Hi,
I have Dataset class which holds its entire data in a Numpy array in memory and returns a tuple of two Numpy arrays via getitem.
The data item returned by getitem has shape (3, 244, 244) and the target item is a 1 dimensional array.
I’m using the default Dataloader with batch size of 64 which in turn uses default_collate to arrange the batches. When using numpy.stack instead of torch.stack in the default_collate function I see a speedup of over 10 times (from ~1s to ~0.1s per batch) in the collate function.
Specifically, when changing line 68 in utils/data/_utils/collate.py from
return default_collate([torch.as_tensor(b) for b in batch])

to
return torch.as_tensor(numpy.stack(batch))

How is that?