import os import time import datetime from typing import Union, List import torch from torch.utils import data from src import u2net_full from train_utils import train_one_epoch, evaluate, get_params_groups, create_lr_scheduler from my_dataset import DUTSDataset import transforms as T class SODPresetTrain: def __init__(self, base_size: Union[int, List[int]], crop_size: int, hflip_prob=0.5, mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)): self.transforms = T.Compose([ T.ToTensor(), T.Resize(base_size, resize_mask=True), T.RandomCrop(crop_size), T.RandomHorizontalFlip(hflip_prob), T.Normalize(mean=mean, std=std) ]) def __call__(self, img, target): return self.transforms(img, target) class SODPresetEval: def __init__(self, base_size: Union[int, List[int]], mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)): self.transforms = T.Compose([ T.ToTensor(), T.Resize(base_size, resize_mask=False), T.Normalize(mean=mean, std=std), ]) def __call__(self, img, target): return self.transforms(img, target) def main(args): device = torch.device(args.device if torch.cuda.is_available() else "cpu") batch_size = args.batch_size # 用来保存训练以及验证过程中信息 results_file = "results{}.txt".format(datetime.datetime.now().strftime("%Y%m%d-%H%M%S")) train_dataset = DUTSDataset(args.data_path, train=True, transforms=SODPresetTrain([320, 320], crop_size=288)) val_dataset = DUTSDataset(args.data_path, train=False, transforms=SODPresetEval([320, 320])) num_workers = min([os.cpu_count(), batch_size if batch_size > 1 else 0, 8]) train_data_loader = data.DataLoader(train_dataset, batch_size=batch_size, num_workers=num_workers, shuffle=True, pin_memory=True, collate_fn=train_dataset.collate_fn) val_data_loader = data.DataLoader(val_dataset, batch_size=1, # must be 1 num_workers=num_workers, pin_memory=True, collate_fn=val_dataset.collate_fn) model = u2net_full() model.to(device) params_group = get_params_groups(model, weight_decay=args.weight_decay) optimizer = torch.optim.AdamW(params_group, lr=args.lr, weight_decay=args.weight_decay) lr_scheduler = create_lr_scheduler(optimizer, len(train_data_loader), args.epochs, warmup=True, warmup_epochs=2) scaler = torch.cuda.amp.GradScaler() if args.amp else None if args.resume: checkpoint = torch.load(args.resume, map_location='cpu') model.load_state_dict(checkpoint['model']) optimizer.load_state_dict(checkpoint['optimizer']) lr_scheduler.load_state_dict(checkpoint['lr_scheduler']) args.start_epoch = checkpoint['epoch'] + 1 if args.amp: scaler.load_state_dict(checkpoint["scaler"]) current_mae, current_f1 = 1.0, 0.0 start_time = time.time() for epoch in range(args.start_epoch, args.epochs): mean_loss, lr = train_one_epoch(model, optimizer, train_data_loader, device, epoch, lr_scheduler=lr_scheduler, print_freq=args.print_freq, scaler=scaler) save_file = {"model": model.state_dict(), "optimizer": optimizer.state_dict(), "lr_scheduler": lr_scheduler.state_dict(), "epoch": epoch, "args": args} if args.amp: save_file["scaler"] = scaler.state_dict() if epoch % args.eval_interval == 0 or epoch == args.epochs - 1: # 每间隔eval_interval个epoch验证一次，减少验证频率节省训练时间 mae_metric, f1_metric = evaluate(model, val_data_loader, device=device) mae_info, f1_info = mae_metric.compute(), f1_metric.compute() print(f"[epoch: {epoch}] val_MAE: {mae_info:.3f} val_maxF1: {f1_info:.3f}") # write into txt with open(results_file, "a") as f: # 记录每个epoch对应的train_loss、lr以及验证集各指标 write_info = f"[epoch: {epoch}] train_loss: {mean_loss:.4f} lr: {lr:.6f} " \ f"MAE: {mae_info:.3f} maxF1: {f1_info:.3f} \n" f.write(write_info) # save_best if current_mae >= mae_info and current_f1 <= f1_info: torch.save(save_file, "save_weights/model_best.pth") # only save latest 10 epoch weights if os.path.exists(f"save_weights/model_{epoch-10}.pth"): os.remove(f"save_weights/model_{epoch-10}.pth") torch.save(save_file, f"save_weights/model_{epoch}.pth") total_time = time.time() - start_time total_time_str = str(datetime.timedelta(seconds=int(total_time))) print("training time {}".format(total_time_str)) def parse_args(): import argparse parser = argparse.ArgumentParser(description="pytorch u2net training") parser.add_argument("--data-path", default="./", help="DUTS root") parser.add_argument("--device", default="cuda", help="training device") parser.add_argument("-b", "--batch-size", default=16, type=int) parser.add_argument('--wd', '--weight-decay', default=1e-4, type=float, metavar='W', help='weight decay (default: 1e-4)', dest='weight_decay') parser.add_argument("--epochs", default=360, type=int, metavar="N", help="number of total epochs to train") parser.add_argument("--eval-interval", default=10, type=int, help="validation interval default 10 Epochs") parser.add_argument('--lr', default=0.001, type=float, help='initial learning rate') parser.add_argument('--print-freq', default=50, type=int, help='print frequency') parser.add_argument('--resume', default='', help='resume from checkpoint') parser.add_argument('--start-epoch', default=0, type=int, metavar='N', help='start epoch') # Mixed precision training parameters parser.add_argument("--amp", action='store_true', help="Use torch.cuda.amp for mixed precision training") args = parser.parse_args() return args if __name__ == '__main__': args = parse_args() if not os.path.exists("./save_weights"): os.mkdir("./save_weights") main(args)