Avoid deeply nested control flow statements.

Open

          with tf.variable_scope('AuxLogits'):
            # 17 x 17 x 1024
            aux_logits = end_points['Mixed_6h']
            aux_logits = slim.avg_pool2d(aux_logits, [5, 5], stride=3,
                                         padding='VALID',

Function __init__ has 6 arguments (exceeds 4 allowed). Consider refactoring.

Open

  def __init__(self,

Avoid deeply nested control flow statements.

Open

            with self.test_session() as sess:
              tf.set_random_seed(0)
              inputs = create_test_input(1, height, width, 3)
              # Dense feature extraction followed by subsampling.
              output = resnet_utils.stack_blocks_dense(inputs,

Avoid deeply nested control flow statements.

Open

            if output_stride is None:
              factor = 1
            else:
              factor = nominal_stride // output_stride
            output = resnet_utils.subsample(output, factor)

Function _visualize_boxes has 6 arguments (exceeds 4 allowed). Consider refactoring.

Open

def _visualize_boxes(image, boxes, classes, scores, category_index, **kwargs):

Avoid deeply nested control flow statements.

Open

          if output_stride is not None and current_stride == output_stride:
            net = block.unit_fn(net, rate=rate, **dict(unit, stride=1))
            rate *= unit.get('stride', 1)

          else:

Function deploy has 6 arguments (exceeds 4 allowed). Consider refactoring.

Open

def deploy(config,

Avoid deeply nested control flow statements.

Open

          with tf.variable_scope(tf.get_variable_scope(),
                                 reuse=True if i > 0 else None):
            outputs = model_fn(*args, **kwargs)
          clones.append(Clone(outputs, clone_scope, clone_device))

Function _configure_optimizer has a Cognitive Complexity of 8 (exceeds 5 allowed). Consider refactoring.

Open

def _configure_optimizer(learning_rate):
  """Configures the optimizer used for training.

  Args:
    learning_rate: A scalar or `Tensor` learning rate.

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Function _sample_obs has 6 arguments (exceeds 4 allowed). Consider refactoring.

Open

  def _sample_obs(self,

Function tf_episode_batch has a Cognitive Complexity of 8 (exceeds 5 allowed). Consider refactoring.

Open

  def tf_episode_batch(self, batch_size):
    """A batch of episodes as TF Tensors.

    Same as episode_batch with the difference that the return values are TF
    Tensors.

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Avoid deeply nested control flow statements.

Open

          if not agnostic_mode:
            if classes[i] in category_index.keys():
              class_name = category_index[classes[i]]['name']
            else:
              class_name = 'N/A'

Function evaluate_checkpoint_repeatedly has 6 arguments (exceeds 4 allowed). Consider refactoring.

Open

def evaluate_checkpoint_repeatedly(checkpoint_dir,

Function compute_average_reward has 6 arguments (exceeds 4 allowed). Consider refactoring.

Open

def compute_average_reward(sess, env_base, step_fn, gamma, num_steps,

Function prediction_to_keypoints_argmax has 6 arguments (exceeds 4 allowed). Consider refactoring.

Open

def prediction_to_keypoints_argmax(

Function main has a Cognitive Complexity of 8 (exceeds 5 allowed). Consider refactoring.

Open

def main(unused_argv):
  flags.mark_flag_as_required('model_dir')
  flags.mark_flag_as_required('pipeline_config_path')
  config = tf_estimator.RunConfig(model_dir=FLAGS.model_dir)

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Function __init__ has 6 arguments (exceeds 4 allowed). Consider refactoring.

Open

  def __init__(self,

Function env_transition has 6 arguments (exceeds 4 allowed). Consider refactoring.

Open

def env_transition(agent, state, action, transition_type, environment_steps,

Function ctrl_rewards has 6 arguments (exceeds 4 allowed). Consider refactoring.

Open

def ctrl_rewards(states,

Avoid deeply nested control flow statements.

Open

          if not tf.io.gfile.exists(wav_file):
            tfm.build(flac_file, wav_file)
          wav_filesize = os.path.getsize(wav_file)