Minor improvement (RandAug)

higher/smart_aug/dataug.py

@@ -187,11 +187,11 @@ class Data_augV5(nn.Module): #Optimisation jointe (mag, proba)
             Ensure that the parameters value stays in the right intevals. This should be called after each update of those parameters.
 
             Args:
-                soft (bool): Wether to use a softmax function for TF probabilites. Not Recommended as it tends to lock the probabilities, preventing them to be learned. (default: False)
+                soft (bool): Wether to use a softmax function for TF probabilites. Tends to lock the probabilities if the learning rate is low, preventing them to be learned. (default: False)
         """
         if not self._fixed_prob:
             if soft :
-                self._params['prob'].data=F.softmax(self._params['prob'].data, dim=0) #Trop 'soft', bloque en dist uniforme si lr trop faible
+                self._params['prob'].data=F.softmax(self._params['prob'].data, dim=0)
             else:
                 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
@@ -269,6 +269,14 @@ class Data_augV5(nn.Module): #Optimisation jointe (mag, proba)
         """
         self._data_augmentation=mode
 
+    def is_augmenting(self):
+        """ Return wether data augmentation is applied.
+
+            Returns:
+                bool : True if data augmentation is applied.
+        """
+        return self._data_augmentation
+
     def __getitem__(self, key):
         """Access to the learnable parameters
         Args:
@@ -588,6 +596,14 @@ class Data_augV7(nn.Module): #Proba sequentielles
         """
         self._data_augmentation=mode
 
+    def is_augmenting(self):
+        """ Return wether data augmentation is applied.
+
+            Returns:
+                bool : True if data augmentation is applied.
+        """
+        return self._data_augmentation
+
     def __getitem__(self, key):
         """Access to the learnable parameters
         Args:
@@ -659,6 +675,8 @@ class RandAug(nn.Module): #RandAugment = UniformFx-MagFxSh + rapide
         })
         self._shared_mag = True
         self._fixed_mag = True
+        self._fixed_prob=True
+        self._fixed_mix=True
 
         self._params['mag'].data = self._params['mag'].data.clamp(min=TF.PARAMETER_MIN, max=TF.PARAMETER_MAX)
 
@@ -753,6 +771,14 @@ class RandAug(nn.Module): #RandAugment = UniformFx-MagFxSh + rapide
         """
         self._data_augmentation=mode
 
+    def is_augmenting(self):
+        """ Return wether data augmentation is applied.
+
+            Returns:
+                bool : True if data augmentation is applied.
+        """
+        return self._data_augmentation
+
     def __getitem__(self, key):
         """Access to the learnable parameters
         Args:
@@ -796,7 +822,7 @@ class Higher_model(nn.Module):
         """
         super(Higher_model, self).__init__()
 
-        self._name = model.__str__()
+        self._name = model.__class__.__name__ #model.__str__()
         self._mods = nn.ModuleDict({
             'original': model,
             'functional': higher.patch.monkeypatch(model, device=None, copy_initial_weights=True)