question in lrp_layers.py

[S200331082]

hi @kaifishr
Thanks for your implementation. I'm trying to reimplement lrp on Resnet50, but it has a BatchNorm2D layer in the backbone, I'm a freshman in python and I don't know how to code the RelevancePropagationBatchNorm2D in lrp_layers.py. Can you just give me some ideas？ Thanks a lot.

[kaifishr]

As BatchNorm2D consists of two consecutive affine linear transformations I would try to weight the relevance scores by the weight parameters of the batch normalization layer learned during the training.

[zbb2022hust]

Hello, I also meet problems when calculating relevance via the BatchNorm1D layer. I'm not professional on math, but I'm in urgent to use this method to evaluate my FCN model in data-driven fault diagnosis task. Could you add the RelevancePropagationBatchNorm1d/2d in lrp_layers or explain more clear on how to calculate this?
Thanks!
Best regards.

[peacefulotter]

Bumping this as BatchNorm2D is required for ResNet models!