<h3 dir="auto">System information</h3>
<ul dir="auto">
<li><strong>Have I written custom code (as opposed to using a stock example script provided in TensorFlow)</strong>:<br>
Yes</li>
<li><strong>OS Platform and Distribution (e.g., Linux Ubuntu 16.04)</strong>:<br>
Ubuntu 16.04</li>
<li><strong>TensorFlow installed from (source or binary)</strong>:<br>
binary(By pip)</li>
<li><strong>TensorFlow version (use command below)</strong>:<br>
1.4.1</li>
<li><strong>Python version</strong>:<br>
3.5.2</li>
<li><strong>Bazel version (if compiling from source)</strong>:</li>
<li><strong>GCC/Compiler version (if compiling from source)</strong>:</li>
<li><strong>CUDA/cuDNN version</strong>:<br>
8.0.61/6.0.21</li>
<li><strong>GPU model and memory</strong>:<br>
GTX 1080 Ti, 11172MiB</li>
<li><strong>Exact command to reproduce</strong>:<br>
python main.py</li>
</ul>
<h3 dir="auto">Describe the problem</h3>
<p dir="auto">I train my RFCN by tensorflow. My project need very high speed. So I use the profile and I find that non_max_suppression is on CPU? Is there a GPU version?I think if you calculate all pairs of boxes IOU first, then just for-loop once will ultimately boost speed, there have some trick in it, just see the source code in <a href="https://github.com/rbgirshick/py-faster-rcnn/tree/master/lib/nms">https://github.com/rbgirshick/py-faster-rcnn/tree/master/lib/nms</a>. I think cuda version of NMS is faster than CPU version.</p>
<p dir="auto">It appears that tf.image.non_max_suppression currently takes about 200ms for about 8000 boxes, runs on a single CPU thread and doesn't have a GPU implementation.</p>
<h3 dir="auto">Environment info</h3>
<p dir="auto">Operating System:<br>
Ubuntu 16.04</p>
<p dir="auto">Installed version of CUDA and cuDNN:<br>
(please attach the output of <code class="notranslate">ls -l /path/to/cuda/lib/libcud*</code>):<br>
8.0, 5.1.5</p>
<p dir="auto">If installed from binary pip package, provide:</p>
<ol dir="auto">
<li>A link to the pip package you installed:</li>
<li>The output from <code class="notranslate">python -c "import tensorflow; print(tensorflow.__version__)"</code>.<br>
0.12.0-rc1</li>
</ol>