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zhanghang1989 / ResNeSt

ResNeSt: Split-Attention Networks
https://arxiv.org/abs/2004.08955
Apache License 2.0
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Fail to Reproduce the FCN baseline on Pascal-Context & Cityscapes with openseg.pytorch. #58

Open PkuRainBow opened 4 years ago

PkuRainBow commented 4 years ago

Really excellent work!

In fact, we have tried to integerate the ResNeSt backbone in our own code-base openseg.pytorch.

Here is the backbone configuration resnest_models.py and the running script run_r_101_d_8_fcn.sh. (Here we have checked that the ImageNet pre-trained checkpoint is loaded)

All the other settings are kept the same as the settings of FCN w/ ResNet-101. We conduct experiments on both Cityscapes and Pascal-Context. However, we find that the final performance of only achieves 73.3% on Cityscapes (FCN w/ ResNet-101) and 36.9% on Pascal-Context (FCN w/ ResNet-200). In fact, both numbers are far from the reported performance and we guess there must be something wrong with our usage.

We used to check the resnest_models.py for multiple times and fail to find anything different. Besides, we are conducting experiments based on PyTorch0.4.1 and Inplace-ABN.

It would be great if anyone could help check the possible reasons~ Many thanks!

chongruo commented 4 years ago

One possible reason is that the input mean and std are not set correctly. I found the std is [1,1,1] in openseg.pytorch https://github.com/openseg-group/openseg.pytorch/blob/6787d215ccc28714b265c48bd9cfc41b41c81e28/configs/cityscapes/R_101_D_8.json#L70.

The mean and std values in our setting are

PIXEL_MEAN: [123.68, 116.779, 103.939] PIXEL_STD: [58.393, 57.12, 57.375]

Please also change the input order to 'RGB'.

(update) Sorry that these values are not for the segmentation. Please check the new reply below

PkuRainBow commented 4 years ago

@chongruo Thanks, I will fix the mentioned issues and re-run the experiments soon.

Update:

I have fixed the mentioned two issues and re-run the experiments on both Cityscapes and PASCAL-Context. According to the intermediate logs, it seems the performance is close to my previous experiments that achieve poor performance.

It would get great if you could help to check the other possible reasons~

zhanghang1989 commented 4 years ago

Thanks for interest in ResNeSt. Sorry for the delayed reply, just saw this issue.

Our code implementation has been released at https://github.com/zhanghang1989/PyTorch-Encoding

The Cityscaples models are pending by legal, but other models have been released.

zhanghang1989 commented 4 years ago

One possible reason is that the input mean and std are not set correctly. I found the std is [1,1,1] in openseg.pytorch https://github.com/openseg-group/openseg.pytorch/blob/6787d215ccc28714b265c48bd9cfc41b41c81e28/configs/cityscapes/R_101_D_8.json#L70.

The mean and std values in our setting are

PIXEL_MEAN: [123.68, 116.779, 103.939] PIXEL_STD: [58.393, 57.12, 57.375]

Please also change the input order to 'RGB'.

This reply is not correct. Our mean and std are:

https://github.com/zhanghang1989/ResNeSt/blob/master/scripts/torch/verify.py#L72-L73

zhanghang1989 commented 4 years ago

Really excellent work!

In fact, we have tried to integerate the ResNeSt backbone in our own code-base openseg.pytorch.

Here is the backbone configuration resnest_models.py and the running script run_r_101_d_8_fcn.sh. (Here we have checked that the ImageNet pre-trained checkpoint is loaded)

All the other settings are kept the same as the settings of FCN w/ ResNet-101. We conduct experiments on both Cityscapes and Pascal-Context. However, we find that the final performance of only achieves 73.3% on Cityscapes (FCN w/ ResNet-101) and 36.9% on Pascal-Context (FCN w/ ResNet-200). In fact, both numbers are far from the reported performance and we guess there must be something wrong with our usage.

We used to check the resnest_models.py for multiple times and fail to find anything different. Besides, we are conducting experiments based on PyTorch0.4.1 and Inplace-ABN.

It would be great if anyone could help check the possible reasons~ Many thanks!

This looks wired, because it seems worse than ResNet baseline. Could you double check whether the pretrained weights are loaded. We also have released our implementation at https://hangzhang.org/PyTorch-Encoding/model_zoo/segmentation.html It would be great if you can take a look. Thanks!

PkuRainBow commented 4 years ago

@zhanghang1989 We have checked the loaded weights multiple times and we still find that the weights loading seems OK. Here we print the name of the variables that are loaded with the pre-trained checkpoints as below (just now).

The loaded checkpoint is resnest101-966fb78c.pth, which is downloaded following https://github.com/zhanghang1989/PyTorch-Encoding/blob/69ba6789f1c2f6e596880c5b2563baf5f60e815b/encoding/models/model_store.py#L13

It would be great if you could help to check the possible reasons~ We also have checked the architecture configuration resnest_models.py multiple times and failed to find any difference.

2020-05-20 05:55:07,819 INFO [module_helper.py, 152] Loading pretrained model:./pretrained_model/resnest101-966fb78c.pth 2020-05-20 05:55:07,960 INFO [module_helper.py, 187] Matched Keys List: 2020-05-20 05:55:07,960 INFO [module_helper.py, 189] conv1.0.weight 2020-05-20 05:55:07,960 INFO [module_helper.py, 189] conv1.1.weight 2020-05-20 05:55:07,960 INFO [module_helper.py, 189] conv1.1.bias 2020-05-20 05:55:07,960 INFO [module_helper.py, 189] conv1.1.running_mean 2020-05-20 05:55:07,960 INFO [module_helper.py, 189] conv1.1.running_var 2020-05-20 05:55:07,960 INFO [module_helper.py, 189] conv1.3.weight 2020-05-20 05:55:07,960 INFO [module_helper.py, 189] conv1.4.weight 2020-05-20 05:55:07,960 INFO [module_helper.py, 189] conv1.4.bias 2020-05-20 05:55:07,960 INFO [module_helper.py, 189] conv1.4.running_mean 2020-05-20 05:55:07,960 INFO [module_helper.py, 189] conv1.4.running_var 2020-05-20 05:55:07,960 INFO [module_helper.py, 189] conv1.6.weight 2020-05-20 05:55:07,960 INFO [module_helper.py, 189] bn1.weight 2020-05-20 05:55:07,960 INFO [module_helper.py, 189] bn1.bias 2020-05-20 05:55:07,960 INFO [module_helper.py, 189] bn1.running_mean 2020-05-20 05:55:07,960 INFO [module_helper.py, 189] bn1.running_var 2020-05-20 05:55:07,960 INFO [module_helper.py, 189] layer1.0.conv1.weight 2020-05-20 05:55:07,960 INFO [module_helper.py, 189] layer1.0.bn1.weight 2020-05-20 05:55:07,960 INFO [module_helper.py, 189] layer1.0.bn1.bias 2020-05-20 05:55:07,961 INFO [module_helper.py, 189] layer1.0.bn1.running_mean 2020-05-20 05:55:07,961 INFO [module_helper.py, 189] layer1.0.bn1.running_var 2020-05-20 05:55:07,961 INFO [module_helper.py, 189] layer1.0.conv2.conv.weight 2020-05-20 05:55:07,961 INFO [module_helper.py, 189] layer1.0.conv2.bn0.weight 2020-05-20 05:55:07,961 INFO [module_helper.py, 189] layer1.0.conv2.bn0.bias 2020-05-20 05:55:07,961 INFO [module_helper.py, 189] layer1.0.conv2.bn0.running_mean 2020-05-20 05:55:07,961 INFO [module_helper.py, 189] layer1.0.conv2.bn0.running_var 2020-05-20 05:55:07,961 INFO [module_helper.py, 189] layer1.0.conv2.fc1.weight 2020-05-20 05:55:07,961 INFO [module_helper.py, 189] layer1.0.conv2.fc1.bias 2020-05-20 05:55:07,961 INFO [module_helper.py, 189] layer1.0.conv2.bn1.weight 2020-05-20 05:55:07,961 INFO [module_helper.py, 189] layer1.0.conv2.bn1.bias 2020-05-20 05:55:07,961 INFO [module_helper.py, 189] layer1.0.conv2.bn1.running_mean 2020-05-20 05:55:07,961 INFO [module_helper.py, 189] layer1.0.conv2.bn1.running_var 2020-05-20 05:55:07,961 INFO [module_helper.py, 189] layer1.0.conv2.fc2.weight 2020-05-20 05:55:07,961 INFO [module_helper.py, 189] layer1.0.conv2.fc2.bias 2020-05-20 05:55:07,961 INFO [module_helper.py, 189] layer1.0.conv3.weight 2020-05-20 05:55:07,961 INFO [module_helper.py, 189] layer1.0.bn3.weight 2020-05-20 05:55:07,961 INFO [module_helper.py, 189] layer1.0.bn3.bias 2020-05-20 05:55:07,961 INFO [module_helper.py, 189] layer1.0.bn3.running_mean 2020-05-20 05:55:07,961 INFO [module_helper.py, 189] layer1.0.bn3.running_var 2020-05-20 05:55:07,961 INFO [module_helper.py, 189] layer1.0.downsample.1.weight 2020-05-20 05:55:07,961 INFO [module_helper.py, 189] layer1.0.downsample.2.weight 2020-05-20 05:55:07,961 INFO [module_helper.py, 189] layer1.0.downsample.2.bias 2020-05-20 05:55:07,961 INFO [module_helper.py, 189] layer1.0.downsample.2.running_mean 2020-05-20 05:55:07,961 INFO [module_helper.py, 189] layer1.0.downsample.2.running_var 2020-05-20 05:55:07,962 INFO [module_helper.py, 189] layer1.1.conv1.weight 2020-05-20 05:55:07,962 INFO [module_helper.py, 189] layer1.1.bn1.weight 2020-05-20 05:55:07,962 INFO [module_helper.py, 189] layer1.1.bn1.bias 2020-05-20 05:55:07,962 INFO [module_helper.py, 189] layer1.1.bn1.running_mean 2020-05-20 05:55:07,962 INFO [module_helper.py, 189] layer1.1.bn1.running_var 2020-05-20 05:55:07,962 INFO [module_helper.py, 189] layer1.1.conv2.conv.weight 2020-05-20 05:55:07,962 INFO [module_helper.py, 189] 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layer2.1.conv2.bn0.running_mean 2020-05-20 05:55:07,965 INFO [module_helper.py, 189] layer2.1.conv2.bn0.running_var 2020-05-20 05:55:07,965 INFO [module_helper.py, 189] layer2.1.conv2.fc1.weight 2020-05-20 05:55:07,965 INFO [module_helper.py, 189] layer2.1.conv2.fc1.bias 2020-05-20 05:55:07,965 INFO [module_helper.py, 189] layer2.1.conv2.bn1.weight 2020-05-20 05:55:07,965 INFO [module_helper.py, 189] layer2.1.conv2.bn1.bias 2020-05-20 05:55:07,965 INFO [module_helper.py, 189] layer2.1.conv2.bn1.running_mean 2020-05-20 05:55:07,965 INFO [module_helper.py, 189] layer2.1.conv2.bn1.running_var 2020-05-20 05:55:07,965 INFO [module_helper.py, 189] layer2.1.conv2.fc2.weight 2020-05-20 05:55:07,965 INFO [module_helper.py, 189] layer2.1.conv2.fc2.bias 2020-05-20 05:55:07,965 INFO [module_helper.py, 189] layer2.1.conv3.weight 2020-05-20 05:55:07,965 INFO [module_helper.py, 189] layer2.1.bn3.weight 2020-05-20 05:55:07,965 INFO [module_helper.py, 189] layer2.1.bn3.bias 2020-05-20 05:55:07,965 INFO 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05:55:07,966 INFO [module_helper.py, 189] layer2.2.conv2.fc1.bias 2020-05-20 05:55:07,966 INFO [module_helper.py, 189] layer2.2.conv2.bn1.weight 2020-05-20 05:55:07,966 INFO [module_helper.py, 189] layer2.2.conv2.bn1.bias 2020-05-20 05:55:07,966 INFO [module_helper.py, 189] layer2.2.conv2.bn1.running_mean 2020-05-20 05:55:07,966 INFO [module_helper.py, 189] layer2.2.conv2.bn1.running_var 2020-05-20 05:55:07,966 INFO [module_helper.py, 189] layer2.2.conv2.fc2.weight 2020-05-20 05:55:07,966 INFO [module_helper.py, 189] layer2.2.conv2.fc2.bias 2020-05-20 05:55:07,966 INFO [module_helper.py, 189] layer2.2.conv3.weight 2020-05-20 05:55:07,966 INFO [module_helper.py, 189] layer2.2.bn3.weight 2020-05-20 05:55:07,966 INFO [module_helper.py, 189] layer2.2.bn3.bias 2020-05-20 05:55:07,966 INFO [module_helper.py, 189] layer2.2.bn3.running_mean 2020-05-20 05:55:07,966 INFO [module_helper.py, 189] layer2.2.bn3.running_var 2020-05-20 05:55:07,966 INFO [module_helper.py, 189] layer2.3.conv1.weight 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[module_helper.py, 189] layer3.18.bn1.bias 2020-05-20 05:55:07,984 INFO [module_helper.py, 189] layer3.18.bn1.running_mean 2020-05-20 05:55:07,984 INFO [module_helper.py, 189] layer3.18.bn1.running_var 2020-05-20 05:55:07,984 INFO [module_helper.py, 189] layer3.18.conv2.conv.weight 2020-05-20 05:55:07,984 INFO [module_helper.py, 189] layer3.18.conv2.bn0.weight 2020-05-20 05:55:07,984 INFO [module_helper.py, 189] layer3.18.conv2.bn0.bias 2020-05-20 05:55:07,985 INFO [module_helper.py, 189] layer3.18.conv2.bn0.running_mean 2020-05-20 05:55:07,985 INFO [module_helper.py, 189] layer3.18.conv2.bn0.running_var 2020-05-20 05:55:07,985 INFO [module_helper.py, 189] layer3.18.conv2.fc1.weight 2020-05-20 05:55:07,985 INFO [module_helper.py, 189] layer3.18.conv2.fc1.bias 2020-05-20 05:55:07,985 INFO [module_helper.py, 189] layer3.18.conv2.bn1.weight 2020-05-20 05:55:07,985 INFO [module_helper.py, 189] layer3.18.conv2.bn1.bias 2020-05-20 05:55:07,985 INFO [module_helper.py, 189] 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05:55:07,990 INFO [module_helper.py, 189] layer4.1.conv2.fc1.weight 2020-05-20 05:55:07,990 INFO [module_helper.py, 189] layer4.1.conv2.fc1.bias 2020-05-20 05:55:07,990 INFO [module_helper.py, 189] layer4.1.conv2.bn1.weight 2020-05-20 05:55:07,990 INFO [module_helper.py, 189] layer4.1.conv2.bn1.bias 2020-05-20 05:55:07,990 INFO [module_helper.py, 189] layer4.1.conv2.bn1.running_mean 2020-05-20 05:55:07,990 INFO [module_helper.py, 189] layer4.1.conv2.bn1.running_var 2020-05-20 05:55:07,990 INFO [module_helper.py, 189] layer4.1.conv2.fc2.weight 2020-05-20 05:55:07,990 INFO [module_helper.py, 189] layer4.1.conv2.fc2.bias 2020-05-20 05:55:07,991 INFO [module_helper.py, 189] layer4.1.conv3.weight 2020-05-20 05:55:07,991 INFO [module_helper.py, 189] layer4.1.bn3.weight 2020-05-20 05:55:07,991 INFO [module_helper.py, 189] layer4.1.bn3.bias 2020-05-20 05:55:07,991 INFO [module_helper.py, 189] layer4.1.bn3.running_mean 2020-05-20 05:55:07,991 INFO [module_helper.py, 189] layer4.1.bn3.running_var 2020-05-20 05:55:07,991 INFO [module_helper.py, 189] layer4.2.conv1.weight 2020-05-20 05:55:07,991 INFO [module_helper.py, 189] layer4.2.bn1.weight 2020-05-20 05:55:07,991 INFO [module_helper.py, 189] layer4.2.bn1.bias 2020-05-20 05:55:07,991 INFO [module_helper.py, 189] layer4.2.bn1.running_mean 2020-05-20 05:55:07,991 INFO [module_helper.py, 189] layer4.2.bn1.running_var 2020-05-20 05:55:07,991 INFO [module_helper.py, 189] layer4.2.conv2.conv.weight 2020-05-20 05:55:07,991 INFO [module_helper.py, 189] layer4.2.conv2.bn0.weight 2020-05-20 05:55:07,991 INFO [module_helper.py, 189] layer4.2.conv2.bn0.bias 2020-05-20 05:55:07,991 INFO [module_helper.py, 189] layer4.2.conv2.bn0.running_mean 2020-05-20 05:55:07,991 INFO [module_helper.py, 189] layer4.2.conv2.bn0.running_var 2020-05-20 05:55:07,991 INFO [module_helper.py, 189] layer4.2.conv2.fc1.weight 2020-05-20 05:55:07,991 INFO [module_helper.py, 189] layer4.2.conv2.fc1.bias 2020-05-20 05:55:07,991 INFO [module_helper.py, 189] layer4.2.conv2.bn1.weight 2020-05-20 05:55:07,991 INFO [module_helper.py, 189] layer4.2.conv2.bn1.bias 2020-05-20 05:55:07,991 INFO [module_helper.py, 189] layer4.2.conv2.bn1.running_mean 2020-05-20 05:55:07,991 INFO [module_helper.py, 189] layer4.2.conv2.bn1.running_var 2020-05-20 05:55:07,991 INFO [module_helper.py, 189] layer4.2.conv2.fc2.weight 2020-05-20 05:55:07,991 INFO [module_helper.py, 189] layer4.2.conv2.fc2.bias 2020-05-20 05:55:07,991 INFO [module_helper.py, 189] layer4.2.conv3.weight 2020-05-20 05:55:07,991 INFO [module_helper.py, 189] layer4.2.bn3.weight 2020-05-20 05:55:07,991 INFO [module_helper.py, 189] layer4.2.bn3.bias 2020-05-20 05:55:07,991 INFO [module_helper.py, 189] layer4.2.bn3.running_mean 2020-05-20 05:55:07,991 INFO [module_helper.py, 189] layer4.2.bn3.running_var

`

zhanghang1989 commented 4 years ago

BTW, the model definition already support dilation.

If making some changes to the model definition, I usually use this function to check the model output:

def _AssertTensorClose(a, b, atol=1e-3, rtol=1e-3):
    npa, npb = a.cpu().detach().numpy(), b.cpu().detach().numpy()
    assert np.allclose(npa, npb, atol=atol), \
        'Tensor close check failed\n{}\n{}\nadiff={}, rdiff={}'.format(
            a, b, np.abs(npa - npb).max(), np.abs((npa - npb) / np.fmax(npa, 1e-5)).max())