AntoineH/smart_augmentation
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Ajout fonctionnalitees apprentissage parametre optimisateur + mix dist

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Harle, Antoine (Contracteur) 2020-01-16 16:38:15 -05:00
parent 75901b69b4
commit cd4b0405b9
3 changed files with 70 additions and 37 deletions
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21
higher/dataug.py
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@ -545,11 +545,17 @@ class Data_augV5(nn.Module): #Optimisation jointe (mag, proba)
self._shared_mag = shared_mag
self._fixed_mag = fixed_mag
self._fixed_mix=True
if mix_dist is None: #Learn Mix dist
self._fixed_mix = False
mix_dist=0.5
init_mag = float(TF.PARAMETER_MAX) if self._fixed_mag else float(TF.PARAMETER_MAX)/2
self._params = nn.ParameterDict({
"prob": nn.Parameter(torch.ones(self._nb_tf)/self._nb_tf), #Distribution prob uniforme
"mag" : nn.Parameter(torch.tensor(init_mag) if self._shared_mag
else torch.tensor(init_mag).repeat(self._nb_tf)), #[0, PARAMETER_MAX]
"mix_dist": nn.Parameter(torch.tensor(mix_dist).clamp(min=0.0,max=0.999))
})
for tf in TF.TF_no_grad :
@ -560,9 +566,9 @@ class Data_augV5(nn.Module): #Optimisation jointe (mag, proba)
self._fixed_prob=fixed_prob
self._samples = []
self._mix_dist = False
if mix_dist != 0.0:
if mix_dist != 0.0: #Mix dist
self._mix_dist = True
self._mix_factor = max(min(mix_dist, 0.999), 0.0)
#self._mix_factor = max(min(mix_dist, 0.999), 0.0)
#Mag regularisation
if not self._fixed_mag:
@ -588,7 +594,9 @@ class Data_augV5(nn.Module): #Optimisation jointe (mag, proba)
self._distrib = uniforme_dist
else:
prob = self._params["prob"].detach() if self._fixed_prob else self._params["prob"]
self._distrib = (self._mix_factor*prob+(1-self._mix_factor)*uniforme_dist)#.softmax(dim=1) #Mix distrib reel / uniforme avec mix_factor
mix_dist = self._params["mix_dist"].detach() if self._fixed_mix else self._params["mix_dist"]
#self._distrib = (self._mix_factor*prob+(1-self._mix_factor)*uniforme_dist)#.softmax(dim=1) #Mix distrib reel / uniforme avec mix_factor
self._distrib = (mix_dist*prob+(1-mix_dist)*uniforme_dist)#.softmax(dim=1) #Mix distrib reel / uniforme avec mix_factor
cat_distrib= Categorical(probs=torch.ones((batch_size, self._nb_tf), device=device)*self._distrib)
sample = cat_distrib.sample()
@ -638,6 +646,9 @@ class Data_augV5(nn.Module): #Optimisation jointe (mag, proba)
self._params['mag'].data = self._params['mag'].data.clamp(min=TF.PARAMETER_MIN, max=TF.PARAMETER_MAX)
#self._params['mag'].data = F.relu(self._params['mag'].data) - F.relu(self._params['mag'].data - TF.PARAMETER_MAX)
if not self._fixed_mix:
self._params['mix_dist'].data = self._params['mix_dist'].data.clamp(min=0.0, max=0.999)
def loss_weight(self):
if len(self._samples)==0 : return 1 #Pas d'echantillon = pas de ponderation
@ -692,8 +703,10 @@ class Data_augV5(nn.Module): #Optimisation jointe (mag, proba)
if self._shared_mag: mag_param+= 'Sh'
if not self._mix_dist:
return "Data_augV5(Uniform%s-%dTFx%d-%s)" % (dist_param, self._nb_tf, self._N_seqTF, mag_param)
elif self._fixed_mix:
return "Data_augV5(Mix%.1f%s-%dTFx%d-%s)" % (self._params['mix_dist'].item(),dist_param, self._nb_tf, self._N_seqTF, mag_param)
else:
return "Data_augV5(Mix%.1f%s-%dTFx%d-%s)" % (self._mix_factor,dist_param, self._nb_tf, self._N_seqTF, mag_param)
return "Data_augV5(Mix%s-%dTFx%d-%s)" % (dist_param, self._nb_tf, self._N_seqTF, mag_param)
class Data_augV6(nn.Module): #Optimisation sequentielle #Mauvais resultats