def create_model(ema=False):
# Network definition
net = UNet('efficientnet-b3',
encoder_weights='imagenet',
in_channels=1,
classes=num_classes)
model = net.cuda()
if ema:
for param in model.parameters():
param.detach_()
return model
def Inference(FLAGS):
with open(FLAGS.root_path + '/test.list', 'r') as f:
image_list = f.readlines()
image_list = sorted(
[item.replace('\n', '').split(".")[0] for item in image_list])
snapshot_path = "../model/{}/{}".format(FLAGS.exp, FLAGS.model)
test_save_path = "../model/{}/{}_Prediction/".format(
FLAGS.exp, FLAGS.model)
if os.path.exists(test_save_path):
shutil.rmtree(test_save_path)
os.makedirs(test_save_path)
net = UNet('efficientnet-b3',
encoder_weights='imagenet',
in_channels=1,
classes=4).cuda()
save_mode_path = os.path.join(snapshot_path,
'{}_best_model.pth'.format(FLAGS.model))
net.load_state_dict(torch.load(save_mode_path))
print("init weight from {}".format(save_mode_path))
net.eval()
first_total = 0.0
second_total = 0.0
third_total = 0.0
for case in tqdm(image_list):
first_metric, second_metric, third_metric = test_single_volume(
case, net, test_save_path)
first_total += np.asarray(first_metric)
second_total += np.asarray(second_metric)
third_total += np.asarray(third_metric)
avg_metric = [
first_total / len(image_list), second_total / len(image_list),
third_total / len(image_list)
]
return avg_metric
def train(args, snapshot_path):
base_lr = args.base_lr
num_classes = 4
batch_size = args.batch_size
max_iterations = args.max_iterations
def worker_init_fn(worker_id):
random.seed(args.seed + worker_id)
model = UNet('efficientnet-b3',
encoder_weights='imagenet',
in_channels=1,
classes=num_classes).cuda()
DAN = FCDiscriminator(num_classes=num_classes)
DAN = DAN.cuda()
db_train = ACDC(base_dir=args.root_path,
split="train",
num=None,
transform=transforms.Compose(
[RandomGenerator(args.patch_size)]))
labeled_idxs = list(range(0, args.labeled_num))
unlabeled_idxs = list(range(args.labeled_num, 1312))
batch_sampler = TwoStreamBatchSampler(labeled_idxs, unlabeled_idxs,
batch_size,
batch_size - args.labeled_bs)
trainloader = DataLoader(db_train,
batch_sampler=batch_sampler,
num_workers=16,
pin_memory=True,
worker_init_fn=worker_init_fn)
db_val = ACDC(base_dir=args.root_path, split="val")
valloader = DataLoader(db_val, batch_size=1, shuffle=False, num_workers=1)
model.train()
optimizer = optim.SGD(model.parameters(),
lr=base_lr,
momentum=0.9,
weight_decay=0.0001)
DAN_optimizer = optim.Adam(DAN.parameters(),
lr=args.DAN_lr,
betas=(0.9, 0.99))
ce_loss = CrossEntropyLoss()
dice_loss = losses.DiceLoss(num_classes)
writer = SummaryWriter(snapshot_path + '/log')
logging.info("{} iterations per epoch".format(len(trainloader)))
iter_num = 0
max_epoch = max_iterations // len(trainloader) + 1
best_performance = 0.0
iterator = tqdm(range(max_epoch), ncols=70)
for epoch_num in iterator:
for i_batch, sampled_batch in enumerate(trainloader):
volume_batch, label_batch = sampled_batch['image'], sampled_batch[
'label']
volume_batch, label_batch = volume_batch.cuda(), label_batch.cuda()
DAN_target = torch.tensor([0] * args.batch_size).cuda()
DAN_target[:args.labeled_bs] = 1
model.train()
DAN.eval()
outputs = model(volume_batch)
outputs_soft = torch.softmax(outputs, dim=1)
loss_ce = ce_loss(outputs[:args.labeled_bs],
label_batch[:][:args.labeled_bs].long())
loss_dice = dice_loss(outputs_soft[:args.labeled_bs],
label_batch[:args.labeled_bs].unsqueeze(1))
supervised_loss = 0.5 * (loss_dice + loss_ce)
consistency_weight = get_current_consistency_weight(iter_num //
150)
DAN_outputs = DAN(outputs_soft[args.labeled_bs:],
volume_batch[args.labeled_bs:])
consistency_loss = F.cross_entropy(
DAN_outputs, (DAN_target[:args.labeled_bs]).long())
loss = supervised_loss + consistency_weight * consistency_loss
optimizer.zero_grad()
loss.backward()
optimizer.step()
model.eval()
DAN.train()
with torch.no_grad():
outputs = model(volume_batch)
outputs_soft = torch.softmax(outputs, dim=1)
DAN_outputs = DAN(outputs_soft, volume_batch)
DAN_loss = F.cross_entropy(DAN_outputs, DAN_target.long())
DAN_optimizer.zero_grad()
DAN_loss.backward()
DAN_optimizer.step()
lr_ = base_lr * (1.0 - iter_num / max_iterations)**0.9
for param_group in optimizer.param_groups:
param_group['lr'] = lr_
iter_num = iter_num + 1
writer.add_scalar('info/lr', lr_, iter_num)
writer.add_scalar('info/total_loss', loss, iter_num)
writer.add_scalar('info/loss_ce', loss_ce, iter_num)
writer.add_scalar('info/loss_dice', loss_dice, iter_num)
writer.add_scalar('info/consistency_loss', consistency_loss,
iter_num)
writer.add_scalar('info/consistency_weight', consistency_weight,
iter_num)
logging.info(
'iteration %d : loss : %f, loss_ce: %f, loss_dice: %f' %
(iter_num, loss.item(), loss_ce.item(), loss_dice.item()))
if iter_num % 20 == 0:
image = volume_batch[1, 0:1, :, :]
writer.add_image('train/Image', image, iter_num)
outputs = torch.argmax(torch.softmax(outputs, dim=1),
dim=1,
keepdim=True)
writer.add_image('train/Prediction', outputs[1, ...] * 50,
iter_num)
labs = label_batch[1, ...].unsqueeze(0) * 50
writer.add_image('train/GroundTruth', labs, iter_num)
if iter_num > 0 and iter_num % 200 == 0:
model.eval()
first_total, second_total, third_total = 0.0, 0.0, 0.0
for i_batch, sampled_batch in enumerate(valloader):
first, second, third = test_single_volume(
sampled_batch["image"], sampled_batch["label"], model)
first_total += np.asarray(first)
second_total += np.asarray(second)
third_total += np.asarray(third)
first_total, second_total, third_total = first_total / \
len(db_val), second_total / \
len(db_val), third_total/len(db_val)
writer.add_scalar('info/val_one_dice', first_total[0],
iter_num)
writer.add_scalar('info/val_one_hd95', first_total[1],
iter_num)
writer.add_scalar('info/val_two_dice', second_total[0],
iter_num)
writer.add_scalar('info/val_two_hd95', second_total[1],
iter_num)
writer.add_scalar('info/val_three_dice', third_total[0],
iter_num)
writer.add_scalar('info/val_three_hd95', third_total[1],
iter_num)
performance = (first_total[0] + second_total[0] +
third_total[0]) / 3
mean_hd95 = (first_total[1] + second_total[1] +
third_total[1]) / 3
writer.add_scalar('info/val_mean_dice', performance, iter_num)
writer.add_scalar('info/val_mean_hd95', mean_hd95, iter_num)
if performance > best_performance:
best_performance = performance
save_mode_path = os.path.join(
snapshot_path, 'iter_{}_dice_{}.pth'.format(
iter_num, round(best_performance, 4)))
save_best = os.path.join(
snapshot_path, '{}_best_model.pth'.format(args.model))
torch.save(model.state_dict(), save_mode_path)
torch.save(model.state_dict(), save_best)
logging.info('iteration %d : mean_dice : %f mean_hd95 : %f' %
(iter_num, performance, mean_hd95))
model.train()
if iter_num % 3000 == 0:
save_mode_path = os.path.join(snapshot_path,
'iter_' + str(iter_num) + '.pth')
torch.save(model.state_dict(), save_mode_path)
logging.info("save model to {}".format(save_mode_path))
if iter_num >= max_iterations:
break
if iter_num >= max_iterations:
iterator.close()
break
writer.close()
return "Training Finished!"