# coding=utf-8
# 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 tf.contrib.layers."""

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

import math

import numpy as np

import tensorflow.compat.v1 as tf

import tensorflow.compat.v1.losses as losses
from tf_slim import layers as layers_lib
from tf_slim.layers import layers as _layers
from tf_slim.layers import regularizers
from tf_slim.ops import arg_scope as arg_scope_lib
from tf_slim.ops import variables
# pylint: disable=g-direct-tensorflow-import
from tensorflow.python.client import session
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import errors_impl
from tensorflow.python.framework import ops
from tensorflow.python.framework import random_seed
from tensorflow.python.framework import sparse_tensor
from tensorflow.python.framework import tensor_shape
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import gradient_checker
from tensorflow.python.ops import gradients_impl
from tensorflow.python.ops import init_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import nn_ops
from tensorflow.python.ops import partitioned_variables
from tensorflow.python.ops import random_ops
from tensorflow.python.ops import sparse_ops
from tensorflow.python.ops import state_ops
from tensorflow.python.ops import template
from tensorflow.python.ops import variable_scope
from tensorflow.python.ops import variables as variables_lib
from tensorflow.python.platform import test


def setUpModule():
  tf.disable_eager_execution()


arg_scope = arg_scope_lib.arg_scope


class AvgPool2DTest(test.TestCase):

  def testInvalidDataFormat(self):
    height, width = 3, 6
    images = np.random.uniform(size=(5, height, width, 3))
    with self.assertRaisesRegexp(ValueError,
                                 'data_format has to be either NCHW or NHWC.'):
      _layers.avg_pool2d(images, [3, 3], data_format='CHWN')

  def testCreateAvgPool(self):
    height, width = 3, 6
    images = np.random.uniform(size=(5, height, width, 3))
    output = _layers.avg_pool2d(images, [3, 3])
    self.assertEqual(output.op.name, 'AvgPool2D/AvgPool')
    self.assertListEqual(output.get_shape().as_list(), [5, 1, 2, 3])

  def testCreateAvgPoolNCHW(self):
    height, width = 3, 6
    images = np.random.uniform(size=(5, 2, height, width))
    output = _layers.avg_pool2d(images, [3, 3], data_format='NCHW')
    self.assertListEqual(output.get_shape().as_list(), [5, 2, 1, 2])

  def testCollectOutputs(self):
    height, width = 3, 6
    images = random_ops.random_uniform((5, height, width, 3), seed=1)
    output = _layers.avg_pool2d(images, [3, 3], outputs_collections='outputs')
    output_collected = ops.get_collection('outputs')[0]
    self.assertEqual(output_collected.aliases, ['AvgPool2D'])
    self.assertEqual(output_collected, output)

  def testCreateSquareAvgPool(self):
    height, width = 3, 6
    images = random_ops.random_uniform((5, height, width, 3), seed=1)
    output = _layers.avg_pool2d(images, 3)
    self.assertEqual(output.op.name, 'AvgPool2D/AvgPool')
    self.assertListEqual(output.get_shape().as_list(), [5, 1, 2, 3])

  def testCreateAvgPoolWithScope(self):
    height, width = 3, 6
    images = random_ops.random_uniform((5, height, width, 3), seed=1)
    output = _layers.avg_pool2d(images, [3, 3], scope='pool1')
    self.assertEqual(output.op.name, 'pool1/AvgPool')

  def testCreateAvgPoolWithSamePadding(self):
    height, width = 3, 6
    images = random_ops.random_uniform((5, height, width, 3), seed=1)
    output = _layers.avg_pool2d(images, [3, 3], padding='SAME')
    self.assertListEqual(output.get_shape().as_list(), [5, 2, 3, 3])

  def testCreateAvgPoolWithSamePaddingNCHW(self):
    height, width = 3, 6
    images = random_ops.random_uniform((5, 3, height, width), seed=1)
    output = _layers.avg_pool2d(
        images, [3, 3], padding='SAME', data_format='NCHW')
    self.assertListEqual(output.get_shape().as_list(), [5, 3, 2, 3])

  def testCreateAvgPoolStrideWithSamePadding(self):
    height, width = 3, 6
    images = random_ops.random_uniform((5, height, width, 3), seed=1)
    output = _layers.avg_pool2d(images, [3, 3], stride=1, padding='SAME')
    self.assertListEqual(output.get_shape().as_list(), [5, height, width, 3])

  def testGlobalAvgPool(self):
    height, width = 3, 6
    images = random_ops.random_uniform((5, height, width, 3), seed=1)
    output = _layers.avg_pool2d(images, images.get_shape()[1:3], stride=1)
    self.assertListEqual(output.get_shape().as_list(), [5, 1, 1, 3])


class AvgPool3DTest(test.TestCase):

  def testInvalidDataFormat(self):
    depth, height, width = 3, 6, 9
    images = np.random.uniform(size=(5, depth, height, width, 3))
    with self.assertRaisesRegexp(
        ValueError, 'data_format has to be either NCDHW or NDHWC.'):
      _layers.avg_pool3d(images, [3, 3, 3], data_format='CDHWN')

  def testCreateAvgPool(self):
    depth, height, width = 3, 6, 9
    images = np.random.uniform(size=(5, depth, height, width, 3))
    output = _layers.avg_pool3d(images, [3, 3, 3])
    self.assertEqual(output.op.name, 'AvgPool3D/AvgPool3D')
    self.assertListEqual(output.get_shape().as_list(), [5, 1, 2, 4, 3])

  def testCreateAvgPoolNCDHW(self):
    depth, height, width = 3, 6, 9
    images = np.random.uniform(size=(5, 2, depth, height, width))
    output = _layers.avg_pool3d(images, [3, 3, 3], data_format='NCDHW')
    self.assertEqual(output.op.name, 'AvgPool3D/transpose_1')
    self.assertListEqual(output.get_shape().as_list(), [5, 2, 1, 2, 4])

  def testCollectOutputs(self):
    depth, height, width = 3, 6, 9
    images = random_ops.random_uniform((5, depth, height, width, 3), seed=1)
    output = _layers.avg_pool3d(
        images, [3, 3, 3], outputs_collections='outputs')
    output_collected = ops.get_collection('outputs')[0]
    self.assertEqual(output_collected.aliases, ['AvgPool3D'])
    self.assertEqual(output_collected, output)

  def testCreateSquareAvgPool(self):
    depth, height, width = 3, 6, 9
    images = random_ops.random_uniform((5, depth, height, width, 3), seed=1)
    output = _layers.avg_pool3d(images, 3)
    self.assertEqual(output.op.name, 'AvgPool3D/AvgPool3D')
    self.assertListEqual(output.get_shape().as_list(), [5, 1, 2, 4, 3])

  def testCreateAvgPoolWithScope(self):
    depth, height, width = 3, 6, 9
    images = random_ops.random_uniform((5, depth, height, width, 3), seed=1)
    output = _layers.avg_pool3d(images, [3, 3, 3], scope='pool1')
    self.assertEqual(output.op.name, 'pool1/AvgPool3D')

  def testCreateAvgPoolWithSamePadding(self):
    depth, height, width = 3, 6, 9
    images = random_ops.random_uniform((5, depth, height, width, 3), seed=1)
    output = _layers.avg_pool3d(images, [3, 3, 3], padding='SAME')
    self.assertListEqual(output.get_shape().as_list(), [5, 2, 3, 5, 3])

  def testCreateAvgPoolWithSamePaddingNCDHW(self):
    depth, height, width = 3, 6, 9
    images = random_ops.random_uniform((5, 3, depth, height, width), seed=1)
    output = _layers.avg_pool3d(
        images, [3, 3, 3], padding='SAME', data_format='NCDHW')
    self.assertListEqual(output.get_shape().as_list(), [5, 3, 2, 3, 5])

  def testCreateAvgPoolStrideWithSamePadding(self):
    depth, height, width = 3, 6, 9
    images = random_ops.random_uniform((5, depth, height, width, 3), seed=1)
    output = _layers.avg_pool3d(images, [3, 3, 3], stride=1, padding='SAME')
    self.assertListEqual(output.get_shape().as_list(),
                         [5, depth, height, width, 3])

  def testGlobalAvgPool(self):
    depth, height, width = 3, 6, 9
    images = random_ops.random_uniform((5, depth, height, width, 3), seed=1)
    output = _layers.avg_pool3d(images, images.get_shape()[1:4], stride=1)
    self.assertListEqual(output.get_shape().as_list(), [5, 1, 1, 1, 3])


class PoolTest(test.TestCase):

  def testCreatePool(self):
    height, width = 3, 3
    images = np.random.uniform(size=(5, height, width, 3))
    output = _layers.pool(images, [3, 3], pooling_type='AVG')
    self.assertEqual(output.op.name, 'avg_pool')
    self.assertListEqual(output.get_shape().as_list(), [5, 1, 1, 3])

  def testCreatePoolNCHW(self):
    height, width = 3, 3
    images = np.random.uniform(size=(5, 3, height, width))
    output = _layers.pool(
        images, [3, 3], pooling_type='AVG', data_format='NCHW')
    self.assertEqual(output.op.name, 'avg_pool')
    self.assertListEqual(output.get_shape().as_list(), [5, 3, 1, 1])

  def testCollectOutputs(self):
    height, width = 3, 3
    images = random_ops.random_uniform((5, height, width, 3), seed=1)
    output = _layers.pool(
        images, [3, 3], pooling_type='AVG', outputs_collections='outputs')
    output_collected = ops.get_collection('outputs')[0]
    self.assertEqual(output_collected.aliases, ['avg_pool'])
    self.assertEqual(output_collected, output)

  def testCreateSquareAvgPool(self):
    height, width = 3, 3
    images = random_ops.random_uniform((5, height, width, 3), seed=1)
    output = _layers.pool(images, 3, pooling_type='AVG')
    self.assertEqual(output.op.name, 'avg_pool')
    self.assertEqual(output.get_shape().as_list(), [5, 1, 1, 3])

  def testCreateMaxPoolWithScope(self):
    height, width = 3, 3
    images = random_ops.random_uniform((5, height, width, 3), seed=1)
    output = _layers.pool(images, [3, 3], pooling_type='MAX', scope='pool1')
    self.assertEqual(output.op.name, 'pool1')

  def testCreateMaxPoolWithSamePadding(self):
    height, width = 3, 3
    images = random_ops.random_uniform((5, height, width, 3), seed=1)
    output = _layers.pool(images, [3, 3], pooling_type='MAX', padding='SAME')
    self.assertEqual(output.get_shape().as_list(), [5, 3, 3, 3])

  def testCreateAvgPoolStrideWithSamePadding(self):
    height, width = 3, 3
    images = random_ops.random_uniform((5, height, width, 3), seed=1)
    output = _layers.pool(
        images, [3, 3], stride=1, padding='SAME', pooling_type='AVG')
    self.assertEqual(output.get_shape().as_list(), [5, height, width, 3])

  def testGlobalAvgPool(self):
    height, width = 3, 3
    images = random_ops.random_uniform((5, height, width, 3), seed=1)
    output = _layers.pool(
        images, images.get_shape()[1:3], stride=1, pooling_type='AVG')
    self.assertEqual(output.get_shape().as_list(), [5, 1, 1, 3])

  def testAvgPoolWithStride(self):
    height, width = 5, 8
    images = random_ops.random_uniform((5, height, width, 3), seed=1)
    output = _layers.pool(images, [2, 3], stride=[1, 2], pooling_type='AVG')
    self.assertEqual(output.get_shape().as_list(), [5, 4, 3, 3])

  def testAvgPoolWithDilation(self):
    height, width = 5, 8
    images = random_ops.random_uniform((5, height, width, 3), seed=1)
    output = _layers.pool(
        images, [2, 3], dilation_rate=[1, 2], pooling_type='AVG')
    self.assertEqual(output.get_shape().as_list(), [5, 4, 4, 3])

  def testAvgPoolWithDilationNCHW(self):
    height, width = 5, 8
    images = random_ops.random_uniform((5, 3, height, width), seed=1)
    output = _layers.pool(
        images, [2, 3],
        dilation_rate=[1, 2],
        pooling_type='AVG',
        data_format='NCHW')
    self.assertEqual(output.get_shape().as_list(), [5, 3, 4, 4])


class BiasAddTest(test.TestCase):

  def testCreate(self):
    height, width = 3, 3
    with self.cached_session():
      images = np.random.uniform(size=(5, height, width, 3))
      output = _layers.bias_add(images)
      self.assertEqual(output.op.name, 'BiasAdd/BiasAdd')
      self.assertListEqual(output.get_shape().as_list(), [5, height, width, 3])

  def testCreateWithActivation(self):
    height, width = 3, 3
    with self.cached_session():
      images = random_ops.random_uniform((5, height, width, 3), seed=1)
      output = _layers.bias_add(images, activation_fn=nn_ops.relu)
      self.assertEqual(output.op.name, 'BiasAdd/Relu')
      self.assertListEqual(output.get_shape().as_list(), [5, height, width, 3])

  def testCreateDimensions(self):
    dims = (2, 3, 4)
    shape = [5, 2, 3, 4]
    with self.cached_session():
      for d in dims:
        input_shape = shape[:d]
        inputs = random_ops.random_uniform(input_shape, seed=1)
        output = _layers.bias_add(inputs)
        self.assertListEqual(output.get_shape().as_list(), input_shape)
        biases = variables.get_variables_by_name('biases')[-1]
        self.assertListEqual(biases.get_shape().as_list(), [input_shape[-1]])


class ConvolutionTest(test.TestCase):

  def testInvalidShape(self):
    with self.cached_session():
      images_2d = random_ops.random_uniform((5, 7, 9, 3), seed=1)
      with self.assertRaisesRegexp(
          ValueError, 'Convolution expects input with rank 5, got 4'):
        layers_lib.convolution3d(images_2d, 32, 3)
      images_3d = random_ops.random_uniform((5, 6, 7, 9, 3), seed=1)
      with self.assertRaisesRegexp(
          ValueError, 'Convolution expects input with rank 4, got 5'):
        layers_lib.convolution2d(images_3d, 32, 3)

  def testInvalidDataFormat(self):
    height, width = 7, 9
    with self.cached_session():
      images = random_ops.random_uniform((5, height, width, 3), seed=1)
      with self.assertRaisesRegexp(ValueError, 'data_format'):
        layers_lib.convolution2d(images, 32, 3, data_format='CHWN')

  def testCreateConv(self):
    height, width = 7, 9
    with self.cached_session():
      images = np.random.uniform(size=(5, height, width, 4)).astype(np.float32)
      output = layers_lib.convolution2d(images, 32, [3, 3])
      self.assertEqual(output.op.name, 'Conv/Relu')
      self.assertListEqual(output.get_shape().as_list(), [5, height, width, 32])
      weights = variables.get_variables_by_name('weights')[0]
      self.assertListEqual(weights.get_shape().as_list(), [3, 3, 4, 32])
      biases = variables.get_variables_by_name('biases')[0]
      self.assertListEqual(biases.get_shape().as_list(), [32])

  def testCreateConvNCHW(self):
    height, width = 7, 9
    with self.cached_session():
      images = np.random.uniform(size=(5, 4, height, width)).astype(np.float32)
      output = layers_lib.convolution2d(images, 32, [3, 3], data_format='NCHW')
      self.assertEqual(output.op.name, 'Conv/Relu')
      self.assertListEqual(output.get_shape().as_list(), [5, 32, height, width])
      weights = variables.get_variables_by_name('weights')[0]
      self.assertListEqual(weights.get_shape().as_list(), [3, 3, 4, 32])
      biases = variables.get_variables_by_name('biases')[0]
      self.assertListEqual(biases.get_shape().as_list(), [32])

  def testCreateSquareConv(self):
    height, width = 7, 9
    with self.cached_session():
      images = random_ops.random_uniform((5, height, width, 3), seed=1)
      output = layers_lib.convolution2d(images, 32, 3)
      self.assertEqual(output.op.name, 'Conv/Relu')
      self.assertListEqual(output.get_shape().as_list(), [5, height, width, 32])

  def testCreateConvWithTensorShape(self):
    height, width = 7, 9
    with self.cached_session():
      images = random_ops.random_uniform((5, height, width, 3), seed=1)
      output = layers_lib.convolution2d(images, 32, images.get_shape()[1:3])
      self.assertEqual(output.op.name, 'Conv/Relu')
      self.assertListEqual(output.get_shape().as_list(), [5, height, width, 32])

  def testCreateFullyConv(self):
    height, width = 7, 9
    with self.cached_session():
      images = random_ops.random_uniform((5, height, width, 32), seed=1)
      output = layers_lib.convolution2d(
          images, 64, images.get_shape()[1:3], padding='VALID')
      self.assertEqual(output.op.name, 'Conv/Relu')
      self.assertListEqual(output.get_shape().as_list(), [5, 1, 1, 64])
      biases = variables.get_variables_by_name('biases')[0]
      self.assertListEqual(biases.get_shape().as_list(), [64])

  def testFullyConvWithCustomGetter(self):
    height, width = 7, 9
    with self.cached_session():
      called = [0]

      def custom_getter(getter, *args, **kwargs):
        called[0] += 1
        return getter(*args, **kwargs)

      with variable_scope.variable_scope('test', custom_getter=custom_getter):
        images = random_ops.random_uniform((5, height, width, 32), seed=1)
        layers_lib.convolution2d(images, 64, images.get_shape()[1:3])
      self.assertEqual(called[0], 2)  # Custom getter called twice.

  def testCreateVerticalConv(self):
    height, width = 7, 9
    with self.cached_session():
      images = random_ops.random_uniform((5, height, width, 4), seed=1)
      output = layers_lib.convolution2d(images, 32, [3, 1])
      self.assertEqual(output.op.name, 'Conv/Relu')
      self.assertListEqual(output.get_shape().as_list(), [5, height, width, 32])
      weights = variables.get_variables_by_name('weights')[0]
      self.assertListEqual(weights.get_shape().as_list(), [3, 1, 4, 32])
      biases = variables.get_variables_by_name('biases')[0]
      self.assertListEqual(biases.get_shape().as_list(), [32])

  def testCreateHorizontalConv(self):
    height, width = 7, 9
    with self.cached_session():
      images = random_ops.random_uniform((5, height, width, 4), seed=1)
      output = layers_lib.convolution2d(images, 32, [1, 3])
      self.assertEqual(output.op.name, 'Conv/Relu')
      self.assertListEqual(output.get_shape().as_list(), [5, height, width, 32])
      weights = variables.get_variables_by_name('weights')[0]
      self.assertListEqual(weights.get_shape().as_list(), [1, 3, 4, 32])

  def testCreateConvWithStride(self):
    height, width = 6, 8
    with self.cached_session():
      images = random_ops.random_uniform((5, height, width, 3), seed=1)
      output = layers_lib.convolution2d(images, 32, [3, 3], stride=2)
      self.assertEqual(output.op.name, 'Conv/Relu')
      self.assertListEqual(output.get_shape().as_list(),
                           [5, height / 2, width / 2, 32])

  def testCreateConvCreatesWeightsAndBiasesVars(self):
    height, width = 7, 9
    images = random_ops.random_uniform((5, height, width, 3), seed=1)
    with self.cached_session():
      self.assertFalse(variables.get_variables('conv1/weights'))
      self.assertFalse(variables.get_variables('conv1/biases'))
      layers_lib.convolution2d(images, 32, [3, 3], scope='conv1')
      self.assertTrue(variables.get_variables('conv1/weights'))
      self.assertTrue(variables.get_variables('conv1/biases'))

  def testCreateConvWithScope(self):
    height, width = 7, 9
    with self.cached_session():
      images = random_ops.random_uniform((5, height, width, 3), seed=1)
      output = layers_lib.convolution2d(images, 32, [3, 3], scope='conv1')
      self.assertEqual(output.op.name, 'conv1/Relu')

  def testCreateConvWithCollection(self):
    height, width = 7, 9
    images = random_ops.random_uniform((5, height, width, 3), seed=1)
    with ops.name_scope('fe'):
      conv = layers_lib.convolution2d(
          images, 32, [3, 3], outputs_collections='outputs', scope='Conv')
    output_collected = ops.get_collection('outputs')[0]
    self.assertEqual(output_collected.aliases, ['Conv'])
    self.assertEqual(output_collected, conv)

  def testCreateConvWithoutActivation(self):
    height, width = 7, 9
    with self.cached_session():
      images = random_ops.random_uniform((5, height, width, 3), seed=1)
      output = layers_lib.convolution2d(images, 32, [3, 3], activation_fn=None)
      self.assertEqual(output.op.name, 'Conv/BiasAdd')

  def testCreateConvValid(self):
    height, width = 7, 9
    with self.cached_session():
      images = random_ops.random_uniform((5, height, width, 3), seed=1)
      output = layers_lib.convolution2d(images, 32, [3, 3], padding='VALID')
      self.assertListEqual(output.get_shape().as_list(), [5, 5, 7, 32])

  def testCreateConvWithWD(self):
    height, width = 7, 9
    weight_decay = 0.01
    with self.cached_session() as sess:
      images = random_ops.random_uniform((5, height, width, 3), seed=1)
      regularizer = regularizers.l2_regularizer(weight_decay)
      layers_lib.convolution2d(
          images, 32, [3, 3], weights_regularizer=regularizer)
      l2_loss = nn_ops.l2_loss(variables.get_variables_by_name('weights')[0])
      wd = ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES)[0]
      self.assertEqual(wd.op.name, 'Conv/kernel/Regularizer/l2_regularizer')
      sess.run(variables_lib.global_variables_initializer())
      self.assertAlmostEqual(sess.run(wd), weight_decay * l2_loss.eval())

  def testCreateConvNoRegularizers(self):
    height, width = 7, 9
    with self.cached_session():
      images = random_ops.random_uniform((5, height, width, 3), seed=1)
      layers_lib.convolution2d(images, 32, [3, 3])
      self.assertEqual(
          ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES), [])

  def testReuseVars(self):
    height, width = 7, 9
    with self.cached_session():
      images = random_ops.random_uniform((5, height, width, 3), seed=1)
      layers_lib.convolution2d(images, 32, [3, 3], scope='conv1')
      self.assertEqual(len(variables.get_variables()), 2)
      layers_lib.convolution2d(images, 32, [3, 3], scope='conv1', reuse=True)
      self.assertEqual(len(variables.get_variables()), 2)

  def testNonReuseVars(self):
    height, width = 7, 9
    with self.cached_session():
      images = random_ops.random_uniform((5, height, width, 3), seed=1)
      layers_lib.convolution2d(images, 32, [3, 3])
      self.assertEqual(len(variables.get_variables()), 2)
      layers_lib.convolution2d(images, 32, [3, 3])
      self.assertEqual(len(variables.get_variables()), 4)

  def testReuseConvWithWD(self):
    height, width = 7, 9
    with self.cached_session():
      images = random_ops.random_uniform((5, height, width, 3), seed=1)
      weight_decay = regularizers.l2_regularizer(0.01)
      with arg_scope(
          [layers_lib.convolution2d], weights_regularizer=weight_decay):
        layers_lib.convolution2d(images, 32, [3, 3], scope='conv1')
        self.assertEqual(len(variables.get_variables()), 2)
        self.assertEqual(
            len(ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES)), 1)
        layers_lib.convolution2d(images, 32, [3, 3], scope='conv1', reuse=True)
        self.assertEqual(len(variables.get_variables()), 2)
        self.assertEqual(
            len(ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES)), 1)

  def testConvWithBatchNorm(self):
    height, width = 7, 9
    with self.cached_session():
      images = random_ops.random_uniform((5, height, width, 32), seed=1)
      with arg_scope(
          [layers_lib.convolution2d],
          normalizer_fn=_layers.batch_norm,
          normalizer_params={
              'decay': 0.9
          }):
        net = layers_lib.convolution2d(images, 32, [3, 3])
        net = layers_lib.convolution2d(net, 32, [3, 3])
      self.assertEqual(len(variables.get_variables()), 8)
      self.assertEqual(len(variables.get_variables('Conv/BatchNorm')), 3)
      self.assertEqual(len(variables.get_variables('Conv_1/BatchNorm')), 3)

  def testReuseConvWithBatchNorm(self):
    height, width = 7, 9
    with self.cached_session():
      images = random_ops.random_uniform((5, height, width, 32), seed=1)
      with arg_scope(
          [layers_lib.convolution2d],
          normalizer_fn=_layers.batch_norm,
          normalizer_params={
              'decay': 0.9
          }):
        net = layers_lib.convolution2d(images, 32, [3, 3], scope='Conv')
        net = layers_lib.convolution2d(
            net, 32, [3, 3], scope='Conv', reuse=True)
      self.assertEqual(len(variables.get_variables()), 4)
      self.assertEqual(len(variables.get_variables('Conv/BatchNorm')), 3)
      self.assertEqual(len(variables.get_variables('Conv_1/BatchNorm')), 0)

  def testCreateConvCreatesWeightsAndBiasesVarsWithRateTwo(self):
    height, width = 7, 9
    images = random_ops.random_uniform((5, height, width, 3), seed=1)
    with self.cached_session():
      self.assertFalse(variables.get_variables('conv1/weights'))
      self.assertFalse(variables.get_variables('conv1/biases'))
      layers_lib.convolution2d(images, 32, [3, 3], rate=2, scope='conv1')
      self.assertTrue(variables.get_variables('conv1/weights'))
      self.assertTrue(variables.get_variables('conv1/biases'))

  def testOutputSizeWithRateTwoSamePadding(self):
    num_filters = 32
    input_size = [5, 10, 12, 3]
    expected_size = [5, 10, 12, num_filters]

    images = random_ops.random_uniform(input_size, seed=1)
    output = layers_lib.convolution2d(
        images, num_filters, [3, 3], rate=2, padding='SAME')
    self.assertListEqual(list(output.get_shape().as_list()), expected_size)
    with self.cached_session() as sess:
      sess.run(variables_lib.global_variables_initializer())
      self.assertEqual(output.op.name, 'Conv/Relu')
      self.assertListEqual(list(output.eval().shape), expected_size)

  def testOutputSizeWithRateTwoValidPadding(self):
    num_filters = 32
    input_size = [5, 10, 12, 3]
    expected_size = [5, 6, 8, num_filters]

    images = random_ops.random_uniform(input_size, seed=1)
    output = layers_lib.convolution2d(
        images, num_filters, [3, 3], rate=2, padding='VALID')
    self.assertListEqual(list(output.get_shape().as_list()), expected_size)
    with self.cached_session() as sess:
      sess.run(variables_lib.global_variables_initializer())
      self.assertEqual(output.op.name, 'Conv/Relu')
      self.assertListEqual(list(output.eval().shape), expected_size)

  def testOutputSizeWithRateTwoThreeValidPadding(self):
    num_filters = 32
    input_size = [5, 10, 12, 3]
    expected_size = [5, 6, 6, num_filters]

    images = random_ops.random_uniform(input_size, seed=1)
    output = layers_lib.convolution2d(
        images, num_filters, [3, 3], rate=[2, 3], padding='VALID')
    self.assertListEqual(list(output.get_shape().as_list()), expected_size)
    with self.cached_session() as sess:
      sess.run(variables_lib.global_variables_initializer())
      self.assertEqual(output.op.name, 'Conv/Relu')
      self.assertListEqual(list(output.eval().shape), expected_size)

  def testDynamicOutputSizeWithRateOneValidPadding(self):
    num_filters = 32
    input_size = [5, 9, 11, 3]
    expected_size = [None, None, None, num_filters]
    expected_size_dynamic = [5, 7, 9, num_filters]

    with self.cached_session():
      images = array_ops.placeholder(np.float32,
                                     [None, None, None, input_size[3]])
      output = layers_lib.convolution2d(
          images, num_filters, [3, 3], rate=1, padding='VALID')
      variables_lib.global_variables_initializer().run()
      self.assertEqual(output.op.name, 'Conv/Relu')
      self.assertListEqual(output.get_shape().as_list(), expected_size)
      eval_output = output.eval({images: np.zeros(input_size, np.float32)})
      self.assertListEqual(list(eval_output.shape), expected_size_dynamic)

  def testDynamicOutputSizeWithRateOneValidPaddingNCHW(self):
    if test.is_gpu_available(cuda_only=True):
      num_filters = 32
      input_size = [5, 3, 9, 11]
      expected_size = [None, num_filters, None, None]
      expected_size_dynamic = [5, num_filters, 7, 9]

      with self.session(use_gpu=True):
        images = array_ops.placeholder(np.float32,
                                       [None, input_size[1], None, None])
        output = layers_lib.convolution2d(
            images,
            num_filters, [3, 3],
            rate=1,
            padding='VALID',
            data_format='NCHW')
        variables_lib.global_variables_initializer().run()
        self.assertEqual(output.op.name, 'Conv/Relu')
        self.assertListEqual(output.get_shape().as_list(), expected_size)
        eval_output = output.eval({images: np.zeros(input_size, np.float32)})
        self.assertListEqual(list(eval_output.shape), expected_size_dynamic)

  def testDynamicOutputSizeWithRateTwoValidPadding(self):
    num_filters = 32
    input_size = [5, 9, 11, 3]
    expected_size = [None, None, None, num_filters]
    expected_size_dynamic = [5, 5, 7, num_filters]

    with self.cached_session():
      images = array_ops.placeholder(np.float32,
                                     [None, None, None, input_size[3]])
      output = layers_lib.convolution2d(
          images, num_filters, [3, 3], rate=2, padding='VALID')
      variables_lib.global_variables_initializer().run()
      self.assertEqual(output.op.name, 'Conv/Relu')
      self.assertListEqual(output.get_shape().as_list(), expected_size)
      eval_output = output.eval({images: np.zeros(input_size, np.float32)})
      self.assertListEqual(list(eval_output.shape), expected_size_dynamic)

  def testWithScope(self):
    num_filters = 32
    input_size = [5, 9, 11, 3]
    expected_size = [5, 5, 7, num_filters]

    images = random_ops.random_uniform(input_size, seed=1)
    output = layers_lib.convolution2d(
        images, num_filters, [3, 3], rate=2, padding='VALID', scope='conv7')
    with self.cached_session() as sess:
      sess.run(variables_lib.global_variables_initializer())
      self.assertEqual(output.op.name, 'conv7/Relu')
      self.assertListEqual(list(output.eval().shape), expected_size)

  def testWithScopeWithoutActivation(self):
    num_filters = 32
    input_size = [5, 9, 11, 3]
    expected_size = [5, 5, 7, num_filters]

    images = random_ops.random_uniform(input_size, seed=1)
    output = layers_lib.convolution2d(
        images,
        num_filters, [3, 3],
        rate=2,
        padding='VALID',
        activation_fn=None,
        scope='conv7')
    with self.cached_session() as sess:
      sess.run(variables_lib.global_variables_initializer())
      self.assertEqual(output.op.name, 'conv7/BiasAdd')
      self.assertListEqual(list(output.eval().shape), expected_size)


class Convolution2dTransposeTests(test.TestCase):

  def testTrainableFlagIsPassedOn(self):
    for trainable in [True, False]:
      with ops.Graph().as_default():
        num_filters = 32
        input_size = [5, 10, 12, 3]

        images = random_ops.random_uniform(input_size, seed=1)
        layers_lib.conv2d_transpose(
            images, num_filters, [3, 3], stride=1, trainable=trainable)
        model_variables = variables.get_model_variables()
        trainable_variables = variables_lib.trainable_variables()
        for model_variable in model_variables:
          self.assertEqual(trainable, model_variable in trainable_variables)

  def testInvalidDataFormat(self):
    height, width = 7, 9
    with self.cached_session():
      images = random_ops.random_uniform((5, height, width, 3), seed=1)
      with self.assertRaisesRegexp(
          ValueError, 'data_format has to be either NCHW or NHWC.'):
        _layers.convolution2d_transpose(images, 32, 3, data_format='CHWN')

  def testOutputSizeWithStrideOneSamePaddingNCHW(self):
    # `NCHW` data format is only supported for `GPU` device.
    if test.is_gpu_available(cuda_only=True):
      with self.session(use_gpu=True) as sess:
        num_filters = 32
        input_size = [5, 3, 10, 12]
        expected_size = [5, num_filters, 10, 12]

        images = random_ops.random_uniform(input_size, seed=1)
        output = layers_lib.conv2d_transpose(
            images,
            num_filters, [3, 3],
            stride=1,
            padding='SAME',
            data_format='NCHW')
        self.assertEqual(output.op.name, 'Conv2d_transpose/Relu')

        sess.run(variables_lib.global_variables_initializer())
        self.assertListEqual(list(output.eval().shape), expected_size)

  def testOutputSizeWithStrideOneValidPaddingNCHW(self):
    if test.is_gpu_available(cuda_only=True):
      with self.session(use_gpu=True) as sess:
        num_filters = 32
        input_size = [5, 3, 10, 12]
        expected_size = [5, num_filters, 12, 14]

        images = random_ops.random_uniform(input_size, seed=1)
        output = layers_lib.conv2d_transpose(
            images,
            num_filters, [3, 3],
            stride=1,
            padding='VALID',
            data_format='NCHW')
        self.assertEqual(output.op.name, 'Conv2d_transpose/Relu')

        sess.run(variables_lib.global_variables_initializer())
        self.assertListEqual(list(output.eval().shape), expected_size)

  def testOutputSizeWithStrideTwoValidPaddingNCHW(self):
    if test.is_gpu_available(cuda_only=True):
      with self.session(use_gpu=True) as sess:
        num_filters = 32
        input_size = [5, 3, 9, 11]
        expected_size = [5, num_filters, 19, 23]

        images = random_ops.random_uniform(input_size, seed=1)
        output = layers_lib.conv2d_transpose(
            images,
            num_filters, [3, 3],
            stride=[2, 2],
            padding='VALID',
            data_format='NCHW')
        self.assertEqual(output.op.name, 'Conv2d_transpose/Relu')
        self.assertListEqual(list(output.get_shape().as_list()), expected_size)

        sess.run(variables_lib.global_variables_initializer())
        self.assertListEqual(list(output.eval().shape), expected_size)

  def testOutputSizeWith1x1StrideTwoSamePaddingNCHW(self):
    if test.is_gpu_available(cuda_only=True):
      with self.session(use_gpu=True) as sess:
        num_filters = 1
        input_size = [1, 1, 1, 1]
        expected_size = [1, num_filters, 2, 2]

        images = random_ops.random_uniform(input_size, seed=1)
        output = layers_lib.conv2d_transpose(
            images,
            num_filters, [2, 2],
            stride=[2, 2],
            padding='SAME',
            data_format='NCHW')
        self.assertListEqual(list(output.get_shape().as_list()), expected_size)

        sess.run(variables_lib.global_variables_initializer())
        self.assertEqual(output.op.name, 'Conv2d_transpose/Relu')
        self.assertListEqual(list(output.eval().shape), expected_size)

  def testOutputSizeWith1x1StrideTwoValidPaddingNCHW(self):
    if test.is_gpu_available(cuda_only=True):
      with self.session(use_gpu=True) as sess:
        num_filters = 1
        input_size = [1, 1, 1, 1]
        expected_size = [1, num_filters, 2, 2]

        images = random_ops.random_uniform(input_size, seed=1)
        output = layers_lib.conv2d_transpose(
            images,
            num_filters, [2, 2],
            stride=[2, 2],
            padding='VALID',
            data_format='NCHW')
        sess.run(variables_lib.global_variables_initializer())
        self.assertEqual(output.op.name, 'Conv2d_transpose/Relu')
        self.assertListEqual(list(output.eval().shape), expected_size)

  def testOutputSizeWith2x2StrideTwoSamePaddingNCHW(self):
    if test.is_gpu_available(cuda_only=True):
      with self.session(use_gpu=True) as sess:
        num_filters = 1
        input_size = [1, 1, 2, 2]
        expected_size = [1, num_filters, 4, 4]

        images = random_ops.random_uniform(input_size, seed=1)
        output = layers_lib.conv2d_transpose(
            images,
            num_filters, [2, 2],
            stride=[2, 2],
            padding='SAME',
            data_format='NCHW')
        sess.run(variables_lib.global_variables_initializer())
        self.assertEqual(output.op.name, 'Conv2d_transpose/Relu')
        self.assertListEqual(list(output.eval().shape), expected_size)

  def testOutputSizeWith2x2StrideTwoValidPaddingNCHW(self):
    if test.is_gpu_available(cuda_only=True):
      with self.session(use_gpu=True) as sess:
        num_filters = 1
        input_size = [1, 1, 2, 2]
        expected_size = [1, num_filters, 4, 4]

        images = random_ops.random_uniform(input_size, seed=1)
        output = layers_lib.conv2d_transpose(
            images,
            num_filters, [2, 2],
            stride=[2, 2],
            padding='VALID',
            data_format='NCHW')
        sess.run(variables_lib.global_variables_initializer())
        self.assertEqual(output.op.name, 'Conv2d_transpose/Relu')
        self.assertListEqual(list(output.eval().shape), expected_size)

  def testOutputSizeWithStride2x1NCHW(self):
    if test.is_gpu_available(cuda_only=True):
      with self.session(use_gpu=True) as sess:
        num_filters = 1
        input_size = [1, 1, 3, 2]
        expected_size = [1, num_filters, 6, 5]

        images = random_ops.random_uniform(input_size, seed=1)
        output = layers_lib.conv2d_transpose(
            images,
            num_filters, [2, 4],
            stride=[2, 1],
            padding='VALID',
            data_format='NCHW')
        sess.run(variables_lib.global_variables_initializer())
        self.assertEqual(output.op.name, 'Conv2d_transpose/Relu')
        self.assertListEqual(list(output.eval().shape), expected_size)

  def testOutputSizeWithStride2x4NCHW(self):
    if test.is_gpu_available(cuda_only=True):
      with self.session(use_gpu=True) as sess:
        num_filters = 1
        input_size = [1, 1, 3, 2]
        expected_size = [1, num_filters, 6, 8]

        images = random_ops.random_uniform(input_size, seed=1)
        output = layers_lib.conv2d_transpose(
            images,
            num_filters, [2, 4],
            stride=[2, 4],
            padding='VALID',
            data_format='NCHW')
        sess.run(variables_lib.global_variables_initializer())
        self.assertEqual(output.op.name, 'Conv2d_transpose/Relu')
        self.assertListEqual(list(output.eval().shape), expected_size)

  def testOutputSizeWithStride2x5NCHW(self):
    if test.is_gpu_available(cuda_only=True):
      with self.session(use_gpu=True) as sess:
        num_filters = 1
        input_size = [1, 1, 3, 2]
        expected_size = [1, num_filters, 6, 10]

        images = random_ops.random_uniform(input_size, seed=1)
        output = layers_lib.conv2d_transpose(
            images,
            num_filters, [2, 4],
            stride=[2, 5],
            padding='VALID',
            data_format='NCHW')
        sess.run(variables_lib.global_variables_initializer())
        self.assertEqual(output.op.name, 'Conv2d_transpose/Relu')
        self.assertListEqual(list(output.eval().shape), expected_size)

  def testOutputSizeWithStrideOneSamePadding(self):
    num_filters = 32
    input_size = [5, 10, 12, 3]
    expected_size = [5, 10, 12, num_filters]

    images = random_ops.random_uniform(input_size, seed=1)
    output = layers_lib.conv2d_transpose(
        images, num_filters, [3, 3], stride=1, padding='SAME')
    self.assertEqual(output.op.name, 'Conv2d_transpose/Relu')

    with self.cached_session() as sess:
      sess.run(variables_lib.global_variables_initializer())
      self.assertListEqual(list(output.eval().shape), expected_size)

  def testOutputSizeWithStrideOneValidPadding(self):
    num_filters = 32
    input_size = [5, 10, 12, 3]
    expected_size = [5, 12, 14, num_filters]

    images = random_ops.random_uniform(input_size, seed=1)
    output = layers_lib.conv2d_transpose(
        images, num_filters, [3, 3], stride=1, padding='VALID')
    self.assertEqual(output.op.name, 'Conv2d_transpose/Relu')

    with self.cached_session() as sess:
      sess.run(variables_lib.global_variables_initializer())
      self.assertListEqual(list(output.eval().shape), expected_size)

  def testOutputSizeWithStrideTwoValidPadding(self):
    num_filters = 32
    input_size = [5, 9, 11, 3]
    expected_size = [5, 19, 23, num_filters]

    images = random_ops.random_uniform(input_size, seed=1)
    output = layers_lib.conv2d_transpose(
        images, num_filters, [3, 3], stride=[2, 2], padding='VALID')
    self.assertEqual(output.op.name, 'Conv2d_transpose/Relu')
    self.assertListEqual(list(output.get_shape().as_list()), expected_size)

    with self.cached_session() as sess:
      sess.run(variables_lib.global_variables_initializer())
      self.assertListEqual(list(output.eval().shape), expected_size)

  def testOutputSizeWith1x1StrideTwoSamePadding(self):
    num_filters = 1
    input_size = [1, 1, 1, 1]
    expected_size = [1, 2, 2, num_filters]

    images = random_ops.random_uniform(input_size, seed=1)
    output = layers_lib.conv2d_transpose(
        images, num_filters, [2, 2], stride=[2, 2], padding='SAME')
    self.assertListEqual(list(output.get_shape().as_list()), expected_size)

    with self.cached_session() as sess:
      sess.run(variables_lib.global_variables_initializer())
      self.assertEqual(output.op.name, 'Conv2d_transpose/Relu')
      self.assertListEqual(list(output.eval().shape), expected_size)

  def testOutputSizeWith1x1StrideTwoValidPadding(self):
    num_filters = 1
    input_size = [1, 1, 1, 1]
    expected_size = [1, 2, 2, num_filters]

    images = random_ops.random_uniform(input_size, seed=1)
    output = layers_lib.conv2d_transpose(
        images, num_filters, [2, 2], stride=[2, 2], padding='VALID')
    with self.cached_session() as sess:
      sess.run(variables_lib.global_variables_initializer())
      self.assertEqual(output.op.name, 'Conv2d_transpose/Relu')
      self.assertListEqual(list(output.eval().shape), expected_size)

  def testOutputSizeWith2x2StrideTwoSamePadding(self):
    num_filters = 1
    input_size = [1, 2, 2, 1]
    expected_size = [1, 4, 4, num_filters]

    images = random_ops.random_uniform(input_size, seed=1)
    output = layers_lib.conv2d_transpose(
        images, num_filters, [2, 2], stride=[2, 2], padding='SAME')
    with self.cached_session() as sess:
      sess.run(variables_lib.global_variables_initializer())
      self.assertEqual(output.op.name, 'Conv2d_transpose/Relu')
      self.assertListEqual(list(output.eval().shape), expected_size)

  def testOutputSizeWith2x2StrideTwoValidPadding(self):
    num_filters = 1
    input_size = [1, 2, 2, 1]
    expected_size = [1, 4, 4, num_filters]

    images = random_ops.random_uniform(input_size, seed=1)
    output = layers_lib.conv2d_transpose(
        images, num_filters, [2, 2], stride=[2, 2], padding='VALID')
    with self.cached_session() as sess:
      sess.run(variables_lib.global_variables_initializer())
      self.assertEqual(output.op.name, 'Conv2d_transpose/Relu')
      self.assertListEqual(list(output.eval().shape), expected_size)

  def testOutputSizeWithStride2x1(self):
    num_filters = 1
    input_size = [1, 3, 2, 1]
    expected_size = [1, 6, 5, num_filters]

    images = random_ops.random_uniform(input_size, seed=1)
    output = layers_lib.conv2d_transpose(
        images, num_filters, [2, 4], stride=[2, 1], padding='VALID')
    with self.cached_session() as sess:
      sess.run(variables_lib.global_variables_initializer())
      self.assertEqual(output.op.name, 'Conv2d_transpose/Relu')
      self.assertListEqual(list(output.eval().shape), expected_size)

  def testOutputSizeWithStride2x4(self):
    num_filters = 1
    input_size = [1, 3, 2, 1]
    expected_size = [1, 6, 8, num_filters]

    images = random_ops.random_uniform(input_size, seed=1)
    output = layers_lib.conv2d_transpose(
        images, num_filters, [2, 4], stride=[2, 4], padding='VALID')
    with self.cached_session() as sess:
      sess.run(variables_lib.global_variables_initializer())
      self.assertEqual(output.op.name, 'Conv2d_transpose/Relu')
      self.assertListEqual(list(output.eval().shape), expected_size)

  def testOutputSizeWithStride2x5(self):
    num_filters = 1
    input_size = [1, 3, 2, 1]
    expected_size = [1, 6, 10, num_filters]

    images = random_ops.random_uniform(input_size, seed=1)
    output = layers_lib.conv2d_transpose(
        images, num_filters, [2, 4], stride=[2, 5], padding='VALID')
    with self.cached_session() as sess:
      sess.run(variables_lib.global_variables_initializer())
      self.assertEqual(output.op.name, 'Conv2d_transpose/Relu')
      self.assertListEqual(list(output.eval().shape), expected_size)

  def testOutputSizeRandomSizesAndStridesValidPadding(self):
    np.random.seed(0)
    max_image_size = 10

    for _ in range(10):
      num_filters = 1
      input_size = [
          1,
          np.random.randint(1, max_image_size),
          np.random.randint(1, max_image_size), 1
      ]
      filter_size = [
          np.random.randint(1, input_size[1] + 1),
          np.random.randint(1, input_size[2] + 1)
      ]
      stride = [np.random.randint(1, 3), np.random.randint(1, 3)]

      ops.reset_default_graph()
      graph = ops.Graph()
      with graph.as_default():
        images = random_ops.random_uniform(input_size, seed=1)
        transpose = layers_lib.conv2d_transpose(
            images, num_filters, filter_size, stride=stride, padding='VALID')
        conv = layers_lib.conv2d(
            transpose, num_filters, filter_size, stride=stride, padding='VALID')

        with self.session(graph=graph) as sess:
          sess.run(variables_lib.global_variables_initializer())
          self.assertListEqual(list(conv.eval().shape), input_size)

  def testDynamicOutputSizeWithStrideTwoValidPadding(self):
    num_filters = 32
    input_size = [5, 9, 11, 3]
    expected_size = [None, None, None, num_filters]
    expected_size_dynamic = [5, 19, 23, num_filters]

    images = array_ops.placeholder(np.float32,
                                   [None, None, None, input_size[3]])
    output = layers_lib.conv2d_transpose(
        images, num_filters, [3, 3], stride=[2, 2], padding='VALID')
    self.assertListEqual(output.get_shape().as_list(), expected_size)

    with self.cached_session() as sess:
      sess.run(variables_lib.global_variables_initializer())
      self.assertEqual(output.op.name, 'Conv2d_transpose/Relu')
      eval_output = output.eval({images: np.zeros(input_size, np.float32)})
      self.assertListEqual(list(eval_output.shape), expected_size_dynamic)

  def testDynamicOutputSizeWithStrideTwoSamePadding(self):
    num_filters = 32
    input_size = [5, 9, 11, 3]
    expected_size = [None, None, None, num_filters]
    expected_size_dynamic = [5, 18, 22, num_filters]

    with self.cached_session():
      images = array_ops.placeholder(np.float32,
                                     [None, None, None, input_size[3]])
      output = layers_lib.conv2d_transpose(
          images, num_filters, [3, 3], stride=[2, 2], padding='SAME')
      variables_lib.global_variables_initializer().run()
      self.assertEqual(output.op.name, 'Conv2d_transpose/Relu')
      self.assertListEqual(output.get_shape().as_list(), expected_size)
      eval_output = output.eval({images: np.zeros(input_size, np.float32)})
      self.assertListEqual(list(eval_output.shape), expected_size_dynamic)

  def testWithScope(self):
    num_filters = 32
    input_size = [5, 9, 11, 3]
    expected_size = [5, 19, 23, num_filters]

    images = random_ops.random_uniform(input_size, seed=1)
    output = layers_lib.conv2d_transpose(
        images, num_filters, [3, 3], stride=2, padding='VALID', scope='conv7')
    self.assertEqual(output.op.name, 'conv7/Relu')

    with self.cached_session() as sess:
      sess.run(variables_lib.global_variables_initializer())
      self.assertListEqual(list(output.eval().shape), expected_size)

  def testWithScopeWithoutActivation(self):
    num_filters = 32
    input_size = [5, 9, 11, 3]
    expected_size = [5, 19, 23, num_filters]

    images = random_ops.random_uniform(input_size, seed=1)
    output = layers_lib.conv2d_transpose(
        images,
        num_filters, [3, 3],
        stride=2,
        padding='VALID',
        activation_fn=None,
        scope='conv7')
    self.assertEqual(output.op.name, 'conv7/BiasAdd')

    with self.cached_session() as sess:
      sess.run(variables_lib.global_variables_initializer())
      self.assertListEqual(list(output.eval().shape), expected_size)

  def testDeconvWithoutBiasesProducesConv2dTranspose(self):
    num_filters = 32
    input_size = [5, 9, 11, 3]
    expected_size = [5, 19, 23, num_filters]
    stride = 2
    padding = 'VALID'

    with self.cached_session() as sess:
      images = random_ops.random_uniform(input_size, seed=1)
      output_deconv = layers_lib.conv2d_transpose(
          images,
          num_filters, [3, 3],
          stride=stride,
          padding=padding,
          activation_fn=None,
          scope='conv7')

      weights = variables.get_variables_by_name('conv7/weights')[0]
      output_conv2d_transpose = nn_ops.conv2d_transpose(
          images,
          weights,
          expected_size, [1, stride, stride, 1],
          padding=padding)

      sess.run(variables_lib.global_variables_initializer())

      output_deconv, output_conv2d_transpose = sess.run(
          [output_deconv, output_conv2d_transpose])

      self.assertTrue(
          np.isclose(output_deconv, output_conv2d_transpose, 1e-5, 1e-5).all())


class ConvolutionInPlaneTest(test.TestCase):

  def testHorzConvWithBlankImage(self):
    image = array_ops.ones((1, 10, 10, 1))
    horz_gradients = layers_lib.conv2d_in_plane(
        image,
        weights_initializer=init_ops.constant_initializer([1, -1]),
        kernel_size=[1, 2],
        padding='VALID',
        activation_fn=None)
    init_op = variables_lib.global_variables_initializer()

    with self.cached_session() as sess:
      sess.run(init_op)
      result = sess.run(horz_gradients)
      expected = np.zeros((1, 10, 9, 1))

      self.assertAllClose(result, expected, rtol=1e-5, atol=1e-5)

  def testHorzConvWithBlankImageAndPlaceholder(self):
    image = array_ops.placeholder(dtypes.float32, shape=(None, None, None, 1))
    horz_gradients = layers_lib.conv2d_in_plane(
        image,
        weights_initializer=init_ops.constant_initializer([1, -1]),
        kernel_size=[1, 2],
        padding='VALID',
        activation_fn=None)
    init_op = variables_lib.global_variables_initializer()

    with self.cached_session() as sess:
      sess.run(init_op)
      result = sess.run(
          horz_gradients, feed_dict={
              image: np.ones((1, 10, 10, 1))
          })
      expected = np.zeros((1, 10, 9, 1))

      self.assertAllClose(result, expected, rtol=1e-5, atol=1e-5)

  def testHorzConvWithRandomImageMultiBatch(self):
    np.random.seed(1)
    image = np.random.rand(5, 10, 10, 1)
    expected = image[:, :, 0:-1, :] - image[:, :, 1:, :]

    tf_image = constant_op.constant(image, dtype=dtypes.float32)
    horz_gradients = layers_lib.conv2d_in_plane(
        tf_image,
        weights_initializer=init_ops.constant_initializer([1, -1]),
        kernel_size=[1, 2],
        padding='VALID',
        activation_fn=None)
    init_op = variables_lib.global_variables_initializer()

    with self.cached_session() as sess:
      sess.run(init_op)
      result = sess.run(horz_gradients)

      self.assertAllClose(result, expected, rtol=1e-5, atol=1e-5)

  def testHorzConvWithRandomImageMultiBatchMultiChannel(self):
    np.random.seed(1)
    image = np.random.rand(5, 10, 10, 7)
    expected = image[:, :, 0:-1, :] - image[:, :, 1:, :]

    tf_image = constant_op.constant(image, dtype=dtypes.float32)
    horz_gradients = layers_lib.conv2d_in_plane(
        tf_image,
        weights_initializer=init_ops.constant_initializer([1, -1]),
        kernel_size=[1, 2],
        padding='VALID',
        activation_fn=None)
    init_op = variables_lib.global_variables_initializer()

    with self.cached_session() as sess:
      sess.run(init_op)
      result = sess.run(horz_gradients)

      self.assertAllClose(result, expected, rtol=1e-5, atol=1e-5)

  def testHorzConvWithVaryingImage(self):
    image = np.asmatrix(('1.0 2.0 3.0;' '1.1 2.0 4.0;' '-4.3 0.0 8.9'))

    expected = np.asmatrix(('-1.0 -1.0;' '-0.9 -2.0;' '-4.3 -8.9'))
    expected = np.reshape(np.asarray(expected), (1, 3, 2, 1))

    tf_image = constant_op.constant(
        image, shape=(1, 3, 3, 1), dtype=dtypes.float32)
    horz_gradients = layers_lib.conv2d_in_plane(
        tf_image,
        weights_initializer=init_ops.constant_initializer([1, -1]),
        kernel_size=[1, 2],
        padding='VALID',
        activation_fn=None)
    init_op = variables_lib.global_variables_initializer()

    with self.cached_session() as sess:
      sess.run(init_op)
      result = sess.run(horz_gradients)

      self.assertAllClose(result, expected, rtol=1e-5, atol=1e-5)

  def testVertConvWithBlankImage(self):
    image = array_ops.ones((1, 10, 10, 1))
    vert_gradients = layers_lib.conv2d_in_plane(
        image,
        weights_initializer=init_ops.constant_initializer([1, -1]),
        kernel_size=[2, 1],
        padding='VALID',
        activation_fn=None)
    init_op = variables_lib.global_variables_initializer()

    with self.cached_session() as sess:
      sess.run(init_op)
      result = sess.run(vert_gradients)
      expected = np.zeros((1, 9, 10, 1))

      self.assertAllClose(result, expected, rtol=1e-5, atol=1e-5)

  def testVertConvWithVaryingImage(self):
    image = np.asmatrix(('1.0 2.0 3.0;' '1.1 2.0 4.0;' '-4.3 0.0 8.9'))

    expected = np.asmatrix(('-0.1 0.0 -1.0;' ' 5.4 2.0 -4.9'))
    expected = np.reshape(np.asarray(expected), (1, 2, 3, 1))

    tf_image = constant_op.constant(
        image, shape=(1, 3, 3, 1), dtype=dtypes.float32)
    vert_gradients = layers_lib.conv2d_in_plane(
        tf_image,
        weights_initializer=init_ops.constant_initializer([1, -1]),
        kernel_size=[2, 1],
        padding='VALID',
        activation_fn=None)
    init_op = variables_lib.global_variables_initializer()

    with self.cached_session() as sess:
      sess.run(init_op)
      result = sess.run(vert_gradients)

      self.assertAllClose(result, expected, rtol=1e-5, atol=1e-5)

  def testConv1dShape(self):
    width = 7
    with self.cached_session():
      images = random_ops.random_uniform((5, width, 3), seed=1)
      output = layers_lib.convolution1d(images, 32, 3)
      self.assertEqual(output.op.name, 'Conv/Relu')
      self.assertListEqual(output.get_shape().as_list(), [5, width, 32])

  def testConvInferSpatialDims(self):
    depth, height, width = 7, 9, 11
    with self.cached_session():
      images = np.random.uniform(size=(5, width, 4)).astype(np.float32)
      output = layers_lib.convolution(images, 32, [3])
      self.assertListEqual(output.get_shape().as_list(), [5, width, 32])
      images = np.random.uniform(size=(5, height, width, 4)).astype(np.float32)
      output = layers_lib.convolution(images, 32, [3, 3])
      self.assertListEqual(output.get_shape().as_list(), [5, height, width, 32])
      images = np.random.uniform(size=(5, depth, height, width,
                                       4)).astype(np.float32)
      output = layers_lib.convolution(images, 32, [3, 3, 3])
      self.assertListEqual(output.get_shape().as_list(),
                           [5, depth, height, width, 32])


class DenseToSparseTest(test.TestCase):

  def testDenseFromConstantToSparse(self):
    expected_constant = np.reshape(np.arange(24, dtype=np.int64), (3, 4, 2))
    tensor = constant_op.constant(expected_constant)
    sparse = _layers.dense_to_sparse(tensor)
    dense = sparse_ops.sparse_to_dense(sparse.indices, sparse.dense_shape,
                                       sparse.values)
    with self.cached_session() as sess:
      constant = sess.run(dense)
      self.assertAllEqual(expected_constant, constant)


class DropoutTest(test.TestCase):

  def testCreateDropout(self):
    height, width = 3, 3
    with self.cached_session():
      images = np.random.uniform(size=(5, height, width, 3))
      output = _layers.dropout(images)
      self.assertEqual(output.op.name.lower(), 'dropout/dropout_1/mul_1')
      output.get_shape().assert_is_compatible_with(
          ops.convert_to_tensor(images).get_shape())

  def testCreateDropoutWithConstantTrue(self):
    height, width = 3, 3
    with self.cached_session():
      is_training = constant_op.constant(True)
      images = random_ops.random_uniform((5, height, width, 3), seed=1)
      output = _layers.dropout(images, is_training=is_training)
      output.get_shape().assert_is_compatible_with(images.get_shape())

  def testCreateDropoutWithConstantFalse(self):
    height, width = 3, 3
    with self.cached_session():
      is_training = constant_op.constant(False)
      images = random_ops.random_uniform((5, height, width, 3), seed=1)
      output = _layers.dropout(images, is_training=is_training)
      output.get_shape().assert_is_compatible_with(images.get_shape())

  def testCreateDropoutWithPlaceholder(self):
    height, width = 3, 3
    tf.reset_default_graph()
    with self.cached_session():
      is_training = array_ops.placeholder(dtype=dtypes.bool, shape=[])
      images = random_ops.random_uniform((5, height, width, 3), seed=1)
      # this verifies that that we've inserted cond properly.
      output = _layers.dropout(images, is_training=is_training)
      # In control_flow_v2 the op is called "If" and it is behind
      # identity op. In legacy mode cond we just go by name.
      # Might need to do something more robust here eventually.
      is_cond_op = (output.op.inputs[0].op.type == 'If' or
                    output.op.name == 'Dropout/cond/Merge')
      self.assertTrue(is_cond_op,
                      'Expected cond_op got ' + repr(output))
      output.get_shape().assert_is_compatible_with(images.get_shape())

  def testCollectOutputs(self):
    height, width = 3, 3
    with self.cached_session():
      images = random_ops.random_uniform((5, height, width, 3), seed=1)
      output = _layers.dropout(images, outputs_collections='outputs')
      c_output = ops.get_collection('outputs')[0]
      self.assertEqual(c_output.aliases, ['Dropout'])
      self.assertEqual(c_output, output)

  def testDropout(self):
    height, width = 10, 10
    with self.cached_session() as sess:
      images = random_ops.random_uniform(
          (5, height, width, 3), seed=1, name='images')
      num_elem_initial = math_ops.reduce_mean(
          math_ops.cast(images > 0, dtypes.float32))
      output = _layers.dropout(images)
      num_elem = math_ops.reduce_mean(math_ops.cast(output > 0, dtypes.float32))
      num_elem, num_elem_initial = sess.run([num_elem, num_elem_initial])
      self.assertLess(num_elem, num_elem_initial / 2 + 0.1)
      self.assertGreater(num_elem, num_elem_initial / 2 - 0.1)

  def testDropoutSeed(self):
    """Test that providing the same seed produces the same result."""
    height, width = 10, 10
    with self.cached_session() as sess:
      images = random_ops.random_uniform(
          (5, height, width, 3), seed=1, name='images')
      output1 = _layers.dropout(images, seed=1)
      output2 = _layers.dropout(images, seed=1)
      self.assertAllEqual(*sess.run([output1, output2]))

  def testCreateDropoutNoTraining(self):
    height, width = 3, 3
    with self.cached_session() as sess:
      images = random_ops.random_uniform(
          (5, height, width, 3), seed=1, name='images')
      num_elem_initial = math_ops.reduce_mean(
          math_ops.cast(images > 0, dtypes.float32))
      output = _layers.dropout(images, is_training=False)
      num_elem = math_ops.reduce_mean(math_ops.cast(output > 0, dtypes.float32))
      num_elem, num_elem_initial = sess.run([num_elem, num_elem_initial])
      self.assertEqual(num_elem, num_elem_initial)
      outputs, inputs = sess.run([output, images])
      self.assertAllClose(outputs, inputs)

  def testCreateFCFollowByDropout(self):
    height, width = 3, 3
    with self.cached_session() as sess:
      images = random_ops.random_uniform(
          (5, height, width, 3), seed=1, name='images')
      output = _layers.fully_connected(images, 50)
      num_elem_initial = math_ops.reduce_mean(
          math_ops.cast(output > 0, dtypes.float32))
      output = _layers.dropout(output)
      num_elem = math_ops.reduce_mean(math_ops.cast(output > 0, dtypes.float32))
      sess.run(variables_lib.global_variables_initializer())
      num_elem, num_elem_initial = sess.run([num_elem, num_elem_initial])
      self.assertLess(num_elem, num_elem_initial / 2 + 0.1)
      self.assertGreater(num_elem, num_elem_initial / 2 - 0.1)

  def testCreateFCWithDropout(self):
    height, width = 3, 3
    with self.cached_session() as sess:
      images = random_ops.random_uniform(
          (5, height, width, 3), seed=1, name='images')
      output = _layers.fully_connected(
          images, 50, normalizer_fn=_layers.dropout)
      num_elem = math_ops.reduce_mean(math_ops.cast(output > 0, dtypes.float32))
      sess.run(variables_lib.global_variables_initializer())
      num_elem = sess.run(num_elem)
      self.assertLess(num_elem, 0.5)
      self.assertGreater(num_elem, 0.1)


class FlattenTest(test.TestCase):

  def testUnknownLastDim(self):
    with ops.Graph().as_default() as g, self.session(g):
      inputs = array_ops.placeholder(dtype=dtypes.float32)
      inputs.set_shape(tensor_shape.TensorShape((5, None)))
      output = _layers.flatten(inputs)
      self.assertEqual(output.get_shape().as_list(), [5, None])

  def testCollectOutputs(self):
    height, width = 3, 3
    with self.cached_session():
      images = np.random.uniform(size=(5, height, width, 3))
      output = _layers.flatten(images, outputs_collections='outputs')
      c_output = ops.get_collection('outputs')[0]
      self.assertEqual(c_output.aliases, ['Flatten'])
      self.assertEqual(c_output, output)

  def testFlatten4D(self):
    height, width = 3, 3
    with self.cached_session():
      images = random_ops.random_uniform(
          (5, height, width, 3), seed=1, name='images')
      output = _layers.flatten(images)
      self.assertEqual(output.get_shape().num_elements(),
                       images.get_shape().num_elements())
      self.assertEqual(output.get_shape()[0], images.get_shape()[0])

  def testFlatten3D(self):
    height, width = 3, 3
    with self.cached_session():
      images = random_ops.random_uniform(
          (5, height, width), seed=1, name='images')
      output = _layers.flatten(images)
      self.assertEqual(output.get_shape().num_elements(),
                       images.get_shape().num_elements())
      self.assertEqual(output.get_shape()[0], images.get_shape()[0])

  def testFlatten0D(self):
    with self.cached_session():
      scalars = random_ops.random_uniform((5,), seed=1, name='scalars')
      output = _layers.flatten(scalars)
      self.assertEqual(output.shape, (5, 1))

  def testFlattenBatchSize(self):
    height, width = 3, 3
    with self.cached_session() as sess:
      images = random_ops.random_uniform(
          (5, height, width, 3), seed=1, name='images')
      inputs = array_ops.placeholder(dtypes.int32, (None, height, width, 3))
      output = _layers.flatten(inputs)
      self.assertEqual(output.get_shape().as_list(), [None, height * width * 3])
      output = sess.run(output, {inputs: images.eval()})
      self.assertEqual(output.size, images.get_shape().num_elements())
      self.assertEqual(output.shape[0], images.get_shape()[0])

  def testUnknownDims(self):
    height = width = depth = 3
    with self.cached_session() as sess:
      images = random_ops.random_uniform(
          (5, height, width, depth), seed=1, name='images')
      inputs = array_ops.placeholder(dtypes.int32, (None, None, None, None))
      output = _layers.flatten(inputs)
      output = sess.run(output, {inputs: images.eval()})
      self.assertEqual(output.size, images.get_shape().num_elements())
      self.assertEqual(output.shape[0], images.get_shape()[0])


def _sparsify(array, threshold=0.5):
  array[array < threshold] = 0
  non_zero = np.where(array)
  indices = np.vstack(non_zero).T
  values = array[non_zero]
  shape = array.shape
  return indices, values, shape


class PartialFlattenTest(test.TestCase):

  def testDensePartialFlatten(self):
    """Test `_inner_flatten` on `Tensor`s."""
    shape = [2, 3, 4, 5, 6]
    np.random.seed(5446)
    inputs = np.random.randint(0, 100, size=shape)

    for new_rank in [1, 2, 3, 4, 5]:
      expected_new_shape = (
          shape[:new_rank - 1] + [np.prod(shape[new_rank - 1:])])
      expected_flattened = np.reshape(inputs, expected_new_shape)

      flattened_t = _layers._inner_flatten(inputs, new_rank)
      static_shape = flattened_t.get_shape().as_list()
      self.assertEqual(static_shape, expected_new_shape)
      with self.cached_session() as sess:
        flattened = sess.run(flattened_t)
      np.testing.assert_array_equal(expected_flattened, flattened)

  def testSparsePartialFlatten(self):
    """Test `_inner_flatten` on `SparseTensor`s."""
    shape = [4, 3, 11, 6]
    np.random.seed(10301)
    random_ = np.random.rand(*shape)
    indices, values, _ = _sparsify(random_)

    for new_rank in [1, 2, 3]:
      expected_shape = (shape[:new_rank - 1] + [np.prod(shape[new_rank - 1:])])
      reshaped_random_ = np.reshape(random_, expected_shape)
      expected_indices, expected_values, _ = _sparsify(reshaped_random_)

      inputs_t = sparse_tensor.SparseTensor(indices, values, shape)

      flattened_t = _layers._inner_flatten(inputs_t, new_rank)

      with self.cached_session() as sess:
        flattened = sess.run(flattened_t)

      np.testing.assert_array_equal(expected_indices, flattened.indices)
      np.testing.assert_array_equal(expected_values, flattened.values)
      np.testing.assert_array_equal(expected_shape, flattened.dense_shape)

  def testIncompleteShape(self):
    """Test `_inner_flatten` shape inference for incomplete shapes."""
    shape = [2, None, 4, None, 5, 6]
    inputs = array_ops.placeholder(dtypes.int32)
    inputs.set_shape(shape)

    flattened1 = _layers._inner_flatten(inputs, 1)
    self.assertEqual([None], flattened1.get_shape().as_list())

    flattened2 = _layers._inner_flatten(inputs, 2)
    self.assertEqual([2, None], flattened2.get_shape().as_list())

    flattened3 = _layers._inner_flatten(inputs, 3)
    self.assertEqual([2, None, None], flattened3.get_shape().as_list())

    flattened4 = _layers._inner_flatten(inputs, 4)
    self.assertEqual([2, None, 4, None], flattened4.get_shape().as_list())

    flattened5 = _layers._inner_flatten(inputs, 5)
    self.assertEqual([2, None, 4, None, 30], flattened5.get_shape().as_list())

  def testDenseFlattenRankAssertion(self):
    """Test `_inner_flatten` rank assertion for dense tensors."""
    shape = [2, 3]
    new_rank = 3
    inputs = array_ops.placeholder(dtypes.int32)
    inputs.set_shape(shape)

    with self.assertRaisesRegexp(ValueError,
                                 'inputs has rank less than new_rank'):
      _layers._inner_flatten(inputs, new_rank)

  def testSparseFlattenRankAssertion(self):
    """Test `_inner_flatten` rank assertion for sparse tensors."""
    shape = [2, 3]
    new_rank = 3
    np.random.seed(10301)
    random_ = np.random.rand(*shape)
    indices, values, _ = _sparsify(random_)
    inputs = sparse_tensor.SparseTensor(indices, values, shape)

    with self.assertRaisesRegexp(ValueError,
                                 'Inputs has rank less than new_rank'):
      _layers._inner_flatten(inputs, new_rank)


class FCTest(test.TestCase):

  def testCreateFC(self):
    height, width = 3, 3
    for layer_fn in (_layers.fully_connected, layers_lib.relu):
      with ops.Graph().as_default() as g, self.session(g):
        inputs = np.random.uniform(size=(5, height * width * 3))
        output = layer_fn(inputs, 32)
        self.assertEqual(output.op.name, 'fully_connected/Relu')
        self.assertListEqual(output.get_shape().as_list(), [5, 32])
        weights = variables.get_variables_by_name('weights')[0]
        self.assertListEqual(weights.get_shape().as_list(), [3 * 3 * 3, 32])
        biases = variables.get_variables_by_name('biases')[0]
        self.assertListEqual(biases.get_shape().as_list(), [32])

  def testCreateFCWithScope(self):
    height, width = 3, 3
    with self.cached_session():
      inputs = random_ops.random_uniform((5, height * width * 3), seed=1)
      output = _layers.fully_connected(inputs, 32, scope='fc1')
      self.assertEqual(output.op.name, 'fc1/Relu')

  def testCreateFCWithCollection(self):
    height, width = 3, 3
    inputs = random_ops.random_uniform((5, height * width * 3), seed=1)
    with ops.name_scope('fe'):
      fc = _layers.fully_connected(
          inputs, 7, outputs_collections='outputs', scope='fc')
    output_collected = ops.get_collection('outputs')[0]
    self.assertEqual(output_collected.aliases, ['fc'])
    self.assertEqual(output_collected, fc)

  def testCreateFcCreatesWeightsAndBiasesVars(self):
    height, width = 3, 3
    inputs = random_ops.random_uniform((5, height * width * 3), seed=1)
    with self.cached_session():
      self.assertFalse(variables.get_variables('fc1/weights'))
      self.assertFalse(variables.get_variables('fc1/biases'))
      _layers.fully_connected(inputs, 32, scope='fc1')
      self.assertTrue(variables.get_variables('fc1/weights'))
      self.assertTrue(variables.get_variables('fc1/biases'))

  def testReuseVars(self):
    height, width = 3, 3
    inputs = random_ops.random_uniform((5, height * width * 3), seed=1)
    with self.cached_session():
      _layers.fully_connected(inputs, 32, scope='fc1')
      self.assertEqual(len(variables.get_variables('fc1')), 2)
      _layers.fully_connected(inputs, 32, scope='fc1', reuse=True)
      self.assertEqual(len(variables.get_variables('fc1')), 2)

  def testNonReuseVars(self):
    height, width = 3, 3
    inputs = random_ops.random_uniform((5, height * width * 3), seed=1)
    with self.cached_session():
      _layers.fully_connected(inputs, 32)
      self.assertEqual(len(variables.get_variables('fully_connected')), 2)
      _layers.fully_connected(inputs, 32)
      self.assertEqual(len(variables.get_variables('fully_connected')), 4)

  def testReuseWithRegularizer(self):
    height, width = 3, 3
    regularizer = lambda x: math_ops.reduce_sum(x) * 1e-3
    inputs = random_ops.random_uniform((5, height * width * 3), seed=1)

    _layers.fully_connected(
        inputs, 32, scope='fc1', weights_regularizer=regularizer)
    self.assertEqual(
        len(ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES)), 1)
    self.assertEqual(len(losses.get_regularization_losses()), 1)
    _layers.fully_connected(
        inputs, 32, scope='fc1', weights_regularizer=regularizer, reuse=True)
    self.assertEqual(
        len(ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES)), 1)
    self.assertEqual(len(losses.get_regularization_losses()), 1)

    with variable_scope.variable_scope('outer', reuse=False):
      _layers.fully_connected(inputs, 32, weights_regularizer=regularizer)
      self.assertEqual(
          len(ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES)), 2)
      self.assertEqual(len(losses.get_regularization_losses()), 2)
    with variable_scope.variable_scope('outer', reuse=True):
      _layers.fully_connected(inputs, 32, weights_regularizer=regularizer)
      self.assertEqual(
          len(ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES)), 2)
      self.assertEqual(len(losses.get_regularization_losses()), 2)

  def testCreateFCWithoutActivation(self):
    height, width = 3, 3
    with self.cached_session():
      inputs = random_ops.random_uniform((5, height * width * 3), seed=1)
      output = _layers.fully_connected(inputs, 32, activation_fn=None)
      self.assertEqual(output.op.name, 'fully_connected/BiasAdd')

  def testCreateFCWithWD(self):
    height, width = 3, 3
    with self.cached_session() as sess:
      inputs = random_ops.random_uniform((5, height * width * 3), seed=1)
      weight_decay = regularizers.l2_regularizer(0.01)
      _layers.fully_connected(inputs, 32, weights_regularizer=weight_decay)
      wd = ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES)[0]
      self.assertEqual(wd.op.name,
                       'fully_connected/kernel/Regularizer/l2_regularizer')
      sess.run(variables_lib.global_variables_initializer())
      self.assertLess(sess.run(wd), 0.4)

  def testCreateFCWithBD(self):
    height, width = 3, 3
    with self.cached_session() as sess:
      inputs = random_ops.random_uniform((5, height * width * 3), seed=1)
      bias_decay = regularizers.l2_regularizer(0.01)
      _layers.fully_connected(inputs, 32, biases_regularizer=bias_decay)
      wd = ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES)[0]
      self.assertEqual(wd.op.name,
                       'fully_connected/bias/Regularizer/l2_regularizer')
      sess.run(variables_lib.global_variables_initializer())
      self.assertLess(sess.run(wd), 0.4)

  def testCreateNoRegularizers(self):
    height, width = 3, 3
    with self.cached_session():
      inputs = random_ops.random_uniform((5, height * width * 3), seed=1)
      _layers.fully_connected(inputs, 32)
      self.assertEqual(
          ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES), [])

  def testReuseFCWithWD(self):
    height, width = 3, 3
    with self.cached_session():
      inputs = random_ops.random_uniform((5, height * width * 3), seed=1)
      weight_decay = regularizers.l2_regularizer(0.01)
      _layers.fully_connected(
          inputs, 32, weights_regularizer=weight_decay, scope='FC')
      self.assertEqual(len(variables.get_variables()), 2)
      self.assertEqual(
          len(ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES)), 1)
      _layers.fully_connected(
          inputs, 32, weights_regularizer=weight_decay, scope='FC', reuse=True)
      self.assertEqual(len(variables.get_variables()), 2)
      self.assertEqual(
          len(ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES)), 1)

  def testFCWithBatchNorm(self):
    height, width = 3, 3
    with self.cached_session():
      images = random_ops.random_uniform((5, height * width * 3), seed=1)
      with arg_scope(
          [_layers.fully_connected],
          normalizer_fn=_layers.batch_norm,
          normalizer_params={
              'decay': 0.9
          }):
        net = _layers.fully_connected(images, 27)
        net = _layers.fully_connected(net, 27)
      self.assertEqual(len(variables.get_variables()), 8)
      self.assertEqual(
          len(variables.get_variables('fully_connected/BatchNorm')), 3)
      self.assertEqual(
          len(variables.get_variables('fully_connected_1/BatchNorm')), 3)

  def testReuseFCWithBatchNorm(self):
    height, width = 3, 3
    with self.cached_session():
      images = random_ops.random_uniform((5, height * width * 3), seed=1)
      with arg_scope(
          [_layers.fully_connected],
          normalizer_fn=_layers.batch_norm,
          normalizer_params={
              'decay': 0.9
          }):
        net = _layers.fully_connected(images, 27, scope='fc1')
        net = _layers.fully_connected(net, 27, scope='fc1', reuse=True)
      self.assertEqual(len(variables.get_variables()), 4)
      self.assertEqual(len(variables.get_variables('fc1/BatchNorm')), 3)


class BatchNormTest(test.TestCase):

  def _addBesselsCorrection(self, sample_size, expected_var):
    correction_factor = sample_size / (sample_size - 1)
    expected_var *= correction_factor
    return expected_var, correction_factor

  def testBatchNormCenterFalse(self):
    a = array_ops.placeholder(dtype=dtypes.float32, shape=(10, 10, 10, 10))
    # Test that center=False builds a valid graph.
    _layers.batch_norm(
        a, center=False, data_format='NCHW', zero_debias_moving_mean=True)

  def testUnknownShape(self):
    with ops.Graph().as_default() as g, self.session(g):
      inputs = array_ops.placeholder(dtype=dtypes.float32)
      with self.assertRaisesRegexp(ValueError, 'undefined rank'):
        _layers.batch_norm(inputs)

  def testInvalidDataFormat(self):
    with ops.Graph().as_default() as g, self.session(g):
      inputs = array_ops.placeholder(dtype=dtypes.float32)
      with self.assertRaisesRegexp(
          ValueError, 'data_format has to be either NCHW or NHWC.'):
        _layers.batch_norm(inputs, data_format='CHWN')

  def testUnknownChannelsDimNHWC(self):
    with ops.Graph().as_default() as g, self.session(g):
      inputs = array_ops.placeholder(dtype=dtypes.float32)
      inputs.set_shape(tensor_shape.TensorShape((5, 3, 3, None)))
      with self.assertRaisesRegexp(ValueError, 'undefined'):
        _layers.batch_norm(inputs, data_format='NHWC')

  def testUnknownChannelsDimNCHW(self):
    with ops.Graph().as_default() as g, self.session(g):
      inputs = array_ops.placeholder(dtype=dtypes.float32)
      inputs.set_shape(tensor_shape.TensorShape((5, None, 3, 3)))
      with self.assertRaisesRegexp(ValueError, 'undefined'):
        _layers.batch_norm(inputs, data_format='NCHW')

  def _testCreateOp(self, fused, dtype=None):
    if dtype is None:
      dtype = dtypes.float32
    height, width = 3, 3
    with self.cached_session():
      images = np.random.uniform(size=(5, height, width, 3)).astype(
          dtype.as_numpy_dtype)
      output = _layers.batch_norm(images, fused=fused)
      expected_name = ('BatchNorm/FusedBatchNorm'
                       if fused else 'BatchNorm/batchnorm')
      self.assertTrue(output.op.name.startswith(expected_name))
      self.assertListEqual(output.get_shape().as_list(), [5, height, width, 3])
      self.assertEqual(
          ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES), [])

  def testCreateOpDefault(self):
    self._testCreateOp(False)

  def testCreateOpFused(self):
    self._testCreateOp(True)

  def testCreateOpFusedFloat16(self):
    self._testCreateOp(True, dtypes.float16)

  def _testCreateOpBetaRegularizer(self, fused=True):
    height, width = 3, 3
    with self.cached_session():
      reg = lambda x: 0.1 * math_ops.reduce_sum(x)
      images = np.random.uniform(size=(5, height, width, 3)).astype('f')
      _layers.batch_norm(images, param_regularizers={'beta': reg}, fused=fused)
      self.assertEqual(
          len(ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES)), 1)
      beta_decay = ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES)[0]
      self.assertEqual(beta_decay.op.name, 'BatchNorm/beta/Regularizer/mul')

  def testCreateOpBetaRegularizerFused(self):
    self._testCreateOpBetaRegularizer(fused=True)

  def testCreateOpBetaRegularizerNonFused(self):
    self._testCreateOpBetaRegularizer(fused=False)

  def _testCreateOpGammaRegularizer(self, fused=True):
    height, width = 3, 3
    with self.cached_session():
      reg = lambda x: 0.1 * math_ops.reduce_sum(x)
      images = np.random.uniform(size=(5, height, width, 3)).astype('f')
      _layers.batch_norm(
          images, param_regularizers={'gamma': reg}, scale=True, fused=fused)
      self.assertEqual(
          len(ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES)), 1)
      gamma_decay = ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES)[0]
      self.assertEqual(gamma_decay.op.name, 'BatchNorm/gamma/Regularizer/mul')

  def testCreateOpGammaRegularizerFused(self):
    self._testCreateOpGammaRegularizer(fused=True)

  def testCreateOpGammaRegularizerNonFused(self):
    self._testCreateOpGammaRegularizer(fused=False)

  def testCreateVariables(self):
    height, width = 3, 3
    with self.cached_session():
      images = random_ops.random_uniform((5, height, width, 3), seed=1)
      _layers.batch_norm(images, scale=True)
      beta = variables.get_variables_by_name('beta')[0]
      gamma = variables.get_variables_by_name('gamma')[0]
      self.assertEqual(beta.op.name, 'BatchNorm/beta')
      self.assertEqual(gamma.op.name, 'BatchNorm/gamma')
      moving_mean = variables.get_variables_by_name('moving_mean')[0]
      moving_variance = variables.get_variables_by_name('moving_variance')[0]
      self.assertEqual(moving_mean.op.name, 'BatchNorm/moving_mean')
      self.assertEqual(moving_variance.op.name, 'BatchNorm/moving_variance')

  def testMovingAverageVariables(self):
    height, width = 3, 3
    with self.cached_session():
      images = random_ops.random_uniform((5, height, width, 3), seed=1)
      _layers.batch_norm(images, scale=True)
      self.assertEqual(len(variables.get_model_variables()), 4)
      moving_mean = variables.get_variables_by_name('moving_mean')[0]
      moving_variance = variables.get_variables_by_name('moving_variance')[0]
      self.assertEqual(moving_mean.op.name, 'BatchNorm/moving_mean')
      self.assertEqual(moving_variance.op.name, 'BatchNorm/moving_variance')

  def testMovingAverageVariablesZeroDebias(self):
    height, width = 3, 3
    with self.cached_session():
      images = random_ops.random_uniform((5, height, width, 3), seed=1)
      _layers.batch_norm(
          images, scale=True, zero_debias_moving_mean=True, fused=False)
      self.assertEqual(len(variables.get_model_variables()), 6)
      moving_mean = variables.get_variables_by_name('moving_mean')[0]
      moving_variance = variables.get_variables_by_name('moving_variance')[0]
      biased = variables.get_variables_by_name('biased')[0]
      local_step = variables.get_variables_by_name('local_step')[0]
      self.assertEqual(moving_mean.op.name, 'BatchNorm/moving_mean')
      self.assertEqual(moving_variance.op.name, 'BatchNorm/moving_variance')
      self.assertEqual(biased.op.name, 'BatchNorm/BatchNorm/moving_mean/biased')
      self.assertEqual(local_step.op.name,
                       'BatchNorm/BatchNorm/moving_mean/local_step')

  def testUpdatesCollection(self):
    height, width = 3, 3
    with self.cached_session():
      images = random_ops.random_uniform((5, height, width, 3), seed=1)
      _layers.batch_norm(images, updates_collections='my_update_ops')
      update_layers = ops.get_collection('my_update_ops')
      update_moving_mean = update_layers[0]
      update_moving_variance = update_layers[1]
      self.assertStartsWith(update_moving_mean.op.name,
                            'BatchNorm/AssignMovingAvg')
      self.assertStartsWith(update_moving_variance.op.name,
                            'BatchNorm/AssignMovingAvg_1')

  def testVariablesCollections(self):
    variables_collections = {
        'beta': ['beta'],
        'gamma': ['gamma'],
        'moving_mean': ['moving_mean'],
        'moving_variance': ['moving_variance'],
    }
    images = random_ops.random_uniform((5, 5, 5, 3), seed=1)
    _layers.batch_norm(
        images, scale=True, variables_collections=variables_collections)
    for var_name, collection_names in variables_collections.items():
      collection = ops.get_collection(collection_names[0])
      self.assertEqual(len(collection), 1)
      var_name_in_collection = collection[0].op.name
      self.assertEqual(var_name_in_collection, 'BatchNorm/' + var_name)

  def testReuseVariables(self):
    height, width = 3, 3
    with self.cached_session():
      images = random_ops.random_uniform((5, height, width, 3), seed=1)
      _layers.batch_norm(images, scale=True, scope='bn')
      _layers.batch_norm(images, scale=True, scope='bn', reuse=True)
      beta = variables.get_variables_by_name('beta')
      gamma = variables.get_variables_by_name('gamma')
      self.assertEqual(len(beta), 1)
      self.assertEqual(len(gamma), 1)
      moving_mean = variables.get_variables_by_name('moving_mean')
      moving_variance = variables.get_variables_by_name('moving_variance')
      moving_vars = moving_mean + moving_variance
      self.assertEqual(len(moving_vars), 2)

  def testReuseUpdateOps(self):
    height, width = 3, 3
    with self.cached_session():
      images = random_ops.random_uniform((5, height, width, 3), seed=1)
      with arg_scope([_layers.batch_norm], updates_collections='update_ops'):
        _layers.batch_norm(images, scope='bn')
        self.assertEqual(len(ops.get_collection('update_ops')), 2)
        _layers.batch_norm(images, scope='bn', reuse=True)
        self.assertEqual(len(ops.get_collection('update_ops')), 4)

  def testCreateMovingVars(self):
    height, width = 3, 3
    with self.cached_session():
      images = random_ops.random_uniform((5, height, width, 3), seed=1)
      _ = _layers.batch_norm(images)
      moving_mean = variables.get_variables('BatchNorm/moving_mean')
      self.assertEqual(len(moving_mean), 1)
      self.assertEqual(moving_mean[0].op.name, 'BatchNorm/moving_mean')
      moving_variance = variables.get_variables('BatchNorm/moving_variance')
      self.assertEqual(len(moving_variance), 1)
      self.assertEqual(moving_variance[0].op.name, 'BatchNorm/moving_variance')

  def testZeroDebiasMovingMean(self):
    height, width = 3, 3
    batch_size = 10
    channels = 3
    np.random.seed(1)
    image_shape = (batch_size, height, width, channels)
    axis = (0, 1, 2)
    image_values = np.random.rand(*image_shape)
    expected_mean = np.mean(image_values, axis=axis)
    expected_var = np.var(image_values, axis=axis)

    images = constant_op.constant(
        image_values, shape=image_shape, dtype=dtypes.float32)
    output = _layers.batch_norm(
        images,
        decay=0.1,
        updates_collections=None,
        zero_debias_moving_mean=True,
        fused=False)
    moving_mean = variables.get_variables_by_name('BatchNorm/moving_mean')[0]
    moving_variance = variables.get_variables_by_name('moving_variance')[0]
    biased = variables.get_variables_by_name('biased')[0]
    local_step = variables.get_variables_by_name('local_step')[0]
    with self.cached_session() as sess:
      sess.run(variables_lib.global_variables_initializer())
      self.assertAllClose(local_step.eval(), 0)
      self.assertAllClose(moving_mean.eval(), [0] * channels)
      self.assertAllClose(biased.eval(), [0] * channels)
      self.assertAllClose(moving_variance.eval(), [1] * channels)
      for i in range(10):
        self.assertAllClose(local_step.eval(), i)
        sess.run([output])
        # In this case moving_mean == expected_mean after each update
        self.assertAllClose(moving_mean.eval(), expected_mean)

      # After 10 updates with decay 0.1 moving_mean == expected_mean,
      # biased == expected_mean and moving_variance == expected_var.
      self.assertAllClose(moving_mean.eval(), expected_mean)
      self.assertAllClose(moving_variance.eval(), expected_var)
      self.assertAllClose(biased.eval(), expected_mean)

  def _testNoneUpdatesCollections(self,
                                  fused,
                                  data_format='NHWC',
                                  zero_debias_moving_mean=False):
    height, width = 2, 2
    batch_size = 10
    channels = 3
    np.random.seed(1)
    use_gpu = fused
    with self.session(use_gpu=use_gpu) as sess:
      if data_format == 'NHWC':
        image_shape = (batch_size, height, width, channels)
        axis = (0, 1, 2)
      else:
        image_shape = (batch_size, channels, height, width)
        axis = (0, 2, 3)
      image_values = np.random.rand(*image_shape)
      expected_mean = np.mean(image_values, axis=axis)
      expected_var = np.var(image_values, axis=axis)
      if fused:
        # Add Bessel's correction
        expected_var, _ = self._addBesselsCorrection(
            batch_size * height * width, expected_var)
      images = constant_op.constant(
          image_values, shape=image_shape, dtype=dtypes.float32)
      output = _layers.batch_norm(
          images,
          decay=0.1,
          updates_collections=None,
          fused=fused,
          data_format=data_format,
          zero_debias_moving_mean=zero_debias_moving_mean)
      # updates_ops are not added to UPDATE_OPS collection.
      self.assertEqual(ops.get_collection(ops.GraphKeys.UPDATE_OPS), [])
      # Initialize all variables
      sess.run(variables_lib.global_variables_initializer())
      moving_mean = variables.get_variables('BatchNorm/moving_mean')[0]
      moving_variance = variables.get_variables('BatchNorm/moving_variance')[0]
      mean, variance = sess.run([moving_mean, moving_variance])
      # After initialization moving_mean == 0 and moving_variance == 1.
      self.assertAllClose(mean, [0] * channels)
      self.assertAllClose(variance, [1] * channels)
      for _ in range(10):
        sess.run([output])
        if zero_debias_moving_mean:
          # In this case moving_mean == expected_mean after update
          self.assertAllClose(moving_mean.eval(), expected_mean)
      mean = moving_mean.eval()
      variance = moving_variance.eval()
      # After 10 updates with decay 0.1 moving_mean == expected_mean and
      # moving_variance == expected_var.
      self.assertAllClose(mean, expected_mean)
      self.assertAllClose(variance, expected_var)

  def testNoneUpdatesCollectionsNHWC(self):
    self._testNoneUpdatesCollections(False, data_format='NHWC')
    print(tf.all_variables())

  def testNoneUpdatesCollectionsNCHW(self):
    self._testNoneUpdatesCollections(False, data_format='NCHW')

  def testNoneUpdatesCollectionsNHWCZeroDebias(self):
    self._testNoneUpdatesCollections(
        False, data_format='NHWC', zero_debias_moving_mean=True)

  def testNoneUpdatesCollectionsNCHWZeroDebias(self):
    self._testNoneUpdatesCollections(
        False, data_format='NCHW', zero_debias_moving_mean=True)

  def testNoneUpdatesCollectionsFusedNCHW(self):
    if test.is_gpu_available(cuda_only=True):
      self._testNoneUpdatesCollections(True, data_format='NCHW')

  def testNoneUpdatesCollectionsFusedNHWC(self):
    self._testNoneUpdatesCollections(True, data_format='NHWC')

  def testNoneUpdatesCollectionsFusedNCHWZeroDebias(self):
    if test.is_gpu_available(cuda_only=True):
      self._testNoneUpdatesCollections(
          True, data_format='NCHW', zero_debias_moving_mean=True)

  def testNoneUpdatesCollectionsFusedNHWCZeroDebias(self):
    self._testNoneUpdatesCollections(
        True, data_format='NHWC', zero_debias_moving_mean=True)

  def _testDelayedUpdateMovingVars(self,
                                   fused,
                                   data_format='NHWC',
                                   zero_debias_moving_mean=False):
    height, width = 2, 2
    batch_size = 10
    channels = 3
    np.random.seed(1)
    use_gpu = fused
    with self.session(use_gpu=use_gpu) as sess:
      if data_format == 'NHWC':
        image_shape = (batch_size, height, width, channels)
        axis = (0, 1, 2)
      else:
        image_shape = (batch_size, channels, height, width)
        axis = (0, 2, 3)
      image_values = np.random.rand(*image_shape)
      expected_mean = np.mean(image_values, axis=axis)
      expected_var = np.var(image_values, axis=axis)
      if fused:
        # Add Bessel's correction
        expected_var, correction_factor = self._addBesselsCorrection(
            batch_size * height * width, expected_var)
      images = constant_op.constant(
          image_values, shape=image_shape, dtype=dtypes.float32)
      output = _layers.batch_norm(
          images,
          decay=0.1,
          fused=fused,
          data_format=data_format,
          zero_debias_moving_mean=zero_debias_moving_mean)
      update_ops = ops.get_collection(ops.GraphKeys.UPDATE_OPS)
      # updates_ops are added to UPDATE_OPS collection.
      self.assertEqual(len(update_ops), 2)
      with ops.control_dependencies(update_ops):
        barrier = control_flow_ops.no_op(name='barrier')
      output = control_flow_ops.with_dependencies([barrier], output)
      # Initialize all variables
      sess.run(variables_lib.global_variables_initializer())
      moving_mean = variables.get_variables('BatchNorm/moving_mean')[0]
      moving_variance = variables.get_variables('BatchNorm/moving_variance')[0]
      mean, variance = sess.run([moving_mean, moving_variance])
      # After initialization moving_mean == 0 and moving_variance == 1.
      self.assertAllClose(mean, [0] * channels)
      self.assertAllClose(variance, [1] * channels)
      for _ in range(10):
        sess.run([output])
        if zero_debias_moving_mean:
          # In this case moving_mean == expected_mean after update
          self.assertAllClose(moving_mean.eval(), expected_mean)

      mean = moving_mean.eval()
      variance = moving_variance.eval()
      # After 10 updates with decay 0.1 moving_mean == expected_mean and
      # moving_variance == expected_var.
      self.assertAllClose(mean, expected_mean)
      if fused:
        # Add Bessel's correction
        moving_variance_corrected = moving_variance / correction_factor
        correct_moving_variance = state_ops.assign(moving_variance,
                                                   moving_variance_corrected)
        sess.run(correct_moving_variance)
      self.assertAllClose(variance, expected_var)

  def testDelayedUpdateMovingVarsNHWC(self):
    self._testDelayedUpdateMovingVars(False, data_format='NHWC')

  def testDelayedUpdateMovingVarsNCHW(self):
    self._testDelayedUpdateMovingVars(False, data_format='NCHW')

  def testDelayedUpdateMovingVarsFusedNCHW(self):
    if test.is_gpu_available(cuda_only=True):
      self._testDelayedUpdateMovingVars(True, data_format='NCHW')

  def testDelayedUpdateMovingVarsFusedNHWC(self):
    self._testDelayedUpdateMovingVars(True, data_format='NHWC')

  def testDelayedUpdateMovingVars(self):
    self._testDelayedUpdateMovingVars(False)

  def _testEvalMovingVars(self, zero_debias_moving_mean=False):
    height, width = 3, 3
    with self.cached_session() as sess:
      image_shape = (10, height, width, 3)
      image_values = np.random.rand(*image_shape)
      expected_mean = np.mean(image_values, axis=(0, 1, 2))
      expected_var = np.var(image_values, axis=(0, 1, 2))
      images = constant_op.constant(
          image_values, shape=image_shape, dtype=dtypes.float32)
      output = _layers.batch_norm(images, decay=0.1, is_training=False)
      self.assertEqual(ops.get_collection(ops.GraphKeys.UPDATE_OPS), [])
      # Initialize all variables
      sess.run(variables_lib.global_variables_initializer())
      moving_mean = variables.get_variables('BatchNorm/moving_mean')[0]
      moving_variance = variables.get_variables('BatchNorm/moving_variance')[0]
      mean, variance = sess.run([moving_mean, moving_variance])
      # After initialization moving_mean == 0 and moving_variance == 1.
      self.assertAllClose(mean, [0] * 3)
      self.assertAllClose(variance, [1] * 3)
      # Simulate assignment from saver restore.
      init_assigns = [
          state_ops.assign(moving_mean, expected_mean),
          state_ops.assign(moving_variance, expected_var)
      ]
      sess.run(init_assigns)
      for _ in range(10):
        sess.run([output], {images: np.random.rand(*image_shape)})
      mean = moving_mean.eval()
      variance = moving_variance.eval()
      # Although we feed different images, the moving_mean and moving_variance
      # shouldn't change.
      self.assertAllClose(mean, expected_mean)
      self.assertAllClose(variance, expected_var)

  def testEvalMovingVars(self):
    self._testEvalMovingVars()

  def testEvalMovingVarsZeroDebias(self):
    self._testEvalMovingVars(True)

  def testEvalMovingVarsWithPartitioner(self):
    # This test makes sure that the moving-mean and moving-variance logic works
    # when `batch_norm` is called within a variable-scope that has a variable
    # partitioner.
    partitioner = partitioned_variables.fixed_size_partitioner(2, axis=0)
    with variable_scope.variable_scope(
        variable_scope.get_variable_scope(), partitioner=partitioner):
      self.testEvalMovingVars()

  def _testReuseVars(self, fused, zero_debias_moving_mean=False):
    height, width = 3, 3
    batch_size = 10
    channels = 3
    with self.cached_session() as sess:
      image_shape = (batch_size, height, width, channels)
      image_values = np.random.rand(*image_shape)
      expected_mean = np.mean(image_values, axis=(0, 1, 2))
      expected_var = np.var(image_values, axis=(0, 1, 2))
      if fused:
        # Add Bessel's correction
        expected_var, correction_factor = self._addBesselsCorrection(
            batch_size * height * width, expected_var)
      images = constant_op.constant(
          image_values, shape=image_shape, dtype=dtypes.float32)
      output_train = _layers.batch_norm(
          images,
          decay=0.1,
          is_training=True,
          scope='BN',
          fused=fused,
          zero_debias_moving_mean=zero_debias_moving_mean)
      output_eval = _layers.batch_norm(
          images,
          decay=0.1,
          is_training=False,
          scope='BN',
          reuse=True,
          fused=fused,
          zero_debias_moving_mean=zero_debias_moving_mean)
      # Initialize all variables
      sess.run(variables_lib.global_variables_initializer())
      moving_mean = variables.get_variables('BN/moving_mean')[0]
      moving_variance = variables.get_variables('BN/moving_variance')[0]
      mean, variance = sess.run([moving_mean, moving_variance])
      # After initialization moving_mean == 0 and moving_variance == 1.
      self.assertAllClose(mean, [0] * channels)
      self.assertAllClose(variance, [1] * channels)
      update_ops = ops.get_collection(ops.GraphKeys.UPDATE_OPS)
      with ops.control_dependencies(update_ops):
        barrier = control_flow_ops.no_op(name='barrier')
      train_op = control_flow_ops.with_dependencies([barrier], output_train)
      # Before updates the outputs are different for train and eval.
      self.assertFalse(
          np.allclose(sess.run([output_train]), sess.run([output_eval])))
      for _ in range(10):
        sess.run([train_op])
      mean = moving_mean.eval()
      variance = moving_variance.eval()
      # After 10 updates with decay 0.1 moving_mean == expected_mean and
      # moving_variance == expected_var.
      self.assertAllClose(mean, expected_mean)
      if fused:
        # Add Bessel's correction
        moving_variance_corrected = moving_variance / correction_factor
        correct_moving_variance = state_ops.assign(moving_variance,
                                                   moving_variance_corrected)
        sess.run(correct_moving_variance)
      self.assertAllClose(variance, expected_var)
      # After convergence output_train and output_eval should be the same.
      self.assertAllClose(sess.run([output_train]), sess.run([output_eval]))

  def testReuseVarsDefault(self):
    self._testReuseVars(False)

  def testReuseVarsFused(self):
    self._testReuseVars(True)

  def testReuseVarsDefaultZeroDebias(self):
    self._testReuseVars(False, True)

  def testReuseVarsFusedZeroDebias(self):
    self._testReuseVars(True, True)

  def _testIsTrainingVariable(self,
                              fused,
                              data_format='NHWC',
                              zero_debias_moving_mean=False):
    height, width = 2, 2
    batch_size = 10
    channels = 3
    np.random.seed(1)
    use_gpu = fused
    np.random.seed(1)
    with self.session(use_gpu=use_gpu) as sess:
      if data_format == 'NHWC':
        image_shape = (batch_size, height, width, channels)
        axis = (0, 1, 2)
      else:
        image_shape = (batch_size, channels, height, width)
        axis = (0, 2, 3)
      image_values = np.random.rand(*image_shape)
      expected_mean = np.mean(image_values, axis=axis)
      expected_var = np.var(image_values, axis=axis)
      if fused:
        # Add Bessel's correction
        expected_var, correction_factor = self._addBesselsCorrection(
            batch_size * height * width, expected_var)
      images = constant_op.constant(
          image_values, shape=image_shape, dtype=dtypes.float32)
      # NB:  tf.identity is required, because variables can't be fed.
      is_training = tf.identity(tf.Variable(True))
      output = _layers.batch_norm(
          images,
          decay=0.1,
          is_training=is_training,
          fused=fused,
          data_format=data_format,
          zero_debias_moving_mean=zero_debias_moving_mean)
      # Initialize all variables
      sess.run(variables_lib.global_variables_initializer())
      moving_mean = variables.get_variables('BatchNorm/moving_mean')[0]
      moving_variance = variables.get_variables('BatchNorm/moving_variance')[0]
      mean, variance = sess.run([moving_mean, moving_variance])
      # After initialization moving_mean == 0 and moving_variance == 1.
      self.assertAllClose(mean, [0] * channels)
      self.assertAllClose(variance, [1] * channels)
      # Before updates the outputs are different depending of is_training.
      output_true = sess.run([output], {is_training: True})
      output_false = sess.run([output], {is_training: False})
      self.assertFalse(np.allclose(output_true, output_false))
      update_ops = ops.get_collection(ops.GraphKeys.UPDATE_OPS)
      with ops.control_dependencies(update_ops):
        barrier = control_flow_ops.no_op(name='barrier')
      train_op = control_flow_ops.with_dependencies([barrier], output)
      for _ in range(10):
        sess.run([train_op])
      mean = moving_mean.eval()
      variance = moving_variance.eval()
      # After 10 updates with decay 0.1 moving_mean == expected_mean and
      # moving_variance == expected_var.
      self.assertAllClose(mean, expected_mean)
      self.assertAllClose(variance, expected_var)
      # After updates to convergence the outputs don't depend on is_training.
      output_true = sess.run([output], {is_training: True})
      if fused:
        # Add Bessel's correction
        moving_variance_corrected = moving_variance / correction_factor
        correct_moving_variance = state_ops.assign(moving_variance,
                                                   moving_variance_corrected)
        sess.run(correct_moving_variance)
      output_false = sess.run([output], {is_training: False})
      self.assertAllClose(output_true, output_false)

  def testIsTrainingVariableNHWC(self):
    self._testIsTrainingVariable(False, data_format='NHWC')

  def testIsTrainingVariableNCHW(self):
    self._testIsTrainingVariable(False, data_format='NCHW')

  def testIsTrainingVariableNHWCZeroDebias(self):
    self._testIsTrainingVariable(
        False, data_format='NHWC', zero_debias_moving_mean=True)

  def testIsTrainingVariableNCHWZeroDebias(self):
    self._testIsTrainingVariable(
        False, data_format='NCHW', zero_debias_moving_mean=True)

  def testIsTrainingVariableFusedNCHW(self):
    if test.is_gpu_available(cuda_only=True):
      self._testIsTrainingVariable(True, data_format='NCHW')

  def testIsTrainingVariableFusedNHWC(self):
    self._testIsTrainingVariable(True, data_format='NHWC')

  def testIsTrainingVariableFusedNCHWZeroDebias(self):
    if test.is_gpu_available(cuda_only=True):
      self._testIsTrainingVariable(
          True, data_format='NCHW', zero_debias_moving_mean=True)

  def testIsTrainingVariableFusedNHWCZeroDebias(self):
    self._testIsTrainingVariable(
        True, data_format='NHWC', zero_debias_moving_mean=True)

  def testNoUpdatesWhenIsTrainingFalse(self):
    height, width = 3, 3
    with self.cached_session() as sess:
      image_shape = (10, height, width, 3)
      image_values = np.random.rand(*image_shape)
      images = constant_op.constant(
          image_values, shape=image_shape, dtype=dtypes.float32)
      output = _layers.batch_norm(images, decay=0.1, is_training=False)
      update_ops = ops.get_collection(ops.GraphKeys.UPDATE_OPS)
      # updates_ops are not added to UPDATE_OPS collection.
      self.assertEqual(len(update_ops), 0)
      # Initialize all variables
      sess.run(variables_lib.global_variables_initializer())
      moving_mean = variables.get_variables('BatchNorm/moving_mean')[0]
      moving_variance = variables.get_variables('BatchNorm/moving_variance')[0]
      mean, variance = sess.run([moving_mean, moving_variance])
      # After initialization moving_mean == 0 and moving_variance == 1.
      self.assertAllClose(mean, [0] * 3)
      self.assertAllClose(variance, [1] * 3)
      # When is_training is False batch_norm doesn't update moving_vars.
      for _ in range(10):
        sess.run([output])
      self.assertAllClose(moving_mean.eval(), [0] * 3)
      self.assertAllClose(moving_variance.eval(), [1] * 3)

  def testNoneUpdatesCollectionNoTraining(self):
    height, width = 3, 3
    with self.cached_session() as sess:
      image_shape = (10, height, width, 3)
      image_values = np.random.rand(*image_shape)
      images = constant_op.constant(
          image_values, shape=image_shape, dtype=dtypes.float32)
      output = _layers.batch_norm(
          images, decay=0.1, updates_collections=None, is_training=False)
      # updates_ops are not added to UPDATE_OPS collection.
      self.assertEqual(ops.get_collection(ops.GraphKeys.UPDATE_OPS), [])
      # Initialize all variables
      sess.run(variables_lib.global_variables_initializer())
      moving_mean = variables.get_variables('BatchNorm/moving_mean')[0]
      moving_variance = variables.get_variables('BatchNorm/moving_variance')[0]
      mean, variance = sess.run([moving_mean, moving_variance])
      # After initialization moving_mean == 0 and moving_variance == 1.
      self.assertAllClose(mean, [0] * 3)
      self.assertAllClose(variance, [1] * 3)
      # When is_training is False batch_norm doesn't update moving_vars.
      for _ in range(10):
        sess.run([output])
      self.assertAllClose(moving_mean.eval(), [0] * 3)
      self.assertAllClose(moving_variance.eval(), [1] * 3)

  def _testNoneUpdatesCollectionIsTrainingVariable(self,
                                                   fused,
                                                   data_format='NHWC'):
    height, width = 2, 2
    batch_size = 10
    channels = 3
    np.random.seed(1)
    use_gpu = fused
    with self.session(use_gpu=use_gpu) as sess:
      if data_format == 'NHWC':
        image_shape = (batch_size, height, width, channels)
        axis = (0, 1, 2)
      else:
        image_shape = (batch_size, channels, height, width)
        axis = (0, 2, 3)
      image_values = np.random.rand(*image_shape)
      expected_mean = np.mean(image_values, axis=axis)
      expected_var = np.var(image_values, axis=axis)
      if fused:
        # Add Bessel's correction
        expected_var, correction_factor = self._addBesselsCorrection(
            batch_size * height * width, expected_var)
      images = constant_op.constant(
          image_values, shape=image_shape, dtype=dtypes.float32)
      # NB: tf.identity is required because variables cant be fed.
      # Ref: https://github.com/tensorflow/tensorflow/issues/19884
      is_training = tf.identity(tf.Variable(True))

      output = _layers.batch_norm(
          images,
          decay=0.1,
          updates_collections=None,
          is_training=is_training,
          fused=fused,
          data_format=data_format)
      # updates_ops are not added to UPDATE_OPS collection.
      self.assertEqual(ops.get_collection(ops.GraphKeys.UPDATE_OPS), [])
      # Initialize all variables
      sess.run(variables_lib.global_variables_initializer())
      moving_mean = variables.get_variables('BatchNorm/moving_mean')[0]
      moving_variance = variables.get_variables('BatchNorm/moving_variance')[0]
      mean, variance = sess.run([moving_mean, moving_variance], {
          is_training: True})
      # After initialization moving_mean == 0 and moving_variance == 1.
      self.assertAllClose(mean, [0] * channels)
      self.assertAllClose(variance, [1] * channels)
      # When is_training is False batch_norm doesn't update moving_vars.
      for _ in range(10):
        sess.run([output], {is_training: False})
      self.assertAllClose(moving_mean.eval(), [0] * channels)
      self.assertAllClose(moving_variance.eval(), [1] * channels)
      # Before updates the outputs are different depending of is_training.
      output_true = sess.run([output], {is_training: True})
      output_false = sess.run([output], {is_training: False})
      self.assertFalse(np.allclose(output_true, output_false))
      # When is_training is True update moving_vars.
      for _ in range(10):
        sess.run([output], {is_training: True})
      # After 10 updates with decay 0.1 moving_mean == expected_mean and
      # moving_variance == expected_var.
      self.assertAllClose(moving_mean.eval(), expected_mean)
      self.assertAllClose(moving_variance.eval(), expected_var)
      # After updates to convergence the outputs don't depend on is_training.
      output_true = sess.run([output], {is_training: True})
      if fused:
        # Add Bessel's correction
        moving_variance_corrected = moving_variance / correction_factor
        correct_moving_variance = state_ops.assign(moving_variance,
                                                   moving_variance_corrected)
        sess.run(correct_moving_variance, {is_training: True})
      output_false = sess.run([output], {is_training: False})
      self.assertTrue(np.allclose(output_true, output_false))

  def testNoneUpdatesCollectionIsTrainingVariableNHWC(self):
    self._testNoneUpdatesCollectionIsTrainingVariable(False, data_format='NHWC')

  def testNoneUpdatesCollectionIsTrainingVariableNCHW(self):
    self._testNoneUpdatesCollectionIsTrainingVariable(False, data_format='NCHW')

  def testNoneUpdatesCollectionIsTrainingVariableFusedNCHW(self):
    if test.is_gpu_available(cuda_only=True):
      with tf.Graph().as_default():
        self._testNoneUpdatesCollectionIsTrainingVariable(
            True, data_format='NCHW')

  def testNoneUpdatesCollectionIsTrainingVariableFusedNHWC(self):
    self._testNoneUpdatesCollectionIsTrainingVariable(True, data_format='NHWC')

  def _testTrainMovingVars(self, fused, data_format='NHWC'):
    # Test that the gradients are stable while the moving_mean is updated.
    # Since the moving_mean is used as shift to compute the tf.momments, the
    # gradients could diverge, this test checks that gradients remains stable
    # while the moving_mean is updated.
    height, width = 7, 7
    batch_size = 10
    channels = 32
    np.random.seed(1)
    use_gpu = fused
    with self.session(use_gpu=use_gpu) as sess:
      if data_format == 'NHWC':
        image_shape = (batch_size, height, width, channels)
        axis = (0, 1, 2)
      else:
        image_shape = (batch_size, channels, height, width)
        axis = (0, 2, 3)
      image_values = np.random.rand(*image_shape) + 256
      expected_mean = np.mean(image_values, axis=axis)
      expected_var = np.var(image_values, axis=axis)
      if fused:
        # Add Bessel's correction
        expected_var, _ = self._addBesselsCorrection(
            batch_size * height * width, expected_var)
      images = constant_op.constant(
          image_values, shape=image_shape, dtype=dtypes.float32)
      output = _layers.batch_norm(
          images,
          decay=0.2,
          updates_collections=None,
          is_training=True,
          fused=fused,
          data_format=data_format)
      self.assertEqual(ops.get_collection(ops.GraphKeys.UPDATE_OPS), [])

      objective = math_ops.reduce_sum(output)

      [images_gradients] = gradients_impl.gradients(objective, images)
      # Initialize all variables
      sess.run(variables_lib.global_variables_initializer())
      moving_mean = variables.get_variables('BatchNorm/moving_mean')[0]
      moving_variance = variables.get_variables('BatchNorm/moving_variance')[0]
      mean, variance = sess.run([moving_mean, moving_variance])
      # After initialization moving_mean == 0 and moving_variance == 1.
      self.assertAllClose(mean, [0] * channels)
      self.assertAllClose(variance, [1] * channels)

      # Initial input gradients.
      images_gradients_value = sess.run(images_gradients)
      for _ in range(10):
        np_output, new_images_gradients = sess.run([output, images_gradients])
        # The outputs should be close to 0.0 mean and 1.0 variance
        self.assertAllClose(
            np.mean(np_output, axis=axis), [0] * channels,
            rtol=0.001,
            atol=0.001)
        self.assertAllClose(
            np.var(np_output, axis=axis), [1] * channels, rtol=0.01, atol=0.01)
        # The gradients should change slowly while updating moving_mean.
        max_diff = np.max(np.abs(images_gradients_value - new_images_gradients))
        self.assertGreaterEqual(max_diff, 0.0)
        self.assertLess(max_diff, 5e-5)
      self.assertAllClose(moving_mean.eval(), expected_mean)
      self.assertAllClose(moving_variance.eval(), expected_var)

  def testTrainMovingVarsNHWC(self):
    self._testTrainMovingVars(False, data_format='NHWC')

  def testTrainMovingVarsNCHW(self):
    self._testTrainMovingVars(False, data_format='NCHW')

  def testTrainMovingVarsFusedNCHW(self):
    if test.is_gpu_available(cuda_only=True):
      self._testTrainMovingVars(True, data_format='NCHW')

  def testTrainMovingVarsFusedNHWC(self):
    self._testTrainMovingVars(True, data_format='NHWC')

  def testCustomInitializer(self):
    height, width = 3, 3
    channels = 3
    with self.cached_session() as sess:
      images = (np.ones((5, height, width, channels)) * 9.0).astype('f')
      beta = init_ops.constant_initializer(
          (np.ones(channels) * 5.0).astype('f'))
      gamma = init_ops.constant_initializer(
          (np.ones(channels) * 2.0).astype('f'))
      mean = init_ops.constant_initializer(
          (np.ones(channels) * 5.0).astype('f'))
      variance = init_ops.constant_initializer(
          (np.ones(channels) * 4.0).astype('f'))
      output = _layers.batch_norm(
          images,
          is_training=False,
          scale=True,
          epsilon=0.0,
          param_initializers={
              'beta': beta,
              'gamma': gamma,
              'moving_mean': mean,
              'moving_variance': variance,
          })
      sess.run(variables_lib.global_variables_initializer())
      outs = sess.run(output)
      self.assertAllClose(outs, images)

  def _runBatchNormalizationWithFormat(self, shape, data_format, is_training):
    channels = shape[-1]
    with self.session(use_gpu=True) as sess:
      images = np.arange(np.product(shape), dtype=np.float32).reshape(shape)
      beta = init_ops.constant_initializer(
          np.arange(2, channels + 2, dtype=np.float32))
      gamma = init_ops.constant_initializer(
          np.arange(10, channels + 10, dtype=np.float32) * 2.0)
      mean = init_ops.constant_initializer(
          np.arange(3, channels + 3, dtype=np.float32) * 5.0)
      variance = init_ops.constant_initializer(
          np.arange(1, channels + 1, dtype=np.float32) * 4.0)
      if data_format == 'NCHW':
        # Reshape inputs from NHWC to NCHW format.
        images = array_ops.transpose(
            images, [0, len(shape) - 1] + list(range(1,
                                                     len(shape) - 1)))
      output = _layers.batch_norm(
          images,
          is_training=is_training,
          scale=True,
          epsilon=0.5,
          param_initializers={
              'beta': beta,
              'gamma': gamma,
              'moving_mean': mean,
              'moving_variance': variance,
          },
          data_format=data_format)
      if data_format == 'NCHW':
        # Reshape outputs from NCHW back to NHWC format.
        output = array_ops.transpose(output,
                                     [0] + list(range(2, len(shape))) + [1])
      sess.run(variables_lib.global_variables_initializer())
      return sess.run(output)

  def testNHWCAndNCHWInferenceProduceSameOutput(self):
    if test.is_gpu_available(cuda_only=True):
      for shape in [[7, 3, 5], [5, 2, 3, 4], [11, 3, 2, 4, 5]]:
        nhwc = self._runBatchNormalizationWithFormat(
            data_format='NHWC', shape=shape, is_training=False)
        nchw = self._runBatchNormalizationWithFormat(
            data_format='NCHW', shape=shape, is_training=False)
        self.assertAllClose(nhwc, nchw, atol=1e-4, rtol=1e-4)

  def testNHWCAndNCHWTrainingProduceSameOutput(self):
    if test.is_gpu_available(cuda_only=True):
      for shape in [[7, 3, 5], [5, 2, 3, 4], [11, 3, 2, 4, 5]]:
        nhwc = self._runBatchNormalizationWithFormat(
            data_format='NHWC', shape=shape, is_training=True)
        nchw = self._runBatchNormalizationWithFormat(
            data_format='NCHW', shape=shape, is_training=True)
        self.assertAllClose(nhwc, nchw, atol=1e-4, rtol=1e-4)

  def testBatchNormBeta(self):
    # Test case for 11673
    with self.cached_session() as sess:
      a_32 = array_ops.placeholder(dtypes.float32, shape=(10, 10, 10, 10))
      _layers.batch_norm(
          a_32, center=False, data_format='NCHW', zero_debias_moving_mean=True)
      a_16 = array_ops.placeholder(dtypes.float16, shape=(10, 10, 10, 10))
      _layers.batch_norm(
          a_16, center=False, data_format='NCHW', zero_debias_moving_mean=True)
      sess.run(variables_lib.global_variables_initializer())

  def is_float_var(self, v):
    if v.dtype == dtypes.float32_ref:
      return True
    if v.dtype == tf.float32 and v.op.outputs[0].dtype == tf.resource:
      return True
    return False

  def testVariablesAreFloat32(self):
    height, width = 3, 3
    with self.cached_session():
      images = random_ops.random_uniform(
          (5, height, width, 3), seed=1, dtype=dtypes.float16)
      _layers.batch_norm(images, scale=True)
      beta = variables.get_variables_by_name('beta')[0]
      gamma = variables.get_variables_by_name('gamma')[0]
      self.assertTrue(self.is_float_var(beta))
      self.assertTrue(self.is_float_var(gamma))
      moving_mean = variables.get_variables_by_name('moving_mean')[0]
      moving_variance = variables.get_variables_by_name('moving_variance')[0]
      self.assertTrue(self.is_float_var(moving_mean))
      self.assertTrue(self.is_float_var(moving_variance))

  def _runFusedBatchNorm(self, shape, dtype):
    channels = shape[1]
    images = np.arange(np.product(shape), dtype=dtype).reshape(shape)
    beta = init_ops.constant_initializer(
        np.arange(2, channels + 2, dtype=np.float32))
    gamma = init_ops.constant_initializer(
        np.arange(10, channels + 10, dtype=np.float32) * 2.0)
    mean = init_ops.constant_initializer(
        np.arange(3, channels + 3, dtype=np.float32) * 5.0)
    variance = init_ops.constant_initializer(
        np.arange(1, channels + 1, dtype=np.float32) * 4.0)
    output = _layers.batch_norm(
        images,
        fused=True,
        is_training=True,
        scale=True,
        epsilon=0.5,
        param_initializers={
            'beta': beta,
            'gamma': gamma,
            'moving_mean': mean,
            'moving_variance': variance,
        },
        data_format='NCHW')
    with self.session(use_gpu=True) as sess:
      sess.run(variables_lib.global_variables_initializer())
      return sess.run(output)

  def testFusedBatchNormFloat16MatchesFloat32(self):
    if test.is_gpu_available(cuda_only=True):
      shape = [5, 4, 2, 3]
      res_32 = self._runFusedBatchNorm(shape, np.float32)
      res_16 = self._runFusedBatchNorm(shape, np.float16)
      self.assertAllClose(res_32, res_16, rtol=1e-3)

  def testAdjustmentCreated(self):
    # Tests that the adjustment is appropriately passed to and used by the core
    # BN layer.
    all_adjustments = []

    def _create_adjustment(shape):
      adjustments = [array_ops.ones(shape[-1:]), array_ops.zeros(shape[-1:])]
      all_adjustments.extend(adjustments)
      return adjustments

    depth = 8
    images = array_ops.zeros([10, 5, 5, depth])
    output = _layers.batch_norm(
        images, is_training=True, adjustment=_create_adjustment)
    self.assertListEqual(output.shape.as_list(), images.shape.as_list())
    self.assertEqual(len(all_adjustments), 2)
    self.assertListEqual(all_adjustments[0].shape.as_list(), [depth])
    self.assertListEqual(all_adjustments[1].shape.as_list(), [depth])


class LayerNormTest(test.TestCase):

  def testUnknownShape(self):
    with ops.Graph().as_default() as g, self.session(g):
      inputs = array_ops.placeholder(dtype=dtypes.float32)
      with self.assertRaisesRegexp(ValueError, 'undefined rank'):
        _layers.layer_norm(inputs)

  def testParamsDimsNotFullyDefined(self):
    with ops.Graph().as_default() as g, self.session(g):
      inputs = array_ops.placeholder(dtype=dtypes.float32)
      inputs.set_shape(tensor_shape.TensorShape((5, 3, 3, None)))
      with self.assertRaisesRegexp(ValueError, 'is not fully defined'):
        _layers.layer_norm(inputs)

  def testCreateOp(self):
    height, width = 3, 3
    with self.cached_session():
      images = np.random.uniform(size=(5, height, width, 3))
      output = _layers.layer_norm(images)
      self.assertTrue(output.op.name.startswith('LayerNorm/batchnorm'))
      self.assertListEqual(output.get_shape().as_list(), [5, height, width, 3])

  def testCreateVariables(self):
    height, width = 3, 3
    with self.cached_session():
      images = random_ops.random_uniform((5, height, width, 3), seed=1)
      _layers.layer_norm(images)
      beta = variables.get_variables_by_name('beta')[0]
      gamma = variables.get_variables_by_name('gamma')[0]
      self.assertEqual(beta.op.name, 'LayerNorm/beta')
      self.assertEqual(gamma.op.name, 'LayerNorm/gamma')

  def testReuseVariables(self):
    height, width = 3, 3
    with self.cached_session():
      images = random_ops.random_uniform((5, height, width, 3), seed=1)
      _layers.layer_norm(images, scope='ln')
      _layers.layer_norm(images, scope='ln', reuse=True)
      beta = variables.get_variables_by_name('beta')
      gamma = variables.get_variables_by_name('gamma')
      self.assertEqual(len(beta), 1)
      self.assertEqual(len(gamma), 1)

  def testReuseVars(self):
    height, width = 3, 3
    with self.cached_session() as sess:
      image_shape = (10, height, width, 3)
      image_values = np.random.rand(*image_shape)
      images = constant_op.constant(
          image_values, shape=image_shape, dtype=dtypes.float32)
      output_train = _layers.layer_norm(images, scope='LN')
      output_eval = _layers.layer_norm(images, scope='LN', reuse=True)
      # Initialize all variables
      sess.run(variables_lib.global_variables_initializer())
      # output_train and output_eval should be the same.
      self.assertAllClose(sess.run([output_train]), sess.run([output_eval]))

  def doOutputTest(self,
                   input_shape,
                   tol=1e-5,
                   begin_norm_axis=1,
                   dtype=dtypes.float64):
    eps = 1e-12 if dtype != dtypes.float16 else 1e-3
    expected_mean = np.zeros(input_shape[:begin_norm_axis])
    expected_var_uncorrected = np.ones(input_shape[:begin_norm_axis])
    sigma_list = [1.0, 0.1]
    if dtype == dtypes.float16:
      # This causes the variance to underflow in float16, and requires that
      # variance_epsilon be set appropriately to avoid NaNs in the output.
      sigma_list.append(1e-4)
    # Note that the mean:variance ratio must be limited to the representable
    # range for float16.
    for mu in [0.0, 1e2 if dtype != dtypes.float16 else 1e1]:
      for sigma in sigma_list:
        expected_var = expected_var_uncorrected / (1.0 + eps / sigma**2)
        input_values = np.random.randn(*input_shape) * sigma + mu
        with ops.Graph().as_default() as g:
          with self.session(graph=g) as sess:
            inputs = constant_op.constant(
                input_values, shape=input_shape, dtype=dtype)
            output_t = _layers.layer_norm(
                inputs, begin_norm_axis=begin_norm_axis, scope='LN')
            # Initialize all variables
            sess.run(variables_lib.global_variables_initializer())
            # The mean and variance of the output should be close to 0 and 1
            # respectively.
            if begin_norm_axis < 0:
              begin_norm_axis = len(input_shape) + begin_norm_axis
            moments_axis = tuple(range(begin_norm_axis, len(input_shape)))
            with variable_scope.variable_scope('LN', reuse=True):
              beta_var = variable_scope.get_variable('beta', dtype=dtype)
              gamma_var = variable_scope.get_variable('gamma', dtype=dtype)
            outputs, beta, gamma = sess.run((output_t, beta_var, gamma_var))
            # Make sure that there are no NaNs
            self.assertFalse(np.isnan(outputs).any())
            if outputs.dtype != np.float64:
              # Cast to float64 before computing mean/variance to avoid
              # overflow and precision issues.
              outputs = outputs.astype(np.float64)
            mean = np.mean(outputs, axis=moments_axis)
            var = np.var(outputs, axis=moments_axis)
            # Layer-norm implemented in numpy
            expected_out = (
                (gamma * (input_values - np.mean(
                    input_values, axis=moments_axis, keepdims=True)) /
                 np.sqrt(eps + np.var(
                     input_values, axis=moments_axis, keepdims=True))) + beta)
            self.assertAllClose(expected_mean, mean, atol=tol, rtol=tol)
            self.assertAllClose(expected_var, var, atol=tol)
            # The full computation gets a bigger tolerance
            self.assertAllClose(expected_out, outputs, atol=5 * tol)

  def testOutput2DInput(self):
    self.doOutputTest((10, 300))

  def testOutput2DInputDegenerateNormAxis(self):
    with self.assertRaisesRegexp(ValueError, r'must be < rank\(inputs\)'):
      self.doOutputTest((10, 300), begin_norm_axis=2)

  def testOutput4DInput(self):
    self.doOutputTest((100, 10, 10, 3))

  def testOutput4DInputNormOnInnermostAxis(self):
    # Equivalent tests
    self.doOutputTest(
        (100, 10, 10, 3), begin_norm_axis=3, tol=1e-4, dtype=dtypes.float64)
    self.doOutputTest(
        (100, 10, 10, 3), begin_norm_axis=-1, tol=1e-4, dtype=dtypes.float64)

  def testOutputSmallInput(self):
    self.doOutputTest((10, 10, 10, 30))

  def testOutputSmallInputNormOnInnermostAxis(self):
    self.doOutputTest((10, 10, 10, 30), begin_norm_axis=3)

  def testOutputBigInput(self):
    self.doOutputTest((1, 100, 100, 1))

  def testOutputBigInputFloat32(self):
    self.doOutputTest((1, 100, 1000, 1), tol=1e-4, dtype=dtypes.float32)

  def testOutputBigInputFloat16(self):
    self.doOutputTest((1, 100, 1000, 1), tol=5e-2, dtype=dtypes.float16)


class GDNTest(test.TestCase):

  def _runGDN(self, x, shape, inverse, data_format):
    inputs = array_ops.placeholder(dtypes.float32, shape)
    outputs = _layers.gdn(inputs, inverse=inverse, data_format=data_format)
    with self.cached_session() as sess:
      variables_lib.global_variables_initializer().run()
      y, = sess.run([outputs], {inputs: x})
    return y

  def testInvalidDataFormat(self):
    x = np.random.uniform(size=(1, 2, 3, 4))
    with self.assertRaises(ValueError):
      self._runGDN(x, x.shape, False, 'NHWC')

  def testUnknownDim(self):
    x = np.random.uniform(size=(1, 2, 3, 4))
    with self.assertRaises(ValueError):
      self._runGDN(x, 4 * [None], False, 'channels_last')

  def testChannelsLast(self):
    for ndim in [3, 4, 5]:
      x = np.random.uniform(size=(1, 2, 3, 4)[:ndim])
      y = self._runGDN(x, x.shape, False, 'channels_last')
      self.assertEqual(x.shape, y.shape)
      self.assertAllClose(y, x / np.sqrt(1 + .1 * (x**2)), rtol=0, atol=1e-6)

  def testChannelsFirst(self):
    # `bias_add` doesn't support NCHW on CPU.
    if test.is_gpu_available(cuda_only=True):
      for ndim in [3, 4, 5]:
        x = np.random.uniform(size=(4, 3, 2, 1)[:ndim])
        y = self._runGDN(x, x.shape, False, 'channels_first')
        self.assertEqual(x.shape, y.shape)
        self.assertAllClose(y, x / np.sqrt(1 + .1 * (x**2)), rtol=0, atol=1e-6)

  def testWrongDims(self):
    for ndim in [1, 2, 6]:
      x = np.random.uniform(size=(1, 2, 3, 4, 3, 2)[:ndim])
      with self.assertRaises(ValueError):
        self._runGDN(x, x.shape, False, 'channels_last')

  def testIGDN(self):
    x = np.random.uniform(size=(1, 2, 3, 4))
    y = self._runGDN(x, x.shape, True, 'channels_last')
    self.assertEqual(x.shape, y.shape)
    self.assertAllClose(y, x * np.sqrt(1 + .1 * (x**2)), rtol=0, atol=1e-6)


class ImagesToSequenceTest(test.TestCase):

  def testInvalidDataFormat(self):
    height, width = 7, 11
    images = np.random.uniform(size=(5, height, width, 2))
    with self.assertRaisesRegexp(ValueError,
                                 'data_format has to be either NCHW or NHWC.'):
      _layers.images_to_sequence(images, data_format='CHWN')

  def testImagesToSequenceDims(self):
    height, width = 7, 11
    images = np.random.uniform(size=(2, height, width, 5)).astype(np.float32)
    output = _layers.images_to_sequence(images)
    self.assertListEqual(output.get_shape().as_list(), [11, 14, 5])

  def testImagesToSequenceNCHW(self):
    height, width = 7, 11
    images = np.random.uniform(size=(2, 5, height, width)).astype(np.float32)
    output = _layers.images_to_sequence(images, data_format='NCHW')
    self.assertListEqual(output.get_shape().as_list(), [11, 14, 5])


class MaxPool2DTest(test.TestCase):

  def testInvalidDataFormat(self):
    height, width = 3, 6
    images = np.random.uniform(size=(5, height, width, 3))
    with self.assertRaisesRegexp(ValueError,
                                 'data_format has to be either NCHW or NHWC.'):
      _layers.max_pool2d(images, [3, 3], data_format='CHWN')

  def testCreateMaxPool(self):
    height, width = 3, 6
    images = np.random.uniform(size=(5, height, width, 3)).astype(np.float32)
    output = _layers.max_pool2d(images, [3, 3])
    self.assertEqual(output.op.name, 'MaxPool2D/MaxPool')
    self.assertListEqual(output.get_shape().as_list(), [5, 1, 2, 3])

  def testCreateMaxPoolNCHW(self):
    height, width = 3, 6
    images = np.random.uniform(size=(5, 3, height, width)).astype(np.float32)
    output = _layers.max_pool2d(images, [3, 3], data_format='NCHW')
    self.assertListEqual(output.get_shape().as_list(), [5, 3, 1, 2])

  def testCollectOutputs(self):
    height, width = 3, 6
    images = random_ops.random_uniform((5, height, width, 3), seed=1)
    output = _layers.max_pool2d(images, [3, 3], outputs_collections='outputs')
    output_collected = ops.get_collection('outputs')[0]
    self.assertEqual(output_collected.aliases, ['MaxPool2D'])
    self.assertEqual(output_collected, output)

  def testCreateSquareMaxPool(self):
    height, width = 3, 6
    images = random_ops.random_uniform((5, height, width, 3), seed=1)
    output = _layers.max_pool2d(images, 3)
    self.assertEqual(output.op.name, 'MaxPool2D/MaxPool')
    self.assertListEqual(output.get_shape().as_list(), [5, 1, 2, 3])

  def testCreateMaxPoolWithScope(self):
    height, width = 3, 6
    images = random_ops.random_uniform((5, height, width, 3), seed=1)
    output = _layers.max_pool2d(images, [3, 3], scope='pool1')
    self.assertEqual(output.op.name, 'pool1/MaxPool')

  def testCreateMaxPoolWithSamePadding(self):
    height, width = 3, 6
    images = random_ops.random_uniform((5, height, width, 3), seed=1)
    output = _layers.max_pool2d(images, [3, 3], padding='SAME')
    self.assertListEqual(output.get_shape().as_list(), [5, 2, 3, 3])

  def testCreateMaxPoolWithSamePaddingNCHW(self):
    height, width = 3, 6
    images = random_ops.random_uniform((5, 3, height, width), seed=1)
    output = _layers.max_pool2d(
        images, [3, 3], padding='SAME', data_format='NCHW')
    self.assertListEqual(output.get_shape().as_list(), [5, 3, 2, 3])

  def testCreateMaxPoolStrideWithSamePadding(self):
    height, width = 3, 6
    images = random_ops.random_uniform((5, height, width, 3), seed=1)
    output = _layers.max_pool2d(images, [3, 3], stride=1, padding='SAME')
    self.assertListEqual(output.get_shape().as_list(), [5, height, width, 3])

  def testGlobalMaxPool(self):
    height, width = 3, 6
    images = random_ops.random_uniform((5, height, width, 3), seed=1)
    output = _layers.max_pool2d(images, images.get_shape()[1:3], stride=1)
    self.assertListEqual(output.get_shape().as_list(), [5, 1, 1, 3])


class MaxPool3DTest(test.TestCase):

  def testInvalidDataFormat(self):
    depth, height, width = 3, 6, 9
    images = np.random.uniform(size=(5, depth, height, width, 3))
    with self.assertRaisesRegexp(
        ValueError, 'data_format has to be either NCDHW or NDHWC.'):
      _layers.max_pool3d(images, [3, 3, 3], data_format='CDHWN')

  def testCreateMaxPool(self):
    depth, height, width = 3, 6, 9
    images = np.random.uniform(size=(5, depth, height, width, 3)).astype(
        np.float32)
    output = _layers.max_pool3d(images, [3, 3, 3])
    self.assertEqual(output.op.name, 'MaxPool3D/MaxPool3D')
    self.assertListEqual(output.get_shape().as_list(), [5, 1, 2, 4, 3])

  def testCreateMaxPoolNCDHW(self):
    depth, height, width = 3, 6, 9
    images = np.random.uniform(size=(5, 3, depth, height, width)).astype(
        np.float32)
    output = _layers.max_pool3d(images, [3, 3, 3], data_format='NCDHW')
    self.assertEqual(output.op.name, 'MaxPool3D/transpose_1')
    self.assertListEqual(output.get_shape().as_list(), [5, 3, 1, 2, 4])

  def testCollectOutputs(self):
    depth, height, width = 3, 6, 9
    images = random_ops.random_uniform((5, depth, height, width, 3), seed=1)
    output = _layers.max_pool3d(
        images, [3, 3, 3], outputs_collections='outputs')
    output_collected = ops.get_collection('outputs')[0]
    self.assertEqual(output_collected.aliases, ['MaxPool3D'])
    self.assertEqual(output_collected, output)

  def testCreateSquareMaxPool(self):
    depth, height, width = 3, 6, 9
    images = random_ops.random_uniform((5, depth, height, width, 3), seed=1)
    output = _layers.max_pool3d(images, 3)
    self.assertEqual(output.op.name, 'MaxPool3D/MaxPool3D')
    self.assertListEqual(output.get_shape().as_list(), [5, 1, 2, 4, 3])

  def testCreateMaxPoolWithScope(self):
    depth, height, width = 3, 6, 9
    images = random_ops.random_uniform((5, depth, height, width, 3), seed=1)
    output = _layers.max_pool3d(images, [3, 3, 3], scope='pool1')
    self.assertEqual(output.op.name, 'pool1/MaxPool3D')

  def testCreateMaxPoolWithSamePadding(self):
    depth, height, width = 3, 6, 9
    images = random_ops.random_uniform((5, depth, height, width, 3), seed=1)
    output = _layers.max_pool3d(images, [3, 3, 3], padding='SAME')
    self.assertListEqual(output.get_shape().as_list(), [5, 2, 3, 5, 3])

  def testCreateMaxPoolWithSamePaddingNCDHW(self):
    depth, height, width = 3, 6, 9
    images = random_ops.random_uniform((5, 3, depth, height, width), seed=1)
    output = _layers.max_pool3d(
        images, [3, 3, 3], padding='SAME', data_format='NCDHW')
    self.assertListEqual(output.get_shape().as_list(), [5, 3, 2, 3, 5])

  def testCreateMaxPoolStrideWithSamePadding(self):
    depth, height, width = 3, 6, 9
    images = random_ops.random_uniform((5, depth, height, width, 3), seed=1)
    output = _layers.max_pool3d(images, [3, 3, 3], stride=1, padding='SAME')
    self.assertListEqual(output.get_shape().as_list(),
                         [5, depth, height, width, 3])

  def testGlobalMaxPool(self):
    depth, height, width = 3, 6, 9
    images = random_ops.random_uniform((5, depth, height, width, 3), seed=1)
    output = _layers.max_pool3d(images, images.get_shape()[1:4], stride=1)
    self.assertListEqual(output.get_shape().as_list(), [5, 1, 1, 1, 3])


class OneHotEncodingTest(test.TestCase):

  def testOneHotEncodingCreate(self):
    with self.cached_session():
      labels = np.array([0, 1, 2])
      output = _layers.one_hot_encoding(labels, num_classes=3)
      self.assertEqual(output.op.name, 'OneHotEncoding/one_hot')
      self.assertListEqual(output.get_shape().as_list(), [3, 3])

  def testCollectOutputs(self):
    with self.cached_session():
      labels = constant_op.constant([0, 1, 2])
      output = _layers.one_hot_encoding(
          labels, num_classes=3, outputs_collections='outputs')
      c_output = ops.get_collection('outputs')[0]
      self.assertEqual(c_output.aliases, ['OneHotEncoding'])
      self.assertEqual(c_output, output)

  def testOneHotEncoding(self):
    with self.cached_session():
      labels = constant_op.constant([0, 1, 2])
      one_hot_labels = constant_op.constant([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
      output = _layers.one_hot_encoding(labels, num_classes=3)
      self.assertAllClose(output.eval(), one_hot_labels.eval())

  def testOneHotEncodingInt32(self):
    with self.cached_session():
      labels = constant_op.constant([0, 1, 2], dtype=dtypes.int32)
      one_hot_labels = constant_op.constant([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
      output = _layers.one_hot_encoding(labels, num_classes=3)
      self.assertAllClose(output.eval(), one_hot_labels.eval())


class RepeatTests(test.TestCase):

  def testRepeat(self):
    height, width = 3, 3
    with self.cached_session():
      images = np.random.uniform(size=(5, height, width, 3)).astype(np.float32)
      output = _layers.repeat(images, 3, layers_lib.conv2d, 32, [3, 3])
      self.assertEqual(output.op.name, 'Repeat/convolution2d_3/Relu')
      self.assertListEqual(output.get_shape().as_list(), [5, 3, 3, 32])

  def testRepeatWithScope(self):
    height, width = 3, 3
    with self.cached_session():
      images = random_ops.random_uniform(
          (5, height, width, 3), seed=1, name='images')
      output = _layers.repeat(
          images, 3, layers_lib.conv2d, 32, [3, 3], scope='conv1')
      self.assertEqual(output.op.name, 'conv1/conv1_3/Relu')
      self.assertListEqual(output.get_shape().as_list(), [5, 3, 3, 32])


class SeparableConv2dTest(test.TestCase):

  def testCreateConvInt32(self):
    height, width = 3, 3
    with self.cached_session():
      images = random_ops.random_uniform(
          (5, height, width, 3), seed=1, dtype=dtypes.int32, maxval=12345)
      with self.assertRaisesRegexp(TypeError, 'non-floating point type'):
        layers_lib.separable_conv2d(images, 32, [3, 3], 2)

  def testCreateConvFloat32(self):
    height, width = 3, 3
    with self.cached_session():
      images = random_ops.random_uniform(
          (5, height, width, 3), seed=1, dtype=dtypes.float32)
      output = layers_lib.separable_conv2d(images, 32, [3, 3], 2)
      self.assertEqual(output.op.name, 'SeparableConv2d/Relu')
      self.assertListEqual(output.get_shape().as_list(), [5, height, width, 32])

  def testCreateDepthwiseConv(self):
    height, width = 3, 3
    with self.cached_session():
      images = random_ops.random_uniform((5, height, width, 3), seed=1)
      output = layers_lib.separable_conv2d(images, None, [3, 3], 2)
      self.assertEqual(output.op.name, 'SeparableConv2d/Relu')
      self.assertListEqual(output.get_shape().as_list(), [5, height, width, 6])

  def testCreateConvCreatesWeightsAndBiasesVars(self):
    height, width = 3, 3
    images = random_ops.random_uniform((5, height, width, 3), seed=1)
    with self.cached_session():
      self.assertFalse(variables.get_variables('conv1/depthwise_weights'))
      self.assertFalse(variables.get_variables('conv1/pointwise_weights'))
      self.assertFalse(variables.get_variables('conv1/biases'))
      layers_lib.separable_conv2d(images, 32, [3, 3], 4, scope='conv1')
      self.assertTrue(variables.get_variables('conv1/depthwise_weights'))
      self.assertTrue(variables.get_variables('conv1/pointwise_weights'))
      self.assertTrue(variables.get_variables('conv1/biases'))

  def testCreateAtrousConvCreatesWeightsAndBiasesVars(self):
    height, width = 3, 3
    images = random_ops.random_uniform((5, height, width, 3), seed=1)
    with self.cached_session():
      self.assertFalse(variables.get_variables('conv1/depthwise_weights'))
      self.assertFalse(variables.get_variables('conv1/pointwise_weights'))
      self.assertFalse(variables.get_variables('conv1/biases'))
      layers_lib.separable_conv2d(images, 32, [3, 3], 4, rate=2, scope='conv1')
      self.assertTrue(variables.get_variables('conv1/depthwise_weights'))
      self.assertTrue(variables.get_variables('conv1/pointwise_weights'))
      self.assertTrue(variables.get_variables('conv1/biases'))

  def testCreateDepthwiseConvCreatesWeightsAndBiasesVars(self):
    height, width = 3, 3
    images = random_ops.random_uniform((5, height, width, 3), seed=1)
    with self.cached_session():
      self.assertFalse(variables.get_variables('conv1/depthwise_weights'))
      self.assertFalse(variables.get_variables('conv1/pointwise_weights'))
      self.assertFalse(variables.get_variables('conv1/biases'))
      layers_lib.separable_conv2d(images, None, [3, 3], 4, scope='conv1')
      self.assertTrue(variables.get_variables('conv1/depthwise_weights'))
      self.assertFalse(variables.get_variables('conv1/pointwise_weights'))
      self.assertTrue(variables.get_variables('conv1/biases'))

  def testCreateConvWithScope(self):
    height, width = 3, 3
    with self.cached_session():
      images = random_ops.random_uniform((5, height, width, 3), seed=1)
      output = layers_lib.separable_conv2d(images, 32, [3, 3], 6, scope='conv1')
      self.assertEqual(output.op.name, 'conv1/Relu')

  def testCreateConvWithoutActivation(self):
    height, width = 3, 3
    with self.cached_session():
      images = random_ops.random_uniform((5, height, width, 3), seed=1)
      output = layers_lib.separable_conv2d(
          images, 32, [3, 3], 8, activation_fn=None)
      self.assertEqual(output.op.name, 'SeparableConv2d/BiasAdd')

  def testCreateConvValid(self):
    height, width = 3, 3
    with self.cached_session():
      images = random_ops.random_uniform((5, height, width, 3), seed=1)
      output = layers_lib.separable_conv2d(
          images, 32, [3, 3], 2, padding='VALID')
      self.assertListEqual(output.get_shape().as_list(), [5, 1, 1, 32])

  def testCreateAtrousConvValid(self):
    height, width = 5, 5
    with self.cached_session():
      images = random_ops.random_uniform((5, height, width, 3), seed=1)
      output = layers_lib.separable_conv2d(
          images, 32, [3, 3], 2, padding='VALID', rate=2)
      self.assertListEqual(output.get_shape().as_list(), [5, 1, 1, 32])

  def testCreateDepthwiseConvValid(self):
    height, width = 3, 3
    with self.cached_session():
      images = random_ops.random_uniform((5, height, width, 3), seed=1)
      output = layers_lib.separable_conv2d(
          images, None, [3, 3], 2, padding='VALID')
      self.assertListEqual(output.get_shape().as_list(), [5, 1, 1, 6])

  def testCreateAtrousDepthwiseConvValid(self):
    height, width = 5, 5
    with self.cached_session():
      images = random_ops.random_uniform((5, height, width, 3), seed=1)
      output = layers_lib.separable_conv2d(
          images, None, [3, 3], 2, padding='VALID', rate=2)
      self.assertListEqual(output.get_shape().as_list(), [5, 1, 1, 6])

  def testCreateConvWithWeightDecay(self):
    random_seed.set_random_seed(0)
    height, width = 3, 3
    with self.cached_session() as sess:
      images = random_ops.random_uniform((5, height, width, 3), seed=1)
      regularizer = regularizers.l2_regularizer(0.01)
      layers_lib.separable_conv2d(
          images,
          32, [3, 3],
          2,
          weights_regularizer=regularizer,
          weights_initializer=init_ops.ones_initializer())
      self.assertEqual(
          len(ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES)), 2)
      weight_decay = ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES)[0]
      self.assertEqual(
          weight_decay.op.name,
          'SeparableConv2d/depthwise_kernel/Regularizer/l2_regularizer')
      sess.run(variables_lib.global_variables_initializer())
      depth_weight_one = sess.run(weight_decay)
      weight_decay = ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES)[1]
      self.assertEqual(
          weight_decay.op.name,
          'SeparableConv2d/pointwise_kernel/Regularizer/l2_regularizer')
      pointwise_weight_one = sess.run(weight_decay)

      regularizer = regularizers.l2_regularizer(1.0)
      layers_lib.separable_conv2d(
          images,
          32, [3, 3],
          2,
          weights_regularizer=regularizer,
          weights_initializer=init_ops.ones_initializer())
      self.assertEqual(
          len(ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES)), 4)
      weight_decay = ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES)[2]
      sess.run(variables_lib.global_variables_initializer())
      depth_weight_two = sess.run(weight_decay)
      weight_decay = ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES)[3]
      pointwise_weight_two = sess.run(weight_decay)

      self.assertAllClose(
          [100.0 * depth_weight_one, 100.0 * pointwise_weight_one],
          [depth_weight_two, pointwise_weight_two])

  def testReuseConvWithWeightDecay(self):
    height, width = 3, 3
    with self.cached_session():
      images = random_ops.random_uniform((5, height, width, 3), seed=1)
      regularizer = regularizers.l2_regularizer(0.01)
      layers_lib.separable_conv2d(
          images, 32, [3, 3], 2, weights_regularizer=regularizer, scope='conv1')
      self.assertEqual(
          len(ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES)), 2)
      layers_lib.separable_conv2d(
          images,
          32, [3, 3],
          2,
          weights_regularizer=regularizer,
          scope='conv1',
          reuse=True)
      self.assertEqual(
          len(ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES)), 2)

  def testConvWithBatchNorm(self):
    height, width = 3, 3
    batch_norm_collection = 'moving_vars'
    normalizer_params = {
        'variables_collections': {
            'beta': [batch_norm_collection],
            'gamma': [batch_norm_collection],
            'moving_mean': [batch_norm_collection],
            'moving_variance': [batch_norm_collection],
        }
    }
    images = random_ops.random_uniform((5, height, width, 3), seed=1)
    net = layers_lib.separable_conv2d(
        images,
        8, [3, 3],
        2,
        normalizer_fn=_layers.batch_norm,
        normalizer_params=normalizer_params,
        scope='conv1')
    net = layers_lib.separable_conv2d(
        net,
        32, [3, 3],
        2,
        normalizer_fn=_layers.batch_norm,
        normalizer_params=normalizer_params,
        scope='conv2')
    self.assertEqual(len(ops.get_collection(batch_norm_collection)), 6)
    self.assertEqual(len(variables.get_variables('conv1/BatchNorm')), 3)
    self.assertEqual(len(variables.get_variables('conv2/BatchNorm')), 3)

  def testConvWithInputsViaPlaceHolder(self):
    height, width = 3, 3
    images_placeholder = array_ops.placeholder(
        dtypes.float32, shape=(None, None, None, 3))
    net = layers_lib.separable_conv2d(
        images_placeholder,
        8, [3, 3],
        2,
        normalizer_fn=_layers.batch_norm,
        normalizer_params={},
        scope='conv1')
    init_op = variables_lib.global_variables_initializer()
    with self.cached_session() as sess:
      images = np.random.rand(5, height, width, 3)
      sess.run(init_op)
      sess.run(net, feed_dict={images_placeholder: images})

  def testTrainableFlagIsPassedOn(self):
    for trainable in [True, False]:
      for num_filters in [None, 8]:
        with ops.Graph().as_default():
          input_size = [5, 10, 12, 3]

          images = random_ops.random_uniform(input_size, seed=1)
          layers_lib.separable_conv2d(
              images, num_filters, [3, 3], 1, trainable=trainable)
          model_variables = variables.get_model_variables()
          trainable_variables = variables_lib.trainable_variables()
          for model_variable in model_variables:
            self.assertEqual(trainable, model_variable in trainable_variables)

  def testSepConvNCHW(self):
    for num_filters, correct_output_filters in zip((None, 5), (6, 5)):
      with self.cached_session():
        batch, height, width = 4, 10, 12
        kernel_dim, stride = 3, 2
        images = random_ops.random_uniform((batch, 3, height, width), seed=1)
        output = layers_lib.separable_conv2d(
            images,
            num_outputs=num_filters,
            kernel_size=[kernel_dim, kernel_dim],
            depth_multiplier=2,
            stride=stride,
            padding='VALID',
            data_format='NCHW')
        self.assertListEqual(output.get_shape().as_list(), [
            batch, correct_output_filters, (height - kernel_dim + 1) // stride,
            (width - kernel_dim + 1) // stride
        ])


class ScaleGradientTests(test.TestCase):
  """Simple tests of the scale_gradient function."""

  def testBasic(self):
    with self.cached_session():
      x = np.array([42], np.float32)
      gradient_scale = np.array([2], np.float32)

      x = ops.convert_to_tensor(x)
      y = layers_lib.scale_gradient(x, gradient_scale)

      np.testing.assert_array_equal(x.eval(), y.eval())
      g_x, = gradients_impl.gradients(y, [x], [np.array([3], np.float32)])
      np.testing.assert_array_equal([3 * 2], g_x.eval())


class SequenceToImagesTest(test.TestCase):

  def testImagesToSequenceDims(self):
    num_batches = 14
    num_time_steps = 11
    num_channels = 5
    desired_height = 7
    sequence = np.random.uniform(size=(num_time_steps,
                                       num_batches,
                                       num_channels)).astype(np.float32)
    output = _layers.sequence_to_images(sequence, desired_height)
    self.assertListEqual(output.get_shape().as_list(), [2, 7, 11, 5])

  def testImagesToSequenceNCHW(self):
    num_batches = 14
    num_time_steps = 11
    num_channels = 5
    desired_height = 7
    sequence = np.random.uniform(size=(num_time_steps,
                                       num_batches,
                                       num_channels)).astype(np.float32)
    output = _layers.sequence_to_images(sequence,
                                        desired_height,
                                        output_data_format='channels_first')
    self.assertListEqual(output.get_shape().as_list(), [2, 5, 7, 11])


class SoftmaxTests(test.TestCase):

  def setUp(self):
    super(SoftmaxTests, self).setUp()
    self.low = 1 / (1 + math.e)
    self.high = math.e / (1 + math.e)

  def testSoftmax2D(self):
    logits = constant_op.constant([[0.0, 1], [1, 1], [1, 0]])
    prediction = _layers.softmax(logits)
    exp_prediction = np.array([[self.low, self.high], [0.5, 0.5],
                               [self.high, self.low]])

    with self.cached_session() as sess:
      prediction = sess.run(prediction)
      self.assertAllClose(exp_prediction, prediction)

  def testSoftmax3D(self):
    logits = np.ones((2, 3, 2))
    logits[0, 0, 0] = 0
    logits[1, 1, 1] = 0
    logits = constant_op.constant(logits)
    exp_prediction = 0.5 * np.ones((2, 3, 2))
    exp_prediction[0, 0, 0] = self.low
    exp_prediction[0, 0, 1] = self.high
    exp_prediction[1, 1, 0] = self.high
    exp_prediction[1, 1, 1] = self.low

    prediction = _layers.softmax(logits)
    with self.cached_session() as sess:
      prediction = sess.run(prediction)
      self.assertAllClose(exp_prediction, prediction)

  def testSoftmax3DUnknownSize(self):
    logits = np.ones((2, 3, 2))
    logits[0, 0, 0] = 0
    logits[1, 1, 1] = 0
    logit_placeholder = array_ops.placeholder(
        dtypes.float32, shape=(None, None, 2))
    feed_dict = {logit_placeholder: logits}
    exp_prediction = 0.5 * np.ones((2, 3, 2))
    exp_prediction[0, 0, 0] = self.low
    exp_prediction[0, 0, 1] = self.high
    exp_prediction[1, 1, 0] = self.high
    exp_prediction[1, 1, 1] = self.low

    prediction = _layers.softmax(logit_placeholder)
    with self.cached_session() as sess:
      prediction = sess.run(prediction, feed_dict=feed_dict)
      self.assertAllClose(exp_prediction, prediction)

  def testSoftmaxUndefinedNthDimension(self):
    logits = array_ops.placeholder(dtypes.float32)
    with self.assertRaises(ValueError):
      _layers.softmax(logits)


class SpatialSoftmaxTests(test.TestCase):

  def _SpatialSoftmax(self, x_loc, y_loc, height, width, batch_size, nchannels):
    # Convert specified activation locations to range [-1, 1].
    height_lin = np.linspace(-1, 1, height)
    width_lin = np.linspace(-1, 1, width)
    x_lin = np.expand_dims(np.array([height_lin[i] for i in x_loc]), 1)
    y_lin = np.expand_dims(np.array([width_lin[i] for i in y_loc]), 1)
    np_keypoints = np.array(
        [np.concatenate([x_lin, y_lin], axis=1) for i in range(batch_size)])
    np_keypoints = np.reshape(np_keypoints, [-1, nchannels * 2])
    return np_keypoints

  def testSpatialSoftmaxShape(self):
    batch_shape = (2, 35, 30, 2)
    features = array_ops.placeholder(dtypes.float32, shape=batch_shape)
    np_features = np.zeros(batch_shape, dtype=np.float32)
    spatial_softmax = _layers.spatial_softmax(features)
    with self.cached_session() as sess:
      sess.run(variables_lib.global_variables_initializer())
      feed_dict = {features: np_features}
      keypoints = sess.run(spatial_softmax, feed_dict)
      self.assertAllEqual(keypoints.shape, (batch_shape[0], batch_shape[3] * 2))

  def testSpatialSoftmaxShapeNCHW(self):
    batch_shape = (2, 2, 35, 35)
    features = array_ops.placeholder(dtypes.float32, shape=batch_shape)
    np_features = np.zeros(batch_shape, dtype=np.float32)
    spatial_softmax = _layers.spatial_softmax(features, data_format='NCHW')
    with self.cached_session() as sess:
      sess.run(variables_lib.global_variables_initializer())
      feed_dict = {features: np_features}
      keypoints = sess.run(spatial_softmax, feed_dict)
      self.assertAllEqual(keypoints.shape, (batch_shape[0], batch_shape[1] * 2))

  def testTwoMaxActivationsSameChannel(self):
    batch_size, height, width, nchannels = (2, 35, 35, 1)
    batch_shape = (batch_size, height, width, nchannels)

    # Put high equal activations on different locations in the same channel.
    features = array_ops.placeholder(dtypes.float32, shape=batch_shape)
    spatial_softmax = _layers.spatial_softmax(features)
    np_features = np.zeros(batch_shape, dtype=np.float32)
    x0, y0 = (10, 10)
    x1, y1 = (20, 20)
    avg_x = (x0 + x1) // 2
    avg_y = (y0 + y1) // 2
    np_features[:, x0, y0, :] = 100.
    np_features[:, x1, y1, :] = 100.
    x_loc = [avg_x]
    y_loc = [avg_y]

    np_keypoints = self._SpatialSoftmax(x_loc, y_loc, height, width, batch_size,
                                        nchannels)

    # Make sure expected location keypoints matches actual location keypoints.
    with self.cached_session() as sess:
      sess.run(variables_lib.global_variables_initializer())
      feed_dict = {features: np_features}
      keypoints = sess.run(spatial_softmax, feed_dict)
      self.assertAllClose(keypoints, np_keypoints)

  def testMaxActivationsAtEdges(self):
    batch_size, height, width, nchannels = (2, 35, 35, 4)
    batch_shape = (batch_size, height, width, nchannels)

    # Put high activations on edges of spatial extent.
    features = array_ops.placeholder(dtypes.float32, shape=batch_shape)
    spatial_softmax = _layers.spatial_softmax(features)
    np_features = np.zeros(batch_shape, dtype=np.float32)

    edges = [(0, 0), (0, width - 1), (height - 1, 0), (height - 1, width - 1)]
    x_loc, y_loc = zip(*edges)
    for c in range(nchannels):
      np_features[:, x_loc[c], y_loc[c], c] = 100.

    np_keypoints = self._SpatialSoftmax(x_loc, y_loc, height, width, batch_size,
                                        nchannels)

    # Make sure expected location keypoints matches actual location keypoints.
    with self.cached_session() as sess:
      sess.run(variables_lib.global_variables_initializer())
      feed_dict = {features: np_features}
      keypoints = sess.run(spatial_softmax, feed_dict)
      self.assertAllClose(keypoints, np_keypoints)

  def testSpatialSoftmaxVariableSized(self):
    batch_size = 2
    nchannels = 2
    height1, width1 = (35, 30)
    height2, width2 = (20, 20)
    batch_shape1 = (batch_size, height1, width1, nchannels)
    batch_shape2 = (batch_size, height2, width2, nchannels)
    variable_sized_shape = (None, None, None, 2)

    # Put high activations on single spatial locations.
    features = array_ops.placeholder(dtypes.float32, shape=variable_sized_shape)
    spatial_softmax = _layers.spatial_softmax(features)
    np_features1 = np.zeros(batch_shape1, dtype=np.float32)
    np_features2 = np.zeros(batch_shape2, dtype=np.float32)
    x_loc = [15, 2]
    y_loc = [10, 9]
    for c in range(nchannels):
      np_features1[:, x_loc[c], y_loc[c], c] = 100.
      np_features2[:, x_loc[c], y_loc[c], c] = 100.

    np_keypoints1 = self._SpatialSoftmax(x_loc, y_loc, height1, width1,
                                         batch_size, nchannels)
    np_keypoints2 = self._SpatialSoftmax(x_loc, y_loc, height2, width2,
                                         batch_size, nchannels)

    # Make sure expected location keypoints matches actual location keypoints.
    with self.cached_session() as sess:
      sess.run(variables_lib.global_variables_initializer())
      feed_dict = {features: np_features1}
      tf_keypoints1 = sess.run(spatial_softmax, feed_dict)
      self.assertAllClose(tf_keypoints1, np_keypoints1)

      feed_dict = {features: np_features2}
      tf_keypoints2 = sess.run(spatial_softmax, feed_dict)
      self.assertAllClose(tf_keypoints2, np_keypoints2)

  def testSpatialSoftmax(self):
    batch_size, height, width, nchannels = (2, 35, 35, 2)
    batch_shape = (batch_size, height, width, nchannels)

    # Put high activations on single spatial locations.
    features = array_ops.placeholder(dtypes.float32, shape=batch_shape)
    spatial_softmax = _layers.spatial_softmax(features)
    np_features = np.zeros(batch_shape, dtype=np.float32)
    x_loc = [15, 2]
    y_loc = [10, 28]
    for c in range(nchannels):
      np_features[:, x_loc[c], y_loc[c], c] = 100.

    np_keypoints = self._SpatialSoftmax(x_loc, y_loc, height, width, batch_size,
                                        nchannels)

    # Make sure expected location keypoints matches actual location keypoints.
    with self.cached_session() as sess:
      sess.run(variables_lib.global_variables_initializer())
      feed_dict = {features: np_features}
      keypoints = sess.run(spatial_softmax, feed_dict)
      self.assertAllClose(keypoints, np_keypoints)

  def testSpatialSoftmaxNCHW(self):
    batch_size, nchannels, height, width = (2, 2, 35, 35)
    batch_shape = (batch_size, nchannels, height, width)

    # Put high activations on single spatial locations.
    features = array_ops.placeholder(dtypes.float32, shape=batch_shape)
    spatial_softmax = _layers.spatial_softmax(features, data_format='NCHW')
    np_features = np.zeros(batch_shape, dtype=np.float32)
    x_loc = [15, 2]
    y_loc = [10, 28]
    for c in range(nchannels):
      np_features[:, c, x_loc[c], y_loc[c]] = 100.

    np_keypoints = self._SpatialSoftmax(x_loc, y_loc, height, width, batch_size,
                                        nchannels)

    # Make sure expected location keypoints matches actual location keypoints.
    with self.cached_session() as sess:
      sess.run(variables_lib.global_variables_initializer())
      feed_dict = {features: np_features}
      keypoints = sess.run(spatial_softmax, feed_dict)
      self.assertAllClose(keypoints, np_keypoints)

  def testSpatialSoftmaxToFullyConnected(self):
    batch_shape = (2, 35, 35, 2)
    features = array_ops.placeholder(dtypes.float32, shape=batch_shape)
    spatial_softmax = _layers.spatial_softmax(features)
    net = _layers.fully_connected(spatial_softmax, 10)
    np_features = np.zeros(batch_shape, dtype=np.float32)
    with self.cached_session() as sess:
      sess.run(variables_lib.global_variables_initializer())
      feed_dict = {features: np_features}
      sess.run(net, feed_dict)


class StackTests(test.TestCase):

  def testStackFullyConnected(self):
    height, width = 3, 3
    with self.cached_session():
      images = np.random.uniform(size=(5, height * width * 3))
      output = _layers.stack(images, _layers.fully_connected, [10, 20, 30])
      self.assertEqual(output.op.name, 'Stack/fully_connected_3/Relu')
      self.assertListEqual(output.get_shape().as_list(), [5, 30])

  def testStackFullyConnectedFailOnReuse(self):
    height, width = 3, 3
    with self.cached_session():
      with variable_scope.variable_scope('test', reuse=True):
        images = np.random.uniform(size=(5, height * width * 3))
        with self.assertRaises(ValueError):
          _layers.stack(images, _layers.fully_connected, [10, 20, 30])

  def testStackRelu(self):
    height, width = 3, 3
    with self.cached_session():
      images = random_ops.random_uniform(
          (5, height * width * 3), seed=1, name='images')
      output = _layers.stack(images, layers_lib.relu, [10, 20, 30])
      self.assertEqual(output.op.name, 'Stack/fully_connected_3/Relu')
      self.assertListEqual(output.get_shape().as_list(), [5, 30])

  def testStackElu(self):
    height, width = 3, 3
    with self.cached_session():
      images = random_ops.random_uniform(
          (5, height * width * 3), seed=1, name='images')
      output = _layers.stack(images, layers_lib.elu, [10, 20, 30])
      self.assertEqual(output.op.name, 'Stack/fully_connected_3/Elu')
      self.assertListEqual(output.get_shape().as_list(), [5, 30])

  def testStackConvolution2d(self):
    height, width = 3, 3
    with self.cached_session():
      images = random_ops.random_uniform(
          (5, height, width, 3), seed=1, name='images')
      output = _layers.stack(
          images,
          layers_lib.convolution2d, [10, 20, 30],
          kernel_size=[3, 3],
          padding='SAME')
      self.assertEqual(output.op.name, 'Stack/convolution2d_3/Relu')
      self.assertListEqual(output.get_shape().as_list(), [5, 3, 3, 30])

  def testStackWithScope(self):
    height, width = 3, 3
    with self.cached_session():
      images = random_ops.random_uniform(
          (5, height, width, 3), seed=1, name='images')
      output = _layers.stack(
          images,
          layers_lib.convolution2d, [10, 20, 30],
          kernel_size=[3, 3],
          padding='SAME',
          scope='conv1')
      self.assertEqual(output.op.name, 'conv1/conv1_3/Relu')
      self.assertListEqual(output.get_shape().as_list(), [5, 3, 3, 30])


class UnitNormTests(test.TestCase):

  def testUnitNormWithRandomMatrix(self):
    height, width = 2, 3

    for dim in range(3):
      random_seed.set_random_seed(0)
      image = random_ops.random_uniform((height, width, 3))
      output = _layers.unit_norm(image, dim=dim, epsilon=1e-6)
      norms = math_ops.sqrt(
          math_ops.reduce_sum(math_ops.square(output), axis=dim))

      shape = [height, width, 3]
      del shape[dim]
      expected = np.ones(shape)

      with self.cached_session():
        actual = norms.eval()
        self.assertAllClose(expected, actual, 1e-4, 1e-4)

  def testDimEqualToRankRaisesError(self):
    height, width = 2, 3

    random_seed.set_random_seed(0)
    image = random_ops.random_uniform((height, width, 3))

    with self.assertRaises(ValueError):
      _layers.unit_norm(image, dim=3, epsilon=1e-6)

  def testUnknownRankRaisesError(self):
    image = array_ops.placeholder(dtypes.float32)
    with self.assertRaises(ValueError):
      _layers.unit_norm(image, dim=2)

  def testKnownRankUnknownDimsSucceeds(self):
    height, width = 2, 3

    for dim in range(3):
      placeholder_value = np.ones((height, width, 3))
      shape = [height, width, 3]
      del shape[dim]
      expected = np.ones(shape)

      image = array_ops.placeholder(dtypes.float32, (None, None, 3))
      output = _layers.unit_norm(image, dim=dim, epsilon=1e-6)
      norms = math_ops.sqrt(
          math_ops.reduce_sum(math_ops.square(output), axis=dim))

      with self.cached_session():
        actual = norms.eval({image: placeholder_value})
        self.assertAllClose(expected, actual, 1e-4, 1e-4)


class PoincareNormalizeTest(test.TestCase):

  def _PoincareNormalize(self, x, dim, epsilon=1e-5):
    if isinstance(dim, list):
      norm = np.linalg.norm(x, axis=tuple(dim))
      for d in dim:
        norm = np.expand_dims(norm, d)
      norm_x = ((1. - epsilon) * x) / norm
    else:
      norm = np.expand_dims(np.apply_along_axis(np.linalg.norm, dim, x), dim)
      norm_x = ((1. - epsilon) * x) / norm
    return np.where(norm > 1.0 - epsilon, norm_x, x)

  def testPoincareNormalize(self):
    x_shape = [20, 7, 3]
    epsilon = 1e-5
    tol = 1e-6
    np.random.seed(1)
    x_np = np.random.random_sample(x_shape).astype(np.float32)
    for dim in range(len(x_shape)):
      y_np = self._PoincareNormalize(x_np, dim, epsilon)
      with self.cached_session():
        x_tf = constant_op.constant(x_np, name='x')
        y_tf = _layers.poincare_normalize(x_tf, dim, epsilon)
        y_tf_eval = y_tf.eval()
        norm = np.linalg.norm(y_np, axis=dim)
        self.assertLessEqual(norm.max(), 1. - epsilon + tol)
        norm = np.linalg.norm(y_tf_eval, axis=dim)
        self.assertLessEqual(norm.max(), 1. - epsilon + tol)
        self.assertAllClose(y_np, y_tf_eval)

  def testPoincareNormalizeDimArray(self):
    x_shape = [20, 7, 3]
    epsilon = 1e-5
    tol = 1e-6
    np.random.seed(1)
    x_np = np.random.random_sample(x_shape).astype(np.float32)
    dim = [1, 2]
    y_np = self._PoincareNormalize(x_np, dim, epsilon)
    with self.cached_session():
      x_tf = constant_op.constant(x_np, name='x')
      y_tf = _layers.poincare_normalize(x_tf, dim, epsilon)
      y_tf_eval = y_tf.eval()
      norm = np.linalg.norm(y_np, axis=tuple(dim))
      self.assertLess(norm.max(), 1. - epsilon + tol)
      norm = np.linalg.norm(y_tf_eval, axis=tuple(dim))
      self.assertLess(norm.max(), 1. - epsilon + tol)
      self.assertAllClose(y_np, y_tf_eval, rtol=1e-6, atol=1e-6)

  def testPoincareNormalizeGradient(self):
    x_shape = [20, 7, 3]
    np.random.seed(1)
    x_np = np.random.random_sample(x_shape).astype(np.float64)
    for dim in range(len(x_shape)):
      with self.cached_session():
        x_tf = constant_op.constant(x_np, name='x')
        y_tf = _layers.poincare_normalize(x_tf, dim)
        err = gradient_checker.compute_gradient_error(x_tf, x_shape, y_tf,
                                                      x_shape)
      print('PoinCareNormalize gradient err = %g ' % err)
      self.assertLess(err, 1e-4)


# TODO(b/28426988): Add separate tests for non-legacy versions.
class LegacyFullyConnectedTest(test.TestCase):

  def setUp(self):
    super(LegacyFullyConnectedTest, self).setUp()
    random_seed.set_random_seed(1234)
    self.input = constant_op.constant([[1., 2., 3.], [-4., 15., -6.]])
    self.input_3_dim_arr = [[[1., 1.1, 1.2], [2., 2.1, 2.2], [3., 3.1, 3.2],
                             [4., 4.1, 4.2]], [[5., 5.1, 5.2], [6., 6.1, 6.2],
                                               [7., 7.1, 7.2], [8., 8.1, 8.2]]]
    self.input_3_dim = constant_op.constant(self.input_3_dim_arr)

    assert not ops.get_collection(ops.GraphKeys.SUMMARIES)

  def _fully_connected_basic_use(self, x, num_output_units, expected_shape):
    output = _layers.legacy_fully_connected(
        x, num_output_units, activation_fn=nn_ops.relu)

    with session.Session() as sess:
      with self.assertRaises(errors_impl.FailedPreconditionError):
        sess.run(output)

      variables_lib.global_variables_initializer().run()
      out_value, shape_value = sess.run([output, array_ops.shape(output)])

    self.assertAllClose(shape_value, expected_shape)
    self.assertEqual(output.get_shape().as_list(), expected_shape)
    self.assertTrue(np.all(out_value >= 0), 'Relu should have all values >= 0.')

    self.assertEqual(2,
                     len(ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)))
    self.assertEqual(
        0, len(ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES)))

  def test_fully_connected_basic_use(self):
    self._fully_connected_basic_use(self.input, 8, [2, 8])

  def test_fully_connected_basic_use_multi_dim(self):
    for last_dim in [1, 3]:
      self.setUp()
      self._fully_connected_basic_use(self.input_3_dim, last_dim,
                                      [2, 4, last_dim])

  def test_relu_layer_basic_use(self):
    output = layers_lib.legacy_relu(self.input, 8)

    with session.Session() as sess:
      with self.assertRaises(errors_impl.FailedPreconditionError):
        sess.run(output)

      variables_lib.global_variables_initializer().run()
      out_value = sess.run(output)

    self.assertEqual(output.get_shape().as_list(), [2, 8])
    self.assertTrue(np.all(out_value >= 0), 'Relu should have all values >= 0.')

    self.assertEqual(2,
                     len(ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)))
    self.assertEqual(
        0, len(ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES)))

  def test_variable_reuse_with_scope(self):
    with variable_scope.variable_scope('test') as vs:
      output1 = layers_lib.legacy_relu(self.input, 8)
      output2 = layers_lib.legacy_relu(self.input, 8)

    with variable_scope.variable_scope(vs, reuse=True):
      output3 = layers_lib.legacy_relu(self.input, 8)

    with session.Session() as sess:
      variables_lib.global_variables_initializer().run()
      out_value1, out_value2, out_value3 = sess.run([output1, output2, output3])

    self.assertFalse(np.allclose(out_value1, out_value2))
    self.assertAllClose(out_value1, out_value3)

  def test_variable_reuse_with_template(self):
    tmpl1 = template.make_template(
        'test', _layers.legacy_fully_connected, num_output_units=8)
    output1 = tmpl1(self.input)
    output2 = tmpl1(self.input)

    with session.Session() as sess:
      variables_lib.global_variables_initializer().run()
      out_value1, out_value2 = sess.run([output1, output2])
    self.assertAllClose(out_value1, out_value2)

  def _custom_initializers(self, x, num_output_units, expected_outputs):
    output = layers_lib.legacy_relu(
        x,
        num_output_units,
        weight_init=init_ops.constant_initializer(2.0),
        bias_init=init_ops.constant_initializer(1.0))

    with session.Session() as sess:
      variables_lib.global_variables_initializer().run()
      out_value = sess.run(output)

    self.assertAllClose(np.array(expected_outputs), out_value)

  def test_custom_initializers(self):
    self._custom_initializers(self.input, 2, [[13.0, 13.0], [11.0, 11.0]])

  def test_custom_initializers_multi_dim(self):
    self._custom_initializers(
        self.input_3_dim, 2,
        [[[7.6, 7.6], [13.6, 13.6], [19.6, 19.6], [25.6, 25.6]],
         [[31.6, 31.6], [37.6, 37.6], [43.6, 43.6], [49.6, 49.6]]])

  def test_custom_collections(self):
    layers_lib.legacy_relu(
        self.input,
        2,
        weight_collections=['unbiased'],
        bias_collections=['biased'],
        output_collections=['output'])

    self.assertEqual(1, len(ops.get_collection('unbiased')))
    self.assertEqual(1, len(ops.get_collection('biased')))
    self.assertEqual(1, len(ops.get_collection('output')))
    self.assertEqual(2, len(ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)))

  def test_all_custom_collections(self):
    layers_lib.legacy_relu(
        self.input,
        2,
        weight_collections=['unbiased', 'all'],
        bias_collections=['biased', 'all'])

    self.assertEqual(1, len(ops.get_collection('unbiased')))
    self.assertEqual(1, len(ops.get_collection('biased')))
    self.assertEqual(
        ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES),
        ops.get_collection('all'))

  def test_no_bias(self):
    layers_lib.legacy_relu(self.input, 2, bias_init=None)
    self.assertEqual(1, len(ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)))

  def test_no_activation(self):
    y = _layers.legacy_fully_connected(self.input, 2)
    self.assertEqual(2, len(ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)))
    self.assertEqual('BiasAdd', y.op.type)

  def test_no_activation_no_bias(self):
    y = _layers.legacy_fully_connected(self.input, 2, bias_init=None)
    self.assertEqual(1, len(ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)))
    self.assertEqual('MatMul', y.op.type)

  def test_regularizer(self):
    tensor = constant_op.constant(5.0)

    def test_fn(_):
      return tensor

    _layers.legacy_fully_connected(self.input, 2, weight_regularizer=test_fn)

    regs = [
        ops.convert_to_tensor(r)
        for r in ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES)
    ]
    self.assertEqual([tensor], regs)

  def test_regularizer_with_multiple_variables(self):
    tensor = constant_op.constant(5.0)

    def test_fn(_):
      return tensor

    _layers.legacy_fully_connected(self.input, 2, weight_regularizer=test_fn)
    _layers.legacy_fully_connected(self.input, 2, weight_regularizer=test_fn)

    regs = [
        ops.convert_to_tensor(r)
        for r in ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES)
    ]
    self.assertEqual([tensor, tensor], regs)

  def test_regularizer_with_variable_reuse(self):
    tensor = constant_op.constant(5.0)

    def test_fn(_):
      return tensor

    with variable_scope.variable_scope('test') as vs:
      _layers.legacy_fully_connected(self.input, 2, weight_regularizer=test_fn)

    with variable_scope.variable_scope(vs, reuse=True):
      _layers.legacy_fully_connected(self.input, 2, weight_regularizer=test_fn)

    regs = [
        ops.convert_to_tensor(r)
        for r in ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES)
    ]
    self.assertEqual([tensor], regs)

  def test_empty_x_results_in_empty_output(self):
    # Empty x is common if someone masks their input with tf.boolean_mask in
    # order to drop missing entries, and in a particular batch all entries are
    # missing.
    with self.cached_session():
      x = np.array([]).reshape(0, 3)
      self.assertEqual(0, array_ops.size(x).eval())
      y = _layers.legacy_fully_connected(x, 2, activation_fn=nn_ops.softmax)
      variables_lib.global_variables_initializer().run()
      expected_y = np.array([]).reshape(0, 2)
      np.testing.assert_array_equal(expected_y, y.eval())

  def test_shapes_variable_first_dim(self):
    # first dimension is not known statically.
    x = array_ops.placeholder(dtypes.float32, shape=[None, 4, 3])
    y = _layers.legacy_fully_connected(x, 1)
    # in the output we still only know the 2nd and 3rd dimensions statically.
    self.assertEqual(y.get_shape().as_list(), [None, 4, 1])
    with self.cached_session() as sess:
      variables_lib.global_variables_initializer().run()
      # we can feed in input with first dimension 2
      shape_value = sess.run(
          array_ops.shape(y), feed_dict={
              x: self.input_3_dim_arr
          })
      self.assertAllClose(shape_value, [2, 4, 1])
      # we can feed in input with first dimension 1
      shape_value = sess.run(
          array_ops.shape(y), feed_dict={
              x: [self.input_3_dim_arr[0]]
          })
      self.assertAllClose(shape_value, [1, 4, 1])
      # we cannot feed in input with inconsistent dimensions
      with self.assertRaises(ValueError):
        sess.run(array_ops.shape(y), feed_dict={x: [[[]]]})

  def _unknown_dim_invalid_input(self, last_dim):
    x = array_ops.placeholder(dtypes.float32, shape=[3, last_dim])
    _layers.legacy_fully_connected(x, 2, activation_fn=None)

  def test_known_dim_valid_input(self):
    self._unknown_dim_invalid_input(last_dim=3)

  def test_unknown_dim_invalid_input(self):
    with self.assertRaisesRegexp(
        ValueError, 'last dimension of x must be known but is None'):
      self._unknown_dim_invalid_input(last_dim=None)

  def test_1d_invalid_input(self):
    with self.cached_session():
      with self.assertRaisesRegexp(ValueError,
                                   'rank of x must be at least 2 not: 1'):
        x = constant_op.constant([[]], shape=[0])
        _layers.legacy_fully_connected(x, 2, activation_fn=nn_ops.softmax)


class MaxOutTest(test.TestCase):

  def test_simple(self):
    inputs = random_ops.random_uniform((64, 10, 36), seed=1)
    graph = _layers.maxout(inputs, num_units=3)
    self.assertEqual(graph.get_shape().as_list(), [64, 10, 3])

  def test_fully_connected(self):
    inputs = random_ops.random_uniform((64, 50), seed=1)
    graph = _layers.fully_connected(inputs, 50)
    graph = _layers.maxout(graph, num_units=10)
    self.assertEqual(graph.get_shape().as_list(), [64, 10])

  def test_nchw(self):
    inputs = random_ops.random_uniform((10, 100, 100, 3), seed=1)
    graph = _layers.conv2d(inputs, 10, 3, padding='SAME')
    graph = _layers.maxout(graph, num_units=1)
    self.assertEqual(graph.get_shape().as_list(), [10, 100, 100, 1])

  def test_invalid_shape(self):
    inputs = random_ops.random_uniform((10, 100, 100, 3), seed=1)
    graph = _layers.conv2d(inputs, 3, 10)
    with self.assertRaisesRegexp(ValueError, 'number of features'):
      graph = _layers.maxout(graph, num_units=2)


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