Conv_783[FLOAT, 64x1x5x5] %onnx::Conv_786[FLOAT, 64x32x1x1] %onnx::Conv_789[FLOAT, 64x32x1x1] %onnx::Conv_792[FLOAT, 64x1x5x5] %onnx::Conv_795[FLOAT, 112x32x1x1] %onnx::Conv_796[FLOAT, 112] %onnx::Conv_798[FLOAT, 112x112x1x1] %onnx::Conv_801[FLOAT, 112x1x3x3] %onnx::Conv_804[FLOAT, 112x112x1x1] %onnx::Conv_807[FLOAT, 336x112x1x1] %onnx::Conv_808[FLOAT, 336] %onnx::Conv_810[FLOAT, 336x1x5x5] %onnx::Conv_813[FLOAT, 112x336x1x1] %onnx::Conv_816[FLOAT, 112x112x1x1] %onnx::Conv_819[FLOAT, 112x1x5x5] %onnx::Conv_822[FLOAT, 184x112x1x1] %onnx::Conv_823[FLOAT, 184] %onnx::Conv_825[FLOAT, 184x92x1x1] %onnx::Conv_828[FLOAT, 184x1x3x3] %onnx::Conv_831[FLOAT, 184x92x1x1] %onnx::Conv_834[FLOAT, 184x184x1x1] %onnx::Conv_837[FLOAT, 184x1x3x3] %onnx::Conv_840[FLOAT, 184x184x1x1] %onnx::Conv_843[FLOAT, 1104x184x1x1] %onnx::Conv_844[FLOAT, 1104] %onnx::Conv_846[FLOAT, 1104x1x5x5] %onnx::Conv_849[FLOAT, 184x1104x1x1] %onnx::Conv_852[FLOAT, 184x92x1x1] %onnx::Conv_855[FLOAT, 184x1x5x5] %onnx::Conv_858[FLOAT, 352x92x1x1] %onnx::Conv_859[FLOAT, 352] %onnx::Conv_861[FLOAT, 1504x352x1x1] %onnx::Conv_862[FLOAT, 1504] ) { %onnx::Conv_856 = Identity(%onnx::Conv_823) %onnx::Conv_853 = Identity(%onnx::Conv_823) %onnx::Conv_850 = Identity(%onnx::Conv_823) %onnx::Conv_847 = Identity(%onnx::Conv_844) %onnx::Conv_841 = Identity(%onnx::Conv_823) %onnx::Conv_838 = Identity(%onnx::Conv_823) %onnx::Conv_835 = Identity(%onnx::Conv_823) %onnx::Conv_832 = Identity(%onnx::Conv_823) %onnx::Conv_829 = Identity(%onnx::Conv_823) %onnx::Conv_826 = Identity(%onnx::Conv_823) %onnx::Conv_820 = Identity(%onnx::Conv_796) %onnx::Conv_817 = Identity(%onnx::Conv_796) %onnx::Conv_814 = Identity(%onnx::Conv_796) %onnx::Conv_811 = Identity(%onnx::Conv_808) %onnx::Conv_805 = Identity(%onnx::Conv_796) %onnx::Conv_802 = Identity(%onnx::Conv_796) %onnx::Conv_799 = Identity(%onnx::Conv_796) %onnx::Conv_793 = Identity(%onnx::Conv_769) %onnx::Conv_790 = Identity(%onnx::Conv_769) %onnx::Conv_787 = Identity(%onnx::Conv_769) %onnx::Conv_784 = Identity(%onnx::Conv_769) %onnx::Conv_781 = Identity(%onnx::Conv_769) %onnx::Conv_778 = Identity(%onnx::Conv_769) %onnx::Conv_775 = Identity(%onnx::Conv_772) %onnx::Conv_766 = Identity(%onnx::Conv_763) %onnx::Conv_760 = Identity(%onnx::Conv_742) %onnx::Conv_757 = Identity(%onnx::Conv_742) %onnx::Conv_754 = Identity(%onnx::Conv_742) %onnx::Conv_751 = Identity(%onnx::Conv_742) %onnx::Conv_748 = Identity(%onnx::Conv_742) %onnx::Conv_745 = Identity(%onnx::Conv_742) %onnx::Conv_739 = Identity(%onnx::Conv_709) %onnx::Conv_736 = Identity(%onnx::Conv_709) %onnx::Conv_733 = Identity(%onnx::Conv_706) %onnx::Conv_730 = Identity(%onnx::Conv_706) %onnx::Conv_727 = Identity(%onnx::Conv_706) %onnx::Conv_724 = Identity(%onnx::Conv_706) %onnx::Conv_721 = Identity(%onnx::Conv_718) %onnx::Conv_715 = Identity(%onnx::Conv_706) %onnx::Conv_712 = Identity(%onnx::Conv_709) %onnx::Conv_703 = Identity(%onnx::Conv_700) %onnx::Conv_697 = Identity(%onnx::Conv_688) %onnx::Conv_694 = Identity(%onnx::Conv_688) %onnx::Conv_691 = Identity(%onnx::Conv_688) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_687, %onnx::Conv_688) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_690, %onnx::Conv_691) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.0/shuffle/Reshape_output_0 = Reshape(%/cells.0/nl/Relu_output_0, %/cells.0/shuffle/Constant_output_0) %/cells.0/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.0/shuffle/Reshape_output_0) %/cells.0/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.0/shuffle/Reshape_1_output_0 = Reshape(%/cells.0/shuffle/Transpose_output_0, %/cells.0/shuffle/Constant_1_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.0/shuffle/Reshape_1_output_0, %onnx::Conv_693, %onnx::Conv_694) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_696, %onnx::Conv_697) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_699, %onnx::Conv_700) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 48, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.1/nl/Relu_output_0, %onnx::Conv_702, %onnx::Conv_703) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_705, %onnx::Conv_706) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_708, %onnx::Conv_709) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.2/nl/Relu_output_0, %onnx::Conv_711, %onnx::Conv_712) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_714, %onnx::Conv_715) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_717, %onnx::Conv_718) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.3/nl/Relu_output_0, %onnx::Conv_720, %onnx::Conv_721) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_723, %onnx::Conv_724) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_726, %onnx::Conv_727) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.4/shuffle/Reshape_output_0 = Reshape(%/cells.4/nl/Relu_output_0, %/cells.4/shuffle/Constant_output_0) %/cells.4/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.4/shuffle/Reshape_output_0) %/cells.4/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.4/shuffle/Reshape_1_output_0 = Reshape(%/cells.4/shuffle/Transpose_output_0, %/cells.4/shuffle/Constant_1_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.4/shuffle/Reshape_1_output_0, %onnx::Conv_729, %onnx::Conv_730) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_732, %onnx::Conv_733) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/Add_output_0, %onnx::Conv_735, %onnx::Conv_736) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.5/nl/Relu_output_0, %onnx::Conv_738, %onnx::Conv_739) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_741, %onnx::Conv_742) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_744, %onnx::Conv_745) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.6/shuffle/Reshape_output_0 = Reshape(%/cells.6/nl/Relu_output_0, %/cells.6/shuffle/Constant_output_0) %/cells.6/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.6/shuffle/Reshape_output_0) %/cells.6/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.6/shuffle/Reshape_1_output_0 = Reshape(%/cells.6/shuffle/Transpose_output_0, %/cells.6/shuffle/Constant_1_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.6/shuffle/Reshape_1_output_0, %onnx::Conv_747, %onnx::Conv_748) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_750, %onnx::Conv_751) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_753, %onnx::Conv_754) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.7/shuffle/Reshape_output_0 = Reshape(%/cells.7/nl/Relu_output_0, %/cells.7/shuffle/Constant_output_0) %/cells.7/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.7/shuffle/Reshape_output_0) %/cells.7/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.7/shuffle/Reshape_1_output_0 = Reshape(%/cells.7/shuffle/Transpose_output_0, %/cells.7/shuffle/Constant_1_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.7/shuffle/Reshape_1_output_0, %onnx::Conv_756, %onnx::Conv_757) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_759, %onnx::Conv_760) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_762, %onnx::Conv_763) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.9/nl/Relu_output_0, %onnx::Conv_765, %onnx::Conv_766) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_768, %onnx::Conv_769) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_771, %onnx::Conv_772) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.10/nl/Relu_output_0, %onnx::Conv_774, %onnx::Conv_775) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_777, %onnx::Conv_778) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_780, %onnx::Conv_781) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.11/shuffle/Reshape_output_0 = Reshape(%/cells.11/nl/Relu_output_0, %/cells.11/shuffle/Constant_output_0) %/cells.11/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.11/shuffle/Reshape_output_0) %/cells.11/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.11/shuffle/Reshape_1_output_0 = Reshape(%/cells.11/shuffle/Transpose_output_0, %/cells.11/shuffle/Constant_1_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.11/shuffle/Reshape_1_output_0, %onnx::Conv_783, %onnx::Conv_784) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_786, %onnx::Conv_787) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_789, %onnx::Conv_790) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.13/shuffle/Reshape_output_0 = Reshape(%/cells.13/nl/Relu_output_0, %/cells.13/shuffle/Constant_output_0) %/cells.13/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.13/shuffle/Reshape_output_0) %/cells.13/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.13/shuffle/Reshape_1_output_0 = Reshape(%/cells.13/shuffle/Transpose_output_0, %/cells.13/shuffle/Constant_1_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.13/shuffle/Reshape_1_output_0, %onnx::Conv_792, %onnx::Conv_793) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_795, %onnx::Conv_796) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_798, %onnx::Conv_799) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.14/nl/Relu_output_0, %onnx::Conv_801, %onnx::Conv_802) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_804, %onnx::Conv_805) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_807, %onnx::Conv_808) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.15/nl/Relu_output_0, %onnx::Conv_810, %onnx::Conv_811) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_813, %onnx::Conv_814) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_816, %onnx::Conv_817) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.17/nl/Relu_output_0, %onnx::Conv_819, %onnx::Conv_820) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_822, %onnx::Conv_823) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_825, %onnx::Conv_826) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.18/shuffle/Reshape_output_0 = Reshape(%/cells.18/nl/Relu_output_0, %/cells.18/shuffle/Constant_output_0) %/cells.18/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.18/shuffle/Reshape_output_0) %/cells.18/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.18/shuffle/Reshape_1_output_0 = Reshape(%/cells.18/shuffle/Transpose_output_0, %/cells.18/shuffle/Constant_1_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.18/shuffle/Reshape_1_output_0, %onnx::Conv_828, %onnx::Conv_829) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_831, %onnx::Conv_832) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_834, %onnx::Conv_835) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.19/nl/Relu_output_0, %onnx::Conv_837, %onnx::Conv_838) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_840, %onnx::Conv_841) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_843, %onnx::Conv_844) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.20/nl/Relu_output_0, %onnx::Conv_846, %onnx::Conv_847) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_849, %onnx::Conv_850) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_852, %onnx::Conv_853) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.21/shuffle/Reshape_output_0 = Reshape(%/cells.21/nl/Relu_output_0, %/cells.21/shuffle/Constant_output_0) %/cells.21/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.21/shuffle/Reshape_output_0) %/cells.21/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.21/shuffle/Reshape_1_output_0 = Reshape(%/cells.21/shuffle/Transpose_output_0, %/cells.21/shuffle/Constant_1_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.21/shuffle/Reshape_1_output_0, %onnx::Conv_855, %onnx::Conv_856) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_858, %onnx::Conv_859) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_861, %onnx::Conv_862) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %685 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %685 }	val_accuracy	59,739,776	1,551,540	{'zcp_synflow': 67.63575919381243, 'zcp_zen': 60.29612350463867, 'zcp_epe_nas': 23.381722047838124, 'zcp_fisher': 0.1026507318019867, 'zcp_flops': 59739776.0, 'zcp_grad_norm': 22.80610466003418, 'zcp_grasp': 0.0155792236328125, 'zcp_jacov': -16.05125799199406, 'zcp_l2_norm': 531.8972778320312, 'zcp_nwot': 212.1161803150811, 'zcp_params': 1551540.0, 'zcp_plain': -0.0017536563100293279, 'zcp_snip': 32.440223693847656, 'lat_1080ti_1': 0.49675374666552585, 'lat_1080ti_32': 0.5544357293995191, 'lat_1080ti_64': 0.4814832217551407, 'lat_2080ti_1': 0.6010999044331543, 'lat_2080ti_32': 0.5972936915682798, 'lat_2080ti_64': 0.5051080274172801, 'lat_essential_ph_1': 0.24528301886792453, 'lat_eyeriss': 0.36927181706489665, 'lat_fpga': 0.3179132981723547, 'lat_gold_6226': 0.23072368334668816, 'lat_gold_6240': 0.43306038275667563, 'lat_pixel2': 0.2391304347826087, 'lat_pixel3': 0.4183806222793255, 'lat_raspi4': 0.4096586508998817, 'lat_samsung_a50': 0.15789473684210525, 'lat_samsung_s7': 0.13385826771653545, 'lat_silver_4114': 0.39429349839020256, 'lat_silver_4210r': 0.40681565922983937, 'lat_titan_rtx_1': 0.554699656915989, 'lat_titan_rtx_32': 0.554315318529631, 'lat_titan_rtx_64': 0.5191687075693051, 'lat_titanx_1': 0.2934760966726758, 'lat_titanx_32': 0.5516559475530239, 'lat_titanx_64': 0.4936071344817176, 'lat_titanxp_1': 0.510511376262991, 'lat_titanxp_32': 0.5596382620771495, 'lat_titanxp_64': 0.5110287635110322}
FBNet_4387	FBNet	4387	4387	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_622[FLOAT, 16x3x3x3] %onnx::Conv_623[FLOAT, 16] %onnx::Conv_625[FLOAT, 96x16x1x1] %onnx::Conv_626[FLOAT, 96] %onnx::Conv_628[FLOAT, 96x1x5x5] %onnx::Conv_631[FLOAT, 16x96x1x1] %onnx::Conv_634[FLOAT, 48x16x1x1] %onnx::Conv_635[FLOAT, 48] %onnx::Conv_637[FLOAT, 48x1x5x5] %onnx::Conv_640[FLOAT, 24x48x1x1] %onnx::Conv_641[FLOAT, 24] %onnx::Conv_643[FLOAT, 72x24x1x1] %onnx::Conv_644[FLOAT, 72] %onnx::Conv_646[FLOAT, 72x1x3x3] %onnx::Conv_649[FLOAT, 24x72x1x1] %onnx::Conv_652[FLOAT, 24x24x1x1] %onnx::Conv_655[FLOAT, 24x1x3x3] %onnx::Conv_658[FLOAT, 24x24x1x1] %onnx::Conv_661[FLOAT, 72x24x1x1] %onnx::Conv_664[FLOAT, 72x1x3x3] %onnx::Conv_667[FLOAT, 24x72x1x1] %onnx::Conv_670[FLOAT, 24x12x1x1] %onnx::Conv_673[FLOAT, 24x1x3x3] %onnx::Conv_676[FLOAT, 32x12x1x1] %onnx::Conv_677[FLOAT, 32] %onnx::Conv_679[FLOAT, 192x32x1x1] %onnx::Conv_680[FLOAT, 192] %onnx::Conv_682[FLOAT, 192x1x5x5] %onnx::Conv_685[FLOAT, 32x192x1x1] %onnx::Conv_688[FLOAT, 32x16x1x1] %onnx::Conv_691[FLOAT, 32x1x3x3] %onnx::Conv_694[FLOAT, 32x16x1x1] %onnx::Conv_697[FLOAT, 192x32x1x1] %onnx::Conv_700[FLOAT, 192x1x3x3] %onnx::Conv_703[FLOAT, 32x192x1x1] %onnx::Conv_706[FLOAT, 192x32x1x1] %onnx::Conv_709[FLOAT, 192x1x5x5] %onnx::Conv_712[FLOAT, 64x192x1x1] %onnx::Conv_713[FLOAT, 64] %onnx::Conv_715[FLOAT, 192x64x1x1] %onnx::Conv_718[FLOAT, 192x1x5x5] %onnx::Conv_721[FLOAT, 64x192x1x1] %onnx::Conv_724[FLOAT, 64x64x1x1] %onnx::Conv_727[FLOAT, 64x1x3x3] %onnx::Conv_730[FLOAT, 64x64x1x1] %onnx::Conv_733[FLOAT, 64x64x1x1] %onnx::Conv_736[FLOAT, 64x1x3x3] %onnx::Conv_739[FLOAT, 112x64x1x1] %onnx::Conv_740[FLOAT, 112] %onnx::Conv_742[FLOAT, 112x56x1x1] %onnx::Conv_745[FLOAT, 112x1x3x3] %onnx::Conv_748[FLOAT, 112x56x1x1] %onnx::Conv_751[FLOAT, 112x112x1x1] %onnx::Conv_754[FLOAT, 112x1x5x5] %onnx::Conv_757[FLOAT, 112x112x1x1] %onnx::Conv_760[FLOAT, 112x112x1x1] %onnx::Conv_763[FLOAT, 112x1x3x3] %onnx::Conv_766[FLOAT, 184x112x1x1] %onnx::Conv_767[FLOAT, 184] %onnx::Conv_769[FLOAT, 552x184x1x1] %onnx::Conv_770[FLOAT, 552] %onnx::Conv_772[FLOAT, 552x1x5x5] %onnx::Conv_775[FLOAT, 184x552x1x1] %onnx::Conv_778[FLOAT, 1104x184x1x1] %onnx::Conv_779[FLOAT, 1104] %onnx::Conv_781[FLOAT, 1104x1x3x3] %onnx::Conv_784[FLOAT, 184x1104x1x1] %onnx::Conv_787[FLOAT, 552x184x1x1] %onnx::Conv_790[FLOAT, 552x1x5x5] %onnx::Conv_793[FLOAT, 184x552x1x1] %onnx::Conv_796[FLOAT, 184x184x1x1] %onnx::Conv_799[FLOAT, 184x1x5x5] %onnx::Conv_802[FLOAT, 352x184x1x1] %onnx::Conv_803[FLOAT, 352] %onnx::Conv_805[FLOAT, 1504x352x1x1] %onnx::Conv_806[FLOAT, 1504] ) { %onnx::Conv_800 = Identity(%onnx::Conv_767) %onnx::Conv_797 = Identity(%onnx::Conv_767) %onnx::Conv_794 = Identity(%onnx::Conv_767) %onnx::Conv_791 = Identity(%onnx::Conv_770) %onnx::Conv_788 = Identity(%onnx::Conv_770) %onnx::Conv_785 = Identity(%onnx::Conv_767) %onnx::Conv_782 = Identity(%onnx::Conv_779) %onnx::Conv_776 = Identity(%onnx::Conv_767) %onnx::Conv_773 = Identity(%onnx::Conv_770) %onnx::Conv_764 = Identity(%onnx::Conv_740) %onnx::Conv_761 = Identity(%onnx::Conv_740) %onnx::Conv_758 = Identity(%onnx::Conv_740) %onnx::Conv_755 = Identity(%onnx::Conv_740) %onnx::Conv_752 = Identity(%onnx::Conv_740) %onnx::Conv_749 = Identity(%onnx::Conv_740) %onnx::Conv_746 = Identity(%onnx::Conv_740) %onnx::Conv_743 = Identity(%onnx::Conv_740) %onnx::Conv_737 = Identity(%onnx::Conv_713) %onnx::Conv_734 = Identity(%onnx::Conv_713) %onnx::Conv_731 = Identity(%onnx::Conv_713) %onnx::Conv_728 = Identity(%onnx::Conv_713) %onnx::Conv_725 = Identity(%onnx::Conv_713) %onnx::Conv_722 = Identity(%onnx::Conv_713) %onnx::Conv_719 = Identity(%onnx::Conv_680) %onnx::Conv_716 = Identity(%onnx::Conv_680) %onnx::Conv_710 = Identity(%onnx::Conv_680) %onnx::Conv_707 = Identity(%onnx::Conv_680) %onnx::Conv_704 = Identity(%onnx::Conv_677) %onnx::Conv_701 = Identity(%onnx::Conv_680) %onnx::Conv_698 = Identity(%onnx::Conv_680) %onnx::Conv_695 = Identity(%onnx::Conv_677) %onnx::Conv_692 = Identity(%onnx::Conv_677) %onnx::Conv_689 = Identity(%onnx::Conv_677) %onnx::Conv_686 = Identity(%onnx::Conv_677) %onnx::Conv_683 = Identity(%onnx::Conv_680) %onnx::Conv_674 = Identity(%onnx::Conv_641) %onnx::Conv_671 = Identity(%onnx::Conv_641) %onnx::Conv_668 = Identity(%onnx::Conv_641) %onnx::Conv_665 = Identity(%onnx::Conv_644) %onnx::Conv_662 = Identity(%onnx::Conv_644) %onnx::Conv_659 = Identity(%onnx::Conv_641) %onnx::Conv_656 = Identity(%onnx::Conv_641) %onnx::Conv_653 = Identity(%onnx::Conv_641) %onnx::Conv_650 = Identity(%onnx::Conv_641) %onnx::Conv_647 = Identity(%onnx::Conv_644) %onnx::Conv_638 = Identity(%onnx::Conv_635) %onnx::Conv_632 = Identity(%onnx::Conv_623) %onnx::Conv_629 = Identity(%onnx::Conv_626) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_622, %onnx::Conv_623) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_625, %onnx::Conv_626) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.0/nl/Relu_output_0, %onnx::Conv_628, %onnx::Conv_629) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_631, %onnx::Conv_632) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_634, %onnx::Conv_635) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 48, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.1/nl/Relu_output_0, %onnx::Conv_637, %onnx::Conv_638) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_640, %onnx::Conv_641) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_643, %onnx::Conv_644) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.2/nl/Relu_output_0, %onnx::Conv_646, %onnx::Conv_647) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_649, %onnx::Conv_650) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_652, %onnx::Conv_653) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.3/nl/Relu_output_0, %onnx::Conv_655, %onnx::Conv_656) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_658, %onnx::Conv_659) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_661, %onnx::Conv_662) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.4/nl/Relu_output_0, %onnx::Conv_664, %onnx::Conv_665) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_667, %onnx::Conv_668) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/Add_output_0, %onnx::Conv_670, %onnx::Conv_671) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.5/shuffle/Reshape_output_0 = Reshape(%/cells.5/nl/Relu_output_0, %/cells.5/shuffle/Constant_output_0) %/cells.5/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.5/shuffle/Reshape_output_0) %/cells.5/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.5/shuffle/Reshape_1_output_0 = Reshape(%/cells.5/shuffle/Transpose_output_0, %/cells.5/shuffle/Constant_1_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.5/shuffle/Reshape_1_output_0, %onnx::Conv_673, %onnx::Conv_674) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_676, %onnx::Conv_677) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_679, %onnx::Conv_680) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.6/nl/Relu_output_0, %onnx::Conv_682, %onnx::Conv_683) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_685, %onnx::Conv_686) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_688, %onnx::Conv_689) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.7/shuffle/Reshape_output_0 = Reshape(%/cells.7/nl/Relu_output_0, %/cells.7/shuffle/Constant_output_0) %/cells.7/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.7/shuffle/Reshape_output_0) %/cells.7/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.7/shuffle/Reshape_1_output_0 = Reshape(%/cells.7/shuffle/Transpose_output_0, %/cells.7/shuffle/Constant_1_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.7/shuffle/Reshape_1_output_0, %onnx::Conv_691, %onnx::Conv_692) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_694, %onnx::Conv_695) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_697, %onnx::Conv_698) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.8/nl/Relu_output_0, %onnx::Conv_700, %onnx::Conv_701) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_703, %onnx::Conv_704) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_706, %onnx::Conv_707) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.9/nl/Relu_output_0, %onnx::Conv_709, %onnx::Conv_710) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_712, %onnx::Conv_713) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_715, %onnx::Conv_716) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.10/nl/Relu_output_0, %onnx::Conv_718, %onnx::Conv_719) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_721, %onnx::Conv_722) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_724, %onnx::Conv_725) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.11/nl/Relu_output_0, %onnx::Conv_727, %onnx::Conv_728) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_730, %onnx::Conv_731) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_733, %onnx::Conv_734) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.13/nl/Relu_output_0, %onnx::Conv_736, %onnx::Conv_737) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_739, %onnx::Conv_740) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_742, %onnx::Conv_743) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.14/shuffle/Reshape_output_0 = Reshape(%/cells.14/nl/Relu_output_0, %/cells.14/shuffle/Constant_output_0) %/cells.14/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.14/shuffle/Reshape_output_0) %/cells.14/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.14/shuffle/Reshape_1_output_0 = Reshape(%/cells.14/shuffle/Transpose_output_0, %/cells.14/shuffle/Constant_1_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.14/shuffle/Reshape_1_output_0, %onnx::Conv_745, %onnx::Conv_746) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_748, %onnx::Conv_749) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_751, %onnx::Conv_752) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.15/nl/Relu_output_0, %onnx::Conv_754, %onnx::Conv_755) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_757, %onnx::Conv_758) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_760, %onnx::Conv_761) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.17/nl/Relu_output_0, %onnx::Conv_763, %onnx::Conv_764) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_766, %onnx::Conv_767) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_769, %onnx::Conv_770) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 552, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.18/nl/Relu_output_0, %onnx::Conv_772, %onnx::Conv_773) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_775, %onnx::Conv_776) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_778, %onnx::Conv_779) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.19/nl/Relu_output_0, %onnx::Conv_781, %onnx::Conv_782) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_784, %onnx::Conv_785) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_787, %onnx::Conv_788) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 552, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.20/nl/Relu_output_0, %onnx::Conv_790, %onnx::Conv_791) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_793, %onnx::Conv_794) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_796, %onnx::Conv_797) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.21/nl/Relu_output_0, %onnx::Conv_799, %onnx::Conv_800) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_802, %onnx::Conv_803) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_805, %onnx::Conv_806) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %620 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %620 }	val_accuracy	64,594,048	1,856,652	{'zcp_synflow': 76.91901983538749, 'zcp_zen': 67.26091766357422, 'zcp_epe_nas': 16.532026960989786, 'zcp_fisher': 0.1145751103758812, 'zcp_flops': 64594048.0, 'zcp_grad_norm': 25.455442428588867, 'zcp_grasp': -0.0839996337890625, 'zcp_jacov': -16.048592792492755, 'zcp_l2_norm': 613.0245361328125, 'zcp_nwot': 213.7828202060258, 'zcp_params': 1856652.0, 'zcp_plain': 0.0015253762248903513, 'zcp_snip': 47.00305938720703, 'lat_1080ti_1': 0.6044559345158294, 'lat_1080ti_32': 0.5624895210714919, 'lat_1080ti_64': 0.48971067372650645, 'lat_2080ti_1': 0.5814599923746465, 'lat_2080ti_32': 0.527701224293775, 'lat_2080ti_64': 0.50414298223892, 'lat_essential_ph_1': 0.3018867924528302, 'lat_eyeriss': 0.4758219793519521, 'lat_fpga': 0.4266814435131951, 'lat_gold_6226': 0.3239336574642247, 'lat_gold_6240': 0.46381429285786085, 'lat_pixel2': 0.41304347826086957, 'lat_pixel3': 0.4285793416947649, 'lat_raspi4': 0.41977044158166765, 'lat_samsung_a50': 0.18947368421052632, 'lat_samsung_s7': 0.16535433070866143, 'lat_silver_4114': 0.5411767461564335, 'lat_silver_4210r': 0.5081863227099322, 'lat_titan_rtx_1': 0.5501196585061816, 'lat_titan_rtx_32': 0.5130730390013006, 'lat_titan_rtx_64': 0.5133560205674953, 'lat_titanx_1': 0.2926058223642429, 'lat_titanx_32': 0.49739313588607914, 'lat_titanx_64': 0.4761197755803911, 'lat_titanxp_1': 0.5094258920986372, 'lat_titanxp_32': 0.5077624590867991, 'lat_titanxp_64': 0.4948544768538674}
FBNet_373	FBNet	373	373	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_577[FLOAT, 16x3x3x3] %onnx::Conv_578[FLOAT, 16] %onnx::Conv_580[FLOAT, 96x16x1x1] %onnx::Conv_581[FLOAT, 96] %onnx::Conv_583[FLOAT, 96x1x3x3] %onnx::Conv_586[FLOAT, 16x96x1x1] %onnx::Conv_589[FLOAT, 24x16x1x1] %onnx::Conv_590[FLOAT, 24] %onnx::Conv_592[FLOAT, 72x24x1x1] %onnx::Conv_593[FLOAT, 72] %onnx::Conv_595[FLOAT, 72x1x5x5] %onnx::Conv_598[FLOAT, 24x72x1x1] %onnx::Conv_601[FLOAT, 24x12x1x1] %onnx::Conv_604[FLOAT, 24x1x3x3] %onnx::Conv_607[FLOAT, 24x12x1x1] %onnx::Conv_610[FLOAT, 24x24x1x1] %onnx::Conv_613[FLOAT, 24x1x5x5] %onnx::Conv_616[FLOAT, 24x24x1x1] %onnx::Conv_619[FLOAT, 24x12x1x1] %onnx::Conv_622[FLOAT, 24x1x5x5] %onnx::Conv_625[FLOAT, 32x12x1x1] %onnx::Conv_626[FLOAT, 32] %onnx::Conv_628[FLOAT, 96x32x1x1] %onnx::Conv_631[FLOAT, 96x1x3x3] %onnx::Conv_634[FLOAT, 32x96x1x1] %onnx::Conv_637[FLOAT, 32x32x1x1] %onnx::Conv_640[FLOAT, 32x1x3x3] %onnx::Conv_643[FLOAT, 32x32x1x1] %onnx::Conv_646[FLOAT, 96x32x1x1] %onnx::Conv_649[FLOAT, 96x1x3x3] %onnx::Conv_652[FLOAT, 32x96x1x1] %onnx::Conv_655[FLOAT, 96x32x1x1] %onnx::Conv_658[FLOAT, 96x1x5x5] %onnx::Conv_661[FLOAT, 64x96x1x1] %onnx::Conv_662[FLOAT, 64] %onnx::Conv_664[FLOAT, 64x32x1x1] %onnx::Conv_667[FLOAT, 64x1x3x3] %onnx::Conv_670[FLOAT, 64x32x1x1] %onnx::Conv_673[FLOAT, 64x64x1x1] %onnx::Conv_676[FLOAT, 64x1x3x3] %onnx::Conv_679[FLOAT, 64x64x1x1] %onnx::Conv_682[FLOAT, 192x64x1x1] %onnx::Conv_683[FLOAT, 192] %onnx::Conv_685[FLOAT, 192x1x3x3] %onnx::Conv_688[FLOAT, 112x192x1x1] %onnx::Conv_689[FLOAT, 112] %onnx::Conv_691[FLOAT, 112x112x1x1] %onnx::Conv_694[FLOAT, 112x1x5x5] %onnx::Conv_697[FLOAT, 112x112x1x1] %onnx::Conv_700[FLOAT, 112x112x1x1] %onnx::Conv_703[FLOAT, 112x1x5x5] %onnx::Conv_706[FLOAT, 112x112x1x1] %onnx::Conv_709[FLOAT, 336x112x1x1] %onnx::Conv_710[FLOAT, 336] %onnx::Conv_712[FLOAT, 336x1x5x5] %onnx::Conv_715[FLOAT, 112x336x1x1] %onnx::Conv_718[FLOAT, 672x112x1x1] %onnx::Conv_719[FLOAT, 672] %onnx::Conv_721[FLOAT, 672x1x5x5] %onnx::Conv_724[FLOAT, 184x672x1x1] %onnx::Conv_725[FLOAT, 184] %onnx::Conv_727[FLOAT, 1104x184x1x1] %onnx::Conv_728[FLOAT, 1104] %onnx::Conv_730[FLOAT, 1104x1x5x5] %onnx::Conv_733[FLOAT, 184x1104x1x1] %onnx::Conv_736[FLOAT, 184x184x1x1] %onnx::Conv_739[FLOAT, 184x1x5x5] %onnx::Conv_742[FLOAT, 352x184x1x1] %onnx::Conv_743[FLOAT, 352] %onnx::Conv_745[FLOAT, 1504x352x1x1] %onnx::Conv_746[FLOAT, 1504] ) { %onnx::Conv_740 = Identity(%onnx::Conv_725) %onnx::Conv_737 = Identity(%onnx::Conv_725) %onnx::Conv_734 = Identity(%onnx::Conv_725) %onnx::Conv_731 = Identity(%onnx::Conv_728) %onnx::Conv_722 = Identity(%onnx::Conv_719) %onnx::Conv_716 = Identity(%onnx::Conv_689) %onnx::Conv_713 = Identity(%onnx::Conv_710) %onnx::Conv_707 = Identity(%onnx::Conv_689) %onnx::Conv_704 = Identity(%onnx::Conv_689) %onnx::Conv_701 = Identity(%onnx::Conv_689) %onnx::Conv_698 = Identity(%onnx::Conv_689) %onnx::Conv_695 = Identity(%onnx::Conv_689) %onnx::Conv_692 = Identity(%onnx::Conv_689) %onnx::Conv_686 = Identity(%onnx::Conv_683) %onnx::Conv_680 = Identity(%onnx::Conv_662) %onnx::Conv_677 = Identity(%onnx::Conv_662) %onnx::Conv_674 = Identity(%onnx::Conv_662) %onnx::Conv_671 = Identity(%onnx::Conv_662) %onnx::Conv_668 = Identity(%onnx::Conv_662) %onnx::Conv_665 = Identity(%onnx::Conv_662) %onnx::Conv_659 = Identity(%onnx::Conv_581) %onnx::Conv_656 = Identity(%onnx::Conv_581) %onnx::Conv_653 = Identity(%onnx::Conv_626) %onnx::Conv_650 = Identity(%onnx::Conv_581) %onnx::Conv_647 = Identity(%onnx::Conv_581) %onnx::Conv_644 = Identity(%onnx::Conv_626) %onnx::Conv_641 = Identity(%onnx::Conv_626) %onnx::Conv_638 = Identity(%onnx::Conv_626) %onnx::Conv_635 = Identity(%onnx::Conv_626) %onnx::Conv_632 = Identity(%onnx::Conv_581) %onnx::Conv_629 = Identity(%onnx::Conv_581) %onnx::Conv_623 = Identity(%onnx::Conv_590) %onnx::Conv_620 = Identity(%onnx::Conv_590) %onnx::Conv_617 = Identity(%onnx::Conv_590) %onnx::Conv_614 = Identity(%onnx::Conv_590) %onnx::Conv_611 = Identity(%onnx::Conv_590) %onnx::Conv_608 = Identity(%onnx::Conv_590) %onnx::Conv_605 = Identity(%onnx::Conv_590) %onnx::Conv_602 = Identity(%onnx::Conv_590) %onnx::Conv_599 = Identity(%onnx::Conv_590) %onnx::Conv_596 = Identity(%onnx::Conv_593) %onnx::Conv_587 = Identity(%onnx::Conv_578) %onnx::Conv_584 = Identity(%onnx::Conv_581) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_577, %onnx::Conv_578) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_580, %onnx::Conv_581) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.0/nl/Relu_output_0, %onnx::Conv_583, %onnx::Conv_584) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_586, %onnx::Conv_587) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_589, %onnx::Conv_590) %/cells.1/relu/Relu_output_0 = Relu(%/cells.1/conv/Conv_output_0) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/relu/Relu_output_0, %onnx::Conv_592, %onnx::Conv_593) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.2/nl/Relu_output_0, %onnx::Conv_595, %onnx::Conv_596) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_598, %onnx::Conv_599) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/relu/Relu_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_601, %onnx::Conv_602) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.3/shuffle/Reshape_output_0 = Reshape(%/cells.3/nl/Relu_output_0, %/cells.3/shuffle/Constant_output_0) %/cells.3/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.3/shuffle/Reshape_output_0) %/cells.3/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.3/shuffle/Reshape_1_output_0 = Reshape(%/cells.3/shuffle/Transpose_output_0, %/cells.3/shuffle/Constant_1_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.3/shuffle/Reshape_1_output_0, %onnx::Conv_604, %onnx::Conv_605) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_607, %onnx::Conv_608) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_610, %onnx::Conv_611) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.4/nl/Relu_output_0, %onnx::Conv_613, %onnx::Conv_614) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_616, %onnx::Conv_617) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/Add_output_0, %onnx::Conv_619, %onnx::Conv_620) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.5/shuffle/Reshape_output_0 = Reshape(%/cells.5/nl/Relu_output_0, %/cells.5/shuffle/Constant_output_0) %/cells.5/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.5/shuffle/Reshape_output_0) %/cells.5/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.5/shuffle/Reshape_1_output_0 = Reshape(%/cells.5/shuffle/Transpose_output_0, %/cells.5/shuffle/Constant_1_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.5/shuffle/Reshape_1_output_0, %onnx::Conv_622, %onnx::Conv_623) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_625, %onnx::Conv_626) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_628, %onnx::Conv_629) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.6/nl/Relu_output_0, %onnx::Conv_631, %onnx::Conv_632) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_634, %onnx::Conv_635) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_637, %onnx::Conv_638) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.7/nl/Relu_output_0, %onnx::Conv_640, %onnx::Conv_641) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_643, %onnx::Conv_644) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_646, %onnx::Conv_647) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.8/nl/Relu_output_0, %onnx::Conv_649, %onnx::Conv_650) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_652, %onnx::Conv_653) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_655, %onnx::Conv_656) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.9/nl/Relu_output_0, %onnx::Conv_658, %onnx::Conv_659) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_661, %onnx::Conv_662) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_664, %onnx::Conv_665) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.10/shuffle/Reshape_output_0 = Reshape(%/cells.10/nl/Relu_output_0, %/cells.10/shuffle/Constant_output_0) %/cells.10/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.10/shuffle/Reshape_output_0) %/cells.10/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.10/shuffle/Reshape_1_output_0 = Reshape(%/cells.10/shuffle/Transpose_output_0, %/cells.10/shuffle/Constant_1_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.10/shuffle/Reshape_1_output_0, %onnx::Conv_667, %onnx::Conv_668) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_670, %onnx::Conv_671) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_673, %onnx::Conv_674) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.12/nl/Relu_output_0, %onnx::Conv_676, %onnx::Conv_677) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_679, %onnx::Conv_680) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_682, %onnx::Conv_683) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.13/nl/Relu_output_0, %onnx::Conv_685, %onnx::Conv_686) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_688, %onnx::Conv_689) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_691, %onnx::Conv_692) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.14/nl/Relu_output_0, %onnx::Conv_694, %onnx::Conv_695) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_697, %onnx::Conv_698) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_700, %onnx::Conv_701) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.15/nl/Relu_output_0, %onnx::Conv_703, %onnx::Conv_704) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_706, %onnx::Conv_707) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_709, %onnx::Conv_710) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.16/nl/Relu_output_0, %onnx::Conv_712, %onnx::Conv_713) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_715, %onnx::Conv_716) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.15/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_718, %onnx::Conv_719) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.17/nl/Relu_output_0, %onnx::Conv_721, %onnx::Conv_722) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_724, %onnx::Conv_725) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_727, %onnx::Conv_728) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.20/nl/Relu_output_0, %onnx::Conv_730, %onnx::Conv_731) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_733, %onnx::Conv_734) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_736, %onnx::Conv_737) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.21/nl/Relu_output_0, %onnx::Conv_739, %onnx::Conv_740) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_742, %onnx::Conv_743) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_745, %onnx::Conv_746) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %575 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %575 }	val_accuracy	57,856,128	1,682,748	{'zcp_synflow': 72.35616147629247, 'zcp_zen': 60.384822845458984, 'zcp_epe_nas': 0.00015999920000638146, 'zcp_fisher': 0.15093658864498138, 'zcp_flops': 57856128.0, 'zcp_grad_norm': 19.597076416015625, 'zcp_grasp': 0.11472511291503906, 'zcp_jacov': -16.059414336938968, 'zcp_l2_norm': 541.3841552734375, 'zcp_nwot': 209.60414806686907, 'zcp_params': 1682748.0, 'zcp_plain': -0.004441497381776571, 'zcp_snip': 34.93146896362305, 'lat_1080ti_1': 0.4142721306552117, 'lat_1080ti_32': 0.33976362142710076, 'lat_1080ti_64': 0.2807318137264432, 'lat_2080ti_1': 0.40506393478676095, 'lat_2080ti_32': 0.34603350997785726, 'lat_2080ti_64': 0.2736297316141646, 'lat_essential_ph_1': 0.32075471698113206, 'lat_eyeriss': 0.31615718922487324, 'lat_fpga': 0.2945803936314429, 'lat_gold_6226': 0.24726174390620548, 'lat_gold_6240': 0.3753322783831945, 'lat_pixel2': 0.4782608695652174, 'lat_pixel3': 0.3215636897260128, 'lat_raspi4': 0.3225714273193657, 'lat_samsung_a50': 0.12631578947368421, 'lat_samsung_s7': 0.15748031496062992, 'lat_silver_4114': 0.48365406017504203, 'lat_silver_4210r': 0.38758724072593337, 'lat_titan_rtx_1': 0.3796695385119114, 'lat_titan_rtx_32': 0.35058357782236543, 'lat_titan_rtx_64': 0.27904328498969594, 'lat_titanx_1': 0.20739000316002576, 'lat_titanx_32': 0.2921121288655931, 'lat_titanx_64': 0.30857844715998334, 'lat_titanxp_1': 0.37809261268526256, 'lat_titanxp_32': 0.34074892110407967, 'lat_titanxp_64': 0.2776968497136992}
FBNet_1440	FBNet	1440	1440	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_658[FLOAT, 16x3x3x3] %onnx::Conv_659[FLOAT, 16] %onnx::Conv_661[FLOAT, 16x8x1x1] %onnx::Conv_664[FLOAT, 16x1x5x5] %onnx::Conv_667[FLOAT, 16x8x1x1] %onnx::Conv_670[FLOAT, 96x16x1x1] %onnx::Conv_671[FLOAT, 96] %onnx::Conv_673[FLOAT, 96x1x5x5] %onnx::Conv_676[FLOAT, 24x96x1x1] %onnx::Conv_677[FLOAT, 24] %onnx::Conv_679[FLOAT, 24x24x1x1] %onnx::Conv_682[FLOAT, 24x1x3x3] %onnx::Conv_685[FLOAT, 24x24x1x1] %onnx::Conv_688[FLOAT, 144x24x1x1] %onnx::Conv_689[FLOAT, 144] %onnx::Conv_691[FLOAT, 144x1x5x5] %onnx::Conv_694[FLOAT, 32x144x1x1] %onnx::Conv_695[FLOAT, 32] %onnx::Conv_697[FLOAT, 96x32x1x1] %onnx::Conv_700[FLOAT, 96x1x5x5] %onnx::Conv_703[FLOAT, 32x96x1x1] %onnx::Conv_706[FLOAT, 96x32x1x1] %onnx::Conv_709[FLOAT, 96x1x5x5] %onnx::Conv_712[FLOAT, 32x96x1x1] %onnx::Conv_715[FLOAT, 192x32x1x1] %onnx::Conv_716[FLOAT, 192] %onnx::Conv_718[FLOAT, 192x1x5x5] %onnx::Conv_721[FLOAT, 32x192x1x1] %onnx::Conv_724[FLOAT, 192x32x1x1] %onnx::Conv_727[FLOAT, 192x1x5x5] %onnx::Conv_730[FLOAT, 64x192x1x1] %onnx::Conv_731[FLOAT, 64] %onnx::Conv_733[FLOAT, 64x32x1x1] %onnx::Conv_736[FLOAT, 64x1x5x5] %onnx::Conv_739[FLOAT, 64x32x1x1] %onnx::Conv_742[FLOAT, 64x32x1x1] %onnx::Conv_745[FLOAT, 64x1x5x5] %onnx::Conv_748[FLOAT, 64x32x1x1] %onnx::Conv_751[FLOAT, 384x64x1x1] %onnx::Conv_752[FLOAT, 384] %onnx::Conv_754[FLOAT, 384x1x5x5] %onnx::Conv_757[FLOAT, 64x384x1x1] %onnx::Conv_760[FLOAT, 192x64x1x1] %onnx::Conv_763[FLOAT, 192x1x3x3] %onnx::Conv_766[FLOAT, 112x192x1x1] %onnx::Conv_767[FLOAT, 112] %onnx::Conv_769[FLOAT, 672x112x1x1] %onnx::Conv_770[FLOAT, 672] %onnx::Conv_772[FLOAT, 672x1x3x3] %onnx::Conv_775[FLOAT, 112x672x1x1] %onnx::Conv_778[FLOAT, 336x112x1x1] %onnx::Conv_779[FLOAT, 336] %onnx::Conv_781[FLOAT, 336x1x3x3] %onnx::Conv_784[FLOAT, 112x336x1x1] %onnx::Conv_787[FLOAT, 112x112x1x1] %onnx::Conv_790[FLOAT, 112x1x3x3] %onnx::Conv_793[FLOAT, 112x112x1x1] %onnx::Conv_796[FLOAT, 112x112x1x1] %onnx::Conv_799[FLOAT, 112x1x5x5] %onnx::Conv_802[FLOAT, 184x112x1x1] %onnx::Conv_803[FLOAT, 184] %onnx::Conv_805[FLOAT, 184x184x1x1] %onnx::Conv_808[FLOAT, 184x1x3x3] %onnx::Conv_811[FLOAT, 184x184x1x1] %onnx::Conv_814[FLOAT, 184x92x1x1] %onnx::Conv_817[FLOAT, 184x1x5x5] %onnx::Conv_820[FLOAT, 184x92x1x1] %onnx::Conv_823[FLOAT, 184x184x1x1] %onnx::Conv_826[FLOAT, 184x1x5x5] %onnx::Conv_829[FLOAT, 184x184x1x1] %onnx::Conv_832[FLOAT, 184x92x1x1] %onnx::Conv_835[FLOAT, 184x1x5x5] %onnx::Conv_838[FLOAT, 352x92x1x1] %onnx::Conv_839[FLOAT, 352] %onnx::Conv_841[FLOAT, 1504x352x1x1] %onnx::Conv_842[FLOAT, 1504] ) { %onnx::Conv_836 = Identity(%onnx::Conv_803) %onnx::Conv_833 = Identity(%onnx::Conv_803) %onnx::Conv_830 = Identity(%onnx::Conv_803) %onnx::Conv_827 = Identity(%onnx::Conv_803) %onnx::Conv_824 = Identity(%onnx::Conv_803) %onnx::Conv_821 = Identity(%onnx::Conv_803) %onnx::Conv_818 = Identity(%onnx::Conv_803) %onnx::Conv_815 = Identity(%onnx::Conv_803) %onnx::Conv_812 = Identity(%onnx::Conv_803) %onnx::Conv_809 = Identity(%onnx::Conv_803) %onnx::Conv_806 = Identity(%onnx::Conv_803) %onnx::Conv_800 = Identity(%onnx::Conv_767) %onnx::Conv_797 = Identity(%onnx::Conv_767) %onnx::Conv_794 = Identity(%onnx::Conv_767) %onnx::Conv_791 = Identity(%onnx::Conv_767) %onnx::Conv_788 = Identity(%onnx::Conv_767) %onnx::Conv_785 = Identity(%onnx::Conv_767) %onnx::Conv_782 = Identity(%onnx::Conv_779) %onnx::Conv_776 = Identity(%onnx::Conv_767) %onnx::Conv_773 = Identity(%onnx::Conv_770) %onnx::Conv_764 = Identity(%onnx::Conv_716) %onnx::Conv_761 = Identity(%onnx::Conv_716) %onnx::Conv_758 = Identity(%onnx::Conv_731) %onnx::Conv_755 = Identity(%onnx::Conv_752) %onnx::Conv_749 = Identity(%onnx::Conv_731) %onnx::Conv_746 = Identity(%onnx::Conv_731) %onnx::Conv_743 = Identity(%onnx::Conv_731) %onnx::Conv_740 = Identity(%onnx::Conv_731) %onnx::Conv_737 = Identity(%onnx::Conv_731) %onnx::Conv_734 = Identity(%onnx::Conv_731) %onnx::Conv_728 = Identity(%onnx::Conv_716) %onnx::Conv_725 = Identity(%onnx::Conv_716) %onnx::Conv_722 = Identity(%onnx::Conv_695) %onnx::Conv_719 = Identity(%onnx::Conv_716) %onnx::Conv_713 = Identity(%onnx::Conv_695) %onnx::Conv_710 = Identity(%onnx::Conv_671) %onnx::Conv_707 = Identity(%onnx::Conv_671) %onnx::Conv_704 = Identity(%onnx::Conv_695) %onnx::Conv_701 = Identity(%onnx::Conv_671) %onnx::Conv_698 = Identity(%onnx::Conv_671) %onnx::Conv_692 = Identity(%onnx::Conv_689) %onnx::Conv_686 = Identity(%onnx::Conv_677) %onnx::Conv_683 = Identity(%onnx::Conv_677) %onnx::Conv_680 = Identity(%onnx::Conv_677) %onnx::Conv_674 = Identity(%onnx::Conv_671) %onnx::Conv_668 = Identity(%onnx::Conv_659) %onnx::Conv_665 = Identity(%onnx::Conv_659) %onnx::Conv_662 = Identity(%onnx::Conv_659) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_658, %onnx::Conv_659) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_661, %onnx::Conv_662) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.0/shuffle/Reshape_output_0 = Reshape(%/cells.0/nl/Relu_output_0, %/cells.0/shuffle/Constant_output_0) %/cells.0/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.0/shuffle/Reshape_output_0) %/cells.0/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.0/shuffle/Reshape_1_output_0 = Reshape(%/cells.0/shuffle/Transpose_output_0, %/cells.0/shuffle/Constant_1_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.0/shuffle/Reshape_1_output_0, %onnx::Conv_664, %onnx::Conv_665) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_667, %onnx::Conv_668) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_670, %onnx::Conv_671) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.1/nl/Relu_output_0, %onnx::Conv_673, %onnx::Conv_674) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_676, %onnx::Conv_677) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_679, %onnx::Conv_680) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.4/nl/Relu_output_0, %onnx::Conv_682, %onnx::Conv_683) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_685, %onnx::Conv_686) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/Add_output_0, %onnx::Conv_688, %onnx::Conv_689) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.5/nl/Relu_output_0, %onnx::Conv_691, %onnx::Conv_692) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_694, %onnx::Conv_695) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_697, %onnx::Conv_698) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.6/nl/Relu_output_0, %onnx::Conv_700, %onnx::Conv_701) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_703, %onnx::Conv_704) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_706, %onnx::Conv_707) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.7/nl/Relu_output_0, %onnx::Conv_709, %onnx::Conv_710) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_712, %onnx::Conv_713) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_715, %onnx::Conv_716) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.8/nl/Relu_output_0, %onnx::Conv_718, %onnx::Conv_719) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_721, %onnx::Conv_722) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_724, %onnx::Conv_725) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.9/nl/Relu_output_0, %onnx::Conv_727, %onnx::Conv_728) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_730, %onnx::Conv_731) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_733, %onnx::Conv_734) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.10/shuffle/Reshape_output_0 = Reshape(%/cells.10/nl/Relu_output_0, %/cells.10/shuffle/Constant_output_0) %/cells.10/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.10/shuffle/Reshape_output_0) %/cells.10/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.10/shuffle/Reshape_1_output_0 = Reshape(%/cells.10/shuffle/Transpose_output_0, %/cells.10/shuffle/Constant_1_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.10/shuffle/Reshape_1_output_0, %onnx::Conv_736, %onnx::Conv_737) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_739, %onnx::Conv_740) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_742, %onnx::Conv_743) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.11/shuffle/Reshape_output_0 = Reshape(%/cells.11/nl/Relu_output_0, %/cells.11/shuffle/Constant_output_0) %/cells.11/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.11/shuffle/Reshape_output_0) %/cells.11/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.11/shuffle/Reshape_1_output_0 = Reshape(%/cells.11/shuffle/Transpose_output_0, %/cells.11/shuffle/Constant_1_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.11/shuffle/Reshape_1_output_0, %onnx::Conv_745, %onnx::Conv_746) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_748, %onnx::Conv_749) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_751, %onnx::Conv_752) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.12/nl/Relu_output_0, %onnx::Conv_754, %onnx::Conv_755) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_757, %onnx::Conv_758) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_760, %onnx::Conv_761) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.13/nl/Relu_output_0, %onnx::Conv_763, %onnx::Conv_764) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_766, %onnx::Conv_767) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_769, %onnx::Conv_770) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.14/nl/Relu_output_0, %onnx::Conv_772, %onnx::Conv_773) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_775, %onnx::Conv_776) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_778, %onnx::Conv_779) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.15/nl/Relu_output_0, %onnx::Conv_781, %onnx::Conv_782) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_784, %onnx::Conv_785) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_787, %onnx::Conv_788) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.16/nl/Relu_output_0, %onnx::Conv_790, %onnx::Conv_791) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_793, %onnx::Conv_794) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.15/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_796, %onnx::Conv_797) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.17/nl/Relu_output_0, %onnx::Conv_799, %onnx::Conv_800) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_802, %onnx::Conv_803) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_805, %onnx::Conv_806) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.18/nl/Relu_output_0, %onnx::Conv_808, %onnx::Conv_809) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_811, %onnx::Conv_812) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_814, %onnx::Conv_815) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.19/shuffle/Reshape_output_0 = Reshape(%/cells.19/nl/Relu_output_0, %/cells.19/shuffle/Constant_output_0) %/cells.19/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.19/shuffle/Reshape_output_0) %/cells.19/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.19/shuffle/Reshape_1_output_0 = Reshape(%/cells.19/shuffle/Transpose_output_0, %/cells.19/shuffle/Constant_1_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.19/shuffle/Reshape_1_output_0, %onnx::Conv_817, %onnx::Conv_818) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_820, %onnx::Conv_821) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_823, %onnx::Conv_824) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.20/nl/Relu_output_0, %onnx::Conv_826, %onnx::Conv_827) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_829, %onnx::Conv_830) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_832, %onnx::Conv_833) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.21/shuffle/Reshape_output_0 = Reshape(%/cells.21/nl/Relu_output_0, %/cells.21/shuffle/Constant_output_0) %/cells.21/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.21/shuffle/Reshape_output_0) %/cells.21/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.21/shuffle/Reshape_1_output_0 = Reshape(%/cells.21/shuffle/Transpose_output_0, %/cells.21/shuffle/Constant_1_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.21/shuffle/Reshape_1_output_0, %onnx::Conv_835, %onnx::Conv_836) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_838, %onnx::Conv_839) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_841, %onnx::Conv_842) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %656 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %656 }	val_accuracy	65,825,152	1,415,420	{'zcp_synflow': 77.75211531540195, 'zcp_zen': 69.51578521728516, 'zcp_epe_nas': 10.5744969304873, 'zcp_fisher': 0.11800754815340042, 'zcp_flops': 65825152.0, 'zcp_grad_norm': 25.65627670288086, 'zcp_grasp': 0.0069141387939453125, 'zcp_jacov': -16.055516181779577, 'zcp_l2_norm': 606.4366455078125, 'zcp_nwot': 212.2262550894392, 'zcp_params': 1415420.0, 'zcp_plain': 0.004903733730316162, 'zcp_snip': 43.18825912475586, 'lat_1080ti_1': 0.537499059129485, 'lat_1080ti_32': 0.5003034808653247, 'lat_1080ti_64': 0.43760711147684805, 'lat_2080ti_1': 0.6285092019544727, 'lat_2080ti_32': 0.5212000527716993, 'lat_2080ti_64': 0.4337585198004947, 'lat_essential_ph_1': 0.22641509433962265, 'lat_eyeriss': 0.398987768873893, 'lat_fpga': 0.3773241373850919, 'lat_gold_6226': 0.25766177031159154, 'lat_gold_6240': 0.3961687761728221, 'lat_pixel2': 0.2391304347826087, 'lat_pixel3': 0.40701041519192255, 'lat_raspi4': 0.38503644208293303, 'lat_samsung_a50': 0.15789473684210525, 'lat_samsung_s7': 0.13385826771653545, 'lat_silver_4114': 0.4017731822214511, 'lat_silver_4210r': 0.4585506195653167, 'lat_titan_rtx_1': 0.5807812751300663, 'lat_titan_rtx_32': 0.5218673906442798, 'lat_titan_rtx_64': 0.4641072168225973, 'lat_titanx_1': 0.3091018673599486, 'lat_titanx_32': 0.48437835215467323, 'lat_titanx_64': 0.4127557150202376, 'lat_titanxp_1': 0.5644286386102288, 'lat_titanxp_32': 0.5124634172068122, 'lat_titanxp_64': 0.43743518091711747}
FBNet_2528	FBNet	2528	2528	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_741[FLOAT, 16x3x3x3] %onnx::Conv_742[FLOAT, 16] %onnx::Conv_744[FLOAT, 16x8x1x1] %onnx::Conv_747[FLOAT, 16x1x3x3] %onnx::Conv_750[FLOAT, 16x8x1x1] %onnx::Conv_753[FLOAT, 16x16x1x1] %onnx::Conv_756[FLOAT, 16x1x3x3] %onnx::Conv_759[FLOAT, 24x16x1x1] %onnx::Conv_760[FLOAT, 24] %onnx::Conv_762[FLOAT, 24x12x1x1] %onnx::Conv_765[FLOAT, 24x1x5x5] %onnx::Conv_768[FLOAT, 24x12x1x1] %onnx::Conv_771[FLOAT, 24x24x1x1] %onnx::Conv_774[FLOAT, 24x1x3x3] %onnx::Conv_777[FLOAT, 24x24x1x1] %onnx::Conv_780[FLOAT, 72x24x1x1] %onnx::Conv_781[FLOAT, 72] %onnx::Conv_783[FLOAT, 72x1x3x3] %onnx::Conv_786[FLOAT, 24x72x1x1] %onnx::Conv_789[FLOAT, 24x24x1x1] %onnx::Conv_792[FLOAT, 24x1x5x5] %onnx::Conv_795[FLOAT, 32x24x1x1] %onnx::Conv_796[FLOAT, 32] %onnx::Conv_798[FLOAT, 32x32x1x1] %onnx::Conv_801[FLOAT, 32x1x5x5] %onnx::Conv_804[FLOAT, 32x32x1x1] %onnx::Conv_807[FLOAT, 192x32x1x1] %onnx::Conv_808[FLOAT, 192] %onnx::Conv_810[FLOAT, 192x1x5x5] %onnx::Conv_813[FLOAT, 32x192x1x1] %onnx::Conv_816[FLOAT, 32x32x1x1] %onnx::Conv_819[FLOAT, 32x1x3x3] %onnx::Conv_822[FLOAT, 64x32x1x1] %onnx::Conv_823[FLOAT, 64] %onnx::Conv_825[FLOAT, 384x64x1x1] %onnx::Conv_826[FLOAT, 384] %onnx::Conv_828[FLOAT, 384x1x3x3] %onnx::Conv_831[FLOAT, 64x384x1x1] %onnx::Conv_834[FLOAT, 192x64x1x1] %onnx::Conv_837[FLOAT, 192x1x5x5] %onnx::Conv_840[FLOAT, 64x192x1x1] %onnx::Conv_843[FLOAT, 64x32x1x1] %onnx::Conv_846[FLOAT, 64x1x3x3] %onnx::Conv_849[FLOAT, 64x32x1x1] %onnx::Conv_852[FLOAT, 64x32x1x1] %onnx::Conv_855[FLOAT, 64x1x5x5] %onnx::Conv_858[FLOAT, 112x32x1x1] %onnx::Conv_859[FLOAT, 112] %onnx::Conv_861[FLOAT, 336x112x1x1] %onnx::Conv_862[FLOAT, 336] %onnx::Conv_864[FLOAT, 336x1x5x5] %onnx::Conv_867[FLOAT, 112x336x1x1] %onnx::Conv_870[FLOAT, 112x56x1x1] %onnx::Conv_873[FLOAT, 112x1x3x3] %onnx::Conv_876[FLOAT, 112x56x1x1] %onnx::Conv_879[FLOAT, 672x112x1x1] %onnx::Conv_880[FLOAT, 672] %onnx::Conv_882[FLOAT, 672x1x3x3] %onnx::Conv_885[FLOAT, 112x672x1x1] %onnx::Conv_888[FLOAT, 112x56x1x1] %onnx::Conv_891[FLOAT, 112x1x3x3] %onnx::Conv_894[FLOAT, 184x56x1x1] %onnx::Conv_895[FLOAT, 184] %onnx::Conv_897[FLOAT, 184x92x1x1] %onnx::Conv_900[FLOAT, 184x1x5x5] %onnx::Conv_903[FLOAT, 184x92x1x1] %onnx::Conv_906[FLOAT, 184x92x1x1] %onnx::Conv_909[FLOAT, 184x1x5x5] %onnx::Conv_912[FLOAT, 184x92x1x1] %onnx::Conv_915[FLOAT, 552x184x1x1] %onnx::Conv_916[FLOAT, 552] %onnx::Conv_918[FLOAT, 552x1x3x3] %onnx::Conv_921[FLOAT, 184x552x1x1] %onnx::Conv_924[FLOAT, 184x184x1x1] %onnx::Conv_927[FLOAT, 184x1x3x3] %onnx::Conv_930[FLOAT, 352x184x1x1] %onnx::Conv_931[FLOAT, 352] %onnx::Conv_933[FLOAT, 1504x352x1x1] %onnx::Conv_934[FLOAT, 1504] ) { %onnx::Conv_928 = Identity(%onnx::Conv_895) %onnx::Conv_925 = Identity(%onnx::Conv_895) %onnx::Conv_922 = Identity(%onnx::Conv_895) %onnx::Conv_919 = Identity(%onnx::Conv_916) %onnx::Conv_913 = Identity(%onnx::Conv_895) %onnx::Conv_910 = Identity(%onnx::Conv_895) %onnx::Conv_907 = Identity(%onnx::Conv_895) %onnx::Conv_904 = Identity(%onnx::Conv_895) %onnx::Conv_901 = Identity(%onnx::Conv_895) %onnx::Conv_898 = Identity(%onnx::Conv_895) %onnx::Conv_892 = Identity(%onnx::Conv_859) %onnx::Conv_889 = Identity(%onnx::Conv_859) %onnx::Conv_886 = Identity(%onnx::Conv_859) %onnx::Conv_883 = Identity(%onnx::Conv_880) %onnx::Conv_877 = Identity(%onnx::Conv_859) %onnx::Conv_874 = Identity(%onnx::Conv_859) %onnx::Conv_871 = Identity(%onnx::Conv_859) %onnx::Conv_868 = Identity(%onnx::Conv_859) %onnx::Conv_865 = Identity(%onnx::Conv_862) %onnx::Conv_856 = Identity(%onnx::Conv_823) %onnx::Conv_853 = Identity(%onnx::Conv_823) %onnx::Conv_850 = Identity(%onnx::Conv_823) %onnx::Conv_847 = Identity(%onnx::Conv_823) %onnx::Conv_844 = Identity(%onnx::Conv_823) %onnx::Conv_841 = Identity(%onnx::Conv_823) %onnx::Conv_838 = Identity(%onnx::Conv_808) %onnx::Conv_835 = Identity(%onnx::Conv_808) %onnx::Conv_832 = Identity(%onnx::Conv_823) %onnx::Conv_829 = Identity(%onnx::Conv_826) %onnx::Conv_820 = Identity(%onnx::Conv_796) %onnx::Conv_817 = Identity(%onnx::Conv_796) %onnx::Conv_814 = Identity(%onnx::Conv_796) %onnx::Conv_811 = Identity(%onnx::Conv_808) %onnx::Conv_805 = Identity(%onnx::Conv_796) %onnx::Conv_802 = Identity(%onnx::Conv_796) %onnx::Conv_799 = Identity(%onnx::Conv_796) %onnx::Conv_793 = Identity(%onnx::Conv_760) %onnx::Conv_790 = Identity(%onnx::Conv_760) %onnx::Conv_787 = Identity(%onnx::Conv_760) %onnx::Conv_784 = Identity(%onnx::Conv_781) %onnx::Conv_778 = Identity(%onnx::Conv_760) %onnx::Conv_775 = Identity(%onnx::Conv_760) %onnx::Conv_772 = Identity(%onnx::Conv_760) %onnx::Conv_769 = Identity(%onnx::Conv_760) %onnx::Conv_766 = Identity(%onnx::Conv_760) %onnx::Conv_763 = Identity(%onnx::Conv_760) %onnx::Conv_757 = Identity(%onnx::Conv_742) %onnx::Conv_754 = Identity(%onnx::Conv_742) %onnx::Conv_751 = Identity(%onnx::Conv_742) %onnx::Conv_748 = Identity(%onnx::Conv_742) %onnx::Conv_745 = Identity(%onnx::Conv_742) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_741, %onnx::Conv_742) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_744, %onnx::Conv_745) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.0/shuffle/Reshape_output_0 = Reshape(%/cells.0/nl/Relu_output_0, %/cells.0/shuffle/Constant_output_0) %/cells.0/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.0/shuffle/Reshape_output_0) %/cells.0/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.0/shuffle/Reshape_1_output_0 = Reshape(%/cells.0/shuffle/Transpose_output_0, %/cells.0/shuffle/Constant_1_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.0/shuffle/Reshape_1_output_0, %onnx::Conv_747, %onnx::Conv_748) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_750, %onnx::Conv_751) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_753, %onnx::Conv_754) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.1/nl/Relu_output_0, %onnx::Conv_756, %onnx::Conv_757) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_759, %onnx::Conv_760) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_762, %onnx::Conv_763) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.2/shuffle/Reshape_output_0 = Reshape(%/cells.2/nl/Relu_output_0, %/cells.2/shuffle/Constant_output_0) %/cells.2/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.2/shuffle/Reshape_output_0) %/cells.2/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.2/shuffle/Reshape_1_output_0 = Reshape(%/cells.2/shuffle/Transpose_output_0, %/cells.2/shuffle/Constant_1_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.2/shuffle/Reshape_1_output_0, %onnx::Conv_765, %onnx::Conv_766) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_768, %onnx::Conv_769) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_771, %onnx::Conv_772) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.3/nl/Relu_output_0, %onnx::Conv_774, %onnx::Conv_775) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_777, %onnx::Conv_778) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_780, %onnx::Conv_781) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.4/nl/Relu_output_0, %onnx::Conv_783, %onnx::Conv_784) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_786, %onnx::Conv_787) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/Add_output_0, %onnx::Conv_789, %onnx::Conv_790) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.5/nl/Relu_output_0, %onnx::Conv_792, %onnx::Conv_793) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_795, %onnx::Conv_796) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_798, %onnx::Conv_799) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.6/nl/Relu_output_0, %onnx::Conv_801, %onnx::Conv_802) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_804, %onnx::Conv_805) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_807, %onnx::Conv_808) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.8/nl/Relu_output_0, %onnx::Conv_810, %onnx::Conv_811) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_813, %onnx::Conv_814) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_816, %onnx::Conv_817) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.9/nl/Relu_output_0, %onnx::Conv_819, %onnx::Conv_820) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_822, %onnx::Conv_823) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_825, %onnx::Conv_826) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.10/nl/Relu_output_0, %onnx::Conv_828, %onnx::Conv_829) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_831, %onnx::Conv_832) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_834, %onnx::Conv_835) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.11/nl/Relu_output_0, %onnx::Conv_837, %onnx::Conv_838) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_840, %onnx::Conv_841) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_843, %onnx::Conv_844) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.12/shuffle/Reshape_output_0 = Reshape(%/cells.12/nl/Relu_output_0, %/cells.12/shuffle/Constant_output_0) %/cells.12/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.12/shuffle/Reshape_output_0) %/cells.12/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.12/shuffle/Reshape_1_output_0 = Reshape(%/cells.12/shuffle/Transpose_output_0, %/cells.12/shuffle/Constant_1_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.12/shuffle/Reshape_1_output_0, %onnx::Conv_846, %onnx::Conv_847) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_849, %onnx::Conv_850) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_852, %onnx::Conv_853) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.13/shuffle/Reshape_output_0 = Reshape(%/cells.13/nl/Relu_output_0, %/cells.13/shuffle/Constant_output_0) %/cells.13/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.13/shuffle/Reshape_output_0) %/cells.13/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.13/shuffle/Reshape_1_output_0 = Reshape(%/cells.13/shuffle/Transpose_output_0, %/cells.13/shuffle/Constant_1_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.13/shuffle/Reshape_1_output_0, %onnx::Conv_855, %onnx::Conv_856) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_858, %onnx::Conv_859) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_861, %onnx::Conv_862) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.14/nl/Relu_output_0, %onnx::Conv_864, %onnx::Conv_865) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_867, %onnx::Conv_868) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_870, %onnx::Conv_871) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.15/shuffle/Reshape_output_0 = Reshape(%/cells.15/nl/Relu_output_0, %/cells.15/shuffle/Constant_output_0) %/cells.15/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.15/shuffle/Reshape_output_0) %/cells.15/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.15/shuffle/Reshape_1_output_0 = Reshape(%/cells.15/shuffle/Transpose_output_0, %/cells.15/shuffle/Constant_1_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.15/shuffle/Reshape_1_output_0, %onnx::Conv_873, %onnx::Conv_874) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_876, %onnx::Conv_877) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_879, %onnx::Conv_880) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.16/nl/Relu_output_0, %onnx::Conv_882, %onnx::Conv_883) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_885, %onnx::Conv_886) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.15/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_888, %onnx::Conv_889) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.17/shuffle/Reshape_output_0 = Reshape(%/cells.17/nl/Relu_output_0, %/cells.17/shuffle/Constant_output_0) %/cells.17/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.17/shuffle/Reshape_output_0) %/cells.17/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.17/shuffle/Reshape_1_output_0 = Reshape(%/cells.17/shuffle/Transpose_output_0, %/cells.17/shuffle/Constant_1_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.17/shuffle/Reshape_1_output_0, %onnx::Conv_891, %onnx::Conv_892) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_894, %onnx::Conv_895) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_897, %onnx::Conv_898) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.18/shuffle/Reshape_output_0 = Reshape(%/cells.18/nl/Relu_output_0, %/cells.18/shuffle/Constant_output_0) %/cells.18/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.18/shuffle/Reshape_output_0) %/cells.18/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.18/shuffle/Reshape_1_output_0 = Reshape(%/cells.18/shuffle/Transpose_output_0, %/cells.18/shuffle/Constant_1_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.18/shuffle/Reshape_1_output_0, %onnx::Conv_900, %onnx::Conv_901) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_903, %onnx::Conv_904) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_906, %onnx::Conv_907) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.19/shuffle/Reshape_output_0 = Reshape(%/cells.19/nl/Relu_output_0, %/cells.19/shuffle/Constant_output_0) %/cells.19/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.19/shuffle/Reshape_output_0) %/cells.19/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.19/shuffle/Reshape_1_output_0 = Reshape(%/cells.19/shuffle/Transpose_output_0, %/cells.19/shuffle/Constant_1_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.19/shuffle/Reshape_1_output_0, %onnx::Conv_909, %onnx::Conv_910) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_912, %onnx::Conv_913) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_915, %onnx::Conv_916) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 552, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.20/nl/Relu_output_0, %onnx::Conv_918, %onnx::Conv_919) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_921, %onnx::Conv_922) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_924, %onnx::Conv_925) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.21/nl/Relu_output_0, %onnx::Conv_927, %onnx::Conv_928) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_930, %onnx::Conv_931) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_933, %onnx::Conv_934) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %739 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %739 }	val_accuracy	54,932,864	1,484,900	{'zcp_synflow': 74.95154099253742, 'zcp_zen': 66.30712890625, 'zcp_epe_nas': 7.924297340099852, 'zcp_fisher': 0.08635241538286209, 'zcp_flops': 54932864.0, 'zcp_grad_norm': 21.04476547241211, 'zcp_grasp': 0.14464187622070312, 'zcp_jacov': -16.051712331239905, 'zcp_l2_norm': 576.2957153320312, 'zcp_nwot': 207.50197248345543, 'zcp_params': 1484900.0, 'zcp_plain': 0.0019389111548662186, 'zcp_snip': 36.876277923583984, 'lat_1080ti_1': 0.836630305556328, 'lat_1080ti_32': 0.5523171157814372, 'lat_1080ti_64': 0.3492324701372168, 'lat_2080ti_1': 0.7893587476653491, 'lat_2080ti_32': 0.6158039112048125, 'lat_2080ti_64': 0.4139646884683264, 'lat_essential_ph_1': 0.2641509433962264, 'lat_eyeriss': 0.2537179322611995, 'lat_fpga': 0.32465193599778336, 'lat_gold_6226': 0.23258990461928883, 'lat_gold_6240': 0.474637260681841, 'lat_pixel2': 0.1956521739130435, 'lat_pixel3': 0.273714527222636, 'lat_raspi4': 0.2685384248959267, 'lat_samsung_a50': 0.35789473684210527, 'lat_samsung_s7': 0.14173228346456693, 'lat_silver_4114': 0.6517034389078161, 'lat_silver_4210r': 0.587361018635603, 'lat_titan_rtx_1': 0.7411304871513582, 'lat_titan_rtx_32': 0.6204641899992929, 'lat_titan_rtx_64': 0.460493755935729, 'lat_titanx_1': 0.3891930783501865, 'lat_titanx_32': 0.5071708048472289, 'lat_titanx_64': 0.38440402353918746, 'lat_titanxp_1': 0.6872294763796786, 'lat_titanxp_32': 0.5733463235210344, 'lat_titanxp_64': 0.38706262277843406}
FBNet_3158	FBNet	3158	3158	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_597[FLOAT, 16x3x3x3] %onnx::Conv_598[FLOAT, 16] %onnx::Conv_600[FLOAT, 16x8x1x1] %onnx::Conv_603[FLOAT, 16x1x3x3] %onnx::Conv_606[FLOAT, 24x8x1x1] %onnx::Conv_607[FLOAT, 24] %onnx::Conv_609[FLOAT, 144x24x1x1] %onnx::Conv_610[FLOAT, 144] %onnx::Conv_612[FLOAT, 144x1x3x3] %onnx::Conv_615[FLOAT, 24x144x1x1] %onnx::Conv_618[FLOAT, 24x24x1x1] %onnx::Conv_621[FLOAT, 24x1x5x5] %onnx::Conv_624[FLOAT, 24x24x1x1] %onnx::Conv_627[FLOAT, 32x24x1x1] %onnx::Conv_628[FLOAT, 32] %onnx::Conv_630[FLOAT, 96x32x1x1] %onnx::Conv_631[FLOAT, 96] %onnx::Conv_633[FLOAT, 96x1x3x3] %onnx::Conv_636[FLOAT, 32x96x1x1] %onnx::Conv_639[FLOAT, 192x32x1x1] %onnx::Conv_640[FLOAT, 192] %onnx::Conv_642[FLOAT, 192x1x3x3] %onnx::Conv_645[FLOAT, 32x192x1x1] %onnx::Conv_648[FLOAT, 192x32x1x1] %onnx::Conv_651[FLOAT, 192x1x3x3] %onnx::Conv_654[FLOAT, 32x192x1x1] %onnx::Conv_657[FLOAT, 32x16x1x1] %onnx::Conv_660[FLOAT, 32x1x3x3] %onnx::Conv_663[FLOAT, 64x16x1x1] %onnx::Conv_664[FLOAT, 64] %onnx::Conv_666[FLOAT, 384x64x1x1] %onnx::Conv_667[FLOAT, 384] %onnx::Conv_669[FLOAT, 384x1x5x5] %onnx::Conv_672[FLOAT, 64x384x1x1] %onnx::Conv_675[FLOAT, 64x64x1x1] %onnx::Conv_678[FLOAT, 64x1x3x3] %onnx::Conv_681[FLOAT, 64x64x1x1] %onnx::Conv_684[FLOAT, 64x32x1x1] %onnx::Conv_687[FLOAT, 64x1x5x5] %onnx::Conv_690[FLOAT, 64x32x1x1] %onnx::Conv_693[FLOAT, 384x64x1x1] %onnx::Conv_696[FLOAT, 384x1x5x5] %onnx::Conv_699[FLOAT, 112x384x1x1] %onnx::Conv_700[FLOAT, 112] %onnx::Conv_702[FLOAT, 672x112x1x1] %onnx::Conv_703[FLOAT, 672] %onnx::Conv_705[FLOAT, 672x1x3x3] %onnx::Conv_708[FLOAT, 112x672x1x1] %onnx::Conv_711[FLOAT, 112x112x1x1] %onnx::Conv_714[FLOAT, 112x1x5x5] %onnx::Conv_717[FLOAT, 112x112x1x1] %onnx::Conv_720[FLOAT, 112x112x1x1] %onnx::Conv_723[FLOAT, 112x1x3x3] %onnx::Conv_726[FLOAT, 184x112x1x1] %onnx::Conv_727[FLOAT, 184] %onnx::Conv_729[FLOAT, 1104x184x1x1] %onnx::Conv_730[FLOAT, 1104] %onnx::Conv_732[FLOAT, 1104x1x3x3] %onnx::Conv_735[FLOAT, 184x1104x1x1] %onnx::Conv_738[FLOAT, 184x92x1x1] %onnx::Conv_741[FLOAT, 184x1x3x3] %onnx::Conv_744[FLOAT, 184x92x1x1] %onnx::Conv_747[FLOAT, 552x184x1x1] %onnx::Conv_748[FLOAT, 552] %onnx::Conv_750[FLOAT, 552x1x3x3] %onnx::Conv_753[FLOAT, 184x552x1x1] %onnx::Conv_756[FLOAT, 552x184x1x1] %onnx::Conv_759[FLOAT, 552x1x3x3] %onnx::Conv_762[FLOAT, 352x552x1x1] %onnx::Conv_763[FLOAT, 352] %onnx::Conv_765[FLOAT, 1504x352x1x1] %onnx::Conv_766[FLOAT, 1504] ) { %onnx::Conv_760 = Identity(%onnx::Conv_748) %onnx::Conv_757 = Identity(%onnx::Conv_748) %onnx::Conv_754 = Identity(%onnx::Conv_727) %onnx::Conv_751 = Identity(%onnx::Conv_748) %onnx::Conv_745 = Identity(%onnx::Conv_727) %onnx::Conv_742 = Identity(%onnx::Conv_727) %onnx::Conv_739 = Identity(%onnx::Conv_727) %onnx::Conv_736 = Identity(%onnx::Conv_727) %onnx::Conv_733 = Identity(%onnx::Conv_730) %onnx::Conv_724 = Identity(%onnx::Conv_700) %onnx::Conv_721 = Identity(%onnx::Conv_700) %onnx::Conv_718 = Identity(%onnx::Conv_700) %onnx::Conv_715 = Identity(%onnx::Conv_700) %onnx::Conv_712 = Identity(%onnx::Conv_700) %onnx::Conv_709 = Identity(%onnx::Conv_700) %onnx::Conv_706 = Identity(%onnx::Conv_703) %onnx::Conv_697 = Identity(%onnx::Conv_667) %onnx::Conv_694 = Identity(%onnx::Conv_667) %onnx::Conv_691 = Identity(%onnx::Conv_664) %onnx::Conv_688 = Identity(%onnx::Conv_664) %onnx::Conv_685 = Identity(%onnx::Conv_664) %onnx::Conv_682 = Identity(%onnx::Conv_664) %onnx::Conv_679 = Identity(%onnx::Conv_664) %onnx::Conv_676 = Identity(%onnx::Conv_664) %onnx::Conv_673 = Identity(%onnx::Conv_664) %onnx::Conv_670 = Identity(%onnx::Conv_667) %onnx::Conv_661 = Identity(%onnx::Conv_628) %onnx::Conv_658 = Identity(%onnx::Conv_628) %onnx::Conv_655 = Identity(%onnx::Conv_628) %onnx::Conv_652 = Identity(%onnx::Conv_640) %onnx::Conv_649 = Identity(%onnx::Conv_640) %onnx::Conv_646 = Identity(%onnx::Conv_628) %onnx::Conv_643 = Identity(%onnx::Conv_640) %onnx::Conv_637 = Identity(%onnx::Conv_628) %onnx::Conv_634 = Identity(%onnx::Conv_631) %onnx::Conv_625 = Identity(%onnx::Conv_607) %onnx::Conv_622 = Identity(%onnx::Conv_607) %onnx::Conv_619 = Identity(%onnx::Conv_607) %onnx::Conv_616 = Identity(%onnx::Conv_607) %onnx::Conv_613 = Identity(%onnx::Conv_610) %onnx::Conv_604 = Identity(%onnx::Conv_598) %onnx::Conv_601 = Identity(%onnx::Conv_598) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_597, %onnx::Conv_598) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_600, %onnx::Conv_601) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.1/shuffle/Reshape_output_0 = Reshape(%/cells.1/nl/Relu_output_0, %/cells.1/shuffle/Constant_output_0) %/cells.1/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.1/shuffle/Reshape_output_0) %/cells.1/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.1/shuffle/Reshape_1_output_0 = Reshape(%/cells.1/shuffle/Transpose_output_0, %/cells.1/shuffle/Constant_1_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.1/shuffle/Reshape_1_output_0, %onnx::Conv_603, %onnx::Conv_604) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_606, %onnx::Conv_607) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_609, %onnx::Conv_610) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.2/nl/Relu_output_0, %onnx::Conv_612, %onnx::Conv_613) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_615, %onnx::Conv_616) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_618, %onnx::Conv_619) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.3/nl/Relu_output_0, %onnx::Conv_621, %onnx::Conv_622) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_624, %onnx::Conv_625) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.5/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [2, 2]](%/cells.3/Add_output_0, %onnx::Conv_627, %onnx::Conv_628) %/cells.5/relu/Relu_output_0 = Relu(%/cells.5/conv/Conv_output_0) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/relu/Relu_output_0, %onnx::Conv_630, %onnx::Conv_631) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.6/nl/Relu_output_0, %onnx::Conv_633, %onnx::Conv_634) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_636, %onnx::Conv_637) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/relu/Relu_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_639, %onnx::Conv_640) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.7/nl/Relu_output_0, %onnx::Conv_642, %onnx::Conv_643) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_645, %onnx::Conv_646) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_648, %onnx::Conv_649) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.8/nl/Relu_output_0, %onnx::Conv_651, %onnx::Conv_652) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_654, %onnx::Conv_655) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_657, %onnx::Conv_658) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.9/shuffle/Reshape_output_0 = Reshape(%/cells.9/nl/Relu_output_0, %/cells.9/shuffle/Constant_output_0) %/cells.9/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.9/shuffle/Reshape_output_0) %/cells.9/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.9/shuffle/Reshape_1_output_0 = Reshape(%/cells.9/shuffle/Transpose_output_0, %/cells.9/shuffle/Constant_1_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.9/shuffle/Reshape_1_output_0, %onnx::Conv_660, %onnx::Conv_661) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_663, %onnx::Conv_664) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_666, %onnx::Conv_667) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.10/nl/Relu_output_0, %onnx::Conv_669, %onnx::Conv_670) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_672, %onnx::Conv_673) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_675, %onnx::Conv_676) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.11/nl/Relu_output_0, %onnx::Conv_678, %onnx::Conv_679) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_681, %onnx::Conv_682) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_684, %onnx::Conv_685) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.12/shuffle/Reshape_output_0 = Reshape(%/cells.12/nl/Relu_output_0, %/cells.12/shuffle/Constant_output_0) %/cells.12/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.12/shuffle/Reshape_output_0) %/cells.12/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.12/shuffle/Reshape_1_output_0 = Reshape(%/cells.12/shuffle/Transpose_output_0, %/cells.12/shuffle/Constant_1_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.12/shuffle/Reshape_1_output_0, %onnx::Conv_687, %onnx::Conv_688) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_690, %onnx::Conv_691) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_693, %onnx::Conv_694) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.13/nl/Relu_output_0, %onnx::Conv_696, %onnx::Conv_697) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_699, %onnx::Conv_700) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_702, %onnx::Conv_703) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.15/nl/Relu_output_0, %onnx::Conv_705, %onnx::Conv_706) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_708, %onnx::Conv_709) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_711, %onnx::Conv_712) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.16/nl/Relu_output_0, %onnx::Conv_714, %onnx::Conv_715) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_717, %onnx::Conv_718) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.15/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_720, %onnx::Conv_721) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.17/nl/Relu_output_0, %onnx::Conv_723, %onnx::Conv_724) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_726, %onnx::Conv_727) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_729, %onnx::Conv_730) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.18/nl/Relu_output_0, %onnx::Conv_732, %onnx::Conv_733) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_735, %onnx::Conv_736) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_738, %onnx::Conv_739) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.19/shuffle/Reshape_output_0 = Reshape(%/cells.19/nl/Relu_output_0, %/cells.19/shuffle/Constant_output_0) %/cells.19/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.19/shuffle/Reshape_output_0) %/cells.19/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.19/shuffle/Reshape_1_output_0 = Reshape(%/cells.19/shuffle/Transpose_output_0, %/cells.19/shuffle/Constant_1_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.19/shuffle/Reshape_1_output_0, %onnx::Conv_741, %onnx::Conv_742) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_744, %onnx::Conv_745) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_747, %onnx::Conv_748) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 552, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.20/nl/Relu_output_0, %onnx::Conv_750, %onnx::Conv_751) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_753, %onnx::Conv_754) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_756, %onnx::Conv_757) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 552, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.21/nl/Relu_output_0, %onnx::Conv_759, %onnx::Conv_760) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_762, %onnx::Conv_763) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_765, %onnx::Conv_766) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %595 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %595 }	val_accuracy	69,874,816	2,084,404	{'zcp_synflow': 70.52662494408344, 'zcp_zen': 64.60386657714844, 'zcp_epe_nas': 33.78948064830971, 'zcp_fisher': 0.06074092909693718, 'zcp_flops': 69874816.0, 'zcp_grad_norm': 21.253433227539062, 'zcp_grasp': -0.012530326843261719, 'zcp_jacov': -16.07688050077756, 'zcp_l2_norm': 621.638916015625, 'zcp_nwot': 210.97576921237444, 'zcp_params': 2084404.0, 'zcp_plain': -0.0032224440947175026, 'zcp_snip': 33.41200256347656, 'lat_1080ti_1': 0.43671508069409676, 'lat_1080ti_32': 0.3807730946463703, 'lat_1080ti_64': 0.308686589433666, 'lat_2080ti_1': 0.44560513254753975, 'lat_2080ti_32': 0.3998039096939515, 'lat_2080ti_64': 0.3325352685175014, 'lat_essential_ph_1': 0.32075471698113206, 'lat_eyeriss': 0.4360754038838866, 'lat_fpga': 0.5468488704618094, 'lat_gold_6226': 0.40414689121853964, 'lat_gold_6240': 0.5579059751670675, 'lat_pixel2': 0.30434782608695654, 'lat_pixel3': 0.3849168380826895, 'lat_raspi4': 0.4488854086257134, 'lat_samsung_a50': 0.2, 'lat_samsung_s7': 0.16535433070866143, 'lat_silver_4114': 0.48845722268774855, 'lat_silver_4210r': 0.44644073029434006, 'lat_titan_rtx_1': 0.4105330064228817, 'lat_titan_rtx_32': 0.3827722208561438, 'lat_titan_rtx_64': 0.32675079727187983, 'lat_titanx_1': 0.22022444660867807, 'lat_titanx_32': 0.3197328375227549, 'lat_titanx_64': 0.2864743851003796, 'lat_titanxp_1': 0.40655490573248393, 'lat_titanxp_32': 0.3554087315032655, 'lat_titanxp_64': 0.3019909341724524}
FBNet_3699	FBNet	3699	3699	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_686[FLOAT, 16x3x3x3] %onnx::Conv_687[FLOAT, 16] %onnx::Conv_689[FLOAT, 16x16x1x1] %onnx::Conv_692[FLOAT, 16x1x3x3] %onnx::Conv_695[FLOAT, 16x16x1x1] %onnx::Conv_698[FLOAT, 48x16x1x1] %onnx::Conv_699[FLOAT, 48] %onnx::Conv_701[FLOAT, 48x1x5x5] %onnx::Conv_704[FLOAT, 24x48x1x1] %onnx::Conv_705[FLOAT, 24] %onnx::Conv_707[FLOAT, 24x12x1x1] %onnx::Conv_710[FLOAT, 24x1x3x3] %onnx::Conv_713[FLOAT, 24x12x1x1] %onnx::Conv_716[FLOAT, 24x12x1x1] %onnx::Conv_719[FLOAT, 24x1x5x5] %onnx::Conv_722[FLOAT, 24x12x1x1] %onnx::Conv_725[FLOAT, 72x24x1x1] %onnx::Conv_726[FLOAT, 72] %onnx::Conv_728[FLOAT, 72x1x3x3] %onnx::Conv_731[FLOAT, 24x72x1x1] %onnx::Conv_734[FLOAT, 144x24x1x1] %onnx::Conv_735[FLOAT, 144] %onnx::Conv_737[FLOAT, 144x1x5x5] %onnx::Conv_740[FLOAT, 32x144x1x1] %onnx::Conv_741[FLOAT, 32] %onnx::Conv_743[FLOAT, 192x32x1x1] %onnx::Conv_744[FLOAT, 192] %onnx::Conv_746[FLOAT, 192x1x5x5] %onnx::Conv_749[FLOAT, 32x192x1x1] %onnx::Conv_752[FLOAT, 32x16x1x1] %onnx::Conv_755[FLOAT, 32x1x5x5] %onnx::Conv_758[FLOAT, 32x16x1x1] %onnx::Conv_761[FLOAT, 32x32x1x1] %onnx::Conv_764[FLOAT, 32x1x3x3] %onnx::Conv_767[FLOAT, 32x32x1x1] %onnx::Conv_770[FLOAT, 192x32x1x1] %onnx::Conv_773[FLOAT, 192x1x3x3] %onnx::Conv_776[FLOAT, 64x192x1x1] %onnx::Conv_777[FLOAT, 64] %onnx::Conv_779[FLOAT, 384x64x1x1] %onnx::Conv_780[FLOAT, 384] %onnx::Conv_782[FLOAT, 384x1x3x3] %onnx::Conv_785[FLOAT, 64x384x1x1] %onnx::Conv_788[FLOAT, 192x64x1x1] %onnx::Conv_791[FLOAT, 192x1x3x3] %onnx::Conv_794[FLOAT, 64x192x1x1] %onnx::Conv_797[FLOAT, 192x64x1x1] %onnx::Conv_800[FLOAT, 192x1x5x5] %onnx::Conv_803[FLOAT, 64x192x1x1] %onnx::Conv_806[FLOAT, 64x32x1x1] %onnx::Conv_809[FLOAT, 64x1x3x3] %onnx::Conv_812[FLOAT, 112x32x1x1] %onnx::Conv_813[FLOAT, 112] %onnx::Conv_815[FLOAT, 112x112x1x1] %onnx::Conv_818[FLOAT, 112x1x5x5] %onnx::Conv_821[FLOAT, 112x112x1x1] %onnx::Conv_824[FLOAT, 336x112x1x1] %onnx::Conv_825[FLOAT, 336] %onnx::Conv_827[FLOAT, 336x1x5x5] %onnx::Conv_830[FLOAT, 112x336x1x1] %onnx::Conv_833[FLOAT, 672x112x1x1] %onnx::Conv_834[FLOAT, 672] %onnx::Conv_836[FLOAT, 672x1x5x5] %onnx::Conv_839[FLOAT, 184x672x1x1] %onnx::Conv_840[FLOAT, 184] %onnx::Conv_842[FLOAT, 184x92x1x1] %onnx::Conv_845[FLOAT, 184x1x5x5] %onnx::Conv_848[FLOAT, 184x92x1x1] %onnx::Conv_851[FLOAT, 1104x184x1x1] %onnx::Conv_852[FLOAT, 1104] %onnx::Conv_854[FLOAT, 1104x1x3x3] %onnx::Conv_857[FLOAT, 184x1104x1x1] %onnx::Conv_860[FLOAT, 184x184x1x1] %onnx::Conv_863[FLOAT, 184x1x3x3] %onnx::Conv_866[FLOAT, 184x184x1x1] %onnx::Conv_869[FLOAT, 184x184x1x1] %onnx::Conv_872[FLOAT, 184x1x3x3] %onnx::Conv_875[FLOAT, 352x184x1x1] %onnx::Conv_876[FLOAT, 352] %onnx::Conv_878[FLOAT, 1504x352x1x1] %onnx::Conv_879[FLOAT, 1504] ) { %onnx::Conv_873 = Identity(%onnx::Conv_840) %onnx::Conv_870 = Identity(%onnx::Conv_840) %onnx::Conv_867 = Identity(%onnx::Conv_840) %onnx::Conv_864 = Identity(%onnx::Conv_840) %onnx::Conv_861 = Identity(%onnx::Conv_840) %onnx::Conv_858 = Identity(%onnx::Conv_840) %onnx::Conv_855 = Identity(%onnx::Conv_852) %onnx::Conv_849 = Identity(%onnx::Conv_840) %onnx::Conv_846 = Identity(%onnx::Conv_840) %onnx::Conv_843 = Identity(%onnx::Conv_840) %onnx::Conv_837 = Identity(%onnx::Conv_834) %onnx::Conv_831 = Identity(%onnx::Conv_813) %onnx::Conv_828 = Identity(%onnx::Conv_825) %onnx::Conv_822 = Identity(%onnx::Conv_813) %onnx::Conv_819 = Identity(%onnx::Conv_813) %onnx::Conv_816 = Identity(%onnx::Conv_813) %onnx::Conv_810 = Identity(%onnx::Conv_777) %onnx::Conv_807 = Identity(%onnx::Conv_777) %onnx::Conv_804 = Identity(%onnx::Conv_777) %onnx::Conv_801 = Identity(%onnx::Conv_744) %onnx::Conv_798 = Identity(%onnx::Conv_744) %onnx::Conv_795 = Identity(%onnx::Conv_777) %onnx::Conv_792 = Identity(%onnx::Conv_744) %onnx::Conv_789 = Identity(%onnx::Conv_744) %onnx::Conv_786 = Identity(%onnx::Conv_777) %onnx::Conv_783 = Identity(%onnx::Conv_780) %onnx::Conv_774 = Identity(%onnx::Conv_744) %onnx::Conv_771 = Identity(%onnx::Conv_744) %onnx::Conv_768 = Identity(%onnx::Conv_741) %onnx::Conv_765 = Identity(%onnx::Conv_741) %onnx::Conv_762 = Identity(%onnx::Conv_741) %onnx::Conv_759 = Identity(%onnx::Conv_741) %onnx::Conv_756 = Identity(%onnx::Conv_741) %onnx::Conv_753 = Identity(%onnx::Conv_741) %onnx::Conv_750 = Identity(%onnx::Conv_741) %onnx::Conv_747 = Identity(%onnx::Conv_744) %onnx::Conv_738 = Identity(%onnx::Conv_735) %onnx::Conv_732 = Identity(%onnx::Conv_705) %onnx::Conv_729 = Identity(%onnx::Conv_726) %onnx::Conv_723 = Identity(%onnx::Conv_705) %onnx::Conv_720 = Identity(%onnx::Conv_705) %onnx::Conv_717 = Identity(%onnx::Conv_705) %onnx::Conv_714 = Identity(%onnx::Conv_705) %onnx::Conv_711 = Identity(%onnx::Conv_705) %onnx::Conv_708 = Identity(%onnx::Conv_705) %onnx::Conv_702 = Identity(%onnx::Conv_699) %onnx::Conv_696 = Identity(%onnx::Conv_687) %onnx::Conv_693 = Identity(%onnx::Conv_687) %onnx::Conv_690 = Identity(%onnx::Conv_687) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_686, %onnx::Conv_687) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_689, %onnx::Conv_690) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.0/nl/Relu_output_0, %onnx::Conv_692, %onnx::Conv_693) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_695, %onnx::Conv_696) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_698, %onnx::Conv_699) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 48, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.1/nl/Relu_output_0, %onnx::Conv_701, %onnx::Conv_702) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_704, %onnx::Conv_705) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_707, %onnx::Conv_708) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.2/shuffle/Reshape_output_0 = Reshape(%/cells.2/nl/Relu_output_0, %/cells.2/shuffle/Constant_output_0) %/cells.2/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.2/shuffle/Reshape_output_0) %/cells.2/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.2/shuffle/Reshape_1_output_0 = Reshape(%/cells.2/shuffle/Transpose_output_0, %/cells.2/shuffle/Constant_1_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.2/shuffle/Reshape_1_output_0, %onnx::Conv_710, %onnx::Conv_711) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_713, %onnx::Conv_714) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_716, %onnx::Conv_717) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.3/shuffle/Reshape_output_0 = Reshape(%/cells.3/nl/Relu_output_0, %/cells.3/shuffle/Constant_output_0) %/cells.3/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.3/shuffle/Reshape_output_0) %/cells.3/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.3/shuffle/Reshape_1_output_0 = Reshape(%/cells.3/shuffle/Transpose_output_0, %/cells.3/shuffle/Constant_1_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.3/shuffle/Reshape_1_output_0, %onnx::Conv_719, %onnx::Conv_720) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_722, %onnx::Conv_723) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_725, %onnx::Conv_726) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.4/nl/Relu_output_0, %onnx::Conv_728, %onnx::Conv_729) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_731, %onnx::Conv_732) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/Add_output_0, %onnx::Conv_734, %onnx::Conv_735) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.5/nl/Relu_output_0, %onnx::Conv_737, %onnx::Conv_738) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_740, %onnx::Conv_741) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_743, %onnx::Conv_744) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.6/nl/Relu_output_0, %onnx::Conv_746, %onnx::Conv_747) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_749, %onnx::Conv_750) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_752, %onnx::Conv_753) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.7/shuffle/Reshape_output_0 = Reshape(%/cells.7/nl/Relu_output_0, %/cells.7/shuffle/Constant_output_0) %/cells.7/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.7/shuffle/Reshape_output_0) %/cells.7/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.7/shuffle/Reshape_1_output_0 = Reshape(%/cells.7/shuffle/Transpose_output_0, %/cells.7/shuffle/Constant_1_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.7/shuffle/Reshape_1_output_0, %onnx::Conv_755, %onnx::Conv_756) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_758, %onnx::Conv_759) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_761, %onnx::Conv_762) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.8/nl/Relu_output_0, %onnx::Conv_764, %onnx::Conv_765) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_767, %onnx::Conv_768) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_770, %onnx::Conv_771) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.9/nl/Relu_output_0, %onnx::Conv_773, %onnx::Conv_774) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_776, %onnx::Conv_777) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_779, %onnx::Conv_780) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.10/nl/Relu_output_0, %onnx::Conv_782, %onnx::Conv_783) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_785, %onnx::Conv_786) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_788, %onnx::Conv_789) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.11/nl/Relu_output_0, %onnx::Conv_791, %onnx::Conv_792) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_794, %onnx::Conv_795) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_797, %onnx::Conv_798) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.12/nl/Relu_output_0, %onnx::Conv_800, %onnx::Conv_801) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_803, %onnx::Conv_804) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_806, %onnx::Conv_807) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.13/shuffle/Reshape_output_0 = Reshape(%/cells.13/nl/Relu_output_0, %/cells.13/shuffle/Constant_output_0) %/cells.13/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.13/shuffle/Reshape_output_0) %/cells.13/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.13/shuffle/Reshape_1_output_0 = Reshape(%/cells.13/shuffle/Transpose_output_0, %/cells.13/shuffle/Constant_1_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.13/shuffle/Reshape_1_output_0, %onnx::Conv_809, %onnx::Conv_810) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_812, %onnx::Conv_813) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_815, %onnx::Conv_816) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.14/nl/Relu_output_0, %onnx::Conv_818, %onnx::Conv_819) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_821, %onnx::Conv_822) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_824, %onnx::Conv_825) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.15/nl/Relu_output_0, %onnx::Conv_827, %onnx::Conv_828) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_830, %onnx::Conv_831) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_833, %onnx::Conv_834) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.17/nl/Relu_output_0, %onnx::Conv_836, %onnx::Conv_837) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_839, %onnx::Conv_840) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_842, %onnx::Conv_843) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.18/shuffle/Reshape_output_0 = Reshape(%/cells.18/nl/Relu_output_0, %/cells.18/shuffle/Constant_output_0) %/cells.18/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.18/shuffle/Reshape_output_0) %/cells.18/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.18/shuffle/Reshape_1_output_0 = Reshape(%/cells.18/shuffle/Transpose_output_0, %/cells.18/shuffle/Constant_1_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.18/shuffle/Reshape_1_output_0, %onnx::Conv_845, %onnx::Conv_846) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_848, %onnx::Conv_849) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_851, %onnx::Conv_852) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.19/nl/Relu_output_0, %onnx::Conv_854, %onnx::Conv_855) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_857, %onnx::Conv_858) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_860, %onnx::Conv_861) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.20/nl/Relu_output_0, %onnx::Conv_863, %onnx::Conv_864) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_866, %onnx::Conv_867) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_869, %onnx::Conv_870) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.21/nl/Relu_output_0, %onnx::Conv_872, %onnx::Conv_873) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_875, %onnx::Conv_876) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_878, %onnx::Conv_879) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %684 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %684 }	val_accuracy	67,239,040	1,833,748	{'zcp_synflow': 79.09363227558384, 'zcp_zen': 70.05848693847656, 'zcp_epe_nas': 0.00015999920000638146, 'zcp_fisher': 0.18091824650764465, 'zcp_flops': 67239040.0, 'zcp_grad_norm': 26.445016860961914, 'zcp_grasp': -0.0217742919921875, 'zcp_jacov': -16.06500081718157, 'zcp_l2_norm': 641.7221069335938, 'zcp_nwot': 212.5795416907652, 'zcp_params': 1833748.0, 'zcp_plain': 0.0005786491092294455, 'zcp_snip': 43.802120208740234, 'lat_1080ti_1': 0.7767961916917676, 'lat_1080ti_32': 0.6199264390324425, 'lat_1080ti_64': 0.4596230987780822, 'lat_2080ti_1': 0.7292720101227121, 'lat_2080ti_32': 0.609651768241881, 'lat_2080ti_64': 0.47871395768288705, 'lat_essential_ph_1': 0.32075471698113206, 'lat_eyeriss': 0.4467845157977741, 'lat_fpga': 0.3840429847910625, 'lat_gold_6226': 0.3675043339774003, 'lat_gold_6240': 0.5679108313709996, 'lat_pixel2': 0.34782608695652173, 'lat_pixel3': 0.4278036479401028, 'lat_raspi4': 0.42827259606513196, 'lat_samsung_a50': 0.2, 'lat_samsung_s7': 0.1889763779527559, 'lat_silver_4114': 0.5847647257646341, 'lat_silver_4210r': 0.6160908737275022, 'lat_titan_rtx_1': 0.6868592524903239, 'lat_titan_rtx_32': 0.5826986676501572, 'lat_titan_rtx_64': 0.5159986658522466, 'lat_titanx_1': 0.3730651677054543, 'lat_titanx_32': 0.5742314849379704, 'lat_titanx_64': 0.45188760896207836, 'lat_titanxp_1': 0.6476222985756015, 'lat_titanxp_32': 0.5618887077970278, 'lat_titanxp_64': 0.46592611919983135}
FBNet_1421	FBNet	1421	1421	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_668[FLOAT, 16x3x3x3] %onnx::Conv_669[FLOAT, 16] %onnx::Conv_671[FLOAT, 96x16x1x1] %onnx::Conv_672[FLOAT, 96] %onnx::Conv_674[FLOAT, 96x1x5x5] %onnx::Conv_677[FLOAT, 16x96x1x1] %onnx::Conv_680[FLOAT, 16x8x1x1] %onnx::Conv_683[FLOAT, 16x1x5x5] %onnx::Conv_686[FLOAT, 24x8x1x1] %onnx::Conv_687[FLOAT, 24] %onnx::Conv_689[FLOAT, 24x12x1x1] %onnx::Conv_692[FLOAT, 24x1x3x3] %onnx::Conv_695[FLOAT, 24x12x1x1] %onnx::Conv_698[FLOAT, 24x12x1x1] %onnx::Conv_701[FLOAT, 24x1x3x3] %onnx::Conv_704[FLOAT, 24x12x1x1] %onnx::Conv_707[FLOAT, 24x24x1x1] %onnx::Conv_710[FLOAT, 24x1x5x5] %onnx::Conv_713[FLOAT, 24x24x1x1] %onnx::Conv_716[FLOAT, 24x12x1x1] %onnx::Conv_719[FLOAT, 24x1x3x3] %onnx::Conv_722[FLOAT, 32x12x1x1] %onnx::Conv_723[FLOAT, 32] %onnx::Conv_725[FLOAT, 32x32x1x1] %onnx::Conv_728[FLOAT, 32x1x5x5] %onnx::Conv_731[FLOAT, 32x32x1x1] %onnx::Conv_734[FLOAT, 32x16x1x1] %onnx::Conv_737[FLOAT, 32x1x3x3] %onnx::Conv_740[FLOAT, 32x16x1x1] %onnx::Conv_743[FLOAT, 32x16x1x1] %onnx::Conv_746[FLOAT, 32x1x3x3] %onnx::Conv_749[FLOAT, 64x16x1x1] %onnx::Conv_750[FLOAT, 64] %onnx::Conv_752[FLOAT, 64x64x1x1] %onnx::Conv_755[FLOAT, 64x1x3x3] %onnx::Conv_758[FLOAT, 64x64x1x1] %onnx::Conv_761[FLOAT, 384x64x1x1] %onnx::Conv_762[FLOAT, 384] %onnx::Conv_764[FLOAT, 384x1x3x3] %onnx::Conv_767[FLOAT, 64x384x1x1] %onnx::Conv_770[FLOAT, 64x64x1x1] %onnx::Conv_773[FLOAT, 64x1x5x5] %onnx::Conv_776[FLOAT, 112x64x1x1] %onnx::Conv_777[FLOAT, 112] %onnx::Conv_779[FLOAT, 112x112x1x1] %onnx::Conv_782[FLOAT, 112x1x3x3] %onnx::Conv_785[FLOAT, 112x112x1x1] %onnx::Conv_788[FLOAT, 112x112x1x1] %onnx::Conv_791[FLOAT, 112x1x3x3] %onnx::Conv_794[FLOAT, 112x112x1x1] %onnx::Conv_797[FLOAT, 336x112x1x1] %onnx::Conv_798[FLOAT, 336] %onnx::Conv_800[FLOAT, 336x1x3x3] %onnx::Conv_803[FLOAT, 184x336x1x1] %onnx::Conv_804[FLOAT, 184] %onnx::Conv_806[FLOAT, 184x184x1x1] %onnx::Conv_809[FLOAT, 184x1x5x5] %onnx::Conv_812[FLOAT, 184x184x1x1] %onnx::Conv_815[FLOAT, 552x184x1x1] %onnx::Conv_816[FLOAT, 552] %onnx::Conv_818[FLOAT, 552x1x3x3] %onnx::Conv_821[FLOAT, 184x552x1x1] %onnx::Conv_824[FLOAT, 1104x184x1x1] %onnx::Conv_825[FLOAT, 1104] %onnx::Conv_827[FLOAT, 1104x1x5x5] %onnx::Conv_830[FLOAT, 184x1104x1x1] %onnx::Conv_833[FLOAT, 184x92x1x1] %onnx::Conv_836[FLOAT, 184x1x3x3] %onnx::Conv_839[FLOAT, 352x92x1x1] %onnx::Conv_840[FLOAT, 352] %onnx::Conv_842[FLOAT, 1504x352x1x1] %onnx::Conv_843[FLOAT, 1504] ) { %onnx::Conv_837 = Identity(%onnx::Conv_804) %onnx::Conv_834 = Identity(%onnx::Conv_804) %onnx::Conv_831 = Identity(%onnx::Conv_804) %onnx::Conv_828 = Identity(%onnx::Conv_825) %onnx::Conv_822 = Identity(%onnx::Conv_804) %onnx::Conv_819 = Identity(%onnx::Conv_816) %onnx::Conv_813 = Identity(%onnx::Conv_804) %onnx::Conv_810 = Identity(%onnx::Conv_804) %onnx::Conv_807 = Identity(%onnx::Conv_804) %onnx::Conv_801 = Identity(%onnx::Conv_798) %onnx::Conv_795 = Identity(%onnx::Conv_777) %onnx::Conv_792 = Identity(%onnx::Conv_777) %onnx::Conv_789 = Identity(%onnx::Conv_777) %onnx::Conv_786 = Identity(%onnx::Conv_777) %onnx::Conv_783 = Identity(%onnx::Conv_777) %onnx::Conv_780 = Identity(%onnx::Conv_777) %onnx::Conv_774 = Identity(%onnx::Conv_750) %onnx::Conv_771 = Identity(%onnx::Conv_750) %onnx::Conv_768 = Identity(%onnx::Conv_750) %onnx::Conv_765 = Identity(%onnx::Conv_762) %onnx::Conv_759 = Identity(%onnx::Conv_750) %onnx::Conv_756 = Identity(%onnx::Conv_750) %onnx::Conv_753 = Identity(%onnx::Conv_750) %onnx::Conv_747 = Identity(%onnx::Conv_723) %onnx::Conv_744 = Identity(%onnx::Conv_723) %onnx::Conv_741 = Identity(%onnx::Conv_723) %onnx::Conv_738 = Identity(%onnx::Conv_723) %onnx::Conv_735 = Identity(%onnx::Conv_723) %onnx::Conv_732 = Identity(%onnx::Conv_723) %onnx::Conv_729 = Identity(%onnx::Conv_723) %onnx::Conv_726 = Identity(%onnx::Conv_723) %onnx::Conv_720 = Identity(%onnx::Conv_687) %onnx::Conv_717 = Identity(%onnx::Conv_687) %onnx::Conv_714 = Identity(%onnx::Conv_687) %onnx::Conv_711 = Identity(%onnx::Conv_687) %onnx::Conv_708 = Identity(%onnx::Conv_687) %onnx::Conv_705 = Identity(%onnx::Conv_687) %onnx::Conv_702 = Identity(%onnx::Conv_687) %onnx::Conv_699 = Identity(%onnx::Conv_687) %onnx::Conv_696 = Identity(%onnx::Conv_687) %onnx::Conv_693 = Identity(%onnx::Conv_687) %onnx::Conv_690 = Identity(%onnx::Conv_687) %onnx::Conv_684 = Identity(%onnx::Conv_669) %onnx::Conv_681 = Identity(%onnx::Conv_669) %onnx::Conv_678 = Identity(%onnx::Conv_669) %onnx::Conv_675 = Identity(%onnx::Conv_672) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_668, %onnx::Conv_669) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_671, %onnx::Conv_672) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.0/nl/Relu_output_0, %onnx::Conv_674, %onnx::Conv_675) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_677, %onnx::Conv_678) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_680, %onnx::Conv_681) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.1/shuffle/Reshape_output_0 = Reshape(%/cells.1/nl/Relu_output_0, %/cells.1/shuffle/Constant_output_0) %/cells.1/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.1/shuffle/Reshape_output_0) %/cells.1/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.1/shuffle/Reshape_1_output_0 = Reshape(%/cells.1/shuffle/Transpose_output_0, %/cells.1/shuffle/Constant_1_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.1/shuffle/Reshape_1_output_0, %onnx::Conv_683, %onnx::Conv_684) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_686, %onnx::Conv_687) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_689, %onnx::Conv_690) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.2/shuffle/Reshape_output_0 = Reshape(%/cells.2/nl/Relu_output_0, %/cells.2/shuffle/Constant_output_0) %/cells.2/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.2/shuffle/Reshape_output_0) %/cells.2/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.2/shuffle/Reshape_1_output_0 = Reshape(%/cells.2/shuffle/Transpose_output_0, %/cells.2/shuffle/Constant_1_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.2/shuffle/Reshape_1_output_0, %onnx::Conv_692, %onnx::Conv_693) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_695, %onnx::Conv_696) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_698, %onnx::Conv_699) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.3/shuffle/Reshape_output_0 = Reshape(%/cells.3/nl/Relu_output_0, %/cells.3/shuffle/Constant_output_0) %/cells.3/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.3/shuffle/Reshape_output_0) %/cells.3/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.3/shuffle/Reshape_1_output_0 = Reshape(%/cells.3/shuffle/Transpose_output_0, %/cells.3/shuffle/Constant_1_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.3/shuffle/Reshape_1_output_0, %onnx::Conv_701, %onnx::Conv_702) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_704, %onnx::Conv_705) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_707, %onnx::Conv_708) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.4/nl/Relu_output_0, %onnx::Conv_710, %onnx::Conv_711) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_713, %onnx::Conv_714) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/Add_output_0, %onnx::Conv_716, %onnx::Conv_717) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.5/shuffle/Reshape_output_0 = Reshape(%/cells.5/nl/Relu_output_0, %/cells.5/shuffle/Constant_output_0) %/cells.5/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.5/shuffle/Reshape_output_0) %/cells.5/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.5/shuffle/Reshape_1_output_0 = Reshape(%/cells.5/shuffle/Transpose_output_0, %/cells.5/shuffle/Constant_1_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.5/shuffle/Reshape_1_output_0, %onnx::Conv_719, %onnx::Conv_720) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_722, %onnx::Conv_723) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_725, %onnx::Conv_726) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.7/nl/Relu_output_0, %onnx::Conv_728, %onnx::Conv_729) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_731, %onnx::Conv_732) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_734, %onnx::Conv_735) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.8/shuffle/Reshape_output_0 = Reshape(%/cells.8/nl/Relu_output_0, %/cells.8/shuffle/Constant_output_0) %/cells.8/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.8/shuffle/Reshape_output_0) %/cells.8/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.8/shuffle/Reshape_1_output_0 = Reshape(%/cells.8/shuffle/Transpose_output_0, %/cells.8/shuffle/Constant_1_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.8/shuffle/Reshape_1_output_0, %onnx::Conv_737, %onnx::Conv_738) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_740, %onnx::Conv_741) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_743, %onnx::Conv_744) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.9/shuffle/Reshape_output_0 = Reshape(%/cells.9/nl/Relu_output_0, %/cells.9/shuffle/Constant_output_0) %/cells.9/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.9/shuffle/Reshape_output_0) %/cells.9/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.9/shuffle/Reshape_1_output_0 = Reshape(%/cells.9/shuffle/Transpose_output_0, %/cells.9/shuffle/Constant_1_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.9/shuffle/Reshape_1_output_0, %onnx::Conv_746, %onnx::Conv_747) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_749, %onnx::Conv_750) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_752, %onnx::Conv_753) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.11/nl/Relu_output_0, %onnx::Conv_755, %onnx::Conv_756) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_758, %onnx::Conv_759) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_761, %onnx::Conv_762) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.12/nl/Relu_output_0, %onnx::Conv_764, %onnx::Conv_765) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_767, %onnx::Conv_768) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_770, %onnx::Conv_771) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.13/nl/Relu_output_0, %onnx::Conv_773, %onnx::Conv_774) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_776, %onnx::Conv_777) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_779, %onnx::Conv_780) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.15/nl/Relu_output_0, %onnx::Conv_782, %onnx::Conv_783) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_785, %onnx::Conv_786) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_788, %onnx::Conv_789) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.16/nl/Relu_output_0, %onnx::Conv_791, %onnx::Conv_792) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_794, %onnx::Conv_795) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.15/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_797, %onnx::Conv_798) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.17/nl/Relu_output_0, %onnx::Conv_800, %onnx::Conv_801) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_803, %onnx::Conv_804) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_806, %onnx::Conv_807) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.18/nl/Relu_output_0, %onnx::Conv_809, %onnx::Conv_810) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_812, %onnx::Conv_813) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_815, %onnx::Conv_816) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 552, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.19/nl/Relu_output_0, %onnx::Conv_818, %onnx::Conv_819) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_821, %onnx::Conv_822) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_824, %onnx::Conv_825) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.20/nl/Relu_output_0, %onnx::Conv_827, %onnx::Conv_828) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_830, %onnx::Conv_831) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_833, %onnx::Conv_834) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.21/shuffle/Reshape_output_0 = Reshape(%/cells.21/nl/Relu_output_0, %/cells.21/shuffle/Constant_output_0) %/cells.21/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.21/shuffle/Reshape_output_0) %/cells.21/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.21/shuffle/Reshape_1_output_0 = Reshape(%/cells.21/shuffle/Transpose_output_0, %/cells.21/shuffle/Constant_1_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.21/shuffle/Reshape_1_output_0, %onnx::Conv_836, %onnx::Conv_837) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_839, %onnx::Conv_840) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_842, %onnx::Conv_843) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %666 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %666 }	val_accuracy	46,427,264	1,711,196	{'zcp_synflow': 67.99876448060378, 'zcp_zen': 59.306697845458984, 'zcp_epe_nas': 11.708196176375765, 'zcp_fisher': 0.11872810870409012, 'zcp_flops': 46427264.0, 'zcp_grad_norm': 19.790069580078125, 'zcp_grasp': -0.11985206604003906, 'zcp_jacov': -16.056779897131634, 'zcp_l2_norm': 533.5463256835938, 'zcp_nwot': 205.411183116369, 'zcp_params': 1711196.0, 'zcp_plain': 0.007086068391799927, 'zcp_snip': 31.414392471313477, 'lat_1080ti_1': 0.4757786811081264, 'lat_1080ti_32': 0.476905077043379, 'lat_1080ti_64': 0.3023247460290819, 'lat_2080ti_1': 0.5421278471282317, 'lat_2080ti_32': 0.4730883642235048, 'lat_2080ti_64': 0.29483984831369947, 'lat_essential_ph_1': 0.1509433962264151, 'lat_eyeriss': 0.22998001210276325, 'lat_fpga': 0.1977260808042826, 'lat_gold_6226': 0.20700612506637409, 'lat_gold_6240': 0.4177106758806218, 'lat_pixel2': 0.21739130434782608, 'lat_pixel3': 0.24727729356306039, 'lat_raspi4': 0.2752892511689725, 'lat_samsung_a50': 0.10526315789473684, 'lat_samsung_s7': 0.10236220472440945, 'lat_silver_4114': 0.5011574247406649, 'lat_silver_4210r': 0.4508467264004035, 'lat_titan_rtx_1': 0.5237262205931308, 'lat_titan_rtx_32': 0.4824387569936267, 'lat_titan_rtx_64': 0.33743888100313296, 'lat_titanx_1': 0.2767435011251649, 'lat_titanx_32': 0.3849026133715081, 'lat_titanx_64': 0.28144283142007714, 'lat_titanxp_1': 0.4867789245982813, 'lat_titanxp_32': 0.44768809503593465, 'lat_titanxp_64': 0.30963495008156966}
FBNet_506	FBNet	506	506	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_640[FLOAT, 16x3x3x3] %onnx::Conv_641[FLOAT, 16] %onnx::Conv_643[FLOAT, 48x16x1x1] %onnx::Conv_644[FLOAT, 48] %onnx::Conv_646[FLOAT, 48x1x5x5] %onnx::Conv_649[FLOAT, 16x48x1x1] %onnx::Conv_652[FLOAT, 16x16x1x1] %onnx::Conv_655[FLOAT, 16x1x3x3] %onnx::Conv_658[FLOAT, 24x16x1x1] %onnx::Conv_659[FLOAT, 24] %onnx::Conv_661[FLOAT, 72x24x1x1] %onnx::Conv_662[FLOAT, 72] %onnx::Conv_664[FLOAT, 72x1x3x3] %onnx::Conv_667[FLOAT, 24x72x1x1] %onnx::Conv_670[FLOAT, 24x24x1x1] %onnx::Conv_673[FLOAT, 24x1x3x3] %onnx::Conv_676[FLOAT, 24x24x1x1] %onnx::Conv_679[FLOAT, 24x24x1x1] %onnx::Conv_682[FLOAT, 24x1x5x5] %onnx::Conv_685[FLOAT, 24x24x1x1] %onnx::Conv_688[FLOAT, 72x24x1x1] %onnx::Conv_691[FLOAT, 72x1x3x3] %onnx::Conv_694[FLOAT, 32x72x1x1] %onnx::Conv_695[FLOAT, 32] %onnx::Conv_697[FLOAT, 192x32x1x1] %onnx::Conv_698[FLOAT, 192] %onnx::Conv_700[FLOAT, 192x1x3x3] %onnx::Conv_703[FLOAT, 32x192x1x1] %onnx::Conv_706[FLOAT, 32x32x1x1] %onnx::Conv_709[FLOAT, 32x1x3x3] %onnx::Conv_712[FLOAT, 32x32x1x1] %onnx::Conv_715[FLOAT, 96x32x1x1] %onnx::Conv_716[FLOAT, 96] %onnx::Conv_718[FLOAT, 96x1x3x3] %onnx::Conv_721[FLOAT, 32x96x1x1] %onnx::Conv_724[FLOAT, 32x16x1x1] %onnx::Conv_727[FLOAT, 32x1x3x3] %onnx::Conv_730[FLOAT, 64x16x1x1] %onnx::Conv_731[FLOAT, 64] %onnx::Conv_733[FLOAT, 64x64x1x1] %onnx::Conv_736[FLOAT, 64x1x5x5] %onnx::Conv_739[FLOAT, 64x64x1x1] %onnx::Conv_742[FLOAT, 64x32x1x1] %onnx::Conv_745[FLOAT, 64x1x3x3] %onnx::Conv_748[FLOAT, 64x32x1x1] %onnx::Conv_751[FLOAT, 64x32x1x1] %onnx::Conv_754[FLOAT, 64x1x3x3] %onnx::Conv_757[FLOAT, 64x32x1x1] %onnx::Conv_760[FLOAT, 192x64x1x1] %onnx::Conv_763[FLOAT, 192x1x5x5] %onnx::Conv_766[FLOAT, 112x192x1x1] %onnx::Conv_767[FLOAT, 112] %onnx::Conv_769[FLOAT, 112x112x1x1] %onnx::Conv_772[FLOAT, 112x1x3x3] %onnx::Conv_775[FLOAT, 112x112x1x1] %onnx::Conv_778[FLOAT, 672x112x1x1] %onnx::Conv_779[FLOAT, 672] %onnx::Conv_781[FLOAT, 672x1x5x5] %onnx::Conv_784[FLOAT, 112x672x1x1] %onnx::Conv_787[FLOAT, 112x56x1x1] %onnx::Conv_790[FLOAT, 112x1x5x5] %onnx::Conv_793[FLOAT, 184x56x1x1] %onnx::Conv_794[FLOAT, 184] %onnx::Conv_796[FLOAT, 184x184x1x1] %onnx::Conv_799[FLOAT, 184x1x3x3] %onnx::Conv_802[FLOAT, 184x184x1x1] %onnx::Conv_805[FLOAT, 184x184x1x1] %onnx::Conv_808[FLOAT, 184x1x3x3] %onnx::Conv_811[FLOAT, 184x184x1x1] %onnx::Conv_814[FLOAT, 552x184x1x1] %onnx::Conv_815[FLOAT, 552] %onnx::Conv_817[FLOAT, 552x1x5x5] %onnx::Conv_820[FLOAT, 352x552x1x1] %onnx::Conv_821[FLOAT, 352] %onnx::Conv_823[FLOAT, 1504x352x1x1] %onnx::Conv_824[FLOAT, 1504] ) { %onnx::Conv_818 = Identity(%onnx::Conv_815) %onnx::Conv_812 = Identity(%onnx::Conv_794) %onnx::Conv_809 = Identity(%onnx::Conv_794) %onnx::Conv_806 = Identity(%onnx::Conv_794) %onnx::Conv_803 = Identity(%onnx::Conv_794) %onnx::Conv_800 = Identity(%onnx::Conv_794) %onnx::Conv_797 = Identity(%onnx::Conv_794) %onnx::Conv_791 = Identity(%onnx::Conv_767) %onnx::Conv_788 = Identity(%onnx::Conv_767) %onnx::Conv_785 = Identity(%onnx::Conv_767) %onnx::Conv_782 = Identity(%onnx::Conv_779) %onnx::Conv_776 = Identity(%onnx::Conv_767) %onnx::Conv_773 = Identity(%onnx::Conv_767) %onnx::Conv_770 = Identity(%onnx::Conv_767) %onnx::Conv_764 = Identity(%onnx::Conv_698) %onnx::Conv_761 = Identity(%onnx::Conv_698) %onnx::Conv_758 = Identity(%onnx::Conv_731) %onnx::Conv_755 = Identity(%onnx::Conv_731) %onnx::Conv_752 = Identity(%onnx::Conv_731) %onnx::Conv_749 = Identity(%onnx::Conv_731) %onnx::Conv_746 = Identity(%onnx::Conv_731) %onnx::Conv_743 = Identity(%onnx::Conv_731) %onnx::Conv_740 = Identity(%onnx::Conv_731) %onnx::Conv_737 = Identity(%onnx::Conv_731) %onnx::Conv_734 = Identity(%onnx::Conv_731) %onnx::Conv_728 = Identity(%onnx::Conv_695) %onnx::Conv_725 = Identity(%onnx::Conv_695) %onnx::Conv_722 = Identity(%onnx::Conv_695) %onnx::Conv_719 = Identity(%onnx::Conv_716) %onnx::Conv_713 = Identity(%onnx::Conv_695) %onnx::Conv_710 = Identity(%onnx::Conv_695) %onnx::Conv_707 = Identity(%onnx::Conv_695) %onnx::Conv_704 = Identity(%onnx::Conv_695) %onnx::Conv_701 = Identity(%onnx::Conv_698) %onnx::Conv_692 = Identity(%onnx::Conv_662) %onnx::Conv_689 = Identity(%onnx::Conv_662) %onnx::Conv_686 = Identity(%onnx::Conv_659) %onnx::Conv_683 = Identity(%onnx::Conv_659) %onnx::Conv_680 = Identity(%onnx::Conv_659) %onnx::Conv_677 = Identity(%onnx::Conv_659) %onnx::Conv_674 = Identity(%onnx::Conv_659) %onnx::Conv_671 = Identity(%onnx::Conv_659) %onnx::Conv_668 = Identity(%onnx::Conv_659) %onnx::Conv_665 = Identity(%onnx::Conv_662) %onnx::Conv_656 = Identity(%onnx::Conv_641) %onnx::Conv_653 = Identity(%onnx::Conv_641) %onnx::Conv_650 = Identity(%onnx::Conv_641) %onnx::Conv_647 = Identity(%onnx::Conv_644) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_640, %onnx::Conv_641) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_643, %onnx::Conv_644) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 48, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.0/nl/Relu_output_0, %onnx::Conv_646, %onnx::Conv_647) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_649, %onnx::Conv_650) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_652, %onnx::Conv_653) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.1/nl/Relu_output_0, %onnx::Conv_655, %onnx::Conv_656) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_658, %onnx::Conv_659) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_661, %onnx::Conv_662) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.2/nl/Relu_output_0, %onnx::Conv_664, %onnx::Conv_665) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_667, %onnx::Conv_668) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_670, %onnx::Conv_671) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.3/nl/Relu_output_0, %onnx::Conv_673, %onnx::Conv_674) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_676, %onnx::Conv_677) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_679, %onnx::Conv_680) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.4/nl/Relu_output_0, %onnx::Conv_682, %onnx::Conv_683) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_685, %onnx::Conv_686) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/Add_output_0, %onnx::Conv_688, %onnx::Conv_689) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.5/nl/Relu_output_0, %onnx::Conv_691, %onnx::Conv_692) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_694, %onnx::Conv_695) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_697, %onnx::Conv_698) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.6/nl/Relu_output_0, %onnx::Conv_700, %onnx::Conv_701) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_703, %onnx::Conv_704) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_706, %onnx::Conv_707) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.7/nl/Relu_output_0, %onnx::Conv_709, %onnx::Conv_710) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_712, %onnx::Conv_713) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_715, %onnx::Conv_716) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.8/nl/Relu_output_0, %onnx::Conv_718, %onnx::Conv_719) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_721, %onnx::Conv_722) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_724, %onnx::Conv_725) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.9/shuffle/Reshape_output_0 = Reshape(%/cells.9/nl/Relu_output_0, %/cells.9/shuffle/Constant_output_0) %/cells.9/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.9/shuffle/Reshape_output_0) %/cells.9/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.9/shuffle/Reshape_1_output_0 = Reshape(%/cells.9/shuffle/Transpose_output_0, %/cells.9/shuffle/Constant_1_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.9/shuffle/Reshape_1_output_0, %onnx::Conv_727, %onnx::Conv_728) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_730, %onnx::Conv_731) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_733, %onnx::Conv_734) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.10/nl/Relu_output_0, %onnx::Conv_736, %onnx::Conv_737) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_739, %onnx::Conv_740) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_742, %onnx::Conv_743) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.11/shuffle/Reshape_output_0 = Reshape(%/cells.11/nl/Relu_output_0, %/cells.11/shuffle/Constant_output_0) %/cells.11/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.11/shuffle/Reshape_output_0) %/cells.11/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.11/shuffle/Reshape_1_output_0 = Reshape(%/cells.11/shuffle/Transpose_output_0, %/cells.11/shuffle/Constant_1_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.11/shuffle/Reshape_1_output_0, %onnx::Conv_745, %onnx::Conv_746) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_748, %onnx::Conv_749) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_751, %onnx::Conv_752) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.12/shuffle/Reshape_output_0 = Reshape(%/cells.12/nl/Relu_output_0, %/cells.12/shuffle/Constant_output_0) %/cells.12/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.12/shuffle/Reshape_output_0) %/cells.12/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.12/shuffle/Reshape_1_output_0 = Reshape(%/cells.12/shuffle/Transpose_output_0, %/cells.12/shuffle/Constant_1_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.12/shuffle/Reshape_1_output_0, %onnx::Conv_754, %onnx::Conv_755) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_757, %onnx::Conv_758) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_760, %onnx::Conv_761) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.13/nl/Relu_output_0, %onnx::Conv_763, %onnx::Conv_764) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_766, %onnx::Conv_767) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_769, %onnx::Conv_770) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.15/nl/Relu_output_0, %onnx::Conv_772, %onnx::Conv_773) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_775, %onnx::Conv_776) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_778, %onnx::Conv_779) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.16/nl/Relu_output_0, %onnx::Conv_781, %onnx::Conv_782) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_784, %onnx::Conv_785) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.15/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_787, %onnx::Conv_788) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.17/shuffle/Reshape_output_0 = Reshape(%/cells.17/nl/Relu_output_0, %/cells.17/shuffle/Constant_output_0) %/cells.17/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.17/shuffle/Reshape_output_0) %/cells.17/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.17/shuffle/Reshape_1_output_0 = Reshape(%/cells.17/shuffle/Transpose_output_0, %/cells.17/shuffle/Constant_1_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.17/shuffle/Reshape_1_output_0, %onnx::Conv_790, %onnx::Conv_791) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_793, %onnx::Conv_794) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_796, %onnx::Conv_797) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.18/nl/Relu_output_0, %onnx::Conv_799, %onnx::Conv_800) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_802, %onnx::Conv_803) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_805, %onnx::Conv_806) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.19/nl/Relu_output_0, %onnx::Conv_808, %onnx::Conv_809) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_811, %onnx::Conv_812) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_814, %onnx::Conv_815) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 552, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.21/nl/Relu_output_0, %onnx::Conv_817, %onnx::Conv_818) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_820, %onnx::Conv_821) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_823, %onnx::Conv_824) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %638 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %638 }	val_accuracy	55,233,920	1,457,628	{'zcp_synflow': 75.11332231185307, 'zcp_zen': 62.85075759887695, 'zcp_epe_nas': 32.44329470320523, 'zcp_fisher': 0.0905551090836525, 'zcp_flops': 55233920.0, 'zcp_grad_norm': 22.724929809570312, 'zcp_grasp': -0.05493736267089844, 'zcp_jacov': -16.069311013124455, 'zcp_l2_norm': 543.855224609375, 'zcp_nwot': 209.40420514901766, 'zcp_params': 1457628.0, 'zcp_plain': -0.007476273458451033, 'zcp_snip': 39.20109176635742, 'lat_1080ti_1': 0.6628523315562076, 'lat_1080ti_32': 0.4246599457077283, 'lat_1080ti_64': 0.3518273763340882, 'lat_2080ti_1': 0.5692989730858571, 'lat_2080ti_32': 0.4405841908767998, 'lat_2080ti_64': 0.3609842731950396, 'lat_essential_ph_1': 0.20754716981132076, 'lat_eyeriss': 0.2593108759833494, 'lat_fpga': 0.3128964268397669, 'lat_gold_6226': 0.17116021644662946, 'lat_gold_6240': 0.30176446412699015, 'lat_pixel2': 0.15217391304347827, 'lat_pixel3': 0.2645698778082003, 'lat_raspi4': 0.3173046280827166, 'lat_samsung_a50': 0.2, 'lat_samsung_s7': 0.13385826771653545, 'lat_silver_4114': 0.3444227063431895, 'lat_silver_4210r': 0.3750134224772763, 'lat_titan_rtx_1': 0.5527677428483908, 'lat_titan_rtx_32': 0.4412365163197365, 'lat_titan_rtx_64': 0.3669996096274256, 'lat_titanx_1': 0.29743219423493805, 'lat_titanx_32': 0.392050094736273, 'lat_titanx_64': 0.3488958085600013, 'lat_titanxp_1': 0.5728619437800648, 'lat_titanxp_32': 0.42734326574940806, 'lat_titanxp_64': 0.3556087280716067}
FBNet_4130	FBNet	4130	4130	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_541[FLOAT, 16x3x3x3] %onnx::Conv_542[FLOAT, 16] %onnx::Conv_544[FLOAT, 48x16x1x1] %onnx::Conv_545[FLOAT, 48] %onnx::Conv_547[FLOAT, 48x1x3x3] %onnx::Conv_550[FLOAT, 16x48x1x1] %onnx::Conv_553[FLOAT, 16x16x1x1] %onnx::Conv_556[FLOAT, 16x1x3x3] %onnx::Conv_559[FLOAT, 24x16x1x1] %onnx::Conv_560[FLOAT, 24] %onnx::Conv_562[FLOAT, 144x24x1x1] %onnx::Conv_563[FLOAT, 144] %onnx::Conv_565[FLOAT, 144x1x3x3] %onnx::Conv_568[FLOAT, 24x144x1x1] %onnx::Conv_571[FLOAT, 144x24x1x1] %onnx::Conv_574[FLOAT, 144x1x3x3] %onnx::Conv_577[FLOAT, 32x144x1x1] %onnx::Conv_578[FLOAT, 32] %onnx::Conv_580[FLOAT, 32x32x1x1] %onnx::Conv_583[FLOAT, 32x1x5x5] %onnx::Conv_586[FLOAT, 32x32x1x1] %onnx::Conv_589[FLOAT, 32x16x1x1] %onnx::Conv_592[FLOAT, 32x1x5x5] %onnx::Conv_595[FLOAT, 64x16x1x1] %onnx::Conv_596[FLOAT, 64] %onnx::Conv_598[FLOAT, 64x32x1x1] %onnx::Conv_601[FLOAT, 64x1x3x3] %onnx::Conv_604[FLOAT, 64x32x1x1] %onnx::Conv_607[FLOAT, 384x64x1x1] %onnx::Conv_608[FLOAT, 384] %onnx::Conv_610[FLOAT, 384x1x3x3] %onnx::Conv_613[FLOAT, 64x384x1x1] %onnx::Conv_616[FLOAT, 64x64x1x1] %onnx::Conv_619[FLOAT, 64x1x3x3] %onnx::Conv_622[FLOAT, 64x64x1x1] %onnx::Conv_625[FLOAT, 192x64x1x1] %onnx::Conv_626[FLOAT, 192] %onnx::Conv_628[FLOAT, 192x1x5x5] %onnx::Conv_631[FLOAT, 112x192x1x1] %onnx::Conv_632[FLOAT, 112] %onnx::Conv_634[FLOAT, 112x112x1x1] %onnx::Conv_637[FLOAT, 112x1x3x3] %onnx::Conv_640[FLOAT, 112x112x1x1] %onnx::Conv_643[FLOAT, 112x112x1x1] %onnx::Conv_646[FLOAT, 112x1x5x5] %onnx::Conv_649[FLOAT, 112x112x1x1] %onnx::Conv_652[FLOAT, 112x112x1x1] %onnx::Conv_655[FLOAT, 112x1x5x5] %onnx::Conv_658[FLOAT, 112x112x1x1] %onnx::Conv_661[FLOAT, 112x56x1x1] %onnx::Conv_664[FLOAT, 112x1x5x5] %onnx::Conv_667[FLOAT, 184x56x1x1] %onnx::Conv_668[FLOAT, 184] %onnx::Conv_670[FLOAT, 552x184x1x1] %onnx::Conv_671[FLOAT, 552] %onnx::Conv_673[FLOAT, 552x1x5x5] %onnx::Conv_676[FLOAT, 184x552x1x1] %onnx::Conv_679[FLOAT, 552x184x1x1] %onnx::Conv_682[FLOAT, 552x1x5x5] %onnx::Conv_685[FLOAT, 184x552x1x1] %onnx::Conv_688[FLOAT, 184x184x1x1] %onnx::Conv_691[FLOAT, 184x1x3x3] %onnx::Conv_694[FLOAT, 352x184x1x1] %onnx::Conv_695[FLOAT, 352] %onnx::Conv_697[FLOAT, 1504x352x1x1] %onnx::Conv_698[FLOAT, 1504] ) { %onnx::Conv_692 = Identity(%onnx::Conv_668) %onnx::Conv_689 = Identity(%onnx::Conv_668) %onnx::Conv_686 = Identity(%onnx::Conv_668) %onnx::Conv_683 = Identity(%onnx::Conv_671) %onnx::Conv_680 = Identity(%onnx::Conv_671) %onnx::Conv_677 = Identity(%onnx::Conv_668) %onnx::Conv_674 = Identity(%onnx::Conv_671) %onnx::Conv_665 = Identity(%onnx::Conv_632) %onnx::Conv_662 = Identity(%onnx::Conv_632) %onnx::Conv_659 = Identity(%onnx::Conv_632) %onnx::Conv_656 = Identity(%onnx::Conv_632) %onnx::Conv_653 = Identity(%onnx::Conv_632) %onnx::Conv_650 = Identity(%onnx::Conv_632) %onnx::Conv_647 = Identity(%onnx::Conv_632) %onnx::Conv_644 = Identity(%onnx::Conv_632) %onnx::Conv_641 = Identity(%onnx::Conv_632) %onnx::Conv_638 = Identity(%onnx::Conv_632) %onnx::Conv_635 = Identity(%onnx::Conv_632) %onnx::Conv_629 = Identity(%onnx::Conv_626) %onnx::Conv_623 = Identity(%onnx::Conv_596) %onnx::Conv_620 = Identity(%onnx::Conv_596) %onnx::Conv_617 = Identity(%onnx::Conv_596) %onnx::Conv_614 = Identity(%onnx::Conv_596) %onnx::Conv_611 = Identity(%onnx::Conv_608) %onnx::Conv_605 = Identity(%onnx::Conv_596) %onnx::Conv_602 = Identity(%onnx::Conv_596) %onnx::Conv_599 = Identity(%onnx::Conv_596) %onnx::Conv_593 = Identity(%onnx::Conv_578) %onnx::Conv_590 = Identity(%onnx::Conv_578) %onnx::Conv_587 = Identity(%onnx::Conv_578) %onnx::Conv_584 = Identity(%onnx::Conv_578) %onnx::Conv_581 = Identity(%onnx::Conv_578) %onnx::Conv_575 = Identity(%onnx::Conv_563) %onnx::Conv_572 = Identity(%onnx::Conv_563) %onnx::Conv_569 = Identity(%onnx::Conv_560) %onnx::Conv_566 = Identity(%onnx::Conv_563) %onnx::Conv_557 = Identity(%onnx::Conv_542) %onnx::Conv_554 = Identity(%onnx::Conv_542) %onnx::Conv_551 = Identity(%onnx::Conv_542) %onnx::Conv_548 = Identity(%onnx::Conv_545) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_541, %onnx::Conv_542) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_544, %onnx::Conv_545) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 48, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.0/nl/Relu_output_0, %onnx::Conv_547, %onnx::Conv_548) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_550, %onnx::Conv_551) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_553, %onnx::Conv_554) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.1/nl/Relu_output_0, %onnx::Conv_556, %onnx::Conv_557) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_559, %onnx::Conv_560) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_562, %onnx::Conv_563) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.4/nl/Relu_output_0, %onnx::Conv_565, %onnx::Conv_566) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_568, %onnx::Conv_569) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/Add_output_0, %onnx::Conv_571, %onnx::Conv_572) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.5/nl/Relu_output_0, %onnx::Conv_574, %onnx::Conv_575) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_577, %onnx::Conv_578) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_580, %onnx::Conv_581) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.7/nl/Relu_output_0, %onnx::Conv_583, %onnx::Conv_584) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_586, %onnx::Conv_587) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_589, %onnx::Conv_590) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.9/shuffle/Reshape_output_0 = Reshape(%/cells.9/nl/Relu_output_0, %/cells.9/shuffle/Constant_output_0) %/cells.9/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.9/shuffle/Reshape_output_0) %/cells.9/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.9/shuffle/Reshape_1_output_0 = Reshape(%/cells.9/shuffle/Transpose_output_0, %/cells.9/shuffle/Constant_1_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.9/shuffle/Reshape_1_output_0, %onnx::Conv_592, %onnx::Conv_593) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_595, %onnx::Conv_596) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_598, %onnx::Conv_599) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.10/shuffle/Reshape_output_0 = Reshape(%/cells.10/nl/Relu_output_0, %/cells.10/shuffle/Constant_output_0) %/cells.10/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.10/shuffle/Reshape_output_0) %/cells.10/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.10/shuffle/Reshape_1_output_0 = Reshape(%/cells.10/shuffle/Transpose_output_0, %/cells.10/shuffle/Constant_1_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.10/shuffle/Reshape_1_output_0, %onnx::Conv_601, %onnx::Conv_602) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_604, %onnx::Conv_605) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_607, %onnx::Conv_608) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.11/nl/Relu_output_0, %onnx::Conv_610, %onnx::Conv_611) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_613, %onnx::Conv_614) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_616, %onnx::Conv_617) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.12/nl/Relu_output_0, %onnx::Conv_619, %onnx::Conv_620) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_622, %onnx::Conv_623) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_625, %onnx::Conv_626) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.13/nl/Relu_output_0, %onnx::Conv_628, %onnx::Conv_629) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_631, %onnx::Conv_632) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_634, %onnx::Conv_635) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.14/nl/Relu_output_0, %onnx::Conv_637, %onnx::Conv_638) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_640, %onnx::Conv_641) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_643, %onnx::Conv_644) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.15/nl/Relu_output_0, %onnx::Conv_646, %onnx::Conv_647) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_649, %onnx::Conv_650) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_652, %onnx::Conv_653) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.16/nl/Relu_output_0, %onnx::Conv_655, %onnx::Conv_656) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_658, %onnx::Conv_659) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.15/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_661, %onnx::Conv_662) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.17/shuffle/Reshape_output_0 = Reshape(%/cells.17/nl/Relu_output_0, %/cells.17/shuffle/Constant_output_0) %/cells.17/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.17/shuffle/Reshape_output_0) %/cells.17/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.17/shuffle/Reshape_1_output_0 = Reshape(%/cells.17/shuffle/Transpose_output_0, %/cells.17/shuffle/Constant_1_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.17/shuffle/Reshape_1_output_0, %onnx::Conv_664, %onnx::Conv_665) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_667, %onnx::Conv_668) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_670, %onnx::Conv_671) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 552, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.18/nl/Relu_output_0, %onnx::Conv_673, %onnx::Conv_674) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_676, %onnx::Conv_677) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_679, %onnx::Conv_680) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 552, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.19/nl/Relu_output_0, %onnx::Conv_682, %onnx::Conv_683) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_685, %onnx::Conv_686) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_688, %onnx::Conv_689) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.21/nl/Relu_output_0, %onnx::Conv_691, %onnx::Conv_692) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_694, %onnx::Conv_695) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_697, %onnx::Conv_698) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %539 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %539 }	val_accuracy	49,825,664	1,463,820	{'zcp_synflow': 67.5633486476816, 'zcp_zen': 58.074005126953125, 'zcp_epe_nas': 28.59685840183039, 'zcp_fisher': 0.061444107443094254, 'zcp_flops': 49825664.0, 'zcp_grad_norm': 16.150787353515625, 'zcp_grasp': -0.0635080337524414, 'zcp_jacov': -16.06373257237611, 'zcp_l2_norm': 517.5015869140625, 'zcp_nwot': 209.08012784278495, 'zcp_params': 1463820.0, 'zcp_plain': 0.0030478311236947775, 'zcp_snip': 27.31925392150879, 'lat_1080ti_1': 0.24891554907533073, 'lat_1080ti_32': 0.29605283263550575, 'lat_1080ti_64': 0.23361568160108281, 'lat_2080ti_1': 0.278542326753951, 'lat_2080ti_32': 0.3033687264993608, 'lat_2080ti_64': 0.2809776641074206, 'lat_essential_ph_1': 0.1509433962264151, 'lat_eyeriss': 0.21316450589551258, 'lat_fpga': 0.22965792259966944, 'lat_gold_6226': 0.16063185444118983, 'lat_gold_6240': 0.1410783970696184, 'lat_pixel2': 0.13043478260869565, 'lat_pixel3': 0.17350794600135677, 'lat_raspi4': 0.19404977512170787, 'lat_samsung_a50': 0.09473684210526316, 'lat_samsung_s7': 0.06299212598425197, 'lat_silver_4114': 0.13195541174077263, 'lat_silver_4210r': 0.10707033974141372, 'lat_titan_rtx_1': 0.25536039193102417, 'lat_titan_rtx_32': 0.2545379782937082, 'lat_titan_rtx_64': 0.2552248372750012, 'lat_titanx_1': 0.13283535023838658, 'lat_titanx_32': 0.23282010418916846, 'lat_titanx_64': 0.21729652116838521, 'lat_titanxp_1': 0.2498060956878932, 'lat_titanxp_32': 0.23352721315111477, 'lat_titanxp_64': 0.24165073806912934}
FBNet_3186	FBNet	3186	3186	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_523[FLOAT, 16x3x3x3] %onnx::Conv_524[FLOAT, 16] %onnx::Conv_526[FLOAT, 96x16x1x1] %onnx::Conv_527[FLOAT, 96] %onnx::Conv_529[FLOAT, 96x1x3x3] %onnx::Conv_532[FLOAT, 16x96x1x1] %onnx::Conv_535[FLOAT, 96x16x1x1] %onnx::Conv_538[FLOAT, 96x1x3x3] %onnx::Conv_541[FLOAT, 24x96x1x1] %onnx::Conv_542[FLOAT, 24] %onnx::Conv_544[FLOAT, 24x24x1x1] %onnx::Conv_547[FLOAT, 24x1x3x3] %onnx::Conv_550[FLOAT, 24x24x1x1] %onnx::Conv_553[FLOAT, 24x24x1x1] %onnx::Conv_556[FLOAT, 24x1x3x3] %onnx::Conv_559[FLOAT, 32x24x1x1] %onnx::Conv_560[FLOAT, 32] %onnx::Conv_562[FLOAT, 192x32x1x1] %onnx::Conv_563[FLOAT, 192] %onnx::Conv_565[FLOAT, 192x1x3x3] %onnx::Conv_568[FLOAT, 32x192x1x1] %onnx::Conv_571[FLOAT, 32x32x1x1] %onnx::Conv_574[FLOAT, 32x1x5x5] %onnx::Conv_577[FLOAT, 32x32x1x1] %onnx::Conv_580[FLOAT, 32x16x1x1] %onnx::Conv_583[FLOAT, 32x1x3x3] %onnx::Conv_586[FLOAT, 32x16x1x1] %onnx::Conv_589[FLOAT, 192x32x1x1] %onnx::Conv_592[FLOAT, 192x1x3x3] %onnx::Conv_595[FLOAT, 64x192x1x1] %onnx::Conv_596[FLOAT, 64] %onnx::Conv_598[FLOAT, 384x64x1x1] %onnx::Conv_599[FLOAT, 384] %onnx::Conv_601[FLOAT, 384x1x5x5] %onnx::Conv_604[FLOAT, 64x384x1x1] %onnx::Conv_607[FLOAT, 64x64x1x1] %onnx::Conv_610[FLOAT, 64x1x5x5] %onnx::Conv_613[FLOAT, 64x64x1x1] %onnx::Conv_616[FLOAT, 192x64x1x1] %onnx::Conv_619[FLOAT, 192x1x3x3] %onnx::Conv_622[FLOAT, 64x192x1x1] %onnx::Conv_625[FLOAT, 112x64x1x1] %onnx::Conv_626[FLOAT, 112] %onnx::Conv_628[FLOAT, 672x112x1x1] %onnx::Conv_629[FLOAT, 672] %onnx::Conv_631[FLOAT, 672x1x3x3] %onnx::Conv_634[FLOAT, 112x672x1x1] %onnx::Conv_637[FLOAT, 672x112x1x1] %onnx::Conv_640[FLOAT, 672x1x3x3] %onnx::Conv_643[FLOAT, 112x672x1x1] %onnx::Conv_646[FLOAT, 112x112x1x1] %onnx::Conv_649[FLOAT, 112x1x5x5] %onnx::Conv_652[FLOAT, 112x112x1x1] %onnx::Conv_655[FLOAT, 184x112x1x1] %onnx::Conv_656[FLOAT, 184] %onnx::Conv_658[FLOAT, 1104x184x1x1] %onnx::Conv_659[FLOAT, 1104] %onnx::Conv_661[FLOAT, 1104x1x3x3] %onnx::Conv_664[FLOAT, 184x1104x1x1] %onnx::Conv_667[FLOAT, 1104x184x1x1] %onnx::Conv_670[FLOAT, 1104x1x5x5] %onnx::Conv_673[FLOAT, 184x1104x1x1] %onnx::Conv_676[FLOAT, 552x184x1x1] %onnx::Conv_677[FLOAT, 552] %onnx::Conv_679[FLOAT, 552x1x5x5] %onnx::Conv_682[FLOAT, 352x552x1x1] %onnx::Conv_683[FLOAT, 352] %onnx::Conv_685[FLOAT, 1504x352x1x1] %onnx::Conv_686[FLOAT, 1504] ) { %onnx::Conv_680 = Identity(%onnx::Conv_677) %onnx::Conv_674 = Identity(%onnx::Conv_656) %onnx::Conv_671 = Identity(%onnx::Conv_659) %onnx::Conv_668 = Identity(%onnx::Conv_659) %onnx::Conv_665 = Identity(%onnx::Conv_656) %onnx::Conv_662 = Identity(%onnx::Conv_659) %onnx::Conv_653 = Identity(%onnx::Conv_626) %onnx::Conv_650 = Identity(%onnx::Conv_626) %onnx::Conv_647 = Identity(%onnx::Conv_626) %onnx::Conv_644 = Identity(%onnx::Conv_626) %onnx::Conv_641 = Identity(%onnx::Conv_629) %onnx::Conv_638 = Identity(%onnx::Conv_629) %onnx::Conv_635 = Identity(%onnx::Conv_626) %onnx::Conv_632 = Identity(%onnx::Conv_629) %onnx::Conv_623 = Identity(%onnx::Conv_596) %onnx::Conv_620 = Identity(%onnx::Conv_563) %onnx::Conv_617 = Identity(%onnx::Conv_563) %onnx::Conv_614 = Identity(%onnx::Conv_596) %onnx::Conv_611 = Identity(%onnx::Conv_596) %onnx::Conv_608 = Identity(%onnx::Conv_596) %onnx::Conv_605 = Identity(%onnx::Conv_596) %onnx::Conv_602 = Identity(%onnx::Conv_599) %onnx::Conv_593 = Identity(%onnx::Conv_563) %onnx::Conv_590 = Identity(%onnx::Conv_563) %onnx::Conv_587 = Identity(%onnx::Conv_560) %onnx::Conv_584 = Identity(%onnx::Conv_560) %onnx::Conv_581 = Identity(%onnx::Conv_560) %onnx::Conv_578 = Identity(%onnx::Conv_560) %onnx::Conv_575 = Identity(%onnx::Conv_560) %onnx::Conv_572 = Identity(%onnx::Conv_560) %onnx::Conv_569 = Identity(%onnx::Conv_560) %onnx::Conv_566 = Identity(%onnx::Conv_563) %onnx::Conv_557 = Identity(%onnx::Conv_542) %onnx::Conv_554 = Identity(%onnx::Conv_542) %onnx::Conv_551 = Identity(%onnx::Conv_542) %onnx::Conv_548 = Identity(%onnx::Conv_542) %onnx::Conv_545 = Identity(%onnx::Conv_542) %onnx::Conv_539 = Identity(%onnx::Conv_527) %onnx::Conv_536 = Identity(%onnx::Conv_527) %onnx::Conv_533 = Identity(%onnx::Conv_524) %onnx::Conv_530 = Identity(%onnx::Conv_527) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_523, %onnx::Conv_524) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_526, %onnx::Conv_527) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.0/nl/Relu_output_0, %onnx::Conv_529, %onnx::Conv_530) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_532, %onnx::Conv_533) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_535, %onnx::Conv_536) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.1/nl/Relu_output_0, %onnx::Conv_538, %onnx::Conv_539) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_541, %onnx::Conv_542) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_544, %onnx::Conv_545) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.4/nl/Relu_output_0, %onnx::Conv_547, %onnx::Conv_548) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_550, %onnx::Conv_551) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/Add_output_0, %onnx::Conv_553, %onnx::Conv_554) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.5/nl/Relu_output_0, %onnx::Conv_556, %onnx::Conv_557) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_559, %onnx::Conv_560) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_562, %onnx::Conv_563) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.6/nl/Relu_output_0, %onnx::Conv_565, %onnx::Conv_566) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_568, %onnx::Conv_569) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_571, %onnx::Conv_572) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.7/nl/Relu_output_0, %onnx::Conv_574, %onnx::Conv_575) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_577, %onnx::Conv_578) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_580, %onnx::Conv_581) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.8/shuffle/Reshape_output_0 = Reshape(%/cells.8/nl/Relu_output_0, %/cells.8/shuffle/Constant_output_0) %/cells.8/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.8/shuffle/Reshape_output_0) %/cells.8/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.8/shuffle/Reshape_1_output_0 = Reshape(%/cells.8/shuffle/Transpose_output_0, %/cells.8/shuffle/Constant_1_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.8/shuffle/Reshape_1_output_0, %onnx::Conv_583, %onnx::Conv_584) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_586, %onnx::Conv_587) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_589, %onnx::Conv_590) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.9/nl/Relu_output_0, %onnx::Conv_592, %onnx::Conv_593) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_595, %onnx::Conv_596) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_598, %onnx::Conv_599) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.10/nl/Relu_output_0, %onnx::Conv_601, %onnx::Conv_602) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_604, %onnx::Conv_605) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_607, %onnx::Conv_608) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.11/nl/Relu_output_0, %onnx::Conv_610, %onnx::Conv_611) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_613, %onnx::Conv_614) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_616, %onnx::Conv_617) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.12/nl/Relu_output_0, %onnx::Conv_619, %onnx::Conv_620) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_622, %onnx::Conv_623) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_625, %onnx::Conv_626) %/cells.13/relu/Relu_output_0 = Relu(%/cells.13/conv/Conv_output_0) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/relu/Relu_output_0, %onnx::Conv_628, %onnx::Conv_629) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.14/nl/Relu_output_0, %onnx::Conv_631, %onnx::Conv_632) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_634, %onnx::Conv_635) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/relu/Relu_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_637, %onnx::Conv_638) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.15/nl/Relu_output_0, %onnx::Conv_640, %onnx::Conv_641) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_643, %onnx::Conv_644) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_646, %onnx::Conv_647) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.16/nl/Relu_output_0, %onnx::Conv_649, %onnx::Conv_650) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_652, %onnx::Conv_653) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.15/Add_output_0) %/cells.17/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [2, 2]](%/cells.16/Add_output_0, %onnx::Conv_655, %onnx::Conv_656) %/cells.17/relu/Relu_output_0 = Relu(%/cells.17/conv/Conv_output_0) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/relu/Relu_output_0, %onnx::Conv_658, %onnx::Conv_659) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.18/nl/Relu_output_0, %onnx::Conv_661, %onnx::Conv_662) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_664, %onnx::Conv_665) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/relu/Relu_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_667, %onnx::Conv_668) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.19/nl/Relu_output_0, %onnx::Conv_670, %onnx::Conv_671) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_673, %onnx::Conv_674) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_676, %onnx::Conv_677) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 552, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.21/nl/Relu_output_0, %onnx::Conv_679, %onnx::Conv_680) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_682, %onnx::Conv_683) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_685, %onnx::Conv_686) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %521 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %521 }	val_accuracy	77,516,544	2,382,476	{'zcp_synflow': 73.51041714964312, 'zcp_zen': 63.63251495361328, 'zcp_epe_nas': 38.75065888142216, 'zcp_fisher': 0.07977709174156189, 'zcp_flops': 77516544.0, 'zcp_grad_norm': 20.05245590209961, 'zcp_grasp': -0.1407756805419922, 'zcp_jacov': -16.06254678391811, 'zcp_l2_norm': 640.3583374023438, 'zcp_nwot': 212.55493040558625, 'zcp_params': 2382476.0, 'zcp_plain': 0.007813770323991776, 'zcp_snip': 38.67026138305664, 'lat_1080ti_1': 0.30654661387194204, 'lat_1080ti_32': 0.29371095570958605, 'lat_1080ti_64': 0.2778724037983332, 'lat_2080ti_1': 0.3072730714151731, 'lat_2080ti_32': 0.26934298409460483, 'lat_2080ti_64': 0.26769523721705835, 'lat_essential_ph_1': 0.3584905660377358, 'lat_eyeriss': 0.5177232134670747, 'lat_fpga': 0.6384884077618024, 'lat_gold_6226': 0.5209330490835137, 'lat_gold_6240': 0.5315844305207265, 'lat_pixel2': 0.3695652173913043, 'lat_pixel3': 0.4319760355138179, 'lat_raspi4': 0.5266018528950728, 'lat_samsung_a50': 0.22105263157894736, 'lat_samsung_s7': 0.1968503937007874, 'lat_silver_4114': 0.5404072150468601, 'lat_silver_4210r': 0.505980926303393, 'lat_titan_rtx_1': 0.28904994402879997, 'lat_titan_rtx_32': 0.2612911755305575, 'lat_titan_rtx_64': 0.24095989888198474, 'lat_titanx_1': 0.1621813805491138, 'lat_titanx_32': 0.22535604685744492, 'lat_titanx_64': 0.25781244169669015, 'lat_titanxp_1': 0.29094471716896486, 'lat_titanxp_32': 0.2480968052477061, 'lat_titanxp_64': 0.2534400263753959}
FBNet_3190	FBNet	3190	3190	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_661[FLOAT, 16x3x3x3] %onnx::Conv_662[FLOAT, 16] %onnx::Conv_664[FLOAT, 16x16x1x1] %onnx::Conv_667[FLOAT, 16x1x3x3] %onnx::Conv_670[FLOAT, 24x16x1x1] %onnx::Conv_671[FLOAT, 24] %onnx::Conv_673[FLOAT, 24x24x1x1] %onnx::Conv_676[FLOAT, 24x1x3x3] %onnx::Conv_679[FLOAT, 24x24x1x1] %onnx::Conv_682[FLOAT, 24x24x1x1] %onnx::Conv_685[FLOAT, 24x1x3x3] %onnx::Conv_688[FLOAT, 24x24x1x1] %onnx::Conv_691[FLOAT, 24x12x1x1] %onnx::Conv_694[FLOAT, 24x1x5x5] %onnx::Conv_697[FLOAT, 24x12x1x1] %onnx::Conv_700[FLOAT, 32x24x1x1] %onnx::Conv_701[FLOAT, 32] %onnx::Conv_703[FLOAT, 32x16x1x1] %onnx::Conv_706[FLOAT, 32x1x3x3] %onnx::Conv_709[FLOAT, 32x16x1x1] %onnx::Conv_712[FLOAT, 96x32x1x1] %onnx::Conv_713[FLOAT, 96] %onnx::Conv_715[FLOAT, 96x1x3x3] %onnx::Conv_718[FLOAT, 32x96x1x1] %onnx::Conv_721[FLOAT, 96x32x1x1] %onnx::Conv_724[FLOAT, 96x1x5x5] %onnx::Conv_727[FLOAT, 32x96x1x1] %onnx::Conv_730[FLOAT, 32x32x1x1] %onnx::Conv_733[FLOAT, 32x1x5x5] %onnx::Conv_736[FLOAT, 64x32x1x1] %onnx::Conv_737[FLOAT, 64] %onnx::Conv_739[FLOAT, 64x32x1x1] %onnx::Conv_742[FLOAT, 64x1x5x5] %onnx::Conv_745[FLOAT, 64x32x1x1] %onnx::Conv_748[FLOAT, 64x64x1x1] %onnx::Conv_751[FLOAT, 64x1x5x5] %onnx::Conv_754[FLOAT, 64x64x1x1] %onnx::Conv_757[FLOAT, 64x32x1x1] %onnx::Conv_760[FLOAT, 64x1x3x3] %onnx::Conv_763[FLOAT, 64x32x1x1] %onnx::Conv_766[FLOAT, 192x64x1x1] %onnx::Conv_767[FLOAT, 192] %onnx::Conv_769[FLOAT, 192x1x5x5] %onnx::Conv_772[FLOAT, 112x192x1x1] %onnx::Conv_773[FLOAT, 112] %onnx::Conv_775[FLOAT, 336x112x1x1] %onnx::Conv_776[FLOAT, 336] %onnx::Conv_778[FLOAT, 336x1x3x3] %onnx::Conv_781[FLOAT, 112x336x1x1] %onnx::Conv_784[FLOAT, 336x112x1x1] %onnx::Conv_787[FLOAT, 336x1x5x5] %onnx::Conv_790[FLOAT, 112x336x1x1] %onnx::Conv_793[FLOAT, 112x56x1x1] %onnx::Conv_796[FLOAT, 112x1x5x5] %onnx::Conv_799[FLOAT, 184x56x1x1] %onnx::Conv_800[FLOAT, 184] %onnx::Conv_802[FLOAT, 552x184x1x1] %onnx::Conv_803[FLOAT, 552] %onnx::Conv_805[FLOAT, 552x1x5x5] %onnx::Conv_808[FLOAT, 184x552x1x1] %onnx::Conv_811[FLOAT, 184x184x1x1] %onnx::Conv_814[FLOAT, 184x1x3x3] %onnx::Conv_817[FLOAT, 184x184x1x1] %onnx::Conv_820[FLOAT, 184x92x1x1] %onnx::Conv_823[FLOAT, 184x1x5x5] %onnx::Conv_826[FLOAT, 184x92x1x1] %onnx::Conv_829[FLOAT, 552x184x1x1] %onnx::Conv_832[FLOAT, 552x1x3x3] %onnx::Conv_835[FLOAT, 352x552x1x1] %onnx::Conv_836[FLOAT, 352] %onnx::Conv_838[FLOAT, 1504x352x1x1] %onnx::Conv_839[FLOAT, 1504] ) { %onnx::Conv_833 = Identity(%onnx::Conv_803) %onnx::Conv_830 = Identity(%onnx::Conv_803) %onnx::Conv_827 = Identity(%onnx::Conv_800) %onnx::Conv_824 = Identity(%onnx::Conv_800) %onnx::Conv_821 = Identity(%onnx::Conv_800) %onnx::Conv_818 = Identity(%onnx::Conv_800) %onnx::Conv_815 = Identity(%onnx::Conv_800) %onnx::Conv_812 = Identity(%onnx::Conv_800) %onnx::Conv_809 = Identity(%onnx::Conv_800) %onnx::Conv_806 = Identity(%onnx::Conv_803) %onnx::Conv_797 = Identity(%onnx::Conv_773) %onnx::Conv_794 = Identity(%onnx::Conv_773) %onnx::Conv_791 = Identity(%onnx::Conv_773) %onnx::Conv_788 = Identity(%onnx::Conv_776) %onnx::Conv_785 = Identity(%onnx::Conv_776) %onnx::Conv_782 = Identity(%onnx::Conv_773) %onnx::Conv_779 = Identity(%onnx::Conv_776) %onnx::Conv_770 = Identity(%onnx::Conv_767) %onnx::Conv_764 = Identity(%onnx::Conv_737) %onnx::Conv_761 = Identity(%onnx::Conv_737) %onnx::Conv_758 = Identity(%onnx::Conv_737) %onnx::Conv_755 = Identity(%onnx::Conv_737) %onnx::Conv_752 = Identity(%onnx::Conv_737) %onnx::Conv_749 = Identity(%onnx::Conv_737) %onnx::Conv_746 = Identity(%onnx::Conv_737) %onnx::Conv_743 = Identity(%onnx::Conv_737) %onnx::Conv_740 = Identity(%onnx::Conv_737) %onnx::Conv_734 = Identity(%onnx::Conv_701) %onnx::Conv_731 = Identity(%onnx::Conv_701) %onnx::Conv_728 = Identity(%onnx::Conv_701) %onnx::Conv_725 = Identity(%onnx::Conv_713) %onnx::Conv_722 = Identity(%onnx::Conv_713) %onnx::Conv_719 = Identity(%onnx::Conv_701) %onnx::Conv_716 = Identity(%onnx::Conv_713) %onnx::Conv_710 = Identity(%onnx::Conv_701) %onnx::Conv_707 = Identity(%onnx::Conv_701) %onnx::Conv_704 = Identity(%onnx::Conv_701) %onnx::Conv_698 = Identity(%onnx::Conv_671) %onnx::Conv_695 = Identity(%onnx::Conv_671) %onnx::Conv_692 = Identity(%onnx::Conv_671) %onnx::Conv_689 = Identity(%onnx::Conv_671) %onnx::Conv_686 = Identity(%onnx::Conv_671) %onnx::Conv_683 = Identity(%onnx::Conv_671) %onnx::Conv_680 = Identity(%onnx::Conv_671) %onnx::Conv_677 = Identity(%onnx::Conv_671) %onnx::Conv_674 = Identity(%onnx::Conv_671) %onnx::Conv_668 = Identity(%onnx::Conv_662) %onnx::Conv_665 = Identity(%onnx::Conv_662) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_661, %onnx::Conv_662) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_664, %onnx::Conv_665) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.1/nl/Relu_output_0, %onnx::Conv_667, %onnx::Conv_668) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_670, %onnx::Conv_671) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_673, %onnx::Conv_674) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.2/nl/Relu_output_0, %onnx::Conv_676, %onnx::Conv_677) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_679, %onnx::Conv_680) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_682, %onnx::Conv_683) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.3/nl/Relu_output_0, %onnx::Conv_685, %onnx::Conv_686) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_688, %onnx::Conv_689) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_691, %onnx::Conv_692) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.4/shuffle/Reshape_output_0 = Reshape(%/cells.4/nl/Relu_output_0, %/cells.4/shuffle/Constant_output_0) %/cells.4/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.4/shuffle/Reshape_output_0) %/cells.4/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.4/shuffle/Reshape_1_output_0 = Reshape(%/cells.4/shuffle/Transpose_output_0, %/cells.4/shuffle/Constant_1_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.4/shuffle/Reshape_1_output_0, %onnx::Conv_694, %onnx::Conv_695) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_697, %onnx::Conv_698) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [2, 2]](%/cells.4/Add_output_0, %onnx::Conv_700, %onnx::Conv_701) %/cells.5/relu/Relu_output_0 = Relu(%/cells.5/conv/Conv_output_0) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/relu/Relu_output_0, %onnx::Conv_703, %onnx::Conv_704) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.6/shuffle/Reshape_output_0 = Reshape(%/cells.6/nl/Relu_output_0, %/cells.6/shuffle/Constant_output_0) %/cells.6/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.6/shuffle/Reshape_output_0) %/cells.6/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.6/shuffle/Reshape_1_output_0 = Reshape(%/cells.6/shuffle/Transpose_output_0, %/cells.6/shuffle/Constant_1_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.6/shuffle/Reshape_1_output_0, %onnx::Conv_706, %onnx::Conv_707) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_709, %onnx::Conv_710) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/relu/Relu_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_712, %onnx::Conv_713) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.7/nl/Relu_output_0, %onnx::Conv_715, %onnx::Conv_716) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_718, %onnx::Conv_719) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_721, %onnx::Conv_722) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.8/nl/Relu_output_0, %onnx::Conv_724, %onnx::Conv_725) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_727, %onnx::Conv_728) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_730, %onnx::Conv_731) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.9/nl/Relu_output_0, %onnx::Conv_733, %onnx::Conv_734) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_736, %onnx::Conv_737) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_739, %onnx::Conv_740) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.10/shuffle/Reshape_output_0 = Reshape(%/cells.10/nl/Relu_output_0, %/cells.10/shuffle/Constant_output_0) %/cells.10/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.10/shuffle/Reshape_output_0) %/cells.10/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.10/shuffle/Reshape_1_output_0 = Reshape(%/cells.10/shuffle/Transpose_output_0, %/cells.10/shuffle/Constant_1_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.10/shuffle/Reshape_1_output_0, %onnx::Conv_742, %onnx::Conv_743) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_745, %onnx::Conv_746) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_748, %onnx::Conv_749) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.11/nl/Relu_output_0, %onnx::Conv_751, %onnx::Conv_752) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_754, %onnx::Conv_755) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_757, %onnx::Conv_758) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.12/shuffle/Reshape_output_0 = Reshape(%/cells.12/nl/Relu_output_0, %/cells.12/shuffle/Constant_output_0) %/cells.12/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.12/shuffle/Reshape_output_0) %/cells.12/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.12/shuffle/Reshape_1_output_0 = Reshape(%/cells.12/shuffle/Transpose_output_0, %/cells.12/shuffle/Constant_1_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.12/shuffle/Reshape_1_output_0, %onnx::Conv_760, %onnx::Conv_761) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_763, %onnx::Conv_764) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_766, %onnx::Conv_767) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.13/nl/Relu_output_0, %onnx::Conv_769, %onnx::Conv_770) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_772, %onnx::Conv_773) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_775, %onnx::Conv_776) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.14/nl/Relu_output_0, %onnx::Conv_778, %onnx::Conv_779) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_781, %onnx::Conv_782) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_784, %onnx::Conv_785) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.15/nl/Relu_output_0, %onnx::Conv_787, %onnx::Conv_788) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_790, %onnx::Conv_791) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_793, %onnx::Conv_794) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.17/shuffle/Reshape_output_0 = Reshape(%/cells.17/nl/Relu_output_0, %/cells.17/shuffle/Constant_output_0) %/cells.17/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.17/shuffle/Reshape_output_0) %/cells.17/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.17/shuffle/Reshape_1_output_0 = Reshape(%/cells.17/shuffle/Transpose_output_0, %/cells.17/shuffle/Constant_1_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.17/shuffle/Reshape_1_output_0, %onnx::Conv_796, %onnx::Conv_797) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_799, %onnx::Conv_800) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_802, %onnx::Conv_803) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 552, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.18/nl/Relu_output_0, %onnx::Conv_805, %onnx::Conv_806) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_808, %onnx::Conv_809) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_811, %onnx::Conv_812) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.19/nl/Relu_output_0, %onnx::Conv_814, %onnx::Conv_815) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_817, %onnx::Conv_818) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_820, %onnx::Conv_821) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.20/shuffle/Reshape_output_0 = Reshape(%/cells.20/nl/Relu_output_0, %/cells.20/shuffle/Constant_output_0) %/cells.20/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.20/shuffle/Reshape_output_0) %/cells.20/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.20/shuffle/Reshape_1_output_0 = Reshape(%/cells.20/shuffle/Transpose_output_0, %/cells.20/shuffle/Constant_1_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.20/shuffle/Reshape_1_output_0, %onnx::Conv_823, %onnx::Conv_824) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_826, %onnx::Conv_827) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_829, %onnx::Conv_830) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 552, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.21/nl/Relu_output_0, %onnx::Conv_832, %onnx::Conv_833) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_835, %onnx::Conv_836) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_838, %onnx::Conv_839) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %659 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %659 }	val_accuracy	45,337,472	1,591,004	{'zcp_synflow': 73.36723873849202, 'zcp_zen': 62.68036651611328, 'zcp_epe_nas': 36.89986648005886, 'zcp_fisher': 0.06457121670246124, 'zcp_flops': 45337472.0, 'zcp_grad_norm': 16.451833724975586, 'zcp_grasp': -0.0011277198791503906, 'zcp_jacov': -16.06609679094344, 'zcp_l2_norm': 542.6666259765625, 'zcp_nwot': 201.58892815025615, 'zcp_params': 1591004.0, 'zcp_plain': 0.0010169363813474774, 'zcp_snip': 26.561967849731445, 'lat_1080ti_1': 0.5727716840952639, 'lat_1080ti_32': 0.43593835760443445, 'lat_1080ti_64': 0.16899570550510287, 'lat_2080ti_1': 0.5751008152877816, 'lat_2080ti_32': 0.4294547376790619, 'lat_2080ti_64': 0.2075233371577075, 'lat_essential_ph_1': 0.1509433962264151, 'lat_eyeriss': 0.13066400161370154, 'lat_fpga': 0.2065031318338792, 'lat_gold_6226': 0.1876564727188341, 'lat_gold_6240': 0.38683177977471783, 'lat_pixel2': 0.2391304347826087, 'lat_pixel3': 0.1537595362382098, 'lat_raspi4': 0.22881060183792673, 'lat_samsung_a50': 0.07368421052631578, 'lat_samsung_s7': 0.07874015748031496, 'lat_silver_4114': 0.4049300127986382, 'lat_silver_4210r': 0.3647817925124687, 'lat_titan_rtx_1': 0.5479887721444768, 'lat_titan_rtx_32': 0.435560090832291, 'lat_titan_rtx_64': 0.2602565115397762, 'lat_titanx_1': 0.28609604007756223, 'lat_titanx_32': 0.32615681125520657, 'lat_titanx_64': 0.15920196968828565, 'lat_titanxp_1': 0.5160156527506233, 'lat_titanxp_32': 0.39620667998428916, 'lat_titanxp_64': 0.2074932369775594}
FBNet_4133	FBNet	4133	4133	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_614[FLOAT, 16x3x3x3] %onnx::Conv_615[FLOAT, 16] %onnx::Conv_617[FLOAT, 48x16x1x1] %onnx::Conv_618[FLOAT, 48] %onnx::Conv_620[FLOAT, 48x1x3x3] %onnx::Conv_623[FLOAT, 16x48x1x1] %onnx::Conv_626[FLOAT, 16x8x1x1] %onnx::Conv_629[FLOAT, 16x1x3x3] %onnx::Conv_632[FLOAT, 24x8x1x1] %onnx::Conv_633[FLOAT, 24] %onnx::Conv_635[FLOAT, 72x24x1x1] %onnx::Conv_636[FLOAT, 72] %onnx::Conv_638[FLOAT, 72x1x5x5] %onnx::Conv_641[FLOAT, 24x72x1x1] %onnx::Conv_644[FLOAT, 72x24x1x1] %onnx::Conv_647[FLOAT, 72x1x5x5] %onnx::Conv_650[FLOAT, 24x72x1x1] %onnx::Conv_653[FLOAT, 144x24x1x1] %onnx::Conv_654[FLOAT, 144] %onnx::Conv_656[FLOAT, 144x1x5x5] %onnx::Conv_659[FLOAT, 24x144x1x1] %onnx::Conv_662[FLOAT, 72x24x1x1] %onnx::Conv_665[FLOAT, 72x1x5x5] %onnx::Conv_668[FLOAT, 32x72x1x1] %onnx::Conv_669[FLOAT, 32] %onnx::Conv_671[FLOAT, 32x32x1x1] %onnx::Conv_674[FLOAT, 32x1x5x5] %onnx::Conv_677[FLOAT, 32x32x1x1] %onnx::Conv_680[FLOAT, 32x16x1x1] %onnx::Conv_683[FLOAT, 32x1x5x5] %onnx::Conv_686[FLOAT, 32x16x1x1] %onnx::Conv_689[FLOAT, 96x32x1x1] %onnx::Conv_690[FLOAT, 96] %onnx::Conv_692[FLOAT, 96x1x5x5] %onnx::Conv_695[FLOAT, 64x96x1x1] %onnx::Conv_696[FLOAT, 64] %onnx::Conv_698[FLOAT, 384x64x1x1] %onnx::Conv_699[FLOAT, 384] %onnx::Conv_701[FLOAT, 384x1x5x5] %onnx::Conv_704[FLOAT, 64x384x1x1] %onnx::Conv_707[FLOAT, 192x64x1x1] %onnx::Conv_708[FLOAT, 192] %onnx::Conv_710[FLOAT, 192x1x3x3] %onnx::Conv_713[FLOAT, 64x192x1x1] %onnx::Conv_716[FLOAT, 64x64x1x1] %onnx::Conv_719[FLOAT, 64x1x3x3] %onnx::Conv_722[FLOAT, 112x64x1x1] %onnx::Conv_723[FLOAT, 112] %onnx::Conv_725[FLOAT, 672x112x1x1] %onnx::Conv_726[FLOAT, 672] %onnx::Conv_728[FLOAT, 672x1x5x5] %onnx::Conv_731[FLOAT, 112x672x1x1] %onnx::Conv_734[FLOAT, 672x112x1x1] %onnx::Conv_737[FLOAT, 672x1x3x3] %onnx::Conv_740[FLOAT, 112x672x1x1] %onnx::Conv_743[FLOAT, 112x56x1x1] %onnx::Conv_746[FLOAT, 112x1x5x5] %onnx::Conv_749[FLOAT, 184x56x1x1] %onnx::Conv_750[FLOAT, 184] %onnx::Conv_752[FLOAT, 1104x184x1x1] %onnx::Conv_753[FLOAT, 1104] %onnx::Conv_755[FLOAT, 1104x1x3x3] %onnx::Conv_758[FLOAT, 184x1104x1x1] %onnx::Conv_761[FLOAT, 184x92x1x1] %onnx::Conv_764[FLOAT, 184x1x5x5] %onnx::Conv_767[FLOAT, 184x92x1x1] %onnx::Conv_770[FLOAT, 184x184x1x1] %onnx::Conv_773[FLOAT, 184x1x5x5] %onnx::Conv_776[FLOAT, 184x184x1x1] %onnx::Conv_779[FLOAT, 184x184x1x1] %onnx::Conv_782[FLOAT, 184x1x3x3] %onnx::Conv_785[FLOAT, 352x184x1x1] %onnx::Conv_786[FLOAT, 352] %onnx::Conv_788[FLOAT, 1504x352x1x1] %onnx::Conv_789[FLOAT, 1504] ) { %onnx::Conv_783 = Identity(%onnx::Conv_750) %onnx::Conv_780 = Identity(%onnx::Conv_750) %onnx::Conv_777 = Identity(%onnx::Conv_750) %onnx::Conv_774 = Identity(%onnx::Conv_750) %onnx::Conv_771 = Identity(%onnx::Conv_750) %onnx::Conv_768 = Identity(%onnx::Conv_750) %onnx::Conv_765 = Identity(%onnx::Conv_750) %onnx::Conv_762 = Identity(%onnx::Conv_750) %onnx::Conv_759 = Identity(%onnx::Conv_750) %onnx::Conv_756 = Identity(%onnx::Conv_753) %onnx::Conv_747 = Identity(%onnx::Conv_723) %onnx::Conv_744 = Identity(%onnx::Conv_723) %onnx::Conv_741 = Identity(%onnx::Conv_723) %onnx::Conv_738 = Identity(%onnx::Conv_726) %onnx::Conv_735 = Identity(%onnx::Conv_726) %onnx::Conv_732 = Identity(%onnx::Conv_723) %onnx::Conv_729 = Identity(%onnx::Conv_726) %onnx::Conv_720 = Identity(%onnx::Conv_696) %onnx::Conv_717 = Identity(%onnx::Conv_696) %onnx::Conv_714 = Identity(%onnx::Conv_696) %onnx::Conv_711 = Identity(%onnx::Conv_708) %onnx::Conv_705 = Identity(%onnx::Conv_696) %onnx::Conv_702 = Identity(%onnx::Conv_699) %onnx::Conv_693 = Identity(%onnx::Conv_690) %onnx::Conv_687 = Identity(%onnx::Conv_669) %onnx::Conv_684 = Identity(%onnx::Conv_669) %onnx::Conv_681 = Identity(%onnx::Conv_669) %onnx::Conv_678 = Identity(%onnx::Conv_669) %onnx::Conv_675 = Identity(%onnx::Conv_669) %onnx::Conv_672 = Identity(%onnx::Conv_669) %onnx::Conv_666 = Identity(%onnx::Conv_636) %onnx::Conv_663 = Identity(%onnx::Conv_636) %onnx::Conv_660 = Identity(%onnx::Conv_633) %onnx::Conv_657 = Identity(%onnx::Conv_654) %onnx::Conv_651 = Identity(%onnx::Conv_633) %onnx::Conv_648 = Identity(%onnx::Conv_636) %onnx::Conv_645 = Identity(%onnx::Conv_636) %onnx::Conv_642 = Identity(%onnx::Conv_633) %onnx::Conv_639 = Identity(%onnx::Conv_636) %onnx::Conv_630 = Identity(%onnx::Conv_615) %onnx::Conv_627 = Identity(%onnx::Conv_615) %onnx::Conv_624 = Identity(%onnx::Conv_615) %onnx::Conv_621 = Identity(%onnx::Conv_618) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_614, %onnx::Conv_615) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_617, %onnx::Conv_618) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 48, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.0/nl/Relu_output_0, %onnx::Conv_620, %onnx::Conv_621) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_623, %onnx::Conv_624) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_626, %onnx::Conv_627) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.1/shuffle/Reshape_output_0 = Reshape(%/cells.1/nl/Relu_output_0, %/cells.1/shuffle/Constant_output_0) %/cells.1/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.1/shuffle/Reshape_output_0) %/cells.1/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.1/shuffle/Reshape_1_output_0 = Reshape(%/cells.1/shuffle/Transpose_output_0, %/cells.1/shuffle/Constant_1_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.1/shuffle/Reshape_1_output_0, %onnx::Conv_629, %onnx::Conv_630) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_632, %onnx::Conv_633) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_635, %onnx::Conv_636) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.2/nl/Relu_output_0, %onnx::Conv_638, %onnx::Conv_639) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_641, %onnx::Conv_642) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_644, %onnx::Conv_645) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.3/nl/Relu_output_0, %onnx::Conv_647, %onnx::Conv_648) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_650, %onnx::Conv_651) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_653, %onnx::Conv_654) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.4/nl/Relu_output_0, %onnx::Conv_656, %onnx::Conv_657) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_659, %onnx::Conv_660) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/Add_output_0, %onnx::Conv_662, %onnx::Conv_663) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.5/nl/Relu_output_0, %onnx::Conv_665, %onnx::Conv_666) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_668, %onnx::Conv_669) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_671, %onnx::Conv_672) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.6/nl/Relu_output_0, %onnx::Conv_674, %onnx::Conv_675) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_677, %onnx::Conv_678) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_680, %onnx::Conv_681) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.8/shuffle/Reshape_output_0 = Reshape(%/cells.8/nl/Relu_output_0, %/cells.8/shuffle/Constant_output_0) %/cells.8/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.8/shuffle/Reshape_output_0) %/cells.8/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.8/shuffle/Reshape_1_output_0 = Reshape(%/cells.8/shuffle/Transpose_output_0, %/cells.8/shuffle/Constant_1_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.8/shuffle/Reshape_1_output_0, %onnx::Conv_683, %onnx::Conv_684) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_686, %onnx::Conv_687) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_689, %onnx::Conv_690) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.9/nl/Relu_output_0, %onnx::Conv_692, %onnx::Conv_693) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_695, %onnx::Conv_696) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_698, %onnx::Conv_699) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.10/nl/Relu_output_0, %onnx::Conv_701, %onnx::Conv_702) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_704, %onnx::Conv_705) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_707, %onnx::Conv_708) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.11/nl/Relu_output_0, %onnx::Conv_710, %onnx::Conv_711) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_713, %onnx::Conv_714) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_716, %onnx::Conv_717) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.13/nl/Relu_output_0, %onnx::Conv_719, %onnx::Conv_720) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_722, %onnx::Conv_723) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_725, %onnx::Conv_726) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.14/nl/Relu_output_0, %onnx::Conv_728, %onnx::Conv_729) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_731, %onnx::Conv_732) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_734, %onnx::Conv_735) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.16/nl/Relu_output_0, %onnx::Conv_737, %onnx::Conv_738) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_740, %onnx::Conv_741) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_743, %onnx::Conv_744) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.17/shuffle/Reshape_output_0 = Reshape(%/cells.17/nl/Relu_output_0, %/cells.17/shuffle/Constant_output_0) %/cells.17/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.17/shuffle/Reshape_output_0) %/cells.17/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.17/shuffle/Reshape_1_output_0 = Reshape(%/cells.17/shuffle/Transpose_output_0, %/cells.17/shuffle/Constant_1_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.17/shuffle/Reshape_1_output_0, %onnx::Conv_746, %onnx::Conv_747) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_749, %onnx::Conv_750) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_752, %onnx::Conv_753) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.18/nl/Relu_output_0, %onnx::Conv_755, %onnx::Conv_756) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_758, %onnx::Conv_759) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_761, %onnx::Conv_762) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.19/shuffle/Reshape_output_0 = Reshape(%/cells.19/nl/Relu_output_0, %/cells.19/shuffle/Constant_output_0) %/cells.19/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.19/shuffle/Reshape_output_0) %/cells.19/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.19/shuffle/Reshape_1_output_0 = Reshape(%/cells.19/shuffle/Transpose_output_0, %/cells.19/shuffle/Constant_1_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.19/shuffle/Reshape_1_output_0, %onnx::Conv_764, %onnx::Conv_765) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_767, %onnx::Conv_768) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_770, %onnx::Conv_771) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.20/nl/Relu_output_0, %onnx::Conv_773, %onnx::Conv_774) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_776, %onnx::Conv_777) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_779, %onnx::Conv_780) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.21/nl/Relu_output_0, %onnx::Conv_782, %onnx::Conv_783) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_785, %onnx::Conv_786) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_788, %onnx::Conv_789) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %612 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %612 }	val_accuracy	79,477,888	1,817,380	{'zcp_synflow': 74.17407787101082, 'zcp_zen': 65.09272766113281, 'zcp_epe_nas': 33.12056602554353, 'zcp_fisher': 0.09519956260919571, 'zcp_flops': 79477888.0, 'zcp_grad_norm': 23.64584732055664, 'zcp_grasp': -0.4182853698730469, 'zcp_jacov': -16.064573590884034, 'zcp_l2_norm': 597.318603515625, 'zcp_nwot': 214.90561423992776, 'zcp_params': 1817380.0, 'zcp_plain': -9.864951425697654e-05, 'zcp_snip': 37.6973991394043, 'lat_1080ti_1': 0.43013416172930563, 'lat_1080ti_32': 0.5497473498770447, 'lat_1080ti_64': 0.6113104714915149, 'lat_2080ti_1': 0.5096174468787479, 'lat_2080ti_32': 0.5374006469439685, 'lat_2080ti_64': 0.5731923673834497, 'lat_essential_ph_1': 0.3584905660377358, 'lat_eyeriss': 0.5600645480718095, 'lat_fpga': 0.5790775685490653, 'lat_gold_6226': 0.37865868947975545, 'lat_gold_6240': 0.42914162639014813, 'lat_pixel2': 0.34782608695652173, 'lat_pixel3': 0.6206714479725676, 'lat_raspi4': 0.590336539488976, 'lat_samsung_a50': 0.23157894736842105, 'lat_samsung_s7': 0.18110236220472442, 'lat_silver_4114': 0.43689741053649017, 'lat_silver_4210r': 0.4356309014847945, 'lat_titan_rtx_1': 0.4819097732736914, 'lat_titan_rtx_32': 0.5132626691987067, 'lat_titan_rtx_64': 0.5534912336597463, 'lat_titanx_1': 0.2487977124854431, 'lat_titanx_32': 0.5622414903247778, 'lat_titanx_64': 0.5988211526300882, 'lat_titanxp_1': 0.4594071750275382, 'lat_titanxp_32': 0.5333757322450853, 'lat_titanxp_64': 0.5901485431837681}
FBNet_601	FBNet	601	601	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_605[FLOAT, 16x3x3x3] %onnx::Conv_606[FLOAT, 16] %onnx::Conv_608[FLOAT, 48x16x1x1] %onnx::Conv_609[FLOAT, 48] %onnx::Conv_611[FLOAT, 48x1x5x5] %onnx::Conv_614[FLOAT, 16x48x1x1] %onnx::Conv_617[FLOAT, 48x16x1x1] %onnx::Conv_620[FLOAT, 48x1x5x5] %onnx::Conv_623[FLOAT, 24x48x1x1] %onnx::Conv_624[FLOAT, 24] %onnx::Conv_626[FLOAT, 72x24x1x1] %onnx::Conv_627[FLOAT, 72] %onnx::Conv_629[FLOAT, 72x1x3x3] %onnx::Conv_632[FLOAT, 24x72x1x1] %onnx::Conv_635[FLOAT, 24x24x1x1] %onnx::Conv_638[FLOAT, 24x1x5x5] %onnx::Conv_641[FLOAT, 24x24x1x1] %onnx::Conv_644[FLOAT, 24x24x1x1] %onnx::Conv_647[FLOAT, 24x1x5x5] %onnx::Conv_650[FLOAT, 32x24x1x1] %onnx::Conv_651[FLOAT, 32] %onnx::Conv_653[FLOAT, 192x32x1x1] %onnx::Conv_654[FLOAT, 192] %onnx::Conv_656[FLOAT, 192x1x5x5] %onnx::Conv_659[FLOAT, 32x192x1x1] %onnx::Conv_662[FLOAT, 32x16x1x1] %onnx::Conv_665[FLOAT, 32x1x3x3] %onnx::Conv_668[FLOAT, 32x16x1x1] %onnx::Conv_671[FLOAT, 96x32x1x1] %onnx::Conv_672[FLOAT, 96] %onnx::Conv_674[FLOAT, 96x1x5x5] %onnx::Conv_677[FLOAT, 32x96x1x1] %onnx::Conv_680[FLOAT, 96x32x1x1] %onnx::Conv_683[FLOAT, 96x1x3x3] %onnx::Conv_686[FLOAT, 64x96x1x1] %onnx::Conv_687[FLOAT, 64] %onnx::Conv_689[FLOAT, 64x32x1x1] %onnx::Conv_692[FLOAT, 64x1x3x3] %onnx::Conv_695[FLOAT, 64x32x1x1] %onnx::Conv_698[FLOAT, 192x64x1x1] %onnx::Conv_701[FLOAT, 192x1x5x5] %onnx::Conv_704[FLOAT, 64x192x1x1] %onnx::Conv_707[FLOAT, 64x64x1x1] %onnx::Conv_710[FLOAT, 64x1x5x5] %onnx::Conv_713[FLOAT, 64x64x1x1] %onnx::Conv_716[FLOAT, 384x64x1x1] %onnx::Conv_717[FLOAT, 384] %onnx::Conv_719[FLOAT, 384x1x3x3] %onnx::Conv_722[FLOAT, 112x384x1x1] %onnx::Conv_723[FLOAT, 112] %onnx::Conv_725[FLOAT, 112x56x1x1] %onnx::Conv_728[FLOAT, 112x1x5x5] %onnx::Conv_731[FLOAT, 112x56x1x1] %onnx::Conv_734[FLOAT, 336x112x1x1] %onnx::Conv_735[FLOAT, 336] %onnx::Conv_737[FLOAT, 336x1x5x5] %onnx::Conv_740[FLOAT, 112x336x1x1] %onnx::Conv_743[FLOAT, 336x112x1x1] %onnx::Conv_746[FLOAT, 336x1x3x3] %onnx::Conv_749[FLOAT, 112x336x1x1] %onnx::Conv_752[FLOAT, 184x112x1x1] %onnx::Conv_753[FLOAT, 184] %onnx::Conv_755[FLOAT, 552x184x1x1] %onnx::Conv_756[FLOAT, 552] %onnx::Conv_758[FLOAT, 552x1x3x3] %onnx::Conv_761[FLOAT, 184x552x1x1] %onnx::Conv_764[FLOAT, 552x184x1x1] %onnx::Conv_767[FLOAT, 552x1x3x3] %onnx::Conv_770[FLOAT, 184x552x1x1] %onnx::Conv_773[FLOAT, 1104x184x1x1] %onnx::Conv_774[FLOAT, 1104] %onnx::Conv_776[FLOAT, 1104x1x3x3] %onnx::Conv_779[FLOAT, 352x1104x1x1] %onnx::Conv_780[FLOAT, 352] %onnx::Conv_782[FLOAT, 1504x352x1x1] %onnx::Conv_783[FLOAT, 1504] ) { %onnx::Conv_777 = Identity(%onnx::Conv_774) %onnx::Conv_771 = Identity(%onnx::Conv_753) %onnx::Conv_768 = Identity(%onnx::Conv_756) %onnx::Conv_765 = Identity(%onnx::Conv_756) %onnx::Conv_762 = Identity(%onnx::Conv_753) %onnx::Conv_759 = Identity(%onnx::Conv_756) %onnx::Conv_750 = Identity(%onnx::Conv_723) %onnx::Conv_747 = Identity(%onnx::Conv_735) %onnx::Conv_744 = Identity(%onnx::Conv_735) %onnx::Conv_741 = Identity(%onnx::Conv_723) %onnx::Conv_738 = Identity(%onnx::Conv_735) %onnx::Conv_732 = Identity(%onnx::Conv_723) %onnx::Conv_729 = Identity(%onnx::Conv_723) %onnx::Conv_726 = Identity(%onnx::Conv_723) %onnx::Conv_720 = Identity(%onnx::Conv_717) %onnx::Conv_714 = Identity(%onnx::Conv_687) %onnx::Conv_711 = Identity(%onnx::Conv_687) %onnx::Conv_708 = Identity(%onnx::Conv_687) %onnx::Conv_705 = Identity(%onnx::Conv_687) %onnx::Conv_702 = Identity(%onnx::Conv_654) %onnx::Conv_699 = Identity(%onnx::Conv_654) %onnx::Conv_696 = Identity(%onnx::Conv_687) %onnx::Conv_693 = Identity(%onnx::Conv_687) %onnx::Conv_690 = Identity(%onnx::Conv_687) %onnx::Conv_684 = Identity(%onnx::Conv_672) %onnx::Conv_681 = Identity(%onnx::Conv_672) %onnx::Conv_678 = Identity(%onnx::Conv_651) %onnx::Conv_675 = Identity(%onnx::Conv_672) %onnx::Conv_669 = Identity(%onnx::Conv_651) %onnx::Conv_666 = Identity(%onnx::Conv_651) %onnx::Conv_663 = Identity(%onnx::Conv_651) %onnx::Conv_660 = Identity(%onnx::Conv_651) %onnx::Conv_657 = Identity(%onnx::Conv_654) %onnx::Conv_648 = Identity(%onnx::Conv_624) %onnx::Conv_645 = Identity(%onnx::Conv_624) %onnx::Conv_642 = Identity(%onnx::Conv_624) %onnx::Conv_639 = Identity(%onnx::Conv_624) %onnx::Conv_636 = Identity(%onnx::Conv_624) %onnx::Conv_633 = Identity(%onnx::Conv_624) %onnx::Conv_630 = Identity(%onnx::Conv_627) %onnx::Conv_621 = Identity(%onnx::Conv_609) %onnx::Conv_618 = Identity(%onnx::Conv_609) %onnx::Conv_615 = Identity(%onnx::Conv_606) %onnx::Conv_612 = Identity(%onnx::Conv_609) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_605, %onnx::Conv_606) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_608, %onnx::Conv_609) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 48, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.0/nl/Relu_output_0, %onnx::Conv_611, %onnx::Conv_612) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_614, %onnx::Conv_615) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_617, %onnx::Conv_618) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 48, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.1/nl/Relu_output_0, %onnx::Conv_620, %onnx::Conv_621) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_623, %onnx::Conv_624) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_626, %onnx::Conv_627) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.2/nl/Relu_output_0, %onnx::Conv_629, %onnx::Conv_630) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_632, %onnx::Conv_633) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_635, %onnx::Conv_636) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.3/nl/Relu_output_0, %onnx::Conv_638, %onnx::Conv_639) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_641, %onnx::Conv_642) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_644, %onnx::Conv_645) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.5/nl/Relu_output_0, %onnx::Conv_647, %onnx::Conv_648) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_650, %onnx::Conv_651) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_653, %onnx::Conv_654) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.6/nl/Relu_output_0, %onnx::Conv_656, %onnx::Conv_657) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_659, %onnx::Conv_660) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_662, %onnx::Conv_663) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.7/shuffle/Reshape_output_0 = Reshape(%/cells.7/nl/Relu_output_0, %/cells.7/shuffle/Constant_output_0) %/cells.7/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.7/shuffle/Reshape_output_0) %/cells.7/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.7/shuffle/Reshape_1_output_0 = Reshape(%/cells.7/shuffle/Transpose_output_0, %/cells.7/shuffle/Constant_1_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.7/shuffle/Reshape_1_output_0, %onnx::Conv_665, %onnx::Conv_666) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_668, %onnx::Conv_669) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_671, %onnx::Conv_672) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.8/nl/Relu_output_0, %onnx::Conv_674, %onnx::Conv_675) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_677, %onnx::Conv_678) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_680, %onnx::Conv_681) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.9/nl/Relu_output_0, %onnx::Conv_683, %onnx::Conv_684) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_686, %onnx::Conv_687) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_689, %onnx::Conv_690) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.10/shuffle/Reshape_output_0 = Reshape(%/cells.10/nl/Relu_output_0, %/cells.10/shuffle/Constant_output_0) %/cells.10/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.10/shuffle/Reshape_output_0) %/cells.10/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.10/shuffle/Reshape_1_output_0 = Reshape(%/cells.10/shuffle/Transpose_output_0, %/cells.10/shuffle/Constant_1_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.10/shuffle/Reshape_1_output_0, %onnx::Conv_692, %onnx::Conv_693) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_695, %onnx::Conv_696) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_698, %onnx::Conv_699) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.11/nl/Relu_output_0, %onnx::Conv_701, %onnx::Conv_702) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_704, %onnx::Conv_705) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_707, %onnx::Conv_708) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.12/nl/Relu_output_0, %onnx::Conv_710, %onnx::Conv_711) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_713, %onnx::Conv_714) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_716, %onnx::Conv_717) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.13/nl/Relu_output_0, %onnx::Conv_719, %onnx::Conv_720) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_722, %onnx::Conv_723) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_725, %onnx::Conv_726) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.14/shuffle/Reshape_output_0 = Reshape(%/cells.14/nl/Relu_output_0, %/cells.14/shuffle/Constant_output_0) %/cells.14/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.14/shuffle/Reshape_output_0) %/cells.14/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.14/shuffle/Reshape_1_output_0 = Reshape(%/cells.14/shuffle/Transpose_output_0, %/cells.14/shuffle/Constant_1_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.14/shuffle/Reshape_1_output_0, %onnx::Conv_728, %onnx::Conv_729) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_731, %onnx::Conv_732) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_734, %onnx::Conv_735) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.15/nl/Relu_output_0, %onnx::Conv_737, %onnx::Conv_738) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_740, %onnx::Conv_741) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_743, %onnx::Conv_744) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.16/nl/Relu_output_0, %onnx::Conv_746, %onnx::Conv_747) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_749, %onnx::Conv_750) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.15/Add_output_0) %/cells.17/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [2, 2]](%/cells.16/Add_output_0, %onnx::Conv_752, %onnx::Conv_753) %/cells.17/relu/Relu_output_0 = Relu(%/cells.17/conv/Conv_output_0) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/relu/Relu_output_0, %onnx::Conv_755, %onnx::Conv_756) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 552, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.18/nl/Relu_output_0, %onnx::Conv_758, %onnx::Conv_759) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_761, %onnx::Conv_762) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/relu/Relu_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_764, %onnx::Conv_765) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 552, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.20/nl/Relu_output_0, %onnx::Conv_767, %onnx::Conv_768) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_770, %onnx::Conv_771) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_773, %onnx::Conv_774) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.21/nl/Relu_output_0, %onnx::Conv_776, %onnx::Conv_777) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_779, %onnx::Conv_780) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_782, %onnx::Conv_783) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %603 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %603 }	val_accuracy	68,137,984	2,085,692	{'zcp_synflow': 77.38764737569123, 'zcp_zen': 66.38729858398438, 'zcp_epe_nas': 11.719836952701604, 'zcp_fisher': 0.10722104460000992, 'zcp_flops': 68137984.0, 'zcp_grad_norm': 24.510969161987305, 'zcp_grasp': -0.014612197875976562, 'zcp_jacov': -16.072812816077835, 'zcp_l2_norm': 613.9210205078125, 'zcp_nwot': 210.33952857086288, 'zcp_params': 2085692.0, 'zcp_plain': -0.005292781628668308, 'zcp_snip': 45.565185546875, 'lat_1080ti_1': 0.4814847281163523, 'lat_1080ti_32': 0.3378871779346511, 'lat_1080ti_64': 0.304130388770672, 'lat_2080ti_1': 0.49954391929269293, 'lat_2080ti_32': 0.3776067405297894, 'lat_2080ti_64': 0.33004076338313876, 'lat_essential_ph_1': 0.24528301886792453, 'lat_eyeriss': 0.4111638824198192, 'lat_fpga': 0.48626050129132464, 'lat_gold_6226': 0.377792599385387, 'lat_gold_6240': 0.49857347167713245, 'lat_pixel2': 0.30434782608695654, 'lat_pixel3': 0.41399385194937577, 'lat_raspi4': 0.45110025738530846, 'lat_samsung_a50': 0.17894736842105263, 'lat_samsung_s7': 0.5275590551181102, 'lat_silver_4114': 0.5152459403425584, 'lat_silver_4210r': 0.5289075509848272, 'lat_titan_rtx_1': 0.4740050597130643, 'lat_titan_rtx_32': 0.39790872527106697, 'lat_titan_rtx_64': 0.3445898019069674, 'lat_titanx_1': 0.269802422940401, 'lat_titanx_32': 0.36131378324623165, 'lat_titanx_64': 0.3056815579655632, 'lat_titanxp_1': 0.4663068025680386, 'lat_titanxp_32': 0.4032759968130785, 'lat_titanxp_64': 0.32960760420990354}
FBNet_66	FBNet	66	66	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_614[FLOAT, 16x3x3x3] %onnx::Conv_615[FLOAT, 16] %onnx::Conv_617[FLOAT, 96x16x1x1] %onnx::Conv_618[FLOAT, 96] %onnx::Conv_620[FLOAT, 96x1x3x3] %onnx::Conv_623[FLOAT, 16x96x1x1] %onnx::Conv_626[FLOAT, 48x16x1x1] %onnx::Conv_627[FLOAT, 48] %onnx::Conv_629[FLOAT, 48x1x5x5] %onnx::Conv_632[FLOAT, 24x48x1x1] %onnx::Conv_633[FLOAT, 24] %onnx::Conv_635[FLOAT, 24x12x1x1] %onnx::Conv_638[FLOAT, 24x1x5x5] %onnx::Conv_641[FLOAT, 24x12x1x1] %onnx::Conv_644[FLOAT, 144x24x1x1] %onnx::Conv_645[FLOAT, 144] %onnx::Conv_647[FLOAT, 144x1x5x5] %onnx::Conv_650[FLOAT, 32x144x1x1] %onnx::Conv_651[FLOAT, 32] %onnx::Conv_653[FLOAT, 192x32x1x1] %onnx::Conv_654[FLOAT, 192] %onnx::Conv_656[FLOAT, 192x1x5x5] %onnx::Conv_659[FLOAT, 32x192x1x1] %onnx::Conv_662[FLOAT, 32x32x1x1] %onnx::Conv_665[FLOAT, 32x1x3x3] %onnx::Conv_668[FLOAT, 32x32x1x1] %onnx::Conv_671[FLOAT, 96x32x1x1] %onnx::Conv_674[FLOAT, 96x1x3x3] %onnx::Conv_677[FLOAT, 64x96x1x1] %onnx::Conv_678[FLOAT, 64] %onnx::Conv_680[FLOAT, 192x64x1x1] %onnx::Conv_683[FLOAT, 192x1x3x3] %onnx::Conv_686[FLOAT, 64x192x1x1] %onnx::Conv_689[FLOAT, 64x32x1x1] %onnx::Conv_692[FLOAT, 64x1x3x3] %onnx::Conv_695[FLOAT, 64x32x1x1] %onnx::Conv_698[FLOAT, 384x64x1x1] %onnx::Conv_699[FLOAT, 384] %onnx::Conv_701[FLOAT, 384x1x5x5] %onnx::Conv_704[FLOAT, 64x384x1x1] %onnx::Conv_707[FLOAT, 64x64x1x1] %onnx::Conv_710[FLOAT, 64x1x3x3] %onnx::Conv_713[FLOAT, 112x64x1x1] %onnx::Conv_714[FLOAT, 112] %onnx::Conv_716[FLOAT, 336x112x1x1] %onnx::Conv_717[FLOAT, 336] %onnx::Conv_719[FLOAT, 336x1x3x3] %onnx::Conv_722[FLOAT, 112x336x1x1] %onnx::Conv_725[FLOAT, 336x112x1x1] %onnx::Conv_728[FLOAT, 336x1x3x3] %onnx::Conv_731[FLOAT, 112x336x1x1] %onnx::Conv_734[FLOAT, 112x56x1x1] %onnx::Conv_737[FLOAT, 112x1x5x5] %onnx::Conv_740[FLOAT, 112x56x1x1] %onnx::Conv_743[FLOAT, 112x112x1x1] %onnx::Conv_746[FLOAT, 112x1x5x5] %onnx::Conv_749[FLOAT, 184x112x1x1] %onnx::Conv_750[FLOAT, 184] %onnx::Conv_752[FLOAT, 1104x184x1x1] %onnx::Conv_753[FLOAT, 1104] %onnx::Conv_755[FLOAT, 1104x1x5x5] %onnx::Conv_758[FLOAT, 184x1104x1x1] %onnx::Conv_761[FLOAT, 552x184x1x1] %onnx::Conv_762[FLOAT, 552] %onnx::Conv_764[FLOAT, 552x1x3x3] %onnx::Conv_767[FLOAT, 184x552x1x1] %onnx::Conv_770[FLOAT, 184x92x1x1] %onnx::Conv_773[FLOAT, 184x1x5x5] %onnx::Conv_776[FLOAT, 184x92x1x1] %onnx::Conv_779[FLOAT, 552x184x1x1] %onnx::Conv_782[FLOAT, 552x1x3x3] %onnx::Conv_785[FLOAT, 352x552x1x1] %onnx::Conv_786[FLOAT, 352] %onnx::Conv_788[FLOAT, 1504x352x1x1] %onnx::Conv_789[FLOAT, 1504] ) { %onnx::Conv_783 = Identity(%onnx::Conv_762) %onnx::Conv_780 = Identity(%onnx::Conv_762) %onnx::Conv_777 = Identity(%onnx::Conv_750) %onnx::Conv_774 = Identity(%onnx::Conv_750) %onnx::Conv_771 = Identity(%onnx::Conv_750) %onnx::Conv_768 = Identity(%onnx::Conv_750) %onnx::Conv_765 = Identity(%onnx::Conv_762) %onnx::Conv_759 = Identity(%onnx::Conv_750) %onnx::Conv_756 = Identity(%onnx::Conv_753) %onnx::Conv_747 = Identity(%onnx::Conv_714) %onnx::Conv_744 = Identity(%onnx::Conv_714) %onnx::Conv_741 = Identity(%onnx::Conv_714) %onnx::Conv_738 = Identity(%onnx::Conv_714) %onnx::Conv_735 = Identity(%onnx::Conv_714) %onnx::Conv_732 = Identity(%onnx::Conv_714) %onnx::Conv_729 = Identity(%onnx::Conv_717) %onnx::Conv_726 = Identity(%onnx::Conv_717) %onnx::Conv_723 = Identity(%onnx::Conv_714) %onnx::Conv_720 = Identity(%onnx::Conv_717) %onnx::Conv_711 = Identity(%onnx::Conv_678) %onnx::Conv_708 = Identity(%onnx::Conv_678) %onnx::Conv_705 = Identity(%onnx::Conv_678) %onnx::Conv_702 = Identity(%onnx::Conv_699) %onnx::Conv_696 = Identity(%onnx::Conv_678) %onnx::Conv_693 = Identity(%onnx::Conv_678) %onnx::Conv_690 = Identity(%onnx::Conv_678) %onnx::Conv_687 = Identity(%onnx::Conv_678) %onnx::Conv_684 = Identity(%onnx::Conv_654) %onnx::Conv_681 = Identity(%onnx::Conv_654) %onnx::Conv_675 = Identity(%onnx::Conv_618) %onnx::Conv_672 = Identity(%onnx::Conv_618) %onnx::Conv_669 = Identity(%onnx::Conv_651) %onnx::Conv_666 = Identity(%onnx::Conv_651) %onnx::Conv_663 = Identity(%onnx::Conv_651) %onnx::Conv_660 = Identity(%onnx::Conv_651) %onnx::Conv_657 = Identity(%onnx::Conv_654) %onnx::Conv_648 = Identity(%onnx::Conv_645) %onnx::Conv_642 = Identity(%onnx::Conv_633) %onnx::Conv_639 = Identity(%onnx::Conv_633) %onnx::Conv_636 = Identity(%onnx::Conv_633) %onnx::Conv_630 = Identity(%onnx::Conv_627) %onnx::Conv_624 = Identity(%onnx::Conv_615) %onnx::Conv_621 = Identity(%onnx::Conv_618) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_614, %onnx::Conv_615) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_617, %onnx::Conv_618) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.0/nl/Relu_output_0, %onnx::Conv_620, %onnx::Conv_621) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_623, %onnx::Conv_624) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_626, %onnx::Conv_627) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 48, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.1/nl/Relu_output_0, %onnx::Conv_629, %onnx::Conv_630) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_632, %onnx::Conv_633) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_635, %onnx::Conv_636) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.2/shuffle/Reshape_output_0 = Reshape(%/cells.2/nl/Relu_output_0, %/cells.2/shuffle/Constant_output_0) %/cells.2/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.2/shuffle/Reshape_output_0) %/cells.2/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.2/shuffle/Reshape_1_output_0 = Reshape(%/cells.2/shuffle/Transpose_output_0, %/cells.2/shuffle/Constant_1_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.2/shuffle/Reshape_1_output_0, %onnx::Conv_638, %onnx::Conv_639) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_641, %onnx::Conv_642) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_644, %onnx::Conv_645) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.5/nl/Relu_output_0, %onnx::Conv_647, %onnx::Conv_648) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_650, %onnx::Conv_651) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_653, %onnx::Conv_654) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.7/nl/Relu_output_0, %onnx::Conv_656, %onnx::Conv_657) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_659, %onnx::Conv_660) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_662, %onnx::Conv_663) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.8/nl/Relu_output_0, %onnx::Conv_665, %onnx::Conv_666) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_668, %onnx::Conv_669) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_671, %onnx::Conv_672) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.9/nl/Relu_output_0, %onnx::Conv_674, %onnx::Conv_675) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_677, %onnx::Conv_678) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_680, %onnx::Conv_681) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.10/nl/Relu_output_0, %onnx::Conv_683, %onnx::Conv_684) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_686, %onnx::Conv_687) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_689, %onnx::Conv_690) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.11/shuffle/Reshape_output_0 = Reshape(%/cells.11/nl/Relu_output_0, %/cells.11/shuffle/Constant_output_0) %/cells.11/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.11/shuffle/Reshape_output_0) %/cells.11/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.11/shuffle/Reshape_1_output_0 = Reshape(%/cells.11/shuffle/Transpose_output_0, %/cells.11/shuffle/Constant_1_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.11/shuffle/Reshape_1_output_0, %onnx::Conv_692, %onnx::Conv_693) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_695, %onnx::Conv_696) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_698, %onnx::Conv_699) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.12/nl/Relu_output_0, %onnx::Conv_701, %onnx::Conv_702) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_704, %onnx::Conv_705) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_707, %onnx::Conv_708) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.13/nl/Relu_output_0, %onnx::Conv_710, %onnx::Conv_711) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_713, %onnx::Conv_714) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_716, %onnx::Conv_717) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.14/nl/Relu_output_0, %onnx::Conv_719, %onnx::Conv_720) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_722, %onnx::Conv_723) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_725, %onnx::Conv_726) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.15/nl/Relu_output_0, %onnx::Conv_728, %onnx::Conv_729) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_731, %onnx::Conv_732) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_734, %onnx::Conv_735) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.16/shuffle/Reshape_output_0 = Reshape(%/cells.16/nl/Relu_output_0, %/cells.16/shuffle/Constant_output_0) %/cells.16/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.16/shuffle/Reshape_output_0) %/cells.16/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.16/shuffle/Reshape_1_output_0 = Reshape(%/cells.16/shuffle/Transpose_output_0, %/cells.16/shuffle/Constant_1_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.16/shuffle/Reshape_1_output_0, %onnx::Conv_737, %onnx::Conv_738) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_740, %onnx::Conv_741) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.15/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_743, %onnx::Conv_744) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.17/nl/Relu_output_0, %onnx::Conv_746, %onnx::Conv_747) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_749, %onnx::Conv_750) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_752, %onnx::Conv_753) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.18/nl/Relu_output_0, %onnx::Conv_755, %onnx::Conv_756) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_758, %onnx::Conv_759) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_761, %onnx::Conv_762) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 552, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.19/nl/Relu_output_0, %onnx::Conv_764, %onnx::Conv_765) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_767, %onnx::Conv_768) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_770, %onnx::Conv_771) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.20/shuffle/Reshape_output_0 = Reshape(%/cells.20/nl/Relu_output_0, %/cells.20/shuffle/Constant_output_0) %/cells.20/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.20/shuffle/Reshape_output_0) %/cells.20/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.20/shuffle/Reshape_1_output_0 = Reshape(%/cells.20/shuffle/Transpose_output_0, %/cells.20/shuffle/Constant_1_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.20/shuffle/Reshape_1_output_0, %onnx::Conv_773, %onnx::Conv_774) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_776, %onnx::Conv_777) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_779, %onnx::Conv_780) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 552, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.21/nl/Relu_output_0, %onnx::Conv_782, %onnx::Conv_783) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_785, %onnx::Conv_786) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_788, %onnx::Conv_789) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %612 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %612 }	val_accuracy	66,479,232	2,045,892	{'zcp_synflow': 73.82079191240197, 'zcp_zen': 67.26435089111328, 'zcp_epe_nas': 14.558953050800438, 'zcp_fisher': 0.15029571950435638, 'zcp_flops': 66479232.0, 'zcp_grad_norm': 22.536182403564453, 'zcp_grasp': -0.1351451873779297, 'zcp_jacov': -16.053792345789823, 'zcp_l2_norm': 629.9716186523438, 'zcp_nwot': 211.45930278114525, 'zcp_params': 2045892.0, 'zcp_plain': 0.004411468282341957, 'zcp_snip': 43.47357940673828, 'lat_1080ti_1': 0.45445825106497895, 'lat_1080ti_32': 0.44964953702079846, 'lat_1080ti_64': 0.3623804509183883, 'lat_2080ti_1': 0.46697422332512595, 'lat_2080ti_32': 0.3819963290368187, 'lat_2080ti_64': 0.3524333665031555, 'lat_essential_ph_1': 0.33962264150943394, 'lat_eyeriss': 0.4275484568976582, 'lat_fpga': 0.4297687489486339, 'lat_gold_6226': 0.3944090526117808, 'lat_gold_6240': 0.558116622590362, 'lat_pixel2': 0.30434782608695654, 'lat_pixel3': 0.4054733245973974, 'lat_raspi4': 0.4646394150569667, 'lat_samsung_a50': 0.18947368421052632, 'lat_samsung_s7': 0.16535433070866143, 'lat_silver_4114': 0.6049634081642248, 'lat_silver_4210r': 0.4774611223232096, 'lat_titan_rtx_1': 0.4611419354971438, 'lat_titan_rtx_32': 0.37897760895431254, 'lat_titan_rtx_64': 0.3774025542312929, 'lat_titanx_1': 0.2513137383039451, 'lat_titanx_32': 0.3743412045512117, 'lat_titanx_64': 0.3766156088101098, 'lat_titanxp_1': 0.4446027374083972, 'lat_titanxp_32': 0.4175499889950094, 'lat_titanxp_64': 0.3818294834541672}
FBNet_1785	FBNet	1785	1785	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_625[FLOAT, 16x3x3x3] %onnx::Conv_626[FLOAT, 16] %onnx::Conv_628[FLOAT, 96x16x1x1] %onnx::Conv_629[FLOAT, 96] %onnx::Conv_631[FLOAT, 96x1x5x5] %onnx::Conv_634[FLOAT, 16x96x1x1] %onnx::Conv_637[FLOAT, 16x8x1x1] %onnx::Conv_640[FLOAT, 16x1x3x3] %onnx::Conv_643[FLOAT, 24x8x1x1] %onnx::Conv_644[FLOAT, 24] %onnx::Conv_646[FLOAT, 72x24x1x1] %onnx::Conv_647[FLOAT, 72] %onnx::Conv_649[FLOAT, 72x1x3x3] %onnx::Conv_652[FLOAT, 24x72x1x1] %onnx::Conv_655[FLOAT, 72x24x1x1] %onnx::Conv_658[FLOAT, 72x1x3x3] %onnx::Conv_661[FLOAT, 24x72x1x1] %onnx::Conv_664[FLOAT, 72x24x1x1] %onnx::Conv_667[FLOAT, 72x1x5x5] %onnx::Conv_670[FLOAT, 32x72x1x1] %onnx::Conv_671[FLOAT, 32] %onnx::Conv_673[FLOAT, 96x32x1x1] %onnx::Conv_676[FLOAT, 96x1x3x3] %onnx::Conv_679[FLOAT, 32x96x1x1] %onnx::Conv_682[FLOAT, 32x16x1x1] %onnx::Conv_685[FLOAT, 32x1x5x5] %onnx::Conv_688[FLOAT, 32x16x1x1] %onnx::Conv_691[FLOAT, 32x32x1x1] %onnx::Conv_694[FLOAT, 32x1x5x5] %onnx::Conv_697[FLOAT, 32x32x1x1] %onnx::Conv_700[FLOAT, 64x32x1x1] %onnx::Conv_701[FLOAT, 64] %onnx::Conv_703[FLOAT, 64x32x1x1] %onnx::Conv_706[FLOAT, 64x1x5x5] %onnx::Conv_709[FLOAT, 64x32x1x1] %onnx::Conv_712[FLOAT, 64x32x1x1] %onnx::Conv_715[FLOAT, 64x1x3x3] %onnx::Conv_718[FLOAT, 64x32x1x1] %onnx::Conv_721[FLOAT, 112x64x1x1] %onnx::Conv_722[FLOAT, 112] %onnx::Conv_724[FLOAT, 672x112x1x1] %onnx::Conv_725[FLOAT, 672] %onnx::Conv_727[FLOAT, 672x1x3x3] %onnx::Conv_730[FLOAT, 112x672x1x1] %onnx::Conv_733[FLOAT, 336x112x1x1] %onnx::Conv_734[FLOAT, 336] %onnx::Conv_736[FLOAT, 336x1x5x5] %onnx::Conv_739[FLOAT, 112x336x1x1] %onnx::Conv_742[FLOAT, 112x112x1x1] %onnx::Conv_745[FLOAT, 112x1x5x5] %onnx::Conv_748[FLOAT, 112x112x1x1] %onnx::Conv_751[FLOAT, 672x112x1x1] %onnx::Conv_754[FLOAT, 672x1x3x3] %onnx::Conv_757[FLOAT, 184x672x1x1] %onnx::Conv_758[FLOAT, 184] %onnx::Conv_760[FLOAT, 184x184x1x1] %onnx::Conv_763[FLOAT, 184x1x5x5] %onnx::Conv_766[FLOAT, 184x184x1x1] %onnx::Conv_769[FLOAT, 1104x184x1x1] %onnx::Conv_770[FLOAT, 1104] %onnx::Conv_772[FLOAT, 1104x1x3x3] %onnx::Conv_775[FLOAT, 184x1104x1x1] %onnx::Conv_778[FLOAT, 184x92x1x1] %onnx::Conv_781[FLOAT, 184x1x5x5] %onnx::Conv_784[FLOAT, 184x92x1x1] %onnx::Conv_787[FLOAT, 184x184x1x1] %onnx::Conv_790[FLOAT, 184x1x5x5] %onnx::Conv_793[FLOAT, 352x184x1x1] %onnx::Conv_794[FLOAT, 352] %onnx::Conv_796[FLOAT, 1504x352x1x1] %onnx::Conv_797[FLOAT, 1504] ) { %onnx::Conv_791 = Identity(%onnx::Conv_758) %onnx::Conv_788 = Identity(%onnx::Conv_758) %onnx::Conv_785 = Identity(%onnx::Conv_758) %onnx::Conv_782 = Identity(%onnx::Conv_758) %onnx::Conv_779 = Identity(%onnx::Conv_758) %onnx::Conv_776 = Identity(%onnx::Conv_758) %onnx::Conv_773 = Identity(%onnx::Conv_770) %onnx::Conv_767 = Identity(%onnx::Conv_758) %onnx::Conv_764 = Identity(%onnx::Conv_758) %onnx::Conv_761 = Identity(%onnx::Conv_758) %onnx::Conv_755 = Identity(%onnx::Conv_725) %onnx::Conv_752 = Identity(%onnx::Conv_725) %onnx::Conv_749 = Identity(%onnx::Conv_722) %onnx::Conv_746 = Identity(%onnx::Conv_722) %onnx::Conv_743 = Identity(%onnx::Conv_722) %onnx::Conv_740 = Identity(%onnx::Conv_722) %onnx::Conv_737 = Identity(%onnx::Conv_734) %onnx::Conv_731 = Identity(%onnx::Conv_722) %onnx::Conv_728 = Identity(%onnx::Conv_725) %onnx::Conv_719 = Identity(%onnx::Conv_701) %onnx::Conv_716 = Identity(%onnx::Conv_701) %onnx::Conv_713 = Identity(%onnx::Conv_701) %onnx::Conv_710 = Identity(%onnx::Conv_701) %onnx::Conv_707 = Identity(%onnx::Conv_701) %onnx::Conv_704 = Identity(%onnx::Conv_701) %onnx::Conv_698 = Identity(%onnx::Conv_671) %onnx::Conv_695 = Identity(%onnx::Conv_671) %onnx::Conv_692 = Identity(%onnx::Conv_671) %onnx::Conv_689 = Identity(%onnx::Conv_671) %onnx::Conv_686 = Identity(%onnx::Conv_671) %onnx::Conv_683 = Identity(%onnx::Conv_671) %onnx::Conv_680 = Identity(%onnx::Conv_671) %onnx::Conv_677 = Identity(%onnx::Conv_629) %onnx::Conv_674 = Identity(%onnx::Conv_629) %onnx::Conv_668 = Identity(%onnx::Conv_647) %onnx::Conv_665 = Identity(%onnx::Conv_647) %onnx::Conv_662 = Identity(%onnx::Conv_644) %onnx::Conv_659 = Identity(%onnx::Conv_647) %onnx::Conv_656 = Identity(%onnx::Conv_647) %onnx::Conv_653 = Identity(%onnx::Conv_644) %onnx::Conv_650 = Identity(%onnx::Conv_647) %onnx::Conv_641 = Identity(%onnx::Conv_626) %onnx::Conv_638 = Identity(%onnx::Conv_626) %onnx::Conv_635 = Identity(%onnx::Conv_626) %onnx::Conv_632 = Identity(%onnx::Conv_629) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_625, %onnx::Conv_626) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_628, %onnx::Conv_629) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.0/nl/Relu_output_0, %onnx::Conv_631, %onnx::Conv_632) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_634, %onnx::Conv_635) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_637, %onnx::Conv_638) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.1/shuffle/Reshape_output_0 = Reshape(%/cells.1/nl/Relu_output_0, %/cells.1/shuffle/Constant_output_0) %/cells.1/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.1/shuffle/Reshape_output_0) %/cells.1/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.1/shuffle/Reshape_1_output_0 = Reshape(%/cells.1/shuffle/Transpose_output_0, %/cells.1/shuffle/Constant_1_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.1/shuffle/Reshape_1_output_0, %onnx::Conv_640, %onnx::Conv_641) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_643, %onnx::Conv_644) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_646, %onnx::Conv_647) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.2/nl/Relu_output_0, %onnx::Conv_649, %onnx::Conv_650) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_652, %onnx::Conv_653) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_655, %onnx::Conv_656) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.3/nl/Relu_output_0, %onnx::Conv_658, %onnx::Conv_659) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_661, %onnx::Conv_662) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_664, %onnx::Conv_665) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.5/nl/Relu_output_0, %onnx::Conv_667, %onnx::Conv_668) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_670, %onnx::Conv_671) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_673, %onnx::Conv_674) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.6/nl/Relu_output_0, %onnx::Conv_676, %onnx::Conv_677) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_679, %onnx::Conv_680) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_682, %onnx::Conv_683) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.7/shuffle/Reshape_output_0 = Reshape(%/cells.7/nl/Relu_output_0, %/cells.7/shuffle/Constant_output_0) %/cells.7/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.7/shuffle/Reshape_output_0) %/cells.7/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.7/shuffle/Reshape_1_output_0 = Reshape(%/cells.7/shuffle/Transpose_output_0, %/cells.7/shuffle/Constant_1_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.7/shuffle/Reshape_1_output_0, %onnx::Conv_685, %onnx::Conv_686) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_688, %onnx::Conv_689) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_691, %onnx::Conv_692) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.8/nl/Relu_output_0, %onnx::Conv_694, %onnx::Conv_695) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_697, %onnx::Conv_698) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [2, 2]](%/cells.8/Add_output_0, %onnx::Conv_700, %onnx::Conv_701) %/cells.9/relu/Relu_output_0 = Relu(%/cells.9/conv/Conv_output_0) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/relu/Relu_output_0, %onnx::Conv_703, %onnx::Conv_704) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.10/shuffle/Reshape_output_0 = Reshape(%/cells.10/nl/Relu_output_0, %/cells.10/shuffle/Constant_output_0) %/cells.10/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.10/shuffle/Reshape_output_0) %/cells.10/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.10/shuffle/Reshape_1_output_0 = Reshape(%/cells.10/shuffle/Transpose_output_0, %/cells.10/shuffle/Constant_1_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.10/shuffle/Reshape_1_output_0, %onnx::Conv_706, %onnx::Conv_707) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_709, %onnx::Conv_710) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/relu/Relu_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_712, %onnx::Conv_713) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.11/shuffle/Reshape_output_0 = Reshape(%/cells.11/nl/Relu_output_0, %/cells.11/shuffle/Constant_output_0) %/cells.11/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.11/shuffle/Reshape_output_0) %/cells.11/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.11/shuffle/Reshape_1_output_0 = Reshape(%/cells.11/shuffle/Transpose_output_0, %/cells.11/shuffle/Constant_1_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.11/shuffle/Reshape_1_output_0, %onnx::Conv_715, %onnx::Conv_716) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_718, %onnx::Conv_719) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.13/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_721, %onnx::Conv_722) %/cells.13/relu/Relu_output_0 = Relu(%/cells.13/conv/Conv_output_0) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/relu/Relu_output_0, %onnx::Conv_724, %onnx::Conv_725) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.14/nl/Relu_output_0, %onnx::Conv_727, %onnx::Conv_728) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_730, %onnx::Conv_731) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/relu/Relu_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_733, %onnx::Conv_734) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.15/nl/Relu_output_0, %onnx::Conv_736, %onnx::Conv_737) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_739, %onnx::Conv_740) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_742, %onnx::Conv_743) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.16/nl/Relu_output_0, %onnx::Conv_745, %onnx::Conv_746) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_748, %onnx::Conv_749) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.15/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_751, %onnx::Conv_752) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.17/nl/Relu_output_0, %onnx::Conv_754, %onnx::Conv_755) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_757, %onnx::Conv_758) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_760, %onnx::Conv_761) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.18/nl/Relu_output_0, %onnx::Conv_763, %onnx::Conv_764) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_766, %onnx::Conv_767) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_769, %onnx::Conv_770) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.19/nl/Relu_output_0, %onnx::Conv_772, %onnx::Conv_773) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_775, %onnx::Conv_776) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_778, %onnx::Conv_779) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.20/shuffle/Reshape_output_0 = Reshape(%/cells.20/nl/Relu_output_0, %/cells.20/shuffle/Constant_output_0) %/cells.20/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.20/shuffle/Reshape_output_0) %/cells.20/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.20/shuffle/Reshape_1_output_0 = Reshape(%/cells.20/shuffle/Transpose_output_0, %/cells.20/shuffle/Constant_1_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.20/shuffle/Reshape_1_output_0, %onnx::Conv_781, %onnx::Conv_782) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_784, %onnx::Conv_785) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_787, %onnx::Conv_788) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.21/nl/Relu_output_0, %onnx::Conv_790, %onnx::Conv_791) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_793, %onnx::Conv_794) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_796, %onnx::Conv_797) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %623 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %623 }	val_accuracy	67,633,280	1,858,052	{'zcp_synflow': 72.67340134917355, 'zcp_zen': 63.154354095458984, 'zcp_epe_nas': 11.866542124594956, 'zcp_fisher': 0.11199109256267548, 'zcp_flops': 67633280.0, 'zcp_grad_norm': 22.453025817871094, 'zcp_grasp': -0.07316970825195312, 'zcp_jacov': -16.080514111672898, 'zcp_l2_norm': 583.971435546875, 'zcp_nwot': 211.9651758692302, 'zcp_params': 1858052.0, 'zcp_plain': -0.004415757022798061, 'zcp_snip': 41.07036209106445, 'lat_1080ti_1': 0.45657324545637545, 'lat_1080ti_32': 0.4665115088770615, 'lat_1080ti_64': 0.3914734270916839, 'lat_2080ti_1': 0.49007927625971803, 'lat_2080ti_32': 0.44288541728676056, 'lat_2080ti_64': 0.3943755315880876, 'lat_essential_ph_1': 0.39622641509433965, 'lat_eyeriss': 0.39574202776300577, 'lat_fpga': 0.4349835244758012, 'lat_gold_6226': 0.2913601098991771, 'lat_gold_6240': 0.5097152699444617, 'lat_pixel2': 0.2826086956521739, 'lat_pixel3': 0.3927027821795556, 'lat_raspi4': 0.38791502918926674, 'lat_samsung_a50': 0.16842105263157894, 'lat_samsung_s7': 0.2047244094488189, 'lat_silver_4114': 0.39344646158057706, 'lat_silver_4210r': 0.34893772065207534, 'lat_titan_rtx_1': 0.47030209656496297, 'lat_titan_rtx_32': 0.41163789234616516, 'lat_titan_rtx_64': 0.3963796239948425, 'lat_titanx_1': 0.25864513783468723, 'lat_titanx_32': 0.39904954719035823, 'lat_titanx_64': 0.35602968209024505, 'lat_titanxp_1': 0.45969043287099537, 'lat_titanxp_32': 0.4113264382218249, 'lat_titanxp_64': 0.38764796842762717}
FBNet_4252	FBNet	4252	4252	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_687[FLOAT, 16x3x3x3] %onnx::Conv_688[FLOAT, 16] %onnx::Conv_690[FLOAT, 48x16x1x1] %onnx::Conv_691[FLOAT, 48] %onnx::Conv_693[FLOAT, 48x1x5x5] %onnx::Conv_696[FLOAT, 16x48x1x1] %onnx::Conv_699[FLOAT, 16x16x1x1] %onnx::Conv_702[FLOAT, 16x1x5x5] %onnx::Conv_705[FLOAT, 24x16x1x1] %onnx::Conv_706[FLOAT, 24] %onnx::Conv_708[FLOAT, 24x24x1x1] %onnx::Conv_711[FLOAT, 24x1x5x5] %onnx::Conv_714[FLOAT, 24x24x1x1] %onnx::Conv_717[FLOAT, 144x24x1x1] %onnx::Conv_718[FLOAT, 144] %onnx::Conv_720[FLOAT, 144x1x3x3] %onnx::Conv_723[FLOAT, 24x144x1x1] %onnx::Conv_726[FLOAT, 24x12x1x1] %onnx::Conv_729[FLOAT, 24x1x3x3] %onnx::Conv_732[FLOAT, 32x12x1x1] %onnx::Conv_733[FLOAT, 32] %onnx::Conv_735[FLOAT, 192x32x1x1] %onnx::Conv_736[FLOAT, 192] %onnx::Conv_738[FLOAT, 192x1x3x3] %onnx::Conv_741[FLOAT, 32x192x1x1] %onnx::Conv_744[FLOAT, 32x32x1x1] %onnx::Conv_747[FLOAT, 32x1x3x3] %onnx::Conv_750[FLOAT, 32x32x1x1] %onnx::Conv_753[FLOAT, 192x32x1x1] %onnx::Conv_756[FLOAT, 192x1x5x5] %onnx::Conv_759[FLOAT, 32x192x1x1] %onnx::Conv_762[FLOAT, 32x16x1x1] %onnx::Conv_765[FLOAT, 32x1x5x5] %onnx::Conv_768[FLOAT, 64x16x1x1] %onnx::Conv_769[FLOAT, 64] %onnx::Conv_771[FLOAT, 64x64x1x1] %onnx::Conv_774[FLOAT, 64x1x5x5] %onnx::Conv_777[FLOAT, 64x64x1x1] %onnx::Conv_780[FLOAT, 64x32x1x1] %onnx::Conv_783[FLOAT, 64x1x5x5] %onnx::Conv_786[FLOAT, 64x32x1x1] %onnx::Conv_789[FLOAT, 64x32x1x1] %onnx::Conv_792[FLOAT, 64x1x3x3] %onnx::Conv_795[FLOAT, 64x32x1x1] %onnx::Conv_798[FLOAT, 112x64x1x1] %onnx::Conv_799[FLOAT, 112] %onnx::Conv_801[FLOAT, 112x112x1x1] %onnx::Conv_804[FLOAT, 112x1x3x3] %onnx::Conv_807[FLOAT, 112x112x1x1] %onnx::Conv_810[FLOAT, 112x56x1x1] %onnx::Conv_813[FLOAT, 112x1x5x5] %onnx::Conv_816[FLOAT, 112x56x1x1] %onnx::Conv_819[FLOAT, 672x112x1x1] %onnx::Conv_820[FLOAT, 672] %onnx::Conv_822[FLOAT, 672x1x3x3] %onnx::Conv_825[FLOAT, 112x672x1x1] %onnx::Conv_828[FLOAT, 112x56x1x1] %onnx::Conv_831[FLOAT, 112x1x3x3] %onnx::Conv_834[FLOAT, 184x56x1x1] %onnx::Conv_835[FLOAT, 184] %onnx::Conv_837[FLOAT, 184x184x1x1] %onnx::Conv_840[FLOAT, 184x1x5x5] %onnx::Conv_843[FLOAT, 184x184x1x1] %onnx::Conv_846[FLOAT, 184x184x1x1] %onnx::Conv_849[FLOAT, 184x1x3x3] %onnx::Conv_852[FLOAT, 184x184x1x1] %onnx::Conv_855[FLOAT, 184x184x1x1] %onnx::Conv_858[FLOAT, 184x1x3x3] %onnx::Conv_861[FLOAT, 184x184x1x1] %onnx::Conv_864[FLOAT, 184x184x1x1] %onnx::Conv_867[FLOAT, 184x1x5x5] %onnx::Conv_870[FLOAT, 352x184x1x1] %onnx::Conv_871[FLOAT, 352] %onnx::Conv_873[FLOAT, 1504x352x1x1] %onnx::Conv_874[FLOAT, 1504] ) { %onnx::Conv_868 = Identity(%onnx::Conv_835) %onnx::Conv_865 = Identity(%onnx::Conv_835) %onnx::Conv_862 = Identity(%onnx::Conv_835) %onnx::Conv_859 = Identity(%onnx::Conv_835) %onnx::Conv_856 = Identity(%onnx::Conv_835) %onnx::Conv_853 = Identity(%onnx::Conv_835) %onnx::Conv_850 = Identity(%onnx::Conv_835) %onnx::Conv_847 = Identity(%onnx::Conv_835) %onnx::Conv_844 = Identity(%onnx::Conv_835) %onnx::Conv_841 = Identity(%onnx::Conv_835) %onnx::Conv_838 = Identity(%onnx::Conv_835) %onnx::Conv_832 = Identity(%onnx::Conv_799) %onnx::Conv_829 = Identity(%onnx::Conv_799) %onnx::Conv_826 = Identity(%onnx::Conv_799) %onnx::Conv_823 = Identity(%onnx::Conv_820) %onnx::Conv_817 = Identity(%onnx::Conv_799) %onnx::Conv_814 = Identity(%onnx::Conv_799) %onnx::Conv_811 = Identity(%onnx::Conv_799) %onnx::Conv_808 = Identity(%onnx::Conv_799) %onnx::Conv_805 = Identity(%onnx::Conv_799) %onnx::Conv_802 = Identity(%onnx::Conv_799) %onnx::Conv_796 = Identity(%onnx::Conv_769) %onnx::Conv_793 = Identity(%onnx::Conv_769) %onnx::Conv_790 = Identity(%onnx::Conv_769) %onnx::Conv_787 = Identity(%onnx::Conv_769) %onnx::Conv_784 = Identity(%onnx::Conv_769) %onnx::Conv_781 = Identity(%onnx::Conv_769) %onnx::Conv_778 = Identity(%onnx::Conv_769) %onnx::Conv_775 = Identity(%onnx::Conv_769) %onnx::Conv_772 = Identity(%onnx::Conv_769) %onnx::Conv_766 = Identity(%onnx::Conv_733) %onnx::Conv_763 = Identity(%onnx::Conv_733) %onnx::Conv_760 = Identity(%onnx::Conv_733) %onnx::Conv_757 = Identity(%onnx::Conv_736) %onnx::Conv_754 = Identity(%onnx::Conv_736) %onnx::Conv_751 = Identity(%onnx::Conv_733) %onnx::Conv_748 = Identity(%onnx::Conv_733) %onnx::Conv_745 = Identity(%onnx::Conv_733) %onnx::Conv_742 = Identity(%onnx::Conv_733) %onnx::Conv_739 = Identity(%onnx::Conv_736) %onnx::Conv_730 = Identity(%onnx::Conv_706) %onnx::Conv_727 = Identity(%onnx::Conv_706) %onnx::Conv_724 = Identity(%onnx::Conv_706) %onnx::Conv_721 = Identity(%onnx::Conv_718) %onnx::Conv_715 = Identity(%onnx::Conv_706) %onnx::Conv_712 = Identity(%onnx::Conv_706) %onnx::Conv_709 = Identity(%onnx::Conv_706) %onnx::Conv_703 = Identity(%onnx::Conv_688) %onnx::Conv_700 = Identity(%onnx::Conv_688) %onnx::Conv_697 = Identity(%onnx::Conv_688) %onnx::Conv_694 = Identity(%onnx::Conv_691) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_687, %onnx::Conv_688) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_690, %onnx::Conv_691) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 48, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.0/nl/Relu_output_0, %onnx::Conv_693, %onnx::Conv_694) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_696, %onnx::Conv_697) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_699, %onnx::Conv_700) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.1/nl/Relu_output_0, %onnx::Conv_702, %onnx::Conv_703) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_705, %onnx::Conv_706) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_708, %onnx::Conv_709) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.3/nl/Relu_output_0, %onnx::Conv_711, %onnx::Conv_712) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_714, %onnx::Conv_715) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_717, %onnx::Conv_718) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.4/nl/Relu_output_0, %onnx::Conv_720, %onnx::Conv_721) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_723, %onnx::Conv_724) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/Add_output_0, %onnx::Conv_726, %onnx::Conv_727) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.5/shuffle/Reshape_output_0 = Reshape(%/cells.5/nl/Relu_output_0, %/cells.5/shuffle/Constant_output_0) %/cells.5/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.5/shuffle/Reshape_output_0) %/cells.5/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.5/shuffle/Reshape_1_output_0 = Reshape(%/cells.5/shuffle/Transpose_output_0, %/cells.5/shuffle/Constant_1_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.5/shuffle/Reshape_1_output_0, %onnx::Conv_729, %onnx::Conv_730) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_732, %onnx::Conv_733) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_735, %onnx::Conv_736) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.6/nl/Relu_output_0, %onnx::Conv_738, %onnx::Conv_739) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_741, %onnx::Conv_742) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_744, %onnx::Conv_745) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.7/nl/Relu_output_0, %onnx::Conv_747, %onnx::Conv_748) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_750, %onnx::Conv_751) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_753, %onnx::Conv_754) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.8/nl/Relu_output_0, %onnx::Conv_756, %onnx::Conv_757) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_759, %onnx::Conv_760) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_762, %onnx::Conv_763) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.9/shuffle/Reshape_output_0 = Reshape(%/cells.9/nl/Relu_output_0, %/cells.9/shuffle/Constant_output_0) %/cells.9/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.9/shuffle/Reshape_output_0) %/cells.9/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.9/shuffle/Reshape_1_output_0 = Reshape(%/cells.9/shuffle/Transpose_output_0, %/cells.9/shuffle/Constant_1_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.9/shuffle/Reshape_1_output_0, %onnx::Conv_765, %onnx::Conv_766) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_768, %onnx::Conv_769) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_771, %onnx::Conv_772) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.10/nl/Relu_output_0, %onnx::Conv_774, %onnx::Conv_775) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_777, %onnx::Conv_778) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_780, %onnx::Conv_781) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.11/shuffle/Reshape_output_0 = Reshape(%/cells.11/nl/Relu_output_0, %/cells.11/shuffle/Constant_output_0) %/cells.11/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.11/shuffle/Reshape_output_0) %/cells.11/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.11/shuffle/Reshape_1_output_0 = Reshape(%/cells.11/shuffle/Transpose_output_0, %/cells.11/shuffle/Constant_1_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.11/shuffle/Reshape_1_output_0, %onnx::Conv_783, %onnx::Conv_784) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_786, %onnx::Conv_787) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_789, %onnx::Conv_790) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.12/shuffle/Reshape_output_0 = Reshape(%/cells.12/nl/Relu_output_0, %/cells.12/shuffle/Constant_output_0) %/cells.12/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.12/shuffle/Reshape_output_0) %/cells.12/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.12/shuffle/Reshape_1_output_0 = Reshape(%/cells.12/shuffle/Transpose_output_0, %/cells.12/shuffle/Constant_1_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.12/shuffle/Reshape_1_output_0, %onnx::Conv_792, %onnx::Conv_793) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_795, %onnx::Conv_796) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_798, %onnx::Conv_799) %/cells.13/relu/Relu_output_0 = Relu(%/cells.13/conv/Conv_output_0) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/relu/Relu_output_0, %onnx::Conv_801, %onnx::Conv_802) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.14/nl/Relu_output_0, %onnx::Conv_804, %onnx::Conv_805) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_807, %onnx::Conv_808) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/relu/Relu_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_810, %onnx::Conv_811) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.15/shuffle/Reshape_output_0 = Reshape(%/cells.15/nl/Relu_output_0, %/cells.15/shuffle/Constant_output_0) %/cells.15/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.15/shuffle/Reshape_output_0) %/cells.15/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.15/shuffle/Reshape_1_output_0 = Reshape(%/cells.15/shuffle/Transpose_output_0, %/cells.15/shuffle/Constant_1_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.15/shuffle/Reshape_1_output_0, %onnx::Conv_813, %onnx::Conv_814) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_816, %onnx::Conv_817) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_819, %onnx::Conv_820) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.16/nl/Relu_output_0, %onnx::Conv_822, %onnx::Conv_823) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_825, %onnx::Conv_826) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.15/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_828, %onnx::Conv_829) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.17/shuffle/Reshape_output_0 = Reshape(%/cells.17/nl/Relu_output_0, %/cells.17/shuffle/Constant_output_0) %/cells.17/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.17/shuffle/Reshape_output_0) %/cells.17/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.17/shuffle/Reshape_1_output_0 = Reshape(%/cells.17/shuffle/Transpose_output_0, %/cells.17/shuffle/Constant_1_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.17/shuffle/Reshape_1_output_0, %onnx::Conv_831, %onnx::Conv_832) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_834, %onnx::Conv_835) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_837, %onnx::Conv_838) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.18/nl/Relu_output_0, %onnx::Conv_840, %onnx::Conv_841) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_843, %onnx::Conv_844) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_846, %onnx::Conv_847) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.19/nl/Relu_output_0, %onnx::Conv_849, %onnx::Conv_850) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_852, %onnx::Conv_853) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_855, %onnx::Conv_856) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.20/nl/Relu_output_0, %onnx::Conv_858, %onnx::Conv_859) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_861, %onnx::Conv_862) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_864, %onnx::Conv_865) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.21/nl/Relu_output_0, %onnx::Conv_867, %onnx::Conv_868) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_870, %onnx::Conv_871) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_873, %onnx::Conv_874) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %685 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %685 }	val_accuracy	55,179,136	1,305,556	{'zcp_synflow': 76.58299639792494, 'zcp_zen': 65.03718566894531, 'zcp_epe_nas': 0.00015999920000638146, 'zcp_fisher': 0.11485544592142105, 'zcp_flops': 55179136.0, 'zcp_grad_norm': 25.35411262512207, 'zcp_grasp': 0.001934051513671875, 'zcp_jacov': -16.070917733294216, 'zcp_l2_norm': 556.9221801757812, 'zcp_nwot': 210.53732972285957, 'zcp_params': 1305556.0, 'zcp_plain': 0.0008928455645218492, 'zcp_snip': 40.10200119018555, 'lat_1080ti_1': 0.632799722447362, 'lat_1080ti_32': 0.535913106865463, 'lat_1080ti_64': 0.45518688419969533, 'lat_2080ti_1': 0.689613713723655, 'lat_2080ti_32': 0.6020297067230208, 'lat_2080ti_64': 0.5145501170115568, 'lat_essential_ph_1': 0.20754716981132076, 'lat_eyeriss': 0.2974162433755708, 'lat_fpga': 0.31004660643782345, 'lat_gold_6226': 0.1138444685581651, 'lat_gold_6240': 0.3375537315024534, 'lat_pixel2': 0.2826086956521739, 'lat_pixel3': 0.2889943341563631, 'lat_raspi4': 0.2861854870276947, 'lat_samsung_a50': 0.11578947368421053, 'lat_samsung_s7': 0.06299212598425197, 'lat_silver_4114': 0.35821756404001914, 'lat_silver_4210r': 0.40676974685100664, 'lat_titan_rtx_1': 0.6532951989156159, 'lat_titan_rtx_32': 0.5796324114039093, 'lat_titan_rtx_64': 0.5268522056856104, 'lat_titanx_1': 0.34418233158341005, 'lat_titanx_32': 0.5353431463124011, 'lat_titanx_64': 0.4459889446462736, 'lat_titanxp_1': 0.6270484566237947, 'lat_titanxp_32': 0.5604631152276665, 'lat_titanxp_64': 0.497343190622182}
FBNet_1722	FBNet	1722	1722	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_549[FLOAT, 16x3x3x3] %onnx::Conv_550[FLOAT, 16] %onnx::Conv_552[FLOAT, 16x16x1x1] %onnx::Conv_555[FLOAT, 16x1x3x3] %onnx::Conv_558[FLOAT, 16x16x1x1] %onnx::Conv_561[FLOAT, 16x16x1x1] %onnx::Conv_564[FLOAT, 16x1x3x3] %onnx::Conv_567[FLOAT, 24x16x1x1] %onnx::Conv_568[FLOAT, 24] %onnx::Conv_570[FLOAT, 144x24x1x1] %onnx::Conv_571[FLOAT, 144] %onnx::Conv_573[FLOAT, 144x1x3x3] %onnx::Conv_576[FLOAT, 24x144x1x1] %onnx::Conv_579[FLOAT, 144x24x1x1] %onnx::Conv_582[FLOAT, 144x1x3x3] %onnx::Conv_585[FLOAT, 32x144x1x1] %onnx::Conv_586[FLOAT, 32] %onnx::Conv_588[FLOAT, 32x32x1x1] %onnx::Conv_591[FLOAT, 32x1x3x3] %onnx::Conv_594[FLOAT, 32x32x1x1] %onnx::Conv_597[FLOAT, 32x32x1x1] %onnx::Conv_600[FLOAT, 32x1x5x5] %onnx::Conv_603[FLOAT, 32x32x1x1] %onnx::Conv_606[FLOAT, 96x32x1x1] %onnx::Conv_607[FLOAT, 96] %onnx::Conv_609[FLOAT, 96x1x3x3] %onnx::Conv_612[FLOAT, 32x96x1x1] %onnx::Conv_615[FLOAT, 32x16x1x1] %onnx::Conv_618[FLOAT, 32x1x3x3] %onnx::Conv_621[FLOAT, 64x16x1x1] %onnx::Conv_622[FLOAT, 64] %onnx::Conv_624[FLOAT, 192x64x1x1] %onnx::Conv_625[FLOAT, 192] %onnx::Conv_627[FLOAT, 192x1x5x5] %onnx::Conv_630[FLOAT, 64x192x1x1] %onnx::Conv_633[FLOAT, 64x64x1x1] %onnx::Conv_636[FLOAT, 64x1x3x3] %onnx::Conv_639[FLOAT, 64x64x1x1] %onnx::Conv_642[FLOAT, 64x64x1x1] %onnx::Conv_645[FLOAT, 64x1x5x5] %onnx::Conv_648[FLOAT, 112x64x1x1] %onnx::Conv_649[FLOAT, 112] %onnx::Conv_651[FLOAT, 112x112x1x1] %onnx::Conv_654[FLOAT, 112x1x5x5] %onnx::Conv_657[FLOAT, 112x112x1x1] %onnx::Conv_660[FLOAT, 336x112x1x1] %onnx::Conv_661[FLOAT, 336] %onnx::Conv_663[FLOAT, 336x1x5x5] %onnx::Conv_666[FLOAT, 112x336x1x1] %onnx::Conv_669[FLOAT, 672x112x1x1] %onnx::Conv_670[FLOAT, 672] %onnx::Conv_672[FLOAT, 672x1x5x5] %onnx::Conv_675[FLOAT, 112x672x1x1] %onnx::Conv_678[FLOAT, 112x112x1x1] %onnx::Conv_681[FLOAT, 112x1x5x5] %onnx::Conv_684[FLOAT, 184x112x1x1] %onnx::Conv_685[FLOAT, 184] %onnx::Conv_687[FLOAT, 184x92x1x1] %onnx::Conv_690[FLOAT, 184x1x3x3] %onnx::Conv_693[FLOAT, 184x92x1x1] %onnx::Conv_696[FLOAT, 552x184x1x1] %onnx::Conv_697[FLOAT, 552] %onnx::Conv_699[FLOAT, 552x1x5x5] %onnx::Conv_702[FLOAT, 184x552x1x1] %onnx::Conv_705[FLOAT, 184x184x1x1] %onnx::Conv_708[FLOAT, 184x1x3x3] %onnx::Conv_711[FLOAT, 352x184x1x1] %onnx::Conv_712[FLOAT, 352] %onnx::Conv_714[FLOAT, 1504x352x1x1] %onnx::Conv_715[FLOAT, 1504] ) { %onnx::Conv_709 = Identity(%onnx::Conv_685) %onnx::Conv_706 = Identity(%onnx::Conv_685) %onnx::Conv_703 = Identity(%onnx::Conv_685) %onnx::Conv_700 = Identity(%onnx::Conv_697) %onnx::Conv_694 = Identity(%onnx::Conv_685) %onnx::Conv_691 = Identity(%onnx::Conv_685) %onnx::Conv_688 = Identity(%onnx::Conv_685) %onnx::Conv_682 = Identity(%onnx::Conv_649) %onnx::Conv_679 = Identity(%onnx::Conv_649) %onnx::Conv_676 = Identity(%onnx::Conv_649) %onnx::Conv_673 = Identity(%onnx::Conv_670) %onnx::Conv_667 = Identity(%onnx::Conv_649) %onnx::Conv_664 = Identity(%onnx::Conv_661) %onnx::Conv_658 = Identity(%onnx::Conv_649) %onnx::Conv_655 = Identity(%onnx::Conv_649) %onnx::Conv_652 = Identity(%onnx::Conv_649) %onnx::Conv_646 = Identity(%onnx::Conv_622) %onnx::Conv_643 = Identity(%onnx::Conv_622) %onnx::Conv_640 = Identity(%onnx::Conv_622) %onnx::Conv_637 = Identity(%onnx::Conv_622) %onnx::Conv_634 = Identity(%onnx::Conv_622) %onnx::Conv_631 = Identity(%onnx::Conv_622) %onnx::Conv_628 = Identity(%onnx::Conv_625) %onnx::Conv_619 = Identity(%onnx::Conv_586) %onnx::Conv_616 = Identity(%onnx::Conv_586) %onnx::Conv_613 = Identity(%onnx::Conv_586) %onnx::Conv_610 = Identity(%onnx::Conv_607) %onnx::Conv_604 = Identity(%onnx::Conv_586) %onnx::Conv_601 = Identity(%onnx::Conv_586) %onnx::Conv_598 = Identity(%onnx::Conv_586) %onnx::Conv_595 = Identity(%onnx::Conv_586) %onnx::Conv_592 = Identity(%onnx::Conv_586) %onnx::Conv_589 = Identity(%onnx::Conv_586) %onnx::Conv_583 = Identity(%onnx::Conv_571) %onnx::Conv_580 = Identity(%onnx::Conv_571) %onnx::Conv_577 = Identity(%onnx::Conv_568) %onnx::Conv_574 = Identity(%onnx::Conv_571) %onnx::Conv_565 = Identity(%onnx::Conv_550) %onnx::Conv_562 = Identity(%onnx::Conv_550) %onnx::Conv_559 = Identity(%onnx::Conv_550) %onnx::Conv_556 = Identity(%onnx::Conv_550) %onnx::Conv_553 = Identity(%onnx::Conv_550) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_549, %onnx::Conv_550) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_552, %onnx::Conv_553) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.0/nl/Relu_output_0, %onnx::Conv_555, %onnx::Conv_556) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_558, %onnx::Conv_559) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_561, %onnx::Conv_562) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.1/nl/Relu_output_0, %onnx::Conv_564, %onnx::Conv_565) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_567, %onnx::Conv_568) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_570, %onnx::Conv_571) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.2/nl/Relu_output_0, %onnx::Conv_573, %onnx::Conv_574) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_576, %onnx::Conv_577) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_579, %onnx::Conv_580) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.5/nl/Relu_output_0, %onnx::Conv_582, %onnx::Conv_583) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_585, %onnx::Conv_586) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_588, %onnx::Conv_589) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.6/nl/Relu_output_0, %onnx::Conv_591, %onnx::Conv_592) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_594, %onnx::Conv_595) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_597, %onnx::Conv_598) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.7/nl/Relu_output_0, %onnx::Conv_600, %onnx::Conv_601) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_603, %onnx::Conv_604) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_606, %onnx::Conv_607) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.8/nl/Relu_output_0, %onnx::Conv_609, %onnx::Conv_610) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_612, %onnx::Conv_613) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_615, %onnx::Conv_616) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.9/shuffle/Reshape_output_0 = Reshape(%/cells.9/nl/Relu_output_0, %/cells.9/shuffle/Constant_output_0) %/cells.9/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.9/shuffle/Reshape_output_0) %/cells.9/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.9/shuffle/Reshape_1_output_0 = Reshape(%/cells.9/shuffle/Transpose_output_0, %/cells.9/shuffle/Constant_1_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.9/shuffle/Reshape_1_output_0, %onnx::Conv_618, %onnx::Conv_619) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_621, %onnx::Conv_622) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_624, %onnx::Conv_625) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.10/nl/Relu_output_0, %onnx::Conv_627, %onnx::Conv_628) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_630, %onnx::Conv_631) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_633, %onnx::Conv_634) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.11/nl/Relu_output_0, %onnx::Conv_636, %onnx::Conv_637) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_639, %onnx::Conv_640) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_642, %onnx::Conv_643) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.13/nl/Relu_output_0, %onnx::Conv_645, %onnx::Conv_646) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_648, %onnx::Conv_649) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_651, %onnx::Conv_652) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.14/nl/Relu_output_0, %onnx::Conv_654, %onnx::Conv_655) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_657, %onnx::Conv_658) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_660, %onnx::Conv_661) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.15/nl/Relu_output_0, %onnx::Conv_663, %onnx::Conv_664) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_666, %onnx::Conv_667) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_669, %onnx::Conv_670) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.16/nl/Relu_output_0, %onnx::Conv_672, %onnx::Conv_673) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_675, %onnx::Conv_676) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.15/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_678, %onnx::Conv_679) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.17/nl/Relu_output_0, %onnx::Conv_681, %onnx::Conv_682) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_684, %onnx::Conv_685) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_687, %onnx::Conv_688) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.19/shuffle/Reshape_output_0 = Reshape(%/cells.19/nl/Relu_output_0, %/cells.19/shuffle/Constant_output_0) %/cells.19/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.19/shuffle/Reshape_output_0) %/cells.19/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.19/shuffle/Reshape_1_output_0 = Reshape(%/cells.19/shuffle/Transpose_output_0, %/cells.19/shuffle/Constant_1_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.19/shuffle/Reshape_1_output_0, %onnx::Conv_690, %onnx::Conv_691) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_693, %onnx::Conv_694) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_696, %onnx::Conv_697) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 552, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.20/nl/Relu_output_0, %onnx::Conv_699, %onnx::Conv_700) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_702, %onnx::Conv_703) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_705, %onnx::Conv_706) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.21/nl/Relu_output_0, %onnx::Conv_708, %onnx::Conv_709) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_711, %onnx::Conv_712) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_714, %onnx::Conv_715) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %547 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %547 }	val_accuracy	57,804,160	1,450,380	{'zcp_synflow': 71.88857557215896, 'zcp_zen': 60.51887512207031, 'zcp_epe_nas': 0.00015999920000638146, 'zcp_fisher': 0.058871086686849594, 'zcp_flops': 57804160.0, 'zcp_grad_norm': 16.41106414794922, 'zcp_grasp': -0.012205123901367188, 'zcp_jacov': -16.050637614202437, 'zcp_l2_norm': 535.7154541015625, 'zcp_nwot': 209.99771387796426, 'zcp_params': 1450380.0, 'zcp_plain': -0.0016251534689217806, 'zcp_snip': 27.162607192993164, 'lat_1080ti_1': 0.31703952528813356, 'lat_1080ti_32': 0.3390396377267368, 'lat_1080ti_64': 0.25658517522273433, 'lat_2080ti_1': 0.3448022089013573, 'lat_2080ti_32': 0.3338375681339714, 'lat_2080ti_64': 0.31422175398062063, 'lat_essential_ph_1': 0.2830188679245283, 'lat_eyeriss': 0.26047530852878076, 'lat_fpga': 0.3251565916939608, 'lat_gold_6226': 0.2740398259278054, 'lat_gold_6240': 0.17178615265828004, 'lat_pixel2': 0.17391304347826086, 'lat_pixel3': 0.2551051337370119, 'lat_raspi4': 0.25801457154316254, 'lat_samsung_a50': 0.10526315789473684, 'lat_samsung_s7': 0.12598425196850394, 'lat_silver_4114': 0.21325638309676143, 'lat_silver_4210r': 0.1605388994460641, 'lat_titan_rtx_1': 0.3292413373789428, 'lat_titan_rtx_32': 0.3064009663207019, 'lat_titan_rtx_64': 0.3065591722470224, 'lat_titanx_1': 0.16913698456940293, 'lat_titanx_32': 0.30110642516702973, 'lat_titanx_64': 0.25307802548161656, 'lat_titanxp_1': 0.30563271805815967, 'lat_titanxp_32': 0.3035583349676875, 'lat_titanxp_64': 0.2805930326695522}
FBNet_4088	FBNet	4088	4088	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_734[FLOAT, 16x3x3x3] %onnx::Conv_735[FLOAT, 16] %onnx::Conv_737[FLOAT, 48x16x1x1] %onnx::Conv_738[FLOAT, 48] %onnx::Conv_740[FLOAT, 48x1x3x3] %onnx::Conv_743[FLOAT, 16x48x1x1] %onnx::Conv_746[FLOAT, 96x16x1x1] %onnx::Conv_747[FLOAT, 96] %onnx::Conv_749[FLOAT, 96x1x3x3] %onnx::Conv_752[FLOAT, 24x96x1x1] %onnx::Conv_753[FLOAT, 24] %onnx::Conv_755[FLOAT, 24x12x1x1] %onnx::Conv_758[FLOAT, 24x1x5x5] %onnx::Conv_761[FLOAT, 24x12x1x1] %onnx::Conv_764[FLOAT, 24x24x1x1] %onnx::Conv_767[FLOAT, 24x1x5x5] %onnx::Conv_770[FLOAT, 24x24x1x1] %onnx::Conv_773[FLOAT, 24x24x1x1] %onnx::Conv_776[FLOAT, 24x1x3x3] %onnx::Conv_779[FLOAT, 24x24x1x1] %onnx::Conv_782[FLOAT, 24x12x1x1] %onnx::Conv_785[FLOAT, 24x1x3x3] %onnx::Conv_788[FLOAT, 32x12x1x1] %onnx::Conv_789[FLOAT, 32] %onnx::Conv_791[FLOAT, 192x32x1x1] %onnx::Conv_792[FLOAT, 192] %onnx::Conv_794[FLOAT, 192x1x3x3] %onnx::Conv_797[FLOAT, 32x192x1x1] %onnx::Conv_800[FLOAT, 192x32x1x1] %onnx::Conv_803[FLOAT, 192x1x5x5] %onnx::Conv_806[FLOAT, 32x192x1x1] %onnx::Conv_809[FLOAT, 32x16x1x1] %onnx::Conv_812[FLOAT, 32x1x5x5] %onnx::Conv_815[FLOAT, 32x16x1x1] %onnx::Conv_818[FLOAT, 64x32x1x1] %onnx::Conv_819[FLOAT, 64] %onnx::Conv_821[FLOAT, 64x32x1x1] %onnx::Conv_824[FLOAT, 64x1x5x5] %onnx::Conv_827[FLOAT, 64x32x1x1] %onnx::Conv_830[FLOAT, 64x64x1x1] %onnx::Conv_833[FLOAT, 64x1x5x5] %onnx::Conv_836[FLOAT, 64x64x1x1] %onnx::Conv_839[FLOAT, 192x64x1x1] %onnx::Conv_842[FLOAT, 192x1x3x3] %onnx::Conv_845[FLOAT, 64x192x1x1] %onnx::Conv_848[FLOAT, 64x32x1x1] %onnx::Conv_851[FLOAT, 64x1x3x3] %onnx::Conv_854[FLOAT, 112x32x1x1] %onnx::Conv_855[FLOAT, 112] %onnx::Conv_857[FLOAT, 112x56x1x1] %onnx::Conv_860[FLOAT, 112x1x5x5] %onnx::Conv_863[FLOAT, 112x56x1x1] %onnx::Conv_866[FLOAT, 336x112x1x1] %onnx::Conv_867[FLOAT, 336] %onnx::Conv_869[FLOAT, 336x1x3x3] %onnx::Conv_872[FLOAT, 112x336x1x1] %onnx::Conv_875[FLOAT, 112x112x1x1] %onnx::Conv_878[FLOAT, 112x1x5x5] %onnx::Conv_881[FLOAT, 112x112x1x1] %onnx::Conv_884[FLOAT, 184x112x1x1] %onnx::Conv_885[FLOAT, 184] %onnx::Conv_887[FLOAT, 184x92x1x1] %onnx::Conv_890[FLOAT, 184x1x5x5] %onnx::Conv_893[FLOAT, 184x92x1x1] %onnx::Conv_896[FLOAT, 184x92x1x1] %onnx::Conv_899[FLOAT, 184x1x3x3] %onnx::Conv_902[FLOAT, 184x92x1x1] %onnx::Conv_905[FLOAT, 184x184x1x1] %onnx::Conv_908[FLOAT, 184x1x5x5] %onnx::Conv_911[FLOAT, 184x184x1x1] %onnx::Conv_914[FLOAT, 184x184x1x1] %onnx::Conv_917[FLOAT, 184x1x3x3] %onnx::Conv_920[FLOAT, 352x184x1x1] %onnx::Conv_921[FLOAT, 352] %onnx::Conv_923[FLOAT, 1504x352x1x1] %onnx::Conv_924[FLOAT, 1504] ) { %onnx::Conv_918 = Identity(%onnx::Conv_885) %onnx::Conv_915 = Identity(%onnx::Conv_885) %onnx::Conv_912 = Identity(%onnx::Conv_885) %onnx::Conv_909 = Identity(%onnx::Conv_885) %onnx::Conv_906 = Identity(%onnx::Conv_885) %onnx::Conv_903 = Identity(%onnx::Conv_885) %onnx::Conv_900 = Identity(%onnx::Conv_885) %onnx::Conv_897 = Identity(%onnx::Conv_885) %onnx::Conv_894 = Identity(%onnx::Conv_885) %onnx::Conv_891 = Identity(%onnx::Conv_885) %onnx::Conv_888 = Identity(%onnx::Conv_885) %onnx::Conv_882 = Identity(%onnx::Conv_855) %onnx::Conv_879 = Identity(%onnx::Conv_855) %onnx::Conv_876 = Identity(%onnx::Conv_855) %onnx::Conv_873 = Identity(%onnx::Conv_855) %onnx::Conv_870 = Identity(%onnx::Conv_867) %onnx::Conv_864 = Identity(%onnx::Conv_855) %onnx::Conv_861 = Identity(%onnx::Conv_855) %onnx::Conv_858 = Identity(%onnx::Conv_855) %onnx::Conv_852 = Identity(%onnx::Conv_819) %onnx::Conv_849 = Identity(%onnx::Conv_819) %onnx::Conv_846 = Identity(%onnx::Conv_819) %onnx::Conv_843 = Identity(%onnx::Conv_792) %onnx::Conv_840 = Identity(%onnx::Conv_792) %onnx::Conv_837 = Identity(%onnx::Conv_819) %onnx::Conv_834 = Identity(%onnx::Conv_819) %onnx::Conv_831 = Identity(%onnx::Conv_819) %onnx::Conv_828 = Identity(%onnx::Conv_819) %onnx::Conv_825 = Identity(%onnx::Conv_819) %onnx::Conv_822 = Identity(%onnx::Conv_819) %onnx::Conv_816 = Identity(%onnx::Conv_789) %onnx::Conv_813 = Identity(%onnx::Conv_789) %onnx::Conv_810 = Identity(%onnx::Conv_789) %onnx::Conv_807 = Identity(%onnx::Conv_789) %onnx::Conv_804 = Identity(%onnx::Conv_792) %onnx::Conv_801 = Identity(%onnx::Conv_792) %onnx::Conv_798 = Identity(%onnx::Conv_789) %onnx::Conv_795 = Identity(%onnx::Conv_792) %onnx::Conv_786 = Identity(%onnx::Conv_753) %onnx::Conv_783 = Identity(%onnx::Conv_753) %onnx::Conv_780 = Identity(%onnx::Conv_753) %onnx::Conv_777 = Identity(%onnx::Conv_753) %onnx::Conv_774 = Identity(%onnx::Conv_753) %onnx::Conv_771 = Identity(%onnx::Conv_753) %onnx::Conv_768 = Identity(%onnx::Conv_753) %onnx::Conv_765 = Identity(%onnx::Conv_753) %onnx::Conv_762 = Identity(%onnx::Conv_753) %onnx::Conv_759 = Identity(%onnx::Conv_753) %onnx::Conv_756 = Identity(%onnx::Conv_753) %onnx::Conv_750 = Identity(%onnx::Conv_747) %onnx::Conv_744 = Identity(%onnx::Conv_735) %onnx::Conv_741 = Identity(%onnx::Conv_738) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_734, %onnx::Conv_735) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_737, %onnx::Conv_738) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 48, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.0/nl/Relu_output_0, %onnx::Conv_740, %onnx::Conv_741) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_743, %onnx::Conv_744) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_746, %onnx::Conv_747) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.1/nl/Relu_output_0, %onnx::Conv_749, %onnx::Conv_750) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_752, %onnx::Conv_753) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_755, %onnx::Conv_756) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.2/shuffle/Reshape_output_0 = Reshape(%/cells.2/nl/Relu_output_0, %/cells.2/shuffle/Constant_output_0) %/cells.2/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.2/shuffle/Reshape_output_0) %/cells.2/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.2/shuffle/Reshape_1_output_0 = Reshape(%/cells.2/shuffle/Transpose_output_0, %/cells.2/shuffle/Constant_1_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.2/shuffle/Reshape_1_output_0, %onnx::Conv_758, %onnx::Conv_759) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_761, %onnx::Conv_762) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_764, %onnx::Conv_765) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.3/nl/Relu_output_0, %onnx::Conv_767, %onnx::Conv_768) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_770, %onnx::Conv_771) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_773, %onnx::Conv_774) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.4/nl/Relu_output_0, %onnx::Conv_776, %onnx::Conv_777) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_779, %onnx::Conv_780) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/Add_output_0, %onnx::Conv_782, %onnx::Conv_783) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.5/shuffle/Reshape_output_0 = Reshape(%/cells.5/nl/Relu_output_0, %/cells.5/shuffle/Constant_output_0) %/cells.5/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.5/shuffle/Reshape_output_0) %/cells.5/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.5/shuffle/Reshape_1_output_0 = Reshape(%/cells.5/shuffle/Transpose_output_0, %/cells.5/shuffle/Constant_1_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.5/shuffle/Reshape_1_output_0, %onnx::Conv_785, %onnx::Conv_786) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_788, %onnx::Conv_789) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_791, %onnx::Conv_792) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.6/nl/Relu_output_0, %onnx::Conv_794, %onnx::Conv_795) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_797, %onnx::Conv_798) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_800, %onnx::Conv_801) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.7/nl/Relu_output_0, %onnx::Conv_803, %onnx::Conv_804) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_806, %onnx::Conv_807) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_809, %onnx::Conv_810) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.8/shuffle/Reshape_output_0 = Reshape(%/cells.8/nl/Relu_output_0, %/cells.8/shuffle/Constant_output_0) %/cells.8/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.8/shuffle/Reshape_output_0) %/cells.8/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.8/shuffle/Reshape_1_output_0 = Reshape(%/cells.8/shuffle/Transpose_output_0, %/cells.8/shuffle/Constant_1_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.8/shuffle/Reshape_1_output_0, %onnx::Conv_812, %onnx::Conv_813) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_815, %onnx::Conv_816) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [2, 2]](%/cells.8/Add_output_0, %onnx::Conv_818, %onnx::Conv_819) %/cells.9/relu/Relu_output_0 = Relu(%/cells.9/conv/Conv_output_0) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/relu/Relu_output_0, %onnx::Conv_821, %onnx::Conv_822) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.10/shuffle/Reshape_output_0 = Reshape(%/cells.10/nl/Relu_output_0, %/cells.10/shuffle/Constant_output_0) %/cells.10/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.10/shuffle/Reshape_output_0) %/cells.10/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.10/shuffle/Reshape_1_output_0 = Reshape(%/cells.10/shuffle/Transpose_output_0, %/cells.10/shuffle/Constant_1_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.10/shuffle/Reshape_1_output_0, %onnx::Conv_824, %onnx::Conv_825) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_827, %onnx::Conv_828) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/relu/Relu_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_830, %onnx::Conv_831) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.11/nl/Relu_output_0, %onnx::Conv_833, %onnx::Conv_834) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_836, %onnx::Conv_837) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_839, %onnx::Conv_840) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.12/nl/Relu_output_0, %onnx::Conv_842, %onnx::Conv_843) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_845, %onnx::Conv_846) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_848, %onnx::Conv_849) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.13/shuffle/Reshape_output_0 = Reshape(%/cells.13/nl/Relu_output_0, %/cells.13/shuffle/Constant_output_0) %/cells.13/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.13/shuffle/Reshape_output_0) %/cells.13/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.13/shuffle/Reshape_1_output_0 = Reshape(%/cells.13/shuffle/Transpose_output_0, %/cells.13/shuffle/Constant_1_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.13/shuffle/Reshape_1_output_0, %onnx::Conv_851, %onnx::Conv_852) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_854, %onnx::Conv_855) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_857, %onnx::Conv_858) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.14/shuffle/Reshape_output_0 = Reshape(%/cells.14/nl/Relu_output_0, %/cells.14/shuffle/Constant_output_0) %/cells.14/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.14/shuffle/Reshape_output_0) %/cells.14/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.14/shuffle/Reshape_1_output_0 = Reshape(%/cells.14/shuffle/Transpose_output_0, %/cells.14/shuffle/Constant_1_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.14/shuffle/Reshape_1_output_0, %onnx::Conv_860, %onnx::Conv_861) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_863, %onnx::Conv_864) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_866, %onnx::Conv_867) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.15/nl/Relu_output_0, %onnx::Conv_869, %onnx::Conv_870) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_872, %onnx::Conv_873) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_875, %onnx::Conv_876) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.16/nl/Relu_output_0, %onnx::Conv_878, %onnx::Conv_879) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_881, %onnx::Conv_882) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.15/Add_output_0) %/cells.17/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [2, 2]](%/cells.16/Add_output_0, %onnx::Conv_884, %onnx::Conv_885) %/cells.17/relu/Relu_output_0 = Relu(%/cells.17/conv/Conv_output_0) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/relu/Relu_output_0, %onnx::Conv_887, %onnx::Conv_888) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.18/shuffle/Reshape_output_0 = Reshape(%/cells.18/nl/Relu_output_0, %/cells.18/shuffle/Constant_output_0) %/cells.18/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.18/shuffle/Reshape_output_0) %/cells.18/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.18/shuffle/Reshape_1_output_0 = Reshape(%/cells.18/shuffle/Transpose_output_0, %/cells.18/shuffle/Constant_1_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.18/shuffle/Reshape_1_output_0, %onnx::Conv_890, %onnx::Conv_891) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_893, %onnx::Conv_894) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/relu/Relu_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_896, %onnx::Conv_897) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.19/shuffle/Reshape_output_0 = Reshape(%/cells.19/nl/Relu_output_0, %/cells.19/shuffle/Constant_output_0) %/cells.19/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.19/shuffle/Reshape_output_0) %/cells.19/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.19/shuffle/Reshape_1_output_0 = Reshape(%/cells.19/shuffle/Transpose_output_0, %/cells.19/shuffle/Constant_1_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.19/shuffle/Reshape_1_output_0, %onnx::Conv_899, %onnx::Conv_900) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_902, %onnx::Conv_903) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_905, %onnx::Conv_906) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.20/nl/Relu_output_0, %onnx::Conv_908, %onnx::Conv_909) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_911, %onnx::Conv_912) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_914, %onnx::Conv_915) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.21/nl/Relu_output_0, %onnx::Conv_917, %onnx::Conv_918) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_920, %onnx::Conv_921) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_923, %onnx::Conv_924) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %732 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %732 }	val_accuracy	47,066,880	1,180,308	{'zcp_synflow': 75.70092367697909, 'zcp_zen': 64.5880355834961, 'zcp_epe_nas': 0.00015999920000638146, 'zcp_fisher': 0.24249312281608582, 'zcp_flops': 47066880.0, 'zcp_grad_norm': 21.83704948425293, 'zcp_grasp': -1.0365219116210938, 'zcp_jacov': -16.068195836461648, 'zcp_l2_norm': 538.7059936523438, 'zcp_nwot': 209.20539581245393, 'zcp_params': 1180308.0, 'zcp_plain': -0.0003343012067489326, 'zcp_snip': 43.20347213745117, 'lat_1080ti_1': 0.7404529416964746, 'lat_1080ti_32': 0.6168362975125905, 'lat_1080ti_64': 0.44831212100007156, 'lat_2080ti_1': 0.7777808355857709, 'lat_2080ti_32': 0.6342572869367956, 'lat_2080ti_64': 0.46261016856571113, 'lat_essential_ph_1': 0.18867924528301888, 'lat_eyeriss': 0.2235343736819907, 'lat_fpga': 0.20681977854520622, 'lat_gold_6226': 0.10327271942181147, 'lat_gold_6240': 0.36503225860024746, 'lat_pixel2': 0.17391304347826086, 'lat_pixel3': 0.22945174562683276, 'lat_raspi4': 0.22512808358241818, 'lat_samsung_a50': 0.09473684210526316, 'lat_samsung_s7': 0.08661417322834646, 'lat_silver_4114': 0.45243691670057923, 'lat_silver_4210r': 0.5118879132697004, 'lat_titan_rtx_1': 0.7310807954077247, 'lat_titan_rtx_32': 0.623068004078214, 'lat_titan_rtx_64': 0.5105391349431023, 'lat_titanx_1': 0.37652095062593754, 'lat_titanx_32': 0.5127538319942693, 'lat_titanx_64': 0.42160428145208995, 'lat_titanxp_1': 0.6619356792964624, 'lat_titanxp_32': 0.5648547660331913, 'lat_titanxp_64': 0.45175696020416045}
FBNet_4006	FBNet	4006	4006	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_658[FLOAT, 16x3x3x3] %onnx::Conv_659[FLOAT, 16] %onnx::Conv_661[FLOAT, 16x16x1x1] %onnx::Conv_664[FLOAT, 16x1x5x5] %onnx::Conv_667[FLOAT, 16x16x1x1] %onnx::Conv_670[FLOAT, 16x16x1x1] %onnx::Conv_673[FLOAT, 16x1x5x5] %onnx::Conv_676[FLOAT, 24x16x1x1] %onnx::Conv_677[FLOAT, 24] %onnx::Conv_679[FLOAT, 72x24x1x1] %onnx::Conv_680[FLOAT, 72] %onnx::Conv_682[FLOAT, 72x1x5x5] %onnx::Conv_685[FLOAT, 24x72x1x1] %onnx::Conv_688[FLOAT, 24x24x1x1] %onnx::Conv_691[FLOAT, 24x1x5x5] %onnx::Conv_694[FLOAT, 24x24x1x1] %onnx::Conv_697[FLOAT, 72x24x1x1] %onnx::Conv_700[FLOAT, 72x1x3x3] %onnx::Conv_703[FLOAT, 24x72x1x1] %onnx::Conv_706[FLOAT, 32x24x1x1] %onnx::Conv_707[FLOAT, 32] %onnx::Conv_709[FLOAT, 192x32x1x1] %onnx::Conv_710[FLOAT, 192] %onnx::Conv_712[FLOAT, 192x1x3x3] %onnx::Conv_715[FLOAT, 32x192x1x1] %onnx::Conv_718[FLOAT, 96x32x1x1] %onnx::Conv_719[FLOAT, 96] %onnx::Conv_721[FLOAT, 96x1x3x3] %onnx::Conv_724[FLOAT, 32x96x1x1] %onnx::Conv_727[FLOAT, 32x32x1x1] %onnx::Conv_730[FLOAT, 32x1x5x5] %onnx::Conv_733[FLOAT, 32x32x1x1] %onnx::Conv_736[FLOAT, 96x32x1x1] %onnx::Conv_739[FLOAT, 96x1x3x3] %onnx::Conv_742[FLOAT, 64x96x1x1] %onnx::Conv_743[FLOAT, 64] %onnx::Conv_745[FLOAT, 64x32x1x1] %onnx::Conv_748[FLOAT, 64x1x5x5] %onnx::Conv_751[FLOAT, 64x32x1x1] %onnx::Conv_754[FLOAT, 64x64x1x1] %onnx::Conv_757[FLOAT, 64x1x3x3] %onnx::Conv_760[FLOAT, 64x64x1x1] %onnx::Conv_763[FLOAT, 64x32x1x1] %onnx::Conv_766[FLOAT, 64x1x5x5] %onnx::Conv_769[FLOAT, 64x32x1x1] %onnx::Conv_772[FLOAT, 384x64x1x1] %onnx::Conv_773[FLOAT, 384] %onnx::Conv_775[FLOAT, 384x1x5x5] %onnx::Conv_778[FLOAT, 112x384x1x1] %onnx::Conv_779[FLOAT, 112] %onnx::Conv_781[FLOAT, 336x112x1x1] %onnx::Conv_782[FLOAT, 336] %onnx::Conv_784[FLOAT, 336x1x5x5] %onnx::Conv_787[FLOAT, 112x336x1x1] %onnx::Conv_790[FLOAT, 112x112x1x1] %onnx::Conv_793[FLOAT, 112x1x3x3] %onnx::Conv_796[FLOAT, 112x112x1x1] %onnx::Conv_799[FLOAT, 336x112x1x1] %onnx::Conv_802[FLOAT, 336x1x3x3] %onnx::Conv_805[FLOAT, 112x336x1x1] %onnx::Conv_808[FLOAT, 112x56x1x1] %onnx::Conv_811[FLOAT, 112x1x5x5] %onnx::Conv_814[FLOAT, 184x56x1x1] %onnx::Conv_815[FLOAT, 184] %onnx::Conv_817[FLOAT, 552x184x1x1] %onnx::Conv_818[FLOAT, 552] %onnx::Conv_820[FLOAT, 552x1x5x5] %onnx::Conv_823[FLOAT, 184x552x1x1] %onnx::Conv_826[FLOAT, 1104x184x1x1] %onnx::Conv_827[FLOAT, 1104] %onnx::Conv_829[FLOAT, 1104x1x5x5] %onnx::Conv_832[FLOAT, 184x1104x1x1] %onnx::Conv_835[FLOAT, 184x184x1x1] %onnx::Conv_838[FLOAT, 184x1x5x5] %onnx::Conv_841[FLOAT, 184x184x1x1] %onnx::Conv_844[FLOAT, 184x184x1x1] %onnx::Conv_847[FLOAT, 184x1x5x5] %onnx::Conv_850[FLOAT, 352x184x1x1] %onnx::Conv_851[FLOAT, 352] %onnx::Conv_853[FLOAT, 1504x352x1x1] %onnx::Conv_854[FLOAT, 1504] ) { %onnx::Conv_848 = Identity(%onnx::Conv_815) %onnx::Conv_845 = Identity(%onnx::Conv_815) %onnx::Conv_842 = Identity(%onnx::Conv_815) %onnx::Conv_839 = Identity(%onnx::Conv_815) %onnx::Conv_836 = Identity(%onnx::Conv_815) %onnx::Conv_833 = Identity(%onnx::Conv_815) %onnx::Conv_830 = Identity(%onnx::Conv_827) %onnx::Conv_824 = Identity(%onnx::Conv_815) %onnx::Conv_821 = Identity(%onnx::Conv_818) %onnx::Conv_812 = Identity(%onnx::Conv_779) %onnx::Conv_809 = Identity(%onnx::Conv_779) %onnx::Conv_806 = Identity(%onnx::Conv_779) %onnx::Conv_803 = Identity(%onnx::Conv_782) %onnx::Conv_800 = Identity(%onnx::Conv_782) %onnx::Conv_797 = Identity(%onnx::Conv_779) %onnx::Conv_794 = Identity(%onnx::Conv_779) %onnx::Conv_791 = Identity(%onnx::Conv_779) %onnx::Conv_788 = Identity(%onnx::Conv_779) %onnx::Conv_785 = Identity(%onnx::Conv_782) %onnx::Conv_776 = Identity(%onnx::Conv_773) %onnx::Conv_770 = Identity(%onnx::Conv_743) %onnx::Conv_767 = Identity(%onnx::Conv_743) %onnx::Conv_764 = Identity(%onnx::Conv_743) %onnx::Conv_761 = Identity(%onnx::Conv_743) %onnx::Conv_758 = Identity(%onnx::Conv_743) %onnx::Conv_755 = Identity(%onnx::Conv_743) %onnx::Conv_752 = Identity(%onnx::Conv_743) %onnx::Conv_749 = Identity(%onnx::Conv_743) %onnx::Conv_746 = Identity(%onnx::Conv_743) %onnx::Conv_740 = Identity(%onnx::Conv_719) %onnx::Conv_737 = Identity(%onnx::Conv_719) %onnx::Conv_734 = Identity(%onnx::Conv_707) %onnx::Conv_731 = Identity(%onnx::Conv_707) %onnx::Conv_728 = Identity(%onnx::Conv_707) %onnx::Conv_725 = Identity(%onnx::Conv_707) %onnx::Conv_722 = Identity(%onnx::Conv_719) %onnx::Conv_716 = Identity(%onnx::Conv_707) %onnx::Conv_713 = Identity(%onnx::Conv_710) %onnx::Conv_704 = Identity(%onnx::Conv_677) %onnx::Conv_701 = Identity(%onnx::Conv_680) %onnx::Conv_698 = Identity(%onnx::Conv_680) %onnx::Conv_695 = Identity(%onnx::Conv_677) %onnx::Conv_692 = Identity(%onnx::Conv_677) %onnx::Conv_689 = Identity(%onnx::Conv_677) %onnx::Conv_686 = Identity(%onnx::Conv_677) %onnx::Conv_683 = Identity(%onnx::Conv_680) %onnx::Conv_674 = Identity(%onnx::Conv_659) %onnx::Conv_671 = Identity(%onnx::Conv_659) %onnx::Conv_668 = Identity(%onnx::Conv_659) %onnx::Conv_665 = Identity(%onnx::Conv_659) %onnx::Conv_662 = Identity(%onnx::Conv_659) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_658, %onnx::Conv_659) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_661, %onnx::Conv_662) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.0/nl/Relu_output_0, %onnx::Conv_664, %onnx::Conv_665) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_667, %onnx::Conv_668) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_670, %onnx::Conv_671) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.1/nl/Relu_output_0, %onnx::Conv_673, %onnx::Conv_674) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_676, %onnx::Conv_677) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_679, %onnx::Conv_680) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.2/nl/Relu_output_0, %onnx::Conv_682, %onnx::Conv_683) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_685, %onnx::Conv_686) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_688, %onnx::Conv_689) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.3/nl/Relu_output_0, %onnx::Conv_691, %onnx::Conv_692) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_694, %onnx::Conv_695) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_697, %onnx::Conv_698) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.4/nl/Relu_output_0, %onnx::Conv_700, %onnx::Conv_701) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_703, %onnx::Conv_704) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [2, 2]](%/cells.4/Add_output_0, %onnx::Conv_706, %onnx::Conv_707) %/cells.5/relu/Relu_output_0 = Relu(%/cells.5/conv/Conv_output_0) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/relu/Relu_output_0, %onnx::Conv_709, %onnx::Conv_710) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.6/nl/Relu_output_0, %onnx::Conv_712, %onnx::Conv_713) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_715, %onnx::Conv_716) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/relu/Relu_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_718, %onnx::Conv_719) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.7/nl/Relu_output_0, %onnx::Conv_721, %onnx::Conv_722) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_724, %onnx::Conv_725) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_727, %onnx::Conv_728) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.8/nl/Relu_output_0, %onnx::Conv_730, %onnx::Conv_731) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_733, %onnx::Conv_734) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_736, %onnx::Conv_737) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.9/nl/Relu_output_0, %onnx::Conv_739, %onnx::Conv_740) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_742, %onnx::Conv_743) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_745, %onnx::Conv_746) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.10/shuffle/Reshape_output_0 = Reshape(%/cells.10/nl/Relu_output_0, %/cells.10/shuffle/Constant_output_0) %/cells.10/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.10/shuffle/Reshape_output_0) %/cells.10/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.10/shuffle/Reshape_1_output_0 = Reshape(%/cells.10/shuffle/Transpose_output_0, %/cells.10/shuffle/Constant_1_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.10/shuffle/Reshape_1_output_0, %onnx::Conv_748, %onnx::Conv_749) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_751, %onnx::Conv_752) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_754, %onnx::Conv_755) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.11/nl/Relu_output_0, %onnx::Conv_757, %onnx::Conv_758) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_760, %onnx::Conv_761) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_763, %onnx::Conv_764) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.12/shuffle/Reshape_output_0 = Reshape(%/cells.12/nl/Relu_output_0, %/cells.12/shuffle/Constant_output_0) %/cells.12/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.12/shuffle/Reshape_output_0) %/cells.12/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.12/shuffle/Reshape_1_output_0 = Reshape(%/cells.12/shuffle/Transpose_output_0, %/cells.12/shuffle/Constant_1_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.12/shuffle/Reshape_1_output_0, %onnx::Conv_766, %onnx::Conv_767) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_769, %onnx::Conv_770) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_772, %onnx::Conv_773) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.13/nl/Relu_output_0, %onnx::Conv_775, %onnx::Conv_776) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_778, %onnx::Conv_779) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_781, %onnx::Conv_782) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.14/nl/Relu_output_0, %onnx::Conv_784, %onnx::Conv_785) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_787, %onnx::Conv_788) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_790, %onnx::Conv_791) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.15/nl/Relu_output_0, %onnx::Conv_793, %onnx::Conv_794) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_796, %onnx::Conv_797) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_799, %onnx::Conv_800) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.16/nl/Relu_output_0, %onnx::Conv_802, %onnx::Conv_803) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_805, %onnx::Conv_806) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.15/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_808, %onnx::Conv_809) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.17/shuffle/Reshape_output_0 = Reshape(%/cells.17/nl/Relu_output_0, %/cells.17/shuffle/Constant_output_0) %/cells.17/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.17/shuffle/Reshape_output_0) %/cells.17/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.17/shuffle/Reshape_1_output_0 = Reshape(%/cells.17/shuffle/Transpose_output_0, %/cells.17/shuffle/Constant_1_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.17/shuffle/Reshape_1_output_0, %onnx::Conv_811, %onnx::Conv_812) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_814, %onnx::Conv_815) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_817, %onnx::Conv_818) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 552, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.18/nl/Relu_output_0, %onnx::Conv_820, %onnx::Conv_821) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_823, %onnx::Conv_824) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_826, %onnx::Conv_827) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.19/nl/Relu_output_0, %onnx::Conv_829, %onnx::Conv_830) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_832, %onnx::Conv_833) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_835, %onnx::Conv_836) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.20/nl/Relu_output_0, %onnx::Conv_838, %onnx::Conv_839) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_841, %onnx::Conv_842) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_844, %onnx::Conv_845) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.21/nl/Relu_output_0, %onnx::Conv_847, %onnx::Conv_848) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_850, %onnx::Conv_851) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_853, %onnx::Conv_854) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %656 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %656 }	val_accuracy	64,772,736	1,882,788	{'zcp_synflow': 85.00591221776914, 'zcp_zen': 72.51466369628906, 'zcp_epe_nas': 12.553763779735043, 'zcp_fisher': 0.13719302415847778, 'zcp_flops': 64772736.0, 'zcp_grad_norm': 25.580547332763672, 'zcp_grasp': -0.10321998596191406, 'zcp_jacov': -16.053334721106495, 'zcp_l2_norm': 649.6470336914062, 'zcp_nwot': 209.8639257974921, 'zcp_params': 1882788.0, 'zcp_plain': -0.0030134536791592836, 'zcp_snip': 44.29522705078125, 'lat_1080ti_1': 0.6426132833839503, 'lat_1080ti_32': 0.5768314432724118, 'lat_1080ti_64': 0.4233988619502823, 'lat_2080ti_1': 0.720506947296087, 'lat_2080ti_32': 0.600994238951876, 'lat_2080ti_64': 0.42144049403249545, 'lat_essential_ph_1': 0.1509433962264151, 'lat_eyeriss': 0.4038563805402232, 'lat_fpga': 0.44319654855084645, 'lat_gold_6226': 0.32570052365841623, 'lat_gold_6240': 0.4802391886267132, 'lat_pixel2': 0.2608695652173913, 'lat_pixel3': 0.37767847850195774, 'lat_raspi4': 0.36890197393666324, 'lat_samsung_a50': 0.16842105263157894, 'lat_samsung_s7': 0.09448818897637795, 'lat_silver_4114': 0.5042166953247228, 'lat_silver_4210r': 0.5468690409118179, 'lat_titan_rtx_1': 0.6822512391212281, 'lat_titan_rtx_32': 0.5926209155933458, 'lat_titan_rtx_64': 0.46456549420422205, 'lat_titanx_1': 0.3658692093946968, 'lat_titanx_32': 0.5172336922477779, 'lat_titanx_64': 0.44287675766420825, 'lat_titanxp_1': 0.657070919312509, 'lat_titanxp_32': 0.5680612422134979, 'lat_titanxp_64': 0.4306261487483907}
FBNet_826	FBNet	826	826	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_686[FLOAT, 16x3x3x3] %onnx::Conv_687[FLOAT, 16] %onnx::Conv_689[FLOAT, 48x16x1x1] %onnx::Conv_690[FLOAT, 48] %onnx::Conv_692[FLOAT, 48x1x3x3] %onnx::Conv_695[FLOAT, 16x48x1x1] %onnx::Conv_698[FLOAT, 96x16x1x1] %onnx::Conv_699[FLOAT, 96] %onnx::Conv_701[FLOAT, 96x1x3x3] %onnx::Conv_704[FLOAT, 24x96x1x1] %onnx::Conv_705[FLOAT, 24] %onnx::Conv_707[FLOAT, 24x12x1x1] %onnx::Conv_710[FLOAT, 24x1x3x3] %onnx::Conv_713[FLOAT, 24x12x1x1] %onnx::Conv_716[FLOAT, 144x24x1x1] %onnx::Conv_717[FLOAT, 144] %onnx::Conv_719[FLOAT, 144x1x5x5] %onnx::Conv_722[FLOAT, 24x144x1x1] %onnx::Conv_725[FLOAT, 24x24x1x1] %onnx::Conv_728[FLOAT, 24x1x3x3] %onnx::Conv_731[FLOAT, 24x24x1x1] %onnx::Conv_734[FLOAT, 72x24x1x1] %onnx::Conv_735[FLOAT, 72] %onnx::Conv_737[FLOAT, 72x1x3x3] %onnx::Conv_740[FLOAT, 32x72x1x1] %onnx::Conv_741[FLOAT, 32] %onnx::Conv_743[FLOAT, 32x16x1x1] %onnx::Conv_746[FLOAT, 32x1x3x3] %onnx::Conv_749[FLOAT, 32x16x1x1] %onnx::Conv_752[FLOAT, 192x32x1x1] %onnx::Conv_753[FLOAT, 192] %onnx::Conv_755[FLOAT, 192x1x3x3] %onnx::Conv_758[FLOAT, 32x192x1x1] %onnx::Conv_761[FLOAT, 192x32x1x1] %onnx::Conv_764[FLOAT, 192x1x3x3] %onnx::Conv_767[FLOAT, 32x192x1x1] %onnx::Conv_770[FLOAT, 32x32x1x1] %onnx::Conv_773[FLOAT, 32x1x3x3] %onnx::Conv_776[FLOAT, 64x32x1x1] %onnx::Conv_777[FLOAT, 64] %onnx::Conv_779[FLOAT, 192x64x1x1] %onnx::Conv_782[FLOAT, 192x1x5x5] %onnx::Conv_785[FLOAT, 64x192x1x1] %onnx::Conv_788[FLOAT, 64x32x1x1] %onnx::Conv_791[FLOAT, 64x1x3x3] %onnx::Conv_794[FLOAT, 64x32x1x1] %onnx::Conv_797[FLOAT, 192x64x1x1] %onnx::Conv_800[FLOAT, 192x1x5x5] %onnx::Conv_803[FLOAT, 64x192x1x1] %onnx::Conv_806[FLOAT, 64x64x1x1] %onnx::Conv_809[FLOAT, 64x1x3x3] %onnx::Conv_812[FLOAT, 112x64x1x1] %onnx::Conv_813[FLOAT, 112] %onnx::Conv_815[FLOAT, 336x112x1x1] %onnx::Conv_816[FLOAT, 336] %onnx::Conv_818[FLOAT, 336x1x3x3] %onnx::Conv_821[FLOAT, 112x336x1x1] %onnx::Conv_824[FLOAT, 336x112x1x1] %onnx::Conv_827[FLOAT, 336x1x3x3] %onnx::Conv_830[FLOAT, 112x336x1x1] %onnx::Conv_833[FLOAT, 672x112x1x1] %onnx::Conv_834[FLOAT, 672] %onnx::Conv_836[FLOAT, 672x1x3x3] %onnx::Conv_839[FLOAT, 112x672x1x1] %onnx::Conv_842[FLOAT, 336x112x1x1] %onnx::Conv_845[FLOAT, 336x1x3x3] %onnx::Conv_848[FLOAT, 184x336x1x1] %onnx::Conv_849[FLOAT, 184] %onnx::Conv_851[FLOAT, 184x92x1x1] %onnx::Conv_854[FLOAT, 184x1x3x3] %onnx::Conv_857[FLOAT, 184x92x1x1] %onnx::Conv_860[FLOAT, 184x184x1x1] %onnx::Conv_863[FLOAT, 184x1x3x3] %onnx::Conv_866[FLOAT, 184x184x1x1] %onnx::Conv_869[FLOAT, 184x92x1x1] %onnx::Conv_872[FLOAT, 184x1x3x3] %onnx::Conv_875[FLOAT, 352x92x1x1] %onnx::Conv_876[FLOAT, 352] %onnx::Conv_878[FLOAT, 1504x352x1x1] %onnx::Conv_879[FLOAT, 1504] ) { %onnx::Conv_873 = Identity(%onnx::Conv_849) %onnx::Conv_870 = Identity(%onnx::Conv_849) %onnx::Conv_867 = Identity(%onnx::Conv_849) %onnx::Conv_864 = Identity(%onnx::Conv_849) %onnx::Conv_861 = Identity(%onnx::Conv_849) %onnx::Conv_858 = Identity(%onnx::Conv_849) %onnx::Conv_855 = Identity(%onnx::Conv_849) %onnx::Conv_852 = Identity(%onnx::Conv_849) %onnx::Conv_846 = Identity(%onnx::Conv_816) %onnx::Conv_843 = Identity(%onnx::Conv_816) %onnx::Conv_840 = Identity(%onnx::Conv_813) %onnx::Conv_837 = Identity(%onnx::Conv_834) %onnx::Conv_831 = Identity(%onnx::Conv_813) %onnx::Conv_828 = Identity(%onnx::Conv_816) %onnx::Conv_825 = Identity(%onnx::Conv_816) %onnx::Conv_822 = Identity(%onnx::Conv_813) %onnx::Conv_819 = Identity(%onnx::Conv_816) %onnx::Conv_810 = Identity(%onnx::Conv_777) %onnx::Conv_807 = Identity(%onnx::Conv_777) %onnx::Conv_804 = Identity(%onnx::Conv_777) %onnx::Conv_801 = Identity(%onnx::Conv_753) %onnx::Conv_798 = Identity(%onnx::Conv_753) %onnx::Conv_795 = Identity(%onnx::Conv_777) %onnx::Conv_792 = Identity(%onnx::Conv_777) %onnx::Conv_789 = Identity(%onnx::Conv_777) %onnx::Conv_786 = Identity(%onnx::Conv_777) %onnx::Conv_783 = Identity(%onnx::Conv_753) %onnx::Conv_780 = Identity(%onnx::Conv_753) %onnx::Conv_774 = Identity(%onnx::Conv_741) %onnx::Conv_771 = Identity(%onnx::Conv_741) %onnx::Conv_768 = Identity(%onnx::Conv_741) %onnx::Conv_765 = Identity(%onnx::Conv_753) %onnx::Conv_762 = Identity(%onnx::Conv_753) %onnx::Conv_759 = Identity(%onnx::Conv_741) %onnx::Conv_756 = Identity(%onnx::Conv_753) %onnx::Conv_750 = Identity(%onnx::Conv_741) %onnx::Conv_747 = Identity(%onnx::Conv_741) %onnx::Conv_744 = Identity(%onnx::Conv_741) %onnx::Conv_738 = Identity(%onnx::Conv_735) %onnx::Conv_732 = Identity(%onnx::Conv_705) %onnx::Conv_729 = Identity(%onnx::Conv_705) %onnx::Conv_726 = Identity(%onnx::Conv_705) %onnx::Conv_723 = Identity(%onnx::Conv_705) %onnx::Conv_720 = Identity(%onnx::Conv_717) %onnx::Conv_714 = Identity(%onnx::Conv_705) %onnx::Conv_711 = Identity(%onnx::Conv_705) %onnx::Conv_708 = Identity(%onnx::Conv_705) %onnx::Conv_702 = Identity(%onnx::Conv_699) %onnx::Conv_696 = Identity(%onnx::Conv_687) %onnx::Conv_693 = Identity(%onnx::Conv_690) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_686, %onnx::Conv_687) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_689, %onnx::Conv_690) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 48, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.0/nl/Relu_output_0, %onnx::Conv_692, %onnx::Conv_693) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_695, %onnx::Conv_696) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_698, %onnx::Conv_699) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.1/nl/Relu_output_0, %onnx::Conv_701, %onnx::Conv_702) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_704, %onnx::Conv_705) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_707, %onnx::Conv_708) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.2/shuffle/Reshape_output_0 = Reshape(%/cells.2/nl/Relu_output_0, %/cells.2/shuffle/Constant_output_0) %/cells.2/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.2/shuffle/Reshape_output_0) %/cells.2/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.2/shuffle/Reshape_1_output_0 = Reshape(%/cells.2/shuffle/Transpose_output_0, %/cells.2/shuffle/Constant_1_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.2/shuffle/Reshape_1_output_0, %onnx::Conv_710, %onnx::Conv_711) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_713, %onnx::Conv_714) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_716, %onnx::Conv_717) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.3/nl/Relu_output_0, %onnx::Conv_719, %onnx::Conv_720) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_722, %onnx::Conv_723) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_725, %onnx::Conv_726) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.4/nl/Relu_output_0, %onnx::Conv_728, %onnx::Conv_729) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_731, %onnx::Conv_732) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/Add_output_0, %onnx::Conv_734, %onnx::Conv_735) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.5/nl/Relu_output_0, %onnx::Conv_737, %onnx::Conv_738) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_740, %onnx::Conv_741) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_743, %onnx::Conv_744) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.6/shuffle/Reshape_output_0 = Reshape(%/cells.6/nl/Relu_output_0, %/cells.6/shuffle/Constant_output_0) %/cells.6/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.6/shuffle/Reshape_output_0) %/cells.6/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.6/shuffle/Reshape_1_output_0 = Reshape(%/cells.6/shuffle/Transpose_output_0, %/cells.6/shuffle/Constant_1_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.6/shuffle/Reshape_1_output_0, %onnx::Conv_746, %onnx::Conv_747) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_749, %onnx::Conv_750) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_752, %onnx::Conv_753) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.7/nl/Relu_output_0, %onnx::Conv_755, %onnx::Conv_756) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_758, %onnx::Conv_759) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_761, %onnx::Conv_762) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.8/nl/Relu_output_0, %onnx::Conv_764, %onnx::Conv_765) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_767, %onnx::Conv_768) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_770, %onnx::Conv_771) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.9/nl/Relu_output_0, %onnx::Conv_773, %onnx::Conv_774) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_776, %onnx::Conv_777) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_779, %onnx::Conv_780) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.10/nl/Relu_output_0, %onnx::Conv_782, %onnx::Conv_783) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_785, %onnx::Conv_786) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_788, %onnx::Conv_789) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.11/shuffle/Reshape_output_0 = Reshape(%/cells.11/nl/Relu_output_0, %/cells.11/shuffle/Constant_output_0) %/cells.11/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.11/shuffle/Reshape_output_0) %/cells.11/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.11/shuffle/Reshape_1_output_0 = Reshape(%/cells.11/shuffle/Transpose_output_0, %/cells.11/shuffle/Constant_1_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.11/shuffle/Reshape_1_output_0, %onnx::Conv_791, %onnx::Conv_792) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_794, %onnx::Conv_795) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_797, %onnx::Conv_798) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.12/nl/Relu_output_0, %onnx::Conv_800, %onnx::Conv_801) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_803, %onnx::Conv_804) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_806, %onnx::Conv_807) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.13/nl/Relu_output_0, %onnx::Conv_809, %onnx::Conv_810) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_812, %onnx::Conv_813) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_815, %onnx::Conv_816) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.14/nl/Relu_output_0, %onnx::Conv_818, %onnx::Conv_819) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_821, %onnx::Conv_822) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_824, %onnx::Conv_825) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.15/nl/Relu_output_0, %onnx::Conv_827, %onnx::Conv_828) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_830, %onnx::Conv_831) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_833, %onnx::Conv_834) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.16/nl/Relu_output_0, %onnx::Conv_836, %onnx::Conv_837) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_839, %onnx::Conv_840) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.15/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_842, %onnx::Conv_843) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.17/nl/Relu_output_0, %onnx::Conv_845, %onnx::Conv_846) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_848, %onnx::Conv_849) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_851, %onnx::Conv_852) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.18/shuffle/Reshape_output_0 = Reshape(%/cells.18/nl/Relu_output_0, %/cells.18/shuffle/Constant_output_0) %/cells.18/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.18/shuffle/Reshape_output_0) %/cells.18/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.18/shuffle/Reshape_1_output_0 = Reshape(%/cells.18/shuffle/Transpose_output_0, %/cells.18/shuffle/Constant_1_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.18/shuffle/Reshape_1_output_0, %onnx::Conv_854, %onnx::Conv_855) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_857, %onnx::Conv_858) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_860, %onnx::Conv_861) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.19/nl/Relu_output_0, %onnx::Conv_863, %onnx::Conv_864) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_866, %onnx::Conv_867) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_869, %onnx::Conv_870) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.21/shuffle/Reshape_output_0 = Reshape(%/cells.21/nl/Relu_output_0, %/cells.21/shuffle/Constant_output_0) %/cells.21/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.21/shuffle/Reshape_output_0) %/cells.21/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.21/shuffle/Reshape_1_output_0 = Reshape(%/cells.21/shuffle/Transpose_output_0, %/cells.21/shuffle/Constant_1_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.21/shuffle/Reshape_1_output_0, %onnx::Conv_872, %onnx::Conv_873) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_875, %onnx::Conv_876) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_878, %onnx::Conv_879) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %684 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %684 }	val_accuracy	73,853,312	1,404,948	{'zcp_synflow': 76.33529162498564, 'zcp_zen': 67.6063003540039, 'zcp_epe_nas': 0.00015999920000638146, 'zcp_fisher': 0.18388396501541138, 'zcp_flops': 73853312.0, 'zcp_grad_norm': 26.407691955566406, 'zcp_grasp': -0.09899139404296875, 'zcp_jacov': -16.064035142902597, 'zcp_l2_norm': 610.1244506835938, 'zcp_nwot': 216.08485103953063, 'zcp_params': 1404948.0, 'zcp_plain': -0.0002752191503532231, 'zcp_snip': 49.09975051879883, 'lat_1080ti_1': 0.645686062914999, 'lat_1080ti_32': 0.6900355606681844, 'lat_1080ti_64': 0.5756415433452214, 'lat_2080ti_1': 0.704620142268873, 'lat_2080ti_32': 0.6786951743469388, 'lat_2080ti_64': 0.611175606381992, 'lat_essential_ph_1': 0.33962264150943394, 'lat_eyeriss': 0.4727687822052705, 'lat_fpga': 0.52541584618886, 'lat_gold_6226': 0.28884983312462736, 'lat_gold_6240': 0.4439710403748642, 'lat_pixel2': 0.3695652173913043, 'lat_pixel3': 0.47927105648606516, 'lat_raspi4': 0.4560835800865931, 'lat_samsung_a50': 0.2, 'lat_samsung_s7': 0.2204724409448819, 'lat_silver_4114': 0.6327807097821527, 'lat_silver_4210r': 0.5782601074607161, 'lat_titan_rtx_1': 0.6877633434880182, 'lat_titan_rtx_32': 0.6757575429601764, 'lat_titan_rtx_64': 0.6314138230956403, 'lat_titanx_1': 0.3586581207504687, 'lat_titanx_32': 0.6582686241103951, 'lat_titanx_64': 0.563980054258466, 'lat_titanxp_1': 0.6247895553398205, 'lat_titanxp_32': 0.6753104544507479, 'lat_titanxp_64': 0.6000041404345915}
FBNet_3250	FBNet	3250	3250	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_677[FLOAT, 16x3x3x3] %onnx::Conv_678[FLOAT, 16] %onnx::Conv_680[FLOAT, 48x16x1x1] %onnx::Conv_681[FLOAT, 48] %onnx::Conv_683[FLOAT, 48x1x3x3] %onnx::Conv_686[FLOAT, 16x48x1x1] %onnx::Conv_689[FLOAT, 24x16x1x1] %onnx::Conv_690[FLOAT, 24] %onnx::Conv_692[FLOAT, 24x12x1x1] %onnx::Conv_695[FLOAT, 24x1x3x3] %onnx::Conv_698[FLOAT, 24x12x1x1] %onnx::Conv_701[FLOAT, 144x24x1x1] %onnx::Conv_702[FLOAT, 144] %onnx::Conv_704[FLOAT, 144x1x3x3] %onnx::Conv_707[FLOAT, 24x144x1x1] %onnx::Conv_710[FLOAT, 144x24x1x1] %onnx::Conv_713[FLOAT, 144x1x5x5] %onnx::Conv_716[FLOAT, 24x144x1x1] %onnx::Conv_719[FLOAT, 24x12x1x1] %onnx::Conv_722[FLOAT, 24x1x3x3] %onnx::Conv_725[FLOAT, 32x12x1x1] %onnx::Conv_726[FLOAT, 32] %onnx::Conv_728[FLOAT, 192x32x1x1] %onnx::Conv_729[FLOAT, 192] %onnx::Conv_731[FLOAT, 192x1x3x3] %onnx::Conv_734[FLOAT, 32x192x1x1] %onnx::Conv_737[FLOAT, 96x32x1x1] %onnx::Conv_738[FLOAT, 96] %onnx::Conv_740[FLOAT, 96x1x3x3] %onnx::Conv_743[FLOAT, 32x96x1x1] %onnx::Conv_746[FLOAT, 96x32x1x1] %onnx::Conv_749[FLOAT, 96x1x5x5] %onnx::Conv_752[FLOAT, 32x96x1x1] %onnx::Conv_755[FLOAT, 32x32x1x1] %onnx::Conv_758[FLOAT, 32x1x3x3] %onnx::Conv_761[FLOAT, 64x32x1x1] %onnx::Conv_762[FLOAT, 64] %onnx::Conv_764[FLOAT, 64x64x1x1] %onnx::Conv_767[FLOAT, 64x1x5x5] %onnx::Conv_770[FLOAT, 64x64x1x1] %onnx::Conv_773[FLOAT, 384x64x1x1] %onnx::Conv_774[FLOAT, 384] %onnx::Conv_776[FLOAT, 384x1x5x5] %onnx::Conv_779[FLOAT, 64x384x1x1] %onnx::Conv_782[FLOAT, 192x64x1x1] %onnx::Conv_785[FLOAT, 192x1x3x3] %onnx::Conv_788[FLOAT, 64x192x1x1] %onnx::Conv_791[FLOAT, 64x32x1x1] %onnx::Conv_794[FLOAT, 64x1x5x5] %onnx::Conv_797[FLOAT, 112x32x1x1] %onnx::Conv_798[FLOAT, 112] %onnx::Conv_800[FLOAT, 336x112x1x1] %onnx::Conv_801[FLOAT, 336] %onnx::Conv_803[FLOAT, 336x1x5x5] %onnx::Conv_806[FLOAT, 112x336x1x1] %onnx::Conv_809[FLOAT, 336x112x1x1] %onnx::Conv_812[FLOAT, 336x1x3x3] %onnx::Conv_815[FLOAT, 112x336x1x1] %onnx::Conv_818[FLOAT, 112x112x1x1] %onnx::Conv_821[FLOAT, 112x1x5x5] %onnx::Conv_824[FLOAT, 112x112x1x1] %onnx::Conv_827[FLOAT, 336x112x1x1] %onnx::Conv_830[FLOAT, 336x1x5x5] %onnx::Conv_833[FLOAT, 184x336x1x1] %onnx::Conv_834[FLOAT, 184] %onnx::Conv_836[FLOAT, 184x92x1x1] %onnx::Conv_839[FLOAT, 184x1x5x5] %onnx::Conv_842[FLOAT, 184x92x1x1] %onnx::Conv_845[FLOAT, 184x184x1x1] %onnx::Conv_848[FLOAT, 184x1x3x3] %onnx::Conv_851[FLOAT, 184x184x1x1] %onnx::Conv_854[FLOAT, 184x184x1x1] %onnx::Conv_857[FLOAT, 184x1x5x5] %onnx::Conv_860[FLOAT, 184x184x1x1] %onnx::Conv_863[FLOAT, 1104x184x1x1] %onnx::Conv_864[FLOAT, 1104] %onnx::Conv_866[FLOAT, 1104x1x3x3] %onnx::Conv_869[FLOAT, 352x1104x1x1] %onnx::Conv_870[FLOAT, 352] %onnx::Conv_872[FLOAT, 1504x352x1x1] %onnx::Conv_873[FLOAT, 1504] ) { %onnx::Conv_867 = Identity(%onnx::Conv_864) %onnx::Conv_861 = Identity(%onnx::Conv_834) %onnx::Conv_858 = Identity(%onnx::Conv_834) %onnx::Conv_855 = Identity(%onnx::Conv_834) %onnx::Conv_852 = Identity(%onnx::Conv_834) %onnx::Conv_849 = Identity(%onnx::Conv_834) %onnx::Conv_846 = Identity(%onnx::Conv_834) %onnx::Conv_843 = Identity(%onnx::Conv_834) %onnx::Conv_840 = Identity(%onnx::Conv_834) %onnx::Conv_837 = Identity(%onnx::Conv_834) %onnx::Conv_831 = Identity(%onnx::Conv_801) %onnx::Conv_828 = Identity(%onnx::Conv_801) %onnx::Conv_825 = Identity(%onnx::Conv_798) %onnx::Conv_822 = Identity(%onnx::Conv_798) %onnx::Conv_819 = Identity(%onnx::Conv_798) %onnx::Conv_816 = Identity(%onnx::Conv_798) %onnx::Conv_813 = Identity(%onnx::Conv_801) %onnx::Conv_810 = Identity(%onnx::Conv_801) %onnx::Conv_807 = Identity(%onnx::Conv_798) %onnx::Conv_804 = Identity(%onnx::Conv_801) %onnx::Conv_795 = Identity(%onnx::Conv_762) %onnx::Conv_792 = Identity(%onnx::Conv_762) %onnx::Conv_789 = Identity(%onnx::Conv_762) %onnx::Conv_786 = Identity(%onnx::Conv_729) %onnx::Conv_783 = Identity(%onnx::Conv_729) %onnx::Conv_780 = Identity(%onnx::Conv_762) %onnx::Conv_777 = Identity(%onnx::Conv_774) %onnx::Conv_771 = Identity(%onnx::Conv_762) %onnx::Conv_768 = Identity(%onnx::Conv_762) %onnx::Conv_765 = Identity(%onnx::Conv_762) %onnx::Conv_759 = Identity(%onnx::Conv_726) %onnx::Conv_756 = Identity(%onnx::Conv_726) %onnx::Conv_753 = Identity(%onnx::Conv_726) %onnx::Conv_750 = Identity(%onnx::Conv_738) %onnx::Conv_747 = Identity(%onnx::Conv_738) %onnx::Conv_744 = Identity(%onnx::Conv_726) %onnx::Conv_741 = Identity(%onnx::Conv_738) %onnx::Conv_735 = Identity(%onnx::Conv_726) %onnx::Conv_732 = Identity(%onnx::Conv_729) %onnx::Conv_723 = Identity(%onnx::Conv_690) %onnx::Conv_720 = Identity(%onnx::Conv_690) %onnx::Conv_717 = Identity(%onnx::Conv_690) %onnx::Conv_714 = Identity(%onnx::Conv_702) %onnx::Conv_711 = Identity(%onnx::Conv_702) %onnx::Conv_708 = Identity(%onnx::Conv_690) %onnx::Conv_705 = Identity(%onnx::Conv_702) %onnx::Conv_699 = Identity(%onnx::Conv_690) %onnx::Conv_696 = Identity(%onnx::Conv_690) %onnx::Conv_693 = Identity(%onnx::Conv_690) %onnx::Conv_687 = Identity(%onnx::Conv_678) %onnx::Conv_684 = Identity(%onnx::Conv_681) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_677, %onnx::Conv_678) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_680, %onnx::Conv_681) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 48, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.0/nl/Relu_output_0, %onnx::Conv_683, %onnx::Conv_684) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_686, %onnx::Conv_687) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_689, %onnx::Conv_690) %/cells.1/relu/Relu_output_0 = Relu(%/cells.1/conv/Conv_output_0) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/relu/Relu_output_0, %onnx::Conv_692, %onnx::Conv_693) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.2/shuffle/Reshape_output_0 = Reshape(%/cells.2/nl/Relu_output_0, %/cells.2/shuffle/Constant_output_0) %/cells.2/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.2/shuffle/Reshape_output_0) %/cells.2/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.2/shuffle/Reshape_1_output_0 = Reshape(%/cells.2/shuffle/Transpose_output_0, %/cells.2/shuffle/Constant_1_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.2/shuffle/Reshape_1_output_0, %onnx::Conv_695, %onnx::Conv_696) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_698, %onnx::Conv_699) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/relu/Relu_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_701, %onnx::Conv_702) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.3/nl/Relu_output_0, %onnx::Conv_704, %onnx::Conv_705) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_707, %onnx::Conv_708) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_710, %onnx::Conv_711) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.4/nl/Relu_output_0, %onnx::Conv_713, %onnx::Conv_714) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_716, %onnx::Conv_717) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/Add_output_0, %onnx::Conv_719, %onnx::Conv_720) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.5/shuffle/Reshape_output_0 = Reshape(%/cells.5/nl/Relu_output_0, %/cells.5/shuffle/Constant_output_0) %/cells.5/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.5/shuffle/Reshape_output_0) %/cells.5/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.5/shuffle/Reshape_1_output_0 = Reshape(%/cells.5/shuffle/Transpose_output_0, %/cells.5/shuffle/Constant_1_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.5/shuffle/Reshape_1_output_0, %onnx::Conv_722, %onnx::Conv_723) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_725, %onnx::Conv_726) %/cells.6/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_728, %onnx::Conv_729) %/cells.6/nl/Relu_output_0 = Relu(%/cells.6/conv1/Conv_output_0) %/cells.6/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.6/nl/Relu_output_0, %onnx::Conv_731, %onnx::Conv_732) %/cells.6/nl_1/Relu_output_0 = Relu(%/cells.6/conv2/Conv_output_0) %/cells.6/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/nl_1/Relu_output_0, %onnx::Conv_734, %onnx::Conv_735) %/cells.6/Add_output_0 = Add(%/cells.6/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.6/Add_output_0, %onnx::Conv_737, %onnx::Conv_738) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.7/nl/Relu_output_0, %onnx::Conv_740, %onnx::Conv_741) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_743, %onnx::Conv_744) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.6/Add_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_746, %onnx::Conv_747) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 96, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.8/nl/Relu_output_0, %onnx::Conv_749, %onnx::Conv_750) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_752, %onnx::Conv_753) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_755, %onnx::Conv_756) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.9/nl/Relu_output_0, %onnx::Conv_758, %onnx::Conv_759) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_761, %onnx::Conv_762) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_764, %onnx::Conv_765) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.10/nl/Relu_output_0, %onnx::Conv_767, %onnx::Conv_768) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_770, %onnx::Conv_771) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_773, %onnx::Conv_774) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.11/nl/Relu_output_0, %onnx::Conv_776, %onnx::Conv_777) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_779, %onnx::Conv_780) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_782, %onnx::Conv_783) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.12/nl/Relu_output_0, %onnx::Conv_785, %onnx::Conv_786) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_788, %onnx::Conv_789) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_791, %onnx::Conv_792) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.13/shuffle/Reshape_output_0 = Reshape(%/cells.13/nl/Relu_output_0, %/cells.13/shuffle/Constant_output_0) %/cells.13/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.13/shuffle/Reshape_output_0) %/cells.13/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.13/shuffle/Reshape_1_output_0 = Reshape(%/cells.13/shuffle/Transpose_output_0, %/cells.13/shuffle/Constant_1_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.13/shuffle/Reshape_1_output_0, %onnx::Conv_794, %onnx::Conv_795) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_797, %onnx::Conv_798) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_800, %onnx::Conv_801) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.14/nl/Relu_output_0, %onnx::Conv_803, %onnx::Conv_804) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_806, %onnx::Conv_807) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.15/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_809, %onnx::Conv_810) %/cells.15/nl/Relu_output_0 = Relu(%/cells.15/conv1/Conv_output_0) %/cells.15/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.15/nl/Relu_output_0, %onnx::Conv_812, %onnx::Conv_813) %/cells.15/nl_1/Relu_output_0 = Relu(%/cells.15/conv2/Conv_output_0) %/cells.15/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/nl_1/Relu_output_0, %onnx::Conv_815, %onnx::Conv_816) %/cells.15/Add_output_0 = Add(%/cells.15/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.15/Add_output_0, %onnx::Conv_818, %onnx::Conv_819) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.16/nl/Relu_output_0, %onnx::Conv_821, %onnx::Conv_822) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_824, %onnx::Conv_825) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.15/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_827, %onnx::Conv_828) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 336, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.17/nl/Relu_output_0, %onnx::Conv_830, %onnx::Conv_831) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_833, %onnx::Conv_834) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_836, %onnx::Conv_837) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.18/shuffle/Reshape_output_0 = Reshape(%/cells.18/nl/Relu_output_0, %/cells.18/shuffle/Constant_output_0) %/cells.18/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.18/shuffle/Reshape_output_0) %/cells.18/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.18/shuffle/Reshape_1_output_0 = Reshape(%/cells.18/shuffle/Transpose_output_0, %/cells.18/shuffle/Constant_1_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.18/shuffle/Reshape_1_output_0, %onnx::Conv_839, %onnx::Conv_840) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_842, %onnx::Conv_843) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_845, %onnx::Conv_846) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.19/nl/Relu_output_0, %onnx::Conv_848, %onnx::Conv_849) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_851, %onnx::Conv_852) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_854, %onnx::Conv_855) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.20/nl/Relu_output_0, %onnx::Conv_857, %onnx::Conv_858) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_860, %onnx::Conv_861) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_863, %onnx::Conv_864) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.21/nl/Relu_output_0, %onnx::Conv_866, %onnx::Conv_867) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_869, %onnx::Conv_870) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_872, %onnx::Conv_873) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %675 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %675 }	val_accuracy	78,288,000	1,941,836	{'zcp_synflow': 82.2774199955852, 'zcp_zen': 72.78775024414062, 'zcp_epe_nas': 15.067862713540924, 'zcp_fisher': 0.1708516925573349, 'zcp_flops': 78288000.0, 'zcp_grad_norm': 26.107114791870117, 'zcp_grasp': 0.18115997314453125, 'zcp_jacov': -16.069400645785223, 'zcp_l2_norm': 664.616943359375, 'zcp_nwot': 215.96528591039402, 'zcp_params': 1941836.0, 'zcp_plain': -0.0004349767114035785, 'zcp_snip': 45.46179962158203, 'lat_1080ti_1': 0.6889229783680841, 'lat_1080ti_32': 0.7499621155273083, 'lat_1080ti_64': 0.6388603638049042, 'lat_2080ti_1': 0.7444993521360788, 'lat_2080ti_32': 0.7591294973760129, 'lat_2080ti_64': 0.6625186144042071, 'lat_essential_ph_1': 0.2641509433962264, 'lat_eyeriss': 0.5674362312728074, 'lat_fpga': 0.5903680028498205, 'lat_gold_6226': 0.3589129748502316, 'lat_gold_6240': 0.5315013382091647, 'lat_pixel2': 0.34782608695652173, 'lat_pixel3': 0.5696736052903197, 'lat_raspi4': 0.60688286099827, 'lat_samsung_a50': 0.23157894736842105, 'lat_samsung_s7': 0.1889763779527559, 'lat_silver_4114': 0.589634965512613, 'lat_silver_4210r': 0.6611535223797804, 'lat_titan_rtx_1': 0.7026156955058046, 'lat_titan_rtx_32': 0.7133646769254569, 'lat_titan_rtx_64': 0.6915627458250638, 'lat_titanx_1': 0.37303001549267595, 'lat_titanx_32': 0.7039732252731236, 'lat_titanx_64': 0.6344050936510495, 'lat_titanxp_1': 0.6950488194584868, 'lat_titanxp_32': 0.7388736358550448, 'lat_titanxp_64': 0.6320384746162059}
FBNet_3686	FBNet	3686	3686	graph main_graph ( %input.1[FLOAT, 1x3x32x32] %fc.weight[FLOAT, 100x1504] %fc.bias[FLOAT, 100] %onnx::Conv_696[FLOAT, 16x3x3x3] %onnx::Conv_697[FLOAT, 16] %onnx::Conv_699[FLOAT, 16x8x1x1] %onnx::Conv_702[FLOAT, 16x1x5x5] %onnx::Conv_705[FLOAT, 16x8x1x1] %onnx::Conv_708[FLOAT, 16x8x1x1] %onnx::Conv_711[FLOAT, 16x1x3x3] %onnx::Conv_714[FLOAT, 24x8x1x1] %onnx::Conv_715[FLOAT, 24] %onnx::Conv_717[FLOAT, 144x24x1x1] %onnx::Conv_718[FLOAT, 144] %onnx::Conv_720[FLOAT, 144x1x3x3] %onnx::Conv_723[FLOAT, 24x144x1x1] %onnx::Conv_726[FLOAT, 24x12x1x1] %onnx::Conv_729[FLOAT, 24x1x3x3] %onnx::Conv_732[FLOAT, 24x12x1x1] %onnx::Conv_735[FLOAT, 24x24x1x1] %onnx::Conv_738[FLOAT, 24x1x5x5] %onnx::Conv_741[FLOAT, 24x24x1x1] %onnx::Conv_744[FLOAT, 72x24x1x1] %onnx::Conv_745[FLOAT, 72] %onnx::Conv_747[FLOAT, 72x1x3x3] %onnx::Conv_750[FLOAT, 32x72x1x1] %onnx::Conv_751[FLOAT, 32] %onnx::Conv_753[FLOAT, 32x16x1x1] %onnx::Conv_756[FLOAT, 32x1x3x3] %onnx::Conv_759[FLOAT, 32x16x1x1] %onnx::Conv_762[FLOAT, 32x32x1x1] %onnx::Conv_765[FLOAT, 32x1x3x3] %onnx::Conv_768[FLOAT, 32x32x1x1] %onnx::Conv_771[FLOAT, 192x32x1x1] %onnx::Conv_772[FLOAT, 192] %onnx::Conv_774[FLOAT, 192x1x3x3] %onnx::Conv_777[FLOAT, 64x192x1x1] %onnx::Conv_778[FLOAT, 64] %onnx::Conv_780[FLOAT, 192x64x1x1] %onnx::Conv_783[FLOAT, 192x1x5x5] %onnx::Conv_786[FLOAT, 64x192x1x1] %onnx::Conv_789[FLOAT, 64x32x1x1] %onnx::Conv_792[FLOAT, 64x1x3x3] %onnx::Conv_795[FLOAT, 64x32x1x1] %onnx::Conv_798[FLOAT, 64x64x1x1] %onnx::Conv_801[FLOAT, 64x1x3x3] %onnx::Conv_804[FLOAT, 64x64x1x1] %onnx::Conv_807[FLOAT, 384x64x1x1] %onnx::Conv_808[FLOAT, 384] %onnx::Conv_810[FLOAT, 384x1x5x5] %onnx::Conv_813[FLOAT, 112x384x1x1] %onnx::Conv_814[FLOAT, 112] %onnx::Conv_816[FLOAT, 672x112x1x1] %onnx::Conv_817[FLOAT, 672] %onnx::Conv_819[FLOAT, 672x1x5x5] %onnx::Conv_822[FLOAT, 112x672x1x1] %onnx::Conv_825[FLOAT, 112x56x1x1] %onnx::Conv_828[FLOAT, 112x1x3x3] %onnx::Conv_831[FLOAT, 112x56x1x1] %onnx::Conv_834[FLOAT, 112x56x1x1] %onnx::Conv_837[FLOAT, 112x1x5x5] %onnx::Conv_840[FLOAT, 184x56x1x1] %onnx::Conv_841[FLOAT, 184] %onnx::Conv_843[FLOAT, 552x184x1x1] %onnx::Conv_844[FLOAT, 552] %onnx::Conv_846[FLOAT, 552x1x3x3] %onnx::Conv_849[FLOAT, 184x552x1x1] %onnx::Conv_852[FLOAT, 184x184x1x1] %onnx::Conv_855[FLOAT, 184x1x5x5] %onnx::Conv_858[FLOAT, 184x184x1x1] %onnx::Conv_861[FLOAT, 184x184x1x1] %onnx::Conv_864[FLOAT, 184x1x3x3] %onnx::Conv_867[FLOAT, 184x184x1x1] %onnx::Conv_870[FLOAT, 1104x184x1x1] %onnx::Conv_871[FLOAT, 1104] %onnx::Conv_873[FLOAT, 1104x1x5x5] %onnx::Conv_876[FLOAT, 352x1104x1x1] %onnx::Conv_877[FLOAT, 352] %onnx::Conv_879[FLOAT, 1504x352x1x1] %onnx::Conv_880[FLOAT, 1504] ) { %onnx::Conv_874 = Identity(%onnx::Conv_871) %onnx::Conv_868 = Identity(%onnx::Conv_841) %onnx::Conv_865 = Identity(%onnx::Conv_841) %onnx::Conv_862 = Identity(%onnx::Conv_841) %onnx::Conv_859 = Identity(%onnx::Conv_841) %onnx::Conv_856 = Identity(%onnx::Conv_841) %onnx::Conv_853 = Identity(%onnx::Conv_841) %onnx::Conv_850 = Identity(%onnx::Conv_841) %onnx::Conv_847 = Identity(%onnx::Conv_844) %onnx::Conv_838 = Identity(%onnx::Conv_814) %onnx::Conv_835 = Identity(%onnx::Conv_814) %onnx::Conv_832 = Identity(%onnx::Conv_814) %onnx::Conv_829 = Identity(%onnx::Conv_814) %onnx::Conv_826 = Identity(%onnx::Conv_814) %onnx::Conv_823 = Identity(%onnx::Conv_814) %onnx::Conv_820 = Identity(%onnx::Conv_817) %onnx::Conv_811 = Identity(%onnx::Conv_808) %onnx::Conv_805 = Identity(%onnx::Conv_778) %onnx::Conv_802 = Identity(%onnx::Conv_778) %onnx::Conv_799 = Identity(%onnx::Conv_778) %onnx::Conv_796 = Identity(%onnx::Conv_778) %onnx::Conv_793 = Identity(%onnx::Conv_778) %onnx::Conv_790 = Identity(%onnx::Conv_778) %onnx::Conv_787 = Identity(%onnx::Conv_778) %onnx::Conv_784 = Identity(%onnx::Conv_772) %onnx::Conv_781 = Identity(%onnx::Conv_772) %onnx::Conv_775 = Identity(%onnx::Conv_772) %onnx::Conv_769 = Identity(%onnx::Conv_751) %onnx::Conv_766 = Identity(%onnx::Conv_751) %onnx::Conv_763 = Identity(%onnx::Conv_751) %onnx::Conv_760 = Identity(%onnx::Conv_751) %onnx::Conv_757 = Identity(%onnx::Conv_751) %onnx::Conv_754 = Identity(%onnx::Conv_751) %onnx::Conv_748 = Identity(%onnx::Conv_745) %onnx::Conv_742 = Identity(%onnx::Conv_715) %onnx::Conv_739 = Identity(%onnx::Conv_715) %onnx::Conv_736 = Identity(%onnx::Conv_715) %onnx::Conv_733 = Identity(%onnx::Conv_715) %onnx::Conv_730 = Identity(%onnx::Conv_715) %onnx::Conv_727 = Identity(%onnx::Conv_715) %onnx::Conv_724 = Identity(%onnx::Conv_715) %onnx::Conv_721 = Identity(%onnx::Conv_718) %onnx::Conv_712 = Identity(%onnx::Conv_697) %onnx::Conv_709 = Identity(%onnx::Conv_697) %onnx::Conv_706 = Identity(%onnx::Conv_697) %onnx::Conv_703 = Identity(%onnx::Conv_697) %onnx::Conv_700 = Identity(%onnx::Conv_697) %/stem/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %onnx::Conv_696, %onnx::Conv_697) %/stem/relu/Relu_output_0 = Relu(%/stem/conv/Conv_output_0) %/cells.0/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/stem/relu/Relu_output_0, %onnx::Conv_699, %onnx::Conv_700) %/cells.0/nl/Relu_output_0 = Relu(%/cells.0/conv1/Conv_output_0) %/cells.0/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.0/shuffle/Reshape_output_0 = Reshape(%/cells.0/nl/Relu_output_0, %/cells.0/shuffle/Constant_output_0) %/cells.0/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.0/shuffle/Reshape_output_0) %/cells.0/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.0/shuffle/Reshape_1_output_0 = Reshape(%/cells.0/shuffle/Transpose_output_0, %/cells.0/shuffle/Constant_1_output_0) %/cells.0/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.0/shuffle/Reshape_1_output_0, %onnx::Conv_702, %onnx::Conv_703) %/cells.0/nl_1/Relu_output_0 = Relu(%/cells.0/conv2/Conv_output_0) %/cells.0/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/nl_1/Relu_output_0, %onnx::Conv_705, %onnx::Conv_706) %/cells.0/Add_output_0 = Add(%/cells.0/conv3/Conv_output_0, %/stem/relu/Relu_output_0) %/cells.1/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.0/Add_output_0, %onnx::Conv_708, %onnx::Conv_709) %/cells.1/nl/Relu_output_0 = Relu(%/cells.1/conv1/Conv_output_0) %/cells.1/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.1/shuffle/Reshape_output_0 = Reshape(%/cells.1/nl/Relu_output_0, %/cells.1/shuffle/Constant_output_0) %/cells.1/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.1/shuffle/Reshape_output_0) %/cells.1/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.1/shuffle/Reshape_1_output_0 = Reshape(%/cells.1/shuffle/Transpose_output_0, %/cells.1/shuffle/Constant_1_output_0) %/cells.1/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 16, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.1/shuffle/Reshape_1_output_0, %onnx::Conv_711, %onnx::Conv_712) %/cells.1/nl_1/Relu_output_0 = Relu(%/cells.1/conv2/Conv_output_0) %/cells.1/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/nl_1/Relu_output_0, %onnx::Conv_714, %onnx::Conv_715) %/cells.2/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.1/conv3/Conv_output_0, %onnx::Conv_717, %onnx::Conv_718) %/cells.2/nl/Relu_output_0 = Relu(%/cells.2/conv1/Conv_output_0) %/cells.2/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 144, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.2/nl/Relu_output_0, %onnx::Conv_720, %onnx::Conv_721) %/cells.2/nl_1/Relu_output_0 = Relu(%/cells.2/conv2/Conv_output_0) %/cells.2/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/nl_1/Relu_output_0, %onnx::Conv_723, %onnx::Conv_724) %/cells.2/Add_output_0 = Add(%/cells.2/conv3/Conv_output_0, %/cells.1/conv3/Conv_output_0) %/cells.3/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.2/Add_output_0, %onnx::Conv_726, %onnx::Conv_727) %/cells.3/nl/Relu_output_0 = Relu(%/cells.3/conv1/Conv_output_0) %/cells.3/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.3/shuffle/Reshape_output_0 = Reshape(%/cells.3/nl/Relu_output_0, %/cells.3/shuffle/Constant_output_0) %/cells.3/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.3/shuffle/Reshape_output_0) %/cells.3/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.3/shuffle/Reshape_1_output_0 = Reshape(%/cells.3/shuffle/Transpose_output_0, %/cells.3/shuffle/Constant_1_output_0) %/cells.3/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.3/shuffle/Reshape_1_output_0, %onnx::Conv_729, %onnx::Conv_730) %/cells.3/nl_1/Relu_output_0 = Relu(%/cells.3/conv2/Conv_output_0) %/cells.3/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/nl_1/Relu_output_0, %onnx::Conv_732, %onnx::Conv_733) %/cells.3/Add_output_0 = Add(%/cells.3/conv3/Conv_output_0, %/cells.2/Add_output_0) %/cells.4/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.3/Add_output_0, %onnx::Conv_735, %onnx::Conv_736) %/cells.4/nl/Relu_output_0 = Relu(%/cells.4/conv1/Conv_output_0) %/cells.4/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 24, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.4/nl/Relu_output_0, %onnx::Conv_738, %onnx::Conv_739) %/cells.4/nl_1/Relu_output_0 = Relu(%/cells.4/conv2/Conv_output_0) %/cells.4/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/nl_1/Relu_output_0, %onnx::Conv_741, %onnx::Conv_742) %/cells.4/Add_output_0 = Add(%/cells.4/conv3/Conv_output_0, %/cells.3/Add_output_0) %/cells.5/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.4/Add_output_0, %onnx::Conv_744, %onnx::Conv_745) %/cells.5/nl/Relu_output_0 = Relu(%/cells.5/conv1/Conv_output_0) %/cells.5/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 72, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.5/nl/Relu_output_0, %onnx::Conv_747, %onnx::Conv_748) %/cells.5/nl_1/Relu_output_0 = Relu(%/cells.5/conv2/Conv_output_0) %/cells.5/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/nl_1/Relu_output_0, %onnx::Conv_750, %onnx::Conv_751) %/cells.7/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.5/conv3/Conv_output_0, %onnx::Conv_753, %onnx::Conv_754) %/cells.7/nl/Relu_output_0 = Relu(%/cells.7/conv1/Conv_output_0) %/cells.7/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.7/shuffle/Reshape_output_0 = Reshape(%/cells.7/nl/Relu_output_0, %/cells.7/shuffle/Constant_output_0) %/cells.7/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.7/shuffle/Reshape_output_0) %/cells.7/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.7/shuffle/Reshape_1_output_0 = Reshape(%/cells.7/shuffle/Transpose_output_0, %/cells.7/shuffle/Constant_1_output_0) %/cells.7/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.7/shuffle/Reshape_1_output_0, %onnx::Conv_756, %onnx::Conv_757) %/cells.7/nl_1/Relu_output_0 = Relu(%/cells.7/conv2/Conv_output_0) %/cells.7/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/nl_1/Relu_output_0, %onnx::Conv_759, %onnx::Conv_760) %/cells.7/Add_output_0 = Add(%/cells.7/conv3/Conv_output_0, %/cells.5/conv3/Conv_output_0) %/cells.8/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.7/Add_output_0, %onnx::Conv_762, %onnx::Conv_763) %/cells.8/nl/Relu_output_0 = Relu(%/cells.8/conv1/Conv_output_0) %/cells.8/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 32, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.8/nl/Relu_output_0, %onnx::Conv_765, %onnx::Conv_766) %/cells.8/nl_1/Relu_output_0 = Relu(%/cells.8/conv2/Conv_output_0) %/cells.8/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/nl_1/Relu_output_0, %onnx::Conv_768, %onnx::Conv_769) %/cells.8/Add_output_0 = Add(%/cells.8/conv3/Conv_output_0, %/cells.7/Add_output_0) %/cells.9/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.8/Add_output_0, %onnx::Conv_771, %onnx::Conv_772) %/cells.9/nl/Relu_output_0 = Relu(%/cells.9/conv1/Conv_output_0) %/cells.9/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%/cells.9/nl/Relu_output_0, %onnx::Conv_774, %onnx::Conv_775) %/cells.9/nl_1/Relu_output_0 = Relu(%/cells.9/conv2/Conv_output_0) %/cells.9/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/nl_1/Relu_output_0, %onnx::Conv_777, %onnx::Conv_778) %/cells.10/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.9/conv3/Conv_output_0, %onnx::Conv_780, %onnx::Conv_781) %/cells.10/nl/Relu_output_0 = Relu(%/cells.10/conv1/Conv_output_0) %/cells.10/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 192, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.10/nl/Relu_output_0, %onnx::Conv_783, %onnx::Conv_784) %/cells.10/nl_1/Relu_output_0 = Relu(%/cells.10/conv2/Conv_output_0) %/cells.10/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/nl_1/Relu_output_0, %onnx::Conv_786, %onnx::Conv_787) %/cells.10/Add_output_0 = Add(%/cells.10/conv3/Conv_output_0, %/cells.9/conv3/Conv_output_0) %/cells.11/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.10/Add_output_0, %onnx::Conv_789, %onnx::Conv_790) %/cells.11/nl/Relu_output_0 = Relu(%/cells.11/conv1/Conv_output_0) %/cells.11/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.11/shuffle/Reshape_output_0 = Reshape(%/cells.11/nl/Relu_output_0, %/cells.11/shuffle/Constant_output_0) %/cells.11/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.11/shuffle/Reshape_output_0) %/cells.11/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.11/shuffle/Reshape_1_output_0 = Reshape(%/cells.11/shuffle/Transpose_output_0, %/cells.11/shuffle/Constant_1_output_0) %/cells.11/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.11/shuffle/Reshape_1_output_0, %onnx::Conv_792, %onnx::Conv_793) %/cells.11/nl_1/Relu_output_0 = Relu(%/cells.11/conv2/Conv_output_0) %/cells.11/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/nl_1/Relu_output_0, %onnx::Conv_795, %onnx::Conv_796) %/cells.11/Add_output_0 = Add(%/cells.11/conv3/Conv_output_0, %/cells.10/Add_output_0) %/cells.12/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.11/Add_output_0, %onnx::Conv_798, %onnx::Conv_799) %/cells.12/nl/Relu_output_0 = Relu(%/cells.12/conv1/Conv_output_0) %/cells.12/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.12/nl/Relu_output_0, %onnx::Conv_801, %onnx::Conv_802) %/cells.12/nl_1/Relu_output_0 = Relu(%/cells.12/conv2/Conv_output_0) %/cells.12/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/nl_1/Relu_output_0, %onnx::Conv_804, %onnx::Conv_805) %/cells.12/Add_output_0 = Add(%/cells.12/conv3/Conv_output_0, %/cells.11/Add_output_0) %/cells.13/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.12/Add_output_0, %onnx::Conv_807, %onnx::Conv_808) %/cells.13/nl/Relu_output_0 = Relu(%/cells.13/conv1/Conv_output_0) %/cells.13/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 384, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.13/nl/Relu_output_0, %onnx::Conv_810, %onnx::Conv_811) %/cells.13/nl_1/Relu_output_0 = Relu(%/cells.13/conv2/Conv_output_0) %/cells.13/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/nl_1/Relu_output_0, %onnx::Conv_813, %onnx::Conv_814) %/cells.14/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.13/conv3/Conv_output_0, %onnx::Conv_816, %onnx::Conv_817) %/cells.14/nl/Relu_output_0 = Relu(%/cells.14/conv1/Conv_output_0) %/cells.14/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 672, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.14/nl/Relu_output_0, %onnx::Conv_819, %onnx::Conv_820) %/cells.14/nl_1/Relu_output_0 = Relu(%/cells.14/conv2/Conv_output_0) %/cells.14/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/nl_1/Relu_output_0, %onnx::Conv_822, %onnx::Conv_823) %/cells.14/Add_output_0 = Add(%/cells.14/conv3/Conv_output_0, %/cells.13/conv3/Conv_output_0) %/cells.16/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.14/Add_output_0, %onnx::Conv_825, %onnx::Conv_826) %/cells.16/nl/Relu_output_0 = Relu(%/cells.16/conv1/Conv_output_0) %/cells.16/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.16/shuffle/Reshape_output_0 = Reshape(%/cells.16/nl/Relu_output_0, %/cells.16/shuffle/Constant_output_0) %/cells.16/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.16/shuffle/Reshape_output_0) %/cells.16/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.16/shuffle/Reshape_1_output_0 = Reshape(%/cells.16/shuffle/Transpose_output_0, %/cells.16/shuffle/Constant_1_output_0) %/cells.16/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.16/shuffle/Reshape_1_output_0, %onnx::Conv_828, %onnx::Conv_829) %/cells.16/nl_1/Relu_output_0 = Relu(%/cells.16/conv2/Conv_output_0) %/cells.16/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/nl_1/Relu_output_0, %onnx::Conv_831, %onnx::Conv_832) %/cells.16/Add_output_0 = Add(%/cells.16/conv3/Conv_output_0, %/cells.14/Add_output_0) %/cells.17/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.16/Add_output_0, %onnx::Conv_834, %onnx::Conv_835) %/cells.17/nl/Relu_output_0 = Relu(%/cells.17/conv1/Conv_output_0) %/cells.17/shuffle/Constant_output_0 = Constant[value = <Tensor>]() %/cells.17/shuffle/Reshape_output_0 = Reshape(%/cells.17/nl/Relu_output_0, %/cells.17/shuffle/Constant_output_0) %/cells.17/shuffle/Transpose_output_0 = Transpose[perm = [0, 2, 1, 3, 4]](%/cells.17/shuffle/Reshape_output_0) %/cells.17/shuffle/Constant_1_output_0 = Constant[value = <Tensor>]() %/cells.17/shuffle/Reshape_1_output_0 = Reshape(%/cells.17/shuffle/Transpose_output_0, %/cells.17/shuffle/Constant_1_output_0) %/cells.17/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 112, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [2, 2]](%/cells.17/shuffle/Reshape_1_output_0, %onnx::Conv_837, %onnx::Conv_838) %/cells.17/nl_1/Relu_output_0 = Relu(%/cells.17/conv2/Conv_output_0) %/cells.17/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 2, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/nl_1/Relu_output_0, %onnx::Conv_840, %onnx::Conv_841) %/cells.18/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.17/conv3/Conv_output_0, %onnx::Conv_843, %onnx::Conv_844) %/cells.18/nl/Relu_output_0 = Relu(%/cells.18/conv1/Conv_output_0) %/cells.18/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 552, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.18/nl/Relu_output_0, %onnx::Conv_846, %onnx::Conv_847) %/cells.18/nl_1/Relu_output_0 = Relu(%/cells.18/conv2/Conv_output_0) %/cells.18/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/nl_1/Relu_output_0, %onnx::Conv_849, %onnx::Conv_850) %/cells.18/Add_output_0 = Add(%/cells.18/conv3/Conv_output_0, %/cells.17/conv3/Conv_output_0) %/cells.19/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.18/Add_output_0, %onnx::Conv_852, %onnx::Conv_853) %/cells.19/nl/Relu_output_0 = Relu(%/cells.19/conv1/Conv_output_0) %/cells.19/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.19/nl/Relu_output_0, %onnx::Conv_855, %onnx::Conv_856) %/cells.19/nl_1/Relu_output_0 = Relu(%/cells.19/conv2/Conv_output_0) %/cells.19/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/nl_1/Relu_output_0, %onnx::Conv_858, %onnx::Conv_859) %/cells.19/Add_output_0 = Add(%/cells.19/conv3/Conv_output_0, %/cells.18/Add_output_0) %/cells.20/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.19/Add_output_0, %onnx::Conv_861, %onnx::Conv_862) %/cells.20/nl/Relu_output_0 = Relu(%/cells.20/conv1/Conv_output_0) %/cells.20/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 184, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%/cells.20/nl/Relu_output_0, %onnx::Conv_864, %onnx::Conv_865) %/cells.20/nl_1/Relu_output_0 = Relu(%/cells.20/conv2/Conv_output_0) %/cells.20/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/nl_1/Relu_output_0, %onnx::Conv_867, %onnx::Conv_868) %/cells.20/Add_output_0 = Add(%/cells.20/conv3/Conv_output_0, %/cells.19/Add_output_0) %/cells.21/conv1/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.20/Add_output_0, %onnx::Conv_870, %onnx::Conv_871) %/cells.21/nl/Relu_output_0 = Relu(%/cells.21/conv1/Conv_output_0) %/cells.21/conv2/Conv_output_0 = Conv[dilations = [1, 1], group = 1104, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]](%/cells.21/nl/Relu_output_0, %onnx::Conv_873, %onnx::Conv_874) %/cells.21/nl_1/Relu_output_0 = Relu(%/cells.21/conv2/Conv_output_0) %/cells.21/conv3/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/nl_1/Relu_output_0, %onnx::Conv_876, %onnx::Conv_877) %/header/conv/Conv_output_0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%/cells.21/conv3/Conv_output_0, %onnx::Conv_879, %onnx::Conv_880) %/header/relu/Relu_output_0 = Relu(%/header/conv/Conv_output_0) %/avgpool/GlobalAveragePool_output_0 = GlobalAveragePool(%/header/relu/Relu_output_0) %/Constant_output_0 = Constant[value = <Tensor>]() %/Reshape_output_0 = Reshape(%/avgpool/GlobalAveragePool_output_0, %/Constant_output_0) %694 = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias) return %694 }	val_accuracy	65,281,664	2,033,604	{'zcp_synflow': 71.64857177507007, 'zcp_zen': 64.73391723632812, 'zcp_epe_nas': 0.00015999920000638146, 'zcp_fisher': 0.20650014281272888, 'zcp_flops': 65281664.0, 'zcp_grad_norm': 21.267210006713867, 'zcp_grasp': -0.13774681091308594, 'zcp_jacov': -16.06149689292758, 'zcp_l2_norm': 593.7361450195312, 'zcp_nwot': 210.70142664875428, 'zcp_params': 2033604.0, 'zcp_plain': 0.0014588702470064163, 'zcp_snip': 42.05717849731445, 'lat_1080ti_1': 0.6832231510282144, 'lat_1080ti_32': 0.601378112242564, 'lat_1080ti_64': 0.4298723571396253, 'lat_2080ti_1': 0.6703582777571414, 'lat_2080ti_32': 0.5871699116715798, 'lat_2080ti_64': 0.4600348175972655, 'lat_essential_ph_1': 0.3018867924528302, 'lat_eyeriss': 0.37901820915775764, 'lat_fpga': 0.4471150516035187, 'lat_gold_6226': 0.33369705843786907, 'lat_gold_6240': 0.525490233919915, 'lat_pixel2': 0.32608695652173914, 'lat_pixel3': 0.40366380580558253, 'lat_raspi4': 0.4819317453233667, 'lat_samsung_a50': 0.17894736842105263, 'lat_samsung_s7': 0.16535433070866143, 'lat_silver_4114': 0.6292656902142562, 'lat_silver_4210r': 0.5348366285917002, 'lat_titan_rtx_1': 0.6293433507102802, 'lat_titan_rtx_32': 0.5804486809249383, 'lat_titan_rtx_64': 0.478987218076895, 'lat_titanx_1': 0.3337744044300114, 'lat_titanx_32': 0.4958679264430853, 'lat_titanx_64': 0.40080325327622274, 'lat_titanxp_1': 0.6328458816397775, 'lat_titanxp_32': 0.529883571098058, 'lat_titanxp_64': 0.43376120364592796}

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