Closed titu1992 closed 4 years ago
Nope, the trained 'Semi-Inf-Net-100.pth' and the pre-trained 'Inf-Net_Pseduo' totally matches. Besides, I have checked the each item in ''Semi-Inf-Net-100.pth" but fail to find the param_name that contains total_ops
and total_params
. May you provide training details and testing details?
Also I have the same issue too. I trained well (myTrain_lungInf.py) but when I tested (myTest_lungInf.py), mismatch occured.
size mismatch for resnet.layer4.2.conv3.weight: copying a param with shape torch.Size([2048, 512, 1, 1]) from checkpoint, the shape in current model is torch.Size([2048, 832, 1, 1]).
I have replied to your e-mail, please check it. @aytek03
I run 100 epoch and I got Inf-Net-100.pth. Then I changed this code in Res2Net.py (in backbone)
def res2net50_v1b_26w_4s(pretrained=False, **kwargs):
model.load_state_dict(model_zoo.load_url(model_urls['res2net50_v1b_26w_4s']))
because name 'model_lung_infection' is not defined in this code.
Then I changed ResNet50 to Res2Net50 but I have this error code in myTest_lungInf.py:
RuntimeError: Error(s) in loading state_dict for Inf_Net: Unexpected key(s) in state_dict: "total_ops", "total_params",..
we do not have total_ops and total_params.
Have do we solve sir?
I find the error in this line:
model.load_state_dict(torch.load('./Snapshots/save_weights/Inf-Net/Inf-Net-100.pth'))
When I directly download the pre-trained weights from your .pth, it works but when I use my trained .pth, I got an error.
Why? I changed to Res2net50 and trained but this system does not work.
I think THOP may result in an error. You better close off it. If you solve this problem, please notify me.
Yes it works sir. When I close THOP settings in MyTrain_LungInf.py then I run MyTest_LungInf.py. Then I got the result images.
Have a nice day! Thank you for your attention to our work.
我也遇到了这个问题,我把计算参数的代码放到了train的后面,这样解决了这个问题并且还能计算参数
我也遇到了这个问题,我把计算参数的代码放到了train的后面,这样解决了这个问题并且还能计算参数
Can you share this code?
我也遇到了这个问题,我把计算参数的代码放到了train的后面,这样解决了这个问题并且还能计算参数
请问能展示你的解决问题部分的代码吗?同样遇到这个问题,调整了几次都不成功。谢谢了
我也遇到了这个问题,我把计算参数的代码放到了train的后面,这样解决了这个问题并且还能计算参数
请问能展示你的解决问题部分的代码吗?同样遇到这个问题,调整了几次都不成功。谢谢了
你可以把计算参数的这一段去掉试试
我也遇到了这个问题,我把计算参数的代码放到了train的后面,这样解决了这个问题并且还能计算参数
请问能展示你的解决问题部分的代码吗?同样遇到这个问题,调整了几次都不成功。谢谢了
你可以把计算参数的这一段去掉试试
感谢作者,拉取新代码修改部分问题后,PseudoGenerator和test都跑通了,非常感谢!
You are welcome!
harvey notifications@github.com 于2020年8月31日周一 下午11:12写道:
我也遇到了这个问题,我把计算参数的代码放到了train的后面,这样解决了这个问题并且还能计算参数
请问能展示你的解决问题部分的代码吗?同样遇到这个问题,调整了几次都不成功。谢谢了
你可以把计算参数的这一段去掉试试
感谢作者,拉取新代码修改部分问题后,PseudoGenerator和test都跑通了,非常感谢!
— You are receiving this because you modified the open/close state. Reply to this email directly, view it on GitHub https://github.com/DengPingFan/Inf-Net/issues/15#issuecomment-683841554, or unsubscribe https://github.com/notifications/unsubscribe-auth/AJEUADOUSJQQLSWJFQVUBA3SDO4XRANCNFSM4NXP7KAA .
Hi, Thank you for this helpful compilation. I am able to generate my weights after training MyTrain_LungInf.py with backbone = 'Res2Net50' However when I am trying to run the MyTest_LungInf.py I am facing the below issue although I have placed the pre-trained weights of 'Inf-Net_Pseduo' in the reuired path. Is it because of some mis-match between the trained 'Semi-Inf-Net-100.pth' and the pre-trained 'Inf-Net_Pseduo'? How could we fix this?
Traceback (most recent call last): File "MyTest_LungInf.py", line 65, in
inference()
File "MyTest_LungInf.py", line 39, in inference
model.load_state_dict(torch.load(opt.pth_path))
File "/home/user/Virtualenv/lib/python3.6/site-packages/torch/nn/modules/module.py", line 839, in load_state_dict
self.class.name, "\n\t".join(error_msgs)))
RuntimeError: Error(s) in loading state_dict for Inf_Net:
Unexpected key(s) in state_dict: "total_ops", "total_params", "resnet.total_ops", "resnet.total_params", "resnet.conv1.total_ops", "resnet.conv1.total_params", "resnet.layer1.total_ops", "resnet.layer1.total_params", "resnet.layer1.0.total_ops", "resnet.layer1.0.total_params", "resnet.layer1.0.convs.total_ops", "resnet.layer1.0.convs.total_params", "resnet.layer1.0.bns.total_ops", "resnet.layer1.0.bns.total_params", "resnet.layer1.0.downsample.total_ops", "resnet.layer1.0.downsample.total_params", "resnet.layer1.1.total_ops", "resnet.layer1.1.total_params", "resnet.layer1.1.convs.total_ops", "resnet.layer1.1.convs.total_params", "resnet.layer1.1.bns.total_ops", "resnet.layer1.1.bns.total_params", "resnet.layer1.2.total_ops", "resnet.layer1.2.total_params", "resnet.layer1.2.convs.total_ops", "resnet.layer1.2.convs.total_params", "resnet.layer1.2.bns.total_ops", "resnet.layer1.2.bns.total_params", "resnet.layer2.total_ops", "resnet.layer2.total_params", "resnet.layer2.0.total_ops", "resnet.layer2.0.total_params", "resnet.layer2.0.convs.total_ops", "resnet.layer2.0.convs.total_params", "resnet.layer2.0.bns.total_ops", "resnet.layer2.0.bns.total_params", "resnet.layer2.0.downsample.total_ops", "resnet.layer2.0.downsample.total_params", "resnet.layer2.1.total_ops", "resnet.layer2.1.total_params", "resnet.layer2.1.convs.total_ops", "resnet.layer2.1.convs.total_params", "resnet.layer2.1.bns.total_ops", "resnet.layer2.1.bns.total_params", "resnet.layer2.2.total_ops", "resnet.layer2.2.total_params", "resnet.layer2.2.convs.total_ops", "resnet.layer2.2.convs.total_params", "resnet.layer2.2.bns.total_ops", "resnet.layer2.2.bns.total_params", "resnet.layer2.3.total_ops", "resnet.layer2.3.total_params", "resnet.layer2.3.convs.total_ops", "resnet.layer2.3.convs.total_params", "resnet.layer2.3.bns.total_ops", "resnet.layer2.3.bns.total_params", "resnet.layer3.total_ops", "resnet.layer3.total_params", "resnet.layer3.0.total_ops", "resnet.layer3.0.total_params", "resnet.layer3.0.convs.total_ops", "resnet.layer3.0.convs.total_params", "resnet.layer3.0.bns.total_ops", "resnet.layer3.0.bns.total_params", "resnet.layer3.0.downsample.total_ops", "resnet.layer3.0.downsample.total_params", "resnet.layer3.1.total_ops", "resnet.layer3.1.total_params", "resnet.layer3.1.convs.total_ops", "resnet.layer3.1.convs.total_params", "resnet.layer3.1.bns.total_ops", "resnet.layer3.1.bns.total_params", "resnet.layer3.2.total_ops", "resnet.layer3.2.total_params", "resnet.layer3.2.convs.total_ops", "resnet.layer3.2.convs.total_params", "resnet.layer3.2.bns.total_ops", "resnet.layer3.2.bns.total_params", "resnet.layer3.3.total_ops", "resnet.layer3.3.total_params", "resnet.layer3.3.convs.total_ops", "resnet.layer3.3.convs.total_params", "resnet.layer3.3.bns.total_ops", "resnet.layer3.3.bns.total_params", "resnet.layer3.4.total_ops", "resnet.layer3.4.total_params", "resnet.layer3.4.convs.total_ops", "resnet.layer3.4.convs.total_params", "resnet.layer3.4.bns.total_ops", "resnet.layer3.4.bns.total_params", "resnet.layer3.5.total_ops", "resnet.layer3.5.total_params", "resnet.layer3.5.convs.total_ops", "resnet.layer3.5.convs.total_params", "resnet.layer3.5.bns.total_ops", "resnet.layer3.5.bns.total_params", "resnet.layer4.total_ops", "resnet.layer4.total_params", "resnet.layer4.0.total_ops", "resnet.layer4.0.total_params", "resnet.layer4.0.convs.total_ops", "resnet.layer4.0.convs.total_params", "resnet.layer4.0.bns.total_ops", "resnet.layer4.0.bns.total_params", "resnet.layer4.0.downsample.total_ops", "resnet.layer4.0.downsample.total_params", "resnet.layer4.1.total_ops", "resnet.layer4.1.total_params"