# Copyright 2015 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 initializers."""

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

import numpy as np

from tensorflow.contrib import layers
from tensorflow.contrib.layers.python.layers import initializers
from tensorflow.contrib.layers.python.layers import regularizers
from tensorflow.python.client import session
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.ops import variable_scope
from tensorflow.python.ops import variables
from tensorflow.python.platform import test


class InitializerTest(test.TestCase):

  def test_xavier_wrong_dtype(self):
    with self.assertRaisesRegexp(
        TypeError, 'Cannot create initializer for non-floating point type.'):
      initializers.xavier_initializer(dtype=dtypes.int32)

    self.assertIsNone(regularizers.l1_regularizer(0.)(None))

  def _test_xavier(self, initializer, shape, variance, uniform):
    with session.Session() as sess:
      var = variable_scope.get_variable(
          name='test',
          shape=shape,
          dtype=dtypes.float32,
          initializer=initializer(
              uniform=uniform, seed=1))
      sess.run(variables.global_variables_initializer())
      values = var.eval()
      self.assertAllClose(np.var(values), variance, 1e-3, 1e-3)

  def test_xavier_uniform(self):
    self._test_xavier(initializers.xavier_initializer, [100, 40],
                      2. / (100. + 40.), True)

  def test_xavier_normal(self):
    self._test_xavier(initializers.xavier_initializer, [100, 40],
                      2. / (100. + 40.), False)

  def test_xavier_scalar(self):
    self._test_xavier(initializers.xavier_initializer, [], 0.0, True)

  def test_xavier_conv2d_uniform(self):
    self._test_xavier(layers.xavier_initializer_conv2d, [100, 40, 5, 7],
                      2. / (100. * 40 * (5 + 7)), True)

  def test_xavier_conv2d_normal(self):
    self._test_xavier(layers.xavier_initializer_conv2d, [100, 40, 5, 7],
                      2. / (100. * 40 * (5 + 7)), False)


class VarianceScalingInitializerTest(test.TestCase):

  def test_wrong_dtype(self):
    with self.assertRaisesRegexp(
        TypeError, 'Cannot create initializer for non-floating point type.'):
      initializers.variance_scaling_initializer(dtype=dtypes.int32)
    initializer = initializers.variance_scaling_initializer()
    with self.assertRaisesRegexp(
        TypeError, 'Cannot create initializer for non-floating point type.'):
      initializer([], dtype=dtypes.int32)

  def _test_variance(self, initializer, shape, variance, factor, mode, uniform):
    with ops.Graph().as_default() as g:
      with self.session(graph=g) as sess:
        var = variable_scope.get_variable(
            name='test',
            shape=shape,
            dtype=dtypes.float32,
            initializer=initializer(
                factor=factor, mode=mode, uniform=uniform, seed=1))
        sess.run(variables.global_variables_initializer())
        values = var.eval()
        self.assertAllClose(np.var(values), variance, 1e-3, 1e-3)

  def test_fan_in(self):
    for uniform in [False, True]:
      self._test_variance(
          initializers.variance_scaling_initializer,
          shape=[100, 40],
          variance=2. / 100.,
          factor=2.0,
          mode='FAN_IN',
          uniform=uniform)

  def test_fan_out(self):
    for uniform in [False, True]:
      self._test_variance(
          initializers.variance_scaling_initializer,
          shape=[100, 40],
          variance=2. / 40.,
          factor=2.0,
          mode='FAN_OUT',
          uniform=uniform)

  def test_fan_avg(self):
    for uniform in [False, True]:
      self._test_variance(
          initializers.variance_scaling_initializer,
          shape=[100, 40],
          variance=4. / (100. + 40.),
          factor=2.0,
          mode='FAN_AVG',
          uniform=uniform)

  def test_conv2d_fan_in(self):
    for uniform in [False, True]:
      self._test_variance(
          initializers.variance_scaling_initializer,
          shape=[100, 40, 5, 7],
          variance=2. / (100. * 40. * 5.),
          factor=2.0,
          mode='FAN_IN',
          uniform=uniform)

  def test_conv2d_fan_out(self):
    for uniform in [False, True]:
      self._test_variance(
          initializers.variance_scaling_initializer,
          shape=[100, 40, 5, 7],
          variance=2. / (100. * 40. * 7.),
          factor=2.0,
          mode='FAN_OUT',
          uniform=uniform)

  def test_conv2d_fan_avg(self):
    for uniform in [False, True]:
      self._test_variance(
          initializers.variance_scaling_initializer,
          shape=[100, 40, 5, 7],
          variance=2. / (100. * 40. * (5. + 7.)),
          factor=2.0,
          mode='FAN_AVG',
          uniform=uniform)

  def test_xavier_uniform(self):
    self._test_variance(
        initializers.variance_scaling_initializer,
        shape=[100, 40],
        variance=2. / (100. + 40.),
        factor=1.0,
        mode='FAN_AVG',
        uniform=True)

  def test_xavier_normal(self):
    self._test_variance(
        initializers.variance_scaling_initializer,
        shape=[100, 40],
        variance=2. / (100. + 40.),
        factor=1.0,
        mode='FAN_AVG',
        uniform=False)

  def test_xavier_scalar(self):
    self._test_variance(
        initializers.variance_scaling_initializer,
        shape=[],
        variance=0.0,
        factor=1.0,
        mode='FAN_AVG',
        uniform=False)

  def test_xavier_conv2d_uniform(self):
    self._test_variance(
        initializers.variance_scaling_initializer,
        shape=[100, 40, 5, 7],
        variance=2. / (100. * 40. * (5. + 7.)),
        factor=1.0,
        mode='FAN_AVG',
        uniform=True)

  def test_xavier_conv2d_normal(self):
    self._test_variance(
        initializers.variance_scaling_initializer,
        shape=[100, 40, 5, 7],
        variance=2. / (100. * 40. * (5. + 7.)),
        factor=1.0,
        mode='FAN_AVG',
        uniform=True)

  def test_1d_shape_fan_in(self):
    for uniform in [False, True]:
      self._test_variance(
          initializers.variance_scaling_initializer,
          shape=[100],
          variance=2. / 100.,
          factor=2.0,
          mode='FAN_IN',
          uniform=uniform)

  def test_1d_shape_fan_out(self):
    for uniform in [False, True]:
      self._test_variance(
          initializers.variance_scaling_initializer,
          shape=[100],
          variance=2. / 100.,
          factor=2.0,
          mode='FAN_OUT',
          uniform=uniform)

  def test_1d_shape_fan_avg(self):
    for uniform in [False, True]:
      self._test_variance(
          initializers.variance_scaling_initializer,
          shape=[100],
          variance=4. / (100. + 100.),
          factor=2.0,
          mode='FAN_AVG',
          uniform=uniform)


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