def clone_model(self, model, id2label):
""" clone only part of params """
# deal with data parallel model
new_model: FewShotSeqLabeler
old_model: FewShotSeqLabeler
if self.opt.local_rank != -1 or self.n_gpu > 1 and hasattr(model, 'module'): # the model is parallel class here
old_model = model.module
else:
old_model = model
emission_dict = old_model.emission_scorer.state_dict()
old_num_tags = len(self.get_value_from_order_dict(emission_dict, 'label_reps'))
config = {'num_tags': len(id2label), 'id2label': id2label}
if 'num_anchors' in old_model.config:
config['num_anchors'] = old_model.config['num_anchors'] # Use previous model's random anchors.
# get a new instance for different domain
new_model = make_model(opt=self.opt, config=config)
new_model = prepare_model(self.opt, new_model, self.device, self.n_gpu)
if self.opt.local_rank != -1 or self.n_gpu > 1:
sub_new_model = new_model.module
else:
sub_new_model = new_model
''' copy weights and stuff '''
if old_model.opt.task == 'sl' and old_model.transition_scorer:
# copy one-by-one because target transition and decoder will be left un-assigned
sub_new_model.context_embedder.load_state_dict(old_model.context_embedder.state_dict())
sub_new_model.emission_scorer.load_state_dict(old_model.emission_scorer.state_dict())
for param_name in ['backoff_trans_mat', 'backoff_start_trans_mat', 'backoff_end_trans_mat']:
sub_new_model.transition_scorer.state_dict()[param_name].copy_(
old_model.transition_scorer.state_dict()[param_name].data)
else:
sub_new_model.load_state_dict(old_model.state_dict())
return new_model
def clone_model(self, model):
# deal with data parallel model
best_model: FewShotLearner
old_model: FewShotLearner
if self.opt.local_rank != -1 or self.n_gpu > 1: # the model is parallel class here
old_model = model.module
else:
old_model = model
# get a new instance for different domain (cpu version to save resource)
config = {'num_tags': old_model.config['num_tags']}
if 'num_anchors' in old_model.config:
config['num_anchors'] = old_model.config['num_anchors'] # Use previous model's random anchors.
best_model = make_model(opt=old_model.opt, config=config)
# copy weights and stuff
best_model.load_state_dict(old_model.state_dict())
return best_model
def clone_model(self, model, id2label, mat_type='test'):
""" clone only part of params """
# deal with data parallel model
new_model: FewShotSeqLabeler
old_model: FewShotSeqLabeler
if self.opt.local_rank != -1 or self.n_gpu > 1 and hasattr(
model, 'module'): # the model is parallel class here
old_model = model.module
else:
old_model = model
emission_dict = old_model.emission_scorer.state_dict()
old_num_tags = len(
self.get_value_from_order_dict(emission_dict, 'label_reps'))
# get a new instance for different domain
new_model = make_model(opt=self.opt,
num_tags=len(id2label),
trans_r=self.opt.trans_r,
id2label=id2label,
random_num_tags=old_num_tags // 3,
mat_type=mat_type)
new_model = prepare_model(self.opt, new_model, self.device, self.n_gpu)
if self.opt.local_rank != -1 or self.n_gpu > 1:
sub_new_model = new_model.module
else:
sub_new_model = new_model
# copy weights and stuff
# target transition and decoder will be left un-assigned
sub_new_model.context_embedder.load_state_dict(
old_model.context_embedder.state_dict())
sub_new_model.emission_scorer.load_state_dict(
old_model.emission_scorer.state_dict())
if old_model.transition_scorer:
for param_name in [
'backoff_trans_mat', 'backoff_start_trans_mat',
'backoff_end_trans_mat'
]:
sub_new_model.transition_scorer.state_dict()[param_name].copy_(
old_model.transition_scorer.state_dict()[param_name].data)
return new_model
def clone_model(self, model):
# deal with data parallel model
best_model: FewShotSeqLabeler
old_model: FewShotSeqLabeler
if self.opt.local_rank != -1 or self.n_gpu > 1: # the model is parallel class here
old_model = model.module
else:
old_model = model
# get a new instance for different domain (cpu version to save resource)
old_num_tags = len(
self.get_value_from_order_dict(old_model.state_dict(),
'label_reps'))
best_model = make_model(opt=old_model.opt,
num_tags=old_model.transition_scorer.num_tags
if old_model.transition_scorer else 1,
trans_r=old_model.transition_scorer.r
if old_model.transition_scorer else -1,
random_num_tags=old_num_tags // 3,
mat_type='train')
# copy weights and stuff
best_model.load_state_dict(old_model.state_dict())
return best_model
def main():
""" to start the experiment """
''' set option '''
parser = argparse.ArgumentParser()
parser = define_args(parser, basic_args, train_args, test_args, preprocess_args, model_args)
opt = parser.parse_args()
print('Args:\n', json.dumps(vars(opt), indent=2))
opt = option_check(opt)
''' device & environment '''
device, n_gpu = set_device_environment(opt)
os.makedirs(opt.output_dir, exist_ok=True)
logger.info("Environment: device {}, n_gpu {}".format(device, n_gpu))
''' data & feature '''
data_loader = FewShotRawDataLoader(debugging=opt.do_debug)
preprocessor = make_preprocessor(opt)
if opt.do_train:
train_features, train_label2id, train_id2label, train_trans_mat, \
dev_features, dev_label2id, dev_id2label, dev_trans_mat = \
get_training_data_and_feature(opt, data_loader, preprocessor)
# todo: remove the train label mask out of opt.
if opt.mask_transition:
opt.train_label_mask = make_label_mask(opt, opt.train_path, train_label2id)
opt.dev_label_mask = make_label_mask(opt, opt.dev_path, dev_label2id)
opt.train_trans_mat = [torch.Tensor(item).to(device) for item in train_trans_mat]
opt.dev_trans_mat = [torch.Tensor(item).to(device) for item in dev_trans_mat]
else:
train_features, train_label2id, train_id2label, dev_features, dev_label2id, dev_id2label = [None] * 6
if opt.mask_transition:
opt.train_label_mask = None
opt.dev_label_mask = None
if opt.do_predict:
test_features, test_label2id, test_id2label, test_trans_mat = get_testing_data_feature(opt, data_loader, preprocessor)
if opt.mask_transition:
opt.test_label_mask = make_label_mask(opt, opt.test_path, test_label2id)
opt.test_trans_mat = [torch.Tensor(item).to(device) for item in test_trans_mat]
else:
test_features, test_label2id, test_id2label = [None] * 3
if opt.mask_transition:
opt.test_label_mask = None
''' over fitting test '''
if opt.do_overfit_test:
test_features, test_label2id, test_id2label = train_features, train_label2id, train_id2label
dev_features, dev_label2id, dev_id2label = train_features, train_label2id, train_id2label
''' select training & testing mode '''
trainer_class = SchemaFewShotTrainer if opt.use_schema else FewShotTrainer
tester_class = SchemaFewShotTester if opt.use_schema else FewShotTester
''' training '''
best_model = None
if opt.do_train:
logger.info("***** Perform training *****")
training_model = make_model(opt, num_tags=len(train_label2id), trans_r=1) # trans_r is 1 for training
training_model = prepare_model(opt, training_model, device, n_gpu)
if opt.mask_transition:
training_model.label_mask = opt.train_label_mask.to(device)
if opt.upper_lr > 0: # use different learning rate for upper structure parameter
param_to_optimize, optimizer = prepare_few_shot_optimizer(opt, training_model, len(train_features))
else:
param_to_optimize, optimizer = prepare_optimizer(opt, training_model, len(train_features))
tester = tester_class(opt, device, n_gpu)
trainer = trainer_class(opt, optimizer, param_to_optimize, device, n_gpu, tester=tester)
if opt.warmup_epoch > 0:
training_model.no_embedder_grad = True
if opt.upper_lr > 0: # use different learning rate for upper structure parameter
stage_1_param_to_optimize, stage_1_optimizer = prepare_few_shot_optimizer(opt, training_model, len(train_features))
else:
stage_1_param_to_optimize, stage_1_optimizer = prepare_optimizer(opt, training_model, len(train_features))
stage_1_trainer = trainer_class(opt, stage_1_optimizer, stage_1_param_to_optimize, device, n_gpu, tester=None)
trained_model, best_dev_score, test_score = stage_1_trainer.do_train(
training_model, train_features, opt.warmup_epoch)
training_model = trained_model
training_model.no_embedder_grad = False
print('========== Stage one training finished! ==========')
trained_model, best_dev_score, test_score = trainer.do_train(
training_model, train_features, opt.num_train_epochs,
dev_features, dev_id2label, test_features, test_id2label, best_dev_score_now=0)
# decide the best model
if not opt.eval_when_train: # select best among check points
best_model, best_score, test_score_then = trainer.select_model_from_check_point(
train_id2label, dev_features, dev_id2label, test_features, test_id2label, rm_cpt=opt.delete_checkpoint)
else: # best model is selected during training
best_model = trained_model
logger.info('dev:{}, test:{}'.format(best_dev_score, test_score))
print('dev:{}, test:{}'.format(best_dev_score, test_score))
''' testing '''
if opt.do_predict:
logger.info("***** Perform testing *****")
tester = tester_class(opt, device, n_gpu)
if not best_model:
if not opt.saved_model_path:
raise ValueError("No model trained and no trained model file given!")
if os.path.isdir(opt.saved_model_path):
all_cpt_file = list(filter(lambda x: '.cpt.pl' in x, os.listdir(opt.saved_model_path)))
all_cpt_file = sorted(all_cpt_file,
key=lambda x: int(x.replace('model.step', '').replace('.cpt.pl', '')))
max_score = 0
for cpt_file in all_cpt_file:
cpt_model = load_model(os.path.join(opt.saved_model_path, cpt_file))
testing_model = tester.clone_model(cpt_model, test_id2label)
if opt.mask_transition:
testing_model.label_mask = opt.test_label_mask.to(device)
test_score = tester.do_test(testing_model, test_features, test_id2label, log_mark='test_pred')
if test_score > max_score:
max_score = test_score
logger.info('cpt_file:{} - test:{}'.format(cpt_file, test_score))
print('max_score:{}'.format(max_score))
else:
if not os.path.exists(opt.saved_model_path):
logger.info('The model is not exits')
raise ValueError('The model is not exits')
best_model = load_model(opt.saved_model_path)
if not os.path.isdir(opt.saved_model_path):
testing_model = tester.clone_model(best_model, test_id2label) # copy reusable params
if opt.mask_transition:
testing_model.label_mask = opt.test_label_mask.to(device)
test_score = tester.do_test(testing_model, test_features, test_id2label, log_mark='test_pred')
logger.info('test:{}'.format(test_score))
print('test:{}'.format(test_score))
def main():
""" to start the experiment """
''' set option '''
parser = argparse.ArgumentParser()
parser = define_args(parser, basic_args, train_args, test_args,
preprocess_args, model_args)
opt = parser.parse_args()
print('Args:\n', json.dumps(vars(opt), indent=2))
opt = option_check(opt)
''' device & environment '''
device, n_gpu = set_device_environment(opt)
os.makedirs(opt.output_dir, exist_ok=True)
logger.info("Environment: device {}, n_gpu {}".format(device, n_gpu))
''' data & feature '''
data_loader = FewShotRawDataLoader(opt)
preprocessor = make_preprocessor(opt)
if opt.do_train:
train_features, (train_slot_label2id, train_slot_id2label), (train_intent_label2id, train_intent_id2label), \
dev_features, (dev_slot_label2id, dev_slot_id2label), (dev_intent_label2id, dev_intent_id2label) = \
get_training_data_and_feature(opt, data_loader, preprocessor)
if opt.mask_transition and opt.task in ['slot_filling', 'slu']:
opt.train_label_mask = make_label_mask(opt, opt.train_path,
train_slot_label2id)
opt.dev_label_mask = make_label_mask(opt, opt.dev_path,
dev_slot_label2id)
else:
train_features, train_slot_label2id, train_slot_id2label, train_intent_label2id, train_intent_id2label, \
dev_features, dev_slot_label2id, dev_slot_id2label, dev_intent_label2id, dev_intent_id2label = [None] * 10
if opt.mask_transition and opt.task in ['slot_filling', 'slu']:
opt.train_label_mask = None
opt.dev_label_mask = None
if opt.do_predict:
test_features, (test_slot_label2id, test_slot_id2label), (
test_intent_label2id,
test_intent_id2label) = get_testing_data_feature(
opt, data_loader, preprocessor)
if opt.mask_transition and opt.task in ['slot_filling', 'slu']:
opt.test_label_mask = make_label_mask(opt, opt.test_path,
test_slot_label2id)
else:
test_features, test_slot_label2id, test_slot_id2label, test_intent_label2id, test_intent_id2label = [
None
] * 6
if opt.mask_transition and opt.task in ['slot_filling', 'slu']:
opt.test_label_mask = None
''' over fitting test '''
if opt.do_overfit_test:
test_features, (test_slot_label2id, test_slot_id2label), (test_intent_label2id, test_intent_id2label) = \
train_features, (train_slot_label2id, train_slot_id2label), (train_intent_label2id, train_intent_id2label)
dev_features, (dev_slot_label2id, dev_slot_id2label), (dev_intent_label2id, dev_intent_id2label) = \
train_features, (train_slot_label2id, train_slot_id2label), (train_intent_label2id, train_intent_id2label)
train_id2label_map = {
'slot': train_slot_id2label,
'intent': train_intent_id2label
}
dev_id2label_map = {
'slot': dev_slot_id2label,
'intent': dev_intent_id2label
}
test_id2label_map = {
'slot': test_slot_id2label,
'intent': test_intent_id2label
}
''' select training & testing mode '''
trainer_class = SchemaFewShotTrainer if opt.use_schema else FewShotTrainer
tester_class = SchemaFewShotTester if opt.use_schema else FewShotTester
''' training '''
best_model = None
if opt.do_train:
logger.info("***** Perform training *****")
if opt.restore_cpt: # restart training from a check point.
training_model = load_model(
opt.saved_model_path
) # restore optimizer param is not support now.
opt = training_model.opt
opt.warmup_epoch = -1
else:
training_model = make_model(
opt,
config={
# 'num_tags': len(train_slot_label2id) if opt.task in ['slot_filling', 'slu'] else 0,
'num_tags': {
'slot': len(train_slot_label2id),
'intent': len(train_intent_label2id)
},
'id2label': train_id2label_map
})
# ================== fine-tune =====================
if opt.finetune and not opt.restore_cpt:
logger.info(
"***** Start fine-tuning target domain with only support data *****"
)
# train & dev as fine-tune train
# test as fine-tune dev
ft_preprocessor = make_preprocessor(opt, finetune=opt.finetune)
if opt.ft_dataset == 'origin':
ft_train_features, (ft_train_slot_label2id, ft_train_slot_id2label), \
(ft_train_intent_label2id, ft_train_intent_id2label) = get_training_data_feature(
opt, data_loader, ft_preprocessor)
ft_test_features, (ft_test_slot_label2id, ft_test_slot_id2label), \
(ft_test_intent_label2id, ft_test_intent_id2label) = get_testing_data_feature(
opt, data_loader, ft_preprocessor)
elif opt.ft_dataset == 'shuffle':
ft_features, (ft_train_slot_label2id, ft_train_slot_id2label), \
(ft_train_intent_label2id, ft_train_intent_id2label) = get_support_data_feature(
opt, data_loader, ft_preprocessor)
(ft_test_slot_label2id, ft_test_slot_id2label), (ft_test_intent_label2id, ft_test_intent_id2label) = \
(ft_train_slot_label2id, ft_train_slot_id2label), (ft_train_intent_label2id, ft_train_intent_id2label)
random.seed(opt.seed)
random.shuffle(ft_features)
train_num = int(len(ft_features) * opt.ft_td_rate)
ft_train_features, ft_test_features = ft_features[:train_num], ft_features[
train_num:]
else:
raise NotImplementedError
ft_label2id = {
'slot': ft_train_slot_label2id,
'intent': ft_train_intent_label2id
}
ft_id2label = {
'slot': ft_train_slot_id2label,
'intent': ft_train_intent_id2label
}
ft_test_id2label = {
'slot': ft_test_slot_id2label,
'intent': ft_test_intent_id2label
}
finetune_model = make_finetune_model(opt,
config={
'label2id': ft_label2id,
'id2label': ft_id2label
})
finetune_model = prepare_model(opt, finetune_model, device, n_gpu)
ft_upper_structures = ['intent', 'slot']
ft_param_to_optimize, ft_optimizer, ft_scheduler = prepare_optimizer(
opt, finetune_model, len(ft_train_features),
ft_upper_structures)
ft_tester = FineTuneTester(opt, device, n_gpu)
ft_trainer = FineTuneTrainer(opt,
ft_optimizer,
ft_scheduler,
ft_param_to_optimize,
device,
n_gpu,
tester=ft_tester)
ft_trained_model, ft_best_dev_score, ft_test_score = ft_trainer.do_train(
finetune_model, ft_train_features, opt.ft_num_train_epochs,
ft_test_features, ft_test_id2label)
print('========== Fine-tuning finished! ==========')
# ============ init embedder parameters to training_model ===========
training_model = prepare_model(opt, training_model, device, n_gpu)
if opt.mask_transition and opt.task in ['slot_filling', 'slu']:
training_model.label_mask = opt.train_label_mask.to(device)
# prepare a set of name subseuqence/mark to use different learning rate for part of params
training_model.context_embedder.embedder.load_state_dict(
ft_trained_model.context_embedder.embedder.state_dict())
# remove saved model file
all_cpt_file = list(
filter(lambda x: '.cpt.pl' in x, os.listdir(opt.output_dir)))
for cpt_file in all_cpt_file:
os.unlink(os.path.join(opt.output_dir, cpt_file))
else:
training_model = prepare_model(opt, training_model, device, n_gpu)
if opt.mask_transition and opt.task in ['slot_filling', 'slu']:
training_model.label_mask = opt.train_label_mask.to(device)
# prepare a set of name subseuqence/mark to use different learning rate for part of params
upper_structures = [
'backoff', 'scale_rate', 'f_theta', 'phi', 'start_reps',
'end_reps', 'biaffine', 'relation'
]
metric_params = ['intent', 'slot', 'metric', 'slu_rnn_encoder']
if not opt.no_up_metric_params:
upper_structures.extend(metric_params)
param_to_optimize, optimizer, scheduler = prepare_optimizer(
opt, training_model, len(train_features), upper_structures)
tester = tester_class(opt, device, n_gpu)
trainer = trainer_class(opt,
optimizer,
scheduler,
param_to_optimize,
device,
n_gpu,
tester=tester)
if opt.warmup_epoch > 0:
training_model.no_embedder_grad = True
stage_1_param_to_optimize, stage_1_optimizer, stage_1_scheduler = prepare_optimizer(
opt, training_model, len(train_features), upper_structures)
stage_1_trainer = trainer_class(opt,
stage_1_optimizer,
stage_1_scheduler,
stage_1_param_to_optimize,
device,
n_gpu,
tester=None)
trained_model, best_dev_score, test_score = stage_1_trainer.do_train(
training_model, train_features, opt.warmup_epoch)
training_model = trained_model
training_model.no_embedder_grad = False
print('========== Warmup training finished! ==========')
trained_model, best_dev_score, test_score = trainer.do_train(
training_model,
train_features,
opt.num_train_epochs,
dev_features,
dev_id2label_map,
test_features,
test_id2label_map,
best_dev_score_now=0)
# decide the best model
if not opt.eval_when_train: # select best among check points
best_model, best_score, test_score_then = trainer.select_model_from_check_point(
train_id2label_map,
dev_features,
dev_id2label_map,
test_features,
test_id2label_map,
rm_cpt=opt.delete_checkpoint)
else: # best model is selected during training
best_model = trained_model
logger.info('dev:{}, test:{}'.format(best_dev_score, test_score))
print('dev:{}, test:{}'.format(best_dev_score, test_score))
''' testing '''
if opt.do_predict:
logger.info("***** Perform testing *****")
print("***** Perform testing *****")
tester = tester_class(opt, device, n_gpu)
if not best_model: # no trained model load it from disk.
if not opt.saved_model_path or not os.path.exists(
opt.saved_model_path):
raise ValueError(
"No model trained and no trained model file given (or not exist)"
)
if os.path.isdir(
opt.saved_model_path): # eval a list of checkpoints
max_score = eval_check_points(opt, tester, test_features,
test_id2label_map, device)
print('best check points scores:{}'.format(max_score))
exit(0)
else:
best_model = load_model(opt.saved_model_path)
''' test the best model '''
testing_model = tester.clone_model(
best_model, test_id2label_map) # copy reusable params
if opt.mask_transition and opt.task in ['slot_filling', 'slu']:
testing_model.label_mask = opt.test_label_mask.to(device)
test_score = tester.do_test(testing_model,
test_features,
test_id2label_map,
log_mark='test_pred')
logger.info('test:{}'.format(test_score))
print('test:{}'.format(test_score))
def main():
""" to start the experiment """
''' set option '''
parser = argparse.ArgumentParser()
parser = define_args(parser, basic_args, train_args, test_args,
preprocess_args, model_args)
opt = parser.parse_args()
print('Args:\n', json.dumps(vars(opt), indent=2))
opt = option_check(opt)
''' device & environment '''
device, n_gpu = set_device_environment(opt)
os.makedirs(opt.output_dir, exist_ok=True)
logger.info("Environment: device {}, n_gpu {}".format(device, n_gpu))
''' data & feature '''
data_loader = FewShotRawDataLoader(opt)
preprocessor = make_preprocessor(opt)
if opt.do_train:
train_features, train_label2id_map, train_id2label_map, dev_features, dev_label2id_map, dev_id2label_map = \
get_training_data_and_feature(opt, data_loader, preprocessor)
if opt.mask_transition and 'sl' in opt.task:
opt.train_label_mask = make_label_mask(opt, opt.train_path,
train_label2id_map['sl'])
opt.dev_label_mask = make_label_mask(opt, opt.dev_path,
dev_label2id_map['sl'])
else:
train_features, train_label2id_map, train_id2label_map, dev_features, dev_label2id_map, dev_id2label_map = \
[None] * 6
if opt.mask_transition and 'sl' in opt.task:
opt.train_label_mask = None
opt.dev_label_mask = None
if opt.do_predict:
test_features, test_label2id_map, test_id2label_map = get_testing_data_feature(
opt, data_loader, preprocessor)
if opt.mask_transition and 'sl' in opt.task:
opt.test_label_mask = make_label_mask(opt, opt.test_path,
test_label2id_map['sl'])
else:
test_features, test_label2id_map, test_id2label_map = [None] * 3
if opt.mask_transition and 'sl' in opt.task:
opt.test_label_mask = None
''' over fitting test '''
if opt.do_overfit_test:
test_features, test_label2id_map, test_id2label_map = train_features, train_label2id_map, train_id2label_map
dev_features, dev_label2id_map, dev_id2label_map = train_features, train_label2id_map, train_id2label_map
''' select training & testing mode '''
trainer_class = SchemaFewShotTrainer if opt.use_schema else FewShotTrainer
tester_class = SchemaFewShotTester if opt.use_schema else FewShotTester
''' training '''
best_model = None
if opt.do_train:
logger.info("***** Perform training *****")
if opt.restore_cpt: # restart training from a check point.
training_model = load_model(
opt.saved_model_path
) # restore optimizer param is not support now.
opt = training_model.opt
opt.warmup_epoch = -1
else:
training_model = make_model(opt,
config={
'num_tags':
len(train_label2id_map['sl']) if
'sl' in train_label2id_map else 0
})
training_model = prepare_model(opt, training_model, device, n_gpu)
if opt.mask_transition and 'sl' in opt.task:
training_model.label_mask = opt.train_label_mask.to(device)
# prepare a set of name subseuqence/mark to use different learning rate for part of params
upper_structures = [
'backoff', 'scale_rate', 'f_theta', 'phi', 'start_reps',
'end_reps', 'biaffine'
]
param_to_optimize, optimizer, scheduler = prepare_optimizer(
opt, training_model, len(train_features), upper_structures)
tester = tester_class(opt, device, n_gpu)
trainer = trainer_class(opt,
optimizer,
scheduler,
param_to_optimize,
device,
n_gpu,
tester=tester)
if opt.warmup_epoch > 0:
training_model.no_embedder_grad = True
stage_1_param_to_optimize, stage_1_optimizer, stage_1_scheduler = prepare_optimizer(
opt, training_model, len(train_features), upper_structures)
stage_1_trainer = trainer_class(opt,
stage_1_optimizer,
stage_1_scheduler,
stage_1_param_to_optimize,
device,
n_gpu,
tester=None)
trained_model, best_dev_score, test_score = stage_1_trainer.do_train(
training_model, train_features, opt.warmup_epoch)
training_model = trained_model
training_model.no_embedder_grad = False
print('========== Warmup training finished! ==========')
trained_model, best_dev_score, test_score = trainer.do_train(
training_model,
train_features,
opt.num_train_epochs,
dev_features,
dev_id2label_map,
test_features,
test_id2label_map,
best_dev_score_now=0)
# decide the best model
if not opt.eval_when_train: # select best among check points
best_model, best_score, test_score_then = trainer.select_model_from_check_point(
train_id2label_map,
dev_features,
dev_id2label_map,
test_features,
test_id2label_map,
rm_cpt=opt.delete_checkpoint)
else: # best model is selected during training
best_model = trained_model
logger.info('dev:{}, test:{}'.format(best_dev_score, test_score))
print('dev:{}, test:{}'.format(best_dev_score, test_score))
''' testing '''
if opt.do_predict:
logger.info("***** Perform testing *****")
print("***** Perform testing *****")
tester = tester_class(opt, device, n_gpu)
if not best_model: # no trained model load it from disk.
if not opt.saved_model_path or not os.path.exists(
opt.saved_model_path):
raise ValueError(
"No model trained and no trained model file given (or not exist)"
)
if os.path.isdir(
opt.saved_model_path): # eval a list of checkpoints
max_score = eval_check_points(opt, tester, test_features,
test_id2label_map, device)
print('best check points scores:{}'.format(max_score))
exit(0)
else:
best_model = load_model(opt.saved_model_path)
''' test the best model '''
testing_model = tester.clone_model(
best_model, test_id2label_map) # copy reusable params
if opt.mask_transition and 'sl' in opt.task:
testing_model.label_mask = opt.test_label_mask.to(device)
test_score = tester.do_test(testing_model,
test_features,
test_id2label_map,
log_mark='test_pred')
logger.info('test:{}'.format(test_score))
print('test:{}'.format(test_score))