mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-06 00:57:33 +08:00
aa6931bee9
* 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
|
||
|
||
在部署前，需确认以下两个步骤
|
||
|
||
- 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)
|
||
- 2. FastDeploy Python whl包安装，参考[FastDeploy Python安装](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
|
||
|
||
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
|
||
|
||
```bash
|
||
#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
|
||
|
||
<img width="640" src="https://user-images.githubusercontent.com/19977378/209955620-657bdd1d-574c-40a2-b05d-42b9e5a15ae8.png">
|
||
|
||
## YOLOv5Seg Python接口
|
||
|
||
```python
|
||
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
|
||
|
||
```python
|
||
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](../../../../../docs/api/vision_results/)
|
||
|
||
## 其它文档
|
||
|
||
- [YOLOv5Seg 模型介绍](..)
|
||
- [YOLOv5Seg C++部署](../cpp)
|
||
- [模型预测结果说明](../../../../../docs/api/vision_results/)
|
||
- [如何切换模型推理后端引擎](../../../../../docs/cn/faq/how_to_change_backend.md)
|