Resultats experience effet nbTF + outil comparaison resultat

higher/test_dataug.py

@@ -11,7 +11,36 @@ BATCH_SIZE = 300
 #TEST_SIZE = 300
 TEST_SIZE = 10000
 
+tf_names = [
+    ## Geometric TF ##
+    'Identity',
+    'FlipUD',
+    'FlipLR',
+    #'Rotate',
+    #'TranslateX',
+    #'TranslateY',
+    #'ShearX',
+    #'ShearY',
+
+    ## Color TF (Expect image in the range of [0, 1]) ##
+    #'Contrast',
+    #'Color',
+    'Brightness',
+    #'Sharpness',
+    #'Posterize',
+    #'Solarize', #=>Image entre [0,1] #Pas opti pour des batch
+
+    #Non fonctionnel
+    #'Auto_Contrast', #Pas opti pour des batch (Super lent)
+    #'Equalize',
+]
+
 #ATTENTION : Dataug (Kornia) Expect image in the range of [0, 1]
+#transform_train = torchvision.transforms.Compose([
+#    torchvision.transforms.RandomHorizontalFlip(),
+#    torchvision.transforms.ToTensor(),
+    #torchvision.transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)), #CIFAR10
+#])
 transform = torchvision.transforms.Compose([
     torchvision.transforms.ToTensor(),
     #torchvision.transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)), #CIFAR10
@@ -31,6 +60,9 @@ data_test = torchvision.datasets.MNIST(
 data_train = torchvision.datasets.CIFAR10(
     "./data", train=True, download=True, transform=transform
 )
+#data_val = torchvision.datasets.CIFAR10(
+#    "./data", train=True, download=True, transform=transform
+#)
 data_test = torchvision.datasets.CIFAR10(
     "./data", train=False, download=True, transform=transform
 )
@@ -81,7 +113,7 @@ def train_classic(model, epochs=1):
     dl_val_it = iter(dl_val)
     log = []
     for epoch in range(epochs):
-        print_torch_mem("Start epoch")
+        #print_torch_mem("Start epoch")
         t0 = time.process_time()
         for i, (features, labels) in enumerate(dl_train):
             #print_torch_mem("Start iter")
@@ -132,8 +164,8 @@ def train_classic_higher(model, epochs=1):
     #with higher.innerloop_ctx(model, optim, copy_initial_weights=True, track_higher_grads=False) as (fmodel, diffopt):
 
     for epoch in range(epochs):
-        print_torch_mem("Start epoch "+str(epoch))
+        #print_torch_mem("Start epoch "+str(epoch))
-        print("Fast param ",len(fmodel._fast_params))
+        #print("Fast param ",len(fmodel._fast_params))
         t0 = time.process_time()
         for i, (features, labels) in enumerate(dl_train):
             #print_torch_mem("Start iter")
@@ -702,8 +734,8 @@ def run_dist_dataugV2(model, epochs=1, inner_it=0, dataug_epoch_start=0, print_f
 ##########################################
 if __name__ == "__main__":
 
-    n_inner_iter = 0
+    n_inner_iter = 10
-    epochs = 2
+    epochs = 100
     dataug_epoch_start=0
 
     #### Classic ####
@@ -714,7 +746,8 @@ if __name__ == "__main__":
     #model.augment(mode=False)
 
     print(str(model), 'on', device_name)
-    log= train_classic_higher(model=model, epochs=epochs)
+    log= train_classic(model=model, epochs=epochs)
+    #log= train_classic_higher(model=model, epochs=epochs)
 
     ####
     plot_res(log, fig_name="res/{}-{} epochs".format(str(model),epochs))
@@ -728,11 +761,13 @@ if __name__ == "__main__":
     print('-'*9)
     '''
     #### Augmented Model ####
-    '''
+    #'''
+    tf_dict = {k: TF.TF_dict[k] for k in tf_names}
+    #tf_dict = TF.TF_dict
     aug_model = Augmented_model(Data_augV4(TF_dict=TF.TF_dict, mix_dist=0.0), LeNet(3,10)).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=1, loss_patience=10)
+    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=10)
 
     ####
     plot_res(log, fig_name="res/{}-{} epochs (dataug:{})- {} in_it".format(str(aug_model),epochs,dataug_epoch_start,n_inner_iter))
@@ -744,13 +779,14 @@ if __name__ == "__main__":
         json.dump(out, f, indent=True)
         print('Log :\"',f.name, '\" saved !')
     print('-'*9)
-    '''
+    #'''
     ## TF number tests ##
+    '''
     res_folder="res/TF_nb_tests/"
     epochs= 200
     inner_its = [0, 10]
     dataug_epoch_starts= [0, -1]
-    max_TF_nb = len(TF.TF_dict)
+    TF_nb = [14] #range(1,len(TF.TF_dict)+1)
     
     try:
         os.mkdir(res_folder)
@@ -762,14 +798,14 @@ if __name__ == "__main__":
         print("---Starting inner_it", n_inner_iter,"---")
         for dataug_epoch_start in dataug_epoch_starts:
             print("---Starting dataug", dataug_epoch_start,"---")
-            for i in range(1,max_TF_nb):
+            for i in TF_nb:
                 keys = list(TF.TF_dict.keys())[0:i]
                 ntf_dict = {k: TF.TF_dict[k] for k in keys}
 
                 aug_model = Augmented_model(Data_augV4(TF_dict=ntf_dict, mix_dist=0.0), LeNet(3,10)).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=1, loss_patience=10)
+                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=10)
 
                 ####
                 plot_res(log, fig_name=res_folder+"{}-{} epochs (dataug:{})- {} in_it".format(str(aug_model),epochs,dataug_epoch_start,n_inner_iter))
@@ -781,3 +817,5 @@ if __name__ == "__main__":
                     json.dump(out, f, indent=True)
                     print('Log :\"',f.name, '\" saved !')
                 print('-'*9)
+
+    '''