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

""" AutoAugment and RandAugment
Implementation adapted from:
    https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/autoaugment.py
Papers: https://arxiv.org/abs/1805.09501, https://arxiv.org/abs/1906.11172, and https://arxiv.org/abs/1909.13719

Hacked together by 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)

# This signifies the max integer that the controller RNN could predict for the
# augmentation scheme.
_MAX_LEVEL = 10.

_HPARAMS_DEFAULT = dict(
    translate_const=250,
    img_mean=_FILL,
)

_RANDOM_INTERPOLATION = (Image.BILINEAR, Image.BICUBIC)


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 / _MAX_LEVEL) * 30.
    level = _randomly_negate(level)
    return level,


def _enhance_level_to_arg(level, _hparams):
    # range [0.1, 1.9]
    return (level / _MAX_LEVEL) * 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]
    level = (level / _MAX_LEVEL) * .9
    level = 1.0 + _randomly_negate(level)
    return 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,


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,


def _translate_rel_level_to_arg(level, hparams):
    # default range [-0.45, 0.45]
    translate_pct = hparams.get('translate_pct', 0.45)
    level = (level / _MAX_LEVEL) * 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 / _MAX_LEVEL) * 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 / _MAX_LEVEL) * 4) + 4,


def _solarize_level_to_arg(level, _hparams):
    # range [0, 256]
    # intensity/severity of augmentation decreases with level
    return int((level / _MAX_LEVEL) * 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 / _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
    '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.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=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.
        self.magnitude_std = self.hparams.get('magnitude_std', 0)

    def __call__(self, img):
        if self.prob < 1.0 and 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))  # 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):
    # 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 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 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
        '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 = _MAX_LEVEL  # default to _MAX_LEVEL 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 injected via hparams for now
            hparams.setdefault('magnitude_std', float(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:
    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

    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 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'
    ops = augmix_ops(magnitude=magnitude, hparams=hparams)
    return AugMixAugment(ops, alpha=alpha, width=width, depth=depth, blended=blended)