det_model_fn_test.py

# Lint as: python3
# Copyright 2020 Google Research. 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.
# ==============================================================================
"""Tests for det_model_fn."""
import tensorflow as tf
import det_model_fn


def legacy_focal_loss(logits, targets, alpha, gamma, normalizer, _=0):
  """A legacy focal loss that does not support label smoothing."""
  with tf.name_scope('focal_loss'):
    positive_label_mask = tf.equal(targets, 1.0)
    cross_entropy = (
        tf.nn.sigmoid_cross_entropy_with_logits(labels=targets, logits=logits))

    neg_logits = -1.0 * logits
    modulator = tf.exp(gamma * targets * neg_logits -
                       gamma * tf.math.log1p(tf.exp(neg_logits)))
    loss = modulator * cross_entropy
    weighted_loss = tf.where(positive_label_mask, alpha * loss,
                             (1.0 - alpha) * loss)
    weighted_loss /= normalizer
  return weighted_loss


class FocalLossTest(tf.test.TestCase):

  def test_focal_loss(self):
    tf.random.set_seed(1111)
    y_pred = tf.random.uniform([4, 32, 32, 90])
    y_true = tf.ones([4, 32, 32, 90])
    alpha, gamma, n = 0.25, 1.5, 100
    legacy_output = legacy_focal_loss(y_pred, y_true, alpha, gamma, n)
    new_output = det_model_fn.focal_loss(y_pred, y_true, alpha, gamma, n)
    self.assertAllClose(legacy_output, new_output)

  def test_focal_loss_with_label_smoothing(self):
    tf.random.set_seed(1111)
    shape = [2, 2, 2, 2]
    y_pred = tf.random.uniform(shape)

    # A binary classification target [0.0, 1.0] becomes [.1, .9]
    #  with smoothing .2
    y_true = tf.ones(shape) * [0.0, 1.0]
    y_true_presmoothed = tf.ones(shape) * [0.1, 0.9]

    alpha, gamma, n = 1, 0, 100
    presmoothed = det_model_fn.focal_loss(y_pred, y_true_presmoothed, alpha,
                                          gamma, n, 0)
    alpha, gamma, n = 0.9, 0, 100
    unsmoothed = det_model_fn.focal_loss(y_pred, y_true, alpha, gamma, n, 0.2)

    self.assertAllClose(presmoothed, unsmoothed)


if __name__ == '__main__':
  tf.test.main()