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| """Anomaly Map Generator for the STFPM 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 Dict, Tuple, Union | |
| 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], | |
| ): | |
| self.distance = torch.nn.PairwiseDistance(p=2, keepdim=True) | |
| self.image_size = image_size if isinstance(image_size, tuple) else tuple(image_size) | |
| def compute_layer_map(self, teacher_features: Tensor, student_features: Tensor) -> Tensor: | |
| """Compute the layer map based on cosine similarity. | |
| Args: | |
| teacher_features (Tensor): Teacher features | |
| student_features (Tensor): Student features | |
| Returns: | |
| Anomaly score based on cosine similarity. | |
| """ | |
| norm_teacher_features = F.normalize(teacher_features) | |
| norm_student_features = F.normalize(student_features) | |
| layer_map = 0.5 * torch.norm(norm_teacher_features - norm_student_features, p=2, dim=-3, keepdim=True) ** 2 | |
| layer_map = F.interpolate(layer_map, size=self.image_size, align_corners=False, mode="bilinear") | |
| return layer_map | |
| def compute_anomaly_map( | |
| self, teacher_features: Dict[str, Tensor], student_features: Dict[str, Tensor] | |
| ) -> torch.Tensor: | |
| """Compute the overall anomaly map via element-wise production the interpolated anomaly maps. | |
| Args: | |
| teacher_features (Dict[str, Tensor]): Teacher features | |
| student_features (Dict[str, Tensor]): Student features | |
| Returns: | |
| Final anomaly map | |
| """ | |
| batch_size = list(teacher_features.values())[0].shape[0] | |
| anomaly_map = torch.ones(batch_size, 1, self.image_size[0], self.image_size[1]) | |
| for layer in teacher_features.keys(): | |
| layer_map = self.compute_layer_map(teacher_features[layer], student_features[layer]) | |
| anomaly_map = anomaly_map.to(layer_map.device) | |
| anomaly_map *= layer_map | |
| return anomaly_map | |
| def __call__(self, **kwds: Dict[str, Tensor]) -> torch.Tensor: | |
| """Returns anomaly map. | |
| Expects `teach_features` and `student_features` keywords to be passed explicitly. | |
| Example: | |
| >>> anomaly_map_generator = AnomalyMapGenerator(image_size=tuple(hparams.model.input_size)) | |
| >>> output = self.anomaly_map_generator( | |
| teacher_features=teacher_features, | |
| student_features=student_features | |
| ) | |
| Raises: | |
| ValueError: `teach_features` and `student_features` keys are not found | |
| Returns: | |
| torch.Tensor: anomaly map | |
| """ | |
| if not ("teacher_features" in kwds and "student_features" in kwds): | |
| raise ValueError(f"Expected keys `teacher_features` and `student_features. Found {kwds.keys()}") | |
| teacher_features: Dict[str, Tensor] = kwds["teacher_features"] | |
| student_features: Dict[str, Tensor] = kwds["student_features"] | |
| return self.compute_anomaly_map(teacher_features, student_features) | |