That is what I thought, so I created a clean Jupyter Notebook environment with Gradient and re-uploaded my images but I still encounter the same error. It seems like it doesn’t matter if I start from a clean slate or new data set since “.ipynb_checkpoints” get created dynamically. And yes, you are correct that this is an issue with “lesson1.ipynb”.
Including code that is raising error:
arch=resnet34 data = ImageClassifierData.from_paths(PATH, tfms=tfms_from_model(arch, sz)) learn = ConvLearner.pretrained(arch, data, precompute=True) learn.fit(0.01, 2)
Including the error snippet:
OSError Traceback (most recent call last)
<ipython-input-16-e6c87b20ce86> in <module>()
1 arch=resnet34
2 data = ImageClassifierData.from_paths(PATH, tfms=tfms_from_model(arch, sz))
----> 3 learn = ConvLearner.pretrained(arch, data, precompute=True)
4 learn.fit(0.01, 2)
/notebooks/courses/dl1/fastai/conv_learner.py in pretrained(cls, f, data, ps, xtra_fc, xtra_cut, custom_head, precompute, pretrained, **kwargs)
112 models = ConvnetBuilder(f, data.c, data.is_multi, data.is_reg,
113 ps=ps, xtra_fc=xtra_fc, xtra_cut=xtra_cut, custom_head=custom_head, pretrained=pretrained)
--> 114 return cls(data, models, precompute, **kwargs)
115
116 @classmethod
/notebooks/courses/dl1/fastai/conv_learner.py in __init__(self, data, models, precompute, **kwargs)
98 if hasattr(data, 'is_multi') and not data.is_reg and self.metrics is None:
99 self.metrics = [accuracy_thresh(0.5)] if self.data.is_multi else [accuracy]
--> 100 if precompute: self.save_fc1()
101 self.freeze()
102 self.precompute = precompute
/notebooks/courses/dl1/fastai/conv_learner.py in save_fc1(self)
166 m=self.models.top_model
167 if len(self.activations[0])!=len(self.data.trn_ds):
--> 168 predict_to_bcolz(m, self.data.fix_dl, act)
169 if len(self.activations[1])!=len(self.data.val_ds):
170 predict_to_bcolz(m, self.data.val_dl, val_act)
/notebooks/courses/dl1/fastai/model.py in predict_to_bcolz(m, gen, arr, workers)
15 lock=threading.Lock()
16 m.eval()
---> 17 for x,*_ in tqdm(gen):
18 y = to_np(m(VV(x)).data)
19 with lock:
/opt/conda/envs/fastai/lib/python3.6/site-packages/tqdm/_tqdm.py in __iter__(self)
928 fp_write=getattr(self.fp, 'write', sys.stderr.write))
929
--> 930 for obj in iterable:
931 yield obj
932 # Update and possibly print the progressbar.
/notebooks/courses/dl1/fastai/dataloader.py in __iter__(self)
86 # avoid py3.6 issue where queue is infinite and can result in memory exhaustion
87 for c in chunk_iter(iter(self.batch_sampler), self.num_workers*10):
---> 88 for batch in e.map(self.get_batch, c):
89 yield get_tensor(batch, self.pin_memory, self.half)
90
/opt/conda/envs/fastai/lib/python3.6/concurrent/futures/_base.py in result_iterator()
584 # Careful not to keep a reference to the popped future
585 if timeout is None:
--> 586 yield fs.pop().result()
587 else:
588 yield fs.pop().result(end_time - time.time())
/opt/conda/envs/fastai/lib/python3.6/concurrent/futures/_base.py in result(self, timeout)
430 raise CancelledError()
431 elif self._state == FINISHED:
--> 432 return self.__get_result()
433 else:
434 raise TimeoutError()
/opt/conda/envs/fastai/lib/python3.6/concurrent/futures/_base.py in __get_result(self)
382 def __get_result(self):
383 if self._exception:
--> 384 raise self._exception
385 else:
386 return self._result
/opt/conda/envs/fastai/lib/python3.6/concurrent/futures/thread.py in run(self)
54
55 try:
---> 56 result = self.fn(*self.args, **self.kwargs)
57 except BaseException as exc:
58 self.future.set_exception(exc)
/notebooks/courses/dl1/fastai/dataloader.py in get_batch(self, indices)
73
74 def get_batch(self, indices):
---> 75 res = self.np_collate([self.dataset[i] for i in indices])
76 if self.transpose: res[0] = res[0].T
77 if self.transpose_y: res[1] = res[1].T
/notebooks/courses/dl1/fastai/dataloader.py in <listcomp>(.0)
73
74 def get_batch(self, indices):
---> 75 res = self.np_collate([self.dataset[i] for i in indices])
76 if self.transpose: res[0] = res[0].T
77 if self.transpose_y: res[1] = res[1].T
/notebooks/courses/dl1/fastai/dataset.py in __getitem__(self, idx)
166 xs,ys = zip(*[self.get1item(i) for i in range(*idx.indices(self.n))])
167 return np.stack(xs),ys
--> 168 return self.get1item(idx)
169
170 def __len__(self): return self.n
/notebooks/courses/dl1/fastai/dataset.py in get1item(self, idx)
159
160 def get1item(self, idx):
--> 161 x,y = self.get_x(idx),self.get_y(idx)
162 return self.get(self.transform, x, y)
163
/notebooks/courses/dl1/fastai/dataset.py in get_x(self, i)
238 super().__init__(transform)
239 def get_sz(self): return self.transform.sz
--> 240 def get_x(self, i): return open_image(os.path.join(self.path, self.fnames[i]))
241 def get_n(self): return len(self.fnames)
242
/notebooks/courses/dl1/fastai/dataset.py in open_image(fn)
221 raise OSError('No such file or directory: {}'.format(fn))
222 elif os.path.isdir(fn):
--> 223 raise OSError('Is a directory: {}'.format(fn))
224 else:
225 #res = np.array(Image.open(fn), dtype=np.float32)/255
OSError: Is a directory: /storage/magicornot/train/gourmet/.ipynb_checkpoints```
