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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
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