====== Instructions for KSNN conversion tool ======

Introduction and guide on how to use the KSNN conversion tool.

===== Get the conversion tool =====

The conversion tool is integrated in the NPU SDK.

```shell
$ git lfs install
$ git lfs clone --recursive https://github.com/khadas/aml_npu_sdk.git
```
The KSNN conversion tool is under ''acuity-toolkit/python'',

```shell
$ cd aml_npu_sdk/acuity-toolkit/python && ls
$ convert  data  outputs
```

===== Conversion example =====

Choose tensorflow MobileNet SSD as an example.

1. Get the frozen model,

```shell
$ cd aml_npu_sdk/acuity-toolkit/python
$ wget https://github.com/yan-wyb/models-zoo/raw/master/tensorflow/mobilenet_ssd/ssd_mobilenet_v1_coco_2017_11_17.pb
```

2. Convert,

```shell
# uint8
$ ./convert --model-name mobilenet_ssd \
>           --platform tensorflow \
>           --model /home/yan/yan/Yan/models-zoo/tensorflow/mobilenet_ssd/ssd_mobilenet_v1_coco_2017_11_17.pb \
>           --input-size-list '300,300,3' \
>           --inputs FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_0/BatchNorm/batchnorm/mul_1 \
>           --outputs "'concat concat_1'" \
>           --mean-values '127.5 127.5 127.5 0.007843137' \
>           --quantized-dtype asymmetric_affine \
>           --source-files ./data/dataset/dataset0.txt \
>           --kboard VIM3 --print-level 0

# int8
$ ./convert --model-name mobilenet_ssd \
>           --platform tensorflow \
>           --model /home/yan/yan/Yan/models-zoo/tensorflow/mobilenet_ssd/ssd_mobilenet_v1_coco_2017_11_17.pb \
>           --input-size-list '300,300,3' \
>           --inputs FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_0/BatchNorm/batchnorm/mul_1 \
>           --outputs "'concat concat_1'" \
>           --mean-values '127.5 127.5 127.5 0.007843137' \
>           --quantized-dtype dynamic_fixed_point \
>           --qtype int8 \
>           --source-files ./data/dataset/dataset0.txt \
>           --kboard VIM3 --print-level 0

# int16
$ ./convert --model-name mobilenet_ssd \
>           --platform tensorflow \
>           --model /home/yan/yan/Yan/models-zoo/tensorflow/mobilenet_ssd/ssd_mobilenet_v1_coco_2017_11_17.pb \
>           --input-size-list '300,300,3' \
>           --inputs FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_0/BatchNorm/batchnorm/mul_1 \
>           --outputs "'concat concat_1'" \
>           --mean-values '127.5 127.5 127.5 0.007843137' \
>           --quantized-dtype dynamic_fixed_point \
>           --qtype int16 \
>           --source-files ./data/dataset/dataset0.txt \
>           --kboard VIM3 --print-level 0
```

During conversion, if you need to view detailed information, you can modify ''--print-level 0'' to ''--print-level 1''.

3. Model file generated after conversion.

```shell
$ cd aml_npu_sdk/acuity-toolkit/python
$ ls outputs/mobilenet_ssd/
mobilenet_ssd.nb  libnn_mobilenet_ssd.so
```

===== More =====

  - [[ksnn-usage|KSNN Usage]]
  - [[ksnn-api|KSNN API Documentation]]