"""DFM: Deep Feature Kernel Density Estimation."""

# 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
from typing import List, Union

import torch
from omegaconf import DictConfig, ListConfig
from pytorch_lightning.utilities.cli import MODEL_REGISTRY
from torch import Tensor

from anomalib.models.components import AnomalyModule

from .torch_model import DFMModel

logger = logging.getLogger(__name__)


@MODEL_REGISTRY
class Dfm(AnomalyModule):
    """DFM: Deep Featured Kernel Density Estimation.

    Args:
        backbone (str): Backbone CNN network
        layer (str): Layer to extract features from the backbone CNN
        pooling_kernel_size (int, optional): Kernel size to pool features extracted from the CNN.
            Defaults to 4.
        pca_level (float, optional): Ratio from which number of components for PCA are calculated.
            Defaults to 0.97.
        score_type (str, optional): Scoring type. Options are `fre` and `nll`. Defaults to "fre".
        nll: for Gaussian modeling, fre: pca feature reconstruction error
    """

    def __init__(
        self,
        backbone: str,
        layer: str,
        pooling_kernel_size: int = 4,
        pca_level: float = 0.97,
        score_type: str = "fre",
    ):
        super().__init__()

        self.model: DFMModel = DFMModel(
            backbone=backbone,
            layer=layer,
            pooling_kernel_size=pooling_kernel_size,
            n_comps=pca_level,
            score_type=score_type,
        )
        self.embeddings: List[Tensor] = []

    @staticmethod
    def configure_optimizers() -> None:  # pylint: disable=arguments-differ
        """DFM doesn't require optimization, therefore returns no optimizers."""
        return None

    def training_step(self, batch, _):  # pylint: disable=arguments-differ
        """Training Step of DFM.

        For each batch, features are extracted from the CNN.

        Args:
          batch (Dict[str, Tensor]): Input batch
          _: Index of the batch.

        Returns:
          Deep CNN features.
        """
        embedding = self.model.get_features(batch["image"]).squeeze()

        # NOTE: `self.embedding` appends each batch embedding to
        #   store the training set embedding. We manually append these
        #   values mainly due to the new order of hooks introduced after PL v1.4.0
        #   https://github.com/PyTorchLightning/pytorch-lightning/pull/7357
        self.embeddings.append(embedding)

    def on_validation_start(self) -> None:
        """Fit a PCA transformation and a Gaussian model to dataset."""
        # NOTE: Previous anomalib versions fit Gaussian at the end of the epoch.
        #   This is not possible anymore with PyTorch Lightning v1.4.0 since validation
        #   is run within train epoch.
        logger.info("Aggregating the embedding extracted from the training set.")
        embeddings = torch.vstack(self.embeddings)

        logger.info("Fitting a PCA and a Gaussian model to dataset.")
        self.model.fit(embeddings)

    def validation_step(self, batch, _):  # pylint: disable=arguments-differ
        """Validation Step of DFM.

        Similar to the training step, features are extracted from the CNN for each batch.

        Args:
          batch (List[Dict[str, Any]]): Input batch

        Returns:
          Dictionary containing FRE anomaly scores and ground-truth.
        """
        batch["pred_scores"] = self.model(batch["image"])

        return batch


class DfmLightning(Dfm):
    """DFM: Deep Featured Kernel Density Estimation.

    Args:
        hparams (Union[DictConfig, ListConfig]): Model params
    """

    def __init__(self, hparams: Union[DictConfig, ListConfig]) -> None:
        super().__init__(
            backbone=hparams.model.backbone,
            layer=hparams.model.layer,
            pooling_kernel_size=hparams.model.pooling_kernel_size,
            pca_level=hparams.model.pca_level,
            score_type=hparams.model.score_type,
        )
        self.hparams: Union[DictConfig, ListConfig]  # type: ignore
        self.save_hyperparameters(hparams)

"""DFM: Deep Feature Kernel Density Estimation."""

# 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
from typing import List, Union

import torch
from omegaconf import DictConfig, ListConfig
from pytorch_lightning.utilities.cli import MODEL_REGISTRY
from torch import Tensor

from anomalib.models.components import AnomalyModule

from .torch_model import DFMModel

logger = logging.getLogger(__name__)


@MODEL_REGISTRY
class Dfm(AnomalyModule):
    """DFM: Deep Featured Kernel Density Estimation.

    Args:
        backbone (str): Backbone CNN network
        layer (str): Layer to extract features from the backbone CNN
        pooling_kernel_size (int, optional): Kernel size to pool features extracted from the CNN.
            Defaults to 4.
        pca_level (float, optional): Ratio from which number of components for PCA are calculated.
            Defaults to 0.97.
        score_type (str, optional): Scoring type. Options are `fre` and `nll`. Defaults to "fre".
        nll: for Gaussian modeling, fre: pca feature reconstruction error
    """

    def __init__(
        self,
        backbone: str,
        layer: str,
        pooling_kernel_size: int = 4,
        pca_level: float = 0.97,
        score_type: str = "fre",
    ):
        super().__init__()

        self.model: DFMModel = DFMModel(
            backbone=backbone,
            layer=layer,
            pooling_kernel_size=pooling_kernel_size,
            n_comps=pca_level,
            score_type=score_type,
        )
        self.embeddings: List[Tensor] = []

    @staticmethod
    def configure_optimizers() -> None:  # pylint: disable=arguments-differ
        """DFM doesn't require optimization, therefore returns no optimizers."""
        return None

    def training_step(self, batch, _):  # pylint: disable=arguments-differ
        """Training Step of DFM.

        For each batch, features are extracted from the CNN.

        Args:
          batch (Dict[str, Tensor]): Input batch
          _: Index of the batch.

        Returns:
          Deep CNN features.
        """
        embedding = self.model.get_features(batch["image"]).squeeze()

        # NOTE: `self.embedding` appends each batch embedding to
        #   store the training set embedding. We manually append these
        #   values mainly due to the new order of hooks introduced after PL v1.4.0
        #   https://github.com/PyTorchLightning/pytorch-lightning/pull/7357
        self.embeddings.append(embedding)

    def on_validation_start(self) -> None:
        """Fit a PCA transformation and a Gaussian model to dataset."""
        # NOTE: Previous anomalib versions fit Gaussian at the end of the epoch.
        #   This is not possible anymore with PyTorch Lightning v1.4.0 since validation
        #   is run within train epoch.
        logger.info("Aggregating the embedding extracted from the training set.")
        embeddings = torch.vstack(self.embeddings)

        logger.info("Fitting a PCA and a Gaussian model to dataset.")
        self.model.fit(embeddings)

    def validation_step(self, batch, _):  # pylint: disable=arguments-differ
        """Validation Step of DFM.

        Similar to the training step, features are extracted from the CNN for each batch.

        Args:
          batch (List[Dict[str, Any]]): Input batch

        Returns:
          Dictionary containing FRE anomaly scores and ground-truth.
        """
        batch["pred_scores"] = self.model(batch["image"])

        return batch


class DfmLightning(Dfm):
    """DFM: Deep Featured Kernel Density Estimation.

    Args:
        hparams (Union[DictConfig, ListConfig]): Model params
    """

    def __init__(self, hparams: Union[DictConfig, ListConfig]) -> None:
        super().__init__(
            backbone=hparams.model.backbone,
            layer=hparams.model.layer,
            pooling_kernel_size=hparams.model.pooling_kernel_size,
            pca_level=hparams.model.pca_level,
            score_type=hparams.model.score_type,
        )
        self.hparams: Union[DictConfig, ListConfig]  # type: ignore
        self.save_hyperparameters(hparams)

DFM: Deep Feature Kernel Density Estimation. # 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. DFM: Deep Featured Kernel Density Estimation.      Args:         backbone (str): Backbone CNN network         layer (str): Layer to extract features from the backbone CNN         pooling_kernel_size (int, optional): Kernel size to pool features extracted from the CNN.             Defaults to 4.         pca_level (float, optional): Ratio from which number of components for PCA are calculated.             Defaults to 0.97.         score_type (str, optional): Scoring type. Options are `fre` and `nll`. Defaults to "fre".         nll: for Gaussian modeling, fre: pca feature reconstruction error # pylint: disable=arguments-differ DFM doesn't require optimization, therefore returns no optimizers. # pylint: disable=arguments-differ Training Step of DFM.          For each batch, features are extracted from the CNN.          Args:           batch (Dict[str, Tensor]): Input batch           _: Index of the batch.          Returns:           Deep CNN features. # NOTE: `self.embedding` appends each batch embedding to #   store the training set embedding. We manually append these #   values mainly due to the new order of hooks introduced after PL v1.4.0 #   https://github.com/PyTorchLightning/pytorch-lightning/pull/7357 Fit a PCA transformation and a Gaussian model to dataset. # NOTE: Previous anomalib versions fit Gaussian at the end of the epoch. #   This is not possible anymore with PyTorch Lightning v1.4.0 since validation #   is run within train epoch. # pylint: disable=arguments-differ Validation Step of DFM.          Similar to the training step, features are extracted from the CNN for each batch.          Args:           batch (List[Dict[str, Any]]): Input batch          Returns:           Dictionary containing FRE anomaly scores and ground-truth. DFM: Deep Featured Kernel Density Estimation.      Args:         hparams (Union[DictConfig, ListConfig]): Model params # type: ignore