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import numpy as np
from pytorch_grad_cam.base_cam import BaseCAM
# https://arxiv.org/abs/1710.11063
class GradCAMPlusPlus(BaseCAM):
def __init__(self, model, target_layers, use_cuda=False,
reshape_transform=None):
super(GradCAMPlusPlus, self).__init__(model, target_layers, use_cuda,
reshape_transform)
def get_cam_weights(self,
input_tensor,
target_layers,
target_category,
activations,
grads):
grads_power_2 = grads**2
grads_power_3 = grads_power_2 * grads
# Equation 19 in https://arxiv.org/abs/1710.11063
sum_activations = np.sum(activations, axis=(2, 3))
eps = 0.000001
aij = grads_power_2 / (2 * grads_power_2 +
sum_activations[:, :, None, None] * grads_power_3 + eps)
# Now bring back the ReLU from eq.7 in the paper,
# And zero out aijs where the activations are 0
aij = np.where(grads != 0, aij, 0)
weights = np.maximum(grads, 0) * aij
weights = np.sum(weights, axis=(2, 3))
return weights
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