Skip to content

Latest commit

 

History

History
155 lines (107 loc) · 4.08 KB

README.md

File metadata and controls

155 lines (107 loc) · 4.08 KB
Raw

NanoDet OpenVINO Demo

This fold provides NanoDet inference code using Intel's OpenVINO Toolkit. Most of the implements in this fold are same as demo_ncnn.

Install OpenVINO Toolkit

Go to OpenVINO HomePage

Download a suitable version and install.

Follow the official Get Started Guides: https://docs.openvinotoolkit.org/latest/get_started_guides.html

Set the Environment Variables

Windows:

Run this command in cmd. (Every time before using OpenVINO)

<INSTSLL_DIR>\openvino_2021\bin\setupvars.bat

Or set the system environment variables once for all:

Name Value
INTEL_OPENVINO_DIR <INSTSLL_DIR>\openvino_2021
INTEL_CVSDK_DIR %INTEL_OPENVINO_DIR%
InferenceEngine_DIR %INTEL_OPENVINO_DIR%\deployment_tools\inference_engine\share
HDDL_INSTALL_DIR %INTEL_OPENVINO_DIR%\deployment_tools\inference_engine\external\hddl
ngraph_DIR %INTEL_OPENVINO_DIR%\deployment_tools\ngraph\cmake

And add this to Path

%INTEL_OPENVINO_DIR%\deployment_tools\inference_engine\bin\intel64\Debug;%INTEL_OPENVINO_DIR%\deployment_tools\inference_engine\bin\intel64\Release;%HDDL_INSTALL_DIR%\bin;%INTEL_OPENVINO_DIR%\deployment_tools\inference_engine\external\tbb\bin;%INTEL_OPENVINO_DIR%\deployment_tools\ngraph\lib

Linux

Run this command in shell. (Every time before using OpenVINO)

source /opt/intel/openvino_2021/bin/setupvars.sh

Or edit .bashrc

vi ~/.bashrc

Add this line to the end of the file

source /opt/intel/openvino_2021/bin/setupvars.sh

Convert model

  1. Export ONNX model

    python ./tools/export_onnx.py --cfg_path ${CONFIG_PATH} --model_path ${PYTORCH_MODEL_PATH}

  2. Convert to OpenVINO

    cd <INSTSLL_DIR>/openvino_2021/deployment_tools/model_optimizer

    Install requirements for convert tool

    sudo ./install_prerequisites/install_prerequisites_onnx.sh

    Then convert model. Notice: mean_values and scale_values should be the same with your training settings in YAML config file.

    python3 mo.py --input_model ${ONNX_MODEL} --mean_values [103.53,116.28,123.675] --scale_values [57.375,57.12,58.395] --output output --data_type FP32 --output_dir ${OUTPUT_DIR}

Build

Windows

<OPENVINO_INSTSLL_DIR>\openvino_2021\bin\setupvars.bat
mkdir -p build
cd build
cmake ..
msbuild nanodet_demo.vcxproj /p:configuration=release /p:platform=x64

Linux

source /opt/intel/openvino_2021/bin/setupvars.sh
mkdir build
cd build
cmake ..
make

Run demo

You can convert the model to openvino or use the converted model

First, move nanodet openvino model files to the build folder and rename the files to nanodet.xml, nanodet.mapping, nanodet.bin.

Then run these commands:

Webcam

./nanodet_demo 0 0

Inference images

./nanodet_demo 1 ${IMAGE_FOLDER}/*.jpg

Inference video

./nanodet_demo 2 ${VIDEO_PATH}

Benchmark

./nanodet_demo 3 0
Model Resolution COCO mAP CPU Latency (i7-8700)
NanoDet-Plus-m 320*320 27.0 5.25ms / 190FPS
NanoDet-Plus-m 416*416 30.4 8.32ms / 120FPS
NanoDet-Plus-m-1.5x 320*320 29.9 7.21ms / 139FPS
NanoDet-Plus-m-1.5x 416*416 34.1 11.50ms / 87FPS

Custom model

If you want to use custom model, please make sure the hyperparameters in nanodet_openvino.h are the same with your training config file.

int input_size[2] = {416, 416}; // input height and width
int num_class = 80; // number of classes. 80 for COCO
int reg_max = 7; // `reg_max` set in the training config. Default: 7.
std::vector<int> strides = { 8, 16, 32, 64 }; // strides of the multi-level feature.