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151 lines
4.6 KiB
151 lines
4.6 KiB
import torch.utils.data
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from data.transforms import *
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from data.distributed_sampler import OrderedDistributedSampler
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from data.mixup import FastCollateMixup
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def fast_collate(batch):
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targets = torch.tensor([b[1] for b in batch], dtype=torch.int64)
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batch_size = len(targets)
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tensor = torch.zeros((batch_size, *batch[0][0].shape), dtype=torch.uint8)
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for i in range(batch_size):
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tensor[i] += torch.from_numpy(batch[i][0])
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return tensor, targets
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class PrefetchLoader:
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def __init__(self,
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loader,
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rand_erase_prob=0.,
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rand_erase_mode='const',
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mean=IMAGENET_DEFAULT_MEAN,
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std=IMAGENET_DEFAULT_STD):
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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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if rand_erase_prob > 0.:
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self.random_erasing = RandomErasing(
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probability=rand_erase_prob, mode=rand_erase_mode)
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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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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 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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def create_loader(
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dataset,
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input_size,
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batch_size,
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is_training=False,
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use_prefetcher=True,
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rand_erase_prob=0.,
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rand_erase_mode='const',
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interpolation='bilinear',
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mean=IMAGENET_DEFAULT_MEAN,
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std=IMAGENET_DEFAULT_STD,
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num_workers=1,
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distributed=False,
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crop_pct=None,
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collate_fn=None,
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tf_preprocessing=False,
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):
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if isinstance(input_size, tuple):
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img_size = input_size[-2:]
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else:
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img_size = input_size
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if tf_preprocessing and use_prefetcher:
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from data.tf_preprocessing import TfPreprocessTransform
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transform = TfPreprocessTransform(is_training=is_training, size=img_size)
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else:
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if is_training:
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transform = transforms_imagenet_train(
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img_size,
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interpolation=interpolation,
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use_prefetcher=use_prefetcher,
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mean=mean,
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std=std)
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else:
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transform = transforms_imagenet_eval(
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img_size,
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interpolation=interpolation,
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use_prefetcher=use_prefetcher,
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mean=mean,
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std=std,
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crop_pct=crop_pct)
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dataset.transform = transform
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sampler = None
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if distributed:
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if is_training:
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sampler = torch.utils.data.distributed.DistributedSampler(dataset)
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else:
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# This will add extra duplicate entries to result in equal num
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# of samples per-process, will slightly alter validation results
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sampler = OrderedDistributedSampler(dataset)
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if collate_fn is None:
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collate_fn = fast_collate if use_prefetcher else torch.utils.data.dataloader.default_collate
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loader = torch.utils.data.DataLoader(
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dataset,
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batch_size=batch_size,
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shuffle=sampler is None and is_training,
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num_workers=num_workers,
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sampler=sampler,
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collate_fn=collate_fn,
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drop_last=is_training,
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)
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if use_prefetcher:
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loader = PrefetchLoader(
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loader,
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rand_erase_prob=rand_erase_prob if is_training else 0.,
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rand_erase_mode=rand_erase_mode,
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mean=mean,
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std=std)
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return loader
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