[BUG] RuntimeError: cannot pin 'torch.cuda.FloatTensor' only dense CPU tensors can be pinned

Describe the bug When trying to train the NBEATS model on GPU, I get the error:

---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-21-cd62ef13c2ee> in <module>
     10     ts_mad,
     11     ts_bcn
---> 12 ], verbose=True)

~/Environments/base/lib/python3.7/site-packages/darts/utils/torch.py in decorator(self, *args, **kwargs)
     63         with fork_rng():
     64             manual_seed(self._random_instance.randint(0, high=MAX_TORCH_SEED_VALUE))
---> 65             return decorated(self, *args, **kwargs)
     66     return decorator

~/Environments/base/lib/python3.7/site-packages/darts/models/torch_forecasting_model.py in fit(self, series, past_covariates, future_covariates, val_series, val_past_covariates, val_future_covariates, verbose, epochs)
    429         logger.info('Train dataset contains {} samples.'.format(len(train_dataset)))
    430 
--> 431         self.fit_from_dataset(train_dataset, val_dataset, verbose, epochs)
    432 
    433     @random_method

~/Environments/base/lib/python3.7/site-packages/darts/utils/torch.py in decorator(self, *args, **kwargs)
     63         with fork_rng():
     64             manual_seed(self._random_instance.randint(0, high=MAX_TORCH_SEED_VALUE))
---> 65             return decorated(self, *args, **kwargs)
     66     return decorator

~/Environments/base/lib/python3.7/site-packages/darts/models/torch_forecasting_model.py in fit_from_dataset(self, train_dataset, val_dataset, verbose, epochs)
    517 
    518         # Train model
--> 519         self._train(train_loader, val_loader, tb_writer, verbose, train_num_epochs)
    520 
    521         # Close tensorboard writer

~/Environments/base/lib/python3.7/site-packages/darts/models/torch_forecasting_model.py in _train(self, train_loader, val_loader, tb_writer, verbose, epochs)
    790             total_loss = 0
    791 
--> 792             for batch_idx, train_batch in enumerate(train_loader):
    793                 self.model.train()
    794                 output = self._produce_train_output(train_batch[:-1])

~/Environments/base/lib/python3.7/site-packages/torch/utils/data/dataloader.py in __next__(self)
    515             if self._sampler_iter is None:
    516                 self._reset()
--> 517             data = self._next_data()
    518             self._num_yielded += 1
    519             if self._dataset_kind == _DatasetKind.Iterable and \

~/Environments/base/lib/python3.7/site-packages/torch/utils/data/dataloader.py in _next_data(self)
    557         data = self._dataset_fetcher.fetch(index)  # may raise StopIteration
    558         if self._pin_memory:
--> 559             data = _utils.pin_memory.pin_memory(data)
    560         return data
    561 

~/Environments/base/lib/python3.7/site-packages/torch/utils/data/_utils/pin_memory.py in pin_memory(data)
     53         return type(data)(*(pin_memory(sample) for sample in data))
     54     elif isinstance(data, container_abcs.Sequence):
---> 55         return [pin_memory(sample) for sample in data]
     56     elif hasattr(data, "pin_memory"):
     57         return data.pin_memory()

~/Environments/base/lib/python3.7/site-packages/torch/utils/data/_utils/pin_memory.py in <listcomp>(.0)
     53         return type(data)(*(pin_memory(sample) for sample in data))
     54     elif isinstance(data, container_abcs.Sequence):
---> 55         return [pin_memory(sample) for sample in data]
     56     elif hasattr(data, "pin_memory"):
     57         return data.pin_memory()

~/Environments/base/lib/python3.7/site-packages/torch/utils/data/_utils/pin_memory.py in pin_memory(data)
     45 def pin_memory(data):
     46     if isinstance(data, torch.Tensor):
---> 47         return data.pin_memory()
     48     elif isinstance(data, string_classes):
     49         return data

RuntimeError: cannot pin 'torch.cuda.FloatTensor' only dense CPU tensors can be pinned

To Reproduce

ts = TimeSeries.from_series(series.dropna().astype('float32'), freq='D')

model = NBEATSModel(
    input_chunk_length=365, 
    output_chunk_length=51,
    torch_device_str='cuda',
    force_reset=True
)

model.fit(ts, verbose=True)

System (please complete the following information):

• Python version: 3.7.7
• darts version: 0.10.0

[hrzn]

The bug has been solved in v0.10.1. Thanks again for reporting.

[Ayesha-An]

Running semantic segmentation using UNET

RuntimeError Traceback (most recent call last) Input In [18], in <cell line: 2>() 1 model = create_model(params) ----> 2 model = train_and_validate(model, train_data, test_data, params)

Input In [16], in train_and_validate(model, train_dataset, val_dataset, params) 17 optimizer = torch.optim.Adam(model.parameters(), lr=params[“lr”]) 18 for epoch in range(1, params[“epochs”] + 1): —> 19 train(train_loader, model, criterion, optimizer, epoch, params) 20 validate(val_loader, model, criterion, epoch, params) 21 return model

Input In [13], in train(train_loader, model, criterion, optimizer, epoch, params) 3 model.train() 4 stream = tqdm(train_loader) ----> 5 for i, (images, target) in enumerate(stream, start=1): 6 images = images.to(params[“device”], non_blocking=True) 7 target = target.to(params[“device”], non_blocking=True)

File ~\Anaconda3\envs\py38\lib\site-packages\tqdm\std.py:1195, in tqdm.iter(self) 1192 time = self._time 1194 try: -> 1195 for obj in iterable: 1196 yield obj 1197 # Update and possibly print the progressbar. 1198 # Note: does not call self.update(1) for speed optimisation.

File ~\Anaconda3\envs\py38\lib\site-packages\torch\utils\data\dataloader.py:681, in _BaseDataLoaderIter.next(self) 678 if self._sampler_iter is None: 679 # TODO(https://github.com/pytorch/pytorch/issues/76750) 680 self._reset() # type: ignore[call-arg] –> 681 data = self._next_data() 682 self._num_yielded += 1 683 if self._dataset_kind == _DatasetKind.Iterable and
684 self._IterableDataset_len_called is not None and
685 self._num_yielded > self._IterableDataset_len_called:

File ~\Anaconda3\envs\py38\lib\site-packages\torch\utils\data\dataloader.py:723, in _SingleProcessDataLoaderIter._next_data(self) 721 data = self._dataset_fetcher.fetch(index) # may raise StopIteration 722 if self._pin_memory: –> 723 data = _utils.pin_memory.pin_memory(data, self._pin_memory_device) 724 return data

File ~\Anaconda3\envs\py38\lib\site-packages\torch\utils\data_utils\pin_memory.py:65, in pin_memory(data, device) 63 elif isinstance(data, collections.abc.Sequence): 64 try: —> 65 return type(data)([pin_memory(sample, device) for sample in data]) # type: ignore[call-arg] 66 except TypeError: 67 # The sequence type may not support __init__(iterable) (e.g., range). 68 return [pin_memory(sample, device) for sample in data]

File ~\Anaconda3\envs\py38\lib\site-packages\torch\utils\data_utils\pin_memory.py:65, in <listcomp>(.0) 63 elif isinstance(data, collections.abc.Sequence): 64 try: —> 65 return type(data)([pin_memory(sample, device) for sample in data]) # type: ignore[call-arg] 66 except TypeError: 67 # The sequence type may not support __init__(iterable) (e.g., range). 68 return [pin_memory(sample, device) for sample in data]

File ~\Anaconda3\envs\py38\lib\site-packages\torch\utils\data_utils\pin_memory.py:50, in pin_memory(data, device) 48 def pin_memory(data, device=None): 49 if isinstance(data, torch.Tensor): —> 50 return data.pin_memory(device) 51 elif isinstance(data, string_classes): 52 return data

RuntimeError: cannot pin ‘torch.cuda.FloatTensor’ only dense CPU tensors can be pinned

torch 1.12.1 python 3.8