Create wrapper.py

src/models/backbones/wrapper.py

@@ -0,0 +1,82 @@
+import os
+from functools import reduce
+
+import torch
+import torch.nn as nn
+
+from .mobilenetv2 import MobileNetV2
+
+
+class BaseBackbone(nn.Module):
+    """ Superclass of Replaceable Backbone Model for Semantic Estimation
+    """
+
+    def __init__(self, in_channels):
+        super(BaseBackbone, self).__init__()
+        self.in_channels = in_channels
+
+        self.model = None
+        self.enc_channels = []
+
+    def forward(self, x):
+        raise NotImplementedError
+
+    def load_pretrained_ckpt(self):
+        raise NotImplementedError
+
+
+class MobileNetV2Backbone(BaseBackbone):
+    """ MobileNetV2 Backbone 
+    """
+
+    def __init__(self, in_channels):
+        super(MobileNetV2Backbone, self).__init__(in_channels)
+
+        self.model = MobileNetV2(self.in_channels, alpha=1.0, expansion=6, num_classes=None)
+        self.enc_channels = [16, 24, 32, 96, 1280]
+
+    def forward(self, x):
+        # x = reduce(lambda x, n: self.model.features[n](x), list(range(0, 2)), x)
+        x = self.model.features[0](x)
+        x = self.model.features[1](x)
+        enc2x = x
+
+        # x = reduce(lambda x, n: self.model.features[n](x), list(range(2, 4)), x)
+        x = self.model.features[2](x)
+        x = self.model.features[3](x)
+        enc4x = x
+
+        # x = reduce(lambda x, n: self.model.features[n](x), list(range(4, 7)), x)
+        x = self.model.features[4](x)
+        x = self.model.features[5](x)
+        x = self.model.features[6](x)
+        enc8x = x
+
+        # x = reduce(lambda x, n: self.model.features[n](x), list(range(7, 14)), x)
+        x = self.model.features[7](x)
+        x = self.model.features[8](x)
+        x = self.model.features[9](x)
+        x = self.model.features[10](x)
+        x = self.model.features[11](x)
+        x = self.model.features[12](x)
+        x = self.model.features[13](x)
+        enc16x = x
+
+        # x = reduce(lambda x, n: self.model.features[n](x), list(range(14, 19)), x)
+        x = self.model.features[14](x)
+        x = self.model.features[15](x)
+        x = self.model.features[16](x)
+        x = self.model.features[17](x)
+        x = self.model.features[18](x)
+        enc32x = x
+        return [enc2x, enc4x, enc8x, enc16x, enc32x]
+
+    def load_pretrained_ckpt(self):
+        # the pre-trained model is provided by https://github.com/thuyngch/Human-Segmentation-PyTorch 
+        ckpt_path = './pretrained/mobilenetv2_human_seg.ckpt'
+        if not os.path.exists(ckpt_path):
+            print('cannot find the pretrained mobilenetv2 backbone')
+            exit()
+        
+        ckpt = torch.load(ckpt_path)
+        self.model.load_state_dict(ckpt)