Describe the bug
In the 23_tutorial.vision.ipynb notebook, it fails to train the Segmentation model in the section titled: “Segmentation - Using the high-level API”
To Reproduce
Steps to reproduce the behavior:
- Open 23_tutorial.vision.ipynb in colab
- Run all
Expected behavior
The model to train successfully after calling learn.fine_tune()
Error
TypeError: no implementation found for ‘torch.nn.functional.cross_entropy’ on types that implement torch_function: [<class ‘fastai.torch_core.TensorImage’>, <class ‘fastai.torch_core.TensorMask’>]
click to view full stack trace
```
TypeError Traceback (most recent call last)
in ()
1 learn = unet_learner(dls, resnet34)
----> 2 learn.fine_tune(8)
17 frames
/usr/local/lib/python3.6/dist-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()
/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)
188
189 def _do_epoch(self):
–> 190 self._do_epoch_train()
191 self._do_epoch_validate()
192
/usr/local/lib/python3.6/dist-packages/fastai/learner.py in _do_epoch_train(self)
180 def _do_epoch_train(self):
181 self.dl = self.dls.train
–> 182 self._with_events(self.all_batches, ‘train’, CancelTrainException)
183
184 def _do_epoch_validate(self, ds_idx=1, dl=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 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)
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_one_batch(self)
163 self.pred = self.model(*self.xb)
164 self(‘after_pred’)
–> 165 if len(self.yb): self.loss = self.loss_func(self.pred, *self.yb)
166 self(‘after_loss’)
167 if not self.training or not len(self.yb): return
/usr/local/lib/python3.6/dist-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
/usr/local/lib/python3.6/dist-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(),
/usr/local/lib/python3.6/dist-packages/torch/nn/modules/loss.py in forward(self, input, target)
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
964
/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py in cross_entropy(input, target, weight, size_average, ignore_index, reduce, reduction)
2463 cross_entropy, tens_ops, input, target, weight=weight,
2464 size_average=size_average, ignore_index=ignore_index, reduce=reduce,
-> 2465 reduction=reduction)
2466 if size_average is not None or reduce is not None:
2467 reduction = _Reduction.legacy_get_string(size_average, reduce)
/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.nn.functional.cross_entropy’ on types that implement torch_function: [<class ‘fastai.torch_core.TensorImage’>, <class ‘fastai.torch_core.TensorMask’>]
</details>