Some remaining questions with fine-pruning-celeba.py

[HazardFY]

Thanks for your answers. For the first question before, I find that I didn't download the latest version of the code. But I still have some questions about https://github.com/VinAIResearch/Warping-based_Backdoor_Attack-release/tree/main/defenses/fine_pruning/fine-pruning-celeba.py

1. https://github.com/VinAIResearch/Warping-based_Backdoor_Attack-release/blob/main/defenses/fine_pruning/fine-pruning-celeba.py#L83 opt.input_width is not assigned.
2. When I apply the fine_pruning method in ResNet50, I find that I have to redefine the last bn layer with code nn.BatchNorm2d(pruning_mask.shape[0] - num_pruned) and then load the bn's params data in the way of https://github.com/VinAIResearch/Warping-based_Backdoor_Attack-release/blob/main/defenses/fine_pruning/fine-pruning-celeba.py#L150](url). Otherwise, the output of redefined last conv layer doesn't match the dimension of input of the last bn layer. Finally before using net_pruned in the eval function, I used net_pruned.eval() to fix params of the redefined last bn layer.(The Resnet50 which I used is torchvision.models.resnet50(), so the dimension of the concrete layer may be different, but I think redefining bn layer perhaps is also needed in your code)