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import queue as Queue
import threading
import torch
from torch.utils.data import DataLoader
class PrefetchGenerator(threading.Thread):
"""A general prefetch generator.
Ref:
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.
Ref:
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.
Ref:
https://github.com/NVIDIA/apex/issues/304#
It may consums 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) # self.batch is a dict
except StopIteration:
self.batch = None
return None
# put tensors to gpu
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()
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