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kaiwang960112 / Self-Cure-Network

This is a novel and easy method for annotation uncertainties.
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python test.py #47

Closed MaQingya closed 6 months ago

MaQingya commented 3 years ago

When I run: Traceback (most recent call last): File "test.py", line 52, in res18.load_state_dict(checkpoint['model_state_dict']) KeyError: 'model_state_dict'

The code:

模型存储路径

model_save_path = "./models/ijba_res18_naive.pth.tar"#修改为你自己保存下来的模型文件 img_path = "./test.jpg"#待测试照片位置

res18 = Res18Feature(pretrained = False) checkpoint = torch.load(model_save_path) res18.load_state_dict(checkpoint['model_state_dict'])

I am a beginner of facial expression recognition, can you help me?

7109029214 commented 3 years ago

Replace

res18.load_state_dict(checkpoint['model_state_dict'])

To

res18.load_state_dict(checkpoint['state_dict'])
yangjian1218 commented 2 years ago

@7109029214 按你的方式改完后,新出问题: RuntimeError: Error(s) in loading state_dict for Res18Feature: Missing key(s) in state_dict: "features.0.weight", "features.1.weight", "features.1.bias", "features.1.running_mean", "features.1.running_var", "features.4.0.conv1.weight", "features.4.0.bn1.weight", "features.4.0.bn1.bias", "features.4.0.bn1.running_mean", "features.4.0.bn1.running_var", "features.4.0.conv2.weight", "features.4.0.bn2.weight", "features.4.0.bn2.bias", "features.4.0.bn2.running_mean", "features.4.0.bn2.running_var", "features.4.1.conv1.weight", "features.4.1.bn1.weight", "features.4.1.bn1.bias", "features.4.1.bn1.running_mean", "features.4.1.bn1.running_var", "features.4.1.conv2.weight", "features.4.1.bn2.weight", "features.4.1.bn2.bias", "features.4.1.bn2.running_mean", "features.4.1.bn2.running_var", "features.5.0.conv1.weight", "features.5.0.bn1.weight", "features.5.0.bn1.bias", "features.5.0.bn1.running_mean", "features.5.0.bn1.running_var", "features.5.0.conv2.weight", "features.5.0.bn2.weight", "features.5.0.bn2.bias", "features.5.0.bn2.running_mean", "features.5.0.bn2.running_var", "features.5.0.downsample.0.weight", "features.5.0.downsample.1.weight", "features.5.0.downsample.1.bias", "features.5.0.downsample.1.running_mean", "features.5.0.downsample.1.running_var", "features.5.1.conv1.weight", "features.5.1.bn1.weight", "features.5.1.bn1.bias", "features.5.1.bn1.running_mean", "features.5.1.bn1.running_var", "features.5.1.conv2.weight", "features.5.1.bn2.weight", "features.5.1.bn2.bias", "features.5.1.bn2.running_mean", "features.5.1.bn2.running_var", "features.6.0.conv1.weight", "features.6.0.bn1.weight", "features.6.0.bn1.bias", "features.6.0.bn1.running_mean", "features.6.0.bn1.running_var", "features.6.0.conv2.weight", "features.6.0.bn2.weight", "features.6.0.bn2.bias", "features.6.0.bn2.running_mean", "features.6.0.bn2.running_var", "features.6.0.downsample.0.weight", "features.6.0.downsample.1.weight", "features.6.0.downsample.1.bias", "features.6.0.downsample.1.running_mean", "features.6.0.downsample.1.running_var", "features.6.1.conv1.weight", "features.6.1.bn1.weight", "features.6.1.bn1.bias", "features.6.1.bn1.running_mean", "features.6.1.bn1.running_var", "features.6.1.conv2.weight", "features.6.1.bn2.weight", "features.6.1.bn2.bias", "features.6.1.bn2.running_mean", "features.6.1.bn2.running_var", "features.7.0.conv1.weight", "features.7.0.bn1.weight", "features.7.0.bn1.bias", "features.7.0.bn1.running_mean", "features.7.0.bn1.running_var", "features.7.0.conv2.weight", "features.7.0.bn2.weight", "features.7.0.bn2.bias", "features.7.0.bn2.running_mean", "features.7.0.bn2.running_var", "features.7.0.downsample.0.weight", "features.7.0.downsample.1.weight", "features.7.0.downsample.1.bias", "features.7.0.downsample.1.running_mean", "features.7.0.downsample.1.running_var", "features.7.1.conv1.weight", "features.7.1.bn1.weight", "features.7.1.bn1.bias", "features.7.1.bn1.running_mean", "features.7.1.bn1.running_var", "features.7.1.conv2.weight", "features.7.1.bn2.weight", "features.7.1.bn2.bias", "features.7.1.bn2.running_mean", "features.7.1.bn2.running_var", "fc.weight", "fc.bias", "alpha.0.weight", "alpha.0.bias". Unexpected key(s) in state_dict: "module.conv1.weight", "module.bn1.weight", "module.bn1.bias", "module.bn1.running_mean", "module.bn1.running_var", "module.layer1.0.conv1.weight", "module.layer1.0.bn1.weight", "module.layer1.0.bn1.bias", "module.layer1.0.bn1.running_mean", "module.layer1.0.bn1.running_var", "module.layer1.0.conv2.weight", "module.layer1.0.bn2.weight", "module.layer1.0.bn2.bias", "module.layer1.0.bn2.running_mean", "module.layer1.0.bn2.running_var", "module.layer1.1.conv1.weight", "module.layer1.1.bn1.weight", "module.layer1.1.bn1.bias", "module.layer1.1.bn1.running_mean", "module.layer1.1.bn1.running_var", "module.layer1.1.conv2.weight", "module.layer1.1.bn2.weight", "module.layer1.1.bn2.bias", "module.layer1.1.bn2.running_mean", "module.layer1.1.bn2.running_var", "module.layer2.0.conv1.weight", "module.layer2.0.bn1.weight", "module.layer2.0.bn1.bias", "module.layer2.0.bn1.running_mean", "module.layer2.0.bn1.running_var", "module.layer2.0.conv2.weight", "module.layer2.0.bn2.weight", "module.layer2.0.bn2.bias", "module.layer2.0.bn2.running_mean", "module.layer2.0.bn2.running_var", "module.layer2.0.downsample.0.weight", "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.conv1.weight", "module.layer2.1.bn1.weight", "module.layer2.1.bn1.bias", "module.layer2.1.bn1.running_mean", "module.layer2.1.bn1.running_var", "module.layer2.1.conv2.weight", "module.layer2.1.bn2.weight", "module.layer2.1.bn2.bias", "module.layer2.1.bn2.running_mean", "module.layer2.1.bn2.running_var", "module.layer3.0.conv1.weight", "module.layer3.0.bn1.weight", "module.layer3.0.bn1.bias", "module.layer3.0.bn1.running_mean", "module.layer3.0.bn1.running_var", "module.layer3.0.conv2.weight", "module.layer3.0.bn2.weight", "module.layer3.0.bn2.bias", "module.layer3.0.bn2.running_mean", "module.layer3.0.bn2.running_var", "module.layer3.0.downsample.0.weight", "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.conv1.weight", "module.layer3.1.bn1.weight", "module.layer3.1.bn1.bias", "module.layer3.1.bn1.running_mean", "module.layer3.1.bn1.running_var", "module.layer3.1.conv2.weight", "module.layer3.1.bn2.weight", "module.layer3.1.bn2.bias", "module.layer3.1.bn2.running_mean", "module.layer3.1.bn2.running_var", "module.layer4.0.conv1.weight", "module.layer4.0.bn1.weight", "module.layer4.0.bn1.bias", "module.layer4.0.bn1.running_mean", "module.layer4.0.bn1.running_var", "module.layer4.0.conv2.weight", "module.layer4.0.bn2.weight", "module.layer4.0.bn2.bias", "module.layer4.0.bn2.running_mean", "module.layer4.0.bn2.running_var", "module.layer4.0.downsample.0.weight", "module.layer4.0.downsample.1.weight", "module.layer4.0.downsample.1.bias", "module.layer4.0.downsample.1.running_mean", "module.layer4.0.downsample.1.running_var", "module.layer4.1.conv1.weight", "module.layer4.1.bn1.weight", "module.layer4.1.bn1.bias", "module.layer4.1.bn1.running_mean", "module.layer4.1.bn1.running_var", "module.layer4.1.conv2.weight", "module.layer4.1.bn2.weight", "module.layer4.1.bn2.bias", "module.layer4.1.bn2.running_mean", "module.layer4.1.bn2.running_var", "module.feature.weight", "module.feature.bias", "module.fc.weight", "module.fc.bias".

Zhuoyi416 commented 2 years ago

@7109029214 按你的方式改完后,新出问题: RuntimeError: Error(s) in loading state_dict for Res18Feature: Missing key(s) in state_dict: "features.0.weight", "features.1.weight", "features.1.bias", "features.1.running_mean", "features.1.running_var", "features.4.0.conv1.weight", "features.4.0.bn1.weight", "features.4.0.bn1.bias", "features.4.0.bn1.running_mean", "features.4.0.bn1.running_var", "features.4.0.conv2.weight", "features.4.0.bn2.weight", "features.4.0.bn2.bias", "features.4.0.bn2.running_mean", "features.4.0.bn2.running_var", "features.4.1.conv1.weight", "features.4.1.bn1.weight", "features.4.1.bn1.bias", "features.4.1.bn1.running_mean", "features.4.1.bn1.running_var", "features.4.1.conv2.weight", "features.4.1.bn2.weight", "features.4.1.bn2.bias", "features.4.1.bn2.running_mean", "features.4.1.bn2.running_var", "features.5.0.conv1.weight", "features.5.0.bn1.weight", "features.5.0.bn1.bias", "features.5.0.bn1.running_mean", "features.5.0.bn1.running_var", "features.5.0.conv2.weight", "features.5.0.bn2.weight", "features.5.0.bn2.bias", "features.5.0.bn2.running_mean", "features.5.0.bn2.running_var", "features.5.0.downsample.0.weight", "features.5.0.downsample.1.weight", "features.5.0.downsample.1.bias", "features.5.0.downsample.1.running_mean", "features.5.0.downsample.1.running_var", "features.5.1.conv1.weight", "features.5.1.bn1.weight", "features.5.1.bn1.bias", "features.5.1.bn1.running_mean", "features.5.1.bn1.running_var", "features.5.1.conv2.weight", "features.5.1.bn2.weight", "features.5.1.bn2.bias", "features.5.1.bn2.running_mean", "features.5.1.bn2.running_var", "features.6.0.conv1.weight", "features.6.0.bn1.weight", "features.6.0.bn1.bias", "features.6.0.bn1.running_mean", "features.6.0.bn1.running_var", "features.6.0.conv2.weight", "features.6.0.bn2.weight", "features.6.0.bn2.bias", "features.6.0.bn2.running_mean", "features.6.0.bn2.running_var", "features.6.0.downsample.0.weight", "features.6.0.downsample.1.weight", "features.6.0.downsample.1.bias", "features.6.0.downsample.1.running_mean", "features.6.0.downsample.1.running_var", "features.6.1.conv1.weight", "features.6.1.bn1.weight", "features.6.1.bn1.bias", "features.6.1.bn1.running_mean", "features.6.1.bn1.running_var", "features.6.1.conv2.weight", "features.6.1.bn2.weight", "features.6.1.bn2.bias", "features.6.1.bn2.running_mean", "features.6.1.bn2.running_var", "features.7.0.conv1.weight", "features.7.0.bn1.weight", "features.7.0.bn1.bias", "features.7.0.bn1.running_mean", "features.7.0.bn1.running_var", "features.7.0.conv2.weight", "features.7.0.bn2.weight", "features.7.0.bn2.bias", "features.7.0.bn2.running_mean", "features.7.0.bn2.running_var", "features.7.0.downsample.0.weight", "features.7.0.downsample.1.weight", "features.7.0.downsample.1.bias", "features.7.0.downsample.1.running_mean", "features.7.0.downsample.1.running_var", "features.7.1.conv1.weight", "features.7.1.bn1.weight", "features.7.1.bn1.bias", "features.7.1.bn1.running_mean", "features.7.1.bn1.running_var", "features.7.1.conv2.weight", "features.7.1.bn2.weight", "features.7.1.bn2.bias", "features.7.1.bn2.running_mean", "features.7.1.bn2.running_var", "fc.weight", "fc.bias", "alpha.0.weight", "alpha.0.bias". Unexpected key(s) in state_dict: "module.conv1.weight", "module.bn1.weight", "module.bn1.bias", "module.bn1.running_mean", "module.bn1.running_var", "module.layer1.0.conv1.weight", "module.layer1.0.bn1.weight", "module.layer1.0.bn1.bias", "module.layer1.0.bn1.running_mean", "module.layer1.0.bn1.running_var", "module.layer1.0.conv2.weight", "module.layer1.0.bn2.weight", "module.layer1.0.bn2.bias", "module.layer1.0.bn2.running_mean", "module.layer1.0.bn2.running_var", "module.layer1.1.conv1.weight", "module.layer1.1.bn1.weight", "module.layer1.1.bn1.bias", "module.layer1.1.bn1.running_mean", "module.layer1.1.bn1.running_var", "module.layer1.1.conv2.weight", "module.layer1.1.bn2.weight", "module.layer1.1.bn2.bias", "module.layer1.1.bn2.running_mean", "module.layer1.1.bn2.running_var", "module.layer2.0.conv1.weight", "module.layer2.0.bn1.weight", "module.layer2.0.bn1.bias", "module.layer2.0.bn1.running_mean", "module.layer2.0.bn1.running_var", "module.layer2.0.conv2.weight", "module.layer2.0.bn2.weight", "module.layer2.0.bn2.bias", "module.layer2.0.bn2.running_mean", "module.layer2.0.bn2.running_var", "module.layer2.0.downsample.0.weight", "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.conv1.weight", "module.layer2.1.bn1.weight", "module.layer2.1.bn1.bias", "module.layer2.1.bn1.running_mean", "module.layer2.1.bn1.running_var", "module.layer2.1.conv2.weight", "module.layer2.1.bn2.weight", "module.layer2.1.bn2.bias", "module.layer2.1.bn2.running_mean", "module.layer2.1.bn2.running_var", "module.layer3.0.conv1.weight", "module.layer3.0.bn1.weight", "module.layer3.0.bn1.bias", "module.layer3.0.bn1.running_mean", "module.layer3.0.bn1.running_var", "module.layer3.0.conv2.weight", "module.layer3.0.bn2.weight", "module.layer3.0.bn2.bias", "module.layer3.0.bn2.running_mean", "module.layer3.0.bn2.running_var", "module.layer3.0.downsample.0.weight", "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.conv1.weight", "module.layer3.1.bn1.weight", "module.layer3.1.bn1.bias", "module.layer3.1.bn1.running_mean", "module.layer3.1.bn1.running_var", "module.layer3.1.conv2.weight", "module.layer3.1.bn2.weight", "module.layer3.1.bn2.bias", "module.layer3.1.bn2.running_mean", "module.layer3.1.bn2.running_var", "module.layer4.0.conv1.weight", "module.layer4.0.bn1.weight", "module.layer4.0.bn1.bias", "module.layer4.0.bn1.running_mean", "module.layer4.0.bn1.running_var", "module.layer4.0.conv2.weight", "module.layer4.0.bn2.weight", "module.layer4.0.bn2.bias", "module.layer4.0.bn2.running_mean", "module.layer4.0.bn2.running_var", "module.layer4.0.downsample.0.weight", "module.layer4.0.downsample.1.weight", "module.layer4.0.downsample.1.bias", "module.layer4.0.downsample.1.running_mean", "module.layer4.0.downsample.1.running_var", "module.layer4.1.conv1.weight", "module.layer4.1.bn1.weight", "module.layer4.1.bn1.bias", "module.layer4.1.bn1.running_mean", "module.layer4.1.bn1.running_var", "module.layer4.1.conv2.weight", "module.layer4.1.bn2.weight", "module.layer4.1.bn2.bias", "module.layer4.1.bn2.running_mean", "module.layer4.1.bn2.running_var", "module.feature.weight", "module.feature.bias", "module.fc.weight", "module.fc.bias".

hello,did u solve this problem?I met the same problem.

kaiwang960112 commented 2 years ago

load的时候，把keys检查下，就差一个module.

Zhuoyi416 @.***> 於 2022年4月12日週二 下午4:05寫道：

@7109029214 https://github.com/7109029214 按你的方式改完后,新出问题: RuntimeError: Error(s) in loading state_dict for Res18Feature: Missing key(s) in state_dict: "features.0.weight", "features.1.weight", "features.1.bias", "features.1.running_mean", "features.1.running_var", "features.4.0.conv1.weight", "features.4.0.bn1.weight", "features.4.0.bn1.bias", "features.4.0.bn1.running_mean", "features.4.0.bn1.running_var", "features.4.0.conv2.weight", "features.4.0.bn2.weight", "features.4.0.bn2.bias", "features.4.0.bn2.running_mean", "features.4.0.bn2.running_var", "features.4.1.conv1.weight", "features.4.1.bn1.weight", "features.4.1.bn1.bias", "features.4.1.bn1.running_mean", "features.4.1.bn1.running_var", "features.4.1.conv2.weight", "features.4.1.bn2.weight", "features.4.1.bn2.bias", "features.4.1.bn2.running_mean", "features.4.1.bn2.running_var", "features.5.0.conv1.weight", "features.5.0.bn1.weight", "features.5.0.bn1.bias", "features.5.0.bn1.running_mean", "features.5.0.bn1.running_var", "features.5.0.conv2.weight", "features.5.0.bn2.weight", "features.5.0.bn2.bias", "features.5.0.bn2.running_mean", "features.5.0.bn2.running_var", "features.5.0.downsample.0.weight", "features.5.0.downsample.1.weight", "features.5.0.downsample.1.bias", "features.5.0.downsample.1.running_mean", "features.5.0.downsample.1.running_var", "features.5.1.conv1.weight", "features.5.1.bn1.weight", "features.5.1.bn1.bias", "features.5.1.bn1.running_mean", "features.5.1.bn1.running_var", "features.5.1.conv2.weight", "features.5.1.bn2.weight", "features.5.1.bn2.bias", "features.5.1.bn2.running_mean", "features.5.1.bn2.running_var", "features.6.0.conv1.weight", "features.6.0.bn1.weight", "features.6.0.bn1.bias", "features.6.0.bn1.running_mean", "features.6.0.bn1.running_var", "features.6.0.conv2.weight", "features.6.0.bn2.weight", "features.6.0.bn2.bias", "features.6.0.bn2.running_mean", "features.6.0.bn2.running_var", "features.6.0.downsample.0.weight", "features.6.0.downsample.1.weight", "features.6.0.downsample.1.bias", "features.6.0.downsample.1.running_mean", "features.6.0.downsample.1.running_var", "features.6.1.conv1.weight", "features.6.1.bn1.weight", "features.6.1.bn1.bias", "features.6.1.bn1.running_mean", "features.6.1.bn1.running_var", "features.6.1.conv2.weight", "features.6.1.bn2.weight", "features.6.1.bn2.bias", "features.6.1.bn2.running_mean", "features.6.1.bn2.running_var", "features.7.0.conv1.weight", "features.7.0.bn1.weight", "features.7.0.bn1.bias", "features.7.0.bn1.running_mean", "features.7.0.bn1.running_var", "features.7.0.conv2.weight", "features.7.0.bn2.weight", "features.7.0.bn2.bias", "features.7.0.bn2.running_mean", "features.7.0.bn2.running_var", "features.7.0.downsample.0.weight", "features.7.0.downsample.1.weight", "features.7.0.downsample.1.bias", "features.7.0.downsample.1.running_mean", "features.7.0.downsample.1.running_var", "features.7.1.conv1.weight", "features.7.1.bn1.weight", "features.7.1.bn1.bias", "features.7.1.bn1.running_mean", "features.7.1.bn1.running_var", "features.7.1.conv2.weight", "features.7.1.bn2.weight", "features.7.1.bn2.bias", "features.7.1.bn2.running_mean", "features.7.1.bn2.running_var", "fc.weight", "fc.bias", "alpha.0.weight", "alpha.0.bias". Unexpected key(s) in state_dict: "module.conv1.weight", "module.bn1.weight", "module.bn1.bias", "module.bn1.running_mean", "module.bn1.running_var", "module.layer1.0.conv1.weight", "module.layer1.0.bn1.weight", "module.layer1.0.bn1.bias", "module.layer1.0.bn1.running_mean", "module.layer1.0.bn1.running_var", "module.layer1.0.conv2.weight", "module.layer1.0.bn2.weight", "module.layer1.0.bn2.bias", "module.layer1.0.bn2.running_mean", "module.layer1.0.bn2.running_var", "module.layer1.1.conv1.weight", "module.layer1.1.bn1.weight", "module.layer1.1.bn1.bias", "module.layer1.1.bn1.running_mean", "module.layer1.1.bn1.running_var", "module.layer1.1.conv2.weight", "module.layer1.1.bn2.weight", "module.layer1.1.bn2.bias", "module.layer1.1.bn2.running_mean", "module.layer1.1.bn2.running_var", "module.layer2.0.conv1.weight", "module.layer2.0.bn1.weight", "module.layer2.0.bn1.bias", "module.layer2.0.bn1.running_mean", "module.layer2.0.bn1.running_var", "module.layer2.0.conv2.weight", "module.layer2.0.bn2.weight", "module.layer2.0.bn2.bias", "module.layer2.0.bn2.running_mean", "module.layer2.0.bn2.running_var", "module.layer2.0.downsample.0.weight", "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.conv1.weight", "module.layer2.1.bn1.weight", "module.layer2.1.bn1.bias", "module.layer2.1.bn1.running_mean", "module.layer2.1.bn1.running_var", "module.layer2.1.conv2.weight", "module.layer2.1.bn2.weight", "module.layer2.1.bn2.bias", "module.layer2.1.bn2.running_mean", "module.layer2.1.bn2.running_var", "module.layer3.0.conv1.weight", "module.layer3.0.bn1.weight", "module.layer3.0.bn1.bias", "module.layer3.0.bn1.running_mean", "module.layer3.0.bn1.running_var", "module.layer3.0.conv2.weight", "module.layer3.0.bn2.weight", "module.layer3.0.bn2.bias", "module.layer3.0.bn2.running_mean", "module.layer3.0.bn2.running_var", "module.layer3.0.downsample.0.weight", "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.conv1.weight", "module.layer3.1.bn1.weight", "module.layer3.1.bn1.bias", "module.layer3.1.bn1.running_mean", "module.layer3.1.bn1.running_var", "module.layer3.1.conv2.weight", "module.layer3.1.bn2.weight", "module.layer3.1.bn2.bias", "module.layer3.1.bn2.running_mean", "module.layer3.1.bn2.running_var", "module.layer4.0.conv1.weight", "module.layer4.0.bn1.weight", "module.layer4.0.bn1.bias", "module.layer4.0.bn1.running_mean", "module.layer4.0.bn1.running_var", "module.layer4.0.conv2.weight", "module.layer4.0.bn2.weight", "module.layer4.0.bn2.bias", "module.layer4.0.bn2.running_mean", "module.layer4.0.bn2.running_var", "module.layer4.0.downsample.0.weight", "module.layer4.0.downsample.1.weight", "module.layer4.0.downsample.1.bias", "module.layer4.0.downsample.1.running_mean", "module.layer4.0.downsample.1.running_var", "module.layer4.1.conv1.weight", "module.layer4.1.bn1.weight", "module.layer4.1.bn1.bias", "module.layer4.1.bn1.running_mean", "module.layer4.1.bn1.running_var", "module.layer4.1.conv2.weight", "module.layer4.1.bn2.weight", "module.layer4.1.bn2.bias", "module.layer4.1.bn2.running_mean", "module.layer4.1.bn2.running_var", "module.feature.weight", "module.feature.bias", "module.fc.weight", "module.fc.bias".

hello,did u solve this problem?I met the same problem.

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dreamlychina commented 2 years ago

怎么可能只差一个module，你仔细看下好不，features.0.weight 和 module.conv1.weight 怎么差一个module？？？