# 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 normalization layers."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from absl.testing import parameterized
import numpy as np

from tensorflow.python import keras
from tensorflow.python.eager import backprop
from tensorflow.python.eager import context
from tensorflow.python.eager import def_function
from tensorflow.python.eager import wrap_function
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import ops
from tensorflow.python.framework import test_util as tf_test_util
from tensorflow.python.keras import keras_parameterized
from tensorflow.python.keras import testing_utils
from tensorflow.python.keras.layers import normalization
from tensorflow.python.keras.layers import normalization_v2
from tensorflow.python.keras.mixed_precision.experimental import policy
from tensorflow.python.keras.optimizer_v2 import rmsprop as rmsprop_v2
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import gradient_checker_v2
from tensorflow.python.ops import math_ops
from tensorflow.python.platform import test
from tensorflow.python.training import gradient_descent


class BatchNormalizationTest(keras_parameterized.TestCase):

  @keras_parameterized.run_all_keras_modes
  def test_basic_batchnorm(self):
    testing_utils.layer_test(
        keras.layers.BatchNormalization,
        kwargs={
            'momentum': 0.9,
            'epsilon': 0.1,
            'gamma_regularizer': keras.regularizers.l2(0.01),
            'beta_regularizer': keras.regularizers.l2(0.01)
        },
        input_shape=(3, 4, 2))
    testing_utils.layer_test(
        keras.layers.BatchNormalization,
        kwargs={
            'gamma_initializer': 'ones',
            'beta_initializer': 'ones',
            'moving_mean_initializer': 'zeros',
            'moving_variance_initializer': 'ones'
        },
        input_shape=(3, 4, 2))
    testing_utils.layer_test(
        keras.layers.BatchNormalization,
        kwargs={'scale': False,
                'center': False},
        input_shape=(3, 3))
    testing_utils.layer_test(
        keras.layers.BatchNormalization,
        kwargs={
            'momentum': 0.9,
            'epsilon': 0.1,
            'gamma_regularizer': keras.regularizers.l2(0.01),
            'beta_regularizer': keras.regularizers.l2(0.01),
            'dtype': 'float64'
        },
        input_shape=(3, 2, 2, 2), input_dtype='float64')

  @tf_test_util.run_in_graph_and_eager_modes
  def test_batchnorm_weights(self):
    layer = keras.layers.BatchNormalization(scale=False, center=False)
    layer.build((None, 3, 4))
    self.assertEqual(len(layer.trainable_weights), 0)
    self.assertEqual(len(layer.weights), 2)

    layer = keras.layers.BatchNormalization()
    layer.build((None, 3, 4))
    self.assertEqual(len(layer.trainable_weights), 2)
    self.assertEqual(len(layer.weights), 4)

  @tf_test_util.run_in_graph_and_eager_modes
  def test_batchnorm_regularization(self):
    layer = keras.layers.BatchNormalization(
        gamma_regularizer='l1', beta_regularizer='l1')
    layer.build((None, 3, 4))
    self.assertEqual(len(layer.losses), 2)
    max_norm = keras.constraints.max_norm
    layer = keras.layers.BatchNormalization(
        gamma_constraint=max_norm, beta_constraint=max_norm)
    layer.build((None, 3, 4))
    self.assertEqual(layer.gamma.constraint, max_norm)
    self.assertEqual(layer.beta.constraint, max_norm)

  @keras_parameterized.run_all_keras_modes
  def test_batchnorm_convnet(self):
    if test.is_gpu_available(cuda_only=True):
      with self.session(use_gpu=True):
        model = keras.models.Sequential()
        norm = keras.layers.BatchNormalization(
            axis=1, input_shape=(3, 4, 4), momentum=0.8)
        model.add(norm)
        model.compile(
            loss='mse',
            optimizer=gradient_descent.GradientDescentOptimizer(0.01),
            run_eagerly=testing_utils.should_run_eagerly())

        # centered on 5.0, variance 10.0
        x = np.random.normal(loc=5.0, scale=10.0, size=(1000, 3, 4, 4))
        model.fit(x, x, epochs=4, verbose=0)
        out = model.predict(x)
        out -= np.reshape(keras.backend.eval(norm.beta), (1, 3, 1, 1))
        out /= np.reshape(keras.backend.eval(norm.gamma), (1, 3, 1, 1))

        np.testing.assert_allclose(np.mean(out, axis=(0, 2, 3)), 0.0, atol=1e-1)
        np.testing.assert_allclose(np.std(out, axis=(0, 2, 3)), 1.0, atol=1e-1)

  @keras_parameterized.run_all_keras_modes
  def test_batchnorm_convnet_channel_last(self):
    model = keras.models.Sequential()
    norm = keras.layers.BatchNormalization(
        axis=-1, input_shape=(4, 4, 3), momentum=0.8)
    model.add(norm)
    model.compile(
        loss='mse',
        optimizer=gradient_descent.GradientDescentOptimizer(0.01),
        run_eagerly=testing_utils.should_run_eagerly())

    # centered on 5.0, variance 10.0
    x = np.random.normal(loc=5.0, scale=10.0, size=(1000, 4, 4, 3))
    model.fit(x, x, epochs=4, verbose=0)
    out = model.predict(x)
    out -= np.reshape(keras.backend.eval(norm.beta), (1, 1, 1, 3))