mirror of
https://github.com/AntoineHX/smart_augmentation.git
synced 2025-05-04 12:10:45 +02:00
Ajout des B_TF + chgt mag initiale
This commit is contained in:
Harle, Antoine (Contracteur)
parent
758d6e9b78
commit
12ff3ee115
3 changed files with 29 additions and 17 deletions
|
|
@ -548,8 +548,8 @@ class Data_augV5(nn.Module): #Optimisation jointe (mag, proba)
|
|||
#self._fixed_mag=5 #[0, PARAMETER_MAX]
|
||||
self._params = nn.ParameterDict({
|
||||
"prob": nn.Parameter(torch.ones(self._nb_tf)/self._nb_tf), #Distribution prob uniforme
|
||||
"mag" : nn.Parameter(torch.tensor(0.5) if self._shared_mag
|
||||
else torch.tensor(0.5).expand(self._nb_tf)), #[0, PARAMETER_MAX]
|
||||
"mag" : nn.Parameter(torch.tensor(float(TF.PARAMETER_MAX)) if self._shared_mag
|
||||
else torch.tensor(float(TF.PARAMETER_MAX)).expand(self._nb_tf)), #[0, PARAMETER_MAX]
|
||||
})
|
||||
|
||||
#for t in TF.TF_no_mag: self._params['mag'][self._TF.index(t)].data-=self._params['mag'][self._TF.index(t)].data #Mag inutile pour les TF ignore_mag
|
||||
|
|
@ -631,7 +631,7 @@ class Data_augV5(nn.Module): #Optimisation jointe (mag, proba)
|
|||
self._params['prob'].data = self._params['prob']/sum(self._params['prob']) #Contrainte sum(p)=1
|
||||
|
||||
#self._params['mag'].data = self._params['mag'].data.clamp(min=0.0,max=TF.PARAMETER_MAX) #Bloque une fois au extreme
|
||||
self._params['mag'].data = F.relu(self._params['mag'].data) - F.relu(self._params['mag'].data - TF.PARAMETER_MAX) #Bloque a PARAMETER_MAX
|
||||
self._params['mag'].data = F.relu(self._params['mag'].data) - F.relu(self._params['mag'].data - TF.PARAMETER_MAX)
|
||||
|
||||
def loss_weight(self):
|
||||
# 1 seule TF
|
||||
|
|
|
|||
|
|
@ -6,21 +6,27 @@ from train_utils import *
|
|||
tf_names = [
|
||||
## Geometric TF ##
|
||||
'Identity',
|
||||
#'FlipUD',
|
||||
'FlipUD',
|
||||
'FlipLR',
|
||||
'Rotate',
|
||||
#'TranslateX',
|
||||
#'TranslateY',
|
||||
'TranslateX',
|
||||
'TranslateY',
|
||||
'ShearX',
|
||||
'ShearY',
|
||||
|
||||
#'BRotate',
|
||||
#'BTranslateX',
|
||||
#'BTranslateY',
|
||||
#'BShearX',
|
||||
#'BShearY',
|
||||
|
||||
## Color TF (Expect image in the range of [0, 1]) ##
|
||||
'Contrast',
|
||||
'Color',
|
||||
'Brightness',
|
||||
'Sharpness',
|
||||
'Posterize',
|
||||
#'Solarize', #=>Image entre [0,1] #Pas opti pour des batch
|
||||
'Solarize', #=>Image entre [0,1] #Pas opti pour des batch
|
||||
|
||||
#Non fonctionnel
|
||||
#'Auto_Contrast', #Pas opti pour des batch (Super lent)
|
||||
|
|
@ -37,7 +43,7 @@ else:
|
|||
##########################################
|
||||
if __name__ == "__main__":
|
||||
|
||||
n_inner_iter = 0
|
||||
n_inner_iter = 10
|
||||
epochs = 100
|
||||
dataug_epoch_start=0
|
||||
|
||||
|
|
@ -68,11 +74,11 @@ if __name__ == "__main__":
|
|||
t0 = time.process_time()
|
||||
tf_dict = {k: TF.TF_dict[k] for k in tf_names}
|
||||
#tf_dict = TF.TF_dict
|
||||
aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=2, mix_dist=0.5, fixed_mag=True, shared_mag=True), LeNet(3,10)).to(device)
|
||||
aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=1, mix_dist=0.0, fixed_mag=False, shared_mag=False), LeNet(3,10)).to(device)
|
||||
#aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=2, mix_dist=0.5, fixed_mag=True, shared_mag=True), WideResNet(num_classes=10, wrn_size=160)).to(device)
|
||||
print(str(aug_model), 'on', device_name)
|
||||
#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=None)
|
||||
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=None)
|
||||
|
||||
####
|
||||
print('-'*9)
|
||||
|
|
@ -91,16 +97,16 @@ if __name__ == "__main__":
|
|||
'''
|
||||
#### TF tests ####
|
||||
#'''
|
||||
res_folder="res/good_TF_tests/"
|
||||
epochs= 100
|
||||
inner_its = [0, 10]
|
||||
dist_mix = [0.0, 0.5]
|
||||
res_folder="res/brutus-tests/"
|
||||
epochs= 150
|
||||
inner_its = [0, 1, 10]
|
||||
dist_mix = [0.0, 0.5, 1]
|
||||
dataug_epoch_starts= [0]
|
||||
tf_dict = {k: TF.TF_dict[k] for k in tf_names}
|
||||
TF_nb = [len(tf_dict)] #range(10,len(TF.TF_dict)+1) #[len(TF.TF_dict)]
|
||||
N_seq_TF= [1]#[1, 2, 3, 4]
|
||||
mag_setup = [(True,True)]#[(True,True), (False,True), (False, False)]
|
||||
nb_run= 1
|
||||
N_seq_TF= [1, 2, 3, 4]
|
||||
mag_setup = [(True,True), (False,True), (False, False)]
|
||||
nb_run= 3
|
||||
|
||||
try:
|
||||
os.mkdir(res_folder)
|
||||
|
|
|
|||
|
|
@ -39,6 +39,12 @@ TF_dict={ #Dataugv5
|
|||
'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_floats(size=(x.shape[0],), mag=mag, maxval=0.3), zero_pos=1))),
|
||||
|
||||
'BRotate': (lambda x, mag: rotate(x, angle=rand_floats(size=x.shape[0], mag=mag, maxval=30*3))),
|
||||
'BTranslateX': (lambda x, mag: translate(x, translation=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, maxval=20*3), zero_pos=0))),
|
||||
'BTranslateY': (lambda x, mag: translate(x, translation=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, maxval=20*3), zero_pos=1))),
|
||||
'BShearX': (lambda x, mag: shear(x, shear=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, maxval=0.3*3), zero_pos=0))),
|
||||
'BShearY': (lambda x, mag: shear(x, shear=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, maxval=0.3*3), zero_pos=1))),
|
||||
|
||||
## Color TF (Expect image in the range of [0, 1]) ##
|
||||
'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_floats(size=x.shape[0], mag=mag, minval=0.1, maxval=1.9))),
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue