print("start training......")
for epoch in range(num_epochs):
trainer.set_train(model)
optimizer.zero_grad()
tbar = tqdm(dataloader_train); train_loss = 0
for i_batch, sample_batched in enumerate(tbar):
if evaluation: break
scheduler(optimizer, i_batch, epoch, best_pred)
loss = trainer.train(sample_batched, model, global_fixed)
train_loss += loss.item()
score_train, score_train_global, score_train_local = trainer.get_scores()
if mode == 1: tbar.set_description('Train loss: %.3f; global mIoU: %.3f' % (train_loss / (i_batch + 1), np.mean(np.nan_to_num(score_train_global["iou"]))))
else: tbar.set_description('Train loss: %.3f; agg mIoU: %.3f' % (train_loss / (i_batch + 1), np.mean(np.nan_to_num(score_train["iou"]))))
score_train, score_train_global, score_train_local = trainer.get_scores()
trainer.reset_metrics()
# torch.cuda.empty_cache()
if epoch % 1 == 0:
with torch.no_grad():
model.eval()
print("evaluating...")
if test: tbar = tqdm(dataloader_test)
else: tbar = tqdm(dataloader_val)
for i_batch, sample_batched in enumerate(tbar):
predictions, predictions_global, predictions_local = evaluator.eval_test(sample_batched, model, global_fixed)
score_val, score_val_global, score_val_local = evaluator.get_scores()
# use [1:] since class0 is not considered in deep_globe metric
if mode == 1: tbar.set_description('global mIoU: %.3f' % (np.mean(np.nan_to_num(score_val_global["iou"])[1:])))
def main(seed=25):
seed_everything(25)
device = torch.device('cuda:0')
# arguments
args = Args().parse()
n_class = args.n_class
img_path_train = args.img_path_train
mask_path_train = args.mask_path_train
img_path_val = args.img_path_val
mask_path_val = args.mask_path_val
model_path = os.path.join(args.model_path, args.task_name) # save model
log_path = args.log_path
output_path = args.output_path
if not os.path.exists(model_path):
os.makedirs(model_path)
if not os.path.exists(log_path):
os.makedirs(log_path)
if not os.path.exists(output_path):
os.makedirs(output_path)
task_name = args.task_name
print(task_name)
###################################
evaluation = args.evaluation
test = evaluation and False
print("evaluation:", evaluation, "test:", test)
###################################
print("preparing datasets and dataloaders......")
batch_size = args.batch_size
num_workers = args.num_workers
config = args.config
data_time = AverageMeter("DataTime", ':3.3f')
batch_time = AverageMeter("BatchTime", ':3.3f')
dataset_train = DoiDataset(img_path_train,
config,
train=True,
root_mask=mask_path_train)
dataloader_train = DataLoader(dataset_train,
batch_size=batch_size,
shuffle=True,
num_workers=num_workers)
dataset_val = DoiDataset(img_path_val,
config,
train=True,
root_mask=mask_path_val)
dataloader_val = DataLoader(dataset_val,
batch_size=batch_size,
shuffle=False,
num_workers=num_workers)
###################################
print("creating models......")
model = DoiNet(n_class, config['min_descriptor'] + 6, 4)
model = create_model_load_weights(model,
evaluation=False,
ckpt_path=args.ckpt_path)
model.to(device)
###################################
num_epochs = args.epochs
learning_rate = args.lr
optimizer = get_optimizer(model, learning_rate=learning_rate)
scheduler = LR_Scheduler(args.scheduler, learning_rate, num_epochs,
len(dataloader_train))
##################################
criterion_node = nn.CrossEntropyLoss()
criterion_edge = nn.BCELoss()
alpha = args.alpha
writer = SummaryWriter(log_dir=log_path + task_name)
f_log = open(log_path + task_name + ".log", 'w')
#######################################
trainer = Trainer(criterion_node,
criterion_edge,
optimizer,
n_class,
device,
alpha=alpha)
evaluator = Evaluator(n_class, device)
best_pred = 0.0
print("start training......")
log = task_name + '\n'
for k, v in args.__dict__.items():
log += str(k) + ' = ' + str(v) + '\n'
print(log)
f_log.write(log)
f_log.flush()
for epoch in range(num_epochs):
optimizer.zero_grad()
tbar = tqdm(dataloader_train)
train_loss = 0
train_loss_edge = 0
train_loss_node = 0
start_time = time.time()
for i_batch, sample in enumerate(tbar):
data_time.update(time.time() - start_time)
if evaluation: # evaluation pattern: no training
break
scheduler(optimizer, i_batch, epoch, best_pred)
loss, loss_node, loss_edge = trainer.train(sample, model)
train_loss += loss.item()
train_loss_node += loss_node.item()
train_loss_edge += loss_edge.item()
train_scores_node, train_scores_edge = trainer.get_scores()
batch_time.update(time.time() - start_time)
start_time = time.time()
if i_batch % 2 == 0:
tbar.set_description(
'Train loss: %.4f (loss_node=%.4f loss_edge=%.4f); F1 node: %.4f F1 edge: %.4f; data time: %.2f; batch time: %.2f'
% (train_loss / (i_batch + 1), train_loss_node /
(i_batch + 1), train_loss_edge /
(i_batch + 1), train_scores_node["macro_f1"],
train_scores_edge["macro_f1"], data_time.avg,
batch_time.avg))
trainer.reset_metrics()
data_time.reset()
batch_time.reset()
if epoch % 1 == 0:
with torch.no_grad():
model.eval()
print("evaluating...")
tbar = tqdm(dataloader_val)
start_time = time.time()
for i_batch, sample in enumerate(tbar):
data_time.update(time.time() - start_time)
pred_node, pred_edge = evaluator.eval(sample, model)
val_scores_node, val_scores_edge = evaluator.get_scores()
batch_time.update(time.time() - start_time)
tbar.set_description(
'F1 node: %.4f F1 edge: %.4f; data time: %.2f; batch time: %.2f'
% (val_scores_node["macro_f1"],
val_scores_edge["macro_f1"], data_time.avg,
batch_time.avg))
start_time = time.time()
data_time.reset()
batch_time.reset()
val_scores_node, val_scores_node = evaluator.get_scores()
evaluator.reset_metrics()
best_pred = save_model(model, model_path, val_scores_node,
val_scores_edge, alpha, task_name, epoch,
best_pred)
write_log(f_log, train_scores_node, train_scores_edge,
val_scores_node, val_scores_edge, epoch, num_epochs)
write_summaryWriter(writer, train_loss / len(dataloader_train),
optimizer, train_scores_node,
train_scores_edge, val_scores_node,
val_scores_edge, epoch)
f_log.close()