Flatten()的Torchvision.transforms实现

Question

我有灰度图像，但我需要将其转换为一维向量的数据集，我如何做到这一点？我在转换中找不到合适的方法：

train_dataset = torchvision.datasets.ImageFolder(root='./data',train=True, transform=transforms.ToTensor())
test_dataset = torchvision.datasets.ImageFolder(root='./data',train=False, transform=transforms.ToTensor())

train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=4, shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=4, shuffle=False)

Answer 1

下面是如何使用Lambda完成此操作

import torch
from torchvision.datasets import MNIST
import torchvision.transforms as T

# without flatten
dataset = MNIST(root='.', download=True, transform=T.ToTensor())
print(dataset[0][0].shape)
# >>> torch.Size([1, 28, 28])

# with flatten (using Lambda, but you can do it in many other ways)
dataset_flatten = MNIST(root='.', download=True, transform=T.Compose([T.ToTensor(), T.Lambda(lambda x: torch.flatten(x))]))
print(dataset_flatten[0][0].shape)
# >>> torch.Size([784])