161 lines
6.6 KiB
Python
161 lines
6.6 KiB
Python
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import os
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import time
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import datetime
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from typing import Union, List
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import torch
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from torch.utils import data
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from src import u2net_full
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from train_utils import train_one_epoch, evaluate, get_params_groups, create_lr_scheduler
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from my_dataset import DUTSDataset
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import transforms as T
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class SODPresetTrain:
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def __init__(self, base_size: Union[int, List[int]], crop_size: int,
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hflip_prob=0.5, 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.Resize(base_size, resize_mask=True),
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T.RandomCrop(crop_size),
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T.RandomHorizontalFlip(hflip_prob),
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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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class SODPresetEval:
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def __init__(self, base_size: Union[int, List[int]], 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.Resize(base_size, resize_mask=False),
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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 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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# 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 = DUTSDataset(args.data_path, train=True, transforms=SODPresetTrain([320, 320], crop_size=288))
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val_dataset = DUTSDataset(args.data_path, train=False, transforms=SODPresetEval([320, 320]))
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num_workers = min([os.cpu_count(), batch_size if batch_size > 1 else 0, 8])
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train_data_loader = 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_data_loader = data.DataLoader(val_dataset,
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batch_size=1, # must be 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 = u2net_full()
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model.to(device)
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params_group = get_params_groups(model, weight_decay=args.weight_decay)
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optimizer = torch.optim.AdamW(params_group, lr=args.lr, weight_decay=args.weight_decay)
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lr_scheduler = create_lr_scheduler(optimizer, len(train_data_loader), args.epochs,
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warmup=True, warmup_epochs=2)
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scaler = torch.cuda.amp.GradScaler() if args.amp else None
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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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current_mae, current_f1 = 1.0, 0.0
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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_data_loader, device, epoch,
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lr_scheduler=lr_scheduler, print_freq=args.print_freq, scaler=scaler)
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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 epoch % args.eval_interval == 0 or epoch == args.epochs - 1:
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# 每间隔eval_interval个epoch验证一次，减少验证频率节省训练时间
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mae_metric, f1_metric = evaluate(model, val_data_loader, device=device)
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mae_info, f1_info = mae_metric.compute(), f1_metric.compute()
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print(f"[epoch: {epoch}] val_MAE: {mae_info:.3f} val_maxF1: {f1_info:.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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write_info = f"[epoch: {epoch}] train_loss: {mean_loss:.4f} lr: {lr:.6f} " \
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f"MAE: {mae_info:.3f} maxF1: {f1_info:.3f} \n"
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f.write(write_info)
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# save_best
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if current_mae >= mae_info and current_f1 <= f1_info:
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torch.save(save_file, "save_weights/model_best.pth")
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# only save latest 10 epoch weights
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if os.path.exists(f"save_weights/model_{epoch-10}.pth"):
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os.remove(f"save_weights/model_{epoch-10}.pth")
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torch.save(save_file, f"save_weights/model_{epoch}.pth")
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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 u2net training")
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parser.add_argument("--data-path", default="./", help="DUTS root")
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parser.add_argument("--device", default="cuda", help="training device")
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parser.add_argument("-b", "--batch-size", default=16, type=int)
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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("--epochs", default=360, type=int, metavar="N",
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help="number of total epochs to train")
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parser.add_argument("--eval-interval", default=10, type=int, help="validation interval default 10 Epochs")
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parser.add_argument('--lr', default=0.001, type=float, help='initial learning rate')
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parser.add_argument('--print-freq', default=50, 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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# Mixed precision training parameters
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parser.add_argument("--amp", action='store_true',
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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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