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mohitzsh / Adversarial-Semisupervised-Semantic-Segmentation

Pytorch Implementation of "Adversarial Learning For Semi-Supervised Semantic Segmentation" for ICLR 2018 Reproducibility Challenge
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Problem with loading checkpoint in generator #9

Closed jeya-maria-jose closed 4 years ago

jeya-maria-jose commented 6 years ago

File "eval2.py", line 109, in main() File "eval2.py", line 80, in main generator.load_state_dict(saved_net) File "/usr/local/lib/python2.7/dist-packages/torch/nn/modules/module.py", line 721, in load_state_dict self.class.name, "\n\t".join(error_msgs))) RuntimeError: Error(s) in loading state_dict for ResNet: Missing key(s) in state_dict: "conv1.weight", "bn1.running_var", "bn1.bias", "bn1.weight", "bn1.running_mean", "layer1.0.conv1.weight", "layer1.0.bn1.running_var", "layer1.0.bn1.bias", "layer1.0.bn1.weight", "layer1.0.bn1.running_mean", "layer1.0.conv2.weight", "layer1.0.bn2.running_var", "layer1.0.bn2.bias", "layer1.0.bn2.weight", "layer1.0.bn2.running_mean", "layer1.0.conv3.weight", "layer1.0.bn3.running_var", "layer1.0.bn3.bias", "layer1.0.bn3.weight", "layer1.0.bn3.running_mean", "layer1.0.downsample.0.weight", "layer1.0.downsample.1.running_var", "layer1.0.downsample.1.bias", "layer1.0.downsample.1.weight", "layer1.0.downsample.1.running_mean", "layer1.1.conv1.weight", "layer1.1.bn1.running_var", "layer1.1.bn1.bias", "layer1.1.bn1.weight", "layer1.1.bn1.running_mean", "layer1.1.conv2.weight", "layer1.1.bn2.running_var", "layer1.1.bn2.bias", "layer1.1.bn2.weight", "layer1.1.bn2.running_mean", "layer1.1.conv3.weight", "layer1.1.bn3.running_var", "layer1.1.bn3.bias", "layer1.1.bn3.weight", "layer1.1.bn3.running_mean", "layer1.2.conv1.weight", "layer1.2.bn1.running_var", "layer1.2.bn1.bias", "layer1.2.bn1.weight", "layer1.2.bn1.running_mean", "layer1.2.conv2.weight", "layer1.2.bn2.running_var", "layer1.2.bn2.bias", "layer1.2.bn2.weight", "layer1.2.bn2.running_mean", "layer1.2.conv3.weight", "layer1.2.bn3.running_var", "layer1.2.bn3.bias", "layer1.2.bn3.weight", "layer1.2.bn3.running_mean", "layer2.0.conv1.weight", "layer2.0.bn1.running_var", "layer2.0.bn1.bias", "layer2.0.bn1.weight", "layer2.0.bn1.running_mean", "layer2.0.conv2.weight", "layer2.0.bn2.running_var", "layer2.0.bn2.bias", "layer2.0.bn2.weight", "layer2.0.bn2.running_mean", "layer2.0.conv3.weight", "layer2.0.bn3.running_var", "layer2.0.bn3.bias", "layer2.0.bn3.weight", "layer2.0.bn3.running_mean", "layer2.0.downsample.0.weight", "layer2.0.downsample.1.running_var", "layer2.0.downsample.1.bias", "layer2.0.downsample.1.weight", "layer2.0.downsample.1.running_mean", "layer2.1.conv1.weight", "layer2.1.bn1.running_var", "layer2.1.bn1.bias", "layer2.1.bn1.weight", "layer2.1.bn1.running_mean", "layer2.1.conv2.weight", "layer2.1.bn2.running_var", "layer2.1.bn2.bias", "layer2.1.bn2.weight", "layer2.1.bn2.running_mean", "layer2.1.conv3.weight", "layer2.1.bn3.running_var", "layer2.1.bn3.bias", "layer2.1.bn3.weight", "layer2.1.bn3.running_mean", "layer2.2.conv1.weight", "layer2.2.bn1.running_var", "layer2.2.bn1.bias", "layer2.2.bn1.weight", "layer2.2.bn1.running_mean", "layer2.2.conv2.weight", "layer2.2.bn2.running_var", "layer2.2.bn2.bias", "layer2.2.bn2.weight", "layer2.2.bn2.running_mean", "layer2.2.conv3.weight", "layer2.2.bn3.running_var", "layer2.2.bn3.bias", "layer2.2.bn3.weight", "layer2.2.bn3.running_mean", "layer2.3.conv1.weight", "layer2.3.bn1.running_var", "layer2.3.bn1.bias", "layer2.3.bn1.weight", "layer2.3.bn1.running_mean", "layer2.3.conv2.weight", "layer2.3.bn2.running_var", "layer2.3.bn2.bias", "layer2.3.bn2.weight", "layer2.3.bn2.running_mean", "layer2.3.conv3.weight", "layer2.3.bn3.running_var", "layer2.3.bn3.bias", "layer2.3.bn3.weight", "layer2.3.bn3.running_mean", "layer3.0.conv1.weight", "layer3.0.bn1.running_var", "layer3.0.bn1.bias", "layer3.0.bn1.weight", "layer3.0.bn1.running_mean", "layer3.0.conv2.weight", "layer3.0.bn2.running_var", "layer3.0.bn2.bias", "layer3.0.bn2.weight", "layer3.0.bn2.running_mean", "layer3.0.conv3.weight", "layer3.0.bn3.running_var", "layer3.0.bn3.bias", "layer3.0.bn3.weight", "layer3.0.bn3.running_mean", "layer3.0.downsample.0.weight", "layer3.0.downsample.1.running_var", "layer3.0.downsample.1.bias", "layer3.0.downsample.1.weight", "layer3.0.downsample.1.running_mean", "layer3.1.conv1.weight", "layer3.1.bn1.running_var", "layer3.1.bn1.bias", "layer3.1.bn1.weight", "layer3.1.bn1.running_mean", "layer3.1.conv2.weight", "layer3.1.bn2.running_var", "layer3.1.bn2.bias", "layer3.1.bn2.weight", "layer3.1.bn2.running_mean", "layer3.1.conv3.weight", "layer3.1.bn3.running_var", "layer3.1.bn3.bias", "layer3.1.bn3.weight", "layer3.1.bn3.running_mean", "layer3.2.conv1.weight", "layer3.2.bn1.running_var", "layer3.2.bn1.bias", "layer3.2.bn1.weight", "layer3.2.bn1.running_mean", "layer3.2.conv2.weight", "layer3.2.bn2.running_var", "layer3.2.bn2.bias", "layer3.2.bn2.weight", "layer3.2.bn2.running_mean", "layer3.2.conv3.weight", "layer3.2.bn3.running_var", "layer3.2.bn3.bias", "layer3.2.bn3.weight", "layer3.2.bn3.running_mean", "layer3.3.conv1.weight", "layer3.3.bn1.running_var", "layer3.3.bn1.bias", "layer3.3.bn1.weight", "layer3.3.bn1.running_mean", "layer3.3.conv2.weight", "layer3.3.bn2.running_var", "layer3.3.bn2.bias", "layer3.3.bn2.weight", "layer3.3.bn2.running_mean", "layer3.3.conv3.weight", "layer3.3.bn3.running_var", "layer3.3.bn3.bias", "layer3.3.bn3.weight", "layer3.3.bn3.running_mean", "layer3.4.conv1.weight", "layer3.4.bn1.running_var", "layer3.4.bn1.bias", "layer3.4.bn1.weight", "layer3.4.bn1.running_mean", "layer3.4.conv2.weight", "layer3.4.bn2.running_var", "layer3.4.bn2.bias", "layer3.4.bn2.weight", "layer3.4.bn2.running_mean", "layer3.4.conv3.weight", "layer3.4.bn3.running_var", "layer3.4.bn3.bias", "layer3.4.bn3.weight", "layer3.4.bn3.running_mean", "layer3.5.conv1.weight", "layer3.5.bn1.running_var", "layer3.5.bn1.bias", "layer3.5.bn1.weight", "layer3.5.bn1.running_mean", "layer3.5.conv2.weight", "layer3.5.bn2.running_var", "layer3.5.bn2.bias", "layer3.5.bn2.weight", "layer3.5.bn2.running_mean", "layer3.5.conv3.weight", "layer3.5.bn3.running_var", "layer3.5.bn3.bias", "layer3.5.bn3.weight", "layer3.5.bn3.running_mean", "layer3.6.conv1.weight", "layer3.6.bn1.running_var", "layer3.6.bn1.bias", "layer3.6.bn1.weight", "layer3.6.bn1.running_mean", "layer3.6.conv2.weight", "layer3.6.bn2.running_var", "layer3.6.bn2.bias", "layer3.6.bn2.weight", "layer3.6.bn2.running_mean", "layer3.6.conv3.weight", "layer3.6.bn3.running_var", "layer3.6.bn3.bias", "layer3.6.bn3.weight", "layer3.6.bn3.running_mean", "layer3.7.conv1.weight", "layer3.7.bn1.running_var", "layer3.7.bn1.bias", "layer3.7.bn1.weight", "layer3.7.bn1.running_mean", "layer3.7.conv2.weight", "layer3.7.bn2.running_var", "layer3.7.bn2.bias", "layer3.7.bn2.weight", "layer3.7.bn2.running_mean", "layer3.7.conv3.weight", "layer3.7.bn3.running_var", "layer3.7.bn3.bias", "layer3.7.bn3.weight", "layer3.7.bn3.running_mean", "layer3.8.conv1.weight", "layer3.8.bn1.running_var", "layer3.8.bn1.bias", "layer3.8.bn1.weight", "layer3.8.bn1.running_mean", "layer3.8.conv2.weight", "layer3.8.bn2.running_var", "layer3.8.bn2.bias", "layer3.8.bn2.weight", "layer3.8.bn2.running_mean", "layer3.8.conv3.weight", "layer3.8.bn3.running_var", "layer3.8.bn3.bias", "layer3.8.bn3.weight", "layer3.8.bn3.running_mean", "layer3.9.conv1.weight", "layer3.9.bn1.running_var", "layer3.9.bn1.bias", "layer3.9.bn1.weight", "layer3.9.bn1.running_mean", "layer3.9.conv2.weight", "layer3.9.bn2.running_var", "layer3.9.bn2.bias", "layer3.9.bn2.weight", "layer3.9.bn2.running_mean", "layer3.9.conv3.weight", "layer3.9.bn3.running_var", "layer3.9.bn3.bias", "layer3.9.bn3.weight", "layer3.9.bn3.running_mean", "layer3.10.conv1.weight", "layer3.10.bn1.running_var", "layer3.10.bn1.bias", "layer3.10.bn1.weight", "layer3.10.bn1.running_mean", "layer3.10.conv2.weight", "layer3.10.bn2.running_var", "layer3.10.bn2.bias", "layer3.10.bn2.weight", "layer3.10.bn2.running_mean", "layer3.10.conv3.weight", "layer3.10.bn3.running_var", "layer3.10.bn3.bias", "layer3.10.bn3.weight", "layer3.10.bn3.running_mean", "layer3.11.conv1.weight", "layer3.11.bn1.running_var", "layer3.11.bn1.bias", "layer3.11.bn1.weight", "layer3.11.bn1.running_mean", "layer3.11.conv2.weight", "layer3.11.bn2.running_var", "layer3.11.bn2.bias", "layer3.11.bn2.weight", "layer3.11.bn2.running_mean", "layer3.11.conv3.weight", "layer3.11.bn3.running_var", "layer3.11.bn3.bias", "layer3.11.bn3.weight", "layer3.11.bn3.running_mean", "layer3.12.conv1.weight", "layer3.12.bn1.running_var", "layer3.12.bn1.bias", "layer3.12.bn1.weight", "layer3.12.bn1.running_mean", "layer3.12.conv2.weight", "layer3.12.bn2.running_var", "layer3.12.bn2.bias", "layer3.12.bn2.weight", "layer3.12.bn2.running_mean", "layer3.12.conv3.weight", "layer3.12.bn3.running_var", "layer3.12.bn3.bias", "layer3.12.bn3.weight", "layer3.12.bn3.running_mean", "layer3.13.conv1.weight", "layer3.13.bn1.running_var", "layer3.13.bn1.bias", "layer3.13.bn1.weight", "layer3.13.bn1.running_mean", "layer3.13.conv2.weight", "layer3.13.bn2.running_var", "layer3.13.bn2.bias", "layer3.13.bn2.weight", "layer3.13.bn2.running_mean", "layer3.13.conv3.weight", "layer3.13.bn3.running_var", "layer3.13.bn3.bias", "layer3.13.bn3.weight", "layer3.13.bn3.running_mean", "layer3.14.conv1.weight", "layer3.14.bn1.running_var", "layer3.14.bn1.bias", "layer3.14.bn1.weight", "layer3.14.bn1.running_mean", "layer3.14.conv2.weight", "layer3.14.bn2.running_var", "layer3.14.bn2.bias", "layer3.14.bn2.weight", "layer3.14.bn2.running_mean", "layer3.14.conv3.weight", "layer3.14.bn3.running_var", "layer3.14.bn3.bias", "layer3.14.bn3.weight", "layer3.14.bn3.running_mean", "layer3.15.conv1.weight", "layer3.15.bn1.running_var", "layer3.15.bn1.bias", "layer3.15.bn1.weight", "layer3.15.bn1.running_mean", "layer3.15.conv2.weight", "layer3.15.bn2.running_var", "layer3.15.bn2.bias", "layer3.15.bn2.weight", "layer3.15.bn2.running_mean", "layer3.15.conv3.weight", "layer3.15.bn3.running_var", "layer3.15.bn3.bias", "layer3.15.bn3.weight", "layer3.15.bn3.running_mean", "layer3.16.conv1.weight", "layer3.16.bn1.running_var", "layer3.16.bn1.bias", "layer3.16.bn1.weight", "layer3.16.bn1.running_mean", "layer3.16.conv2.weight", "layer3.16.bn2.running_var", "layer3.16.bn2.bias", "layer3.16.bn2.weight", "layer3.16.bn2.running_mean", "layer3.16.conv3.weight", "layer3.16.bn3.running_var", "layer3.16.bn3.bias", "layer3.16.bn3.weight", "layer3.16.bn3.running_mean", "layer3.17.conv1.weight", "layer3.17.bn1.running_var", "layer3.17.bn1.bias", "layer3.17.bn1.weight", "layer3.17.bn1.running_mean", "layer3.17.conv2.weight", "layer3.17.bn2.running_var", "layer3.17.bn2.bias", "layer3.17.bn2.weight", "layer3.17.bn2.running_mean", "layer3.17.conv3.weight", "layer3.17.bn3.running_var", "layer3.17.bn3.bias", "layer3.17.bn3.weight", "layer3.17.bn3.running_mean", "layer3.18.conv1.weight", "layer3.18.bn1.running_var", "layer3.18.bn1.bias", "layer3.18.bn1.weight", "layer3.18.bn1.running_mean", "layer3.18.conv2.weight", "layer3.18.bn2.running_var", "layer3.18.bn2.bias", "layer3.18.bn2.weight", "layer3.18.bn2.running_mean", "layer3.18.conv3.weight", "layer3.18.bn3.running_var", "layer3.18.bn3.bias", "layer3.18.bn3.weight", "layer3.18.bn3.running_mean", "layer3.19.conv1.weight", "layer3.19.bn1.running_var", "layer3.19.bn1.bias", "layer3.19.bn1.weight", "layer3.19.bn1.running_mean", "layer3.19.conv2.weight", "layer3.19.bn2.running_var", "layer3.19.bn2.bias", "layer3.19.bn2.weight", "layer3.19.bn2.running_mean", "layer3.19.conv3.weight", "layer3.19.bn3.running_var", "layer3.19.bn3.bias", "layer3.19.bn3.weight", "layer3.19.bn3.running_mean", "layer3.20.conv1.weight", "layer3.20.bn1.running_var", "layer3.20.bn1.bias", "layer3.20.bn1.weight", "layer3.20.bn1.running_mean", "layer3.20.conv2.weight", "layer3.20.bn2.running_var", "layer3.20.bn2.bias", "layer3.20.bn2.weight", "layer3.20.bn2.running_mean", "layer3.20.conv3.weight", "layer3.20.bn3.running_var", "layer3.20.bn3.bias", "layer3.20.bn3.weight", "layer3.20.bn3.running_mean", "layer3.21.conv1.weight", "layer3.21.bn1.running_var", "layer3.21.bn1.bias", "layer3.21.bn1.weight", "layer3.21.bn1.running_mean", "layer3.21.conv2.weight", "layer3.21.bn2.running_var", "layer3.21.bn2.bias", "layer3.21.bn2.weight", "layer3.21.bn2.running_mean", "layer3.21.conv3.weight", "layer3.21.bn3.running_var", "layer3.21.bn3.bias", "layer3.21.bn3.weight", "layer3.21.bn3.running_mean", "layer3.22.conv1.weight", "layer3.22.bn1.running_var", "layer3.22.bn1.bias", "layer3.22.bn1.weight", "layer3.22.bn1.running_mean", "layer3.22.conv2.weight", "layer3.22.bn2.running_var", "layer3.22.bn2.bias", "layer3.22.bn2.weight", "layer3.22.bn2.running_mean", "layer3.22.conv3.weight", "layer3.22.bn3.running_var", "layer3.22.bn3.bias", "layer3.22.bn3.weight", "layer3.22.bn3.running_mean", "layer4.0.conv1.weight", "layer4.0.bn1.running_var", "layer4.0.bn1.bias", "layer4.0.bn1.weight", "layer4.0.bn1.running_mean", "layer4.0.conv2.weight", "layer4.0.bn2.running_var", "layer4.0.bn2.bias", "layer4.0.bn2.weight", "layer4.0.bn2.running_mean", "layer4.0.conv3.weight", "layer4.0.bn3.running_var", "layer4.0.bn3.bias", "layer4.0.bn3.weight", "layer4.0.bn3.running_mean", "layer4.0.downsample.0.weight", "layer4.0.downsample.1.running_var", "layer4.0.downsample.1.bias", "layer4.0.downsample.1.weight", "layer4.0.downsample.1.running_mean", "layer4.1.conv1.weight", "layer4.1.bn1.running_var", "layer4.1.bn1.bias", "layer4.1.bn1.weight", "layer4.1.bn1.running_mean", "layer4.1.conv2.weight", "layer4.1.bn2.running_var", "layer4.1.bn2.bias", "layer4.1.bn2.weight", "layer4.1.bn2.running_mean", "layer4.1.conv3.weight", "layer4.1.bn3.running_var", "layer4.1.bn3.bias", "layer4.1.bn3.weight", "layer4.1.bn3.running_mean", "layer4.2.conv1.weight", "layer4.2.bn1.running_var", "layer4.2.bn1.bias", "layer4.2.bn1.weight", "layer4.2.bn1.running_mean", "layer4.2.conv2.weight", "layer4.2.bn2.running_var", "layer4.2.bn2.bias", "layer4.2.bn2.weight", "layer4.2.bn2.running_mean", "layer4.2.conv3.weight", "layer4.2.bn3.running_var", "layer4.2.bn3.bias", "layer4.2.bn3.weight", "layer4.2.bn3.running_mean", "layer5.conv2d_list.0.bias", "layer5.conv2d_list.0.weight", "layer5.conv2d_list.1.bias", "layer5.conv2d_list.1.weight", "layer5.conv2d_list.2.bias", "layer5.conv2d_list.2.weight", "layer5.conv2d_list.3.bias", "layer5.conv2d_list.3.weight". Unexpected key(s) in state_dict: "".

jeya-maria-jose commented 6 years ago

The snapshot is loading and I am able print and check the tensors in the checkpoint also . But the error arises when the generator.load_state_dict(saved_net) command gets executed.