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Update Auto/RandAugment comments, README, more.

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* Add a weighted choice option for RandAugment
* Adjust magnitude noise/std naming, config
This commit is contained in:
Ross Wightman 2019-11-22 13:00:24 -08:00
parent 4243f076f1
commit 31453b039e
3 changed files with 102 additions and 28 deletions
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1
README.md
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@ -69,6 +69,7 @@ Several (less common) features that I often utilize in my projects are included.
* Training schedules and techniques that provide competitive results (Cosine LR, Random Erasing, Label Smoothing, etc)
* Mixup (as in https://arxiv.org/abs/1710.09412) - currently implementing/testing
* An inference script that dumps output to CSV is provided as an example
* AutoAugment (https://arxiv.org/abs/1805.09501) and RandAugment (https://arxiv.org/abs/1909.13719) ImageNet configurations modeled after impl for EfficientNet training (https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/autoaugment.py)
## Results

127
timm/data/auto_augment.py
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@ -1,7 +1,7 @@
""" Auto Augment
""" AutoAugment and RandAugment
Implementation adapted from:
https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/autoaugment.py
Papers: https://arxiv.org/abs/1805.09501 and https://arxiv.org/abs/1906.11172
Papers: https://arxiv.org/abs/1805.09501, https://arxiv.org/abs/1906.11172, and https://arxiv.org/abs/1909.13719
Hacked together by Ross Wightman
"""
@ -288,18 +288,18 @@ class AutoAugmentOp:
resample=hparams['interpolation'] if 'interpolation' in hparams else _RANDOM_INTERPOLATION,
)
# If magnitude_noise is > 0, we introduce some randomness
# If magnitude_std is > 0, we introduce some randomness
# in the usually fixed policy and sample magnitude from a normal distribution
# with mean `magnitude` and std-dev of `magnitude_noise`.
# with mean `magnitude` and std-dev of `magnitude_std`.
# NOTE This is my own hack, being tested, not in papers or reference impls.
self.magnitude_noise = self.hparams.get('magnitude_noise', 0)
self.magnitude_std = self.hparams.get('magnitude_std', 0)
def __call__(self, img):
if random.random() > self.prob:
return img
magnitude = self.magnitude
if self.magnitude_noise and self.magnitude_noise > 0:
magnitude = random.gauss(magnitude, self.magnitude_noise)
if self.magnitude_std and self.magnitude_std > 0:
magnitude = random.gauss(magnitude, self.magnitude_std)
magnitude = min(_MAX_LEVEL, max(0, magnitude)) # clip to valid range
level_args = self.level_fn(magnitude, self.hparams) if self.level_fn is not None else tuple()
return self.aug_fn(img, *level_args, **self.kwargs)
@ -464,16 +464,32 @@ class AutoAugment:
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:
key, val = cs[:2]
if key == 'noise':
# noise param injected via hparams for now
hparams.setdefault('magnitude_noise', float(val))
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)
@ -498,6 +514,36 @@ _RAND_TRANSFORMS = [
]
# 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,
'PosterizeTpu': 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
@ -506,33 +552,60 @@ def rand_augment_ops(magnitude=10, hparams=None, transforms=None):
class RandAugment:
def __init__(self, ops, num_layers=2):
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):
for _ in range(self.num_layers):
op = random.choice(self.ops)
# 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):
magnitude = 10
num_layers = 2
"""
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:
key, val = cs[:2]
if key == 'noise':
# noise param injected via hparams for now
hparams.setdefault('magnitude_noise', float(val))
elif key == 'm':
magnitude = int(val)
elif key == 'n':
num_layers = int(val)
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)
return RandAugment(ra_ops, num_layers)
choice_weights = None if weight_idx is None else _select_rand_weights(weight_idx)
return RandAugment(ra_ops, num_layers, choice_weights=choice_weights)

2
timm/data/transforms.py
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@ -190,7 +190,7 @@ def transforms_imagenet_train(
)
if interpolation and interpolation != 'random':
aa_params['interpolation'] = _pil_interp(interpolation)
if 'rand' in auto_augment:
if auto_augment.startswith('rand'):
tfl += [rand_augment_transform(auto_augment, aa_params)]
else:
tfl += [auto_augment_transform(auto_augment, aa_params)]