Basic information and examples about how to use Amlogic NPU SDK for VIM3.

We provided a docker image which contains the required environment to convert the model.

Follow Docker official docs to install Docker: Install Docker Engine on Ubuntu.

Follow the command below to get Docker image:

docker pull docker pull numbqq/npu-vim3

Get source: khadas/aml_npu_sdk

mkdir workspace && cd workspace
git clone --recursive https://github.com/khadas/aml_npu_sdk

Enter inside SDK directory aml_npu_sdk

$ cd aml_npu_sdk
$ ls
acuity-toolkit  android_sdk  convert-in-docker.sh  Dockerfile  docs  LICENSE  linux_sdk  README.md

The SDK contains the Android SDK, conversion and compilation tools, and manuals.

acuity-toolkit    # Conversion tool, used to convert AI models
android_sdk       # Android SDK
docs              # Conversion-related documents collection

acuity-toolkit directory contains the conversion tool,

$ ls acuity-toolkit
bin  demo  python  ReadMe.txt  requirements.txt

demo directory is where we can do model conversion,

bin                   # Conversion is a collection of various tools used, most of which are not open source.
demo                  # Conversion script directory, convert AI model location
demo_hybird           # Mixed Input Conversion Tool
mulity_input_demo     # mulity input demo
python                # Used to convert the model and data corresponding to the Python API
ReadMe.txt            # ReadMe.txt file explains how to convert and use
requirements.txt      # Conversion tool dependent environment

Convert the demo model in docker:

./convert-in-docker.sh

The script convert-in-docker.sh will enter docker container and then execute the conversion scripts below:

• acuity-toolkit/demo/0_import_model.sh
• acuity-toolkit/demo/1_quantize_model.sh
• acuity-toolkit/demo/2_export_case_code.sh

The conversion scripts are in the acuity-toolkit/demo directory,

$ ls acuity-toolkit/demo/*.sh -1
acuity-toolkit/demo/0_import_model.sh
acuity-toolkit/demo/1_quantize_model.sh
acuity-toolkit/demo/2_export_case_code.sh
acuity-toolkit/demo/inference.sh

• 0_import_model.sh - Import model script. Now, it supports loading Tensorflow, Caffe, Tensorflow Lite, Onnx, Keras, Pytorch, and Darknet models.
• 1_quantize_model.sh - Quantize model script. Now, it can quantize model in int8, int16 and uint8.
• 2_export_case_code.sh - Export model script. If you use VIM3, please set optimize to VIPNANOQI_PID0X88. If use VIM3L, please set optimize to VIPNANOQI_PID0X99.

After the conversion is completed, you can see the converted code in the xxxx_nbg_unify directory. Converted model is xxxx.nb. Here is the built-in model as an example.

$ ls acuity-toolkit/demo/mobilenet_tf_nbg_unify
BUILD
makefile.linux
mobilenettf.vcxproj
main.c
mobilenet_tf.nb
nbg_meta.json
vnn_global.h
vnn_mobilenettf.h
vnn_post_process.h
vnn_pre_process.h
vnn_mobilenettf.c
vnn_post_process.c
vnn_pre_process.c

For the conversion parameters and settings, please refer to:

• khadas/aml_npu_sdk/tree/master/docs/en
• Model Transcoding and Running User Guide