Python absl.flags.multi_flags_validator() Examples
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code examples of absl.flags.multi_flags_validator().
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Example #1
Source File: _device.py From models with Apache License 2.0 | 6 votes |
def require_cloud_storage(flag_names): """Register a validator to check directory flags. Args: flag_names: An iterable of strings containing the names of flags to be checked. """ msg = "TPU requires GCS path for {}".format(", ".join(flag_names)) @flags.multi_flags_validator(["tpu"] + flag_names, message=msg) def _path_check(flag_values): # pylint: disable=missing-docstring if flag_values["tpu"] is None: return True valid_flags = True for key in flag_names: if not flag_values[key].startswith("gs://"): tf.compat.v1.logging.error("{} must be a GCS path.".format(key)) valid_flags = False return valid_flags
Example #2
Source File: _device.py From nsfw with Apache License 2.0 | 6 votes |
def require_cloud_storage(flag_names): """Register a validator to check directory flags. Args: flag_names: An iterable of strings containing the names of flags to be checked. """ msg = "TPU requires GCS path for {}".format(", ".join(flag_names)) @flags.multi_flags_validator(["tpu"] + flag_names, message=msg) def _path_check(flag_values): # pylint: disable=missing-docstring if flag_values["tpu"] is None: return True valid_flags = True for key in flag_names: if not flag_values[key].startswith("gs://"): tf.logging.error("{} must be a GCS path.".format(key)) valid_flags = False return valid_flags
Example #3
Source File: _device.py From ml-on-gcp with Apache License 2.0 | 6 votes |
def require_cloud_storage(flag_names): """Register a validator to check directory flags. Args: flag_names: An iterable of strings containing the names of flags to be checked. """ msg = "TPU requires GCS path for {}".format(", ".join(flag_names)) @flags.multi_flags_validator(["tpu"] + flag_names, message=msg) def _path_check(flag_values): # pylint: disable=missing-docstring if flag_values["tpu"] is None: return True valid_flags = True for key in flag_names: if not flag_values[key].startswith("gs://"): tf.logging.error("{} must be a GCS path.".format(key)) valid_flags = False return valid_flags
Example #4
Source File: _device.py From ml-on-gcp with Apache License 2.0 | 6 votes |
def require_cloud_storage(flag_names): """Register a validator to check directory flags. Args: flag_names: An iterable of strings containing the names of flags to be checked. """ msg = "TPU requires GCS path for {}".format(", ".join(flag_names)) @flags.multi_flags_validator(["tpu"] + flag_names, message=msg) def _path_check(flag_values): # pylint: disable=missing-docstring if flag_values["tpu"] is None: return True valid_flags = True for key in flag_names: if not flag_values[key].startswith("gs://"): tf.logging.error("{} must be a GCS path.".format(key)) valid_flags = False return valid_flags
Example #5
Source File: _device.py From ml-on-gcp with Apache License 2.0 | 6 votes |
def require_cloud_storage(flag_names): """Register a validator to check directory flags. Args: flag_names: An iterable of strings containing the names of flags to be checked. """ msg = "TPU requires GCS path for {}".format(", ".join(flag_names)) @flags.multi_flags_validator(["tpu"] + flag_names, message=msg) def _path_check(flag_values): # pylint: disable=missing-docstring if flag_values["tpu"] is None: return True valid_flags = True for key in flag_names: if not flag_values[key].startswith("gs://"): tf.logging.error("{} must be a GCS path.".format(key)) valid_flags = False return valid_flags
Example #6
Source File: _device.py From ml-on-gcp with Apache License 2.0 | 6 votes |
def require_cloud_storage(flag_names): """Register a validator to check directory flags. Args: flag_names: An iterable of strings containing the names of flags to be checked. """ msg = "TPU requires GCS path for {}".format(", ".join(flag_names)) @flags.multi_flags_validator(["tpu"] + flag_names, message=msg) def _path_check(flag_values): # pylint: disable=missing-docstring if flag_values["tpu"] is None: return True valid_flags = True for key in flag_names: if not flag_values[key].startswith("gs://"): tf.logging.error("{} must be a GCS path.".format(key)) valid_flags = False return valid_flags
Example #7
Source File: _device.py From models with Apache License 2.0 | 6 votes |
def require_cloud_storage(flag_names): """Register a validator to check directory flags. Args: flag_names: An iterable of strings containing the names of flags to be checked. """ msg = "TPU requires GCS path for {}".format(", ".join(flag_names)) @flags.multi_flags_validator(["tpu"] + flag_names, message=msg) def _path_check(flag_values): # pylint: disable=missing-docstring if flag_values["tpu"] is None: return True valid_flags = True for key in flag_names: if not flag_values[key].startswith("gs://"): tf.logging.error("{} must be a GCS path.".format(key)) valid_flags = False return valid_flags
Example #8
Source File: _device.py From models with Apache License 2.0 | 6 votes |
def require_cloud_storage(flag_names): """Register a validator to check directory flags. Args: flag_names: An iterable of strings containing the names of flags to be checked. """ msg = "TPU requires GCS path for {}".format(", ".join(flag_names)) @flags.multi_flags_validator(["tpu"] + flag_names, message=msg) def _path_check(flag_values): # pylint: disable=missing-docstring if flag_values["tpu"] is None: return True valid_flags = True for key in flag_names: if not flag_values[key].startswith("gs://"): tf.logging.error("{} must be a GCS path.".format(key)) valid_flags = False return valid_flags
Example #9
Source File: _device.py From models with Apache License 2.0 | 6 votes |
def require_cloud_storage(flag_names): """Register a validator to check directory flags. Args: flag_names: An iterable of strings containing the names of flags to be checked. """ msg = "TPU requires GCS path for {}".format(", ".join(flag_names)) @flags.multi_flags_validator(["tpu"] + flag_names, message=msg) def _path_check(flag_values): # pylint: disable=missing-docstring if flag_values["tpu"] is None: return True valid_flags = True for key in flag_names: if not flag_values[key].startswith("gs://"): tf.logging.error("{} must be a GCS path.".format(key)) valid_flags = False return valid_flags
Example #10
Source File: _device.py From Live-feed-object-device-identification-using-Tensorflow-and-OpenCV with Apache License 2.0 | 6 votes |
def require_cloud_storage(flag_names): """Register a validator to check directory flags. Args: flag_names: An iterable of strings containing the names of flags to be checked. """ msg = "TPU requires GCS path for {}".format(", ".join(flag_names)) @flags.multi_flags_validator(["tpu"] + flag_names, message=msg) def _path_check(flag_values): # pylint: disable=missing-docstring if flag_values["tpu"] is None: return True valid_flags = True for key in flag_names: if not flag_values[key].startswith("gs://"): tf.compat.v1.logging.error("{} must be a GCS path.".format(key)) valid_flags = False return valid_flags
Example #11
Source File: _device.py From g-tensorflow-models with Apache License 2.0 | 6 votes |
def require_cloud_storage(flag_names): """Register a validator to check directory flags. Args: flag_names: An iterable of strings containing the names of flags to be checked. """ msg = "TPU requires GCS path for {}".format(", ".join(flag_names)) @flags.multi_flags_validator(["tpu"] + flag_names, message=msg) def _path_check(flag_values): # pylint: disable=missing-docstring if flag_values["tpu"] is None: return True valid_flags = True for key in flag_names: if not flag_values[key].startswith("gs://"): tf.logging.error("{} must be a GCS path.".format(key)) valid_flags = False return valid_flags
Example #12
Source File: _device.py From models with Apache License 2.0 | 6 votes |
def require_cloud_storage(flag_names): """Register a validator to check directory flags. Args: flag_names: An iterable of strings containing the names of flags to be checked. """ msg = "TPU requires GCS path for {}".format(", ".join(flag_names)) @flags.multi_flags_validator(["tpu"] + flag_names, message=msg) def _path_check(flag_values): # pylint: disable=missing-docstring if flag_values["tpu"] is None: return True valid_flags = True for key in flag_names: if not flag_values[key].startswith("gs://"): logging.error("%s must be a GCS path.", key) valid_flags = False return valid_flags
Example #13
Source File: _device.py From multilabel-image-classification-tensorflow with MIT License | 6 votes |
def require_cloud_storage(flag_names): """Register a validator to check directory flags. Args: flag_names: An iterable of strings containing the names of flags to be checked. """ msg = "TPU requires GCS path for {}".format(", ".join(flag_names)) @flags.multi_flags_validator(["tpu"] + flag_names, message=msg) def _path_check(flag_values): # pylint: disable=missing-docstring if flag_values["tpu"] is None: return True valid_flags = True for key in flag_names: if not flag_values[key].startswith("gs://"): tf.logging.error("{} must be a GCS path.".format(key)) valid_flags = False return valid_flags
Example #14
Source File: benchmark_main.py From g-tensorflow-models with Apache License 2.0 | 4 votes |
def define_keras_benchmark_flags(): """Add flags for keras built-in application models.""" flags_core.define_base(hooks=False) flags_core.define_performance() flags_core.define_image() flags_core.define_benchmark() flags.adopt_module_key_flags(flags_core) flags_core.set_defaults( data_format="channels_last", use_synthetic_data=True, batch_size=32, train_epochs=2) flags.DEFINE_enum( name="model", default=None, enum_values=MODELS.keys(), case_sensitive=False, help=flags_core.help_wrap( "Model to be benchmarked.")) flags.DEFINE_integer( name="num_train_images", default=1000, help=flags_core.help_wrap( "The number of synthetic images for training. The default value is " "1000.")) flags.DEFINE_integer( name="num_eval_images", default=50, help=flags_core.help_wrap( "The number of synthetic images for evaluation. The default value is " "50.")) flags.DEFINE_boolean( name="eager", default=False, help=flags_core.help_wrap( "To enable eager execution. Note that if eager execution is enabled, " "only one GPU is utilized even if multiple GPUs are provided and " "multi_gpu_model is used.")) flags.DEFINE_boolean( name="dist_strat", default=False, help=flags_core.help_wrap( "To enable distribution strategy for model training and evaluation. " "Number of GPUs used for distribution strategy can be set by the " "argument --num_gpus.")) flags.DEFINE_list( name="callbacks", default=["ExamplesPerSecondCallback", "LoggingMetricCallback"], help=flags_core.help_wrap( "A list of (case insensitive) strings to specify the names of " "callbacks. For example: `--callbacks ExamplesPerSecondCallback," "LoggingMetricCallback`")) @flags.multi_flags_validator( ["eager", "dist_strat"], message="Both --eager and --dist_strat were set. Only one can be " "defined, as DistributionStrategy is not supported in Eager " "execution currently.") # pylint: disable=unused-variable def _check_eager_dist_strat(flag_dict): return not(flag_dict["eager"] and flag_dict["dist_strat"])
Example #15
Source File: benchmark_main.py From multilabel-image-classification-tensorflow with MIT License | 4 votes |
def define_keras_benchmark_flags(): """Add flags for keras built-in application models.""" flags_core.define_base(hooks=False) flags_core.define_performance() flags_core.define_image() flags_core.define_benchmark() flags.adopt_module_key_flags(flags_core) flags_core.set_defaults( data_format="channels_last", use_synthetic_data=True, batch_size=32, train_epochs=2) flags.DEFINE_enum( name="model", default=None, enum_values=MODELS.keys(), case_sensitive=False, help=flags_core.help_wrap( "Model to be benchmarked.")) flags.DEFINE_integer( name="num_train_images", default=1000, help=flags_core.help_wrap( "The number of synthetic images for training. The default value is " "1000.")) flags.DEFINE_integer( name="num_eval_images", default=50, help=flags_core.help_wrap( "The number of synthetic images for evaluation. The default value is " "50.")) flags.DEFINE_boolean( name="eager", default=False, help=flags_core.help_wrap( "To enable eager execution. Note that if eager execution is enabled, " "only one GPU is utilized even if multiple GPUs are provided and " "multi_gpu_model is used.")) flags.DEFINE_boolean( name="dist_strat", default=False, help=flags_core.help_wrap( "To enable distribution strategy for model training and evaluation. " "Number of GPUs used for distribution strategy can be set by the " "argument --num_gpus.")) flags.DEFINE_list( name="callbacks", default=["ExamplesPerSecondCallback", "LoggingMetricCallback"], help=flags_core.help_wrap( "A list of (case insensitive) strings to specify the names of " "callbacks. For example: `--callbacks ExamplesPerSecondCallback," "LoggingMetricCallback`")) @flags.multi_flags_validator( ["eager", "dist_strat"], message="Both --eager and --dist_strat were set. Only one can be " "defined, as DistributionStrategy is not supported in Eager " "execution currently.") # pylint: disable=unused-variable def _check_eager_dist_strat(flag_dict): return not(flag_dict["eager"] and flag_dict["dist_strat"])