How to use variable-sized input to Dataloader

[InnovArul (Arul)]

A simple way would be to use a custom collate function and collect the variable-sized data to make a batch.
There are other advanced solutions with padded_sequences, as well.

DataLoader for various length of data

This is how I solved it: def collate_fn_padd(batch): ''' Padds batch of variable length note: it converts things ToTensor manually here since the ToTensor transform assume it takes in images rather than arbitrary tensors. ''' ## get sequence lengths lengths = torch.tensor([ t.shape[0] for t in batch ]).to(device) ## padd batch = [ torch.Tensor(t).to(device) for t in batch ] batch = torch.nn.utils.rnn.pad_sequence(batch) ## compute mask mask = (ba…