From fac5a5f3af9f17ad3bb9f3e9cb2d73c6a954f5a3 Mon Sep 17 00:00:00 2001
From: qian-dan <756328797@qq.com>
Date: Wed, 16 Jul 2025 16:32:36 +0800
Subject: [PATCH] modify onnx_demo

Signed-off-by: qian-dan <756328797@qq.com>
---
 docs/sample_code/golden/onnx_demo.py | 11 ++++++-----
 1 file changed, 6 insertions(+), 5 deletions(-)

diff --git a/docs/sample_code/golden/onnx_demo.py b/docs/sample_code/golden/onnx_demo.py
index 959ecb8126..0c2980c9a0 100644
--- a/docs/sample_code/golden/onnx_demo.py
+++ b/docs/sample_code/golden/onnx_demo.py
@@ -34,7 +34,8 @@ def generate_random_input_data_and_run_model(args):
 
     if args.inDataFile:  # User specifies input
         input_data_files = args.inDataFile.split(':')
-        assert len(input_data_files) == len(input_tensors), "Shape of input data is not compatible with the model!"
+        if not len(input_data_files) == len(input_tensors):
+            raise ValueError("Shape of input data is not compatible with the model!")
         for i, input_tensor in enumerate(input_tensors):
             tensor_type = input_tensor.type[7:-1]
             if tensor_type == "float":
@@ -48,12 +49,12 @@ def generate_random_input_data_and_run_model(args):
             if args.inputShape:
                 stable_shape = ast.literal_eval(args.inputShape)
                 shape_info = stable_shape[i]
-                assert len(input_data) == np.prod(
-                    shape_info), "Shape of input data is not compatible with the input shape!"
+                if not len(input_data) == np.prod(shape_info):
+                    raise ValueError("Shape of input data is not compatible with the input shape!")
                 input_data = input_data.reshape(shape_info)
             else:
-                assert len(input_data) == np.prod(
-                    input_tensor.shape), "Shape of input data is not compatible with the model!"
+                if not len(input_data) == np.prod(input_tensor.shape):
+                    raise ValueError("Shape of input data is not compatible with the model!")
                 input_data = input_data.reshape(input_tensor.shape)
             input_dict[input_tensor.name] = input_data
     else:  # Generate random input and save
-- 
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