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https://github.com/SamSamhuns/yolov5_export_cpu
Exporting YOLOv5 for CPU inference with ONNX and OpenVINO
https://github.com/SamSamhuns/yolov5_export_cpu
cpu-inference-api object-detection onnx-inference openvino-inference-engine yolov5
Last synced: 2 months ago
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Exporting YOLOv5 for CPU inference with ONNX and OpenVINO
- Host: GitHub
- URL: https://github.com/SamSamhuns/yolov5_export_cpu
- Owner: SamSamhuns
- License: mit
- Created: 2021-06-29T06:35:26.000Z (over 3 years ago)
- Default Branch: master
- Last Pushed: 2024-08-07T06:31:14.000Z (5 months ago)
- Last Synced: 2024-08-08T09:55:22.418Z (5 months ago)
- Topics: cpu-inference-api, object-detection, onnx-inference, openvino-inference-engine, yolov5
- Language: Python
- Homepage:
- Size: 5.57 MB
- Stars: 35
- Watchers: 2
- Forks: 9
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
Awesome Lists containing this project
- awesome-oneapi - yolov5_export_cpu - The project provides documentation on exporting YOLOv5 models for fast CPU inference using Intel's OpenVINO framework (Table of Contents / AI - Computer Vision)
- awesome-openvino - yolov5_export_cpu - The project provides documentation on exporting YOLOv5 models for fast CPU inference using Intel's OpenVINO framework (Table of content / AI Computer Vision)
README
# YOLOv5 CPU Export and OpenVINO Inference
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Documentation on exporting YOLOv5 models for fast CPU inference using Intel's OpenVINO framework (Tested on commits up to June 6, 2022 in docker).
## Google Colab Conversion
Convert yolov5 model to IR format with Google Colab. [](https://colab.research.google.com/drive/1K8gnZEka47Gbcp1eJbBaSe3GxngJdvio?usp=sharing) (Recommended)
## 1. Clone and set up the Official YOLOv5 GitHub repository
Setup
All package installations should be done in a virtualenv or conda env to prevent package conflict errors.
- Install required requirements for onnx and openvino Inference
```bash
pip install --upgrade pip
pip install -r inf_requirements.txt
```
- Clone and install requirements for yolov5 repository
```bash
git clone https://github.com/ultralytics/yolov5 # clone repo
cd yolov5
pip install -r requirements.txt # base requirements
```
## 2. Export a Trained YOLOv5 Model as ONNX
Export
Export a pre-trained or custom trained YOLOv5 model to generate the respective ONNX, TorchScript and CoreML formats of the model. The pre-trained `yolov5s.pt` is the lightest and fastest model for CPU inference. Other slower but more accurate models include `yolov5m.pt, yolov5l.pt` and `yolov5x.pt`. All available model details at Ultralytics YOLOv5 [README](https://github.com/ultralytics/yolov5#pretrained-checkpoints).
A custom training checkpoint i.e. `runs/exp/weights/best.pt` can be used for conversion as well.
- Export a pre-trained light yolov5s.pt model at 640x640 with batch size 1
```bash
python export.py --weights yolov5s.pt --include onnx --img 640 --batch 1
```
- Export a custom checkpoint for dynamic input shape {BATCH_SIZE, 3, HEIGHT, WIDTH}. Note, for CPU inference mode, BATCH_SIZE must be set to 1. Install onnx-simplifier for simplifying onnx exports
```bash
pip install onnx-simplifier==0.3.10
python export.py --weights runs/exp/weights/best.pt --include onnx --dynamic --simplify
```
- Cd to `yolov5_export_cpu` dir and move the onnx model to `yolov5_export_cpu/models` directory
```bash
mv yolov5_export_cpu/models/
```
## 3. Test YOLOv5 ONNX model inference
ONNX inference
```bash
python detect_onnx.py -m image -i
python detect_onnx.py -m video -i
# python detect_onnx.py -h for more info
```
Optional: To convert the all frames in the `output` directory into a mp4 video using `ffmpeg`, use `ffmpeg -r 25 -start_number 00001 -i output/frame_onnx_%5d.jpg -vcodec libx264 -y -an onnx_result.mp4`
## 4. Export ONNX to OpenVINO
**Recommended Option A**
### Option A. Use OpenVINO's python dev library
A1. Install OpenVINO python dev library
Instructions for setting OpenVINO available [here](https://docs.openvino.ai/latest/openvino_docs_install_guides_install_dev_tools.html)
```bash
# install required OpenVINO lib to convert ONNX to OpenVINO IR
pip install openvino-dev[onnx]
```
A2. Export ONNX to OpenVINO IR
This will create the OpenVINO Intermediate Model Representation (IR) model files (xml and bin) in the directory `models/yolov5_openvino`.
**Important Note:** --input_shape must be provided and match the img shape used to export ONNX model. Batching might not supported for CPU inference
```bash
# export onnx to OpenVINO IR
mo \
--progress \
--input_shape [1,3,640,640] \
--input_model models/yolov5s.onnx \
--output_dir models/yolov5_openvino
```
[Full OpenVINO export options](https://docs.openvinotoolkit.org/latest/openvino_docs_MO_DG_prepare_model_convert_model_Converting_Model_General.html)
### Option B. Use OpenVINO Docker
B1. Download Docker and OpenVINO Docker Image
[Install docker](https://docs.docker.com/get-docker/) in your system if not already installed.
Pass the docker run command below in a terminal which will automatically download the OpenVINO Docker Image and run it. The `models` directory containing the ONNX model must be in the current working directory.
```bash
docker run -it --rm \
-v $PWD/models:/home/openvino/models \
openvino/ubuntu18_dev:latest \
/bin/bash -c "cd /home/openvino/; bash"
```
B2. Export ONNX model to an OpenVINO IR representation
This will create the OpenVINO Intermediate Model Representation (IR) model files (xml and bin) in the directory `models/yolov5_openvino` which will be available in the host system outside the docker container.
**Important Note:** --input_shape must be provided and match the img shape used to export ONNX model. Batching might not supported for CPU inference
```bash
# inside the OpenVINO docker container
mo \
--progress \
--input_shape [1,3,640,640] \
--input_model models/yolov5s.onnx \
--output_dir models/yolov5_openvino
# exit OpenVINO docker container
exit
```
[Full OpenVINO export options](https://docs.openvinotoolkit.org/latest/openvino_docs_MO_DG_prepare_model_convert_model_Converting_Model_General.html)
## 5. Test YOLOv5 OpenVINO IR model CPU inference
OpenVINO model inference
```bash
python detect_openvino.py -m image -i
python detect_openvino.py -m video -i
# python detect_openvino.py -h for more info
```
Optional: To convert the all frames in the `output` directory into a mp4 video using `ffmpeg`, use `ffmpeg -r 25 -start_number 00001 -i output/frame_openvino_%5d.jpg -vcodec libx264 -y -an openvino_result.mp4`