gradcam.py

import torch
import numpy as np

class GradCAM:
    def __init__(self, model, target_layer):
        self.model = model
        self.gradients = None
        self.activations = None

        target_layer.register_forward_hook(self._fwd)
        target_layer.register_backward_hook(self._bwd)

    def _fwd(self, m, i, o):
        self.activations = o

    def _bwd(self, m, gi, go):
        self.gradients = go[0]

    def generate(self, img_tensor, class_idx):
        out = self.model(img_tensor.unsqueeze(0))
        score = out["class"][:, class_idx].sum()
        self.model.zero_grad()
        score.backward()

        w = self.gradients.mean(dim=(2,3), keepdim=True)
        cam = (w * self.activations).sum(dim=1)
        cam = torch.relu(cam)
        cam = cam / (cam.max() + 1e-8)

        return cam.detach().cpu().numpy()[0]