LRP throws RuntimeError 'hook 'backward_hook_activation' has changed the size of value'

🐛 Bug

I am trying to use LRP on a GoogleNet with a modified fc layer. Using the original GoogleNet from the Pytorch models library fails too.

To Reproduce

Steps to reproduce the behavior:

1. Load GoogleNet Model from Pytorch Model Library and put it in eval mode
2. Initialize LRP as described in the API reference
3. Use real or dummy tensor to perform LRP on
4. Run attribute method with said tensor and an arbitrary target

model_ft = models.googlenet(pretrained=True, transform_input=False) num_ftrs = model_ft.fc.in_features model_ft.fc = nn.Linear(num_ftrs, 20) model_ft.to(device).eval()

lrp = LRP(model_ft)

image_tensor = torch.rand(1,3,224,224)

attributions = lrp.attribute(image_tensor.to(device), target = 1)

RuntimeError: hook ‘backward_hook_activation’ has changed the size of value

Expected behavior

No error should be thrown and attributions should be calculated

Environment

Describe the environment used for Captum

 - Pytorch: 1.9.0+cu102
 - Captum: 0.4.0
 - torchvision: 0.10.0+cu102
 - OS (e.g., Linux): Google Collab
 - How you installed Captum / PyTorch (`conda`, `pip`, source): Google Collab
 - Build command you used (if compiling from source):
 - Python version: 3.7.12
 - CUDA/cuDNN version: 11.1.105
 - GPU models and configuration:
 - Any other relevant information:

[NarineK]

@filiprejmus, can it be that some of the linear activations are being reused in googlenet ? If they are being reused then the hooks don’t work properly. Perhaps you can change it the way that the linear activations or the activation blocks containing them aren’t reused. PyTorch hooks don’t tell us in which order are the hooks executed. If they are reused we can’t exactly tell where are they called from in the execution graph.

[martynanna]

Hello @filiprejmus , were you able to solve the problem? I’m getting the same error with a custom altered VGG16 net