Proper relative paths handling

examples/rknn_convert/rknn_convert.py

@@ -16,17 +16,17 @@ def parse_model_config(yaml_config_file):
     return model_configs
 
 
-def convert_model(model_path, out_path, pre_compile):
-    if os.path.isfile(model_path):
-        yaml_config_file = model_path
-        model_path = os.path.dirname(yaml_config_file)
+def convert_model(config_path, out_path, pre_compile):
+    if os.path.isfile(config_path):
+        config_file = os.path.abspath(config_path)
+        config_path = os.path.dirname(config_file)
     else:
-        yaml_config_file = os.path.join(model_path, 'model_config.yml')
-    if not os.path.exists(yaml_config_file):
-        print('model config {} not exist!'.format(yaml_config_file))
+        config_file = os.path.join(config_path, 'model_config.yml')
+    if not os.path.exists(config_file):
+        print('model config {} not exist!'.format(config_file))
         exit(-1)
 
-    model_configs = parse_model_config(yaml_config_file)
+    model_configs = parse_model_config(config_file)
 
     exported_rknn_model_path_list = []
 
@@ -39,7 +39,7 @@ def convert_model(model_path, out_path, pre_compile):
 
         print('--> Loading model...')
         if model['platform'] == 'tensorflow':
-            model_file_path = os.path.join(model_path, model['model_file_path'])
+            model_file_path = os.path.join(config_path, model['model_file_path'])
             input_size_list = []
             for input_size_str in model['subgraphs']['input-size-list']:
                 input_size = list(map(int, input_size_str.split(',')))
@@ -50,22 +50,22 @@ def convert_model(model_path, out_path, pre_compile):
                                  outputs=model['subgraphs']['outputs'],
                                  input_size_list=input_size_list)
         elif model['platform'] == 'tflite':
-            model_file_path = os.path.join(model_path, model['model_file_path'])
+            model_file_path = os.path.join(config_path, model['model_file_path'])
             rknn.load_tflite(model=model_file_path)
         elif model['platform'] == 'caffe':
-            prototxt_file_path = os.path.join(model_path,model['prototxt_file_path'])
-            caffemodel_file_path = os.path.join(model_path,model['caffemodel_file_path'])
+            prototxt_file_path = os.path.join(config_path,model['prototxt_file_path'])
+            caffemodel_file_path = os.path.join(config_path,model['caffemodel_file_path'])
             rknn.load_caffe(model=prototxt_file_path, proto='caffe', blobs=caffemodel_file_path)
         elif model['platform'] == 'onnx':
-            model_file_path = os.path.join(model_path, model['model_file_path'])
+            model_file_path = os.path.join(config_path, model['model_file_path'])
             rknn.load_onnx(model=model_file_path)
         else:
             print("Platform {:} is not supported! Moving on.".format(model['platform']))
             continue
         print('done')
 
         if model['quantize']:
-            dataset_path = os.path.join(model_path, model['dataset'])
+            dataset_path = os.path.join(config_path, model['dataset'])
         else:
             dataset_path = './dataset'
 
@@ -83,8 +83,8 @@ def convert_model(model_path, out_path, pre_compile):
 
 
 if __name__ == '__main__':
-    model_path = sys.argv[1]
+    config_path = sys.argv[1]
     out_path = sys.argv[2]
     pre_compile = sys.argv[3] in ['true', '1', 'True']
 
-    convert_model(model_path, out_path, pre_compile)
+    convert_model(config_path, out_path, pre_compile)