Spaces:
Runtime error
Runtime error
File size: 3,131 Bytes
8e542dc |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 |
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()
|