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"""STFPM: Student-Teacher Feature Pyramid Matching for Unsupervised Anomaly Detection.
https://arxiv.org/abs/2103.04257
"""
# 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 torch
from pytorch_lightning.callbacks import EarlyStopping
from torch import optim
from anomalib.models.components import AnomalyModule
from anomalib.models.stfpm.torch_model import STFPMModel
logger = logging.getLogger(__name__)
__all__ = ["StfpmLightning"]
class StfpmLightning(AnomalyModule):
"""PL Lightning Module for the STFPM algorithm."""
def __init__(self, hparams):
super().__init__(hparams)
logger.info("Initializing Stfpm Lightning model.")
self.model = STFPMModel(
layers=hparams.model.layers,
input_size=hparams.model.input_size,
tile_size=hparams.dataset.tiling.tile_size,
tile_stride=hparams.dataset.tiling.stride,
backbone=hparams.model.backbone,
apply_tiling=hparams.dataset.tiling.apply,
)
self.loss_val = 0
def configure_callbacks(self):
"""Configure model-specific callbacks."""
early_stopping = EarlyStopping(
monitor=self.hparams.model.early_stopping.metric,
patience=self.hparams.model.early_stopping.patience,
mode=self.hparams.model.early_stopping.mode,
)
return [early_stopping]
def configure_optimizers(self) -> torch.optim.Optimizer:
"""Configure optimizers by creating an SGD optimizer.
Returns:
(Optimizer): SGD optimizer
"""
return optim.SGD(
params=self.model.student_model.parameters(),
lr=self.hparams.model.lr,
momentum=self.hparams.model.momentum,
weight_decay=self.hparams.model.weight_decay,
)
def training_step(self, batch, _): # pylint: disable=arguments-differ
"""Training Step of STFPM.
For each batch, teacher and student and teacher features are extracted from the CNN.
Args:
batch (Tensor): Input batch
_: Index of the batch.
Returns:
Hierarchical feature map
"""
self.model.teacher_model.eval()
teacher_features, student_features = self.model.forward(batch["image"])
loss = self.loss_val + self.model.loss(teacher_features, student_features)
self.loss_val = 0
return {"loss": loss}
def validation_step(self, batch, _): # pylint: disable=arguments-differ
"""Validation Step of STFPM.
Similar to the training step, student/teacher features are extracted from the CNN for each batch, and
anomaly map is computed.
Args:
batch (Tensor): Input batch
_: Index of the batch.
Returns:
Dictionary containing images, anomaly maps, true labels and masks.
These are required in `validation_epoch_end` for feature concatenation.
"""
batch["anomaly_maps"] = self.model(batch["image"])
return batch
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