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MindYOLO部署

依赖

pip install -r requirement.txt

MindSpore Lite环境准备

参考：Lite环境配置
注意：MindSpore Lite适配的python环境为3.7，请在安装Lite前准备好python3.7的环境

  1. 根据环境，下载配套的tar.gz包和whl包
  2. 解压tar.gz包并安装对应版本的whl包
tar -zxvf mindspore_lite-2.0.0a0-cp37-cp37m-{os}_{platform}_64.tar.gz
pip install mindspore_lite-2.0.0a0-cp37-cp37m-{os}_{platform}_64.whl
  1. 配置Lite的环境变量 LITE_HOME为tar.gz解压出的文件夹路径，推荐使用绝对路径
export LITE_HOME=/path/to/mindspore-lite-{version}-{os}-{platform}
export LD_LIBRARY_PATH=$LITE_HOME/runtime/lib:$LITE_HOME/tools/converter/lib:$LD_LIBRARY_PATH
export PATH=$LITE_HOME/tools/converter/converter:$LITE_HOME/tools/benchmark:$PATH

快速开始

模型转换

ckpt模型转为mindir模型，此步骤可在CPU/Ascend910上运行

python ./deploy/export.py --config ./path_to_config/model.yaml --weight ./path_to_ckpt/weight.ckpt --per_batch_size 1 --file_format MINDIR --device_target [CPU/Ascend]
e.g.
# 在CPU上运行
python ./deploy/export.py --config ./configs/yolov5/yolov5n.yaml --weight yolov5n_300e_mAP273-9b16bd7b.ckpt --per_batch_size 1 --file_format MINDIR --device_target CPU
# 在Ascend上运行
python ./deploy/export.py --config ./configs/yolov5/yolov5n.yaml --weight yolov5n_300e_mAP273-9b16bd7b.ckpt --per_batch_size 1 --file_format MINDIR --device_target Ascend

Lite Test

python deploy/test.py --model_type Lite --model_path ./path_to_mindir/weight.mindir --config ./path_to_config/yolo.yaml
e.g.
python deploy/test.py --model_type Lite --model_path ./yolov5n.mindir --config ./configs/yolov5/yolov5n.yaml

Lite Predict

python ./deploy/predict.py --model_type Lite --model_path ./path_to_mindir/weight.mindir --config ./path_to_conifg/yolo.yaml --image_path ./path_to_image/image.jpg
e.g.
python deploy/predict.py --model_type Lite --model_path ./yolov5n.mindir --config ./configs/yolov5/yolov5n.yaml --image_path ./coco/image/val2017/image.jpg

脚本说明

  • predict.py 支持单张图片推理
  • test.py 支持COCO数据集推理
  • 注意：当前只支持在Ascend 310上推理

MindX部署

查看 MINDX

MindIR部署

查看 MINDIR

ONNX部署

注意: 仅部分模型支持导出ONNX并使用ONNXRuntime进行部署
查看 ONNX

标准和支持的模型库

Name Scale Context ImageSize Dataset Box mAP (%) Params FLOPs Recipe Download
YOLOv8 N D310x1-G 640 MS COCO 2017 37.2 3.2M 8.7G yaml ckpt
mindir
YOLOv8 S D310x1-G 640 MS COCO 2017 44.6 11.2M 28.6G yaml ckpt
mindir
YOLOv8 M D310x1-G 640 MS COCO 2017 50.5 25.9M 78.9G yaml ckpt
mindir
YOLOv8 L D310x1-G 640 MS COCO 2017 52.8 43.7M 165.2G yaml ckpt
mindir
YOLOv8 X D310x1-G 640 MS COCO 2017 53.7 68.2M 257.8G yaml ckpt
mindir
YOLOv7 Tiny D310x1-G 640 MS COCO 2017 37.5 6.2M 13.8G yaml ckpt
mindir
YOLOv7 L D310x1-G 640 MS COCO 2017 50.8 36.9M 104.7G yaml ckpt
mindir
YOLOv7 X D310x1-G 640 MS COCO 2017 52.4 71.3M 189.9G yaml ckpt
mindir
YOLOv5 N D310x1-G 640 MS COCO 2017 27.3 1.9M 4.5G yaml ckpt
mindir
YOLOv5 S D310x1-G 640 MS COCO 2017 37.6 7.2M 16.5G yaml ckpt
mindir
YOLOv5 M D310x1-G 640 MS COCO 2017 44.9 21.2M 49.0G yaml ckpt
mindir
YOLOv5 L D310x1-G 640 MS COCO 2017 48.5 46.5M 109.1G yaml ckpt
mindir
YOLOv5 X D310x1-G 640 MS COCO 2017 50.5 86.7M 205.7G yaml ckpt
mindir
YOLOv4 CSPDarknet53 D310x1-G 608 MS COCO 2017 45.4 27.6M 52G yaml ckpt
mindir
YOLOv4 CSPDarknet53(silu) D310x1-G 640 MS COCO 2017 45.8 27.6M 52G yaml ckpt
mindir
YOLOv3 Darknet53 D310x1-G 640 MS COCO 2017 45.5 61.9M 156.4G yaml ckpt
mindir
YOLOX N D310x1-G 416 MS COCO 2017 24.1 0.9M 1.1G yaml ckpt
mindir
YOLOX Tiny D310x1-G 416 MS COCO 2017 33.3 5.1M 6.5G yaml ckpt
mindir
YOLOX S D310x1-G 640 MS COCO 2017 40.7 9.0M 26.8G yaml ckpt
mindir
YOLOX M D310x1-G 640 MS COCO 2017 46.7 25.3M 73.8G yaml ckpt
mindir
YOLOX L D310x1-G 640 MS COCO 2017 49.2 54.2M 155.6G yaml ckpt
mindir
YOLOX X D310x1-G 640 MS COCO 2017 51.6 99.1M 281.9G yaml ckpt
mindir
YOLOX Darknet53 D310x1-G 640 MS COCO 2017 47.7 63.7M 185.3G yaml ckpt
mindir