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Adding RandAugment to AutoAugment impl, some tweaks to AA included

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pull/52/head
Ross Wightman 5 years ago
parent 4748c6dff2
commit 4243f076f1
3 changed files with 255 additions and 72 deletions
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2
timm/data/__init__.py
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@ -4,3 +4,5 @@ from .dataset import Dataset, DatasetTar
from .transforms import *
from .loader import create_loader, create_transform
from .mixup import mixup_target, FastCollateMixup
from .auto_augment import RandAugment, AutoAugment, rand_augment_ops, auto_augment_policy,\
rand_augment_transform, auto_augment_transform

316
timm/data/auto_augment.py
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@ -7,11 +7,13 @@ Hacked together by Ross Wightman
"""
import random
import math
import re
from PIL import Image, ImageOps, ImageEnhance
import PIL
import numpy as np
_PIL_VER = tuple([int(x) for x in PIL.__version__.split('.')[:2]])
_FILL = (128, 128, 128)
@ -25,11 +27,11 @@ _HPARAMS_DEFAULT = dict(
img_mean=_FILL,
)
_RANDOM_INTERPOLATION = (Image.NEAREST, Image.BILINEAR, Image.BICUBIC)
_RANDOM_INTERPOLATION = (Image.BILINEAR, Image.BICUBIC)
def _interpolation(kwargs):
interpolation = kwargs.pop('resample', Image.NEAREST)
interpolation = kwargs.pop('resample', Image.BILINEAR)
if isinstance(interpolation, (list, tuple)):
return random.choice(interpolation)
else:
@ -140,7 +142,6 @@ def solarize_add(img, add, thresh=128, **__):
def posterize(img, bits_to_keep, **__):
if bits_to_keep >= 8:
return img
bits_to_keep = max(1, bits_to_keep) # prevent all 0 images
return ImageOps.posterize(img, bits_to_keep)
@ -165,61 +166,89 @@ def _randomly_negate(v):
return -v if random.random() > 0.5 else v
def _rotate_level_to_arg(level):
def _rotate_level_to_arg(level, _hparams):
# range [-30, 30]
level = (level / _MAX_LEVEL) * 30.
level = _randomly_negate(level)
return (level,)
return level,
def _enhance_level_to_arg(level):
def _enhance_level_to_arg(level, _hparams):
# range [0.1, 1.9]
return ((level / _MAX_LEVEL) * 1.8 + 0.1,)
return (level / _MAX_LEVEL) * 1.8 + 0.1,
def _shear_level_to_arg(level):
def _shear_level_to_arg(level, _hparams):
# range [-0.3, 0.3]
level = (level / _MAX_LEVEL) * 0.3
level = _randomly_negate(level)
return (level,)
return level,
def _translate_abs_level_to_arg(level, translate_const):
def _translate_abs_level_to_arg(level, hparams):
translate_const = hparams['translate_const']
level = (level / _MAX_LEVEL) * float(translate_const)
level = _randomly_negate(level)
return (level,)
return level,
def _translate_rel_level_to_arg(level):
def _translate_rel_level_to_arg(level, _hparams):
# range [-0.45, 0.45]
level = (level / _MAX_LEVEL) * 0.45
level = _randomly_negate(level)
return (level,)
def level_to_arg(hparams):
return {
'AutoContrast': lambda level: (),
'Equalize': lambda level: (),
'Invert': lambda level: (),
'Rotate': _rotate_level_to_arg,
# FIXME these are both different from original impl as I believe there is a bug,
# not sure what is the correct alternative, hence 2 options that look better
'Posterize': lambda level: (int((level / _MAX_LEVEL) * 4) + 4,), # range [4, 8]
'Posterize2': lambda level: (4 - int((level / _MAX_LEVEL) * 4),), # range [4, 0]
'Solarize': lambda level: (int((level / _MAX_LEVEL) * 256),), # range [0, 256]
'SolarizeAdd': lambda level: (int((level / _MAX_LEVEL) * 110),), # range [0, 110]
'Color': _enhance_level_to_arg,
'Contrast': _enhance_level_to_arg,
'Brightness': _enhance_level_to_arg,
'Sharpness': _enhance_level_to_arg,
'ShearX': _shear_level_to_arg,
'ShearY': _shear_level_to_arg,
'TranslateX': lambda level: _translate_abs_level_to_arg(level, hparams['translate_const']),
'TranslateY': lambda level: _translate_abs_level_to_arg(level, hparams['translate_const']),
'TranslateXRel': lambda level: _translate_rel_level_to_arg(level),
'TranslateYRel': lambda level: _translate_rel_level_to_arg(level),
}
return level,
def _posterize_original_level_to_arg(level, _hparams):
# As per original AutoAugment paper description
# range [4, 8], 'keep 4 up to 8 MSB of image'
return int((level / _MAX_LEVEL) * 4) + 4,
def _posterize_research_level_to_arg(level, _hparams):
# As per Tensorflow models research and UDA impl
# range [4, 0], 'keep 4 down to 0 MSB of original image'
return 4 - int((level / _MAX_LEVEL) * 4),
def _posterize_tpu_level_to_arg(level, _hparams):
# As per Tensorflow TPU EfficientNet impl
# range [0, 4], 'keep 0 up to 4 MSB of original image'
return int((level / _MAX_LEVEL) * 4),
def _solarize_level_to_arg(level, _hparams):
# range [0, 256]
return int((level / _MAX_LEVEL) * 256),
def _solarize_add_level_to_arg(level, _hparams):
# range [0, 110]
return int((level / _MAX_LEVEL) * 110),
LEVEL_TO_ARG = {
'AutoContrast': None,
'Equalize': None,
'Invert': None,
'Rotate': _rotate_level_to_arg,
# There are several variations of the posterize level scaling in various Tensorflow/Google repositories/papers
'PosterizeOriginal': _posterize_original_level_to_arg,
'PosterizeResearch': _posterize_research_level_to_arg,
'PosterizeTpu': _posterize_tpu_level_to_arg,
'Solarize': _solarize_level_to_arg,
'SolarizeAdd': _solarize_add_level_to_arg,
'Color': _enhance_level_to_arg,
'Contrast': _enhance_level_to_arg,
'Brightness': _enhance_level_to_arg,
'Sharpness': _enhance_level_to_arg,
'ShearX': _shear_level_to_arg,
'ShearY': _shear_level_to_arg,
'TranslateX': _translate_abs_level_to_arg,
'TranslateY': _translate_abs_level_to_arg,
'TranslateXRel': _translate_rel_level_to_arg,
'TranslateYRel': _translate_rel_level_to_arg,
}
NAME_TO_OP = {
@ -227,8 +256,9 @@ NAME_TO_OP = {
'Equalize': equalize,
'Invert': invert,
'Rotate': rotate,
'Posterize': posterize,
'Posterize2': posterize,
'PosterizeOriginal': posterize,
'PosterizeResearch': posterize,
'PosterizeTpu': posterize,
'Solarize': solarize,
'SolarizeAdd': solarize_add,
'Color': color,
@ -246,35 +276,37 @@ NAME_TO_OP = {
class AutoAugmentOp:
def __init__(self, name, prob, magnitude, hparams={}):
def __init__(self, name, prob=0.5, magnitude=10, hparams=None):
hparams = hparams or _HPARAMS_DEFAULT
self.aug_fn = NAME_TO_OP[name]
self.level_fn = level_to_arg(hparams)[name]
self.level_fn = LEVEL_TO_ARG[name]
self.prob = prob
self.magnitude = magnitude
# If std deviation of magnitude is > 0, we introduce some randomness
# in the usually fixed policy and sample magnitude from normal dist
# with mean magnitude and std-dev of magnitude_std.
# NOTE This is being tested as it's not in paper or reference impl.
self.magnitude_std = 0.5 # FIXME add arg/hparam
self.kwargs = {
'fillcolor': hparams['img_mean'] if 'img_mean' in hparams else _FILL,
'resample': hparams['interpolation'] if 'interpolation' in hparams else _RANDOM_INTERPOLATION
}
self.hparams = hparams.copy()
self.kwargs = dict(
fillcolor=hparams['img_mean'] if 'img_mean' in hparams else _FILL,
resample=hparams['interpolation'] if 'interpolation' in hparams else _RANDOM_INTERPOLATION,
)
# If magnitude_noise is > 0, we introduce some randomness
# in the usually fixed policy and sample magnitude from a normal distribution
# with mean `magnitude` and std-dev of `magnitude_noise`.
# NOTE This is my own hack, being tested, not in papers or reference impls.
self.magnitude_noise = self.hparams.get('magnitude_noise', 0)
def __call__(self, img):
if self.prob < random.random():
if random.random() > self.prob:
return img
magnitude = self.magnitude
if self.magnitude_std and self.magnitude_std > 0:
magnitude = random.gauss(magnitude, self.magnitude_std)
magnitude = min(_MAX_LEVEL, max(0, magnitude))
level_args = self.level_fn(magnitude)
if self.magnitude_noise and self.magnitude_noise > 0:
magnitude = random.gauss(magnitude, self.magnitude_noise)
magnitude = min(_MAX_LEVEL, max(0, magnitude)) # clip to valid range
level_args = self.level_fn(magnitude, self.hparams) if self.level_fn is not None else tuple()
return self.aug_fn(img, *level_args, **self.kwargs)
def auto_augment_policy_v0(hparams=_HPARAMS_DEFAULT):
# ImageNet policy from TPU EfficientNet impl, cannot find
# a paper reference.
def auto_augment_policy_v0(hparams):
# ImageNet v0 policy from TPU EfficientNet impl, cannot find a paper reference.
policy = [
[('Equalize', 0.8, 1), ('ShearY', 0.8, 4)],
[('Color', 0.4, 9), ('Equalize', 0.6, 3)],
@ -288,7 +320,7 @@ def auto_augment_policy_v0(hparams=_HPARAMS_DEFAULT):
[('Invert', 0.4, 9), ('Rotate', 0.6, 0)],
[('Equalize', 1.0, 9), ('ShearY', 0.6, 3)],
[('Color', 0.4, 7), ('Equalize', 0.6, 0)],
[('Posterize', 0.4, 6), ('AutoContrast', 0.4, 7)],
[('PosterizeTpu', 0.4, 6), ('AutoContrast', 0.4, 7)],
[('Solarize', 0.6, 8), ('Color', 0.6, 9)],
[('Solarize', 0.2, 4), ('Rotate', 0.8, 9)],
[('Rotate', 1.0, 7), ('TranslateYRel', 0.8, 9)],
@ -298,27 +330,93 @@ def auto_augment_policy_v0(hparams=_HPARAMS_DEFAULT):
[('Equalize', 0.8, 4), ('Equalize', 0.0, 8)],
[('Equalize', 1.0, 4), ('AutoContrast', 0.6, 2)],
[('ShearY', 0.4, 7), ('SolarizeAdd', 0.6, 7)],
[('Posterize', 0.8, 2), ('Solarize', 0.6, 10)],
[('PosterizeTpu', 0.8, 2), ('Solarize', 0.6, 10)], # This results in black image with Tpu posterize
[('Solarize', 0.6, 8), ('Equalize', 0.6, 1)],
[('Color', 0.8, 6), ('Rotate', 0.4, 5)],
]
pc = [[AutoAugmentOp(*a, hparams) for a in sp] for sp in policy]
pc = [[AutoAugmentOp(*a, hparams=hparams) for a in sp] for sp in policy]
return pc
def auto_augment_policy_original(hparams=_HPARAMS_DEFAULT):
def auto_augment_policy_v0r(hparams):
# ImageNet v0 policy from TPU EfficientNet impl, with research variation of Posterize
policy = [
[('Equalize', 0.8, 1), ('ShearY', 0.8, 4)],
[('Color', 0.4, 9), ('Equalize', 0.6, 3)],
[('Color', 0.4, 1), ('Rotate', 0.6, 8)],
[('Solarize', 0.8, 3), ('Equalize', 0.4, 7)],
[('Solarize', 0.4, 2), ('Solarize', 0.6, 2)],
[('Color', 0.2, 0), ('Equalize', 0.8, 8)],
[('Equalize', 0.4, 8), ('SolarizeAdd', 0.8, 3)],
[('ShearX', 0.2, 9), ('Rotate', 0.6, 8)],
[('Color', 0.6, 1), ('Equalize', 1.0, 2)],
[('Invert', 0.4, 9), ('Rotate', 0.6, 0)],
[('Equalize', 1.0, 9), ('ShearY', 0.6, 3)],
[('Color', 0.4, 7), ('Equalize', 0.6, 0)],
[('PosterizeResearch', 0.4, 6), ('AutoContrast', 0.4, 7)],
[('Solarize', 0.6, 8), ('Color', 0.6, 9)],
[('Solarize', 0.2, 4), ('Rotate', 0.8, 9)],
[('Rotate', 1.0, 7), ('TranslateYRel', 0.8, 9)],
[('ShearX', 0.0, 0), ('Solarize', 0.8, 4)],
[('ShearY', 0.8, 0), ('Color', 0.6, 4)],
[('Color', 1.0, 0), ('Rotate', 0.6, 2)],
[('Equalize', 0.8, 4), ('Equalize', 0.0, 8)],
[('Equalize', 1.0, 4), ('AutoContrast', 0.6, 2)],
[('ShearY', 0.4, 7), ('SolarizeAdd', 0.6, 7)],
[('PosterizeResearch', 0.8, 2), ('Solarize', 0.6, 10)],
[('Solarize', 0.6, 8), ('Equalize', 0.6, 1)],
[('Color', 0.8, 6), ('Rotate', 0.4, 5)],
]
pc = [[AutoAugmentOp(*a, hparams=hparams) for a in sp] for sp in policy]
return pc
def auto_augment_policy_original(hparams):
# ImageNet policy from https://arxiv.org/abs/1805.09501
policy = [
[('Posterize', 0.4, 8), ('Rotate', 0.6, 9)],
[('PosterizeOriginal', 0.4, 8), ('Rotate', 0.6, 9)],
[('Solarize', 0.6, 5), ('AutoContrast', 0.6, 5)],
[('Equalize', 0.8, 8), ('Equalize', 0.6, 3)],
[('PosterizeOriginal', 0.6, 7), ('PosterizeOriginal', 0.6, 6)],
[('Equalize', 0.4, 7), ('Solarize', 0.2, 4)],
[('Equalize', 0.4, 4), ('Rotate', 0.8, 8)],
[('Solarize', 0.6, 3), ('Equalize', 0.6, 7)],
[('PosterizeOriginal', 0.8, 5), ('Equalize', 1.0, 2)],
[('Rotate', 0.2, 3), ('Solarize', 0.6, 8)],
[('Equalize', 0.6, 8), ('PosterizeOriginal', 0.4, 6)],
[('Rotate', 0.8, 8), ('Color', 0.4, 0)],
[('Rotate', 0.4, 9), ('Equalize', 0.6, 2)],
[('Equalize', 0.0, 7), ('Equalize', 0.8, 8)],
[('Invert', 0.6, 4), ('Equalize', 1.0, 8)],
[('Color', 0.6, 4), ('Contrast', 1.0, 8)],
[('Rotate', 0.8, 8), ('Color', 1.0, 2)],
[('Color', 0.8, 8), ('Solarize', 0.8, 7)],
[('Sharpness', 0.4, 7), ('Invert', 0.6, 8)],
[('ShearX', 0.6, 5), ('Equalize', 1.0, 9)],
[('Color', 0.4, 0), ('Equalize', 0.6, 3)],
[('Equalize', 0.4, 7), ('Solarize', 0.2, 4)],
[('Solarize', 0.6, 5), ('AutoContrast', 0.6, 5)],
[('Invert', 0.6, 4), ('Equalize', 1.0, 8)],
[('Color', 0.6, 4), ('Contrast', 1.0, 8)],
[('Equalize', 0.8, 8), ('Equalize', 0.6, 3)],
]
pc = [[AutoAugmentOp(*a, hparams=hparams) for a in sp] for sp in policy]
return pc
def auto_augment_policy_originalr(hparams):
# ImageNet policy from https://arxiv.org/abs/1805.09501 with research posterize variation
policy = [
[('PosterizeResearch', 0.4, 8), ('Rotate', 0.6, 9)],
[('Solarize', 0.6, 5), ('AutoContrast', 0.6, 5)],
[('Equalize', 0.8, 8), ('Equalize', 0.6, 3)],
[('Posterize', 0.6, 7), ('Posterize', 0.6, 6)],
[('PosterizeResearch', 0.6, 7), ('PosterizeResearch', 0.6, 6)],
[('Equalize', 0.4, 7), ('Solarize', 0.2, 4)],
[('Equalize', 0.4, 4), ('Rotate', 0.8, 8)],
[('Solarize', 0.6, 3), ('Equalize', 0.6, 7)],
[('Posterize', 0.8, 5), ('Equalize', 1.0, 2)],
[('PosterizeResearch', 0.8, 5), ('Equalize', 1.0, 2)],
[('Rotate', 0.2, 3), ('Solarize', 0.6, 8)],
[('Equalize', 0.6, 8), ('Posterize', 0.4, 6)],
[('Equalize', 0.6, 8), ('PosterizeResearch', 0.4, 6)],
[('Rotate', 0.8, 8), ('Color', 0.4, 0)],
[('Rotate', 0.4, 9), ('Equalize', 0.6, 2)],
[('Equalize', 0.0, 7), ('Equalize', 0.8, 8)],
@ -335,15 +433,20 @@ def auto_augment_policy_original(hparams=_HPARAMS_DEFAULT):
[('Color', 0.6, 4), ('Contrast', 1.0, 8)],
[('Equalize', 0.8, 8), ('Equalize', 0.6, 3)],
]
pc = [[AutoAugmentOp(*a, hparams) for a in sp] for sp in policy]
pc = [[AutoAugmentOp(*a, hparams=hparams) for a in sp] for sp in policy]
return pc
def auto_augment_policy(name='v0', hparams=_HPARAMS_DEFAULT):
def auto_augment_policy(name='v0', hparams=None):
hparams = hparams or _HPARAMS_DEFAULT
if name == 'original':
return auto_augment_policy_original(hparams)
elif name == 'originalr':
return auto_augment_policy_originalr(hparams)
elif name == 'v0':
return auto_augment_policy_v0(hparams)
elif name == 'v0r':
return auto_augment_policy_v0r(hparams)
else:
assert False, 'Unknown AA policy (%s)' % name
@ -358,3 +461,78 @@ class AutoAugment:
for op in sub_policy:
img = op(img)
return img
def auto_augment_transform(config_str, hparams):
config = config_str.split('-')
policy_name = config[0]
config = config[1:]
for c in config:
cs = re.split(r'(\d.*)', c)
if len(cs) >= 2:
key, val = cs[:2]
if key == 'noise':
# noise param injected via hparams for now
hparams.setdefault('magnitude_noise', float(val))
aa_policy = auto_augment_policy(policy_name, hparams=hparams)
return AutoAugment(aa_policy)
_RAND_TRANSFORMS = [
'AutoContrast',
'Equalize',
'Invert',
'Rotate',
'PosterizeTpu',
'Solarize',
'SolarizeAdd',
'Color',
'Contrast',
'Brightness',
'Sharpness',
'ShearX',
'ShearY',
'TranslateXRel',
'TranslateYRel',
#'Cutout' # FIXME I implement this as random erasing separately
]
def rand_augment_ops(magnitude=10, hparams=None, transforms=None):
hparams = hparams or _HPARAMS_DEFAULT
transforms = transforms or _RAND_TRANSFORMS
return [AutoAugmentOp(
name, prob=0.5, magnitude=magnitude, hparams=hparams) for name in transforms]
class RandAugment:
def __init__(self, ops, num_layers=2):
self.ops = ops
self.num_layers = num_layers
def __call__(self, img):
for _ in range(self.num_layers):
op = random.choice(self.ops)
img = op(img)
return img
def rand_augment_transform(config_str, hparams):
magnitude = 10
num_layers = 2
config = config_str.split('-')
assert config[0] == 'rand'
config = config[1:]
for c in config:
cs = re.split(r'(\d.*)', c)
if len(cs) >= 2:
key, val = cs[:2]
if key == 'noise':
# noise param injected via hparams for now
hparams.setdefault('magnitude_noise', float(val))
elif key == 'm':
magnitude = int(val)
elif key == 'n':
num_layers = int(val)
ra_ops = rand_augment_ops(magnitude=magnitude, hparams=hparams)
return RandAugment(ra_ops, num_layers)

9
timm/data/transforms.py
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@ -9,7 +9,7 @@ import numpy as np
from .constants import DEFAULT_CROP_PCT, IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from .random_erasing import RandomErasing
from .auto_augment import AutoAugment, auto_augment_policy
from .auto_augment import auto_augment_transform, rand_augment_transform
class ToNumpy:
@ -179,6 +179,7 @@ def transforms_imagenet_train(
transforms.RandomHorizontalFlip()
]
if auto_augment:
assert isinstance(auto_augment, str)
if isinstance(img_size, tuple):
img_size_min = min(img_size)
else:
@ -189,8 +190,10 @@ def transforms_imagenet_train(
)
if interpolation and interpolation != 'random':
aa_params['interpolation'] = _pil_interp(interpolation)
aa_policy = auto_augment_policy(auto_augment, aa_params)
tfl += [AutoAugment(aa_policy)]
if 'rand' in auto_augment:
tfl += [rand_augment_transform(auto_augment, aa_params)]
else:
tfl += [auto_augment_transform(auto_augment, aa_params)]
else:
# color jitter is enabled when not using AA
if isinstance(color_jitter, (list, tuple)):

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