git clone https://github.com/Daipuwei/Mini-VGG-CIFAR10

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

Get Docker.

$ docker pull yanwyb/npu:v1
$ docker run -it --name vim4-npu1 -v $(pwd):/home/khadas/npu \
				-v /etc/localtime:/etc/localtime:ro \
				-v /etc/timezone:/etc/timezone:ro \
				yanwyb/npu:v1

Download Tool from khadas/vim4_npu_sdk.git.

$ git clone https://gitlab.com/khadas/vim4_npu_sdk.git

Keras model can convert rknn model directly. But this demo is to convert tensorflow model(.pb) first, and convert tnesorflow model to rknn model. We use this tool to convert.

git clone https://github.com/amir-abdi/keras_to_tensorflow.git

Enter vim4npu_sdk/demo and modify convert_adla.sh as follows.

convert_adla.sh

#!/bin/bash
 
ACUITY_PATH=../bin/
#ACUITY_PATH=../python/tvm/
adla_convert=${ACUITY_PATH}adla_convert
 
 
if [ ! -e "$adla_convert" ]; then
    adla_convert=${ACUITY_PATH}adla_convert.py
fi
 
$adla_convert --model-type tensorflow \
        --model ./model_source/vgg16/vgg16.pb \
        --inputs image_input --input-shapes 32,32,3 \
        --outputs dense_2/Softmax \
        --inference-input-type float32 \
		--inference-output-type float32 \
        --quantize-dtype int8 --outdir tensorflow_output \
        --channel-mean-value "0,0,0,255" \
        --source-file vgg16_dataset.txt \
        --iterations 500 \
        --batch-size 1 \
        --target-platform PRODUCT_PID0XA003

Run convert_adla.sh to generate VIM4 model. The converted model is xxx.adla in tensorflow_output.

$ bash convert_adla.sh

Clone the source code from our khadas/vim4_npu_applications.

$ git clone https://github.com/khadas/vim4npu_applications.git

$ sudo apt update
$ sudo apt install libopencv-dev python3-opencv cmake

Put vgg16_int8.adla in vim4_npu_applications/vgg16/data/.

# Compile
$ cd vim4npu_applications/vgg16
$ mkdir build
$ cd build
$ cmake ..
$ make
 
# Run
$ sudo ./vgg16 -m ../data/vgg16_int8.adla -p ../data/airplane.jpeg