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import torch
import torch.nn as nn
from spikingjelly.clock_driven.neuron import MultiStepParametricLIFNode, MultiStepLIFNode
from timm.models.layers import to_2tuple, trunc_normal_, DropPath
from timm.models.registry import register_model
from timm.models.vision_transformer import _cfg
from functools import partial
from timm.models import create_model
from .delay_synaptic_func_inter import DelayConv
__all__ = ['delay_QKFormer']
class MLP(nn.Module):
def __init__(self, in_features, hidden_features=None, out_features=None, drop=0.):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
self.mlp1_conv = nn.Conv2d(in_features, hidden_features, kernel_size=1, stride=1)
self.mlp1_bn = nn.BatchNorm2d(hidden_features)
self.mlp1_lif = MultiStepLIFNode(tau=2.0, detach_reset=True, backend='cupy')
self.mlp2_conv = nn.Conv2d(hidden_features, out_features, kernel_size=1, stride=1)
self.mlp2_bn = nn.BatchNorm2d(out_features)
self.mlp2_lif = MultiStepLIFNode(tau=2.0, detach_reset=True, backend='cupy')
self.c_hidden = hidden_features
self.c_output = out_features
def forward(self, x):
T, B, C, H, W = x.shape
x = self.mlp1_conv(x.flatten(0, 1))
x = self.mlp1_bn(x).reshape(T, B, self.c_hidden, H, W)
x = self.mlp1_lif(x)
x = self.mlp2_conv(x.flatten(0, 1))
x = self.mlp2_bn(x).reshape(T, B, C, H, W)
x = self.mlp2_lif(x)
return x
class Token_QK_Attention(nn.Module):
def __init__(self,
dim,
num_heads=8,
qkv_bias=False,
qk_scale=None,
attn_drop=0.,
proj_drop=0.,
sr_ratio=1,
k=16):
super().__init__()
assert dim % num_heads == 0, f"dim {dim} should be divided by num_heads {num_heads}."
self.dim = dim
self.num_heads = num_heads
self.q_conv = nn.Conv1d(dim, dim, kernel_size=1, stride=1, bias=False)
self.q_bn = nn.BatchNorm1d(dim)
self.q_lif = MultiStepLIFNode(tau=2.0, detach_reset=True, backend='cupy')
# self.k_conv = nn.Conv1d(dim, dim, kernel_size=1, stride=1, bias=False)
self.k_proj_delay = DelayConv(in_c=self.dim, k=k)
self.k_bn = nn.BatchNorm1d(dim)
self.k_lif = MultiStepLIFNode(tau=2.0, detach_reset=True, backend='cupy')
self.attn_lif = MultiStepLIFNode(tau=2.0, v_threshold=0.5, detach_reset=True, backend='cupy')
self.proj_conv = nn.Conv1d(dim, dim, kernel_size=1, stride=1)
self.proj_bn = nn.BatchNorm1d(dim)
self.proj_lif = MultiStepLIFNode(tau=2.0, detach_reset=True, backend='cupy')
def forward(self, x):
T, B, C, H, W = x.shape
x = x.flatten(3)
T, B, C, N = x.shape
x_for_qkv = x.flatten(0, 1)
q_conv_out = self.q_conv(x_for_qkv)
q_conv_out = self.q_bn(q_conv_out).reshape(T, B, C, N)
q_conv_out = self.q_lif(q_conv_out)
q = q_conv_out.unsqueeze(2).reshape(T, B, self.num_heads, C // self.num_heads, N)
# k_conv_out = self.k_conv(x_for_qkv)
k_conv_out = self.k_proj_delay(x_for_qkv.reshape(T,B,C,N))
k_conv_out = self.k_bn(k_conv_out).reshape(T, B, C, N)
k_conv_out = self.k_lif(k_conv_out)
k = k_conv_out.unsqueeze(2).reshape(T, B, self.num_heads, C // self.num_heads, N)
q = torch.sum(q, dim=3, keepdim=True)
attn = self.attn_lif(q)
x = torch.mul(attn, k)
x = x.flatten(2, 3)
x = self.proj_bn(self.proj_conv(x.flatten(0, 1))).reshape(T, B, C, H, W)
x = self.proj_lif(x)
return x
class Spiking_Self_Attention(nn.Module):
def __init__(self,
dim,
num_heads=8,
qkv_bias=False,
qk_scale=None,
attn_drop=0.,
proj_drop=0.,
sr_ratio=1,
k=16):
super().__init__()
assert dim % num_heads == 0, f"dim {dim} should be divided by num_heads {num_heads}."
self.dim = dim
self.num_heads = num_heads
head_dim = dim // num_heads
self.scale = 0.125
self.q_conv = nn.Conv1d(dim, dim, kernel_size=1, stride=1, bias=False)
self.q_bn = nn.BatchNorm1d(dim)
self.q_lif = MultiStepLIFNode(tau=2.0, detach_reset=True, backend='cupy')
# self.k_conv = nn.Conv1d(dim, dim, kernel_size=1, stride=1, bias=False)
self.k_proj_delay = DelayConv(in_c=self.dim, k=k)
self.k_bn = nn.BatchNorm1d(dim)
self.k_lif = MultiStepLIFNode(tau=2.0, detach_reset=True, backend='cupy')
# self.v_conv = nn.Conv1d(dim, dim, kernel_size=1, stride=1, bias=False)
self.v_proj_delay = DelayConv(in_c=self.dim, k=k)
self.v_bn = nn.BatchNorm1d(dim)
self.v_lif = MultiStepLIFNode(tau=2.0, detach_reset=True, backend='cupy')
self.attn_lif = MultiStepLIFNode(tau=2.0, v_threshold=0.5, detach_reset=True, backend='cupy')
self.proj_conv = nn.Conv1d(dim, dim, kernel_size=1, stride=1)
self.proj_bn = nn.BatchNorm1d(dim)
self.proj_lif = MultiStepLIFNode(tau=2.0, detach_reset=True, backend='cupy')
self.qkv_mp = nn.MaxPool1d(4)
def forward(self, x):
T, B, C, H, W = x.shape
x = x.flatten(3)
T, B, C, N = x.shape
x_for_qkv = x.flatten(0, 1)
q_conv_out = self.q_conv(x_for_qkv)
q_conv_out = self.q_bn(q_conv_out).reshape(T, B, C, N).contiguous()
q_conv_out = self.q_lif(q_conv_out)
q = q_conv_out.transpose(-1, -2).reshape(T, B, N, self.num_heads, C // self.num_heads).permute(0, 1, 3, 2,
4).contiguous()
k_conv_out = self.k_proj_delay(x_for_qkv.reshape(T,B,C,N))
k_conv_out = self.k_bn(k_conv_out).reshape(T, B, C, N).contiguous()
k_conv_out = self.k_lif(k_conv_out)
k = k_conv_out.transpose(-1, -2).reshape(T, B, N, self.num_heads, C // self.num_heads).permute(0, 1, 3, 2,
4).contiguous()
v_conv_out = self.v_proj_delay(x_for_qkv.reshape(T,B,C,N))
v_conv_out = self.v_bn(v_conv_out).reshape(T, B, C, N).contiguous()
v_conv_out = self.v_lif(v_conv_out)
v = v_conv_out.transpose(-1, -2).reshape(T, B, N, self.num_heads, C // self.num_heads).permute(0, 1, 3, 2,
4).contiguous()
x = k.transpose(-2, -1) @ v
x = (q @ x) * self.scale
x = x.transpose(3, 4).reshape(T, B, C, N).contiguous()
x = self.attn_lif(x)
x = x.flatten(0, 1)
x = self.proj_lif(self.proj_bn(self.proj_conv(x))).reshape(T, B, C, H, W)
return x
class TokenSpikingTransformer(nn.Module):
def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
drop_path=0., norm_layer=nn.LayerNorm, sr_ratio=1):
super().__init__()
self.tssa = Token_QK_Attention(dim, num_heads)
mlp_hidden_dim = int(dim * mlp_ratio)
self.mlp = MLP(in_features= dim, hidden_features=mlp_hidden_dim, drop=drop)
def forward(self, x):
x = x + self.tssa(x)
x = x + self.mlp(x)
return x
class SpikingTransformer(nn.Module):
def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
drop_path=0., norm_layer=nn.LayerNorm, sr_ratio=1):
super().__init__()
self.ssa = Spiking_Self_Attention(dim, num_heads)
mlp_hidden_dim = int(dim * mlp_ratio)
self.mlp = MLP(in_features= dim, hidden_features=mlp_hidden_dim, drop=drop)
def forward(self, x):
x = x + self.ssa(x)
x = x + self.mlp(x)
return x
class PatchEmbedInit(nn.Module):
def __init__(self, img_size_h=128, img_size_w=128, patch_size=4, in_channels=2, embed_dims=256):
super().__init__()
self.image_size = [img_size_h, img_size_w]
patch_size = to_2tuple(patch_size)
self.patch_size = patch_size
self.C = in_channels
self.H, self.W = self.image_size[0] // patch_size[0], self.image_size[1] // patch_size[1]
self.num_patches = self.H * self.W
self.proj_conv = nn.Conv2d(in_channels, embed_dims // 8, kernel_size=3, stride=1, padding=1, bias=False)
self.proj_bn = nn.BatchNorm2d(embed_dims // 8)
self.proj_lif = MultiStepLIFNode(tau=2.0, detach_reset=True, backend='cupy')
self.proj1_conv = nn.Conv2d(embed_dims // 8, embed_dims // 4, kernel_size=3, stride=1, padding=1, bias=False)
self.proj1_bn = nn.BatchNorm2d(embed_dims // 4)
self.maxpool1 = torch.nn.MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
self.proj1_lif = MultiStepLIFNode(tau=2.0, detach_reset=True, backend='cupy')
self.proj2_conv = nn.Conv2d(embed_dims//4, embed_dims // 2, kernel_size=3, stride=1, padding=1, bias=False)
self.proj2_bn = nn.BatchNorm2d(embed_dims // 2)
self.maxpool2 = torch.nn.MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
self.proj2_lif = MultiStepLIFNode(tau=2.0, detach_reset=True, backend='cupy')
self.proj3_conv = nn.Conv2d(embed_dims // 2, embed_dims, kernel_size=3, stride=1, padding=1, bias=False)
self.proj3_bn = nn.BatchNorm2d(embed_dims)
self.maxpool3 = torch.nn.MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
self.proj3_lif = MultiStepLIFNode(tau=2.0, detach_reset=True, backend='cupy')
self.proj_res_conv = nn.Conv2d(embed_dims // 4, embed_dims, kernel_size=1, stride=4, padding=0, bias=False)
self.proj_res_bn = nn.BatchNorm2d(embed_dims)
self.proj_res_lif = MultiStepLIFNode(tau=2.0, detach_reset=True, backend='cupy')
def forward(self, x):
T, B, C, H, W = x.shape
# Downsampling + Res
# x_feat = x.flatten(0, 1)
x = self.proj_conv(x.flatten(0, 1))
x = self.proj_bn(x).reshape(T, B, -1, H, W)
x = self.proj_lif(x).flatten(0, 1).contiguous()
x = self.proj1_conv(x)
x = self.proj1_bn(x)
x = self.maxpool1(x)
_, _, H1, W1 = x.shape
x = x.reshape(T, B, -1, H1, W1).contiguous()
x = self.proj1_lif(x).flatten(0, 1).contiguous()
x_feat = x
x = self.proj2_conv(x)
x = self.proj2_bn(x)
x = self.maxpool2(x)
_, _, H2, W2 = x.shape
x = x.reshape(T, B, -1, H2, W2).contiguous()
x = self.proj2_lif(x).flatten(0, 1).contiguous()
x = self.proj3_conv(x)
x = self.proj3_bn(x)
x = self.maxpool3(x)
_, _, H3, W3 = x.shape
x = x.reshape(T, B, -1, H3, W3).contiguous()
x = self.proj3_lif(x)
x_feat = self.proj_res_conv(x_feat)
x_feat = self.proj_res_bn(x_feat)
_, _, Hres, Wres = x_feat.shape
x_feat = x_feat.reshape(T, B, -1, Hres, Wres).contiguous()
x_feat = self.proj_res_lif(x_feat)
x = x + x_feat # shortcut
return x
class PatchEmbeddingStage(nn.Module):
def __init__(self, img_size_h=128, img_size_w=128, patch_size=4, in_channels=2, embed_dims=256):
super().__init__()
self.image_size = [img_size_h, img_size_w]
patch_size = to_2tuple(patch_size)
self.patch_size = patch_size
self.C = in_channels
self.H, self.W = self.image_size[0] // patch_size[0], self.image_size[1] // patch_size[1]
self.num_patches = self.H * self.W
self.proj_conv = nn.Conv2d(embed_dims//2, embed_dims, kernel_size=3, stride=1, padding=1, bias=False)
self.proj_bn = nn.BatchNorm2d(embed_dims)
self.proj_lif = MultiStepLIFNode(tau=2.0, detach_reset=True, backend='cupy')
self.proj4_conv = nn.Conv2d(embed_dims, embed_dims, kernel_size=3, stride=1, padding=1, bias=False)
self.proj4_bn = nn.BatchNorm2d(embed_dims)
self.proj4_maxpool = torch.nn.MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
self.proj4_lif = MultiStepLIFNode(tau=2.0, detach_reset=True, backend='cupy')
self.proj_res_conv = nn.Conv2d(embed_dims//2, embed_dims, kernel_size=1, stride=2, padding=0, bias=False)
self.proj_res_bn = nn.BatchNorm2d(embed_dims)
self.proj_res_lif = MultiStepLIFNode(tau=2.0, detach_reset=True, backend='cupy')
def forward(self, x):
T, B, C, H, W = x.shape
# Downsampling + Res
x = x.flatten(0, 1).contiguous()
x_feat = x
x = self.proj_conv(x)
x = self.proj_bn(x).reshape(T, B, -1, H, W).contiguous()
x = self.proj_lif(x).flatten(0, 1).contiguous()
x = self.proj4_conv(x)
x = self.proj4_bn(x)
x = self.proj4_maxpool(x)
_, _, H4, W4 = x.shape
x = x.reshape(T, B, -1, H4, W4).contiguous()
x = self.proj4_lif(x)
x_feat = self.proj_res_conv(x_feat)
x_feat = self.proj_res_bn(x_feat)
_, _, Hres, Wres = x_feat.shape
x_feat = x_feat.reshape(T, B, -1, Hres, Wres).contiguous()
x_feat = self.proj_res_lif(x_feat)
x = x + x_feat # shortcut
return x
class vit_snn(nn.Module):
def __init__(self,
img_size_h=128, img_size_w=128, patch_size=16, in_channels=2, num_classes=11,
embed_dims=[64, 128, 256], num_heads=[1, 2, 4], mlp_ratios=[4, 4, 4], qkv_bias=False, qk_scale=None,
drop_rate=0., attn_drop_rate=0., drop_path_rate=0., norm_layer=nn.LayerNorm,
depths=[6, 8, 6], sr_ratios=[8, 4, 2], T=4, pretrained_cfg=None, in_chans = 3, no_weight_decay = None
):
super().__init__()
self.num_classes = num_classes
self.depths = depths
self.T = T
num_heads = [16, 16, 16]
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depths)] # stochastic depth decay rule
#
patch_embed1 = PatchEmbedInit(img_size_h=img_size_h,
img_size_w=img_size_w,
patch_size=patch_size,
in_channels=in_channels,
embed_dims=embed_dims // 2)
stage1 = nn.ModuleList([TokenSpikingTransformer(
dim=embed_dims // 2, num_heads=num_heads[0], mlp_ratio=mlp_ratios, qkv_bias=qkv_bias,
qk_scale=qk_scale, drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[j],
norm_layer=norm_layer, sr_ratio=sr_ratios)
for j in range(1)])
patch_embed2 = PatchEmbeddingStage(img_size_h=img_size_h,
img_size_w=img_size_w,
patch_size=patch_size,
in_channels=in_channels,
embed_dims=embed_dims)
stage2 = nn.ModuleList([SpikingTransformer(
dim=embed_dims, num_heads=num_heads[1], mlp_ratio=mlp_ratios, qkv_bias=qkv_bias,
qk_scale=qk_scale, drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[j],
norm_layer=norm_layer, sr_ratio=sr_ratios)
for j in range(1)])
setattr(self, f"patch_embed1", patch_embed1)
setattr(self, f"stage1", stage1)
setattr(self, f"patch_embed2", patch_embed2)
setattr(self, f"stage2", stage2)
# classification head
self.head = nn.Linear(embed_dims, num_classes) if num_classes > 0 else nn.Identity()
self.apply(self._init_weights)
@torch.jit.ignore
def no_weight_decay(self):
return {'pose_embed'}
@torch.jit.ignore
def _get_pos_embed(self, pos_embed, patch_embed, H, W):
return None
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
def forward_features(self, x):
stage1 = getattr(self, f"stage1")
patch_embed1 = getattr(self, f"patch_embed1")
stage2 = getattr(self, f"stage2")
patch_embed2 = getattr(self, f"patch_embed2")
x = patch_embed1(x)
for blk in stage1:
x = blk(x)
x = patch_embed2(x)
for blk in stage2:
x = blk(x)
return x.flatten(3).mean(3)
def forward(self, x):
x = x.permute(1, 0, 2, 3, 4) # [T, N, 2, *, *]
# print("torch.unique", torch.unique(x))
# print("torch.count_nonzero", torch.count_nonzero(x))
# print("numel()", x.numel())
x = self.forward_features(x)
x = self.head(x.mean(0))
return x
@register_model
def delay_QKFormer(pretrained=False, **kwargs):
model = vit_snn(
patch_size=16, embed_dims=256, num_heads=16, mlp_ratios=4,
in_channels=2, num_classes=101, qkv_bias=False,
norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=4, sr_ratios=1,
**kwargs
)
model.default_cfg = _cfg()
return model
from timm.models import create_model
if __name__ == '__main__':
x = torch.randn(1, 1, 2, 128, 128).cuda()
model = create_model(
'delay_QKFormer',
pretrained=False,
drop_rate=0,
drop_path_rate=0.1,
drop_block_rate=None,
).cuda()
model.eval()
from torchinfo import summary
summary(model, input_size=(1, 1, 2, 128, 128))
# y = model(x)
# print(y.shape)
# print('Test Good!')
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