zauberzeug/l4t-tkdnn-darknet repository overview

⁠l4t-tkdnn

tkDNN and darknet for Jetson Nano (Linux for Tegra, l4t) with Docker. At this stage, the Container is tested with a YOLOv4-Tiny net with three output layers.

Code: https://github.com/zauberzeug/l4t-tkdnn-darknet⁠

Image: https://hub.docker.com/repository/docker/zauberzeug/l4t-tkdnn-darknet⁠

⁠Usage

⁠Dockerfile

Most of the time you will use this image as base for your own Dockerfile:

FROM zauberzeug/l4t-tkdnn-darknet:nano-r32.5.0

...

⁠Docker Run

docker run --rm -it --runtime=nvidia -e NVIDIA_VISIBLE_DEVICES=all zauberzeug/l4t-tkdnn-darknet:nano-r32.5.0

⁠Docker Compose

version: "3.3"

services:
  tkdnn:
    image: "zauberzeug/l4t-tkdnn-darknet:nano-r32.5.0"
    environment:
      - NVIDIA_VISIBLE_DEVICES=all
    build:
      args:
        MAKEFLAGS: "-j6"
    command: "/bin/bash"
    volumes:
      - ./test_data:/model
      - ./tkdnn_python/darknet_rt.py:/tkDNN/darknet_rt.py
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              capabilities: [gpu, utility]

⁠Build

We use drone to automatically build this image. If you want to do it by hand, execute

docker build --build-arg MAKEFLAGS=-j6 -t l4t-tkdnn-darknet:latest .

⁠Demo

⁠Prequesites

This Repo uses the tkDNN Repository⁠. Although tkDNN and darknet are already compiled, the weights have to be exported manually. Therefore, ./darknet export <path-to-cfg-file> <path-to-weights> layers has to be executed inside /tkDNN/darknet.

If data like a custom .cfgfile or test images should be available on startup, you can place those files inside the test_data directory. For the demo to work with custom data, the needed files are a cfg file called training.cfg, a weightfile called some_weightfile and a txt file called names.txt including the classnames. If you want to use different filenames for the yolo4tiny demo, you have to adapt the /tkdnn/tests/yolo4tiny.cpp file and recompile tkdnn.

After the weights are exported, the .rt file has to be created by calling ./test_yolo4tinyinside the build directory. Of course this step can be done with any other net available inside that directory. Precision is set beforehand by export TKDNN_MODE=FP16 or FP32. INT8 is not supported by Jetson Nano.

⁠Run Demo

The demo can be run like explained in the tkDNN Repo: https://github.com/ceccocats/tkDNN#run-the-demo⁠

This demo additionally includes a Python Wrapper like suggested here: https://github.com/ceccocats/tkDNN/pull/44⁠

This wrapper is based on this pull request and can be run by calling python3 darknet_rt.py ./darknet_fp16.rt --file=example.jpg

In general, the file takes 4 arguments:

python3 darknet_rt.py <network-rt-file> <width> <height> <path-to-file>

where:

• <network-rt-file> : The generated .rt file
• <width>: width of the provided file. If not provided, it defaults to 1600.
• <height>: height of the provided file. If not provided, it defaults to 1200.
• <path-to-file>: Path to the used image or video file. Has to be provided by using --file=*