Python object_detection.models.feature_map_generators.multi_resolution_feature_maps() Examples

def _build_feature_map_generator(self, feature_map_layout, use_keras,
                                   pool_residual=False):
    if use_keras:
      return feature_map_generators.KerasMultiResolutionFeatureMaps(
          feature_map_layout=feature_map_layout,
          depth_multiplier=1,
          min_depth=32,
          insert_1x1_conv=True,
          freeze_batchnorm=False,
          is_training=True,
          conv_hyperparams=self._build_conv_hyperparams(),
          name='FeatureMaps'
      )
    else:
      def feature_map_generator(image_features):
        return feature_map_generators.multi_resolution_feature_maps(
            feature_map_layout=feature_map_layout,
            depth_multiplier=1,
            min_depth=32,
            insert_1x1_conv=True,
            image_features=image_features,
            pool_residual=pool_residual)
      return feature_map_generator 

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.get_shape().assert_has_rank(4)
    shape_assert = tf.Assert(
        tf.logical_and(tf.greater_equal(tf.shape(preprocessed_inputs)[1], 33),
                       tf.greater_equal(tf.shape(preprocessed_inputs)[2], 33)),
        ['image size must at least be 33 in both height and width.'])

    feature_map_layout = {
        'from_layer': ['Conv2d_11_pointwise', 'Conv2d_13_pointwise', '', '',
                       '', ''],
        'layer_depth': [-1, -1, 512, 256, 256, 128],
    }

    with tf.control_dependencies([shape_assert]):
      with slim.arg_scope(self._conv_hyperparams):
        with tf.variable_scope('MobilenetV1',
                               reuse=self._reuse_weights) as scope:
          _, image_features = mobilenet_v1.mobilenet_v1_base(
              preprocessed_inputs,
              final_endpoint='Conv2d_13_pointwise',
              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_get_expected_feature_map_shapes_with_embedded_ssd_mobilenet_v1(
      self):
    image_features = {
        'Conv2d_11_pointwise': tf.random_uniform([4, 16, 16, 512],
                                                 dtype=tf.float32),
        'Conv2d_13_pointwise': tf.random_uniform([4, 8, 8, 1024],
                                                 dtype=tf.float32),
    }

    feature_maps = feature_map_generators.multi_resolution_feature_maps(
        feature_map_layout=EMBEDDED_SSD_MOBILENET_V1_LAYOUT,
        depth_multiplier=1,
        min_depth=32,
        insert_1x1_conv=True,
        image_features=image_features)

    expected_feature_map_shapes = {
        'Conv2d_11_pointwise': (4, 16, 16, 512),
        'Conv2d_13_pointwise': (4, 8, 8, 1024),
        'Conv2d_13_pointwise_2_Conv2d_2_3x3_s2_512': (4, 4, 4, 512),
        'Conv2d_13_pointwise_2_Conv2d_3_3x3_s2_256': (4, 2, 2, 256),
        'Conv2d_13_pointwise_2_Conv2d_4_2x2_s2_256': (4, 1, 1, 256)}

    init_op = tf.global_variables_initializer()
    with self.test_session() as sess:
      sess.run(init_op)
      out_feature_maps = sess.run(feature_maps)
      out_feature_map_shapes = dict(
          (key, value.shape) for key, value in out_feature_maps.items())
      self.assertDictEqual(out_feature_map_shapes, expected_feature_map_shapes) 

def test_get_expected_feature_map_shapes_with_inception_v2(self):
    image_features = {
        'Mixed_3c': tf.random_uniform([4, 28, 28, 256], dtype=tf.float32),
        'Mixed_4c': tf.random_uniform([4, 14, 14, 576], dtype=tf.float32),
        'Mixed_5c': tf.random_uniform([4, 7, 7, 1024], dtype=tf.float32)
    }
    feature_maps = feature_map_generators.multi_resolution_feature_maps(
        feature_map_layout=INCEPTION_V2_LAYOUT,
        depth_multiplier=1,
        min_depth=32,
        insert_1x1_conv=True,
        image_features=image_features)

    expected_feature_map_shapes = {
        'Mixed_3c': (4, 28, 28, 256),
        'Mixed_4c': (4, 14, 14, 576),
        'Mixed_5c': (4, 7, 7, 1024),
        'Mixed_5c_2_Conv2d_3_3x3_s2_512': (4, 4, 4, 512),
        'Mixed_5c_2_Conv2d_4_3x3_s2_256': (4, 2, 2, 256),
        'Mixed_5c_2_Conv2d_5_3x3_s2_256': (4, 1, 1, 256)}

    init_op = tf.global_variables_initializer()
    with self.test_session() as sess:
      sess.run(init_op)
      out_feature_maps = sess.run(feature_maps)
      out_feature_map_shapes = dict(
          (key, value.shape) for key, value in out_feature_maps.items())
      self.assertDictEqual(out_feature_map_shapes, expected_feature_map_shapes) 

def test_get_expected_feature_map_shapes_with_inception_v2(self):
    image_features = {
        'Mixed_3c': tf.random_uniform([4, 28, 28, 256], dtype=tf.float32),
        'Mixed_4c': tf.random_uniform([4, 14, 14, 576], dtype=tf.float32),
        'Mixed_5c': tf.random_uniform([4, 7, 7, 1024], dtype=tf.float32)
    }
    feature_maps = feature_map_generators.multi_resolution_feature_maps(
        feature_map_layout=INCEPTION_V2_LAYOUT,
        depth_multiplier=1,
        min_depth=32,
        insert_1x1_conv=True,
        image_features=image_features)

    expected_feature_map_shapes = {
        'Mixed_3c': (4, 28, 28, 256),
        'Mixed_4c': (4, 14, 14, 576),
        'Mixed_5c': (4, 7, 7, 1024),
        'Mixed_5c_2_Conv2d_3_3x3_s2_512': (4, 4, 4, 512),
        'Mixed_5c_2_Conv2d_4_3x3_s2_256': (4, 2, 2, 256),
        'Mixed_5c_2_Conv2d_5_3x3_s2_256': (4, 1, 1, 256)}

    init_op = tf.global_variables_initializer()
    with self.test_session() as sess:
      sess.run(init_op)
      out_feature_maps = sess.run(feature_maps)
      out_feature_map_shapes = dict(
          (key, value.shape) for key, value in out_feature_maps.iteritems())
      self.assertDictEqual(out_feature_map_shapes, expected_feature_map_shapes) 

def test_get_expected_feature_map_shapes_with_inception_v3(self):
    image_features = {
        'Mixed_5d': tf.random_uniform([4, 35, 35, 256], dtype=tf.float32),
        'Mixed_6e': tf.random_uniform([4, 17, 17, 576], dtype=tf.float32),
        'Mixed_7c': tf.random_uniform([4, 8, 8, 1024], dtype=tf.float32)
    }

    feature_maps = feature_map_generators.multi_resolution_feature_maps(
        feature_map_layout=INCEPTION_V3_LAYOUT,
        depth_multiplier=1,
        min_depth=32,
        insert_1x1_conv=True,
        image_features=image_features)

    expected_feature_map_shapes = {
        'Mixed_5d': (4, 35, 35, 256),
        'Mixed_6e': (4, 17, 17, 576),
        'Mixed_7c': (4, 8, 8, 1024),
        'Mixed_7c_2_Conv2d_3_3x3_s2_512': (4, 4, 4, 512),
        'Mixed_7c_2_Conv2d_4_3x3_s2_256': (4, 2, 2, 256),
        'Mixed_7c_2_Conv2d_5_3x3_s2_128': (4, 1, 1, 128)}

    init_op = tf.global_variables_initializer()
    with self.test_session() as sess:
      sess.run(init_op)
      out_feature_maps = sess.run(feature_maps)
      out_feature_map_shapes = dict(
          (key, value.shape) for key, value in out_feature_maps.iteritems())
      self.assertDictEqual(out_feature_map_shapes, expected_feature_map_shapes) 

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.get_shape().assert_has_rank(4)
    shape_assert = tf.Assert(
        tf.logical_and(tf.greater_equal(tf.shape(preprocessed_inputs)[1], 33),
                       tf.greater_equal(tf.shape(preprocessed_inputs)[2], 33)),
        ['image size must at least be 33 in both height and width.'])

    feature_map_layout = {
        'from_layer': ['Conv2d_11_pointwise', 'Conv2d_13_pointwise', '', '',
                       '', ''],
        'layer_depth': [-1, -1, 512, 256, 256, 128],
    }

    with tf.control_dependencies([shape_assert]):
      with slim.arg_scope(self._conv_hyperparams):
        with tf.variable_scope('MobilenetV1',
                               reuse=self._reuse_weights) as scope:
          _, image_features = mobilenet_v1.mobilenet_v1_base(
              preprocessed_inputs,
              final_endpoint='Conv2d_13_pointwise',
              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.get_shape().assert_has_rank(4)
    shape_assert = tf.Assert(
        tf.logical_and(tf.greater_equal(tf.shape(preprocessed_inputs)[1], 33),
                       tf.greater_equal(tf.shape(preprocessed_inputs)[2], 33)),
        ['image size must at least be 33 in both height and width.'])

    feature_map_layout = {
        'from_layer': ['Mixed_4c', 'Mixed_5c', '', '', '', ''],
        'layer_depth': [-1, -1, 512, 256, 256, 128],
    }

    with tf.control_dependencies([shape_assert]):
      with slim.arg_scope(self._conv_hyperparams):
        with tf.variable_scope('InceptionV2',
                               reuse=self._reuse_weights) as scope:
          _, image_features = inception_v2.inception_v2_base(
              preprocessed_inputs,
              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_get_expected_feature_map_shapes_with_inception_v2(self):
    image_features = {
        'Mixed_3c': tf.random_uniform([4, 28, 28, 256], dtype=tf.float32),
        'Mixed_4c': tf.random_uniform([4, 14, 14, 576], dtype=tf.float32),
        'Mixed_5c': tf.random_uniform([4, 7, 7, 1024], dtype=tf.float32)
    }
    feature_maps = feature_map_generators.multi_resolution_feature_maps(
        feature_map_layout=INCEPTION_V2_LAYOUT,
        depth_multiplier=1,
        min_depth=32,
        insert_1x1_conv=True,
        image_features=image_features)

    expected_feature_map_shapes = {
        'Mixed_3c': (4, 28, 28, 256),
        'Mixed_4c': (4, 14, 14, 576),
        'Mixed_5c': (4, 7, 7, 1024),
        'Mixed_5c_2_Conv2d_3_3x3_s2_512': (4, 4, 4, 512),
        'Mixed_5c_2_Conv2d_4_3x3_s2_256': (4, 2, 2, 256),
        'Mixed_5c_2_Conv2d_5_3x3_s2_256': (4, 1, 1, 256)}

    init_op = tf.global_variables_initializer()
    with self.test_session() as sess:
      sess.run(init_op)
      out_feature_maps = sess.run(feature_maps)
      out_feature_map_shapes = dict(
          (key, value.shape) for key, value in out_feature_maps.items())
      self.assertDictEqual(out_feature_map_shapes, expected_feature_map_shapes) 

def test_get_expected_feature_map_shapes_with_inception_v3(self):
    image_features = {
        'Mixed_5d': tf.random_uniform([4, 35, 35, 256], dtype=tf.float32),
        'Mixed_6e': tf.random_uniform([4, 17, 17, 576], dtype=tf.float32),
        'Mixed_7c': tf.random_uniform([4, 8, 8, 1024], dtype=tf.float32)
    }

    feature_maps = feature_map_generators.multi_resolution_feature_maps(
        feature_map_layout=INCEPTION_V3_LAYOUT,
        depth_multiplier=1,
        min_depth=32,
        insert_1x1_conv=True,
        image_features=image_features)

    expected_feature_map_shapes = {
        'Mixed_5d': (4, 35, 35, 256),
        'Mixed_6e': (4, 17, 17, 576),
        'Mixed_7c': (4, 8, 8, 1024),
        'Mixed_7c_2_Conv2d_3_3x3_s2_512': (4, 4, 4, 512),
        'Mixed_7c_2_Conv2d_4_3x3_s2_256': (4, 2, 2, 256),
        'Mixed_7c_2_Conv2d_5_3x3_s2_128': (4, 1, 1, 128)}

    init_op = tf.global_variables_initializer()
    with self.test_session() as sess:
      sess.run(init_op)
      out_feature_maps = sess.run(feature_maps)
      out_feature_map_shapes = dict(
          (key, value.shape) for key, value in out_feature_maps.items())
      self.assertDictEqual(out_feature_map_shapes, expected_feature_map_shapes) 

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.get_shape().assert_has_rank(4)
    shape_assert = tf.Assert(
        tf.logical_and(tf.greater_equal(tf.shape(preprocessed_inputs)[1], 33),
                       tf.greater_equal(tf.shape(preprocessed_inputs)[2], 33)),
        ['image size must at least be 33 in both height and width.'])

    feature_map_layout = {
        'from_layer': ['Conv2d_11_pointwise', 'Conv2d_13_pointwise', '', '',
                       '', ''],
        'layer_depth': [-1, -1, 512, 256, 256, 128],
    }

    with tf.control_dependencies([shape_assert]):
      with slim.arg_scope(self._conv_hyperparams):
        with tf.variable_scope('MobilenetV1',
                               reuse=self._reuse_weights) as scope:
          _, image_features = mobilenet_v1.mobilenet_v1_base(
              preprocessed_inputs,
              final_endpoint='Conv2d_13_pointwise',
              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.get_shape().assert_has_rank(4)
    shape_assert = tf.Assert(
        tf.logical_and(tf.greater_equal(tf.shape(preprocessed_inputs)[1], 33),
                       tf.greater_equal(tf.shape(preprocessed_inputs)[2], 33)),
        ['image size must at least be 33 in both height and width.'])

    feature_map_layout = {
        'from_layer': ['Mixed_4c', 'Mixed_5c', '', '', '', ''],
        'layer_depth': [-1, -1, 512, 256, 256, 128],
    }

    with tf.control_dependencies([shape_assert]):
      with slim.arg_scope(self._conv_hyperparams):
        with tf.variable_scope('InceptionV2',
                               reuse=self._reuse_weights) as scope:
          _, image_features = inception_v2.inception_v2_base(
              preprocessed_inputs,
              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_get_expected_feature_map_shapes_with_inception_v3(self):
    image_features = {
        'Mixed_5d': tf.random_uniform([4, 35, 35, 256], dtype=tf.float32),
        'Mixed_6e': tf.random_uniform([4, 17, 17, 576], dtype=tf.float32),
        'Mixed_7c': tf.random_uniform([4, 8, 8, 1024], dtype=tf.float32)
    }

    feature_maps = feature_map_generators.multi_resolution_feature_maps(
        feature_map_layout=INCEPTION_V3_LAYOUT,
        depth_multiplier=1,
        min_depth=32,
        insert_1x1_conv=True,
        image_features=image_features)

    expected_feature_map_shapes = {
        'Mixed_5d': (4, 35, 35, 256),
        'Mixed_6e': (4, 17, 17, 576),
        'Mixed_7c': (4, 8, 8, 1024),
        'Mixed_7c_2_Conv2d_3_3x3_s2_512': (4, 4, 4, 512),
        'Mixed_7c_2_Conv2d_4_3x3_s2_256': (4, 2, 2, 256),
        'Mixed_7c_2_Conv2d_5_3x3_s2_128': (4, 1, 1, 128)}

    init_op = tf.global_variables_initializer()
    with self.test_session() as sess:
      sess.run(init_op)
      out_feature_maps = sess.run(feature_maps)
      out_feature_map_shapes = dict(
          (key, value.shape) for key, value in out_feature_maps.items())
      self.assertDictEqual(out_feature_map_shapes, expected_feature_map_shapes) 

def test_get_expected_feature_map_shapes_with_inception_v2(self):
    image_features = {
        'Mixed_3c': tf.random_uniform([4, 28, 28, 256], dtype=tf.float32),
        'Mixed_4c': tf.random_uniform([4, 14, 14, 576], dtype=tf.float32),
        'Mixed_5c': tf.random_uniform([4, 7, 7, 1024], dtype=tf.float32)
    }
    feature_maps = feature_map_generators.multi_resolution_feature_maps(
        feature_map_layout=INCEPTION_V2_LAYOUT,
        depth_multiplier=1,
        min_depth=32,
        insert_1x1_conv=True,
        image_features=image_features)

    expected_feature_map_shapes = {
        'Mixed_3c': (4, 28, 28, 256),
        'Mixed_4c': (4, 14, 14, 576),
        'Mixed_5c': (4, 7, 7, 1024),
        'Mixed_5c_2_Conv2d_3_3x3_s2_512': (4, 4, 4, 512),
        'Mixed_5c_2_Conv2d_4_3x3_s2_256': (4, 2, 2, 256),
        'Mixed_5c_2_Conv2d_5_3x3_s2_256': (4, 1, 1, 256)}

    init_op = tf.global_variables_initializer()
    with self.test_session() as sess:
      sess.run(init_op)
      out_feature_maps = sess.run(feature_maps)
      out_feature_map_shapes = dict(
          (key, value.shape) for key, value in out_feature_maps.items())
      self.assertDictEqual(out_feature_map_shapes, expected_feature_map_shapes) 

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.get_shape().assert_has_rank(4)
    shape_assert = tf.Assert(
        tf.logical_and(tf.greater_equal(tf.shape(preprocessed_inputs)[1], 33),
                       tf.greater_equal(tf.shape(preprocessed_inputs)[2], 33)),
        ['image size must at least be 33 in both height and width.'])

    feature_map_layout = {
        'from_layer': ['Mixed_4c', 'Mixed_5c', '', '', '', ''],
        'layer_depth': [-1, -1, 512, 256, 256, 128],
    }

    with tf.control_dependencies([shape_assert]):
      with slim.arg_scope(self._conv_hyperparams):
        with tf.variable_scope('InceptionV2',
                               reuse=self._reuse_weights) as scope:
          _, image_features = inception_v2.inception_v2_base(
              preprocessed_inputs,
              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_get_expected_feature_map_shapes_with_inception_v3(self):
    image_features = {
        'Mixed_5d': tf.random_uniform([4, 35, 35, 256], dtype=tf.float32),
        'Mixed_6e': tf.random_uniform([4, 17, 17, 576], dtype=tf.float32),
        'Mixed_7c': tf.random_uniform([4, 8, 8, 1024], dtype=tf.float32)
    }

    feature_maps = feature_map_generators.multi_resolution_feature_maps(
        feature_map_layout=INCEPTION_V3_LAYOUT,
        depth_multiplier=1,
        min_depth=32,
        insert_1x1_conv=True,
        image_features=image_features)

    expected_feature_map_shapes = {
        'Mixed_5d': (4, 35, 35, 256),
        'Mixed_6e': (4, 17, 17, 576),
        'Mixed_7c': (4, 8, 8, 1024),
        'Mixed_7c_2_Conv2d_3_3x3_s2_512': (4, 4, 4, 512),
        'Mixed_7c_2_Conv2d_4_3x3_s2_256': (4, 2, 2, 256),
        'Mixed_7c_2_Conv2d_5_3x3_s2_128': (4, 1, 1, 128)}

    init_op = tf.global_variables_initializer()
    with self.test_session() as sess:
      sess.run(init_op)
      out_feature_maps = sess.run(feature_maps)
      out_feature_map_shapes = dict(
          (key, value.shape) for key, value in out_feature_maps.items())
      self.assertDictEqual(out_feature_map_shapes, expected_feature_map_shapes) 

def test_get_expected_feature_map_shapes_with_inception_v2(self):
    image_features = {
        'Mixed_3c': tf.random_uniform([4, 28, 28, 256], dtype=tf.float32),
        'Mixed_4c': tf.random_uniform([4, 14, 14, 576], dtype=tf.float32),
        'Mixed_5c': tf.random_uniform([4, 7, 7, 1024], dtype=tf.float32)
    }
    feature_maps = feature_map_generators.multi_resolution_feature_maps(
        feature_map_layout=INCEPTION_V2_LAYOUT,
        depth_multiplier=1,
        min_depth=32,
        insert_1x1_conv=True,
        image_features=image_features)

    expected_feature_map_shapes = {
        'Mixed_3c': (4, 28, 28, 256),
        'Mixed_4c': (4, 14, 14, 576),
        'Mixed_5c': (4, 7, 7, 1024),
        'Mixed_5c_2_Conv2d_3_3x3_s2_512': (4, 4, 4, 512),
        'Mixed_5c_2_Conv2d_4_3x3_s2_256': (4, 2, 2, 256),
        'Mixed_5c_2_Conv2d_5_3x3_s2_256': (4, 1, 1, 256)}

    init_op = tf.global_variables_initializer()
    with self.test_session() as sess:
      sess.run(init_op)
      out_feature_maps = sess.run(feature_maps)
      out_feature_map_shapes = dict(
          (key, value.shape) for key, value in out_feature_maps.items())
      self.assertDictEqual(out_feature_map_shapes, expected_feature_map_shapes) 

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.get_shape().assert_has_rank(4)
    shape_assert = tf.Assert(
        tf.logical_and(tf.greater_equal(tf.shape(preprocessed_inputs)[1], 33),
                       tf.greater_equal(tf.shape(preprocessed_inputs)[2], 33)),
        ['image size must at least be 33 in both height and width.'])

    feature_map_layout = {
        'from_layer': ['Mixed_4c', 'Mixed_5c', '', '', '', ''],
        'layer_depth': [-1, -1, 512, 256, 256, 128],
    }

    with tf.control_dependencies([shape_assert]):
      with slim.arg_scope(self._conv_hyperparams):
        with tf.variable_scope('InceptionV2',
                               reuse=self._reuse_weights) as scope:
          _, image_features = inception_v2.inception_v2_base(
              preprocessed_inputs,
              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.get_shape().assert_has_rank(4)
    shape_assert = tf.Assert(
        tf.logical_and(tf.greater_equal(tf.shape(preprocessed_inputs)[1], 33),
                       tf.greater_equal(tf.shape(preprocessed_inputs)[2], 33)),
        ['image size must at least be 33 in both height and width.'])

    feature_map_layout = {
        'from_layer': ['Conv2d_11_pointwise', 'Conv2d_13_pointwise', '', '',
                       '', ''],
        'layer_depth': [-1, -1, 512, 256, 256, 128],
    }

    with tf.control_dependencies([shape_assert]):
      with slim.arg_scope(self._conv_hyperparams):
        with tf.variable_scope('MobilenetV1',
                               reuse=self._reuse_weights) as scope:
          _, image_features = mobilenet_v1.mobilenet_v1_base(
              preprocessed_inputs,
              final_endpoint='Conv2d_13_pointwise',
              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_get_expected_feature_map_shapes_with_inception_v3(self):
    image_features = {
        'Mixed_5d': tf.random_uniform([4, 35, 35, 256], dtype=tf.float32),
        'Mixed_6e': tf.random_uniform([4, 17, 17, 576], dtype=tf.float32),
        'Mixed_7c': tf.random_uniform([4, 8, 8, 1024], dtype=tf.float32)
    }

    feature_maps = feature_map_generators.multi_resolution_feature_maps(
        feature_map_layout=INCEPTION_V3_LAYOUT,
        depth_multiplier=1,
        min_depth=32,
        insert_1x1_conv=True,
        image_features=image_features)

    expected_feature_map_shapes = {
        'Mixed_5d': (4, 35, 35, 256),
        'Mixed_6e': (4, 17, 17, 576),
        'Mixed_7c': (4, 8, 8, 1024),
        'Mixed_7c_2_Conv2d_3_3x3_s2_512': (4, 4, 4, 512),
        'Mixed_7c_2_Conv2d_4_3x3_s2_256': (4, 2, 2, 256),
        'Mixed_7c_2_Conv2d_5_3x3_s2_128': (4, 1, 1, 128)}

    init_op = tf.global_variables_initializer()
    with self.test_session() as sess:
      sess.run(init_op)
      out_feature_maps = sess.run(feature_maps)
      out_feature_map_shapes = dict(
          (key, value.shape) for key, value in out_feature_maps.items())
      self.assertDictEqual(out_feature_map_shapes, expected_feature_map_shapes) 

def test_get_expected_feature_map_shapes_with_inception_v2(self):
    image_features = {
        'Mixed_3c': tf.random_uniform([4, 28, 28, 256], dtype=tf.float32),
        'Mixed_4c': tf.random_uniform([4, 14, 14, 576], dtype=tf.float32),
        'Mixed_5c': tf.random_uniform([4, 7, 7, 1024], dtype=tf.float32)
    }
    feature_maps = feature_map_generators.multi_resolution_feature_maps(
        feature_map_layout=INCEPTION_V2_LAYOUT,
        depth_multiplier=1,
        min_depth=32,
        insert_1x1_conv=True,
        image_features=image_features)

    expected_feature_map_shapes = {
        'Mixed_3c': (4, 28, 28, 256),
        'Mixed_4c': (4, 14, 14, 576),
        'Mixed_5c': (4, 7, 7, 1024),
        'Mixed_5c_2_Conv2d_3_3x3_s2_512': (4, 4, 4, 512),
        'Mixed_5c_2_Conv2d_4_3x3_s2_256': (4, 2, 2, 256),
        'Mixed_5c_2_Conv2d_5_3x3_s2_256': (4, 1, 1, 256)}

    init_op = tf.global_variables_initializer()
    with self.test_session() as sess:
      sess.run(init_op)
      out_feature_maps = sess.run(feature_maps)
      out_feature_map_shapes = dict(
          (key, value.shape) for key, value in out_feature_maps.items())
      self.assertDictEqual(out_feature_map_shapes, expected_feature_map_shapes) 

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):
      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': ['Conv2d_11_pointwise', 'Conv2d_13_pointwise', '', '',
                       '', ''],
        '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):
      # TODO: Enable fused batch norm once quantization supports it.
      with slim.arg_scope([slim.batch_norm], fused=False):
        with tf.variable_scope('MobilenetV1',
                               reuse=self._reuse_weights) as scope:
          _, 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,
              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_get_expected_feature_map_shapes_with_embedded_ssd_mobilenet_v1(
      self):
    image_features = {
        'Conv2d_11_pointwise': tf.random_uniform([4, 16, 16, 512],
                                                 dtype=tf.float32),
        'Conv2d_13_pointwise': tf.random_uniform([4, 8, 8, 1024],
                                                 dtype=tf.float32),
    }

    feature_maps = feature_map_generators.multi_resolution_feature_maps(
        feature_map_layout=EMBEDDED_SSD_MOBILENET_V1_LAYOUT,
        depth_multiplier=1,
        min_depth=32,
        insert_1x1_conv=True,
        image_features=image_features)

    expected_feature_map_shapes = {
        'Conv2d_11_pointwise': (4, 16, 16, 512),
        'Conv2d_13_pointwise': (4, 8, 8, 1024),
        'Conv2d_13_pointwise_2_Conv2d_2_3x3_s2_512': (4, 4, 4, 512),
        'Conv2d_13_pointwise_2_Conv2d_3_3x3_s2_256': (4, 2, 2, 256),
        'Conv2d_13_pointwise_2_Conv2d_4_2x2_s2_256': (4, 1, 1, 256)}

    init_op = tf.global_variables_initializer()
    with self.test_session() as sess:
      sess.run(init_op)
      out_feature_maps = sess.run(feature_maps)
      out_feature_map_shapes = dict(
          (key, value.shape) for key, value in out_feature_maps.items())
      self.assertDictEqual(out_feature_map_shapes, expected_feature_map_shapes) 

def test_get_expected_feature_map_shapes_with_inception_v3(self):
    image_features = {
        'Mixed_5d': tf.random_uniform([4, 35, 35, 256], dtype=tf.float32),
        'Mixed_6e': tf.random_uniform([4, 17, 17, 576], dtype=tf.float32),
        'Mixed_7c': tf.random_uniform([4, 8, 8, 1024], dtype=tf.float32)
    }

    feature_maps = feature_map_generators.multi_resolution_feature_maps(
        feature_map_layout=INCEPTION_V3_LAYOUT,
        depth_multiplier=1,
        min_depth=32,
        insert_1x1_conv=True,
        image_features=image_features)

    expected_feature_map_shapes = {
        'Mixed_5d': (4, 35, 35, 256),
        'Mixed_6e': (4, 17, 17, 576),
        'Mixed_7c': (4, 8, 8, 1024),
        'Mixed_7c_2_Conv2d_3_3x3_s2_512': (4, 4, 4, 512),
        'Mixed_7c_2_Conv2d_4_3x3_s2_256': (4, 2, 2, 256),
        'Mixed_7c_2_Conv2d_5_3x3_s2_128': (4, 1, 1, 128)}

    init_op = tf.global_variables_initializer()
    with self.test_session() as sess:
      sess.run(init_op)
      out_feature_maps = sess.run(feature_maps)
      out_feature_map_shapes = dict(
          (key, value.shape) for key, value in out_feature_maps.items())
      self.assertDictEqual(out_feature_map_shapes, expected_feature_map_shapes) 

def test_get_expected_feature_map_shapes_with_inception_v2(self):
    image_features = {
        'Mixed_3c': tf.random_uniform([4, 28, 28, 256], dtype=tf.float32),
        'Mixed_4c': tf.random_uniform([4, 14, 14, 576], dtype=tf.float32),
        'Mixed_5c': tf.random_uniform([4, 7, 7, 1024], dtype=tf.float32)
    }
    feature_maps = feature_map_generators.multi_resolution_feature_maps(
        feature_map_layout=INCEPTION_V2_LAYOUT,
        depth_multiplier=1,
        min_depth=32,
        insert_1x1_conv=True,
        image_features=image_features)

    expected_feature_map_shapes = {
        'Mixed_3c': (4, 28, 28, 256),
        'Mixed_4c': (4, 14, 14, 576),
        'Mixed_5c': (4, 7, 7, 1024),
        'Mixed_5c_2_Conv2d_3_3x3_s2_512': (4, 4, 4, 512),
        'Mixed_5c_2_Conv2d_4_3x3_s2_256': (4, 2, 2, 256),
        'Mixed_5c_2_Conv2d_5_3x3_s2_256': (4, 1, 1, 256)}

    init_op = tf.global_variables_initializer()
    with self.test_session() as sess:
      sess.run(init_op)
      out_feature_maps = sess.run(feature_maps)
      out_feature_map_shapes = dict(
          (key, value.shape) for key, value in out_feature_maps.items())
      self.assertDictEqual(out_feature_map_shapes, expected_feature_map_shapes) 

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.get_shape().assert_has_rank(4)
    shape_assert = tf.Assert(
        tf.logical_and(tf.greater_equal(tf.shape(preprocessed_inputs)[1], 33),
                       tf.greater_equal(tf.shape(preprocessed_inputs)[2], 33)),
        ['image size must at least be 33 in both height and width.'])

    feature_map_layout = {
        'from_layer': ['Mixed_5d', 'Mixed_6e', 'Mixed_7c', '', '', ''],
        'layer_depth': [-1, -1, -1, 512, 256, 128],
    }

    with tf.control_dependencies([shape_assert]):
      with slim.arg_scope(self._conv_hyperparams):
        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_get_expected_feature_map_shapes_with_embedded_ssd_mobilenet_v1(
      self):
    image_features = {
        'Conv2d_11_pointwise': tf.random_uniform([4, 16, 16, 512],
                                                 dtype=tf.float32),
        'Conv2d_13_pointwise': tf.random_uniform([4, 8, 8, 1024],
                                                 dtype=tf.float32),
    }

    feature_maps = feature_map_generators.multi_resolution_feature_maps(
        feature_map_layout=EMBEDDED_SSD_MOBILENET_V1_LAYOUT,
        depth_multiplier=1,
        min_depth=32,
        insert_1x1_conv=True,
        image_features=image_features)

    expected_feature_map_shapes = {
        'Conv2d_11_pointwise': (4, 16, 16, 512),
        'Conv2d_13_pointwise': (4, 8, 8, 1024),
        'Conv2d_13_pointwise_2_Conv2d_2_3x3_s2_512': (4, 4, 4, 512),
        'Conv2d_13_pointwise_2_Conv2d_3_3x3_s2_256': (4, 2, 2, 256),
        'Conv2d_13_pointwise_2_Conv2d_4_2x2_s2_256': (4, 1, 1, 256)}

    init_op = tf.global_variables_initializer()
    with self.test_session() as sess:
      sess.run(init_op)
      out_feature_maps = sess.run(feature_maps)
      out_feature_map_shapes = dict(
          (key, value.shape) for key, value in out_feature_maps.items())
      self.assertDictEqual(out_feature_map_shapes, expected_feature_map_shapes)