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340 lines
16 KiB
340 lines
16 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 / Copyright 2019, 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 y1 * lam + y2 * (1. - lam)
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def rand_bbox(img_shape, lam, margin=0., count=None):
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""" Standard CutMix bounding-box
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Generates a random square bbox based on lambda value. This impl includes
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support for enforcing a border margin as percent of bbox dimensions.
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Args:
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img_shape (tuple): Image shape as tuple
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lam (float): Cutmix lambda value
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margin (float): Percentage of bbox dimension to enforce as margin (reduce amount of box outside image)
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count (int): Number of bbox to generate
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"""
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ratio = np.sqrt(1 - lam)
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img_h, img_w = img_shape[-2:]
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cut_h, cut_w = int(img_h * ratio), int(img_w * ratio)
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margin_y, margin_x = int(margin * cut_h), int(margin * cut_w)
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cy = np.random.randint(0 + margin_y, img_h - margin_y, size=count)
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cx = np.random.randint(0 + margin_x, img_w - margin_x, size=count)
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yl = np.clip(cy - cut_h // 2, 0, img_h)
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yh = np.clip(cy + cut_h // 2, 0, img_h)
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xl = np.clip(cx - cut_w // 2, 0, img_w)
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xh = np.clip(cx + cut_w // 2, 0, img_w)
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return yl, yh, xl, xh
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def rand_bbox_minmax(img_shape, minmax, count=None):
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""" Min-Max CutMix bounding-box
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Inspired by Darknet cutmix impl, generates a random rectangular bbox
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based on min/max percent values applied to each dimension of the input image.
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Typical defaults for minmax are usually in the .2-.3 for min and .8-.9 range for max.
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Args:
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img_shape (tuple): Image shape as tuple
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minmax (tuple or list): Min and max bbox ratios (as percent of image size)
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count (int): Number of bbox to generate
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"""
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assert len(minmax) == 2
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img_h, img_w = img_shape[-2:]
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cut_h = np.random.randint(int(img_h * minmax[0]), int(img_h * minmax[1]), size=count)
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cut_w = np.random.randint(int(img_w * minmax[0]), int(img_w * minmax[1]), size=count)
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yl = np.random.randint(0, img_h - cut_h, size=count)
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xl = np.random.randint(0, img_w - cut_w, size=count)
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yu = yl + cut_h
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xu = xl + cut_w
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return yl, yu, xl, xu
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def cutmix_bbox_and_lam(img_shape, lam, ratio_minmax=None, correct_lam=True, count=None):
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""" Generate bbox and apply lambda correction.
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"""
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if ratio_minmax is not None:
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yl, yu, xl, xu = rand_bbox_minmax(img_shape, ratio_minmax, count=count)
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else:
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yl, yu, xl, xu = rand_bbox(img_shape, lam, count=count)
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if correct_lam or ratio_minmax is not None:
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bbox_area = (yu - yl) * (xu - xl)
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lam = 1. - bbox_area / float(img_shape[-2] * img_shape[-1])
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return (yl, yu, xl, xu), lam
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class Mixup:
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""" Mixup/Cutmix that applies different params to each element or whole batch
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Args:
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mixup_alpha (float): mixup alpha value, mixup is active if > 0.
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cutmix_alpha (float): cutmix alpha value, cutmix is active if > 0.
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cutmix_minmax (List[float]): cutmix min/max image ratio, cutmix is active and uses this vs alpha if not None.
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prob (float): probability of applying mixup or cutmix per batch or element
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switch_prob (float): probability of switching to cutmix instead of mixup when both are active
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mode (str): how to apply mixup/cutmix params (per 'batch', 'pair' (pair of elements), 'elem' (element)
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correct_lam (bool): apply lambda correction when cutmix bbox clipped by image borders
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label_smoothing (float): apply label smoothing to the mixed target tensor
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num_classes (int): number of classes for target
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"""
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def __init__(self, mixup_alpha=1., cutmix_alpha=0., cutmix_minmax=None, prob=1.0, switch_prob=0.5,
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mode='batch', correct_lam=True, label_smoothing=0., num_classes=0):
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assert num_classes > 0, 'num_classes must be set for target generation'
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self.mixup_alpha = mixup_alpha
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self.cutmix_alpha = cutmix_alpha
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self.cutmix_minmax = cutmix_minmax
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if self.cutmix_minmax is not None:
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assert len(self.cutmix_minmax) == 2
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# force cutmix alpha == 1.0 when minmax active to keep logic simple & safe
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self.cutmix_alpha = 1.0
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self.mix_prob = prob
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self.switch_prob = switch_prob
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self.label_smoothing = label_smoothing
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self.num_classes = num_classes
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self.mode = mode
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self.correct_lam = correct_lam # correct lambda based on clipped area for cutmix
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self.mixup_enabled = True # set to false to disable mixing (intended tp be set by train loop)
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def _params_per_elem(self, batch_size):
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lam = np.ones(batch_size, dtype=np.float32)
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use_cutmix = np.zeros(batch_size, dtype=np.bool)
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if self.mixup_enabled:
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if self.mixup_alpha > 0. and self.cutmix_alpha > 0.:
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use_cutmix = np.random.rand(batch_size) < self.switch_prob
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lam_mix = np.where(
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use_cutmix,
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np.random.beta(self.cutmix_alpha, self.cutmix_alpha, size=batch_size),
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np.random.beta(self.mixup_alpha, self.mixup_alpha, size=batch_size))
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elif self.mixup_alpha > 0.:
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lam_mix = np.random.beta(self.mixup_alpha, self.mixup_alpha, size=batch_size)
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elif self.cutmix_alpha > 0.:
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use_cutmix = np.ones(batch_size, dtype=np.bool)
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lam_mix = np.random.beta(self.cutmix_alpha, self.cutmix_alpha, size=batch_size)
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else:
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assert False, "One of mixup_alpha > 0., cutmix_alpha > 0., cutmix_minmax not None should be true."
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lam = np.where(np.random.rand(batch_size) < self.mix_prob, lam_mix.astype(np.float32), lam)
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return lam, use_cutmix
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def _params_per_batch(self):
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lam = 1.
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use_cutmix = False
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if self.mixup_enabled and np.random.rand() < self.mix_prob:
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if self.mixup_alpha > 0. and self.cutmix_alpha > 0.:
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use_cutmix = np.random.rand() < self.switch_prob
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lam_mix = np.random.beta(self.cutmix_alpha, self.cutmix_alpha) if use_cutmix else \
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np.random.beta(self.mixup_alpha, self.mixup_alpha)
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elif self.mixup_alpha > 0.:
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lam_mix = np.random.beta(self.mixup_alpha, self.mixup_alpha)
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elif self.cutmix_alpha > 0.:
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use_cutmix = True
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lam_mix = np.random.beta(self.cutmix_alpha, self.cutmix_alpha)
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else:
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assert False, "One of mixup_alpha > 0., cutmix_alpha > 0., cutmix_minmax not None should be true."
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lam = float(lam_mix)
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return lam, use_cutmix
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def _mix_elem(self, x):
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batch_size = len(x)
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lam_batch, use_cutmix = self._params_per_elem(batch_size)
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x_orig = x.clone() # need to keep an unmodified original for mixing source
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for i in range(batch_size):
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j = batch_size - i - 1
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lam = lam_batch[i]
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if lam != 1.:
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if use_cutmix[i]:
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(yl, yh, xl, xh), lam = cutmix_bbox_and_lam(
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x[i].shape, lam, ratio_minmax=self.cutmix_minmax, correct_lam=self.correct_lam)
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x[i][:, yl:yh, xl:xh] = x_orig[j][:, yl:yh, xl:xh]
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lam_batch[i] = lam
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else:
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x[i] = x[i] * lam + x_orig[j] * (1 - lam)
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return torch.tensor(lam_batch, device=x.device, dtype=x.dtype).unsqueeze(1)
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def _mix_pair(self, x):
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batch_size = len(x)
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lam_batch, use_cutmix = self._params_per_elem(batch_size // 2)
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x_orig = x.clone() # need to keep an unmodified original for mixing source
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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 = lam_batch[i]
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if lam != 1.:
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if use_cutmix[i]:
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(yl, yh, xl, xh), lam = cutmix_bbox_and_lam(
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x[i].shape, lam, ratio_minmax=self.cutmix_minmax, correct_lam=self.correct_lam)
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x[i][:, yl:yh, xl:xh] = x_orig[j][:, yl:yh, xl:xh]
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x[j][:, yl:yh, xl:xh] = x_orig[i][:, yl:yh, xl:xh]
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lam_batch[i] = lam
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else:
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x[i] = x[i] * lam + x_orig[j] * (1 - lam)
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x[j] = x[j] * lam + x_orig[i] * (1 - lam)
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lam_batch = np.concatenate((lam_batch, lam_batch[::-1]))
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return torch.tensor(lam_batch, device=x.device, dtype=x.dtype).unsqueeze(1)
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def _mix_batch(self, x):
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lam, use_cutmix = self._params_per_batch()
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if lam == 1.:
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return 1.
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if use_cutmix:
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(yl, yh, xl, xh), lam = cutmix_bbox_and_lam(
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x.shape, lam, ratio_minmax=self.cutmix_minmax, correct_lam=self.correct_lam)
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x[:, :, yl:yh, xl:xh] = x.flip(0)[:, :, yl:yh, xl:xh]
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else:
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x_flipped = x.flip(0).mul_(1. - lam)
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x.mul_(lam).add_(x_flipped)
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return lam
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def __call__(self, x, target):
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assert len(x) % 2 == 0, 'Batch size should be even when using this'
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if self.mode == 'elem':
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lam = self._mix_elem(x)
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elif self.mode == 'pair':
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lam = self._mix_pair(x)
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else:
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lam = self._mix_batch(x)
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target = mixup_target(target, self.num_classes, lam, self.label_smoothing, x.device)
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return x, target
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def blend(a, b, lam, is_tensor=False, round_output=True):
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if is_tensor:
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blend = a.to(dtype=torch.float32) * lam + b.to(dtype=torch.float32) * (1 - lam)
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if round_output:
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torch.round(blend, out=blend)
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else:
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blend = a.astype(np.float32) * lam + b.astype(np.float32) * (1 - lam)
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if round_output:
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np.rint(blend, out=blend)
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return blend
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class FastCollateMixup(Mixup):
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""" Fast Collate w/ Mixup/Cutmix that applies different params to each element or whole batch
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A Mixup impl that's performed while collating the batches.
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"""
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def _mix_elem_collate(self, output, batch, half=False, is_tensor=False):
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batch_size = len(batch)
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num_elem = batch_size // 2 if half else batch_size
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assert len(output) == num_elem
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lam_batch, use_cutmix = self._params_per_elem(num_elem)
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round_output = output.dtype == torch.uint8
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for i in range(num_elem):
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j = batch_size - i - 1
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lam = lam_batch[i]
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mixed = batch[i][0]
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if lam != 1.:
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if use_cutmix[i]:
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if not half:
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mixed = mixed.clone() if is_tensor else mixed.copy() # don't want to modify while iterating
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(yl, yh, xl, xh), lam = cutmix_bbox_and_lam(
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output.shape, lam, ratio_minmax=self.cutmix_minmax, correct_lam=self.correct_lam)
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mixed[:, yl:yh, xl:xh] = batch[j][0][:, yl:yh, xl:xh]
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lam_batch[i] = lam
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else:
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mixed = blend(mixed, batch[j][0], lam, is_tensor, round_output)
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mixed = mixed.to(dtype=output.dtype) if is_tensor else torch.from_numpy(mixed.astype(np.uint8))
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output[i].copy_(mixed)
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if half:
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lam_batch = np.concatenate((lam_batch, np.ones(num_elem)))
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return torch.tensor(lam_batch).unsqueeze(1)
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def _mix_pair_collate(self, output, batch, is_tensor=False):
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batch_size = len(batch)
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lam_batch, use_cutmix = self._params_per_elem(batch_size // 2)
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round_output = output.dtype == torch.uint8
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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 = lam_batch[i]
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mixed_i = batch[i][0]
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mixed_j = batch[j][0]
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assert 0 <= lam <= 1.0
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if lam < 1.:
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if use_cutmix[i]:
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(yl, yh, xl, xh), lam = cutmix_bbox_and_lam(
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output.shape, lam, ratio_minmax=self.cutmix_minmax, correct_lam=self.correct_lam)
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patch_i = mixed_i[:, yl:yh, xl:xh]
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patch_i = patch_i.clone() if is_tensor else patch_i.copy() # don't want to modify while iterating
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mixed_i[:, yl:yh, xl:xh] = mixed_j[:, yl:yh, xl:xh]
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mixed_j[:, yl:yh, xl:xh] = patch_i
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lam_batch[i] = lam
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else:
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mixed_temp = blend(mixed_i, mixed_j, lam, is_tensor, round_output)
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mixed_j = blend(mixed_j, mixed_i, lam, is_tensor, round_output)
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mixed_i = mixed_temp
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if is_tensor:
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mixed_i = mixed_i.to(dtype=output.dtype)
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mixed_j = mixed_j.to(dtype=output.dtype)
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else:
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mixed_i = torch.from_numpy(mixed_i.astype(np.uint8))
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mixed_j = torch.from_numpy(mixed_j.astype(np.uint8))
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output[i].copy_(mixed_i)
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output[j].copy_(mixed_j)
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lam_batch = np.concatenate((lam_batch, lam_batch[::-1]))
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return torch.tensor(lam_batch).unsqueeze(1)
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def _mix_batch_collate(self, output, batch, is_tensor=False):
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batch_size = len(batch)
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lam, use_cutmix = self._params_per_batch()
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round_output = output.dtype == torch.uint8
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if use_cutmix:
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(yl, yh, xl, xh), lam = cutmix_bbox_and_lam(
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output.shape, lam, ratio_minmax=self.cutmix_minmax, correct_lam=self.correct_lam)
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for i in range(batch_size):
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j = batch_size - i - 1
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mixed = batch[i][0]
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if lam != 1.:
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if use_cutmix:
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mixed = mixed.clone() if is_tensor else mixed.copy() # don't want to modify while iterating
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mixed[:, yl:yh, xl:xh] = batch[j][0][:, yl:yh, xl:xh]
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else:
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mixed = blend(mixed, batch[j][0], lam, is_tensor, round_output)
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mixed = mixed.to(dtype=output.dtype) if is_tensor else torch.from_numpy(mixed.astype(np.uint8))
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output[i].copy_(mixed)
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return lam
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def __call__(self, batch, _=None):
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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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half = 'half' in self.mode
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if half:
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batch_size //= 2
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is_tensor = isinstance(batch[0][0], torch.Tensor)
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output_dtype = batch[0][0].dtype if is_tensor else torch.uint8 # always uint8 if numpy src
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output = torch.zeros((batch_size, *batch[0][0].shape), dtype=output_dtype)
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if self.mode == 'elem' or self.mode == 'half':
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lam = self._mix_elem_collate(output, batch, half=half, is_tensor=is_tensor)
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elif self.mode == 'pair':
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lam = self._mix_pair_collate(output, batch, is_tensor=is_tensor)
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else:
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lam = self._mix_batch_collate(output, batch, is_tensor=is_tensor)
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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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target = target[:batch_size]
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return output, target
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