AntoineH/smart_augmentation
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Test doxygen

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Harle, Antoine (Contracteur) 2020-01-24 15:10:08 -05:00
parent f507ff4741
commit 99f15b8946
4 changed files with 54 additions and 25 deletions
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57
higher/smart_aug/transformations.py
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@ -18,15 +18,18 @@ import torch
import kornia
import random
#TF that don't have use for magnitude parameter.
TF_no_mag={'Identity', 'FlipUD', 'FlipLR', 'Random', 'RandBlend'}
#TF which implemetation doesn't allow gradient propagaition.
TF_no_grad={'Solarize', 'Posterize', '=Solarize', '=Posterize'}
#TF for which magnitude should be ignored (Magnitude fixed).
TF_ignore_mag= TF_no_mag | TF_no_grad
TF_no_mag={'Identity', 'FlipUD', 'FlipLR', 'Random', 'RandBlend'} #TF that don't have use for magnitude parameter.
TF_no_grad={'Solarize', 'Posterize', '=Solarize', '=Posterize'} #TF which implemetation doesn't allow gradient propagaition.
TF_ignore_mag= TF_no_mag | TF_no_grad #TF for which magnitude should be ignored (Magnitude fixed).
# What is the max 'level' a transform could be predicted
PARAMETER_MAX = 1
# What is the min 'level' a transform could be predicted
PARAMETER_MIN = 0.1
PARAMETER_MAX = 1 # What is the max 'level' a transform could be predicted
PARAMETER_MIN = 0.1 # What is the min 'level' a transform could be predicted
### Available TF for Dataug ###
# Dictionnary mapping tranformations identifiers to their function.
# 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.
TF_dict={ #Dataugv5+
@ -416,13 +419,16 @@ def blend(x,y,alpha):
return res
#Not working
def auto_contrast(x): #PAS OPTIMISE POUR DES BATCH #EXTRA LENT
# Optimisation : Application de LUT efficace / Calcul d'histogramme par batch/channel
print("Warning : Pas encore check !")
(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
def auto_contrast(x):
"""NOT TESTED - EXTRA SLOW
"""
# Optimisation : Application de LUT efficace / Calcul d'histogramme par batch/channel
print("Warning : Pas encore check !")
(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)
@ -449,19 +455,22 @@ def auto_contrast(x): #PAS OPTIMISE POUR DES BATCH #EXTRA LENT
chan[chan==ix]=n_ix
x[im_idx, chan_idx]=chan
#print('End',x[0])
return float_image(x)
#print('End',x[0])
return float_image(x)
def equalize(x): #PAS OPTIMISE POUR DES BATCH
raise Exception(self, "not implemented")
# 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
def equalize(x):
""" NOT WORKING
"""
raise Exception(self, "not implemented")
# 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)
return float_image(x)