RandomRotFlip(),
RandomCrop(patch_size),
ToTensor(),
]))
def worker_init_fn(worker_id):
random.seed(args.seed + worker_id)
trainloader = DataLoader(db_train,
batch_size=batch_size,
shuffle=True,
num_workers=4,
pin_memory=True,
worker_init_fn=worker_init_fn)
net.train()
optimizer = optim.SGD(net.parameters(),
lr=base_lr,
momentum=0.9,
weight_decay=0.0001)
writer = SummaryWriter(snapshot_path + '/log', flush_secs=2)
logging.info("{} itertations per epoch".format(len(trainloader)))
iter_num = 0
alpha = 1.0
max_epoch = max_iterations // len(trainloader) + 1
lr_ = base_lr
net.train()
for epoch_num in tqdm(range(max_epoch), ncols=70):
time1 = time.time()
def main():
###################
# init parameters #
###################
args = get_args()
# training path
train_data_path = args.root_path
# writer
idx = args.save.rfind('/')
log_dir = args.writer_dir + args.save[idx:]
writer = SummaryWriter(log_dir)
batch_size = args.batch_size * args.ngpu
max_iterations = args.max_iterations
base_lr = args.base_lr
#patch_size = (112, 112, 112)
#patch_size = (160, 160, 160)
patch_size = (64, 128, 128)
num_classes = 2
# random
if args.deterministic:
cudnn.benchmark = False
cudnn.deterministic = True
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
## make logger file
if os.path.exists(args.save):
shutil.rmtree(args.save)
os.makedirs(args.save, exist_ok=True)
snapshot_path = args.save
logging.basicConfig(filename=snapshot_path+"/log.txt", level=logging.INFO,
format='[%(asctime)s.%(msecs)03d] %(message)s', datefmt='%H:%M:%S')
logging.getLogger().addHandler(logging.StreamHandler(sys.stdout))
logging.info(str(args))
net = VNet(n_channels=1, n_classes=num_classes, normalization='batchnorm', has_dropout=True, use_tm=args.use_tm)
net = net.cuda()
#db_train = LAHeart(base_dir=train_data_path,
# split='train',
# transform = transforms.Compose([
# RandomRotFlip(),
# RandomCrop(patch_size),
# ToTensor(),
# ]))
db_train = ABUS(base_dir=args.root_path,
split='train',
use_dismap=args.use_dismap,
transform = transforms.Compose([RandomRotFlip(use_dismap=args.use_dismap), RandomCrop(patch_size, use_dismap=args.use_dismap), ToTensor(use_dismap=args.use_dismap)]))
def worker_init_fn(worker_id):
random.seed(args.seed+worker_id)
trainloader = DataLoader(db_train, batch_size=batch_size, shuffle=True, num_workers=4, pin_memory=True, worker_init_fn=worker_init_fn)
net.train()
optimizer = optim.SGD(net.parameters(), lr=base_lr, momentum=0.9, weight_decay=0.0001)
#gdl = GeneralizedDiceLoss()
logging.info("{} itertations per epoch".format(len(trainloader)))
iter_num = 0
alpha = 1.0
max_epoch = max_iterations//len(trainloader)+1
lr_ = base_lr
net.train()
for epoch_num in tqdm(range(max_epoch), ncols=70):
time1 = time.time()
for i_batch, sampled_batch in enumerate(trainloader):
time2 = time.time()
# print('fetch data cost {}'.format(time2-time1))
volume_batch, label_batch, dis_map_batch = sampled_batch['image'], sampled_batch['label'], sampled_batch['dis_map']
volume_batch, label_batch, dis_map_batch = volume_batch.cuda(), label_batch.cuda(), dis_map_batch.cuda()
#print('volume_batch.shape: ', volume_batch.shape)
if args.use_tm:
outputs, tm = net(volume_batch)
tm = torch.sigmoid(tm)
else:
outputs = net(volume_batch)
#print('volume_batch.shape: ', volume_batch.shape)
#print('outputs.shape, ', outputs.shape)
loss_seg = F.cross_entropy(outputs, label_batch)
outputs_soft = F.softmax(outputs, dim=1)
#print(outputs_soft.shape)
#print(label_batch.shape)
#loss_seg_dice = gdl(outputs_soft, label_batch)
loss_seg_dice = dice_loss(outputs_soft[:, 1, :, :, :], label_batch == 1)
#with torch.no_grad():
# # defalut using compute_sdf; however, compute_sdf1_1 is also worth to try;
# gt_sdf_npy = compute_sdf(label_batch.cpu().numpy(), outputs_soft.shape)
# gt_sdf = torch.from_numpy(gt_sdf_npy).float().cuda(outputs_soft.device.index)
# print('gt_sdf.shape: ', gt_sdf.shape)
#loss_boundary = boundary_loss(outputs_soft, gt_sdf)
#print('dis_map.shape: ', dis_map_batch.shape)
loss_boundary = boundary_loss(outputs_soft, dis_map_batch)
if args.use_tm:
loss_threshold = threshold_loss(outputs_soft[:, 1, :, :, :], tm[:, 0, ...], label_batch == 1)
loss_th = (0.1 * loss_seg + 0.9 * loss_seg_dice) + 3 * loss_threshold
loss = alpha*(loss_th) + (1 - alpha) * loss_boundary
else:
loss = alpha * loss_seg_dice + (1-alpha) * loss_boundary
optimizer.zero_grad()
loss.backward()
optimizer.step()
out = outputs_soft.max(1)[1]
dice = GeneralizedDiceLoss.dice_coeficient(out, label_batch)
iter_num = iter_num + 1
writer.add_scalar('train/lr', lr_, iter_num)
writer.add_scalar('train/loss_seg', loss_seg, iter_num)
writer.add_scalar('train/loss_seg_dice', loss_seg_dice, iter_num)
writer.add_scalar('train/alpha', alpha, iter_num)
writer.add_scalar('train/loss', loss, iter_num)
writer.add_scalar('train/dice', dice, iter_num)
if args.use_tm:
writer.add_scalar('train/loss_threshold', loss_threshold, iter_num)
if args.use_dismap:
writer.add_scalar('train/loss_dis', loss_boundary, iter_num)
logging.info('iteration %d : loss : %f' % (iter_num, loss.item()))
logging.info('iteration %d : alpha : %f' % (iter_num, alpha))
if iter_num % 50 == 0:
image = volume_batch[0, 0:1, :, 30:71:10, :].permute(2,0,1,3)
image = (image + 0.5) * 0.5
grid_image = make_grid(image, 5)
writer.add_image('train/Image', grid_image, iter_num)
#outputs_soft = F.softmax(outputs, 1) #batchsize x num_classes x w x h x d
image = outputs_soft[0, 1:2, :, 30:71:10, :].permute(2,0,1,3)
grid_image = make_grid(image, 5, normalize=False)
grid_image = grid_image.cpu().detach().numpy().transpose((1,2,0))
gt = label_batch[0, :, 30:71:10, :].unsqueeze(0).permute(2,0,1,3)
grid_gt = make_grid(gt, 5, normalize=False)
grid_gt = grid_gt.cpu().detach().numpy().transpose((1,2,0))
image_tm = dis_map_batch[0, :, :, 30:71:10, :].permute(2,0,1,3)
#image_tm = tm[0, :, :, 30:71:10, :].permute(2,0,1,3)
grid_tm = make_grid(image_tm, 5, normalize=False)
grid_tm = grid_tm.cpu().detach().numpy().transpose((1,2,0))
fig = plt.figure()
ax = fig.add_subplot(311)
ax.imshow(grid_gt[:, :, 0], 'gray')
ax = fig.add_subplot(312)
cs = ax.imshow(grid_image[:, :, 0], 'hot', vmin=0., vmax=1.)
fig.colorbar(cs, ax=ax, shrink=0.9)
ax = fig.add_subplot(313)
cs = ax.imshow(grid_tm[:, :, 0], 'hot', vmin=0, vmax=1.)
fig.colorbar(cs, ax=ax, shrink=0.9)
writer.add_figure('train/prediction_results', fig, iter_num)
fig.clear()
## change lr
if iter_num % 5000 == 0:
lr_ = base_lr * 0.1 ** (iter_num // 5000)
for param_group in optimizer.param_groups:
param_group['lr'] = lr_
if iter_num % 1000 == 0:
save_mode_path = os.path.join(snapshot_path, 'iter_' + str(iter_num) + '.pth')
torch.save(net.state_dict(), save_mode_path)
logging.info("save model to {}".format(save_mode_path))
if iter_num > max_iterations:
break
time1 = time.time()
alpha -= 0.005
if alpha <= 0.01:
alpha = 0.01
if iter_num > max_iterations:
break
save_mode_path = os.path.join(snapshot_path, 'iter_'+str(max_iterations+1)+'.pth')
torch.save(net.state_dict(), save_mode_path)
logging.info("save model to {}".format(save_mode_path))
writer.close()
def main():
###################
# init parameters #
###################
args = get_args()
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
# training path
train_data_path = args.root_path
# writer
idx = args.save.rfind('/')
log_dir = args.writer_dir + args.save[idx:]
writer = SummaryWriter(log_dir)
batch_size = args.batch_size * args.ngpu
max_iterations = args.max_iterations
base_lr = args.base_lr
patch_size = (64, 128, 128)
num_classes = 2
# random
if args.deterministic:
cudnn.benchmark = False
cudnn.deterministic = True
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
## make logger file
if os.path.exists(args.save):
shutil.rmtree(args.save)
os.makedirs(args.save, exist_ok=True)
snapshot_path = args.save
logging.basicConfig(filename=snapshot_path+"/log.txt", level=logging.INFO,
format='[%(asctime)s.%(msecs)03d] %(message)s', datefmt='%H:%M:%S')
logging.getLogger().addHandler(logging.StreamHandler(sys.stdout))
logging.info(str(args))
# network
if args.arch == 'vnet':
net = VNet(n_channels=1, n_classes=num_classes, normalization='batchnorm', has_dropout=True, use_tm=args.use_tm)
elif args.arch == 'd2unet':
net = D2UNet()
else:
raise(NotImplementedError('model {} not implement'.format(args.arch)))
net = net.cuda()
# dataset
def worker_init_fn(worker_id):
random.seed(args.seed+worker_id)
db_train = ABUS(base_dir=args.root_path,
split='train',
fold=args.fold,
transform = transforms.Compose([RandomRotFlip(), RandomCrop(patch_size), ToTensor()]))
db_val = ABUS(base_dir=args.root_path,
split='val',
fold=args.fold,
transform = transforms.Compose([CenterCrop(patch_size), ToTensor()]))
trainloader = DataLoader(db_train, batch_size=batch_size, shuffle=True, num_workers=4, pin_memory=True, worker_init_fn=worker_init_fn)
# optimizer
optimizer = optim.SGD(net.parameters(), lr=base_lr, momentum=0.9, weight_decay=0.0001)
gdl = GeneralizedDiceLoss()
logging.info("{} itertations per epoch".format(len(trainloader)))
# training
iter_num = 0
max_epoch = max_iterations//len(trainloader)+1
lr_ = base_lr
net.train()
for epoch_num in tqdm(range(max_epoch), ncols=70):
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()
if args.use_tm:
outputs, tm = net(volume_batch)
tm = torch.sigmoid(tm)
else:
outputs = net(volume_batch)
loss_seg = F.cross_entropy(outputs, label_batch)
outputs_soft = F.softmax(outputs, dim=1)
loss_seg_dice = dice_loss(outputs_soft[:, 1, :, :, :], label_batch == 1)
if args.use_tm:
loss_threshold = threshold_loss(outputs_soft[:, 1, :, :, :], tm[:, 0, ...], label_batch == 1)
loss = loss_seg_dice + 3 * loss_threshold
else:
loss = loss_seg_dice
optimizer.zero_grad()
loss.backward()
optimizer.step()
# visualization on tensorboard
out = outputs_soft.max(1)[1]
dice = GeneralizedDiceLoss.dice_coeficient(out, label_batch)
iter_num = iter_num + 1
writer.add_scalar('train/lr', lr_, iter_num)
writer.add_scalar('train/loss_seg', loss_seg, iter_num)
writer.add_scalar('train/loss_seg_dice', loss_seg_dice, iter_num)
writer.add_scalar('train/loss', loss, iter_num)
writer.add_scalar('train/dice', dice, iter_num)
if args.use_tm:
writer.add_scalar('train/loss_threshold', loss_threshold, iter_num)
logging.info('iteration %d : loss : %f' % (iter_num, loss.item()))
if iter_num % 50 == 0:
nrow = 5
image = volume_batch[0, 0:1, :, 30:71:10, :].permute(2,0,1,3)
image = (image + 0.5) * 0.5
grid_image = make_grid(image, nrow=nrow)
writer.add_image('train/Image', grid_image, iter_num)
#outputs_soft = F.softmax(outputs, 1) #batchsize x num_classes x w x h x d
image = outputs_soft[0, 1:2, :, 30:71:10, :].permute(2,0,1,3)
grid_image = make_grid(image, nrow=nrow, normalize=False)
grid_image = grid_image.cpu().detach().numpy().transpose((1,2,0))
gt = label_batch[0, :, 30:71:10, :].unsqueeze(0).permute(2,0,1,3)
grid_gt = make_grid(gt, nrow=nrow, normalize=False)
grid_gt = grid_gt.cpu().detach().numpy().transpose((1,2,0))
if args.use_tm:
image_tm = tm[0, :, :, 30:71:10, :].permute(2,0,1,3)
else:
image_tm = gt
grid_tm = make_grid(image_tm, nrow=nrow, normalize=False)
grid_tm = grid_tm.cpu().detach().numpy().transpose((1,2,0))
fig = plt.figure()
ax = fig.add_subplot(311)
ax.imshow(grid_gt[:, :, 0], 'gray')
ax = fig.add_subplot(312)
cs = ax.imshow(grid_image[:, :, 0], 'hot', vmin=0., vmax=1.)
fig.colorbar(cs, ax=ax, shrink=0.9)
ax = fig.add_subplot(313)
cs = ax.imshow(grid_tm[:, :, 0], 'hot', vmin=0, vmax=1.)
fig.colorbar(cs, ax=ax, shrink=0.9)
writer.add_figure('train/prediction_results', fig, iter_num)
fig.clear()
## change lr
if iter_num % 2500 == 0:
lr_ = base_lr * 0.1 ** (iter_num // 2500)
for param_group in optimizer.param_groups:
param_group['lr'] = lr_
if iter_num % 1000 == 0 and iter_num > 5000:
save_mode_path = os.path.join(snapshot_path, 'iter_' + str(iter_num) + '.pth')
torch.save(net.state_dict(), save_mode_path)
logging.info("save model to {}".format(save_mode_path))
if iter_num > max_iterations:
break
time1 = time.time()
if iter_num > max_iterations:
break
save_mode_path = os.path.join(snapshot_path, 'iter_'+str(max_iterations+1)+'.pth')
torch.save(net.state_dict(), save_mode_path)
logging.info("save model to {}".format(save_mode_path))
writer.close()
def main():
###################
# init parameters #
###################
args = get_args()
# training path
train_data_path = args.root_path
# writer
idx = args.save.rfind('/')
log_dir = args.writer_dir + args.save[idx:]
writer = SummaryWriter(log_dir)
batch_size = args.batch_size * args.ngpu
max_iterations = args.max_iterations
base_lr = args.base_lr
patch_size = (112, 112, 80)
num_classes = 2
# random
if args.deterministic:
cudnn.benchmark = False
cudnn.deterministic = True
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
## make logger file
if os.path.exists(args.save):
shutil.rmtree(args.save)
os.makedirs(args.save, exist_ok=True)
snapshot_path = args.save
logging.basicConfig(filename=snapshot_path+"/log.txt", level=logging.INFO,
format='[%(asctime)s.%(msecs)03d] %(message)s', datefmt='%H:%M:%S')
logging.getLogger().addHandler(logging.StreamHandler(sys.stdout))
logging.info(str(args))
net = VNet(n_channels=1, n_classes=num_classes, normalization='batchnorm', has_dropout=True)
net = net.cuda()
db_train = LAHeart(base_dir=train_data_path,
split='train',
num=16,
transform = transforms.Compose([
RandomRotFlip(),
RandomCrop(patch_size),
ToTensor(),
]))
db_test = LAHeart(base_dir=train_data_path,
split='test',
transform = transforms.Compose([
CenterCrop(patch_size),
ToTensor()
]))
def worker_init_fn(worker_id):
random.seed(args.seed+worker_id)
trainloader = DataLoader(db_train, batch_size=batch_size, shuffle=True, num_workers=4, pin_memory=True, worker_init_fn=worker_init_fn)
net.train()
optimizer = optim.SGD(net.parameters(), lr=base_lr, momentum=0.9, weight_decay=0.0001)
logging.info("{} itertations per epoch".format(len(trainloader)))
iter_num = 0
max_epoch = max_iterations//len(trainloader)+1
lr_ = base_lr
net.train()
for epoch_num in tqdm(range(max_epoch), ncols=70):
time1 = time.time()
for i_batch, sampled_batch in enumerate(trainloader):
time2 = time.time()
# print('fetch data cost {}'.format(time2-time1))
volume_batch, label_batch = sampled_batch['image'], sampled_batch['label']
volume_batch, label_batch = volume_batch.cuda(), label_batch.cuda()
outputs = net(volume_batch)
loss_seg = F.cross_entropy(outputs, label_batch)
outputs_soft = F.softmax(outputs, dim=1)
loss_seg_dice = dice_loss(outputs_soft[:, 1, :, :, :], label_batch == 1)
loss = 0.5*(loss_seg+loss_seg_dice)
optimizer.zero_grad()
loss.backward()
optimizer.step()
iter_num = iter_num + 1
writer.add_scalar('lr', lr_, iter_num)
writer.add_scalar('loss/loss_seg', loss_seg, iter_num)
writer.add_scalar('loss/loss_seg_dice', loss_seg_dice, iter_num)
writer.add_scalar('loss/loss', loss, iter_num)
logging.info('iteration %d : loss : %f' % (iter_num, loss.item()))
if iter_num % 50 == 0:
image = volume_batch[0, 0:1, :, :, 20:61:10].permute(3,0,1,2).repeat(1,3,1,1)
grid_image = make_grid(image, 5, normalize=True)
writer.add_image('train/Image', grid_image, iter_num)
outputs_soft = F.softmax(outputs, 1)
image = outputs_soft[0, 1:2, :, :, 20:61:10].permute(3, 0, 1, 2).repeat(1, 3, 1, 1)
grid_image = make_grid(image, 5, normalize=False)
writer.add_image('train/Predicted_label', grid_image, iter_num)
image = label_batch[0, :, :, 20:61:10].unsqueeze(0).permute(3, 0, 1, 2).repeat(1, 3, 1, 1)
grid_image = make_grid(image, 5, normalize=False)
writer.add_image('train/Groundtruth_label', grid_image, iter_num)
## change lr
if iter_num % 2500 == 0:
lr_ = base_lr * 0.1 ** (iter_num // 2500)
for param_group in optimizer.param_groups:
param_group['lr'] = lr_
if iter_num % 1000 == 0:
save_mode_path = os.path.join(snapshot_path, 'iter_' + str(iter_num) + '.pth')
torch.save(net.state_dict(), save_mode_path)
logging.info("save model to {}".format(save_mode_path))
if iter_num > max_iterations:
break
time1 = time.time()
if iter_num > max_iterations:
break
save_mode_path = os.path.join(snapshot_path, 'iter_'+str(max_iterations+1)+'.pth')
torch.save(net.state_dict(), save_mode_path)
logging.info("save model to {}".format(save_mode_path))
writer.close()
def main():
###################
# init parameters #
###################
args = get_args()
# training path
train_data_path = args.root_path
# writer
idx = args.save.rfind('/')
log_dir = args.writer_dir + args.save[idx:]
writer = SummaryWriter(log_dir)
batch_size = args.batch_size * args.ngpu
max_iterations = args.max_iterations
base_lr = args.base_lr
patch_size = (112, 112, 80)
num_classes = 2
# random
if args.deterministic:
cudnn.benchmark = False
cudnn.deterministic = True
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
## make logger file
if os.path.exists(args.save):
shutil.rmtree(args.save)
os.makedirs(args.save, exist_ok=True)
snapshot_path = args.save
logging.basicConfig(filename=snapshot_path + "/log.txt",
level=logging.INFO,
format='[%(asctime)s.%(msecs)03d] %(message)s',
datefmt='%H:%M:%S')
logging.getLogger().addHandler(logging.StreamHandler(sys.stdout))
logging.info(str(args))
#training set
db_train = LAHeart(base_dir=train_data_path,
split='train',
num=16,
transform=transforms.Compose([
RandomRotFlip(),
RandomCrop(patch_size),
ToTensor()
]))
def worker_init_fn(worker_id):
random.seed(args.seed + worker_id)
trainloader = DataLoader(db_train,
batch_size=batch_size,
shuffle=True,
num_workers=4,
pin_memory=True,
worker_init_fn=worker_init_fn)
net = VNet(n_channels=1,
n_classes=num_classes,
normalization='batchnorm',
has_dropout=True)
net = net.cuda()
net.train()
optimizer = optim.SGD(net.parameters(),
lr=base_lr,
momentum=0.9,
weight_decay=0.0001)
logging.info("{} itertations per epoch".format(len(trainloader)))
iter_num = 0
alpha = 1.0
max_epoch = max_iterations // len(trainloader) + 1
lr_ = base_lr
net.train()
for epoch_num in tqdm(range(max_epoch), ncols=70):
time1 = time.time()
for i_batch, sampled_batch in enumerate(trainloader):
time2 = time.time()
# print('fetch data cost {}'.format(time2-time1))
# volume_batch.shape=(b,1,x,y,z) label_patch.shape=(b,x,y,z)
volume_batch, label_batch = sampled_batch['image'], sampled_batch[
'label']
volume_batch, label_batch = volume_batch.cuda(), label_batch.cuda()
outputs = net(volume_batch)
loss_seg = F.cross_entropy(outputs, label_batch)
outputs_soft = F.softmax(outputs, dim=1)
loss_seg_dice = dice_loss(outputs_soft[:, 1, :, :, :],
label_batch == 1)
# compute gt_signed distance function and boundary loss
with torch.no_grad():
# defalut using compute_sdf; however, compute_sdf1_1 is also worth to try;
gt_sdf_npy = compute_sdf(label_batch.cpu().numpy(),
outputs_soft.shape)
gt_sdf = torch.from_numpy(gt_sdf_npy).float().cuda(
outputs_soft.device.index)
# show signed distance map for debug
# import matplotlib.pyplot as plt
# plt.figure()
# plt.subplot(121), plt.imshow(gt_sdf_npy[0,1,:,:,40]), plt.colorbar()
# plt.subplot(122), plt.imshow(np.uint8(label_batch.cpu().numpy()[0,:,:,40]>0)), plt.colorbar()
# plt.show()
loss_boundary = boundary_loss(outputs_soft, gt_sdf)
loss = alpha * (loss_seg + loss_seg_dice) + (1 -
alpha) * loss_boundary
optimizer.zero_grad()
loss.backward()
optimizer.step()
iter_num = iter_num + 1
writer.add_scalar('lr', lr_, iter_num)
writer.add_scalar('loss/loss_seg', loss_seg, iter_num)
writer.add_scalar('loss/loss_seg_dice', loss_seg_dice, iter_num)
writer.add_scalar('loss/loss_boundary', loss_boundary, iter_num)
writer.add_scalar('loss/loss', loss, iter_num)
writer.add_scalar('loss/alpha', alpha, iter_num)
logging.info('iteration %d : alpha : %f' % (iter_num, alpha))
logging.info('iteration %d : loss_seg_dice : %f' %
(iter_num, loss_seg_dice.item()))
logging.info('iteration %d : loss_boundary : %f' %
(iter_num, loss_boundary.item()))
logging.info('iteration %d : loss : %f' % (iter_num, loss.item()))
if iter_num % 2 == 0:
image = volume_batch[0, 0:1, :, :,
20:61:10].permute(3, 0, 1,
2).repeat(1, 3, 1, 1)
grid_image = make_grid(image, 5, normalize=True)
writer.add_image('train/Image', grid_image, iter_num)
image = outputs_soft[0, 1:2, :, :,
20:61:10].permute(3, 0, 1,
2).repeat(1, 3, 1, 1)
grid_image = make_grid(image, 5, normalize=False)
writer.add_image('train/Predicted_label', grid_image, iter_num)
image = label_batch[0, :, :, 20:61:10].unsqueeze(0).permute(
3, 0, 1, 2).repeat(1, 3, 1, 1)
grid_image = make_grid(image, 5, normalize=False)
writer.add_image('train/Groundtruth_label', grid_image,
iter_num)
image = gt_sdf[0, 1:2, :, :,
20:61:10].permute(3, 0, 1,
2).repeat(1, 3, 1, 1)
grid_image = make_grid(image, 5, normalize=True)
writer.add_image('train/gt_sdf', grid_image, iter_num)
## change lr
if iter_num % 2500 == 0:
lr_ = base_lr * 0.1**(iter_num // 2500)
for param_group in optimizer.param_groups:
param_group['lr'] = lr_
if iter_num % 1000 == 0:
save_mode_path = os.path.join(snapshot_path,
'iter_' + str(iter_num) + '.pth')
torch.save(net.state_dict(), save_mode_path)
logging.info("save model to {}".format(save_mode_path))
if iter_num > max_iterations:
break
time1 = time.time()
alpha -= 0.01
if alpha <= 0.01:
alpha = 0.01
if iter_num > max_iterations:
break
save_mode_path = os.path.join(snapshot_path,
'iter_' + str(max_iterations + 1) + '.pth')
torch.save(net.state_dict(), save_mode_path)
logging.info("save model to {}".format(save_mode_path))
writer.close()
def experiment(exp_identifier,
max_epoch,
training_data,
testing_data,
batch_size=2,
supervised_only=False,
K=2,
T=0.5,
alpha=1,
mixup_mode='all',
Lambda=1,
Lambda_ramp=None,
base_lr=0.01,
change_lr=None,
aug_factor=1,
from_saved=None,
always_do_validation=True,
decay=0):
'''
max_epoch: epochs to run. Going through labeled data once is one epoch.
batch_size: batch size of labeled data. Unlabeled data is of the same size.
training_data: data for train_epoch, list of dicts of numpy array.
training_data: data for validation, list of dicts of numpy array.
supervised_only: if True, only do supervised training on LABELLED_INDEX; otherwise, use both LABELLED_INDEX and UNLABELLED_INDEX
Hyperparameters
---------------
K: repeats of each unlabelled data
T: temperature of sharpening
alpha: mixup hyperparameter of beta distribution
mixup_mode: how mixup is performed --
'__': no mix up
'ww': x and u both mixed up with w(x+u)
'xx': both with x
'xu': x with x, u with u
'uu': both with u
... _ means no, x means with x, u means with u, w means with w(x+u)
Lambda: loss = loss_x + Lambda * loss_u, relative weight for unsupervised loss
base_lr: initial learning rate
Lambda_ramp: callable or None. Lambda is ignored if this is not None. In this case, Lambda = Lambda_ramp(epoch).
change_lr: dict, {epoch: change_multiplier}
'''
print(
f"Experiment {exp_identifier}: max_epoch = {max_epoch}, batch_size = {batch_size}, supervised_only = {supervised_only},"
f"K = {K}, T = {T}, alpha = {alpha}, mixup_mode = {mixup_mode}, Lambda = {Lambda}, Lambda_ramp = {Lambda_ramp}, base_lr = {base_lr}, aug_factor = {aug_factor}."
)
net = VNet(n_channels=1,
n_classes=2,
normalization='batchnorm',
has_dropout=True)
eval_net = VNet(n_channels=1,
n_classes=2,
normalization='batchnorm',
has_dropout=True)
if from_saved is not None:
net.load_state_dict(torch.load(from_saved))
if GPU:
net = net.cuda()
eval_net.cuda()
## eval_net is not updating
for param in eval_net.parameters():
param.detach_()
net.train()
eval_net.train()
optimizer = optim.SGD(net.parameters(),
lr=base_lr,
momentum=0.9,
weight_decay=0.0001)
x_criterion = soft_cross_entropy #supervised loss is 0.5*(x_criterion + dice_loss)
u_criterion = nn.MSELoss() #unsupervised loss
training_losses = []
testing_losses = []
testing_accuracy = [] #dice accuracy
patch_size = (112, 112, 80)
testing_data = [
shape_transform(CenterCrop(patch_size)(sample))
for sample in testing_data
]
t0 = time.time()
lr = base_lr
for epoch in range(max_epoch):
labelled_index = np.random.permutation(LABELLED_INDEX)
unlabelled_index = np.random.permutation(
UNLABELLED_INDEX)[:len(labelled_index)]
labelled_data = [training_data[i] for i in labelled_index]
unlabelled_data = [training_data[i]
for i in unlabelled_index] #size = 16
##data transformation: rotation, flip, random_crop
labelled_data = [
shape_transform(RandomRotFlip()(RandomCrop(patch_size)(sample)))
for sample in labelled_data
]
unlabelled_data = [
shape_transform(RandomRotFlip()(RandomCrop(patch_size)(sample)))
for sample in unlabelled_data
]
if Lambda_ramp is not None:
Lambda = Lambda_ramp(epoch)
print(f"Lambda ramp: Lambda = {Lambda}")
if change_lr is not None:
if epoch in change_lr:
lr_ = lr * change_lr[epoch]
print(
f"Learning rate decay at epoch {epoch}, from {lr} to {lr_}"
)
lr = lr_
#change learning rate.
for param_group in optimizer.param_groups:
param_group['lr'] = lr_
training_loss = train_epoch(net=net,
eval_net=eval_net,
labelled_data=labelled_data,
unlabelled_data=unlabelled_data,
batch_size=batch_size,
supervised_only=supervised_only,
optimizer=optimizer,
x_criterion=x_criterion,
u_criterion=u_criterion,
K=K,
T=T,
alpha=alpha,
mixup_mode=mixup_mode,
Lambda=Lambda,
aug_factor=aug_factor,
decay=decay)
training_losses.append(training_loss)
if always_do_validation or epoch % 50 == 0:
testing_dice_loss, accuracy = validation(net=net,
testing_data=testing_data,
x_criterion=x_criterion)
testing_losses.append(testing_dice_loss)
testing_accuracy.append(accuracy)
print(
f"Epoch {epoch+1}/{max_epoch}, time used: {time.time()-t0:.2f}, training loss: {training_loss:.6f}, testing dice_loss: {testing_dice_loss:.6f}, testing accuracy: {100.0*accuracy:.2f}% "
)
save_path = f"../saved/{exp_identifier}.pth"
torch.save(net.state_dict(), save_path)
print(f"Experiment {exp_identifier} finished. Model saved as {save_path}")
return training_losses, testing_losses, testing_accuracy