code: # coding=utf-8
  # Copyright 2020 The Trax Authors.
  #
  # 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.
  
  # Lint as: python3
  """Tests for normalization layers."""
  
  from absl.testing import absltest
  import numpy as onp
  
  from trax.layers import base
  from trax.layers import normalization
  from trax.math import numpy as np
  from trax.shapes import ShapeDtype
  
  
  class NormalizationLayerTest(absltest.TestCase):
  
    def test_batch_norm_shape(self):
      input_signature = ShapeDtype((29, 5, 7, 20))
      result_shape = base.check_shape_agreement(normalization.BatchNorm(),
                                                input_signature)
      self.assertEqual(result_shape, input_signature.shape)
  
    def test_batch_norm(self):
      input_shape = (2, 3, 4)
      input_dtype = np.float32
      input_signature = ShapeDtype(input_shape, input_dtype)
      eps = 1e-5
      inp1 = np.reshape(np.arange(np.prod(input_shape), dtype=input_dtype),
                        input_shape)
      m1 = 11.5  # Mean of this random input.
      v1 = 47.9167  # Variance of this random input.
      layer = normalization.BatchNorm(axis=(0, 1, 2))
      _, _ = layer.init(input_signature)
      state = layer.state
      onp.testing.assert_allclose(state[0], 0)
      onp.testing.assert_allclose(state[1], 1)
      self.assertEqual(state[2], 0)
      out = layer(inp1)
      state = layer.state
      onp.testing.assert_allclose(state[0], m1 * 0.001)
      onp.testing.assert_allclose(state[1], 0.999 + v1 * 0.001, rtol=1e-6)
      self.assertEqual(state[2], 1)
      onp.testing.assert_allclose(out, (inp1 - m1) / np.sqrt(v1 + eps),
                                  rtol=1e-6)
  
    def test_layer_norm_shape(self):
      input_signature = ShapeDtype((29, 5, 7, 20))
      result_shape = base.check_shape_agreement(
          normalization.LayerNorm(), input_signature)
      self.assertEqual(result_shape, input_signature.shape)
  
    def test_frn_shape(self):
      B, H, W, C = 64, 5, 7, 3  # pylint: disable=invalid-name
      input_signature = ShapeDtype((B, H, W, C))
      result_shape = base.check_shape_agreement(
          normalization.FilterResponseNorm(), input_signature)
      self.assertEqual(result_shape, input_signature.shape)
  
      result_shape = base.check_shape_agreement(
          normalization.FilterResponseNorm(learn_epsilon=False),
          input_signature)
      self.assertEqual(result_shape, input_signature.shape)
  
  
  if __name__ == '__main__':
    absltest.main()
  
apis: numpy.testing.assert_allclose