import argparse
import logging
import os
import random
import shutil
import sys
import time
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from tensorboardX import SummaryWriter
from torch.nn import BCEWithLogitsLoss
from torch.nn.modules.loss import CrossEntropyLoss
from torch.utils.data import DataLoader
from torchvision import transforms
from torchvision.utils import make_grid
from tqdm import tqdm
from dataloaders import utils
from dataloaders.dataset import BaseDataSets, RandomGenerator
from networks.net_factory import net_factory
from utils import losses, metrics, ramps
from val_2D import test_single_volume, test_single_volume_ds
parser = argparse.ArgumentParser()
parser.add_argument('--root_path', type=str,
default='../data/ACDC', help='Name of Experiment')
parser.add_argument('--exp', type=str,
default='ACDC/Fully_Supervised', help='experiment_name')
parser.add_argument('--model', type=str,
default='unet', help='model_name')
parser.add_argument('--num_classes', type=int, default=4,
help='output channel of network')
parser.add_argument('--max_iterations', type=int,
default=30000, help='maximum epoch number to train')
parser.add_argument('--batch_size', type=int, default=24,
help='batch_size per gpu')
parser.add_argument('--deterministic', type=int, default=1,
help='whether use deterministic training')
parser.add_argument('--base_lr', type=float, default=0.01,
help='segmentation network learning rate')
parser.add_argument('--patch_size', type=list, default=[256, 256],
help='patch size of network input')
parser.add_argument('--seed', type=int, default=1337, help='random seed')
parser.add_argument('--labeled_num', type=int, default=50,
help='labeled data')
args = parser.parse_args()
def patients_to_slices(dataset, patiens_num):
ref_dict = None
if "ACDC" in dataset:
ref_dict = {"3": 68, "7": 136,
"14": 256, "21": 396, "28": 512, "35": 664, "140": 1312}
elif "Prostate":
ref_dict = {"2": 27, "4": 53, "8": 120,
"12": 179, "16": 256, "21": 312, "42": 623}
else:
print("Error")
return ref_dict[str(patiens_num)]
def train(args, snapshot_path):
base_lr = args.base_lr
num_classes = args.num_classes
batch_size = args.batch_size
max_iterations = args.max_iterations
labeled_slice = patients_to_slices(args.root_path, args.labeled_num)
model = net_factory(net_type=args.model, in_chns=1, class_num=num_classes)
db_train = BaseDataSets(base_dir=args.root_path, split="train", num=labeled_slice, transform=transforms.Compose([
RandomGenerator(args.patch_size)
]))
db_val = BaseDataSets(base_dir=args.root_path, split="val")
def worker_init_fn(worker_id):
random.seed(args.seed + worker_id)
trainloader = DataLoader(db_train, batch_size=batch_size, shuffle=True,
num_workers=16, pin_memory=True, worker_init_fn=worker_init_fn)
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)
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()
outputs = model(volume_batch)
outputs_soft = torch.softmax(outputs, dim=1)
loss_ce = ce_loss(outputs, label_batch[:].long())
loss_dice = dice_loss(outputs_soft, label_batch.unsqueeze(1))
loss = 0.5 * (loss_dice + loss_ce)
optimizer.zero_grad()
loss.backward()
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)
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()
metric_list = 0.0
for i_batch, sampled_batch in enumerate(valloader):
metric_i = test_single_volume(
sampled_batch["image"], sampled_batch["label"], model, classes=num_classes)
metric_list += np.array(metric_i)
metric_list = metric_list / len(db_val)
for class_i in range(num_classes-1):
writer.add_scalar('info/val_{}_dice'.format(class_i+1),
metric_list[class_i, 0], iter_num)
writer.add_scalar('info/val_{}_hd95'.format(class_i+1),
metric_list[class_i, 1], iter_num)
performance = np.mean(metric_list, axis=0)[0]
mean_hd95 = np.mean(metric_list, axis=0)[1]
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!"
if __name__ == "__main__":
if not args.deterministic:
cudnn.benchmark = True
cudnn.deterministic = False
else:
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)
snapshot_path = "../model/{}_{}_labeled/{}".format(
args.exp, args.labeled_num, args.model)
if not os.path.exists(snapshot_path):
os.makedirs(snapshot_path)
if os.path.exists(snapshot_path + '/code'):
shutil.rmtree(snapshot_path + '/code')
shutil.copytree('.', snapshot_path + '/code',
shutil.ignore_patterns(['.git', '__pycache__']))
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))
train(args, snapshot_path)