AntoineH/smart_augmentation
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Ameliorations mineurs + clean up

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Harle, Antoine (Contracteur) 2020-01-17 11:08:59 -05:00
parent cd4b0405b9
commit 5dd0e6ad82
3 changed files with 35 additions and 69 deletions
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41
higher/dataug.py
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@ -537,19 +537,30 @@ class Data_augV5(nn.Module): #Optimisation jointe (mag, proba)
self._data_augmentation = True
#TF
self._TF_dict = TF_dict
self._TF= list(self._TF_dict.keys())
self._nb_tf= len(self._TF)
self._N_seqTF = N_TF
#Mag
self._shared_mag = shared_mag
self._fixed_mag = fixed_mag
#Distribution
self._fixed_prob=fixed_prob
self._samples = []
self._mix_dist = False
if mix_dist != 0.0: #Mix dist
self._mix_dist = True
self._fixed_mix=True
if mix_dist is None: #Learn Mix dist
self._fixed_mix = False
mix_dist=0.5
#Params
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
@ -562,14 +573,6 @@ class Data_augV5(nn.Module): #Optimisation jointe (mag, proba)
if tf in self._TF: 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
#Distribution
self._fixed_prob=fixed_prob
self._samples = []
self._mix_dist = False
if mix_dist != 0.0: #Mix dist
self._mix_dist = True
#self._mix_factor = max(min(mix_dist, 0.999), 0.0)
#Mag regularisation
if not self._fixed_mag:
if self._shared_mag :
@ -595,7 +598,6 @@ class Data_augV5(nn.Module): #Optimisation jointe (mag, proba)
else:
prob = self._params["prob"].detach() if self._fixed_prob else self._params["prob"]
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)
@ -613,14 +615,13 @@ class Data_augV5(nn.Module): #Optimisation jointe (mag, proba)
for tf_idx in range(self._nb_tf):
mask = sampled_TF==tf_idx #Create selection mask
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
magnitude=self._params["mag"] if self._shared_mag else self._params["mag"][tf_idx]
if self._fixed_mag: magnitude=magnitude.detach() #Fmodel tente systematiquement de tracker les gradient de tout les param
tf=self._TF[tf_idx]
#print(magnitude)
#In place
#x[mask]=self._TF_dict[tf](x=smp_x, mag=magnitude)
@ -638,13 +639,11 @@ class Data_augV5(nn.Module): #Optimisation jointe (mag, proba)
if soft :
self._params['prob'].data=F.softmax(self._params['prob'].data, dim=0) #Trop 'soft', bloque en dist uniforme si lr trop faible
else:
#self._params['prob'].data = F.relu(self._params['prob'].data)
self._params['prob'].data = self._params['prob'].data.clamp(min=1/(self._nb_tf*100),max=1.0)
self._params['prob'].data = self._params['prob']/sum(self._params['prob']) #Contrainte sum(p)=1
if not self._fixed_mag:
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)
@ -653,12 +652,6 @@ class Data_augV5(nn.Module): #Optimisation jointe (mag, proba)
if len(self._samples)==0 : return 1 #Pas d'echantillon = pas de ponderation
prob = self._params["prob"].detach() if self._fixed_prob else self._params["prob"]
# 1 seule TF
#self._sample = self._samples[-1]
#w_loss = torch.zeros((self._sample.shape[0],self._nb_tf), device=self._sample.device)
#w_loss.scatter_(dim=1, index=self._sample.view(-1,1), value=1)
#w_loss = w_loss * self._params["prob"]/self._distrib #Ponderation par les proba (divisee par la distrib pour pas diminuer la loss)
#w_loss = torch.sum(w_loss,dim=1)
#Plusieurs TF sequentielles (Attention ne prend pas en compte ordre !)
w_loss = torch.zeros((self._samples[0].shape[0],self._nb_tf), device=self._samples[0].device)
@ -672,7 +665,7 @@ class Data_augV5(nn.Module): #Optimisation jointe (mag, proba)
return w_loss
def reg_loss(self, reg_factor=0.005):
if self._fixed_mag: # or self._fixed_prob: #Pas de regularisation si trop peu de DOF
if self._fixed_mag:
return torch.tensor(0)
else:
#return reg_factor * F.l1_loss(self._params['mag'][self._reg_mask], target=self._reg_tgt, reduction='mean')
@ -1109,9 +1102,6 @@ class Augmented_model(nn.Module):
self.augment(mode=True)
#def initialize(self):
# self._mods['model'].initialize()
def forward(self, x):
return self._mods['model'](self._mods['data_aug'](x))
@ -1128,7 +1118,6 @@ class Augmented_model(nn.Module):
def eval(self):
return self.train(mode=False)
#super(Augmented_model, self).eval()
def items(self):
"""Return an iterable of the ModuleDict key/value pairs.