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yiskw713 / video_feature_extractor

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Size mismatch in extract.py #7

Closed gauravmarwal closed 2 years ago

gauravmarwal commented 2 years ago

Can you please help me out with this issue.

------------------------Loading Model------------------------

ResNet50 will be used as a model. Traceback (most recent call last): File "/content/drive/MyDrive/Colab_Notebooks/extract1.py", line 225, in main() File "/content/drive/MyDrive/Colab_Notebooks/extract1.py", line 203, in main model.load_state_dict(state_dict,strict=False) File "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py", line 1483, in load_state_dict self.class.name, "\n\t".join(error_msgs))) RuntimeError: Error(s) in loading state_dict for ResNet: size mismatch for conv1.weight: copying a param with shape torch.Size([64, 3, 7, 7]) from checkpoint, the shape in current model is torch.Size([64, 3, 7, 7, 7]). size mismatch for layer1.0.conv1.weight: copying a param with shape torch.Size([64, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 64, 1, 1, 1]). size mismatch for layer1.0.conv2.weight: copying a param with shape torch.Size([64, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3, 3]). size mismatch for layer1.0.conv3.weight: copying a param with shape torch.Size([256, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 64, 1, 1, 1]). size mismatch for layer1.0.downsample.0.weight: copying a param with shape torch.Size([256, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 64, 1, 1, 1]). size mismatch for layer1.1.conv1.weight: copying a param with shape torch.Size([64, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 256, 1, 1, 1]). size mismatch for layer1.1.conv2.weight: copying a param with shape torch.Size([64, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3, 3]). size mismatch for layer1.1.conv3.weight: copying a param with shape torch.Size([256, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 64, 1, 1, 1]). size mismatch for layer1.2.conv1.weight: copying a param with shape torch.Size([64, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 256, 1, 1, 1]). size mismatch for layer1.2.conv2.weight: copying a param with shape torch.Size([64, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3, 3]). size mismatch for layer1.2.conv3.weight: copying a param with shape torch.Size([256, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 64, 1, 1, 1]). size mismatch for layer2.0.conv1.weight: copying a param with shape torch.Size([128, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([128, 256, 1, 1, 1]). size mismatch for layer2.0.conv2.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3, 3]). size mismatch for layer2.0.conv3.weight: copying a param with shape torch.Size([512, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([512, 128, 1, 1, 1]). size mismatch for layer2.0.downsample.0.weight: copying a param with shape torch.Size([512, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([512, 256, 1, 1, 1]). size mismatch for layer2.1.conv1.weight: copying a param with shape torch.Size([128, 512, 1, 1]) from checkpoint, the shape in current model is torch.Size([128, 512, 1, 1, 1]). size mismatch for layer2.1.conv2.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3, 3]). size mismatch for layer2.1.conv3.weight: copying a param with shape torch.Size([512, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([512, 128, 1, 1, 1]). size mismatch for layer2.2.conv1.weight: copying a param with shape torch.Size([128, 512, 1, 1]) from checkpoint, the shape in current model is torch.Size([128, 512, 1, 1, 1]). size mismatch for layer2.2.conv2.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3, 3]). size mismatch for layer2.2.conv3.weight: copying a param with shape torch.Size([512, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([512, 128, 1, 1, 1]). size mismatch for layer2.3.conv1.weight: copying a param with shape torch.Size([128, 512, 1, 1]) from checkpoint, the shape in current model is torch.Size([128, 512, 1, 1, 1]). size mismatch for layer2.3.conv2.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3, 3]). size mismatch for layer2.3.conv3.weight: copying a param with shape torch.Size([512, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([512, 128, 1, 1, 1]). size mismatch for layer3.0.conv1.weight: copying a param with shape torch.Size([256, 512, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 512, 1, 1, 1]). size mismatch for layer3.0.conv2.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3, 3]). size mismatch for layer3.0.conv3.weight: copying a param with shape torch.Size([1024, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([1024, 256, 1, 1, 1]). size mismatch for layer3.0.downsample.0.weight: copying a param with shape torch.Size([1024, 512, 1, 1]) from checkpoint, the shape in current model is torch.Size([1024, 512, 1, 1, 1]). size mismatch for layer3.1.conv1.weight: copying a param with shape torch.Size([256, 1024, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 1024, 1, 1, 1]). size mismatch for layer3.1.conv2.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3, 3]). size mismatch for layer3.1.conv3.weight: copying a param with shape torch.Size([1024, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([1024, 256, 1, 1, 1]). size mismatch for layer3.2.conv1.weight: copying a param with shape torch.Size([256, 1024, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 1024, 1, 1, 1]). size mismatch for layer3.2.conv2.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3, 3]). size mismatch for layer3.2.conv3.weight: copying a param with shape torch.Size([1024, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([1024, 256, 1, 1, 1]). size mismatch for layer3.3.conv1.weight: copying a param with shape torch.Size([256, 1024, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 1024, 1, 1, 1]). size mismatch for layer3.3.conv2.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3, 3]). size mismatch for layer3.3.conv3.weight: copying a param with shape torch.Size([1024, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([1024, 256, 1, 1, 1]). size mismatch for layer3.4.conv1.weight: copying a param with shape torch.Size([256, 1024, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 1024, 1, 1, 1]). size mismatch for layer3.4.conv2.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3, 3]). size mismatch for layer3.4.conv3.weight: copying a param with shape torch.Size([1024, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([1024, 256, 1, 1, 1]). size mismatch for layer3.5.conv1.weight: copying a param with shape torch.Size([256, 1024, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 1024, 1, 1, 1]). size mismatch for layer3.5.conv2.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3, 3]). size mismatch for layer3.5.conv3.weight: copying a param with shape torch.Size([1024, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([1024, 256, 1, 1, 1]). size mismatch for layer4.0.conv1.weight: copying a param with shape torch.Size([512, 1024, 1, 1]) from checkpoint, the shape in current model is torch.Size([512, 1024, 1, 1, 1]). size mismatch for layer4.0.conv2.weight: copying a param with shape torch.Size([512, 512, 3, 3]) from checkpoint, the shape in current model is torch.Size([512, 512, 3, 3, 3]). size mismatch for layer4.0.conv3.weight: copying a param with shape torch.Size([2048, 512, 1, 1]) from checkpoint, the shape in current model is torch.Size([2048, 512, 1, 1, 1]). size mismatch for layer4.0.downsample.0.weight: copying a param with shape torch.Size([2048, 1024, 1, 1]) from checkpoint, the shape in current model is torch.Size([2048, 1024, 1, 1, 1]). size mismatch for layer4.1.conv1.weight: copying a param with shape torch.Size([512, 2048, 1, 1]) from checkpoint, the shape in current model is torch.Size([512, 2048, 1, 1, 1]). size mismatch for layer4.1.conv2.weight: copying a param with shape torch.Size([512, 512, 3, 3]) from checkpoint, the shape in current model is torch.Size([512, 512, 3, 3, 3]). size mismatch for layer4.1.conv3.weight: copying a param with shape torch.Size([2048, 512, 1, 1]) from checkpoint, the shape in current model is torch.Size([2048, 512, 1, 1, 1]). size mismatch for layer4.2.conv1.weight: copying a param with shape torch.Size([512, 2048, 1, 1]) from checkpoint, the shape in current model is torch.Size([512, 2048, 1, 1, 1]). size mismatch for layer4.2.conv2.weight: copying a param with shape torch.Size([512, 512, 3, 3]) from checkpoint, the shape in current model is torch.Size([512, 512, 3, 3, 3]). size mismatch for layer4.2.conv3.weight: copying a param with shape torch.Size([2048, 512, 1, 1]) from checkpoint, the shape in current model is torch.Size([2048, 512, 1, 1, 1]).