Fix LeNet Tensorflow

higher/datasets.py

@@ -39,8 +39,9 @@ data_test = torchvision.datasets.CIFAR10(
 )
 #'''
 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))
+#train_subset_indices=range(BATCH_SIZE*10)
+#val_subset_indices=range(BATCH_SIZE*10, BATCH_SIZE*20)
 
 dl_train = torch.utils.data.DataLoader(data_train, batch_size=BATCH_SIZE, shuffle=False, sampler=SubsetRandomSampler(train_subset_indices))
 dl_val = torch.utils.data.DataLoader(data_train, batch_size=BATCH_SIZE, shuffle=False, sampler=SubsetRandomSampler(val_subset_indices))

higher/test_dataug.py

@@ -6,11 +6,11 @@ from train_utils import *
 tf_names = [
     ## Geometric TF ##
     'Identity',
-    'FlipUD',
+    #'FlipUD',
     'FlipLR',
     'Rotate',
-    'TranslateX',
-    'TranslateY',
+    #'TranslateX',
+    #'TranslateY',
     'ShearX',
     'ShearY',
 
@@ -20,7 +20,7 @@ tf_names = [
     'Brightness',
     'Sharpness',
     'Posterize',
-    'Solarize', #=>Image entre [0,1] #Pas opti pour des batch
+    #'Solarize', #=>Image entre [0,1] #Pas opti pour des batch
 
     #Non fonctionnel
     #'Auto_Contrast', #Pas opti pour des batch (Super lent)
@@ -37,14 +37,14 @@ else:
 ##########################################
 if __name__ == "__main__":
 
-    n_inner_iter = 10
-    epochs = 200
+    n_inner_iter = 0
+    epochs = 100
     dataug_epoch_start=0
 
     #### Classic ####
     '''
-    #model = LeNet(3,10).to(device)
-    model = WideResNet(num_classes=10, wrn_size=16).to(device)
+    model = LeNet(3,10).to(device)
+    #model = WideResNet(num_classes=10, wrn_size=16).to(device)
     #model = Augmented_model(Data_augV3(mix_dist=0.0), LeNet(3,10)).to(device)
     #model.augment(mode=False)
 
@@ -68,11 +68,11 @@ if __name__ == "__main__":
     t0 = time.process_time()
     tf_dict = {k: TF.TF_dict[k] for k in tf_names}
     #tf_dict = TF.TF_dict
-    aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=2, mix_dist=0.5, fixed_mag=False, shared_mag=True), LeNet(3,10)).to(device)
-    #aug_model = Augmented_model(Data_augV4(TF_dict=tf_dict, N_TF=2, mix_dist=0.0), WideResNet(num_classes=10, wrn_size=160)).to(device)
+    aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=2, mix_dist=0.5, fixed_mag=True, shared_mag=True), LeNet(3,10)).to(device)
+    #aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=2, mix_dist=0.5, fixed_mag=True, shared_mag=True), WideResNet(num_classes=10, wrn_size=160)).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=None)
 
     ####
     print('-'*9)
@@ -91,16 +91,16 @@ if __name__ == "__main__":
     '''
     #### TF tests ####
     #'''
-    res_folder="res/brutus-tests/"
-    epochs= 150
-    inner_its = [0, 1, 10]
+    res_folder="res/good_TF_tests/"
+    epochs= 100
+    inner_its = [0, 10]
     dist_mix = [0.0, 0.5]
     dataug_epoch_starts= [0]
     tf_dict = {k: TF.TF_dict[k] for k in tf_names}
     TF_nb = [len(tf_dict)] #range(10,len(TF.TF_dict)+1) #[len(TF.TF_dict)]
-    N_seq_TF= [1,2,3,4]#[2, 3, 4, 6]
-    mag_setup = [(True,True), (False,True), (False, False)]
-    nb_run= 3
+    N_seq_TF= [1]#[1, 2, 3, 4]
+    mag_setup = [(True,True)]#[(True,True), (False,True), (False, False)]
+    nb_run= 1
     
     try:
         os.mkdir(res_folder)