Using TF-TRT, I’m testing for deep learning performance improvements with Jetson TX2 and Jetson AGX Xavier.
I created a TF-TRT converter and applied it to BlitzNet(https://github.com/dvornikita/blitznet) network.
The TF-TRT converter operates in the following operation:
- Load the frozen graph
- import trt_graph
- save the graph to ‘SavedModel’
First, it was tested on a PC and applied by Jetson, but the performance between TX2 and Xavier is too extreme.
Common sense, Xavier is fast and TX2 is slow. but the TF-TRT converter result is the TX2 is faster.
TF-TRT Convert Result.
Jetson Xavier : about 25min
Jetson TX2 : about 8min
And… SavedModel(TF-TRT Graph) Restore Result
Jetson Xavier : about 10~11min
Jetson TX2 : about 14sec
Is this a normal situation?
I’m really confused.
And Jetosn System
- Jetson AGX Xavier (MAX-N)
JetPack 4.1.1DP, TensorFlow 1.12.0(TensorFlow for JetPack), CUDA 10, TensorRT 5
- Jetson TX2 (MAX-N)
JetPack 3.3, TensorFlow 1.9.0(TensorFlow for JetPack), CUDA 9, TensorRT 4