Evite redefinition inutile de prob dist + Fix mineur transformation

higher/smart_aug/dataug.py

@@ -19,7 +19,7 @@ import copy
 
 import transformations as TF
 
-
+### Data augmenter ###
 class Data_augV5(nn.Module): #Optimisation jointe (mag, proba)
     """Data augmentation module with learnable parameters.
 
@@ -125,7 +125,6 @@ class Data_augV5(nn.Module): #Optimisation jointe (mag, proba)
 
             x = copy.deepcopy(x) #Evite de modifier les echantillons par reference (Problematique pour des utilisations paralleles)
             
-            for _ in range(self._N_seqTF):
             ## Echantillonage ##
             uniforme_dist = torch.ones(1,self._nb_tf,device=device).softmax(dim=1)
 
@@ -137,6 +136,9 @@ class Data_augV5(nn.Module): #Optimisation jointe (mag, proba)
                 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)
+            
+            for _ in range(self._N_seqTF):
+                
                 sample = cat_distrib.sample()
                 self._samples.append(sample)
 
@@ -205,12 +207,12 @@ class Data_augV5(nn.Module): #Optimisation jointe (mag, proba)
             Compute the weights for the loss of each inputs depending on wich TF was applied to them.
             Should be applied to the loss before reduction.
             
-            Do nottake into account the order of application of the TF. See Data_augV7.
+            Do not take into account the order of application of the TF. See Data_augV7.
 
             Returns:
                 Tensor : Loss weights.
         """
-        if len(self._samples)==0 : return 1 #Pas d'echantillon = pas de ponderation
+        if len(self._samples)==0 : return torch.tensor(1, device=self._params["prob"].device) #Pas d'echantillon = pas de ponderation
 
         prob = self._params["prob"].detach() if self._fixed_prob else self._params["prob"]
         
@@ -769,6 +771,7 @@ class RandAug(nn.Module): #RandAugment = UniformFx-MagFxSh + rapide
         """
         return "RandAug(%dTFx%d-Mag%d)" % (self._nb_tf, self._N_seqTF, self.mag)
 
+### Models ###
 import higher
 class Higher_model(nn.Module):
     """Model wrapper for higher gradient tracking.