RuntimeError: Error(s) in loading state_dict for Net

Hi Mr. Zhang: When I test pre-trained model on MINC-2500 using: python main.py --dataset minc --model deepten --nclass 23 --resume deepten_minc.pth --eval, I got the following errors:

=> loading checkpoint 'deepten_minc.pth'
Traceback (most recent call last):
  File "main.py", line 174, in <module>
    main()
  File "main.py", line 72, in main
    model.load_state_dict(checkpoint['state_dict'])
  File "/Users/pro/anaconda3/lib/python3.6/site-packages/torch/nn/modules/module.py", line 719, in load_state_dict
    self.__class__.__name__, "\n\t".join(error_msgs)))
RuntimeError: Error(s) in loading state_dict for Net:
	Missing key(s) in state_dict: "pretrained.conv1.weight", "pretrained.bn1.weight", "pretrained.bn1.bias", "pretrained.bn1.running_mean", "pretrained.bn1.running_var", "pretrained.layer1.0.conv1.weight", "pretrained.layer1.0.bn1.weight", "pretrained.layer1.0.bn1.bias", "pretrained.layer1.0.bn1.running_mean", "pretrained.layer1.0.bn1.running_var", "pretrained.layer1.0.conv2.weight", "pretrained.layer1.0.bn2.weight", "pretrained.layer1.0.bn2.bias", "pretrained.layer1.0.bn2.running_mean", "pretrained.layer1.0.bn2.running_var", "pretrained.layer1.0.conv3.weight", "pretrained.layer1.0.bn3.weight", "pretrained.layer1.0.bn3.bias", "pretrained.layer1.0.bn3.running_mean", "pretrained.layer1.0.bn3.running_var", "pretrained.layer1.0.downsample.0.weight", "pretrained.layer1.0.downsample.1.weight", "pretrained.layer1.0.downsample.1.bias", "pretrained.layer1.0.downsample.1.running_mean", "pretrained.layer1.0.downsample.1.running_var", "pretrained.layer1.1.conv1.weight", "pretrained.layer1.1.bn1.weight", "pretrained.layer1.1.bn1.bias", "pretrained.layer1.1.bn1.running_mean", "pretrained.layer1.1.bn1.running_var", "pretrained.layer1.1.conv2.weight", "pretrained.layer1.1.bn2.weight", "pretrained.layer1.1.bn2.bias", "pretrained.layer1.1.bn2.running_mean", "pretrained.layer1.1.bn2.running_var", "pretrained.layer1.1.conv3.weight", "pretrained.layer1.1.bn3.weight", "pretrained.layer1.1.bn3.bias", "pretrained.layer1.1.bn3.running_mean", "pretrained.layer1.1.bn3.running_var", "pretrained.layer1.2.conv1.weight", "pretrained.layer1.2.bn1.weight", "pretrained.layer1.2.bn1.bias", "pretrained.layer1.2.bn1.running_mean", "pretrained.layer1.2.bn1.running_var", "pretrained.layer1.2.conv2.weight", "pretrained.layer1.2.bn2.weight", "pretrained.layer1.2.bn2.bias", "pretrained.layer1.2.bn2.running_mean", "pretrained.layer1.2.bn2.running_var", "pretrained.layer1.2.conv3.weight", "pretrained.layer1.2.bn3.weight", "pretrained.layer1.2.bn3.bias", "pretrained.layer1.2.bn3.running_mean", "pretrained.layer1.2.bn3.running_var", "pretrained.layer2.0.conv1.weight", "pretrained.layer2.0.bn1.weight", "pretrained.layer2.0.bn1.bias", "pretrained.layer2.0.bn1.running_mean", "pretrained.layer2.0.bn1.running_var", "pretrained.layer2.0.conv2.weight", "pretrained.layer2.0.bn2.weight", "pretrained.layer2.0.bn2.bias", "pretrained.layer2.0.bn2.running_mean", "pretrained.layer2.0.bn2.running_var", "pretrained.layer2.0.conv3.weight", "pretrained.layer2.0.bn3.weight", "pretrained.layer2.0.bn3.bias", "pretrained.layer2.0.bn3.running_mean", "pretrained.layer2.0.bn3.running_var", "pretrained.layer2.0.downsample.0.weight", "pretrained.layer2.0.downsample.1.weight", "pretrained.layer2.0.downsample.1.bias", "pretrained.layer2.0.downsample.1.running_mean", "pretrained.layer2.0.downsample.1.running_var", "pretrained.layer2.1.conv1.weight", "pretrained.layer2.1.bn1.weight", "pretrained.layer2.1.bn1.bias", "pretrained.layer2.1.bn1.running_mean", "pretrained.layer2.1.bn1.running_var", "pretrained.layer2.1.conv2.weight", "pretrained.layer2.1.bn2.weight", "pretrained.layer2.1.bn2.bias", "pretrained.layer2.1.bn2.running_mean", "pretrained.layer2.1.bn2.running_var", "pretrained.layer2.1.conv3.weight", "pretrained.layer2.1.bn3.weight", "pretrained.layer2.1.bn3.bias", "pretrained.layer2.1.bn3.running_mean", "pretrained.layer2.1.bn3.running_var", "pretrained.layer2.2.conv1.weight", "pretrained.layer2.2.bn1.weight", "pretrained.layer2.2.bn1.bias", "pretrained.layer2.2.bn1.running_mean", "pretrained.layer2.2.bn1.running_var", "pretrained.layer2.2.conv2.weight", "pretrained.layer2.2.bn2.weight", "pretrained.layer2.2.bn2.bias", "pretrained.layer2.2.bn2.running_mean", "pretrained.layer2.2.bn2.running_var", "pretrained.layer2.2.conv3.weight", "pretrained.layer2.2.bn3.weight", "pretrained.layer2.2.bn3.bias", "pretrained.layer2.2.bn3.running_mean", "pretrained.layer2.2.bn3.running_var", "pretrained.layer2.3.conv1.weight", "pretrained.layer2.3.bn1.weight", "pretrained.layer2.3.bn1.bias", "pretrained.layer2.3.bn1.running_mean", "pretrained.layer2.3.bn1.running_var", "pretrained.layer2.3.conv2.weight", "pretrained.layer2.3.bn2.weight", "pretrained.layer2.3.bn2.bias", "pretrained.layer2.3.bn2.running_mean", "pretrained.layer2.3.bn2.running_var", "pretrained.layer2.3.conv3.weight", "pretrained.layer2.3.bn3.weight", "pretrained.layer2.3.bn3.bias", "pretrained.layer2.3.bn3.running_mean", "pretrained.layer2.3.bn3.running_var", "pretrained.layer3.0.conv1.weight", "pretrained.layer3.0.bn1.weight", "pretrained.layer3.0.bn1.bias", "pretrained.layer3.0.bn1.running_mean", "pretrained.layer3.0.bn1.running_var", "pretrained.layer3.0.conv2.weight", "pretrained.layer3.0.bn2.weight", "pretrained.layer3.0.bn2.bias", "pretrained.layer3.0.bn2.running_mean", "pretrained.layer3.0.bn2.running_var", "pretrained.layer3.0.conv3.weight", "pretrained.layer3.0.bn3.weight", "pretrained.layer3.0.bn3.bias", "pretrained.layer3.0.bn3.running_mean", "pretrained.layer3.0.bn3.running_var", "pretrained.layer3.0.downsample.0.weight", "pretrained.layer3.0.downsample.1.weight", "pretrained.layer3.0.downsample.1.bias", "pretrained.layer3.0.downsample.1.running_mean", "pretrained.layer3.0.downsample.1.running_var", "pretrained.layer3.1.conv1.weight", "pretrained.layer3.1.bn1.weight", "pretrained.layer3.1.bn1.bias", "pretrained.layer3.1.bn1.running_mean", "pretrained.layer3.1.bn1.running_var", "pretrained.layer3.1.conv2.weight", "pretrained.layer3.1.bn2.weight", "pretrained.layer3.1.bn2.bias", "pretrained.layer3.1.bn2.running_mean", "pretrained.layer3.1.bn2.running_var", "pretrained.layer3.1.conv3.weight", "pretrained.layer3.1.bn3.weight", "pretrained.layer3.1.bn3.bias", "pretrained.layer3.1.bn3.running_mean", "pretrained.layer3.1.bn3.running_var", "pretrained.layer3.2.conv1.weight", "pretrained.layer3.2.bn1.weight", "pretrained.layer3.2.bn1.bias", "pretrained.layer3.2.bn1.running_mean", "pretrained.layer3.2.bn1.running_var", "pretrained.layer3.2.conv2.weight", "pretrained.layer3.2.bn2.weight", "pretrained.layer3.2.bn2.bias", "pretrained.layer3.2.bn2.running_mean", "pretrained.layer3.2.bn2.running_var", "pretrained.layer3.2.conv3.weight", "pretrained.layer3.2.bn3.weight", "pretrained.layer3.2.bn3.bias", "pretrained.layer3.2.bn3.running_mean", "pretrained.layer3.2.bn3.running_var", "pretrained.layer3.3.conv1.weight", "pretrained.layer3.3.bn1.weight", "pretrained.layer3.3.bn1.bias", "pretrained.layer3.3.bn1.running_mean", "pretrained.layer3.3.bn1.running_var", "pretrained.layer3.3.conv2.weight", "pretrained.layer3.3.bn2.weight", "pretrained.layer3.3.bn2.bias", "pretrained.layer3.3.bn2.running_mean", "pretrained.layer3.3.bn2.running_var", "pretrained.layer3.3.conv3.weight", "pretrained.layer3.3.bn3.weight", "pretrained.layer3.3.bn3.bias", "pretrained.layer3.3.bn3.running_mean", "pretrained.layer3.3.bn3.running_var", "pretrained.layer3.4.conv1.weight", "pretrained.layer3.4.bn1.weight", "pretrained.layer3.4.bn1.bias", "pretrained.layer3.4.bn1.running_mean", "pretrained.layer3.4.bn1.running_var", "pretrained.layer3.4.conv2.weight", "pretrained.layer3.4.bn2.weight", "pretrained.layer3.4.bn2.bias", "pretrained.layer3.4.bn2.running_mean", "pretrained.layer3.4.bn2.running_var", "pretrained.layer3.4.conv3.weight", "pretrained.layer3.4.bn3.weight", "pretrained.layer3.4.bn3.bias", "pretrained.layer3.4.bn3.running_mean", "pretrained.layer3.4.bn3.running_var", "pretrained.layer3.5.conv1.weight", "pretrained.layer3.5.bn1.weight", "pretrained.layer3.5.bn1.bias", "pretrained.layer3.5.bn1.running_mean", "pretrained.layer3.5.bn1.running_var", "pretrained.layer3.5.conv2.weight", "pretrained.layer3.5.bn2.weight", "pretrained.layer3.5.bn2.bias", "pretrained.layer3.5.bn2.running_mean", "pretrained.layer3.5.bn2.running_var", "pretrained.layer3.5.conv3.weight", "pretrained.layer3.5.bn3.weight", "pretrained.layer3.5.bn3.bias", "pretrained.layer3.5.bn3.running_mean", "pretrained.layer3.5.bn3.running_var", "pretrained.layer4.0.conv1.weight", "pretrained.layer4.0.bn1.weight", "pretrained.layer4.0.bn1.bias", "pretrained.layer4.0.bn1.running_mean", "pretrained.layer4.0.bn1.running_var", "pretrained.layer4.0.conv2.weight", "pretrained.layer4.0.bn2.weight", "pretrained.layer4.0.bn2.bias", "pretrained.layer4.0.bn2.running_mean", "pretrained.layer4.0.bn2.running_var", "pretrained.layer4.0.conv3.weight", "pretrained.layer4.0.bn3.weight", "pretrained.layer4.0.bn3.bias", "pretrained.layer4.0.bn3.running_mean", "pretrained.layer4.0.bn3.running_var", "pretrained.layer4.0.downsample.0.weight", "pretrained.layer4.0.downsample.1.weight", "pretrained.layer4.0.downsample.1.bias", "pretrained.layer4.0.downsample.1.running_mean", "pretrained.layer4.0.downsample.1.running_var", "pretrained.layer4.1.conv1.weight", "pretrained.layer4.1.bn1.weight", "pretrained.layer4.1.bn1.bias", "pretrained.layer4.1.bn1.running_mean", "pretrained.layer4.1.bn1.running_var", "pretrained.layer4.1.conv2.weight", "pretrained.layer4.1.bn2.weight", "pretrained.layer4.1.bn2.bias", "pretrained.layer4.1.bn2.running_mean", "pretrained.layer4.1.bn2.running_var", "pretrained.layer4.1.conv3.weight", "pretrained.layer4.1.bn3.weight", "pretrained.layer4.1.bn3.bias", "pretrained.layer4.1.bn3.running_mean", "pretrained.layer4.1.bn3.running_var", "pretrained.layer4.2.conv1.weight", "pretrained.layer4.2.bn1.weight", "pretrained.layer4.2.bn1.bias", "pretrained.layer4.2.bn1.running_mean", "pretrained.layer4.2.bn1.running_var", "pretrained.layer4.2.conv2.weight", "pretrained.layer4.2.bn2.weight", "pretrained.layer4.2.bn2.bias", "pretrained.layer4.2.bn2.running_mean", "pretrained.layer4.2.bn2.running_var", "pretrained.layer4.2.conv3.weight", "pretrained.layer4.2.bn3.weight", "pretrained.layer4.2.bn3.bias", "pretrained.layer4.2.bn3.running_mean", "pretrained.layer4.2.bn3.running_var", "pretrained.fc.weight", "pretrained.fc.bias", "head.0.weight", "head.0.bias", "head.1.weight", "head.1.bias", "head.1.running_mean", "head.1.running_var", "head.3.codewords", "head.3.scale", "head.6.weight", "head.6.bias".
	Unexpected key(s) in state_dict: "module.pretrained.conv1.weight", "module.pretrained.bn1.weight", "module.pretrained.bn1.bias", "module.pretrained.bn1.running_mean", "module.pretrained.bn1.running_var", "module.pretrained.bn1.num_batches_tracked", "module.pretrained.layer1.0.conv1.weight", "module.pretrained.layer1.0.bn1.weight", "module.pretrained.layer1.0.bn1.bias", "module.pretrained.layer1.0.bn1.running_mean", "module.pretrained.layer1.0.bn1.running_var", "module.pretrained.layer1.0.bn1.num_batches_tracked", "module.pretrained.layer1.0.conv2.weight", "module.pretrained.layer1.0.bn2.weight", "module.pretrained.layer1.0.bn2.bias", "module.pretrained.layer1.0.bn2.running_mean", "module.pretrained.layer1.0.bn2.running_var", "module.pretrained.layer1.0.bn2.num_batches_tracked", "module.pretrained.layer1.0.conv3.weight", "module.pretrained.layer1.0.bn3.weight", "module.pretrained.layer1.0.bn3.bias", "module.pretrained.layer1.0.bn3.running_mean", "module.pretrained.layer1.0.bn3.running_var", "module.pretrained.layer1.0.bn3.num_batches_tracked", "module.pretrained.layer1.0.downsample.0.weight", "module.pretrained.layer1.0.downsample.1.weight", "module.pretrained.layer1.0.downsample.1.bias", "module.pretrained.layer1.0.downsample.1.running_mean", "module.pretrained.layer1.0.downsample.1.running_var", "module.pretrained.layer1.0.downsample.1.num_batches_tracked", "module.pretrained.layer1.1.conv1.weight", "module.pretrained.layer1.1.bn1.weight", "module.pretrained.layer1.1.bn1.bias", "module.pretrained.layer1.1.bn1.running_mean", "module.pretrained.layer1.1.bn1.running_var", "module.pretrained.layer1.1.bn1.num_batches_tracked", "module.pretrained.layer1.1.conv2.weight", "module.pretrained.layer1.1.bn2.weight", "module.pretrained.layer1.1.bn2.bias", "module.pretrained.layer1.1.bn2.running_mean", "module.pretrained.layer1.1.bn2.running_var", "module.pretrained.layer1.1.bn2.num_batches_tracked", "module.pretrained.layer1.1.conv3.weight", "module.pretrained.layer1.1.bn3.weight", "module.pretrained.layer1.1.bn3.bias", "module.pretrained.layer1.1.bn3.running_mean", "module.pretrained.layer1.1.bn3.running_var", "module.pretrained.layer1.1.bn3.num_batches_tracked", "module.pretrained.layer1.2.conv1.weight", "module.pretrained.layer1.2.bn1.weight", "module.pretrained.layer1.2.bn1.bias", "module.pretrained.layer1.2.bn1.running_mean", "module.pretrained.layer1.2.bn1.running_var", "module.pretrained.layer1.2.bn1.num_batches_tracked", "module.pretrained.layer1.2.conv2.weight", "module.pretrained.layer1.2.bn2.weight", "module.pretrained.layer1.2.bn2.bias", "module.pretrained.layer1.2.bn2.running_mean", "module.pretrained.layer1.2.bn2.running_var", "module.pretrained.layer1.2.bn2.num_batches_tracked", "module.pretrained.layer1.2.conv3.weight", "module.pretrained.layer1.2.bn3.weight", "module.pretrained.layer1.2.bn3.bias", "module.pretrained.layer1.2.bn3.running_mean", "module.pretrained.layer1.2.bn3.running_var", "module.pretrained.layer1.2.bn3.num_batches_tracked", "module.pretrained.layer2.0.conv1.weight", "module.pretrained.layer2.0.bn1.weight", "module.pretrained.layer2.0.bn1.bias", "module.pretrained.layer2.0.bn1.running_mean", "module.pretrained.layer2.0.bn1.running_var", "module.pretrained.layer2.0.bn1.num_batches_tracked", "module.pretrained.layer2.0.conv2.weight", "module.pretrained.layer2.0.bn2.weight", "module.pretrained.layer2.0.bn2.bias", "module.pretrained.layer2.0.bn2.running_mean", "module.pretrained.layer2.0.bn2.running_var", "module.pretrained.layer2.0.bn2.num_batches_tracked", "module.pretrained.layer2.0.conv3.weight", "module.pretrained.layer2.0.bn3.weight", "module.pretrained.layer2.0.bn3.bias", "module.pretrained.layer2.0.bn3.running_mean", "module.pretrained.layer2.0.bn3.running_var", "module.pretrained.layer2.0.bn3.num_batches_tracked", "module.pretrained.layer2.0.downsample.0.weight", "module.pretrained.layer2.0.downsample.1.weight", "module.pretrained.layer2.0.downsample.1.bias", "module.pretrained.layer2.0.downsample.1.running_mean", "module.pretrained.layer2.0.downsample.1.running_var", "module.pretrained.layer2.0.downsample.1.num_batches_tracked", "module.pretrained.layer2.1.conv1.weight", "module.pretrained.layer2.1.bn1.weight", "module.pretrained.layer2.1.bn1.bias", "module.pretrained.layer2.1.bn1.running_mean", "module.pretrained.layer2.1.bn1.running_var", "module.pretrained.layer2.1.bn1.num_batches_tracked", "module.pretrained.layer2.1.conv2.weight", "module.pretrained.layer2.1.bn2.weight", "module.pretrained.layer2.1.bn2.bias", "module.pretrained.layer2.1.bn2.running_mean", "module.pretrained.layer2.1.bn2.running_var", "module.pretrained.layer2.1.bn2.num_batches_tracked", "module.pretrained.layer2.1.conv3.weight", "module.pretrained.layer2.1.bn3.weight", "module.pretrained.layer2.1.bn3.bias", "module.pretrained.layer2.1.bn3.running_mean", "module.pretrained.layer2.1.bn3.running_var", "module.pretrained.layer2.1.bn3.num_batches_tracked", "module.pretrained.layer2.2.conv1.weight", "module.pretrained.layer2.2.bn1.weight", "module.pretrained.layer2.2.bn1.bias", "module.pretrained.layer2.2.bn1.running_mean", "module.pretrained.layer2.2.bn1.running_var", "module.pretrained.layer2.2.bn1.num_batches_tracked", "module.pretrained.layer2.2.conv2.weight", "module.pretrained.layer2.2.bn2.weight", "module.pretrained.layer2.2.bn2.bias", "module.pretrained.layer2.2.bn2.running_mean", "module.pretrained.layer2.2.bn2.running_var", "module.pretrained.layer2.2.bn2.num_batches_tracked", "module.pretrained.layer2.2.conv3.weight", "module.pretrained.layer2.2.bn3.weight", "module.pretrained.layer2.2.bn3.bias", "module.pretrained.layer2.2.bn3.running_mean", "module.pretrained.layer2.2.bn3.running_var", "module.pretrained.layer2.2.bn3.num_batches_tracked", "module.pretrained.layer2.3.conv1.weight", "module.pretrained.layer2.3.bn1.weight", "module.pretrained.layer2.3.bn1.bias", "module.pretrained.layer2.3.bn1.running_mean", "module.pretrained.layer2.3.bn1.running_var", "module.pretrained.layer2.3.bn1.num_batches_tracked", "module.pretrained.layer2.3.conv2.weight", "module.pretrained.layer2.3.bn2.weight", "module.pretrained.layer2.3.bn2.bias", "module.pretrained.layer2.3.bn2.running_mean", "module.pretrained.layer2.3.bn2.running_var", "module.pretrained.layer2.3.bn2.num_batches_tracked", "module.pretrained.layer2.3.conv3.weight", "module.pretrained.layer2.3.bn3.weight", "module.pretrained.layer2.3.bn3.bias", "module.pretrained.layer2.3.bn3.running_mean", "module.pretrained.layer2.3.bn3.running_var", "module.pretrained.layer2.3.bn3.num_batches_tracked", "module.pretrained.layer3.0.conv1.weight", "module.pretrained.layer3.0.bn1.weight", "module.pretrained.layer3.0.bn1.bias", "module.pretrained.layer3.0.bn1.running_mean", "module.pretrained.layer3.0.bn1.running_var", "module.pretrained.layer3.0.bn1.num_batches_tracked", "module.pretrained.layer3.0.conv2.weight", "module.pretrained.layer3.0.bn2.weight", "module.pretrained.layer3.0.bn2.bias", "module.pretrained.layer3.0.bn2.running_mean", "module.pretrained.layer3.0.bn2.running_var", "module.pretrained.layer3.0.bn2.num_batches_tracked", "module.pretrained.layer3.0.conv3.weight", "module.pretrained.layer3.0.bn3.weight", "module.pretrained.layer3.0.bn3.bias", "module.pretrained.layer3.0.bn3.running_mean", "module.pretrained.layer3.0.bn3.running_var", "module.pretrained.layer3.0.bn3.num_batches_tracked", "module.pretrained.layer3.0.downsample.0.weight", "module.pretrained.layer3.0.downsample.1.weight", "module.pretrained.layer3.0.downsample.1.bias", "module.pretrained.layer3.0.downsample.1.running_mean", "module.pretrained.layer3.0.downsample.1.running_var", "module.pretrained.layer3.0.downsample.1.num_batches_tracked", "module.pretrained.layer3.1.conv1.weight", "module.pretrained.layer3.1.bn1.weight", "module.pretrained.layer3.1.bn1.bias", 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I have successfully done the prior instructions but I don’t know why I missed these keys and had those unexpected keys. It seems like something got messed up in deepten_minc.pth. Could you help me solve this problems? Thanks! my_env: pytorch 0.4.1, anaconda3, python 3.6, macOS

[pratyushmaini]

Adding model = nn.DataParallel(model) before loading should fix it

[KanchanIIT]

you can use strict=False in load_state_dict. This can solved the issue.

model.load_state_dict(checkpoint['state_dict'], strict=False)