[Deeplab] The iou of unit8 tflite have a sharp decline

Prerequisites

Please answer the following questions for yourself before submitting an issue.

• [N ] I am using the latest TensorFlow Model Garden release and TensorFlow 2.
• [ Y] I am reporting the issue to the correct repository. (Model Garden official or research directory)
• [Y ] I checked to make sure that this issue has not already been filed.

1. The entire URL of the file you are using

https://github.com/tensorflow/models/tree/master/research/deeplab

2. Describe the bug

The mIou of uint8 tflite have a sharp decline

3. Steps to reproduce

using Quantization-aware training on my own datasets. The iou of ckpt is 87.98.
Convert ckpt to pb file, the iou of pb model is 87.84
Convert pb to tflite, <inference_type=FLOAT>,Then, the iou of float tflite is 87.89
Convet pb to tflite, <inference_type=QUANTIZED_UINT8>,Then, the iou of float tflite is 54.6
the convert_tflite code is followed strictly "https://github.com/tensorflow/models/blob/master/research/deeplab/g3doc/quantize.md"
'tflite_convert
--graph_def_file=${OUTPUT_DIR}/frozen_inference_graph.pb
--output_file=${OUTPUT_DIR}/frozen_inference_graph.tflite
--output_format=TFLITE
--input_shape=1,321,321,3
--input_arrays="MobilenetV2/MobilenetV2/input"
--inference_type=QUANTIZED_UINT8
--inference_input_type=QUANTIZED_UINT8
--std_dev_values=128
--mean_values=128
--change_concat_input_ranges=true
--output_arrays="ArgMax"'

4. Expected behavior

I want to know how to quantize the mobilenetv2 deeplab so that its iou has a small drop.

5. Additional context

None

6. System information

• OS Platform and Distribution (e.g., Linux Ubuntu 16.04):
• Mobile device name if the issue happens on a mobile device:
• TensorFlow installed from (source or binary):
• TensorFlow version (use command below):1.15.0
• Python version:3.6
• Bazel version (if compiling from source):
• GCC/Compiler version (if compiling from source):
• CUDA/cuDNN version:
• GPU model and memory: