def __init__(self, cfg):
# Initializing variables if they don't exist...
# what is the fraction of training samples with scaling augmentation?
cfg['scaleratio'] = cfg.get('scaleratio', 0.6)
# Randomly rotates an image with respect to the image center within the
# range [-rotate_max_deg_abs; rotate_max_deg_abs] to augment training data
cfg['rotate_max_deg_abs'] = cfg.get('rotate_max_deg_abs', 45)
cfg['rotateratio'] = cfg.get(
'rotateratio', 0.4
) # what is the fraction of training samples with rotation augmentation?
# Randomly adds brightness within the range [-brightness_dif, brightness_dif]
# to augment training data
cfg['brightness_dif'] = cfg.get('brightness_dif', 0.3)
cfg['brightnessratio'] = cfg.get(
'brightnessratio', 0.0
) # what is the fraction of training samples with brightness augmentation?
# Randomly applies x = (x - mean) * contrast_factor + mean`` to each
# color channel within the range [contrast_factor_lo, contrast_factor_up]
# to augment training data
cfg['contrast_factor_lo'] = cfg.get('contrast_factor_lo', 0.5)
cfg['contrast_factor_up'] = cfg.get('contrast_factor_up', 2.0)
cfg['contrastratio'] = cfg.get(
'contrastratio', 0.2
) # what is the fraction of training samples with contrast augmentation?
# Randomly adjusts saturation within range 1 + [-saturation_max_dif, saturation_max_dif]
# to augment training data
cfg['saturation_max_dif'] = cfg.get('saturation_max_dif', 0.5)
cfg['saturationratio'] = cfg.get(
'saturationratio', 0.0
) # what is the fraction of training samples with saturation augmentation?
# Randomly applies gaussian noise N(0, noise_sigma^2) to an image
# to augment training data
cfg['noise_sigma'] = cfg.get('noise_sigma', 0.1)
cfg['noiseratio'] = cfg.get(
'noiseratio', 0.0
) # what is the fraction of training samples with noise augmentation?
# Randomly applies gaussian blur to an image with a random window size
# within the range [0, 2 * blur_max_window_size + 1] to augment training data
cfg['blur_max_window_size'] = cfg.get('blur_max_window_size', 10)
cfg['blurratio'] = cfg.get(
'blurratio', 0.2
) # what is the fraction of training samples with blur augmentation?
# Whether image is RGB or RBG. If None, contrast augmentation uses the mean per-channel.
cfg['is_rgb'] = cfg.get('is_rgb', True)
# Clips image to [0, 255] even when data type is not uint8
cfg['to_clip'] = cfg.get('to_clip', True)
# Number of processes to use per core during training
cfg['processratio'] = cfg.get('processratio', 6)
# Number of datapoints to prefetch at a time during training
cfg['num_prefetch'] = cfg.get('num_prefetch', 100)
self.cfg = cfg
self.scaling = RandomResize(
xrange=(self.cfg['scale_jitter_lo'] * self.cfg['global_scale'],
self.cfg['scale_jitter_up'] * self.cfg['global_scale']),
aspect_ratio_thres=0.0)
self.scaling_apply = RandomApplyAug(self.scaling,
self.cfg['scaleratio'])
self.cropping = RandomCropping(
wmin=self.cfg['minsize'],
hmin=self.cfg['minsize'],
wmax=self.cfg['leftwidth'] + self.cfg['rightwidth'] +
self.cfg['minsize'],
hmax=self.cfg['topheight'] + self.cfg['bottomheight'] +
self.cfg['minsize'])
self.rotation = Affine(rotate_max_deg=self.cfg['rotate_max_deg_abs'])
self.brightness = Brightness(self.cfg['brightness_dif'])
self.contrast = Contrast(
(self.cfg['contrast_factor_lo'], self.cfg['contrast_factor_up']),
rgb=self.cfg['is_rgb'],
clip=self.cfg['to_clip'])
self.saturation = Saturation(self.cfg['saturation_max_dif'],
rgb=self.cfg['is_rgb'])
self.gaussian_noise = GaussianNoise(sigma=self.cfg['noise_sigma'])
self.gaussian_blur = GaussianBlur(
max_size=self.cfg['blur_max_window_size'])
self.augmentors = [
RandomApplyAug(self.cropping, self.cfg['cropratio']),
RandomApplyAug(self.rotation, self.cfg['rotateratio']),
RandomApplyAug(self.brightness, self.cfg['brightnessratio']),
RandomApplyAug(self.contrast, self.cfg['contrastratio']),
RandomApplyAug(self.saturation, self.cfg['saturationratio']),
RandomApplyAug(self.gaussian_noise, self.cfg['noiseratio']),
RandomApplyAug(self.gaussian_blur, self.cfg['blurratio']),
self.scaling_apply
]
self.has_gt = True
self.set_shuffle(cfg.shuffle)
p = Pose(cfg=self.cfg, shuffle=self.set_shuffle)
self.data = p.load_dataset()
self.num_images = len(self.data)
df = self.get_dataflow(self.cfg)
df.reset_state()
self.aug = iter(df)
def __init__(self, cfg):
# First, initializing variables (if they don't exist)
# what is the fraction of training samples with scaling augmentation?
cfg["scaleratio"] = cfg.get("scaleratio", 0.6)
# loading defaults for rotation range!
# Randomly rotates an image with respect to the image center within the
# range [-rotate_max_deg_abs; rotate_max_deg_abs] to augment training data
if cfg.get("rotation", True): # i.e. pm 25 degrees
if type(cfg.get("rotation", False)) == int:
cfg["rotation"] = cfg.get("rotation", 25)
else:
cfg["rotation"] = 25
# cfg["rotateratio"] = cfg.get(
# "rotratio", 0.4
# ) # what is the fraction of training samples with rotation augmentation?
else:
cfg["rotratio"] = 0.0
cfg["rotation"] = 0
# Randomly adds brightness within the range [-brightness_dif, brightness_dif]
# to augment training data
cfg["brightness_dif"] = cfg.get("brightness_dif", 0.3)
cfg["brightnessratio"] = cfg.get(
"brightnessratio", 0.0
) # what is the fraction of training samples with brightness augmentation?
# Randomly applies x = (x - mean) * contrast_factor + mean`` to each
# color channel within the range [contrast_factor_lo, contrast_factor_up]
# to augment training data
cfg["contrast_factor_lo"] = cfg.get("contrast_factor_lo", 0.5)
cfg["contrast_factor_up"] = cfg.get("contrast_factor_up", 2.0)
cfg["contrastratio"] = cfg.get(
"contrastratio", 0.2
) # what is the fraction of training samples with contrast augmentation?
# Randomly adjusts saturation within range 1 + [-saturation_max_dif, saturation_max_dif]
# to augment training data
cfg["saturation_max_dif"] = cfg.get("saturation_max_dif", 0.5)
cfg["saturationratio"] = cfg.get(
"saturationratio", 0.0
) # what is the fraction of training samples with saturation augmentation?
# Randomly applies gaussian noise N(0, noise_sigma^2) to an image
# to augment training data
cfg["noise_sigma"] = cfg.get("noise_sigma", 0.1)
cfg["noiseratio"] = cfg.get(
"noiseratio", 0.0
) # what is the fraction of training samples with noise augmentation?
# Randomly applies gaussian blur to an image with a random window size
# within the range [0, 2 * blur_max_window_size + 1] to augment training data
cfg["blur_max_window_size"] = cfg.get("blur_max_window_size", 10)
cfg["blurratio"] = cfg.get(
"blurratio", 0.2
) # what is the fraction of training samples with blur augmentation?
# Whether image is RGB or RBG. If None, contrast augmentation uses the mean per-channel.
cfg["is_rgb"] = cfg.get("is_rgb", True)
# Clips image to [0, 255] even when data type is not uint8
cfg["to_clip"] = cfg.get("to_clip", True)
# Number of processes to use per core during training
cfg["processratio"] = cfg.get("processratio", 1)
# Number of datapoints to prefetch at a time during training
cfg["num_prefetch"] = cfg.get("num_prefetch", 50)
# Auto cropping is new (was not in Nature Neuroscience 2018 paper, but introduced in Nath et al. Nat. Protocols 2019)
# and boosts performance by 2X, particularly on challenging datasets, like the cheetah in Nath et al.
# Parameters for augmentation with regard to cropping:
# what is the minimal frames size for cropping plus/minus ie.. [-100,100]^2 for an arb. joint
cfg["minsize"] = cfg.get("minsize", 100)
cfg["leftwidth"] = cfg.get("leftwidth", 400)
cfg["rightwidth"] = cfg.get("rightwidth", 400)
cfg["topheight"] = cfg.get("topheight", 400)
cfg["bottomheight"] = cfg.get("bottomheight", 400)
cfg["cropratio"] = cfg.get("cropratio", 0.4)
self.cfg = cfg
self.scaling = RandomResize(
xrange=(
self.cfg["scale_jitter_lo"] * self.cfg["global_scale"],
self.cfg["scale_jitter_up"] * self.cfg["global_scale"],
),
aspect_ratio_thres=0.0,
)
self.scaling_apply = RandomApplyAug(self.scaling,
self.cfg["scaleratio"])
self.cropping = RandomCropping(
wmin=self.cfg["minsize"],
hmin=self.cfg["minsize"],
wmax=self.cfg["leftwidth"] + self.cfg["rightwidth"] +
self.cfg["minsize"],
hmax=self.cfg["topheight"] + self.cfg["bottomheight"] +
self.cfg["minsize"],
)
self.rotation = Affine(rotate_max_deg=self.cfg["rotation"])
self.brightness = Brightness(self.cfg["brightness_dif"])
self.contrast = Contrast(
(self.cfg["contrast_factor_lo"], self.cfg["contrast_factor_up"]),
rgb=self.cfg["is_rgb"],
clip=self.cfg["to_clip"],
)
self.saturation = Saturation(self.cfg["saturation_max_dif"],
rgb=self.cfg["is_rgb"])
self.gaussian_noise = GaussianNoise(sigma=self.cfg["noise_sigma"])
self.gaussian_blur = GaussianBlur(
max_size=self.cfg["blur_max_window_size"])
self.augmentors = [
RandomApplyAug(self.cropping, self.cfg["cropratio"]),
RandomApplyAug(self.rotation, self.cfg["rotratio"]),
RandomApplyAug(self.brightness, self.cfg["brightnessratio"]),
RandomApplyAug(self.contrast, self.cfg["contrastratio"]),
RandomApplyAug(self.saturation, self.cfg["saturationratio"]),
RandomApplyAug(self.gaussian_noise, self.cfg["noiseratio"]),
RandomApplyAug(self.gaussian_blur, self.cfg["blurratio"]),
self.scaling_apply,
]
self.has_gt = True
self.set_shuffle(cfg.shuffle)
p = Pose(cfg=self.cfg, shuffle=self.set_shuffle)
self.data = p.load_dataset()
self.num_images = len(self.data)
df = self.get_dataflow(self.cfg)
df.reset_state()
self.aug = iter(df)