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pytorch-image-models/timm/data/auto_augment.py

882 lines
31 KiB

""" AutoAugment, RandAugment, and AugMix for PyTorch
This code implements the searched ImageNet policies with various tweaks and improvements and
does not include any of the search code.
AA and RA Implementation adapted from:
https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/autoaugment.py
AugMix adapted from:
https://github.com/google-research/augmix
Papers:
AutoAugment: Learning Augmentation Policies from Data - https://arxiv.org/abs/1805.09501
Learning Data Augmentation Strategies for Object Detection - https://arxiv.org/abs/1906.11172
RandAugment: Practical automated data augmentation... - https://arxiv.org/abs/1909.13719
AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty - https://arxiv.org/abs/1912.02781
Hacked together by / Copyright 2020 Ross Wightman
"""
import random
import math
import re
from PIL import Image, ImageOps, ImageEnhance, ImageChops
import PIL
import numpy as np
_PIL_VER = tuple([int(x) for x in PIL.__version__.split('.')[:2]])
_FILL = (128, 128, 128)
_LEVEL_DENOM = 10. # denominator for conversion from 'Mx' magnitude scale to fractional aug level for op arguments
_HPARAMS_DEFAULT = dict(
translate_const=250,
img_mean=_FILL,
)
_RANDOM_INTERPOLATION = (Image.BILINEAR, Image.BICUBIC)
def _pil_interp(method):
def _convert(m):
if method == 'bicubic':
return Image.BICUBIC
elif method == 'lanczos':
return Image.LANCZOS
elif method == 'hamming':
return Image.HAMMING
else:
return Image.BILINEAR
if isinstance(method, (list, tuple)):
return [_convert(m) if isinstance(m, str) else m for m in method]
else:
return _convert(method) if isinstance(method, str) else method
def _interpolation(kwargs):
interpolation = kwargs.pop('resample', Image.BILINEAR)
if isinstance(interpolation, (list, tuple)):
return random.choice(interpolation)
else:
return interpolation
def _check_args_tf(kwargs):
if 'fillcolor' in kwargs and _PIL_VER < (5, 0):
kwargs.pop('fillcolor')
kwargs['resample'] = _interpolation(kwargs)
def shear_x(img, factor, **kwargs):
_check_args_tf(kwargs)
return img.transform(img.size, Image.AFFINE, (1, factor, 0, 0, 1, 0), **kwargs)
def shear_y(img, factor, **kwargs):
_check_args_tf(kwargs)
return img.transform(img.size, Image.AFFINE, (1, 0, 0, factor, 1, 0), **kwargs)
def translate_x_rel(img, pct, **kwargs):
pixels = pct * img.size[0]
_check_args_tf(kwargs)
return img.transform(img.size, Image.AFFINE, (1, 0, pixels, 0, 1, 0), **kwargs)
def translate_y_rel(img, pct, **kwargs):
pixels = pct * img.size[1]
_check_args_tf(kwargs)
return img.transform(img.size, Image.AFFINE, (1, 0, 0, 0, 1, pixels), **kwargs)
def translate_x_abs(img, pixels, **kwargs):
_check_args_tf(kwargs)
return img.transform(img.size, Image.AFFINE, (1, 0, pixels, 0, 1, 0), **kwargs)
def translate_y_abs(img, pixels, **kwargs):
_check_args_tf(kwargs)
return img.transform(img.size, Image.AFFINE, (1, 0, 0, 0, 1, pixels), **kwargs)
def rotate(img, degrees, **kwargs):
_check_args_tf(kwargs)
if _PIL_VER >= (5, 2):
return img.rotate(degrees, **kwargs)
elif _PIL_VER >= (5, 0):
w, h = img.size
post_trans = (0, 0)
rotn_center = (w / 2.0, h / 2.0)
angle = -math.radians(degrees)
matrix = [
round(math.cos(angle), 15),
round(math.sin(angle), 15),
0.0,
round(-math.sin(angle), 15),
round(math.cos(angle), 15),
0.0,
]
def transform(x, y, matrix):
(a, b, c, d, e, f) = matrix
return a * x + b * y + c, d * x + e * y + f
matrix[2], matrix[5] = transform(
-rotn_center[0] - post_trans[0], -rotn_center[1] - post_trans[1], matrix
)
matrix[2] += rotn_center[0]
matrix[5] += rotn_center[1]
return img.transform(img.size, Image.AFFINE, matrix, **kwargs)
else:
return img.rotate(degrees, resample=kwargs['resample'])
def auto_contrast(img, **__):
return ImageOps.autocontrast(img)
def invert(img, **__):
return ImageOps.invert(img)
def equalize(img, **__):
return ImageOps.equalize(img)
def solarize(img, thresh, **__):
return ImageOps.solarize(img, thresh)
def solarize_add(img, add, thresh=128, **__):
lut = []
for i in range(256):
if i < thresh:
lut.append(min(255, i + add))
else:
lut.append(i)
if img.mode in ("L", "RGB"):
if img.mode == "RGB" and len(lut) == 256:
lut = lut + lut + lut
return img.point(lut)
else:
return img
def posterize(img, bits_to_keep, **__):
if bits_to_keep >= 8:
return img
return ImageOps.posterize(img, bits_to_keep)
def contrast(img, factor, **__):
return ImageEnhance.Contrast(img).enhance(factor)
def color(img, factor, **__):
return ImageEnhance.Color(img).enhance(factor)
def brightness(img, factor, **__):
return ImageEnhance.Brightness(img).enhance(factor)
def sharpness(img, factor, **__):
return ImageEnhance.Sharpness(img).enhance(factor)
def _randomly_negate(v):
"""With 50% prob, negate the value"""
return -v if random.random() > 0.5 else v
def _rotate_level_to_arg(level, _hparams):
# range [-30, 30]
level = (level / _LEVEL_DENOM) * 30.
level = _randomly_negate(level)
return level,
def _enhance_level_to_arg(level, _hparams):
# range [0.1, 1.9]
return (level / _LEVEL_DENOM) * 1.8 + 0.1,
def _enhance_increasing_level_to_arg(level, _hparams):
# the 'no change' level is 1.0, moving away from that towards 0. or 2.0 increases the enhancement blend
# range [0.1, 1.9] if level <= _LEVEL_DENOM
level = (level / _LEVEL_DENOM) * .9
level = max(0.1, 1.0 + _randomly_negate(level)) # keep it >= 0.1
return level,
def _shear_level_to_arg(level, _hparams):
# range [-0.3, 0.3]
level = (level / _LEVEL_DENOM) * 0.3
level = _randomly_negate(level)
return level,
def _translate_abs_level_to_arg(level, hparams):
translate_const = hparams['translate_const']
level = (level / _LEVEL_DENOM) * float(translate_const)
level = _randomly_negate(level)
return level,
def _translate_rel_level_to_arg(level, hparams):
# default range [-0.45, 0.45]
translate_pct = hparams.get('translate_pct', 0.45)
level = (level / _LEVEL_DENOM) * translate_pct
level = _randomly_negate(level)
return level,
def _posterize_level_to_arg(level, _hparams):
# As per Tensorflow TPU EfficientNet impl
# range [0, 4], 'keep 0 up to 4 MSB of original image'
# intensity/severity of augmentation decreases with level
return int((level / _LEVEL_DENOM) * 4),
def _posterize_increasing_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',
# intensity/severity of augmentation increases with level
return 4 - _posterize_level_to_arg(level, hparams)[0],
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'
# intensity/severity of augmentation decreases with level
return int((level / _LEVEL_DENOM) * 4) + 4,
def _solarize_level_to_arg(level, _hparams):
# range [0, 256]
# intensity/severity of augmentation decreases with level
return int((level / _LEVEL_DENOM) * 256),
def _solarize_increasing_level_to_arg(level, _hparams):
# range [0, 256]
# intensity/severity of augmentation increases with level
return 256 - _solarize_level_to_arg(level, _hparams)[0],
def _solarize_add_level_to_arg(level, _hparams):
# range [0, 110]
return int((level / _LEVEL_DENOM) * 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
'Posterize': _posterize_level_to_arg,
'PosterizeIncreasing': _posterize_increasing_level_to_arg,
'PosterizeOriginal': _posterize_original_level_to_arg,
'Solarize': _solarize_level_to_arg,
'SolarizeIncreasing': _solarize_increasing_level_to_arg,
'SolarizeAdd': _solarize_add_level_to_arg,
'Color': _enhance_level_to_arg,
'ColorIncreasing': _enhance_increasing_level_to_arg,
'Contrast': _enhance_level_to_arg,
'ContrastIncreasing': _enhance_increasing_level_to_arg,
'Brightness': _enhance_level_to_arg,
'BrightnessIncreasing': _enhance_increasing_level_to_arg,
'Sharpness': _enhance_level_to_arg,
'SharpnessIncreasing': _enhance_increasing_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 = {
'AutoContrast': auto_contrast,
'Equalize': equalize,
'Invert': invert,
'Rotate': rotate,
'Posterize': posterize,
'PosterizeIncreasing': posterize,
'PosterizeOriginal': posterize,
'Solarize': solarize,
'SolarizeIncreasing': solarize,
'SolarizeAdd': solarize_add,
'Color': color,
'ColorIncreasing': color,
'Contrast': contrast,
'ContrastIncreasing': contrast,
'Brightness': brightness,
'BrightnessIncreasing': brightness,
'Sharpness': sharpness,
'SharpnessIncreasing': sharpness,
'ShearX': shear_x,
'ShearY': shear_y,
'TranslateX': translate_x_abs,
'TranslateY': translate_y_abs,
'TranslateXRel': translate_x_rel,
'TranslateYRel': translate_y_rel,
}
class AugmentOp:
def __init__(self, name, prob=0.5, magnitude=10, hparams=None):
hparams = hparams or _HPARAMS_DEFAULT
self.name = name
self.aug_fn = NAME_TO_OP[name]
self.level_fn = LEVEL_TO_ARG[name]
self.prob = prob
self.magnitude = magnitude
self.hparams = hparams.copy()
self.kwargs = dict(
fillcolor=hparams['img_mean'] if 'img_mean' in hparams else _FILL,
resample=_pil_interp(hparams['interpolation']) if 'interpolation' in hparams else _RANDOM_INTERPOLATION,
)
# If magnitude_std 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_std`.
# NOTE This is my own hack, being tested, not in papers or reference impls.
# If magnitude_std is inf, we sample magnitude from a uniform distribution
self.magnitude_std = self.hparams.get('magnitude_std', 0)
self.magnitude_max = self.hparams.get('magnitude_max', None)
def __call__(self, img):
if self.prob < 1.0 and random.random() > self.prob:
return img
magnitude = self.magnitude
if self.magnitude_std > 0:
# magnitude randomization enabled
if self.magnitude_std == float('inf'):
magnitude = random.uniform(0, magnitude)
elif self.magnitude_std > 0:
magnitude = random.gauss(magnitude, self.magnitude_std)
# default upper_bound for the timm RA impl is _LEVEL_DENOM (10)
# setting magnitude_max overrides this to allow M > 10 (behaviour closer to Google TF RA impl)
upper_bound = self.magnitude_max or _LEVEL_DENOM
magnitude = max(0., min(magnitude, upper_bound))
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 __repr__(self):
fs = self.__class__.__name__ + f'(name={self.name}, p={self.prob}'
fs += f', m={self.magnitude}, mstd={self.magnitude_std}'
if self.magnitude_max is not None:
fs += f', mmax={self.magnitude_max}'
fs += ')'
return fs
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)],
[('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)],
[('Posterize', 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)],
[('Posterize', 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 = [[AugmentOp(*a, hparams=hparams) for a in sp] for sp in policy]
return pc
def auto_augment_policy_v0r(hparams):
# ImageNet v0 policy from TPU EfficientNet impl, with variation of Posterize used
# in Google research implementation (number of bits discarded increases with magnitude)
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)],
[('PosterizeIncreasing', 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)],
[('PosterizeIncreasing', 0.8, 2), ('Solarize', 0.6, 10)],
[('Solarize', 0.6, 8), ('Equalize', 0.6, 1)],
[('Color', 0.8, 6), ('Rotate', 0.4, 5)],
]
pc = [[AugmentOp(*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 = [
[('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 = [[AugmentOp(*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 = [
[('PosterizeIncreasing', 0.4, 8), ('Rotate', 0.6, 9)],
[('Solarize', 0.6, 5), ('AutoContrast', 0.6, 5)],
[('Equalize', 0.8, 8), ('Equalize', 0.6, 3)],
[('PosterizeIncreasing', 0.6, 7), ('PosterizeIncreasing', 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)],
[('PosterizeIncreasing', 0.8, 5), ('Equalize', 1.0, 2)],
[('Rotate', 0.2, 3), ('Solarize', 0.6, 8)],
[('Equalize', 0.6, 8), ('PosterizeIncreasing', 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 = [[AugmentOp(*a, hparams=hparams) for a in sp] for sp in policy]
return pc
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
class AutoAugment:
def __init__(self, policy):
self.policy = policy
def __call__(self, img):
sub_policy = random.choice(self.policy)
for op in sub_policy:
img = op(img)
return img
def __repr__(self):
fs = self.__class__.__name__ + f'(policy='
for p in self.policy:
fs += '\n\t['
fs += ', '.join([str(op) for op in p])
fs += ']'
fs += ')'
return fs
def auto_augment_transform(config_str, hparams):
"""
Create a AutoAugment transform
:param config_str: String defining configuration of auto augmentation. Consists of multiple sections separated by
dashes ('-'). The first section defines the AutoAugment policy (one of 'v0', 'v0r', 'original', 'originalr').
The remaining sections, not order sepecific determine
'mstd' - float std deviation of magnitude noise applied
Ex 'original-mstd0.5' results in AutoAugment with original policy, magnitude_std 0.5
:param hparams: Other hparams (kwargs) for the AutoAugmentation scheme
:return: A PyTorch compatible Transform
"""
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:
continue
key, val = cs[:2]
if key == 'mstd':
# noise param injected via hparams for now
hparams.setdefault('magnitude_std', float(val))
else:
assert False, 'Unknown AutoAugment config section'
aa_policy = auto_augment_policy(policy_name, hparams=hparams)
return AutoAugment(aa_policy)
_RAND_TRANSFORMS = [
'AutoContrast',
'Equalize',
'Invert',
'Rotate',
'Posterize',
'Solarize',
'SolarizeAdd',
'Color',
'Contrast',
'Brightness',
'Sharpness',
'ShearX',
'ShearY',
'TranslateXRel',
'TranslateYRel',
#'Cutout' # NOTE I've implement this as random erasing separately
]
_RAND_INCREASING_TRANSFORMS = [
'AutoContrast',
'Equalize',
'Invert',
'Rotate',
'PosterizeIncreasing',
'SolarizeIncreasing',
'SolarizeAdd',
'ColorIncreasing',
'ContrastIncreasing',
'BrightnessIncreasing',
'SharpnessIncreasing',
'ShearX',
'ShearY',
'TranslateXRel',
'TranslateYRel',
#'Cutout' # NOTE I've implement this as random erasing separately
]
# These experimental weights are based loosely on the relative improvements mentioned in paper.
# They may not result in increased performance, but could likely be tuned to so.
_RAND_CHOICE_WEIGHTS_0 = {
'Rotate': 0.3,
'ShearX': 0.2,
'ShearY': 0.2,
'TranslateXRel': 0.1,
'TranslateYRel': 0.1,
'Color': .025,
'Sharpness': 0.025,
'AutoContrast': 0.025,
'Solarize': .005,
'SolarizeAdd': .005,
'Contrast': .005,
'Brightness': .005,
'Equalize': .005,
'Posterize': 0,
'Invert': 0,
}
def _select_rand_weights(weight_idx=0, transforms=None):
transforms = transforms or _RAND_TRANSFORMS
assert weight_idx == 0 # only one set of weights currently
rand_weights = _RAND_CHOICE_WEIGHTS_0
probs = [rand_weights[k] for k in transforms]
probs /= np.sum(probs)
return probs
def rand_augment_ops(magnitude=10, hparams=None, transforms=None):
hparams = hparams or _HPARAMS_DEFAULT
transforms = transforms or _RAND_TRANSFORMS
return [AugmentOp(
name, prob=0.5, magnitude=magnitude, hparams=hparams) for name in transforms]
class RandAugment:
def __init__(self, ops, num_layers=2, choice_weights=None):
self.ops = ops
self.num_layers = num_layers
self.choice_weights = choice_weights
def __call__(self, img):
# no replacement when using weighted choice
ops = np.random.choice(
self.ops, self.num_layers, replace=self.choice_weights is None, p=self.choice_weights)
for op in ops:
img = op(img)
return img
def __repr__(self):
fs = self.__class__.__name__ + f'(n={self.num_layers}, ops='
for op in self.ops:
fs += f'\n\t{op}'
fs += ')'
return fs
def rand_augment_transform(config_str, hparams):
"""
Create a RandAugment transform
:param config_str: String defining configuration of random augmentation. Consists of multiple sections separated by
dashes ('-'). The first section defines the specific variant of rand augment (currently only 'rand'). The remaining
sections, not order sepecific determine
'm' - integer magnitude of rand augment
'n' - integer num layers (number of transform ops selected per image)
'w' - integer probabiliy weight index (index of a set of weights to influence choice of op)
'mstd' - float std deviation of magnitude noise applied, or uniform sampling if infinity (or > 100)
'mmax' - set upper bound for magnitude to something other than default of _LEVEL_DENOM (10)
'inc' - integer (bool), use augmentations that increase in severity with magnitude (default: 0)
Ex 'rand-m9-n3-mstd0.5' results in RandAugment with magnitude 9, num_layers 3, magnitude_std 0.5
'rand-mstd1-w0' results in magnitude_std 1.0, weights 0, default magnitude of 10 and num_layers 2
:param hparams: Other hparams (kwargs) for the RandAugmentation scheme
:return: A PyTorch compatible Transform
"""
magnitude = _LEVEL_DENOM # default to _LEVEL_DENOM for magnitude (currently 10)
num_layers = 2 # default to 2 ops per image
weight_idx = None # default to no probability weights for op choice
transforms = _RAND_TRANSFORMS
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:
continue
key, val = cs[:2]
if key == 'mstd':
# noise param / randomization of magnitude values
mstd = float(val)
if mstd > 100:
# use uniform sampling in 0 to magnitude if mstd is > 100
mstd = float('inf')
hparams.setdefault('magnitude_std', mstd)
elif key == 'mmax':
# clip magnitude between [0, mmax] instead of default [0, _LEVEL_DENOM]
hparams.setdefault('magnitude_max', int(val))
elif key == 'inc':
if bool(val):
transforms = _RAND_INCREASING_TRANSFORMS
elif key == 'm':
magnitude = int(val)
elif key == 'n':
num_layers = int(val)
elif key == 'w':
weight_idx = int(val)
else:
assert False, 'Unknown RandAugment config section'
ra_ops = rand_augment_ops(magnitude=magnitude, hparams=hparams, transforms=transforms)
choice_weights = None if weight_idx is None else _select_rand_weights(weight_idx)
return RandAugment(ra_ops, num_layers, choice_weights=choice_weights)
_AUGMIX_TRANSFORMS = [
'AutoContrast',
'ColorIncreasing', # not in paper
'ContrastIncreasing', # not in paper
'BrightnessIncreasing', # not in paper
'SharpnessIncreasing', # not in paper
'Equalize',
'Rotate',
'PosterizeIncreasing',
'SolarizeIncreasing',
'ShearX',
'ShearY',
'TranslateXRel',
'TranslateYRel',
]
def augmix_ops(magnitude=10, hparams=None, transforms=None):
hparams = hparams or _HPARAMS_DEFAULT
transforms = transforms or _AUGMIX_TRANSFORMS
return [AugmentOp(
name, prob=1.0, magnitude=magnitude, hparams=hparams) for name in transforms]
class AugMixAugment:
""" AugMix Transform
Adapted and improved from impl here: https://github.com/google-research/augmix/blob/master/imagenet.py
From paper: 'AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty -
https://arxiv.org/abs/1912.02781
"""
def __init__(self, ops, alpha=1., width=3, depth=-1, blended=False):
self.ops = ops
self.alpha = alpha
self.width = width
self.depth = depth
self.blended = blended # blended mode is faster but not well tested
def _calc_blended_weights(self, ws, m):
ws = ws * m
cump = 1.
rws = []
for w in ws[::-1]:
alpha = w / cump
cump *= (1 - alpha)
rws.append(alpha)
return np.array(rws[::-1], dtype=np.float32)
def _apply_blended(self, img, mixing_weights, m):
# This is my first crack and implementing a slightly faster mixed augmentation. Instead
# of accumulating the mix for each chain in a Numpy array and then blending with original,
# it recomputes the blending coefficients and applies one PIL image blend per chain.
# TODO the results appear in the right ballpark but they differ by more than rounding.
img_orig = img.copy()
ws = self._calc_blended_weights(mixing_weights, m)
for w in ws:
depth = self.depth if self.depth > 0 else np.random.randint(1, 4)
ops = np.random.choice(self.ops, depth, replace=True)
img_aug = img_orig # no ops are in-place, deep copy not necessary
for op in ops:
img_aug = op(img_aug)
img = Image.blend(img, img_aug, w)
return img
def _apply_basic(self, img, mixing_weights, m):
# This is a literal adaptation of the paper/official implementation without normalizations and
# PIL <-> Numpy conversions between every op. It is still quite CPU compute heavy compared to the
# typical augmentation transforms, could use a GPU / Kornia implementation.
img_shape = img.size[0], img.size[1], len(img.getbands())
mixed = np.zeros(img_shape, dtype=np.float32)
for mw in mixing_weights:
depth = self.depth if self.depth > 0 else np.random.randint(1, 4)
ops = np.random.choice(self.ops, depth, replace=True)
img_aug = img # no ops are in-place, deep copy not necessary
for op in ops:
img_aug = op(img_aug)
mixed += mw * np.asarray(img_aug, dtype=np.float32)
np.clip(mixed, 0, 255., out=mixed)
mixed = Image.fromarray(mixed.astype(np.uint8))
return Image.blend(img, mixed, m)
def __call__(self, img):
mixing_weights = np.float32(np.random.dirichlet([self.alpha] * self.width))
m = np.float32(np.random.beta(self.alpha, self.alpha))
if self.blended:
mixed = self._apply_blended(img, mixing_weights, m)
else:
mixed = self._apply_basic(img, mixing_weights, m)
return mixed
def __repr__(self):
fs = self.__class__.__name__ + f'(alpha={self.alpha}, width={self.width}, depth={self.depth}, ops='
for op in self.ops:
fs += f'\n\t{op}'
fs += ')'
return fs
def augment_and_mix_transform(config_str, hparams):
""" Create AugMix PyTorch transform
:param config_str: String defining configuration of random augmentation. Consists of multiple sections separated by
dashes ('-'). The first section defines the specific variant of rand augment (currently only 'rand'). The remaining
sections, not order sepecific determine
'm' - integer magnitude (severity) of augmentation mix (default: 3)
'w' - integer width of augmentation chain (default: 3)
'd' - integer depth of augmentation chain (-1 is random [1, 3], default: -1)
'b' - integer (bool), blend each branch of chain into end result without a final blend, less CPU (default: 0)
'mstd' - float std deviation of magnitude noise applied (default: 0)
Ex 'augmix-m5-w4-d2' results in AugMix with severity 5, chain width 4, chain depth 2
:param hparams: Other hparams (kwargs) for the Augmentation transforms
:return: A PyTorch compatible Transform
"""
magnitude = 3
width = 3
depth = -1
alpha = 1.
blended = False
config = config_str.split('-')
assert config[0] == 'augmix'
config = config[1:]
for c in config:
cs = re.split(r'(\d.*)', c)
if len(cs) < 2:
continue
key, val = cs[:2]
if key == 'mstd':
# noise param injected via hparams for now
hparams.setdefault('magnitude_std', float(val))
elif key == 'm':
magnitude = int(val)
elif key == 'w':
width = int(val)
elif key == 'd':
depth = int(val)
elif key == 'a':
alpha = float(val)
elif key == 'b':
blended = bool(val)
else:
assert False, 'Unknown AugMix config section'
hparams.setdefault('magnitude_std', float('inf')) # default to uniform sampling (if not set via mstd arg)
ops = augmix_ops(magnitude=magnitude, hparams=hparams)
return AugMixAugment(ops, alpha=alpha, width=width, depth=depth, blended=blended)