Avoid deeply nested control flow statements.

Open

          if prediction[i] in retrieval_solution[key]:
            num_correct += 1
          already_predicted.add(prediction[i])

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

Open

def test_retrieval(datasets, net, epoch, writer=None, model_directory=None,
                   precompute_whitening=None, data_root='data', multiscale=[1.],
                   test_image_size=1024):
  """Testing step.

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Avoid deeply nested control flow statements.

Open

          for image_id in row[1].split(' '):
            ground_truth_ids.append(image_id)
        else:

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Open

          if tf.summary.should_record_summaries().numpy():
            last_summary_step_value = global_step_value
            last_summary_time = current_time

          # Print to console if running locally.

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          if (last_summary_step_value is not None and
              last_summary_time is not None):
            tf.summary.scalar(
                'global_steps_per_sec',
                (global_step_value - last_summary_step_value) /

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

Open

def main(argv):
  if len(argv) > 1:
    raise RuntimeError('Too many command-line arguments.')

  # Parse dataset to obtain query/index images, and ground-truth.

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

Open

def _build_train_tfrecord_dataset(csv_path,
                                  clean_csv_path,
                                  image_dir,
                                  generate_train_validation_splits,
                                  validation_split_size,

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

Open

  def __init__(self,

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            if i < self.current_line:
              continue
            self.current_line = i

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            if not self.legacy_mode or i == self.middle_frame_index:
              self.smooth_loss += 1.0 / (2**s) * self.depth_smoothness(
                  self.disp[i][s], self.images[s][:, :, :, 3 * i:3 * (i + 1)])

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

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            if i > j:
              # Need to inverse egomotion when going back in sequence.
              egomotion_mult *= -1
            # For compatiblity with SfMLearner, interpret all egomotion vectors
            # as pointing toward the middle frame.  Note that unlike SfMLearner,

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          if backbone[cell_num] == 3 and nas_remove_os32_stride:
            stride = 1
          filter_scaling *= hparams.filter_scaling_rate

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            if len(words) < self.config.max_sentence_length:
              yield words
      self.current_file = 0

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          if model_options.aspp_with_concat_projection:
            concat_logits = slim.conv2d(
                concat_logits, depth, 1, scope=CONCAT_PROJECTION_SCOPE)
            concat_logits = slim.dropout(
                concat_logits,

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

Open

  def _parse_function(self, example_proto):
    """Function to parse the example proto.

    Args:
      example_proto: Proto in the format of tf.Example.

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Avoid deeply nested control flow statements.

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          if (model_options.add_image_level_feature and
              model_options.aspp_with_squeeze_and_excitation):
            concat_logits *= image_feature

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            if self.ssim_weight > 0:
              self.ssim_error[s][key] = self.ssim(self.warped_image[s][key],
                                                  target)
              # TODO(rezama): This should be min_pool2d().
              ssim_mask = slim.avg_pool2d(self.warp_mask[s][key], 3, 1, 'VALID')

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

Open

def _decoder_with_sum_merge(decoder_features_list,

Avoid deeply nested control flow statements.

Open

          if example:
            s = example['folder_name']
            frame = example['file_name']
            if np.random.random() < 0.1:
              val_f.write('%s %s\n' % (s, frame))