Fin script example

higher/smart_aug/smart_aug_example.py

@@ -0,0 +1,77 @@
+""" Example use of smart augmentation.
+
+"""
+
+from model import *
+from dataug import *
+from train_utils import *
+
+# Use available TF (see transformations.py)
+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
+]
+
+
+device = torch.device('cuda') #Select device to use
+
+if device == torch.device('cpu'):
+    device_name = 'CPU'
+else:
+    device_name = torch.cuda.get_device_name(device)
+
+##########################################
+if __name__ == "__main__":
+
+    #Parameters
+    n_inner_iter = 1
+    epochs = 150
+    optim_param={
+        'Meta':{
+            'optim':'Adam',
+            'lr':1e-2, #1e-2
+        },
+        'Inner':{
+            'optim': 'SGD',
+            'lr':1e-2, #1e-2
+            'momentum':0.9, #0.9
+        }
+    }
+
+    #Models
+    model = LeNet(3,10)
+    #model = ResNet(num_classes=10)
+    #model = MobileNetV2(num_classes=10)
+    #model = WideResNet(num_classes=10, wrn_size=32)
+
+    #Smart_aug initialisation
+    tf_dict = {k: TF.TF_dict[k] for k in tf_names}
+    model = Higher_model(model) #run_dist_dataugV3
+    aug_model = Augmented_model(
+        Data_augV5(TF_dict=tf_dict, 
+            N_TF=3, 
+            mix_dist=0.8, 
+            fixed_prob=False, 
+            fixed_mag=False, 
+            shared_mag=False), 
+        model).to(device)
+
+    print("{} on {} for {} epochs - {} inner_it".format(str(aug_model), device_name, epochs, n_inner_iter))
+
+    # Training
+    trained_model = run_simple_smartaug(model=aug_model, epochs=epochs, inner_it=n_inner_iter, opt_param=optim_param)