Spaces:
Build error
Build error
File size: 7,293 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 |
"""Normality model of DFKDE."""
# 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.
import logging
import random
from typing import List, Optional, Tuple
import torch
import torchvision
from torch import Tensor, nn
from anomalib.models.components import PCA, FeatureExtractor, GaussianKDE
logger = logging.getLogger(__name__)
class DfkdeModel(nn.Module):
"""Normality Model for the DFKDE algorithm.
Args:
backbone (str): Pre-trained model backbone.
n_comps (int, optional): Number of PCA components. Defaults to 16.
pre_processing (str, optional): Preprocess features before passing to KDE.
Options are between `norm` and `scale`. Defaults to "scale".
filter_count (int, optional): Number of training points to fit the KDE model. Defaults to 40000.
threshold_steepness (float, optional): Controls how quickly the value saturates around zero. Defaults to 0.05.
threshold_offset (float, optional): Offset of the density function from 0. Defaults to 12.0.
"""
def __init__(
self,
backbone: str,
n_comps: int = 16,
pre_processing: str = "scale",
filter_count: int = 40000,
threshold_steepness: float = 0.05,
threshold_offset: float = 12.0,
):
super().__init__()
self.n_components = n_comps
self.pre_processing = pre_processing
self.filter_count = filter_count
self.threshold_steepness = threshold_steepness
self.threshold_offset = threshold_offset
_backbone = getattr(torchvision.models, backbone)
self.feature_extractor = FeatureExtractor(backbone=_backbone(pretrained=True), layers=["avgpool"]).eval()
self.pca_model = PCA(n_components=self.n_components)
self.kde_model = GaussianKDE()
self.register_buffer("max_length", Tensor(torch.Size([])))
self.max_length = Tensor(torch.Size([]))
def get_features(self, batch: Tensor) -> Tensor:
"""Extract features from the pretrained network.
Args:
batch (Tensor): Image batch.
Returns:
Tensor: Tensor containing extracted features.
"""
self.feature_extractor.eval()
layer_outputs = self.feature_extractor(batch)
layer_outputs = torch.cat(list(layer_outputs.values())).detach()
return layer_outputs
def fit(self, embeddings: List[Tensor]) -> bool:
"""Fit a kde model to embeddings.
Args:
embeddings (Tensor): Input embeddings to fit the model.
Returns:
Boolean confirming whether the training is successful.
"""
_embeddings = torch.vstack(embeddings)
if _embeddings.shape[0] < self.n_components:
logger.info("Not enough features to commit. Not making a model.")
return False
# if max training points is non-zero and smaller than number of staged features, select random subset
if self.filter_count and _embeddings.shape[0] > self.filter_count:
# pylint: disable=not-callable
selected_idx = torch.tensor(random.sample(range(_embeddings.shape[0]), self.filter_count))
selected_features = _embeddings[selected_idx]
else:
selected_features = _embeddings
feature_stack = self.pca_model.fit_transform(selected_features)
feature_stack, max_length = self.preprocess(feature_stack)
self.max_length = max_length
self.kde_model.fit(feature_stack)
return True
def preprocess(self, feature_stack: Tensor, max_length: Optional[Tensor] = None) -> Tuple[Tensor, Tensor]:
"""Pre-process the CNN features.
Args:
feature_stack (Tensor): Features extracted from CNN
max_length (Optional[Tensor]): Used to unit normalize the feature_stack vector. If ``max_len`` is not
provided, the length is calculated from the ``feature_stack``. Defaults to None.
Returns:
(Tuple): Stacked features and length
"""
if max_length is None:
max_length = torch.max(torch.linalg.norm(feature_stack, ord=2, dim=1))
if self.pre_processing == "norm":
feature_stack /= torch.linalg.norm(feature_stack, ord=2, dim=1)[:, None]
elif self.pre_processing == "scale":
feature_stack /= max_length
else:
raise RuntimeError("Unknown pre-processing mode. Available modes are: Normalized and Scale.")
return feature_stack, max_length
def evaluate(self, features: Tensor, as_log_likelihood: Optional[bool] = False) -> Tensor:
"""Compute the KDE scores.
The scores calculated from the KDE model are converted to densities. If `as_log_likelihood` is set to true then
the log of the scores are calculated.
Args:
features (Tensor): Features to which the PCA model is fit.
as_log_likelihood (Optional[bool], optional): If true, gets log likelihood scores. Defaults to False.
Returns:
(Tensor): Score
"""
features = self.pca_model.transform(features)
features, _ = self.preprocess(features, self.max_length)
# Scores are always assumed to be passed as a density
kde_scores = self.kde_model(features)
# add small constant to avoid zero division in log computation
kde_scores += 1e-300
if as_log_likelihood:
kde_scores = torch.log(kde_scores)
return kde_scores
def predict(self, features: Tensor) -> Tensor:
"""Predicts the probability that the features belong to the anomalous class.
Args:
features (Tensor): Feature from which the output probabilities are detected.
Returns:
Detection probabilities
"""
densities = self.evaluate(features, as_log_likelihood=True)
probabilities = self.to_probability(densities)
return probabilities
def to_probability(self, densities: Tensor) -> Tensor:
"""Converts density scores to anomaly probabilities (see https://www.desmos.com/calculator/ifju7eesg7).
Args:
densities (Tensor): density of an image.
Returns:
probability that image with {density} is anomalous
"""
return 1 / (1 + torch.exp(self.threshold_steepness * (densities - self.threshold_offset)))
def forward(self, batch: Tensor) -> Tensor:
"""Prediction by normality model.
Args:
batch (Tensor): Input images.
Returns:
Tensor: Predictions
"""
feature_vector = self.get_features(batch)
return self.predict(feature_vector.view(feature_vector.shape[:2]))
|