Python nets.inception_resnet_v2.inception_resnet_v2_arg_scope() Examples

def _extract_proposal_features(self, preprocessed_inputs, scope):
    """Extracts first stage RPN features.

    Extracts features using the first half of the Inception Resnet v2 network.
    We construct the network in `align_feature_maps=True` mode, which means
    that all VALID paddings in the network are changed to SAME padding so that
    the feature maps are aligned.

    Args:
      preprocessed_inputs: A [batch, height, width, channels] float32 tensor
        representing a batch of images.
      scope: A scope name.

    Returns:
      rpn_feature_map: A tensor with shape [batch, height, width, depth]
    Raises:
      InvalidArgumentError: If the spatial size of `preprocessed_inputs`
        (height or width) is less than 33.
      ValueError: If the created network is missing the required activation.
    """
    if len(preprocessed_inputs.get_shape().as_list()) != 4:
      raise ValueError('`preprocessed_inputs` must be 4 dimensional, got a '
                       'tensor of shape %s' % preprocessed_inputs.get_shape())

    with slim.arg_scope(inception_resnet_v2.inception_resnet_v2_arg_scope(
        weight_decay=self._weight_decay)):
      # Forces is_training to False to disable batch norm update.
      with slim.arg_scope([slim.batch_norm],
                          is_training=self._train_batch_norm):
        with tf.variable_scope('InceptionResnetV2',
                               reuse=self._reuse_weights) as scope:
          return inception_resnet_v2.inception_resnet_v2_base(
              preprocessed_inputs, final_endpoint='PreAuxLogits',
              scope=scope, output_stride=self._first_stage_features_stride,
              align_feature_maps=True) 

def _extract_proposal_features(self, preprocessed_inputs, scope):
    """Extracts first stage RPN features.

    Extracts features using the first half of the Inception Resnet v2 network.
    We construct the network in `align_feature_maps=True` mode, which means
    that all VALID paddings in the network are changed to SAME padding so that
    the feature maps are aligned.

    Args:
      preprocessed_inputs: A [batch, height, width, channels] float32 tensor
        representing a batch of images.
      scope: A scope name.

    Returns:
      rpn_feature_map: A tensor with shape [batch, height, width, depth]
    Raises:
      InvalidArgumentError: If the spatial size of `preprocessed_inputs`
        (height or width) is less than 33.
      ValueError: If the created network is missing the required activation.
    """
    if len(preprocessed_inputs.get_shape().as_list()) != 4:
      raise ValueError('`preprocessed_inputs` must be 4 dimensional, got a '
                       'tensor of shape %s' % preprocessed_inputs.get_shape())

    with slim.arg_scope(inception_resnet_v2.inception_resnet_v2_arg_scope(
        weight_decay=self._weight_decay)):
      # Forces is_training to False to disable batch norm update.
      with slim.arg_scope([slim.batch_norm],
                          is_training=self._train_batch_norm):
        with tf.variable_scope('InceptionResnetV2',
                               reuse=self._reuse_weights) as scope:
          return inception_resnet_v2.inception_resnet_v2_base(
              preprocessed_inputs, final_endpoint='PreAuxLogits',
              scope=scope, output_stride=self._first_stage_features_stride,
              align_feature_maps=True) 

def _extract_proposal_features(self, preprocessed_inputs, scope):
    """Extracts first stage RPN features.

    Extracts features using the first half of the Inception Resnet v2 network.
    We construct the network in `align_feature_maps=True` mode, which means
    that all VALID paddings in the network are changed to SAME padding so that
    the feature maps are aligned.

    Args:
      preprocessed_inputs: A [batch, height, width, channels] float32 tensor
        representing a batch of images.
      scope: A scope name.

    Returns:
      rpn_feature_map: A tensor with shape [batch, height, width, depth]
    Raises:
      InvalidArgumentError: If the spatial size of `preprocessed_inputs`
        (height or width) is less than 33.
      ValueError: If the created network is missing the required activation.
    """
    if len(preprocessed_inputs.get_shape().as_list()) != 4:
      raise ValueError('`preprocessed_inputs` must be 4 dimensional, got a '
                       'tensor of shape %s' % preprocessed_inputs.get_shape())

    with slim.arg_scope(inception_resnet_v2.inception_resnet_v2_arg_scope(
        weight_decay=self._weight_decay)):
      # Forces is_training to False to disable batch norm update.
      with slim.arg_scope([slim.batch_norm], is_training=False):
        with tf.variable_scope('InceptionResnetV2',
                               reuse=self._reuse_weights) as scope:
          rpn_feature_map, _ = (
              inception_resnet_v2.inception_resnet_v2_base(
                  preprocessed_inputs, final_endpoint='PreAuxLogits',
                  scope=scope, output_stride=self._first_stage_features_stride,
                  align_feature_maps=True))
    return rpn_feature_map 

def _extract_proposal_features(self, preprocessed_inputs, scope):
    """Extracts first stage RPN features.

    Extracts features using the first half of the Inception Resnet v2 network.
    We construct the network in `align_feature_maps=True` mode, which means
    that all VALID paddings in the network are changed to SAME padding so that
    the feature maps are aligned.

    Args:
      preprocessed_inputs: A [batch, height, width, channels] float32 tensor
        representing a batch of images.
      scope: A scope name.

    Returns:
      rpn_feature_map: A tensor with shape [batch, height, width, depth]
    Raises:
      InvalidArgumentError: If the spatial size of `preprocessed_inputs`
        (height or width) is less than 33.
      ValueError: If the created network is missing the required activation.
    """
    if len(preprocessed_inputs.get_shape().as_list()) != 4:
      raise ValueError('`preprocessed_inputs` must be 4 dimensional, got a '
                       'tensor of shape %s' % preprocessed_inputs.get_shape())

    with slim.arg_scope(inception_resnet_v2.inception_resnet_v2_arg_scope(
        weight_decay=self._weight_decay)):
      # Forces is_training to False to disable batch norm update.
      with slim.arg_scope([slim.batch_norm], is_training=False):
        with tf.variable_scope('InceptionResnetV2',
                               reuse=self._reuse_weights) as scope:
          rpn_feature_map, _ = (
              inception_resnet_v2.inception_resnet_v2_base(
                  preprocessed_inputs, final_endpoint='PreAuxLogits',
                  scope=scope, output_stride=self._first_stage_features_stride,
                  align_feature_maps=True))
    return rpn_feature_map 

def _extract_proposal_features(self, preprocessed_inputs, scope):
    """Extracts first stage RPN features.

    Extracts features using the first half of the Inception Resnet v2 network.
    We construct the network in `align_feature_maps=True` mode, which means
    that all VALID paddings in the network are changed to SAME padding so that
    the feature maps are aligned.

    Args:
      preprocessed_inputs: A [batch, height, width, channels] float32 tensor
        representing a batch of images.
      scope: A scope name.

    Returns:
      rpn_feature_map: A tensor with shape [batch, height, width, depth]
    Raises:
      InvalidArgumentError: If the spatial size of `preprocessed_inputs`
        (height or width) is less than 33.
      ValueError: If the created network is missing the required activation.
    """
    if len(preprocessed_inputs.get_shape().as_list()) != 4:
      raise ValueError('`preprocessed_inputs` must be 4 dimensional, got a '
                       'tensor of shape %s' % preprocessed_inputs.get_shape())

    with slim.arg_scope(inception_resnet_v2.inception_resnet_v2_arg_scope(
        weight_decay=self._weight_decay)):
      # Forces is_training to False to disable batch norm update.
      with slim.arg_scope([slim.batch_norm],
                          is_training=self._train_batch_norm):
        with tf.variable_scope('InceptionResnetV2',
                               reuse=self._reuse_weights) as scope:
          rpn_feature_map, _ = (
              inception_resnet_v2.inception_resnet_v2_base(
                  preprocessed_inputs, final_endpoint='PreAuxLogits',
                  scope=scope, output_stride=self._first_stage_features_stride,
                  align_feature_maps=True))
    return rpn_feature_map 

def _extract_proposal_features(self, preprocessed_inputs, scope):
    """Extracts first stage RPN features.

    Extracts features using the first half of the Inception Resnet v2 network.
    We construct the network in `align_feature_maps=True` mode, which means
    that all VALID paddings in the network are changed to SAME padding so that
    the feature maps are aligned.

    Args:
      preprocessed_inputs: A [batch, height, width, channels] float32 tensor
        representing a batch of images.
      scope: A scope name.

    Returns:
      rpn_feature_map: A tensor with shape [batch, height, width, depth]
    Raises:
      InvalidArgumentError: If the spatial size of `preprocessed_inputs`
        (height or width) is less than 33.
      ValueError: If the created network is missing the required activation.
    """
    if len(preprocessed_inputs.get_shape().as_list()) != 4:
      raise ValueError('`preprocessed_inputs` must be 4 dimensional, got a '
                       'tensor of shape %s' % preprocessed_inputs.get_shape())

    with slim.arg_scope(inception_resnet_v2.inception_resnet_v2_arg_scope(
        weight_decay=self._weight_decay)):
      # Forces is_training to False to disable batch norm update.
      with slim.arg_scope([slim.batch_norm], is_training=False):
        with tf.variable_scope('InceptionResnetV2',
                               reuse=self._reuse_weights) as scope:
          rpn_feature_map, _ = (
              inception_resnet_v2.inception_resnet_v2_base(
                  preprocessed_inputs, final_endpoint='PreAuxLogits',
                  scope=scope, output_stride=self._first_stage_features_stride,
                  align_feature_maps=True))
    return rpn_feature_map 

def graph_small(x, target_class_input, i, x_max, x_min, grad):
  eps = 2.0 * FLAGS.max_epsilon / 255.0
  alpha = eps / 28
  momentum = FLAGS.momentum
  num_classes = 1001

  with slim.arg_scope(inception_v3.inception_v3_arg_scope()):
    logits_v3, end_points_v3 = inception_v3.inception_v3(
        x, num_classes=num_classes, is_training=False)

  with slim.arg_scope(inception_resnet_v2.inception_resnet_v2_arg_scope()):
    logits_ensadv_res_v2, end_points_ensadv_res_v2 = inception_resnet_v2.inception_resnet_v2(
        x, num_classes=num_classes, is_training=False, scope='EnsAdvInceptionResnetV2')
            
  one_hot_target_class = tf.one_hot(target_class_input, num_classes)

  logits = (logits_v3 + 2 * logits_ensadv_res_v2) / 3
  auxlogits = (end_points_v3['AuxLogits'] + 2 * end_points_ensadv_res_v2['AuxLogits']) / 3
  cross_entropy = tf.losses.softmax_cross_entropy(one_hot_target_class,
                                                  logits,
                                                  label_smoothing=0.0,
                                                  weights=1.0)
  cross_entropy += tf.losses.softmax_cross_entropy(one_hot_target_class,
                                                   auxlogits,
                                                   label_smoothing=0.0,
                                                   weights=0.4)
  noise = tf.gradients(cross_entropy, x)[0]
  noise = noise / tf.reshape(tf.contrib.keras.backend.std(tf.reshape(noise, [FLAGS.batch_size, -1]), axis=1), [FLAGS.batch_size, 1, 1, 1])
  noise = momentum * grad + noise
  noise = noise / tf.reshape(tf.contrib.keras.backend.std(tf.reshape(noise, [FLAGS.batch_size, -1]), axis=1), [FLAGS.batch_size, 1, 1, 1])
  x = x - alpha * tf.clip_by_value(tf.round(noise), -2, 2)
  x = tf.clip_by_value(x, x_min, x_max)
  i = tf.add(i, 1)
  return x, target_class_input, i, x_max, x_min, noise 

def _extract_proposal_features(self, preprocessed_inputs, scope):
    """Extracts first stage RPN features.

    Extracts features using the first half of the Inception Resnet v2 network.
    We construct the network in `align_feature_maps=True` mode, which means
    that all VALID paddings in the network are changed to SAME padding so that
    the feature maps are aligned.

    Args:
      preprocessed_inputs: A [batch, height, width, channels] float32 tensor
        representing a batch of images.
      scope: A scope name.

    Returns:
      rpn_feature_map: A tensor with shape [batch, height, width, depth]
    Raises:
      InvalidArgumentError: If the spatial size of `preprocessed_inputs`
        (height or width) is less than 33.
      ValueError: If the created network is missing the required activation.
    """
    if len(preprocessed_inputs.get_shape().as_list()) != 4:
      raise ValueError('`preprocessed_inputs` must be 4 dimensional, got a '
                       'tensor of shape %s' % preprocessed_inputs.get_shape())

    with slim.arg_scope(inception_resnet_v2.inception_resnet_v2_arg_scope(
        weight_decay=self._weight_decay)):
      # Forces is_training to False to disable batch norm update.
      with slim.arg_scope([slim.batch_norm], is_training=False):
        with tf.variable_scope('InceptionResnetV2',
                               reuse=self._reuse_weights) as scope:
          rpn_feature_map, _ = (
              inception_resnet_v2.inception_resnet_v2_base(
                  preprocessed_inputs, final_endpoint='PreAuxLogits',
                  scope=scope, output_stride=self._first_stage_features_stride,
                  align_feature_maps=True))
    return rpn_feature_map 

def _extract_proposal_features(self, preprocessed_inputs, scope):
    """Extracts first stage RPN features.

    Extracts features using the first half of the Inception Resnet v2 network.
    We construct the network in `align_feature_maps=True` mode, which means
    that all VALID paddings in the network are changed to SAME padding so that
    the feature maps are aligned.

    Args:
      preprocessed_inputs: A [batch, height, width, channels] float32 tensor
        representing a batch of images.
      scope: A scope name.

    Returns:
      rpn_feature_map: A tensor with shape [batch, height, width, depth]
    Raises:
      InvalidArgumentError: If the spatial size of `preprocessed_inputs`
        (height or width) is less than 33.
      ValueError: If the created network is missing the required activation.
    """
    if len(preprocessed_inputs.get_shape().as_list()) != 4:
      raise ValueError('`preprocessed_inputs` must be 4 dimensional, got a '
                       'tensor of shape %s' % preprocessed_inputs.get_shape())

    with slim.arg_scope(inception_resnet_v2.inception_resnet_v2_arg_scope(
        weight_decay=self._weight_decay)):
      # Forces is_training to False to disable batch norm update.
      with slim.arg_scope([slim.batch_norm],
                          is_training=self._train_batch_norm):
        with tf.variable_scope('InceptionResnetV2',
                               reuse=self._reuse_weights) as scope:
          return inception_resnet_v2.inception_resnet_v2_base(
              preprocessed_inputs, final_endpoint='PreAuxLogits',
              scope=scope, output_stride=self._first_stage_features_stride,
              align_feature_maps=True) 

def _extract_proposal_features(self, preprocessed_inputs, scope):
    """Extracts first stage RPN features.

    Extracts features using the first half of the Inception Resnet v2 network.
    We construct the network in `align_feature_maps=True` mode, which means
    that all VALID paddings in the network are changed to SAME padding so that
    the feature maps are aligned.

    Args:
      preprocessed_inputs: A [batch, height, width, channels] float32 tensor
        representing a batch of images.
      scope: A scope name.

    Returns:
      rpn_feature_map: A tensor with shape [batch, height, width, depth]
    Raises:
      InvalidArgumentError: If the spatial size of `preprocessed_inputs`
        (height or width) is less than 33.
      ValueError: If the created network is missing the required activation.
    """
    if len(preprocessed_inputs.get_shape().as_list()) != 4:
      raise ValueError('`preprocessed_inputs` must be 4 dimensional, got a '
                       'tensor of shape %s' % preprocessed_inputs.get_shape())

    with slim.arg_scope(inception_resnet_v2.inception_resnet_v2_arg_scope(
        weight_decay=self._weight_decay)):
      # Forces is_training to False to disable batch norm update.
      with slim.arg_scope([slim.batch_norm],
                          is_training=self._train_batch_norm):
        with tf.variable_scope('InceptionResnetV2',
                               reuse=self._reuse_weights) as scope:
          rpn_feature_map, _ = (
              inception_resnet_v2.inception_resnet_v2_base(
                  preprocessed_inputs, final_endpoint='PreAuxLogits',
                  scope=scope, output_stride=self._first_stage_features_stride,
                  align_feature_maps=True))
    return rpn_feature_map 

def _extract_proposal_features(self, preprocessed_inputs, scope):
    """Extracts first stage RPN features.

    Extracts features using the first half of the Inception Resnet v2 network.
    We construct the network in `align_feature_maps=True` mode, which means
    that all VALID paddings in the network are changed to SAME padding so that
    the feature maps are aligned.

    Args:
      preprocessed_inputs: A [batch, height, width, channels] float32 tensor
        representing a batch of images.
      scope: A scope name.

    Returns:
      rpn_feature_map: A tensor with shape [batch, height, width, depth]
    Raises:
      InvalidArgumentError: If the spatial size of `preprocessed_inputs`
        (height or width) is less than 33.
      ValueError: If the created network is missing the required activation.
    """
    if len(preprocessed_inputs.get_shape().as_list()) != 4:
      raise ValueError('`preprocessed_inputs` must be 4 dimensional, got a '
                       'tensor of shape %s' % preprocessed_inputs.get_shape())

    with slim.arg_scope(inception_resnet_v2.inception_resnet_v2_arg_scope(
        weight_decay=self._weight_decay)):
      # Forces is_training to False to disable batch norm update.
      with slim.arg_scope([slim.batch_norm],
                          is_training=self._train_batch_norm):
        with tf.variable_scope('InceptionResnetV2',
                               reuse=self._reuse_weights) as scope:
          return inception_resnet_v2.inception_resnet_v2_base(
              preprocessed_inputs, final_endpoint='PreAuxLogits',
              scope=scope, output_stride=self._first_stage_features_stride,
              align_feature_maps=True) 

def _extract_proposal_features(self, preprocessed_inputs, scope):
    """Extracts first stage RPN features.

    Extracts features using the first half of the Inception Resnet v2 network.
    We construct the network in `align_feature_maps=True` mode, which means
    that all VALID paddings in the network are changed to SAME padding so that
    the feature maps are aligned.

    Args:
      preprocessed_inputs: A [batch, height, width, channels] float32 tensor
        representing a batch of images.
      scope: A scope name.

    Returns:
      rpn_feature_map: A tensor with shape [batch, height, width, depth]
    Raises:
      InvalidArgumentError: If the spatial size of `preprocessed_inputs`
        (height or width) is less than 33.
      ValueError: If the created network is missing the required activation.
    """
    if len(preprocessed_inputs.get_shape().as_list()) != 4:
      raise ValueError('`preprocessed_inputs` must be 4 dimensional, got a '
                       'tensor of shape %s' % preprocessed_inputs.get_shape())

    with slim.arg_scope(inception_resnet_v2.inception_resnet_v2_arg_scope(
        weight_decay=self._weight_decay)):
      # Forces is_training to False to disable batch norm update.
      with slim.arg_scope([slim.batch_norm],
                          is_training=self._train_batch_norm):
        with tf.variable_scope('InceptionResnetV2',
                               reuse=self._reuse_weights) as scope:
          return inception_resnet_v2.inception_resnet_v2_base(
              preprocessed_inputs, final_endpoint='PreAuxLogits',
              scope=scope, output_stride=self._first_stage_features_stride,
              align_feature_maps=True) 

def _extract_proposal_features(self, preprocessed_inputs, scope):
    """Extracts first stage RPN features.

    Extracts features using the first half of the Inception Resnet v2 network.
    We construct the network in `align_feature_maps=True` mode, which means
    that all VALID paddings in the network are changed to SAME padding so that
    the feature maps are aligned.

    Args:
      preprocessed_inputs: A [batch, height, width, channels] float32 tensor
        representing a batch of images.
      scope: A scope name.

    Returns:
      rpn_feature_map: A tensor with shape [batch, height, width, depth]
    Raises:
      InvalidArgumentError: If the spatial size of `preprocessed_inputs`
        (height or width) is less than 33.
      ValueError: If the created network is missing the required activation.
    """
    if len(preprocessed_inputs.get_shape().as_list()) != 4:
      raise ValueError('`preprocessed_inputs` must be 4 dimensional, got a '
                       'tensor of shape %s' % preprocessed_inputs.get_shape())

    with slim.arg_scope(inception_resnet_v2.inception_resnet_v2_arg_scope(
        weight_decay=self._weight_decay)):
      # Forces is_training to False to disable batch norm update.
      with slim.arg_scope([slim.batch_norm],
                          is_training=self._train_batch_norm):
        with tf.variable_scope('InceptionResnetV2',
                               reuse=self._reuse_weights) as scope:
          rpn_feature_map, _ = (
              inception_resnet_v2.inception_resnet_v2_base(
                  preprocessed_inputs, final_endpoint='PreAuxLogits',
                  scope=scope, output_stride=self._first_stage_features_stride,
                  align_feature_maps=True))
    return rpn_feature_map 

def _extract_proposal_features(self, preprocessed_inputs, scope):
    """Extracts first stage RPN features.

    Extracts features using the first half of the Inception Resnet v2 network.
    We construct the network in `align_feature_maps=True` mode, which means
    that all VALID paddings in the network are changed to SAME padding so that
    the feature maps are aligned.

    Args:
      preprocessed_inputs: A [batch, height, width, channels] float32 tensor
        representing a batch of images.
      scope: A scope name.

    Returns:
      rpn_feature_map: A tensor with shape [batch, height, width, depth]
    Raises:
      InvalidArgumentError: If the spatial size of `preprocessed_inputs`
        (height or width) is less than 33.
      ValueError: If the created network is missing the required activation.
    """
    if len(preprocessed_inputs.get_shape().as_list()) != 4:
      raise ValueError('`preprocessed_inputs` must be 4 dimensional, got a '
                       'tensor of shape %s' % preprocessed_inputs.get_shape())

    with slim.arg_scope(inception_resnet_v2.inception_resnet_v2_arg_scope(
        weight_decay=self._weight_decay)):
      # Forces is_training to False to disable batch norm update.
      with slim.arg_scope([slim.batch_norm], is_training=False):
        with tf.variable_scope('InceptionResnetV2',
                               reuse=self._reuse_weights) as scope:
          rpn_feature_map, _ = (
              inception_resnet_v2.inception_resnet_v2_base(
                  preprocessed_inputs, final_endpoint='PreAuxLogits',
                  scope=scope, output_stride=self._first_stage_features_stride,
                  align_feature_maps=True))
    return rpn_feature_map 

def _extract_proposal_features(self, preprocessed_inputs, scope):
    """Extracts first stage RPN features.

    Extracts features using the first half of the Inception Resnet v2 network.
    We construct the network in `align_feature_maps=True` mode, which means
    that all VALID paddings in the network are changed to SAME padding so that
    the feature maps are aligned.

    Args:
      preprocessed_inputs: A [batch, height, width, channels] float32 tensor
        representing a batch of images.
      scope: A scope name.

    Returns:
      rpn_feature_map: A tensor with shape [batch, height, width, depth]
    Raises:
      InvalidArgumentError: If the spatial size of `preprocessed_inputs`
        (height or width) is less than 33.
      ValueError: If the created network is missing the required activation.
    """
    if len(preprocessed_inputs.get_shape().as_list()) != 4:
      raise ValueError('`preprocessed_inputs` must be 4 dimensional, got a '
                       'tensor of shape %s' % preprocessed_inputs.get_shape())

    with slim.arg_scope(inception_resnet_v2.inception_resnet_v2_arg_scope(
        weight_decay=self._weight_decay)):
      # Forces is_training to False to disable batch norm update.
      with slim.arg_scope([slim.batch_norm], is_training=False):
        with tf.variable_scope('InceptionResnetV2',
                               reuse=self._reuse_weights) as scope:
          rpn_feature_map, _ = (
              inception_resnet_v2.inception_resnet_v2_base(
                  preprocessed_inputs, final_endpoint='PreAuxLogits',
                  scope=scope, output_stride=self._first_stage_features_stride,
                  align_feature_maps=True))
    return rpn_feature_map 

def _extract_proposal_features(self, preprocessed_inputs, scope):
    """Extracts first stage RPN features.

    Extracts features using the first half of the Inception Resnet v2 network.
    We construct the network in `align_feature_maps=True` mode, which means
    that all VALID paddings in the network are changed to SAME padding so that
    the feature maps are aligned.

    Args:
      preprocessed_inputs: A [batch, height, width, channels] float32 tensor
        representing a batch of images.
      scope: A scope name.

    Returns:
      rpn_feature_map: A tensor with shape [batch, height, width, depth]
    Raises:
      InvalidArgumentError: If the spatial size of `preprocessed_inputs`
        (height or width) is less than 33.
      ValueError: If the created network is missing the required activation.
    """
    if len(preprocessed_inputs.get_shape().as_list()) != 4:
      raise ValueError('`preprocessed_inputs` must be 4 dimensional, got a '
                       'tensor of shape %s' % preprocessed_inputs.get_shape())

    with slim.arg_scope(inception_resnet_v2.inception_resnet_v2_arg_scope(
        weight_decay=self._weight_decay)):
      # Forces is_training to False to disable batch norm update.
      with slim.arg_scope([slim.batch_norm],
                          is_training=self._train_batch_norm):
        with tf.variable_scope('InceptionResnetV2',
                               reuse=self._reuse_weights) as scope:
          rpn_feature_map, _ = (
              inception_resnet_v2.inception_resnet_v2_base(
                  preprocessed_inputs, final_endpoint='PreAuxLogits',
                  scope=scope, output_stride=self._first_stage_features_stride,
                  align_feature_maps=True))
    return rpn_feature_map 

def _extract_proposal_features(self, preprocessed_inputs, scope):
    """Extracts first stage RPN features.

    Extracts features using the first half of the Inception Resnet v2 network.
    We construct the network in `align_feature_maps=True` mode, which means
    that all VALID paddings in the network are changed to SAME padding so that
    the feature maps are aligned.

    Args:
      preprocessed_inputs: A [batch, height, width, channels] float32 tensor
        representing a batch of images.
      scope: A scope name.

    Returns:
      rpn_feature_map: A tensor with shape [batch, height, width, depth]
    Raises:
      InvalidArgumentError: If the spatial size of `preprocessed_inputs`
        (height or width) is less than 33.
      ValueError: If the created network is missing the required activation.
    """
    if len(preprocessed_inputs.get_shape().as_list()) != 4:
      raise ValueError('`preprocessed_inputs` must be 4 dimensional, got a '
                       'tensor of shape %s' % preprocessed_inputs.get_shape())

    with slim.arg_scope(inception_resnet_v2.inception_resnet_v2_arg_scope(
        weight_decay=self._weight_decay)):
      # Forces is_training to False to disable batch norm update.
      with slim.arg_scope([slim.batch_norm],
                          is_training=self._train_batch_norm):
        with tf.variable_scope('InceptionResnetV2',
                               reuse=self._reuse_weights) as scope:
          return inception_resnet_v2.inception_resnet_v2_base(
              preprocessed_inputs, final_endpoint='PreAuxLogits',
              scope=scope, output_stride=self._first_stage_features_stride,
              align_feature_maps=True) 

def _extract_proposal_features(self, preprocessed_inputs, scope):
    """Extracts first stage RPN features.

    Extracts features using the first half of the Inception Resnet v2 network.
    We construct the network in `align_feature_maps=True` mode, which means
    that all VALID paddings in the network are changed to SAME padding so that
    the feature maps are aligned.

    Args:
      preprocessed_inputs: A [batch, height, width, channels] float32 tensor
        representing a batch of images.
      scope: A scope name.

    Returns:
      rpn_feature_map: A tensor with shape [batch, height, width, depth]
    Raises:
      InvalidArgumentError: If the spatial size of `preprocessed_inputs`
        (height or width) is less than 33.
      ValueError: If the created network is missing the required activation.
    """
    if len(preprocessed_inputs.get_shape().as_list()) != 4:
      raise ValueError('`preprocessed_inputs` must be 4 dimensional, got a '
                       'tensor of shape %s' % preprocessed_inputs.get_shape())

    with slim.arg_scope(inception_resnet_v2.inception_resnet_v2_arg_scope(
        weight_decay=self._weight_decay)):
      # Forces is_training to False to disable batch norm update.
      with slim.arg_scope([slim.batch_norm], is_training=False):
        with tf.variable_scope('InceptionResnetV2',
                               reuse=self._reuse_weights) as scope:
          rpn_feature_map, _ = (
              inception_resnet_v2.inception_resnet_v2_base(
                  preprocessed_inputs, final_endpoint='PreAuxLogits',
                  scope=scope, output_stride=self._first_stage_features_stride,
                  align_feature_maps=True))
    return rpn_feature_map 

def _extract_proposal_features(self, preprocessed_inputs, scope):
    """Extracts first stage RPN features.

    Extracts features using the first half of the Inception Resnet v2 network.
    We construct the network in `align_feature_maps=True` mode, which means
    that all VALID paddings in the network are changed to SAME padding so that
    the feature maps are aligned.

    Args:
      preprocessed_inputs: A [batch, height, width, channels] float32 tensor
        representing a batch of images.
      scope: A scope name.

    Returns:
      rpn_feature_map: A tensor with shape [batch, height, width, depth]
    Raises:
      InvalidArgumentError: If the spatial size of `preprocessed_inputs`
        (height or width) is less than 33.
      ValueError: If the created network is missing the required activation.
    """
    if len(preprocessed_inputs.get_shape().as_list()) != 4:
      raise ValueError('`preprocessed_inputs` must be 4 dimensional, got a '
                       'tensor of shape %s' % preprocessed_inputs.get_shape())

    with slim.arg_scope(inception_resnet_v2.inception_resnet_v2_arg_scope(
        weight_decay=self._weight_decay)):
      # Forces is_training to False to disable batch norm update.
      with slim.arg_scope([slim.batch_norm], is_training=False):
        with tf.variable_scope('InceptionResnetV2',
                               reuse=self._reuse_weights) as scope:
          rpn_feature_map, _ = (
              inception_resnet_v2.inception_resnet_v2_base(
                  preprocessed_inputs, final_endpoint='PreAuxLogits',
                  scope=scope, output_stride=self._first_stage_features_stride,
                  align_feature_maps=True))
    return rpn_feature_map 

def _extract_proposal_features(self, preprocessed_inputs, scope):
    """Extracts first stage RPN features.

    Extracts features using the first half of the Inception Resnet v2 network.
    We construct the network in `align_feature_maps=True` mode, which means
    that all VALID paddings in the network are changed to SAME padding so that
    the feature maps are aligned.

    Args:
      preprocessed_inputs: A [batch, height, width, channels] float32 tensor
        representing a batch of images.
      scope: A scope name.

    Returns:
      rpn_feature_map: A tensor with shape [batch, height, width, depth]
    Raises:
      InvalidArgumentError: If the spatial size of `preprocessed_inputs`
        (height or width) is less than 33.
      ValueError: If the created network is missing the required activation.
    """
    if len(preprocessed_inputs.get_shape().as_list()) != 4:
      raise ValueError('`preprocessed_inputs` must be 4 dimensional, got a '
                       'tensor of shape %s' % preprocessed_inputs.get_shape())

    with slim.arg_scope(inception_resnet_v2.inception_resnet_v2_arg_scope(
        weight_decay=self._weight_decay)):
      # Forces is_training to False to disable batch norm update.
      with slim.arg_scope([slim.batch_norm],
                          is_training=self._train_batch_norm):
        with tf.variable_scope('InceptionResnetV2',
                               reuse=self._reuse_weights) as scope:
          return inception_resnet_v2.inception_resnet_v2_base(
              preprocessed_inputs, final_endpoint='PreAuxLogits',
              scope=scope, output_stride=self._first_stage_features_stride,
              align_feature_maps=True) 

def _extract_proposal_features(self, preprocessed_inputs, scope):
    """Extracts first stage RPN features.

    Extracts features using the first half of the Inception Resnet v2 network.
    We construct the network in `align_feature_maps=True` mode, which means
    that all VALID paddings in the network are changed to SAME padding so that
    the feature maps are aligned.

    Args:
      preprocessed_inputs: A [batch, height, width, channels] float32 tensor
        representing a batch of images.
      scope: A scope name.

    Returns:
      rpn_feature_map: A tensor with shape [batch, height, width, depth]
    Raises:
      InvalidArgumentError: If the spatial size of `preprocessed_inputs`
        (height or width) is less than 33.
      ValueError: If the created network is missing the required activation.
    """
    if len(preprocessed_inputs.get_shape().as_list()) != 4:
      raise ValueError('`preprocessed_inputs` must be 4 dimensional, got a '
                       'tensor of shape %s' % preprocessed_inputs.get_shape())

    with slim.arg_scope(inception_resnet_v2.inception_resnet_v2_arg_scope(
        weight_decay=self._weight_decay)):
      # Forces is_training to False to disable batch norm update.
      with slim.arg_scope([slim.batch_norm], is_training=False):
        with tf.variable_scope('InceptionResnetV2',
                               reuse=self._reuse_weights) as scope:
          rpn_feature_map, _ = (
              inception_resnet_v2.inception_resnet_v2_base(
                  preprocessed_inputs, final_endpoint='PreAuxLogits',
                  scope=scope, output_stride=self._first_stage_features_stride,
                  align_feature_maps=True))
    return rpn_feature_map 

def _extract_proposal_features(self, preprocessed_inputs, scope):
    """Extracts first stage RPN features.

    Extracts features using the first half of the Inception Resnet v2 network.
    We construct the network in `align_feature_maps=True` mode, which means
    that all VALID paddings in the network are changed to SAME padding so that
    the feature maps are aligned.

    Args:
      preprocessed_inputs: A [batch, height, width, channels] float32 tensor
        representing a batch of images.
      scope: A scope name.

    Returns:
      rpn_feature_map: A tensor with shape [batch, height, width, depth]
    Raises:
      InvalidArgumentError: If the spatial size of `preprocessed_inputs`
        (height or width) is less than 33.
      ValueError: If the created network is missing the required activation.
    """
    if len(preprocessed_inputs.get_shape().as_list()) != 4:
      raise ValueError('`preprocessed_inputs` must be 4 dimensional, got a '
                       'tensor of shape %s' % preprocessed_inputs.get_shape())

    with slim.arg_scope(inception_resnet_v2.inception_resnet_v2_arg_scope(
        weight_decay=self._weight_decay)):
      # Forces is_training to False to disable batch norm update.
      with slim.arg_scope([slim.batch_norm],
                          is_training=self._train_batch_norm):
        with tf.variable_scope('InceptionResnetV2',
                               reuse=self._reuse_weights) as scope:
          return inception_resnet_v2.inception_resnet_v2_base(
              preprocessed_inputs, final_endpoint='PreAuxLogits',
              scope=scope, output_stride=self._first_stage_features_stride,
              align_feature_maps=True) 

def _extract_proposal_features(self, preprocessed_inputs, scope):
    """Extracts first stage RPN features.

    Extracts features using the first half of the Inception Resnet v2 network.
    We construct the network in `align_feature_maps=True` mode, which means
    that all VALID paddings in the network are changed to SAME padding so that
    the feature maps are aligned.

    Args:
      preprocessed_inputs: A [batch, height, width, channels] float32 tensor
        representing a batch of images.
      scope: A scope name.

    Returns:
      rpn_feature_map: A tensor with shape [batch, height, width, depth]
    Raises:
      InvalidArgumentError: If the spatial size of `preprocessed_inputs`
        (height or width) is less than 33.
      ValueError: If the created network is missing the required activation.
    """
    if len(preprocessed_inputs.get_shape().as_list()) != 4:
      raise ValueError('`preprocessed_inputs` must be 4 dimensional, got a '
                       'tensor of shape %s' % preprocessed_inputs.get_shape())

    with slim.arg_scope(inception_resnet_v2.inception_resnet_v2_arg_scope(
        weight_decay=self._weight_decay)):
      # Forces is_training to False to disable batch norm update.
      with slim.arg_scope([slim.batch_norm],
                          is_training=self._train_batch_norm):
        with tf.variable_scope('InceptionResnetV2',
                               reuse=self._reuse_weights) as scope:
          rpn_feature_map, _ = (
              inception_resnet_v2.inception_resnet_v2_base(
                  preprocessed_inputs, final_endpoint='PreAuxLogits',
                  scope=scope, output_stride=self._first_stage_features_stride,
                  align_feature_maps=True))
    return rpn_feature_map 

def _extract_proposal_features(self, preprocessed_inputs, scope):
    """Extracts first stage RPN features.

    Extracts features using the first half of the Inception Resnet v2 network.
    We construct the network in `align_feature_maps=True` mode, which means
    that all VALID paddings in the network are changed to SAME padding so that
    the feature maps are aligned.

    Args:
      preprocessed_inputs: A [batch, height, width, channels] float32 tensor
        representing a batch of images.
      scope: A scope name.

    Returns:
      rpn_feature_map: A tensor with shape [batch, height, width, depth]
    Raises:
      InvalidArgumentError: If the spatial size of `preprocessed_inputs`
        (height or width) is less than 33.
      ValueError: If the created network is missing the required activation.
    """
    if len(preprocessed_inputs.get_shape().as_list()) != 4:
      raise ValueError('`preprocessed_inputs` must be 4 dimensional, got a '
                       'tensor of shape %s' % preprocessed_inputs.get_shape())

    with slim.arg_scope(inception_resnet_v2.inception_resnet_v2_arg_scope(
        weight_decay=self._weight_decay)):
      # Forces is_training to False to disable batch norm update.
      with slim.arg_scope([slim.batch_norm],
                          is_training=self._train_batch_norm):
        with tf.variable_scope('InceptionResnetV2',
                               reuse=self._reuse_weights) as scope:
          rpn_feature_map, _ = (
              inception_resnet_v2.inception_resnet_v2_base(
                  preprocessed_inputs, final_endpoint='PreAuxLogits',
                  scope=scope, output_stride=self._first_stage_features_stride,
                  align_feature_maps=True))
    return rpn_feature_map 

def _extract_proposal_features(self, preprocessed_inputs, scope):
    """Extracts first stage RPN features.

    Extracts features using the first half of the Inception Resnet v2 network.
    We construct the network in `align_feature_maps=True` mode, which means
    that all VALID paddings in the network are changed to SAME padding so that
    the feature maps are aligned.

    Args:
      preprocessed_inputs: A [batch, height, width, channels] float32 tensor
        representing a batch of images.
      scope: A scope name.

    Returns:
      rpn_feature_map: A tensor with shape [batch, height, width, depth]
    Raises:
      InvalidArgumentError: If the spatial size of `preprocessed_inputs`
        (height or width) is less than 33.
      ValueError: If the created network is missing the required activation.
    """
    if len(preprocessed_inputs.get_shape().as_list()) != 4:
      raise ValueError('`preprocessed_inputs` must be 4 dimensional, got a '
                       'tensor of shape %s' % preprocessed_inputs.get_shape())

    with slim.arg_scope(inception_resnet_v2.inception_resnet_v2_arg_scope(
        weight_decay=self._weight_decay)):
      # Forces is_training to False to disable batch norm update.
      with slim.arg_scope([slim.batch_norm],
                          is_training=self._train_batch_norm):
        with tf.variable_scope('InceptionResnetV2',
                               reuse=self._reuse_weights) as scope:
          return inception_resnet_v2.inception_resnet_v2_base(
              preprocessed_inputs, final_endpoint='PreAuxLogits',
              scope=scope, output_stride=self._first_stage_features_stride,
              align_feature_maps=True) 

def _extract_proposal_features(self, preprocessed_inputs, scope):
    """Extracts first stage RPN features.

    Extracts features using the first half of the Inception Resnet v2 network.
    We construct the network in `align_feature_maps=True` mode, which means
    that all VALID paddings in the network are changed to SAME padding so that
    the feature maps are aligned.

    Args:
      preprocessed_inputs: A [batch, height, width, channels] float32 tensor
        representing a batch of images.
      scope: A scope name.

    Returns:
      rpn_feature_map: A tensor with shape [batch, height, width, depth]
    Raises:
      InvalidArgumentError: If the spatial size of `preprocessed_inputs`
        (height or width) is less than 33.
      ValueError: If the created network is missing the required activation.
    """
    if len(preprocessed_inputs.get_shape().as_list()) != 4:
      raise ValueError('`preprocessed_inputs` must be 4 dimensional, got a '
                       'tensor of shape %s' % preprocessed_inputs.get_shape())

    with slim.arg_scope(inception_resnet_v2.inception_resnet_v2_arg_scope(
        weight_decay=self._weight_decay, trainable=self._is_training)):
      # Forces is_training to False to disable batch norm update.
      with slim.arg_scope([slim.batch_norm], is_training=False):
        with tf.variable_scope('InceptionResnetV2',
                               reuse=self._reuse_weights) as scope:
          rpn_feature_map, _ = (
              inception_resnet_v2.inception_resnet_v2_base(
                  preprocessed_inputs, final_endpoint='PreAuxLogits',
                  scope=scope, output_stride=self._first_stage_features_stride,
                  align_feature_maps=True))
    return rpn_feature_map 

def _extract_proposal_features(self, preprocessed_inputs, scope):
    """Extracts first stage RPN features.

    Extracts features using the first half of the Inception Resnet v2 network.
    We construct the network in `align_feature_maps=True` mode, which means
    that all VALID paddings in the network are changed to SAME padding so that
    the feature maps are aligned.

    Args:
      preprocessed_inputs: A [batch, height, width, channels] float32 tensor
        representing a batch of images.
      scope: A scope name.

    Returns:
      rpn_feature_map: A tensor with shape [batch, height, width, depth]
    Raises:
      InvalidArgumentError: If the spatial size of `preprocessed_inputs`
        (height or width) is less than 33.
      ValueError: If the created network is missing the required activation.
    """
    if len(preprocessed_inputs.get_shape().as_list()) != 4:
      raise ValueError('`preprocessed_inputs` must be 4 dimensional, got a '
                       'tensor of shape %s' % preprocessed_inputs.get_shape())

    with slim.arg_scope(inception_resnet_v2.inception_resnet_v2_arg_scope(
        weight_decay=self._weight_decay)):
      # Forces is_training to False to disable batch norm update.
      with slim.arg_scope([slim.batch_norm],
                          is_training=self._train_batch_norm):
        with tf.variable_scope('InceptionResnetV2',
                               reuse=self._reuse_weights) as scope:
          return inception_resnet_v2.inception_resnet_v2_base(
              preprocessed_inputs, final_endpoint='PreAuxLogits',
              scope=scope, output_stride=self._first_stage_features_stride,
              align_feature_maps=True) 

def _extract_proposal_features(self, preprocessed_inputs, scope):
    """Extracts first stage RPN features.

    Extracts features using the first half of the Inception Resnet v2 network.
    We construct the network in `align_feature_maps=True` mode, which means
    that all VALID paddings in the network are changed to SAME padding so that
    the feature maps are aligned.

    Args:
      preprocessed_inputs: A [batch, height, width, channels] float32 tensor
        representing a batch of images.
      scope: A scope name.

    Returns:
      rpn_feature_map: A tensor with shape [batch, height, width, depth]
    Raises:
      InvalidArgumentError: If the spatial size of `preprocessed_inputs`
        (height or width) is less than 33.
      ValueError: If the created network is missing the required activation.
    """
    if len(preprocessed_inputs.get_shape().as_list()) != 4:
      raise ValueError('`preprocessed_inputs` must be 4 dimensional, got a '
                       'tensor of shape %s' % preprocessed_inputs.get_shape())

    with slim.arg_scope(inception_resnet_v2.inception_resnet_v2_arg_scope(
        weight_decay=self._weight_decay)):
      # Forces is_training to False to disable batch norm update.
      with slim.arg_scope([slim.batch_norm],
                          is_training=self._train_batch_norm):
        with tf.variable_scope('InceptionResnetV2',
                               reuse=self._reuse_weights) as scope:
          return inception_resnet_v2.inception_resnet_v2_base(
              preprocessed_inputs, final_endpoint='PreAuxLogits',
              scope=scope, output_stride=self._first_stage_features_stride,
              align_feature_maps=True) 

def _extract_box_classifier_features(self, proposal_feature_maps, scope):
    """Extracts second stage box classifier features.

    This function reconstructs the "second half" of the Inception ResNet v2
    network after the part defined in `_extract_proposal_features`.

    Args:
      proposal_feature_maps: A 4-D float tensor with shape
        [batch_size * self.max_num_proposals, crop_height, crop_width, depth]
        representing the feature map cropped to each proposal.
      scope: A scope name.

    Returns:
      proposal_classifier_features: A 4-D float tensor with shape
        [batch_size * self.max_num_proposals, height, width, depth]
        representing box classifier features for each proposal.
    """
    with tf.variable_scope('InceptionResnetV2', reuse=self._reuse_weights):
      with slim.arg_scope(inception_resnet_v2.inception_resnet_v2_arg_scope(
          weight_decay=self._weight_decay)):
        # Forces is_training to False to disable batch norm update.
        with slim.arg_scope([slim.batch_norm],
                            is_training=self._train_batch_norm):
          with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d],
                              stride=1, padding='SAME'):
            with tf.variable_scope('Mixed_7a'):
              with tf.variable_scope('Branch_0'):
                tower_conv = slim.conv2d(proposal_feature_maps,
                                         256, 1, scope='Conv2d_0a_1x1')
                tower_conv_1 = slim.conv2d(
                    tower_conv, 384, 3, stride=2,
                    padding='VALID', scope='Conv2d_1a_3x3')
              with tf.variable_scope('Branch_1'):
                tower_conv1 = slim.conv2d(
                    proposal_feature_maps, 256, 1, scope='Conv2d_0a_1x1')
                tower_conv1_1 = slim.conv2d(
                    tower_conv1, 288, 3, stride=2,
                    padding='VALID', scope='Conv2d_1a_3x3')
              with tf.variable_scope('Branch_2'):
                tower_conv2 = slim.conv2d(
                    proposal_feature_maps, 256, 1, scope='Conv2d_0a_1x1')
                tower_conv2_1 = slim.conv2d(tower_conv2, 288, 3,
                                            scope='Conv2d_0b_3x3')
                tower_conv2_2 = slim.conv2d(
                    tower_conv2_1, 320, 3, stride=2,
                    padding='VALID', scope='Conv2d_1a_3x3')
              with tf.variable_scope('Branch_3'):
                tower_pool = slim.max_pool2d(
                    proposal_feature_maps, 3, stride=2, padding='VALID',
                    scope='MaxPool_1a_3x3')
              net = tf.concat(
                  [tower_conv_1, tower_conv1_1, tower_conv2_2, tower_pool], 3)
            net = slim.repeat(net, 9, inception_resnet_v2.block8, scale=0.20)
            net = inception_resnet_v2.block8(net, activation_fn=None)
            proposal_classifier_features = slim.conv2d(
                net, 1536, 1, scope='Conv2d_7b_1x1')
        return proposal_classifier_features 

def _extract_box_classifier_features(self, proposal_feature_maps, scope):
    """Extracts second stage box classifier features.

    This function reconstructs the "second half" of the Inception ResNet v2
    network after the part defined in `_extract_proposal_features`.

    Args:
      proposal_feature_maps: A 4-D float tensor with shape
        [batch_size * self.max_num_proposals, crop_height, crop_width, depth]
        representing the feature map cropped to each proposal.
      scope: A scope name.

    Returns:
      proposal_classifier_features: A 4-D float tensor with shape
        [batch_size * self.max_num_proposals, height, width, depth]
        representing box classifier features for each proposal.
    """
    with tf.variable_scope('InceptionResnetV2', reuse=self._reuse_weights):
      with slim.arg_scope(inception_resnet_v2.inception_resnet_v2_arg_scope(
          weight_decay=self._weight_decay)):
        # Forces is_training to False to disable batch norm update.
        with slim.arg_scope([slim.batch_norm],
                            is_training=self._train_batch_norm):
          with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d],
                              stride=1, padding='SAME'):
            with tf.variable_scope('Mixed_7a'):
              with tf.variable_scope('Branch_0'):
                tower_conv = slim.conv2d(proposal_feature_maps,
                                         256, 1, scope='Conv2d_0a_1x1')
                tower_conv_1 = slim.conv2d(
                    tower_conv, 384, 3, stride=2,
                    padding='VALID', scope='Conv2d_1a_3x3')
              with tf.variable_scope('Branch_1'):
                tower_conv1 = slim.conv2d(
                    proposal_feature_maps, 256, 1, scope='Conv2d_0a_1x1')
                tower_conv1_1 = slim.conv2d(
                    tower_conv1, 288, 3, stride=2,
                    padding='VALID', scope='Conv2d_1a_3x3')
              with tf.variable_scope('Branch_2'):
                tower_conv2 = slim.conv2d(
                    proposal_feature_maps, 256, 1, scope='Conv2d_0a_1x1')
                tower_conv2_1 = slim.conv2d(tower_conv2, 288, 3,
                                            scope='Conv2d_0b_3x3')
                tower_conv2_2 = slim.conv2d(
                    tower_conv2_1, 320, 3, stride=2,
                    padding='VALID', scope='Conv2d_1a_3x3')
              with tf.variable_scope('Branch_3'):
                tower_pool = slim.max_pool2d(
                    proposal_feature_maps, 3, stride=2, padding='VALID',
                    scope='MaxPool_1a_3x3')
              net = tf.concat(
                  [tower_conv_1, tower_conv1_1, tower_conv2_2, tower_pool], 3)
            net = slim.repeat(net, 9, inception_resnet_v2.block8, scale=0.20)
            net = inception_resnet_v2.block8(net, activation_fn=None)
            proposal_classifier_features = slim.conv2d(
                net, 1536, 1, scope='Conv2d_7b_1x1')
        return proposal_classifier_features