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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) | |