I followed the tutorial on transfer learning at https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html#further-learning
This all runs (tho I do have some questions about it, Questions after following transfer learning tutorial), with num_workers=6 in the dataloaders.
Now I am trying to create dataloaders that will give a pair of images; the x is an image with random noise, the y is the original clean image (I would like to train a net that produces a clean image from a noisy one).
It works for num_workers=0. Unfortunately, whenever I use num_workers > 0 I get an error. From searching for a solution, it seems to be quite common. But I have not found anything that makes it work. Is it maybe because I am trying to return a pair of images, rather than an image and a class label? My code is below, along with the error.
In case it matters, the code is run in a Jupyter notebook. I have tried with batch_size=1 just to make sure it is not an out of memory issue (6GB video RAM, more than enough). If there is any further info that would help, let me know and I will add, thanks.
imagenet_stats = ([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
data_dir = 'random_images'
batch_size = 1

transf = {
    'trn': transforms.Compose([
        transforms.RandomResizedCrop(224),
        transforms.RandomHorizontalFlip()
    ]),
    'val': transforms.Compose([
        transforms.Resize(256),
        transforms.CenterCrop(224)
    ]),
}

class NoisyDataset(Dataset):
    def __init__(self, main_dir, transform):
        self.main_dir = main_dir
        self.transform = transform
        all_imgs = os.listdir(main_dir)
        self.total_imgs = natsort.natsorted(all_imgs)

    def __len__(self):
        return len(self.total_imgs)

    def __getitem__(self, idx):
        img_loc = os.path.join(self.main_dir, self.total_imgs[idx])
        image = Image.open(img_loc).convert("RGB")
        image = self.transform(image)
        label = image.copy()
        image = np.array(image) 
        image = random_noise(image, mode='gaussian', mean=0, var=0.05, clip=True)
        image = transforms.ToTensor()(image)
        image = transforms.Normalize(*imagenet_stats)(image)
        label = transforms.ToTensor()(label)
        label = transforms.Normalize(*imagenet_stats)(label)
        return image, label
    
t_ds = NoisyDataset(os.path.join(data_dir, 'train'), transform=transf['trn'])
v_ds = NoisyDataset(os.path.join(data_dir, 'train'), transform=transf['val'])

t_dl = DataLoader(t_ds , batch_size=batch_size, shuffle=True, 
                              num_workers=6, drop_last=False)
v_dl = DataLoader(v_ds , batch_size=2*batch_size, shuffle=False, 
                              num_workers=6, drop_last=False)

# just to check if it works - for num_workers=0 I get
# 0 [<class 'torch.Tensor'>, <class 'torch.Tensor'>]
# 1 [<class 'torch.Tensor'>, <class 'torch.Tensor'>]
# 2 [<class 'torch.Tensor'>, <class 'torch.Tensor'>]
# ... as expected
for idx, batch in enumerate(v_dl):
    print(idx, [type(i) for i in batch])



Empty                                     Traceback (most recent call last)
~\Anaconda3\envs\dsai\lib\site-packages\torch\utils\data\dataloader.py in _try_get_data(self, timeout)
778         try:
–> 779             data = self._data_queue.get(timeout=timeout)
780             return (True, data)
~\Anaconda3\envs\dsai\lib\multiprocessing\queues.py in get(self, block, timeout)
104                     if not self._poll(timeout):
–> 105                         raise Empty
106                 elif not self._poll():
Empty:
During handling of the above exception, another exception occurred:
RuntimeError                              Traceback (most recent call last)
 in 
----> 1 for idx, img in enumerate(v_dl):
2     print(idx, type(img))
~\Anaconda3\envs\dsai\lib\site-packages\torch\utils\data\dataloader.py in next(self)
361
362     def next(self):
–> 363         data = self._next_data()
364         self._num_yielded += 1
365         if self._dataset_kind == _DatasetKind.Iterable and \
~\Anaconda3\envs\dsai\lib\site-packages\torch\utils\data\dataloader.py in _next_data(self)
972
973             assert not self._shutdown and self._tasks_outstanding > 0
–> 974             idx, data = self._get_data()
975             self._tasks_outstanding -= 1
976
~\Anaconda3\envs\dsai\lib\site-packages\torch\utils\data\dataloader.py in _get_data(self)
939         else:
940             while True:
–> 941                 success, data = self._try_get_data()
942                 if success:
943                     return data
~\Anaconda3\envs\dsai\lib\site-packages\torch\utils\data\dataloader.py in _try_get_data(self, timeout)
790             if len(failed_workers) > 0:
791                 pids_str = ', '.join(str(w.pid) for w in failed_workers)
–> 792                 raise RuntimeError(‘DataLoader worker (pid(s) {}) exited unexpectedly’.format(pids_str))
793             if isinstance(e, queue.Empty):
794                 return (False, None)
RuntimeError: DataLoader worker (pid(s) 11752, 8848, 6272, 920, 8248, 11444) exited unexpectedly