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import torch | |
import numpy as np | |
from typing import List, Callable | |
from pytorch_grad_cam.metrics.perturbation_confidence import PerturbationConfidenceMetric | |
def multiply_tensor_with_cam(input_tensor: torch.Tensor, | |
cam: torch.Tensor): | |
""" Multiply an input tensor (after normalization) | |
with a pixel attribution map | |
""" | |
return input_tensor * cam | |
class CamMultImageConfidenceChange(PerturbationConfidenceMetric): | |
def __init__(self): | |
super(CamMultImageConfidenceChange, | |
self).__init__(multiply_tensor_with_cam) | |
class DropInConfidence(CamMultImageConfidenceChange): | |
def __init__(self): | |
super(DropInConfidence, self).__init__() | |
def __call__(self, *args, **kwargs): | |
scores = super(DropInConfidence, self).__call__(*args, **kwargs) | |
scores = -scores | |
return np.maximum(scores, 0) | |
class IncreaseInConfidence(CamMultImageConfidenceChange): | |
def __init__(self): | |
super(IncreaseInConfidence, self).__init__() | |
def __call__(self, *args, **kwargs): | |
scores = super(IncreaseInConfidence, self).__call__(*args, **kwargs) | |
return np.float32(scores > 0) | |