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* 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>
68 lines
2.5 KiB
Markdown
68 lines
2.5 KiB
Markdown
# YOLOv5Seg Python部署示例
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在部署前，需确认以下两个步骤
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- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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- 2. FastDeploy Python whl包安装，参考[FastDeploy Python安装](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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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
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```bash
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#下载部署示例代码
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git clone https://github.com/PaddlePaddle/FastDeploy.git
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cd examples/vision/detection/yolov5seg/python/
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#下载yolov5seg模型文件和测试图片
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wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov5s-seg.onnx
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wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
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# CPU推理
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python infer.py --model yolov5s-seg.onnx --image 000000014439.jpg --device cpu
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# GPU推理
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python infer.py --model yolov5s-seg.onnx --image 000000014439.jpg --device gpu
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# GPU上使用TensorRT推理
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python infer.py --model yolov5s-seg.onnx --image 000000014439.jpg --device gpu --use_trt True
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```
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运行完成可视化结果如下图所示
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<img width="640" src="https://user-images.githubusercontent.com/19977378/209955620-657bdd1d-574c-40a2-b05d-42b9e5a15ae8.png">
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## YOLOv5Seg Python接口
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```python
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fastdeploy.vision.detection.YOLOv5Seg(model_file, params_file=None, runtime_option=None, model_format=ModelFormat.ONNX)
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```
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YOLOv5Seg模型加载和初始化，其中model_file为导出的ONNX模型格式
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**参数**
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> * **model_file**(str): 模型文件路径
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> * **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
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> * **runtime_option**(RuntimeOption): 后端推理配置，默认为None，即采用默认配置
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> * **model_format**(ModelFormat): 模型格式，默认为ONNX
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### predict函数
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```python
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YOLOv5Seg.predict(image_data)
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```
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模型预测结口，输入图像直接输出检测结果。
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**参数**
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> > * **image_data**(np.ndarray): 输入数据，注意需为HWC，BGR格式
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**返回**
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> > 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](../../../../../docs/api/vision_results/)
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## 其它文档
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- [YOLOv5Seg 模型介绍](..)
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- [YOLOv5Seg C++部署](../cpp)
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- [模型预测结果说明](../../../../../docs/api/vision_results/)
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- [如何切换模型推理后端引擎](../../../../../docs/cn/faq/how_to_change_backend.md)
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