Do note that this thread is for the Walk with fastai2 notebooks, not Jeremy’s dev walkthroughs 
(those actually have their own threads if you search for them)
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Hi.
When I try running the notebook 1 of lesson 1 on colab it runs fine but when I try running it on my local computer, it gives me the following error-
AssertionError: Failed to find “re.compile(’/([^/]+)_\d+.*’)” in “D:\DLLibLocation.fastai\data\oxford-iiit-pet\images\British_Shorthair_40.jpg”
the cell was -
pets = DataBlock(blocks=(ImageBlock, CategoryBlock),
get_items=get_image_files,
splitter=RandomSplitter(),
get_y=RegexLabeller(pat = r’/([^/]+)_\d+.*’),
item_tfms=item_tfms,
batch_tfms=batch_tfms)
I am not familiar with RegEx and would appreciate some help as to why the error is absent on colab but I get one when I run it locally.(I have regex library installed too)
This specific regex expression is designed specifically for a linux-based system. It seems as though you are running this on windows. You’d need to adapt your selection based on this new URL/directory structure. This is a good website I use for playing around with regex: https://regex101.com/
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Thank you for the website…I busted my brains at first trying to figure out the answer but now I finally got it. Feels good when you solve a problem something by yourself. Also, I learned a lot about regex. Thanks @muellerzr . 
When I try to create a custom dataset, On running the following code, I get the error-
for i , n in enumerate(classes):
print(n)
path_f=Path(files[i])
download_images(path/n,path_f,max_pics=10)
I get the error-
BrokenProcessPool: A process in the process pool was terminated abruptly while the future was running or pending.
It works fine when I decrease the max_pics to 5 but anything greater than 5 gives me the error. Any idea what might cause this ? I have 8GB ram and 6 GB of VRAM
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Hi @Legnica1241,
Try changing the max_workers in download_images.
For more details check this forum thread:
Has anyone tried rapidsai yet ? Do you know of any resources (books, videos) to help get started with rapids?
I am trying to run 04_Segmentation.ipynb but i get the following error during training
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-25-27d5ab94b4fb> in <module>()
----> 1 learn.fit_one_cycle(10, slice(lr), pct_start=0.9, wd=wd)
22 frames
/usr/local/lib/python3.6/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/python3.6/dist-packages/fastai/learner.py in fit(self, n_epoch, lr, wd, cbs, reset_opt)
203 self.opt.set_hypers(lr=self.lr if lr is None else lr)
204 self.n_epoch = n_epoch
--> 205 self._with_events(self._do_fit, 'fit', CancelFitException, self._end_cleanup)
206
207 def _end_cleanup(self): self.dl,self.xb,self.yb,self.pred,self.loss = None,(None,),(None,),None,None
/usr/local/lib/python3.6/dist-packages/fastai/learner.py in _with_events(self, f, event_type, ex, final)
152
153 def _with_events(self, f, event_type, ex, final=noop):
--> 154 try: self(f'before_{event_type}') ;f()
155 except ex: self(f'after_cancel_{event_type}')
156 finally: self(f'after_{event_type}') ;final()
/usr/local/lib/python3.6/dist-packages/fastai/learner.py in _do_fit(self)
194 for epoch in range(self.n_epoch):
195 self.epoch=epoch
--> 196 self._with_events(self._do_epoch, 'epoch', CancelEpochException)
197
198 def fit(self, n_epoch, lr=None, wd=None, cbs=None, reset_opt=False):
/usr/local/lib/python3.6/dist-packages/fastai/learner.py in _with_events(self, f, event_type, ex, final)
152
153 def _with_events(self, f, event_type, ex, final=noop):
--> 154 try: self(f'before_{event_type}') ;f()
155 except ex: self(f'after_cancel_{event_type}')
156 finally: self(f'after_{event_type}') ;final()
/usr/local/lib/python3.6/dist-packages/fastai/learner.py in _do_epoch(self)
189 def _do_epoch(self):
190 self._do_epoch_train()
--> 191 self._do_epoch_validate()
192
193 def _do_fit(self):
/usr/local/lib/python3.6/dist-packages/fastai/learner.py in _do_epoch_validate(self, ds_idx, dl)
185 if dl is None: dl = self.dls[ds_idx]
186 self.dl = dl
--> 187 with torch.no_grad(): self._with_events(self.all_batches, 'validate', CancelValidException)
188
189 def _do_epoch(self):
/usr/local/lib/python3.6/dist-packages/fastai/learner.py in _with_events(self, f, event_type, ex, final)
152
153 def _with_events(self, f, event_type, ex, final=noop):
--> 154 try: self(f'before_{event_type}') ;f()
155 except ex: self(f'after_cancel_{event_type}')
156 finally: self(f'after_{event_type}') ;final()
/usr/local/lib/python3.6/dist-packages/fastai/learner.py in all_batches(self)
158 def all_batches(self):
159 self.n_iter = len(self.dl)
--> 160 for o in enumerate(self.dl): self.one_batch(*o)
161
162 def _do_one_batch(self):
/usr/local/lib/python3.6/dist-packages/fastai/learner.py in one_batch(self, i, b)
176 self.iter = i
177 self._split(b)
--> 178 self._with_events(self._do_one_batch, 'batch', CancelBatchException)
179
180 def _do_epoch_train(self):
/usr/local/lib/python3.6/dist-packages/fastai/learner.py in _with_events(self, f, event_type, ex, final)
154 try: self(f'before_{event_type}') ;f()
155 except ex: self(f'after_cancel_{event_type}')
--> 156 finally: self(f'after_{event_type}') ;final()
157
158 def all_batches(self):
/usr/local/lib/python3.6/dist-packages/fastai/learner.py in __call__(self, event_name)
130 def ordered_cbs(self, event): return [cb for cb in sort_by_run(self.cbs) if hasattr(cb, event)]
131
--> 132 def __call__(self, event_name): L(event_name).map(self._call_one)
133
134 def _call_one(self, event_name):
/usr/local/lib/python3.6/dist-packages/fastcore/foundation.py in map(self, f, gen, *args, **kwargs)
177 def range(cls, a, b=None, step=None): return cls(range_of(a, b=b, step=step))
178
--> 179 def map(self, f, *args, gen=False, **kwargs): return self._new(map_ex(self, f, *args, gen=gen, **kwargs))
180 def argwhere(self, f, negate=False, **kwargs): return self._new(argwhere(self, f, negate, **kwargs))
181 def filter(self, f=noop, negate=False, gen=False, **kwargs):
/usr/local/lib/python3.6/dist-packages/fastcore/basics.py in map_ex(iterable, f, gen, *args, **kwargs)
604 res = map(g, iterable)
605 if gen: return res
--> 606 return list(res)
607
608 # Cell
/usr/local/lib/python3.6/dist-packages/fastcore/basics.py in __call__(self, *args, **kwargs)
594 if isinstance(v,_Arg): kwargs[k] = args.pop(v.i)
595 fargs = [args[x.i] if isinstance(x, _Arg) else x for x in self.pargs] + args[self.maxi+1:]
--> 596 return self.func(*fargs, **kwargs)
597
598 # Cell
/usr/local/lib/python3.6/dist-packages/fastai/learner.py in _call_one(self, event_name)
134 def _call_one(self, event_name):
135 assert hasattr(event, event_name), event_name
--> 136 [cb(event_name) for cb in sort_by_run(self.cbs)]
137
138 def _bn_bias_state(self, with_bias): return norm_bias_params(self.model, with_bias).map(self.opt.state)
/usr/local/lib/python3.6/dist-packages/fastai/learner.py in <listcomp>(.0)
134 def _call_one(self, event_name):
135 assert hasattr(event, event_name), event_name
--> 136 [cb(event_name) for cb in sort_by_run(self.cbs)]
137
138 def _bn_bias_state(self, with_bias): return norm_bias_params(self.model, with_bias).map(self.opt.state)
/usr/local/lib/python3.6/dist-packages/fastai/callback/core.py in __call__(self, event_name)
42 (self.run_valid and not getattr(self, 'training', False)))
43 res = None
---> 44 if self.run and _run: res = getattr(self, event_name, noop)()
45 if event_name=='after_fit': self.run=True #Reset self.run to True at each end of fit
46 return res
/usr/local/lib/python3.6/dist-packages/fastai/learner.py in after_batch(self)
455 if len(self.yb) == 0: return
456 mets = self._train_mets if self.training else self._valid_mets
--> 457 for met in mets: met.accumulate(self.learn)
458 if not self.training: return
459 self.lrs.append(self.opt.hypers[-1]['lr'])
/usr/local/lib/python3.6/dist-packages/fastai/learner.py in accumulate(self, learn)
377 def accumulate(self, learn):
378 bs = find_bs(learn.yb)
--> 379 self.total += learn.to_detach(self.func(learn.pred, *learn.yb))*bs
380 self.count += bs
381 @property
<ipython-input-19-0f1db32b8c82> in acc_camvid(input, target)
5 target = target.squeeze(1)
6 mask = target != void_code
----> 7 return (input.argmax(dim=1)[mask]==target[mask]).float().mean()
/usr/local/lib/python3.6/dist-packages/torch/tensor.py in wrapped(*args, **kwargs)
23 from torch.overrides import has_torch_function, handle_torch_function
24 if not all(type(t) is Tensor for t in args) and has_torch_function(args):
---> 25 return handle_torch_function(wrapped, args, *args, **kwargs)
26 try:
27 return f(*args, **kwargs)
/usr/local/lib/python3.6/dist-packages/torch/overrides.py in handle_torch_function(public_api, relevant_args, *args, **kwargs)
1069 raise TypeError("no implementation found for '{}' on types that implement "
1070 '__torch_function__: {}'
-> 1071 .format(func_name, list(map(type, overloaded_args))))
1072
1073 def has_torch_function(relevant_args: Iterable[Any]) -> bool:
TypeError: no implementation found for 'torch.tensor.eq' on types that implement __torch_function__: [<class 'fastai.torch_core.TensorImage'>, <class 'fastai.torch_core.TensorMask'>]
So it looks like lesson3-camvid.ipynb also has the same issue. I remember Jeremy tweeting that with the new Pytorch release segmentation doesn’t work, but i’m not sure how to fix this(in terms of going back to an older version and trying to run it). How to i find what the corresponding fastai install with the fastcore and other dependencies ? Thanks for your help in advance
Have you installed the latest version? This should have been fixed in the latest release
Also nice to see you again @barnacl
Thanks @muellerzr for the quick response
i’m using !pip install fastai -q --upgrade and i’m on 2.1.7 which is the latest.
Great to be back
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I had another question- if i were to install a previous fastai version how do i install the correct dependencies, i think you posted about that but i just can’t find it.
Btw i’m on colab.
You’d want to pick your version and then if things start breaking that would come from fastcore. You should check the date (on pip) the fastai version was released and use the fastcore that was released around that time
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Oh dear! Let me try real quick and see too 
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Cool thanks
@barnacl looks like the solution is to do:
valid_fnames = (path/'valid.txt').read_text().split('\n')
If you feel up to it a PR would be most welcome
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Oh wow, I had used a RandomSplitter. Thanks will do that 
Oh shoot wait @barnacl that fixed half the problem, sorry 
I was running my course notebook and hit another issue first. Let me get to yours 
How old is your version? 04_Segmentation trains for me and we use ranger rather than adam. I can’t recreate the issue. If possible could you recreate it in google colab and save it as a gist for me to look at?
Hmm weird, let me check again give me a sec
So this is where i’m cloning from - https://github.com/muellerzr/Practical-Deep-Learning-for-Coders-2.0, just did a new clone to my Drive. The current 04_Segmentation has a config too - config = unet_config(self_attention=True, act_cls=Mish) which i guess has changed?
To make things simple I just used learn = unet_learner(dls, resnet34, metrics=acc_camvid), so i removed ranger and mish.
here is the gist
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