text: # Copyright 2016 The TensorFlow Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  # http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ==============================================================================
  """Tests for slim.nets.resnet_v2."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import numpy as np
  import tensorflow as tf
  
  from tensorflow.contrib.slim.nets import resnet_utils
  from tensorflow.contrib.slim.nets import resnet_v2
  
  slim = tf.contrib.slim
  
  
  def create_test_input(batch_size, height, width, channels):
    """Create test input tensor.
  
    Args:
      batch_size: The number of images per batch or `None` if unknown.
      height: The height of each image or `None` if unknown.
      width: The width of each image or `None` if unknown.
      channels: The number of channels per image or `None` if unknown.
  
    Returns:
      Either a placeholder `Tensor` of dimension
        [batch_size, height, width, channels] if any of the inputs are `None` or a
      constant `Tensor` with the mesh grid values along the spatial dimensions.
    """
    if None in [batch_size, height, width, channels]:
      return tf.placeholder(tf.float32, (batch_size, height, width, channels))
    else:
      return tf.to_float(
          np.tile(
              np.reshape(
                  np.reshape(np.arange(height), [height, 1]) +
                  np.reshape(np.arange(width), [1, width]),
                  [1, height, width, 1]),
              [batch_size, 1, 1, channels]))
  
  
  class ResnetUtilsTest(tf.test.TestCase):
  
    def testSubsampleThreeByThree(self):
      x = tf.reshape(tf.to_float(tf.range(9)), [1, 3, 3, 1])
      x = resnet_utils.subsample(x, 2)
      expected = tf.reshape(tf.constant([0, 2, 6, 8]), [1, 2, 2, 1])
      with self.test_session():
        self.assertAllClose(x.eval(), expected.eval())
  
    def testSubsampleFourByFour(self):
      x = tf.reshape(tf.to_float(tf.range(16)), [1, 4, 4, 1])
      x = resnet_utils.subsample(x, 2)
      expected = tf.reshape(tf.constant([0, 2, 8, 10]), [1, 2, 2, 1])
      with self.test_session():
        self.assertAllClose(x.eval(), expected.eval())
  
    def testConv2DSameEven(self):
      n, n2 = 4, 2
  
      # Input image.
      x = create_test_input(1, n, n, 1)
  
      # Convolution kernel.
      w = create_test_input(1, 3, 3, 1)
      w = tf.reshape(w, [3, 3, 1, 1])
  
      tf.get_variable('Conv/weights', initializer=w)
      tf.get_variable('Conv/biases', initializer=tf.zeros([1]))
      tf.get_variable_scope().reuse_variables()
  
      y1 = slim.conv2d(x, 1, [3, 3], stride=1, scope='Conv')
      y1_expected = tf.to_float([[14, 28, 43, 26],
                                 [28, 48, 66, 37],
                                 [43, 66, 84, 46],
                                 [26, 37, 46, 22]])
      y1_expected = tf.reshape(y1_expected, [1, n, n, 1])
  
      y2 = resnet_utils.subsample(y1, 2)
      y2_expected = tf.to_float([[14, 43],
                                 [43, 84]])
      y2_expected = tf.reshape(y2_expected, [1, n2, n2, 1])
  
      y3 = resnet_utils.conv2d_same(x, 1, 3, stride=2, scope='Conv')
      y3_expected = y2_expected
  
      y4 = slim.conv2d(x, 1, [3, 3], stride=2, scope='Conv')
      y4_expected = tf.to_float([[48, 37],
                                 [37, 22]])
      y4_expected = tf.reshape(y4_expected, [1, n2, n2, 1])
  
      with self.test_session() as sess:
        sess.run(tf.global_variables_initializer())
        self.assertAllClose(y1.eval(), y1_expected.eval())
        self.assertAllClose(y2.eval(), y2_expected.eval())
        self.assertAllClose(y3.eval(), y3_expected.eval())
        self.assertAllClose(y4.eval(), y4_expected.eval())
  
    def testConv2DSameOdd(self):
      n, n2 = 5, 3
  
      # Input image.
      x = create_test_input(1, n, n, 1)
  
      # Convolution kernel.
      w = create_test_input(1, 3, 3, 1)
      w = tf.reshape(w, [3, 3, 1, 1])
  
      tf.get_variable('Conv/weights', initializer=w)
      tf.get_variable('Conv/biases', initializer=tf.zeros([1]))
      tf.get_variable_scope().reuse_variables()
  
      y1 = slim.conv2d(x, 1, [3, 3], stride=1, scope='Conv')
      y1_expected = tf.to_float([[14, 28, 43, 58, 34],
                                 [28, 48, 66, 84, 46],
                                 [43, 66, 84, 102, 55],
                                 [58, 84, 102, 120, 64],
                                 [34, 46, 55, 64, 30]])
      y1_expected = tf.reshape(y1_expected, [1, n, n, 1])
  
      y2 = resnet_utils.subsample(y1, 2)
      y2_expected = tf.to_float([[14, 43, 34],
                                 [43, 84, 55],
                                 [34, 55, 30]])
      y2_expected = tf.reshape(y2_expected, [1, n2, n2, 1])
  
      y3 = resnet_utils.conv2d_same(x, 1, 3, stride=2, scope='Conv')
      y3_expected = y2_expected
  
      y4 = slim.conv2d(x, 1, [3, 3], stride=2, scope='Conv')
      y4_expected = y2_expected
  
      with self.test_session() as sess:
        sess.run(tf.global_variables_initializer())
        self.assertAllClose(y1.eval(), y1_expected.eval())
        self.assertAllClose(y2.eval(), y2_expected.eval())
        self.assertAllClose(y3.eval(), y3_expected.eval())
        self.assertAllClose(y4.eval(), y4_expected.eval())
  
    def _resnet_plain(self, inputs, blocks, output_stride=None, scope=None):
      """A plain ResNet without extra layers before or after the ResNet blocks."""
      with tf.variable_scope(scope, values=[inputs]):
        with slim.arg_scope([slim.conv2d], outputs_collections='end_points'):
          net = resnet_utils.stack_blocks_dense(inputs, blocks, output_stride)
          end_points = slim.utils.convert_collection_to_dict('end_points')
          return net, end_points
  
    def testEndPointsV2(self):
      """Test the end points of a tiny v2 bottleneck network."""
      bottleneck = resnet_v2.bottleneck
      blocks = [resnet_utils.Block('block1', bottleneck, [(4, 1, 1), (4, 1, 2)]),
                resnet_utils.Block('block2', bottleneck, [(8, 2, 1), (8, 2, 1)])]
      inputs = create_test_input(2, 32, 16, 3)
      with slim.arg_scope(resnet_utils.resnet_arg_scope()):
        _, end_points = self._resnet_plain(inputs, blocks, scope='tiny')
      expected = [
          'tiny/block1/unit_1/bottleneck_v2/shortcut',
          'tiny/block1/unit_1/bottleneck_v2/conv1',
          'tiny/block1/unit_1/bottleneck_v2/conv2',
          'tiny/block1/unit_1/bottleneck_v2/conv3',
          'tiny/block1/unit_2/bottleneck_v2/conv1',
          'tiny/block1/unit_2/bottleneck_v2/conv2',
          'tiny/block1/unit_2/bottleneck_v2/conv3',
          'tiny/block2/unit_1/bottleneck_v2/shortcut',
          'tiny/block2/unit_1/bottleneck_v2/conv1',
          'tiny/block2/unit_1/bottleneck_v2/conv2',
          'tiny/block2/unit_1/bottleneck_v2/conv3',
          'tiny/block2/unit_2/bottleneck_v2/conv1',
          'tiny/block2/unit_2/bottleneck_v2/conv2',
          'tiny/block2/unit_2/bottleneck_v2/conv3']
      self.assertItemsEqual(expected, end_points)
  
    def _stack_blocks_nondense(self, net, blocks):
      """A simplified ResNet Block stacker without output stride control."""
      for block in blocks:
        with tf.variable_scope(block.scope, 'block', [net]):
          for i, unit in enumerate(block.args):
            depth, depth_bottleneck, stride = unit
            with tf.variable_scope('unit_%d' % (i + 1), values=[net]):
              net = block.unit_fn(net,
                                  depth=depth,
                                  depth_bottleneck=depth_bottleneck,
                                  stride=stride,
                                  rate=1)
      return net
  
    def _atrousValues(self, bottleneck):
      """Verify the values of dense feature extraction by atrous convolution.
  
      Make sure that dense feature extraction by stack_blocks_dense() followed by
      subsampling gives identical results to feature extraction at the nominal
      network output stride using the simple self._stack_blocks_nondense() above.
  
      Args:
        bottleneck: The bottleneck function.
      """
      blocks = [
          resnet_utils.Block('block1', bottleneck, [(4, 1, 1), (4, 1, 2)]),
          resnet_utils.Block('block2', bottleneck, [(8, 2, 1), (8, 2, 2)]),
          resnet_utils.Block('block3', bottleneck, [(16, 4, 1), (16, 4, 2)]),
          resnet_utils.Block('block4', bottleneck, [(32, 8, 1), (32, 8, 1)])
      ]
      nominal_stride = 8
  
      # Test both odd and even input dimensions.
      height = 30
      width = 31
      with slim.arg_scope(resnet_utils.resnet_arg_scope(is_training=False)):
        for output_stride in [1, 2, 4, 8, None]:
          with tf.Graph().as_default():
            with self.test_session() as sess:
              tf.set_random_seed(0)
              inputs = create_test_input(1, height, width, 3)
              # Dense feature extraction followed by subsampling.
              output = resnet_utils.stack_blocks_dense(inputs,
                                                       blocks,
                                                       output_stride)
              if output_stride is None:
                factor = 1
              else:
                factor = nominal_stride // output_stride
  
              output = resnet_utils.subsample(output, factor)
              # Make the two networks use the same weights.
              tf.get_variable_scope().reuse_variables()
              # Feature extraction at the nominal network rate.
              expected = self._stack_blocks_nondense(inputs, blocks)
              sess.run(tf.global_variables_initializer())
              output, expected = sess.run([output, expected])
              self.assertAllClose(output, expected, atol=1e-4, rtol=1e-4)
  
    def testAtrousValuesBottleneck(self):
      self._atrousValues(resnet_v2.bottleneck)
  
  
  class ResnetCompleteNetworkTest(tf.test.TestCase):
    """Tests with complete small ResNet v2 networks."""
  
    def _resnet_small(self,
                      inputs,
                      num_classes=None,
                      global_pool=True,
                      output_stride=None,
                      include_root_block=True,
                      reuse=None,
                      scope='resnet_v2_small'):
      """A shallow and thin ResNet v2 for faster tests."""
      bottleneck = resnet_v2.bottleneck
      blocks = [
          resnet_utils.Block(
              'block1', bottleneck, [(4, 1, 1)] * 2 + [(4, 1, 2)]),
          resnet_utils.Block(
              'block2', bottleneck, [(8, 2, 1)] * 2 + [(8, 2, 2)]),
          resnet_utils.Block(
              'block3', bottleneck, [(16, 4, 1)] * 2 + [(16, 4, 2)]),
          resnet_utils.Block(
              'block4', bottleneck, [(32, 8, 1)] * 2)]
      return resnet_v2.resnet_v2(inputs, blocks, num_classes, global_pool,
                                 output_stride, include_root_block, reuse, scope)
  
    def testClassificationEndPoints(self):
      global_pool = True
      num_classes = 10
      inputs = create_test_input(2, 224, 224, 3)
      with slim.arg_scope(resnet_utils.resnet_arg_scope()):
        logits, end_points = self._resnet_small(inputs, num_classes, global_pool,
                                                scope='resnet')
      self.assertTrue(logits.op.name.startswith('resnet/logits'))
      self.assertListEqual(logits.get_shape().as_list(), [2, 1, 1, num_classes])
      self.assertTrue('predictions' in end_points)
      self.assertListEqual(end_points['predictions'].get_shape().as_list(),
                           [2, 1, 1, num_classes])
  
    def testClassificationShapes(self):
      global_pool = True
      num_classes = 10
      inputs = create_test_input(2, 224, 224, 3)
      with slim.arg_scope(resnet_utils.resnet_arg_scope()):
        _, end_points = self._resnet_small(inputs, num_classes, global_pool,
                                           scope='resnet')
        endpoint_to_shape = {
            'resnet/block1': [2, 28, 28, 4],
            'resnet/block2': [2, 14, 14, 8],
            'resnet/block3': [2, 7, 7, 16],
            'resnet/block4': [2, 7, 7, 32]}
        for endpoint in endpoint_to_shape:
          shape = endpoint_to_shape[endpoint]
          self.assertListEqual(end_points[endpoint].get_shape().as_list(), shape)
  
    def testFullyConvolutionalEndpointShapes(self):
      global_pool = False
      num_classes = 10
      inputs = create_test_input(2, 321, 321, 3)
      with slim.arg_scope(resnet_utils.resnet_arg_scope()):
        _, end_points = self._resnet_small(inputs, num_classes, global_pool,
                                           scope='resnet')
        endpoint_to_shape = {
            'resnet/block1': [2, 41, 41, 4],
            'resnet/block2': [2, 21, 21, 8],
            'resnet/block3': [2, 11, 11, 16],
            'resnet/block4': [2, 11, 11, 32]}
        for endpoint in endpoint_to_shape:
          shape = endpoint_to_shape[endpoint]
          self.assertListEqual(end_points[endpoint].get_shape().as_list(), shape)
  
    def testRootlessFullyConvolutionalEndpointShapes(self):
      global_pool = False
      num_classes = 10
      inputs = create_test_input(2, 128, 128, 3)
      with slim.arg_scope(resnet_utils.resnet_arg_scope()):
        _, end_points = self._resnet_small(inputs, num_classes, global_pool,
                                           include_root_block=False,
                                           scope='resnet')
        endpoint_to_shape = {
            'resnet/block1': [2, 64, 64, 4],
            'resnet/block2': [2, 32, 32, 8],
            'resnet/block3': [2, 16, 16, 16],
            'resnet/block4': [2, 16, 16, 32]}
        for endpoint in endpoint_to_shape:
          shape = endpoint_to_shape[endpoint]
          self.assertListEqual(end_points[endpoint].get_shape().as_list(), shape)
  
    def testAtrousFullyConvolutionalEndpointShapes(self):
      global_pool = False
      num_classes = 10
      output_stride = 8
      inputs = create_test_input(2, 321, 321, 3)
      with slim.arg_scope(resnet_utils.resnet_arg_scope()):
        _, end_points = self._resnet_small(inputs,
                                           num_classes,
                                           global_pool,
                                           output_stride=output_stride,
                                           scope='resnet')
        endpoint_to_shape = {
            'resnet/block1': [2, 41, 41, 4],
            'resnet/block2': [2, 41, 41, 8],
            'resnet/block3': [2, 41, 41, 16],
            'resnet/block4': [2, 41, 41, 32]}
        for endpoint in endpoint_to_shape:
          shape = endpoint_to_shape[endpoint]
          self.assertListEqual(end_points[endpoint].get_shape().as_list(), shape)
  
    def testAtrousFullyConvolutionalValues(self):
      """Verify dense feature extraction with atrous convolution."""
      nominal_stride = 32
      for output_stride in [4, 8, 16, 32, None]:
        with slim.arg_scope(resnet_utils.resnet_arg_scope(is_training=False)):
          with tf.Graph().as_default():
            with self.test_session() as sess:
              tf.set_random_seed(0)
              inputs = create_test_input(2, 81, 81, 3)
              # Dense feature extraction followed by subsampling.
              output, _ = self._resnet_small(inputs, None, global_pool=False,
                                             output_stride=output_stride)
              if output_stride is None:
                factor = 1
              else:
                factor = nominal_stride // output_stride
              output = resnet_utils.subsample(output, factor)
              # Make the two networks use the same weights.
              tf.get_variable_scope().reuse_variables()
              # Feature extraction at the nominal network rate.
              expected, _ = self._resnet_small(inputs, None, global_pool=False)
              sess.run(tf.global_variables_initializer())
              self.assertAllClose(output.eval(), expected.eval(),
                                  atol=1e-4, rtol=1e-4)
  
    def testUnknownBatchSize(self):
      batch = 2
      height, width = 65, 65
      global_pool = True
      num_classes = 10
      inputs = create_test_input(None, height, width, 3)
      with slim.arg_scope(resnet_utils.resnet_arg_scope()):
        logits, _ = self._resnet_small(inputs, num_classes, global_pool,
                                       scope='resnet')
      self.assertTrue(logits.op.name.startswith('resnet/logits'))
      self.assertListEqual(logits.get_shape().as_list(),
                           [None, 1, 1, num_classes])
      images = create_test_input(batch, height, width, 3)
      with self.test_session() as sess:
        sess.run(tf.global_variables_initializer())
        output = sess.run(logits, {inputs: images.eval()})
        self.assertEqual(output.shape, (batch, 1, 1, num_classes))
  
    def testFullyConvolutionalUnknownHeightWidth(self):
      batch = 2
      height, width = 65, 65
      global_pool = False
      inputs = create_test_input(batch, None, None, 3)
      with slim.arg_scope(resnet_utils.resnet_arg_scope()):
        output, _ = self._resnet_small(inputs, None, global_pool)
      self.assertListEqual(output.get_shape().as_list(),
                           [batch, None, None, 32])
      images = create_test_input(batch, height, width, 3)
      with self.test_session() as sess:
        sess.run(tf.global_variables_initializer())
        output = sess.run(output, {inputs: images.eval()})
        self.assertEqual(output.shape, (batch, 3, 3, 32))
  
    def testAtrousFullyConvolutionalUnknownHeightWidth(self):
      batch = 2
      height, width = 65, 65
      global_pool = False
      output_stride = 8
      inputs = create_test_input(batch, None, None, 3)
      with slim.arg_scope(resnet_utils.resnet_arg_scope()):
        output, _ = self._resnet_small(inputs,
                                       None,
                                       global_pool,
                                       output_stride=output_stride)
      self.assertListEqual(output.get_shape().as_list(),
                           [batch, None, None, 32])
      images = create_test_input(batch, height, width, 3)
      with self.test_session() as sess:
        sess.run(tf.global_variables_initializer())
        output = sess.run(output, {inputs: images.eval()})
        self.assertEqual(output.shape, (batch, 9, 9, 32))
  
  
  if __name__ == '__main__':
    tf.test.main()
  