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bearpaw / pytorch-classification

Classification with PyTorch.
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Error loading pretrained model weights #35

Open raghav1810 opened 5 years ago

raghav1810 commented 5 years ago

When I try resuming from the pretrained model weight I get an error This is what I am running: python cifar.py -a preresnet --depth 110 --epochs 3 --schedule 81 122 --gamma 0.1 --wd 1e-4 --checkpoint checkpoints/cifar10/preresnet-110 --resume 'checkpoint.pth.tar'

and this is the error:

RuntimeError: Error(s) in loading state_dict for DataParallel: Missing key(s) in state_dict: "module.bn.weight", "module.bn.bias", "module.bn.running_mean", "module.bn.running_var". Unexpected key(s) in state_dict: "module.bn1.weight", "module.bn1.bias", "module.bn1.running_mean", "module.bn1.running_var", "module.layer1.0.conv3.weight", "module.layer1.0.bn3.weight", "module.layer1.0.bn3.bias", "module.layer1.0.bn3.running_mean", "module.layer1.0.bn3.running_var", "module.layer1.0.downsample.0.weight", "module.layer1.0.downsample.1.weight", "module.layer1.0.downsample.1.bias", "module.layer1.0.downsample.1.running_mean", "module.layer1.0.downsample.1.running_var", "module.layer1.1.conv3.weight", "module.layer1.1.bn3.weight", "module.layer1.1.bn3.bias", "module.layer1.1.bn3.running_mean", "module.layer1.1.bn3.running_var", "module.layer1.2.conv3.weight", "module.layer1.2.bn3.weight", "module.layer1.2.bn3.bias", "module.layer1.2.bn3.running_mean", "module.layer1.2.bn3.running_var", "module.layer1.3.conv3.weight", "module.layer1.3.bn3.weight", "module.layer1.3.bn3.bias", "module.layer1.3.bn3.running_mean", "module.layer1.3.bn3.running_var", "module.layer1.4.conv3.weight", "module.layer1.4.bn3.weight", "module.layer1.4.bn3.bias", "module.layer1.4.bn3.running_mean", "module.layer1.4.bn3.running_var", "module.layer1.5.conv3.weight", "module.layer1.5.bn3.weight", "module.layer1.5.bn3.bias", "module.layer1.5.bn3.running_mean", "module.layer1.5.bn3.running_var", "module.layer1.6.conv3.weight", "module.layer1.6.bn3.weight", "module.layer1.6.bn3.bias", "module.layer1.6.bn3.running_mean", "module.layer1.6.bn3.running_var", "module.layer1.7.conv3.weight", "module.layer1.7.bn3.weight", "module.layer1.7.bn3.bias", "module.layer1.7.bn3.running_mean", "module.layer1.7.bn3.running_var", "module.layer1.8.conv3.weight", "module.layer1.8.bn3.weight", "module.layer1.8.bn3.bias", "module.layer1.8.bn3.running_mean", "module.layer1.8.bn3.running_var", "module.layer1.9.conv3.weight", "module.layer1.9.bn3.weight", "module.layer1.9.bn3.bias", "module.layer1.9.bn3.running_mean", "module.layer1.9.bn3.running_var", "module.layer1.10.conv3.weight", "module.layer1.10.bn3.weight", "module.layer1.10.bn3.bias", "module.layer1.10.bn3.running_mean", "module.layer1.10.bn3.running_var", "module.layer1.11.conv3.weight", "module.layer1.11.bn3.weight", "module.layer1.11.bn3.bias", "module.layer1.11.bn3.running_mean", "module.layer1.11.bn3.running_var", "module.layer1.12.conv3.weight", "module.layer1.12.bn3.weight", "module.layer1.12.bn3.bias", "module.layer1.12.bn3.running_mean", "module.layer1.12.bn3.running_var", "module.layer1.13.conv3.weight", "module.layer1.13.bn3.weight", "module.layer1.13.bn3.bias", "module.layer1.13.bn3.running_mean", "module.layer1.13.bn3.running_var", "module.layer1.14.conv3.weight", "module.layer1.14.bn3.weight", "module.layer1.14.bn3.bias", "module.layer1.14.bn3.running_mean", "module.layer1.14.bn3.running_var", "module.layer1.15.conv3.weight", "module.layer1.15.bn3.weight", "module.layer1.15.bn3.bias", "module.layer1.15.bn3.running_mean", "module.layer1.15.bn3.running_var", "module.layer1.16.conv3.weight", "module.layer1.16.bn3.weight", "module.layer1.16.bn3.bias", "module.layer1.16.bn3.running_mean", "module.layer1.16.bn3.running_var", "module.layer1.17.conv3.weight", "module.layer1.17.bn3.weight", "module.layer1.17.bn3.bias", "module.layer1.17.bn3.running_mean", "module.layer1.17.bn3.running_var", "module.layer2.0.conv3.weight", "module.layer2.0.bn3.weight", "module.layer2.0.bn3.bias", "module.layer2.0.bn3.running_mean", "module.layer2.0.bn3.running_var", "module.layer2.0.downsample.1.weight", "module.layer2.0.downsample.1.bias", "module.layer2.0.downsample.1.running_mean", "module.layer2.0.downsample.1.running_var", "module.layer2.1.conv3.weight", "module.layer2.1.bn3.weight", "module.layer2.1.bn3.bias", "module.layer2.1.bn3.running_mean", "module.layer2.1.bn3.running_var", "module.layer2.2.conv3.weight", "module.layer2.2.bn3.weight", "module.layer2.2.bn3.bias", "module.layer2.2.bn3.running_mean", "module.layer2.2.bn3.running_var", "module.layer2.3.conv3.weight", "module.layer2.3.bn3.weight", "module.layer2.3.bn3.bias", "module.layer2.3.bn3.running_mean", "module.layer2.3.bn3.running_var", "module.layer2.4.conv3.weight", "module.layer2.4.bn3.weight", "module.layer2.4.bn3.bias", "module.layer2.4.bn3.running_mean", "module.layer2.4.bn3.running_var", "module.layer2.5.conv3.weight", "module.layer2.5.bn3.weight", "module.layer2.5.bn3.bias", "module.layer2.5.bn3.running_mean", "module.layer2.5.bn3.running_var", "module.layer2.6.conv3.weight", "module.layer2.6.bn3.weight", "module.layer2.6.bn3.bias", "module.layer2.6.bn3.running_mean", "module.layer2.6.bn3.running_var", "module.layer2.7.conv3.weight", "module.layer2.7.bn3.weight", "module.layer2.7.bn3.bias", "module.layer2.7.bn3.running_mean", "module.layer2.7.bn3.running_var", "module.layer2.8.conv3.weight", "module.layer2.8.bn3.weight", "module.layer2.8.bn3.bias", "module.layer2.8.bn3.running_mean", "module.layer2.8.bn3.running_var", "module.layer2.9.conv3.weight", "module.layer2.9.bn3.weight", "module.layer2.9.bn3.bias", "module.layer2.9.bn3.running_mean", "module.layer2.9.bn3.running_var", "module.layer2.10.conv3.weight", "module.layer2.10.bn3.weight", "module.layer2.10.bn3.bias", "module.layer2.10.bn3.running_mean", "module.layer2.10.bn3.running_var", "module.layer2.11.conv3.weight", "module.layer2.11.bn3.weight", "module.layer2.11.bn3.bias", "module.layer2.11.bn3.running_mean", "module.layer2.11.bn3.running_var", "module.layer2.12.conv3.weight", "module.layer2.12.bn3.weight", "module.layer2.12.bn3.bias", "module.layer2.12.bn3.running_mean", "module.layer2.12.bn3.running_var", "module.layer2.13.conv3.weight", "module.layer2.13.bn3.weight", "module.layer2.13.bn3.bias", "module.layer2.13.bn3.running_mean", "module.layer2.13.bn3.running_var", "module.layer2.14.conv3.weight", "module.layer2.14.bn3.weight", "module.layer2.14.bn3.bias", "module.layer2.14.bn3.running_mean", "module.layer2.14.bn3.running_var", "module.layer2.15.conv3.weight", "module.layer2.15.bn3.weight", "module.layer2.15.bn3.bias", "module.layer2.15.bn3.running_mean", "module.layer2.15.bn3.running_var", "module.layer2.16.conv3.weight", "module.layer2.16.bn3.weight", "module.layer2.16.bn3.bias", "module.layer2.16.bn3.running_mean", "module.layer2.16.bn3.running_var", "module.layer2.17.conv3.weight", "module.layer2.17.bn3.weight", "module.layer2.17.bn3.bias", "module.layer2.17.bn3.running_mean", "module.layer2.17.bn3.running_var", "module.layer3.0.conv3.weight", "module.layer3.0.bn3.weight", "module.layer3.0.bn3.bias", "module.layer3.0.bn3.running_mean", "module.layer3.0.bn3.running_var", "module.layer3.0.downsample.1.weight", "module.layer3.0.downsample.1.bias", "module.layer3.0.downsample.1.running_mean", "module.layer3.0.downsample.1.running_var", "module.layer3.1.conv3.weight", "module.layer3.1.bn3.weight", "module.layer3.1.bn3.bias", "module.layer3.1.bn3.running_mean", "module.layer3.1.bn3.running_var", "module.layer3.2.conv3.weight", "module.layer3.2.bn3.weight", "module.layer3.2.bn3.bias", "module.layer3.2.bn3.running_mean", "module.layer3.2.bn3.running_var", "module.layer3.3.conv3.weight", "module.layer3.3.bn3.weight", "module.layer3.3.bn3.bias", "module.layer3.3.bn3.running_mean", "module.layer3.3.bn3.running_var", "module.layer3.4.conv3.weight", "module.layer3.4.bn3.weight", "module.layer3.4.bn3.bias", "module.layer3.4.bn3.running_mean", "module.layer3.4.bn3.running_var", "module.layer3.5.conv3.weight", "module.layer3.5.bn3.weight", "module.layer3.5.bn3.bias", "module.layer3.5.bn3.running_mean", "module.layer3.5.bn3.running_var", "module.layer3.6.conv3.weight", "module.layer3.6.bn3.weight", "module.layer3.6.bn3.bias", "module.layer3.6.bn3.running_mean", "module.layer3.6.bn3.running_var", "module.layer3.7.conv3.weight", "module.layer3.7.bn3.weight", "module.layer3.7.bn3.bias", "module.layer3.7.bn3.running_mean", "module.layer3.7.bn3.running_var", "module.layer3.8.conv3.weight", "module.layer3.8.bn3.weight", "module.layer3.8.bn3.bias", "module.layer3.8.bn3.running_mean", "module.layer3.8.bn3.running_var", "module.layer3.9.conv3.weight", "module.layer3.9.bn3.weight", "module.layer3.9.bn3.bias", "module.layer3.9.bn3.running_mean", "module.layer3.9.bn3.running_var", "module.layer3.10.conv3.weight", "module.layer3.10.bn3.weight", "module.layer3.10.bn3.bias", "module.layer3.10.bn3.running_mean", "module.layer3.10.bn3.running_var", "module.layer3.11.conv3.weight", "module.layer3.11.bn3.weight", "module.layer3.11.bn3.bias", "module.layer3.11.bn3.running_mean", "module.layer3.11.bn3.running_var", "module.layer3.12.conv3.weight", "module.layer3.12.bn3.weight", "module.layer3.12.bn3.bias", "module.layer3.12.bn3.running_mean", "module.layer3.12.bn3.running_var", "module.layer3.13.conv3.weight", "module.layer3.13.bn3.weight", "module.layer3.13.bn3.bias", "module.layer3.13.bn3.running_mean", "module.layer3.13.bn3.running_var", "module.layer3.14.conv3.weight", "module.layer3.14.bn3.weight", "module.layer3.14.bn3.bias", "module.layer3.14.bn3.running_mean", "module.layer3.14.bn3.running_var", "module.layer3.15.conv3.weight", "module.layer3.15.bn3.weight", "module.layer3.15.bn3.bias", "module.layer3.15.bn3.running_mean", "module.layer3.15.bn3.running_var", "module.layer3.16.conv3.weight", "module.layer3.16.bn3.weight", "module.layer3.16.bn3.bias", "module.layer3.16.bn3.running_mean", "module.layer3.16.bn3.running_var", "module.layer3.17.conv3.weight", "module.layer3.17.bn3.weight", "module.layer3.17.bn3.bias", "module.layer3.17.bn3.running_mean", "module.layer3.17.bn3.running_var". size mismatch for module.layer1.0.conv1.weight: copying a param with shape torch.Size([16, 16, 1, 1]) from checkpoint, the shape in current model is torch.Size([16, 16, 3, 3]). size mismatch for module.layer1.1.conv1.weight: copying a param with shape torch.Size([16, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([16, 16, 3, 3]). size mismatch for module.layer1.2.conv1.weight: copying a param with shape torch.Size([16, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([16, 16, 3, 3]). size mismatch for module.layer1.3.conv1.weight: copying a param with shape torch.Size([16, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([16, 16, 3, 3]). size mismatch for module.layer1.4.conv1.weight: copying a param with shape torch.Size([16, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([16, 16, 3, 3]). size mismatch for module.layer1.5.conv1.weight: copying a param with shape torch.Size([16, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([16, 16, 3, 3]). size mismatch for module.layer1.6.conv1.weight: copying a param with shape torch.Size([16, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([16, 16, 3, 3]). size mismatch for module.layer1.7.conv1.weight: copying a param with shape torch.Size([16, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([16, 16, 3, 3]). size mismatch for module.layer1.8.conv1.weight: copying a param with shape torch.Size([16, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([16, 16, 3, 3]). size mismatch for module.layer1.9.conv1.weight: copying a param with shape torch.Size([16, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([16, 16, 3, 3]). size mismatch for module.layer1.10.conv1.weight: copying a param with shape torch.Size([16, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([16, 16, 3, 3]). size mismatch for module.layer1.11.conv1.weight: copying a param with shape torch.Size([16, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([16, 16, 3, 3]). size mismatch for module.layer1.12.conv1.weight: copying a param with shape torch.Size([16, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([16, 16, 3, 3]). size mismatch for module.layer1.13.conv1.weight: copying a param with shape torch.Size([16, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([16, 16, 3, 3]). size mismatch for module.layer1.14.conv1.weight: copying a param with shape torch.Size([16, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([16, 16, 3, 3]). size mismatch for module.layer1.15.conv1.weight: copying a param with shape torch.Size([16, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([16, 16, 3, 3]). size mismatch for module.layer1.16.conv1.weight: copying a param with shape torch.Size([16, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([16, 16, 3, 3]). size mismatch for module.layer1.17.conv1.weight: copying a param with shape torch.Size([16, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([16, 16, 3, 3]). size mismatch for module.layer2.0.bn1.weight: copying a param with shape torch.Size([32]) from checkpoint, the shape in current model is torch.Size([16]). size mismatch for module.layer2.0.bn1.bias: copying a param with shape torch.Size([32]) from checkpoint, the shape in current model is torch.Size([16]). size mismatch for module.layer2.0.bn1.running_mean: copying a param with shape torch.Size([32]) from checkpoint, the shape in current model is torch.Size([16]). size mismatch for module.layer2.0.bn1.running_var: copying a param with shape torch.Size([32]) from checkpoint, the shape in current model is torch.Size([16]). size mismatch for module.layer2.0.conv1.weight: copying a param with shape torch.Size([32, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 16, 3, 3]). size mismatch for module.layer2.0.downsample.0.weight: copying a param with shape torch.Size([128, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 16, 1, 1]). size mismatch for module.layer2.1.conv1.weight: copying a param with shape torch.Size([32, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 32, 3, 3]). size mismatch for module.layer2.2.conv1.weight: copying a param with shape torch.Size([32, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 32, 3, 3]). size mismatch for module.layer2.3.conv1.weight: copying a param with shape torch.Size([32, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 32, 3, 3]). size mismatch for module.layer2.4.conv1.weight: copying a param with shape torch.Size([32, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 32, 3, 3]). size mismatch for module.layer2.5.conv1.weight: copying a param with shape torch.Size([32, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 32, 3, 3]). size mismatch for module.layer2.6.conv1.weight: copying a param with shape torch.Size([32, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 32, 3, 3]). size mismatch for module.layer2.7.conv1.weight: copying a param with shape torch.Size([32, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 32, 3, 3]). size mismatch for module.layer2.8.conv1.weight: copying a param with shape torch.Size([32, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 32, 3, 3]). size mismatch for module.layer2.9.conv1.weight: copying a param with shape torch.Size([32, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 32, 3, 3]). size mismatch for module.layer2.10.conv1.weight: copying a param with shape torch.Size([32, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 32, 3, 3]). size mismatch for module.layer2.11.conv1.weight: copying a param with shape torch.Size([32, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 32, 3, 3]). size mismatch for module.layer2.12.conv1.weight: copying a param with shape torch.Size([32, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 32, 3, 3]). size mismatch for module.layer2.13.conv1.weight: copying a param with shape torch.Size([32, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 32, 3, 3]). size mismatch for module.layer2.14.conv1.weight: copying a param with shape torch.Size([32, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 32, 3, 3]). size mismatch for module.layer2.15.conv1.weight: copying a param with shape torch.Size([32, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 32, 3, 3]). size mismatch for module.layer2.16.conv1.weight: copying a param with shape torch.Size([32, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 32, 3, 3]). size mismatch for module.layer2.17.conv1.weight: copying a param with shape torch.Size([32, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 32, 3, 3]). size mismatch for module.layer3.0.bn1.weight: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([32]). size mismatch for module.layer3.0.bn1.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([32]). size mismatch for module.layer3.0.bn1.running_mean: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([32]). size mismatch for module.layer3.0.bn1.running_var: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([32]). size mismatch for module.layer3.0.conv1.weight: copying a param with shape torch.Size([64, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 32, 3, 3]). size mismatch for module.layer3.0.downsample.0.weight: copying a param with shape torch.Size([256, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 32, 1, 1]). size mismatch for module.layer3.1.conv1.weight: copying a param with shape torch.Size([64, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]). size mismatch for module.layer3.2.conv1.weight: copying a param with shape torch.Size([64, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]). size mismatch for module.layer3.3.conv1.weight: copying a param with shape torch.Size([64, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]). size mismatch for module.layer3.4.conv1.weight: copying a param with shape torch.Size([64, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]). size mismatch for module.layer3.5.conv1.weight: copying a param with shape torch.Size([64, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]). size mismatch for module.layer3.6.conv1.weight: copying a param with shape torch.Size([64, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]). size mismatch for module.layer3.7.conv1.weight: copying a param with shape torch.Size([64, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]). size mismatch for module.layer3.8.conv1.weight: copying a param with shape torch.Size([64, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]). size mismatch for module.layer3.9.conv1.weight: copying a param with shape torch.Size([64, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]). size mismatch for module.layer3.10.conv1.weight: copying a param with shape torch.Size([64, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]). size mismatch for module.layer3.11.conv1.weight: copying a param with shape torch.Size([64, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]). size mismatch for module.layer3.12.conv1.weight: copying a param with shape torch.Size([64, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]). size mismatch for module.layer3.13.conv1.weight: copying a param with shape torch.Size([64, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]). size mismatch for module.layer3.14.conv1.weight: copying a param with shape torch.Size([64, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]). size mismatch for module.layer3.15.conv1.weight: copying a param with shape torch.Size([64, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]). size mismatch for module.layer3.16.conv1.weight: copying a param with shape torch.Size([64, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]). size mismatch for module.layer3.17.conv1.weight: copying a param with shape torch.Size([64, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]). size mismatch for module.fc.weight: copying a param with shape torch.Size([100, 256]) from checkpoint, the shape in current model is torch.Size([10, 64]). size mismatch for module.fc.bias: copying a param with shape torch.Size([100]) from checkpoint, the shape in current model is torch.Size([10]).

SecantZhang commented 3 years ago

I know it's a bit late for this reply but for anyone who is looking to this kind of errors of this repo, the pretrained-ResNet110 provided by the author is actually ResNet164, according to another issue in this repo https://github.com/bearpaw/pytorch-classification/issues/36.