def train_model(config):
transformer = volume_normalize('background')
if config.test == True:
layoutdata = dataset[config.data][1]
else:
layoutdata = dataset[config.data][0]
train_db = layoutdata(config, split='train', transform=transformer)
val_db = layoutdata(config, split='val', transform=transformer)
# test_db = layoutdata(config, split='val', transform=transformer)
trainer = SupervisedTrainer(train_db)
trainer.train(train_db, val_db, val_db)
if __name__ == '__main__':
cv2.setNumThreads(0)
config, unparsed = get_config()
config.bert = True
config.if_sample = False
config = layout_arguments(config)
np.random.seed(config.seed)
random.seed(config.seed)
torch.manual_seed(config.seed)
if (config.cuda):
torch.cuda.manual_seed_all(config.seed)
prepare_directories(config)
train_model(config)
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
"--codah_dir",
type=str,
required=True,
help=
"The input data dir. Should contain train.tsv and dev.tsv files for the task."
)
parser.add_argument(
"--bert_model",
default=None,
type=str,
required=True,
help="Bert pre-trained model selected in the list: bert-base-uncased, "
"bert-large-uncased, bert-base-cased, bert-base-multilingual, bert-base-chinese."
)
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("--train_batch_size",
default=8,
type=int,
help="Total batch size for training.")
parser.add_argument("--train_size",
default=0.8,
type=float,
help="Percentage of the data use for training.")
parser.add_argument("--learning_rate",
default=1e-5,
type=float,
help="The initial learning rate for Adam.")
parser.add_argument("--num_train_epochs",
default=6,
type=int,
help="Total number of training epochs to perform.")
parser.add_argument(
"--warmup",
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(
"--categories",
type=str,
default="all",
help=
'String with the categories to be included or excluded separated by "-" ej: "o-i-q-n".'
)
parser.add_argument(
"--exclude_categories",
default=False,
action="store_true",
help=
"the categories listed in `--categories` will be excluded, must not be use if all categories are listed."
)
parser.add_argument(
"--local_model",
default=False,
action="store_true",
help=
"This is to load the bert model from a local ckpt tensorflow index instead of downloading it."
)
parser.add_argument(
"--use_pooled",
default=False,
action="store_true",
help="Use the pooler output instead of the normal cls.")
parser.add_argument("--bert_dir",
type=str,
help="The directori to load ckpt index of bert model.")
parser.add_argument("--use_bert_adam",
default=False,
action="store_true",
help="Use build in BertAdam class instead of Adam.")
args = parser.parse_args()
print(args)
if args.categories == "all":
categories = CodahProcessor.get_all_categories()
else:
categories = set(args.categories.split('-'))
cfg, _ = get_config()
cfg.cuda = True
transformer = volume_normalize('background')
db = layout_coco(cfg, split='train', transform=transformer)
processor = CodahProcessor(path=args.codah_dir,
categories=categories,
exclude=args.exclude_categories)
print(" Initializing tokenizer ")
tokenizer = BertTokenizer.from_pretrained(args.bert_model)
print(" Creating train and dev datasets ")
train_examples, eval_examples = processor.get_train_dev_examples(
args.train_size)
num_train_examples = len(train_examples)
train_data = convert_examples_to_features(train_examples,
processor.get_labels(),
args.max_seq_length, tokenizer,
db)
train_sampler = RandomSampler(train_data)
train_loader = DataLoader(train_data,
sampler=train_sampler,
batch_size=args.train_batch_size)
eval_data = convert_examples_to_features(eval_examples,
processor.get_labels(),
args.max_seq_length, tokenizer,
db)
eval_sampler = RandomSampler(eval_data)
eval_loader = DataLoader(eval_data, sampler=eval_sampler, batch_size=1)
print(" Initializing bert model ")
# model = CodahClasifier(model_type=args.bert_model,
# from_tf=args.local_model,
# tf_dir=args.model_dir,
# use_pooled_output=args.use_pooled,
# freeze_bert=False).cuda()
# model = BertForSequenceClassification.from_pretrained(args.bert_model, num_labels=1)
model = CodahClassifier(args.bert_model, db)
model.cuda()
train_and_validate(model,
train_loader,
eval_loader,
tokenizer,
processor,
args.num_train_epochs,
args.learning_rate,
args.train_batch_size,
num_train_examples,
args.warmup,
print_every=10,
use_bert_adam=args.use_bert_adam)
