chore: normalization-in-onnx as an argument in pytorch2onnx_kneron

tools/pytorch2onnx_kneron.py

@@ -208,17 +208,9 @@ def pytorch2onnx(model,
         print('The outputs are same between Pytorch and ONNX')
 
     if norm_cfg is not None:
+        print("Prepending BatchNorm layer to ONNX as data normalization...")
         mean = norm_cfg['mean']
         std = norm_cfg['std']
-        # TODO: figure out should we add the lines below?
-        '''
-        if all(_ == 128. for _ in mean) and all(_ == 256. for _ in std):
-            print("normalization config perfectly matches kneron "
-                  "quantization requirement; not prepending batch norm.")
-            return
-        print("normalization config does not match kneron "
-              "quantization requirement; prepending batch norm...")
-        '''
         i_n = m.graph.input[0]
         if (
             i_n.type.tensor_type.shape.dim[1].dim_value != len(mean)
@@ -272,6 +264,13 @@ def parse_args():
         'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
         'Note that the quotation marks are necessary and that no white space '
         'is allowed.')
+    parser.add_argument(
+        '--normalization-in-onnx',
+        action='store_true',
+        help='Prepend BatchNorm layer to onnx model as a role of data '
+        'normalization according to the mean and std value in the given'
+        'cfg file.'
+    )
     args = parser.parse_args()
     return args
 
@@ -327,7 +326,10 @@ if __name__ == '__main__':
     else:
         img = _demo_mm_inputs(input_shape)
 
-    norm_cfg = _parse_normalize_cfg(cfg.test_pipeline)
+    if args.normalization_in_onnx:
+        norm_cfg = _parse_normalize_cfg(cfg.test_pipeline)
+    else:
+        norm_cfg = None
     # convert model to onnx file
     pytorch2onnx(
         segmentor,