def main_train(validation_dataset):
logger.critical('Building the model.')
model = desc.make_model(args)
if args.use_gpu:
model.cuda()
# Use the customized data parallel if applicable.
if args.gpu_parallel:
from jactorch.parallel import JacDataParallel
# from jactorch.parallel import UserScatteredJacDataParallel as JacDataParallel
model = JacDataParallel(model, device_ids=args.gpus).cuda()
# Disable the cudnn benchmark.
cudnn.benchmark = False
trainer = TrainerEnv(model, None)
if args.load:
if trainer.load_weights(args.load):
logger.critical(
'Loaded weights from pretrained model: "{}".'.format(
args.load))
from jacinle.utils.meter import GroupMeters
meters = GroupMeters()
logger.critical('Building the data loader.')
validation_dataloader = validation_dataset.make_dataloader(
args.batch_size,
shuffle=False,
drop_last=False,
nr_workers=args.data_workers)
meters.reset()
model.eval()
if not os.path.isdir(args.output_attr_path):
os.makedirs(args.output_attr_path)
validate_attribute(model, validation_dataloader, meters, args.setname,
logger, args.output_attr_path)
logger.critical(
meters.format_simple(args.setname,
{k: v
for k, v in meters.avg.items() if v != 0},
compressed=False))
return meters
def train(self,
data_loader,
nr_epochs,
verbose=True,
meters=None,
early_stop=None,
print_interval=1):
if meters is None:
meters = GroupMeters()
for epoch in range(1, 1 + nr_epochs):
meters.reset()
self.train_epoch(data_loader, meters=meters)
if verbose and epoch % print_interval == 0:
caption = 'Epoch: {}:'.format(epoch)
logger.info(meters.format_simple(caption))
if early_stop is not None:
flag = early_stop(self._model)
if flag:
break
