What wrong when i load state_dict of resnet50.pth with pytorch

1

i load the resnet50.pth and KeyError of ‘state_dict’ pytorch version is 0.4.1

i tried delete/add torch.nn.parallel but it didn’t help and resnet50.pth loaded from pytorch API

model = ResNet(len(CLASSES), pretrained=args.use_imagenet_weights)
if cuda_is_available:
    model = nn.DataParallel(model,  device_ids=[2]).cuda()
if args.model:
    print("Loading model " + args.model)
    state_dict = torch.load(args.model)['state_dict']
    model.load_state_dict(state_dict)

and the error

Loading model resnet50-19c8e357.pth
Traceback (most recent call last):
  File "train.py", line 67, in <module>
    state_dict = torch.load(args.model)['state_dict']
KeyError: 'state_dict'

and when i print(torch.load(args.model).keys())

odict_keys(['conv1.weight', 'bn1.running_mean', 'bn1.running_var', 'bn1.weight', 'bn1.bias', 'layer1.0.conv1.weight', 'layer1.0.bn1.running_mean', 'layer1.0.bn1.running_var', 'layer1.0.bn1.weight', 'layer1.0.bn1.bias', 'layer1.0.conv2.weight', 'layer1.0.bn2.running_mean', 'layer1.0.bn2.running_var', 'layer1.0.bn2.weight', 'layer1.0.bn2.bias', 'layer1.0.conv3.weight', 'layer1.0.bn3.running_mean', 'layer1.0.bn3.running_var', 'layer1.0.bn3.weight', 'layer1.0.bn3.bias', 'layer1.0.downsample.0.weight', 'layer1.0.downsample.1.running_mean', 'layer1.0.downsample.1.running_var', 'layer1.0.downsample.1.weight', 'layer1.0.downsample.1.bias', 'layer1.1.conv1.weight', 'layer1.1.bn1.running_mean', 'layer1.1.bn1.running_var', 'layer1.1.bn1.weight', 'layer1.1.bn1.bias', 'layer1.1.conv2.weight', 'layer1.1.bn2.running_mean', 'layer1.1.bn2.running_var', 'layer1.1.bn2.weight', 'layer1.1.bn2.bias', 'layer1.1.conv3.weight', 'layer1.1.bn3.running_mean', 'layer1.1.bn3.running_var', 'layer1.1.bn3.weight', 'layer1.1.bn3.bias', 'layer1.2.conv1.weight', 'layer1.2.bn1.running_mean', 'layer1.2.bn1.running_var', 'layer1.2.bn1.weight', 'layer1.2.bn1.bias', 'layer1.2.conv2.weight', 'layer1.2.bn2.running_mean', 'layer1.2.bn2.running_var', 'layer1.2.bn2.weight', 'layer1.2.bn2.bias', 'layer1.2.conv3.weight', 'layer1.2.bn3.running_mean', 'layer1.2.bn3.running_var', 'layer1.2.bn3.weight', 'layer1.2.bn3.bias', 'layer2.0.conv1.weight', 'layer2.0.bn1.running_mean', 'layer2.0.bn1.running_var', 'layer2.0.bn1.weight', 'layer2.0.bn1.bias', 'layer2.0.conv2.weight', 'layer2.0.bn2.running_mean', 'layer2.0.bn2.running_var', 'layer2.0.bn2.weight', 'layer2.0.bn2.bias', 'layer2.0.conv3.weight', 'layer2.0.bn3.running_mean', 'layer2.0.bn3.running_var', 'layer2.0.bn3.weight', 'layer2.0.bn3.bias', 'layer2.0.downsample.0.weight', 'layer2.0.downsample.1.running_mean', 'layer2.0.downsample.1.running_var', 'layer2.0.downsample.1.weight', 'layer2.0.downsample.1.bias', 'layer2.1.conv1.weight', 'layer2.1.bn1.running_mean', 'layer2.1.bn1.running_var', 'layer2.1.bn1.weight', 'layer2.1.bn1.bias', 'layer2.1.conv2.weight', 'layer2.1.bn2.running_mean', 'layer2.1.bn2.running_var', 'layer2.1.bn2.weight', 'layer2.1.bn2.bias', 'layer2.1.conv3.weight', 'layer2.1.bn3.running_mean', 'layer2.1.bn3.running_var', 'layer2.1.bn3.weight', 'layer2.1.bn3.bias', 'layer2.2.conv1.weight', 'layer2.2.bn1.running_mean', 'layer2.2.bn1.running_var', 'layer2.2.bn1.weight', 'layer2.2.bn1.bias', 'layer2.2.conv2.weight', 'layer2.2.bn2.running_mean', 'layer2.2.bn2.running_var', 'layer2.2.bn2.weight', 'layer2.2.bn2.bias', 'layer2.2.conv3.weight', 'layer2.2.bn3.running_mean', 'layer2.2.bn3.running_var', 'layer2.2.bn3.weight', 'layer2.2.bn3.bias', 'layer2.3.conv1.weight', 'layer2.3.bn1.running_mean', 'layer2.3.bn1.running_var', 'layer2.3.bn1.weight', 'layer2.3.bn1.bias', 'layer2.3.conv2.weight', 'layer2.3.bn2.running_mean', 'layer2.3.bn2.running_var', 'layer2.3.bn2.weight', 'layer2.3.bn2.bias', 'layer2.3.conv3.weight', 'layer2.3.bn3.running_mean', 'layer2.3.bn3.running_var', 'layer2.3.bn3.weight', 'layer2.3.bn3.bias', 'layer3.0.conv1.weight', 'layer3.0.bn1.running_mean', 'layer3.0.bn1.running_var', 'layer3.0.bn1.weight', 'layer3.0.bn1.bias', 'layer3.0.conv2.weight', 'layer3.0.bn2.running_mean', 'layer3.0.bn2.running_var', 'layer3.0.bn2.weight', 'layer3.0.bn2.bias', 'layer3.0.conv3.weight', 'layer3.0.bn3.running_mean', 'layer3.0.bn3.running_var', 'layer3.0.bn3.weight', 'layer3.0.bn3.bias', 'layer3.0.downsample.0.weight', 'layer3.0.downsample.1.running_mean', 'layer3.0.downsample.1.running_var', 'layer3.0.downsample.1.weight', 'layer3.0.downsample.1.bias', 'layer3.1.conv1.weight', 'layer3.1.bn1.running_mean', 'layer3.1.bn1.running_var', 'layer3.1.bn1.weight', 'layer3.1.bn1.bias', 'layer3.1.conv2.weight', 'layer3.1.bn2.running_mean', 'layer3.1.bn2.running_var', 'layer3.1.bn2.weight', 'layer3.1.bn2.bias', 'layer3.1.conv3.weight', 'layer3.1.bn3.running_mean', 'layer3.1.bn3.running_var', 'layer3.1.bn3.weight', 'layer3.1.bn3.bias', 'layer3.2.conv1.weight', 'layer3.2.bn1.running_mean', 'layer3.2.bn1.running_var', 'layer3.2.bn1.weight', 'layer3.2.bn1.bias', 'layer3.2.conv2.weight', 'layer3.2.bn2.running_mean', 'layer3.2.bn2.running_var', 'layer3.2.bn2.weight', 'layer3.2.bn2.bias', 'layer3.2.conv3.weight', 'layer3.2.bn3.running_mean', 'layer3.2.bn3.running_var', 'layer3.2.bn3.weight', 'layer3.2.bn3.bias', 'layer3.3.conv1.weight', 'layer3.3.bn1.running_mean', 'layer3.3.bn1.running_var', 'layer3.3.bn1.weight', 'layer3.3.bn1.bias', 'layer3.3.conv2.weight', 'layer3.3.bn2.running_mean', 'layer3.3.bn2.running_var', 'layer3.3.bn2.weight', 'layer3.3.bn2.bias', 'layer3.3.conv3.weight', 'layer3.3.bn3.running_mean', 'layer3.3.bn3.running_var', 'layer3.3.bn3.weight', 'layer3.3.bn3.bias', 'layer3.4.conv1.weight', 'layer3.4.bn1.running_mean', 'layer3.4.bn1.running_var', 'layer3.4.bn1.weight', 'layer3.4.bn1.bias', 'layer3.4.conv2.weight', 'layer3.4.bn2.running_mean', 'layer3.4.bn2.running_var', 'layer3.4.bn2.weight', 'layer3.4.bn2.bias', 'layer3.4.conv3.weight', 'layer3.4.bn3.running_mean', 'layer3.4.bn3.running_var', 'layer3.4.bn3.weight', 'layer3.4.bn3.bias', 'layer3.5.conv1.weight', 'layer3.5.bn1.running_mean', 'layer3.5.bn1.running_var', 'layer3.5.bn1.weight', 'layer3.5.bn1.bias', 'layer3.5.conv2.weight', 'layer3.5.bn2.running_mean', 'layer3.5.bn2.running_var', 'layer3.5.bn2.weight', 'layer3.5.bn2.bias', 'layer3.5.conv3.weight', 'layer3.5.bn3.running_mean', 'layer3.5.bn3.running_var', 'layer3.5.bn3.weight', 'layer3.5.bn3.bias', 'layer4.0.conv1.weight', 'layer4.0.bn1.running_mean', 'layer4.0.bn1.running_var', 'layer4.0.bn1.weight', 'layer4.0.bn1.bias', 'layer4.0.conv2.weight', 'layer4.0.bn2.running_mean', 'layer4.0.bn2.running_var', 'layer4.0.bn2.weight', 'layer4.0.bn2.bias', 'layer4.0.conv3.weight', 'layer4.0.bn3.running_mean', 'layer4.0.bn3.running_var', 'layer4.0.bn3.weight', 'layer4.0.bn3.bias', 'layer4.0.downsample.0.weight', 'layer4.0.downsample.1.running_mean', 'layer4.0.downsample.1.running_var', 'layer4.0.downsample.1.weight', 'layer4.0.downsample.1.bias', 'layer4.1.conv1.weight', 'layer4.1.bn1.running_mean', 'layer4.1.bn1.running_var', 'layer4.1.bn1.weight', 'layer4.1.bn1.bias', 'layer4.1.conv2.weight', 'layer4.1.bn2.running_mean', 'layer4.1.bn2.running_var', 'layer4.1.bn2.weight', 'layer4.1.bn2.bias', 'layer4.1.conv3.weight', 'layer4.1.bn3.running_mean', 'layer4.1.bn3.running_var', 'layer4.1.bn3.weight', 'layer4.1.bn3.bias', 'layer4.2.conv1.weight', 'layer4.2.bn1.running_mean', 'layer4.2.bn1.running_var', 'layer4.2.bn1.weight', 'layer4.2.bn1.bias', 'layer4.2.conv2.weight', 'layer4.2.bn2.running_mean', 'layer4.2.bn2.running_var', 'layer4.2.bn2.weight', 'layer4.2.bn2.bias', 'layer4.2.conv3.weight', 'layer4.2.bn3.running_mean', 'layer4.2.bn3.running_var', 'layer4.2.bn3.weight', 'layer4.2.bn3.bias', 'fc.weight', 'fc.bias'])

i just want to successfully run:pleading_face:

2

pay attention to nn.DataParallel. u can print parameters name in the model，to see what is difference between model with and without nn.DataParallel，especially for pytorch version lower than 1. u can try load the model，then use nn.DataParallel to wrap the model

3

See my answer to the same problem here.