AntoineH/smart_augmentation
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Change default data_augV5

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Harle, Antoine (Contracteur) 2020-02-26 12:18:20 -05:00
parent 7ddb4a41b8
commit 2cbe3d09aa
1 changed files with 4 additions and 4 deletions
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8
higher/smart_aug/dataug.py
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@ -52,13 +52,13 @@ class Data_augV5(nn.Module): #Optimisation jointe (mag, proba)
_reg_tgt (Tensor): Target for the magnitude regularisation. Only used when _fixed_mag is set to false (ie. we learn the magnitudes).
_reg_mask (list): Mask selecting the TF considered for the regularisation.
"""
def __init__(self, TF_dict, N_TF=1, mix_dist=0.0, fixed_prob=False, fixed_mag=True, shared_mag=True, TF_ignore_mag=TF.TF_ignore_mag):
def __init__(self, TF_dict, N_TF=1, mix_dist=0.5, fixed_prob=False, fixed_mag=True, shared_mag=True, TF_ignore_mag=TF.TF_ignore_mag):
"""Init Data_augv5.
Args:
TF_dict (dict): A dictionnary containing the data transformations (TF) to be applied. (default: use all available TF from transformations.py)
N_TF (int): Number of TF to be applied sequentially to each inputs. (default: 1)
mix_dist (float): Proportion [0.0, 1.0] of the real distribution used for sampling/selection of the TF. Distribution = (1-mix_dist)*Uniform_distribution + mix_dist*Real_distribution. If None is given, try to learn this parameter. (default: 0)
mix_dist (float): Proportion [0.0, 1.0] of the real distribution used for sampling/selection of the TF. Distribution = (1-mix_dist)*Uniform_distribution + mix_dist*Real_distribution. If None is given, try to learn this parameter. (default: 0.5)
fixed_prob (bool): Wether to lock the TF probabilies. (default: False)
fixed_mag (bool): Wether to lock the TF magnitudes. (default: True)
shared_mag (bool): Wether to share a single magnitude parameters for all TF. (default: True)
@ -106,8 +106,8 @@ class Data_augV5(nn.Module): #Optimisation jointe (mag, proba)
"mix_dist": nn.Parameter(torch.tensor(mix_dist).clamp(min=0.0,max=0.999))
})
#for tf in TF.TF_no_grad :
# if tf in self._TF: self._params['mag'].data[self._TF.index(tf)]=float(TF.PARAMETER_MAX) #TF fixe a max parameter
for tf in self._TF_ignore_mag :
self._params['mag'].data[self._TF.index(tf)]=float(TF.PARAMETER_MAX) #TF fixe a max parameter
#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
#Mag regularisation