| import numpy as np | |
| from pytorch_grad_cam.base_cam import BaseCAM | |
| from pytorch_grad_cam.utils.svd_on_activations import get_2d_projection | |
| # https://ieeexplore.ieee.org/document/9462463 | |
| class LayerCAM(BaseCAM): | |
| def __init__( | |
| self, | |
| model, | |
| target_layers, | |
| use_cuda=False, | |
| reshape_transform=None): | |
| super( | |
| LayerCAM, | |
| self).__init__( | |
| model, | |
| target_layers, | |
| use_cuda, | |
| reshape_transform) | |
| def get_cam_image(self, | |
| input_tensor, | |
| target_layer, | |
| target_category, | |
| activations, | |
| grads, | |
| eigen_smooth): | |
| spatial_weighted_activations = np.maximum(grads, 0) * activations | |
| if eigen_smooth: | |
| cam = get_2d_projection(spatial_weighted_activations) | |
| else: | |
| cam = spatial_weighted_activations.sum(axis=1) | |
| return cam | |