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import torch
from pytorch_grad_cam import GradCAM
from torch import Tensor
from transformers import ViTForImageClassification
def grad_cam(images: Tensor, vit: ViTForImageClassification, use_cuda: bool = False) -> Tensor:
"""Performs the Grad-CAM method on a batch of images (https://arxiv.org/pdf/1610.02391.pdf)."""
# Wrap the ViT model to be compatible with GradCAM
vit = ViTWrapper(vit)
vit.eval()
# Create GradCAM object
cam = GradCAM(
model=vit,
target_layers=[vit.target_layer],
reshape_transform=_reshape_transform,
use_cuda=use_cuda,
)
# Compute GradCAM masks
grayscale_cam = cam(
input_tensor=images,
targets=None,
eigen_smooth=True,
aug_smooth=True,
)
return torch.from_numpy(grayscale_cam)
def _reshape_transform(tensor, height=14, width=14):
result = tensor[:, 1:, :].reshape(tensor.size(0), height, width, tensor.size(2))
# Bring the channels to the first dimension
result = result.transpose(2, 3).transpose(1, 2)
return result
class ViTWrapper(torch.nn.Module):
"""ViT Wrapper to use with Grad-CAM."""
def __init__(self, vit: ViTForImageClassification):
super().__init__()
self.vit = vit
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.vit(x).logits
@property
def target_layer(self):
return self.vit.vit.encoder.layer[-2].layernorm_after
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