Test KL divergence from UDA

higher/datasets.py

@@ -50,6 +50,7 @@ class AugmentedDataset(VisionDataset):
         for idx, img in enumerate(self.sup_data):
             self.sup_data[idx]= Image.fromarray(img) #to PIL Image
 
+        self.unsup_ratio=5 #Batch size unsup = train batch size * unsup_ratio
         self.unsup_data=[]
         self.unsup_targets=[]
 

higher/model.py

@@ -43,7 +43,8 @@ class LeNet(nn.Module):
         #print("Shape ", out.shape)
         out = F.linear(out, self._params["w4"], self._params["b4"])
         #print("Shape ", out.shape)
-        return F.log_softmax(out, dim=1)
+        #return F.log_softmax(out, dim=1)
+        return out
 
     def __getitem__(self, key):
         return self._params[key]

higher/test_brutus.py

@@ -35,7 +35,7 @@ if __name__ == "__main__":
 
 
     n_inner_iter = 1
-    epochs = 200
+    epochs = 100
     dataug_epoch_start=0
 
     tf_dict = {k: TF.TF_dict[k] for k in tf_names}

higher/test_dataug.py

@@ -70,7 +70,7 @@ if __name__ == "__main__":
         'aug_model'
     }
     n_inner_iter = 1
-    epochs = 200
+    epochs = 100
     dataug_epoch_start=0
 
 
@@ -149,12 +149,12 @@ if __name__ == "__main__":
         tf_dict = {k: TF.TF_dict[k] for k in tf_names}
 
         #aug_model = Augmented_model(Data_augV6(TF_dict=tf_dict, N_TF=1, mix_dist=0.0, fixed_prob=False, prob_set_size=2, fixed_mag=True, shared_mag=True), LeNet(3,10)).to(device)
-        #aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=3, mix_dist=0.0, fixed_prob=False, fixed_mag=False, shared_mag=False), LeNet(3,10)).to(device)
+        aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=3, mix_dist=0.0, fixed_prob=False, fixed_mag=False, shared_mag=False), LeNet(3,10)).to(device)
-        aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=3, mix_dist=0.0, fixed_prob=False, fixed_mag=False, shared_mag=False), WideResNet(num_classes=10, wrn_size=32)).to(device)
+        #aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=3, mix_dist=0.0, fixed_prob=False, fixed_mag=False, shared_mag=False), WideResNet(num_classes=10, wrn_size=32)).to(device)
         #aug_model = Augmented_model(RandAug(TF_dict=tf_dict, N_TF=2), WideResNet(num_classes=10, wrn_size=32)).to(device)
         print(str(aug_model), 'on', device_name)
         #run_simple_dataug(inner_it=n_inner_iter, epochs=epochs)
-        log= run_dist_dataugV2(model=aug_model, epochs=epochs, inner_it=n_inner_iter, dataug_epoch_start=dataug_epoch_start, print_freq=10, loss_patience=None)
+        log= run_dist_dataugV2(model=aug_model, epochs=epochs, inner_it=n_inner_iter, dataug_epoch_start=dataug_epoch_start, print_freq=1, KLdiv=True, loss_patience=None)
 
         exec_time=time.process_time() - t0
         ####
@@ -162,7 +162,7 @@ if __name__ == "__main__":
         times = [x["time"] for x in log]
         out = {"Accuracy": max([x["acc"] for x in log]), "Time": (np.mean(times),np.std(times), exec_time), "Device": device_name, "Param_names": aug_model.TF_names(), "Log": log}
         print(str(aug_model),": acc", out["Accuracy"], "in:", out["Time"][0], "+/-", out["Time"][1])
-        filename = "{}-{} epochs (dataug:{})- {} in_it".format(str(aug_model),epochs,dataug_epoch_start,n_inner_iter)
+        filename = "{}-{} epochs (dataug:{})- {} in_it (KLdiv)".format(str(aug_model),epochs,dataug_epoch_start,n_inner_iter)
         with open("res/log/%s.json" % filename, "w+") as f:
             json.dump(out, f, indent=True)
             print('Log :\"',f.name, '\" saved !')

higher/train_utils.py

@@ -542,7 +542,7 @@ def run_dist_dataug(model, epochs=1, inner_it=1, dataug_epoch_start=0):
     print("Copy ", countcopy)
     return log
 
-def run_dist_dataugV2(model, epochs=1, inner_it=0, dataug_epoch_start=0, print_freq=1, loss_patience=None):
+def run_dist_dataugV2(model, epochs=1, inner_it=0, dataug_epoch_start=0, print_freq=1, KLdiv=False, loss_patience=None):
     device = next(model.parameters()).device
     log = []
     countcopy=0
@@ -578,30 +578,51 @@ def run_dist_dataugV2(model, epochs=1, inner_it=0, dataug_epoch_start=0, print_f
 
         for i, (xs, ys) in enumerate(dl_train):
             xs, ys = xs.to(device), ys.to(device)
-            '''
+            
             #Methode exacte
-            final_loss = 0
+            #final_loss = 0
-            for tf_idx in range(fmodel['data_aug']._nb_tf):
+            #for tf_idx in range(fmodel['data_aug']._nb_tf):
-                fmodel['data_aug'].transf_idx=tf_idx
+            #    fmodel['data_aug'].transf_idx=tf_idx
-                logits = fmodel(xs)
+            #    logits = fmodel(xs)
-                loss = F.cross_entropy(logits, ys)
+            #    loss = F.cross_entropy(logits, ys)
-                #loss.backward(retain_graph=True)
+            #    #loss.backward(retain_graph=True)
-                #print('idx', tf_idx)
+            #    final_loss += loss*fmodel['data_aug']['prob'][tf_idx] #Take it in the forward function ? 
-                #print(fmodel['data_aug']['prob'][tf_idx], fmodel['data_aug']['prob'][tf_idx].grad)
+            #loss = final_loss 
-                final_loss += loss*fmodel['data_aug']['prob'][tf_idx] #Take it in the forward function ?
             
-            loss = final_loss 
+            #KLdiv=False
-            '''
+            if(not KLdiv):
             #Methode uniforme
+                logits = fmodel(xs)  # modified `params` can also be passed as a kwarg
+                loss = F.cross_entropy(logits, ys, reduction='none')  # no need to call loss.backwards()
 
-            logits = fmodel(xs)  # modified `params` can also be passed as a kwarg
+                if fmodel._data_augmentation: #Weight loss
-            loss = F.cross_entropy(logits, ys, reduction='none')  # no need to call loss.backwards()
+                    w_loss = fmodel['data_aug'].loss_weight()#.to(device)
+                    loss = loss * w_loss
+                loss = loss.mean()
+            
+            else:
+            #Methode KL div
+                fmodel.augment(mode=False)
+                sup_logits = fmodel(xs)
+                log_sup=F.log_softmax(sup_logits, dim=1)
+                fmodel.augment(mode=True)
+                loss = F.cross_entropy(log_sup, ys)
+
+                if fmodel._data_augmentation:
+                    aug_logits = fmodel(xs)
+                    log_aug=F.log_softmax(aug_logits, dim=1)
+                    #KL div w/ logits
+                    aug_loss = sup_logits*(log_sup-log_aug)
+                    aug_loss=aug_loss.sum(dim=-1)
+                    #aug_loss = F.kl_div(aug_logits, sup_logits, reduction='none') #Similarite predictions (distributions)
+
+                    w_loss = fmodel['data_aug'].loss_weight()#.unsqueeze(dim=1).expand(-1,10) #Weight loss
+                    aug_loss = (w_loss * aug_loss).mean()
+                    unsupp_coeff = 1
+                    loss += aug_loss * unsupp_coeff
+
+                    print('TF Proba :', model['data_aug']['prob'].data)
 
-            if fmodel._data_augmentation: #Weight loss
-                w_loss = fmodel['data_aug'].loss_weight()#.to(device)
-                loss = loss * w_loss
-            loss = loss.mean()
-            #'''
 
             #to visualize computational graph
             #print_graph(loss)
@@ -664,6 +685,7 @@ def run_dist_dataugV2(model, epochs=1, inner_it=0, dataug_epoch_start=0, print_f
             print('TF Mag :', model['data_aug']['mag'].data)
             #print('Mag grad',model['data_aug']['mag'].grad)
             #print('Reg loss:', model['data_aug'].reg_loss().item())
+            print('Aug loss', aug_loss.item())
         #############
         #### Log ####
         #print(type(model['data_aug']) is dataug.Data_augV5)