How to manually define batches for each epoch?

#1

Hi, I am trying to manually define training batches for each epoch such that each batch contains only samples that satisfying specific condition (e.g. first batch are all from class 1 with image size smaller than 128*128, second batch are all from class 2 with image size greater than 256*256 and so on, and batches may have different number of samples). That said, using for batch in dataloader would probably not give me correct grouping of batches. I am wondering if anyone know any good way to manually “assign batches” before each training epoch starts?

Thank you!

#2

What you could do is to create the batches manually. Calling your dataset and then evaluating a conditional to see if the input should be added to the batch.

#3

Thanks for your advice! I implemented it with a batch sampler which defines the batches based on some conditionals (ref: But what are PyTorch DataLoaders really? | Scott Condron’s Blog) which works well.

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