Python absl.flags.DEFINE_enum() Examples
The following are 30
code examples of absl.flags.DEFINE_enum().
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absl.flags
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Example #1
| Source File: flags.py From benchmarks with Apache License 2.0 | 7 votes |
|
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)
Example #2
| Source File: compute_bleu.py From models with Apache License 2.0 | 7 votes |
|
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\"."))
Example #3
| Source File: _misc.py From ml-on-gcp with Apache License 2.0 | 6 votes |
|
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
Example #4
| Source File: compute_bleu.py From multilabel-image-classification-tensorflow with MIT License | 6 votes |
|
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\"."))
Example #5
| Source File: _misc.py From ml-on-gcp with Apache License 2.0 | 6 votes |
|
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
Example #6
| Source File: config.py From trax with Apache License 2.0 | 6 votes |
|
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))
Example #7
| Source File: _misc.py From nsfw with Apache License 2.0 | 6 votes |
|
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
Example #8
| Source File: _misc.py From models with Apache License 2.0 | 6 votes |
|
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
Example #9
| Source File: flags_helpxml_test.py From abseil-py with Apache License 2.0 | 6 votes |
|
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><stable|experimental>: '
'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)
Example #10
| Source File: argparse_flags_test.py From abseil-py with Apache License 2.0 | 6 votes |
|
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)
Example #11
| Source File: _misc.py From models with Apache License 2.0 | 6 votes |
|
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
Example #12
| Source File: compute_bleu.py From models with Apache License 2.0 | 6 votes |
|
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\"."))
Example #13
| Source File: _misc.py From models with Apache License 2.0 | 6 votes |
|
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
Example #14
| Source File: _misc.py From models with Apache License 2.0 | 6 votes |
|
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
Example #15
| Source File: compute_bleu.py From models with Apache License 2.0 | 6 votes |
|
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\"."))
Example #16
| Source File: _misc.py From models with Apache License 2.0 | 6 votes |
|
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
Example #17
| Source File: _misc.py From Live-feed-object-device-identification-using-Tensorflow-and-OpenCV with Apache License 2.0 | 6 votes |
|
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
Example #18
| Source File: movielens_main.py From Live-feed-object-device-identification-using-Tensorflow-and-OpenCV with Apache License 2.0 | 6 votes |
|
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
Example #19
| Source File: wide_deep_run_loop.py From Live-feed-object-device-identification-using-Tensorflow-and-OpenCV with Apache License 2.0 | 6 votes |
|
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."))
Example #20
| Source File: compute_bleu.py From Live-feed-object-device-identification-using-Tensorflow-and-OpenCV with Apache License 2.0 | 6 votes |
|
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\"."))
Example #21
| Source File: flags.py From dlcookbook-dlbs with Apache License 2.0 | 6 votes |
|
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)
Example #22
| Source File: movielens_main.py From g-tensorflow-models with Apache License 2.0 | 6 votes |
|
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
Example #23
| Source File: wide_deep_run_loop.py From g-tensorflow-models with Apache License 2.0 | 6 votes |
|
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."))
Example #24
| Source File: _misc.py From g-tensorflow-models with Apache License 2.0 | 6 votes |
|
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
Example #25
| Source File: compute_bleu.py From g-tensorflow-models with Apache License 2.0 | 6 votes |
|
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\"."))
Example #26
| Source File: _misc.py From models with Apache License 2.0 | 6 votes |
|
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
Example #27
| Source File: compute_bleu.py From models with Apache License 2.0 | 6 votes |
|
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\"."))
Example #28
| Source File: movielens_main.py From multilabel-image-classification-tensorflow with MIT License | 6 votes |
|
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
Example #29
| Source File: wide_deep_run_loop.py From multilabel-image-classification-tensorflow with MIT License | 6 votes |
|
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."))
Example #30
| Source File: _misc.py From multilabel-image-classification-tensorflow with MIT License | 6 votes |
|
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
