Support TFLite and TFJS conversion for CenterNet multi-class keypoints

[biantsh]

Description

Rewrote the _postprocess_keypoints_multi_class() method in center_net_meta_arch.py to not use tf.tensor_scatter_nd_add ops, which seem to:

• Break conversion to TFJS (Error message explicitly says TensorScatterAdd is not supported).
• Not break conversion to TFLite, but inference will fail unless at least one object from all classes is predicted in any given output batch (see this issue raised in tensorflow/tensorflow).

This is a non-breaking change: The model's outputs in the exported SavedModel format remain 100% unchanged after this rewrite. Additionally, no negative impact on latency was observed - my tests yielded a latency improvement of ~3%:

	Before rewrite	After rewrite	Improvement
Latency (batch size 1)	8.63 ms	8.37 ms	3.10%
Latency (batch size 32)	192.29 ms	185.54 ms	3.63%

For this reason, I replaced the code in the existing _postprocess_keypoints_multi_class() method. However, please let me know if I should leave that method as-is and add a new one instead.

Type of change

• Bug fix (non-breaking change which fixes an issue)

Tests

Besides the unit test added in this PR, I ran tests to ensure that the TFLite and TFJS models run successfully without crashing, as well as to test outputs and latency of SavedModels. I have uploaded the models and Python scripts in the drive link below:

https://drive.google.com/drive/folders/1wCUvu_LQN6maRQFAKkAU8l1JnsOYm_-F?usp=sharing

Test Configuration: The scripts were ran in Python 3.10.13 and Tensorflow 2.8.1/2.14.0.

Checklist

• I have signed the Contributor License Agreement.
• I have read guidelines for pull request.
• My code follows the coding guidelines.
• I have performed a self code review of my own code.
• I have commented my code, particularly in hard-to-understand areas.
• I have made corresponding changes to the documentation.
• My changes generate no new warnings.
• I have added tests that prove my fix is effective or that my feature works.

[biantsh]

Hi @jch1, any updates on this? Is this repo still accepting contributions?