Uniform class batch training vs. Not uniform class batch training

vision
1

When you train deep learning model with SGD, batch composes of randomly selected data or sequentially selected data as a default.
I want to compare between uniform class batch training and not uniform class batch training.
More specifically, for CIFAR-10 task, when batch size = 10, each batch has 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 labeled data. Otherwise, Not uniform class batch training means that each batch has 0, 0, 0, 2, 4, 5, 5, 7, 8, 9. You don’t care the order of labels.

2

For the first approach, you could access the targets in the CIFAR10 dataset and store the data indices for each label class.
Then inside a custom Dataset, you could load a sample for each class and return the complete batch in the __getitem__ method.
Note that for this approach you would manually create the batch in Dataset.__getitem__, thus you would have to set batch_size=1 in your DataLoader.

If I understand the second use case correctly, you are just randomly sampling the data, so setting shuffle=True in your DataLoader should work.