[tools/onnx-subgraph] support arbitrary input shape for onnx

tools/onnx_subgraph/single_vs_multiple_onnx.py

@@ -45,6 +45,53 @@ def infer_single_onnx_model(model_file, input_data):
         return output_dict
 
 
+def prepare_initial_input_data(onnx_model_path, default_input_data):
+    """
+    Prepares initial input data for inference.
+    Args:
+        onnx_model_path (str): Path to the ONNX model file.
+        default_input_data (dict): Dictionary containing default input data.
+    Returns:
+        dict: Dictionary with user-specified or default shaped and typed input data.
+    """
+    session = ort.InferenceSession(onnx_model_path)
+    input_info = {input.name: input.shape for input in session.get_inputs()}
+
+    initial_input_data = {}
+    dtype_map = {'f': np.float32, 'i': np.int64}
+
+    for input_name, shape in input_info.items():
+        custom_shape_str = input(
+            f"Enter new shape for input '{input_name}' (comma-separated integers),\
+            or press Enter to use default: ")
+        custom_dtype_str = input(
+            f"Enter data type for input '{input_name}' ('f' for float32, 'i' for int64),\
+            or press Enter to use default: ")
+
+        if not custom_shape_str:
+            new_shape = default_input_data[input_name].shape
+        else:
+            try:
+                new_shape = [int(dim) for dim in custom_shape_str.split(',')]
+            except ValueError:
+                print("Invalid input, please ensure you enter comma-separated integers.")
+                continue
+
+        if not custom_dtype_str:
+            dtype = default_input_data[input_name].dtype
+        else:
+            dtype = dtype_map.get(custom_dtype_str.strip(), None)
+            if dtype is None:
+                print("Invalid data type, set as default, please enter 'f' or 'i'.")
+                dtype = default_input_data[input_name].dtype
+                continue
+
+        input_data = np.random.rand(*new_shape).astype(dtype)
+        initial_input_data[input_name] = input_data
+
+    return initial_input_data
+
+
 if __name__ == "__main__":
     arg_parser = argparse.ArgumentParser()
     arg_parser.add_argument('-s',
@@ -68,8 +115,8 @@ def infer_single_onnx_model(model_file, input_data):
     default_input_data = {
         "x": np.random.rand(1, 3, 256, 256).astype(np.float32),
     }
-
+    initial_input_data = prepare_initial_input_data(args.single, default_input_data)
     # Perform inference using a single ONNX model
     output_single = ModelInference.infer_single_onnx_model(args.single,
-                                                           default_input_data)
+                                                           initial_input_data)
     print("Single model inference completed!")