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79 lines
2.5 KiB
79 lines
2.5 KiB
4 years ago
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import torch.cuda
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from .constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
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from .mixup import FastCollateMixup
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from .random_erasing import RandomErasing
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class PrefetcherCuda:
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def __init__(self,
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loader,
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mean=IMAGENET_DEFAULT_MEAN,
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std=IMAGENET_DEFAULT_STD,
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fp16=False,
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re_prob=0.,
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re_mode='const',
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re_count=1,
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re_num_splits=0):
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self.loader = loader
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self.mean = torch.tensor([x * 255 for x in mean]).cuda().view(1, 3, 1, 1)
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self.std = torch.tensor([x * 255 for x in std]).cuda().view(1, 3, 1, 1)
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self.fp16 = fp16
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if fp16:
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self.mean = self.mean.half()
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self.std = self.std.half()
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if re_prob > 0.:
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self.random_erasing = RandomErasing(
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probability=re_prob, mode=re_mode, max_count=re_count, num_splits=re_num_splits)
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else:
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self.random_erasing = None
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def __iter__(self):
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stream = torch.cuda.Stream()
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first = True
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for next_input, next_target in self.loader:
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with torch.cuda.stream(stream):
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next_input = next_input.cuda(non_blocking=True)
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next_target = next_target.cuda(non_blocking=True)
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if self.fp16:
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next_input = next_input.half().sub_(self.mean).div_(self.std)
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else:
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next_input = next_input.float().sub_(self.mean).div_(self.std)
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if self.random_erasing is not None:
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next_input = self.random_erasing(next_input)
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if not first:
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yield input, target
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else:
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first = False
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torch.cuda.current_stream().wait_stream(stream)
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input = next_input
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target = next_target
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yield input, target
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def __len__(self):
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return len(self.loader)
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@property
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def sampler(self):
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return self.loader.sampler
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@property
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def dataset(self):
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return self.loader.dataset
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@property
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def mixup_enabled(self):
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if isinstance(self.loader.collate_fn, FastCollateMixup):
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return self.loader.collate_fn.mixup_enabled
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else:
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return False
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@mixup_enabled.setter
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def mixup_enabled(self, x):
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if isinstance(self.loader.collate_fn, FastCollateMixup):
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self.loader.collate_fn.mixup_enabled = x
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