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| """Principle Component Analysis (PCA) with PyTorch.""" | |
| # 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 Union | |
| import torch | |
| from torch import Tensor | |
| from anomalib.models.components.base import DynamicBufferModule | |
| class PCA(DynamicBufferModule): | |
| """Principle Component Analysis (PCA). | |
| Args: | |
| n_components (float): Number of components. Can be either integer number of components | |
| or a ratio between 0-1. | |
| """ | |
| def __init__(self, n_components: Union[float, int]): | |
| super().__init__() | |
| self.n_components = n_components | |
| self.register_buffer("singular_vectors", Tensor()) | |
| self.register_buffer("mean", Tensor()) | |
| self.register_buffer("num_components", Tensor()) | |
| self.singular_vectors: Tensor | |
| self.singular_values: Tensor | |
| self.mean: Tensor | |
| self.num_components: Tensor | |
| def fit(self, dataset: Tensor) -> None: | |
| """Fits the PCA model to the dataset. | |
| Args: | |
| dataset (Tensor): Input dataset to fit the model. | |
| """ | |
| mean = dataset.mean(dim=0) | |
| dataset -= mean | |
| _, sig, v_h = torch.linalg.svd(dataset.double()) | |
| num_components: int | |
| if self.n_components <= 1: | |
| variance_ratios = torch.cumsum(sig * sig, dim=0) / torch.sum(sig * sig) | |
| num_components = torch.nonzero(variance_ratios >= self.n_components)[0] | |
| else: | |
| num_components = int(self.n_components) | |
| self.num_components = Tensor([num_components]) | |
| self.singular_vectors = v_h.transpose(-2, -1)[:, :num_components].float() | |
| self.singular_values = sig[:num_components].float() | |
| self.mean = mean | |
| def fit_transform(self, dataset: Tensor) -> Tensor: | |
| """Fit and transform PCA to dataset. | |
| Args: | |
| dataset (Tensor): Dataset to which the PCA if fit and transformed | |
| Returns: | |
| Transformed dataset | |
| """ | |
| mean = dataset.mean(dim=0) | |
| dataset -= mean | |
| num_components = int(self.n_components) | |
| self.num_components = Tensor([num_components]) | |
| v_h = torch.linalg.svd(dataset)[-1] | |
| self.singular_vectors = v_h.transpose(-2, -1)[:, :num_components] | |
| self.mean = mean | |
| return torch.matmul(dataset, self.singular_vectors) | |
| def transform(self, features: Tensor) -> Tensor: | |
| """Transforms the features based on singular vectors calculated earlier. | |
| Args: | |
| features (Tensor): Input features | |
| Returns: | |
| Transformed features | |
| """ | |
| features -= self.mean | |
| return torch.matmul(features, self.singular_vectors) | |
| def inverse_transform(self, features: Tensor) -> Tensor: | |
| """Inverses the transformed features. | |
| Args: | |
| features (Tensor): Transformed features | |
| Returns: Inverse features | |
| """ | |
| inv_features = torch.matmul(features, self.singular_vectors.transpose(-2, -1)) | |
| return inv_features | |
| def forward(self, features: Tensor) -> Tensor: | |
| """Transforms the features. | |
| Args: | |
| features (Tensor): Input features | |
| Returns: | |
| Transformed features | |
| """ | |
| return self.transform(features) | |