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225 lines
8.9 KiB
225 lines
8.9 KiB
""" Mixup and Cutmix
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Papers:
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mixup: Beyond Empirical Risk Minimization (https://arxiv.org/abs/1710.09412)
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CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features (https://arxiv.org/abs/1905.04899)
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Code Reference:
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CutMix: https://github.com/clovaai/CutMix-PyTorch
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Hacked together by Ross Wightman
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"""
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import numpy as np
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import torch
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import math
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import numbers
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from enum import IntEnum
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class MixupMode(IntEnum):
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MIXUP = 0
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CUTMIX = 1
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RANDOM = 2
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@classmethod
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def from_str(cls, value):
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return cls[value.upper()]
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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 y1 * lam + y2 * (1. - lam)
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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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def calc_ratio(lam, minmax=None):
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ratio = math.sqrt(1 - lam)
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if minmax is not None:
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if isinstance(minmax, numbers.Number):
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minmax = (minmax, 1 - minmax)
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ratio = np.clip(ratio, minmax[0], minmax[1])
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return ratio
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def rand_bbox(size, ratio):
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H, W = size[-2:]
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cut_h, cut_w = int(H * ratio), int(W * ratio)
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cy, cx = np.random.randint(H), np.random.randint(W)
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yl, yh = np.clip(cy - cut_h // 2, 0, H), np.clip(cy + cut_h // 2, 0, H)
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xl, xh = np.clip(cx - cut_w // 2, 0, W), np.clip(cx + cut_w // 2, 0, W)
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return yl, yh, xl, xh
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def cutmix_batch(input, target, alpha=0.2, num_classes=1000, smoothing=0.1, disable=False, correct_lam=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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if lam != 1:
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yl, yh, xl, xh = rand_bbox(input.size(), calc_ratio(lam))
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input[:, :, yl:yh, xl:xh] = input.flip(0)[:, :, yl:yh, xl:xh]
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if correct_lam:
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lam = 1 - (yh - yl) * (xh - xl) / (input.shape[-2] * input.shape[-1])
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target = mixup_target(target, num_classes, lam, smoothing)
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return input, target
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def _resolve_mode(mode):
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mode = MixupMode.from_str(mode) if isinstance(mode, str) else mode
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if mode == MixupMode.RANDOM:
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mode = MixupMode(np.random.rand() > 0.5)
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return mode # will be one of cutmix or mixup
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def mix_batch(
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input, target, alpha=0.2, num_classes=1000, smoothing=0.1, disable=False, mode=MixupMode.MIXUP):
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mode = _resolve_mode(mode)
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if mode == MixupMode.CUTMIX:
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return cutmix_batch(input, target, alpha, num_classes, smoothing, disable)
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else:
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return mixup_batch(input, target, alpha, num_classes, smoothing, disable)
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class FastCollateMixup:
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"""Fast Collate Mixup that applies different params to each element + flipped pair
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NOTE once experiments are done, one of the three variants will remain with this class name
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"""
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def __init__(self, mixup_alpha=1., label_smoothing=0.1, num_classes=1000, mode=MixupMode.MIXUP):
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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.mode = MixupMode.from_str(mode) if isinstance(mode, str) else mode
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self.mixup_enabled = True
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self.correct_lam = False # correct lambda based on clipped area for cutmix
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self.ratio_minmax = None # (0.2, 0.8)
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def _do_mix(self, tensor, batch):
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batch_size = len(batch)
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lam_out = torch.ones(batch_size)
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for i in range(batch_size//2):
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j = batch_size - i - 1
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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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if _resolve_mode(self.mode) == MixupMode.CUTMIX:
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mixed_i, mixed_j = batch[i][0].astype(np.float32), batch[j][0].astype(np.float32)
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ratio = calc_ratio(lam, self.ratio_minmax)
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if lam != 1:
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yl, yh, xl, xh = rand_bbox(tensor.size(), ratio)
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mixed_i[:, yl:yh, xl:xh] = batch[j][0][:, yl:yh, xl:xh].astype(np.float32)
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mixed_j[:, yl:yh, xl:xh] = batch[i][0][:, yl:yh, xl:xh].astype(np.float32)
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if self.correct_lam:
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lam_corrected = (yh - yl) * (xh - xl) / (tensor.shape[-2] * tensor.shape[-1])
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lam_out[i] -= lam_corrected
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lam_out[j] -= lam_corrected
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else:
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lam_out[i] = lam
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lam_out[j] = lam
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else:
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mixed_i = batch[i][0].astype(np.float32) * lam + batch[j][0].astype(np.float32) * (1 - lam)
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mixed_j = batch[j][0].astype(np.float32) * lam + batch[i][0].astype(np.float32) * (1 - lam)
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lam_out[i] = lam
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lam_out[j] = lam
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np.round(mixed_i, out=mixed_i)
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np.round(mixed_j, out=mixed_j)
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tensor[i] += torch.from_numpy(mixed_i.astype(np.uint8))
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tensor[j] += torch.from_numpy(mixed_j.astype(np.uint8))
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return lam_out.unsqueeze(1)
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def __call__(self, batch):
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batch_size = len(batch)
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assert batch_size % 2 == 0, 'Batch size should be even when using this'
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tensor = torch.zeros((batch_size, *batch[0][0].shape), dtype=torch.uint8)
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lam = self._do_mix(tensor, batch)
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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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return tensor, target
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class FastCollateMixupElementwise(FastCollateMixup):
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"""Fast Collate Mixup that applies different params to each batch element
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NOTE this is for experimentation, may remove at some point
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"""
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def __init__(self, mixup_alpha=1., label_smoothing=0.1, num_classes=1000, mode=MixupMode.MIXUP):
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super(FastCollateMixupElementwise, self).__init__(mixup_alpha, label_smoothing, num_classes, mode)
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def _do_mix(self, tensor, batch):
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batch_size = len(batch)
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lam_out = torch.ones(batch_size)
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for i in range(batch_size):
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j = batch_size - i - 1
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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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if _resolve_mode(self.mode) == MixupMode.CUTMIX:
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mixed = batch[i][0].astype(np.float32)
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if lam != 1:
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ratio = calc_ratio(lam)
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yl, yh, xl, xh = rand_bbox(tensor.size(), ratio)
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mixed[:, yl:yh, xl:xh] = batch[j][0][:, yl:yh, xl:xh].astype(np.float32)
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if self.correct_lam:
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lam_out[i] -= (yh - yl) * (xh - xl) / (tensor.shape[-2] * tensor.shape[-1])
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else:
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lam_out[i] = lam
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else:
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mixed = batch[i][0].astype(np.float32) * lam + batch[j][0].astype(np.float32) * (1 - lam)
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lam_out[i] = 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 lam_out.unsqueeze(1)
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class FastCollateMixupBatchwise(FastCollateMixup):
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"""Fast Collate Mixup that applies same params to whole batch
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NOTE this is for experimentation, may remove at some point
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"""
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def __init__(self, mixup_alpha=1., label_smoothing=0.1, num_classes=1000, mode=MixupMode.MIXUP):
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super(FastCollateMixupBatchwise, self).__init__(mixup_alpha, label_smoothing, num_classes, mode)
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def _do_mix(self, tensor, batch):
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batch_size = len(batch)
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lam = 1.
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cutmix = _resolve_mode(self.mode) == MixupMode.CUTMIX
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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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if cutmix:
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yl, yh, xl, xh = rand_bbox(batch[0][0].shape, calc_ratio(lam))
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if self.correct_lam:
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lam = 1 - (yh - yl) * (xh - xl) / (tensor.shape[-2] * tensor.shape[-1])
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for i in range(batch_size):
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j = batch_size - i - 1
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if cutmix:
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mixed = batch[i][0].astype(np.float32)
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if lam != 1:
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mixed[:, yl:yh, xl:xh] = batch[j][0][:, yl:yh, xl:xh].astype(np.float32)
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else:
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mixed = batch[i][0].astype(np.float32) * lam + batch[j][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 lam
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