I am currently using fastai v1 for an image segmentation (binary classification for now, but will eventually want to change it to multi-class classification) problem I’m doing at work. To accomplish this, I’m trying to create my own custom pipeline. Because of this, I’ve had to create my own “SegDataset” as I wanted to alter some of “SegmentationDataset” functionality. Here’s some code below:
X_train, X_val, y_train, y_val = train_test_split(x_names, y_names, test_size = 0.2, random_state=21)
data_trn = SegDataset(x=X_train, y=y_train, path=ENGINES, size=sz)
data_val = SegDataset(x=X_val, y=y_val, path=ENGINES, size=sz)data_loader_trn = DataLoader(data_trn, batch_size = bs, num_workers = nw)
data_loader_val = DataLoader(data_val, batch_size = bs, num_workers = nw)x, y = next(iter(data_loader_trn))
databunch = ImageDataBunch(train_dl=data_loader_trn, valid_dl=data_loader_val, path=ENGINES)
databunch.show_batch(rows=3, figsize=(6,6))
learn = ConvLearner(databunch, tvm.resnet34, metrics=[accuracy])
learn.fit(1)
Through examining x and y, I receive the outputs that I expect. The code all works perfectly fine until “learn.fit(1)” where I receive the following error:
Does anyone know where I’m going wrong? Many thanks in advance! 
RuntimeError Traceback (most recent call last)
in
14 # learn.opt_fn = criterion(output, target)
15
—> 16 learn.fit(1)~/.conda/envs/fastaiv1/lib/python3.6/site-packages/fastai/basic_train.py in fit(self, epochs, lr, wd, callbacks)
136 callbacks = [cb(self) for cb in self.callback_fns] + listify(callbacks)
137 fit(epochs, self.model, self.loss_fn, opt=self.opt, data=self.data, metrics=self.metrics,
–> 138 callbacks=self.callbacks+callbacks)
139
140 def create_opt(self, lr:Floats, wd:Floats=0.)->None:~/.conda/envs/fastaiv1/lib/python3.6/site-packages/fastai/basic_train.py in fit(epochs, model, loss_fn, opt, data, callbacks, metrics)
89 except Exception as e:
90 exception = e
—> 91 raise e
92 finally: cb_handler.on_train_end(exception)
93~/.conda/envs/fastaiv1/lib/python3.6/site-packages/fastai/basic_train.py in fit(epochs, model, loss_fn, opt, data, callbacks, metrics)
79 for xb,yb in progress_bar(data.train_dl, parent=pbar):
80 xb, yb = cb_handler.on_batch_begin(xb, yb)
—> 81 loss = loss_batch(model, xb, yb, loss_fn, opt, cb_handler)[0]
82 if cb_handler.on_batch_end(loss): break
83~/.conda/envs/fastaiv1/lib/python3.6/site-packages/fastai/basic_train.py in loss_batch(model, xb, yb, loss_fn, opt, cb_handler, metrics)
21
22 if not loss_fn: return to_detach(out), yb[0].detach()
—> 23 loss = loss_fn(out, *yb)
24 mets = [f(out,*yb).detach().cpu() for f in metrics] if metrics is not None else []
25~/.conda/envs/fastaiv1/lib/python3.6/site-packages/torch/nn/functional.py in cross_entropy(input, target, weight, size_average, ignore_index, reduce, reduction)
1663 if size_average is not None or reduce is not None:
1664 reduction = _Reduction.legacy_get_string(size_average, reduce)
-> 1665 return nll_loss(log_softmax(input, 1), target, weight, None, ignore_index, None, reduction)
1666
1667~/.conda/envs/fastaiv1/lib/python3.6/site-packages/torch/nn/functional.py in nll_loss(input, target, weight, size_average, ignore_index, reduce, reduction)
1520 .format(input.size(0), target.size(0)))
1521 if dim == 2:
-> 1522 return torch._C._nn.nll_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index)
1523 elif dim == 4:
1524 return torch._C._nn.nll_loss2d(input, target, weight, _Reduction.get_enum(reduction), ignore_index)RuntimeError: multi-target not supported at /opt/conda/conda-bld/pytorch-nightly_1539602533843/work/aten/src/THCUNN/generic/ClassNLLCriterion.cu:15