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Kaiseem / DAR-UNet

[JBHI2022] A novel 3D unsupervised domain adaptation framework for cross-modality medical image segmentation
Apache License 2.0
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关于我训练出的pt用于测试的问题 #18

Open QuintinDong opened 11 months ago

QuintinDong commented 11 months ago

感谢作者的工作，抱歉打扰您了。关于我使用您第二阶段训练分割网络的代码，我得到了分割网络best.pt文件，然后我使用权重和您提供的测试代码，出现了key缺失的报错。具体如下： RuntimeError: Error(s) in loading state_dict for DARUnet: Missing key(s) in state_dict: "L1_fromimg.conv_block.0.weight", "L1_fromimg.conv_block.0.bias", "L1_fromimg.conv_block.1.weight", "L1_fromimg.conv_block.1.bias", "L1_fromimg.conv_block.3.weight", "L1_fromimg.conv_block.3.bias", "L1_fromimg.conv_block.4.weight", "L1_fromimg.conv_block.4.bias", "L1_fromimg.conv_block.6.weight", "L1_fromimg.conv_block.6.bias", "L1_fromimg.conv_skip.0.weight", "L1_fromimg.conv_skip.0.bias", "L1_fromimg.conv_skip.1.weight", "L1_fromimg.conv_skip.1.bias", "L1_fromimg.qam.branchs.0.compress.1.weight", "L1_fromimg.qam.branchs.0.compress.2.weight", "L1_fromimg.qam.branchs.0.compress.2.bias", "L1_fromimg.qam.branchs.0.compress.2.running_mean", "L1_fromimg.qam.branchs.0.compress.2.running_var", "L1_fromimg.qam.branchs.1.compress.1.weight", "L1_fromimg.qam.branchs.1.compress.2.weight", 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"L1_up.conv_block.6.weight", "L1_up.conv_block.6.bias", "L1_up.conv_block.8.weight", "L1_up.conv_block.8.bias", "L1_up.conv_skip.0.weight", "L1_up.conv_skip.0.bias", "L1_up.conv_skip.1.weight", "L1_up.conv_skip.1.bias", "L1_up.qam.branchs.0.compress.1.weight", "L1_up.qam.branchs.0.compress.2.weight", "L1_up.qam.branchs.0.compress.2.bias", "L1_up.qam.branchs.0.compress.2.running_mean", "L1_up.qam.branchs.0.compress.2.running_var", "L1_up.qam.branchs.1.compress.1.weight", "L1_up.qam.branchs.1.compress.2.weight", "L1_up.qam.branchs.1.compress.2.bias", "L1_up.qam.branchs.1.compress.2.running_mean", "L1_up.qam.branchs.1.compress.2.running_var", "L1_up.qam.branchs.2.compress.1.weight", "L1_up.qam.branchs.2.compress.2.weight", "L1_up.qam.branchs.2.compress.2.bias", "L1_up.qam.branchs.2.compress.2.running_mean", "L1_up.qam.branchs.2.compress.2.running_var", "L1_up.qam.branchs.3.compress.1.weight", "L1_up.qam.branchs.3.compress.2.weight", "L1_up.qam.branchs.3.compress.2.bias", "L1_up.qam.branchs.3.compress.2.running_mean", "L1_up.qam.branchs.3.compress.2.running_var", "sides.0.weight", "sides.0.bias", "sides.1.weight", "sides.1.bias", "sides.2.weight", "sides.2.bias", "sides.3.weight", "sides.3.bias", "sides.4.weight", "sides.4.bias", "outconv.weight", "outconv.bias". Unexpected key(s) in state_dict: "seg".

当我使用您提供的权重文件ct2mr_converted.pth进行测试是成功的，这是不是说明我在训练过程中改变保存的权重的格式为best.pth? 感谢您的工作，不胜感激！

Kaiseem commented 11 months ago

如果你用你的权重测试的话你可能需要把state_dict = torch.load(f'ct2mr_converted.pth') 改成state_dict = torch.load('best.pt')['seg'], 这部分我没有适配所以麻烦需要你自己手动改一下，作为验证你可以直接打印出来这个state_dict.keys()来确保他们一致性

LaBaicai02 commented 9 months ago

感谢作者的工作，抱歉打扰您了。关于我使用您第二阶段训练分割网络的代码，我得到了分割网络best.pt文件，然后我使用权重和您提供的测试代码，出现了key缺失的报错。具体如下： RuntimeError: Error(s) in loading state_dict for DARUnet: Missing key(s) in state_dict: "L1_fromimg.conv_block.0.weight", "L1_fromimg.conv_block.0.bias", "L1_fromimg.conv_block.1.weight", "L1_fromimg.conv_block.1.bias", "L1_fromimg.conv_block.3.weight", "L1_fromimg.conv_block.3.bias", "L1_fromimg.conv_block.4.weight", "L1_fromimg.conv_block.4.bias", "L1_fromimg.conv_block.6.weight", "L1_fromimg.conv_block.6.bias", "L1_fromimg.conv_skip.0.weight", "L1_fromimg.conv_skip.0.bias", "L1_fromimg.conv_skip.1.weight", "L1_fromimg.conv_skip.1.bias", "L1_fromimg.qam.branchs.0.compress.1.weight", "L1_fromimg.qam.branchs.0.compress.2.weight", "L1_fromimg.qam.branchs.0.compress.2.bias", "L1_fromimg.qam.branchs.0.compress.2.running_mean", "L1_fromimg.qam.branchs.0.compress.2.running_var", "L1_fromimg.qam.branchs.1.compress.1.weight", "L1_fromimg.qam.branchs.1.compress.2.weight", 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"L1_up.qam.branchs.3.compress.2.running_mean", "L1_up.qam.branchs.3.compress.2.running_var", "sides.0.weight", "sides.0.bias", "sides.1.weight", "sides.1.bias", "sides.2.weight", "sides.2.bias", "sides.3.weight", "sides.3.bias", "sides.4.weight", "sides.4.bias", "outconv.weight", "outconv.bias". Unexpected key(s) in state_dict: "seg".

当我使用您提供的权重文件ct2mr_converted.pth进行测试是成功的，这是不是说明我在训练过程中改变保存的权重的格式为best.pth? 感谢您的工作，不胜感激！

您好 我想问下你在测试时，有遇到过monai这个包的报错吗，还是直接可以运行