I have following code which have continuous two steps:

Iterate the original Dataloader and save some feature by update_dataset_memory(save to dict object) .
Add saved feature to the Dataset.

model.eval()
val_dataset = val_loader.dataset
val_memory_loader = Memory_loader(cfg, 'val')
val_memory_loader.update_dataset_memory(val_loader, model, debug=DEBUG)
val_dataset.set_memory_loader(val_memory_loader)
val_loader = loader.construct_loader(cfg, "val", dataset=val_dataset)
for cur_iter, (inputs, labels, _, meta) in enumerate(val_loader):
...

In update_dataset_memory, iterating the original Dataset is ok.
    @torch.no_grad()
    def update_dataset_memory(self, dataloader, model, debug=False):
        for cur_iter, (inputs, _, _, meta) in enumerate(dataloader):
            if isinstance(inputs, (list,)):
                for i in range(len(inputs)):
                    inputs[i] = inputs[i].cuda(non_blocking=True)
            else:
                inputs = inputs.cuda(non_blocking=True)
            feature = model.feature_extract(inputs, meta)['feature']
            memory_boxes, memory_meta = meta['boxes'], meta['metadata']
            self.batch_set_memory(
                feature.cpu(), memory_boxes.cpu(), memory_meta.cpu())

But when I iterate the updated Dataset by map-style Dataloader, it raise ValueError('too many fds').
It seems that if i add extra dict to the Dataset and use it in this context will increase fds obviously(num_workers=2 for all Dataloader).
So, how does it happend and whether this is a proper practice?
  File "/home/nijingcheng/slowfast/tools/train_net_v2.py", line 179, in eval_epoch
    for cur_iter, (inputs, labels, _, meta) in enumerate(val_loader):
  File "/home/nijingcheng/anaconda3/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 279, in __iter__
    return _MultiProcessingDataLoaderIter(self)
  File "/home/nijingcheng/anaconda3/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 719, in __init__
    w.start()
  File "/home/nijingcheng/anaconda3/lib/python3.7/multiprocessing/process.py", line 112, in start
    self._popen = self._Popen(self)
  File "/home/nijingcheng/anaconda3/lib/python3.7/multiprocessing/context.py", line 223, in _Popen
    return _default_context.get_context().Process._Popen(process_obj)
  File "/home/nijingcheng/anaconda3/lib/python3.7/multiprocessing/context.py", line 291, in _Popen
    return Popen(process_obj)
  File "/home/nijingcheng/anaconda3/lib/python3.7/multiprocessing/popen_forkserver.py", line 35, in __init__
    super().__init__(process_obj)
  File "/home/nijingcheng/anaconda3/lib/python3.7/multiprocessing/popen_fork.py", line 20, in __init__
    self._launch(process_obj)
  File "/home/nijingcheng/anaconda3/lib/python3.7/multiprocessing/popen_forkserver.py", line 51, in _launch
    self.sentinel, w = forkserver.connect_to_new_process(self._fds)
  File "/home/nijingcheng/anaconda3/lib/python3.7/multiprocessing/forkserver.py", line 66, in connect_to_new_process
    raise ValueError('too many fds')
ValueError: too many fds