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Salvador tests
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salvador/transformations.py
Executable file
346
salvador/transformations.py
Executable file
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import torch
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import kornia
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import random
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### Available TF for Dataug ###
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'''
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TF_dict={ #Dataugv4
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## Geometric TF ##
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'Identity' : (lambda x, mag: x),
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'FlipUD' : (lambda x, mag: flipUD(x)),
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'FlipLR' : (lambda x, mag: flipLR(x)),
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'Rotate': (lambda x, mag: rotate(x, angle=torch.tensor([rand_int(mag, maxval=30)for _ in x], device=x.device))),
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'TranslateX': (lambda x, mag: translate(x, translation=torch.tensor([[rand_int(mag, maxval=20), 0] for _ in x], device=x.device))),
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'TranslateY': (lambda x, mag: translate(x, translation=torch.tensor([[0, rand_int(mag, maxval=20)] for _ in x], device=x.device))),
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'ShearX': (lambda x, mag: shear(x, shear=torch.tensor([[rand_float(mag, maxval=0.3), 0] for _ in x], device=x.device))),
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'ShearY': (lambda x, mag: shear(x, shear=torch.tensor([[0, rand_float(mag, maxval=0.3)] for _ in x], device=x.device))),
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## Color TF (Expect image in the range of [0, 1]) ##
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'Contrast': (lambda x, mag: contrast(x, contrast_factor=torch.tensor([rand_float(mag, minval=0.1, maxval=1.9) for _ in x], device=x.device))),
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'Color':(lambda x, mag: color(x, color_factor=torch.tensor([rand_float(mag, minval=0.1, maxval=1.9) for _ in x], device=x.device))),
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'Brightness':(lambda x, mag: brightness(x, brightness_factor=torch.tensor([rand_float(mag, minval=0.1, maxval=1.9) for _ in x], device=x.device))),
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'Sharpness':(lambda x, mag: sharpeness(x, sharpness_factor=torch.tensor([rand_float(mag, minval=0.1, maxval=1.9) for _ in x], device=x.device))),
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'Posterize': (lambda x, mag: posterize(x, bits=torch.tensor([rand_int(mag, minval=4, maxval=8) for _ in x], device=x.device))),
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'Solarize': (lambda x, mag: solarize(x, thresholds=torch.tensor([rand_int(mag,minval=1, maxval=256)/256. for _ in x], device=x.device))) , #=>Image entre [0,1] #Pas opti pour des batch
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#Non fonctionnel
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#'Auto_Contrast': (lambda mag: None), #Pas opti pour des batch (Super lent)
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#'Equalize': (lambda mag: None),
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}
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'''
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'''
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TF_dict={ #Dataugv5 #AutoAugment
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## Geometric TF ##
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'Identity' : (lambda x, mag: x),
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'FlipUD' : (lambda x, mag: flipUD(x)),
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'FlipLR' : (lambda x, mag: flipLR(x)),
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'Rotate': (lambda x, mag: rotate(x, angle=rand_floats(size=x.shape[0], mag=mag, maxval=30))),
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'TranslateX': (lambda x, mag: translate(x, translation=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, maxval=20), zero_pos=0))),
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'TranslateY': (lambda x, mag: translate(x, translation=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, maxval=20), zero_pos=1))),
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'ShearX': (lambda x, mag: shear(x, shear=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, maxval=0.3), zero_pos=0))),
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'ShearY': (lambda x, mag: shear(x, shear=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, maxval=0.3), zero_pos=1))),
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## Color TF (Expect image in the range of [0, 1]) ##
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'Contrast': (lambda x, mag: contrast(x, contrast_factor=rand_floats(size=x.shape[0], mag=mag, minval=0.1, maxval=1.9))),
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'Color':(lambda x, mag: color(x, color_factor=rand_floats(size=x.shape[0], mag=mag, minval=0.1, maxval=1.9))),
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'Brightness':(lambda x, mag: brightness(x, brightness_factor=rand_floats(size=x.shape[0], mag=mag, minval=0.1, maxval=1.9))),
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'Sharpness':(lambda x, mag: sharpeness(x, sharpness_factor=rand_floats(size=x.shape[0], mag=mag, minval=0.1, maxval=1.9))),
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'Posterize': (lambda x, mag: posterize(x, bits=rand_floats(size=x.shape[0], mag=mag, minval=4., maxval=8.))),#Perte du gradient
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'Solarize': (lambda x, mag: solarize(x, thresholds=rand_floats(size=x.shape[0], mag=mag, minval=1/256., maxval=256/256.))), #Perte du gradient #=>Image entre [0,1]
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#Non fonctionnel
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#'Auto_Contrast': (lambda mag: None), #Pas opti pour des batch (Super lent)
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#'Equalize': (lambda mag: None),
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}
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'''
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TF_dict={ #Dataugv5
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## Geometric TF ##
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'Identity' : (lambda x, mag: x),
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'FlipUD' : (lambda x, mag: flipUD(x)),
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'FlipLR' : (lambda x, mag: flipLR(x)),
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'Rotate': (lambda x, mag: rotate(x, angle=rand_floats(size=x.shape[0], mag=mag, maxval=30))),
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'TranslateX': (lambda x, mag: translate(x, translation=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, maxval=20), zero_pos=0))),
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'TranslateY': (lambda x, mag: translate(x, translation=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, maxval=20), zero_pos=1))),
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'ShearX': (lambda x, mag: shear(x, shear=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, maxval=0.3), zero_pos=0))),
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'ShearY': (lambda x, mag: shear(x, shear=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, maxval=0.3), zero_pos=1))),
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## Color TF (Expect image in the range of [0, 1]) ##
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'Contrast': (lambda x, mag: contrast(x, contrast_factor=rand_floats(size=x.shape[0], mag=mag, minval=0.1, maxval=1.9))),
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'Color':(lambda x, mag: color(x, color_factor=rand_floats(size=x.shape[0], mag=mag, minval=0.1, maxval=1.9))),
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'Brightness':(lambda x, mag: brightness(x, brightness_factor=rand_floats(size=x.shape[0], mag=mag, minval=0.1, maxval=1.9))),
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'Sharpness':(lambda x, mag: sharpeness(x, sharpness_factor=rand_floats(size=x.shape[0], mag=mag, minval=0.1, maxval=1.9))),
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'Posterize': (lambda x, mag: posterize(x, bits=rand_floats(size=x.shape[0], mag=mag, minval=4., maxval=8.))),#Perte du gradient
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'Solarize': (lambda x, mag: solarize(x, thresholds=rand_floats(size=x.shape[0], mag=mag, minval=1/256., maxval=256/256.))), #Perte du gradient #=>Image entre [0,1]
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#Color TF (Common mag scale)
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'+Contrast': (lambda x, mag: contrast(x, contrast_factor=rand_floats(size=x.shape[0], mag=mag, minval=1.0, maxval=1.9))),
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'+Color':(lambda x, mag: color(x, color_factor=rand_floats(size=x.shape[0], mag=mag, minval=1.0, maxval=1.9))),
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'+Brightness':(lambda x, mag: brightness(x, brightness_factor=rand_floats(size=x.shape[0], mag=mag, minval=1.0, maxval=1.9))),
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'+Sharpness':(lambda x, mag: sharpeness(x, sharpness_factor=rand_floats(size=x.shape[0], mag=mag, minval=1.0, maxval=1.9))),
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'-Contrast': (lambda x, mag: contrast(x, contrast_factor=invScale_rand_floats(size=x.shape[0], mag=mag, minval=0.1, maxval=1.0))),
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'-Color':(lambda x, mag: color(x, color_factor=invScale_rand_floats(size=x.shape[0], mag=mag, minval=0.1, maxval=1.0))),
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'-Brightness':(lambda x, mag: brightness(x, brightness_factor=invScale_rand_floats(size=x.shape[0], mag=mag, minval=0.1, maxval=1.0))),
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'-Sharpness':(lambda x, mag: sharpeness(x, sharpness_factor=invScale_rand_floats(size=x.shape[0], mag=mag, minval=0.1, maxval=1.0))),
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'=Posterize': (lambda x, mag: posterize(x, bits=invScale_rand_floats(size=x.shape[0], mag=mag, minval=4., maxval=8.))),#Perte du gradient
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'=Solarize': (lambda x, mag: solarize(x, thresholds=invScale_rand_floats(size=x.shape[0], mag=mag, minval=1/256., maxval=256/256.))), #Perte du gradient #=>Image entre [0,1]
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'BRotate': (lambda x, mag: rotate(x, angle=rand_floats(size=x.shape[0], mag=mag, maxval=30*3))),
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'BTranslateX': (lambda x, mag: translate(x, translation=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, maxval=20*3), zero_pos=0))),
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'BTranslateY': (lambda x, mag: translate(x, translation=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, maxval=20*3), zero_pos=1))),
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'BShearX': (lambda x, mag: shear(x, shear=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, maxval=0.3*3), zero_pos=0))),
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'BShearY': (lambda x, mag: shear(x, shear=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, maxval=0.3*3), zero_pos=1))),
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'BadTranslateX': (lambda x, mag: translate(x, translation=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, minval=20*2, maxval=20*3), zero_pos=0))),
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'BadTranslateX_neg': (lambda x, mag: translate(x, translation=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, minval=-20*3, maxval=-20*2), zero_pos=0))),
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'BadTranslateY': (lambda x, mag: translate(x, translation=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, minval=20*2, maxval=20*3), zero_pos=1))),
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'BadTranslateY_neg': (lambda x, mag: translate(x, translation=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, minval=-20*3, maxval=-20*2), zero_pos=1))),
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'BadColor':(lambda x, mag: color(x, color_factor=rand_floats(size=x.shape[0], mag=mag, minval=1.9, maxval=2*2))),
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'BadSharpness':(lambda x, mag: sharpeness(x, sharpness_factor=rand_floats(size=x.shape[0], mag=mag, minval=1.9, maxval=2*2))),
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'BadContrast': (lambda x, mag: contrast(x, contrast_factor=rand_floats(size=x.shape[0], mag=mag, minval=1.9, maxval=2*2))),
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'BadBrightness':(lambda x, mag: brightness(x, brightness_factor=rand_floats(size=x.shape[0], mag=mag, minval=1.9, maxval=2*2))),
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#Non fonctionnel
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#'Auto_Contrast': (lambda mag: None), #Pas opti pour des batch (Super lent)
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#'Equalize': (lambda mag: None),
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}
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TF_no_mag={'Identity', 'FlipUD', 'FlipLR'}
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TF_ignore_mag= TF_no_mag | {'Solarize', 'Posterize'}
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def int_image(float_image): #ATTENTION : legere perte d'info (granularite : 1/256 = 0.0039)
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return (float_image*255.).type(torch.uint8)
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def float_image(int_image):
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return int_image.type(torch.float)/255.
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#def rand_inverse(value):
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# return value if random.random() < 0.5 else -value
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#def rand_int(mag, maxval, minval=None): #[(-maxval,minval), maxval]
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# real_max = int_parameter(mag, maxval=maxval)
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# if not minval : minval = -real_max
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# return random.randint(minval, real_max)
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#def rand_float(mag, maxval, minval=None): #[(-maxval,minval), maxval]
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# real_max = float_parameter(mag, maxval=maxval)
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# if not minval : minval = -real_max
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# return random.uniform(minval, real_max)
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def rand_floats(size, mag, maxval, minval=None): #[(-maxval,minval), maxval]
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real_mag = float_parameter(mag, maxval=maxval)
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if not minval : minval = -real_mag
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#return random.uniform(minval, real_max)
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return minval + (real_mag-minval) * torch.rand(size, device=mag.device) #[min_val, real_mag]
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def invScale_rand_floats(size, mag, maxval, minval):
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#Mag=[0,PARAMETER_MAX] => [PARAMETER_MAX, 0] = [maxval, minval]
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real_mag = float_parameter(float(PARAMETER_MAX) - mag, maxval=maxval-minval)+minval
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return real_mag + (maxval-real_mag) * torch.rand(size, device=mag.device) #[real_mag, max_val]
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def zero_stack(tensor, zero_pos):
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if zero_pos==0:
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return torch.stack((tensor, torch.zeros((tensor.shape[0],), device=tensor.device)), dim=1)
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if zero_pos==1:
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return torch.stack((torch.zeros((tensor.shape[0],), device=tensor.device), tensor), dim=1)
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else:
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raise Exception("Invalid zero_pos : ", zero_pos)
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#https://github.com/tensorflow/models/blob/fc2056bce6ab17eabdc139061fef8f4f2ee763ec/research/autoaugment/augmentation_transforms.py#L137
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PARAMETER_MAX = 1 # What is the max 'level' a transform could be predicted
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def float_parameter(level, maxval):
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"""Helper function to scale `val` between 0 and maxval .
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Args:
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level: Level of the operation that will be between [0, `PARAMETER_MAX`].
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maxval: Maximum value that the operation can have. This will be scaled
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to level/PARAMETER_MAX.
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Returns:
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A float that results from scaling `maxval` according to `level`.
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"""
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#return float(level) * maxval / PARAMETER_MAX
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return (level * maxval / PARAMETER_MAX)#.to(torch.float)
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#def int_parameter(level, maxval): #Perte de gradient
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"""Helper function to scale `val` between 0 and maxval .
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Args:
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level: Level of the operation that will be between [0, `PARAMETER_MAX`].
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maxval: Maximum value that the operation can have. This will be scaled
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to level/PARAMETER_MAX.
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Returns:
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An int that results from scaling `maxval` according to `level`.
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"""
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#return int(level * maxval / PARAMETER_MAX)
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# return (level * maxval / PARAMETER_MAX)
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def flipLR(x):
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device = x.device
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(batch_size, channels, h, w) = x.shape
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M =torch.tensor( [[[-1., 0., w-1],
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[ 0., 1., 0.],
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[ 0., 0., 1.]]], device=device).expand(batch_size,-1,-1)
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# warp the original image by the found transform
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return kornia.warp_perspective(x, M, dsize=(h, w))
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def flipUD(x):
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device = x.device
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(batch_size, channels, h, w) = x.shape
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M =torch.tensor( [[[ 1., 0., 0.],
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[ 0., -1., h-1],
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[ 0., 0., 1.]]], device=device).expand(batch_size,-1,-1)
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# warp the original image by the found transform
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return kornia.warp_perspective(x, M, dsize=(h, w))
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def rotate(x, angle):
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return kornia.rotate(x, angle=angle.type(torch.float)) #Kornia ne supporte pas les int
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def translate(x, translation):
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#print(translation)
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return kornia.translate(x, translation=translation.type(torch.float)) #Kornia ne supporte pas les int
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def shear(x, shear):
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return kornia.shear(x, shear=shear)
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def contrast(x, contrast_factor):
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return kornia.adjust_contrast(x, contrast_factor=contrast_factor) #Expect image in the range of [0, 1]
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#https://github.com/python-pillow/Pillow/blob/master/src/PIL/ImageEnhance.py
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def color(x, color_factor):
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(batch_size, channels, h, w) = x.shape
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gray_x = kornia.rgb_to_grayscale(x)
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gray_x = gray_x.repeat_interleave(channels, dim=1)
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return blend(gray_x, x, color_factor).clamp(min=0.0,max=1.0) #Expect image in the range of [0, 1]
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def brightness(x, brightness_factor):
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device = x.device
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return blend(torch.zeros(x.size(), device=device), x, brightness_factor).clamp(min=0.0,max=1.0) #Expect image in the range of [0, 1]
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def sharpeness(x, sharpness_factor):
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device = x.device
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(batch_size, channels, h, w) = x.shape
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k = torch.tensor([[[ 1., 1., 1.],
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[ 1., 5., 1.],
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[ 1., 1., 1.]]], device=device) #Smooth Filter : https://github.com/python-pillow/Pillow/blob/master/src/PIL/ImageFilter.py
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smooth_x = kornia.filter2D(x, kernel=k, border_type='reflect', normalized=True) #Peut etre necessaire de s'occuper du channel Alhpa differement
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return blend(smooth_x, x, sharpness_factor).clamp(min=0.0,max=1.0) #Expect image in the range of [0, 1]
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#https://github.com/python-pillow/Pillow/blob/master/src/PIL/ImageOps.py
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def posterize(x, bits):
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bits = bits.type(torch.uint8) #Perte du gradient
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x = int_image(x) #Expect image in the range of [0, 1]
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mask = ~(2 ** (8 - bits) - 1).type(torch.uint8)
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(batch_size, channels, h, w) = x.shape
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mask = mask.unsqueeze(dim=1).expand(-1,channels).unsqueeze(dim=2).expand(-1,channels, h).unsqueeze(dim=3).expand(-1,channels, h, w) #Il y a forcement plus simple ...
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return float_image(x & mask)
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def auto_contrast(x): #PAS OPTIMISE POUR DES BATCH #EXTRA LENT
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# Optimisation : Application de LUT efficace / Calcul d'histogramme par batch/channel
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print("Warning : Pas encore check !")
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(batch_size, channels, h, w) = x.shape
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x = int_image(x) #Expect image in the range of [0, 1]
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#print('Start',x[0])
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for im_idx, img in enumerate(x.chunk(batch_size, dim=0)): #Operation par image
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#print(img.shape)
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for chan_idx, chan in enumerate(img.chunk(channels, dim=1)): # Operation par channel
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#print(chan.shape)
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hist = torch.histc(chan, bins=256, min=0, max=255) #PAS DIFFERENTIABLE
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# find lowest/highest samples after preprocessing
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for lo in range(256):
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if hist[lo]:
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break
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for hi in range(255, -1, -1):
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if hist[hi]:
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break
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if hi <= lo:
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# don't bother
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pass
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else:
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scale = 255.0 / (hi - lo)
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offset = -lo * scale
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for ix in range(256):
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n_ix = int(ix * scale + offset)
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if n_ix < 0: n_ix = 0
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elif n_ix > 255: n_ix = 255
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chan[chan==ix]=n_ix
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x[im_idx, chan_idx]=chan
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#print('End',x[0])
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return float_image(x)
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def equalize(x): #PAS OPTIMISE POUR DES BATCH
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raise Exception(self, "not implemented")
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# Optimisation : Application de LUT efficace / Calcul d'histogramme par batch/channel
|
||||
(batch_size, channels, h, w) = x.shape
|
||||
x = int_image(x) #Expect image in the range of [0, 1]
|
||||
#print('Start',x[0])
|
||||
for im_idx, img in enumerate(x.chunk(batch_size, dim=0)): #Operation par image
|
||||
#print(img.shape)
|
||||
for chan_idx, chan in enumerate(img.chunk(channels, dim=1)): # Operation par channel
|
||||
#print(chan.shape)
|
||||
hist = torch.histc(chan, bins=256, min=0, max=255) #PAS DIFFERENTIABLE
|
||||
|
||||
return float_image(x)
|
||||
|
||||
def solarize(x, thresholds):
|
||||
batch_size, channels, h, w = x.shape
|
||||
#imgs=[]
|
||||
#for idx, t in enumerate(thresholds): #Operation par image
|
||||
# mask = x[idx] > t #Perte du gradient
|
||||
#In place
|
||||
# inv_x = 1-x[idx][mask]
|
||||
# x[idx][mask]=inv_x
|
||||
#
|
||||
|
||||
#Out of place
|
||||
# im = x[idx]
|
||||
# inv_x = 1-im[mask]
|
||||
|
||||
# imgs.append(im.masked_scatter(mask,inv_x))
|
||||
|
||||
#idxs=torch.tensor(range(x.shape[0]), device=x.device)
|
||||
#idxs=idxs.unsqueeze(dim=1).expand(-1,channels).unsqueeze(dim=2).expand(-1,channels, h).unsqueeze(dim=3).expand(-1,channels, h, w) #Il y a forcement plus simple ...
|
||||
#x=x.scatter(dim=0, index=idxs, src=torch.stack(imgs))
|
||||
#
|
||||
|
||||
thresholds = thresholds.unsqueeze(dim=1).expand(-1,channels).unsqueeze(dim=2).expand(-1,channels, h).unsqueeze(dim=3).expand(-1,channels, h, w) #Il y a forcement plus simple ...
|
||||
#print(thresholds.grad_fn)
|
||||
x=torch.where(x>thresholds,1-x, x)
|
||||
#print(mask.grad_fn)
|
||||
|
||||
#x=x.min(thresholds)
|
||||
#inv_x = 1-x[mask]
|
||||
#x=x.where(x<thresholds,1-x)
|
||||
#x[mask]=inv_x
|
||||
#x=x.masked_scatter(mask, inv_x)
|
||||
|
||||
return x
|
||||
|
||||
#https://github.com/python-pillow/Pillow/blob/9c78c3f97291bd681bc8637922d6a2fa9415916c/src/PIL/Image.py#L2818
|
||||
def blend(x,y,alpha): #out = image1 * (1.0 - alpha) + image2 * alpha
|
||||
#return kornia.add_weighted(src1=x, alpha=(1-alpha), src2=y, beta=alpha, gamma=0) #out=src1∗alpha+src2∗beta+gamma #Ne fonctionne pas pour des batch de alpha
|
||||
|
||||
if not isinstance(x, torch.Tensor):
|
||||
raise TypeError("x should be a tensor. Got {}".format(type(x)))
|
||||
|
||||
if not isinstance(y, torch.Tensor):
|
||||
raise TypeError("y should be a tensor. Got {}".format(type(y)))
|
||||
|
||||
(batch_size, channels, h, w) = x.shape
|
||||
alpha = alpha.unsqueeze(dim=1).expand(-1,channels).unsqueeze(dim=2).expand(-1,channels, h).unsqueeze(dim=3).expand(-1,channels, h, w) #Il y a forcement plus simple ...
|
||||
res = x*(1-alpha) + y*alpha
|
||||
|
||||
return res
|
||||
Loading…
Add table
Add a link
Reference in a new issue