EOFError: Ran out of input

[wang-TJ-20]

Traceback (most recent call last):
File "D:/BEV/translating-images-into-maps-main/translating-images-into-maps-main/train.py", line 951, in
main()
File "D:/BEV/translating-images-into-maps-main/translating-images-into-maps-main/train.py", line 938, in main
train(args, train_loader, model, optimizer, epoch)
File "D:/BEV/translating-images-into-maps-main/translating-images-into-maps-main/train.py", line 53, in train
for i, ((image, calib, grid2d), (cls_map, vis_mask)) in enumerate(dataloader):
File "E:\ananconda\lib\site-packages\torch\utils\data\dataloader.py", line 359, in iter
return self._get_iterator()
File "E:\ananconda\lib\site-packages\torch\utils\data\dataloader.py", line 305, in _get_iterator
return _MultiProcessingDataLoaderIter(self)
File "E:\ananconda\lib\site-packages\torch\utils\data\dataloader.py", line 918, in init
w.start()
File "E:\ananconda\lib\multiprocessing\process.py", line 121, in start
self._popen = self._Popen(self)
File "E:\ananconda\lib\multiprocessing\context.py", line 224, in _Popen
return _default_context.get_context().Process._Popen(process_obj)
File "E:\ananconda\lib\multiprocessing\context.py", line 327, in _Popen
return Popen(process_obj)
File "E:\ananconda\lib\multiprocessing\popen_spawn_win32.py", line 93, in init
reduction.dump(process_obj, to_child)
File "E:\ananconda\lib\multiprocessing\reduction.py", line 60, in dump
ForkingPickler(file, protocol).dump(obj)
TypeError: cannot pickle 'Environment' object
Traceback (most recent call last):
File "", line 1, in
File "E:\ananconda\lib\multiprocessing\spawn.py", line 116, in spawn_main
exitcode = _main(fd, parent_sentinel)
File "E:\ananconda\lib\multiprocessing\spawn.py", line 126, in _main
self = reduction.pickle.load(from_parent)
EOFError: Ran out of input

how to slove this problem

[wang-TJ-20]

@wang-TJ-20
when in Windows, you should set numworks = 0

[avishkarsaha]

I will release the dataloader that works without lmdb's in a few weeks.

[gongyan1]

@avishkarsaha Thank you for your excellent achievements, which have greatly promoted the development of BEV field. Do you plan to publish the dataloader that does not need LMDB recently? Thank you for your time.