def train(arg_list=None):
args, device, checkpoint = init_pipeline(arg_list)
train_loader, val_loader, init_params = load_train_data(args, device)
model, criterion, optimizer = load_model(args, device, checkpoint,
init_params, train_loader)
run_name, metrics = init_metrics(args, checkpoint)
if args.visualize:
metrics.add_network(model, train_loader)
# visualize(model, train_loader, run_name)
util.set_rng_state(checkpoint)
start_epoch = metrics.epoch + 1
for epoch in range(start_epoch, start_epoch + args.epochs):
print(f'Epoch [{epoch}/{start_epoch + args.epochs - 1}]')
metrics.next_epoch()
tr_loss = train_and_validate(model, train_loader, optimizer, criterion,
metrics, Mode.TRAIN, args.binary)
val_loss = train_and_validate(model, val_loader, optimizer, criterion,
metrics, Mode.VAL, args.binary)
is_best = metrics.update_best_metric(val_loss)
util.save_checkpoint(
{
'model_init': init_params,
'state_dict': model.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'rng_state': random.getstate(),
'np_rng_state': np.random.get_state(),
'torch_rng_state': torch.get_rng_state(),
'run_name': run_name,
'metric_obj': metrics.json_repr()
}, run_name, is_best)
return val_loss
def viz():
args, device, checkpoint = init_pipeline()
train_loader, _, init_params = load_train_data(args, device)
init_params = checkpoint.get('model_init', init_params)
model = get_model_initializer(args.model)(*init_params).to(device)
util.load_state_dict(checkpoint, model)
sample_loader = iter(train_loader)
visualize(model, sample_loader)
visualize_trained(model, sample_loader)
def evaluate():
args, device, checkpoint = init_pipeline()
_, val_loader, _, _, _ = load_train_data(args, device)
model = Binary()
# binary_checkpoint = 'AG'
# binary_path = os.path.join('checkpoints', binary_checkpoint, 'model_best.pth.tar')
# bin_model_weights = torch.load(binary_path)
# model.load_state_dict(bin_model_weights['state_dict'])
result = evaluate_model(model, val_loader, device)
print(result)
def train(arg_list=None):
args, device, checkpoint = init_pipeline(arg_list)
train_loader, val_loader, init_params = load_train_data(args, device)
sample_loader = util.get_sample_loader(train_loader)
model, criterion, optimizer, scheduler = load_model(
args, device, init_params, sample_loader)
util.load_state_dict(checkpoint, model, optimizer, scheduler)
metrics = MetricTracker(args, checkpoint)
if not args.no_visualize:
metrics.add_network(model, sample_loader)
visualize(model, sample_loader, metrics.run_name)
util.set_rng_state(checkpoint)
for _ in range(args.epochs):
metrics.next_epoch()
train_and_validate(args, model, train_loader, optimizer, criterion,
metrics, Mode.TRAIN)
train_and_validate(args, model, val_loader, None, criterion, metrics,
Mode.VAL)
if args.scheduler:
scheduler.step()
if not args.no_save:
util.save_checkpoint(
{
"model_init":
init_params,
"model_state_dict":
model.state_dict(),
"optimizer_state_dict":
optimizer.state_dict(),
"scheduler_state_dict":
scheduler.state_dict() if args.scheduler else None,
"rng_state":
random.getstate(),
"np_rng_state":
np.random.get_state(),
"torch_rng_state":
torch.get_rng_state(),
"run_name":
metrics.run_name,
"metric_obj":
metrics.json_repr(),
},
metrics.is_best,
)
torch.set_grad_enabled(True)
if not args.no_visualize:
visualize_trained(model, sample_loader, metrics.run_name)
return metrics
def main():
args, device, checkpoint = init_pipeline()
train_loader, _, _, _, init_params = load_train_data(args, device)
model = Model(*init_params).to(device)
util.load_state_dict(checkpoint, model)
visualize(model, train_loader)