Hi,
I’m beginning my ai adventure and I’m trying to set up a segmentation algorithm, that has two classes: ‘rat’ (as it is dealing with rodents) and background. I’m using the fast ai tutorial on segmentation, however I seem to be getting this error and don’t really know what to do.
Here is the code:
import os
os.environ['CUDA_LAUNCH_BLOCKING'] = "1"
import fastbook
fastbook.setup_book()
from fastbook import *
from fastai.vision.widgets import *
import os
path = os.getcwd()+'/Dataset'
path
codes = np.array(['background','rat'])
codes
fnames = get_image_files(path+'/Images')
def label_func(fn): return path+"/Masks/"+f"M{fn.stem}{fn.suffix}"
rat = DataBlock(blocks=(ImageBlock, MaskBlock(codes)),
get_items = get_image_files,
get_y = label_func,
splitter=RandomSplitter(),
batch_tfms=aug_transforms(size=(120,160)))
dls = rat.dataloaders(path+"/Images", path=path, bs=8)
dls.show_batch(max_n=6)
learn = unet_learner(dls, resnet34,n_out=2)
learn.fine_tune(6)
RuntimeError Traceback (most recent call last)
<ipython-input-10-aaf14b32d67c> in <module>
1 learn = unet_learner(dls, resnet34,n_out=2)
----> 2 learn.fine_tune(6)
/opt/conda/envs/fastai/lib/python3.8/site-packages/fastai/callback/schedule.py in fine_tune(self, epochs, base_lr, freeze_epochs, lr_mult, pct_start, div, **kwargs)
155 "Fine tune with `freeze` for `freeze_epochs` then with `unfreeze` from `epochs` using discriminative LR"
156 self.freeze()
--> 157 self.fit_one_cycle(freeze_epochs, slice(base_lr), pct_start=0.99, **kwargs)
158 base_lr /= 2
159 self.unfreeze()
/opt/conda/envs/fastai/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)
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
/opt/conda/envs/fastai/lib/python3.8/site-packages/fastai/learner.py in fit(self, n_epoch, lr, wd, cbs, reset_opt)
204 self.opt.set_hypers(lr=self.lr if lr is None else lr)
205 self.n_epoch = n_epoch
--> 206 self._with_events(self._do_fit, 'fit', CancelFitException, self._end_cleanup)
207
208 def _end_cleanup(self): self.dl,self.xb,self.yb,self.pred,self.loss = None,(None,),(None,),None,None
/opt/conda/envs/fastai/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()
/opt/conda/envs/fastai/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 def fit(self, n_epoch, lr=None, wd=None, cbs=None, reset_opt=False):
/opt/conda/envs/fastai/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()
/opt/conda/envs/fastai/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
/opt/conda/envs/fastai/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):
/opt/conda/envs/fastai/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()
/opt/conda/envs/fastai/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):
/opt/conda/envs/fastai/lib/python3.8/site-packages/fastai/learner.py in one_batch(self, i, b)
177 self.iter = i
178 self._split(b)
--> 179 self._with_events(self._do_one_batch, 'batch', CancelBatchException)
180
181 def _do_epoch_train(self):
/opt/conda/envs/fastai/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()
/opt/conda/envs/fastai/lib/python3.8/site-packages/fastai/learner.py in _do_one_batch(self)
164 self.pred = self.model(*self.xb)
165 self('after_pred')
--> 166 if len(self.yb): self.loss = self.loss_func(self.pred, *self.yb)
167 self('after_loss')
168 if not self.training or not len(self.yb): return
/opt/conda/envs/fastai/lib/python3.8/site-packages/fastai/losses.py in __call__(self, inp, targ, **kwargs)
31 if targ.dtype in [torch.int8, torch.int16, torch.int32]: targ = targ.long()
32 if self.flatten: inp = inp.view(-1,inp.shape[-1]) if self.is_2d else inp.view(-1)
---> 33 return self.func.__call__(inp, targ.view(-1) if self.flatten else targ, **kwargs)
34
35 # Cell
/opt/conda/envs/fastai/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
725 result = self._slow_forward(*input, **kwargs)
726 else:
--> 727 result = self.forward(*input, **kwargs)
728 for hook in itertools.chain(
729 _global_forward_hooks.values(),
/opt/conda/envs/fastai/lib/python3.8/site-packages/torch/nn/modules/loss.py in forward(self, input, target)
959
960 def forward(self, input: Tensor, target: Tensor) -> Tensor:
--> 961 return F.cross_entropy(input, target, weight=self.weight,
962 ignore_index=self.ignore_index, reduction=self.reduction)
963
/opt/conda/envs/fastai/lib/python3.8/site-packages/torch/nn/functional.py in cross_entropy(input, target, weight, size_average, ignore_index, reduce, reduction)
2460 tens_ops = (input, target)
2461 if any([type(t) is not Tensor for t in tens_ops]) and has_torch_function(tens_ops):
-> 2462 return handle_torch_function(
2463 cross_entropy, tens_ops, input, target, weight=weight,
2464 size_average=size_average, ignore_index=ignore_index, reduce=reduce,
/opt/conda/envs/fastai/lib/python3.8/site-packages/torch/overrides.py in handle_torch_function(public_api, relevant_args, *args, **kwargs)
1061 # Use `public_api` instead of `implementation` so __torch_function__
1062 # implementations can do equality/identity comparisons.
-> 1063 result = overloaded_arg.__torch_function__(public_api, types, args, kwargs)
1064
1065 if result is not NotImplemented:
/opt/conda/envs/fastai/lib/python3.8/site-packages/fastai/torch_core.py in __torch_function__(self, func, types, args, kwargs)
317 # if func.__name__[0]!='_': print(func, types, args, kwargs)
318 # with torch._C.DisableTorchFunction(): ret = _convert(func(*args, **(kwargs or {})), self.__class__)
--> 319 ret = super().__torch_function__(func, types, args=args, kwargs=kwargs)
320 if isinstance(ret, TensorBase): ret.set_meta(self, as_copy=True)
321 return ret
/opt/conda/envs/fastai/lib/python3.8/site-packages/torch/tensor.py in __torch_function__(cls, func, types, args, kwargs)
993
994 with _C.DisableTorchFunction():
--> 995 ret = func(*args, **kwargs)
996 return _convert(ret, cls)
997
/opt/conda/envs/fastai/lib/python3.8/site-packages/torch/nn/functional.py in cross_entropy(input, target, weight, size_average, ignore_index, reduce, reduction)
2466 if size_average is not None or reduce is not None:
2467 reduction = _Reduction.legacy_get_string(size_average, reduce)
-> 2468 return nll_loss(log_softmax(input, 1), target, weight, None, ignore_index, None, reduction)
2469
2470
/opt/conda/envs/fastai/lib/python3.8/site-packages/torch/nn/functional.py in nll_loss(input, target, weight, size_average, ignore_index, reduce, reduction)
2262 .format(input.size(0), target.size(0)))
2263 if dim == 2:
-> 2264 ret = torch._C._nn.nll_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index)
2265 elif dim == 4:
2266 ret = torch._C._nn.nll_loss2d(input, target, weight, _Reduction.get_enum(reduction), ignore_index)
RuntimeError: cuda runtime error (710) : device-side assert triggered at /opt/conda/conda-bld/pytorch_1603729096996/work/aten/src/THCUNN/generic/ClassNLLCriterion.cu:115
thanks for any help 
