| | import queue as Queue
|
| | import threading
|
| | import torch
|
| | from torch.utils.data import DataLoader
|
| |
|
| |
|
| | class PrefetchGenerator(threading.Thread):
|
| | """A general prefetch generator.
|
| |
|
| | Reference: https://stackoverflow.com/questions/7323664/python-generator-pre-fetch
|
| |
|
| | Args:
|
| | generator: Python generator.
|
| | num_prefetch_queue (int): Number of prefetch queue.
|
| | """
|
| |
|
| | def __init__(self, generator, num_prefetch_queue):
|
| | threading.Thread.__init__(self)
|
| | self.queue = Queue.Queue(num_prefetch_queue)
|
| | self.generator = generator
|
| | self.daemon = True
|
| | self.start()
|
| |
|
| | def run(self):
|
| | for item in self.generator:
|
| | self.queue.put(item)
|
| | self.queue.put(None)
|
| |
|
| | def __next__(self):
|
| | next_item = self.queue.get()
|
| | if next_item is None:
|
| | raise StopIteration
|
| | return next_item
|
| |
|
| | def __iter__(self):
|
| | return self
|
| |
|
| |
|
| | class PrefetchDataLoader(DataLoader):
|
| | """Prefetch version of dataloader.
|
| |
|
| | Reference: https://github.com/IgorSusmelj/pytorch-styleguide/issues/5#
|
| |
|
| | TODO:
|
| | Need to test on single gpu and ddp (multi-gpu). There is a known issue in
|
| | ddp.
|
| |
|
| | Args:
|
| | num_prefetch_queue (int): Number of prefetch queue.
|
| | kwargs (dict): Other arguments for dataloader.
|
| | """
|
| |
|
| | def __init__(self, num_prefetch_queue, **kwargs):
|
| | self.num_prefetch_queue = num_prefetch_queue
|
| | super(PrefetchDataLoader, self).__init__(**kwargs)
|
| |
|
| | def __iter__(self):
|
| | return PrefetchGenerator(super().__iter__(), self.num_prefetch_queue)
|
| |
|
| |
|
| | class CPUPrefetcher():
|
| | """CPU prefetcher.
|
| |
|
| | Args:
|
| | loader: Dataloader.
|
| | """
|
| |
|
| | def __init__(self, loader):
|
| | self.ori_loader = loader
|
| | self.loader = iter(loader)
|
| |
|
| | def next(self):
|
| | try:
|
| | return next(self.loader)
|
| | except StopIteration:
|
| | return None
|
| |
|
| | def reset(self):
|
| | self.loader = iter(self.ori_loader)
|
| |
|
| |
|
| | class CUDAPrefetcher():
|
| | """CUDA prefetcher.
|
| |
|
| | Reference: https://github.com/NVIDIA/apex/issues/304#
|
| |
|
| | It may consume more GPU memory.
|
| |
|
| | Args:
|
| | loader: Dataloader.
|
| | opt (dict): Options.
|
| | """
|
| |
|
| | def __init__(self, loader, opt):
|
| | self.ori_loader = loader
|
| | self.loader = iter(loader)
|
| | self.opt = opt
|
| | self.stream = torch.cuda.Stream()
|
| | self.device = torch.device('cuda' if opt['num_gpu'] != 0 else 'cpu')
|
| | self.preload()
|
| |
|
| | def preload(self):
|
| | try:
|
| | self.batch = next(self.loader)
|
| | except StopIteration:
|
| | self.batch = None
|
| | return None
|
| |
|
| | with torch.cuda.stream(self.stream):
|
| | for k, v in self.batch.items():
|
| | if torch.is_tensor(v):
|
| | self.batch[k] = self.batch[k].to(device=self.device, non_blocking=True)
|
| |
|
| | def next(self):
|
| | torch.cuda.current_stream().wait_stream(self.stream)
|
| | batch = self.batch
|
| | self.preload()
|
| | return batch
|
| |
|
| | def reset(self):
|
| | self.loader = iter(self.ori_loader)
|
| | self.preload()
|
| |
|
