我加载了'state_dict'的resnet50.pth和KeyError pytorch版本是0.4.1
我尝试删除/添加torch.nn.parallel，但没有帮助 和pytorch API中加载的resnet50.pth
相关代码
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)
追踪
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'
当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'])
只想运行plz
答案 0 :(得分：1)
您可能是说以下意思吗？
equals根据您的编辑，看来您的模型就是模型本身。没有state_dict。因此，只需使用
state_dict = torch.load(args.model['state_dict'])
答案 1 :(得分：0)
您可以输出已加载模型的密钥吗？
print(torch.load(args.model).keys())