202 lines
7.5 KiB
Python
202 lines
7.5 KiB
Python
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import os
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import time
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import datetime
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import torch
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from src import UNet
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from train_utils import train_one_epoch, evaluate, create_lr_scheduler
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from my_dataset import DriveDataset
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import transforms as T
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class SegmentationPresetTrain:
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def __init__(self, base_size, crop_size, hflip_prob=0.5, vflip_prob=0.5,
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mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)):
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min_size = int(0.5 * base_size)
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max_size = int(1.2 * base_size)
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trans = [T.RandomResize(min_size, max_size)]
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if hflip_prob > 0:
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trans.append(T.RandomHorizontalFlip(hflip_prob))
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if vflip_prob > 0:
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trans.append(T.RandomVerticalFlip(vflip_prob))
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trans.extend([
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T.RandomCrop(crop_size),
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T.ToTensor(),
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T.Normalize(mean=mean, std=std),
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])
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self.transforms = T.Compose(trans)
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def __call__(self, img, target):
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return self.transforms(img, target)
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class SegmentationPresetEval:
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def __init__(self, mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)):
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self.transforms = T.Compose([
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T.ToTensor(),
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T.Normalize(mean=mean, std=std),
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])
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def __call__(self, img, target):
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return self.transforms(img, target)
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def get_transform(train, mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)):
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base_size = 565
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crop_size = 480
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if train:
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return SegmentationPresetTrain(base_size, crop_size, mean=mean, std=std)
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else:
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return SegmentationPresetEval(mean=mean, std=std)
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def create_model(num_classes):
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model = UNet(in_channels=3, num_classes=num_classes, base_c=32)
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return model
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def main(args):
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device = torch.device(args.device if torch.cuda.is_available() else "cpu")
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batch_size = args.batch_size
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# segmentation nun_classes + background
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num_classes = args.num_classes + 1
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# using compute_mean_std.py
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mean = (0.709, 0.381, 0.224)
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std = (0.127, 0.079, 0.043)
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# 用来保存训练以及验证过程中信息
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results_file = "results{}.txt".format(datetime.datetime.now().strftime("%Y%m%d-%H%M%S"))
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train_dataset = DriveDataset(args.data_path,
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train=True,
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transforms=get_transform(train=True, mean=mean, std=std))
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val_dataset = DriveDataset(args.data_path,
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train=False,
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transforms=get_transform(train=False, mean=mean, std=std))
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num_workers = min([os.cpu_count(), batch_size if batch_size > 1 else 0, 8])
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train_loader = torch.utils.data.DataLoader(train_dataset,
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batch_size=batch_size,
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num_workers=num_workers,
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shuffle=True,
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pin_memory=True,
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collate_fn=train_dataset.collate_fn)
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val_loader = torch.utils.data.DataLoader(val_dataset,
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batch_size=1,
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num_workers=num_workers,
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pin_memory=True,
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collate_fn=val_dataset.collate_fn)
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model = create_model(num_classes=num_classes)
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model.to(device)
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params_to_optimize = [p for p in model.parameters() if p.requires_grad]
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optimizer = torch.optim.SGD(
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params_to_optimize,
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lr=args.lr, momentum=args.momentum, weight_decay=args.weight_decay
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)
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scaler = torch.cuda.amp.GradScaler() if args.amp else None
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# 创建学习率更新策略，这里是每个step更新一次(不是每个epoch)
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lr_scheduler = create_lr_scheduler(optimizer, len(train_loader), args.epochs, warmup=True)
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if args.resume:
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checkpoint = torch.load(args.resume, map_location='cpu')
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model.load_state_dict(checkpoint['model'])
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optimizer.load_state_dict(checkpoint['optimizer'])
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lr_scheduler.load_state_dict(checkpoint['lr_scheduler'])
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args.start_epoch = checkpoint['epoch'] + 1
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if args.amp:
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scaler.load_state_dict(checkpoint["scaler"])
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best_dice = 0.
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start_time = time.time()
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for epoch in range(args.start_epoch, args.epochs):
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mean_loss, lr = train_one_epoch(model, optimizer, train_loader, device, epoch, num_classes,
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lr_scheduler=lr_scheduler, print_freq=args.print_freq, scaler=scaler)
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confmat, dice = evaluate(model, val_loader, device=device, num_classes=num_classes)
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val_info = str(confmat)
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print(val_info)
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print(f"dice coefficient: {dice:.3f}")
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# write into txt
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with open(results_file, "a") as f:
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# 记录每个epoch对应的train_loss、lr以及验证集各指标
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train_info = f"[epoch: {epoch}]\n" \
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f"train_loss: {mean_loss:.4f}\n" \
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f"lr: {lr:.6f}\n" \
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f"dice coefficient: {dice:.3f}\n"
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f.write(train_info + val_info + "\n\n")
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if args.save_best is True:
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if best_dice < dice:
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best_dice = dice
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else:
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continue
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save_file = {"model": model.state_dict(),
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"optimizer": optimizer.state_dict(),
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"lr_scheduler": lr_scheduler.state_dict(),
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"epoch": epoch,
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"args": args}
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if args.amp:
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save_file["scaler"] = scaler.state_dict()
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if args.save_best is True:
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torch.save(save_file, "save_weights/best_model.pth")
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else:
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torch.save(save_file, "save_weights/model_{}.pth".format(epoch))
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total_time = time.time() - start_time
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total_time_str = str(datetime.timedelta(seconds=int(total_time)))
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print("training time {}".format(total_time_str))
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def parse_args():
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import argparse
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parser = argparse.ArgumentParser(description="pytorch unet training")
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parser.add_argument("--data-path", default="./", help="DRIVE root")
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# exclude background
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parser.add_argument("--num-classes", default=1, type=int)
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parser.add_argument("--device", default="cuda", help="training device")
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parser.add_argument("-b", "--batch-size", default=4, type=int)
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parser.add_argument("--epochs", default=200, type=int, metavar="N",
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help="number of total epochs to train")
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parser.add_argument('--lr', default=0.01, type=float, help='initial learning rate')
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parser.add_argument('--momentum', default=0.9, type=float, metavar='M',
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help='momentum')
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parser.add_argument('--wd', '--weight-decay', default=1e-4, type=float,
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metavar='W', help='weight decay (default: 1e-4)',
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dest='weight_decay')
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parser.add_argument('--print-freq', default=1, type=int, help='print frequency')
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parser.add_argument('--resume', default='', help='resume from checkpoint')
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parser.add_argument('--start-epoch', default=0, type=int, metavar='N',
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help='start epoch')
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parser.add_argument('--save-best', default=True, type=bool, help='only save best dice weights')
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# Mixed precision training parameters
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parser.add_argument("--amp", default=False, type=bool,
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help="Use torch.cuda.amp for mixed precision training")
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args = parser.parse_args()
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return args
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if __name__ == '__main__':
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args = parse_args()
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if not os.path.exists("./save_weights"):
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os.mkdir("./save_weights")
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main(args)
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