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.
 
@@ -125,18 +125,20 @@ 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)
             
+            ## Echantillonage ##
+            uniforme_dist = torch.ones(1,self._nb_tf,device=device).softmax(dim=1)
+
+            if not self._mix_dist:
+                self._distrib = uniforme_dist        
+            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 = (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):
-                ## Echantillonage ##
+                
-                uniforme_dist = torch.ones(1,self._nb_tf,device=device).softmax(dim=1)
-
-                if not self._mix_dist:
-                    self._distrib = uniforme_dist        
-                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 = (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()
                 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.

higher/smart_aug/transformations.py

@@ -429,31 +429,31 @@ def auto_contrast(x):
     x = int_image(x) #Expect image in the range of [0, 1]
     #print('Start',x[0])
     for im_idx, img in enumerate(x.chunk(batch_size, dim=0)): #Operation par image
-    #print(img.shape)
+        #print(img.shape)
-    for chan_idx, chan in enumerate(img.chunk(channels, dim=1)): # Operation par channel
+        for chan_idx, chan in enumerate(img.chunk(channels, dim=1)): # Operation par channel
-      #print(chan.shape)
+            #print(chan.shape)
-      hist = torch.histc(chan, bins=256, min=0, max=255) #PAS DIFFERENTIABLE
+            hist = torch.histc(chan, bins=256, min=0, max=255) #PAS DIFFERENTIABLE
 
-      # find lowest/highest samples after preprocessing
+            # find lowest/highest samples after preprocessing
-      for lo in range(256):
+            for lo in range(256):
-          if hist[lo]:
+                if hist[lo]:
-              break
+                    break
-      for hi in range(255, -1, -1):
+            for hi in range(255, -1, -1):
-          if hist[hi]:
+                if hist[hi]:
-              break
+                    break
-      if hi <= lo:
+                if hi <= lo:
-          # don't bother
+                    # don't bother
-          pass
+                    pass
-      else:
+            else:
-        scale = 255.0 / (hi - lo)
+                scale = 255.0 / (hi - lo)
-        offset = -lo * scale
+                offset = -lo * scale
-        for ix in range(256):
+                for ix in range(256):
-          n_ix = int(ix * scale + offset)
+                    n_ix = int(ix * scale + offset)
-          if n_ix < 0: n_ix = 0
+                    if n_ix < 0: n_ix = 0
-          elif n_ix > 255: n_ix = 255
+                    elif n_ix > 255: n_ix = 255
 
-          chan[chan==ix]=n_ix
+            chan[chan==ix]=n_ix
-          x[im_idx, chan_idx]=chan
+            x[im_idx, chan_idx]=chan
 
     #print('End',x[0])
     return float_image(x)
@@ -468,9 +468,9 @@ def equalize(x):
     x = int_image(x) #Expect image in the range of [0, 1]
     #print('Start',x[0])
     for im_idx, img in enumerate(x.chunk(batch_size, dim=0)): #Operation par image
-    #print(img.shape)
+        #print(img.shape)
-    for chan_idx, chan in enumerate(img.chunk(channels, dim=1)): # Operation par channel
+        for chan_idx, chan in enumerate(img.chunk(channels, dim=1)): # Operation par channel
-      #print(chan.shape)
+            #print(chan.shape)
-      hist = torch.histc(chan, bins=256, min=0, max=255) #PAS DIFFERENTIABLE
+            hist = torch.histc(chan, bins=256, min=0, max=255) #PAS DIFFERENTIABLE
 
     return float_image(x)