Python absl.flags.mark_flags_as_required() Examples
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Example #1
Source File: cache_tasks_main.py From text-to-text-transfer-transformer with Apache License 2.0 | 6 votes |
def main(_): flags.mark_flags_as_required(["output_cache_dir"]) _import_modules(FLAGS.module_import) t5.data.add_global_cache_dirs( [FLAGS.output_cache_dir] + FLAGS.tasks_additional_cache_dirs) output_dirs = [] pipeline_options = beam.options.pipeline_options.PipelineOptions( FLAGS.pipeline_options) with beam.Pipeline(options=pipeline_options) as pipeline: tf.io.gfile.makedirs(FLAGS.output_cache_dir) output_dirs = run_pipeline( pipeline, FLAGS.tasks, FLAGS.output_cache_dir, FLAGS.max_input_examples, FLAGS.excluded_tasks, FLAGS.module_import, FLAGS.overwrite) # TODO(adarob): Figure out a way to write these when each task completes. for output_dir in output_dirs: with tf.io.gfile.GFile(os.path.join(output_dir, "COMPLETED"), "w") as f: f.write("")
Example #2
Source File: onsets_frames_transcription_create_tfrecords.py From magenta with Apache License 2.0 | 5 votes |
def main(argv): del argv flags.mark_flags_as_required(['csv', 'output_directory']) tf.io.gfile.makedirs(FLAGS.output_directory) with tf.io.gfile.GFile(FLAGS.csv) as f: reader = csv.DictReader(f) splits = collections.defaultdict(list) for row in reader: splits[row['split']].append( (os.path.join(FLAGS.midi_dir, row['midi_filename']), os.path.join(FLAGS.wav_dir, row['audio_filename']))) if sorted(splits.keys()) != sorted(FLAGS.expected_splits.split(',')): raise ValueError('Got unexpected set of splits: %s' % list(splits.keys())) pipeline_options = beam.options.pipeline_options.PipelineOptions( FLAGS.pipeline_options) with beam.Pipeline(options=pipeline_options) as p: for split in splits: split_p = p | 'prepare_split_%s' % split >> beam.Create(splits[split]) split_p |= 'create_examples_%s' % split >> beam.ParDo( CreateExampleDoFn(FLAGS.wav_dir, FLAGS.midi_dir, FLAGS.add_wav_glob)) split_p |= 'write_%s' % split >> beam.io.WriteToTFRecord( os.path.join(FLAGS.output_directory, '%s.tfrecord' % split), coder=beam.coders.ProtoCoder(tf.train.Example), num_shards=FLAGS.num_shards)
Example #3
Source File: run_saved_model.py From delta with Apache License 2.0 | 5 votes |
def define_flags(): ''' define flags for evaluator''' # The GPU devices which are visible for current process flags.DEFINE_string('gpu', '', 'same to CUDA_VISIBLE_DEVICES') flags.DEFINE_string('config', None, help='path to yaml config file') flags.DEFINE_enum('mode', 'eval', ['eval', 'infer', 'eval_and_infer'], 'eval or infer') flags.DEFINE_bool('debug', False, 'debug mode') # https://github.com/abseil/abseil-py/blob/master/absl/flags/_validators.py#L330 flags.mark_flags_as_required(['config', 'mode'])
Example #4
Source File: run_saved_model.py From delta with Apache License 2.0 | 5 votes |
def define_flags(): ''' define flags for evaluator''' # The GPU devices which are visible for current process flags.DEFINE_string('gpu', '', 'same to CUDA_VISIBLE_DEVICES') flags.DEFINE_string('config', None, help='path to yaml config file') flags.DEFINE_enum('mode', 'eval', ['eval', 'infer', 'eval_and_infer'], 'eval or infer') flags.DEFINE_bool('debug', False, 'debug mode') # https://github.com/abseil/abseil-py/blob/master/absl/flags/_validators.py#L330 flags.mark_flags_as_required(['config', 'mode'])
Example #5
Source File: run_saved_model.py From delta with Apache License 2.0 | 5 votes |
def define_flags(): ''' define flags for evaluator''' # The GPU devices which are visible for current process flags.DEFINE_string('gpu', '', 'same to CUDA_VISIBLE_DEVICES') flags.DEFINE_string('config', None, help='path to yaml config file') flags.DEFINE_enum('mode', 'eval', ['eval', 'infer', 'eval_and_infer'], 'eval or infer') flags.DEFINE_bool('debug', False, 'debug mode') # https://github.com/abseil/abseil-py/blob/master/absl/flags/_validators.py#L330 flags.mark_flags_as_required(['config', 'mode'])
Example #6
Source File: run_saved_model.py From delta with Apache License 2.0 | 5 votes |
def define_flags(): ''' define flags for evaluator''' # The GPU devices which are visible for current process flags.DEFINE_string('gpu', '', 'same to CUDA_VISIBLE_DEVICES') flags.DEFINE_string('config', None, help='path to yaml config file') flags.DEFINE_enum('mode', 'eval', ['eval', 'infer', 'eval_and_infer'], 'eval or infer') flags.DEFINE_bool('debug', False, 'debug mode') # https://github.com/abseil/abseil-py/blob/master/absl/flags/_validators.py#L330 flags.mark_flags_as_required(['config', 'mode'])
Example #7
Source File: run_saved_model.py From delta with Apache License 2.0 | 5 votes |
def define_flags(): ''' define flags for evaluator''' # The GPU devices which are visible for current process flags.DEFINE_string('gpu', '', 'same to CUDA_VISIBLE_DEVICES') flags.DEFINE_string('config', None, help='path to yaml config file') flags.DEFINE_enum('mode', 'eval', ['eval', 'infer', 'eval_and_infer'], 'eval or infer') flags.DEFINE_bool('debug', False, 'debug mode') # https://github.com/abseil/abseil-py/blob/master/absl/flags/_validators.py#L330 flags.mark_flags_as_required(['config', 'mode'])
Example #8
Source File: run_saved_model.py From delta with Apache License 2.0 | 5 votes |
def define_flags(): ''' define flags for evaluator''' # The GPU devices which are visible for current process flags.DEFINE_string('gpu', '', 'same to CUDA_VISIBLE_DEVICES') flags.DEFINE_string('config', None, help='path to yaml config file') flags.DEFINE_enum('mode', 'eval', ['eval', 'infer', 'eval_and_infer'], 'eval or infer') flags.DEFINE_bool('debug', False, 'debug mode') # https://github.com/abseil/abseil-py/blob/master/absl/flags/_validators.py#L330 flags.mark_flags_as_required(['config', 'mode'])
Example #9
Source File: run_saved_model.py From delta with Apache License 2.0 | 5 votes |
def define_flags(): ''' define flags for evaluator''' # The GPU devices which are visible for current process flags.DEFINE_string('gpu', '', 'same to CUDA_VISIBLE_DEVICES') flags.DEFINE_string('config', None, help='path to yaml config file') flags.DEFINE_enum('mode', 'eval', ['eval', 'infer', 'eval_and_infer'], 'eval or infer') flags.DEFINE_bool('debug', False, 'debug mode') # https://github.com/abseil/abseil-py/blob/master/absl/flags/_validators.py#L330 flags.mark_flags_as_required(['config', 'mode'])
Example #10
Source File: run_saved_model.py From delta with Apache License 2.0 | 5 votes |
def define_flags(): ''' define flags for evaluator''' # The GPU devices which are visible for current process flags.DEFINE_string('gpu', '', 'same to CUDA_VISIBLE_DEVICES') flags.DEFINE_string('config', None, help='path to yaml config file') flags.DEFINE_enum('mode', 'eval', ['eval', 'infer', 'eval_and_infer'], 'eval or infer') flags.DEFINE_bool('debug', False, 'debug mode') # https://github.com/abseil/abseil-py/blob/master/absl/flags/_validators.py#L330 flags.mark_flags_as_required(['config', 'mode'])
Example #11
Source File: run_saved_model.py From delta with Apache License 2.0 | 5 votes |
def define_flags(): ''' define flags for evaluator''' # The GPU devices which are visible for current process flags.DEFINE_string('gpu', '', 'same to CUDA_VISIBLE_DEVICES') flags.DEFINE_string('config', None, help='path to yaml config file') flags.DEFINE_enum('mode', 'eval', ['eval', 'infer', 'eval_and_infer'], 'eval or infer') flags.DEFINE_bool('debug', False, 'debug mode') # https://github.com/abseil/abseil-py/blob/master/absl/flags/_validators.py#L330 flags.mark_flags_as_required(['config', 'mode'])
Example #12
Source File: run_saved_model.py From delta with Apache License 2.0 | 5 votes |
def define_flags(): ''' define flags for evaluator''' # The GPU devices which are visible for current process flags.DEFINE_string('gpu', '', 'same to CUDA_VISIBLE_DEVICES') flags.DEFINE_string('config', None, help='path to yaml config file') flags.DEFINE_enum('mode', 'eval', ['eval', 'infer', 'eval_and_infer'], 'eval or infer') flags.DEFINE_bool('debug', False, 'debug mode') # https://github.com/abseil/abseil-py/blob/master/absl/flags/_validators.py#L330 flags.mark_flags_as_required(['config', 'mode'])
Example #13
Source File: dump_task.py From text-to-text-transfer-transformer with Apache License 2.0 | 5 votes |
def main(_): flags.mark_flags_as_required(["task"]) if FLAGS.module_import: import_modules(FLAGS.module_import) gin.parse_config_files_and_bindings(FLAGS.gin_file, FLAGS.gin_param) total_examples = 0 tf.enable_eager_execution() task = t5.data.TaskRegistry.get(FLAGS.task) files = task.tfds_dataset.files(FLAGS.split) def _example_to_string(ex): key_to_string = {} for k in ("inputs", "targets"): if k in ex: v = ex[k].numpy() key_to_string[k] = ( " ".join(str(i) for i in v) if FLAGS.tokenize else v.decode("utf-8")) else: v[k] = "" return FLAGS.format_string.format(**key_to_string) for shard_path in files: logging.info("Processing shard: %s", shard_path) ds = task.tfds_dataset.load_shard(shard_path) ds = task.preprocess_text(ds) if FLAGS.tokenize: ds = t5.data.encode_string_features( ds, task.output_features, keys=task.output_features, copy_plaintext=True) ds = task.preprocess_tokens(ds, sequence_length()) for ex in ds: print(_example_to_string(ex)) total_examples += 1 if total_examples == FLAGS.max_examples: return
Example #14
Source File: selfplay.py From training with Apache License 2.0 | 5 votes |
def main(unused_argv): flags.mark_flags_as_required(['bucket_name', 'mode']) if FLAGS.mode == 'cc': run_cc() elif FLAGS.mode == 'tpu': run_tpu(no_resign=False) elif FLAGS.mode == 'tpu_nr': run_tpu(no_resign=True)
Example #15
Source File: data_async_generation.py From multilabel-image-classification-tensorflow with MIT License | 4 votes |
def define_flags(): """Construct flags for the server.""" flags.DEFINE_integer(name="num_workers", default=multiprocessing.cpu_count(), help="Size of the negative generation worker pool.") flags.DEFINE_string(name="data_dir", default=None, help="The data root. (used to construct cache paths.)") flags.DEFINE_string(name="cache_id", default=None, help="The cache_id generated in the main process.") flags.DEFINE_integer(name="num_readers", default=4, help="Number of reader datasets in training. This sets" "how the epoch files are sharded.") flags.DEFINE_integer(name="num_neg", default=None, help="The Number of negative instances to pair with a " "positive instance.") flags.DEFINE_integer(name="num_train_positives", default=None, help="The number of positive training examples.") flags.DEFINE_integer(name="num_items", default=None, help="Number of items from which to select negatives.") flags.DEFINE_integer(name="num_users", default=None, help="The number of unique users. Used for evaluation.") flags.DEFINE_integer(name="epochs_per_cycle", default=1, help="The number of epochs of training data to produce" "at a time.") flags.DEFINE_integer(name="num_cycles", default=None, help="The number of cycles to produce training data " "for.") flags.DEFINE_integer(name="train_batch_size", default=None, help="The batch size with which training TFRecords will " "be chunked.") flags.DEFINE_integer(name="eval_batch_size", default=None, help="The batch size with which evaluation TFRecords " "will be chunked.") flags.DEFINE_boolean(name="redirect_logs", default=False, help="Catch logs and write them to a file. " "(Useful if this is run as a subprocess)") flags.DEFINE_boolean(name="use_tf_logging", default=False, help="Use tf.logging instead of log file.") flags.DEFINE_integer(name="seed", default=None, help="NumPy random seed to set at startup. If not " "specified, a seed will not be set.") flags.DEFINE_boolean(name="ml_perf", default=None, help="Match MLPerf. See ncf_main.py for details.") flags.DEFINE_bool(name="output_ml_perf_compliance_logging", default=None, help="Output the MLPerf compliance logging. See " "ncf_main.py for details.") flags.mark_flags_as_required(["data_dir", "cache_id"])