def main():
args = vars(parser.parse_args())
check_args(args)
set_seeds(2021)
data_cfg = config.DataConfig(args["data_config"])
model_cfg = config.ModelConfig(args["model_config"])
run_cfg = config.RunConfig(args["run_config"],
eval=True,
sanity_check=args["sanity_check"])
output, save_prefix = set_output(args, "evaluate_model_log")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
config.print_configs(args, [data_cfg, model_cfg, run_cfg], device, output)
torch.zeros((1)).to(device)
## Loading a dataset
start = Print(" ".join(['start loading a dataset']), output)
dataset_test = get_dataset_from_configs(data_cfg,
"test",
model_cfg.embedder,
sanity_check=args["sanity_check"])
iterator_test = torch.utils.data.DataLoader(dataset_test,
run_cfg.batch_size,
shuffle=False,
pin_memory=True,
num_workers=4)
end = Print(
" ".join(['loaded',
str(len(dataset_test)), 'dataset_test samples']), output)
Print(" ".join(['elapsed time:', str(end - start)]), output, newline=True)
## initialize a model
start = Print('start initializing a model', output)
model, params = get_model(model_cfg, run_cfg)
get_profile(model, dataset_test, output)
end = Print('end initializing a model', output)
Print(" ".join(['elapsed time:', str(end - start)]), output, newline=True)
## setup trainer configurations
start = Print('start setting trainer configurations', output)
trainer = Trainer(model)
trainer.load_model(args["checkpoint"], output)
trainer.set_device(device)
end = Print('end setting trainer configurations', output)
Print(" ".join(['elapsed time:', str(end - start)]), output, newline=True)
## evaluate a model
start = Print('start evaluating a model', output)
trainer.headline(output)
### validation
for B, batch in enumerate(iterator_test):
trainer.evaluate(batch, device)
if B % 5 == 0:
print('# {:.1%}'.format(B / len(iterator_test)),
end='\r',
file=sys.stderr)
print(' ' * 150, end='\r', file=sys.stderr)
### print log
trainer.save_outputs(save_prefix)
trainer.log(data_cfg.data_idx, output)
end = Print('end evaluating a model', output)
Print(" ".join(['elapsed time:', str(end - start)]), output, newline=True)
if not output == sys.stdout: output.close()
def main():
args = vars(parser.parse_args())
check_args(args)
set_seeds(2021)
data_cfg = config.DataConfig(args["data_config"])
model_cfg = config.ModelConfig(args["model_config"])
run_cfg = config.RunConfig(args["run_config"],
eval=False,
sanity_check=args["sanity_check"])
output, save_prefix = set_output(args, "train_model_log")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
config.print_configs(args, [data_cfg, model_cfg, run_cfg], device, output)
torch.zeros((1)).to(device)
## Loading a dataset
start = Print(" ".join(['start loading a dataset']), output)
dataset_train = get_dataset_from_configs(data_cfg,
"train",
model_cfg.embedder,
sanity_check=args["sanity_check"])
iterator_train = torch.utils.data.DataLoader(dataset_train,
run_cfg.batch_size,
shuffle=True,
pin_memory=True,
num_workers=4)
end = Print(
" ".join(['loaded',
str(len(dataset_train)), 'dataset_train samples']), output)
Print(" ".join(['elapsed time:', str(end - start)]), output, newline=True)
## initialize a model
start = Print('start initializing a model', output)
model, params = get_model(model_cfg, run_cfg)
get_profile(model, dataset_train, output)
end = Print('end initializing a model', output)
Print(" ".join(['elapsed time:', str(end - start)]), output, newline=True)
## setup trainer configurations
start = Print('start setting trainer configurations', output)
trainer = Trainer(model)
trainer.load_model(args["checkpoint"], output)
trainer.set_class_weight(dataset_train.labels, run_cfg)
trainer.set_device(device)
trainer.set_optim_scheduler(run_cfg, params)
end = Print('end setting trainer configurations', output)
Print(" ".join(['elapsed time:', str(end - start)]), output, newline=True)
## train a model
start = Print('start training a model', output)
trainer.headline(output)
for epoch in range(int(trainer.epoch), run_cfg.num_epochs):
### train
for B, batch in enumerate(iterator_train):
trainer.train(batch, device)
if B % 5 == 0:
print('# epoch [{}/{}] train {:.1%}'.format(
epoch + 1, run_cfg.num_epochs, B / len(iterator_train)),
end='\r',
file=sys.stderr)
print(' ' * 150, end='\r', file=sys.stderr)
### print log and save models
trainer.epoch += 1
trainer.save_model(save_prefix)
trainer.log(data_cfg.data_idx, output)
end = Print('end training a model', output)
Print(" ".join(['elapsed time:', str(end - start)]), output, newline=True)
if not output == sys.stdout: output.close()
