def main():
parser = ArgumentParser()
# add task level args
parser = add_task_specific_args(parser)
# add model specific args
parser = Model.add_model_specific_args(parser)
parser = optimization.add_optimizer_specific_args(parser)
parser = Trainer.add_argparse_args(parser)
# task specific default args
parser.set_defaults(num_labels=56)
parser.set_defaults(arc_hidden_size=600, rel_hidden_size=600)
args = parser.parse_args()
if args.build_dataset:
build_distill_dataset(args)
else:
common_train(args,
metric='val_las',
model_class=Model,
build_method=build_method,
task='sdp',
loss_func=sdp_loss)
def build_distill_dataset(args):
model = Model.load_from_checkpoint(args.resume_from_checkpoint,
hparams=args,
loss_func=sdp_loss)
model.eval()
model.freeze()
dataset, metric = build_dataset(model, args.data_dir)
train_dataloader = torch.utils.data.DataLoader(
dataset[datasets.Split.TRAIN],
batch_size=args.batch_size,
collate_fn=collate,
num_workers=args.num_workers)
output = os.path.join(args.data_dir, 'output.npz')
if torch.cuda.is_available():
model.cuda()
map2cpu = lambda x: map2device(x)
map2cuda = lambda x: map2device(x, model.device)
else:
map2cpu = lambda x: x
map2cuda = lambda x: x
with torch.no_grad():
batchs = []
for batch in tqdm(train_dataloader):
batch = map2cuda(batch)
loss, logits = model(**batch)
batch.update(logits=logits)
batchs.append(map2cpu(batch))
numpy.savez(output, data=convert2npy(batchs))
print("Done")
def main():
parser = ArgumentParser()
# add task level args
parser = add_task_specific_args(parser)
# add model specific args
parser = Model.add_model_specific_args(parser)
parser = optimization.add_optimizer_specific_args(parser)
parser = Trainer.add_argparse_args(parser)
# task specific default args
parser.set_defaults(gradient_clip_val=1.0,
num_labels=14,
min_epochs=1,
max_epochs=10)
parser.set_defaults(arc_hidden_size=500, rel_hidden_size=100)
args = parser.parse_args()
if args.build_dataset:
build_distill_dataset(args)
elif args.tune:
tune_train(args, model_class=Model, task_info=task_info)
else:
common_train(args, model_class=Model, task_info=task_info)
def main():
parser = ArgumentParser()
# add task level args
parser = add_task_specific_args(parser)
# add model specific args
parser = Model.add_model_specific_args(parser)
parser = optimization.add_optimizer_specific_args(parser)
parser = Trainer.add_argparse_args(parser)
# task specific default args
parser.set_defaults(num_labels=14, max_epochs=10)
parser.set_defaults(arc_hidden_size=500, rel_hidden_size=100)
args = parser.parse_args()
if args.build_dataset:
build_distill_dataset(args)
else:
common_train(
args,
metric=f'val_{task_info.metric_name}',
model_class=Model,
build_method=build_method,
task=task_info.task_name
)
def main():
parser = ArgumentParser()
# add task level args
parser = add_common_specific_args(parser)
parser = add_tune_specific_args(parser)
parser = add_task_specific_args(parser)
# add model specific args
parser = ViModel.add_model_specific_args(parser)
parser = Model.add_model_specific_args(parser)
parser = optimization.add_optimizer_specific_args(parser)
parser = Trainer.add_argparse_args(parser)
# task specific default args
parser.set_defaults(gradient_clip_val=1.0, min_epochs=1, max_epochs=10)
parser.set_defaults(num_labels=56,
arc_hidden_size=600,
rel_hidden_size=600)
args = parser.parse_args()
if args.use_vi:
model_class = ViModel
model_kwargs = {}
else:
model_class = Model
model_kwargs = {'loss_func': sdp_loss}
if args.build_dataset:
build_distill_dataset(model_class, args, model_kwargs=model_kwargs)
elif args.tune:
tune_train(args,
model_class=model_class,
task_info=task_info,
model_kwargs=model_kwargs)
else:
common_train(args,
model_class=model_class,
task_info=task_info,
model_kwargs=model_kwargs)
