import os
import sys
from sklearn.utils import shuffle
from tqdm import tqdm
from tensorboardX import SummaryWriter
import argparse
import logging
import time
import random
import torch
import torch.optim as optim
from torchvision import transforms
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
from torch.utils.data import DataLoader
from networks.vnet import VNet
from utils import ramps, losses
from dataloaders.la_heart import *
parser = argparse.ArgumentParser()
parser.add_argument('--dataset_name', type=str, default='LA', help='dataset_name')
parser.add_argument('--root_path', type=str, default='/data/omnisky/postgraduate/Yb/data_set/LASet/data',
help='Name of Experiment')
parser.add_argument('--exp', type=str, default='vnet', help='model_name')
parser.add_argument('--model', type=str, default='supervised', help='model_name')
parser.add_argument('--max_iterations', type=int, default=6000, help='maximum epoch number to train')
parser.add_argument('--batch_size', type=int, default=2, help='batch_size per gpu')
parser.add_argument('--labelnum', type=int, default=25, help='trained samples')
parser.add_argument('--max_samples', type=int, default=123, help='all samples')
parser.add_argument('--base_lr', type=float, default=0.01, help='maximum epoch number to train')
parser.add_argument('--deterministic', type=int, default=1, help='whether use deterministic training')
parser.add_argument('--seed', type=int, default=1337, help='random seed')
parser.add_argument('--gpu', type=str, default='1', help='GPU to use')
args = parser.parse_args()
num_classes = 2
patch_size = (112, 112, 80)
snapshot_path = "model/{}_{}_{}_labeled/{}".format(args.dataset_name, args.exp, args.labelnum, args.model)
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
batch_size = args.batch_size * len(args.gpu.split(','))
max_iterations = args.max_iterations
base_lr = args.base_lr
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)
def cal_dice(output, target, eps=1e-3):
output = torch.argmax(output,dim=1)
inter = torch.sum(output * target) + eps
union = torch.sum(output) + torch.sum(target) + eps * 2
dice = 2 * inter / union
return dice
if __name__ == "__main__":
# make logger file
if not os.path.exists(snapshot_path):
os.makedirs(snapshot_path)
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))
model = VNet(n_channels=1, n_classes=num_classes, normalization='batchnorm', has_dropout=True).cuda()
db_train = LAHeart(base_dir=args.root_path,
split='train',
num=args.labelnum,
transform=transforms.Compose([
RandomRotFlip(),
RandomCrop(patch_size),
ToTensor(),
]))
db_test = LAHeart(base_dir=args.root_path,
split='test',
transform=transforms.Compose([
CenterCrop(patch_size),
ToTensor()
]))
def worker_init_fn(worker_id):
random.seed(args.seed + worker_id)
train_loader = DataLoader(db_train, batch_size=args.batch_size,shuffle=True, num_workers=4, pin_memory=True,drop_last=True,
worker_init_fn=worker_init_fn)
test_loader = DataLoader(db_test, batch_size=1,shuffle=False, num_workers=4, pin_memory=True)
model.train()
optimizer = optim.SGD(model.parameters(), lr=base_lr, momentum=0.9, weight_decay=0.0001)
writer = SummaryWriter(snapshot_path + '/log')
logging.info("{} itertations per epoch".format(len(train_loader)))
iter_num = 0
best_dice = 0
max_epoch = max_iterations // len(train_loader) + 1
lr_ = base_lr
model.train()
for epoch_num in tqdm(range(max_epoch), ncols=70):
time1 = time.time()
for i_batch, sampled_batch in enumerate(train_loader):
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 = model(volume_batch)
# calculate the loss
loss_seg = F.cross_entropy(outputs, label_batch)
outputs_soft = F.softmax(outputs, dim=1)
loss_seg_dice = losses.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', loss, iter_num)
writer.add_scalar('loss/loss_seg', loss_seg, iter_num)
writer.add_scalar('loss/loss_seg_dice', loss_seg_dice, iter_num)
logging.info('iteration %d : loss : %f ' % (iter_num, loss.item()))
if iter_num >= 800 and iter_num % 200 == 0:
model.eval()
with torch.no_grad():
dice_sample = 0
for sampled_batch in test_loader:
img, lbl = sampled_batch['image'].cuda(), sampled_batch['label'].cuda()
outputs = model(img)
dice_once = cal_dice(outputs,lbl)
dice_sample += dice_once
dice_sample = dice_sample / len(test_loader)
print('Average center dice:{:.3f}'.format(dice_sample))
if dice_sample > best_dice:
best_dice = dice_sample
save_mode_path = os.path.join(snapshot_path, 'iter_{}_dice_{}.pth'.format(iter_num, best_dice))
save_best_path = 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_path)
logging.info("save best model to {}".format(save_mode_path))
writer.add_scalar('Var_dice/Dice', dice_sample, iter_num)
writer.add_scalar('Var_dice/Best_dice', best_dice, iter_num)
model.train()
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) + '.pth')
torch.save(model.state_dict(), save_mode_path)
logging.info("save model to {}".format(save_mode_path))
writer.close()
Datasets
Models
