DeepExplainer fails for PyTorch Pretrained Alexnet Model

[kracekumar]

from torchvision import transforms
import torchvision.models as models

from torch.utils.data.dataset import Dataset
from torch.utils.data import DataLoader

alexnet = models.alexnet(pretrained=True).eval()
transforms = transforms.Compose([
            transforms.ToTensor(),
            #transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])
        ])
dataloader = DataLoader(ImageDataset('images', transforms=transforms), batch_size=16)
batch = next(iter(dataloader))
images, _ = batch
e = shap.DeepExplainer(alexnet, images[0:1])
shap_values = e.shap_values(images[1:])

The output of the e.shap_values call is below

Warning: unrecognized nn.Module: <class 'type'>
Warning: unrecognized nn.Module: <class 'type'>
Warning: unrecognized nn.Module: <class 'type'>
...

The architecture of alexnet

print(alexnet)
AlexNet(
  (features): Sequential(
    (0): Conv2d(3, 64, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2))
    (1): ReLU(inplace)
    (2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
    (3): Conv2d(64, 192, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
    (4): ReLU(inplace)
    (5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
    (6): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (7): ReLU(inplace)
    (8): Conv2d(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (9): ReLU(inplace)
    (10): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (11): ReLU(inplace)
    (12): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
  )
  (avgpool): AdaptiveAvgPool2d(output_size=(6, 6))
  (classifier): Sequential(
    (0): Dropout(p=0.5)
    (1): Linear(in_features=9216, out_features=4096, bias=True)
    (2): ReLU(inplace)
    (3): Dropout(p=0.5)
    (4): Linear(in_features=4096, out_features=4096, bias=True)
    (5): ReLU(inplace)
    (6): Linear(in_features=4096, out_features=1000, bias=True)
  )
)
# Custom dataset definition
from PIL import Image
class ImageDataset(Dataset):
    def __init__(self, path, transforms):
        self.path = Path(path)
        self.items = list(self.path.glob('*.jpg'))
        self.transforms = transforms

    def __getitem__(self, index):
        path = self.items[index]
        im = Image.open(path).convert('RGB')
        im = np.asarray(im.resize((227,227)))
        return self.transforms(im), path.name
    
    def __len__(self):
        return len(self.items)

transforms = transforms.Compose([
            transforms.ToTensor(),
            #transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])
        ])

Maybe AdaptiveAvgPool2d used in alexnet architecture is missing op_handlers. This happens for pre trained models like vgg16, resnet, inception_v3.

What could be the reason for the failure?

[gabrieltseng]

Hi!

As you noted, the warning you are getting is to let you know one of the modules is not explicitly supported by the deep explainer. The warning had a typo that was fixed, and should now let you know which module caused the error. When this happens, the default is to not change the gradient calculated by PyTorch.

The warning will be thrown by the adaptive average pooling, as you pointed out, but also for the features and classifier modules.

Note that the warning is intentional, and doesn't necessarily mean the deep explainer failed:

For the features and classifier, the warning isn't a problem since the deep explainer will correctly backpropagate though their child modules (and PyTorch will just take that correctly calculated gradient).

I'm not familiar with adaptive average pooling, but the correct way for the deep explainer to backpropagate through an average pooling layer also to leave the gradient unchanged, since it is linear. This means the default behaviour might lead to correct shap values, despite the warnings.

You are welcome to add support for adaptive average pooling! If not, I can do it so that it is explicitly supported.

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