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"""Kernel Density Estimation Classifier."""
# Copyright (C) 2022-2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
import logging
import random
from enum import Enum
import torch
from torch import nn
from anomalib.models.components import PCA, GaussianKDE
logger = logging.getLogger(__name__)
class FeatureScalingMethod(str, Enum):
"""Determines how the feature embeddings are scaled."""
NORM = "norm" # scale to unit vector length
SCALE = "scale" # scale to max length observed in training (preserve relative magnitude)
class KDEClassifier(nn.Module):
"""Classification module for KDE-based anomaly detection.
Args:
n_pca_components (int, optional): Number of PCA components. Defaults to 16.
feature_scaling_method (FeatureScalingMethod, optional): Scaling method applied to features before passing to
KDE. Options are `norm` (normalize to unit vector length) and `scale` (scale to max length observed in
training).
max_training_points (int, optional): Maximum number of training points to fit the KDE model. Defaults to 40000.
"""
def __init__(
self,
n_pca_components: int = 16,
feature_scaling_method: FeatureScalingMethod = FeatureScalingMethod.SCALE,
max_training_points: int = 40000,
) -> None:
super().__init__()
self.n_pca_components = n_pca_components
self.feature_scaling_method = feature_scaling_method
self.max_training_points = max_training_points
self.pca_model = PCA(n_components=self.n_pca_components)
self.kde_model = GaussianKDE()
self.register_buffer("max_length", torch.empty([]))
self.max_length = torch.empty([])
def pre_process(
self,
feature_stack: torch.Tensor,
max_length: torch.Tensor | None = None,
) -> tuple[torch.Tensor, torch.Tensor]:
"""Pre-process the CNN features.
Args:
feature_stack (torch.Tensor): Features extracted from CNN
max_length (Tensor | None): 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.feature_scaling_method == FeatureScalingMethod.NORM:
feature_stack /= torch.linalg.norm(feature_stack, ord=2, dim=1)[:, None]
elif self.feature_scaling_method == FeatureScalingMethod.SCALE:
feature_stack /= max_length
else:
msg = "Unknown pre-processing mode. Available modes are: Normalized and Scale."
raise RuntimeError(msg)
return feature_stack, max_length
def fit(self, embeddings: torch.Tensor) -> bool:
"""Fit a kde model to embeddings.
Args:
embeddings (torch.Tensor): Input embeddings to fit the model.
Returns:
Boolean confirming whether the training is successful.
"""
if embeddings.shape[0] < self.n_pca_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 embeddings.shape[0] > self.max_training_points:
selected_idx = torch.tensor(random.sample(range(embeddings.shape[0]), self.max_training_points))
selected_features = embeddings[selected_idx]
else:
selected_features = embeddings
feature_stack = self.pca_model.fit_transform(selected_features)
feature_stack, max_length = self.pre_process(feature_stack)
self.max_length = max_length
self.kde_model.fit(feature_stack)
return True
def compute_kde_scores(self, features: torch.Tensor, as_log_likelihood: bool | None = False) -> torch.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 (torch.Tensor): Features to which the PCA model is fit.
as_log_likelihood (bool | None, optional): If true, gets log likelihood scores. Defaults to False.
Returns:
(torch.Tensor): Score
"""
features = self.pca_model.transform(features)
features, _ = self.pre_process(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
@staticmethod
def compute_probabilities(scores: torch.Tensor) -> torch.Tensor:
"""Convert density scores to anomaly probabilities (see https://www.desmos.com/calculator/ifju7eesg7).
Args:
scores (torch.Tensor): density of an image.
Returns:
probability that image with {density} is anomalous
"""
return 1 / (1 + torch.exp(0.05 * (scores - 12)))
def predict(self, features: torch.Tensor) -> torch.Tensor:
"""Predicts the probability that the features belong to the anomalous class.
Args:
features (torch.Tensor): Feature from which the output probabilities are detected.
Returns:
Detection probabilities
"""
scores = self.compute_kde_scores(features, as_log_likelihood=True)
return self.compute_probabilities(scores)
def forward(self, features: torch.Tensor) -> torch.Tensor:
"""Make predictions on extracted features."""
return self.predict(features)
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