When one of the input image's resolution (width or height) is not divisiable by 8 (or the downsampling rate in the AER-Net forward process), the error happens.

The error message

The input clear image (550 x 975)

Should I do some resize or crop work with my own dataset both when training and testing? What should I do to make images with random size as a legal input?

Thanks a lot.

Open source code seems to be problematic, e.g. : x1 = self.block(x_down3) x2 = self.block(x1) x3 = self.block(x2) x4 = self.block(x3) x5 = self.block(x4) x6 = self.block(x5) Does this mean that the FABlock of the network is sharing weights? So should it be 1 instead of 6 as stated in the paper, after all the comparison FFANet is not shared. We simply changed the organisation of the dataset: from h5 format to direct feeding of jpg images, and the use of DCNs from MMCV-Full instead of those provided by the authors, yielding results that are only 60% of the paper's results. If shared weights are used, then the number of participants is consistent with the paper but extremely poor, and if no weights are shared, then the number of participants is increased by a factor of 4. What is the problem, please? Looking forward to your answer 开源的代码似乎是有问题的，如: 这里的意思是网络的FABlock是共享权重的么？那么是否应该是1个，而不是论文中说的6个,毕竟对比的FFANet并不是共享的。 x1 = self.block(x_down3) x2 = self.block(x1) x3 = self.block(x2) x4 = self.block(x3) x5 = self.block(x4) x6 = self.block(x5) 我们仅仅是改变了数据集的组织方式：由h5格式改为直接喂入jpg图像，以及使用MMCV-Full的DCN代替作者提供的DCN，得到的结果只有论文结果的60%。 如果使用共享权重，那么参数量和论文一致但是效果极差，如果不共享权重，那么参数量增加4倍。 请问是什么问题呢？期待您的回答

Could you provide other links to pre-trained models apart from Baidu cloud? I am not able to download from Baidu cloud. Or could you share the models in GitHub directly?

In your CR.py file , you select postive and negative samples with the number of batch size. However, how to select one positive sample and multiple negative samples and compute CR loss as shown in your paper? Can you explain or update the code? Thanks for your reply.