models/research/domain_adaptation/domain_separation/losses_test.py

# Copyright 2016 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.
# ==============================================================================
"""Tests for DSN losses."""
from functools import partial

import numpy as np
import tensorflow as tf

import losses
import utils


def MaximumMeanDiscrepancySlow(x, y, sigmas):
  num_samples = x.get_shape().as_list()[0]

  def AverageGaussianKernel(x, y, sigmas):
    result = 0
    for sigma in sigmas:
      dist = tf.reduce_sum(tf.square(x - y))
      result += tf.exp((-1.0 / (2.0 * sigma)) * dist)
    return result / num_samples**2

  total = 0

  for i in range(num_samples):
    for j in range(num_samples):
      total += AverageGaussianKernel(x[i, :], x[j, :], sigmas)
      total += AverageGaussianKernel(y[i, :], y[j, :], sigmas)
      total += -2 * AverageGaussianKernel(x[i, :], y[j, :], sigmas)

  return total


class LogQuaternionLossTest(tf.test.TestCase):

  def test_log_quaternion_loss_batch(self):
    with self.test_session():
      predictions = tf.random_uniform((10, 4), seed=1)
      predictions = tf.nn.l2_normalize(predictions, 1)
      labels = tf.random_uniform((10, 4), seed=1)
      labels = tf.nn.l2_normalize(labels, 1)
      params = {'batch_size': 10, 'use_logging': False}
      x = losses.log_quaternion_loss_batch(predictions, labels, params)
      self.assertTrue(((10,) == tf.shape(x).eval()).all())


class MaximumMeanDiscrepancyTest(tf.test.TestCase):

  def test_mmd_name(self):
    with self.test_session():
      x = tf.random_uniform((2, 3), seed=1)
      kernel = partial(utils.gaussian_kernel_matrix, sigmas=tf.constant([1.]))
      loss = losses.maximum_mean_discrepancy(x, x, kernel)

      self.assertEquals(loss.op.name, 'MaximumMeanDiscrepancy/value')

  def test_mmd_is_zero_when_inputs_are_same(self):
    with self.test_session():
      x = tf.random_uniform((2, 3), seed=1)
      kernel = partial(utils.gaussian_kernel_matrix, sigmas=tf.constant([1.]))
      self.assertEquals(0, losses.maximum_mean_discrepancy(x, x, kernel).eval())

  def test_fast_mmd_is_similar_to_slow_mmd(self):
    with self.test_session():
      x = tf.constant(np.random.normal(size=(2, 3)), tf.float32)
      y = tf.constant(np.random.rand(2, 3), tf.float32)

      cost_old = MaximumMeanDiscrepancySlow(x, y, [1.]).eval()
      kernel = partial(utils.gaussian_kernel_matrix, sigmas=tf.constant([1.]))
      cost_new = losses.maximum_mean_discrepancy(x, y, kernel).eval()

      self.assertAlmostEqual(cost_old, cost_new, delta=1e-5)

  def test_multiple_sigmas(self):
    with self.test_session():
      x = tf.constant(np.random.normal(size=(2, 3)), tf.float32)
      y = tf.constant(np.random.rand(2, 3), tf.float32)

      sigmas = tf.constant([2., 5., 10, 20, 30])
      kernel = partial(utils.gaussian_kernel_matrix, sigmas=sigmas)
      cost_old = MaximumMeanDiscrepancySlow(x, y, [2., 5., 10, 20, 30]).eval()
      cost_new = losses.maximum_mean_discrepancy(x, y, kernel=kernel).eval()

      self.assertAlmostEqual(cost_old, cost_new, delta=1e-5)

  def test_mmd_is_zero_when_distributions_are_same(self):

    with self.test_session():
      x = tf.random_uniform((1000, 10), seed=1)
      y = tf.random_uniform((1000, 10), seed=3)

      kernel = partial(utils.gaussian_kernel_matrix, sigmas=tf.constant([100.]))
      loss = losses.maximum_mean_discrepancy(x, y, kernel=kernel).eval()

      self.assertAlmostEqual(0, loss, delta=1e-4)

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