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Move command-line argparse arguments into main() function

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This commit is contained in:
Tim Rault
2018-11-02 14:42:05 +01:00
parent c9690e57f8
commit 3ff2ec5eb3
3 changed files with 218 additions and 213 deletions
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177
run_classifier_pytorch.py
View File

@@ -37,94 +37,6 @@ logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(messa
level = logging.INFO)
logger = logging.getLogger(__name__)
parser = argparse.ArgumentParser()
## Required parameters
parser.add_argument("--data_dir",
default = None,
type = str,
required = True,
help = "The input data dir. Should contain the .tsv files (or other data files) for the task.")
parser.add_argument("--bert_config_file",
default = None,
type = str,
required = True,
help = "The config json file corresponding to the pre-trained BERT model. \n"
"This specifies the model architecture.")
parser.add_argument("--task_name",
default = None,
type = str,
required = True,
help = "The name of the task to train.")
parser.add_argument("--vocab_file",
default = None,
type = str,
required = True,
help = "The vocabulary file that the BERT model was trained on.")
parser.add_argument("--output_dir",
default = None,
type = str,
required = True,
help = "The output directory where the model checkpoints will be written.")
## Other parameters
parser.add_argument("--init_checkpoint",
default = None,
type = str,
help = "Initial checkpoint (usually from a pre-trained BERT model).")
parser.add_argument("--do_lower_case",
default = False,
action='store_true',
help = "Whether to lower case the input text. Should be True for uncased models and False for cased models.")
parser.add_argument("--max_seq_length",
default = 128,
type = int,
help = "The maximum total input sequence length after WordPiece tokenization. \n"
"Sequences longer than this will be truncated, and sequences shorter \n"
"than this will be padded.")
parser.add_argument("--do_train",
default = False,
action='store_true',
help = "Whether to run training.")
parser.add_argument("--do_eval",
default = False,
action='store_true',
help = "Whether to run eval on the dev set.")
parser.add_argument("--train_batch_size",
default = 32,
type = int,
help = "Total batch size for training.")
parser.add_argument("--eval_batch_size",
default = 8,
type = int,
help = "Total batch size for eval.")
parser.add_argument("--learning_rate",
default = 5e-5,
type = float,
help = "The initial learning rate for Adam.")
parser.add_argument("--num_train_epochs",
default = 3.0,
type = float,
help = "Total number of training epochs to perform.")
parser.add_argument("--warmup_proportion",
default = 0.1,
type = float,
help = "Proportion of training to perform linear learning rate warmup for. "
"E.g., 0.1 = 10%% of training.")
parser.add_argument("--save_checkpoints_steps",
default = 1000,
type = int,
help = "How often to save the model checkpoint.")
parser.add_argument("--no_cuda",
default = False,
action='store_true',
help = "Whether not to use CUDA when available")
parser.add_argument("--local_rank",
type=int,
default=-1,
help = "local_rank for distributed training on gpus")
args = parser.parse_args()
class InputExample(object):
"""A single training/test example for simple sequence classification."""
@@ -428,6 +340,95 @@ def accuracy(out, labels):
return np.sum(outputs==labels)
def main():
parser = argparse.ArgumentParser()
## Required parameters
parser.add_argument("--data_dir",
default=None,
type=str,
required=True,
help="The input data dir. Should contain the .tsv files (or other data files) for the task.")
parser.add_argument("--bert_config_file",
default=None,
type=str,
required=True,
help="The config json file corresponding to the pre-trained BERT model. \n"
"This specifies the model architecture.")
parser.add_argument("--task_name",
default=None,
type=str,
required=True,
help="The name of the task to train.")
parser.add_argument("--vocab_file",
default=None,
type=str,
required=True,
help="The vocabulary file that the BERT model was trained on.")
parser.add_argument("--output_dir",
default=None,
type=str,
required=True,
help="The output directory where the model checkpoints will be written.")
## Other parameters
parser.add_argument("--init_checkpoint",
default=None,
type=str,
help="Initial checkpoint (usually from a pre-trained BERT model).")
parser.add_argument("--do_lower_case",
default=False,
action='store_true',
help="Whether to lower case the input text. Should be True for uncased models and False for cased models.")
parser.add_argument("--max_seq_length",
default=128,
type=int,
help="The maximum total input sequence length after WordPiece tokenization. \n"
"Sequences longer than this will be truncated, and sequences shorter \n"
"than this will be padded.")
parser.add_argument("--do_train",
default=False,
action='store_true',
help="Whether to run training.")
parser.add_argument("--do_eval",
default=False,
action='store_true',
help="Whether to run eval on the dev set.")
parser.add_argument("--train_batch_size",
default=32,
type=int,
help="Total batch size for training.")
parser.add_argument("--eval_batch_size",
default=8,
type=int,
help="Total batch size for eval.")
parser.add_argument("--learning_rate",
default=5e-5,
type=float,
help="The initial learning rate for Adam.")
parser.add_argument("--num_train_epochs",
default=3.0,
type=float,
help="Total number of training epochs to perform.")
parser.add_argument("--warmup_proportion",
default=0.1,
type=float,
help="Proportion of training to perform linear learning rate warmup for. "
"E.g., 0.1 = 10%% of training.")
parser.add_argument("--save_checkpoints_steps",
default=1000,
type=int,
help="How often to save the model checkpoint.")
parser.add_argument("--no_cuda",
default=False,
action='store_true',
help="Whether not to use CUDA when available")
parser.add_argument("--local_rank",
type=int,
default=-1,
help="local_rank for distributed training on gpus")
args = parser.parse_args()
processors = {
"cola": ColaProcessor,
"mnli": MnliProcessor,

99
run_pretraining_pytorch.py
View File

@@ -24,55 +24,6 @@ import optimization
import tensorflow as tf
import argparse
parser = argparse.ArgumentParser()
## Required parameters
parser.add_argument("--bert_config_file", default=None, type=str, required=True,
help="The config json file corresponding to the pre-trained BERT model. "
"This specifies the model architecture.")
parser.add_argument("--input_file", default=None, type=str, required=True,
help="Input TF example files (can be a glob or comma separated).")
parser.add_argument("--output_dir", default=None, type=str, required=True,
help="The output directory where the model checkpoints will be written.")
## Other parameters
parser.add_argument("--init_checkpoint", default=None, type=str,
help="Initial checkpoint (usually from a pre-trained BERT model).")
parser.add_argument("--max_seq_length", default=128, type=int,
help="The maximum total input sequence length after WordPiece tokenization. Sequences longer "
"than this will be truncated, and sequences shorter than this will be padded. "
"Must match data generation.")
parser.add_argument("--max_predictions_per_seq", default=20, type=int,
help="Maximum number of masked LM predictions per sequence. Must match data generation.")
parser.add_argument("--do_train", default=False, action='store_true', help="Whether to run training.")
parser.add_argument("--do_eval", default=False, action='store_true', help="Whether to run eval on the dev set.")
parser.add_argument("--train_batch_size", default=32, type=int, help="Total batch size for training.")
parser.add_argument("--eval_batch_size", default=8, type=int, help="Total batch size for eval.")
parser.add_argument("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.")
parser.add_argument("--num_train_steps", default=100000, type=int, help="Number of training steps.")
parser.add_argument("--num_warmup_steps", default=10000, type=int, help="Number of warmup steps.")
parser.add_argument("--save_checkpoints_steps", default=1000, type=int, help="How often to save the model checkpoint.")
parser.add_argument("--iterations_per_loop", default=1000, type=int,
help="How many steps to make in each estimator call.")
parser.add_argument("--max_eval_steps", default=100, type=int, help="Maximum number of eval steps.")
### BEGIN - TO DELETE EVENTUALLY --> NO SENSE IN PYTORCH ###
parser.add_argument("--use_tpu", default=False, action='store_true', help="Whether to use TPU or GPU/CPU.")
parser.add_argument("--tpu_name", default=None, type=str,
help="The Cloud TPU to use for training. This should be either the name used when creating the "
"Cloud TPU, or a grpc://ip.address.of.tpu:8470 url.")
parser.add_argument("--tpu_zone", default=None, type=str,
help="[Optional] GCE zone where the Cloud TPU is located in. If not specified, we will attempt "
"to automatically detect the GCE project from metadata.")
parser.add_argument("--gcp_project", default=None, type=str,
help="[Optional] Project name for the Cloud TPU-enabled project. If not specified, we will attempt "
"to automatically detect the GCE project from metadata.")
parser.add_argument("--master", default=None, type=str, help="[Optional] TensorFlow master URL.")
parser.add_argument("--num_tpu_cores", default=8, type=int,
help="Only used if `use_tpu` is True. Total number of TPU cores to use.")
### END - TO DELETE EVENTUALLY --> NO SENSE IN PYTORCH ###
args = parser.parse_args()
def model_fn_builder(bert_config, init_checkpoint, learning_rate,
num_train_steps, num_warmup_steps, use_tpu,
@@ -373,6 +324,56 @@ def _decode_record(record, name_to_features):
def main(_):
parser = argparse.ArgumentParser()
## Required parameters
parser.add_argument("--bert_config_file", default=None, type=str, required=True,
help="The config json file corresponding to the pre-trained BERT model. "
"This specifies the model architecture.")
parser.add_argument("--input_file", default=None, type=str, required=True,
help="Input TF example files (can be a glob or comma separated).")
parser.add_argument("--output_dir", default=None, type=str, required=True,
help="The output directory where the model checkpoints will be written.")
## Other parameters
parser.add_argument("--init_checkpoint", default=None, type=str,
help="Initial checkpoint (usually from a pre-trained BERT model).")
parser.add_argument("--max_seq_length", default=128, type=int,
help="The maximum total input sequence length after WordPiece tokenization. Sequences longer "
"than this will be truncated, and sequences shorter than this will be padded. "
"Must match data generation.")
parser.add_argument("--max_predictions_per_seq", default=20, type=int,
help="Maximum number of masked LM predictions per sequence. Must match data generation.")
parser.add_argument("--do_train", default=False, action='store_true', help="Whether to run training.")
parser.add_argument("--do_eval", default=False, action='store_true', help="Whether to run eval on the dev set.")
parser.add_argument("--train_batch_size", default=32, type=int, help="Total batch size for training.")
parser.add_argument("--eval_batch_size", default=8, type=int, help="Total batch size for eval.")
parser.add_argument("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.")
parser.add_argument("--num_train_steps", default=100000, type=int, help="Number of training steps.")
parser.add_argument("--num_warmup_steps", default=10000, type=int, help="Number of warmup steps.")
parser.add_argument("--save_checkpoints_steps", default=1000, type=int,
help="How often to save the model checkpoint.")
parser.add_argument("--iterations_per_loop", default=1000, type=int,
help="How many steps to make in each estimator call.")
parser.add_argument("--max_eval_steps", default=100, type=int, help="Maximum number of eval steps.")
### BEGIN - TO DELETE EVENTUALLY --> NO SENSE IN PYTORCH ###
parser.add_argument("--use_tpu", default=False, action='store_true', help="Whether to use TPU or GPU/CPU.")
parser.add_argument("--tpu_name", default=None, type=str,
help="The Cloud TPU to use for training. This should be either the name used when creating the "
"Cloud TPU, or a grpc://ip.address.of.tpu:8470 url.")
parser.add_argument("--tpu_zone", default=None, type=str,
help="[Optional] GCE zone where the Cloud TPU is located in. If not specified, we will attempt "
"to automatically detect the GCE project from metadata.")
parser.add_argument("--gcp_project", default=None, type=str,
help="[Optional] Project name for the Cloud TPU-enabled project. If not specified, "
"we will attempt to automatically detect the GCE project from metadata.")
parser.add_argument("--master", default=None, type=str, help="[Optional] TensorFlow master URL.")
parser.add_argument("--num_tpu_cores", default=8, type=int,
help="Only used if `use_tpu` is True. Total number of TPU cores to use.")
### END - TO DELETE EVENTUALLY --> NO SENSE IN PYTORCH ###
args = parser.parse_args()
tf.logging.set_verbosity(tf.logging.INFO)
if not args.do_train and not args.do_eval:

155
run_squad_pytorch.py
View File

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