+controle mag reg + Tests fonction mag reg

higher/smart_aug/benchmark.py

@@ -29,11 +29,11 @@ optim_param={
 
 res_folder="../res/benchmark/CIFAR10/"
 #res_folder="../res/HPsearch/"
-epochs= 400
+epochs= 200
 dataug_epoch_start=0
 nb_run= 1
 
-tf_config='../config/base_tf_config.json'
+tf_config='../config/wide_tf_config.json' #'../config/wide_tf_config.json'#'../config/base_tf_config.json'
 TF_loader=TF_loader()
 tf_dict, tf_ignore_mag =TF_loader.load_TF_dict(tf_config)
 
@@ -55,15 +55,16 @@ if __name__ == "__main__":
 
     ### Benchmark ###
     #'''
-    n_inner_iter = 1#[0, 1]
+    inner_its = [3]
     dist_mix = [0.5]
-    N_seq_TF= [3, 4]
+    N_seq_TF= [3]
     mag_setup = [(False, False)] #[(True, True), (False, False)] #(FxSh, Independant)
 
     for model_type in model_list.keys():
             for model_name in model_list[model_type]:
                 for run in range(nb_run):
 
+                    for n_inner_iter in inner_its:
                      for n_tf in N_seq_TF:
                         for dist in dist_mix:
                             for m_setup in mag_setup:

higher/smart_aug/dataug.py

@@ -115,8 +115,17 @@ class Data_augV5(nn.Module): #Optimisation jointe (mag, proba)
             if  self._shared_mag :
                 self._reg_tgt = torch.tensor(TF.PARAMETER_MAX, dtype=torch.float) #Encourage amplitude max
             else:
-                self._reg_mask=[self._TF.index(t) for t in self._TF if t not in self._TF_ignore_mag]
+                TF_mag=[t for t in self._TF if t not in self._TF_ignore_mag] #TF w/ differentiable mag
+                self._reg_mask=[self._TF.index(t) for t in TF_mag]
                 self._reg_tgt=torch.full(size=(len(self._reg_mask),), fill_value=TF.PARAMETER_MAX) #Encourage amplitude max
+                
+                #Prevent Identity
+                #print(TF.TF_identity)
+                #self._reg_tgt=torch.full(size=(len(self._reg_mask),), fill_value=0.0)
+                #for val in TF.TF_identity.keys():
+                #    idx=[self._reg_mask.index(self._TF.index(t)) for t in TF_mag if t in TF.TF_identity[val]]
+                #    self._reg_tgt[idx]=val
+                #print(TF_mag, self._reg_tgt)
 
     def forward(self, x):
         """ Main method of the Data augmentation module.
@@ -247,7 +256,8 @@ class Data_augV5(nn.Module): #Optimisation jointe (mag, proba)
         else:
             #return reg_factor * F.l1_loss(self._params['mag'][self._reg_mask], target=self._reg_tgt, reduction='mean') 
             mags = self._params['mag'] if self._params['mag'].shape==torch.Size([]) else self._params['mag'][self._reg_mask]
-            max_mag_reg = reg_factor * F.mse_loss(mags, target=self._reg_tgt.to(mags.device), reduction='mean')
+            max_mag_reg = reg_factor * F.mse_loss(mags, target=self._reg_tgt.to(mags.device), reduction='mean') #Close to target ?
+            #max_mag_reg = - reg_factor * F.mse_loss(mags, target=self._reg_tgt.to(mags.device), reduction='mean') #Far from target ?
             return max_mag_reg
 
     def train(self, mode=True):

higher/smart_aug/process_res.py

@@ -10,14 +10,15 @@ if __name__ == "__main__":
         #"res/brutus-tests/log/Aug_mod(Data_augV5(Uniform-14TFx3-MagFxSh)-LeNet)-150epochs(dataug:0)-10in_it-2.json",
         #"res/log/Aug_mod(RandAugUDA(18TFx2-Mag1)-LeNet)-100 epochs (dataug:0)- 0 in_it.json",
     ]
-    files = ["../res/benchmark/CIFAR10/log/RandAugment(N%d-M%.2f)-%s-200 epochs -%s.json"%(3,0.17,'wide_resnet50_2', str(run)) for run in range(3)]
+    #files = ["../res/benchmark/CIFAR10/log/RandAugment(N%d-M%.2f)-%s-200 epochs -%s.json"%(3,0.17,'wide_resnet50_2', str(run)) for run in range(3)]
-    files = ["../res/benchmark/CIFAR100/log/Aug_mod(Data_augV5(Mix%.1f-14TFx%d-Mag)-%s)-200 epochs (dataug:0)- 1 in_it-%s.json"%(0.5,3,'wide_resnet50_2', str(run)) for run in range(3)]
+    #files = ["../res/benchmark/CIFAR10/log/Aug_mod(RandAug(14TFx%d-Mag%d)-%s)-200 epochs (dataug:0)- 0 in_it-%s.json"%(2,1,'resnet18', str(run)) for run in range(1)]
+    files = ["../res/benchmark/CIFAR10/log/Aug_mod(Data_augV5(Mix%.1f-14TFx%d-Mag)-%s)-200 epochs (dataug:0)- 3 in_it-%s.json"%(0.5,3,'resnet18', str(run)) for run in range(1)]
 
     for idx, file in enumerate(files):
         #legend+=str(idx)+'-'+file+'\n'
         with open(file) as json_file:
             data = json.load(json_file)
-            plot_resV2(data['Log'], fig_name=file.replace("/log","").replace(".json",""))#, param_names=data['Param_names'])
+            plot_resV2(data['Log'], fig_name=file.replace("/log","").replace(".json",""), param_names=data['Param_names'], f1=True)
             #plot_TF_influence(data['Log'], param_names=data['Param_names'])
     #'''
     ## Loss , Acc, Proba = f(epoch) ##

higher/smart_aug/test_dataug.py

@@ -34,13 +34,15 @@ if __name__ == "__main__":
     }
     #Parameters
     n_inner_iter = 1
-    epochs = 2
+    epochs = 200
     dataug_epoch_start=0
+    Nb_TF_seq=3
     optim_param={
         'Meta':{
             'optim':'Adam',
             'lr':1e-2, #1e-2
             'epoch_start': 2, #0 / 2 (Resnet?)
+            'reg_factor': 0.001,
         },
         'Inner':{
             'optim': 'SGD',
@@ -110,7 +112,7 @@ if __name__ == "__main__":
 
     #### Augmented Model ####
     if 'aug_model' in tasks:
-        tf_config='../config/base_tf_config.json'
+        tf_config='../config/invScale_wide_tf_config.json'#'../config/base_tf_config.json'
         tf_dict, tf_ignore_mag =TF_loader.load_TF_dict(tf_config)
 
         torch.cuda.reset_max_memory_allocated() #reset_peak_stats
@@ -118,15 +120,17 @@ if __name__ == "__main__":
         t0 = time.perf_counter()
 
         model = Higher_model(model, model_name) #run_dist_dataugV3
-        aug_model = Augmented_model(
+        if n_inner_iter !=0:
-            Data_augV5(TF_dict=tf_dict, 
+            aug_model = Augmented_model(
-                N_TF=1, 
+                Data_augV5(TF_dict=tf_dict, 
-                mix_dist=0.5, 
+                    N_TF=Nb_TF_seq, 
-                fixed_prob=False, 
+                    mix_dist=0.5, 
-                fixed_mag=False, 
+                    fixed_prob=False, 
-                shared_mag=False, 
+                    fixed_mag=False, 
-                TF_ignore_mag=tf_ignore_mag), model).to(device)
+                    shared_mag=False, 
-        #aug_model = Augmented_model(RandAug(TF_dict=tf_dict, N_TF=2), model).to(device)
+                    TF_ignore_mag=tf_ignore_mag), model).to(device)
+        else:
+            aug_model = Augmented_model(RandAug(TF_dict=tf_dict, N_TF=Nb_TF_seq), model).to(device)
 
         print("{} on {} for {} epochs - {} inner_it{}".format(str(aug_model), device_name, epochs, n_inner_iter, postfix))
         log= run_dist_dataugV3(model=aug_model,
@@ -134,7 +138,7 @@ if __name__ == "__main__":
              inner_it=n_inner_iter, 
              dataug_epoch_start=dataug_epoch_start, 
              opt_param=optim_param,
-             print_freq=20, 
+             print_freq=10, 
              unsup_loss=1, 
              hp_opt=False,
              save_sample_freq=None)

higher/smart_aug/train_utils.py

@@ -329,7 +329,7 @@ def run_dist_dataugV3(model, opt_param, epochs=1, inner_it=1, dataug_epoch_start
 
             if(high_grad_track and i>0 and i%inner_it==0 and epoch>=opt_param['Meta']['epoch_start']): #Perform Meta step
                 #print("meta")
-                val_loss = compute_vaLoss(model=model, dl_it=dl_val_it, dl=dl_val) + model['data_aug'].reg_loss()
+                val_loss = compute_vaLoss(model=model, dl_it=dl_val_it, dl=dl_val) + model['data_aug'].reg_loss(opt_param['Meta']['reg_factor'])
                 #print_graph(val_loss) #to visualize computational graph
                 val_loss.backward()
 

higher/smart_aug/transformations.py

@@ -31,6 +31,13 @@ PARAMETER_MAX = 1
 # What is the min 'level' a transform could be predicted
 PARAMETER_MIN = 0.1 
 
+#Dict containing the value for wich TF are closer to identity
+#TF_identity={
+#    PARAMETER_MAX:{'Solarize', 'Posterize'},
+#    PARAMETER_MAX/2:{'Contrast','Color','Brightness','Sharpness'},
+#    PARAMETER_MIN:{'Rotate','TranslateX','TranslateY','ShearX','ShearY'},
+#}
+
 class TF_loader(object):
     """ Transformations builder.