Inconsistent inference results between tensorrt 7 and tensorrt 8

When I use the same onnx converted to trt file, the inference results in tensorrt7 and tensorrt8 are not consistent, where tensorrt 8 has the correct result and tensorrt 7 appears to have more bbox, some of which have some position shift.
The model I am using is retinanet,what is the problem and why tensorrt7 and tensorrt8 behave differently on this model?
When I use the ssd model, the results of tensorrt7 and tensorrt8 are basically the same and correct.

Environment
TensorRT Version: 8.0.1.6
NVIDIA GPU: GTX1650TI
NVIDIA Driver Version: 470
CUDA Version: 10.2
CUDNN Version: 8.2.0

Hi,
Request you to share the ONNX model and the script if not shared already so that we can assist you better.
Alongside you can try few things:
https://docs.nvidia.com/deeplearning/tensorrt/quick-start-guide/index.html#onnx-export

  1. validating your model with the below snippet

check_model.py

import sys
import onnx
filename = yourONNXmodel
model = onnx.load(filename)
onnx.checker.check_model(model).
2) Try running your model with trtexec command.
https://github.com/NVIDIA/TensorRT/tree/master/samples/opensource/trtexec
In case you are still facing issue, request you to share the trtexec “”–verbose"" log for further debugging
Thanks!