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65 lines
2.0 KiB
65 lines
2.0 KiB
import torch
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from .constants import *
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from .random_erasing import RandomErasing
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from. mixup import FastCollateMixup
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class FetcherXla:
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def __init__(self):
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pass
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class Fetcher:
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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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device=None,
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dtype=None,
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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.device = torch.device(device)
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self.dtype = dtype or torch.float32
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self.mean = torch.tensor([x * 255 for x in mean], dtype=self.dtype, device=self.device).view(1, 3, 1, 1)
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self.std = torch.tensor([x * 255 for x in std], dtype=self.dtype, device=self.device).view(1, 3, 1, 1)
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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, device=device)
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else:
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self.random_erasing = None
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def __iter__(self):
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for sample, target in self.loader:
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sample = sample.to(device=self.device, dtype=self.dtype).sub_(self.mean).div_(self.std)
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target = target.to(device=self.device)
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if self.random_erasing is not None:
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sample = self.random_erasing(sample)
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yield sample, 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 |