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Ajout RandAugment
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Harle, Antoine (Contracteur)
parent
3c2022de32
commit
4a7e73088d
4 changed files with 249 additions and 37 deletions
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@ -64,6 +64,27 @@ TF_dict={ #Dataugv5
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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] #Pas opti pour des batch
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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] #Pas opti pour des batch
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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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@ -74,14 +95,11 @@ TF_dict={ #Dataugv5
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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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## 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] #Pas opti pour des batch
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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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@ -111,10 +129,15 @@ def float_image(int_image):
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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_max = float_parameter(mag, maxval=maxval)
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if not minval : minval = -real_max
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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_max-minval) * torch.rand(size, device=mag.device)
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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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@ -139,7 +162,7 @@ def float_parameter(level, maxval):
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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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#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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@ -149,7 +172,7 @@ def int_parameter(level, maxval): #Perte de gradient
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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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# return (level * maxval / PARAMETER_MAX)
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def flipLR(x):
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device = x.device
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@ -279,19 +302,19 @@ def solarize(x, thresholds): #PAS OPTIMISE POUR DES BATCH
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for idx, t in enumerate(thresholds): #Operation par image
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mask = x[idx] > t #Perte du gradient
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#In place
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#inv_x = 1-x[idx][mask]
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#x[idx][mask]=inv_x
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inv_x = 1-x[idx][mask]
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x[idx][mask]=inv_x
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#
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#Out of place
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im = x[idx]
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inv_x = 1-im[mask]
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# im = x[idx]
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# inv_x = 1-im[mask]
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imgs.append(im.masked_scatter(mask,inv_x))
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# imgs.append(im.masked_scatter(mask,inv_x))
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idxs=torch.tensor(range(x.shape[0]), device=x.device)
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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 ...
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x=x.scatter(dim=0, index=idxs, src=torch.stack(imgs))
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#idxs=torch.tensor(range(x.shape[0]), device=x.device)
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#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 ...
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#x=x.scatter(dim=0, index=idxs, src=torch.stack(imgs))
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#
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return x
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