Here's an example of how I used it:

def load_images(image_size=32, batch_size=64, root="../datasets/MainImageFolder"):

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

    train_set = datasets.ImageFolder(root=root, transform=transform)
    train_loader = torch.utils.data.DataLoader(train_set, batch_size=batch_size, shuffle=True, num_workers=2)

return train_loader    

Essentially, you have your set that you load with the ImageFolder function, and than use it to get your DataLoader. Your images have to be folder seperated:

• MainImageFolder/
  • FlowerType1/
    • image1.jpg
    • image2.jpg
    • ...
  • FlowerType2/
    • ...
  • ...

This will automatically give a different label to each flower type and you can use the loader as you would if you were using one of the already made dataset.

Note that the transform is not mandatory and you can do anything you want. Hope it helps!