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# Copyright 2024 The TensorFlow Authors. All Rights Reserved.
#
# 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.
"""Utils for yolo task."""
import tensorflow as tf, tf_keras
class ListMetrics:
"""Private class used to cleanly place the matric values for each level."""
def __init__(self, metric_names, name="ListMetrics"):
self.name = name
self._metric_names = metric_names
self._metrics = self.build_metric()
return
def build_metric(self):
metric_names = self._metric_names
metrics = []
for name in metric_names:
metrics.append(tf_keras.metrics.Mean(name, dtype=tf.float32))
return metrics
def update_state(self, loss_metrics):
metrics = self._metrics
for m in metrics:
m.update_state(loss_metrics[m.name])
return
def result(self):
logs = dict()
metrics = self._metrics
for m in metrics:
logs.update({m.name: m.result()})
return logs
def reset_states(self):
metrics = self._metrics
for m in metrics:
m.reset_states()
return