There is no easy way to do it. If you have a look in
create function of the databunch in
fastai/basic_data.py you see this line:
dls = [DataLoader(d, b, shuffle=s, drop_last=s, num_workers=num_workers, **dl_kwargs) for d,b,s in zip(datasets, (bs,val_bs,val_bs,val_bs), (True,False,False,False)) if d is not None]
drop_last defines if you drop the last images. If you override this function with your own you could change the behaviour. But pay attention. Depending on your loss function, the not full batch could screw a bit with your training. As you see the validation and test set dont drop the not complete batch. perhaps this would be the easiest for you to use.