Open Yellow-Champagne opened 5 years ago
duanzhiihao
commented
5 years ago Previously I had that problem too. For me, I just change the line 14 in CC.py to
self.CCN = torch.nn.DataParallel(self.CCN, device_ids=[0]).cuda()
Then everything is working. For details you may refer to Here.
Yellow-Champagne
commented
5 years ago First of all， thank you very much.I tried your method, but it failed.
gjy3035
commented
5 years ago @Yellow-Champagne @duanzhiihao Thanks for your attention!
According to the limited information provided by your, I think it is the problem of keys in ResSFCN.
Our model is trained on multiple GPUs, so the keys contain modules. If you want to train SFCN on single GPU, you should remove modules.
@Yellow-Champagne Can you provide more detailed info for this error? For example, Error location, error log in terminal? Thanks!
Yellow-Champagne
commented
5 years ago Traceback (most recent call last):
File "F:/Object-Detection/Crowd-Counting/node/GCC-SFCN-master/train.py", line 204, in
File "F:\Object-Detection\Crowd-Counting\node\GCC-SFCN-master\models\CC.py", line 11, in init
self.CCN = net()
File "F:\Object-Detection\Crowd-Counting\node\GCC-SFCN-master\models\resSFCN.py", line 33, in init
res.load_state_dict(pre_wts)
File "D:\Anaconda3\lib\site-packages\torch\nn\modules\module.py", line 721, in load_state_dict
self.class.name, "\n\t".join(error_msgs)))
RuntimeError: Error(s) in loading state_dict for ResNet:
Missing key(s) in state_dict: "conv1.weight", "bn1.running_var", "bn1.running_mean", "bn1.weight", "bn1.bias", "layer1.0.conv1.weight", "layer1.0.bn1.running_var", "layer1.0.bn1.running_mean", "layer1.0.bn1.weight", "layer1.0.bn1.bias", "layer1.0.conv2.weight", "layer1.0.bn2.running_var", "layer1.0.bn2.running_mean", "layer1.0.bn2.weight", "layer1.0.bn2.bias", "layer1.0.conv3.weight", "layer1.0.bn3.running_var", "layer1.0.bn3.running_mean", "layer1.0.bn3.weight", "layer1.0.bn3.bias", "layer1.0.downsample.0.weight", "layer1.0.downsample.1.running_var", "layer1.0.downsample.1.running_mean", "layer1.0.downsample.1.weight", "layer1.0.downsample.1.bias", "layer1.1.conv1.weight", "layer1.1.bn1.running_var", "layer1.1.bn1.running_mean", "layer1.1.bn1.weight", "layer1.1.bn1.bias", "layer1.1.conv2.weight", "layer1.1.bn2.running_var", "layer1.1.bn2.running_mean", "layer1.1.bn2.weight", "layer1.1.bn2.bias", "layer1.1.conv3.weight", "layer1.1.bn3.running_var", "layer1.1.bn3.running_mean", "layer1.1.bn3.weight", "layer1.1.bn3.bias", "layer1.2.conv1.weight", "layer1.2.bn1.running_var", "layer1.2.bn1.running_mean", "layer1.2.bn1.weight", "layer1.2.bn1.bias", "layer1.2.conv2.weight", "layer1.2.bn2.running_var", "layer1.2.bn2.running_mean", "layer1.2.bn2.weight", "layer1.2.bn2.bias", "layer1.2.conv3.weight", "layer1.2.bn3.running_var", "layer1.2.bn3.running_mean", "layer1.2.bn3.weight", "layer1.2.bn3.bias", "layer2.0.conv1.weight", "layer2.0.bn1.running_var", "layer2.0.bn1.running_mean", "layer2.0.bn1.weight", "layer2.0.bn1.bias", "layer2.0.conv2.weight", "layer2.0.bn2.running_var", "layer2.0.bn2.running_mean", "layer2.0.bn2.weight", "layer2.0.bn2.bias", "layer2.0.conv3.weight", "layer2.0.bn3.running_var", "layer2.0.bn3.running_mean", "layer2.0.bn3.weight", "layer2.0.bn3.bias", "layer2.0.downsample.0.weight", "layer2.0.downsample.1.running_var", "layer2.0.downsample.1.running_mean", "layer2.0.downsample.1.weight", "layer2.0.downsample.1.bias", "layer2.1.conv1.weight", "layer2.1.bn1.running_var", "layer2.1.bn1.running_mean", "layer2.1.bn1.weight", "layer2.1.bn1.bias", "layer2.1.conv2.weight", "layer2.1.bn2.running_var", "layer2.1.bn2.running_mean", "layer2.1.bn2.weight", "layer2.1.bn2.bias", "layer2.1.conv3.weight", "layer2.1.bn3.running_var", "layer2.1.bn3.running_mean", "layer2.1.bn3.weight", "layer2.1.bn3.bias", "layer2.2.conv1.weight", "layer2.2.bn1.running_var", "layer2.2.bn1.running_mean", "layer2.2.bn1.weight", "layer2.2.bn1.bias", "layer2.2.conv2.weight", "layer2.2.bn2.running_var", "layer2.2.bn2.running_mean", "layer2.2.bn2.weight", "layer2.2.bn2.bias", "layer2.2.conv3.weight", "layer2.2.bn3.running_var", "layer2.2.bn3.running_mean", "layer2.2.bn3.weight", "layer2.2.bn3.bias", "layer2.3.conv1.weight", "layer2.3.bn1.running_var", "layer2.3.bn1.running_mean", "layer2.3.bn1.weight", "layer2.3.bn1.bias", "layer2.3.conv2.weight", "layer2.3.bn2.running_var", "layer2.3.bn2.running_mean", "layer2.3.bn2.weight", "layer2.3.bn2.bias", "layer2.3.conv3.weight", "layer2.3.bn3.running_var", "layer2.3.bn3.running_mean", "layer2.3.bn3.weight", "layer2.3.bn3.bias", "layer3.0.conv1.weight", "layer3.0.bn1.running_var", "layer3.0.bn1.running_mean", "layer3.0.bn1.weight", "layer3.0.bn1.bias", "layer3.0.conv2.weight", "layer3.0.bn2.running_var", "layer3.0.bn2.running_mean", "layer3.0.bn2.weight", "layer3.0.bn2.bias", "layer3.0.conv3.weight", "layer3.0.bn3.running_var", "layer3.0.bn3.running_mean", "layer3.0.bn3.weight", "layer3.0.bn3.bias", "layer3.0.downsample.0.weight", "layer3.0.downsample.1.running_var", "layer3.0.downsample.1.running_mean", "layer3.0.downsample.1.weight", "layer3.0.downsample.1.bias", "layer3.1.conv1.weight", "layer3.1.bn1.running_var", "layer3.1.bn1.running_mean", "layer3.1.bn1.weight", "layer3.1.bn1.bias", "layer3.1.conv2.weight", "layer3.1.bn2.running_var", "layer3.1.bn2.running_mean", "layer3.1.bn2.weight", "layer3.1.bn2.bias", "layer3.1.conv3.weight", "layer3.1.bn3.running_var", "layer3.1.bn3.running_mean", "layer3.1.bn3.weight", "layer3.1.bn3.bias", "layer3.2.conv1.weight", "layer3.2.bn1.running_var", "layer3.2.bn1.running_mean", "layer3.2.bn1.weight", "layer3.2.bn1.bias", "layer3.2.conv2.weight", "layer3.2.bn2.running_var", "layer3.2.bn2.running_mean", "layer3.2.bn2.weight", "layer3.2.bn2.bias", "layer3.2.conv3.weight", "layer3.2.bn3.running_var", "layer3.2.bn3.running_mean", "layer3.2.bn3.weight", "layer3.2.bn3.bias", "layer3.3.conv1.weight", "layer3.3.bn1.running_var", "layer3.3.bn1.running_mean", "layer3.3.bn1.weight", "layer3.3.bn1.bias", "layer3.3.conv2.weight", "layer3.3.bn2.running_var", "layer3.3.bn2.running_mean", "layer3.3.bn2.weight", "layer3.3.bn2.bias", "layer3.3.conv3.weight", "layer3.3.bn3.running_var", "layer3.3.bn3.running_mean", "layer3.3.bn3.weight", "layer3.3.bn3.bias", "layer3.4.conv1.weight", "layer3.4.bn1.running_var", "layer3.4.bn1.running_mean", "layer3.4.bn1.weight", "layer3.4.bn1.bias", "layer3.4.conv2.weight", "layer3.4.bn2.running_var", "layer3.4.bn2.running_mean", "layer3.4.bn2.weight", "layer3.4.bn2.bias", "layer3.4.conv3.weight", "layer3.4.bn3.running_var", "layer3.4.bn3.running_mean", "layer3.4.bn3.weight", "layer3.4.bn3.bias", "layer3.5.conv1.weight", "layer3.5.bn1.running_var", "layer3.5.bn1.running_mean", "layer3.5.bn1.weight", "layer3.5.bn1.bias", "layer3.5.conv2.weight", "layer3.5.bn2.running_var", "layer3.5.bn2.running_mean", "layer3.5.bn2.weight", "layer3.5.bn2.bias", "layer3.5.conv3.weight", "layer3.5.bn3.running_var", "layer3.5.bn3.running_mean", "layer3.5.bn3.weight", "layer3.5.bn3.bias", "layer3.6.conv1.weight", "layer3.6.bn1.running_var", "layer3.6.bn1.running_mean", "layer3.6.bn1.weight", "layer3.6.bn1.bias", "layer3.6.conv2.weight", "layer3.6.bn2.running_var", "layer3.6.bn2.running_mean", "layer3.6.bn2.weight", "layer3.6.bn2.bias", "layer3.6.conv3.weight", "layer3.6.bn3.running_var", "layer3.6.bn3.running_mean", "layer3.6.bn3.weight", "layer3.6.bn3.bias", "layer3.7.conv1.weight", "layer3.7.bn1.running_var", "layer3.7.bn1.running_mean", "layer3.7.bn1.weight", "layer3.7.bn1.bias", "layer3.7.conv2.weight", "layer3.7.bn2.running_var", "layer3.7.bn2.running_mean", "layer3.7.bn2.weight", "layer3.7.bn2.bias", "layer3.7.conv3.weight", "layer3.7.bn3.running_var", "layer3.7.bn3.running_mean", "layer3.7.bn3.weight", "layer3.7.bn3.bias", "layer3.8.conv1.weight", "layer3.8.bn1.running_var", "layer3.8.bn1.running_mean", "layer3.8.bn1.weight", "layer3.8.bn1.bias", "layer3.8.conv2.weight", "layer3.8.bn2.running_var", "layer3.8.bn2.running_mean", "layer3.8.bn2.weight", "layer3.8.bn2.bias", "layer3.8.conv3.weight", "layer3.8.bn3.running_var", "layer3.8.bn3.running_mean", "layer3.8.bn3.weight", "layer3.8.bn3.bias", "layer3.9.conv1.weight", "layer3.9.bn1.running_var", "layer3.9.bn1.running_mean", "layer3.9.bn1.weight", "layer3.9.bn1.bias", "layer3.9.conv2.weight", "layer3.9.bn2.running_var", "layer3.9.bn2.running_mean", "layer3.9.bn2.weight", "layer3.9.bn2.bias", "layer3.9.conv3.weight", "layer3.9.bn3.running_var", "layer3.9.bn3.running_mean", "layer3.9.bn3.weight", "layer3.9.bn3.bias", "layer3.10.conv1.weight", "layer3.10.bn1.running_var", "layer3.10.bn1.running_mean", "layer3.10.bn1.weight", "layer3.10.bn1.bias", "layer3.10.conv2.weight", "layer3.10.bn2.running_var", "layer3.10.bn2.running_mean", "layer3.10.bn2.weight", "layer3.10.bn2.bias", "layer3.10.conv3.weight", "layer3.10.bn3.running_var", "layer3.10.bn3.running_mean", "layer3.10.bn3.weight", "layer3.10.bn3.bias", "layer3.11.conv1.weight", "layer3.11.bn1.running_var", "layer3.11.bn1.running_mean", "layer3.11.bn1.weight", "layer3.11.bn1.bias", "layer3.11.conv2.weight", "layer3.11.bn2.running_var", "layer3.11.bn2.running_mean", "layer3.11.bn2.weight", "layer3.11.bn2.bias", "layer3.11.conv3.weight", "layer3.11.bn3.running_var", "layer3.11.bn3.running_mean", "layer3.11.bn3.weight", "layer3.11.bn3.bias", "layer3.12.conv1.weight", "layer3.12.bn1.running_var", "layer3.12.bn1.running_mean", "layer3.12.bn1.weight", "layer3.12.bn1.bias", "layer3.12.conv2.weight", "layer3.12.bn2.running_var", "layer3.12.bn2.running_mean", "layer3.12.bn2.weight", "layer3.12.bn2.bias", "layer3.12.conv3.weight", "layer3.12.bn3.running_var", "layer3.12.bn3.running_mean", "layer3.12.bn3.weight", "layer3.12.bn3.bias", "layer3.13.conv1.weight", "layer3.13.bn1.running_var", "layer3.13.bn1.running_mean", "layer3.13.bn1.weight", "layer3.13.bn1.bias", "layer3.13.conv2.weight", "layer3.13.bn2.running_var", "layer3.13.bn2.running_mean", "layer3.13.bn2.weight", "layer3.13.bn2.bias", "layer3.13.conv3.weight", "layer3.13.bn3.running_var", "layer3.13.bn3.running_mean", "layer3.13.bn3.weight", "layer3.13.bn3.bias", "layer3.14.conv1.weight", "layer3.14.bn1.running_var", "layer3.14.bn1.running_mean", "layer3.14.bn1.weight", "layer3.14.bn1.bias", "layer3.14.conv2.weight", "layer3.14.bn2.running_var", "layer3.14.bn2.running_mean", "layer3.14.bn2.weight", "layer3.14.bn2.bias", "layer3.14.conv3.weight", "layer3.14.bn3.running_var", "layer3.14.bn3.running_mean", "layer3.14.bn3.weight", "layer3.14.bn3.bias", "layer3.15.conv1.weight", "layer3.15.bn1.running_var", "layer3.15.bn1.running_mean", "layer3.15.bn1.weight", "layer3.15.bn1.bias", "layer3.15.conv2.weight", "layer3.15.bn2.running_var", "layer3.15.bn2.running_mean", "layer3.15.bn2.weight", "layer3.15.bn2.bias", "layer3.15.conv3.weight", "layer3.15.bn3.running_var", "layer3.15.bn3.running_mean", "layer3.15.bn3.weight", "layer3.15.bn3.bias", "layer3.16.conv1.weight", "layer3.16.bn1.running_var", "layer3.16.bn1.running_mean", "layer3.16.bn1.weight", "layer3.16.bn1.bias", "layer3.16.conv2.weight", "layer3.16.bn2.running_var", "layer3.16.bn2.running_mean", "layer3.16.bn2.weight", "layer3.16.bn2.bias", "layer3.16.conv3.weight", "layer3.16.bn3.running_var", "layer3.16.bn3.running_mean", "layer3.16.bn3.weight", "layer3.16.bn3.bias", "layer3.17.conv1.weight", "layer3.17.bn1.running_var", "layer3.17.bn1.running_mean", "layer3.17.bn1.weight", "layer3.17.bn1.bias", "layer3.17.conv2.weight", "layer3.17.bn2.running_var", "layer3.17.bn2.running_mean", "layer3.17.bn2.weight", "layer3.17.bn2.bias", "layer3.17.conv3.weight", "layer3.17.bn3.running_var", "layer3.17.bn3.running_mean", "layer3.17.bn3.weight", "layer3.17.bn3.bias", "layer3.18.conv1.weight", "layer3.18.bn1.running_var", "layer3.18.bn1.running_mean", "layer3.18.bn1.weight", "layer3.18.bn1.bias", "layer3.18.conv2.weight", "layer3.18.bn2.running_var", "layer3.18.bn2.running_mean", "layer3.18.bn2.weight", "layer3.18.bn2.bias", "layer3.18.conv3.weight", "layer3.18.bn3.running_var", "layer3.18.bn3.running_mean", "layer3.18.bn3.weight", "layer3.18.bn3.bias", "layer3.19.conv1.weight", "layer3.19.bn1.running_var", "layer3.19.bn1.running_mean", "layer3.19.bn1.weight", "layer3.19.bn1.bias", "layer3.19.conv2.weight", "layer3.19.bn2.running_var", "layer3.19.bn2.running_mean", "layer3.19.bn2.weight", "layer3.19.bn2.bias", "layer3.19.conv3.weight", "layer3.19.bn3.running_var", "layer3.19.bn3.running_mean", "layer3.19.bn3.weight", "layer3.19.bn3.bias", "layer3.20.conv1.weight", "layer3.20.bn1.running_var", "layer3.20.bn1.running_mean", "layer3.20.bn1.weight", "layer3.20.bn1.bias", "layer3.20.conv2.weight", "layer3.20.bn2.running_var", "layer3.20.bn2.running_mean", "layer3.20.bn2.weight", "layer3.20.bn2.bias", "layer3.20.conv3.weight", "layer3.20.bn3.running_var", "layer3.20.bn3.running_mean", "layer3.20.bn3.weight", "layer3.20.bn3.bias", "layer3.21.conv1.weight", "layer3.21.bn1.running_var", "layer3.21.bn1.running_mean", "layer3.21.bn1.weight", "layer3.21.bn1.bias", "layer3.21.conv2.weight", "layer3.21.bn2.running_var", "layer3.21.bn2.running_mean", "layer3.21.bn2.weight", "layer3.21.bn2.bias", "layer3.21.conv3.weight", "layer3.21.bn3.running_var", "layer3.21.bn3.running_mean", "layer3.21.bn3.weight", "layer3.21.bn3.bias", "layer3.22.conv1.weight", "layer3.22.bn1.running_var", "layer3.22.bn1.running_mean", "layer3.22.bn1.weight", "layer3.22.bn1.bias", "layer3.22.conv2.weight", "layer3.22.bn2.running_var", "layer3.22.bn2.running_mean", "layer3.22.bn2.weight", "layer3.22.bn2.bias", "layer3.22.conv3.weight", "layer3.22.bn3.running_var", "layer3.22.bn3.running_mean", "layer3.22.bn3.weight", "layer3.22.bn3.bias", "layer4.0.conv1.weight", "layer4.0.bn1.running_var", "layer4.0.bn1.running_mean", "layer4.0.bn1.weight", "layer4.0.bn1.bias", "layer4.0.conv2.weight", "layer4.0.bn2.running_var", "layer4.0.bn2.running_mean", "layer4.0.bn2.weight", "layer4.0.bn2.bias", "layer4.0.conv3.weight", "layer4.0.bn3.running_var", "layer4.0.bn3.running_mean", "layer4.0.bn3.weight", "layer4.0.bn3.bias", "layer4.0.downsample.0.weight", "layer4.0.downsample.1.running_var", "layer4.0.downsample.1.running_mean", "layer4.0.downsample.1.weight", "layer4.0.downsample.1.bias", "layer4.1.conv1.weight", "layer4.1.bn1.running_var", "layer4.1.bn1.running_mean", "layer4.1.bn1.weight", "layer4.1.bn1.bias", "layer4.1.conv2.weight", "layer4.1.bn2.running_var", "layer4.1.bn2.running_mean", "layer4.1.bn2.weight", "layer4.1.bn2.bias", "layer4.1.conv3.weight", "layer4.1.bn3.running_var", "layer4.1.bn3.running_mean", "layer4.1.bn3.weight", "layer4.1.bn3.bias", "layer4.2.conv1.weight", "layer4.2.bn1.running_var", "layer4.2.bn1.running_mean", "layer4.2.bn1.weight", "layer4.2.bn1.bias", "layer4.2.conv2.weight", "layer4.2.bn2.running_var", "layer4.2.bn2.running_mean", "layer4.2.bn2.weight", "layer4.2.bn2.bias", "layer4.2.conv3.weight", "layer4.2.bn3.running_var", "layer4.2.bn3.running_mean", "layer4.2.bn3.weight", "layer4.2.bn3.bias", "fc.weight", "fc.bias".
Unexpected key(s) in state_dict: "CCN.module.backend.0.weight", "CCN.module.backend.0.bias", "CCN.module.backend.2.weight", "CCN.module.backend.2.bias", "CCN.module.backend.4.weight", "CCN.module.backend.4.bias", "CCN.module.backend.6.weight", "CCN.module.backend.6.bias", "CCN.module.backend.8.weight", "CCN.module.backend.8.bias", "CCN.module.backend.10.weight", "CCN.module.backend.10.bias", "CCN.module.convDU.conv.0.weight", "CCN.module.convDU.conv.0.bias", "CCN.module.convLR.conv.0.weight", "CCN.module.convLR.conv.0.bias", "CCN.module.frontend.0.weight", "CCN.module.frontend.1.weight", "CCN.module.frontend.1.bias", "CCN.module.frontend.1.running_mean", "CCN.module.frontend.1.running_var", "CCN.module.frontend.4.0.conv1.weight", "CCN.module.frontend.4.0.bn1.weight", "CCN.module.frontend.4.0.bn1.bias", "CCN.module.frontend.4.0.bn1.running_mean", "CCN.module.frontend.4.0.bn1.running_var", "CCN.module.frontend.4.0.conv2.weight", "CCN.module.frontend.4.0.bn2.weight", 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gjy3035
commented
5 years ago Line 33 in resSFCN.py, the purpose is loading resnet 101, so you should download it from this link.
Our provided model is resSFCN trained on GCC, it will be loaded in Line 58 in train.py.
Yellow-Champagne
commented
5 years ago Thank you very much, I have succeeded.
zhangxiaopang88
commented
5 years ago 你好，我之前遇到楼上同样的错误，我也按照您说的方法修改了，但是上个问题解决了，又出现一下错误，您看是哪边出问题了
Traceback (most recent call last):
File "test_myimg.py", line 176, in
zhangxiaopang88
commented
5 years ago @gjy3035
zhangxiaopang88
commented
5 years ago @gjy3035 我把训练的gpu改为[0,1]就解决这个问题了。我测试自己的图片需要把自己的图片生成密度图吗，现在出现以下问题，请问您遇到过吗test_myimg.py:99: UserWarning: volatile was removed and now has no effect. Use with torch.no_grad(): instead.
img = Variable(img[None,:,:,:],volatile=True).cuda()
/home/jan/anaconda3/lib/python3.6/site-packages/torch/tensor.py:287: UserWarning: non-inplace resize is deprecated
warnings.warn("non-inplace resize is deprecated")
Traceback (most recent call last):
File "test_myimg.py", line 176, in
File "/home/jan/anaconda3/lib/python3.6/site-packages/torch/nn/modules/module.py", line 489, in call
result = self.forward(*input, kwargs)
File "/home/jan/anaconda3/lib/python3.6/site-packages/torch/nn/parallel/data_parallel.py", line 143, in forward
outputs = self.parallel_apply(replicas, inputs, kwargs)
File "/home/jan/anaconda3/lib/python3.6/site-packages/torch/nn/parallel/data_parallel.py", line 153, in parallel_apply
return parallel_apply(replicas, inputs, kwargs, self.device_ids[:len(replicas)])
File "/home/jan/anaconda3/lib/python3.6/site-packages/torch/nn/parallel/parallel_apply.py", line 83, in parallel_apply
raise output
File "/home/jan/anaconda3/lib/python3.6/site-packages/torch/nn/parallel/parallel_apply.py", line 59, in _worker
output = module(*input, *kwargs)
File "/home/jan/anaconda3/lib/python3.6/site-packages/torch/nn/modules/module.py", line 489, in call
result = self.forward(input, kwargs)
File "/home/jan/WORKSPACE/CHR/People/FCN_people/GCC-SFCN-master/models/resSFCN.py", line 52, in forward
x = self.convDU(x)
File "/home/jan/anaconda3/lib/python3.6/site-packages/torch/nn/modules/module.py", line 489, in call
result = self.forward(*input, kwargs)
File "/home/jan/WORKSPACE/CHR/People/FCN_people/GCC-SFCN-master/models/layer.py", line 27, in forward
fea_stack.append(self.conv(fea_stack[i-1])+i_fea)
File "/home/jan/anaconda3/lib/python3.6/site-packages/torch/nn/modules/module.py", line 489, in call
result = self.forward(*input, *kwargs)
File "/home/jan/anaconda3/lib/python3.6/site-packages/torch/nn/modules/container.py", line 92, in forward
input = module(input)
File "/home/jan/anaconda3/lib/python3.6/site-packages/torch/nn/modules/module.py", line 489, in call
result = self.forward(input, kwargs)
File "/home/jan/anaconda3/lib/python3.6/site-packages/torch/nn/modules/conv.py", line 320, in forward
self.padding, self.dilation, self.groups)
TypeError: conv2d(): argument 'padding' must be tuple of ints, but found element of type float at pos 1
priya-dwivedi
commented
5 years ago @zhangxiaopang88 : This error is becausing in python 3, division is resulting in a float. Need to // to convert to int. To solve this in layer.py, in line 14 and line 49, change padding to:
padding=((kernel_size[0]-1)//2,(kernel_size[1]-1)//2)
jhxing
commented
4 years ago I met the same problem as you , and I fixed this by change the config.py line23
__C.NET = 'Res101' # net selection: Res101_SFCN, MCNN, AlexNet, VGG, VGG_DECODER, Res50, CSRNet, SANet
Maybe, you have to change the __C.NET to the same as the pre-trained model you are using.
Runtimeerror:Error(s) in loading state_dict for ResNet: Missing key(s) in state_dict: Unexpected key(s) in state_dict: I have encountered the above problem.Weight and residual network do not correspond.