Combing two torchvision.dataset objects into a single DataLoader in PyTorch

Question

I am training a GANS on the Cifar-10 dataset in PyTorch (and hence don't need train/val/test splits), and I want to be able to combine the torchvision.datasets.CIFAR10 in the snippet below to form one single torch.utils.data.DataLoader iterator. My current solution is something like :

import torchvision
import torch
batch_size = 128
cifar_trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=False)
cifar_testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=False)
cifar_dl1 = torch.utils.data.DataLoader(cifar_trainset, batch_size=batch_size, num_workers=12, persistent_workers=True,
                                          shuffle=True, pin_memory=True)
cifar_dl2 = torch.utils.data.DataLoader(cifar_testset, batch_size=batch_size, num_workers=12, persistent_workers=True,
                                          shuffle=True, pin_memory=True)

And then in my training loop I have something like:

for dl in [cifar_dl1, cifar_l2]:
   for data in dl:
      # training

The problem with this approach in a multi-threaded context, where I have found for my setup and this task that the optimal number of workers is 12, is that now I am declaring 24 workers in total which is clearly too many, not to mention the start-up time costs associated with re-iterating over each dataloader in spite of the benefits of the persistent workers flag for each.

Any solutions to this problem much appreciated.

Answer 1

You can use ConcatDataset from torch.utils.data module.

Code Snippet:

import torch    
import torchvision

batch_size = 128

cifar_trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=False)
cifar_testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=False)

cifar_dataset = torch.utils.data.ConcatDataset([cifar_trainset, cifar_testset])

cifar_dataloader = torch.utils.data.DataLoader(cifar_dataset, batch_size=batch_size, num_workers=12, persistent_workers=True,
                                          shuffle=True, pin_memory=True)

for data in cifar_dataloader:
    # training