コード例 #1
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 def testModelHasExpectedNumberOfParameters(self):
   batch_size = 5
   height, width = 224, 224
   inputs = tf.random_uniform((batch_size, height, width, 3))
   with slim.arg_scope(inception.inception_v2_arg_scope()):
     inception.inception_v2_base(inputs)
   total_params, _ = slim.model_analyzer.analyze_vars(
       slim.get_model_variables())
   self.assertAlmostEqual(10173112, total_params)
コード例 #2
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  def testBuildErrorsForDataFormats(self):
    batch_size = 5
    height, width = 224, 224

    inputs = tf.random_uniform((batch_size, height, width, 3))

    # 'NCWH' data format is not supported.
    with self.assertRaises(ValueError):
      _ = inception.inception_v2_base(inputs, data_format='NCWH')

    # 'NCHW' data format is not supported for separable convolution.
    with self.assertRaises(ValueError):
      _ = inception.inception_v2_base(inputs, data_format='NCHW')
コード例 #3
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  def testBuildAndCheckAllEndPointsUptoMixed5c(self):
    batch_size = 5
    height, width = 224, 224

    inputs = tf.random_uniform((batch_size, height, width, 3))
    _, end_points = inception.inception_v2_base(inputs,
                                                final_endpoint='Mixed_5c')
    endpoints_shapes = {'Mixed_3b': [batch_size, 28, 28, 256],
                        'Mixed_3c': [batch_size, 28, 28, 320],
                        'Mixed_4a': [batch_size, 14, 14, 576],
                        'Mixed_4b': [batch_size, 14, 14, 576],
                        'Mixed_4c': [batch_size, 14, 14, 576],
                        'Mixed_4d': [batch_size, 14, 14, 576],
                        'Mixed_4e': [batch_size, 14, 14, 576],
                        'Mixed_5a': [batch_size, 7, 7, 1024],
                        'Mixed_5b': [batch_size, 7, 7, 1024],
                        'Mixed_5c': [batch_size, 7, 7, 1024],
                        'Conv2d_1a_7x7': [batch_size, 112, 112, 64],
                        'MaxPool_2a_3x3': [batch_size, 56, 56, 64],
                        'Conv2d_2b_1x1': [batch_size, 56, 56, 64],
                        'Conv2d_2c_3x3': [batch_size, 56, 56, 192],
                        'MaxPool_3a_3x3': [batch_size, 28, 28, 192]}
    self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys())
    for endpoint_name in endpoints_shapes:
      expected_shape = endpoints_shapes[endpoint_name]
      self.assertTrue(endpoint_name in end_points)
      self.assertListEqual(end_points[endpoint_name].get_shape().as_list(),
                           expected_shape)
コード例 #4
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  def testBuildEndPointsWithUseSeparableConvolutionFalse(self):
    batch_size = 5
    height, width = 224, 224

    inputs = tf.random_uniform((batch_size, height, width, 3))
    _, end_points = inception.inception_v2_base(inputs)

    endpoint_keys = [
        key for key in end_points.keys()
        if key.startswith('Mixed') or key.startswith('Conv')
    ]

    _, end_points_with_replacement = inception.inception_v2_base(
        inputs, use_separable_conv=False)

    # The endpoint shapes must be equal to the original shape even when the
    # separable convolution is replaced with a normal convolution.
    for key in endpoint_keys:
      original_shape = end_points[key].get_shape().as_list()
      self.assertTrue(key in end_points_with_replacement)
      new_shape = end_points_with_replacement[key].get_shape().as_list()
      self.assertListEqual(original_shape, new_shape)
コード例 #5
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 def testBuildOnlyUptoFinalEndpoint(self):
   batch_size = 5
   height, width = 224, 224
   endpoints = ['Conv2d_1a_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1',
                'Conv2d_2c_3x3', 'MaxPool_3a_3x3', 'Mixed_3b', 'Mixed_3c',
                'Mixed_4a', 'Mixed_4b', 'Mixed_4c', 'Mixed_4d', 'Mixed_4e',
                'Mixed_5a', 'Mixed_5b', 'Mixed_5c']
   for index, endpoint in enumerate(endpoints):
     with tf.Graph().as_default():
       inputs = tf.random_uniform((batch_size, height, width, 3))
       out_tensor, end_points = inception.inception_v2_base(
           inputs, final_endpoint=endpoint)
       self.assertTrue(out_tensor.op.name.startswith(
           'InceptionV2/' + endpoint))
       self.assertItemsEqual(endpoints[:index+1], end_points)
コード例 #6
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  def testBuildBaseNetwork(self):
    batch_size = 5
    height, width = 224, 224

    inputs = tf.random_uniform((batch_size, height, width, 3))
    mixed_5c, end_points = inception.inception_v2_base(inputs)
    self.assertTrue(mixed_5c.op.name.startswith('InceptionV2/Mixed_5c'))
    self.assertListEqual(mixed_5c.get_shape().as_list(),
                         [batch_size, 7, 7, 1024])
    expected_endpoints = ['Mixed_3b', 'Mixed_3c', 'Mixed_4a', 'Mixed_4b',
                          'Mixed_4c', 'Mixed_4d', 'Mixed_4e', 'Mixed_5a',
                          'Mixed_5b', 'Mixed_5c', 'Conv2d_1a_7x7',
                          'MaxPool_2a_3x3', 'Conv2d_2b_1x1', 'Conv2d_2c_3x3',
                          'MaxPool_3a_3x3']
    self.assertItemsEqual(end_points.keys(), expected_endpoints)
コード例 #7
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  def testBuildEndPointsNCHWDataFormat(self):
    batch_size = 5
    height, width = 224, 224

    inputs = tf.random_uniform((batch_size, height, width, 3))
    _, end_points = inception.inception_v2_base(inputs)

    endpoint_keys = [
        key for key in end_points.keys()
        if key.startswith('Mixed') or key.startswith('Conv')
    ]

    inputs_in_nchw = tf.random_uniform((batch_size, 3, height, width))
    _, end_points_with_replacement = inception.inception_v2_base(
        inputs_in_nchw, use_separable_conv=False, data_format='NCHW')

    # With the 'NCHW' data format, all endpoint activations have a transposed
    # shape from the original shape with the 'NHWC' layout.
    for key in endpoint_keys:
      transposed_original_shape = tf.transpose(
          end_points[key], [0, 3, 1, 2]).get_shape().as_list()
      self.assertTrue(key in end_points_with_replacement)
      new_shape = end_points_with_replacement[key].get_shape().as_list()
      self.assertListEqual(transposed_original_shape, new_shape)
コード例 #8
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def _construct_model(model_type='resnet_v1_50'):
  """Constructs model for the desired type of CNN.

  Args:
    model_type: Type of model to be used.

  Returns:
    end_points: A dictionary from components of the network to the corresponding
      activations.

  Raises:
    ValueError: If the model_type is not supported.
  """
  # Placeholder input.
  images = array_ops.placeholder(
      dtypes.float32, shape=(1, None, None, 3), name=_INPUT_NODE)

  # Construct model.
  if model_type == 'inception_resnet_v2':
    _, end_points = inception.inception_resnet_v2_base(images)
  elif model_type == 'inception_resnet_v2-same':
    _, end_points = inception.inception_resnet_v2_base(
        images, align_feature_maps=True)
  elif model_type == 'inception_v2':
    _, end_points = inception.inception_v2_base(images)
  elif model_type == 'inception_v2-no-separable-conv':
    _, end_points = inception.inception_v2_base(
        images, use_separable_conv=False)
  elif model_type == 'inception_v3':
    _, end_points = inception.inception_v3_base(images)
  elif model_type == 'inception_v4':
    _, end_points = inception.inception_v4_base(images)
  elif model_type == 'alexnet_v2':
    _, end_points = alexnet.alexnet_v2(images)
  elif model_type == 'vgg_a':
    _, end_points = vgg.vgg_a(images)
  elif model_type == 'vgg_16':
    _, end_points = vgg.vgg_16(images)
  elif model_type == 'mobilenet_v1':
    _, end_points = mobilenet_v1.mobilenet_v1_base(images)
  elif model_type == 'mobilenet_v1_075':
    _, end_points = mobilenet_v1.mobilenet_v1_base(
        images, depth_multiplier=0.75)
  elif model_type == 'resnet_v1_50':
    _, end_points = resnet_v1.resnet_v1_50(
        images, num_classes=None, is_training=False, global_pool=False)
  elif model_type == 'resnet_v1_101':
    _, end_points = resnet_v1.resnet_v1_101(
        images, num_classes=None, is_training=False, global_pool=False)
  elif model_type == 'resnet_v1_152':
    _, end_points = resnet_v1.resnet_v1_152(
        images, num_classes=None, is_training=False, global_pool=False)
  elif model_type == 'resnet_v1_200':
    _, end_points = resnet_v1.resnet_v1_200(
        images, num_classes=None, is_training=False, global_pool=False)
  elif model_type == 'resnet_v2_50':
    _, end_points = resnet_v2.resnet_v2_50(
        images, num_classes=None, is_training=False, global_pool=False)
  elif model_type == 'resnet_v2_101':
    _, end_points = resnet_v2.resnet_v2_101(
        images, num_classes=None, is_training=False, global_pool=False)
  elif model_type == 'resnet_v2_152':
    _, end_points = resnet_v2.resnet_v2_152(
        images, num_classes=None, is_training=False, global_pool=False)
  elif model_type == 'resnet_v2_200':
    _, end_points = resnet_v2.resnet_v2_200(
        images, num_classes=None, is_training=False, global_pool=False)
  else:
    raise ValueError('Unsupported model_type %s.' % model_type)

  return end_points