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