leger chgt mag regularisation

higher/datasets.py

@@ -38,8 +38,8 @@ data_test = torchvision.datasets.CIFAR10(
     "./data", train=False, download=True, transform=transform
 )
 #'''
-train_subset_indices=range(int(len(data_train)/2))
-#train_subset_indices=range(BATCH_SIZE*10)
+#train_subset_indices=range(int(len(data_train)/2))
+train_subset_indices=range(BATCH_SIZE*10)
 val_subset_indices=range(int(len(data_train)/2),len(data_train))
 
 dl_train = torch.utils.data.DataLoader(data_train, batch_size=BATCH_SIZE, shuffle=False, sampler=SubsetRandomSampler(train_subset_indices))

higher/dataug.py

@@ -552,6 +552,8 @@ class Data_augV5(nn.Module): #Optimisation jointe (mag, proba)
                             else torch.tensor(0.5).expand(self._nb_tf)), #[0, PARAMETER_MAX]
         })
 
+        #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._samples = []
         self._mix_dist = False
@@ -561,8 +563,7 @@ class Data_augV5(nn.Module): #Optimisation jointe (mag, proba)
 
         #Mag regularisation
         if not self._fixed_mag:
-            ignore={'Identity', 'FlipUD', 'FlipLR', 'Solarize', 'Posterize'}
-            self._reg_mask=[self._TF.index(t) for t in self._TF if t not in ignore]
+            self._reg_mask=[self._TF.index(t) for t in self._TF if t not in TF.TF_ignore_mag]
             self._reg_tgt = torch.full(size=(len(self._reg_mask),), fill_value=TF.PARAMETER_MAX) #Encourage amplitude max
 
     def forward(self, x):

higher/test_dataug.py

@@ -38,7 +38,7 @@ else:
 if __name__ == "__main__":
 
     n_inner_iter = 10
-    epochs = 200
+    epochs = 2
     dataug_epoch_start=0
 
     #### Classic ####
@@ -84,7 +84,7 @@ if __name__ == "__main__":
         json.dump(out, f, indent=True)
         print('Log :\"',f.name, '\" saved !')
 
-    print('TF influence', TF_influence(log))
+    plot_TF_influence(log, param_names=tf_names)
     print('Execution Time : %.00f '%(time.process_time() - t0))
     print('-'*9)
     #'''

higher/transformations.py

@@ -52,6 +52,9 @@ TF_dict={ #Dataugv5
   #'Equalize': (lambda mag: None),
 }
 
+TF_no_mag={'Identity', 'FlipUD', 'FlipLR'}
+TF_ignore_mag= TF_no_mag | {'Solarize', 'Posterize'}
+
 def int_image(float_image): #ATTENTION : legere perte d'info (granularite : 1/256 = 0.0039)
   return (float_image*255.).type(torch.uint8)
 

higher/utils.py

@@ -254,11 +254,20 @@ def print_torch_mem(add_info=''):
         torch.cuda.max_memory_cached()/ mega_bytes)
     print(string)
 
-def TF_influence(log):
+def plot_TF_influence(log, fig_name='TF_influence', param_names=None):
     proba=[[x["param"][idx]['p'] for x in log] for idx, _ in enumerate(log[0]["param"])]
     mag=[[x["param"][idx]['m'] for x in log] for idx, _ in enumerate(log[0]["param"])]
 
-    return np.mean(proba, axis=1)*np.mean(mag, axis=1) #Pourrait etre interessant de multiplier avant le mean
+    plt.figure()
+
+    mean = np.mean(proba, axis=1)*np.mean(mag, axis=1) #Pourrait etre interessant de multiplier avant le mean
+    std = np.std(proba, axis=1)*np.std(mag, axis=1)
+    plt.bar(param_names, mean, yerr=std)
+
+    plt.xticks(rotation=90)
+    fig_name = fig_name.replace('.',',')
+    plt.savefig(fig_name, bbox_inches='tight')
+    plt.close()
 
 class loss_monitor(): #Voir https://github.com/pytorch/ignite
     def __init__(self, patience, end_train=1):