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Modif interface Data_augv4

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Harle, Antoine (Contracteur) 2019-11-08 11:43:11 -05:00
parent 3ae3e02e59
commit 0066da2e4d
2 changed files with 77 additions and 98 deletions
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169
higher/dataug.py
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@ -320,31 +320,6 @@ class Data_augV4(nn.Module): #Transformations avec mask
#self._TF_matrix={} #self._TF_matrix={}
#self._input_info={'h':0, 'w':0, 'device':None} #Input associe a TF_matrix #self._input_info={'h':0, 'w':0, 'device':None} #Input associe a TF_matrix
'''
self._mag_fct={ #f(mag_normalise)=mag_reelle
## Geometric TF ##
'Identity' : (lambda mag: None),
'FlipUD' : (lambda mag: None),
'FlipLR' : (lambda mag: None),
'Rotate': (lambda mag: random.randint(-int_parameter(mag, maxval=30), int_parameter(mag, maxval=30))),
'TranslateX': (lambda mag: [random.randint(-int_parameter(mag, maxval=20), int_parameter(mag, maxval=20)), 0]),
'TranslateY': (lambda mag: [0, random.randint(-int_parameter(mag, maxval=20), int_parameter(mag, maxval=20))]),
'ShearX': (lambda mag: [random.uniform(-float_parameter(mag, maxval=0.3), float_parameter(mag, maxval=0.3)), 0]),
'ShearY': (lambda mag: [0, random.uniform(-float_parameter(mag, maxval=0.3), float_parameter(mag, maxval=0.3))]),
## Color TF (Expect image in the range of [0, 1]) ##
'Contrast': (lambda mag: random.uniform(0.1, float_parameter(mag, maxval=1.9))),
'Color':(lambda mag: random.uniform(0.1, float_parameter(mag, maxval=1.9))),
'Brightness':(lambda mag: random.uniform(1., float_parameter(mag, maxval=1.9))),
'Sharpness':(lambda mag: random.uniform(0.1, float_parameter(mag, maxval=1.9))),
'Posterize': (lambda mag: random.randint(4, int_parameter(mag, maxval=8))),
'Solarize': (lambda mag: random.randint(1, int_parameter(mag, 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),
}
'''
self._mag_fct = TF_dict self._mag_fct = TF_dict
self._TF=list(self._mag_fct.keys()) self._TF=list(self._mag_fct.keys())
self._nb_tf= len(self._TF) self._nb_tf= len(self._TF)
@ -380,77 +355,8 @@ class Data_augV4(nn.Module): #Transformations avec mask
self._sample = cat_distrib.sample() self._sample = cat_distrib.sample()
## Transformations ## ## Transformations ##
#'''
x = copy.deepcopy(x) #Evite de modifier les echantillons par reference (Problematique pour des utilisations paralleles) x = copy.deepcopy(x) #Evite de modifier les echantillons par reference (Problematique pour des utilisations paralleles)
smps_x=[] x = self.apply_TF(x, self._sample)
masks=[]
for tf_idx in range(self._nb_tf):
mask = self._sample==tf_idx #Create selection mask
smp_x = x[mask] #torch.masked_select() ?
if smp_x.shape[0]!=0: #if there's data to TF
magnitude=self._fixed_mag
tf=self._TF[tf_idx]
## Geometric TF ##
if tf=='Identity':
pass
elif tf=='FlipLR':
smp_x = TF.flipLR(smp_x)
elif tf=='FlipUD':
smp_x = TF.flipUD(smp_x)
elif tf=='Rotate':
smp_x = TF.rotate(smp_x, angle=torch.tensor([self._mag_fct[tf](magnitude) for _ in smp_x], device=device))
elif tf=='TranslateX' or tf=='TranslateY':
smp_x = TF.translate(smp_x, translation=torch.tensor([self._mag_fct[tf](magnitude) for _ in smp_x], device=device))
elif tf=='ShearX' or tf=='ShearY' :
smp_x = TF.shear(smp_x, shear=torch.tensor([self._mag_fct[tf](magnitude) for _ in smp_x], device=device))
## Color TF (Expect image in the range of [0, 1]) ##
elif tf=='Contrast':
smp_x = TF.contrast(smp_x, contrast_factor=torch.tensor([self._mag_fct[tf](magnitude) for _ in smp_x], device=device))
elif tf=='Color':
smp_x = TF.color(smp_x, color_factor=torch.tensor([self._mag_fct[tf](magnitude) for _ in smp_x], device=device))
elif tf=='Brightness':
smp_x = TF.brightness(smp_x, brightness_factor=torch.tensor([self._mag_fct[tf](magnitude) for _ in smp_x], device=device))
elif tf=='Sharpness':
smp_x = TF.sharpeness(smp_x, sharpness_factor=torch.tensor([self._mag_fct[tf](magnitude) for _ in smp_x], device=device))
elif tf=='Posterize':
smp_x = TF.posterize(smp_x, bits=torch.tensor([1 for _ in smp_x], device=device))
elif tf=='Solarize':
smp_x = TF.solarize(smp_x, thresholds=torch.tensor([self._mag_fct[tf](magnitude) for _ in smp_x], device=device))
elif tf=='Equalize':
smp_x = TF.equalize(smp_x)
elif tf=='Auto_Contrast':
smp_x = TF.auto_contrast(smp_x)
else:
raise Exception("Invalid TF requested : ", tf)
x[mask]=smp_x # Refusionner eviter x[mask] : in place
#idx= mask.nonzero()
#print('-'*8)
#print(idx[0], tf_idx)
#print(smp_x[0,])
#x=x.view(-1,3*32*32)
#x=x.scatter(dim=0, index=idx, src=smp_x.view(-1,3*32*32)) #Changement des Tensor mais pas visible sur la visualisation...
#x=x.view(-1,3,32,32)
#print(x[0,])
'''
if len(self._TF_matrix)==0 or self._input_info['h']!=h or self._input_info['w']!=w or self._input_info['device']!=device: #Device different:Pas necessaire de tout recalculer
self.compute_TF_matrix(sample_info={'h': x.shape[2],
'w': x.shape[3],
'device': x.device})
TF_matrix = torch.zeros(batch_size, 3, 3, device=device) #All geom TF
for tf_idx in range(self._nb_tf):
mask = self._sample==tf_idx #Create selection mask
TF_matrix[mask,]=self._TF_matrix[self._TF[tf_idx]]
x=kornia.warp_perspective(x, TF_matrix, dsize=(h, w))
'''
return x return x
''' '''
def compute_TF_matrix(self, magnitude=None, sample_info= None): def compute_TF_matrix(self, magnitude=None, sample_info= None):
@ -489,6 +395,79 @@ class Data_augV4(nn.Module): #Transformations avec mask
else: else:
raise Exception("Invalid TF requested") raise Exception("Invalid TF requested")
''' '''
def apply_TF(self, x, sampled_TF):
device = x.device
smps_x=[]
masks=[]
for tf_idx in range(self._nb_tf):
mask = sampled_TF==tf_idx #Create selection mask
smp_x = x[mask] #torch.masked_select() ?
if smp_x.shape[0]!=0: #if there's data to TF
magnitude=self._fixed_mag
tf=self._TF[tf_idx]
## Geometric TF ##
if tf=='Identity':
pass
elif tf=='FlipLR':
smp_x = TF.flipLR(smp_x)
elif tf=='FlipUD':
smp_x = TF.flipUD(smp_x)
elif tf=='Rotate':
smp_x = TF.rotate(smp_x, angle=torch.tensor([self._mag_fct[tf](magnitude) for _ in smp_x], device=device))
elif tf=='TranslateX' or tf=='TranslateY':
smp_x = TF.translate(smp_x, translation=torch.tensor([self._mag_fct[tf](magnitude) for _ in smp_x], device=device))
elif tf=='ShearX' or tf=='ShearY' :
smp_x = TF.shear(smp_x, shear=torch.tensor([self._mag_fct[tf](magnitude) for _ in smp_x], device=device))
## Color TF (Expect image in the range of [0, 1]) ##
elif tf=='Contrast':
smp_x = TF.contrast(smp_x, contrast_factor=torch.tensor([self._mag_fct[tf](magnitude) for _ in smp_x], device=device))
elif tf=='Color':
smp_x = TF.color(smp_x, color_factor=torch.tensor([self._mag_fct[tf](magnitude) for _ in smp_x], device=device))
elif tf=='Brightness':
smp_x = TF.brightness(smp_x, brightness_factor=torch.tensor([self._mag_fct[tf](magnitude) for _ in smp_x], device=device))
elif tf=='Sharpness':
smp_x = TF.sharpeness(smp_x, sharpness_factor=torch.tensor([self._mag_fct[tf](magnitude) for _ in smp_x], device=device))
elif tf=='Posterize':
smp_x = TF.posterize(smp_x, bits=torch.tensor([1 for _ in smp_x], device=device))
elif tf=='Solarize':
smp_x = TF.solarize(smp_x, thresholds=torch.tensor([self._mag_fct[tf](magnitude) for _ in smp_x], device=device))
elif tf=='Equalize':
smp_x = TF.equalize(smp_x)
elif tf=='Auto_Contrast':
smp_x = TF.auto_contrast(smp_x)
else:
raise Exception("Invalid TF requested : ", tf)
x[mask]=smp_x # Refusionner eviter x[mask] : in place
#idx= mask.nonzero()
#print('-'*8)
#print(idx[0], tf_idx)
#print(smp_x[0,])
#x=x.view(-1,3*32*32)
#x=x.scatter(dim=0, index=idx, src=smp_x.view(-1,3*32*32)) #Changement des Tensor mais pas visible sur la visualisation...
#x=x.view(-1,3,32,32)
#print(x[0,])
'''
if len(self._TF_matrix)==0 or self._input_info['h']!=h or self._input_info['w']!=w or self._input_info['device']!=device: #Device different:Pas necessaire de tout recalculer
self.compute_TF_matrix(sample_info={'h': x.shape[2],
'w': x.shape[3],
'device': x.device})
TF_matrix = torch.zeros(batch_size, 3, 3, device=device) #All geom TF
for tf_idx in range(self._nb_tf):
mask = self._sample==tf_idx #Create selection mask
TF_matrix[mask,]=self._TF_matrix[self._TF[tf_idx]]
x=kornia.warp_perspective(x, TF_matrix, dsize=(h, w))
'''
return x
def adjust_prob(self, soft=False): #Detach from gradient ? def adjust_prob(self, soft=False): #Detach from gradient ?
if soft : if soft :

6
higher/test_dataug.py
View file

@ -646,8 +646,8 @@ def run_dist_dataugV2(model, epochs=1, inner_it=0, dataug_epoch_start=0, print_f
tf = time.process_time() tf = time.process_time()
#viz_sample_data(imgs=xs, labels=ys, fig_name='samples/data_sample_epoch{}_noTF'.format(epoch)) viz_sample_data(imgs=xs, labels=ys, fig_name='samples/data_sample_epoch{}_noTF'.format(epoch))
#viz_sample_data(imgs=aug_model['data_aug'](xs), labels=ys, fig_name='samples/data_sample_epoch{}'.format(epoch)) viz_sample_data(imgs=aug_model['data_aug'](xs), labels=ys, fig_name='samples/data_sample_epoch{}'.format(epoch))
if(not high_grad_track): if(not high_grad_track):
countcopy+=1 countcopy+=1
@ -732,7 +732,7 @@ if __name__ == "__main__":
aug_model = Augmented_model(Data_augV4(TF_dict=TF.TF_dict, mix_dist=0.0), LeNet(3,10)).to(device) aug_model = Augmented_model(Data_augV4(TF_dict=TF.TF_dict, mix_dist=0.0), LeNet(3,10)).to(device)
print(str(aug_model), 'on', device_name) print(str(aug_model), 'on', device_name)
#run_simple_dataug(inner_it=n_inner_iter, epochs=epochs) #run_simple_dataug(inner_it=n_inner_iter, epochs=epochs)
log= run_dist_dataugV2(model=aug_model, epochs=epochs, inner_it=n_inner_iter, dataug_epoch_start=dataug_epoch_start, print_freq=10, loss_patience=10) log= run_dist_dataugV2(model=aug_model, epochs=epochs, inner_it=n_inner_iter, dataug_epoch_start=dataug_epoch_start, print_freq=1, loss_patience=10)
#### ####
plot_res(log, fig_name="res/{}-{} epochs (dataug:{})- {} in_it".format(str(aug_model),epochs,dataug_epoch_start,n_inner_iter)) plot_res(log, fig_name="res/{}-{} epochs (dataug:{})- {} in_it".format(str(aug_model),epochs,dataug_epoch_start,n_inner_iter))