~~tag>VIM4 NPU Docker ~~

====== NPU Model Convert ======

{{indexmenu_n>2}}

<WRAP important >
Only **New VIM4** supports NPU, you can [[../configurations/identify-version|check VIM4 the version]]
</WRAP>

<WRAP tip >
We provided the Docker container for you to convert the model.
</WRAP>

===== Build Docker Environment =====

Follow Docker official docs to install Docker: [[https://docs.docker.com/engine/install/ubuntu/|Install Docker Engine on Ubuntu]].

Follow the script below to get Docker image:

```shell
docker pull numbqq/npu-vim4
```

===== Get Convert Tool =====

```shell
$ git clone https://gitlab.com/khadas/vim4_npu_sdk.git
$ cd vim4_npu_sdk
$ ls
adla-toolkit-binary  adla-toolkit-binary-1.2.0.9  convert-in-docker.sh  Dockerfile  docs  README.md
```

  * ''adla-toolkit-binary/docs'' - SDK documentations
  * ''adla-toolkit-binary/bin'' - SDK tools required for model conversion
  * ''adla-toolkit-binary/demo'' - Conversion examples

===== Convert Model =====

Convert the demo model in docker:

```shell
./convert-in-docker.sh
```

If everything works well you will find the converted files below.

```shell
~/vim4_npu_sdk$ ls adla-toolkit-binary/demo/
caffe_output     darknet_output  dataset.txt   libstdc++_so  mxnet_output  paddle_output   quantized_tflite_output  tflite_output
convert_adla.sh  data            keras_output  model_source  onnx_output   pytorch_output  tensorflow_output
```

Converted model is ''xxxx.adla''.

<WRAP tip >
If you want to convert your own model, just modify script ''adla-toolkit-binary/demo/convert_adla.sh'' and  then execute ''./convert-in-docker.sh'' to convert your model.
</WRAP>


==== Important parameters ====

  * ''model-type'' - Model type used in the conversion.
  * ''model(model files)/weights(model weights file)'' - ''model'' parameter must be set. ''weights'' needs to be set when model-type is caffe/darknet/mxnet.
  * ''inputs/input-shapes'' - ''inputs''  Input node names. ''input-shapes''  The sizes of input nodes. Need to be set when model-type is pytorch/onnx/mxnet/tensorflow/ tflite/paddle.
  * ''outputs'' - Output node names. When model-type is tensorflow, outputs needs to be set.
  * ''dtypes'' - Input type, set the type information corresponding to the input (optional).Default ''float32''.
  * ''quantize-dtype'' - Quantization type. Currently, it supports ''int8'', ''int16'' and ''uint8'' quantification types.
  * ''source-file'' - ''dataset.txt''. The txt file contains paths to the images that need to be quantized. It supports images and npy files.
  * ''channel-mean-value'' - The pre-processing parameters are set according to the pre processing methods used during model training. It includes four values, m1, m2, m3, and scale. The first three are mean-value parameters and the last one is the scale parameter. For the input data with three channels (data1, data2, data3), the pre-processing steps are
    * Out1 = (data1-m1)/scale
    * Out2 = (data2-m2)/scale
    * Out3 = (data3-m3)/scale
  * ''batch-size'' - Set the batch-size for the adla file after conversion. Currently, the default value is 1.
  * ''iterations'' - Optional parameter, if ''dataset.txt'' provides data for multi-groups, and needs to use all data provided for quantization, set ''iterations'' and make sure iterations*batch size = Number of data in the multi-groups.
  * ''outdir'' - Directory for the generated. The default value is the current directory.
  * ''target-platform'' - Specify the target platform for the adla file, it should be ''PRODUCT_PID0XA003'' for VIM4.

===== See Also =====

For more information, please check ''docs/model_conversion_user_guide_1.2.pdf''.