DataLoader worker (pid(s) 35132, 35468) exited unexpectedly

[shireen1512]

Hello,
Im trying to train ocrnet_hr18, and i'm using the jupyter notebook downloaded from colab. by running this line : runner.train() i get the following error :

File c:\Users\Shireen\anaconda3\envs\mmsegmentation\lib\site-packages\torch\utils\data\dataloader.py:630, in _BaseDataLoaderIter.next(self)
627 if self._sampler_iter is None:
628 # TODO(pytorch/pytorch#76750)
629 self._reset() # type: ignore[call-arg]
--> 630 data = self._next_data()
631 self._num_yielded += 1
632 if self._dataset_kind == _DatasetKind.Iterable and
633 self._IterableDataset_len_called is not None and
634 self._num_yielded > self._IterableDataset_len_called:

File c:\Users\Shireen\anaconda3\envs\mmsegmentation\lib\site-packages\torch\utils\data\dataloader.py:1328, in _MultiProcessingDataLoaderIter._next_data(self)
1325 return self._process_data(data)
1327 assert not self._shutdown and self._tasks_outstanding > 0
-> 1328 idx, data = self._get_data()
1329 self._tasks_outstanding -= 1
1330 if self._dataset_kind == _DatasetKind.Iterable:
1331 # Check for _IterableDatasetStopIteration

File c:\Users\Shireen\anaconda3\envs\mmsegmentation\lib\site-packages\torch\utils\data\dataloader.py:1294, in _MultiProcessingDataLoaderIter._get_data(self)
1290 # In this case, self._data_queue is a queue.Queue,. But we don't
1291 # need to call .task_done() because we don't use .join().
1292 else:
1293 while True:
-> 1294 success, data = self._try_get_data()
1295 if success:
1296 return data

File c:\Users\Shireen\anaconda3\envs\mmsegmentation\lib\site-packages\torch\utils\data\dataloader.py:1145, in _MultiProcessingDataLoaderIter._try_get_data(self, timeout)
1143 if len(failed_workers) > 0:
1144 pids_str = ', '.join(str(w.pid) for w in failed_workers)
-> 1145 raise RuntimeError(f'DataLoader worker (pid(s) {pids_str}) exited unexpectedly') from e
1146 if isinstance(e, queue.Empty):
1147 return (False, None)

RuntimeError: DataLoader worker (pid(s) 35132, 35468) exited unexpectedly

---

Here is how i created the config according to colab notebook :

cfg.norm_cfg = dict(type='BN', requires_grad=True)
cfg.crop_size = (256, 256)
cfg.model.data_preprocessor.size = cfg.crop_size
cfg.model.backbone.norm_cfg = cfg.norm_cfg

# Modify num classes of the model in decode/auxiliary head
for head in cfg.model.decode_head:
    head['num_classes'] = 7
    head['norm_cfg'] = cfg.norm_cfg



# Modify dataset type and path
cfg.dataset_type = 'myDataset'
cfg.data_root = "dataset"

cfg.train_dataloader.batch_size = 4

cfg.train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations'),
    dict(type='RandomResize', scale=(320, 240), ratio_range=(0.5, 2.0), keep_ratio=True),
    dict(type='RandomCrop', crop_size=cfg.crop_size, cat_max_ratio=0.75),
    dict(type='RandomFlip', prob=0.5),
    dict(type='PackSegInputs')
]

cfg.test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='Resize', scale=(320, 240), keep_ratio=True),
    dict(type='LoadAnnotations'),
    dict(type='PackSegInputs')
]

cfg.train_dataloader.dataset.type = cfg.dataset_type
cfg.train_dataloader.dataset.data_root = cfg.data_root
cfg.train_dataloader.dataset.data_prefix = dict(img_path=img_dir, seg_map_path=ann_dir)
cfg.train_dataloader.dataset.pipeline = cfg.train_pipeline
cfg.train_dataloader.dataset.ann_file = 'splits/train.txt'

cfg.val_dataloader.dataset.type = cfg.dataset_type
cfg.val_dataloader.dataset.data_root = cfg.data_root
cfg.val_dataloader.dataset.data_prefix = dict(img_path=img_dir, seg_map_path=ann_dir)
cfg.val_dataloader.dataset.pipeline = cfg.test_pipeline
cfg.val_dataloader.dataset.ann_file = 'splits/val.txt'

cfg.test_dataloader = cfg.val_dataloader

# Load the pretrained weights
cfg.load_from = 'ocrnet_hr18_512x1024_40k_cityscapes_20200601_033320-401c5bdd.pth'

# Set up working dir to save files and logs.
cfg.work_dir = '/work_dir/trainwgoogleearth'

cfg.train_cfg.max_iters = 10000
cfg.train_cfg.val_interval = 100
cfg.default_hooks.logger.interval = 10
cfg.default_hooks.checkpoint.interval = 500

# Set seed to facilitate reproducing the result
cfg['randomness'] = dict(seed=0)

# Let's have a look at the final config used for training
print(f'Config:\n{cfg.pretty_text}')

---

could anyone help with the error please?
i have intalled the mmcv with pip, could that be the issue ? here are the versions of everything :

Torch = 2.1.0
Cuda = 12.1
TorchVision= 0.16.0
mmseg = 1.2.2
OpenCV= 4.9.0
MMEngine= 0.10.3
MMCV= 2.1.0
MMCV Compiler= MSVC 192930148
MMCV CUDA Compiler= 12.1

[Zoulinx]

It seems that Jupyter does not support creating subprocesses. Please try setting num_workers=1.