I Try to train on two dataloaders, one attached to a dataset where each __get_item__
call fetches a predefined batch of varying length (thus the batch_size I transfer to the dataloader
object is 1), and one where I sample randomly from a set of sequences, thus __get_item__
call fetches one sample each time.
I’m looking for something like
loader = DataLoader(
batched_dataset,
batch_size=1,
)
loader_tdm = DataLoader(
random_samples_dataset,
batch_size=8,
)
from data.concat_dataloaders import AlternateIterator
loader = AlternateDataloader(loader,loader_tdm)
Is this possible?