You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
901 lines
32 KiB
901 lines
32 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 2019, 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,
|
|
)
|
|
|
|
# Pillow is deprecating the top-level resampling attributes (e.g., Image.BILINEAR) in
|
|
# favor of the Image.Resampling enum. The top-level resampling attributes will be
|
|
# removed in Pillow 10.
|
|
if hasattr(Image, "Resampling"):
|
|
_RANDOM_INTERPOLATION = (Image.Resampling.BILINEAR, Image.Resampling.BICUBIC)
|
|
_DEFAULT_INTERPOLATION = Image.Resampling.BICUBIC
|
|
|
|
_pil_interpolation_to_str = {
|
|
Image.Resampling.NEAREST: 'nearest',
|
|
Image.Resampling.BILINEAR: 'bilinear',
|
|
Image.Resampling.BICUBIC: 'bicubic',
|
|
Image.Resampling.BOX: 'box',
|
|
Image.Resampling.HAMMING: 'hamming',
|
|
Image.Resampling.LANCZOS: 'lanczos',
|
|
}
|
|
else:
|
|
_RANDOM_INTERPOLATION = (Image.BILINEAR, Image.BICUBIC)
|
|
_DEFAULT_INTERPOLATION = Image.BICUBIC
|
|
|
|
_pil_interpolation_to_str = {
|
|
Image.NEAREST: 'nearest',
|
|
Image.BILINEAR: 'bilinear',
|
|
Image.BICUBIC: 'bicubic',
|
|
Image.BOX: 'box',
|
|
Image.HAMMING: 'hamming',
|
|
Image.LANCZOS: 'lanczos',
|
|
}
|
|
|
|
_str_to_pil_interpolation = {b: a for a, b in _pil_interpolation_to_str.items()}
|
|
|
|
|
|
def _pil_interp(method):
|
|
if isinstance(method, (list, tuple)):
|
|
return [_str_to_pil_interpolation(m) if isinstance(m, str) else m for m in method]
|
|
else:
|
|
return _str_to_pil_interpolation(method) if isinstance(method, str) else method
|
|
|
|
|
|
def _interpolation(kwargs):
|
|
interpolation = kwargs.pop('resample', _DEFAULT_INTERPOLATION)
|
|
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)
|