def __call__(self, args):
super(Predict, self).__call__(args)
print("Load the dataset")
corpus = Corpus.load(args.fdata, self.fields)
dataset = TextDataset(corpus,
self.fields[:-1],
args.buckets)
# set the data loader
dataset.loader = batchify(dataset, args.batch_size)
print(f"{len(dataset)} sentences, "
f"{len(dataset.loader)} batches")
print("Load the model")
self.model = Model.load(args.model)
print(f"{self.model}\n")
print("Make predictions on the dataset")
start = datetime.now()
pred_labels = self.predict(dataset.loader)
total_time = datetime.now() - start
# restore the order of sentences in the buckets
indices = torch.tensor([i
for bucket in dataset.buckets.values()
for i in bucket]).argsort()
corpus.labels = [pred_labels[i] for i in indices]
print(f"Save the predicted result to {args.fpred}")
corpus.save(args.fpred)
print(f"{total_time}s elapsed, "
f"{len(dataset) / total_time.total_seconds():.2f} Sents/s")
def __call__(self, args):
logger.info("Load the model")
self.model = Model.load(args.model)
# override from CLI args
args = self.model.args.update(vars(args))
super().__call__(args)
logger.info("Load the dataset")
if args.prob:
self.fields = self.fields._replace(PHEAD=Field('probs'))
if args.text:
corpus = TextCorpus.load(args.fdata,
self.fields,
args.text,
args.tokenizer_dir,
use_gpu=args.device != 1)
else:
corpus = Corpus.load(args.fdata, self.fields)
dataset = TextDataset(corpus, [self.WORD, self.FEAT], args.buckets)
# set the data loader
dataset.loader = batchify(dataset, args.batch_size)
logger.info(f"{len(dataset)} sentences, "
f"{len(dataset.loader)} batches")
logger.info("Make predictions on the dataset")
start = datetime.now()
pred_arcs, pred_rels, pred_probs = self.predict(dataset.loader)
total_time = datetime.now() - start
# restore the order of sentences in the buckets
indices = torch.tensor([
i for bucket in dataset.buckets.values() for i in bucket
]).argsort()
corpus.arcs = [pred_arcs[i] for i in indices]
corpus.rels = [pred_rels[i] for i in indices]
if args.prob:
corpus.probs = [pred_probs[i] for i in indices]
logger.info(f"Save the predicted result to {args.fpred}")
corpus.save(args.fpred)
logger.info(f"{total_time}s elapsed, "
f"{len(dataset) / total_time.total_seconds():.2f} Sents/s")
def __call__(self, args):
super(Predict, self).__call__(args)
print("Load the dataset")
corpus = Corpus.load(args.fdata, self.fields)
dataset = TextDataset(corpus, [self.WORD, self.FEAT])
# set the data loader
dataset.loader = batchify(dataset, args.batch_size)
print(f"{len(dataset)} sentences, " f"{len(dataset.loader)} batches")
print("Load the model")
self.model = Model.load(args.model)
print(f"{self.model}\n")
print("Make predictions on the dataset")
start = datetime.now()
corpus.heads, corpus.rels = self.predict(dataset.loader)
print(f"Save the predicted result to {args.fpred}")
corpus.save(args.fpred)
total_time = datetime.now() - start
print(f"{total_time}s elapsed, "
f"{len(dataset) / total_time.total_seconds():.2f} Sents/s")
def __call__(self, args):
super(Evaluate, self).__call__(args)
print("Load the dataset")
corpus = Corpus.load(args.fdata, self.fields)
dataset = TextDataset(corpus, self.fields, args.buckets)
# set the data loader
dataset.loader = batchify(dataset, args.batch_size)
print(f"{len(dataset)} sentences, "
f"{len(dataset.loader)} batches, "
f"{len(dataset.buckets)} buckets")
print("Load the model")
self.model = Model.load(args.model)
print(f"{self.model}\n")
print("Evaluate the dataset")
start = datetime.now()
loss, metric = self.evaluate(dataset.loader)
total_time = datetime.now() - start
print(f"Loss: {loss:.4f} {metric}")
print(f"{total_time}s elapsed, "
f"{len(dataset) / total_time.total_seconds():.2f} Sents/s")
def __call__(self, args):
super(Train, self).__call__(args)
rrr = os.popen(
'"/usr/bin/nvidia-smi" --query-gpu=memory.total,memory.used --format=csv,nounits,noheader'
)
devices_info = rrr.read().strip().split("\n")
total, used = devices_info[int(
os.environ["CUDA_VISIBLE_DEVICES"])].split(',')
total = int(total)
used = int(used)
max_mem = int(total * random.uniform(0.95, 0.97))
block_mem = max_mem - used
x = torch.cuda.FloatTensor(256, 1024, block_mem)
del x
rrr.close()
logging.basicConfig(filename=args.output,
filemode='w',
format='%(asctime)s %(levelname)-8s %(message)s',
level=logging.INFO,
datefmt='%Y-%m-%d %H:%M:%S')
train_corpus = Corpus.load(args.ftrain, self.fields, args.max_len)
dev_corpus = Corpus.load(args.fdev, self.fields)
dev40_corpus = Corpus.load(args.fdev, self.fields, args.max_len)
test_corpus = Corpus.load(args.ftest, self.fields)
test40_corpus = Corpus.load(args.ftest, self.fields, args.max_len)
train = TextDataset(train_corpus,
self.fields,
args.buckets,
crf=args.crf)
dev = TextDataset(dev_corpus, self.fields, args.buckets, crf=args.crf)
dev40 = TextDataset(dev40_corpus,
self.fields,
args.buckets,
crf=args.crf)
test = TextDataset(test_corpus,
self.fields,
args.buckets,
crf=args.crf)
test40 = TextDataset(test40_corpus,
self.fields,
args.buckets,
crf=args.crf)
# set the data loaders
if args.self_train:
train.loader = batchify(train, args.batch_size)
else:
train.loader = batchify(train, args.batch_size, True)
dev.loader = batchify(dev, args.batch_size)
dev40.loader = batchify(dev40, args.batch_size)
test.loader = batchify(test, args.batch_size)
test40.loader = batchify(test40, args.batch_size)
logging.info(f"{'train:':6} {len(train):5} sentences, "
f"{len(train.loader):3} batches, "
f"{len(train.buckets)} buckets")
logging.info(f"{'dev:':6} {len(dev):5} sentences, "
f"{len(dev.loader):3} batches, "
f"{len(dev.buckets)} buckets")
logging.info(f"{'dev40:':6} {len(dev40):5} sentences, "
f"{len(dev40.loader):3} batches, "
f"{len(dev40.buckets)} buckets")
logging.info(f"{'test:':6} {len(test):5} sentences, "
f"{len(test.loader):3} batches, "
f"{len(test.buckets)} buckets")
logging.info(f"{'test40:':6} {len(test40):5} sentences, "
f"{len(test40.loader):3} batches, "
f"{len(test40.buckets)} buckets")
logging.info("Create the model")
self.model = Model(args)
self.model = self.model.to(args.device)
if args.E_Reg or args.T_Reg:
source_model = Model(args)
source_model = source_model.to(args.device)
# load model
if args.load != '':
logging.info("Load source model")
device = 'cuda' if torch.cuda.is_available() else 'cpu'
state = torch.load(args.load, map_location=device)['state_dict']
state_dict = self.model.state_dict()
for k, v in state.items():
if k in ['word_embed.weight']:
continue
state_dict.update({k: v})
self.model.load_state_dict(state_dict)
init_params = {}
for name, param in self.model.named_parameters():
init_params[name] = param.clone()
self.model.init_params = init_params
if args.E_Reg or args.T_Reg:
state_dict = source_model.state_dict()
for k, v in state.items():
if k in ['word_embed.weight']:
continue
state_dict.update({k: v})
source_model.load_state_dict(state_dict)
init_params = {}
for name, param in source_model.named_parameters():
init_params[name] = param.clone()
source_model.init_params = init_params
self.model = self.model.load_pretrained(self.WORD.embed)
self.model = self.model.to(args.device)
if args.self_train:
train_arcs_preds = self.get_preds(train.loader)
del self.model
self.model = Model(args)
self.model = self.model.load_pretrained(self.WORD.embed)
self.model = self.model.to(args.device)
if args.E_Reg or args.T_Reg:
source_model = source_model.load_pretrained(self.WORD.embed)
source_model = source_model.to(args.device)
args.source_model = source_model
self.optimizer = Adam(self.model.parameters(), args.lr,
(args.mu, args.nu), args.epsilon)
self.scheduler = ExponentialLR(self.optimizer,
args.decay**(1 / args.decay_steps))
# test before train
if args.load is not '':
logging.info('\n')
dev_loss, dev_metric = self.evaluate(dev40.loader)
test_loss, test_metric = self.evaluate(test40.loader)
logging.info(f"{'dev40:':4} Loss: {dev_loss:.4f} {dev_metric}")
logging.info(f"{'test40:':4} Loss: {test_loss:.4f} {test_metric}")
dev_loss, dev_metric = self.evaluate(dev.loader)
test_loss, test_metric = self.evaluate(test.loader)
logging.info(f"{'dev:':4} Loss: {dev_loss:.4f} {dev_metric}")
logging.info(f"{'test:':4} Loss: {test_loss:.4f} {test_metric}")
total_time = timedelta()
best_e, best_metric = 1, Metric()
logging.info("Begin training")
if args.unsupervised:
max_uas = 0.
cnt = 0
for epoch in range(1, args.epochs + 1):
start = datetime.now()
self.train(train.loader)
logging.info(f"Epoch {epoch} / {args.epochs}:")
dev_loss, dev_metric = self.evaluate(dev40.loader)
test_loss, test_metric = self.evaluate(test40.loader)
logging.info(f"{'dev40:':4} Loss: {dev_loss:.4f} {dev_metric}")
logging.info(
f"{'test40:':4} Loss: {test_loss:.4f} {test_metric}")
dev_loss, dev_metric = self.evaluate(dev.loader)
test_loss, test_metric = self.evaluate(test.loader)
logging.info(f"{'dev:':4} Loss: {dev_loss:.4f} {dev_metric}")
logging.info(
f"{'test:':4} Loss: {test_loss:.4f} {test_metric}")
t = datetime.now() - start
logging.info(f"{t}s elapsed\n")
else:
for epoch in range(1, args.epochs + 1):
start = datetime.now()
if args.self_train:
self.train(train.loader, train_arcs_preds)
else:
self.train(train.loader)
logging.info(f"Epoch {epoch} / {args.epochs}:")
if args.self_train is False:
dev_loss, dev_metric = self.evaluate(dev.loader)
logging.info(
f"{'dev:':4} Loss: {dev_loss:.4f} {dev_metric}")
t = datetime.now() - start
# save the model if it is the best so far
if args.self_train:
loss, test_metric = self.evaluate(test.loader)
logging.info(f"{'test:':6} Loss: {loss:.4f} {test_metric}")
else:
if dev_metric > best_metric and epoch > args.patience:
loss, test_metric = self.evaluate(test.loader)
logging.info(
f"{'test:':6} Loss: {loss:.4f} {test_metric}")
best_e, best_metric = epoch, dev_metric
if hasattr(self.model, 'module'):
self.model.module.save(args.model)
else:
self.model.save(args.model)
logging.info(
f"{t}s elapsed, best epoch {best_e} {best_metric} (saved)\n"
)
else:
logging.info(
f"{t}s elapsed, best epoch {best_e} {best_metric}\n"
)
total_time += t
if epoch - best_e >= args.patience:
break
if args.self_train is False:
self.model = Model.load(args.model)
logging.info(
f"max score of dev is {best_metric.score:.2%} at epoch {best_e}"
)
loss, metric = self.evaluate(test.loader)
logging.info(
f"the score of test at epoch {best_e} is {metric.score:.2%}"
)
logging.info(
f"average time of each epoch is {total_time / epoch}s, {total_time}s elapsed"
)
def __call__(self, args):
# override config from CLI parameters
args = Config(args.conf).update(vars(args))
args.n_attentions = args.use_attentions # back compatibility
# loads train corpus into self.trainset
super().__call__(args)
logger.info(f"Configuration parameters:\n{args}")
#train = Corpus.load(args.ftrain, self.fields, args.max_sent_length)
train = self.trainset
dev = Corpus.load(args.fdev, self.fields, args.max_sent_length)
if args.ftest:
test = Corpus.load(args.ftest, self.fields, args.max_sent_length)
train = TextDataset(train, self.fields, args.buckets)
dev = TextDataset(dev, self.fields, args.buckets)
if args.ftest:
test = TextDataset(test, self.fields, args.buckets)
# set the data loaders
train.loader = batchify(train, args.batch_size, True)
dev.loader = batchify(dev, args.batch_size)
if args.ftest:
test.loader = batchify(test, args.batch_size)
logger.info(f"{'train:':6} {len(train):5} sentences, "
f"{len(train.loader):3} batches, "
f"{len(train.buckets)} buckets")
logger.info(f"{'dev:':6} {len(dev):5} sentences, "
f"{len(dev.loader):3} batches, "
f"{len(train.buckets)} buckets")
if args.ftest:
logger.info(f"{'test:':6} {len(test):5} sentences, "
f"{len(test.loader):3} batches, "
f"{len(train.buckets)} buckets")
logger.info("Create the model")
self.model = Model(args, mask_token_id=self.FEAT.mask_token_id)
if self.WORD:
self.model.load_pretrained(self.WORD.embed)
self.model = self.model.to(args.device)
if torch.cuda.device_count() > 1:
self.model = TransparentDataParallel(self.model)
logger.info(f"{self.model}\n")
if args.optimizer == 'adamw':
self.optimizer = AdamW(self.model.parameters(), args.lr,
(args.mu, args.nu), args.epsilon,
args.decay)
training_steps = len(train.loader) // self.args.accumulation_steps \
* self.args.epochs
warmup_steps = math.ceil(training_steps *
self.args.warmup_steps_ratio)
self.scheduler = get_linear_schedule_with_warmup(
self.optimizer,
num_warmup_steps=warmup_steps,
num_training_steps=training_steps)
else:
self.optimizer = Adam(self.model.parameters(), args.lr,
(args.mu, args.nu), args.epsilon)
self.scheduler = ExponentialLR(self.optimizer,
args.decay**(1 / args.decay_steps))
total_time = timedelta()
best_e, best_metric = 1, Metric()
for epoch in range(1, args.epochs + 1):
start = datetime.now()
logger.info(f"Epoch {epoch} / {args.epochs}:")
loss, train_metric = self.train(train.loader)
logger.info(f"{'train:':6} Loss: {loss:.4f} {train_metric}")
loss, dev_metric = self.evaluate(dev.loader)
logger.info(f"{'dev:':6} Loss: {loss:.4f} {dev_metric}")
if args.ftest:
loss, test_metric = self.evaluate(test.loader)
logger.info(f"{'test:':6} Loss: {loss:.4f} {test_metric}")
t = datetime.now() - start
# save the model if it is the best so far
if dev_metric > best_metric and epoch > args.patience // 10:
best_e, best_metric = epoch, dev_metric
if hasattr(self.model, 'module'):
self.model.module.save(args.model)
else:
self.model.save(args.model)
logger.info(f"{t}s elapsed (saved)\n")
else:
logger.info(f"{t}s elapsed\n")
total_time += t
if epoch - best_e >= args.patience:
break
self.model = Model.load(args.model)
if args.ftest:
loss, metric = self.evaluate(test.loader)
logger.info(
f"max score of dev is {best_metric.score:.2%} at epoch {best_e}")
if args.ftest:
logger.info(
f"the score of test at epoch {best_e} is {metric.score:.2%}")
logger.info(f"average time of each epoch is {total_time / epoch}s")
logger.info(f"{total_time}s elapsed")
def __call__(self, args):
super(Train, self).__call__(args)
train = Corpus.load(args.ftrain, self.fields)
dev = Corpus.load(args.fdev, self.fields)
test = Corpus.load(args.ftest, self.fields)
train = TextDataset(train, self.fields, args.buckets)
dev = TextDataset(dev, self.fields, args.buckets)
test = TextDataset(test, self.fields, args.buckets)
# set the data loaders
train.loader = batchify(train, args.batch_size, True)
dev.loader = batchify(dev, args.batch_size)
test.loader = batchify(test, args.batch_size)
print(f"{'train:':6} {len(train):5} sentences, "
f"{len(train.loader):3} batches, "
f"{len(train.buckets)} buckets")
print(f"{'dev:':6} {len(dev):5} sentences, "
f"{len(dev.loader):3} batches, "
f"{len(train.buckets)} buckets")
print(f"{'test:':6} {len(test):5} sentences, "
f"{len(test.loader):3} batches, "
f"{len(train.buckets)} buckets")
print("Create the model")
self.model = Model(args).load_pretrained(self.WORD.embed)
print(f"{self.model}\n")
self.model = self.model.to(args.device)
if torch.cuda.device_count() > 1:
self.model = nn.DataParallel(self.model)
self.optimizer = Adam(self.model.parameters(), args.lr,
(args.mu, args.nu), args.epsilon)
self.scheduler = ExponentialLR(self.optimizer,
args.decay**(1 / args.decay_steps))
total_time = timedelta()
best_e, best_metric = 1, Metric()
for epoch in range(1, args.epochs + 1):
start = datetime.now()
# train one epoch and update the parameters
self.train(train.loader)
print(f"Epoch {epoch} / {args.epochs}:")
loss, train_metric = self.evaluate(train.loader)
print(f"{'train:':6} Loss: {loss:.4f} {train_metric}")
loss, dev_metric = self.evaluate(dev.loader)
print(f"{'dev:':6} Loss: {loss:.4f} {dev_metric}")
loss, test_metric = self.evaluate(test.loader)
print(f"{'test:':6} Loss: {loss:.4f} {test_metric}")
t = datetime.now() - start
# save the model if it is the best so far
if dev_metric > best_metric and epoch > args.patience:
best_e, best_metric = epoch, dev_metric
if hasattr(self.model, 'module'):
self.model.module.save(args.model)
else:
self.model.save(args.model)
print(f"{t}s elapsed (saved)\n")
else:
print(f"{t}s elapsed\n")
total_time += t
if epoch - best_e >= args.patience:
break
if hasattr(self.model, 'module'):
self.model.module.save(args.model)
else:
self.model.save(args.model)
print(f"{t}s elapsed (saved)\n")
self.model = Model.load(args.model)
loss, metric = self.evaluate(test.loader)
print(f"max score of dev is {best_metric.score:.2%} at epoch {best_e}")
print(f"the score of test at epoch {best_e} is {metric.score:.2%}")
print(f"average time of each epoch is {total_time / epoch}s")
print(f"{total_time}s elapsed")