Question

我正在尝试将Torchvision MNIST训练和数据集转换为NumPy数组，但是找不到实际执行转换的文档。

我的目标是获取整个数据集并将其转换为单个NumPy数组，最好不要遍历整个数据集。

我看过How do I turn a Pytorch Dataloader into a numpy array to display image data with matplotlib?，但没有解决我的问题。

所以我的问题是，利用torch.utils.data.DataLoader，我将如何将数据集（训练/测试）转换为两个NumPy数组，以便所有示例都存在？

注意：我现在将批处理大小保留为默认值1；我可以将其设置为用于火车的60,000，用于测试的10,000，但是我宁愿不要使用这种魔术数字。

谢谢。

Answer 1

此任务无需使用from torchvision import datasets, transforms train_set = datasets.MNIST('./data', train=True, download=True) test_set = datasets.MNIST('./data', train=False, download=True) train_set_array = train_set.data.numpy() test_set_array = test_set.data.numpy()。

{{1}}

请注意，在这种情况下，目标被排除在外。

Answer 2

如果我对您的理解正确，那么您希望获得整个MNIST图像的训练数据集（总共60000张图像，每个图像的大小为1x28x28数组，颜色通道为1）作为大小为numpy的数组（60000、1、28 ，28）？

from torchvision import datasets, transforms
from torch.utils.data import DataLoader

# Transform to normalized Tensors 
transform = transforms.Compose([transforms.ToTensor(),
                                transforms.Normalize((0.1307,), (0.3081,))])

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


train_loader = DataLoader(train_dataset, batch_size=len(train_dataset))
# test_loader = DataLoader(test_dataset, batch_size=len(test_dataset))

train_dataset_array = next(iter(train_loader))[0].numpy()
# test_dataset_array = next(iter(test_loader))[0].numpy()

这是结果：

>>> train_dataset_array

array([[[[-0.42421296, -0.42421296, -0.42421296, ..., -0.42421296,
          -0.42421296, -0.42421296],
         [-0.42421296, -0.42421296, -0.42421296, ..., -0.42421296,
          -0.42421296, -0.42421296],
         [-0.42421296, -0.42421296, -0.42421296, ..., -0.42421296,
          -0.42421296, -0.42421296],
         ...,
         [-0.42421296, -0.42421296, -0.42421296, ..., -0.42421296,
          -0.42421296, -0.42421296],
         [-0.42421296, -0.42421296, -0.42421296, ..., -0.42421296,
          -0.42421296, -0.42421296],
         [-0.42421296, -0.42421296, -0.42421296, ..., -0.42421296,
          -0.42421296, -0.42421296]]],


       [[[-0.42421296, -0.42421296, -0.42421296, ..., -0.42421296,
          -0.42421296, -0.42421296],
         [-0.42421296, -0.42421296, -0.42421296, ..., -0.42421296,
          -0.42421296, -0.42421296],
         [-0.42421296, -0.42421296, -0.42421296, ..., -0.42421296,
          -0.42421296, -0.42421296],
         ...,
         [-0.42421296, -0.42421296, -0.42421296, ..., -0.42421296,
          -0.42421296, -0.42421296],
         [-0.42421296, -0.42421296, -0.42421296, ..., -0.42421296,
          -0.42421296, -0.42421296],
         [-0.42421296, -0.42421296, -0.42421296, ..., -0.42421296,
          -0.42421296, -0.42421296]]],


       [[[-0.42421296, -0.42421296, -0.42421296, ..., -0.42421296,
          -0.42421296, -0.42421296],
         [-0.42421296, -0.42421296, -0.42421296, ..., -0.42421296,
          -0.42421296, -0.42421296],
         [-0.42421296, -0.42421296, -0.42421296, ..., -0.42421296,
          -0.42421296, -0.42421296],
         ...,
         [-0.42421296, -0.42421296, -0.42421296, ..., -0.42421296,
          -0.42421296, -0.42421296],
         [-0.42421296, -0.42421296, -0.42421296, ..., -0.42421296,
          -0.42421296, -0.42421296],
         [-0.42421296, -0.42421296, -0.42421296, ..., -0.42421296,
          -0.42421296, -0.42421296]]],


       ...,


       [[[-0.42421296, -0.42421296, -0.42421296, ..., -0.42421296,
          -0.42421296, -0.42421296],
         [-0.42421296, -0.42421296, -0.42421296, ..., -0.42421296,
          -0.42421296, -0.42421296],
         [-0.42421296, -0.42421296, -0.42421296, ..., -0.42421296,
          -0.42421296, -0.42421296],
         ...,
         [-0.42421296, -0.42421296, -0.42421296, ..., -0.42421296,
          -0.42421296, -0.42421296],
         [-0.42421296, -0.42421296, -0.42421296, ..., -0.42421296,
          -0.42421296, -0.42421296],
         [-0.42421296, -0.42421296, -0.42421296, ..., -0.42421296,
          -0.42421296, -0.42421296]]],


       [[[-0.42421296, -0.42421296, -0.42421296, ..., -0.42421296,
          -0.42421296, -0.42421296],
         [-0.42421296, -0.42421296, -0.42421296, ..., -0.42421296,
          -0.42421296, -0.42421296],
         [-0.42421296, -0.42421296, -0.42421296, ..., -0.42421296,
          -0.42421296, -0.42421296],
         ...,
         [-0.42421296, -0.42421296, -0.42421296, ..., -0.42421296,
          -0.42421296, -0.42421296],
         [-0.42421296, -0.42421296, -0.42421296, ..., -0.42421296,
          -0.42421296, -0.42421296],
         [-0.42421296, -0.42421296, -0.42421296, ..., -0.42421296,
          -0.42421296, -0.42421296]]],


       [[[-0.42421296, -0.42421296, -0.42421296, ..., -0.42421296,
          -0.42421296, -0.42421296],
         [-0.42421296, -0.42421296, -0.42421296, ..., -0.42421296,
          -0.42421296, -0.42421296],
         [-0.42421296, -0.42421296, -0.42421296, ..., -0.42421296,
          -0.42421296, -0.42421296],
         ...,
         [-0.42421296, -0.42421296, -0.42421296, ..., -0.42421296,
          -0.42421296, -0.42421296],
         [-0.42421296, -0.42421296, -0.42421296, ..., -0.42421296,
          -0.42421296, -0.42421296],
         [-0.42421296, -0.42421296, -0.42421296, ..., -0.42421296,
          -0.42421296, -0.42421296]]]], dtype=float32)

PyTorch数据集：将整个数据集转换为NumPy

2 个答案: