from torch.nn import DataParallel
model = import_module('models.model_loader')
precise_net, loss = model.get_full_model(
net_name, loss_name, n_classes=5, alpha=alpha)
c_loss = DependentLoss(alpha)
checkpoint = torch.load(precise_net_path)
precise_net.load_state_dict(checkpoint['state_dict'])
precise_net = precise_net.to(DEVICE)
precise_net = DataParallel(precise_net)
precise_net.eval()
################# first get the threshold #####################
composed_transforms_tr = transforms.Compose([
tr.Normalize(mean=(0.12, 0.12, 0.12), std=(0.018, 0.018, 0.018)),
tr.ToTensor2(5)
])
test_files = ["Patient_01"]
'''
eval_dataset = THOR_Data(
transform=composed_transforms_tr,
path=data_path,
file_list=test_files)'''
eval_dataset = THOR_Data(
path=data_path,
file_list=test_files)
eval_loader = DataLoader(
eval_dataset,
batch_size=1,
shuffle=False,
def evaluation(args, net, loss, epoch, save_dir, test_files, saved_thresholds):
start_time = time.time()
net.eval()
eval_loss = []
total_precision = []
total_recall = []
composed_transforms_tr = transforms.Compose([
tr.Normalize(mean=(0.12, 0.12, 0.12), std=(0.018, 0.018, 0.018)),
tr.ToTensor2(args.n_class)
])
eval_dataset = THOR_Data(transform=composed_transforms_tr,
path=args.data_path,
file_list=test_files)
evalloader = DataLoader(eval_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=4)
cur_target = []
cur_predict = []
class_predict = []
class_target = []
for i, sample in enumerate(evalloader):
data = sample['image']
target_c = sample['label_c']
target_s = sample['label_s']
data = data.to(DEVICE)
target_c = target_c.to(DEVICE)
target_s = target_s.to(DEVICE)
with torch.no_grad():
output_s, output_c = net(data)
cur_loss, _, _, c_p = loss(output_s, output_c, target_s, target_c)
eval_loss.append(cur_loss.item())
cur_target.append(torch.argmax(target_s, 1).cpu().numpy())
cur_predict.append(torch.argmax(output_s, 1).cpu().numpy())
class_target.append(target_c.cpu().numpy())
class_predict.append(c_p.cpu().numpy())
cur_precision, cur_recall = metric(np.concatenate(class_predict, 0),
np.concatenate(class_target, 0),
saved_thresholds)
total_precision.append(np.array(cur_precision))
total_recall.append(np.array(cur_recall))
TPVFs, dices, PPVs, FPVFs = segmentation_metrics(
np.concatenate(cur_predict, 0), np.concatenate(cur_target, 0))
logging.info(
'***************************************************************************'
)
logging.info(
'Esophagus --> Global dice is [%.5f], TPR is [%.5f], Precision is [%.5f] '
% (dices[0], TPVFs[0], PPVs[0]))
logging.info(
'heart --> Global dice is [%.5f], TPR is [%.5f], Precision is [%.5f] '
% (dices[1], TPVFs[1], PPVs[1]))
logging.info(
'trachea --> Global dice is [%.5f], TPR is [%.5f], Precision is [%.5f] '
% (dices[2], TPVFs[2], PPVs[2]))
logging.info(
'aorta --> Global dice is [%.5f], TPR is [%.5f], Precision is [%.5f] '
% (dices[3], TPVFs[3], PPVs[3]))
total_precision = np.stack(total_precision, 1)
total_recall = np.stack(total_recall, 1)
logging.info(
'Epoch[%d], [precision=%.4f, -->%.3f, -->%.3f, -->%.3f, -->%.3f], using %.1f s!'
% (epoch, np.mean(total_precision), np.mean(total_precision[0]),
np.mean(total_precision[1]), np.mean(total_precision[2]),
np.mean(total_precision[3]), time.time() - start_time))
logging.info(
'Epoch[%d], [recall=%.4f, -->%.3f, -->%.3f, -->%.3f, -->%.3f], using %.1f s!'
% (epoch, np.mean(total_recall), np.mean(total_recall[0]),
np.mean(total_recall[1]), np.mean(total_recall[2]),
np.mean(total_recall[3]), time.time() - start_time))
logging.info(
'Epoch[%d], [total loss=%.6f], mean_dice=%.4f, using %.1f s!' %
(epoch, np.mean(eval_loss), np.mean(dices), time.time() - start_time))
logging.info(
'***************************************************************************'
)
return np.mean(dices), np.mean(total_precision)
def main(args):
max_precision = 0.
torch.manual_seed(123)
cudnn.benchmark = True
setgpu(args.gpu)
data_path = args.data_path
train_files, test_files = get_cross_validation_paths(args.test_flag)
composed_transforms_tr = transforms.Compose([
tr.Normalize(mean=(0.12, 0.12, 0.12), std=(0.018, 0.018, 0.018)),
tr.ToTensor2(args.n_class)
])
eval_dataset = THOR_Data(transform=composed_transforms_tr,
path=args.data_path,
file_list=test_files,
otsu=args.otsu)
if args.if_dependent == 1:
alpha = get_global_alpha(train_files, data_path)
alpha = torch.from_numpy(alpha).float().to(DEVICE)
alpha.requires_grad = False
else:
alpha = None
model = import_module('models.model_loader')
net, loss = model.get_full_model(args.model_name,
args.loss_name,
n_classes=args.n_class,
alpha=alpha,
if_closs=args.if_closs,
class_weights=torch.FloatTensor(
[1.0, 4.0, 2.0, 5.0, 3.0]).to(DEVICE))
start_epoch = args.start_epoch
save_dir = args.save_dir
logging.info(args)
if args.resume:
checkpoint = torch.load(args.resume)
start_epoch = checkpoint['epoch'] + 1
net.load_state_dict(checkpoint['state_dict'])
net = net.to(DEVICE)
loss = loss.to(DEVICE)
if len(args.gpu.split(',')) > 1 or args.gpu == 'all':
net = DataParallel(net)
optimizer = torch.optim.SGD(net.parameters(),
args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
init_lr = np.copy(args.lr)
def get_lr(epoch):
if args.lr < 0.0001:
return args.lr
if epoch > 0:
args.lr = args.lr * 0.95
logging.info('current learning rate is %f' % args.lr)
return args.lr
composed_transforms_tr = transforms.Compose([
tr.RandomZoom((512, 512)),
tr.RandomHorizontalFlip(),
tr.Normalize(mean=(0.12, 0.12, 0.12), std=(0.018, 0.018, 0.018)),
tr.ToTensor2(args.n_class)
])
train_dataset = THOR_Data(transform=composed_transforms_tr,
path=data_path,
file_list=train_files,
otsu=args.otsu)
trainloader = DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=4)
break_flag = 0.
high_dice = 0.
selected_thresholds = np.zeros((args.n_class - 1, ))
run_id = str(uuid.uuid4())
cur_train_stats_path = train_stats_path.format(run_id)
cur_eval_stats_path = eval_stats_path.format(run_id)
with open(cur_train_stats_path, 'w') as f:
writer = csv.writer(f)
writer.writerow(stats_fields)
with open(cur_eval_stats_path, 'w') as f:
writer = csv.writer(f)
writer.writerow(stats_fields)
for epoch in range(start_epoch, args.epochs + 1):
train_loss, adaptive_thresholds = train(trainloader, net, loss, epoch,
optimizer, get_lr, save_dir,
cur_train_stats_path)
if epoch < args.untest_epoch:
continue
break_flag += 1
eval_dice, eval_precision = evaluation(args, net, loss, epoch,
eval_dataset,
selected_thresholds,
cur_eval_stats_path)
if max_precision <= eval_precision:
selected_thresholds = adaptive_thresholds
max_precision = eval_precision
logging.info(
'************************ dynamic threshold saved successful ************************** !'
)
if eval_dice >= high_dice:
high_dice = eval_dice
break_flag = 0
if len(args.gpu.split(',')) > 1 or args.gpu == 'all':
state_dict = net.module.state_dict()
else:
state_dict = net.state_dict()
torch.save(
{
'epoch': epoch,
'save_dir': save_dir,
'state_dict': state_dict,
'optimizer': optimizer.state_dict(),
'args': args
}, os.path.join(save_dir, '%d.ckpt' % epoch))
logging.info(
'************************ model saved successful ************************** !'
)
if break_flag > args.patient:
break
def main(args):
torch.manual_seed(123)
cudnn.benchmark = True
setgpu(args.gpu)
data_path = args.data_path
train_files, test_files = get_cross_validation_paths(args.test_flag)
model = import_module('models.model_loader')
net_dict, loss = model.get_full_model(
args.model_name,
args.loss_name,
n_classes=args.n_class)
save_dir = args.save_dir
logging.info(args)
for net_name, net in net_dict.items():
net = net.to(DEVICE)
#summary(net, (3, 512, 512))
loss = loss.to(DEVICE)
optimizer_dict = {}
for net_name, net in net_dict.items():
optimizer_dict[net_name] = torch.optim.SGD(
net.parameters(),
args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
'''
for name, param in net.named_parameters():
if param.requires_grad:
print(name)
print(param.shape)
'''
init_lr = np.copy(args.lr)
def get_lr(epoch):
if args.lr < 0.0001:
return args.lr
if epoch > 0:
args.lr = args.lr * 0.95
logging.info('current learning rate is %f' % args.lr)
return args.lr
composed_transforms_tr = transforms.Compose([
tr.RandomZoom((512, 512)),
tr.RandomHorizontalFlip(),
tr.Normalize(mean=(0.12, 0.12, 0.12), std=(0.018, 0.018, 0.018)),
tr.ToTensor2(args.n_class)])
train_dataset = THOR_Data(
transform=composed_transforms_tr, path=data_path, file_list=train_files)
trainloader = DataLoader(
train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=4)
# Pretrain ResUNet101
for epoch_normal in range(args.normal_epochs):
train_normal(trainloader, net_dict, loss, epoch_normal, optimizer_dict, get_lr, save_dir)
print('Pretrain ResUNet101 epoch %d done.' % epoch_normal)
# Save state
net_state_dict = []
for net_name, net in net_dict.items():
net_state_dict.append(net.state_dict())
optimizer_state_dict = []
for net_name, optimizer in optimizer_dict.items():
optimizer_state_dict.append(optimizer.state_dict())
torch.save({
'epoch': epoch_normal,
'save_dir': save_dir,
'state_dict': net_state_dict,
'optimizer': optimizer_state_dict,
'args': args
}, os.path.join(save_dir, '%d.ckpt' % epoch_normal))
# Train ResUNet101_lmser based on ResUNet101
for epoch_lmser in range(args.lmser_epochs):
train_lmser(trainloader, net_dict, loss, epoch_lmser, optimizer_dict, get_lr, save_dir)
print('Train ResUNet101_lmser epoch %d done.' % epoch_lmser)