text: # Copyright 2017 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 MobileNet v1."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import numpy as np
  import tensorflow as tf
  
  from nets import mobilenet_v1
  
  slim = tf.contrib.slim
  
  
  class MobilenetV1Test(tf.test.TestCase):
  
    def testBuildClassificationNetwork(self):
      batch_size = 5
      height, width = 224, 224
      num_classes = 1000
  
      inputs = tf.random_uniform((batch_size, height, width, 3))
      logits, end_points = mobilenet_v1.mobilenet_v1(inputs, num_classes)
      self.assertTrue(logits.op.name.startswith(
          'MobilenetV1/Logits/SpatialSqueeze'))
      self.assertListEqual(logits.get_shape().as_list(),
                           [batch_size, num_classes])
      self.assertTrue('Predictions' in end_points)
      self.assertListEqual(end_points['Predictions'].get_shape().as_list(),
                           [batch_size, num_classes])
  
    def testBuildPreLogitsNetwork(self):
      batch_size = 5
      height, width = 224, 224
      num_classes = None
  
      inputs = tf.random_uniform((batch_size, height, width, 3))
      net, end_points = mobilenet_v1.mobilenet_v1(inputs, num_classes)
      self.assertTrue(net.op.name.startswith('MobilenetV1/Logits/AvgPool'))
      self.assertListEqual(net.get_shape().as_list(), [batch_size, 1, 1, 1024])
      self.assertFalse('Logits' in end_points)
      self.assertFalse('Predictions' in end_points)
  
    def testBuildBaseNetwork(self):
      batch_size = 5
      height, width = 224, 224
  
      inputs = tf.random_uniform((batch_size, height, width, 3))
      net, end_points = mobilenet_v1.mobilenet_v1_base(inputs)
      self.assertTrue(net.op.name.startswith('MobilenetV1/Conv2d_13'))
      self.assertListEqual(net.get_shape().as_list(),
                           [batch_size, 7, 7, 1024])
      expected_endpoints = ['Conv2d_0',
                            'Conv2d_1_depthwise', 'Conv2d_1_pointwise',
                            'Conv2d_2_depthwise', 'Conv2d_2_pointwise',
                            'Conv2d_3_depthwise', 'Conv2d_3_pointwise',
                            'Conv2d_4_depthwise', 'Conv2d_4_pointwise',
                            'Conv2d_5_depthwise', 'Conv2d_5_pointwise',
                            'Conv2d_6_depthwise', 'Conv2d_6_pointwise',
                            'Conv2d_7_depthwise', 'Conv2d_7_pointwise',
                            'Conv2d_8_depthwise', 'Conv2d_8_pointwise',
                            'Conv2d_9_depthwise', 'Conv2d_9_pointwise',
                            'Conv2d_10_depthwise', 'Conv2d_10_pointwise',
                            'Conv2d_11_depthwise', 'Conv2d_11_pointwise',
                            'Conv2d_12_depthwise', 'Conv2d_12_pointwise',
                            'Conv2d_13_depthwise', 'Conv2d_13_pointwise']
      self.assertItemsEqual(end_points.keys(), expected_endpoints)
  
    def testBuildOnlyUptoFinalEndpoint(self):
      batch_size = 5
      height, width = 224, 224
      endpoints = ['Conv2d_0',
                   'Conv2d_1_depthwise', 'Conv2d_1_pointwise',
                   'Conv2d_2_depthwise', 'Conv2d_2_pointwise',
                   'Conv2d_3_depthwise', 'Conv2d_3_pointwise',
                   'Conv2d_4_depthwise', 'Conv2d_4_pointwise',
                   'Conv2d_5_depthwise', 'Conv2d_5_pointwise',
                   'Conv2d_6_depthwise', 'Conv2d_6_pointwise',
                   'Conv2d_7_depthwise', 'Conv2d_7_pointwise',
                   'Conv2d_8_depthwise', 'Conv2d_8_pointwise',
                   'Conv2d_9_depthwise', 'Conv2d_9_pointwise',
                   'Conv2d_10_depthwise', 'Conv2d_10_pointwise',
                   'Conv2d_11_depthwise', 'Conv2d_11_pointwise',
                   'Conv2d_12_depthwise', 'Conv2d_12_pointwise',
                   'Conv2d_13_depthwise', 'Conv2d_13_pointwise']
      for index, endpoint in enumerate(endpoints):
        with tf.Graph().as_default():
          inputs = tf.random_uniform((batch_size, height, width, 3))
          out_tensor, end_points = mobilenet_v1.mobilenet_v1_base(
              inputs, final_endpoint=endpoint)
          self.assertTrue(out_tensor.op.name.startswith(
              'MobilenetV1/' + endpoint))
          self.assertItemsEqual(endpoints[:index+1], end_points.keys())
  
    def testBuildCustomNetworkUsingConvDefs(self):
      batch_size = 5
      height, width = 224, 224
      conv_defs = [
          mobilenet_v1.Conv(kernel=[3, 3], stride=2, depth=32),
          mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=64),
          mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=2, depth=128),
          mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=512)
      ]
  
      inputs = tf.random_uniform((batch_size, height, width, 3))
      net, end_points = mobilenet_v1.mobilenet_v1_base(
          inputs, final_endpoint='Conv2d_3_pointwise', conv_defs=conv_defs)
      self.assertTrue(net.op.name.startswith('MobilenetV1/Conv2d_3'))
      self.assertListEqual(net.get_shape().as_list(),
                           [batch_size, 56, 56, 512])
      expected_endpoints = ['Conv2d_0',
                            'Conv2d_1_depthwise', 'Conv2d_1_pointwise',
                            'Conv2d_2_depthwise', 'Conv2d_2_pointwise',
                            'Conv2d_3_depthwise', 'Conv2d_3_pointwise']
      self.assertItemsEqual(end_points.keys(), expected_endpoints)
  
    def testBuildAndCheckAllEndPointsUptoConv2d_13(self):
      batch_size = 5
      height, width = 224, 224
  
      inputs = tf.random_uniform((batch_size, height, width, 3))
      with slim.arg_scope([slim.conv2d, slim.separable_conv2d],
                          normalizer_fn=slim.batch_norm):
        _, end_points = mobilenet_v1.mobilenet_v1_base(
            inputs, final_endpoint='Conv2d_13_pointwise')
        _, explicit_padding_end_points = mobilenet_v1.mobilenet_v1_base(
            inputs, final_endpoint='Conv2d_13_pointwise',
            use_explicit_padding=True)
      endpoints_shapes = {'Conv2d_0': [batch_size, 112, 112, 32],
                          'Conv2d_1_depthwise': [batch_size, 112, 112, 32],
                          'Conv2d_1_pointwise': [batch_size, 112, 112, 64],
                          'Conv2d_2_depthwise': [batch_size, 56, 56, 64],
                          'Conv2d_2_pointwise': [batch_size, 56, 56, 128],
                          'Conv2d_3_depthwise': [batch_size, 56, 56, 128],
                          'Conv2d_3_pointwise': [batch_size, 56, 56, 128],
                          'Conv2d_4_depthwise': [batch_size, 28, 28, 128],
                          'Conv2d_4_pointwise': [batch_size, 28, 28, 256],
                          'Conv2d_5_depthwise': [batch_size, 28, 28, 256],
                          'Conv2d_5_pointwise': [batch_size, 28, 28, 256],
                          'Conv2d_6_depthwise': [batch_size, 14, 14, 256],
                          'Conv2d_6_pointwise': [batch_size, 14, 14, 512],
                          'Conv2d_7_depthwise': [batch_size, 14, 14, 512],
                          'Conv2d_7_pointwise': [batch_size, 14, 14, 512],
                          'Conv2d_8_depthwise': [batch_size, 14, 14, 512],
                          'Conv2d_8_pointwise': [batch_size, 14, 14, 512],
                          'Conv2d_9_depthwise': [batch_size, 14, 14, 512],
                          'Conv2d_9_pointwise': [batch_size, 14, 14, 512],
                          'Conv2d_10_depthwise': [batch_size, 14, 14, 512],
                          'Conv2d_10_pointwise': [batch_size, 14, 14, 512],
                          'Conv2d_11_depthwise': [batch_size, 14, 14, 512],
                          'Conv2d_11_pointwise': [batch_size, 14, 14, 512],
                          'Conv2d_12_depthwise': [batch_size, 7, 7, 512],
                          'Conv2d_12_pointwise': [batch_size, 7, 7, 1024],
                          'Conv2d_13_depthwise': [batch_size, 7, 7, 1024],
                          'Conv2d_13_pointwise': [batch_size, 7, 7, 1024]}
      self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys())
      for endpoint_name, expected_shape in endpoints_shapes.items():
        self.assertTrue(endpoint_name in end_points)
        self.assertListEqual(end_points[endpoint_name].get_shape().as_list(),
                             expected_shape)
      self.assertItemsEqual(endpoints_shapes.keys(),
                            explicit_padding_end_points.keys())
      for endpoint_name, expected_shape in endpoints_shapes.items():
        self.assertTrue(endpoint_name in explicit_padding_end_points)
        self.assertListEqual(
            explicit_padding_end_points[endpoint_name].get_shape().as_list(),
            expected_shape)
  
    def testOutputStride16BuildAndCheckAllEndPointsUptoConv2d_13(self):
      batch_size = 5
      height, width = 224, 224
      output_stride = 16
  
      inputs = tf.random_uniform((batch_size, height, width, 3))
      with slim.arg_scope([slim.conv2d, slim.separable_conv2d],
                          normalizer_fn=slim.batch_norm):
        _, end_points = mobilenet_v1.mobilenet_v1_base(
            inputs, output_stride=output_stride,
            final_endpoint='Conv2d_13_pointwise')
        _, explicit_padding_end_points = mobilenet_v1.mobilenet_v1_base(
            inputs, output_stride=output_stride,
            final_endpoint='Conv2d_13_pointwise', use_explicit_padding=True)
      endpoints_shapes = {'Conv2d_0': [batch_size, 112, 112, 32],
                          'Conv2d_1_depthwise': [batch_size, 112, 112, 32],
                          'Conv2d_1_pointwise': [batch_size, 112, 112, 64],
                          'Conv2d_2_depthwise': [batch_size, 56, 56, 64],
                          'Conv2d_2_pointwise': [batch_size, 56, 56, 128],
                          'Conv2d_3_depthwise': [batch_size, 56, 56, 128],
                          'Conv2d_3_pointwise': [batch_size, 56, 56, 128],
                          'Conv2d_4_depthwise': [batch_size, 28, 28, 128],
                          'Conv2d_4_pointwise': [batch_size, 28, 28, 256],
                          'Conv2d_5_depthwise': [batch_size, 28, 28, 256],
                          'Conv2d_5_pointwise': [batch_size, 28, 28, 256],
                          'Conv2d_6_depthwise': [batch_size, 14, 14, 256],
                          'Conv2d_6_pointwise': [batch_size, 14, 14, 512],
                          'Conv2d_7_depthwise': [batch_size, 14, 14, 512],
                          'Conv2d_7_pointwise': [batch_size, 14, 14, 512],
                          'Conv2d_8_depthwise': [batch_size, 14, 14, 512],
                          'Conv2d_8_pointwise': [batch_size, 14, 14, 512],
                          'Conv2d_9_depthwise': [batch_size, 14, 14, 512],
                          'Conv2d_9_pointwise': [batch_size, 14, 14, 512],
                          'Conv2d_10_depthwise': [batch_size, 14, 14, 512],
                          'Conv2d_10_pointwise': [batch_size, 14, 14, 512],
                          'Conv2d_11_depthwise': [batch_size, 14, 14, 512],
                          'Conv2d_11_pointwise': [batch_size, 14, 14, 512],
                          'Conv2d_12_depthwise': [batch_size, 14, 14, 512],
                          'Conv2d_12_pointwise': [batch_size, 14, 14, 1024],
                          'Conv2d_13_depthwise': [batch_size, 14, 14, 1024],
                          'Conv2d_13_pointwise': [batch_size, 14, 14, 1024]}
      self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys())
      for endpoint_name, expected_shape in endpoints_shapes.items():
        self.assertTrue(endpoint_name in end_points)
        self.assertListEqual(end_points[endpoint_name].get_shape().as_list(),
                             expected_shape)
      self.assertItemsEqual(endpoints_shapes.keys(),
                            explicit_padding_end_points.keys())
      for endpoint_name, expected_shape in endpoints_shapes.items():
        self.assertTrue(endpoint_name in explicit_padding_end_points)
        self.assertListEqual(
            explicit_padding_end_points[endpoint_name].get_shape().as_list(),
            expected_shape)
  
    def testOutputStride8BuildAndCheckAllEndPointsUptoConv2d_13(self):
      batch_size = 5
      height, width = 224, 224
      output_stride = 8
  
      inputs = tf.random_uniform((batch_size, height, width, 3))
      with slim.arg_scope([slim.conv2d, slim.separable_conv2d],
                          normalizer_fn=slim.batch_norm):
        _, end_points = mobilenet_v1.mobilenet_v1_base(
            inputs, output_stride=output_stride,
            final_endpoint='Conv2d_13_pointwise')
        _, explicit_padding_end_points = mobilenet_v1.mobilenet_v1_base(
            inputs, output_stride=output_stride,
            final_endpoint='Conv2d_13_pointwise', use_explicit_padding=True)
      endpoints_shapes = {'Conv2d_0': [batch_size, 112, 112, 32],
                          'Conv2d_1_depthwise': [batch_size, 112, 112, 32],
                          'Conv2d_1_pointwise': [batch_size, 112, 112, 64],
                          'Conv2d_2_depthwise': [batch_size, 56, 56, 64],
                          'Conv2d_2_pointwise': [batch_size, 56, 56, 128],
                          'Conv2d_3_depthwise': [batch_size, 56, 56, 128],
                          'Conv2d_3_pointwise': [batch_size, 56, 56, 128],
                          'Conv2d_4_depthwise': [batch_size, 28, 28, 128],
                          'Conv2d_4_pointwise': [batch_size, 28, 28, 256],
                          'Conv2d_5_depthwise': [batch_size, 28, 28, 256],
                          'Conv2d_5_pointwise': [batch_size, 28, 28, 256],
                          'Conv2d_6_depthwise': [batch_size, 28, 28, 256],
                          'Conv2d_6_pointwise': [batch_size, 28, 28, 512],
                          'Conv2d_7_depthwise': [batch_size, 28, 28, 512],
                          'Conv2d_7_pointwise': [batch_size, 28, 28, 512],
                          'Conv2d_8_depthwise': [batch_size, 28, 28, 512],
                          'Conv2d_8_pointwise': [batch_size, 28, 28, 512],
                          'Conv2d_9_depthwise': [batch_size, 28, 28, 512],
                          'Conv2d_9_pointwise': [batch_size, 28, 28, 512],
                          'Conv2d_10_depthwise': [batch_size, 28, 28, 512],
                          'Conv2d_10_pointwise': [batch_size, 28, 28, 512],
                          'Conv2d_11_depthwise': [batch_size, 28, 28, 512],
                          'Conv2d_11_pointwise': [batch_size, 28, 28, 512],
                          'Conv2d_12_depthwise': [batch_size, 28, 28, 512],
                          'Conv2d_12_pointwise': [batch_size, 28, 28, 1024],
                          'Conv2d_13_depthwise': [batch_size, 28, 28, 1024],
                          'Conv2d_13_pointwise': [batch_size, 28, 28, 1024]}
      self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys())
      for endpoint_name, expected_shape in endpoints_shapes.items():
        self.assertTrue(endpoint_name in end_points)
        self.assertListEqual(end_points[endpoint_name].get_shape().as_list(),
                             expected_shape)
      self.assertItemsEqual(endpoints_shapes.keys(),
                            explicit_padding_end_points.keys())
      for endpoint_name, expected_shape in endpoints_shapes.items():
        self.assertTrue(endpoint_name in explicit_padding_end_points)
        self.assertListEqual(
            explicit_padding_end_points[endpoint_name].get_shape().as_list(),
            expected_shape)
  
    def testBuildAndCheckAllEndPointsApproximateFaceNet(self):
      batch_size = 5
      height, width = 128, 128
  
      inputs = tf.random_uniform((batch_size, height, width, 3))
      with slim.arg_scope([slim.conv2d, slim.separable_conv2d],
                          normalizer_fn=slim.batch_norm):
        _, end_points = mobilenet_v1.mobilenet_v1_base(
            inputs, final_endpoint='Conv2d_13_pointwise', depth_multiplier=0.75)
        _, explicit_padding_end_points = mobilenet_v1.mobilenet_v1_base(
            inputs, final_endpoint='Conv2d_13_pointwise', depth_multiplier=0.75,
            use_explicit_padding=True)
      # For the Conv2d_0 layer FaceNet has depth=16
      endpoints_shapes = {'Conv2d_0': [batch_size, 64, 64, 24],
                          'Conv2d_1_depthwise': [batch_size, 64, 64, 24],
                          'Conv2d_1_pointwise': [batch_size, 64, 64, 48],
                          'Conv2d_2_depthwise': [batch_size, 32, 32, 48],
                          'Conv2d_2_pointwise': [batch_size, 32, 32, 96],
                          'Conv2d_3_depthwise': [batch_size, 32, 32, 96],
                          'Conv2d_3_pointwise': [batch_size, 32, 32, 96],
                          'Conv2d_4_depthwise': [batch_size, 16, 16, 96],
                          'Conv2d_4_pointwise': [batch_size, 16, 16, 192],
                          'Conv2d_5_depthwise': [batch_size, 16, 16, 192],
                          'Conv2d_5_pointwise': [batch_size, 16, 16, 192],
                          'Conv2d_6_depthwise': [batch_size, 8, 8, 192],
                          'Conv2d_6_pointwise': [batch_size, 8, 8, 384],
                          'Conv2d_7_depthwise': [batch_size, 8, 8, 384],
                          'Conv2d_7_pointwise': [batch_size, 8, 8, 384],
                          'Conv2d_8_depthwise': [batch_size, 8, 8, 384],
                          'Conv2d_8_pointwise': [batch_size, 8, 8, 384],
                          'Conv2d_9_depthwise': [batch_size, 8, 8, 384],
                          'Conv2d_9_pointwise': [batch_size, 8, 8, 384],
                          'Conv2d_10_depthwise': [batch_size, 8, 8, 384],
                          'Conv2d_10_pointwise': [batch_size, 8, 8, 384],
                          'Conv2d_11_depthwise': [batch_size, 8, 8, 384],
                          'Conv2d_11_pointwise': [batch_size, 8, 8, 384],
                          'Conv2d_12_depthwise': [batch_size, 4, 4, 384],
                          'Conv2d_12_pointwise': [batch_size, 4, 4, 768],
                          'Conv2d_13_depthwise': [batch_size, 4, 4, 768],
                          'Conv2d_13_pointwise': [batch_size, 4, 4, 768]}
      self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys())
      for endpoint_name, expected_shape in endpoints_shapes.items():
        self.assertTrue(endpoint_name in end_points)
        self.assertListEqual(end_points[endpoint_name].get_shape().as_list(),
                             expected_shape)
      self.assertItemsEqual(endpoints_shapes.keys(),
                            explicit_padding_end_points.keys())
      for endpoint_name, expected_shape in endpoints_shapes.items():
        self.assertTrue(endpoint_name in explicit_padding_end_points)
        self.assertListEqual(
            explicit_padding_end_points[endpoint_name].get_shape().as_list(),
            expected_shape)
  
    def testModelHasExpectedNumberOfParameters(self):
      batch_size = 5
      height, width = 224, 224
      inputs = tf.random_uniform((batch_size, height, width, 3))
      with slim.arg_scope([slim.conv2d, slim.separable_conv2d],
                          normalizer_fn=slim.batch_norm):
        mobilenet_v1.mobilenet_v1_base(inputs)
        total_params, _ = slim.model_analyzer.analyze_vars(
            slim.get_model_variables())
        self.assertAlmostEqual(3217920, total_params)
  
    def testBuildEndPointsWithDepthMultiplierLessThanOne(self):
      batch_size = 5
      height, width = 224, 224
      num_classes = 1000
  
      inputs = tf.random_uniform((batch_size, height, width, 3))
      _, end_points = mobilenet_v1.mobilenet_v1(inputs, num_classes)
  
      endpoint_keys = [key for key in end_points.keys() if key.startswith('Conv')]
  
      _, end_points_with_multiplier = mobilenet_v1.mobilenet_v1(
          inputs, num_classes, scope='depth_multiplied_net',
          depth_multiplier=0.5)
  
      for key in endpoint_keys:
        original_depth = end_points[key].get_shape().as_list()[3]
        new_depth = end_points_with_multiplier[key].get_shape().as_list()[3]
        self.assertEqual(0.5 * original_depth, new_depth)
  
    def testBuildEndPointsWithDepthMultiplierGreaterThanOne(self):
      batch_size = 5
      height, width = 224, 224
      num_classes = 1000
  
      inputs = tf.random_uniform((batch_size, height, width, 3))
      _, end_points = mobilenet_v1.mobilenet_v1(inputs, num_classes)
  
      endpoint_keys = [key for key in end_points.keys()
                       if key.startswith('Mixed') or key.startswith('Conv')]
  
      _, end_points_with_multiplier = mobilenet_v1.mobilenet_v1(
          inputs, num_classes, scope='depth_multiplied_net',
          depth_multiplier=2.0)
  
      for key in endpoint_keys:
        original_depth = end_points[key].get_shape().as_list()[3]
        new_depth = end_points_with_multiplier[key].get_shape().as_list()[3]
        self.assertEqual(2.0 * original_depth, new_depth)
  
    def testRaiseValueErrorWithInvalidDepthMultiplier(self):
      batch_size = 5
      height, width = 224, 224
      num_classes = 1000
  
      inputs = tf.random_uniform((batch_size, height, width, 3))
      with self.assertRaises(ValueError):
        _ = mobilenet_v1.mobilenet_v1(
            inputs, num_classes, depth_multiplier=-0.1)
      with self.assertRaises(ValueError):
        _ = mobilenet_v1.mobilenet_v1(
            inputs, num_classes, depth_multiplier=0.0)
  
    def testHalfSizeImages(self):
      batch_size = 5
      height, width = 112, 112
      num_classes = 1000
  
      inputs = tf.random_uniform((batch_size, height, width, 3))
      logits, end_points = mobilenet_v1.mobilenet_v1(inputs, num_classes)
      self.assertTrue(logits.op.name.startswith('MobilenetV1/Logits'))
      self.assertListEqual(logits.get_shape().as_list(),
                           [batch_size, num_classes])
      pre_pool = end_points['Conv2d_13_pointwise']
      self.assertListEqual(pre_pool.get_shape().as_list(),
                           [batch_size, 4, 4, 1024])
  
    def testUnknownImageShape(self):
      tf.reset_default_graph()
      batch_size = 2
      height, width = 224, 224
      num_classes = 1000
      input_np = np.random.uniform(0, 1, (batch_size, height, width, 3))
      with self.test_session() as sess:
        inputs = tf.placeholder(tf.float32, shape=(batch_size, None, None, 3))
        logits, end_points = mobilenet_v1.mobilenet_v1(inputs, num_classes)
        self.assertTrue(logits.op.name.startswith('MobilenetV1/Logits'))
        self.assertListEqual(logits.get_shape().as_list(),
                             [batch_size, num_classes])
        pre_pool = end_points['Conv2d_13_pointwise']
        feed_dict = {inputs: input_np}
        tf.global_variables_initializer().run()
        pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict)
        self.assertListEqual(list(pre_pool_out.shape), [batch_size, 7, 7, 1024])
  
    def testGlobalPoolUnknownImageShape(self):
      tf.reset_default_graph()
      batch_size = 1
      height, width = 250, 300
      num_classes = 1000
      input_np = np.random.uniform(0, 1, (batch_size, height, width, 3))
      with self.test_session() as sess:
        inputs = tf.placeholder(tf.float32, shape=(batch_size, None, None, 3))
        logits, end_points = mobilenet_v1.mobilenet_v1(inputs, num_classes,
                                                       global_pool=True)
        self.assertTrue(logits.op.name.startswith('MobilenetV1/Logits'))
        self.assertListEqual(logits.get_shape().as_list(),
                             [batch_size, num_classes])
        pre_pool = end_points['Conv2d_13_pointwise']
        feed_dict = {inputs: input_np}
        tf.global_variables_initializer().run()
        pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict)
        self.assertListEqual(list(pre_pool_out.shape), [batch_size, 8, 10, 1024])
  
    def testUnknowBatchSize(self):
      batch_size = 1
      height, width = 224, 224
      num_classes = 1000
  
      inputs = tf.placeholder(tf.float32, (None, height, width, 3))
      logits, _ = mobilenet_v1.mobilenet_v1(inputs, num_classes)
      self.assertTrue(logits.op.name.startswith('MobilenetV1/Logits'))
      self.assertListEqual(logits.get_shape().as_list(),
                           [None, num_classes])
      images = tf.random_uniform((batch_size, height, width, 3))
  
      with self.test_session() as sess:
        sess.run(tf.global_variables_initializer())
        output = sess.run(logits, {inputs: images.eval()})
        self.assertEquals(output.shape, (batch_size, num_classes))
  
    def testEvaluation(self):
      batch_size = 2
      height, width = 224, 224
      num_classes = 1000
  
      eval_inputs = tf.random_uniform((batch_size, height, width, 3))
      logits, _ = mobilenet_v1.mobilenet_v1(eval_inputs, num_classes,
                                            is_training=False)
      predictions = tf.argmax(logits, 1)
  
      with self.test_session() as sess:
        sess.run(tf.global_variables_initializer())
        output = sess.run(predictions)
        self.assertEquals(output.shape, (batch_size,))
  
    def testTrainEvalWithReuse(self):
      train_batch_size = 5
      eval_batch_size = 2
      height, width = 150, 150
      num_classes = 1000
  
      train_inputs = tf.random_uniform((train_batch_size, height, width, 3))
      mobilenet_v1.mobilenet_v1(train_inputs, num_classes)
      eval_inputs = tf.random_uniform((eval_batch_size, height, width, 3))
      logits, _ = mobilenet_v1.mobilenet_v1(eval_inputs, num_classes,
                                            reuse=True)
      predictions = tf.argmax(logits, 1)
  
      with self.test_session() as sess:
        sess.run(tf.global_variables_initializer())
        output = sess.run(predictions)
        self.assertEquals(output.shape, (eval_batch_size,))
  
    def testLogitsNotSqueezed(self):
      num_classes = 25
      images = tf.random_uniform([1, 224, 224, 3])
      logits, _ = mobilenet_v1.mobilenet_v1(images,
                                            num_classes=num_classes,
                                            spatial_squeeze=False)
  
      with self.test_session() as sess:
        tf.global_variables_initializer().run()
        logits_out = sess.run(logits)
        self.assertListEqual(list(logits_out.shape), [1, 1, 1, num_classes])
  
    def testBatchNormScopeDoesNotHaveIsTrainingWhenItsSetToNone(self):
      sc = mobilenet_v1.mobilenet_v1_arg_scope(is_training=None)
      self.assertNotIn('is_training', sc[slim.arg_scope_func_key(
          slim.batch_norm)])
  
    def testBatchNormScopeDoesHasIsTrainingWhenItsNotNone(self):
      sc = mobilenet_v1.mobilenet_v1_arg_scope(is_training=True)
      self.assertIn('is_training', sc[slim.arg_scope_func_key(slim.batch_norm)])
      sc = mobilenet_v1.mobilenet_v1_arg_scope(is_training=False)
      self.assertIn('is_training', sc[slim.arg_scope_func_key(slim.batch_norm)])
      sc = mobilenet_v1.mobilenet_v1_arg_scope()
      self.assertIn('is_training', sc[slim.arg_scope_func_key(slim.batch_norm)])
  
  if __name__ == '__main__':
    tf.test.main()
  