pytorch读取本地的mnist数据集【实测成功】

仅作为记录,大佬请跳过。

感谢大佬博主——传送门

步骤:

1、博主在mnist数据集官方网站,下载到了笔记本的e盘的data文件夹里:

pytorch读取本地的mnist数据集【实测成功】_第1张图片

2、用pytorch直接读取e盘里的mnist数据集

而不用train_dataset = datasets.MNIST(’./data’, train=True, download=True, transform=transforms.ToTensor())再次下载的方式)

直接上代码

# 加载MNIST数据集

# #************************************a2torchloadlocalminist*********************************************************
class DealDataset(Dataset):
    """
        读取数据、初始化数据
    """

    def __init__(self, folder, data_name, label_name, transform=None):
        (train_set, train_labels) = load_data(folder, data_name,
                                              label_name)  # 其实也可以直接使用torch.load(),读取之后的结果为torch.Tensor形式
        self.train_set = train_set
        self.train_labels = train_labels
        self.transform = transform

    def __getitem__(self, index):
        img, target = self.train_set[index], int(self.train_labels[index])
        if self.transform is not None:
            img = self.transform(img)
        return img, target

    def __len__(self):
        return len(self.train_set)


def load_data(data_folder, data_name, label_name):
    with gzip.open(os.path.join(data_folder, label_name), 'rb') as lbpath:  # rb表示的是读取二进制数据
        y_train = np.frombuffer(lbpath.read(), np.uint8, offset=8)

    with gzip.open(os.path.join(data_folder, data_name), 'rb') as imgpath:
        x_train = np.frombuffer(
            imgpath.read(), np.uint8, offset=16).reshape(len(y_train), 28, 28)
    return (x_train, y_train)


train_dataset = DealDataset(r'E:\第一次实验\data', "train-images-idx3-ubyte.gz",
                           "train-labels-idx1-ubyte.gz", transform=transforms.ToTensor())
test_dataset = DealDataset(r'E:\第一次实验\data', "t10k-images-idx3-ubyte.gz",
                           "t10k-labels-idx1-ubyte.gz", transform=transforms.ToTensor())

# #************************************a2torchloadlocalminist*********************************************************

# train_dataset = datasets.MNIST('./data', train=True, download=True, transform=transforms.ToTensor())
# test_dataset = datasets.MNIST('./data', train=False, download=True, transform=transforms.ToTensor())

即只将最后两行代码,用上面的代码代替;(其余代码不变)

实测效果完全一样。

pytorch读取本地的mnist数据集【实测成功】_第2张图片


其余参考:(博主未尝试)

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