def grouper(iterable, n):
" Collect data into fixed-length chunks or blocks "
# grouper('ABCDEFG', 3) --> ABC DEF"
args = [iter(iterable)] * n
return zip(*args)
if __name__ == "__main__":
from torchvision import transforms
from utils import util
from torch.utils.data import DataLoader
batch_size = 3
labeled_bs = 2
data_path = '/data1/hra/dataset/Pancreas/Pancreas_region'
total_volume_path, total_label_path = util.gen_Pancreas_data_path(
data_path)
train_volume_path, train_label_path, valid_volume_path, valid_label_path \
= util.divide_data2train_valid(total_volume_path, total_label_path, 60, 1124)
labeled_idxs = list(range(12))
unlabeled_idxs = list(range(12, 60))
batch_sampler = TwoStreamBatchSampler(labeled_idxs, unlabeled_idxs,
batch_size, batch_size - labeled_bs)
dataset = Pancreas(train_volume_path,
train_volume_path,
transform=transforms.Compose(
[RandomNoise(),
Rotate(0.5),
Flip(0.5),
ToTensor()]))
def train(gpu,
batch_size,
labeled_bs,
seed1,
seed2,
iter_from,
n_total_iter_from,
n_epochs,
lr,
n_save_iter,
data_path,
model_dir_root_path,
model_pre_trained_dir,
note):
"""
Training 3D U-Net
:param gpu: gpu id
:param batch_size: batch size
:param labeled_bs: labeled batch size
:param seed1: seed 1
:param seed2: seed 2
:param iter_from: iter_from to start training from
:param n_total_iter_from: used for continuing training
:param n_epochs: number of training epochs
:param lr: learning rate
:param n_save_iter: Determines how many epochs before saving model version
:param data_path: data path
:param model_dir_root_path: the model directory root path to save to
:param model_pre_trained_dir: Path to pre-trained model
:param note:
:return:
"""
""" setting """
# gpu
os.environ["CUDA_VISIBLE_DEVICES"] = gpu
# time
now = time.localtime()
now_format = time.strftime("%Y-%m-%d %H:%M:%S", now) # time format
date_now = now_format.split(' ')[0]
time_now = now_format.split(' ')[1]
# save model path
save_path = os.path.join(model_dir_root_path, date_now, time_now)
if not os.path.exists(save_path):
os.makedirs(save_path)
# print setting
print("----------------------------------setting-------------------------------------")
print("lr:%f" % lr)
if model_pre_trained_dir is None:
print("pre-trained dir is None")
else:
print("pre-trained dir:%s" % model_pre_trained_dir)
print("path of saving model:%s" % save_path)
print("----------------------------------setting-------------------------------------")
# save parameters to TXT.
parameter_dict = {"gpu": gpu,
"model_pre_trained_dir": model_pre_trained_dir,
"iter_from": iter_from,
"lr": lr,
"save_path": save_path,
'note': note}
txt_name = 'parameter_log.txt'
path = os.path.join(save_path, txt_name)
with codecs.open(path, mode='a', encoding='utf-8') as file_txt:
for key, value in parameter_dict.items():
file_txt.write(str(key) + ':' + str(value) + '\n')
# logging
logging.basicConfig(filename=save_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(parameter_dict)
# tensorboardX
writer = SummaryWriter(log_dir=save_path)
# label_dict
label_list = [0, 1]
""" data generator """
# load all data path
total_volume_path, total_label_path = util.gen_Pancreas_data_path(data_path)
# 80 data -> 60 data for training
# 80 data -> 20 data for validation
train_volume_path, train_label_path, valid_volume_path, valid_label_path \
= util.divide_data2train_valid(total_volume_path, total_label_path, 60, seed1)
# dataset
# training
labeled_idxs = list(range(12))
unlabeled_idxs = list(range(12, 60))
batch_sampler = TwoStreamBatchSampler(labeled_idxs, unlabeled_idxs, batch_size, batch_size - labeled_bs)
train_dataset = Pancreas(train_volume_path, train_label_path,
transform_views=[transforms.Compose([RandomNoise(), Rotate(0), ToTensor()]),
transforms.Compose([RandomNoise(), Rotate(180), ToTensor()])])
# validation
# valid_dataset = Pancreas(valid_volume_path, valid_label_path,
# transform=transforms.Compose([ToTensor]))
# dataloader
def worker_init_fn(worker_id):
random.seed(seed1 + worker_id)
train_dataloader = DataLoader(train_dataset, batch_sampler=batch_sampler, num_workers=8,
pin_memory=True, worker_init_fn=worker_init_fn)
""" model, optimizer, loss """
model_view1 = UNet3D(1, 2, has_dropout=True).to("cuda:0")
model_view2 = UNet3D(1, 2, has_dropout=True).to("cuda:1")
# model_view3 = UNet3D(1, 2).cuda()
if iter_from != 0:
model_view1.load_state_dict(torch.load("%s/model_view1_%d.ckpt" % (model_pre_trained_dir, iter_from)))
model_view2.load_state_dict(torch.load("%s/model_view2_%d.ckpt" % (model_pre_trained_dir, iter_from)))
# model_view3.load_state_dict(torch.load("%s/model_view3_%d.ckpt" % (model_pre_trained_dir, iter_from)))
optimizer_view1 = torch.optim.Adam(model_view1.parameters(), lr=lr)
optimizer_view2 = torch.optim.Adam(model_view2.parameters(), lr=lr)
# optimizer_view3 = torch.optim.Adam(model_view3.parameters(), lr=lr)
criterion1 = nn.CrossEntropyLoss()
criterion2 = dice_loss
""" training loop """
# TODO: do label fusion (T^-1)
n_total_iter = 0
if n_total_iter_from != 0:
n_total_iter = n_total_iter_from
for epoch in range(n_epochs):
for batch_index, sample_batch_views in enumerate(train_dataloader):
# start_time
start = time.time()
# loading data
for i, sample_batch in enumerate(sample_batch_views):
# sample_batch_name = sample_batch['name']
if i == 0:
device = "cuda:0"
else:
device = "cuda:1"
sample_batch['volume'] = sample_batch['volume'].to(device).float()
sample_batch['label'] = sample_batch['label'].to(device).float()
# labeled data(volume, label)
labeled_volume_batch_view1 = sample_batch_views[0]['volume'][:labeled_bs]
labeled_volume_batch_view2 = sample_batch_views[1]['volume'][:labeled_bs]
label_batch_view1 = sample_batch_views[0]['label'][:labeled_bs]
label_batch_view2 = sample_batch_views[1]['label'][:labeled_bs]
# unlabeled data (volume)
unlabeled_volume_batch_view1 = sample_batch_views[0]['volume'][labeled_bs:]
unlabeled_volume_batch_view2 = sample_batch_views[1]['volume'][labeled_bs:]
# put noise into unlabeled data
noise1 = torch.clamp(torch.rand_like(unlabeled_volume_batch_view1)*0.1, -0.2, 0.2).to('cuda:0')
noise2 = torch.clamp(torch.rand_like(unlabeled_volume_batch_view1) * 0.1, -0.2, 0.2).to('cuda:1')
# noise_unlabeled_volume_batch_view1 = unlabeled_volume_batch_view1 + noise1
# noise_unlabeled_volume_batch_view2 = unlabeled_volume_batch_view2 + noise2
# ------------------
# Train model
# ------------------
# zeros the parameter gradients
optimizer_view1.zero_grad()
optimizer_view2.zero_grad()
# run 3D U-Net model on labeled data with view1 & view2
pred_labeled_view1 = model_view1(labeled_volume_batch_view1)
pred_labeled_view2 = model_view2(labeled_volume_batch_view2)
# run 3D U-Net model on unlabeled data with view1 & view2 (Bayesian)
T = 8
unlabeled_volume_batch_r_view1 = unlabeled_volume_batch_view1.repeat(2, 1, 1, 1, 1)
unlabeled_volume_batch_r_view2 = unlabeled_volume_batch_view2.repeat(2, 1, 1, 1, 1)
stride = unlabeled_volume_batch_r_view1.shape[0] // 2
pred_unlabeled_view1 = torch.zeros([stride * T, 2, 128, 128, 128]).to('cuda:0')
pred_unlabeled_view2 = torch.zeros([stride * T, 2, 128, 128, 128]).to('cuda:1')
for i in range(T//2):
noise_unlabeled_volume_batch_view1 = unlabeled_volume_batch_r_view1 \
+ torch.clamp(torch.rand_like(unlabeled_volume_batch_r_view1) * 0.1, -0.2, 0.2).to('cuda:0')
noise_unlabeled_volume_batch_view2 = unlabeled_volume_batch_r_view2 \
+ torch.clamp(torch.rand_like(unlabeled_volume_batch_r_view1) * 0.1, -0.2, 0.2).to('cuda:1')
with torch.no_grad():
pred_unlabeled_view1[2*stride*i:2*stride*(i+1)] = model_view1(noise_unlabeled_volume_batch_view1)
pred_unlabeled_view2[2*stride*i:2*stride*(i+1)] = model_view2(noise_unlabeled_volume_batch_view2)
pred_unlabeled_view1 = F.softmax(pred_unlabeled_view1, dim=1)
pred_unlabeled_view1 = pred_unlabeled_view1.reshape(T, stride, 2, 128, 128, 128)
pred_unlabeled_view1 = torch.mean(pred_unlabeled_view1, dim=0) # (batch, 2, 128, 128, 128)
uncertainty_view1 = -1.0*torch.sum(pred_unlabeled_view1*torch.log(pred_unlabeled_view1 + 1e-6), dim=1,
keepdim=True) # (batch, 1, 128, 128, 128)
pred_unlabeled_view2 = F.softmax(pred_unlabeled_view2, dim=1)
pred_unlabeled_view2 = pred_unlabeled_view2.reshape(T, stride, 2, 128, 128, 128)
pred_unlabeled_view2 = torch.mean(pred_unlabeled_view2, dim=0) # (batch, 2, 128, 128, 128)
uncertainty_view2 = -1.0 * torch.sum(pred_unlabeled_view2 * torch.log(pred_unlabeled_view2 + 1e-6), dim=1,
keepdim=True) # (batch, 1, 128, 128, 128)
# TODO: label fusion(先要转为同一视角)
# label_fusion_view1 =
# Calculate loss
label_batch_stand_view1 = util.standardized_seg(label_batch_view1, label_list, "cuda:0")
label_batch_stand_view2 = util.standardized_seg(label_batch_view2, label_list, "cuda:1")
loss_1_view1 = criterion1(pred_labeled_view1, label_batch_stand_view1)
loss_1_view2 = criterion1(pred_labeled_view2, label_batch_stand_view2)
label_batch_one_hot_view1 = util.onehot(label_batch_view1, label_list, "cuda:0")
label_batch_one_hot_view2 = util.onehot(label_batch_view2, label_list, "cuda:1")
pred_labeled_softmax_view1 = F.softmax(pred_labeled_view1, dim=1)
pred_labeled_softmax_view2 = F.softmax(pred_labeled_view2, dim=1)
loss_2_view1 = criterion2(pred_labeled_softmax_view1, label_batch_one_hot_view1)
loss_2_view2 = criterion2(pred_labeled_softmax_view2, label_batch_one_hot_view2)
loss_view1 = 0.5 * (loss_1_view1 + loss_2_view1)
loss_view2 = 0.5 * (loss_1_view2 + loss_2_view2)
# backwards and optimize
loss_view1.backward()
optimizer_view1.step()
loss_view2.backward()
optimizer_view2.step()
# ---------------------
# Print log
# ---------------------
# Determine approximate time left
end = time.time()
iter_left = (n_epochs - epoch) * (len(train_dataloader) - batch_index)
time_left = datetime.timedelta(seconds=iter_left * (end - start))
# print log
logging.info("[Epoch: %4d/%d] [n_total_iter: %5d] [Total index: %2d/%d] "
"[loss view1: %f] [loss view2: %f] [ETA: %s]"
% (epoch, n_epochs, n_total_iter+1, batch_index+1, len(train_dataloader),
loss_view1.item(), loss_view2.item(), time_left))
# tensorboardX log writer
writer.add_scalar("loss_view1/loss", loss_view1.item(), global_step=n_total_iter)
writer.add_scalar("loss_view1/loss_CrossEntropy", loss_1_view1.item(), global_step=n_total_iter)
writer.add_scalar("loss_view1/loss_Dice", loss_2_view1.item(), global_step=n_total_iter)
writer.add_scalar("loss_view2/loss", loss_view2.item(), global_step=n_total_iter)
writer.add_scalar("loss_view2/loss_CrossEntropy", loss_1_view2.item(), global_step=n_total_iter)
writer.add_scalar("loss_view2/loss_Dice", loss_2_view2.item(), global_step=n_total_iter)
if n_total_iter % n_save_iter == 0:
# Save model checkpoints
torch.save(model_view1.state_dict(), "%s/model_view1_%d.ckpt" % (save_path, n_total_iter))
torch.save(model_view2.state_dict(), "%s/model_view2_%d.ckpt" % (save_path, n_total_iter))
logging.info("save model : %s/model_view1_%d.ckpt" % (save_path, n_total_iter))
logging.info("save model : %s/model_view2_%d.ckpt" % (save_path, n_total_iter))
n_total_iter += 1
torch.save(model_view1.state_dict(), "%s/model_view1_%d.ckpt" % (save_path, n_total_iter))
torch.save(model_view2.state_dict(), "%s/model_view2_%d.ckpt" % (save_path, n_total_iter))
logging.info("save model : %s/model_view1_%d.ckpt" % (save_path, n_total_iter))
logging.info("save model : %s/model_view2_%d.ckpt" % (save_path, n_total_iter))
writer.close()
def train(gpu, batch_size, seed1, seed2, iter_from, n_total_iter_from,
n_epochs, lr, n_save_iter, data_path, model_dir_root_path,
model_pre_trained_dir, note):
"""
Training 3D U-Net
:param gpu: gpu id
:param batch_size: batch size
:param seed1: seed 1
:param seed2: seed 2
:param iter_from: iter_from to start training from
:param n_total_iter_from: used for continuing training
:param n_epochs: number of training epochs
:param lr: learning rate
:param n_save_iter: Determines how many epochs before saving model version
:param data_path: data path
:param model_dir_root_path: the model directory root path to save to
:param model_pre_trained_dir: Path to pre-trained model
:param note:
:return:
"""
""" setting """
# gpu
os.environ["CUDA_VISIBLE_DEVICES"] = gpu
# time
now = time.localtime()
now_format = time.strftime("%Y-%m-%d %H:%M:%S", now) # time format
date_now = now_format.split(' ')[0]
time_now = now_format.split(' ')[1]
# save model path
save_path = os.path.join(model_dir_root_path, date_now, time_now)
if not os.path.exists(save_path):
os.makedirs(save_path)
# print setting
print(
"----------------------------------setting-------------------------------------"
)
print("lr:%f" % lr)
if model_pre_trained_dir is None:
print("pre-trained dir is None")
else:
print("pre-trained dir:%s" % model_pre_trained_dir)
print("path of saving model:%s" % save_path)
print(
"----------------------------------setting-------------------------------------"
)
# save parameters to TXT.
parameter_dict = {
"gpu": gpu,
"batch size": batch_size,
"model_pre_trained_dir": model_pre_trained_dir,
"data path": data_path,
"iter_from": iter_from,
"lr": lr,
"save_path": save_path,
'note': note
}
txt_name = 'parameter_log.txt'
path = os.path.join(save_path, txt_name)
with codecs.open(path, mode='a', encoding='utf-8') as file_txt:
for key, value in parameter_dict.items():
file_txt.write(str(key) + ':' + str(value) + '\n')
# logging
logging.basicConfig(filename=save_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(parameter_dict)
# tensorboardX
writer = SummaryWriter(log_dir=save_path)
# label_dict
label_list = [0, 1]
# patch size
patch_size = (128, 128, 128)
""" data generator """
# load all data path
total_volume_path, total_label_path = util.gen_Pancreas_data_path(
data_path)
# 82 data -> 62 data for training
# 82 data -> 20 data for validation
train_volume_path, train_label_path, valid_volume_path, valid_label_path \
= util.divide_data2train_valid(total_volume_path, total_label_path, 62, seed1)
train_volume_path = [
i.replace('region', 'region2') for i in train_volume_path
]
train_label_path = [
i.replace('region', 'region2') for i in train_label_path
]
# dataset
# training
# todo: validation
train_volume_path.append(train_volume_path[-1])
train_label_path.append(train_label_path[-1])
total_dataset = Pancreas(train_volume_path,
train_label_path,
transform=transforms.Compose([
RandomCrop(patch_size),
RandomNoise(),
ToTensor()
]))
# validation
valid_dataset = Pancreas(valid_volume_path,
valid_label_path,
transform=transforms.Compose([ToTensor]))
# dataloader
def worker_init_fn(worker_id):
random.seed(seed1 + worker_id)
total_dataloader = DataLoader(total_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=8,
pin_memory=True,
worker_init_fn=worker_init_fn)
""" model, optimizer, loss """
modelS = UNet3D(1, 2, has_dropout=True).cuda()
if iter_from != 0:
modelS.load_state_dict(
torch.load("%s/modelS_%d.ckpt" %
(model_pre_trained_dir, iter_from)))
optimizer_S = torch.optim.Adam(modelS.parameters(), lr=lr)
criterion1 = nn.CrossEntropyLoss()
criterion2 = dice_loss
""" training loop """
n_total_iter = 0
if n_total_iter_from != 0:
n_total_iter = n_total_iter_from
for epoch in range(n_epochs):
for total_index, total_sample in enumerate(total_dataloader):
# start_time
start = time.time()
# generate moving data
total_name = total_sample['name']
total_input = total_sample['volume'].to('cuda').float()
total_seg = total_sample['label'].to('cuda').float()
# ------------------
# Train model
# ------------------
# zeros the parameter gradients
optimizer_S.zero_grad()
# run 3D U-Net model
pred = modelS(total_input)
# Calculate loss
# todo : if wrong, check label_list
total_seg_stand = util.standardized_seg(total_seg, label_list)
loss_1 = criterion1(pred, total_seg_stand)
total_seg_one_hot = util.onehot(total_seg, label_list)
pred_softmax = F.softmax(pred, dim=1)
loss_2 = criterion2(pred_softmax[:, 1, :, :, :],
total_seg_one_hot[:, 1, :, :, :])
loss = 0.5 * (loss_1 + loss_2)
# backwards and optimize
loss.backward()
optimizer_S.step()
# ---------------------
# Print log
# ---------------------
# Determine approximate time left
end = time.time()
iter_left = (n_epochs - epoch) * (len(total_dataloader) -
total_index)
time_left = datetime.timedelta(seconds=iter_left * (end - start))
# print log
logging.info(
"[Epoch: %4d/%d] [n_total_iter: %5d] [Total index: %2d/%d] [loss: %f] [ETA: %s]"
% (epoch, n_epochs, n_total_iter + 1, total_index + 1,
len(total_dataloader), loss.item(), time_left))
# tensorboardX log writer
writer.add_scalar("loss/loss",
loss.item(),
global_step=n_total_iter)
writer.add_scalar("loss/loss_CrossEntropy",
loss_1.item(),
global_step=n_total_iter)
writer.add_scalar("loss/loss_Dice",
loss_2.item(),
global_step=n_total_iter)
if n_total_iter % n_save_iter == 0:
# Save model checkpoints
torch.save(modelS.state_dict(),
"%s/modelS_%d.ckpt" % (save_path, n_total_iter))
logging.info("save model : %s/modelS_%d.ckpt" %
(save_path, n_total_iter))
n_total_iter += 1
torch.save(modelS.state_dict(),
"%s/modelS_%d.ckpt" % (save_path, n_total_iter))
logging.info("save model : %s/modelS_%d.ckpt" % (save_path, n_total_iter))
writer.close()
from utils import util
from dataloaders import PancreasMultiView
import nibabel as nib
import os
path1 = '/home/hra/dataset/Pancreas/Pancreas_region2'
seed = 1997
total_volume_path1, total_label_path1 = util.gen_Pancreas_data_path(path1)
train_volume_path1, train_label_path1, valid_volume_path1, valid_label_path1 = util.divide_data2train_valid(total_volume_path1, total_label_path1, 62, seed)
volume = nib.load(train_label_path1[0]).get_fdata()
label = nib.load(train_label_path1[0]).get_fdata()
name = os.path.basename(train_label_path1[0])
sample = {"volume": volume, "label": label, "name": name}
RC = PancreasMultiView.RandomCrop((128, 128, 128))
r = RC(sample)
print('d')
def train(gpu, batch_size_stage1, batch_size_stage2, labeled_bs, view_num,
n_iter_view_wise, seed1, seed2, iter_from, n_total_iter_from,
n_epochs, lr_stage1, lr_stage2, n_save_iter, data_path,
model_dir_root_path, model_pre_trained_dir, note):
"""
Training 3D U-Net
:param gpu: gpu id
:param batch_size_stage1: batch size for stage 1
:param batch_size_stage2: batch size for stage 2
:param labeled_bs: labeled batch size
:param view_num: number of views
:param n_iter_view_wise: number of iterations for view-wise training
:param seed1: seed 1
:param seed2: seed 2
:param iter_from: iter_from to start training from
:param n_total_iter_from: used for continuing training
:param n_epochs: number of training epochs
:param lr_stage1: for view_wise training
:param lr_stage2: for co-training
:param n_save_iter: Determines how many epochs before saving model version
:param data_path: data path
:param model_dir_root_path: the model directory root path to save to
:param model_pre_trained_dir: Path to pre-trained model
:param note:
:return:
"""
""" setting """
# gpu
os.environ["CUDA_VISIBLE_DEVICES"] = gpu
# time
now = time.localtime()
now_format = time.strftime("%Y-%m-%d %H:%M:%S", now) # time format
date_now = now_format.split(' ')[0]
time_now = now_format.split(' ')[1]
# save model path
save_path = os.path.join(model_dir_root_path, date_now, time_now)
if not os.path.exists(save_path):
os.makedirs(save_path)
# print setting
print(
"----------------------------------setting-------------------------------------"
)
print("lr for stage1 :%f" % lr_stage1)
print("lr for stage2 :%f" % lr_stage2)
if model_pre_trained_dir is None:
print("pre-trained dir is None")
else:
print("pre-trained dir:%s" % model_pre_trained_dir)
print("path of saving model:%s" % save_path)
print(
"----------------------------------setting-------------------------------------"
)
# save parameters to TXT.
parameter_dict = {
"gpu": gpu,
"model_pre_trained_dir": model_pre_trained_dir,
"iter_from": iter_from,
"lr_stage1": lr_stage1,
"lr_stage2": lr_stage2,
"save_path": save_path,
'note': note
}
txt_name = 'parameter_log.txt'
path = os.path.join(save_path, txt_name)
with codecs.open(path, mode='a', encoding='utf-8') as file_txt:
for key, value in parameter_dict.items():
file_txt.write(str(key) + ':' + str(value) + '\n')
# logging
logging.basicConfig(filename=save_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(parameter_dict)
# tensorboardX
writer = SummaryWriter(log_dir=save_path)
# label_dict
label_list = [0, 1]
""" data generator """
# load all data path
total_volume_path, total_label_path = util.gen_Pancreas_data_path(
data_path)
# 82 data -> 62 data for training
# 82 data -> 20 data for validation
train_volume_path, train_label_path, valid_volume_path, valid_label_path \
= util.divide_data2train_valid(total_volume_path, total_label_path, 62, seed1)
# dataset
# training
labeled_idxs = list(range(12))
unlabeled_idxs = list(range(12, 62))
batch_sampler = TwoStreamBatchSampler(labeled_idxs, unlabeled_idxs,
batch_size_stage2,
batch_size_stage2 - labeled_bs)
train_stage1_dataset = Pancreas(
train_volume_path[0:12],
train_label_path[0:12],
transform_views=[
transforms.Compose([RandomNoise(), ToTensor()]),
transforms.Compose([RandomNoise(),
Rotate((0, 2)),
ToTensor()]),
transforms.Compose([RandomNoise(),
Rotate((1, 2)),
ToTensor()])
])
train_stage2_dataset = Pancreas(
train_volume_path,
train_label_path,
transform_views=[
transforms.Compose([RandomNoise(), ToTensor()]),
transforms.Compose([RandomNoise(),
Rotate((0, 2)),
ToTensor()]),
transforms.Compose([RandomNoise(),
Rotate((1, 2)),
ToTensor()])
])
# validation
# valid_dataset = Pancreas(valid_volume_path, valid_label_path,
# transform=transforms.Compose([ToTensor]))
# dataloader
def worker_init_fn(worker_id):
random.seed(seed1 + worker_id)
train_stage1_dataloader = DataLoader(train_stage1_dataset,
batch_size=batch_size_stage1,
shuffle=True,
num_workers=4,
pin_memory=True,
worker_init_fn=worker_init_fn)
train_stage2_dataloader = DataLoader(train_stage2_dataset,
batch_sampler=batch_sampler,
num_workers=4,
pin_memory=True,
worker_init_fn=worker_init_fn)
""" model, optimizer, loss """
model_multi_views = []
for i in range(view_num):
model_view = UNet3D(1, 2, has_dropout=True).to("cuda:%d" % i)
model_multi_views.append(model_view)
if iter_from != 0:
for i in range(view_num):
model_multi_views[i].load_state_dict(
torch.load("%s/model_view%d_%d.ckpt" %
(model_pre_trained_dir, i + 1, iter_from)))
optimizer_stage1_multi_views = []
optimizer_stage2_multi_views = []
for i in range(view_num):
# view-wise training stage, lr=7e-3, m=0.9, weight decay = 4e-5, iterations = 20k (原文)
optimizer_stage1_view = torch.optim.SGD(
model_multi_views[i].parameters(),
lr=lr_stage1,
momentum=0.9,
weight_decay=4e-5)
# optimizer_stage1_view = torch.optim.Adam(model_multi_views[i].parameters(), lr=lr_stage1)
optimizer_stage1_multi_views.append(optimizer_stage1_view)
# co-training stage, constant lr=1e-3, iterations = 5k
optimizer_stage2_view = torch.optim.SGD(
model_multi_views[i].parameters(), lr=lr_stage2)
optimizer_stage2_multi_views.append(optimizer_stage2_view)
criterion1 = nn.CrossEntropyLoss()
criterion2 = dice_loss
n_total_iter = 0
if n_total_iter_from != 0:
n_total_iter = n_total_iter_from
for epoch in range(n_epochs):
if n_total_iter < n_iter_view_wise:
"""
view-wise training
"""
for batch_index, sample_batch_views in enumerate(
train_stage1_dataloader):
# start_time
start = time.time()
# ------------------
# Loading data
# ------------------
for i, sample_batch in enumerate(sample_batch_views):
sample_batch['volume'] = sample_batch['volume'].to(
"cuda:%d" % i).float()
sample_batch['label'] = sample_batch['label'].to(
"cuda:%d" % i).float()
# labeled data(volume, label)
# [view_num * (bs, 1, 128, 128, 128)]
labeled_volume_batch_views = [
sample_batch_views[i]['volume'] for i in range(view_num)
]
# [view_num * (bs, 1, 128, 128, 128)]
label_batch_views = [
sample_batch_views[i]['label'] for i in range(view_num)
]
# ------------------
# Train model
# ------------------
# zeros the parameter gradients
for i in range(view_num):
optimizer_stage1_multi_views[i].zero_grad()
# run 3D U-Net model on labeled data with multi-views
# [view_num * (bs, 2, 128, 128, 128)]
pred_labeled_views = [
model_multi_views[i](labeled_volume_batch_views[i])
for i in range(view_num)
]
pred_labeled_softmax_views = [
F.softmax(pred_labeled_views[i], dim=1)
for i in range(view_num)
]
# supervised loss
loss_sup1_views = []
loss_sup2_views = []
loss_sup_total_views = []
for i in range(view_num):
label_batch_stand_view = util.standardized_seg(
label_batch_views[i], label_list, "cuda:%d" % i)
loss_sup1 = criterion1(pred_labeled_views[i],
label_batch_stand_view)
loss_sup1_views.append(loss_sup1)
label_batch_one_hot_view = util.onehot(
label_batch_views[i], label_list, "cuda:%d" % i)
loss_sup2 = criterion2(
pred_labeled_softmax_views[i][:, 1, :, :, :],
label_batch_one_hot_view[:, 1, :, :, :])
loss_sup2_views.append(loss_sup2)
loss_sup_total_views.append(0.5 * loss_sup1 +
0.5 * loss_sup2)
for i in range(view_num):
loss_sup_total_views[i].backward()
optimizer_stage1_multi_views[i].step()
# ---------------------
# Print log
# ---------------------
# logging, tensorboard
# Determine approximate time left
end = time.time()
iter_left = (n_epochs - epoch) * (
len(train_stage1_dataloader) - batch_index)
time_left = datetime.timedelta(seconds=iter_left *
(end - start))
# print log
logging.info(
"[Epoch: %4d/%d] [n_total_iter: %5d] [labeled volume index: %2d/%d] "
"[loss view1: %f] [loss view2: %f] [loss view3: %f] [ETA: %s]"
% (epoch, n_epochs, n_total_iter + 1, batch_index + 1,
len(train_stage1_dataloader),
loss_sup_total_views[0].item(),
loss_sup_total_views[1].item(),
loss_sup_total_views[2].item(), time_left))
# tensorboardX log writer
for i in range(view_num):
writer.add_scalar("loss_view%d/loss_sup_total" % i,
loss_sup_total_views[i].item(),
global_step=n_total_iter)
writer.add_scalar("loss_view%d/loss_sup_CrossEntropy" % i,
loss_sup1_views[i].item(),
global_step=n_total_iter)
writer.add_scalar("loss_view%d/loss_sup_Dice" % i,
loss_sup2_views[i].item(),
global_step=n_total_iter)
# ---------------------
# Save model
# ---------------------
if n_total_iter % n_save_iter == 0:
# Save model checkpoints
for i in range(view_num):
torch.save(
model_multi_views[i].state_dict(),
"%s/model_view%d_%d.ckpt" %
(save_path, i + 1, n_total_iter))
logging.info("save model : %s/model_view%d_%d.ckpt" %
(save_path, i + 1, n_total_iter))
n_total_iter += 1
else:
"""
co-training
"""
for batch_index, sample_batch_views in enumerate(
train_stage2_dataloader):
# start_time
start = time.time()
# ------------------
# Loading data
# ------------------
for i, sample_batch in enumerate(sample_batch_views):
sample_batch['volume'] = sample_batch['volume'].to(
"cuda:%d" % i).float()
sample_batch['label'] = sample_batch['label'].to(
"cuda:%d" % i).float()
# labeled data(volume, label)
# [view_num * (bs, 1, 128, 128, 128)]
labeled_volume_batch_views = [
sample_batch_views[i]['volume'][:labeled_bs]
for i in range(view_num)
]
# [view_num * (bs, 1, 128, 128, 128)]
label_batch_views = [
sample_batch_views[i]['label'][:labeled_bs]
for i in range(view_num)
]
# unlabeled data (volume)
# [view_num * (bs, 1, 128, 128, 128)]
unlabeled_volume_batch_views = [
sample_batch_views[i]['volume'][labeled_bs:]
for i in range(view_num)
]
# ------------------
# Train model
# ------------------
# zeros the parameter gradients
for i in range(view_num):
optimizer_stage2_multi_views[i].zero_grad()
# run 3D U-Net model on labeled data with multi-views
# [view_num * (bs, 2, 128, 128, 128)]
pred_labeled_views = [
model_multi_views[i](labeled_volume_batch_views[i])
for i in range(view_num)
]
pred_labeled_softmax_views = [
F.softmax(pred_labeled_views[i], dim=1)
for i in range(view_num)
]
# run 3D U-Net model on unlabeled data with multi-views
pred_unlabeled_views = [
model_multi_views[i](unlabeled_volume_batch_views[i])
for i in range(view_num)
]
pred_unlabeled_softmax_views = [
F.softmax(pred_unlabeled_views[i], dim=1)
for i in range(view_num)
]
# run 3D U-Net bayesian model on unlabeled data with multi-views
T = 8
unlabeled_volume_batch_repeat_views = [
unlabeled_volume_batch_views[i].repeat(2, 1, 1, 1, 1)
for i in range(view_num)
]
stride = unlabeled_volume_batch_repeat_views[0].shape[0] // 2
pred_unlabeled_bayes_views = [
torch.zeros([stride * T, 2, 128, 128,
128]).to('cuda:%d' % i)
for i in range(view_num)
]
for t in range(T // 2):
noise_unlabeled_volume_views = [
unlabeled_volume_batch_repeat_views[i] + torch.clamp(
torch.rand_like(
unlabeled_volume_batch_repeat_views[i]) * 0.1,
-0.2, 0.2).to('cuda:%d' % i)
for i in range(view_num)
]
with torch.no_grad():
for i in range(view_num):
pred_unlabeled_bayes_views[
i][2 * stride * t:2 * stride *
(t + 1)] = model_multi_views[i](
noise_unlabeled_volume_views[i])
pred_unlabeled_bayes_views = [
F.softmax(pred_unlabeled_bayes_views[i], dim=1)
for i in range(view_num)
]
pred_unlabeled_bayes_views = [
pred_unlabeled_bayes_views[i].reshape(
T, stride, 2, 128, 128, 128) for i in range(view_num)
]
pred_unlabeled_bayes_views = [
torch.mean(pred_unlabeled_bayes_views[i], dim=0)
for i in range(view_num)
] # [view_num * (bs, 2, 128, 128, 128)]
uncertainty_views = [
-1.0 *
torch.sum(pred_unlabeled_bayes_views[i] *
torch.log(pred_unlabeled_bayes_views[i] + 1e-6),
dim=1,
keepdim=True) for i in range(view_num)
] # [view_num * (bs, 1, 128, 128, 128)]
# turn uncertainty and pred_unlabeled into the same view
rotate_axes = [(0, 2), (1, 2)]
for i in range(view_num):
if i == 0:
continue
else:
pred_unlabeled_softmax_views[
i] = pred_unlabeled_softmax_views[i].rot90(
dims=rotate_axes[i + 1], k=-1)
pred_unlabeled_views[i] = pred_unlabeled_views[
i].rot90(dims=rotate_axes[i + 1], k=-1)
uncertainty_views[i] = uncertainty_views[i].rot90(
dims=rotate_axes[i + 1], k=-1)
# label fusion
pseudo_label_views = []
for i in range(view_num):
top = torch.zeros_like(pred_unlabeled_softmax_views[i]).to(
'cuda:%d' % i)
down = torch.zeros_like(uncertainty_views[i]).to(
'cuda:%d' % i)
for j in range(view_num):
if i == j:
continue
else:
top += (pred_unlabeled_softmax_views[j] /
uncertainty_views[j]).to('cuda:%d' % i)
down += 1 / uncertainty_views[j].to('cuda:%d' % i)
pseudo_label = top / down
pseudo_label = torch.argmax(pseudo_label, dim=1)
pseudo_label_views.append(
pseudo_label) # [view_num * (bs, 128, 128, 128)]
# supervised loss
loss_sup1_views = []
loss_sup2_views = []
loss_sup_total_views = []
for i in range(view_num):
label_batch_stand_view = util.standardized_seg(
label_batch_views[i], label_list, "cuda:%d" % i)
loss_sup1 = criterion1(pred_labeled_views[i],
label_batch_stand_view)
loss_sup1_views.append(loss_sup1)
label_batch_one_hot_view = util.onehot(
label_batch_views[i], label_list, "cuda:%d" % i)
loss_sup2 = criterion2(
pred_labeled_softmax_views[i][:, 1, :, :, :],
label_batch_one_hot_view[:, 1, :, :, :])
loss_sup2_views.append(loss_sup2)
loss_sup_total_views.append(0.5 * loss_sup1 +
0.5 * loss_sup2)
# co-training loss
loss_cot1_views = []
loss_cot2_views = []
loss_cot_total_views = []
for i in range(view_num):
loss_cot1 = criterion1(pred_unlabeled_views[i],
pseudo_label_views[i])
loss_cot1_views.append(loss_cot1)
pseudo_label_view = pseudo_label_views[i].unsqueeze(
dim=1) # (bs, 1, 128, 128, 128)
pseudo_label_one_hot_view = util.onehot(
pseudo_label_view, label_list, "cuda:%d" % i)
loss_cot2 = criterion2(
pred_unlabeled_softmax_views[i][:, 1, :, :, :],
pseudo_label_one_hot_view[:, 1, :, :, :])
loss_cot2_views.append(loss_cot2)
loss_cot_total_views.append(0.5 * loss_cot1 +
0.5 * loss_cot2)
# backwards and optimize
loss_total_views = [
loss_sup_total_views[i] + 0.2 * loss_cot_total_views[i]
for i in range(view_num)
]
for i in range(view_num):
loss_total_views[i].backward()
optimizer_stage2_multi_views[i].step()
# ---------------------
# Print log
# ---------------------
# logging, tensorboard
# Determine approximate time left
end = time.time()
iter_left = (n_epochs - epoch) * (
len(train_stage2_dataloader) - batch_index)
time_left = datetime.timedelta(seconds=iter_left *
(end - start))
# print log
logging.info(
"[Epoch: %4d/%d] [n_total_iter: %5d] [labeled volume index: %2d/%d] "
"[loss view1: %f] [loss view2: %f] [loss view3: %f][ETA: %s]"
% (epoch, n_epochs, n_total_iter + 1, batch_index + 1,
len(train_stage2_dataloader),
loss_total_views[0].item(), loss_total_views[1].item(),
loss_total_views[2].item(), time_left))
# tensorboardX log writer
for i in range(view_num):
writer.add_scalar("loss_view%d/loss_SupCot_total" % i,
loss_total_views[i].item(),
global_step=n_total_iter)
writer.add_scalar("loss_view%d/loss_sup_total" % i,
loss_sup_total_views[i].item(),
global_step=n_total_iter)
writer.add_scalar("loss_view%d/loss_sup_CrossEntropy" % i,
loss_sup1_views[i].item(),
global_step=n_total_iter)
writer.add_scalar("loss_view%d/loss_sup_Dice" % i,
loss_sup2_views[i].item(),
global_step=n_total_iter)
writer.add_scalar("loss_view%d/loss_cot_total" % i,
loss_cot_total_views[i].item(),
global_step=n_total_iter)
writer.add_scalar("loss_view%d/loss_cot_CrossEntropy" % i,
loss_cot1_views[i].item(),
global_step=n_total_iter)
writer.add_scalar("loss_view%d/loss_cot_Dice" % i,
loss_cot2_views[i].item(),
global_step=n_total_iter)
# ---------------------
# save model
# ---------------------
if n_total_iter % n_save_iter == 0:
# Save model checkpoints
for i in range(view_num):
torch.save(
model_multi_views[i].state_dict(),
"%s/model_view%d_%d.ckpt" %
(save_path, i + 1, n_total_iter))
logging.info("save model : %s/model_view%d_%d.ckpt" %
(save_path, i + 1, n_total_iter))
n_total_iter += 1
for i in range(view_num):
torch.save(
model_multi_views[i].state_dict(),
"%s/model_view%d_%d.ckpt" % (save_path, i + 1, n_total_iter))
logging.info("save model : %s/model_view%d_%d.ckpt" %
(save_path, i + 1, n_total_iter))
writer.close()
