Spaces:
Build error
Build error
File size: 3,767 Bytes
c8c12e9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 |
"""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)
|