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More cutmix/mixup overhaul, ready to kick-off some trials.

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pull/218/head
Ross Wightman 4 years ago
parent 92f2d0d65d
commit f471c17c9d
2 changed files with 159 additions and 105 deletions
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226
timm/data/mixup.py
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@ -15,17 +15,6 @@ import numpy as np
import torch
import math
import numbers
from enum import IntEnum
class MixupMode(IntEnum):
MIXUP = 0
CUTMIX = 1
RANDOM = 2
@classmethod
def from_str(cls, value):
return cls[value.upper()]
def one_hot(x, num_classes, on_value=1., off_value=0., device='cuda'):
@ -50,30 +39,49 @@ def mixup_batch(input, target, alpha=0.2, num_classes=1000, smoothing=0.1, disab
return input, target
def calc_ratio(lam, minmax=None):
def rand_bbox(size, lam, border=0., count=None):
ratio = math.sqrt(1 - lam)
if minmax is not None:
if isinstance(minmax, numbers.Number):
minmax = (minmax, 1 - minmax)
ratio = np.clip(ratio, minmax[0], minmax[1])
return ratio
def rand_bbox(size, ratio):
H, W = size[-2:]
cut_h, cut_w = int(H * ratio), int(W * ratio)
cy, cx = np.random.randint(H), np.random.randint(W)
yl, yh = np.clip(cy - cut_h // 2, 0, H), np.clip(cy + cut_h // 2, 0, H)
xl, xh = np.clip(cx - cut_w // 2, 0, W), np.clip(cx + cut_w // 2, 0, W)
img_h, img_w = size[-2:]
cut_h, cut_w = int(img_h * ratio), int(img_w * ratio)
margin_y, margin_x = int(border * cut_h), int(border * cut_w)
cy = np.random.randint(0 + margin_y, img_h - margin_y, size=count)
cx = np.random.randint(0 + margin_x, img_w - margin_x, size=count)
yl = np.clip(cy - cut_h // 2, 0, img_h)
yh = np.clip(cy + cut_h // 2, 0, img_h)
xl = np.clip(cx - cut_w // 2, 0, img_w)
xh = np.clip(cx + cut_w // 2, 0, img_w)
return yl, yh, xl, xh
def rand_bbox_minmax(size, minmax, count=None):
assert len(minmax) == 2
img_h, img_w = size[-2:]
cut_h = np.random.randint(int(img_h * minmax[0]), int(img_h * minmax[1]), size=count)
cut_w = np.random.randint(int(img_w * minmax[0]), int(img_w * minmax[1]), size=count)
yl = np.random.randint(0, img_h - cut_h, size=count)
xl = np.random.randint(0, img_w - cut_w, size=count)
yu = yl + cut_h
xu = xl + cut_w
return yl, yu, xl, xu
def cutmix_bbox_and_lam(img_shape, lam, ratio_minmax=None, correct_lam=True, count=None):
if ratio_minmax is not None:
yl, yu, xl, xu = rand_bbox_minmax(img_shape, ratio_minmax, count=count)
else:
yl, yu, xl, xu = rand_bbox(img_shape, lam, count=count)
if correct_lam or ratio_minmax is not None:
bbox_area = (yu - yl) * (xu - xl)
lam = 1. - bbox_area / (img_shape[-2] * img_shape[-1])
return (yl, yu, xl, xu), lam
def cutmix_batch(input, target, alpha=0.2, num_classes=1000, smoothing=0.1, disable=False, correct_lam=False):
lam = 1.
if not disable:
lam = np.random.beta(alpha, alpha)
if lam != 1:
yl, yh, xl, xh = rand_bbox(input.size(), calc_ratio(lam))
yl, yh, xl, xh = rand_bbox(input.size(), lam)
input[:, :, yl:yh, xl:xh] = input.flip(0)[:, :, yl:yh, xl:xh]
if correct_lam:
lam = 1 - (yh - yl) * (xh - xl) / (input.shape[-2] * input.shape[-1])
@ -81,101 +89,135 @@ def cutmix_batch(input, target, alpha=0.2, num_classes=1000, smoothing=0.1, disa
return input, target
def _resolve_mode(mode):
mode = MixupMode.from_str(mode) if isinstance(mode, str) else mode
if mode == MixupMode.RANDOM:
mode = MixupMode(np.random.rand() > 0.7)
return mode # will be one of cutmix or mixup
def mix_batch(
input, target, alpha=0.2, num_classes=1000, smoothing=0.1, disable=False, mode=MixupMode.MIXUP):
mode = _resolve_mode(mode)
if mode == MixupMode.CUTMIX:
return cutmix_batch(input, target, alpha, num_classes, smoothing, disable)
input, target, mixup_alpha=0.2, cutmix_alpha=0., prob=1.0, switch_prob=.5,
num_classes=1000, smoothing=0.1, disable=False):
# FIXME test this version
if np.random.rand() > prob:
return input, target
use_cutmix = cutmix_alpha > 0. and np.random.rand() <= switch_prob
if use_cutmix:
return cutmix_batch(input, target, cutmix_alpha, num_classes, smoothing, disable)
else:
return mixup_batch(input, target, alpha, num_classes, smoothing, disable)
return mixup_batch(input, target, mixup_alpha, num_classes, smoothing, disable)
class FastCollateMixup:
"""Fast Collate Mixup that applies different params to each element + flipped pair
"""Fast Collate Mixup/Cutmix that applies different params to each element or whole batch
NOTE once experiments are done, one of the three variants will remain with this class name
"""
def __init__(self, mixup_alpha=1., cutmix_alpha=0., cutmix_minmax=None, prob=1.0, switch_prob=0.5,
elementwise=False, correct_lam=True, label_smoothing=0.1, num_classes=1000):
"""
Args:
mixup_alpha (float): mixup alpha value, mixup is active if > 0.
cutmix_alpha (float): cutmix alpha value, cutmix is active if > 0.
cutmix_minmax (float): cutmix min/max image ratio, cutmix is active and uses this vs alpha if not None
prob (float): probability of applying mixup or cutmix per batch or element
switch_prob (float): probability of using cutmix instead of mixup when both active
elementwise (bool): apply mixup/cutmix params per batch element instead of per batch
label_smoothing (float):
num_classes (int):
"""
def __init__(self, mixup_alpha=1., label_smoothing=0.1, num_classes=1000, mode=MixupMode.MIXUP):
self.mixup_alpha = mixup_alpha
self.cutmix_alpha = cutmix_alpha
self.cutmix_minmax = cutmix_minmax
if self.cutmix_minmax is not None:
assert len(self.cutmix_minmax) == 2
# force cutmix alpha == 1.0 when minmax active to keep logic simple & safe
self.cutmix_alpha = 1.0
self.prob = prob
self.switch_prob = switch_prob
self.label_smoothing = label_smoothing
self.num_classes = num_classes
self.mode = MixupMode.from_str(mode) if isinstance(mode, str) else mode
self.mixup_enabled = True
self.correct_lam = True # correct lambda based on clipped area for cutmix
self.ratio_minmax = None # (0.2, 0.8)
self.elementwise = elementwise
self.correct_lam = correct_lam # correct lambda based on clipped area for cutmix
self.mixup_enabled = True # set to false to disable mixing (intended tp be set by train loop)
def _do_mix(self, tensor, batch):
def _mix_elem(self, output, batch):
batch_size = len(batch)
lam_out = torch.ones(batch_size)
for i in range(batch_size):
j = batch_size - i - 1
lam = 1.
lam_out = np.ones(batch_size)
use_cutmix = np.zeros(batch_size).astype(np.bool)
if self.mixup_enabled:
lam = np.random.beta(self.mixup_alpha, self.mixup_alpha)
if self.mixup_alpha > 0. and self.cutmix_alpha > 0.:
use_cutmix = np.random.rand(batch_size) < self.switch_prob
lam_mix = np.where(
use_cutmix,
np.random.beta(self.cutmix_alpha, self.cutmix_alpha, size=batch_size),
np.random.beta(self.mixup_alpha, self.mixup_alpha, size=batch_size))
elif self.mixup_alpha > 0.:
lam_mix = np.random.beta(self.mixup_alpha, self.mixup_alpha, size=batch_size)
elif self.cutmix_alpha > 0.:
use_cutmix = np.ones(batch_size).astype(np.bool)
lam_mix = np.random.beta(self.cutmix_alpha, self.cutmix_alpha, size=batch_size)
else:
assert False, "One of mixup_alpha > 0., cutmix_alpha > 0., cutmix_minmax not None should be true."
lam_out = np.where(np.random.rand(batch_size) < self.prob, lam_mix, lam_out)
if _resolve_mode(self.mode) == MixupMode.CUTMIX:
for i in range(batch_size):
j = batch_size - i - 1
lam = lam_out[i]
mixed = batch[i][0].astype(np.float32)
if lam != 1:
ratio = calc_ratio(lam)
yl, yh, xl, xh = rand_bbox(tensor.size(), ratio)
if lam != 1.:
if use_cutmix[i]:
(yl, yh, xl, xh), lam = cutmix_bbox_and_lam(
output.shape, lam, ratio_minmax=self.cutmix_minmax, correct_lam=self.correct_lam)
mixed[:, yl:yh, xl:xh] = batch[j][0][:, yl:yh, xl:xh].astype(np.float32)
if self.correct_lam:
lam_out[i] -= (yh - yl) * (xh - xl) / (tensor.shape[-2] * tensor.shape[-1])
else:
lam_out[i] = lam
else:
mixed = batch[i][0].astype(np.float32) * lam + batch[j][0].astype(np.float32) * (1 - lam)
mixed = mixed * lam + batch[j][0].astype(np.float32) * (1 - lam)
lam_out[i] = lam
np.round(mixed, out=mixed)
tensor[i] += torch.from_numpy(mixed.astype(np.uint8))
return lam_out.unsqueeze(1)
def __call__(self, batch):
batch_size = len(batch)
assert batch_size % 2 == 0, 'Batch size should be even when using this'
tensor = torch.zeros((batch_size, *batch[0][0].shape), dtype=torch.uint8)
lam = self._do_mix(tensor, batch)
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')
return tensor, target
output[i] += torch.from_numpy(mixed.astype(np.uint8))
return torch.tensor(lam_out).unsqueeze(1)
class FastCollateMixupBatchwise(FastCollateMixup):
"""Fast Collate Mixup that applies same params to whole batch
NOTE this is for experimentation, may remove at some point
"""
def __init__(self, mixup_alpha=1., label_smoothing=0.1, num_classes=1000, mode=MixupMode.MIXUP):
super(FastCollateMixupBatchwise, self).__init__(mixup_alpha, label_smoothing, num_classes, mode)
def _do_mix(self, tensor, batch):
def _mix_batch(self, output, batch):
batch_size = len(batch)
lam = 1.
cutmix = _resolve_mode(self.mode) == MixupMode.CUTMIX
if self.mixup_enabled:
lam = np.random.beta(self.mixup_alpha, self.mixup_alpha)
if cutmix:
yl, yh, xl, xh = rand_bbox(batch[0][0].shape, calc_ratio(lam))
if self.correct_lam:
lam = 1 - (yh - yl) * (xh - xl) / (tensor.shape[-2] * tensor.shape[-1])
use_cutmix = False
if self.mixup_enabled and np.random.rand() < self.prob:
if self.mixup_alpha > 0. and self.cutmix_alpha > 0.:
use_cutmix = np.random.rand() < self.switch_prob
lam_mix = np.random.beta(self.cutmix_alpha, self.cutmix_alpha) if use_cutmix else \
np.random.beta(self.mixup_alpha, self.mixup_alpha)
elif self.mixup_alpha > 0.:
lam_mix = np.random.beta(self.mixup_alpha, self.mixup_alpha)
elif self.cutmix_alpha > 0.:
use_cutmix = True
lam_mix = np.random.beta(self.cutmix_alpha, self.cutmix_alpha)
else:
assert False, "One of mixup_alpha > 0., cutmix_alpha > 0., cutmix_minmax not None should be true."
lam = lam_mix
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
if cutmix:
mixed = batch[i][0].astype(np.float32)
if lam != 1:
if lam != 1.:
if use_cutmix:
mixed[:, yl:yh, xl:xh] = batch[j][0][:, yl:yh, xl:xh].astype(np.float32)
else:
mixed = batch[i][0].astype(np.float32) * lam + batch[j][0].astype(np.float32) * (1 - lam)
mixed = mixed * lam + batch[j][0].astype(np.float32) * (1 - lam)
np.round(mixed, out=mixed)
tensor[i] += torch.from_numpy(mixed.astype(np.uint8))
output[i] += torch.from_numpy(mixed.astype(np.uint8))
return lam
def __call__(self, batch):
batch_size = len(batch)
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)
if self.elementwise:
lam = self._mix_elem(output, batch)
else:
lam = self._mix_batch(output, batch)
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')
return output, target

24
train.py
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@ -157,8 +157,16 @@ parser.add_argument('--resplit', action='store_true', default=False,
help='Do not random erase first (clean) augmentation split')
parser.add_argument('--mixup', type=float, default=0.0,
help='mixup alpha, mixup enabled if > 0. (default: 0.)')
parser.add_argument('--mixup-mode', type=str, default='mixup',
help='Mixup mode. One of "mixup", "cutmix", "random" (default: "mixup")')
parser.add_argument('--cutmix', type=float, default=0.0,
help='cutmix alpha, cutmix enabled if > 0. (default: 0.)')
parser.add_argument('--cutmix-minmax', type=float, nargs='+', default=None,
help='cutmix min/max ratio, overrides alpha and enables cutmix if set (default: None)')
parser.add_argument('--mixup-prob', type=float, default=1.0,
help='Probability of performing mixup or cutmix when either/both is enabled')
parser.add_argument('--mixup-switch-prob', type=float, default=0.5,
help='Probability of switching to cutmix when both mixup and cutmix enabled')
parser.add_argument('--mixup-elem', action='store_true', default=False,
help='Apply mixup/cutmix params uniquely per batch element instead of per batch.')
parser.add_argument('--mixup-off-epoch', default=0, type=int, metavar='N',
help='Turn off mixup after this epoch, disabled if 0 (default: 0)')
parser.add_argument('--smoothing', type=float, default=0.1,
@ -390,9 +398,12 @@ def main():
dataset_train = Dataset(train_dir)
collate_fn = None
if args.prefetcher and args.mixup > 0:
if args.prefetcher and (args.mixup > 0 or args.cutmix > 0. or args.cutmix_minmax is not None):
assert not num_aug_splits # collate conflict (need to support deinterleaving in collate mixup)
collate_fn = FastCollateMixup(args.mixup, args.smoothing, args.num_classes, args.mixup_mode)
collate_fn = FastCollateMixup(
mixup_alpha=args.mixup, cutmix_alpha=args.cutmix, cutmix_minmax=args.cutmix_minmax,
prob=args.mixup_prob, switch_prob=args.mixup_switch_prob, elementwise=args.mixup_elem,
label_smoothing=args.smoothing, num_classes=args.num_classes)
if num_aug_splits > 1:
dataset_train = AugMixDataset(dataset_train, num_splits=num_aug_splits)
@ -555,8 +566,9 @@ def train_epoch(
if args.mixup > 0.:
input, target = mix_batch(
input, target,
alpha=args.mixup, num_classes=args.num_classes, smoothing=args.smoothing,
disable=args.mixup_off_epoch and epoch >= args.mixup_off_epoch, mode=args.mixup_mode)
mixup_alpha=args.mixup, cutmix_alpha=args.cutmix, prob=args.mixup_prob,
switch_prob=args.mixup_switch_prob, num_classes=args.num_classes, smoothing=args.smoothing,
disable=args.mixup_off_epoch and epoch >= args.mixup_off_epoch)
output = model(input)

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