Changed image size from 100x100 to 64x64.

Implemented save model graph method (needs graphviz packaged!).

model.py

@@ -19,8 +19,8 @@
 VALIDATION_SET_SIZE = 0.2            # proportion of full dataset used for the test set
 BATCH_SIZE = 256                     # default training batch size (number of images per batch)
 ROI = [20, 60, 320 - 20, 160 - 22]   # Region if interest
-IMAGE_WIDTH = 100                    # Model input image width
-IMAGE_HEIGHT = 100                   # Model input image height
+IMAGE_WIDTH = 64                     # Model input image width
+IMAGE_HEIGHT = 64                    # Model input image height
 
 # augmentation
 STEERING_ANGLE_CORRECTION = 6.25     # Steering angle correction for left and right images in degree
@@ -195,6 +195,62 @@ def train_model(model, dataset_csv_filename, trained_model=None, nb_epochs=7, nb
     network.safe_model(model + '_model.h5')
 
 
+def save_model_graph(model, filename):
+    """ Saves the model graph to a PNG file.
+
+    :param model:     Supported models are
+                        - LeNet5
+                        - NvidiaFull
+                        - NvidiaLight (lightweight NVIDIA architecture)
+                        - VGG16
+    :param filename:  Filename of PNG file.
+    """
+
+    supported_models = ['LeNet5', 'NvidiaFull', 'NvidiaLight', 'VGG16']
+
+    # check for valid model
+    matching = [s for s in supported_models if model in s]
+
+    if len(matching) == 0:
+        print('Not support model. Check help for supported models.')
+        exit(-1)
+
+    if model == 'LeNet5':
+        # setup LeNet-5 model architecture
+        network = LeNet(input_depth=3, input_height=IMAGE_HEIGHT, input_width=IMAGE_WIDTH,
+                        regression=True, nb_classes=1,
+                        roi=ROI,
+                        steering_angle_correction=STEERING_ANGLE_CORRECTION,
+                        weights_path=None)
+    elif model == 'NvidiaFull':
+        # setup Full NVIDIA CNN model architecture
+        network = NvidiaFull(input_depth=3, input_height=IMAGE_HEIGHT, input_width=IMAGE_WIDTH,
+                             regression=True, nb_classes=1,
+                             roi=ROI,
+                             steering_angle_correction=STEERING_ANGLE_CORRECTION,
+                             weights_path=None)
+    elif model == 'NvidiaLight':
+        # setup lightweight NVIDIA CNN model architecture
+        network = NvidiaLight(input_depth=3, input_height=IMAGE_HEIGHT, input_width=IMAGE_WIDTH,
+                              regression=True, nb_classes=1,
+                              roi=ROI,
+                              steering_angle_correction=STEERING_ANGLE_CORRECTION,
+                              weights_path=None)
+    elif model == 'VGG16':
+        # setup VGG-16 model architecture
+        network = VGG(input_depth=3, input_height=IMAGE_HEIGHT, input_width=IMAGE_WIDTH,
+                      regression=True, nb_classes=1,
+                      roi=ROI,
+                      steering_angle_correction=STEERING_ANGLE_CORRECTION,
+                      weights_path=None)
+    else:
+        print('Not support model. Check help for supported models.')
+        exit(-1)
+
+    print('Safe model graph...', end='', flush=True)
+    network.save_model_graph(filename)
+    print('done')
+
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(description='Model Training and Evaluation')
 
@@ -244,14 +300,15 @@ def train_model(model, dataset_csv_filename, trained_model=None, nb_epochs=7, nb
         '-th', '--show_training_history',
         help='Plots the training history (train loss and validation loss).',
         dest='history_filename',
-        metavar="FILE"
+        metavar='FILE'
     )
 
     parser.add_argument(
         '-smg', '--safe-model-graph',
-        help='Saves the model graph to a PNG file.',
+        help='Saves the model graph to a PNG file (1=model name, 2=filename).',
         dest='safe_model_graph',
-        metavar="FILE"
+        nargs=2,
+        metavar='ARG'
     )
 
     args = parser.parse_args()
@@ -306,5 +363,6 @@ def train_model(model, dataset_csv_filename, trained_model=None, nb_epochs=7, nb
             print('History object file \"{:s}\" not found.'.format(args.history_filename))
 
     elif args.safe_model_graph:
-        # TODO: safes the model graph to a PNG file
-        print('Safe model graph')
+        # safes the model graph to a PNG file
+        save_model_graph(args.safe_model_graph[0], args.safe_model_graph[1])
+