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66 lines
2.0 KiB
66 lines
2.0 KiB
6 years ago
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import torch
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from data.random_erasing import RandomErasingTorch
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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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fp16=False,
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random_erasing=True,
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mean=[0.485, 0.456, 0.406],
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std=[0.229, 0.224, 0.225]):
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self.loader = loader
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self.fp16 = fp16
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self.random_erasing = random_erasing
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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 random_erasing:
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self.random_erasing = RandomErasingTorch(per_pixel=True)
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else:
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self.random_erasing = None
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if self.fp16:
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self.mean = self.mean.half()
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self.std = self.std.half()
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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()
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else:
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next_input = next_input.float()
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next_input = next_input.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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