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# Copyright 2023 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.
"""Losses used for detection models."""
import tensorflow as tf, tf_keras
class FocalLoss(tf_keras.losses.Loss):
"""Implements a Focal loss for classification problems.
Reference:
[Focal Loss for Dense Object Detection](https://arxiv.org/abs/1708.02002).
"""
def __init__(self,
alpha,
gamma,
reduction=tf_keras.losses.Reduction.AUTO,
name=None):
"""Initializes `FocalLoss`.
Args:
alpha: The `alpha` weight factor for binary class imbalance.
gamma: The `gamma` focusing parameter to re-weight loss.
reduction: (Optional) Type of `tf_keras.losses.Reduction` to apply to
loss. Default value is `AUTO`. `AUTO` indicates that the reduction
option will be determined by the usage context. For almost all cases
this defaults to `SUM_OVER_BATCH_SIZE`. When used with
`tf.distribute.Strategy`, outside of built-in training loops such as
`tf.keras` `compile` and `fit`, using `AUTO` or `SUM_OVER_BATCH_SIZE`
will raise an error. Please see this custom training [tutorial](
https://www.tensorflow.org/tutorials/distribute/custom_training) for
more details.
name: Optional name for the op. Defaults to 'retinanet_class_loss'.
"""
self._alpha = alpha
self._gamma = gamma
super(FocalLoss, self).__init__(reduction=reduction, name=name)
def call(self, y_true, y_pred):
"""Invokes the `FocalLoss`.
Args:
y_true: A tensor of size [batch, num_anchors, num_classes]
y_pred: A tensor of size [batch, num_anchors, num_classes]
Returns:
Summed loss float `Tensor`.
"""
with tf.name_scope('focal_loss'):
y_true = tf.cast(y_true, dtype=tf.float32)
y_pred = tf.cast(y_pred, dtype=tf.float32)
positive_label_mask = tf.equal(y_true, 1.0)
cross_entropy = (
tf.nn.sigmoid_cross_entropy_with_logits(labels=y_true, logits=y_pred))
probs = tf.sigmoid(y_pred)
probs_gt = tf.where(positive_label_mask, probs, 1.0 - probs)
# With small gamma, the implementation could produce NaN during back prop.
modulator = tf.pow(1.0 - probs_gt, self._gamma)
loss = modulator * cross_entropy
weighted_loss = tf.where(positive_label_mask, self._alpha * loss,
(1.0 - self._alpha) * loss)
return weighted_loss
def get_config(self):
config = {
'alpha': self._alpha,
'gamma': self._gamma,
}
base_config = super(FocalLoss, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
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