Table of contents

Preparation

Please refer here for the environment construction procedure.

Convert

Convert the model.

Please change the parameter "setting.conf" of the argument according to the model to be converted.

$ cd /home/cvtool/conversion/onnx
$ ./onnx_conversion.sh setting.conf

The model after conversion is output to the following directory.

${OUTPUT_DIR}/${NET_NAME}/${PARSER_OPTION}/[モデル名]

Setting.conf

# Network Name
NET_NAME=mobilenet_v2

# SSD Model or Not (0:not SSD, 1:SSD)
IS_SSD=0

# Path to Directory for ONNX Models
MODEL_DIR=./sample/mobilenet_v2/models

# Path to Directory for DRA Images
DRA_IMAGE_DIR=../../dra_img

# Path to Directory for Output Data
OUTPUT_DIR=./out

# Quantization Mode
#  FIX8  : Fixed-point  8bit
#  FIX16 : Fixed-point 16bit
#  MIX   : FIX8/FIX16 mixed
PARSER_OPTION=MIX

# Input Data Format (0:NHWC, 1:NCHW)
IN_DATA_FORMAT=1

# Input Data Channel
IN_DATA_CHANNEL=3

# Input Data Width
IN_DATA_WIDTH=224

# Input Data Height
IN_DATA_HEIGHT=224

# Input Data Mean Vector
IN_MEAN=103.94,116.78,123.68

# Input Data Scale
# IN_SCALE=1/Scale
IN_SCALE=58.823529411

# RGB or BGR (0:RGB, 1:BGR)
IS_BGR=1

# Output Layers Name
OUT_LAYER=mobilenetv20_output_flatten0_reshape0

#cavalry version
#if not specified -> ""
CAVALRY_VER="2.1.7"

# Unique preprocess
# if use im2bin -> NONE
# if use unique preprocess -> script path
PREPRO=NONE
PREPRO_ARG=""

# Input file data format
IN_DATA_FILEFORMAT=0,0,0,0

# Transpose indices(NONE:without transpose , 0,3,1,2:transpose (EX))
IN_DATA_TRANSPOSE=NONE

• NET_NAME: The name of network

  • Any name can be set.

• IS_SSD: Not used.

• MODEL_DIR: Path to directory which includes .onnx files

  • All .onnx files under the directory are converted.

• IMAGE_DIR: Path to directory which includes image files for optimizing quantization

  • Please put the directory image files for training. Recommended number of image files is 100 to 200.

  • Available image file format is what OpenCV can handle, for example, JPEG, PNG and so on.

  • Any resolution is available.

• OUTPUT_DIR: Path to directory which converted data will be put

• PARSER_OPTION: Quantization mode

  • Select from FIX8/FIX16/MIX (FIX8/FIX16 mixed).

• IN_DATA_CHANNEL: Number of input image channel for target model

• N_DATA_WIDTH: Width of input image for target model

• IN_DATA_HEIGHT: Height of input image for target model

• IN_MEAN: ormalization parameter (mean) of input image

  • In case of setting by numerical value, do not put space between “,” as following.
    IN_MEAN=127.5,127.5,127.5

• IN_SCALE: Normalization parameter (scale) of input image

  • In case of setting different value for each channel, split values by “,”. Do not put space between “,”.

• IS_BGR: Format of input image (RGB or BGR)

• OUT_LAYER: The name of output layer for target network

  • If two or more layers exists, separate layers by “,”.

• PRIORBOX_NODE: Node equivalent to “priorbox”

  • Need to set when IS_SSD=1

• CAVALRY_VER: Version of cavalry to use

• PREPRO: Path of preprocessing script (python script)

  • Refer to “/home/cvtool/ common/prepro.py” for how to create a script

• PREPRO_ARG: Argument of preprocessing script (python script)

• IN_DATA_FILEFORMAT: Input data format

  • Examples : uint8->0,0,0,0, float32->1,2,0,7, float16-> 1,1,0,4

  • When the value of IN_DATA_FILEFORMAT changes from “0,0,0,0”, setting PREPRO is needed.

• N_DATA_TRANSPOSE: Specify when performing TRANSPOSE on the input data

$ graph_surgery.py onnx -m mobilenetv210.onnx -o mobilenetv210_mod.onnx -t Default

Convert sample models

mobilenetv2

Move directory.

$ cd /home/cvtool/conversion/onnx/mobilenetv2

Convert the model.

$ ./onnx_conversion.sh setting.conf

yolov5

Download sample model.

$ cd /home/cvtool/conversion/onnx/yolov5
$ ./setup_yolov5.sh

Convert the model.

$ ./onnx_conversion.sh setting.conf