Instructions for KSNN conversion tool

This document will introduce how to use the KSNN conversion tool.

Get convert tool

The tool is integrated in the NPU conversion tool warehouse of VIM3.

yan@yan-wyb:~$ git clone --recursive

The KSNN conversion tool is under acuity-toolkit/python,

yan@yan-wyb:~$ cd aml_npu_sdk/acuity-toolkit/python && ls
convert  data  outputs

Example for Convert

Choose tensorflow Inception V3 as a exmple.

  1. Get forzen model
yan@yan-wyb:~$ cd aml_npu_sdk/acuity-toolkit/python
yan@yan-wyb:~/aml_npu_sdk/acuity-toolkit/python$ wget
  1. Convert
yan@yan-wyb:~$ cd aml_npu_sdk/acuity-toolkit/python
yan@yan-wyb:~/aml_npu_sdk/acuity-toolkit/python$ ./convert --model-name inception \
>           --platform tensorflow \
>           --model /home/yan/yan/Yan/models-zoo/tensorflow/inception/inception_v3_2016_08_28_frozen.pb \
>           --input-size-list '299,299,3' \
>           --inputs input \
>           --outputs InceptionV3/Predictions/Reshape_1 \
>           --mean-values '128 128 128 0.0078125' \
>           --quantized-dtype asymmetric_affine \
>           --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.

  1. File generated after conversion
yan@yan-wyb:~$ cd aml_npu_sdk/acuity-toolkit/python
yan@yan-wyb:~/aml_npu_sdk/acuity-toolkit/python$ ls outputs/inception/


  1. KSNN Usage

  2. KSNN API Documentation

  3. More conversion parameters can be viewed through -h

yan@yan-wyb:~/aml_npu_sdk/acuity-toolkit/python$ ./convert -h
usage: convert [-h] [--model-name MODEL_NAME] [--print-level PRINT_LEVEL] [--platform PLATFORM] [--kboard KBOARD] [--model MODEL] [--outputs OUTPUTS] [--input-size-list INPUT_SIZE_LIST]
               [--size-with-batch SIZE_WITH_BATCH] [--input-dtype-list INPUT_DTYPE_LIST] [--inputs INPUTS] [--weights WEIGHTS] [--std-values STD_VALUES] [--mean-values MEAN_VALUES]
               [--predef-file PREDEF_FILE] [--config CONFIG] [--proto PROTO] [--convert-engine CONVERT_ENGINE] [--quantized-dtype QUANTIZED_DTYPE] [--qtype QTYPE] [--batch-size BATCH_SIZE]
               [--iterations ITERATIONS] [--device DEVICE] [--hybrid HYBRID] [--algorithm ALGORITHM] [--moving-average-weight MOVING_AVERAGE_WEIGHT] [--divergence-nbins DIVERGENCE_NBINS]

optional arguments:
  -h, --help            show this help message and exit
  --model-name MODEL_NAME
                        the model conversion name you want to use
  --print-level PRINT_LEVEL
                        information printing level(default 0) : 0(just print error)/1(full information)
  --platform PLATFORM   choose you platform : pytorch/caffe/tensorflow/tflite/darknet/onnx/keras
  --kboard KBOARD       Choose khadas board: VIM3/VIM3L
  --model MODEL         Model filename
  --outputs OUTPUTS     Output points of graph
  --input-size-list INPUT_SIZE_LIST
                        Inputs size list for correspoding input points
  --size-with-batch SIZE_WITH_BATCH
                        Describe if the '--input-size-list' contain the highest batch dimension.
  --input-dtype-list INPUT_DTYPE_LIST
                        Input tensors dtype for corresponding input points
  --inputs INPUTS       Inputs points of graph
  --weights WEIGHTS     Weights filename
  --std-values STD_VALUES
                        Standard values for Tensorflow quantmodels
  --mean-values MEAN_VALUES
                        Mean values for quant models
  --predef-file PREDEF_FILE
                        Pre-define file to import some complex models
  --config CONFIG       A json file that describes model information.
  --proto PROTO         Switch protocol used for the Caffe binary protocol buffer file
  --convert-engine CONVERT_ENGINE
                        Convert engine for model
  --quantized-dtype QUANTIZED_DTYPE
                        Quant type :'asymmetric_affine/dynamic_fixed_point/perchannel_symmetric_affine/symmetric_affine/asymmetric_quantized'
  --qtype QTYPE         qtpye: 'uint8/int8/int16'
  --batch-size BATCH_SIZE
                        Integer value which specifies the batch size
  --iterations ITERATIONS
                        Number of iterations to run, integer value.
  --device DEVICE       Specify the compute device. GPU/CPU
  --hybrid HYBRID       Setup a hybrid quantize network
  --algorithm ALGORITHM
                        Quantization algorithm: normal(Default)/kl_divergence/moving_average.
  --moving-average-weight MOVING_AVERAGE_WEIGHT
                        Moving average coefficient.Positive float value.
  --divergence-nbins DIVERGENCE_NBINS
                        KL divergence histogram nbins.Positive integer value.