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78 lines
3.8 KiB
Markdown
78 lines
3.8 KiB
Markdown
# PaddleDetection 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/environment.md)
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- 2. FastDeploy Python whl包安装，参考[FastDeploy Python安装](../../../../../docs/quick_start)
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本目录下提供`infer_xxx.py`快速完成PPYOLOE/PicoDet等模型在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/paddledetection/python/
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#下载PPYOLOE模型文件和测试图片
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wget https://bj.bcebos.com/paddlehub/fastdeploy/ppyoloe_crn_l_300e_coco.tgz
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wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
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tar xvf ppyoloe_crn_l_300e_coco.tgz
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# CPU推理
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python infer_ppyoloe.py --model_dir ppyoloe_crn_l_300e_coco --image 000000014439.jpg --device cpu
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# GPU推理
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python infer_ppyoloe.py --model_dir ppyoloe_crn_l_300e_coco --image 000000014439.jpg --device gpu
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# GPU上使用TensorRT推理 （注意：TensorRT推理第一次运行，有序列化模型的操作，有一定耗时，需要耐心等待）
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python infer_ppyoloe.py --model_dir ppyoloe_crn_l_300e_coco --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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<div align="center">
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<img src="https://user-images.githubusercontent.com/19339784/184326520-7075e907-10ed-4fad-93f8-52d0e35d4964.jpg", width=480px, height=320px />
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</div>
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## PaddleDetection Python接口
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```python
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fastdeploy.vision.detection.PPYOLOE(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
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fastdeploy.vision.detection.PicoDet(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
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fastdeploy.vision.detection.PaddleYOLOX(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
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fastdeploy.vision.detection.YOLOv3(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
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fastdeploy.vision.detection.PPYOLO(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
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fastdeploy.vision.detection.FasterRCNN(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
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fastdeploy.vision.detection.MaskRCNN(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
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```
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PaddleDetection模型加载和初始化，其中model_file， params_file为导出的Paddle部署模型格式, config_file为PaddleDetection同时导出的部署配置yaml文件
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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
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> * **config_file**(str): 推理配置yaml文件路径
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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Paddle
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### predict函数
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PaddleDetection中各个模型，包括PPYOLOE/PicoDet/PaddleYOLOX/YOLOv3/PPYOLO/FasterRCNN，均提供如下同样的成员函数用于进行图像的检测
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> ```python
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> PPYOLOE.predict(image_data, conf_threshold=0.25, nms_iou_threshold=0.5)
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> ```
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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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>
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> **参数**
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>
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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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>
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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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- [PaddleDetection 模型介绍](..)
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- [PaddleDetection 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/runtime/how_to_change_backend.md)
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