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@ -10,11 +10,8 @@ CutMix: https://github.com/clovaai/CutMix-PyTorch
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Hacked together by / Copyright 2020 Ross Wightman
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Hacked together by / Copyright 2020 Ross Wightman
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"""
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"""
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import numpy as np
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import numpy as np
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import torch
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import torch
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import math
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import numbers
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def one_hot(x, num_classes, on_value=1., off_value=0., device='cuda'):
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def one_hot(x, num_classes, on_value=1., off_value=0., device='cuda'):
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@ -30,20 +27,21 @@ def mixup_target(target, num_classes, lam=1., smoothing=0.0, device='cuda'):
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return y1 * lam + y2 * (1. - lam)
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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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def rand_bbox(img_shape, lam, margin=0., count=None):
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lam = 1.
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""" Standard CutMix bounding-box
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if not disable:
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Generates a random square bbox based on lambda value. This impl includes
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lam = np.random.beta(alpha, alpha)
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support for enforcing a border margin as percent of bbox dimensions.
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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 rand_bbox(size, lam, border=0., count=None):
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Args:
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ratio = math.sqrt(1 - lam)
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img_shape (tuple): Image shape as tuple
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img_h, img_w = size[-2:]
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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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cut_h, cut_w = int(img_h * ratio), int(img_w * ratio)
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margin_y, margin_x = int(border * cut_h), int(border * cut_w)
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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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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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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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yl = np.clip(cy - cut_h // 2, 0, img_h)
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@ -53,9 +51,20 @@ def rand_bbox(size, lam, border=0., count=None):
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return yl, yh, xl, xh
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return yl, yh, xl, xh
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def rand_bbox_minmax(size, minmax, count=None):
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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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assert len(minmax) == 2
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img_h, img_w = size[-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_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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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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yl = np.random.randint(0, img_h - cut_h, size=count)
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@ -66,6 +75,8 @@ def rand_bbox_minmax(size, minmax, count=None):
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def cutmix_bbox_and_lam(img_shape, lam, ratio_minmax=None, correct_lam=True, count=None):
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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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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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yl, yu, xl, xu = rand_bbox_minmax(img_shape, ratio_minmax, count=count)
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else:
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else:
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@ -76,52 +87,22 @@ def cutmix_bbox_and_lam(img_shape, lam, ratio_minmax=None, correct_lam=True, cou
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return (yl, yu, xl, xu), lam
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return (yl, yu, xl, xu), lam
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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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class Mixup:
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lam = 1.
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""" Mixup/Cutmix that applies different params to each element or whole batch
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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(), 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) / float(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 mix_batch(
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input, target, mixup_alpha=0.2, cutmix_alpha=0., prob=1.0, switch_prob=.5,
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num_classes=1000, smoothing=0.1, disable=False):
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# FIXME test this version
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if np.random.rand() > prob:
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return input, target
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use_cutmix = cutmix_alpha > 0. and np.random.rand() <= switch_prob
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if use_cutmix:
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return cutmix_batch(input, target, cutmix_alpha, num_classes, smoothing, disable)
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else:
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return mixup_batch(input, target, mixup_alpha, num_classes, smoothing, disable)
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class FastCollateMixup:
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"""Fast Collate Mixup/Cutmix that applies different params to each element or whole batch
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NOTE once experiments are done, one of the three variants will remain with this class name
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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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elementwise (bool): apply mixup/cutmix params per batch element instead of per batch
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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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"""
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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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def __init__(self, mixup_alpha=1., cutmix_alpha=0., cutmix_minmax=None, prob=1.0, switch_prob=0.5,
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elementwise=False, correct_lam=True, label_smoothing=0.1, num_classes=1000):
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elementwise=False, correct_lam=True, label_smoothing=0.1, num_classes=1000):
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"""
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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 (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 using cutmix instead of mixup when both active
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elementwise (bool): apply mixup/cutmix params per batch element instead of per batch
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label_smoothing (float):
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num_classes (int):
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"""
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self.mixup_alpha = mixup_alpha
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self.mixup_alpha = mixup_alpha
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self.cutmix_alpha = cutmix_alpha
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self.cutmix_alpha = cutmix_alpha
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self.cutmix_minmax = cutmix_minmax
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self.cutmix_minmax = cutmix_minmax
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@ -129,7 +110,7 @@ class FastCollateMixup:
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assert len(self.cutmix_minmax) == 2
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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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# 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.cutmix_alpha = 1.0
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self.prob = prob
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self.mix_prob = prob
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self.switch_prob = switch_prob
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self.switch_prob = switch_prob
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self.label_smoothing = label_smoothing
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self.label_smoothing = label_smoothing
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self.num_classes = num_classes
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self.num_classes = num_classes
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@ -137,10 +118,9 @@ class FastCollateMixup:
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self.correct_lam = correct_lam # correct lambda based on clipped area for cutmix
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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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self.mixup_enabled = True # set to false to disable mixing (intended tp be set by train loop)
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def _mix_elem(self, output, batch):
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def _params_per_elem(self, batch_size):
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batch_size = len(batch)
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lam = np.ones(batch_size, dtype=np.float32)
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lam_out = np.ones(batch_size, dtype=np.float32)
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use_cutmix = np.zeros(batch_size, dtype=np.bool)
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use_cutmix = np.zeros(batch_size).astype(np.bool)
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if self.mixup_enabled:
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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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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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use_cutmix = np.random.rand(batch_size) < self.switch_prob
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@ -151,35 +131,17 @@ class FastCollateMixup:
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elif self.mixup_alpha > 0.:
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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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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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elif self.cutmix_alpha > 0.:
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use_cutmix = np.ones(batch_size).astype(np.bool)
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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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lam_mix = np.random.beta(self.cutmix_alpha, self.cutmix_alpha, size=batch_size)
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else:
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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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assert False, "One of mixup_alpha > 0., cutmix_alpha > 0., cutmix_minmax not None should be true."
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lam_out = np.where(np.random.rand(batch_size) < self.prob, lam_mix.astype(np.float32), lam_out)
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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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for i in range(batch_size):
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j = batch_size - i - 1
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lam = lam_out[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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mixed = mixed.copy()
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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_out[i] = lam
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else:
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mixed = mixed.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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output[i] += torch.from_numpy(mixed.astype(np.uint8))
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return torch.tensor(lam_out).unsqueeze(1)
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def _mix_batch(self, output, batch):
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def _params_per_batch(self):
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batch_size = len(batch)
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lam = 1.
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lam = 1.
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use_cutmix = False
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use_cutmix = False
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if self.mixup_enabled and np.random.rand() < self.prob:
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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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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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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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lam_mix = np.random.beta(self.cutmix_alpha, self.cutmix_alpha) if use_cutmix else \
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@ -192,17 +154,84 @@ class FastCollateMixup:
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else:
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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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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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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_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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if use_cutmix:
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(yl, yh, xl, xh), lam = cutmix_bbox_and_lam(
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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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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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lam = self._mix_elem(x) if self.elementwise else self._mix_batch(x)
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target = mixup_target(target, self.num_classes, lam, self.label_smoothing)
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return x, target
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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):
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batch_size = len(batch)
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lam_batch, use_cutmix = self._params_per_elem(batch_size)
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for i in range(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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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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mixed = batch[i][0]
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if lam != 1.:
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if lam != 1.:
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|
if use_cutmix:
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if use_cutmix[i]:
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|
mixed = mixed.copy()
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mixed = mixed.copy()
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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 = mixed.astype(np.float32) * lam + batch[j][0].astype(np.float32) * (1 - lam)
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|
lam_batch[i] = lam
|
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|
|
np.round(mixed, out=mixed)
|
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|
|
output[i] += torch.from_numpy(mixed.astype(np.uint8))
|
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|
|
return torch.tensor(lam_batch).unsqueeze(1)
|
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|
def _mix_batch_collate(self, output, batch):
|
|
|
|
|
|
|
|
batch_size = len(batch)
|
|
|
|
|
|
|
|
lam, use_cutmix = self._params_per_batch()
|
|
|
|
|
|
|
|
if use_cutmix:
|
|
|
|
|
|
|
|
(yl, yh, xl, xh), lam = cutmix_bbox_and_lam(
|
|
|
|
|
|
|
|
output.shape, lam, ratio_minmax=self.cutmix_minmax, correct_lam=self.correct_lam)
|
|
|
|
|
|
|
|
for i in range(batch_size):
|
|
|
|
|
|
|
|
j = batch_size - i - 1
|
|
|
|
|
|
|
|
mixed = batch[i][0]
|
|
|
|
|
|
|
|
if lam != 1.:
|
|
|
|
|
|
|
|
if use_cutmix:
|
|
|
|
|
|
|
|
mixed = mixed.copy() # don't want to modify the original while iterating
|
|
|
|
mixed[:, yl:yh, xl:xh] = batch[j][0][:, yl:yh, xl:xh]
|
|
|
|
mixed[:, yl:yh, xl:xh] = batch[j][0][:, yl:yh, xl:xh]
|
|
|
|
else:
|
|
|
|
else:
|
|
|
|
mixed = mixed.astype(np.float32) * lam + batch[j][0].astype(np.float32) * (1 - lam)
|
|
|
|
mixed = mixed.astype(np.float32) * lam + batch[j][0].astype(np.float32) * (1 - lam)
|
|
|
@ -210,16 +239,15 @@ class FastCollateMixup:
|
|
|
|
output[i] += torch.from_numpy(mixed.astype(np.uint8))
|
|
|
|
output[i] += torch.from_numpy(mixed.astype(np.uint8))
|
|
|
|
return lam
|
|
|
|
return lam
|
|
|
|
|
|
|
|
|
|
|
|
def __call__(self, batch):
|
|
|
|
def __call__(self, batch, _=None):
|
|
|
|
batch_size = len(batch)
|
|
|
|
batch_size = len(batch)
|
|
|
|
assert batch_size % 2 == 0, 'Batch size should be even when using this'
|
|
|
|
assert batch_size % 2 == 0, 'Batch size should be even when using this'
|
|
|
|
output = torch.zeros((batch_size, *batch[0][0].shape), dtype=torch.uint8)
|
|
|
|
output = torch.zeros((batch_size, *batch[0][0].shape), dtype=torch.uint8)
|
|
|
|
if self.elementwise:
|
|
|
|
if self.elementwise:
|
|
|
|
lam = self._mix_elem(output, batch)
|
|
|
|
lam = self._mix_elem_collate(output, batch)
|
|
|
|
else:
|
|
|
|
else:
|
|
|
|
lam = self._mix_batch(output, batch)
|
|
|
|
lam = self._mix_batch_collate(output, batch)
|
|
|
|
target = torch.tensor([b[1] for b in batch], dtype=torch.int64)
|
|
|
|
target = torch.tensor([b[1] for b in batch], dtype=torch.int64)
|
|
|
|
target = mixup_target(target, self.num_classes, lam, self.label_smoothing, device='cpu')
|
|
|
|
target = mixup_target(target, self.num_classes, lam, self.label_smoothing, device='cpu')
|
|
|
|
|
|
|
|
|
|
|
|
return output, target
|
|
|
|
return output, target
|
|
|
|
|
|
|
|
|
|
|
|