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https://github.com/huggingface/pytorch-image-models.git
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Add mmax config key to auto_augment for increasing upper bound of RandAugment magnitude beyond 10. Make AugMix uniform sampling default not override config setting.
This commit is contained in:
Ross Wightman
parent
1042b8a146
commit
3cdaf5ed56
@ -29,9 +29,7 @@ _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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_LEVEL_DENOM = 10. # denominator for conversion from 'Mx' magnitude scale to fractional aug level for op arguments
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_HPARAMS_DEFAULT = dict(
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translate_const=250,
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@ -179,34 +177,34 @@ def _randomly_negate(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 = (level / _LEVEL_DENOM) * 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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return (level / _LEVEL_DENOM) * 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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# range [0.1, 1.9] if level <= _LEVEL_DENOM
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level = (level / _LEVEL_DENOM) * .9
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level = max(0.1, 1.0 + _randomly_negate(level)) # keep it >= 0.1
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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 = (level / _LEVEL_DENOM) * 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 = (level / _LEVEL_DENOM) * float(translate_const)
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level = _randomly_negate(level)
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return level,
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@ -214,7 +212,7 @@ def _translate_abs_level_to_arg(level, hparams):
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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 = (level / _LEVEL_DENOM) * translate_pct
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level = _randomly_negate(level)
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return level,
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@ -223,7 +221,7 @@ 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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return int((level / _LEVEL_DENOM) * 4),
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def _posterize_increasing_level_to_arg(level, hparams):
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@ -237,13 +235,13 @@ 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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return int((level / _LEVEL_DENOM) * 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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return int((level / _LEVEL_DENOM) * 256),
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def _solarize_increasing_level_to_arg(level, _hparams):
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@ -254,7 +252,7 @@ def _solarize_increasing_level_to_arg(level, _hparams):
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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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return int((level / _LEVEL_DENOM) * 110),
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LEVEL_TO_ARG = {
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@ -334,17 +332,22 @@ class AugmentOp:
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# NOTE This is my own hack, being tested, not in papers or reference impls.
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# If magnitude_std is inf, we sample magnitude from a uniform distribution
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self.magnitude_std = self.hparams.get('magnitude_std', 0)
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self.magnitude_max = self.hparams.get('magnitude_max', None)
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def __call__(self, img):
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if self.prob < 1.0 and 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:
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if self.magnitude_std > 0:
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# magnitude randomization enabled
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if self.magnitude_std == float('inf'):
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magnitude = random.uniform(0, magnitude)
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elif 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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# default upper_bound for the timm RA impl is _LEVEL_DENOM (10)
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# setting magnitude_max overrides this to allow M > 10 (behaviour closer to Google TF RA impl)
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upper_bound = self.magnitude_max or _LEVEL_DENOM
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magnitude = max(0., min(magnitude, upper_bound))
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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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@ -642,7 +645,8 @@ def rand_augment_transform(config_str, hparams):
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'm' - integer magnitude of rand augment
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'n' - integer num layers (number of transform ops selected per image)
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'w' - integer probabiliy weight index (index of a set of weights to influence choice of op)
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'mstd' - float std deviation of magnitude noise applied
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'mstd' - float std deviation of magnitude noise applied, or uniform sampling if infinity (or > 100)
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'mmax' - set upper bound for magnitude to something other than default of _LEVEL_DENOM (10)
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'inc' - integer (bool), use augmentations that increase in severity with magnitude (default: 0)
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Ex 'rand-m9-n3-mstd0.5' results in RandAugment with magnitude 9, num_layers 3, magnitude_std 0.5
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'rand-mstd1-w0' results in magnitude_std 1.0, weights 0, default magnitude of 10 and num_layers 2
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@ -651,7 +655,7 @@ def rand_augment_transform(config_str, hparams):
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:return: A PyTorch compatible Transform
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"""
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magnitude = _MAX_LEVEL # default to _MAX_LEVEL for magnitude (currently 10)
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magnitude = _LEVEL_DENOM # default to _LEVEL_DENOM for magnitude (currently 10)
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num_layers = 2 # default to 2 ops per image
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weight_idx = None # default to no probability weights for op choice
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transforms = _RAND_TRANSFORMS
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@ -664,8 +668,15 @@ def rand_augment_transform(config_str, hparams):
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continue
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key, val = cs[:2]
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if key == 'mstd':
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# noise param injected via hparams for now
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hparams.setdefault('magnitude_std', float(val))
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# noise param / randomization of magnitude values
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mstd = float(val)
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if mstd > 100:
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# use uniform sampling in 0 to magnitude if mstd is > 100
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mstd = float('inf')
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hparams.setdefault('magnitude_std', mstd)
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elif key == 'mmax':
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# clip magnitude between [0, mmax] instead of default [0, _LEVEL_DENOM]
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hparams.setdefault('magnitude_max', int(val))
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elif key == 'inc':
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if bool(val):
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transforms = _RAND_INCREASING_TRANSFORMS
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@ -794,7 +805,6 @@ def augment_and_mix_transform(config_str, hparams):
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depth = -1
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alpha = 1.
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blended = False
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hparams['magnitude_std'] = float('inf')
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config = config_str.split('-')
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assert config[0] == 'augmix'
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config = config[1:]
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@ -818,5 +828,6 @@ def augment_and_mix_transform(config_str, hparams):
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blended = bool(val)
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else:
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assert False, 'Unknown AugMix config section'
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hparams.setdefault('magnitude_std', float('inf')) # default to uniform sampling (if not set via mstd arg)
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ops = augmix_ops(magnitude=magnitude, hparams=hparams)
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return AugMixAugment(ops, alpha=alpha, width=width, depth=depth, blended=blended)
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