PyTorch DataLoader shuffle

Issue

I did an experiment and I did not get the result I was expecting.

For the first part, I am using

trainloader = torch.utils.data.DataLoader(trainset, batch_size=128, 
                                          shuffle=False, num_workers=0)

I save trainloader.dataset.targets to the variable a, and trainloader.dataset.data to the variable b before training my model. Then, I train the model using trainloader.
After the training is finished, I save trainloader.dataset.targets to the variable c, and trainloader.dataset.data to the variable d. Finally, I check a == c and b == d and they both give True, which was expected because the shuffle parameter of the DataLoader is False.

For the second part, I am using

trainloader = torch.utils.data.DataLoader(trainset, batch_size=128, 
                                          shuffle=True, num_workers=0)

I save trainloader.dataset.targets to the variable e, and trainloader.dataset.data to the variable f before training my model. Then, I train the model using trainloader. After the training is finished, I save trainloader.dataset.targets to the variable g, and trainloader.dataset.data to the variable h. I expect e == g and f == h to be both False since shuffle=True, but they give True again. What am I missing from the definition of DataLoader class?

Solution

I believe that the data that is stored directly in the trainloader.dataset.data or .target will not be shuffled, the data is only shuffled when the DataLoader is called as a generator or as iterator

You can check it by doing next(iter(trainloader)) a few times without shuffling and with shuffling and they should give different results

import torch
import torchvision

transform = torchvision.transforms.Compose([
        torchvision.transforms.ToTensor(),
        ])
MNIST_dataset = torchvision.datasets.MNIST('~/Desktop/intern/',download = True, train = False,
                                           transform = transform)
dataLoader = torch.utils.data.DataLoader(MNIST_dataset,
                                         batch_size = 128,
                                         shuffle = False,
                                         num_workers = 10)
target = dataLoader.dataset.targets


MNIST_dataset = torchvision.datasets.MNIST('~/Desktop/intern/',download = True, train = False,
                                           transform = transform)

dataLoader_shuffled= torch.utils.data.DataLoader(MNIST_dataset,
                                         batch_size = 128,
                                         shuffle = True,
                                         num_workers = 10)

target_shuffled = dataLoader_shuffled.dataset.targets

print(target == target_shuffled)

_, target = next(iter(dataLoader));
_, target_shuffled = next(iter(dataLoader_shuffled))

print(target == target_shuffled)

This will give :

tensor([True, True, True,  ..., True, True, True])
tensor([False, False, False, False, False, False, False, False, False, False,
        False, False, False, False, False, False, False, False, False, False,
        False, False, False, False, False, False, False, False, False,  True,
        False, False, False, False, False, False, False, False, False, False,
        False, False, False, False, False, False, False, False, False, False,
        False, False, False, False, False, False, False, False, False, False,
        False, False, False, False,  True, False, False, False, False, False,
        False,  True, False, False, False, False, False, False, False, False,
        False, False, False, False, False, False, False, False, False, False,
        False, False, False, False,  True,  True, False, False, False, False,
        False, False, False, False, False, False, False, False, False, False,
        False, False, False, False, False,  True, False, False,  True, False,
        False, False, False, False, False, False, False, False])

However the data and label stored in data and target is a fixed list and since you are trying to access it directly, they will not be shuffled.

Answered By – Simon J

Answer Checked By – Robin (AngularFixing Admin)

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