136 lines
5.7 KiB
Python
136 lines
5.7 KiB
Python
import os
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import argparse
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import torch
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import torch.optim as optim
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from torch.utils.tensorboard import SummaryWriter
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from torchvision import transforms
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from my_dataset import MyDataSet
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from model import mobile_vit_xx_small as create_model
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from utils import read_split_data, train_one_epoch, evaluate
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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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if os.path.exists("./weights") is False:
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os.makedirs("./weights")
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tb_writer = SummaryWriter()
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train_images_path, train_images_label, val_images_path, val_images_label = read_split_data(args.data_path)
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img_size = 224
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data_transform = {
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"train": transforms.Compose([transforms.RandomResizedCrop(img_size),
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transforms.RandomHorizontalFlip(),
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transforms.ToTensor(),
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transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]),
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"val": transforms.Compose([transforms.Resize(int(img_size * 1.143)),
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transforms.CenterCrop(img_size),
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transforms.ToTensor(),
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transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])}
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# 实例化训练数据集
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train_dataset = MyDataSet(images_path=train_images_path,
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images_class=train_images_label,
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transform=data_transform["train"])
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# 实例化验证数据集
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val_dataset = MyDataSet(images_path=val_images_path,
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images_class=val_images_label,
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transform=data_transform["val"])
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batch_size = args.batch_size
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nw = min([os.cpu_count(), batch_size if batch_size > 1 else 0, 8]) # number of workers
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print('Using {} dataloader workers every process'.format(nw))
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train_loader = torch.utils.data.DataLoader(train_dataset,
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batch_size=batch_size,
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shuffle=True,
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pin_memory=True,
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num_workers=nw,
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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=batch_size,
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shuffle=False,
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pin_memory=True,
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num_workers=nw,
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collate_fn=val_dataset.collate_fn)
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model = create_model(num_classes=args.num_classes).to(device)
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if args.weights != "":
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assert os.path.exists(args.weights), "weights file: '{}' not exist.".format(args.weights)
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weights_dict = torch.load(args.weights, map_location=device)
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weights_dict = weights_dict["model"] if "model" in weights_dict else weights_dict
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# 删除有关分类类别的权重
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for k in list(weights_dict.keys()):
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if "classifier" in k:
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del weights_dict[k]
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print(model.load_state_dict(weights_dict, strict=False))
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if args.freeze_layers:
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for name, para in model.named_parameters():
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# 除head外，其他权重全部冻结
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if "classifier" not in name:
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para.requires_grad_(False)
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else:
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print("training {}".format(name))
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pg = [p for p in model.parameters() if p.requires_grad]
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optimizer = optim.AdamW(pg, lr=args.lr, weight_decay=1E-2)
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best_acc = 0.
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for epoch in range(args.epochs):
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# train
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train_loss, train_acc = train_one_epoch(model=model,
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optimizer=optimizer,
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data_loader=train_loader,
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device=device,
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epoch=epoch)
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# validate
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val_loss, val_acc = evaluate(model=model,
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data_loader=val_loader,
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device=device,
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epoch=epoch)
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tags = ["train_loss", "train_acc", "val_loss", "val_acc", "learning_rate"]
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tb_writer.add_scalar(tags[0], train_loss, epoch)
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tb_writer.add_scalar(tags[1], train_acc, epoch)
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tb_writer.add_scalar(tags[2], val_loss, epoch)
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tb_writer.add_scalar(tags[3], val_acc, epoch)
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tb_writer.add_scalar(tags[4], optimizer.param_groups[0]["lr"], epoch)
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if val_acc > best_acc:
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best_acc = val_acc
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torch.save(model.state_dict(), "./weights/best_model.pth")
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torch.save(model.state_dict(), "./weights/latest_model.pth")
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('--num_classes', type=int, default=5)
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parser.add_argument('--epochs', type=int, default=10)
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parser.add_argument('--batch-size', type=int, default=8)
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parser.add_argument('--lr', type=float, default=0.0002)
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# 数据集所在根目录
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# https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz
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parser.add_argument('--data-path', type=str,
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default="/data/flower_photos")
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# 预训练权重路径，如果不想载入就设置为空字符
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parser.add_argument('--weights', type=str, default='./mobilevit_xxs.pt',
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help='initial weights path')
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# 是否冻结权重
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parser.add_argument('--freeze-layers', type=bool, default=False)
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parser.add_argument('--device', default='cuda:0', help='device id (i.e. 0 or 0,1 or cpu)')
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opt = parser.parse_args()
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main(opt)
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