I have tried to provide a single class to segmentation task but it works well with two classes
get_y_fn = lambda x: path_msk/f'{x.stem}{x.suffix}'
src = (ImageFileList.from_folder(path_img)
.label_from_func(get_y_fn)
.random_split_by_pct(0.2))
data = (src.datasets(SegmentationDataset, classes=codes, div=True)
.transform(get_transforms(), size=128, tfm_y=True)
.databunch(bs=32, num_workers=0)
.normalize(imagenet_stats))
learn = Learner.create_unet(data, models.resnet18)
learn.fit_one_cycle(1, slice(1e-2/2))
And the error is:
RuntimeError Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/fastai/basic_train.py in fit(epochs, model, loss_func, opt, data, callbacks, metrics)
93 exception = e
---> 94 raise e
95 finally: cb_handler.on_train_end(exception)
/usr/local/lib/python3.6/dist-packages/fastai/basic_train.py in fit(epochs, model, loss_func, opt, data, callbacks, metrics)
83 xb, yb = cb_handler.on_batch_begin(xb, yb)
---> 84 loss = loss_batch(model, xb, yb, loss_func, opt, cb_handler)
85 if cb_handler.on_batch_end(loss): break
/usr/local/lib/python3.6/dist-packages/fastai/basic_train.py in loss_batch(model, xb, yb, loss_func, opt, cb_handler)
24 if opt is not None:
---> 25 loss = cb_handler.on_backward_begin(loss)
26 loss.backward()
/usr/local/lib/python3.6/dist-packages/fastai/callback.py in on_backward_begin(self, loss)
223 for cb in self.callbacks:
--> 224 a = cb.on_backward_begin(**self.state_dict)
225 if a is not None: self.state_dict['last_loss'] = a
/usr/local/lib/python3.6/dist-packages/fastai/basic_train.py in on_backward_begin(self, smooth_loss, **kwargs)
266 if self.pbar is not None and hasattr(self.pbar,'child'):
--> 267 self.pbar.child.comment = f'{smooth_loss:.4f}'
268
/usr/local/lib/python3.6/dist-packages/torch/tensor.py in __format__(self, format_spec)
377 if self.dim() == 0:
--> 378 return self.item().__format__(format_spec)
379 return object.__format__(self, format_spec)
RuntimeError: CUDA error: device-side assert triggered
I’m trying to remove extra demotion by providing single class. In case two classes I have prediction in form of (32, 2, 128,128) as I noticed the 2 comes from number of classes.