How do you load MNIST images into Pytorch DataLoader?

Here's what I did for pytorch 0.4.1 (should still work in 1.3)

def load_dataset():
    data_path = 'data/train/'
    train_dataset = torchvision.datasets.ImageFolder(
        root=data_path,
        transform=torchvision.transforms.ToTensor()
    )
    train_loader = torch.utils.data.DataLoader(
        train_dataset,
        batch_size=64,
        num_workers=0,
        shuffle=True
    )
    return train_loader

for batch_idx, (data, target) in enumerate(load_dataset()):
    #train network

If you're using mnist, there's already a preset in pytorch via torchvision.
You could do

import torch
import torchvision
import torchvision.transforms as transforms
import pandas as pd

transform = transforms.Compose(
[transforms.ToTensor(),
 transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

mnistTrainSet = torchvision.datasets.MNIST(root='./data', train=True,
                                    download=True, transform=transform)
mnistTrainLoader = torch.utils.data.DataLoader(mnistTrainSet, batch_size=16,
                                      shuffle=True, num_workers=2)

If you want to generalize to a directory of images (same imports as above), you could do

class mnistmTrainingDataset(torch.utils.data.Dataset):

    def __init__(self,text_file,root_dir,transform=transformMnistm):
        """
        Args:
            text_file(string): path to text file
            root_dir(string): directory with all train images
        """
        self.name_frame = pd.read_csv(text_file,sep=" ",usecols=range(1))
        self.label_frame = pd.read_csv(text_file,sep=" ",usecols=range(1,2))
        self.root_dir = root_dir
        self.transform = transform

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

    def __getitem__(self, idx):
        img_name = os.path.join(self.root_dir, self.name_frame.iloc[idx, 0])
        image = Image.open(img_name)
        image = self.transform(image)
        labels = self.label_frame.iloc[idx, 0]
        #labels = labels.reshape(-1, 2)
        sample = {'image': image, 'labels': labels}

        return sample


mnistmTrainSet = mnistmTrainingDataset(text_file ='Downloads/mnist_m/mnist_m_train_labels.txt',
                                   root_dir = 'Downloads/mnist_m/mnist_m_train')

mnistmTrainLoader = torch.utils.data.DataLoader(mnistmTrainSet,batch_size=16,shuffle=True, num_workers=2)

You can then iterate over it like:

for i_batch,sample_batched in enumerate(mnistmTrainLoader,0):
    print("training sample for mnist-m")
    print(i_batch,sample_batched['image'],sample_batched['labels'])

There are a bunch of ways to generalize pytorch for image dataset loading, the method that I know of is subclassing torch.utils.data.dataset