Hey folks,
I’ve been testing my project for a long time, and now I made a progress.
But I got this error that I didn’t find a good solution to:
UserWarning: Using a target size (torch.Size([7])) that is different to the input size (torch.Size([7, 3])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
How am I supposed to update the sizes of the tensors? And by what size?
As seen here:
More info and interesting comments, you can see here:
can you confirm what '/content/gdrive/MyDrive/SID/train' this folder is suppose to point to? I would like to run your notebook to debug further. I think it might be an issue with how cnn_learner is setup but would like to confirm. (thinking maybe brackets or parenthesis missing maybe?)
Can you rerun your code and do:
pip install fastdebug
from fastdebug import *
Before running it? Should give us a more clear error of where it’s stemming from
Thanks Kevin!
I mounted my google drive to that Google Colab. So I can access the folders in my google drive from Google Colab (Basically it adds the folders to that path, you can see in the files explorer on the left).
If you can’t see those files there, let me know. Maybe I gotta give it permission when sharing
Hey Man! Thanks.
Yup, just did so.
Btw, you can copy this notebook to your own drive (File->Save a copy …) and make changes and run it.
But maybe I’ll need to give it just permission to everybody to edit it. Let me know if copying it doesnt work
When asking for assistance on the forums, don’t assume that we will run it and debug ourselves. You should try to convey enough information to get everything relating to the problem, and the gist or Colab notebook serves as a record to see your code and it’s outputs 
As many of us are too busy to run it ourselves, so we just want a quick view as to what you’re doing and how you go the behavior.
In this case you’re also hooking up to your Google drive, which none of us will be able to reproduce since that is your drive specific. We cannot login to that
Don’t get me wrong, I’d do the work myself.
I probably understood it as if you asked me to add something that helps you through to run it. My apologies.
Nope, I’m not able to access them
Alright guys, I suspected that the error arose because for the same target path, there were attached up to 10 different inputs. So I tried to rescale the problem, by attaching for every target object only a single input.
Problem was, that when loading the data, it missed one line in the csv. Any ideas how or why?
It looks like this:
Also the stack trace:
KeyError Traceback (most recent call last)
<ipython-input-38-f142507f9d90> in <module>()
----> 1 learn.fit_one_cycle(1, 0.01)
13 frames
/usr/local/lib/python3.7/dist-packages/fastai/callback/schedule.py in fit_one_cycle(self, n_epoch, lr_max, div, div_final, pct_start, wd, moms, cbs, reset_opt)
110 scheds = {'lr': combined_cos(pct_start, lr_max/div, lr_max, lr_max/div_final),
111 'mom': combined_cos(pct_start, *(self.moms if moms is None else moms))}
--> 112 self.fit(n_epoch, cbs=ParamScheduler(scheds)+L(cbs), reset_opt=reset_opt, wd=wd)
113
114 # Cell
/usr/local/lib/python3.7/dist-packages/fastai/learner.py in fit(self, n_epoch, lr, wd, cbs, reset_opt)
216 self.opt.set_hypers(lr=self.lr if lr is None else lr)
217 self.n_epoch = n_epoch
--> 218 self._with_events(self._do_fit, 'fit', CancelFitException, self._end_cleanup)
219
220 def _end_cleanup(self): self.dl,self.xb,self.yb,self.pred,self.loss = None,(None,),(None,),None,None
/usr/local/lib/python3.7/dist-packages/fastai/learner.py in _with_events(self, f, event_type, ex, final)
158
159 def _with_events(self, f, event_type, ex, final=noop):
--> 160 try: self(f'before_{event_type}'); f()
161 except ex: self(f'after_cancel_{event_type}')
162 self(f'after_{event_type}'); final()
/usr/local/lib/python3.7/dist-packages/fastai/learner.py in _do_fit(self)
207 for epoch in range(self.n_epoch):
208 self.epoch=epoch
--> 209 self._with_events(self._do_epoch, 'epoch', CancelEpochException)
210
211 def fit(self, n_epoch, lr=None, wd=None, cbs=None, reset_opt=False):
/usr/local/lib/python3.7/dist-packages/fastai/learner.py in _with_events(self, f, event_type, ex, final)
158
159 def _with_events(self, f, event_type, ex, final=noop):
--> 160 try: self(f'before_{event_type}'); f()
161 except ex: self(f'after_cancel_{event_type}')
162 self(f'after_{event_type}'); final()
/usr/local/lib/python3.7/dist-packages/fastai/learner.py in _do_epoch(self)
202 def _do_epoch(self):
203 self._do_epoch_train()
--> 204 self._do_epoch_validate()
205
206 def _do_fit(self):
/usr/local/lib/python3.7/dist-packages/fastai/learner.py in _do_epoch_validate(self, ds_idx, dl)
198 if dl is None: dl = self.dls[ds_idx]
199 self.dl = dl
--> 200 with torch.no_grad(): self._with_events(self.all_batches, 'validate', CancelValidException)
201
202 def _do_epoch(self):
/usr/local/lib/python3.7/dist-packages/fastai/learner.py in _with_events(self, f, event_type, ex, final)
158
159 def _with_events(self, f, event_type, ex, final=noop):
--> 160 try: self(f'before_{event_type}'); f()
161 except ex: self(f'after_cancel_{event_type}')
162 self(f'after_{event_type}'); final()
/usr/local/lib/python3.7/dist-packages/fastai/learner.py in all_batches(self)
164 def all_batches(self):
165 self.n_iter = len(self.dl)
--> 166 for o in enumerate(self.dl): self.one_batch(*o)
167
168 def _do_one_batch(self):
/usr/local/lib/python3.7/dist-packages/fastai/data/load.py in __iter__(self)
107 self.before_iter()
108 self.__idxs=self.get_idxs() # called in context of main process (not workers/subprocesses)
--> 109 for b in _loaders[self.fake_l.num_workers==0](self.fake_l):
110 if self.device is not None: b = to_device(b, self.device)
111 yield self.after_batch(b)
/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py in __next__(self)
515 if self._sampler_iter is None:
516 self._reset()
--> 517 data = self._next_data()
518 self._num_yielded += 1
519 if self._dataset_kind == _DatasetKind.Iterable and \
/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py in _next_data(self)
1197 else:
1198 del self._task_info[idx]
-> 1199 return self._process_data(data)
1200
1201 def _try_put_index(self):
/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py in _process_data(self, data)
1223 self._try_put_index()
1224 if isinstance(data, ExceptionWrapper):
-> 1225 data.reraise()
1226 return data
1227
/usr/local/lib/python3.7/dist-packages/torch/_utils.py in reraise(self)
427 # have message field
428 raise self.exc_type(message=msg)
--> 429 raise self.exc_type(msg)
430
431
KeyError: Caught KeyError in DataLoader worker process 0.
Original Traceback (most recent call last):
File "/usr/local/lib/python3.7/dist-packages/fastai/data/transforms.py", line 246, in encodes
return TensorCategory(self.vocab.o2i[o])
KeyError: 'long/10/7.jpg'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/usr/local/lib/python3.7/dist-packages/torch/utils/data/_utils/worker.py", line 202, 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/fastcore/basics.py", line 216, in chunked
res = list(itertools.islice(it, chunk_sz))
File "/usr/local/lib/python3.7/dist-packages/fastai/data/load.py", line 133, in do_item
try: return self.after_item(self.create_item(s))
File "/usr/local/lib/python3.7/dist-packages/fastai/data/load.py", line 140, in create_item
if self.indexed: return self.dataset[s or 0]
File "/usr/local/lib/python3.7/dist-packages/fastai/data/core.py", line 333, in __getitem__
res = tuple([tl[it] for tl in self.tls])
File "/usr/local/lib/python3.7/dist-packages/fastai/data/core.py", line 333, in <listcomp>
res = tuple([tl[it] for tl in self.tls])
File "/usr/local/lib/python3.7/dist-packages/fastai/data/core.py", line 299, in __getitem__
return self._after_item(res) if is_indexer(idx) else res.map(self._after_item)
File "/usr/local/lib/python3.7/dist-packages/fastai/data/core.py", line 261, in _after_item
def _after_item(self, o): return self.tfms(o)
File "/usr/local/lib/python3.7/dist-packages/fastcore/transform.py", line 200, in __call__
def __call__(self, o): return compose_tfms(o, tfms=self.fs, split_idx=self.split_idx)
File "/usr/local/lib/python3.7/dist-packages/fastcore/transform.py", line 150, in compose_tfms
x = f(x, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/fastdebug/fastai/transform.py", line 32, in __call__
transform_error(e, _get_name(self), 'encodes')
File "/usr/local/lib/python3.7/dist-packages/fastdebug/fastai/transform.py", line 24, in transform_error
raise e
File "/usr/local/lib/python3.7/dist-packages/fastdebug/fastai/transform.py", line 30, in __call__
return self._call('encodes', x, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/fastcore/transform.py", line 83, in _call
return self._do_call(getattr(self, fn), x, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/fastcore/transform.py", line 89, in _do_call
return retain_type(f(x, **kwargs), x, ret)
File "/usr/local/lib/python3.7/dist-packages/fastcore/dispatch.py", line 118, in __call__
return f(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/fastai/data/transforms.py", line 248, in encodes
raise KeyError(f"Label '{o}' was not included in the training dataset") from e
KeyError: "There was an issue calling the encodes on transform Categorize:\n\nLabel 'long/10/7.jpg' was not included in the training dataset"