def define_resnet_flags()

in example_zoo/tensorflow/models/keras_imagenet_main/official/resnet/resnet_run_loop.py [0:0]

• 47 lines of code
• 2 McCabe index (conditional complexity)

def define_resnet_flags(resnet_size_choices=None):
  """Add flags and validators for ResNet."""
  flags_core.define_base()
  flags_core.define_performance(num_parallel_calls=False,
                                tf_gpu_thread_mode=True,
                                datasets_num_private_threads=True,
                                datasets_num_parallel_batches=True)
  flags_core.define_image()
  flags_core.define_benchmark()
  flags.adopt_module_key_flags(flags_core)

  flags.DEFINE_enum(
      name='resnet_version', short_name='rv', default='1',
      enum_values=['1', '2'],
      help=flags_core.help_wrap(
          'Version of ResNet. (1 or 2) See README.md for details.'))
  flags.DEFINE_bool(
      name='fine_tune', short_name='ft', default=False,
      help=flags_core.help_wrap(
          'If True do not train any parameters except for the final layer.'))
  flags.DEFINE_string(
      name='pretrained_model_checkpoint_path', short_name='pmcp', default=None,
      help=flags_core.help_wrap(
          'If not None initialize all the network except the final layer with '
          'these values'))
  flags.DEFINE_boolean(
      name='eval_only', default=False,
      help=flags_core.help_wrap('Skip training and only perform evaluation on '
                                'the latest checkpoint.'))
  flags.DEFINE_boolean(
      name='image_bytes_as_serving_input', default=False,
      help=flags_core.help_wrap(
          'If True exports savedmodel with serving signature that accepts '
          'JPEG image bytes instead of a fixed size [HxWxC] tensor that '
          'represents the image. The former is easier to use for serving at '
          'the expense of image resize/cropping being done as part of model '
          'inference. Note, this flag only applies to ImageNet and cannot '
          'be used for CIFAR.'))
  flags.DEFINE_boolean(
      name='turn_off_distribution_strategy', default=False,
      help=flags_core.help_wrap('Set to True to not use distribution '
                                'strategies.'))
  choice_kwargs = dict(
      name='resnet_size', short_name='rs', default='50',
      help=flags_core.help_wrap('The size of the ResNet model to use.'))

  if resnet_size_choices is None:
    flags.DEFINE_string(**choice_kwargs)
  else:
    flags.DEFINE_enum(enum_values=resnet_size_choices, **choice_kwargs)