Nice, thank you!!! I will try later, I will let you know if there is some kind of problem!
I have tried the code @sgugger , summary is working, however show_batch shows the next error:
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-20-3028df996ec0> in <module>
11 dataset1.summary(path_images)
12 dls = dataset1.dataloaders(path_images,bs=bs)
---> 13 dls.show_batch(vmin=0,vmax=1,figsize=(12, 9))
~/anaconda3/envs/seg/lib/python3.7/site-packages/fastai2/data/core.py in show_batch(self, b, max_n, ctxs, show, unique, **kwargs)
95 old_get_idxs = self.get_idxs
96 self.get_idxs = lambda: Inf.zeros
---> 97 if b is None: b = self.one_batch()
98 if not show: return self._pre_show_batch(b, max_n=max_n)
99 show_batch(*self._pre_show_batch(b, max_n=max_n), ctxs=ctxs, max_n=max_n, **kwargs)
~/anaconda3/envs/seg/lib/python3.7/site-packages/fastai2/data/load.py in one_batch(self)
130 def one_batch(self):
131 if self.n is not None and len(self)==0: raise ValueError(f'This DataLoader does not contain any batches')
--> 132 with self.fake_l.no_multiproc(): res = first(self)
133 if hasattr(self, 'it'): delattr(self, 'it')
134 return res
~/anaconda3/envs/seg/lib/python3.7/site-packages/fastcore/utils.py in first(x)
175 def first(x):
176 "First element of `x`, or None if missing"
--> 177 try: return next(iter(x))
178 except StopIteration: return None
179
~/anaconda3/envs/seg/lib/python3.7/site-packages/fastai2/data/load.py in __iter__(self)
96 self.randomize()
97 self.before_iter()
---> 98 for b in _loaders[self.fake_l.num_workers==0](self.fake_l):
99 if self.device is not None: b = to_device(b, self.device)
100 yield self.after_batch(b)
~/anaconda3/envs/seg/lib/python3.7/site-packages/torch/utils/data/dataloader.py in __next__(self)
343
344 def __next__(self):
--> 345 data = self._next_data()
346 self._num_yielded += 1
347 if self._dataset_kind == _DatasetKind.Iterable and \
~/anaconda3/envs/seg/lib/python3.7/site-packages/torch/utils/data/dataloader.py in _next_data(self)
383 def _next_data(self):
384 index = self._next_index() # may raise StopIteration
--> 385 data = self._dataset_fetcher.fetch(index) # may raise StopIteration
386 if self._pin_memory:
387 data = _utils.pin_memory.pin_memory(data)
~/anaconda3/envs/seg/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py in fetch(self, possibly_batched_index)
32 raise StopIteration
33 else:
---> 34 data = next(self.dataset_iter)
35 return self.collate_fn(data)
36
~/anaconda3/envs/seg/lib/python3.7/site-packages/fastai2/data/load.py in create_batches(self, samps)
105 self.it = iter(self.dataset) if self.dataset is not None else None
106 res = filter(lambda o:o is not None, map(self.do_item, samps))
--> 107 yield from map(self.do_batch, self.chunkify(res))
108
109 def new(self, dataset=None, cls=None, **kwargs):
~/anaconda3/envs/seg/lib/python3.7/site-packages/fastai2/data/load.py in do_batch(self, b)
126 def create_item(self, s): return next(self.it) if s is None else self.dataset[s]
127 def create_batch(self, b): return (fa_collate,fa_convert)[self.prebatched](b)
--> 128 def do_batch(self, b): return self.retain(self.create_batch(self.before_batch(b)), b)
129 def to(self, device): self.device = device
130 def one_batch(self):
~/anaconda3/envs/seg/lib/python3.7/site-packages/fastai2/data/load.py in create_batch(self, b)
125 def retain(self, res, b): return retain_types(res, b[0] if is_listy(b) else b)
126 def create_item(self, s): return next(self.it) if s is None else self.dataset[s]
--> 127 def create_batch(self, b): return (fa_collate,fa_convert)[self.prebatched](b)
128 def do_batch(self, b): return self.retain(self.create_batch(self.before_batch(b)), b)
129 def to(self, device): self.device = device
~/anaconda3/envs/seg/lib/python3.7/site-packages/fastai2/data/load.py in fa_collate(t)
44 b = t[0]
45 return (default_collate(t) if isinstance(b, _collate_types)
---> 46 else type(t[0])([fa_collate(s) for s in zip(*t)]) if isinstance(b, Sequence)
47 else default_collate(t))
48
~/anaconda3/envs/seg/lib/python3.7/site-packages/fastai2/data/load.py in <listcomp>(.0)
44 b = t[0]
45 return (default_collate(t) if isinstance(b, _collate_types)
---> 46 else type(t[0])([fa_collate(s) for s in zip(*t)]) if isinstance(b, Sequence)
47 else default_collate(t))
48
~/anaconda3/envs/seg/lib/python3.7/site-packages/fastai2/data/load.py in fa_collate(t)
45 return (default_collate(t) if isinstance(b, _collate_types)
46 else type(t[0])([fa_collate(s) for s in zip(*t)]) if isinstance(b, Sequence)
---> 47 else default_collate(t))
48
49 # Cell
~/anaconda3/envs/seg/lib/python3.7/site-packages/torch/utils/data/_utils/collate.py in default_collate(batch)
79 return [default_collate(samples) for samples in transposed]
80
---> 81 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 'fastai2.vision.core.PILImage'>
Creating a learner with that Dataset gives the exact same error!
Looks like validation set is not getting the AddMasks, Resize, ToTensor and Normalize!
Please stop that behavior where you reply, re-reply, tag, re-tag in an effort to attract attention to your post. I have millions of things to do, two kids at home to homeschool and I donāt have time for those distractions. The only thing you got with this is that I now ignore your messages and if you keep going I will just block you.
Be patient, try to debug yourself, and wait for people in the community to reply. Never tag someone in particular unless there is only that person that can help you. This is not the case here.
1 Like
I tagged you because you updated the tutorial and said that the error in Fastcore ItemTransform was solved.
There is no documentation of how split_idx work and no explanation in fastbook so itās is difficult to understand that.
Sorry for trying to get attention, I am used to see people doing that in forums that doesnāt have the option to ask for attention. If it is bad behaviour in this forum ,Iāll stop doing it!
If you look split_idx is from fastcore. The first hint is that it wasnāt in the fastai2 documentation, it was only referenced. The two libraries go hand in hand. From the fastcore documentation:
Filtering based on the dataset type - By setting the
split_idxflag you can make the transform be used only in a specificDataSourcesubset like in training, but not validation.
Further down:
f the transform has
split_idxthen itās only applied ifsplit_idxparam matches.
With this example:
f.split_idx = 1
test_eq(f(1, split_idx=1),2)
test_eq_type(f(1, split_idx=0), 1)
F is then as follows (from the documentation):
def func(x): return Int(x+1)
def dec (x): return x-1
f = Transform(func,dec)
t = f(1)
So we can see that if we have a split_idx of 1 itāll be applied (and the result is 2) and when it is 0 itās not (and the result is 1)
This was taken under āMain Transform featuresā:
3 Likes
You replied first and I had seen your reply. Itās just that I will look into it when I have time. No need to add the mention in an edit later.
I was not trying to bother you much less, take a look when you have time.
If I have bothered you, I apologize 
Okey.
So, training has index 0 and validation has index 1?
Correct! 
Interesting
So, I donāt understand why I am getting an error if I use split_idx=0 on AlbumentationsSegmentationWrapper. 
I have introduced a bug in fastcore while fixing another. Working on a fix now, and adding more robust tests.
2 Likes
Good to know!
Fixed now, and added enough tests to avoid regression.
There is also an example at the end of the pets tutorial.
1 Like
Thank you very much!! 
Iāll look into the example.
Sorry for my bad behaviour before. 
No problem, just remember that one reply is enough, and not tagging is necessary 
Works with DataBlock API or just on after_item of Datasets.dataloaders()?
It should work with the data block API normally.
I have make some models and I can confirm that is working correctly now. Thank you!!!
1 Like