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FastDeploy/examples/vision/detection/yolov5seg/python/README.md
WJJ1995 aa6931bee9 [Model] Add YOLOv5-seg (#988) 
* add onnx_ort_runtime demo

* rm in requirements

* support batch eval

* fixed MattingResults bug

* move assignment for DetectionResult

* integrated x2paddle

* add model convert readme

* update readme

* re-lint

* add processor api

* Add MattingResult Free

* change valid_cpu_backends order

* add ppocr benchmark

* mv bs from 64 to 32

* fixed quantize.md

* fixed quantize bugs

* Add Monitor for benchmark

* update mem monitor

* Set trt_max_batch_size default 1

* fixed ocr benchmark bug

* support yolov5 in serving

* Fixed yolov5 serving

* Fixed postprocess

* update yolov5 to 7.0

* add poros runtime demos

* update readme

* Support poros abi=1

* rm useless note

* deal with comments

* support pp_trt for ppseg

* fixed symlink problem

* Add is_mini_pad and stride for yolov5

* Add yolo series for paddle format

* fixed bugs

* fixed bug

* support yolov5seg

* fixed bug

* refactor yolov5seg

* fixed bug

* mv Mask int32 to uint8

* add yolov5seg example

* rm log info

* fixed code style

* add yolov5seg example in python

* fixed dtype bug

* update note

* deal with comments

* get sorted index

* add yolov5seg test case

* Add GPL-3.0 License

* add round func

* deal with comments

* deal with commens

Co-authored-by: Jason <jiangjiajun@baidu.com>
2023-01-11 15:36:32 +08:00

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YOLOv5Seg Python部署示例

在部署前，需确认以下两个步骤

本目录下提供infer.py快速完成YOLOv5Seg在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。执行如下脚本即可完成

#下载部署示例代码
git clone https://github.com/PaddlePaddle/FastDeploy.git
cd examples/vision/detection/yolov5seg/python/

#下载yolov5seg模型文件和测试图片
wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov5s-seg.onnx
wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg

# CPU推理
python infer.py --model yolov5s-seg.onnx --image 000000014439.jpg --device cpu
# GPU推理
python infer.py --model yolov5s-seg.onnx --image 000000014439.jpg --device gpu
# GPU上使用TensorRT推理
python infer.py --model yolov5s-seg.onnx --image 000000014439.jpg --device gpu --use_trt True

运行完成可视化结果如下图所示

YOLOv5Seg Python接口

fastdeploy.vision.detection.YOLOv5Seg(model_file, params_file=None, runtime_option=None, model_format=ModelFormat.ONNX)

YOLOv5Seg模型加载和初始化，其中model_file为导出的ONNX模型格式

参数

  • model_file(str): 模型文件路径
  • params_file(str): 参数文件路径，当模型格式为ONNX格式时，此参数无需设定
  • runtime_option(RuntimeOption): 后端推理配置，默认为None，即采用默认配置
  • model_format(ModelFormat): 模型格式，默认为ONNX

predict函数

YOLOv5Seg.predict(image_data)

模型预测结口，输入图像直接输出检测结果。

参数

  • image_data(np.ndarray): 输入数据，注意需为HWC，BGR格式

返回

返回fastdeploy.vision.DetectionResult结构体，结构体说明参考文档视觉模型预测结果

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