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| """Anomaly Map Generator for CFlow model implementation.""" | |
| # Copyright (C) 2020 Intel Corporation | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, | |
| # software distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions | |
| # and limitations under the License. | |
| from typing import List, Tuple, Union, cast | |
| import torch | |
| import torch.nn.functional as F | |
| from omegaconf import ListConfig | |
| from torch import Tensor | |
| class AnomalyMapGenerator: | |
| """Generate Anomaly Heatmap.""" | |
| def __init__( | |
| self, | |
| image_size: Union[ListConfig, Tuple], | |
| pool_layers: List[str], | |
| ): | |
| self.distance = torch.nn.PairwiseDistance(p=2, keepdim=True) | |
| self.image_size = image_size if isinstance(image_size, tuple) else tuple(image_size) | |
| self.pool_layers: List[str] = pool_layers | |
| def compute_anomaly_map( | |
| self, distribution: Union[List[Tensor], List[List]], height: List[int], width: List[int] | |
| ) -> Tensor: | |
| """Compute the layer map based on likelihood estimation. | |
| Args: | |
| distribution: Probability distribution for each decoder block | |
| height: blocks height | |
| width: blocks width | |
| Returns: | |
| Final Anomaly Map | |
| """ | |
| test_map: List[Tensor] = [] | |
| for layer_idx in range(len(self.pool_layers)): | |
| test_norm = torch.tensor(distribution[layer_idx], dtype=torch.double) # pylint: disable=not-callable | |
| test_norm -= torch.max(test_norm) # normalize likelihoods to (-Inf:0] by subtracting a constant | |
| test_prob = torch.exp(test_norm) # convert to probs in range [0:1] | |
| test_mask = test_prob.reshape(-1, height[layer_idx], width[layer_idx]) | |
| # upsample | |
| test_map.append( | |
| F.interpolate( | |
| test_mask.unsqueeze(1), size=self.image_size, mode="bilinear", align_corners=True | |
| ).squeeze() | |
| ) | |
| # score aggregation | |
| score_map = torch.zeros_like(test_map[0]) | |
| for layer_idx in range(len(self.pool_layers)): | |
| score_map += test_map[layer_idx] | |
| score_mask = score_map | |
| # invert probs to anomaly scores | |
| anomaly_map = score_mask.max() - score_mask | |
| return anomaly_map | |
| def __call__(self, **kwargs: Union[List[Tensor], List[int], List[List]]) -> Tensor: | |
| """Returns anomaly_map. | |
| Expects `distribution`, `height` and 'width' keywords to be passed explicitly | |
| Example | |
| >>> anomaly_map_generator = AnomalyMapGenerator(image_size=tuple(hparams.model.input_size), | |
| >>> pool_layers=pool_layers) | |
| >>> output = self.anomaly_map_generator(distribution=dist, height=height, width=width) | |
| Raises: | |
| ValueError: `distribution`, `height` and 'width' keys are not found | |
| Returns: | |
| torch.Tensor: anomaly map | |
| """ | |
| if not ("distribution" in kwargs and "height" in kwargs and "width" in kwargs): | |
| raise KeyError(f"Expected keys `distribution`, `height` and `width`. Found {kwargs.keys()}") | |
| # placate mypy | |
| distribution: List[Tensor] = cast(List[Tensor], kwargs["distribution"]) | |
| height: List[int] = cast(List[int], kwargs["height"]) | |
| width: List[int] = cast(List[int], kwargs["width"]) | |
| return self.compute_anomaly_map(distribution, height, width) | |