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792 lines
28 KiB
792 lines
28 KiB
""" AutoAugment and RandAugment
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Implementation adapted from:
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https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/autoaugment.py
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Papers: https://arxiv.org/abs/1805.09501, https://arxiv.org/abs/1906.11172, and https://arxiv.org/abs/1909.13719
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Hacked together by Ross Wightman
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"""
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import random
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import math
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import re
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from PIL import Image, ImageOps, ImageEnhance, ImageChops
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import PIL
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import numpy as np
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_PIL_VER = tuple([int(x) for x in PIL.__version__.split('.')[:2]])
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_FILL = (128, 128, 128)
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# This signifies the max integer that the controller RNN could predict for the
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# augmentation scheme.
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_MAX_LEVEL = 10.
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_HPARAMS_DEFAULT = dict(
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translate_const=250,
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img_mean=_FILL,
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)
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_RANDOM_INTERPOLATION = (Image.BILINEAR, Image.BICUBIC)
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def _interpolation(kwargs):
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interpolation = kwargs.pop('resample', Image.BILINEAR)
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if isinstance(interpolation, (list, tuple)):
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return random.choice(interpolation)
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else:
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return interpolation
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def _check_args_tf(kwargs):
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if 'fillcolor' in kwargs and _PIL_VER < (5, 0):
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kwargs.pop('fillcolor')
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kwargs['resample'] = _interpolation(kwargs)
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def shear_x(img, factor, **kwargs):
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_check_args_tf(kwargs)
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return img.transform(img.size, Image.AFFINE, (1, factor, 0, 0, 1, 0), **kwargs)
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def shear_y(img, factor, **kwargs):
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_check_args_tf(kwargs)
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return img.transform(img.size, Image.AFFINE, (1, 0, 0, factor, 1, 0), **kwargs)
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def translate_x_rel(img, pct, **kwargs):
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pixels = pct * img.size[0]
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_check_args_tf(kwargs)
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return img.transform(img.size, Image.AFFINE, (1, 0, pixels, 0, 1, 0), **kwargs)
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def translate_y_rel(img, pct, **kwargs):
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pixels = pct * img.size[1]
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_check_args_tf(kwargs)
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return img.transform(img.size, Image.AFFINE, (1, 0, 0, 0, 1, pixels), **kwargs)
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def translate_x_abs(img, pixels, **kwargs):
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_check_args_tf(kwargs)
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return img.transform(img.size, Image.AFFINE, (1, 0, pixels, 0, 1, 0), **kwargs)
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def translate_y_abs(img, pixels, **kwargs):
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_check_args_tf(kwargs)
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return img.transform(img.size, Image.AFFINE, (1, 0, 0, 0, 1, pixels), **kwargs)
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def rotate(img, degrees, **kwargs):
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_check_args_tf(kwargs)
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if _PIL_VER >= (5, 2):
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return img.rotate(degrees, **kwargs)
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elif _PIL_VER >= (5, 0):
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w, h = img.size
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post_trans = (0, 0)
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rotn_center = (w / 2.0, h / 2.0)
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angle = -math.radians(degrees)
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matrix = [
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round(math.cos(angle), 15),
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round(math.sin(angle), 15),
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0.0,
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round(-math.sin(angle), 15),
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round(math.cos(angle), 15),
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0.0,
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]
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def transform(x, y, matrix):
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(a, b, c, d, e, f) = matrix
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return a * x + b * y + c, d * x + e * y + f
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matrix[2], matrix[5] = transform(
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-rotn_center[0] - post_trans[0], -rotn_center[1] - post_trans[1], matrix
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)
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matrix[2] += rotn_center[0]
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matrix[5] += rotn_center[1]
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return img.transform(img.size, Image.AFFINE, matrix, **kwargs)
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else:
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return img.rotate(degrees, resample=kwargs['resample'])
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def auto_contrast(img, **__):
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return ImageOps.autocontrast(img)
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def invert(img, **__):
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return ImageOps.invert(img)
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def equalize(img, **__):
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return ImageOps.equalize(img)
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def solarize(img, thresh, **__):
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return ImageOps.solarize(img, thresh)
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def solarize_add(img, add, thresh=128, **__):
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lut = []
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for i in range(256):
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if i < thresh:
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lut.append(min(255, i + add))
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else:
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lut.append(i)
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if img.mode in ("L", "RGB"):
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if img.mode == "RGB" and len(lut) == 256:
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lut = lut + lut + lut
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return img.point(lut)
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else:
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return img
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def posterize(img, bits_to_keep, **__):
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if bits_to_keep >= 8:
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return img
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return ImageOps.posterize(img, bits_to_keep)
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def contrast(img, factor, **__):
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return ImageEnhance.Contrast(img).enhance(factor)
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def color(img, factor, **__):
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return ImageEnhance.Color(img).enhance(factor)
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def brightness(img, factor, **__):
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return ImageEnhance.Brightness(img).enhance(factor)
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def sharpness(img, factor, **__):
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return ImageEnhance.Sharpness(img).enhance(factor)
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def _randomly_negate(v):
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"""With 50% prob, negate the value"""
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return -v if random.random() > 0.5 else v
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def _rotate_level_to_arg(level, _hparams):
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# range [-30, 30]
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level = (level / _MAX_LEVEL) * 30.
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level = _randomly_negate(level)
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return level,
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def _enhance_level_to_arg(level, _hparams):
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# range [0.1, 1.9]
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return (level / _MAX_LEVEL) * 1.8 + 0.1,
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def _enhance_increasing_level_to_arg(level, _hparams):
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# the 'no change' level is 1.0, moving away from that towards 0. or 2.0 increases the enhancement blend
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# range [0.1, 1.9]
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level = (level / _MAX_LEVEL) * .9
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level = 1.0 + _randomly_negate(level)
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return level,
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def _shear_level_to_arg(level, _hparams):
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# range [-0.3, 0.3]
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level = (level / _MAX_LEVEL) * 0.3
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level = _randomly_negate(level)
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return level,
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def _translate_abs_level_to_arg(level, hparams):
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translate_const = hparams['translate_const']
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level = (level / _MAX_LEVEL) * float(translate_const)
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level = _randomly_negate(level)
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return level,
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def _translate_rel_level_to_arg(level, hparams):
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# default range [-0.45, 0.45]
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translate_pct = hparams.get('translate_pct', 0.45)
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level = (level / _MAX_LEVEL) * translate_pct
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level = _randomly_negate(level)
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return level,
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def _posterize_level_to_arg(level, _hparams):
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# As per Tensorflow TPU EfficientNet impl
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# range [0, 4], 'keep 0 up to 4 MSB of original image'
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# intensity/severity of augmentation decreases with level
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return int((level / _MAX_LEVEL) * 4),
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def _posterize_increasing_level_to_arg(level, hparams):
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# As per Tensorflow models research and UDA impl
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# range [4, 0], 'keep 4 down to 0 MSB of original image',
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# intensity/severity of augmentation increases with level
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return 4 - _posterize_level_to_arg(level, hparams)[0],
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def _posterize_original_level_to_arg(level, _hparams):
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# As per original AutoAugment paper description
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# range [4, 8], 'keep 4 up to 8 MSB of image'
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# intensity/severity of augmentation decreases with level
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return int((level / _MAX_LEVEL) * 4) + 4,
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def _solarize_level_to_arg(level, _hparams):
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# range [0, 256]
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# intensity/severity of augmentation decreases with level
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return int((level / _MAX_LEVEL) * 256),
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def _solarize_increasing_level_to_arg(level, _hparams):
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# range [0, 256]
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# intensity/severity of augmentation increases with level
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return 256 - _solarize_level_to_arg(level, _hparams)[0],
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def _solarize_add_level_to_arg(level, _hparams):
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# range [0, 110]
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return int((level / _MAX_LEVEL) * 110),
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LEVEL_TO_ARG = {
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'AutoContrast': None,
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'Equalize': None,
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'Invert': None,
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'Rotate': _rotate_level_to_arg,
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# There are several variations of the posterize level scaling in various Tensorflow/Google repositories/papers
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'Posterize': _posterize_level_to_arg,
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'PosterizeIncreasing': _posterize_increasing_level_to_arg,
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'PosterizeOriginal': _posterize_original_level_to_arg,
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'Solarize': _solarize_level_to_arg,
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'SolarizeIncreasing': _solarize_increasing_level_to_arg,
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'SolarizeAdd': _solarize_add_level_to_arg,
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'Color': _enhance_level_to_arg,
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'ColorIncreasing': _enhance_increasing_level_to_arg,
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'Contrast': _enhance_level_to_arg,
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'ContrastIncreasing': _enhance_increasing_level_to_arg,
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'Brightness': _enhance_level_to_arg,
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'BrightnessIncreasing': _enhance_increasing_level_to_arg,
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'Sharpness': _enhance_level_to_arg,
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'SharpnessIncreasing': _enhance_increasing_level_to_arg,
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'ShearX': _shear_level_to_arg,
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'ShearY': _shear_level_to_arg,
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'TranslateX': _translate_abs_level_to_arg,
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'TranslateY': _translate_abs_level_to_arg,
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'TranslateXRel': _translate_rel_level_to_arg,
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'TranslateYRel': _translate_rel_level_to_arg,
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}
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NAME_TO_OP = {
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'AutoContrast': auto_contrast,
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'Equalize': equalize,
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'Invert': invert,
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'Rotate': rotate,
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'Posterize': posterize,
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'PosterizeIncreasing': posterize,
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'PosterizeOriginal': posterize,
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'Solarize': solarize,
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'SolarizeIncreasing': solarize,
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'SolarizeAdd': solarize_add,
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'Color': color,
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'ColorIncreasing': color,
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'Contrast': contrast,
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'ContrastIncreasing': contrast,
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'Brightness': brightness,
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'BrightnessIncreasing': brightness,
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'Sharpness': sharpness,
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'SharpnessIncreasing': sharpness,
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'ShearX': shear_x,
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'ShearY': shear_y,
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'TranslateX': translate_x_abs,
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'TranslateY': translate_y_abs,
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'TranslateXRel': translate_x_rel,
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'TranslateYRel': translate_y_rel,
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}
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class AugmentOp:
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def __init__(self, name, prob=0.5, magnitude=10, hparams=None):
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hparams = hparams or _HPARAMS_DEFAULT
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self.aug_fn = NAME_TO_OP[name]
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self.level_fn = LEVEL_TO_ARG[name]
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self.prob = prob
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self.magnitude = magnitude
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self.hparams = hparams.copy()
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self.kwargs = dict(
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fillcolor=hparams['img_mean'] if 'img_mean' in hparams else _FILL,
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resample=hparams['interpolation'] if 'interpolation' in hparams else _RANDOM_INTERPOLATION,
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)
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# If magnitude_std is > 0, we introduce some randomness
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# in the usually fixed policy and sample magnitude from a normal distribution
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# with mean `magnitude` and std-dev of `magnitude_std`.
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# NOTE This is my own hack, being tested, not in papers or reference impls.
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self.magnitude_std = self.hparams.get('magnitude_std', 0)
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def __call__(self, img):
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if not self.prob >= 1.0 or random.random() > self.prob:
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return img
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magnitude = self.magnitude
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if self.magnitude_std and self.magnitude_std > 0:
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magnitude = random.gauss(magnitude, self.magnitude_std)
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magnitude = min(_MAX_LEVEL, max(0, magnitude)) # clip to valid range
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level_args = self.level_fn(magnitude, self.hparams) if self.level_fn is not None else tuple()
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return self.aug_fn(img, *level_args, **self.kwargs)
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def auto_augment_policy_v0(hparams):
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# ImageNet v0 policy from TPU EfficientNet impl, cannot find a paper reference.
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policy = [
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[('Equalize', 0.8, 1), ('ShearY', 0.8, 4)],
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[('Color', 0.4, 9), ('Equalize', 0.6, 3)],
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[('Color', 0.4, 1), ('Rotate', 0.6, 8)],
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[('Solarize', 0.8, 3), ('Equalize', 0.4, 7)],
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[('Solarize', 0.4, 2), ('Solarize', 0.6, 2)],
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[('Color', 0.2, 0), ('Equalize', 0.8, 8)],
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[('Equalize', 0.4, 8), ('SolarizeAdd', 0.8, 3)],
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[('ShearX', 0.2, 9), ('Rotate', 0.6, 8)],
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[('Color', 0.6, 1), ('Equalize', 1.0, 2)],
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[('Invert', 0.4, 9), ('Rotate', 0.6, 0)],
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[('Equalize', 1.0, 9), ('ShearY', 0.6, 3)],
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[('Color', 0.4, 7), ('Equalize', 0.6, 0)],
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[('Posterize', 0.4, 6), ('AutoContrast', 0.4, 7)],
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[('Solarize', 0.6, 8), ('Color', 0.6, 9)],
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[('Solarize', 0.2, 4), ('Rotate', 0.8, 9)],
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[('Rotate', 1.0, 7), ('TranslateYRel', 0.8, 9)],
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[('ShearX', 0.0, 0), ('Solarize', 0.8, 4)],
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[('ShearY', 0.8, 0), ('Color', 0.6, 4)],
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[('Color', 1.0, 0), ('Rotate', 0.6, 2)],
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[('Equalize', 0.8, 4), ('Equalize', 0.0, 8)],
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[('Equalize', 1.0, 4), ('AutoContrast', 0.6, 2)],
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[('ShearY', 0.4, 7), ('SolarizeAdd', 0.6, 7)],
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[('Posterize', 0.8, 2), ('Solarize', 0.6, 10)], # This results in black image with Tpu posterize
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[('Solarize', 0.6, 8), ('Equalize', 0.6, 1)],
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[('Color', 0.8, 6), ('Rotate', 0.4, 5)],
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]
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pc = [[AugmentOp(*a, hparams=hparams) for a in sp] for sp in policy]
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return pc
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def auto_augment_policy_v0r(hparams):
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# ImageNet v0 policy from TPU EfficientNet impl, with variation of Posterize used
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# in Google research implementation (number of bits discarded increases with magnitude)
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policy = [
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[('Equalize', 0.8, 1), ('ShearY', 0.8, 4)],
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[('Color', 0.4, 9), ('Equalize', 0.6, 3)],
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[('Color', 0.4, 1), ('Rotate', 0.6, 8)],
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[('Solarize', 0.8, 3), ('Equalize', 0.4, 7)],
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[('Solarize', 0.4, 2), ('Solarize', 0.6, 2)],
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[('Color', 0.2, 0), ('Equalize', 0.8, 8)],
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[('Equalize', 0.4, 8), ('SolarizeAdd', 0.8, 3)],
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[('ShearX', 0.2, 9), ('Rotate', 0.6, 8)],
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[('Color', 0.6, 1), ('Equalize', 1.0, 2)],
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[('Invert', 0.4, 9), ('Rotate', 0.6, 0)],
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[('Equalize', 1.0, 9), ('ShearY', 0.6, 3)],
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[('Color', 0.4, 7), ('Equalize', 0.6, 0)],
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[('PosterizeIncreasing', 0.4, 6), ('AutoContrast', 0.4, 7)],
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[('Solarize', 0.6, 8), ('Color', 0.6, 9)],
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[('Solarize', 0.2, 4), ('Rotate', 0.8, 9)],
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[('Rotate', 1.0, 7), ('TranslateYRel', 0.8, 9)],
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[('ShearX', 0.0, 0), ('Solarize', 0.8, 4)],
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[('ShearY', 0.8, 0), ('Color', 0.6, 4)],
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[('Color', 1.0, 0), ('Rotate', 0.6, 2)],
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[('Equalize', 0.8, 4), ('Equalize', 0.0, 8)],
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[('Equalize', 1.0, 4), ('AutoContrast', 0.6, 2)],
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[('ShearY', 0.4, 7), ('SolarizeAdd', 0.6, 7)],
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[('PosterizeIncreasing', 0.8, 2), ('Solarize', 0.6, 10)],
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[('Solarize', 0.6, 8), ('Equalize', 0.6, 1)],
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[('Color', 0.8, 6), ('Rotate', 0.4, 5)],
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]
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pc = [[AugmentOp(*a, hparams=hparams) for a in sp] for sp in policy]
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return pc
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def auto_augment_policy_original(hparams):
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# ImageNet policy from https://arxiv.org/abs/1805.09501
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policy = [
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[('PosterizeOriginal', 0.4, 8), ('Rotate', 0.6, 9)],
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[('Solarize', 0.6, 5), ('AutoContrast', 0.6, 5)],
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[('Equalize', 0.8, 8), ('Equalize', 0.6, 3)],
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[('PosterizeOriginal', 0.6, 7), ('PosterizeOriginal', 0.6, 6)],
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[('Equalize', 0.4, 7), ('Solarize', 0.2, 4)],
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[('Equalize', 0.4, 4), ('Rotate', 0.8, 8)],
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[('Solarize', 0.6, 3), ('Equalize', 0.6, 7)],
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[('PosterizeOriginal', 0.8, 5), ('Equalize', 1.0, 2)],
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[('Rotate', 0.2, 3), ('Solarize', 0.6, 8)],
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[('Equalize', 0.6, 8), ('PosterizeOriginal', 0.4, 6)],
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[('Rotate', 0.8, 8), ('Color', 0.4, 0)],
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[('Rotate', 0.4, 9), ('Equalize', 0.6, 2)],
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[('Equalize', 0.0, 7), ('Equalize', 0.8, 8)],
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[('Invert', 0.6, 4), ('Equalize', 1.0, 8)],
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[('Color', 0.6, 4), ('Contrast', 1.0, 8)],
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[('Rotate', 0.8, 8), ('Color', 1.0, 2)],
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[('Color', 0.8, 8), ('Solarize', 0.8, 7)],
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[('Sharpness', 0.4, 7), ('Invert', 0.6, 8)],
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[('ShearX', 0.6, 5), ('Equalize', 1.0, 9)],
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[('Color', 0.4, 0), ('Equalize', 0.6, 3)],
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[('Equalize', 0.4, 7), ('Solarize', 0.2, 4)],
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[('Solarize', 0.6, 5), ('AutoContrast', 0.6, 5)],
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[('Invert', 0.6, 4), ('Equalize', 1.0, 8)],
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[('Color', 0.6, 4), ('Contrast', 1.0, 8)],
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[('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' # FIXME I 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' # FIXME I 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
|
|
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
|
|
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 == '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)
|
|
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):
|
|
self.ops = ops
|
|
self.alpha = alpha
|
|
self.width = width
|
|
self.depth = depth
|
|
self.blended = False
|
|
|
|
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 I've verified the results are 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)
|
|
'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.
|
|
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)
|
|
else:
|
|
assert False, 'Unknown AugMix config section'
|
|
ops = augmix_ops(magnitude=magnitude, hparams=hparams)
|
|
return AugMixAugment(ops, alpha=alpha, width=width, depth=depth)
|