OpenOCR/openrec/preprocess/auto_augment.py

"""AutoAugment, RandAugment, AugMix, and 3-Augment 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

3-Augment based on: https://github.com/facebookresearch/deit/blob/main/README_revenge.md

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
    3-Augment: DeiT III: Revenge of the ViT - https://arxiv.org/abs/2204.07118

Hacked together by / Copyright 2019, Ross Wightman
"""
import math
import random
import re
from functools import partial
from typing import Dict, List, Optional, Union

import numpy as np
import PIL
from PIL import Image, ImageChops, ImageEnhance, ImageFilter, ImageOps

_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,
)

if hasattr(Image, 'Resampling'):
    _RANDOM_INTERPOLATION = (Image.Resampling.BILINEAR,
                             Image.Resampling.BICUBIC)
    _DEFAULT_INTERPOLATION = Image.Resampling.BICUBIC
else:
    _RANDOM_INTERPOLATION = (Image.BILINEAR, Image.BICUBIC)
    _DEFAULT_INTERPOLATION = Image.BICUBIC


def _interpolation(kwargs):
    interpolation = kwargs.pop('resample', _DEFAULT_INTERPOLATION)
    if isinstance(interpolation, (list, tuple)):
        return random.choice(interpolation)
    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)
    if _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)
    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)

    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 gaussian_blur(img, factor, **__):
    img = img.filter(ImageFilter.GaussianBlur(radius=factor))
    return img


def gaussian_blur_rand(img, factor, **__):
    radius_min = 0.1
    radius_max = 2.0
    img = img.filter(
        ImageFilter.GaussianBlur(radius=random.uniform(radius_min, radius_max *
                                                       factor)))
    return img


def desaturate(img, factor, **_):
    factor = min(1., max(0., 1. - factor))
    # enhance factor 0 = grayscale, 1.0 = no-change
    return ImageEnhance.Color(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 _minmax_level_to_arg(level, _hparams, min_val=0., max_val=1.0, clamp=True):
    level = (level / _LEVEL_DENOM)
    level = min_val + (max_val - min_val) * level
    if clamp:
        level = max(min_val, min(max_val, level))
    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 min(256, 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 min(128, 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,
    'Desaturate': partial(_minmax_level_to_arg, min_val=0.5, max_val=1.0),
    'GaussianBlur': partial(_minmax_level_to_arg, min_val=0.1, max_val=2.0),
    'GaussianBlurRand': _minmax_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,
    'Desaturate': desaturate,
    'GaussianBlur': gaussian_blur,
    'GaussianBlurRand': gaussian_blur_rand,
}


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=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'):
                # inf == uniform sampling
                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_3a(hparams):
    policy = [
        [('Solarize', 1.0, 5)],  # 128 solarize threshold @ 5 magnitude
        [('Desaturate', 1.0, 10)],  # grayscale at 10 magnitude
        [('GaussianBlurRand', 1.0, 10)],
    ]
    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)
    if name == 'originalr':
        return auto_augment_policy_originalr(hparams)
    if name == 'v0':
        return auto_augment_policy_v0(hparams)
    if name == 'v0r':
        return auto_augment_policy_v0r(hparams)
    if name == '3a':
        return auto_augment_policy_3a(hparams)
    assert False, f'Unknown AA policy {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__ + '(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: str, hparams: Optional[Dict] = None):
    """Create a AutoAugment transform.

    Args:
        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:
                'mstd' -  float std deviation of magnitude noise applied
            Ex 'original-mstd0.5' results in AutoAugment with original policy, magnitude_std 0.5

        hparams: Other hparams (kwargs) for the AutoAugmentation scheme

    Returns:
         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
]

_RAND_3A = [
    'SolarizeIncreasing',
    'Desaturate',
    'GaussianBlur',
]

_RAND_WEIGHTED_3A = {
    'SolarizeIncreasing': 6,
    'Desaturate': 6,
    'GaussianBlur': 6,
    'Rotate': 3,
    'ShearX': 2,
    'ShearY': 2,
    'PosterizeIncreasing': 1,
    'AutoContrast': 1,
    'ColorIncreasing': 1,
    'SharpnessIncreasing': 1,
    'ContrastIncreasing': 1,
    'BrightnessIncreasing': 1,
    'Equalize': 1,
    'Invert': 1,
}

# 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_WEIGHTED_0 = {
    'Rotate': 3,
    'ShearX': 2,
    'ShearY': 2,
    'TranslateXRel': 1,
    'TranslateYRel': 1,
    'ColorIncreasing': .25,
    'SharpnessIncreasing': 0.25,
    'AutoContrast': 0.25,
    'SolarizeIncreasing': .05,
    'SolarizeAdd': .05,
    'ContrastIncreasing': .05,
    'BrightnessIncreasing': .05,
    'Equalize': .05,
    'PosterizeIncreasing': 0.05,
    'Invert': 0.05,
}


def _get_weighted_transforms(transforms: Dict):
    transforms, probs = list(zip(*transforms.items()))
    probs = np.array(probs)
    probs = probs / np.sum(probs)
    return transforms, probs


def rand_augment_choices(name: str, increasing=True):
    if name == 'weights':
        return _RAND_WEIGHTED_0
    if name == '3aw':
        return _RAND_WEIGHTED_3A
    if name == '3a':
        return _RAND_3A
    return _RAND_INCREASING_TRANSFORMS if increasing else _RAND_TRANSFORMS


def rand_augment_ops(
    magnitude: Union[int, float] = 10,
    prob: float = 0.5,
    hparams: Optional[Dict] = None,
    transforms: Optional[Union[Dict, List]] = None,
):
    hparams = hparams or _HPARAMS_DEFAULT
    transforms = transforms or _RAND_TRANSFORMS
    return [
        AugmentOp(name, prob=prob, 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: str,
    hparams: Optional[Dict] = None,
    transforms: Optional[Union[str, Dict, List]] = None,
):
    """Create a RandAugment transform.

    Args:
        config_str (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)
                'p' - float probability of applying each layer (default 0.5)
                '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)
                't' - str name of transform set to use
            Ex 'rand-m9-n3-mstd0.5' results in RandAugment with magnitude 9, num_layers 3, magnitude_std 0.5
            'rand-mstd1-tweights' results in mag std 1.0, weighted transforms, default mag of 10 and num_layers 2

        hparams (dict): Other hparams (kwargs) for the RandAugmentation scheme

    Returns:
         A PyTorch compatible Transform
    """
    magnitude = _LEVEL_DENOM  # default to _LEVEL_DENOM for magnitude (currently 10)
    num_layers = 2  # default to 2 ops per image
    increasing = False
    prob = 0.5
    config = config_str.split('-')
    assert config[0] == 'rand'
    config = config[1:]
    for c in config:
        if c.startswith('t'):
            # NOTE old 'w' key was removed, 'w0' is not equivalent to 'tweights'
            val = str(c[1:])
            if transforms is None:
                transforms = val
        else:
            # numeric options
            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):
                    increasing = True
            elif key == 'm':
                magnitude = int(val)
            elif key == 'n':
                num_layers = int(val)
            elif key == 'p':
                prob = float(val)
            else:
                assert False, 'Unknown RandAugment config section'

    if isinstance(transforms, str):
        transforms = rand_augment_choices(transforms, increasing=increasing)
    elif transforms is None:
        transforms = _RAND_INCREASING_TRANSFORMS if increasing else _RAND_TRANSFORMS

    choice_weights = None
    if isinstance(transforms, Dict):
        transforms, choice_weights = _get_weighted_transforms(transforms)

    ra_ops = rand_augment_ops(magnitude=magnitude,
                              prob=prob,
                              hparams=hparams,
                              transforms=transforms)
    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: Union[int, float] = 10,
    hparams: Optional[Dict] = None,
    transforms: Optional[Union[str, Dict, List]] = 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: str, hparams: Optional[Dict] = None):
    """Create AugMix PyTorch transform.

    Args:
        config_str (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

        hparams: Other hparams (kwargs) for the Augmentation transforms

    Returns:
         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)