Addons: Implement fast-nms

[fsx950223]

Describe the feature and the current behavior/state.
The fast-nms algorithm has better performance than traditional nms.
Described in paper.
Kernel implementation may provides better performance than python implementation.
Relevant information

• Are you willing to contribute it (yes/no): yes
• Are you willing to maintain it going forward? (yes/no): yes
• Is there a relevant academic paper? (if so, where): https://arxiv.org/pdf/1904.02689.pdf
• Is there already an implementation in another framework? (if so, where): https://github.com/dbolya/yolact/blob/1722387d75210361c1f21c911d0e2420a48c7a23/layers/functions/detection.py#L136
• Was it part of tf.contrib? (if so, where): no

Which API type would this fall under (layer, metric, optimizer, etc.)

Who will benefit with this feature?

Any other info.
Tensorflow Implementation on colab

[WindQAQ]

Would like to see how fast it is compared with tf.image.non_max_suppression.

[SSaishruthi]

Is anyone working on this? If not, I will take a look @seanpmorgan

[seanpmorgan]

Is anyone working on this? If not, I will take a look @seanpmorgan

Not that I'm aware of, but @fsx950223 said they are willing to implement. If there has been no progress then should be fine to start on.

[fsx950223]

I'm a little busy for now, I will try it next week. Thanks.

[SSaishruthi]

I will wait for the next update on this.

[samikama]

@fsx950223 how does this algorithm compare against existing GPU NMS implementations in TF? Do you have any idea?

[fsx950223]

@fsx950223 how does this algorithm compare against existing GPU NMS implementations in TF? Do you have any idea?

Tensorflow benchmark could compare performance between them.

[bhack]

We have an upstream fast nms kernel in Tensorflow Lite https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/kernels/detection_postprocess.cc#L456-L463. @seanpmorgan I think this could be more in the "perimeter" of keras-cv (/cc @tanzhenyu ) as yesterday we have closed the other image processing related issues/PRs.