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- import os
- import argparse
-
- import torch
- import torch.optim as optim
- import torchvision
- from torch.utils.tensorboard import SummaryWriter
- from torchvision import transforms
-
- from my_dataset import MyDataSet
- from model import swin_tiny_patch4_window7_224 as create_model
- from utils import read_split_data, train_one_epoch, evaluate
-
-
- def main(args):
- device = torch.device(args.device if torch.cuda.is_available() else "cpu")
-
- if os.path.exists("./weights") is False:
- os.makedirs("./weights")
-
- tb_writer = SummaryWriter()
-
- train_images_path, train_images_label, val_images_path, val_images_label = read_split_data(args.data_path)
-
- img_size = 224
- data_transform = {
- "train": transforms.Compose([transforms.RandomResizedCrop(img_size),
- transforms.RandomHorizontalFlip(),
- transforms.ToTensor(),
- transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]),
- "val": transforms.Compose([transforms.Resize(int(img_size * 1.143)),
- transforms.CenterCrop(img_size),
- transforms.ToTensor(),
- transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])}
-
- # # 实例化训练数据集
- # train_dataset = MyDataSet(images_path=train_images_path,
- # images_class=train_images_label,
- # transform=data_transform["train"])
- #
- # # 实例化验证数据集
- # val_dataset = MyDataSet(images_path=val_images_path,
- # images_class=val_images_label,
- # transform=data_transform["val"])
-
- train_dataset = torchvision.datasets.CIFAR10(root=args.data_path, train=True, transform=data_transform["train"],
- download=True)
- val_dataset = torchvision.datasets.CIFAR10(root=args.data_path, train=False, transform=data_transform["val"],
- download=True)
-
- batch_size = args.batch_size
- nw = min([os.cpu_count(), batch_size if batch_size > 1 else 0, 8]) # number of workers
- print('Using {} dataloader workers every process'.format(nw))
- train_loader = torch.utils.data.DataLoader(train_dataset,
- batch_size=batch_size,
- shuffle=True)
- # pin_memory=True,
- # num_workers=nw,
- # collate_fn=train_dataset.collate_fn)
-
- val_loader = torch.utils.data.DataLoader(val_dataset,
- batch_size=batch_size,
- shuffle=False)
- # pin_memory=True,
- # num_workers=nw,
- # collate_fn=val_dataset.collate_fn)
-
- model = create_model(num_classes=args.num_classes).to(device)
-
- if args.weights != "":
- assert os.path.exists(args.weights), "weights file: '{}' not exist.".format(args.weights)
- weights_dict = torch.load(args.weights, map_location=device)["model"]
- # 删除有关分类类别的权重
- for k in list(weights_dict.keys()):
- if "head" in k:
- del weights_dict[k]
- print(model.load_state_dict(weights_dict, strict=False))
-
- if args.freeze_layers:
- for name, para in model.named_parameters():
- # 除head外,其他权重全部冻结
- if "head" not in name:
- para.requires_grad_(False)
- else:
- print("training {}".format(name))
-
- pg = [p for p in model.parameters() if p.requires_grad]
- optimizer = optim.AdamW(pg, lr=args.lr, weight_decay=5E-2)
-
- for epoch in range(args.epochs):
- # train
- train_loss, train_acc = train_one_epoch(model=model,
- optimizer=optimizer,
- data_loader=train_loader,
- device=device,
- epoch=epoch)
-
- # validate
- val_loss, val_acc = evaluate(model=model,
- data_loader=val_loader,
- device=device,
- epoch=epoch)
-
- tags = ["train_loss", "train_acc", "val_loss", "val_acc", "learning_rate"]
- tb_writer.add_scalar(tags[0], train_loss, epoch)
- tb_writer.add_scalar(tags[1], train_acc, epoch)
- tb_writer.add_scalar(tags[2], val_loss, epoch)
- tb_writer.add_scalar(tags[3], val_acc, epoch)
- tb_writer.add_scalar(tags[4], optimizer.param_groups[0]["lr"], epoch)
-
- torch.save(model.state_dict(), "./weights/model-{}.pth".format(epoch))
-
-
- if __name__ == '__main__':
- parser = argparse.ArgumentParser()
- parser.add_argument('--num_classes', type=int, default=10)
- parser.add_argument('--epochs', type=int, default=10)
- parser.add_argument('--batch-size', type=int, default=4)
- parser.add_argument('--lr', type=float, default=0.0001)
-
- # 数据集所在根目录
- # https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz
- parser.add_argument('--data-path', type=str,
- default="data")
- # 预训练权重路径,如果不想载入就设置为空字符
- parser.add_argument('--weights', type=str, default='swin_tiny_patch4_window7_224.pth',
- help='initial weights path')
- # 是否冻结权重
- parser.add_argument('--freeze-layers', type=bool, default=False)
- parser.add_argument('--device', default='cpu', help='device id (i.e. 0 or 0,1 or cpu)')
-
- opt = parser.parse_args()
-
- main(opt)
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