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"""Anomalib Metric Collection."""
# 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 torchmetrics import MetricCollection
class AnomalibMetricCollection(MetricCollection):
"""Extends the MetricCollection class for use in the Anomalib pipeline."""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self._update_called = False
self._threshold = 0.5
def set_threshold(self, threshold_value):
"""Update the threshold value for all metrics that have the threshold attribute."""
self._threshold = threshold_value
for metric in self.values():
if hasattr(metric, "threshold"):
metric.threshold = threshold_value
def update(self, *args, **kwargs) -> None:
"""Add data to the metrics."""
super().update(*args, **kwargs)
self._update_called = True
@property
def update_called(self) -> bool:
"""Returns a boolean indicating if the update method has been called at least once."""
return self._update_called
@property
def threshold(self) -> float:
"""Return the value of the anomaly threshold."""
return self._threshold
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