如何以相同顺序重新排列不同的日期加载器？

问题描述

traindir = os.path.join(args.data,"train")
valdir = os.path.join(args.data,"val")
normalize = transforms.normalize(mean=[0.485,0.456,0.406],std=[0.229,0.224,0.225])

train_dataset = datasets.ImageFolder(root=traindir,transform=transforms.Compose([
    transforms.RandomresizedCrop(224),transforms.RandomHorizontalFlip(),transforms.ToTensor(),normalize,]))
val_dataset = datasets.ImageFolder(root=valdir,transform=transforms.Compose([
    transforms.Resize(256),transforms.CenterCrop(224),]))


def worker_init_fn(worker_id):
    # seed=10
    # seed+=worker_id
    # np.random.seed(seed)
    # print(worker_id)
    print(np.random.get_state()[1][0])
    print(np.random.get_state()[1][0]+worker_id)
    np.random.seed(np.random.get_state()[1][0]+worker_id)

def _init_fn(worker_id):
    np.random.seed(42)
train_loader = torch.utils.data.DataLoader(
    dataset=train_dataset,batch_size=args.batch_size,shuffle=True,num_workers=args.workers,pin_memory=True)
train_loader2 = torch.utils.data.DataLoader(
    dataset=train_dataset,pin_memory=True)
train_loader3 = torch.utils.data.DataLoader(
    dataset=train_dataset,pin_memory=True)

val_loader = torch.utils.data.DataLoader(
    dataset=val_dataset,shuffle=False,# batch_size=len(val_dataset),pin_memory=True)
val_loader2 = torch.utils.data.DataLoader(
    dataset=val_dataset,pin_memory=True)
val_loader3 = torch.utils.data.DataLoader(
    dataset=val_dataset,pin_memory=True)


for i,dd in enumerate(zip(train_loader,train_loader2,train_loader3)):

    input1,target1=dd[0]
    input2,target2=dd[1]
    input3,target3=dd[2]

    print("1",target1)
    print("2",target2)
    print("3",target3)

有一个要求，应将同一数据标签的三份副本并行洗净，并读入三个不同的网络中以进行联合训练。但是，如果将shuffle设置为True，则Dateloader的三个副本将为每个批次产生不同的标签。因此，我们不能一起训练

解决方法

暂无找到可以解决该程序问题的有效方法，小编努力寻找整理中！

如果你已经找到好的解决方法，欢迎将解决方案带上本链接一起发送给小编。

小编邮箱:dio#foxmail.com (将#修改为@）

python pytorch shuffle