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Hu Chuqi
2023-01-06 09:34:28 +08:00
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# YOLOX Python部署示例
English | [简体中文](README_CN.md)
# YOLOX Python Deployment Example
在部署前，需确认以下两个步骤
Two steps before deployment
- 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)
- 1. Software and hardware should meet the requirements. Please refer to [FastDeploy Environment Requirements](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
- 2. Install FastDeploy Python whl package. Refer to [FastDeploy Python Installation](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
本目录下提供`infer.py`快速完成YOLOX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
This directory provides examples that `infer.py` fast finishes the deployment of YOLOX on CPU/GPU and GPU accelerated by TensorRT. The script is as follows
```bash
#下载部署示例代码
# Download the example code for deployment
git clone https://github.com/PaddlePaddle/FastDeploy.git
cd examples/vision/detection/yolox/python/
#下载YOLOX模型文件和测试图片
# Download YOLOX model files and test images
wget https://bj.bcebos.com/paddlehub/fastdeploy/yolox_s.onnx
wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
# CPU推理
# CPU inference
python infer.py --model yolox_s.onnx --image 000000014439.jpg --device cpu
# GPU推理
# GPU inference
python infer.py --model yolox_s.onnx --image 000000014439.jpg --device gpu
# GPU上使用TensorRT推理 (TensorRT在SDK中已包含，不需要单独安装)
# TensorRT inference on GPU (TensorRT in SDK. No need Separate installation)
python infer.py --model yolox_s.onnx --image 000000014439.jpg --device gpu --use_trt True
```
运行完成可视化结果如下图所示
The visualized result after running is as follows
<img width="640" src="https://user-images.githubusercontent.com/67993288/184301746-04595d76-454a-4f07-8c7d-6f41418f8ae3.jpg">
## YOLOX Python接口
## YOLOX Python Interface
```python
fastdeploy.vision.detection.YOLOX(model_file, params_file=None, runtime_option=None, model_format=ModelFormat.ONNX)
```
YOLOX模型加载和初始化，其中model_file为导出的ONNX模型格式
YOLOX model loading and initialization, among which model_file is the exported ONNX model format
**参数**
**Parameter**
> * **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
> * **model_file**(str): Model file path
> * **params_file**(str): Parameter file path. No need to set when the model is in ONNX format
> * **runtime_option**(RuntimeOption): Backend inference configuration. None by default, which is the default configuration
> * **model_format**(ModelFormat): Model format. ONNX format by default
### predict函数
### predict function
> ```python
> YOLOX.predict(image_data, conf_threshold=0.25, nms_iou_threshold=0.5)
> ```
>
> 模型预测结口，输入图像直接输出检测结果。
> Model prediction interface. Input images and output results
>
> **参数**
> **Parameter**
>
> > * **image_data**(np.ndarray): 输入数据，注意需为HWC，BGR格式
> > * **conf_threshold**(float): 检测框置信度过滤阈值
> > * **nms_iou_threshold**(float): NMS处理过程中iou阈值
> > * **image_data**(np.ndarray): Input data in HWC or BGR format
> > * **conf_threshold**(float): Filtering threshold of detection box confidence
> > * **nms_iou_threshold**(float): iou threshold during NMS processing
> **返回**
> **Return**
>
> > 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/)
> > Return `fastdeploy.vision.DetectionResult` structure, refer to [Vision Model Prediction Results](../../../../../docs/api/vision_results/) for its description
### 类成员属性
#### 预处理参数
用户可按照自己的实际需求，修改下列预处理参数，从而影响最终的推理和部署效果
### Class Member Property
#### Pre-processing Parameter
Users can modify the following pre-processing parameters to their needs, which affects the final inference and deployment results
> >* **size**(list[int]): 通过此参数修改预处理过程中resize的大小，包含两个整型元素，表示[width, height], 默认值为[640, 640]
> >* **padding_value**(list[float]): 通过此参数可以修改图片在resize时候做填充(padding)的值, 包含三个浮点型元素, 分别表示三个通道的值, 默认值为[114, 114, 114]
> >* **is_decode_exported**(bool): 表示导出的YOLOX的onnx模型文件是否带坐标反算的decode部分, 默认值为`is_decode_exported=False`，官方默认的导出不带decode部分，如果您导出的模型带了decode，请将此参数设置为True
> >* **size**(list[int]): This parameter changes the size of the resize during preprocessing, containing two integer elements for [width, height] with default value [640, 640]
> >* **padding_value**(list[float]): This parameter is used to change the padding value of images during resize, containing three floating-point elements that represent the value of three channels. Default [114, 114, 114]
> >* **is_decode_exported**(bool): The default value is `is_decode_exported=False`. The official default export does not have the decoded part. If you export the model with the decoded part, please set this parameter to true
## 其它文档
## Other Documents
- [YOLOX 模型介绍](..)
- [YOLOX 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)
- [YOLOX Model Description](..)
- [YOLOX C++ Deployment](../cpp)
- [Model Prediction Results](../../../../../docs/api/vision_results/)
- [How to switch the model inference backend engine](../../../../../docs/cn/faq/how_to_change_backend.md)