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last_0603_92.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4dc05f2a61959c1a0e91e1381e461638b4c5ff872f07d0fc383c5425b0e2871b
+size 107986969

model_fasterrcnn.py

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+import torch
+import torch.nn as nn
+# import torch.nn.utils.prune as prune
+
+
+import torchvision.models as models
+import torchvision
+# from torchsummary import summary
+
+class MobileNetV2FeatureExtractor(nn.Module):
+    def __init__(self):
+        super(MobileNetV2FeatureExtractor, self).__init__()
+        self.model = torchvision.models.detection.fasterrcnn_resnet50_fpn_v2(pretrained=False)
+
+        for param in self.model.parameters():
+            param.requires_grad = True
+        self.model = self.model.backbone
+
+    def forward(self, x):
+        return self.model(x)
+class GlobalAvgPool2D(nn.Module):
+    def __init__(self):
+        super(GlobalAvgPool2D, self).__init__()
+
+    def forward(self, x):
+        tensor = x['0']
+        return torch.mean(tensor.view(tensor.size(0), tensor.size(1), -1), dim=2)
+
+
+class LDRNet_fasterrcnn(nn.Module):
+    def __init__(self, points_size=100, classification_list=[1]):
+        super(LDRNet_fasterrcnn, self).__init__()
+
+        self.points_size = points_size
+        self.classification_list = classification_list
+
+        self.backbone = MobileNetV2FeatureExtractor()
+        if len(classification_list) > 0:
+            class_size = sum(self.classification_list)
+        else:
+            class_size = 0
+        self.global_pool = GlobalAvgPool2D()
+        # self.dropout = nn.Dropout(p=0.3) 
+        self.corner = nn.Linear(256, 8)
+        self.border = nn.Linear(256, (points_size - 4) * 2)
+        self.cls = nn.Linear(256, class_size + len(self.classification_list))
+
+    def forward(self, x):
+        x = self.backbone(x)
+        x = self.global_pool(x)
+        # x = self.dropout(x)
+        corner_output = self.corner(x)
+        border_output = self.border(x)
+        cls_output = self.cls(x)
+        return corner_output, border_output, cls_output
+if __name__ == "__main__":
+    import torch
+    # from torchsummary import summary
+    xx = torch.zeros((1, 3, 224, 224))
+    model = LDRNet_fasterrcnn()
+    print(model)
+    y = model(xx)
+    for name, module in model.named_modules():
+        if isinstance(module, torch.nn.Conv2d):
+            prune.l1_unstructured(module, name='weight', amount=0.2)
+        elif isinstance(module, torch.nn.Linear):
+            prune.l1_unstructured(module, name='weight', amount=0.4)
+    # print(y[0].detach().numpy()[0])
+    # summary(model,input_size=(3, 224, 224))
+    total_params = sum(p.numel() for p in model.parameters())
+    total_trainable_params = sum(
+      p.numel() for p in model.parameters() if p.requires_grad
+    )
+    print(f"[INFO]: {total_params:,} total parameters.")
+    print(f"[INFO]: {total_trainable_params:,} trainable parameters.")
+
+