这篇教程Pytorch DataLoader shuffle验证方式写得很实用，希望能帮到您。
shuffle = False时，不打乱数据顺序 shuffle = True，随机打乱 import numpy as npimport h5pyimport torchfrom torch.utils.data import DataLoader, Dataset h5f = h5py.File('train.h5', 'w');data1 = np.array([[1,2,3], [2,5,6], [3,5,6], [4,5,6]])data2 = np.array([[1,1,1], [1,2,6], [1,3,6], [1,4,6]])h5f.create_dataset(str('data'), data=data1)h5f.create_dataset(str('label'), data=data2)class Dataset(Dataset): def __init__(self): h5f = h5py.File('train.h5', 'r') self.data = h5f['data'] self.label = h5f['label'] def __getitem__(self, index): data = torch.from_numpy(self.data[index]) label = torch.from_numpy(self.label[index]) return data, label def __len__(self): assert self.data.shape[0] == self.label.shape[0], "wrong data length" return self.data.shape[0] dataset_train = Dataset()loader_train = DataLoader(dataset=dataset_train, batch_size=2, shuffle = True) for i, data in enumerate(loader_train): train_data, label = data print(train_data) pytorch DataLoader使用细节背景：我一开始是对数据扩增这一块有疑问, 只看到了数据变换(torchvisiom.transforms),但是没看到数据扩增, 后来搞明白了, 数据扩增在pytorch指的是torchvisiom.transforms + torch.utils.data.DataLoader+多个epoch共同作用下完成的, 数据变换共有以下内容 composed = transforms.Compose([transforms.Resize((448, 448)), # resize transforms.RandomCrop(300), # random crop transforms.ToTensor(), transforms.Normalize(mean=[0.5, 0.5, 0.5], # normalize std=[0.5, 0.5, 0.5])]) 简单的数据读取类, 进返回PIL格式的image: class MyDataset(data.Dataset): def __init__(self, labels_file, root_dir, transform=None): with open(labels_file) as csvfile: self.labels_file = list(csv.reader(csvfile)) self.root_dir = root_dir self.transform = transform def __len__(self): return len(self.labels_file) def __getitem__(self, idx): im_name = os.path.join(root_dir, self.labels_file[idx][0]) im = Image.open(im_name) if self.transform: im = self.transform(im) return im 下面是主程序 labels_file = "F:/test_temp/labels.csv"root_dir = "F:/test_temp"dataset_transform = MyDataset(labels_file, root_dir, transform=composed)dataloader = data.DataLoader(dataset_transform, batch_size=1, shuffle=False)"""原始数据集共3张图片, 以batch_size=1, epoch为2 展示所有图片(共6张) """for eopch in range(2): plt.figure(figsize=(6, 6)) for ind, i in enumerate(dataloader): a = i[0, :, :, :].numpy().transpose((1, 2, 0)) plt.subplot(1, 3, ind+1) plt.imshow(a) 
从上述图片总可以看到, 在每个eopch阶段实际上是对原始图片重新使用了transform, , 这就造就了数据的扩增 以上为个人经验，希望能给大家一个参考，也希望大家多多支持51zixue.net。
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