部署¶

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

3. 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