def get_model_path_list(self):
try:
model_lists = find_all_checkpoints(
checkpoint_dir=self.checkpoint_dir,
checkpoint_prefix=self.checkpoint_dir_prefix,
checkpoint_name=self.checkpoint_name)
if self.monitor == 'step':
model_lists = sorted(
model_lists,
key=lambda x: int(x.split("/")[-2].split("-")[-1]))
elif self.monitor == 'metric':
is_reverse = False
if self.sort_mode == 'min': is_reverse = True
model_lists = sorted(
model_lists,
key=lambda x: float(x.split("/")[-2].split("-")[-1][2]),
reverse=is_reverse)
model_lists = model_lists[-self.k_best_checkpoints:]
logger.info(
f"Averaging checkpoints: {[f.split('/')[-2] for f in model_lists]}"
)
return model_lists
except Exception as e:
logger.info("Error in `swa.get_model_path_list")
print(e)
def main():
parser = Argparser.get_training_parser()
group = parser.add_argument_group(title="global pointer",
description="Global pointer")
group.add_argument("--decode_thresh", type=float, default=0.0)
group.add_argument('--pe_dim',
default=64,
type=int,
help='The dim of Positional embedding')
group.add_argument('--use_rope', action='store_true')
opts = parser.parse_args_from_parser(parser)
logger = Logger(opts=opts)
# device
logger.info("initializing device")
opts.device, opts.device_num = prepare_device(opts.device_id)
seed_everything(opts.seed)
config_class, model_class, tokenizer_class = MODEL_CLASSES[opts.model_type]
# data processor
logger.info("initializing data processor")
tokenizer = tokenizer_class.from_pretrained(
opts.pretrained_model_path, do_lower_case=opts.do_lower_case)
train_dataset = load_data(opts.train_input_file, opts.data_dir, "train",
tokenizer, opts.train_max_seq_length)
dev_dataset = load_data(opts.eval_input_file, opts.data_dir, "dev",
tokenizer, opts.eval_max_seq_length)
test_dataset = load_data(opts.test_input_file, opts.data_dir, "test",
tokenizer, opts.test_max_seq_length)
opts.num_labels = train_dataset.num_labels
opts.label2id = CnerDataset.label2id()
opts.id2label = CnerDataset.id2label()
# model
logger.info("initializing model and config")
config, unused_kwargs = config_class.from_pretrained(
opts.pretrained_model_path,
return_unused_kwargs=True,
pe_dim=opts.pe_dim,
use_rope=opts.use_rope,
num_labels=opts.num_labels,
id2label=opts.id2label,
label2id=opts.label2id,
decode_thresh=opts.decode_thresh,
max_seq_length=512)
# FIXED: 默认`from_dict`中，只有config中有键才能设置值，这里强制设置
for key, value in unused_kwargs.items():
setattr(config, key, value)
model = model_class.from_pretrained(opts.pretrained_model_path,
config=config)
model.to(opts.device)
# trainer
logger.info("initializing traniner")
metrics = [
SequenceLabelingScore(CnerDataset.get_labels(),
schema='BIOS',
average='micro')
]
trainer = SequenceLabelingTrainer(opts=opts,
model=model,
tokenizer=tokenizer,
metrics=metrics,
logger=logger)
# do train
if opts.do_train:
trainer.train(train_data=train_dataset, dev_data=dev_dataset)
if opts.do_eval:
checkpoints = []
if opts.checkpoint_predict_code is not None:
checkpoint = os.path.join(opts.output_dir,
opts.checkpoint_predict_code)
check_dir(checkpoint)
checkpoints.append(checkpoint)
if opts.eval_all_checkpoints:
checkpoints = find_all_checkpoints(checkpoint_dir=opts.output_dir)
logger.info("Evaluate the following checkpoints: %s", checkpoints)
for checkpoint in checkpoints:
prefix = checkpoint.split("/")[-1]
model = model_class.from_pretrained(checkpoint, config=config)
model.to(opts.device)
trainer.model = model
trainer.evaluate(dev_data=dev_dataset,
save_result=True,
save_dir=prefix)
if opts.do_predict:
checkpoints = []
if opts.checkpoint_predict_code is not None:
checkpoint = os.path.join(opts.output_dir,
opts.checkpoint_predict_code)
check_dir(checkpoint)
checkpoints.append(checkpoint)
logger.info("Evaluate the following checkpoints: %s", checkpoints)
for checkpoint in checkpoints:
prefix = checkpoint.split("/")[-1]
model = model_class.from_pretrained(checkpoint, config=config)
model.to(opts.device)
trainer.model = model
trainer.predict(test_data=test_dataset,
save_result=True,
save_dir=prefix)
def main():
opts = Argparser().get_training_arguments()
logger = Logger(opts=opts)
# device
logger.info("initializing device")
opts.device, opts.device_num = prepare_device(opts.device_id)
seed_everything(opts.seed)
config_class, model_class, tokenizer_class = MODEL_CLASSES[opts.model_type]
# data processor
logger.info("initializing data processor")
tokenizer = tokenizer_class.from_pretrained(
opts.pretrained_model_path, do_lower_case=opts.do_lower_case)
train_dataset = load_data(opts.train_input_file, opts.data_dir, "train",
tokenizer, opts.train_max_seq_length)
dev_dataset = load_data(opts.eval_input_file, opts.data_dir, "dev",
tokenizer, opts.eval_max_seq_length)
test_dataset = load_data(opts.test_input_file, opts.data_dir, "test",
tokenizer, opts.test_max_seq_length)
opts.num_labels = train_dataset.num_labels
opts.label2id = CnerDataset.label2id()
opts.id2label = CnerDataset.id2label()
# model
logger.info("initializing model and config")
config = config_class.from_pretrained(opts.pretrained_model_path,
num_labels=opts.num_labels,
label2id=opts.label2id,
id2label=opts.id2label)
model = model_class.from_pretrained(opts.pretrained_model_path,
config=config)
model.to(opts.device)
# trainer
logger.info("initializing traniner")
labels = {
label.split('-')[1]
for label in CnerDataset.get_labels() if '-' in label
}
metrics = [
SequenceLabelingScore(labels=labels, average='micro', schema='BIOS')
]
trainer = SequenceLabelingTrainer(opts=opts,
model=model,
tokenizer=tokenizer,
metrics=metrics,
logger=logger)
# do train
if opts.do_train:
trainer.train(train_data=train_dataset, dev_data=dev_dataset)
if opts.do_eval:
checkpoints = []
if opts.checkpoint_predict_code is not None:
checkpoint = os.path.join(opts.output_dir,
opts.checkpoint_predict_code)
check_dir(checkpoint)
checkpoints.append(checkpoint)
if opts.eval_all_checkpoints:
checkpoints = find_all_checkpoints(checkpoint_dir=opts.output_dir)
logger.info("Evaluate the following checkpoints: %s", checkpoints)
for checkpoint in checkpoints:
prefix = checkpoint.split("/")[-1]
model = model_class.from_pretrained(checkpoint, config=config)
model.to(opts.device)
trainer.model = model
trainer.evaluate(dev_data=dev_dataset,
save_result=True,
save_dir=prefix)
if opts.do_predict:
checkpoints = []
if opts.checkpoint_predict_code is not None:
checkpoint = os.path.join(opts.output_dir,
opts.checkpoint_predict_code)
check_dir(checkpoint)
checkpoints.append(checkpoint)
logger.info("Evaluate the following checkpoints: %s", checkpoints)
for checkpoint in checkpoints:
prefix = checkpoint.split("/")[-1]
model = model_class.from_pretrained(checkpoint, config=config)
model.to(opts.device)
trainer.model = model
trainer.predict(test_data=test_dataset,
save_result=True,
save_dir=prefix)
def main():
opts = Argparser().get_training_arguments()
logger = Logger(opts=opts)
# device
logger.info("initializing device")
opts.device, opts.device_num = prepare_device(opts.device_id)
seed_everything(opts.seed)
config_class, model_class, tokenizer_class = MODEL_CLASSES[opts.model_type]
# data processor
logger.info("initializing data processor")
tokenizer = tokenizer_class.from_pretrained(
opts.pretrained_model_path, do_lower_case=opts.do_lower_case)
train_dataset = load_data(opts.train_input_file, opts.data_dir, "train",
tokenizer, opts.train_max_seq_length)
dev_dataset = load_data(opts.eval_input_file, opts.data_dir, "dev",
tokenizer, opts.eval_max_seq_length)
opts.num_labels = train_dataset.num_labels
# model
logger.info("initializing model and config")
config = config_class.from_pretrained(opts.pretrained_model_path,
num_labels=opts.num_labels)
model = model_class.from_pretrained(opts.pretrained_model_path,
config=config)
model.to(opts.device)
# trainer
logger.info("initializing traniner")
trainer = TextClassifierTrainer(
opts=opts,
model=model,
tokenizer=tokenizer,
metrics=[MattewsCorrcoef(num_classes=opts.num_labels)],
logger=logger)
# do train
if opts.do_train:
trainer.train(train_data=train_dataset, dev_data=dev_dataset)
if opts.do_eval:
checkpoints = []
if opts.checkpoint_predict_code is not None:
checkpoint = os.path.join(opts.output_dir,
opts.checkpoint_predict_code)
check_dir(checkpoint)
checkpoints.append(checkpoint)
if opts.eval_all_checkpoints:
checkpoints = find_all_checkpoints(checkpoint_dir=opts.output_dir)
logger.info("Evaluate the following checkpoints: %s", checkpoints)
for checkpoint in checkpoints:
prefix = checkpoint.split("/")[-1]
model = model_class.from_pretrained(checkpoint, config=config)
model.to(opts.device)
trainer.model = model
trainer.evaluate(dev_data=dev_dataset,
save_result=True,
save_dir=prefix)
if opts.do_predict:
test_dataset = load_data(opts.test_input_file, opts.data_dir, "test",
tokenizer, opts.test_max_seq_length)
checkpoints = []
if opts.checkpoint_predict_code is not None:
checkpoint = os.path.join(opts.output_dir,
opts.checkpoint_predict_code)
check_dir(checkpoint)
checkpoints.append(checkpoint)
logger.info("Evaluate the following checkpoints: %s", checkpoints)
for checkpoint in checkpoints:
prefix = checkpoint.split("/")[-1]
model = model_class.from_pretrained(checkpoint, config=config)
model.to(opts.device)
trainer.model = model
trainer.predict(test_data=test_dataset,
save_result=True,
save_dir=prefix)
