How to get results (bonding boxes, class IDs, confidences) of Object detection (Yolo v5) in TensorRT

Hello, I’m trying to do object detection(Yolov5) in TensorRT.
But I’m not sure how to get results.

My workflow:
Model is trained with Yolo v5. It works correctly in Pytorch framework.
Convert the model to ONNX format in Ubuntu PC.
Convert the ONNX-format Model to TensorRT in Jetson nano.

Problem:
I inferred with the TensorRT model. But it returns array of [nan, nan, nan, … ,nan].
How to get bounding boxes, confidences, class IDs?

(I post same article in Jetson forum https://forums.developer.nvidia.com/t/how-to-get-results-bonding-boxes-class-ids-confidences-of-object-detection-yolo-v5-in-tensorrt/195816)

def main():
    SHOW_IMAGE=True

    data_root="../Image/gratin_od/images"
    files = glob.glob(data_root+"/*.bmp")

    print("Got file names")
    files.sort()
    print(files[75])
    all_judges=np.empty(0)


    # Image shape expected by the post-processing

    with get_engine("./models/gratin.onnx", "./models/gratin.trt") as engine, \
        engine.create_execution_context() as context:

        for i in range(1):
            x_cv = cv2.imread(str(files[75]))
            start = time.time()
            x_show = cv2.cvtColor(x_cv, cv2.COLOR_BGR2RGB)


            inputs[0].host = x_show
            output= common.do_inference(context, bindings=bindings, inputs=inputs, outputs=outputs, stream=stream)

            print(output[0])   
            #print>> [nan nan nan ... nan nan nan]
            #How to get bounding box, class id and confidence?


            end = time.time()
            ms = 1000.0*(end-start)
 

            if(SHOW_IMAGE):
                cv2.imshow("image", x_cv)
                cv2.waitKey(0)&0xFF

    print("END")