Python object_detection.utils.shape_utils.check_min_image_dim() Examples

def _extract_features(self, preprocessed_inputs):
    """Extract features from preprocessed inputs.

    Args:
      preprocessed_inputs: a [batch, height, width, channels] float tensor
        representing a batch of images.

    Returns:
      feature_maps: a list of tensors where the ith tensor has shape
        [batch, height_i, width_i, depth_i]
    """
    preprocessed_inputs = shape_utils.check_min_image_dim(
        33, preprocessed_inputs)

    image_features = self.mobilenet_v2(
        ops.pad_to_multiple(preprocessed_inputs, self._pad_to_multiple))

    feature_maps = self.feature_map_generator({
        'layer_15/expansion_output': image_features[0],
        'layer_19': image_features[1]})

    return feature_maps.values() 

def _extract_features(self, preprocessed_inputs):
    """Extract features from preprocessed inputs.

    Args:
      preprocessed_inputs: a [batch, height, width, channels] float tensor
        representing a batch of images.

    Returns:
      feature_maps: a list of tensors where the ith tensor has shape
        [batch, height_i, width_i, depth_i]
    """
    preprocessed_inputs = shape_utils.check_min_image_dim(
        33, preprocessed_inputs)

    image_features = self._mobilenet_v1(
        ops.pad_to_multiple(preprocessed_inputs, self._pad_to_multiple))

    feature_maps = self._feature_map_generator({
        'Conv2d_11_pointwise': image_features[0],
        'Conv2d_13_pointwise': image_features[1]})

    return feature_maps.values() 

def _extract_features(self, preprocessed_inputs):
    """Extract features from preprocessed inputs.

    Args:
      preprocessed_inputs: a [batch, height, width, channels] float tensor
        representing a batch of images.

    Returns:
      feature_maps: a list of tensors where the ith tensor has shape
        [batch, height_i, width_i, depth_i]
    """
    preprocessed_inputs = shape_utils.check_min_image_dim(
        33, preprocessed_inputs)

    image_features = self.mobilenet_v2(
        ops.pad_to_multiple(preprocessed_inputs, self._pad_to_multiple))

    feature_maps = self.feature_map_generator({
        'layer_15/expansion_output': image_features[0],
        'layer_19': image_features[1]})

    return feature_maps.values() 

def _extract_features(self, preprocessed_inputs):
    """Extract features from preprocessed inputs.

    Args:
      preprocessed_inputs: a [batch, height, width, channels] float tensor
        representing a batch of images.

    Returns:
      feature_maps: a list of tensors where the ith tensor has shape
        [batch, height_i, width_i, depth_i]
    """
    preprocessed_inputs = shape_utils.check_min_image_dim(
        33, preprocessed_inputs)

    image_features = self.mobilenet_v2(
        ops.pad_to_multiple(preprocessed_inputs, self._pad_to_multiple))

    feature_maps = self.feature_map_generator({
        'layer_15/expansion_output': image_features[0],
        'layer_19': image_features[1]})

    return feature_maps.values() 

def test_check_min_image_dim_static_shape(self):
    input_tensor = tf.constant(np.zeros([1, 42, 42, 3]))
    _ = shape_utils.check_min_image_dim(33, input_tensor)

    with self.assertRaisesRegexp(
        ValueError, 'image size must be >= 64 in both height and width.'):
      _ = shape_utils.check_min_image_dim(64, input_tensor) 

def extract_features(self, preprocessed_inputs):
    """Extract features from preprocessed inputs.

    Args:
      preprocessed_inputs: a [batch, height, width, channels] float tensor
        representing a batch of images.

    Returns:
      feature_maps: a list of tensors where the ith tensor has shape
        [batch, height_i, width_i, depth_i]
    """
    preprocessed_inputs = shape_utils.check_min_image_dim(
        33, preprocessed_inputs)

    feature_map_layout = {
        'from_layer': ['Mixed_5d', 'Mixed_6e', 'Mixed_7c', '', '', ''
                      ][:self._num_layers],
        'layer_depth': [-1, -1, -1, 512, 256, 128][:self._num_layers],
        'use_explicit_padding': self._use_explicit_padding,
        'use_depthwise': self._use_depthwise,
    }

    with slim.arg_scope(self._conv_hyperparams_fn()):
      with tf.variable_scope('InceptionV3', reuse=self._reuse_weights) as scope:
        _, image_features = inception_v3.inception_v3_base(
            ops.pad_to_multiple(preprocessed_inputs, self._pad_to_multiple),
            final_endpoint='Mixed_7c',
            min_depth=self._min_depth,
            depth_multiplier=self._depth_multiplier,
            scope=scope)
        feature_maps = feature_map_generators.multi_resolution_feature_maps(
            feature_map_layout=feature_map_layout,
            depth_multiplier=self._depth_multiplier,
            min_depth=self._min_depth,
            insert_1x1_conv=True,
            image_features=image_features)

    return feature_maps.values() 

def test_check_min_image_dim_dynamic_shape(self):
    input_placeholder = tf.placeholder(tf.float32, shape=[1, None, None, 3])
    image_tensor = shape_utils.check_min_image_dim(33, input_placeholder)

    with self.test_session() as sess:
      sess.run(image_tensor,
               feed_dict={input_placeholder: np.zeros([1, 42, 42, 3])})
      with self.assertRaises(tf.errors.InvalidArgumentError):
        sess.run(image_tensor,
                 feed_dict={input_placeholder: np.zeros([1, 32, 32, 3])}) 

def test_check_min_image_dim_static_shape(self):
    input_tensor = tf.constant(np.zeros([1, 42, 42, 3]))
    _ = shape_utils.check_min_image_dim(33, input_tensor)

    with self.assertRaisesRegexp(
        ValueError, 'image size must be >= 64 in both height and width.'):
      _ = shape_utils.check_min_image_dim(64, input_tensor) 

def test_check_min_image_dim_dynamic_shape(self):
    input_placeholder = tf.placeholder(tf.float32, shape=[1, None, None, 3])
    image_tensor = shape_utils.check_min_image_dim(33, input_placeholder)

    with self.test_session() as sess:
      sess.run(image_tensor,
               feed_dict={input_placeholder: np.zeros([1, 42, 42, 3])})
      with self.assertRaises(tf.errors.InvalidArgumentError):
        sess.run(image_tensor,
                 feed_dict={input_placeholder: np.zeros([1, 32, 32, 3])}) 

def test_check_min_image_dim_dynamic_shape(self):
    input_placeholder = tf.placeholder(tf.float32, shape=[1, None, None, 3])
    image_tensor = shape_utils.check_min_image_dim(33, input_placeholder)

    with self.test_session() as sess:
      sess.run(image_tensor,
               feed_dict={input_placeholder: np.zeros([1, 42, 42, 3])})
      with self.assertRaises(tf.errors.InvalidArgumentError):
        sess.run(image_tensor,
                 feed_dict={input_placeholder: np.zeros([1, 32, 32, 3])}) 

def extract_features(self, preprocessed_inputs):
    """Extract features from preprocessed inputs.

    Args:
      preprocessed_inputs: a [batch, height, width, channels] float tensor
        representing a batch of images.

    Returns:
      feature_maps: a list of tensors where the ith tensor has shape
        [batch, height_i, width_i, depth_i]
    """
    preprocessed_inputs = shape_utils.check_min_image_dim(
        33, preprocessed_inputs)

    with tf.variable_scope('MobilenetV1',
                           reuse=self._reuse_weights) as scope:
      with slim.arg_scope(
          mobilenet_v1.mobilenet_v1_arg_scope(
              is_training=None, regularize_depthwise=True)):
        with (slim.arg_scope(self._conv_hyperparams_fn())
              if self._override_base_feature_extractor_hyperparams
              else context_manager.IdentityContextManager()):
          _, image_features = mobilenet_v1.mobilenet_v1_base(
              ops.pad_to_multiple(preprocessed_inputs, self._pad_to_multiple),
              final_endpoint='Conv2d_13_pointwise',
              min_depth=self._min_depth,
              depth_multiplier=self._depth_multiplier,
              use_explicit_padding=self._use_explicit_padding,
              scope=scope)
      with slim.arg_scope(self._conv_hyperparams_fn()):
        feature_maps = feature_map_generators.pooling_pyramid_feature_maps(
            base_feature_map_depth=0,
            num_layers=6,
            image_features={
                'image_features': image_features['Conv2d_11_pointwise']
            })
    return feature_maps.values() 

def extract_features(self, preprocessed_inputs):
    """Extract features from preprocessed inputs.

    Args:
      preprocessed_inputs: a [batch, height, width, channels] float tensor
        representing a batch of images.

    Returns:
      feature_maps: a list of tensors where the ith tensor has shape
        [batch, height_i, width_i, depth_i]
    """
    preprocessed_inputs = shape_utils.check_min_image_dim(
        33, preprocessed_inputs)

    feature_map_layout = {
        'from_layer': ['Mixed_4c', 'Mixed_5c', '', '', '', ''],
        'layer_depth': [-1, -1, 512, 256, 256, 128],
        'use_explicit_padding': self._use_explicit_padding,
        'use_depthwise': self._use_depthwise,
    }

    with slim.arg_scope(self._conv_hyperparams_fn()):
      with tf.variable_scope('InceptionV2',
                             reuse=self._reuse_weights) as scope:
        _, image_features = inception_v2.inception_v2_base(
            ops.pad_to_multiple(preprocessed_inputs, self._pad_to_multiple),
            final_endpoint='Mixed_5c',
            min_depth=self._min_depth,
            depth_multiplier=self._depth_multiplier,
            scope=scope)
        feature_maps = feature_map_generators.multi_resolution_feature_maps(
            feature_map_layout=feature_map_layout,
            depth_multiplier=self._depth_multiplier,
            min_depth=self._min_depth,
            insert_1x1_conv=True,
            image_features=image_features)

    return feature_maps.values() 

def extract_features(self, preprocessed_inputs):
    """Extract features from preprocessed inputs.

    Args:
      preprocessed_inputs: a [batch, height, width, channels] float tensor
        representing a batch of images.

    Returns:
      feature_maps: a list of tensors where the ith tensor has shape
        [batch, height_i, width_i, depth_i]
    """
    preprocessed_inputs = shape_utils.check_min_image_dim(
        33, preprocessed_inputs)

    feature_map_layout = {
        'from_layer': ['Mixed_5d', 'Mixed_6e', 'Mixed_7c', '', '', ''],
        'layer_depth': [-1, -1, -1, 512, 256, 128],
        'use_explicit_padding': self._use_explicit_padding,
        'use_depthwise': self._use_depthwise,
    }

    with slim.arg_scope(self._conv_hyperparams_fn()):
      with tf.variable_scope('InceptionV3', reuse=self._reuse_weights) as scope:
        _, image_features = inception_v3.inception_v3_base(
            ops.pad_to_multiple(preprocessed_inputs, self._pad_to_multiple),
            final_endpoint='Mixed_7c',
            min_depth=self._min_depth,
            depth_multiplier=self._depth_multiplier,
            scope=scope)
        feature_maps = feature_map_generators.multi_resolution_feature_maps(
            feature_map_layout=feature_map_layout,
            depth_multiplier=self._depth_multiplier,
            min_depth=self._min_depth,
            insert_1x1_conv=True,
            image_features=image_features)

    return feature_maps.values() 

def test_check_min_image_dim_static_shape(self):
    input_tensor = tf.constant(np.zeros([1, 42, 42, 3]))
    _ = shape_utils.check_min_image_dim(33, input_tensor)

    with self.assertRaisesRegex(
        ValueError, 'image size must be >= 64 in both height and width.'):
      _ = shape_utils.check_min_image_dim(64, input_tensor) 

def test_check_min_image_dim_static_shape(self):
    input_tensor = tf.constant(np.zeros([1, 42, 42, 3]))
    _ = shape_utils.check_min_image_dim(33, input_tensor)

    with self.assertRaisesRegexp(
        ValueError, 'image size must be >= 64 in both height and width.'):
      _ = shape_utils.check_min_image_dim(64, input_tensor) 

def _extract_features(self, preprocessed_inputs):
    """Extract features from preprocessed inputs.

    Args:
      preprocessed_inputs: a [batch, height, width, channels] float tensor
        representing a batch of images.

    Returns:
      feature_maps: a list of tensors where the ith tensor has shape
        [batch, height_i, width_i, depth_i]
    """
    preprocessed_inputs = shape_utils.check_min_image_dim(
        33, preprocessed_inputs)

    image_features = self._mobilenet_v2(
        ops.pad_to_multiple(preprocessed_inputs, self._pad_to_multiple))

    feature_block_list = []
    for level in range(self._fpn_min_level, self._base_fpn_max_level + 1):
      feature_block_list.append(self._feature_blocks[level - 2])

    feature_start_index = len(self._feature_blocks) - self._num_levels
    fpn_input_image_features = [
        (key, image_features[feature_start_index + index])
        for index, key in enumerate(feature_block_list)]
    fpn_features = self._fpn_features_generator(fpn_input_image_features)

    feature_maps = []
    for level in range(self._fpn_min_level, self._base_fpn_max_level + 1):
      feature_maps.append(fpn_features['top_down_{}'.format(
          self._feature_blocks[level - 2])])
    last_feature_map = fpn_features['top_down_{}'.format(
        self._feature_blocks[self._base_fpn_max_level - 2])]

    for coarse_feature_layers in self._coarse_feature_layers:
      for layer in coarse_feature_layers:
        last_feature_map = layer(last_feature_map)
      feature_maps.append(last_feature_map)
    return feature_maps 

def extract_features(self, preprocessed_inputs):
    """Extract features from preprocessed inputs.

    Args:
      preprocessed_inputs: a [batch, height, width, channels] float tensor
        representing a batch of images.

    Returns:
      feature_maps: a list of tensors where the ith tensor has shape
        [batch, height_i, width_i, depth_i]
    """
    preprocessed_inputs = shape_utils.check_min_image_dim(
        33, preprocessed_inputs)

    feature_map_layout = {
        'from_layer': ['Mixed_4c', 'Mixed_5c', '', '', '', ''
                      ][:self._num_layers],
        'layer_depth': [-1, -1, 512, 256, 256, 128][:self._num_layers],
        'use_explicit_padding': self._use_explicit_padding,
        'use_depthwise': self._use_depthwise,
    }

    with slim.arg_scope(self._conv_hyperparams_fn()):
      with tf.variable_scope('InceptionV2',
                             reuse=self._reuse_weights) as scope:
        _, image_features = inception_v2.inception_v2_base(
            ops.pad_to_multiple(preprocessed_inputs, self._pad_to_multiple),
            final_endpoint='Mixed_5c',
            min_depth=self._min_depth,
            depth_multiplier=self._depth_multiplier,
            scope=scope)
        feature_maps = feature_map_generators.multi_resolution_feature_maps(
            feature_map_layout=feature_map_layout,
            depth_multiplier=self._depth_multiplier,
            min_depth=self._min_depth,
            insert_1x1_conv=True,
            image_features=image_features)

    return feature_maps.values() 

def _extract_features(self, preprocessed_inputs):
    """Extract features from preprocessed inputs.

    Args:
      preprocessed_inputs: a [batch, height, width, channels] float tensor
        representing a batch of images.

    Returns:
      feature_maps: a list of tensors where the ith tensor has shape
        [batch, height_i, width_i, depth_i]
    """
    preprocessed_inputs = shape_utils.check_min_image_dim(
        33, preprocessed_inputs)

    image_features = self._mobilenet_v1(
        ops.pad_to_multiple(preprocessed_inputs, self._pad_to_multiple))

    feature_block_list = []
    for level in range(self._fpn_min_level, self._base_fpn_max_level + 1):
      feature_block_list.append(self._feature_blocks[level - 2])

    feature_start_index = len(self._feature_blocks) - self._num_levels
    fpn_input_image_features = [
        (key, image_features[feature_start_index + index])
        for index, key in enumerate(feature_block_list)]
    fpn_features = self._fpn_features_generator(fpn_input_image_features)

    feature_maps = []
    for level in range(self._fpn_min_level, self._base_fpn_max_level + 1):
      feature_maps.append(fpn_features['top_down_{}'.format(
          self._feature_blocks[level - 2])])
    last_feature_map = fpn_features['top_down_{}'.format(
        self._feature_blocks[self._base_fpn_max_level - 2])]

    for coarse_feature_layers in self._coarse_feature_layers:
      for layer in coarse_feature_layers:
        last_feature_map = layer(last_feature_map)
      feature_maps.append(last_feature_map)
    return feature_maps 

def extract_features(self, preprocessed_inputs):
    """Extract features from preprocessed inputs.

    Args:
      preprocessed_inputs: a [batch, height, width, channels] float tensor
        representing a batch of images.

    Returns:
      feature_maps: a list of tensors where the ith tensor has shape
        [batch, height_i, width_i, depth_i]
    """
    preprocessed_inputs = shape_utils.check_min_image_dim(
        33, preprocessed_inputs)

    with tf.variable_scope('MobilenetV1',
                           reuse=self._reuse_weights) as scope:
      with slim.arg_scope(
          mobilenet_v1.mobilenet_v1_arg_scope(
              is_training=None, regularize_depthwise=True)):
        with (slim.arg_scope(self._conv_hyperparams_fn())
              if self._override_base_feature_extractor_hyperparams
              else context_manager.IdentityContextManager()):
          _, image_features = mobilenet_v1.mobilenet_v1_base(
              ops.pad_to_multiple(preprocessed_inputs, self._pad_to_multiple),
              final_endpoint='Conv2d_13_pointwise',
              min_depth=self._min_depth,
              depth_multiplier=self._depth_multiplier,
              use_explicit_padding=self._use_explicit_padding,
              scope=scope)
      with slim.arg_scope(self._conv_hyperparams_fn()):
        feature_maps = feature_map_generators.pooling_pyramid_feature_maps(
            base_feature_map_depth=0,
            num_layers=6,
            image_features={
                'image_features': image_features['Conv2d_11_pointwise']
            })
    return feature_maps.values() 

def test_check_min_image_dim_dynamic_shape(self):
    input_placeholder = tf.placeholder(tf.float32, shape=[1, None, None, 3])
    image_tensor = shape_utils.check_min_image_dim(33, input_placeholder)

    with self.test_session() as sess:
      sess.run(image_tensor,
               feed_dict={input_placeholder: np.zeros([1, 42, 42, 3])})
      with self.assertRaises(tf.errors.InvalidArgumentError):
        sess.run(image_tensor,
                 feed_dict={input_placeholder: np.zeros([1, 32, 32, 3])}) 

def test_check_min_image_dim_static_shape(self):
    input_tensor = tf.constant(np.zeros([1, 42, 42, 3]))
    _ = shape_utils.check_min_image_dim(33, input_tensor)

    with self.assertRaisesRegexp(
        ValueError, 'image size must be >= 64 in both height and width.'):
      _ = shape_utils.check_min_image_dim(64, input_tensor) 

def extract_features(self, preprocessed_inputs):
    """Extract features from preprocessed inputs.

    Args:
      preprocessed_inputs: a [batch, height, width, channels] float tensor
        representing a batch of images.

    Returns:
      feature_maps: a list of tensors where the ith tensor has shape
        [batch, height_i, width_i, depth_i]
    """
    preprocessed_inputs = shape_utils.check_min_image_dim(
        33, preprocessed_inputs)

    feature_map_layout = {
        'from_layer': ['Mixed_5d', 'Mixed_6e', 'Mixed_7c', '', '', ''],
        'layer_depth': [-1, -1, -1, 512, 256, 128],
        'use_explicit_padding': self._use_explicit_padding,
        'use_depthwise': self._use_depthwise,
    }

    with slim.arg_scope(self._conv_hyperparams_fn()):
      with tf.variable_scope('InceptionV3', reuse=self._reuse_weights) as scope:
        _, image_features = inception_v3.inception_v3_base(
            ops.pad_to_multiple(preprocessed_inputs, self._pad_to_multiple),
            final_endpoint='Mixed_7c',
            min_depth=self._min_depth,
            depth_multiplier=self._depth_multiplier,
            scope=scope)
        feature_maps = feature_map_generators.multi_resolution_feature_maps(
            feature_map_layout=feature_map_layout,
            depth_multiplier=self._depth_multiplier,
            min_depth=self._min_depth,
            insert_1x1_conv=True,
            image_features=image_features)

    return feature_maps.values() 

def extract_features(self, preprocessed_inputs):
    """Extract features from preprocessed inputs.

    Args:
      preprocessed_inputs: a [batch, height, width, channels] float tensor
        representing a batch of images.

    Returns:
      feature_maps: a list of tensors where the ith tensor has shape
        [batch, height_i, width_i, depth_i]
    """
    preprocessed_inputs = shape_utils.check_min_image_dim(
        33, preprocessed_inputs)

    feature_map_layout = {
        'from_layer': ['Mixed_4c', 'Mixed_5c', '', '', '', ''],
        'layer_depth': [-1, -1, 512, 256, 256, 128],
        'use_explicit_padding': self._use_explicit_padding,
        'use_depthwise': self._use_depthwise,
    }

    with slim.arg_scope(self._conv_hyperparams_fn()):
      with tf.variable_scope('InceptionV2',
                             reuse=self._reuse_weights) as scope:
        _, image_features = inception_v2.inception_v2_base(
            ops.pad_to_multiple(preprocessed_inputs, self._pad_to_multiple),
            final_endpoint='Mixed_5c',
            min_depth=self._min_depth,
            depth_multiplier=self._depth_multiplier,
            scope=scope)
        feature_maps = feature_map_generators.multi_resolution_feature_maps(
            feature_map_layout=feature_map_layout,
            depth_multiplier=self._depth_multiplier,
            min_depth=self._min_depth,
            insert_1x1_conv=True,
            image_features=image_features)

    return feature_maps.values() 

def extract_features(self, preprocessed_inputs):
    """Extract features from preprocessed inputs.

    Args:
      preprocessed_inputs: a [batch, height, width, channels] float tensor
        representing a batch of images.

    Returns:
      feature_maps: a list of tensors where the ith tensor has shape
        [batch, height_i, width_i, depth_i]
    """
    preprocessed_inputs = shape_utils.check_min_image_dim(
        33, preprocessed_inputs)

    with tf.variable_scope('MobilenetV1',
                           reuse=self._reuse_weights) as scope:
      with slim.arg_scope(
          mobilenet_v1.mobilenet_v1_arg_scope(
              is_training=None, regularize_depthwise=True)):
        with (slim.arg_scope(self._conv_hyperparams_fn())
              if self._override_base_feature_extractor_hyperparams
              else context_manager.IdentityContextManager()):
          _, image_features = mobilenet_v1.mobilenet_v1_base(
              ops.pad_to_multiple(preprocessed_inputs, self._pad_to_multiple),
              final_endpoint='Conv2d_13_pointwise',
              min_depth=self._min_depth,
              depth_multiplier=self._depth_multiplier,
              use_explicit_padding=self._use_explicit_padding,
              scope=scope)
      with slim.arg_scope(self._conv_hyperparams_fn()):
        feature_maps = feature_map_generators.pooling_pyramid_feature_maps(
            base_feature_map_depth=0,
            num_layers=6,
            image_features={
                'image_features': image_features['Conv2d_11_pointwise']
            })
    return feature_maps.values() 

def test_check_min_image_dim_dynamic_shape(self):
    input_placeholder = tf.placeholder(tf.float32, shape=[1, None, None, 3])
    image_tensor = shape_utils.check_min_image_dim(33, input_placeholder)

    with self.test_session() as sess:
      sess.run(image_tensor,
               feed_dict={input_placeholder: np.zeros([1, 42, 42, 3])})
      with self.assertRaises(tf.errors.InvalidArgumentError):
        sess.run(image_tensor,
                 feed_dict={input_placeholder: np.zeros([1, 32, 32, 3])}) 

def test_check_min_image_dim_static_shape(self):
    input_tensor = tf.constant(np.zeros([1, 42, 42, 3]))
    _ = shape_utils.check_min_image_dim(33, input_tensor)

    with self.assertRaisesRegexp(
        ValueError, 'image size must be >= 64 in both height and width.'):
      _ = shape_utils.check_min_image_dim(64, input_tensor) 

def test_check_min_image_dim_static_shape(self):
    input_tensor = tf.constant(np.zeros([1, 42, 42, 3]))
    _ = shape_utils.check_min_image_dim(33, input_tensor)

    with self.assertRaisesRegexp(
        ValueError, 'image size must be >= 64 in both height and width.'):
      _ = shape_utils.check_min_image_dim(64, input_tensor) 

def extract_features(self, preprocessed_inputs):
    """Extract features from preprocessed inputs.

    Args:
      preprocessed_inputs: a [batch, height, width, channels] float tensor
        representing a batch of images.

    Returns:
      feature_maps: a list of tensors where the ith tensor has shape
        [batch, height_i, width_i, depth_i]
    """
    preprocessed_inputs = shape_utils.check_min_image_dim(
        33, preprocessed_inputs)

    feature_map_layout = {
        'from_layer': ['Mixed_4c', 'Mixed_5c', '', '', '', ''],
        'layer_depth': [-1, -1, 512, 256, 256, 128],
        'use_explicit_padding': self._use_explicit_padding,
        'use_depthwise': self._use_depthwise,
    }

    with slim.arg_scope(self._conv_hyperparams_fn()):
      with tf.variable_scope('InceptionV2',
                             reuse=self._reuse_weights) as scope:
        _, image_features = inception_v2.inception_v2_base(
            ops.pad_to_multiple(preprocessed_inputs, self._pad_to_multiple),
            final_endpoint='Mixed_5c',
            min_depth=self._min_depth,
            depth_multiplier=self._depth_multiplier,
            scope=scope)
        feature_maps = feature_map_generators.multi_resolution_feature_maps(
            feature_map_layout=feature_map_layout,
            depth_multiplier=self._depth_multiplier,
            min_depth=self._min_depth,
            insert_1x1_conv=True,
            image_features=image_features)

    return feature_maps.values() 

def test_check_min_image_dim_dynamic_shape(self):
    input_placeholder = tf.placeholder(tf.float32, shape=[1, None, None, 3])
    image_tensor = shape_utils.check_min_image_dim(33, input_placeholder)

    with self.test_session() as sess:
      sess.run(image_tensor,
               feed_dict={input_placeholder: np.zeros([1, 42, 42, 3])})
      with self.assertRaises(tf.errors.InvalidArgumentError):
        sess.run(image_tensor,
                 feed_dict={input_placeholder: np.zeros([1, 32, 32, 3])}) 

def test_check_min_image_dim_static_shape(self):
    input_tensor = tf.constant(np.zeros([1, 42, 42, 3]))
    _ = shape_utils.check_min_image_dim(33, input_tensor)

    with self.assertRaisesRegexp(
        ValueError, 'image size must be >= 64 in both height and width.'):
      _ = shape_utils.check_min_image_dim(64, input_tensor)