Tflite：Error in post process calculation

System information

Model_Task:models-master/research/object_detection
MODEL_name : mobilenetV2_ssdlite_fakequantization
OS:centos 7

== check python ===================================================
python version: 3.6.6
python compiler version: GCC 4.8.5 20150623 (Red Hat 4.8.5-36)
python implementation: CPython
== check os platform ===============================================
os: Linux
os kernel version: #1 SMP Fri Apr 20 16:44:24 UTC 2018
os release version: 3.10.0-862.el7.x86_64
os platform: Linux-3.10.0-862.el7.x86_64-x86_64-with-centos-7.5.1804-Core
linux distribution: ('CentOS Linux', '7.5.1804', 'Core')
linux os distribution: ('centos', '7.5.1804', 'Core')
uname: uname_result(system='Linux', node='VM_12_5_centos', release='3.10.0-862.el7.x86_64', version='#1 SMP Fri Apr 20 16:44:24 UTC 2018', machine='x86_64', processor='x86_64')
architecture: ('64bit', 'ELF')
machine: x86_64
== are we in docker =============================================
No
== compiler =====================================================
c++ (GCC) 4.8.5 20150623 (Red Hat 4.8.5-36)
Copyright (C) 2015 Free Software Foundation, Inc.
This is free software; see the source for copying conditions. There is NO
warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
== check pips ===================================================
numpy 1.14.5
protobuf 3.7.1
tensorflow-gpu 1.10.0
== check for virtualenv =========================================
False
== tensorflow import ============================================
tf.version.VERSION = 1.10.0
tf.version.COMPILER_VERSION = 4.8.5
Sanity check: array([1], dtype=int32)
== cuda libs ===================================================
/usr/local/python3/lib/python3.6/site-packages/torch/lib/libcudart-f7fdd8d7.so.9.0
/usr/local/cuda-9.0/doc/man/man7/libcudart.so.7
/usr/local/cuda-9.0/doc/man/man7/libcudart.7
/usr/local/cuda-9.0/lib64/libcudart_static.a
/usr/local/cuda-9.0/lib64/libcudart.so.9.0.103
CUDA Version 9.0.103
cudnn version:7.0.5
== tensorflow installed from info ==================

== python version ==============================================
(major, minor, micro, releaselevel, serial)
(3, 6, 6, 'final', 0)

== bazel version ===============================================
Build label: 0.28.1
Build time: Fri Jul 19 00:00:00 2019 (1563494400)
Build timestamp: 1563494400
Build timestamp as int: 1563494400

Describe the problem

First of all, the files frozen_inference_graph.pb I generated on the server side can be correctly reasoned out and properly post-processed
result:

However, when I generated the tflite file, I found wrong results. After analyzing the conat and concat_1 layer, that is, before post-processing, the calculated results were correct in tflite, but after post-processing, the values would become abnormal.
The script and results are as follows:
toco --graph_def_file=./tflite_graph_300_300.pb --output_file=./detect_300_300_quantize.tflite --input_shapes=1,300,300,3 --input_arrays=normalized_input_image_tensor --output_arrays='TFLite_Detection_PostProcess','TFLite_Detection_PostProcess:1','TFLite_Detection_PostProcess:2','TFLite_Detection_PostProcess:3' --inference_type=QUANTIZED_UINT8 --inference_input_type=QUANTIZED_UINT8 --output_format=TFLITE --dump_graphviz_dir=./ --default_ranges_min=0 --default_ranges_max=6 --mean_values=128 --std_dev_values=127


Meanwhile, I also tried to generate in different versions of tf. Since I used CUDA9.0, before the main test of 1.13.1, even though I used --allow_custom_ops, there would still be" Op type not registered 'TFLite_Detection_PostProcess' in 1.10.0" .Therefore, I upgraded to 1.11.0, 1.12.0 and 1.14.0. The conversion of error resolution was normal, but after positioning, I found that the calculation results of the post-processing All the above were wrong.But it does make sense to run the official demo.
This is a problem with both quantization and floating inference point reasoning, I suspect "--allow_custom_ops" is successfully converted in tflite, but there are still BUGS causing incorrect calculations, so does anyone have this problem?Your comments are welcome

The tflite_test file / test Code

test_file.zip
this is my test code:
`interpreter = tf.contrib.lite.Interpreter(model_path=model_path)
interpreter.allocate_tensors()

input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
image_np = cv2.imread(image_path)
b,g,r = np.split(image_np,3,axis = 2)
image_np = np.concatenate((r,g,b),axis = 2)
image_np_org = image_np
image_np = cv2.resize(image_np,(400,300))
image_np = np.reshape(image_np,(1,300,400,3))
image_np_org = image_np
interpreter.set_tensor(input_details[0]['index'], image_np)
#Running inference and timing it
interpreter.invoke()
output_dict['detection_scores']=interpreter.get_tensor(xx)
output_dict['detection_boxes']=interpreter.get_tensor(xx)
`