from fastai.vision.data import ImageDataLoaders
from fastai.vision.learner import cnn_learner
from fastai.vision.augment import aug_transforms
import pandas as pd
from fastai import vision
df = pd.read_csv("/data/cats/labels.csv")
data = ImageDataLoaders.from_df(df=df, path="/", label_col=1, bs=100, batch_tfms=[
*aug_transforms(size=224)], valid_pct=0.2)
learn = cnn_learner(data, getattr(vision.models, "resnet18"))
learn.fit_one_cycle(10)
Results in:
RuntimeError Traceback (most recent call last)
<ipython-input-106-427e45e6948b> in <module>
8 data = ImageDataLoaders.from_df(df=df, path="/", label_col=1, bs=100, batch_tfms=[*aug_transforms(size=224)], valid_pct=0.2)
9 learn = cnn_learner(data, getattr(vision.models, "resnet18"))
---> 10 learn.fit_one_cycle(10)
~/.local/lib/python3.8/site-packages/fastcore/logargs.py in _f(*args, **kwargs)
54 init_args.update(log)
55 setattr(inst, 'init_args', init_args)
---> 56 return inst if to_return else f(*args, **kwargs)
57 return _f
~/.local/lib/python3.8/site-packages/fastai/callback/schedule.py in fit_one_cycle(self, n_epoch, lr_max, div, div_final, pct_start, wd, moms, cbs, reset_opt)
111 scheds = {'lr': combined_cos(pct_start, lr_max/div, lr_max, lr_max/div_final),
112 'mom': combined_cos(pct_start, *(self.moms if moms is None else moms))}
--> 113 self.fit(n_epoch, cbs=ParamScheduler(scheds)+L(cbs), reset_opt=reset_opt, wd=wd)
114
115 # Cell
~/.local/lib/python3.8/site-packages/fastcore/logargs.py in _f(*args, **kwargs)
54 init_args.update(log)
55 setattr(inst, 'init_args', init_args)
---> 56 return inst if to_return else f(*args, **kwargs)
57 return _f
~/.local/lib/python3.8/site-packages/fastai/learner.py in fit(self, n_epoch, lr, wd, cbs, reset_opt)
205 self.opt.set_hypers(lr=self.lr if lr is None else lr)
206 self.n_epoch = n_epoch
--> 207 self._with_events(self._do_fit, 'fit', CancelFitException, self._end_cleanup)
208
209 def _end_cleanup(self): self.dl,self.xb,self.yb,self.pred,self.loss = None,(None,),(None,),None,None
~/.local/lib/python3.8/site-packages/fastai/learner.py in _with_events(self, f, event_type, ex, final)
153
154 def _with_events(self, f, event_type, ex, final=noop):
--> 155 try: self(f'before_{event_type}') ;f()
156 except ex: self(f'after_cancel_{event_type}')
157 finally: self(f'after_{event_type}') ;final()
~/.local/lib/python3.8/site-packages/fastai/learner.py in _do_fit(self)
195 for epoch in range(self.n_epoch):
196 self.epoch=epoch
--> 197 self._with_events(self._do_epoch, 'epoch', CancelEpochException)
198
199 @log_args(but='cbs')
~/.local/lib/python3.8/site-packages/fastai/learner.py in _with_events(self, f, event_type, ex, final)
153
154 def _with_events(self, f, event_type, ex, final=noop):
--> 155 try: self(f'before_{event_type}') ;f()
156 except ex: self(f'after_cancel_{event_type}')
157 finally: self(f'after_{event_type}') ;final()
~/.local/lib/python3.8/site-packages/fastai/learner.py in _do_epoch(self)
189
190 def _do_epoch(self):
--> 191 self._do_epoch_train()
192 self._do_epoch_validate()
193
~/.local/lib/python3.8/site-packages/fastai/learner.py in _do_epoch_train(self)
181 def _do_epoch_train(self):
182 self.dl = self.dls.train
--> 183 self._with_events(self.all_batches, 'train', CancelTrainException)
184
185 def _do_epoch_validate(self, ds_idx=1, dl=None):
~/.local/lib/python3.8/site-packages/fastai/learner.py in _with_events(self, f, event_type, ex, final)
153
154 def _with_events(self, f, event_type, ex, final=noop):
--> 155 try: self(f'before_{event_type}') ;f()
156 except ex: self(f'after_cancel_{event_type}')
157 finally: self(f'after_{event_type}') ;final()
~/.local/lib/python3.8/site-packages/fastai/learner.py in all_batches(self)
159 def all_batches(self):
160 self.n_iter = len(self.dl)
--> 161 for o in enumerate(self.dl): self.one_batch(*o)
162
163 def _do_one_batch(self):
~/.local/lib/python3.8/site-packages/fastai/data/load.py in __iter__(self)
100 self.before_iter()
101 self.__idxs=self.get_idxs() # called in context of main process (not workers/subprocesses)
--> 102 for b in _loaders[self.fake_l.num_workers==0](self.fake_l):
103 if self.device is not None: b = to_device(b, self.device)
104 yield self.after_batch(b)
~/.local/lib/python3.8/site-packages/torch/utils/data/dataloader.py in __init__(self, loader)
735 # before it starts, and __del__ tries to join but will get:
736 # AssertionError: can only join a started process.
--> 737 w.start()
738 self._index_queues.append(index_queue)
739 self._workers.append(w)
/usr/lib/python3.8/multiprocessing/process.py in start(self)
119 'daemonic processes are not allowed to have children'
120 _cleanup()
--> 121 self._popen = self._Popen(self)
122 self._sentinel = self._popen.sentinel
123 # Avoid a refcycle if the target function holds an indirect
/usr/lib/python3.8/multiprocessing/context.py in _Popen(process_obj)
222 @staticmethod
223 def _Popen(process_obj):
--> 224 return _default_context.get_context().Process._Popen(process_obj)
225
226 class DefaultContext(BaseContext):
/usr/lib/python3.8/multiprocessing/context.py in _Popen(process_obj)
282 def _Popen(process_obj):
283 from .popen_spawn_posix import Popen
--> 284 return Popen(process_obj)
285
286 class ForkServerProcess(process.BaseProcess):
/usr/lib/python3.8/multiprocessing/popen_spawn_posix.py in __init__(self, process_obj)
30 def __init__(self, process_obj):
31 self._fds = []
---> 32 super().__init__(process_obj)
33
34 def duplicate_for_child(self, fd):
/usr/lib/python3.8/multiprocessing/popen_fork.py in __init__(self, process_obj)
17 self.returncode = None
18 self.finalizer = None
---> 19 self._launch(process_obj)
20
21 def duplicate_for_child(self, fd):
/usr/lib/python3.8/multiprocessing/popen_spawn_posix.py in _launch(self, process_obj)
45 try:
46 reduction.dump(prep_data, fp)
---> 47 reduction.dump(process_obj, fp)
48 finally:
49 set_spawning_popen(None)
/usr/lib/python3.8/multiprocessing/reduction.py in dump(obj, file, protocol)
58 def dump(obj, file, protocol=None):
59 '''Replacement for pickle.dump() using ForkingPickler.'''
---> 60 ForkingPickler(file, protocol).dump(obj)
61
62 #
~/.local/lib/python3.8/site-packages/torch/multiprocessing/reductions.py in reduce_storage(storage)
308 from . import get_sharing_strategy
309 if storage.is_cuda:
--> 310 raise RuntimeError("Cannot pickle CUDA storage; try pickling a CUDA tensor instead")
311 elif get_sharing_strategy() == 'file_system':
312 metadata = storage._share_filename_()
RuntimeError: Cannot pickle CUDA storage; try pickling a CUDA tensor instead
The dataset are images of cats/no-cats, binary label in the second column, first column filename. I have been using that dataset with fastai1.