Increasing fps on Jetson TX2 for a Tensorflow algorithm

Robotics & Edge Computing Jetson Systems Jetson TX2
1

Hi ,

I am working on a Real time Object detection algorithm using Tensorflow with GPU support v1.0.1 on Jetson TX2.
After using the below commands:
$ sudo nvpmodel -m 0
$ sudo ./jetson_clocks.sh

The algorithm is taking 0.8 to 0.9 seconds to detect one frame.

How can I increase the fps to 17-19?

Kindly help.

2

Hi,

We have an object detection sample with DetectNet and TensorRT:

It can reach around 11-fps with TX2 maximized frequency.
Thanks.

3

Hi AastaLLL,

Thanks for your response.
I am concentrating on increasing the fps of my current working algorithm as it is fine tuned for automotive on-road object detection.

Is there any other way I can increase the fps?

Kindly help

4

Hi,

Suppose you want to use a TensorFlow model on Jetson.
Here are some advices for you:

1. Try TensorRT:

2. Profile your model first.
If an op run slowly on GPU(ex. tf.where), try to place it on CPU.

Thanks.

5

Hi AastaLLL,

It take 17 seconds per frame while placed on the CPU whereas 0.8 seconds/frame when run on the GPU.
The code is written in python. I have tensorRT v3.0 installed on Jetson TX2. How do I use UFF format to make it compatible with tensorRT?

I did run the detectnet code from the link shared - GitHub - dusty-nv/jetson-inference: Hello AI World guide to deploying deep-learning inference networks and deep vision primitives with TensorRT and NVIDIA Jetson.
Is there anyway to train the model using our own dataset and checkpoint file ?
For instance -(VGGnet_fast_rcnn_iter_70000.ckpt)

Thanks,
Pratosha

6

Hi,

To launch TensorRT with a TensorFlow model on Jetson, it requires following steps:

1. Convert TensorFlow model to UFF format

  • Require x86 Linux platform
  • Python interface
  • Sample is located at '/usr/local/lib/python2.7/dist-packages/tensorrt/examples/tf_to_trt/'

2. Create an TensorRT engine with UFF file

  • Can be applied on Jetson
  • C++
  • Sample is located at '/usr/src/tensorrt/samples/sampleUffMNIST/'

You can re-train the detectNet model with DIGITs and tutorial can be found here:
https://github.com/dusty-nv/jetson-inference#digits-workflow

Please noticed that detectNet is a caffe-based model and use nvcaffe_parser rather than uff_parser.

Thanks.

7

Hi AastaLLL,

Thanks :) will surely try