def main():
pl.seed_everything(1234)
# ------------
# args
# ------------
parser = get_parser()
parser = BiafDependency.add_model_specific_args(parser)
parser = pl.Trainer.add_argparse_args(parser)
args = parser.parse_args()
# ------------
# model
# ------------
model = BiafDependency(args)
# load pretrained_model
if args.pretrained:
model.load_state_dict(
torch.load(args.pretrained,
map_location=torch.device('cpu'))["state_dict"])
# call backs
checkpoint_callback = ModelCheckpoint(monitor='val_UAS',
dirpath=args.default_root_dir,
save_top_k=10,
save_last=True,
mode='max',
verbose=True)
early_stop_callback = early_stopping.EarlyStopping(monitor='val_UAS',
min_delta=0.00,
patience=10,
verbose=True,
mode='max')
lr_monitor = LearningRateMonitor(logging_interval='step')
print_model = ModelPrintCallback(print_modules=["model"])
callbacks = [
checkpoint_callback, lr_monitor, print_model, early_stop_callback
]
if args.freeze_bert:
callbacks.append(EvalCallback(["model.bert"]))
trainer = pl.Trainer.from_argparse_args(args,
callbacks=callbacks,
replace_sampler_ddp=False)
trainer.fit(model)
trainer.test()
def main():
pl.seed_everything(1234)
# ------------
# args
# ------------
parser = get_parser()
parser = MrcSpanProposal.add_model_specific_args(parser)
parser = pl.Trainer.add_argparse_args(parser)
args = parser.parse_args()
# ------------
# model
# ------------
model = MrcSpanProposal(args)
# load pretrained_model
if args.pretrained:
model.load_state_dict(
torch.load(args.pretrained, map_location=torch.device('cpu'))["state_dict"]
)
# call backs
checkpoint_callback = ModelCheckpoint(
monitor=f'val_top{MrcSpanProposal.acc_topk}_acc',
dirpath=args.default_root_dir,
save_top_k=10,
save_last=True,
mode='max',
verbose=True
)
lr_monitor = LearningRateMonitor(logging_interval='step')
print_model = ModelPrintCallback(print_modules=["model"])
callbacks = [checkpoint_callback, lr_monitor, print_model]
if args.freeze_bert:
callbacks.append(EvalCallback(["model.bert"]))
trainer = pl.Trainer.from_argparse_args(
args,
callbacks=callbacks,
replace_sampler_ddp=False
)
trainer.fit(model)
