Move command-line argparse arguments into main() function
This commit is contained in:
Tim Rault
@@ -39,82 +39,6 @@ logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(messa
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level = logging.INFO)
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logger = logging.getLogger(__name__)
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parser = argparse.ArgumentParser()
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## Required parameters
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parser.add_argument("--bert_config_file", default=None, type=str, required=True,
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help="The config json file corresponding to the pre-trained BERT model. "
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"This specifies the model architecture.")
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parser.add_argument("--vocab_file", default=None, type=str, required=True,
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help="The vocabulary file that the BERT model was trained on.")
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parser.add_argument("--output_dir", default=None, type=str, required=True,
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help="The output directory where the model checkpoints will be written.")
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## Other parameters
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parser.add_argument("--train_file", default=None, type=str, help="SQuAD json for training. E.g., train-v1.1.json")
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parser.add_argument("--predict_file", default=None, type=str,
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help="SQuAD json for predictions. E.g., dev-v1.1.json or test-v1.1.json")
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parser.add_argument("--init_checkpoint", default=None, type=str,
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help="Initial checkpoint (usually from a pre-trained BERT model).")
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parser.add_argument("--do_lower_case", default=True, action='store_true',
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help="Whether to lower case the input text. Should be True for uncased "
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"models and False for cased models.")
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parser.add_argument("--max_seq_length", default=384, type=int,
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help="The maximum total input sequence length after WordPiece tokenization. "
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"Sequences longer than this will be truncated, and sequences shorter than this will be padded.")
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parser.add_argument("--doc_stride", default=128, type=int,
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help="When splitting up a long document into chunks, how much stride to take between chunks.")
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parser.add_argument("--max_query_length", default=64, type=int,
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help="The maximum number of tokens for the question. Questions longer than this will "
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"be truncated to this length.")
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parser.add_argument("--do_train", default=False, action='store_true', help="Whether to run training.")
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parser.add_argument("--do_predict", default=False, action='store_true', help="Whether to run eval on the dev set.")
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parser.add_argument("--train_batch_size", default=32, type=int, help="Total batch size for training.")
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parser.add_argument("--predict_batch_size", default=8, type=int, help="Total batch size for predictions.")
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parser.add_argument("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.")
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parser.add_argument("--num_train_epochs", default=3.0, type=float, help="Total number of training epochs to perform.")
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parser.add_argument("--warmup_proportion", default=0.1, type=float,
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help="Proportion of training to perform linear learning rate warmup for. E.g., 0.1 = 10% "
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"of training.")
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parser.add_argument("--save_checkpoints_steps", default=1000, type=int, help="How often to save the model checkpoint.")
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parser.add_argument("--iterations_per_loop", default=1000, type=int,
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help="How many steps to make in each estimator call.")
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parser.add_argument("--n_best_size", default=20, type=int,
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help="The total number of n-best predictions to generate in the nbest_predictions.json output file.")
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parser.add_argument("--max_answer_length", default=30, type=int,
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help="The maximum length of an answer that can be generated. This is needed because the start "
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"and end predictions are not conditioned on one another.")
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### BEGIN - TO DELETE EVENTUALLY --> NO SENSE IN PYTORCH ###
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# parser.add_argument("--use_tpu", default=False, action='store_true', help="Whether to use TPU or GPU/CPU.")
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# parser.add_argument("--tpu_name", default=None, type=str,
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# help="The Cloud TPU to use for training. This should be either the name used when creating the "
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# "Cloud TPU, or a grpc://ip.address.of.tpu:8470 url.")
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# parser.add_argument("--tpu_zone", default=None, type=str,
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# help="[Optional] GCE zone where the Cloud TPU is located in. If not specified, we will attempt "
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# "to automatically detect the GCE project from metadata.")
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# parser.add_argument("--gcp_project", default=None, type=str,
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# help="[Optional] Project name for the Cloud TPU-enabled project. If not specified, we will attempt "
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# "to automatically detect the GCE project from metadata.")
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# parser.add_argument("--master", default=None, type=str, help="[Optional] TensorFlow master URL.")
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# parser.add_argument("--num_tpu_cores", default=8, type=int, help="Only used if `use_tpu` is True. "
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# "Total number of TPU cores to use.")
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### END - TO DELETE EVENTUALLY --> NO SENSE IN PYTORCH ###
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parser.add_argument("--verbose_logging", default=False, action='store_true',
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help="If true, all of the warnings related to data processing will be printed. "
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"A number of warnings are expected for a normal SQuAD evaluation.")
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parser.add_argument("--no_cuda",
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default = False,
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action='store_true',
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help = "Whether not to use CUDA when available")
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parser.add_argument("--local_rank",
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type=int,
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default=-1,
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help = "local_rank for distributed training on gpus")
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args = parser.parse_args()
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class SquadExample(object):
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"""A single training/test example for simple sequence classification."""
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@@ -744,6 +668,85 @@ def _compute_softmax(scores):
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def main():
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parser = argparse.ArgumentParser()
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## Required parameters
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parser.add_argument("--bert_config_file", default=None, type=str, required=True,
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help="The config json file corresponding to the pre-trained BERT model. "
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"This specifies the model architecture.")
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parser.add_argument("--vocab_file", default=None, type=str, required=True,
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help="The vocabulary file that the BERT model was trained on.")
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parser.add_argument("--output_dir", default=None, type=str, required=True,
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help="The output directory where the model checkpoints will be written.")
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## Other parameters
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parser.add_argument("--train_file", default=None, type=str, help="SQuAD json for training. E.g., train-v1.1.json")
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parser.add_argument("--predict_file", default=None, type=str,
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help="SQuAD json for predictions. E.g., dev-v1.1.json or test-v1.1.json")
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parser.add_argument("--init_checkpoint", default=None, type=str,
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help="Initial checkpoint (usually from a pre-trained BERT model).")
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parser.add_argument("--do_lower_case", default=True, action='store_true',
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help="Whether to lower case the input text. Should be True for uncased "
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"models and False for cased models.")
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parser.add_argument("--max_seq_length", default=384, type=int,
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help="The maximum total input sequence length after WordPiece tokenization. Sequences "
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"longer than this will be truncated, and sequences shorter than this will be padded.")
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parser.add_argument("--doc_stride", default=128, type=int,
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help="When splitting up a long document into chunks, how much stride to take between chunks.")
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parser.add_argument("--max_query_length", default=64, type=int,
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help="The maximum number of tokens for the question. Questions longer than this will "
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"be truncated to this length.")
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parser.add_argument("--do_train", default=False, action='store_true', help="Whether to run training.")
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parser.add_argument("--do_predict", default=False, action='store_true', help="Whether to run eval on the dev set.")
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parser.add_argument("--train_batch_size", default=32, type=int, help="Total batch size for training.")
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parser.add_argument("--predict_batch_size", default=8, type=int, help="Total batch size for predictions.")
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parser.add_argument("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.")
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parser.add_argument("--num_train_epochs", default=3.0, type=float,
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help="Total number of training epochs to perform.")
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parser.add_argument("--warmup_proportion", default=0.1, type=float,
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help="Proportion of training to perform linear learning rate warmup for. E.g., 0.1 = 10% "
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"of training.")
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parser.add_argument("--save_checkpoints_steps", default=1000, type=int,
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help="How often to save the model checkpoint.")
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parser.add_argument("--iterations_per_loop", default=1000, type=int,
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help="How many steps to make in each estimator call.")
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parser.add_argument("--n_best_size", default=20, type=int,
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help="The total number of n-best predictions to generate in the nbest_predictions.json "
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"output file.")
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parser.add_argument("--max_answer_length", default=30, type=int,
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help="The maximum length of an answer that can be generated. This is needed because the start "
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"and end predictions are not conditioned on one another.")
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### BEGIN - TO DELETE EVENTUALLY --> NO SENSE IN PYTORCH ###
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# parser.add_argument("--use_tpu", default=False, action='store_true', help="Whether to use TPU or GPU/CPU.")
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# parser.add_argument("--tpu_name", default=None, type=str,
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# help="The Cloud TPU to use for training. This should be either the name used when creating the "
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# "Cloud TPU, or a grpc://ip.address.of.tpu:8470 url.")
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# parser.add_argument("--tpu_zone", default=None, type=str,
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# help="[Optional] GCE zone where the Cloud TPU is located in. If not specified, we will attempt "
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# "to automatically detect the GCE project from metadata.")
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# parser.add_argument("--gcp_project", default=None, type=str,
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# help="[Optional] Project name for the Cloud TPU-enabled project. If not specified, we will attempt "
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# "to automatically detect the GCE project from metadata.")
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# parser.add_argument("--master", default=None, type=str, help="[Optional] TensorFlow master URL.")
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# parser.add_argument("--num_tpu_cores", default=8, type=int, help="Only used if `use_tpu` is True. "
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# "Total number of TPU cores to use.")
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### END - TO DELETE EVENTUALLY --> NO SENSE IN PYTORCH ###
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parser.add_argument("--verbose_logging", default=False, action='store_true',
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help="If true, all of the warnings related to data processing will be printed. "
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"A number of warnings are expected for a normal SQuAD evaluation.")
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parser.add_argument("--no_cuda",
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default=False,
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action='store_true',
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help="Whether not to use CUDA when available")
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parser.add_argument("--local_rank",
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type=int,
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default=-1,
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help="local_rank for distributed training on gpus")
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args = parser.parse_args()
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if args.local_rank == -1 or args.no_cuda:
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device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
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n_gpu = torch.cuda.device_count()
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