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- import os
- import math
- import argparse
-
- import torch
- import torch.optim as optim
- from torchvision import transforms
- import torch.optim.lr_scheduler as lr_scheduler
-
- from model import efficientnetv2_l as create_model
- from my_dataset import MyDataSet
- from utils import read_split_data, train_one_epoch, evaluate, train_one_epoch2
-
-
- def main(args):
- device = torch.device(args.device if torch.cuda.is_available() else "cpu")
-
- print(args)
- print('Start Tensorboard with "tensorboard --logdir=runs", view at http://localhost:6006/')
- if os.path.exists("./weights") is False:
- os.makedirs("./weights")
- print(1)
- train_images_path, train_images_label, val_images_path, val_images_label = read_split_data(args.data_path)
-
- img_size = {"s": [300, 384], # train_size, val_size
- "m": [384, 480],
- "l": [384, 480]}
- num_model = "l"
-
- data_transform = {
- "train": transforms.Compose([transforms.RandomResizedCrop(img_size[num_model][0]),
- transforms.RandomHorizontalFlip(),
- transforms.ToTensor(),
- transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]),
- "val": transforms.Compose([transforms.Resize(img_size[num_model][1]),
- transforms.CenterCrop(img_size[num_model][1]),
- transforms.ToTensor(),
- transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])])}
-
- # 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"])
-
- batch_size = args.batch_size
- nw = min([os.cpu_count(), batch_size if batch_size > 1 else 0, 12]) # 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)
-
- # 如果存在预训练权重则载入
-
- model = create_model(num_classes=args.num_classes).to(device)
- # model = torch.nn.DataParallel(create_model(num_classes=args.num_classes), device_ids=device_ids)
-
- if args.weights != "":
- if os.path.exists(args.weights):
- weights_dict = torch.load(args.weights, map_location=device)
- load_weights_dict = {k: v for k, v in weights_dict.items()
- if model.state_dict()[k].numel() == v.numel()}
- print(model.load_state_dict(load_weights_dict, strict=False))
- else:
- raise FileNotFoundError("not found weights file: {}".format(args.weights))
-
- # 是否冻结权重
- 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.SGD(pg, lr=args.lr, momentum=0.9, weight_decay=1E-4)
- # Scheduler https://arxiv.org/pdf/1812.01187.pdf
- lf = lambda x: ((1 + math.cos(x * math.pi / args.epochs)) / 2) * (1 - args.lrf) + args.lrf # cosine
- scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
-
- for epoch in range(args.epochs):
- # train
- if args.advTrain:
- train_loss, train_acc = train_one_epoch2(model=model, optimizer=optimizer, data_loader=train_loader, device=device, epoch=epoch)
- else:
- train_loss, train_acc = train_one_epoch(model=model, optimizer=optimizer, data_loader=train_loader, device=device, epoch=epoch)
- scheduler.step()
-
- # validate
- val_loss, val_acc = evaluate(model=model,
- data_loader=val_loader,
- device=device,
- epoch=epoch)
-
- print('epoch: {} train_loss: {} train_acc:{} val_loss: {} val_acc: {}'.format(epoch, train_loss, train_acc, val_loss, val_acc))
-
- torch.save(model.state_dict(), "/model/model.pth".format(epoch))
-
- if __name__ == '__main__':
- parser = argparse.ArgumentParser()
- parser.add_argument('--num_classes', type=int, default=20)
- parser.add_argument('--epochs', type=int, default=300)
- parser.add_argument('--batch-size', type=int, default=8)
- parser.add_argument('--lr', type=float, default=0.01)
- parser.add_argument('--lrf', type=float, default=0.01)
-
- # 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="/dataset/classification_data")
-
- # download model weights
- # 链接: https://pan.baidu.com/s/1uZX36rvrfEss-JGj4yfzbQ 密码: 5gu1
- parser.add_argument('--weights', type=str, default='/code/Test11_efficientnetV2/pre_efficientnetv2-l.pth',
- help='initial weights path')
- parser.add_argument('--freeze-layers', type=bool, default=False)
- parser.add_argument('--device', default='cuda:0', help='device id (i.e. 0 or 0,1 or cpu)')
- parser.add_argument('--advTrain', default=True, help='is advtrain')
-
- opt = parser.parse_args()
-
- main(opt)
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