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trainer.py
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trainer.py
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import logging
import os
import random
import sys
import torch
import torch.nn as nn
import torch.optim as optim
from tensorboardX import SummaryWriter
from torch.nn.modules.loss import CrossEntropyLoss
from torch.utils.data import DataLoader
from torchvision import transforms
from tqdm import tqdm
from utils import DiceLoss
def trainer_synapse(args, model, snapshot_path):
from datasets.dataset_synapse import Synapse_dataset, RandomGenerator
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))
base_lr = args.base_lr
num_classes = args.num_classes
batch_size = args.batch_size * args.n_gpu
# max_iterations = args.max_iterations
db_train = Synapse_dataset(base_dir=args.root_path, list_dir=args.list_dir, split="train",
transform=transforms.Compose(
[RandomGenerator(output_size=[args.img_size, args.img_size])]))
db_val = Synapse_dataset(base_dir=args.root_path, list_dir=args.list_dir, split="val",
transform=transforms.Compose(
[RandomGenerator(output_size=[args.img_size, args.img_size])]))
print("The length of train set is: {}".format(len(db_train)))
def worker_init_fn(worker_id):
random.seed(args.seed + worker_id)
train_loader = DataLoader(db_train, batch_size=batch_size, shuffle=True, num_workers=args.num_workers,
pin_memory=True,
worker_init_fn=worker_init_fn)
val_loader = DataLoader(db_train, batch_size=batch_size, shuffle=False, num_workers=args.num_workers,
pin_memory=True,
worker_init_fn=worker_init_fn)
if args.n_gpu > 1:
model = nn.DataParallel(model)
model.train()
ce_loss = CrossEntropyLoss()
dice_loss = DiceLoss(num_classes)
optimizer = optim.SGD(model.parameters(), lr=base_lr, momentum=0.9, weight_decay=0.0001)
writer = SummaryWriter(snapshot_path + '/log')
iter_num = 0
max_epoch = args.max_epochs
max_iterations = args.max_epochs * len(train_loader) # max_epoch = max_iterations // len(trainloader) + 1
logging.info("{} iterations per epoch. {} max iterations ".format(len(train_loader), max_iterations))
iterator = tqdm(range(max_epoch), ncols=70)
best_loss = 10e10
for epoch_num in iterator:
model.train()
batch_dice_loss = 0
batch_ce_loss = 0
for i_batch, sampled_batch in tqdm(enumerate(train_loader), desc=f"Train: {epoch_num}", total=len(train_loader),
leave=False):
image_batch, label_batch = sampled_batch['image'], sampled_batch['label']
image_batch, label_batch = image_batch.cuda(), label_batch.cuda()
outputs = model(image_batch)
loss_ce = ce_loss(outputs, label_batch[:].long())
loss_dice = dice_loss(outputs, label_batch, softmax=True)
loss = 0.4 * loss_ce + 0.6 * loss_dice
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)
# logging.info('Train: iteration : %d/%d, lr : %f, loss : %f, loss_ce: %f, loss_dice: %f' % (
# iter_num, epoch_num, lr_, loss.item(), loss_ce.item(), loss_dice.item()))
batch_dice_loss += loss_dice.item()
batch_ce_loss += loss_ce.item()
if iter_num % 20 == 0:
image = image_batch[1, 0:1, :, :]
image = (image - image.min()) / (image.max() - image.min())
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)
batch_ce_loss /= len(train_loader)
batch_dice_loss /= len(train_loader)
batch_loss = 0.4 * batch_ce_loss + 0.6 * batch_dice_loss
logging.info('Train epoch: %d : loss : %f, loss_ce: %f, loss_dice: %f' % (
epoch_num, batch_loss, batch_ce_loss, batch_dice_loss))
if (epoch_num + 1) % args.eval_interval == 0:
model.eval()
batch_dice_loss = 0
batch_ce_loss = 0
with torch.no_grad():
for i_batch, sampled_batch in tqdm(enumerate(val_loader), desc=f"Val: {epoch_num}",
total=len(val_loader), leave=False):
image_batch, label_batch = sampled_batch['image'], sampled_batch['label']
image_batch, label_batch = image_batch.cuda(), label_batch.cuda()
outputs = model(image_batch)
loss_ce = ce_loss(outputs, label_batch[:].long())
loss_dice = dice_loss(outputs, label_batch, softmax=True)
batch_dice_loss += loss_dice.item()
batch_ce_loss += loss_ce.item()
batch_ce_loss /= len(val_loader)
batch_dice_loss /= len(val_loader)
batch_loss = 0.4 * batch_ce_loss + 0.6 * batch_dice_loss
logging.info('Val epoch: %d : loss : %f, loss_ce: %f, loss_dice: %f' % (
epoch_num, batch_loss, batch_ce_loss, batch_dice_loss))
if batch_loss < best_loss:
save_mode_path = os.path.join(snapshot_path, 'best_model.pth')
torch.save(model.state_dict(), save_mode_path)
best_loss = batch_loss
else:
save_mode_path = os.path.join(snapshot_path, 'last_model.pth')
torch.save(model.state_dict(), save_mode_path)
logging.info("save model to {}".format(save_mode_path))
writer.close()
return "Training Finished!"