AntoineH/smart_augmentation
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Modif pour shared_mag

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Harle, Antoine (Contracteur) 2019-11-18 14:18:15 -05:00
parent 9ad3f0453b
commit 860d9f1bbb
3 changed files with 10 additions and 8 deletions
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8
higher/dataug.py
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@ -531,7 +531,7 @@ class Data_augV4(nn.Module): #Transformations avec mask
return "Data_augV4(Mix %.1f-%d TF x %d)" % (self._mix_factor, self._nb_tf, self._N_seqTF) return "Data_augV4(Mix %.1f-%d TF x %d)" % (self._mix_factor, self._nb_tf, self._N_seqTF)
class Data_augV5(nn.Module): #Optimisation jointe (mag, proba) class Data_augV5(nn.Module): #Optimisation jointe (mag, proba)
def __init__(self, TF_dict=TF.TF_dict, N_TF=1, mix_dist=0.0, glob_mag=True): def __init__(self, TF_dict=TF.TF_dict, N_TF=1, mix_dist=0.0, shared_mag=True):
super(Data_augV5, self).__init__() super(Data_augV5, self).__init__()
assert len(TF_dict)>0 assert len(TF_dict)>0
@ -542,11 +542,13 @@ class Data_augV5(nn.Module): #Optimisation jointe (mag, proba)
self._nb_tf= len(self._TF) self._nb_tf= len(self._TF)
self._N_seqTF = N_TF self._N_seqTF = N_TF
self._shared_mag = shared_mag
#self._fixed_mag=5 #[0, PARAMETER_MAX] #self._fixed_mag=5 #[0, PARAMETER_MAX]
self._params = nn.ParameterDict({ self._params = nn.ParameterDict({
"prob": nn.Parameter(torch.ones(self._nb_tf)/self._nb_tf), #Distribution prob uniforme "prob": nn.Parameter(torch.ones(self._nb_tf)/self._nb_tf), #Distribution prob uniforme
"mag" : nn.Parameter(torch.tensor(0.5).expand(self._nb_tf) if glob_mag else torch.tensor(0.5).repeat(self._nb_tf)) #[0, PARAMETER_MAX]/10 "mag" : nn.Parameter(torch.tensor(0.5) if shared_mag
else torch.tensor(0.5).expand(self._nb_tf)), #[0, PARAMETER_MAX]/10
}) })
self._samples = [] self._samples = []
@ -591,7 +593,7 @@ class Data_augV5(nn.Module): #Optimisation jointe (mag, proba)
smp_x = x[mask] #torch.masked_select() ? (NEcessite d'expand le mask au meme dim) smp_x = x[mask] #torch.masked_select() ? (NEcessite d'expand le mask au meme dim)
if smp_x.shape[0]!=0: #if there's data to TF if smp_x.shape[0]!=0: #if there's data to TF
magnitude=self._params["mag"][tf_idx]*10 magnitude=self._params["mag"] if self._shared_mag else self._params["mag"][tf_idx]
tf=self._TF[tf_idx] tf=self._TF[tf_idx]
#print(magnitude) #print(magnitude)

2
higher/test_dataug.py
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@ -68,7 +68,7 @@ if __name__ == "__main__":
t0 = time.process_time() t0 = time.process_time()
tf_dict = {k: TF.TF_dict[k] for k in tf_names} tf_dict = {k: TF.TF_dict[k] for k in tf_names}
#tf_dict = TF.TF_dict #tf_dict = TF.TF_dict
aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=1, mix_dist=0.5, glob_mag=False), LeNet(3,10)).to(device) aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=1, mix_dist=0.5, shared_mag=True), LeNet(3,10)).to(device)
#aug_model = Augmented_model(Data_augV4(TF_dict=tf_dict, N_TF=2, mix_dist=0.0), WideResNet(num_classes=10, wrn_size=160)).to(device) #aug_model = Augmented_model(Data_augV4(TF_dict=tf_dict, N_TF=2, mix_dist=0.0), WideResNet(num_classes=10, wrn_size=160)).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)

8
higher/transformations.py
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@ -86,7 +86,7 @@ def zero_stack(tensor, zero_pos):
raise Exception("Invalid zero_pos : ", zero_pos) raise Exception("Invalid zero_pos : ", zero_pos)
#https://github.com/tensorflow/models/blob/fc2056bce6ab17eabdc139061fef8f4f2ee763ec/research/autoaugment/augmentation_transforms.py#L137 #https://github.com/tensorflow/models/blob/fc2056bce6ab17eabdc139061fef8f4f2ee763ec/research/autoaugment/augmentation_transforms.py#L137
PARAMETER_MAX = 10 # What is the max 'level' a transform could be predicted PARAMETER_MAX = 1 # What is the max 'level' a transform could be predicted
def float_parameter(level, maxval): def float_parameter(level, maxval):
"""Helper function to scale `val` between 0 and maxval . """Helper function to scale `val` between 0 and maxval .
Args: Args:
@ -98,7 +98,7 @@ 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.float)
def int_parameter(level, maxval): #Perte de gradient 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 .
@ -135,11 +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.float)) #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.float)) #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)