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"""DFKDE: 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
from omegaconf.dictconfig import DictConfig
from omegaconf.listconfig import ListConfig
from torch import Tensor
from anomalib.models.components import AnomalyModule
from .torch_model import DfkdeModel
logger = logging.getLogger(__name__)
class DfkdeLightning(AnomalyModule):
"""DFKDE: Deep Feature Kernel Density Estimation.
Args:
hparams (Union[DictConfig, ListConfig]): Model params
"""
def __init__(self, hparams: Union[DictConfig, ListConfig]):
super().__init__(hparams)
logger.info("Initializing DFKDE Lightning model.")
threshold_steepness = 0.05
threshold_offset = 12
self.model = DfkdeModel(
backbone=hparams.model.backbone,
filter_count=hparams.model.max_training_points,
threshold_steepness=threshold_steepness,
threshold_offset=threshold_offset,
)
self.embeddings: List[Tensor] = []
@staticmethod
def configure_optimizers(): # pylint: disable=arguments-differ
"""DFKDE doesn't require optimization, therefore returns no optimizers."""
return None
def training_step(self, batch, _batch_idx): # pylint: disable=arguments-differ
"""Training Step of DFKDE. For each batch, features are extracted from the CNN.
Args:
batch (Dict[str, Any]): Batch containing image filename, image, label and mask
_batch_idx: 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 KDE Model to the embedding collected from the training set."""
# 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("Fitting a KDE model to the embedding collected from the training set.")
self.model.fit(self.embeddings)
def validation_step(self, batch, _): # pylint: disable=arguments-differ
"""Validation Step of DFKDE.
Similar to the training step, features are extracted from the CNN for each batch.
Args:
batch: Input batch
Returns:
Dictionary containing probability, prediction and ground truth values.
"""
batch["pred_scores"] = self.model(batch["image"])
return batch
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