Error(s) in loading state_dict for DataParallel:

[cosmolu]

When I download the pre_trained model and resume it. there is an error. model.load_state_dict(checkpoint['state_dict']) It seems that the name are not matched.(e.g. "module.features.0.weight" v.s. "features.module.0.weight") How could I solve it if I wish to use the pre_trained model on Cifar10? Thank you !

Traceback (most recent call last): File "test_0.py", line 130, in model = load_model() File "test_0.py", line 104, in load_model model.load_state_dict(checkpoint['state_dict']) File "/home/cosmo/anaconda3/envs/tf8/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 DataParallel: Missing key(s) in state_dict: "module.features.0.weight", "module.features.0.bias", "module.features.3.weight", "module.features.3.bias", "module.features.6.weight", "module.features.6.bias", "module.features.8.weight", "module.features.8.bias", "module.features.10.weight", "module.features.10.bias", "module.classifier.weight", "module.classifier.bias". Unexpected key(s) in state_dict: "features.module.0.weight", "features.module.0.bias", "features.module.3.weight", "features.module.3.bias", "features.module.6.weight", "features.module.6.bias", "features.module.8.weight", "features.module.8.bias", "features.module.10.weight", "features.module.10.bias", "classifier.weight", "classifier.bias".

[imirzadeh]

Hi,

The problem is the module is load with dataparallel activated and you are trying to load it without data parallel. That's why there's an extra module at the beginning of each key!

Refer to this link for more information: https://discuss.pytorch.org/t/missing-keys-unexpected-keys-in-state-dict-when-loading-self-trained-model/22379

[goncalomordido]

You can also manually updated the dic. Like this:

        state_dict =checkpoint['state_dict']
        from collections import OrderedDict
        new_state_dict = OrderedDict()

        for k, v in state_dict.items():
            if 'module' not in k:
                k = 'module.'+k
            else:
                k = k.replace('features.module.', 'module.features.')
            new_state_dict[k]=v

        model.load_state_dict(new_state_dict)

[xjock]

The mutiple GPUs usage in pytorch is a little difficult. In TF, you just set the os.environ["CUDA_VISIBLE_DEVICES"]='0,1,2,3'

[raghav1810]

Am getting an error similar to this one 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' ('checkpoint.pth.tar' is from the onedrive folder)

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]).

[jiteshm17]

You can also manually updated the dic. Like this:

        state_dict =checkpoint['state_dict']
        from collections import OrderedDict
        new_state_dict = OrderedDict()

        for k, v in state_dict.items():
            if 'module' not in k:
                k = 'module.'+k
            else:
                k = k.replace('features.module.', 'module.features.')
            new_state_dict[k]=v

        model.load_state_dict(new_state_dict)

I suppose this issue can be closed as the referenced post mentions the cause of the error and offers a solution

[yustiks]

You can also manually updated the dic. Like this:

        state_dict =checkpoint['state_dict']
        from collections import OrderedDict
        new_state_dict = OrderedDict()

        for k, v in state_dict.items():
            if 'module' not in k:
                k = 'module.'+k
            else:
                k = k.replace('features.module.', 'module.features.')
            new_state_dict[k]=v

        model.load_state_dict(new_state_dict)

life saver!

[ersamo]

Am getting an error similar to this one 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' ('checkpoint.pth.tar' is from the onedrive folder)

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]).

did you solve it please ?

[Kin-Zhang]

        state_dict =checkpoint['state_dict']
        from collections import OrderedDict
        new_state_dict = OrderedDict()

        for k, v in state_dict.items():
            if 'module' not in k:
                k = 'module.'+k
            else:
                k = k.replace('features.module.', 'module.features.')
            new_state_dict[k]=v

        model.load_state_dict(new_state_dict)

This is the solution!!!! Thanks!!!!!

[bilalghanem]

change: model.load_state_dict(torch.load(path + '/pytorch_model.pt')) to model.load_state_dict(torch.load(path + '/pytorch_model.pt'), strict=False)

[mgrachten]

change: model.load_state_dict(torch.load(path + '/pytorch_model.pt')) to model.load_state_dict(torch.load(path + '/pytorch_model.pt'), strict=False)

Although it will make the RuntimeError go away, don't do this unless you know what you are doing. It will leave any parameters it can't find in the checkpoint with random values. That's not what you want if the issue is caused by a mix-up of parameter names, as was the case for the issue reporter.

[Chenny0808]

Use model.module.state_dict() instead of model.state_dict() in DP mode