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pytorch-lrp's Issues

RuntimeError: shape '[1, 2048, 10, 10]' is invalid for input of size 2048

I'm trying to analyze FaceForensics detector XceptionNet with your LRP implementation, but I get stuck when trying to analyze the first Conv2d layer (the last conv in the network) in the backpass. I'm getting this error message when trying to reshape relevance_in in method conv_nd_inverse (file: inverter_util.py):

relevance_in = relevance_in.view(m.out_shape)
RuntimeError: shape '[1, 2048, 10, 10]' is invalid for input of size 2048

So relevance_in has size torch.Size([1, 2048]) and m.out_shape is [1, 2048, 10, 10]. Why am I getting this error?

NameError: name 'INSERT' is not defined

When I tried to run this code in colab notebook , it occured the problem as "NameError: name 'INSERT' is not defined" during the process of importing setting.py. I wonder the reason behind it because I couldn't find corresponding useful information on the Internet. Thanks.

Question about unet with upsample or ConvTranspose2d layer support

I want to use LRP to explain the semantic segmentation task using Unet model (Pytorch). I tested the LRP in captum but not support nn.Upsample and nn.ConvTranspose2d. I would like to know if the semantic segmentation model like Unet can be supported, and if not, how should it be implemented? Any help would be appreciated!

Question inverter_util.py

In compute_propagated_relevance function,

Do you only support max_pooling, not avg_pool or adaptive_avg_pooling??

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