110 lines
3.4 KiB
Python
110 lines
3.4 KiB
Python
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import torch
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import numpy as np
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from typing import List, Callable
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import numpy as np
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import cv2
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class PerturbationConfidenceMetric:
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def __init__(self, perturbation):
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self.perturbation = perturbation
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def __call__(self, input_tensor: torch.Tensor,
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cams: np.ndarray,
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targets: List[Callable],
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model: torch.nn.Module,
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return_visualization=False,
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return_diff=True):
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if return_diff:
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with torch.no_grad():
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outputs = model(input_tensor)
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scores = [target(output).cpu().numpy()
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for target, output in zip(targets, outputs)]
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scores = np.float32(scores)
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batch_size = input_tensor.size(0)
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perturbated_tensors = []
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for i in range(batch_size):
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cam = cams[i]
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tensor = self.perturbation(input_tensor[i, ...].cpu(),
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torch.from_numpy(cam))
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tensor = tensor.to(input_tensor.device)
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perturbated_tensors.append(tensor.unsqueeze(0))
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perturbated_tensors = torch.cat(perturbated_tensors)
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with torch.no_grad():
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outputs_after_imputation = model(perturbated_tensors)
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scores_after_imputation = [
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target(output).cpu().numpy() for target, output in zip(
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targets, outputs_after_imputation)]
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scores_after_imputation = np.float32(scores_after_imputation)
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if return_diff:
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result = scores_after_imputation - scores
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else:
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result = scores_after_imputation
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if return_visualization:
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return result, perturbated_tensors
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else:
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return result
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class RemoveMostRelevantFirst:
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def __init__(self, percentile, imputer):
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self.percentile = percentile
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self.imputer = imputer
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def __call__(self, input_tensor, mask):
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imputer = self.imputer
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if self.percentile != 'auto':
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threshold = np.percentile(mask.cpu().numpy(), self.percentile)
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binary_mask = np.float32(mask < threshold)
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else:
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_, binary_mask = cv2.threshold(
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np.uint8(mask * 255), 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
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binary_mask = torch.from_numpy(binary_mask)
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binary_mask = binary_mask.to(mask.device)
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return imputer(input_tensor, binary_mask)
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class RemoveLeastRelevantFirst(RemoveMostRelevantFirst):
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def __init__(self, percentile, imputer):
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super(RemoveLeastRelevantFirst, self).__init__(percentile, imputer)
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def __call__(self, input_tensor, mask):
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return super(RemoveLeastRelevantFirst, self).__call__(
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input_tensor, 1 - mask)
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class AveragerAcrossThresholds:
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def __init__(
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self,
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imputer,
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percentiles=[
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10,
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20,
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30,
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40,
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50,
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60,
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70,
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80,
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90]):
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self.imputer = imputer
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self.percentiles = percentiles
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def __call__(self,
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input_tensor: torch.Tensor,
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cams: np.ndarray,
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targets: List[Callable],
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model: torch.nn.Module):
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scores = []
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for percentile in self.percentiles:
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imputer = self.imputer(percentile)
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scores.append(imputer(input_tensor, cams, targets, model))
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return np.mean(np.float32(scores), axis=0)
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