Added options to specify dataset path and whether to overwrite existing model evaluations.

pysegcnn/core/cli.py

@@ -126,7 +126,7 @@ def evaluation_parser():
     # in cross validation
     parser.add_argument('-a', '--aggregate', type=bool,
                         help=('Aggregate the statistics of the different '
-                              'models matching the defined pattern. {}.'
+                              'models matching the defined pattern {}.'
                               .format(default)),
                         default=False, nargs='?', const=True, metavar='')
 
@@ -162,10 +162,23 @@ def evaluation_parser():
                               .format(default)),
                         default=False, nargs='?', const=True, metavar='')
 
+    # optional argument: whether to overwrite existing files
+    parser.add_argument('-o', '--overwrite', type=bool,
+                        help=('Overwrite existing model evaluations {}.'
+                              .format(default)),
+                        default=False, nargs='?', const=True, metavar='')
+
     # optional argument: plot scenes
     parser.add_argument('-plot', '--plot-scenes', type=bool,
                         help=('Save plots for each predicted scene {}.'
                               .format(default)),
                         default=False, nargs='?', const=True, metavar='')
 
+    # optional argument: dataset path
+    parser.add_argument('-ds', '--dataset-path', type=str,
+                        help=('Path to the datasets on the current machine {}.'
+                              'Per default, it is assumed to be same as during'
+                              ' model training .'.format(default)),
+                        default='', metavar='')
+
     return parser

pysegcnn/main/eval.py

@@ -73,10 +73,12 @@ if __name__ == '__main__':
             aggregate=args.aggregate,
             ds=ds,
             ds_split=ds_split,
+            drive_path=args.dataset_path,
             map_labels=args.map_labels,
             predict_scene=args.predict_scene,
             plot_scenes=args.plot_scenes,
-            cm=args.confusion_matrix)
+            cm=args.confusion_matrix,
+            overwrite=args.overwrite)
 
         # evaluate models
         output = inference.evaluate()