How to use transformations of my choice during inference with Test Time Augmentation in fastai? - deep-learning

Question

I am using Test Time Augmentation during inference, like so-
file_path = '/path/to/file.jpg'
dl = learn_.dls.test_dl([file_path])
pred, _ = learn_.tta(dl=dl, n=N_IMAGES)
When I try to add additional transformations of my choice, I am unable to do so.
If I try to add additional transforms using either the item_tfms or batch_tfms parameters following the docs, like this-
pred, _ = learn_.tta(dl=dl,
n=N_IMAGES,
item_tfms=Resize(256),
batch_tfms=Zoom(p=1, draw=2.0))
I get thrown this error-
TypeError: default_collate: batch must contain tensors, numpy arrays, numbers, dicts or lists; found <class 'fastai.vision.core.PILImage'>
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-8-86c798126984> in <module>()
1 # tta
2 dl = learn_.dls.test_dl([file_path])
----> 3 pred, _ = learn_.tta(dl=dl, n=N_IMAGES, item_tfms=Resize(256), batch_tfms=Zoom(p=1, draw=2.0))
4 cat = learn_.dls.vocab[torch.argmax(pred).item()]
5 cat.lstrip()
9 frames
/usr/local/lib/python3.7/dist-packages/torch/_utils.py in reraise(self)
423 # have message field
424 raise self.exc_type(message=msg)
--> 425 raise self.exc_type(msg)
426
427
TypeError: Caught TypeError in DataLoader worker process 0.
Original Traceback (most recent call last):
File "/usr/local/lib/python3.7/dist-packages/torch/utils/data/_utils/worker.py", line 287, in _worker_loop
data = fetcher.fetch(index)
File "/usr/local/lib/python3.7/dist-packages/torch/utils/data/_utils/fetch.py", line 34, in fetch
data = next(self.dataset_iter)
File "/usr/local/lib/python3.7/dist-packages/fastai/data/load.py", line 118, in create_batches
yield from map(self.do_batch, self.chunkify(res))
File "/usr/local/lib/python3.7/dist-packages/fastai/data/load.py", line 144, in do_batch
def do_batch(self, b): return self.retain(self.create_batch(self.before_batch(b)), b)
File "/usr/local/lib/python3.7/dist-packages/fastai/data/load.py", line 143, in create_batch
def create_batch(self, b): return (fa_collate,fa_convert)[self.prebatched](b)
File "/usr/local/lib/python3.7/dist-packages/fastai/data/load.py", line 50, in fa_collate
else type(t[0])([fa_collate(s) for s in zip(*t)]) if isinstance(b, Sequence)
File "/usr/local/lib/python3.7/dist-packages/fastai/data/load.py", line 50, in <listcomp>
else type(t[0])([fa_collate(s) for s in zip(*t)]) if isinstance(b, Sequence)
File "/usr/local/lib/python3.7/dist-packages/fastai/data/load.py", line 51, in fa_collate
else default_collate(t))
File "/usr/local/lib/python3.7/dist-packages/torch/utils/data/_utils/collate.py", line 86, in default_collate
raise TypeError(default_collate_err_msg_format.format(elem_type))
TypeError: default_collate: batch must contain tensors, numpy arrays, numbers, dicts or lists; found <class 'fastai.vision.core.PILImage'>
Is there any way I can use additional transformations during inference time with tta?