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57 lines
2.1 KiB
57 lines
2.1 KiB
""" Mixup
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Paper: `mixup: Beyond Empirical Risk Minimization` - https://arxiv.org/abs/1710.09412
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Hacked together by / Copyright 2020 Ross Wightman
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"""
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import numpy as np
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import torch
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def one_hot(x, num_classes, on_value=1., off_value=0., device='cuda'):
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x = x.long().view(-1, 1)
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return torch.full((x.size()[0], num_classes), off_value, device=device).scatter_(1, x, on_value)
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def mixup_target(target, num_classes, lam=1., smoothing=0.0, device='cuda'):
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off_value = smoothing / num_classes
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on_value = 1. - smoothing + off_value
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y1 = one_hot(target, num_classes, on_value=on_value, off_value=off_value, device=device)
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y2 = one_hot(target.flip(0), num_classes, on_value=on_value, off_value=off_value, device=device)
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return lam*y1 + (1. - lam)*y2
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def mixup_batch(input, target, alpha=0.2, num_classes=1000, smoothing=0.1, disable=False):
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lam = 1.
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if not disable:
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lam = np.random.beta(alpha, alpha)
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input = input.mul(lam).add_(1 - lam, input.flip(0))
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target = mixup_target(target, num_classes, lam, smoothing)
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return input, target
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class FastCollateMixup:
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def __init__(self, mixup_alpha=1., label_smoothing=0.1, num_classes=1000):
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self.mixup_alpha = mixup_alpha
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self.label_smoothing = label_smoothing
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self.num_classes = num_classes
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self.mixup_enabled = True
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def __call__(self, batch):
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batch_size = len(batch)
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lam = 1.
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if self.mixup_enabled:
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lam = np.random.beta(self.mixup_alpha, self.mixup_alpha)
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target = torch.tensor([b[1] for b in batch], dtype=torch.int64)
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target = mixup_target(target, self.num_classes, lam, self.label_smoothing, device='cpu')
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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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mixed = batch[i][0].astype(np.float32) * lam + \
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batch[batch_size - i - 1][0].astype(np.float32) * (1 - lam)
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np.round(mixed, out=mixed)
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tensor[i] += torch.from_numpy(mixed.astype(np.uint8))
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return tensor, target
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