Python flags.DEFINE_enum方法代碼示例

# 需要導入模塊: from absl import flags [as 別名]
# 或者: from absl.flags import DEFINE_enum [as 別名]
def define_flags(specs=None):
  """Define a command line flag for each ParamSpec in flags.param_specs."""
  specs = specs or param_specs
  define_flag = {
      'boolean': absl_flags.DEFINE_boolean,
      'float': absl_flags.DEFINE_float,
      'integer': absl_flags.DEFINE_integer,
      'string': absl_flags.DEFINE_string,
      'enum': absl_flags.DEFINE_enum,
      'list': absl_flags.DEFINE_list
  }
  for name, param_spec in six.iteritems(specs):
    if param_spec.flag_type not in define_flag:
      raise ValueError('Unknown flag_type %s' % param_spec.flag_type)
    else:
      define_flag[param_spec.flag_type](name, param_spec.default_value,
                                        help=param_spec.description,
                                        **param_spec.kwargs) 

# 需要導入模塊: from absl import flags [as 別名]
# 或者: from absl.flags import DEFINE_enum [as 別名]
def define_compute_bleu_flags():
  """Add flags for computing BLEU score."""
  flags.DEFINE_string(
      name="translation", default=None,
      help=flags_core.help_wrap("File containing translated text."))
  flags.mark_flag_as_required("translation")

  flags.DEFINE_string(
      name="reference", default=None,
      help=flags_core.help_wrap("File containing reference translation."))
  flags.mark_flag_as_required("reference")

  flags.DEFINE_enum(
      name="bleu_variant", short_name="bv", default="both",
      enum_values=["both", "uncased", "cased"], case_sensitive=False,
      help=flags_core.help_wrap(
          "Specify one or more BLEU variants to calculate. Variants: \"cased\""
          ", \"uncased\", or \"both\".")) 

# 需要導入模塊: from absl import flags [as 別名]
# 或者: from absl.flags import DEFINE_enum [as 別名]
def define_image(data_format=True):
  """Register image specific flags.

  Args:
    data_format: Create a flag to specify image axis convention.

  Returns:
    A list of flags for core.py to marks as key flags.
  """

  key_flags = []

  if data_format:
    flags.DEFINE_enum(
        name="data_format", short_name="df", default=None,
        enum_values=["channels_first", "channels_last"],
        help=help_wrap(
            "A flag to override the data format used in the model. "
            "channels_first provides a performance boost on GPU but is not "
            "always compatible with CPU. If left unspecified, the data format "
            "will be chosen automatically based on whether TensorFlow was "
            "built for CPU or GPU."))
    key_flags.append("data_format")

  return key_flags 

# 需要導入模塊: from absl import flags [as 別名]
# 或者: from absl.flags import DEFINE_enum [as 別名]
def config_with_absl(self):
    # Run this before calling `app.run(main)` etc
    import absl.flags as absl_FLAGS
    from absl import app, flags as absl_flags

    self.use_absl = True
    self.absl_flags = absl_flags
    absl_defs = { bool: absl_flags.DEFINE_bool,
                  int:  absl_flags.DEFINE_integer,
                  str:  absl_flags.DEFINE_string,
                  'enum': absl_flags.DEFINE_enum }

    for name, val in self.values.items():
      flag_type, meta_args, meta_kwargs = self.meta[name]
      absl_defs[flag_type](name, val, *meta_args, **meta_kwargs)

    app.call_after_init(lambda: self.complete_absl_config(absl_flags)) 

# 需要導入模塊: from absl import flags [as 別名]
# 或者: from absl.flags import DEFINE_enum [as 別名]
def test_flag_help_in_xml_enum(self):
    flags.DEFINE_enum('cc_version', 'stable', ['stable', 'experimental'],
                      'Compiler version to use.', flag_values=self.fv)
    expected_output = (
        '<flag>\n'
        '  <file>tool</file>\n'
        '  <name>cc_version</name>\n'
        '  <meaning>&lt;stable|experimental&gt;: '
        'Compiler version to use.</meaning>\n'
        '  <default>stable</default>\n'
        '  <current>stable</current>\n'
        '  <type>string enum</type>\n'
        '  <enum_value>stable</enum_value>\n'
        '  <enum_value>experimental</enum_value>\n'
        '</flag>\n')
    self._check_flag_help_in_xml('cc_version', 'tool', expected_output) 

# 需要導入模塊: from absl import flags [as 別名]
# 或者: from absl.flags import DEFINE_enum [as 別名]
def setUp(self):
    self._absl_flags = flags.FlagValues()
    flags.DEFINE_bool(
        'absl_bool', None, 'help for --absl_bool.',
        short_name='b', flag_values=self._absl_flags)
    # Add a boolean flag that starts with "no", to verify it can correctly
    # handle the "no" prefixes in boolean flags.
    flags.DEFINE_bool(
        'notice', None, 'help for --notice.',
        flag_values=self._absl_flags)
    flags.DEFINE_string(
        'absl_string', 'default', 'help for --absl_string=%.',
        short_name='s', flag_values=self._absl_flags)
    flags.DEFINE_integer(
        'absl_integer', 1, 'help for --absl_integer.',
        flag_values=self._absl_flags)
    flags.DEFINE_float(
        'absl_float', 1, 'help for --absl_integer.',
        flag_values=self._absl_flags)
    flags.DEFINE_enum(
        'absl_enum', 'apple', ['apple', 'orange'], 'help for --absl_enum.',
        flag_values=self._absl_flags) 

# 需要導入模塊: from absl import flags [as 別名]
# 或者: from absl.flags import DEFINE_enum [as 別名]
def define_movie_flags():
  """Define flags for movie dataset training."""
  wide_deep_run_loop.define_wide_deep_flags()
  flags.DEFINE_enum(
      name="dataset", default=movielens.ML_1M,
      enum_values=movielens.DATASETS, case_sensitive=False,
      help=flags_core.help_wrap("Dataset to be trained and evaluated."))
  flags.adopt_module_key_flags(wide_deep_run_loop)
  flags_core.set_defaults(data_dir="/tmp/movielens-data/",
                          model_dir='/tmp/movie_model',
                          model_type="deep",
                          train_epochs=50,
                          epochs_between_evals=5,
                          inter_op_parallelism_threads=0,
                          intra_op_parallelism_threads=0,
                          batch_size=256)

  @flags.validator("stop_threshold",
                   message="stop_threshold not supported for movielens model")
  def _no_stop(stop_threshold):
    return stop_threshold is None 

# 需要導入模塊: from absl import flags [as 別名]
# 或者: from absl.flags import DEFINE_enum [as 別名]
def define_wide_deep_flags():
  """Add supervised learning flags, as well as wide-deep model type."""
  flags_core.define_base(clean=True, train_epochs=True,
                         epochs_between_evals=True)
  flags_core.define_benchmark()
  flags_core.define_performance(
      num_parallel_calls=False, inter_op=True, intra_op=True,
      synthetic_data=False, max_train_steps=False, dtype=False,
      all_reduce_alg=False)

  flags.adopt_module_key_flags(flags_core)

  flags.DEFINE_enum(
      name="model_type", short_name="mt", default="wide_deep",
      enum_values=['wide', 'deep', 'wide_deep'],
      help="Select model topology.")
  flags.DEFINE_boolean(
      name="download_if_missing", default=True, help=flags_core.help_wrap(
          "Download data to data_dir if it is not already present.")) 

# 需要導入模塊: from absl import flags [as 別名]
# 或者: from absl.flags import DEFINE_enum [as 別名]
def define_flags():
  """Define a command line flag for each ParamSpec in flags.param_specs."""
  define_flag = {
      'boolean': absl_flags.DEFINE_boolean,
      'float': absl_flags.DEFINE_float,
      'integer': absl_flags.DEFINE_integer,
      'string': absl_flags.DEFINE_string,
      'enum': absl_flags.DEFINE_enum,
      'list': absl_flags.DEFINE_list
  }
  for name, param_spec in six.iteritems(param_specs):
    if param_spec.flag_type not in define_flag:
      raise ValueError('Unknown flag_type %s' % param_spec.flag_type)
    else:
      define_flag[param_spec.flag_type](name, param_spec.default_value,
                                        help=param_spec.description,
                                        **param_spec.kwargs) 

# 需要導入模塊: from absl import flags [as 別名]
# 或者: from absl.flags import DEFINE_enum [as 別名]
def define_wide_deep_flags():
  """Add supervised learning flags, as well as wide-deep model type."""
  flags_core.define_base()
  flags_core.define_benchmark()
  flags_core.define_performance(
      num_parallel_calls=False, inter_op=True, intra_op=True,
      synthetic_data=False, max_train_steps=False, dtype=False,
      all_reduce_alg=False)

  flags.adopt_module_key_flags(flags_core)

  flags.DEFINE_enum(
      name="model_type", short_name="mt", default="wide_deep",
      enum_values=['wide', 'deep', 'wide_deep'],
      help="Select model topology.")
  flags.DEFINE_boolean(
      name="download_if_missing", default=True, help=flags_core.help_wrap(
          "Download data to data_dir if it is not already present.")) 

# 需要導入模塊: from absl import flags [as 別名]
# 或者: from absl.flags import DEFINE_enum [as 別名]
def define_compute_bleu_flags():
  """Add flags for computing BLEU score."""
  flags.DEFINE_string(
      name="translation",
      default=None,
      help=flags_core.help_wrap("File containing translated text."))
  flags.mark_flag_as_required("translation")

  flags.DEFINE_string(
      name="reference",
      default=None,
      help=flags_core.help_wrap("File containing reference translation."))
  flags.mark_flag_as_required("reference")

  flags.DEFINE_enum(
      name="bleu_variant",
      short_name="bv",
      default="both",
      enum_values=["both", "uncased", "cased"],
      case_sensitive=False,
      help=flags_core.help_wrap(
          "Specify one or more BLEU variants to calculate. Variants: \"cased\""
          ", \"uncased\", or \"both\"."))