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import argparse |
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import logging |
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import os |
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import random |
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import sys |
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import time |
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import numpy as np |
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import torch |
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import torch.nn as nn |
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import torch.optim as optim |
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from tensorboardX import SummaryWriter |
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from torch.nn.modules.loss import CrossEntropyLoss |
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from torch.utils.data import DataLoader |
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from tqdm import tqdm |
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from utils import DiceLoss |
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from torchvision import transforms |
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from utils import test_single_volume |
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def trainer_synapse(args, model, snapshot_path): |
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from datasets.dataset_synapse import Synapse_dataset, RandomGenerator |
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logging.basicConfig(filename=snapshot_path + "/log.txt", level=logging.INFO, |
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format='[%(asctime)s.%(msecs)03d] %(message)s', datefmt='%H:%M:%S') |
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logging.getLogger().addHandler(logging.StreamHandler(sys.stdout)) |
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logging.info(str(args)) |
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base_lr = args.base_lr |
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num_classes = args.num_classes |
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batch_size = args.batch_size * args.n_gpu |
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db_train = Synapse_dataset(base_dir=args.root_path, list_dir=args.list_dir, split="train", |
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transform=transforms.Compose( |
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[RandomGenerator(output_size=[args.img_size, args.img_size])])) |
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print("The length of train set is: {}".format(len(db_train))) |
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def worker_init_fn(worker_id): |
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random.seed(args.seed + worker_id) |
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trainloader = DataLoader(db_train, batch_size=batch_size, shuffle=True, num_workers=8, pin_memory=True, |
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worker_init_fn=worker_init_fn) |
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if args.n_gpu > 1: |
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model = nn.DataParallel(model) |
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model.train() |
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ce_loss = CrossEntropyLoss() |
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dice_loss = DiceLoss(num_classes) |
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optimizer = optim.SGD(model.parameters(), lr=base_lr, momentum=0.9, weight_decay=0.0001) |
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writer = SummaryWriter(snapshot_path + '/log') |
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iter_num = 0 |
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max_epoch = args.max_epochs |
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max_iterations = args.max_epochs * len(trainloader) |
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logging.info("{} iterations per epoch. {} max iterations ".format(len(trainloader), max_iterations)) |
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best_performance = 0.0 |
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iterator = tqdm(range(max_epoch), ncols=70) |
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for epoch_num in iterator: |
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for i_batch, sampled_batch in enumerate(trainloader): |
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image_batch, label_batch = sampled_batch['image'], sampled_batch['label'] |
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image_batch, label_batch = image_batch.cuda(), label_batch.cuda() |
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outputs = model(image_batch) |
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loss_ce = ce_loss(outputs, label_batch[:].long()) |
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loss_dice = dice_loss(outputs, label_batch, softmax=True) |
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loss = 0.4 * loss_ce + 0.6 * loss_dice |
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optimizer.zero_grad() |
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loss.backward() |
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optimizer.step() |
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lr_ = base_lr * (1.0 - iter_num / max_iterations) ** 0.9 |
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for param_group in optimizer.param_groups: |
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param_group['lr'] = lr_ |
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iter_num = iter_num + 1 |
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writer.add_scalar('info/lr', lr_, iter_num) |
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writer.add_scalar('info/total_loss', loss, iter_num) |
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writer.add_scalar('info/loss_ce', loss_ce, iter_num) |
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logging.info('iteration %d : loss : %f, loss_ce: %f' % (iter_num, loss.item(), loss_ce.item())) |
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if iter_num % 20 == 0: |
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image = image_batch[1, 0:1, :, :] |
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image = (image - image.min()) / (image.max() - image.min()) |
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writer.add_image('train/Image', image, iter_num) |
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outputs = torch.argmax(torch.softmax(outputs, dim=1), dim=1, keepdim=True) |
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writer.add_image('train/Prediction', outputs[1, ...] * 50, iter_num) |
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labs = label_batch[1, ...].unsqueeze(0) * 50 |
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writer.add_image('train/GroundTruth', labs, iter_num) |
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save_interval = 50 |
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if epoch_num > int(max_epoch / 2) and (epoch_num + 1) % save_interval == 0: |
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save_mode_path = os.path.join(snapshot_path, 'epoch_' + str(epoch_num) + '.pth') |
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torch.save(model.state_dict(), save_mode_path) |
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logging.info("save model to {}".format(save_mode_path)) |
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if epoch_num >= max_epoch - 1: |
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save_mode_path = os.path.join(snapshot_path, 'epoch_' + str(epoch_num) + '.pth') |
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torch.save(model.state_dict(), save_mode_path) |
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logging.info("save model to {}".format(save_mode_path)) |
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iterator.close() |
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break |
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writer.close() |
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return "Training Finished!" |
