Backward hook with shap.DeepExplainer on simple models with PyTorch

1

Hi,

I’m trying to implement a Deep Explainer for a Resnet50 imported from Torchvision and executed on cifar100. The basic implementation is not working because of an in-place modification not supported by Pytorch (but probably in Keras/TF). Do you know if there is a way to fix this? The error I saw was a warning in the past, but now it stops my code.

Thanks in advance.

Minimal Reproducible Example:

import torch
from torchvision import models, datasets, transforms
import shap

model = models.resnet50(pretrained=True)

dataset = datasets.CIFAR100(root="./",
                            download=True,
                            train=True,
                            transform=transforms.ToTensor())
    
dataloader  = torch.utils.data.DataLoader(dataset, batch_size=8, shuffle=True, num_workers=1)
    

for images, labels in dataloader: break

e = shap.DeepExplainer(model, images )
shap_values = e.shap_values( images )

Traceback

Traceback (most recent call last):
  File "xai.py", line 339, in <module>
    shap_values = e.shap_values(test_images)
  File "/usr/local/lib/python3.8/dist-packages/shap/explainers/_deep/__init__.py", line 125, in shap_values
    return self.explainer.shap_values(X, ranked_outputs, output_rank_order, check_additivity=check_additivity)
  File "/usr/local/lib/python3.8/dist-packages/shap/explainers/_deep/deep_pytorch.py", line 191, in shap_values
    sample_phis = self.gradient(feature_ind, joint_x)
  File "/usr/local/lib/python3.8/dist-packages/shap/explainers/_deep/deep_pytorch.py", line 107, in gradient
    outputs = self.model(*X)
  File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1110, in _call_impl
    return forward_call(*input, **kwargs)
  File "/work/project/models/resnet.py", line 168, in forward
    x = self.relu(x)    # 32x32
  File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1128, in _call_impl
    result = forward_call(*input, **kwargs)
  File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/activation.py", line 98, in forward
    return F.relu(input, inplace=self.inplace)
  File "/usr/local/lib/python3.8/dist-packages/torch/nn/functional.py", line 1440, in relu
    result = torch.relu_(input)
RuntimeError: Output 0 of BackwardHookFunctionBackward is a view and is being modified inplace. This view was created inside a custom Function (or because an input was returned as-is) and the autograd logic to handle view+inplace would override the custom backward associated with the custom Function, leading to incorrect gradients. This behavior is forbidden. You can fix this by cloning the output of the custom Function.
2

Hi,
I am also getting the same error for a pre-trained GoogleNet. I tried to change the inplace true values to false for the ReLU functions using a class wrapper but this gives a weird shape mismatch error :frowning: