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Stockage code inutile dans old
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Harle, Antoine (Contracteur)
parent
c1ad787d97
commit
f2019aae4a
12 changed files with 2649 additions and 2407 deletions
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@ -1,58 +1,25 @@
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""" PyTorch implementation of some PIL image transformations.
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Those implementation are thinked to take advantages of batched computation of PyTorch on GPU.
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Based on Kornia library.
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See: https://github.com/kornia/kornia
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And PIL.
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See:
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https://github.com/python-pillow/Pillow/blob/master/src/PIL/ImageOps.py
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https://github.com/python-pillow/Pillow/blob/9c78c3f97291bd681bc8637922d6a2fa9415916c/src/PIL/Image.py#L2818
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Inspired from AutoAugment.
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See: https://github.com/tensorflow/models/blob/fc2056bce6ab17eabdc139061fef8f4f2ee763ec/research/autoaugment/augmentation_transforms.py
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"""
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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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# Dictionnary mapping tranformations identifiers to their function.
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# Each value of the dict should be a lambda function taking a (batch of data, magnitude of transformations) tuple as input and returns a batch of data.
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TF_dict={ #Dataugv5
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@ -112,6 +79,9 @@ TF_no_mag={'Identity', 'FlipUD', 'FlipLR', 'Random', 'RandBlend'} #TF that don't
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TF_no_grad={'Solarize', 'Posterize', '=Solarize', '=Posterize'} #TF which implemetation doesn't allow gradient propagaition.
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TF_ignore_mag= TF_no_mag | TF_no_grad #TF for which magnitude should be ignored (Magnitude fixed).
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PARAMETER_MAX = 1 # What is the max 'level' a transform could be predicted
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PARAMETER_MIN = 0.1 # What is the min 'level' a transform could be predicted
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def int_image(float_image):
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"""Convert a float Tensor/Image to an int Tensor/Image.
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@ -121,10 +91,10 @@ def int_image(float_image):
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This will also result in the loss of the gradient associated to input as gradient cannot be tracked on int Tensor.
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Args:
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float_image (torch.float): Image tensor.
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float_image (FloatTensor): Image tensor.
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Returns:
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(torch.uint8) Converted tensor.
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(ByteTensor) Converted tensor.
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"""
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return (float_image*255.).type(torch.uint8)
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@ -132,10 +102,10 @@ def float_image(int_image):
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"""Convert a int Tensor/Image to an float Tensor/Image.
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Args:
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int_image (torch.uint8): Image tensor.
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int_image (ByteTensor): Image tensor.
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Returns:
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(torch.float) Converted tensor.
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(FloatTensor) Converted tensor.
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"""
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return int_image.type(torch.float)/255.
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@ -162,7 +132,7 @@ def rand_floats(size, mag, maxval, minval=None):
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minval (float): Minimum value that can be generated. (default: -maxval)
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Returns:
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Generated batch of float values between [minval, maxval].
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(Tensor) Generated batch of float values between [minval, maxval].
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"""
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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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@ -170,30 +140,52 @@ def rand_floats(size, mag, maxval, minval=None):
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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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"""Generate a batch of random values.
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Similar to rand_floats() except that the mag is used in an inversed scale.
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Mag:[0,PARAMETER_MAX] => [PARAMETER_MAX, 0]
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Args:
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size (int): Number of value to generate.
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mag (float): Level of the operation that will be between [PARAMETER_MIN, PARAMETER_MAX].
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maxval (float): Maximum value that can be generated. This will be scaled to mag/PARAMETER_MAX.
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minval (float): Minimum value that can be generated. (default: -maxval)
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Returns:
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(Tensor) Generated batch of float values between [minval, maxval].
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"""
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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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"""Add a row of zeros to a Tensor.
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This function is intended to be used with single row Tensor, thus returning a 2 dimension Tensor.
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Args:
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tensor (Tensor): Tensor to be stacked with zeros.
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zero_pos (int): Wheter the zeros should be added before or after the Tensor. Either 0 or 1.
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Returns:
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Stacked Tensor.
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"""
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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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PARAMETER_MIN = 0.1
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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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"""Scale level between 0 and maxval.
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Args:
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level (float): Level of the operation that will be between [PARAMETER_MIN, PARAMETER_MAX].
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maxval: Maximum value that the operation can have. This will be scaled 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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# return (level * maxval / PARAMETER_MAX)
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def flipLR(x):
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"""Flip horizontaly/Left-Right images.
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Args:
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x (Tensor): Batch of images.
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Returns:
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(Tensor): Batch of fliped images.
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"""
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device = x.device
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(batch_size, channels, h, w) = x.shape
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@ -222,6 +222,14 @@ def flipLR(x):
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return kornia.warp_perspective(x, M, dsize=(h, w))
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def flipUD(x):
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"""Flip vertically/Up-Down images.
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Args:
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x (Tensor): Batch of images.
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Returns:
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(Tensor): Batch of fliped images.
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"""
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device = x.device
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(batch_size, channels, h, w) = x.shape
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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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"""Rotate images.
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Args:
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x (Tensor): Batch of images.
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angle (Tensor): Angles (degrees) of rotation for each images.
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Returns:
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(Tensor): Batch of rotated images.
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"""
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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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"""Translate images.
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Args:
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x (Tensor): Batch of images.
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translation (Tensor): Distance (pixels) of translation for each images.
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Returns:
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(Tensor): Batch of translated images.
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"""
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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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"""Shear images.
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Args:
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x (Tensor): Batch of images.
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shear (Tensor): Angle of shear for each images.
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Returns:
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(Tensor): Batch of skewed images.
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"""
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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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"""Adjust contast of images.
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Args:
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x (FloatTensor): Batch of images.
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contrast_factor (FloatTensor): Contrast adjust factor per element in the batch.
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0 generates a compleatly black image, 1 does not modify the input image while any other non-negative number modify the brightness by this factor.
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Returns:
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(Tensor): Batch of adjusted images.
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"""
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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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"""Adjust color of images.
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Args:
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x (Tensor): Batch of images.
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color_factor (Tensor): Color factor for each images.
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0.0 gives a black and white image. A factor of 1.0 gives the original image.
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Returns:
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(Tensor): Batch of adjusted images.
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"""
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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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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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"""Adjust brightness of images.
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Args:
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x (Tensor): Batch of images.
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brightness_factor (Tensor): Brightness factor for each images.
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0.0 gives a black image. A factor of 1.0 gives the original image.
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Returns:
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(Tensor): Batch of adjusted images.
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"""
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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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"""Adjust sharpness of images.
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Args:
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x (Tensor): Batch of images.
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sharpness_factor (Tensor): Sharpness factor for each images.
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0.0 gives a black image. A factor of 1.0 gives the original image.
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Returns:
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(Tensor): Batch of adjusted images.
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"""
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device = x.device
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(batch_size, channels, h, w) = x.shape
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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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@ -365,7 +437,6 @@ def solarize(x, thresholds):
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return x
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#https://github.com/python-pillow/Pillow/blob/9c78c3f97291bd681bc8637922d6a2fa9415916c/src/PIL/Image.py#L2818
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def blend(x,y,alpha): #out = image1 * (1.0 - alpha) + image2 * alpha
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#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
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