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Retrocompatibilite avec Dataugv4

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Harle, Antoine (Contracteur) 2019-11-18 13:05:50 -05:00
parent 994d657a28
commit 9ad3f0453b
1 changed files with 22 additions and 47 deletions
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69
higher/transformations.py
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@ -4,32 +4,7 @@ import random
### Available TF for Dataug ### ### Available TF for Dataug ###
''' '''
TF_dict={ #f(mag_normalise)=mag_reelle TF_dict={ #Dataugv4
## Geometric TF ##
'Identity' : (lambda mag: None),
'FlipUD' : (lambda mag: None),
'FlipLR' : (lambda mag: None),
'Rotate': (lambda mag: rand_int(mag,maxval=30)),
'TranslateX': (lambda mag: [rand_int(mag,maxval=20), 0]),
'TranslateY': (lambda mag: [0, rand_int(mag,maxval=20)]),
'ShearX': (lambda mag: [rand_float(mag, maxval=0.3), 0]),
'ShearY': (lambda mag: [0, rand_float(mag, maxval=0.3)]),
## Color TF (Expect image in the range of [0, 1]) ##
'Contrast': (lambda mag: rand_float(mag,minval=0.1, maxval=1.9)),
'Color':(lambda mag: rand_float(mag,minval=0.1, maxval=1.9)),
'Brightness':(lambda mag: rand_float(mag,minval=1., maxval=1.9)),
'Sharpness':(lambda mag: rand_float(mag,minval=0.1, maxval=1.9)),
'Posterize': (lambda mag: rand_int(mag,minval=4, maxval=8)),
'Solarize': (lambda mag: rand_int(mag,minval=1, maxval=256)/256.), #=>Image entre [0,1] #Pas opti pour des batch
#Non fonctionnel
#'Auto_Contrast': (lambda mag: None), #Pas opti pour des batch (Super lent)
#'Equalize': (lambda mag: None),
}
'''
'''
TF_dict={
## Geometric TF ## ## Geometric TF ##
'Identity' : (lambda x, mag: x), 'Identity' : (lambda x, mag: x),
'FlipUD' : (lambda x, mag: flipUD(x)), 'FlipUD' : (lambda x, mag: flipUD(x)),
@ -53,25 +28,28 @@ TF_dict={
#'Equalize': (lambda mag: None), #'Equalize': (lambda mag: None),
} }
''' '''
TF_dict={ TF_dict={ #Dataugv5
## Geometric TF ## ## Geometric TF ##
'Identity' : (lambda x, mag: x), 'Identity' : (lambda x, mag: x),
'FlipUD' : (lambda x, mag: flipUD(x)), 'FlipUD' : (lambda x, mag: flipUD(x)),
'FlipLR' : (lambda x, mag: flipLR(x)), 'FlipLR' : (lambda x, mag: flipLR(x)),
'Rotate': (lambda x, mag: rotate(x, angle=rand_float(size=x.shape[0], mag=mag, maxval=30))), 'Rotate': (lambda x, mag: rotate(x, angle=rand_floats(size=x.shape[0], mag=mag, maxval=30))),
'TranslateX': (lambda x, mag: translate(x, translation=zero_stack(rand_float(size=(x.shape[0],), mag=mag, maxval=20), zero_pos=0))), 'TranslateX': (lambda x, mag: translate(x, translation=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, maxval=20), zero_pos=0))),
'TranslateY': (lambda x, mag: translate(x, translation=zero_stack(rand_float(size=(x.shape[0],), mag=mag, maxval=20), zero_pos=1))), 'TranslateY': (lambda x, mag: translate(x, translation=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, maxval=20), zero_pos=1))),
'ShearX': (lambda x, mag: shear(x, shear=zero_stack(rand_float(size=(x.shape[0],), mag=mag, maxval=0.3), zero_pos=0))), 'ShearX': (lambda x, mag: shear(x, shear=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, maxval=0.3), zero_pos=0))),
'ShearY': (lambda x, mag: shear(x, shear=zero_stack(rand_float(size=(x.shape[0],), mag=mag, maxval=0.3), zero_pos=1))), 'ShearY': (lambda x, mag: shear(x, shear=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, maxval=0.3), zero_pos=1))),
## Color TF (Expect image in the range of [0, 1]) ## ## Color TF (Expect image in the range of [0, 1]) ##
'Contrast': (lambda x, mag: contrast(x, contrast_factor=rand_float(size=x.shape[0], mag=mag, minval=0.1, maxval=1.9))), 'Contrast': (lambda x, mag: contrast(x, contrast_factor=rand_floats(size=x.shape[0], mag=mag, minval=0.1, maxval=1.9))),
'Color':(lambda x, mag: color(x, color_factor=rand_float(size=x.shape[0], mag=mag, minval=0.1, maxval=1.9))), 'Color':(lambda x, mag: color(x, color_factor=rand_floats(size=x.shape[0], mag=mag, minval=0.1, maxval=1.9))),
'Brightness':(lambda x, mag: brightness(x, brightness_factor=rand_float(size=x.shape[0], mag=mag, minval=0.1, maxval=1.9))), 'Brightness':(lambda x, mag: brightness(x, brightness_factor=rand_floats(size=x.shape[0], mag=mag, minval=0.1, maxval=1.9))),
'Sharpness':(lambda x, mag: sharpeness(x, sharpness_factor=rand_float(size=x.shape[0], mag=mag, minval=0.1, maxval=1.9))), 'Sharpness':(lambda x, mag: sharpeness(x, sharpness_factor=rand_floats(size=x.shape[0], mag=mag, minval=0.1, maxval=1.9))),
'Posterize': (lambda x, mag: posterize(x, bits=rand_float(size=x.shape[0], mag=mag, minval=4., maxval=8.))),#Perte du gradient 'Posterize': (lambda x, mag: posterize(x, bits=rand_floats(size=x.shape[0], mag=mag, minval=4., maxval=8.))),#Perte du gradient
'Solarize': (lambda x, mag: solarize(x, thresholds=rand_float(size=x.shape[0], mag=mag, minval=1/256., maxval=256/256.))), #Perte du gradient #=>Image entre [0,1] #Pas opti pour des batch '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
#Non fonctionnel
#'Auto_Contrast': (lambda mag: None), #Pas opti pour des batch (Super lent)
#'Equalize': (lambda mag: None),
} }
def int_image(float_image): #ATTENTION : legere perte d'info (granularite : 1/256 = 0.0039) def int_image(float_image): #ATTENTION : legere perte d'info (granularite : 1/256 = 0.0039)
@ -93,7 +71,7 @@ def rand_float(mag, maxval, minval=None): #[(-maxval,minval), maxval]
if not minval : minval = -real_max if not minval : minval = -real_max
return random.uniform(minval, real_max) return random.uniform(minval, real_max)
def rand_float(size, mag, maxval, minval=None): #[(-maxval,minval), maxval] def rand_floats(size, mag, maxval, minval=None): #[(-maxval,minval), maxval]
real_max = float_parameter(mag, maxval=maxval) real_max = float_parameter(mag, maxval=maxval)
if not minval : minval = -real_max if not minval : minval = -real_max
#return random.uniform(minval, real_max) #return random.uniform(minval, real_max)
@ -122,7 +100,7 @@ def float_parameter(level, maxval):
#return float(level) * maxval / PARAMETER_MAX #return float(level) * maxval / PARAMETER_MAX
return (level * maxval / PARAMETER_MAX)#.to(torch.float32) return (level * maxval / PARAMETER_MAX)#.to(torch.float32)
def int_parameter(level, maxval): def int_parameter(level, maxval): #Perte de gradient
"""Helper function to scale `val` between 0 and maxval . """Helper function to scale `val` between 0 and maxval .
Args: Args:
level: Level of the operation that will be between [0, `PARAMETER_MAX`]. level: Level of the operation that will be between [0, `PARAMETER_MAX`].
@ -132,10 +110,7 @@ def int_parameter(level, maxval):
An int that results from scaling `maxval` according to `level`. An int that results from scaling `maxval` according to `level`.
""" """
#return int(level * maxval / PARAMETER_MAX) #return int(level * maxval / PARAMETER_MAX)
print(level) return (level * maxval / PARAMETER_MAX)
res= (level * maxval / PARAMETER_MAX).to(torch.int8).requires_grad_()#.type(torch.int8)
print(res)
return res
def flipLR(x): def flipLR(x):
device = x.device device = x.device
@ -160,11 +135,11 @@ def flipUD(x):
return kornia.warp_perspective(x, M, dsize=(h, w)) return kornia.warp_perspective(x, M, dsize=(h, w))
def rotate(x, angle): def rotate(x, angle):
return kornia.rotate(x, angle=angle)#.type(torch.float32)) #Kornia ne supporte pas les int return kornia.rotate(x, angle=angle.type(torch.float32)) #Kornia ne supporte pas les int
def translate(x, translation): def translate(x, translation):
#print(translation) #print(translation)
return kornia.translate(x, translation=translation)#.type(torch.float32)) #Kornia ne supporte pas les int return kornia.translate(x, translation=translation.type(torch.float32)) #Kornia ne supporte pas les int
def shear(x, shear): def shear(x, shear):
return kornia.shear(x, shear=shear) return kornia.shear(x, shear=shear)