Table of contents
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| Table of Contents | ||||
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Preparation
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Please refer here for the environment construction procedure.
Conversion
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Convert the model.
Please change the parameter "setting.conf" of the argument according to the model to be converted.
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| Note |
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Pytorch model needs to be converted to ONNX in advance |
Setting.conf
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# 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 |
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If there is a node that is not a constant, it cannot be converted normally. In that case, convert the model using the following command, and perform quantization with the converted model. -m : before conversion model -o : after conversion model |
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$ graph_surgery.py onnx -m mobilenetv210.onnx -o mobilenetv210_mod.onnx -t Default |
Convert sample models
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mobilenetv2
Move directory.
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$ cd /home/cvtool/conversion/onnx/mobilenetv2 |
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| Code Block |
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$ ./onnx_conversion.sh setting.conf |
yolov5
Download sample model.
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$ cd /home/cvtool/conversion/onnx/yolov5 $ ./setup_yolov5.sh |
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