Classes
class	AugmentOp

class	AugMixAugment

class	AutoAugment

class	RandAugment

Functions
	_interpolation (kwargs)

	_check_args_tf (kwargs)

	shear_x (img, factor, **kwargs)

	shear_y (img, factor, **kwargs)

	translate_x_rel (img, pct, **kwargs)

	translate_y_rel (img, pct, **kwargs)

	translate_x_abs (img, pixels, **kwargs)

	translate_y_abs (img, pixels, **kwargs)

	rotate (img, degrees, **kwargs)

	auto_contrast (img, **__)

	invert (img, **__)

	equalize (img, **__)

	solarize (img, thresh, **__)

	solarize_add (img, add, thresh=128, **__)

	posterize (img, bits_to_keep, **__)

	contrast (img, factor, **__)

	color (img, factor, **__)

	brightness (img, factor, **__)

	sharpness (img, factor, **__)

	_randomly_negate (v)

	_rotate_level_to_arg (level, _hparams)

	_enhance_level_to_arg (level, _hparams)

	_enhance_increasing_level_to_arg (level, _hparams)

	_shear_level_to_arg (level, _hparams)

	_translate_abs_level_to_arg (level, hparams)

	_translate_rel_level_to_arg (level, hparams)

	_posterize_level_to_arg (level, _hparams)

	_posterize_increasing_level_to_arg (level, hparams)

	_posterize_original_level_to_arg (level, _hparams)

	_solarize_level_to_arg (level, _hparams)

	_solarize_increasing_level_to_arg (level, _hparams)

	_solarize_add_level_to_arg (level, _hparams)

	auto_augment_policy_v0 (hparams)

	auto_augment_policy_v0r (hparams)

	auto_augment_policy_original (hparams)

	auto_augment_policy_originalr (hparams)

	auto_augment_policy (name="original")

	auto_augment_transform (config_str, hparams)

	_select_rand_weights (weight_idx=0, transforms=None)

	rand_augment_ops (magnitude=10, hparams=None, transforms=None)

	rand_augment_transform (config_str, hparams)

	augmix_ops (magnitude=10, hparams=None, transforms=None)

	augment_and_mix_transform (config_str, hparams)

Variables
	_PIL_VER = tuple([int(x) for x in PIL.__version__.split('.')[:2]])

tuple	_FILL = (128, 128, 128)

int	_MAX_LEVEL = 10.

	_HPARAMS_DEFAULT

tuple	_RANDOM_INTERPOLATION = (Image.BILINEAR, Image.BICUBIC)

dict	LEVEL_TO_ARG

dict	NAME_TO_OP

list	_RAND_TRANSFORMS

dict	_RAND_CHOICE_WEIGHTS_0

list	_AUGMIX_TRANSFORMS

Detailed Description

@author:  liaoxingyu
@contact: sherlockliao01@gmail.com

Function Documentation

◆ _check_args_tf()

fastreid.data.transforms.autoaugment._check_args_tf ( kwargs )

protected

Definition at line 53 of file autoaugment.py.

def _check_args_tf(kwargs):
    if 'fillcolor' in kwargs and _PIL_VER < (5, 0):
        kwargs.pop('fillcolor')
    kwargs['resample'] = _interpolation(kwargs)
 
 

◆ _enhance_increasing_level_to_arg()

fastreid.data.transforms.autoaugment._enhance_increasing_level_to_arg	(		level,
			_hparams )

protected

Definition at line 193 of file autoaugment.py.

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,
 
 

◆ _enhance_level_to_arg()

fastreid.data.transforms.autoaugment._enhance_level_to_arg	(		level,
			_hparams )

protected

Definition at line 188 of file autoaugment.py.

def _enhance_level_to_arg(level, _hparams):
    # range [0.1, 1.9]
    return (level / _MAX_LEVEL) * 1.8 + 0.1,
 
 

◆ _interpolation()

fastreid.data.transforms.autoaugment._interpolation ( kwargs )

protected

Definition at line 45 of file autoaugment.py.

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

◆ _posterize_increasing_level_to_arg()

fastreid.data.transforms.autoaugment._posterize_increasing_level_to_arg	(		level,
			hparams )

protected

Definition at line 230 of file autoaugment.py.

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],
 
 

◆ _posterize_level_to_arg()

fastreid.data.transforms.autoaugment._posterize_level_to_arg	(		level,
			_hparams )

protected

Definition at line 223 of file autoaugment.py.

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),
 
 

◆ _posterize_original_level_to_arg()

fastreid.data.transforms.autoaugment._posterize_original_level_to_arg	(		level,
			_hparams )

protected

Definition at line 237 of file autoaugment.py.

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,
 
 

◆ _randomly_negate()

fastreid.data.transforms.autoaugment._randomly_negate ( v )

protected

With 50% prob, negate the value

Definition at line 176 of file autoaugment.py.

def _randomly_negate(v):
    """With 50% prob, negate the value"""
    return -v if random.random() > 0.5 else v
 
 

◆ _rotate_level_to_arg()

fastreid.data.transforms.autoaugment._rotate_level_to_arg	(		level,
			_hparams )

protected

Definition at line 181 of file autoaugment.py.

def _rotate_level_to_arg(level, _hparams):
    # range [-30, 30]
    level = (level / _MAX_LEVEL) * 30.
    level = _randomly_negate(level)
    return level,
 
 

◆ _select_rand_weights()

fastreid.data.transforms.autoaugment._select_rand_weights	(		weight_idx = 0,
			transforms = None )

protected

Definition at line 600 of file autoaugment.py.

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
 
 

◆ _shear_level_to_arg()

fastreid.data.transforms.autoaugment._shear_level_to_arg	(		level,
			_hparams )

protected

Definition at line 201 of file autoaugment.py.

def _shear_level_to_arg(level, _hparams):
    # range [-0.3, 0.3]
    level = (level / _MAX_LEVEL) * 0.3
    level = _randomly_negate(level)
    return level,
 
 

◆ _solarize_add_level_to_arg()

fastreid.data.transforms.autoaugment._solarize_add_level_to_arg	(		level,
			_hparams )

protected

Definition at line 256 of file autoaugment.py.

def _solarize_add_level_to_arg(level, _hparams):
    # range [0, 110]
    return int((level / _MAX_LEVEL) * 110),
 
 

◆ _solarize_increasing_level_to_arg()

fastreid.data.transforms.autoaugment._solarize_increasing_level_to_arg	(		level,
			_hparams )

protected

Definition at line 250 of file autoaugment.py.

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],
 
 

◆ _solarize_level_to_arg()

fastreid.data.transforms.autoaugment._solarize_level_to_arg	(		level,
			_hparams )

protected

Definition at line 244 of file autoaugment.py.

def _solarize_level_to_arg(level, _hparams):
    # range [0, 256]
    # intensity/severity of augmentation decreases with level
    return int((level / _MAX_LEVEL) * 256),
 
 

◆ _translate_abs_level_to_arg()

fastreid.data.transforms.autoaugment._translate_abs_level_to_arg	(		level,
			hparams )

protected

Definition at line 208 of file autoaugment.py.

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,
 
 

◆ _translate_rel_level_to_arg()

fastreid.data.transforms.autoaugment._translate_rel_level_to_arg	(		level,
			hparams )

protected

Definition at line 215 of file autoaugment.py.

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,
 
 

◆ augment_and_mix_transform()

fastreid.data.transforms.autoaugment.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

Definition at line 769 of file autoaugment.py.

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)

◆ augmix_ops()

fastreid.data.transforms.autoaugment.augmix_ops	(	magnitude = 10,
		hparams = None,
		transforms = None )

Definition at line 695 of file autoaugment.py.

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]
 
 

◆ auto_augment_policy()

fastreid.data.transforms.autoaugment.auto_augment_policy ( name = "original" )

Definition at line 481 of file autoaugment.py.

def auto_augment_policy(name="original"):
    hparams = _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
 
 

◆ auto_augment_policy_original()

fastreid.data.transforms.autoaugment.auto_augment_policy_original ( hparams )

Definition at line 415 of file autoaugment.py.

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
 
 

◆ auto_augment_policy_originalr()

fastreid.data.transforms.autoaugment.auto_augment_policy_originalr ( hparams )

Definition at line 448 of file autoaugment.py.

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
 
 

◆ auto_augment_policy_v0()

fastreid.data.transforms.autoaugment.auto_augment_policy_v0 ( hparams )

Definition at line 348 of file autoaugment.py.

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
 
 

◆ auto_augment_policy_v0r()

fastreid.data.transforms.autoaugment.auto_augment_policy_v0r ( hparams )

Definition at line 381 of file autoaugment.py.

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
 
 

◆ auto_augment_transform()

fastreid.data.transforms.autoaugment.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

Definition at line 513 of file autoaugment.py.

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)
    return AutoAugment(aa_policy)
 
 

◆ auto_contrast()

fastreid.data.transforms.autoaugment.auto_contrast	(		img,
		**	__ )

Definition at line 123 of file autoaugment.py.

def auto_contrast(img, **__):
    return ImageOps.autocontrast(img)

◆ brightness()

fastreid.data.transforms.autoaugment.brightness	(		img,
			factor,
		**	__ )

Definition at line 168 of file autoaugment.py.

def brightness(img, factor, **__):
    return ImageEnhance.Brightness(img).enhance(factor)

◆ color()

fastreid.data.transforms.autoaugment.color	(		img,
			factor,
		**	__ )

Definition at line 164 of file autoaugment.py.

def color(img, factor, **__):
    return ImageEnhance.Color(img).enhance(factor)

◆ contrast()

fastreid.data.transforms.autoaugment.contrast	(		img,
			factor,
		**	__ )

Definition at line 160 of file autoaugment.py.

def contrast(img, factor, **__):
    return ImageEnhance.Contrast(img).enhance(factor)

◆ equalize()

fastreid.data.transforms.autoaugment.equalize	(		img,
		**	__ )

Definition at line 131 of file autoaugment.py.

def equalize(img, **__):
    return ImageOps.equalize(img)

◆ invert()

fastreid.data.transforms.autoaugment.invert	(		img,
		**	__ )

Definition at line 127 of file autoaugment.py.

def invert(img, **__):
    return ImageOps.invert(img)

◆ posterize()

fastreid.data.transforms.autoaugment.posterize	(		img,
			bits_to_keep,
		**	__ )

Definition at line 154 of file autoaugment.py.

def posterize(img, bits_to_keep, **__):
    if bits_to_keep >= 8:
        return img
    return ImageOps.posterize(img, bits_to_keep)
 
 

◆ rand_augment_ops()

fastreid.data.transforms.autoaugment.rand_augment_ops	(	magnitude = 10,
		hparams = None,
		transforms = None )

Definition at line 609 of file autoaugment.py.

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]
 
 

◆ rand_augment_transform()

fastreid.data.transforms.autoaugment.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

Definition at line 631 of file autoaugment.py.

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)
 
 

◆ rotate()

fastreid.data.transforms.autoaugment.rotate	(		img,
			degrees,
		**	kwargs )

Definition at line 91 of file autoaugment.py.

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'])
 
 

◆ sharpness()

fastreid.data.transforms.autoaugment.sharpness	(		img,
			factor,
		**	__ )

Definition at line 172 of file autoaugment.py.

def sharpness(img, factor, **__):
    return ImageEnhance.Sharpness(img).enhance(factor)

◆ shear_x()

fastreid.data.transforms.autoaugment.shear_x	(		img,
			factor,
		**	kwargs )

Definition at line 59 of file autoaugment.py.

def shear_x(img, factor, **kwargs):
    _check_args_tf(kwargs)
    return img.transform(img.size, Image.AFFINE, (1, factor, 0, 0, 1, 0), **kwargs)
 
 

◆ shear_y()

fastreid.data.transforms.autoaugment.shear_y	(		img,
			factor,
		**	kwargs )

Definition at line 64 of file autoaugment.py.

def shear_y(img, factor, **kwargs):
    _check_args_tf(kwargs)
    return img.transform(img.size, Image.AFFINE, (1, 0, 0, factor, 1, 0), **kwargs)
 
 

◆ solarize()

fastreid.data.transforms.autoaugment.solarize	(		img,
			thresh,
		**	__ )

Definition at line 135 of file autoaugment.py.

def solarize(img, thresh, **__):
    return ImageOps.solarize(img, thresh)

◆ solarize_add()

fastreid.data.transforms.autoaugment.solarize_add	(		img,
			add,
			thresh = 128,
		**	__ )

Definition at line 139 of file autoaugment.py.

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
 
 

◆ translate_x_abs()

fastreid.data.transforms.autoaugment.translate_x_abs	(		img,
			pixels,
		**	kwargs )

Definition at line 81 of file autoaugment.py.

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)
 
 

◆ translate_x_rel()

fastreid.data.transforms.autoaugment.translate_x_rel	(		img,
			pct,
		**	kwargs )

Definition at line 69 of file autoaugment.py.

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)
 
 

◆ translate_y_abs()

fastreid.data.transforms.autoaugment.translate_y_abs	(		img,
			pixels,
		**	kwargs )

Definition at line 86 of file autoaugment.py.

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)
 
 

◆ translate_y_rel()

fastreid.data.transforms.autoaugment.translate_y_rel	(		img,
			pct,
		**	kwargs )

Definition at line 75 of file autoaugment.py.

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)
 
 

Variable Documentation

◆ _AUGMIX_TRANSFORMS

list fastreid.data.transforms.autoaugment._AUGMIX_TRANSFORMS

protected

Initial value:

=  [
    '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',
]

Definition at line 678 of file autoaugment.py.

◆ _FILL

tuple fastreid.data.transforms.autoaugment._FILL = (128, 128, 128)

protected

Definition at line 31 of file autoaugment.py.

◆ _HPARAMS_DEFAULT

fastreid.data.transforms.autoaugment._HPARAMS_DEFAULT

protected

Initial value:

=  dict(
    translate_const=57,
    img_mean=_FILL,
)

Definition at line 37 of file autoaugment.py.

◆ _MAX_LEVEL

int fastreid.data.transforms.autoaugment._MAX_LEVEL = 10.

protected

Definition at line 35 of file autoaugment.py.

◆ _PIL_VER

fastreid.data.transforms.autoaugment._PIL_VER = tuple([int(x) for x in PIL.__version__.split('.')[:2]])

protected

Definition at line 29 of file autoaugment.py.

◆ _RAND_CHOICE_WEIGHTS_0

dict fastreid.data.transforms.autoaugment._RAND_CHOICE_WEIGHTS_0

protected

Initial value:

=  {
    '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,
}

Definition at line 581 of file autoaugment.py.

◆ _RAND_TRANSFORMS

list fastreid.data.transforms.autoaugment._RAND_TRANSFORMS

protected

Initial value:

=  [
    '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
]

Definition at line 541 of file autoaugment.py.

◆ _RANDOM_INTERPOLATION

tuple fastreid.data.transforms.autoaugment._RANDOM_INTERPOLATION = (Image.BILINEAR, Image.BICUBIC)

protected

Definition at line 42 of file autoaugment.py.

◆ LEVEL_TO_ARG

dict fastreid.data.transforms.autoaugment.LEVEL_TO_ARG

Initial value:

=  {
    '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,
}

Definition at line 261 of file autoaugment.py.

◆ NAME_TO_OP

dict fastreid.data.transforms.autoaugment.NAME_TO_OP

Initial value:

=  {
    '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,
}

Definition at line 289 of file autoaugment.py.

Classes

Functions

Variables

Detailed Description

Function Documentation

◆ _check_args_tf()

◆ _enhance_increasing_level_to_arg()

◆ _enhance_level_to_arg()

◆ _interpolation()

◆ _posterize_increasing_level_to_arg()

◆ _posterize_level_to_arg()

◆ _posterize_original_level_to_arg()

◆ _randomly_negate()

◆ _rotate_level_to_arg()

◆ _select_rand_weights()

◆ _shear_level_to_arg()

◆ _solarize_add_level_to_arg()

◆ _solarize_increasing_level_to_arg()

◆ _solarize_level_to_arg()

◆ _translate_abs_level_to_arg()

◆ _translate_rel_level_to_arg()

◆ augment_and_mix_transform()

◆ augmix_ops()

◆ auto_augment_policy()

◆ auto_augment_policy_original()

◆ auto_augment_policy_originalr()

◆ auto_augment_policy_v0()

◆ auto_augment_policy_v0r()

◆ auto_augment_transform()

◆ auto_contrast()

◆ brightness()

◆ color()

◆ contrast()

◆ equalize()

◆ invert()

◆ posterize()

◆ rand_augment_ops()

◆ rand_augment_transform()

◆ rotate()

◆ sharpness()

◆ shear_x()

◆ shear_y()

◆ solarize()

◆ solarize_add()

◆ translate_x_abs()

◆ translate_x_rel()

◆ translate_y_abs()

◆ translate_y_rel()

Variable Documentation

◆ _AUGMIX_TRANSFORMS

◆ _FILL

◆ _HPARAMS_DEFAULT

◆ _MAX_LEVEL

◆ _PIL_VER

◆ _RAND_CHOICE_WEIGHTS_0

◆ _RAND_TRANSFORMS

◆ _RANDOM_INTERPOLATION

◆ LEVEL_TO_ARG

◆ NAME_TO_OP