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This commit is contained in:
Hu Chuqi
2023-01-06 09:34:28 +08:00
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# FastestDet Python部署示例
English | [简体中文](README_CN.md)
# FastestDet Python Deployment Example
在部署前,需确认以下两个步骤
Before deployment, two steps require confirmation
- 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FastestDetCPU/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 FastestDet 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/fastestdet/python/
#下载fastestdet模型文件和测试图片
# Download fastestdet model files and test images
wget https://bj.bcebos.com/paddlehub/fastdeploy/FastestDet.onnx
wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
# CPU推理
# CPU inference
python infer.py --model FastestDet.onnx --image 000000014439.jpg --device cpu
# GPU推理
# GPU inference
python infer.py --model FastestDet.onnx --image 000000014439.jpg --device gpu
# GPU上使用TensorRT推理
# TensorRT inference on GPU
python infer.py --model FastestDet.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/44280887/206176291-61eb118b-391b-4431-b79e-a393b9452138.jpg">
## FastestDet Python接口
## FastestDet Python Interface
```python
fastdeploy.vision.detection.FastestDet(model_file, params_file=None, runtime_option=None, model_format=ModelFormat.ONNX)
```
FastestDet模型加载和初始化其中model_file为导出的ONNX模型格式
FastestDet 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
> FastestDet.predict(image_data)
> ```
>
> 模型预测接口,输入图像直接输出检测结果。
> Model prediction interface. Input images and output detection results.
>
> **参数**
> **Parameter**
>
> > * **image_data**(np.ndarray): 输入数据注意需为HWCBGR格式
> > * **image_data**(np.ndarray): Input data in HWC or BGR format
> **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 structure
### 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[352, 352]
> > * **size**(list[int]): This parameter changes the size of the resize used during preprocessing, containing two integer elements for [width, height] with default value [352, 352]
## 其它文档
## Other Documents
- [FastestDet 模型介绍](..)
- [FastestDet 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)
- [FastestDet Model Description](..)
- [FastestDet 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)

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[English](README.md) | 简体中文
# FastestDet 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FastestDet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/fastestdet/python/
#下载fastestdet模型文件和测试图片
wget https://bj.bcebos.com/paddlehub/fastdeploy/FastestDet.onnx
wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
# CPU推理
python infer.py --model FastestDet.onnx --image 000000014439.jpg --device cpu
# GPU推理
python infer.py --model FastestDet.onnx --image 000000014439.jpg --device gpu
# GPU上使用TensorRT推理
python infer.py --model FastestDet.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/44280887/206176291-61eb118b-391b-4431-b79e-a393b9452138.jpg">
## FastestDet Python接口
```python
fastdeploy.vision.detection.FastestDet(model_file, params_file=None, runtime_option=None, model_format=ModelFormat.ONNX)
```
FastestDet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
> FastestDet.predict(image_data)
> ```
>
> 模型预测接口,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HWCBGR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/)
### 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
> > * **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[352, 352]
## 其它文档
- [FastestDet 模型介绍](..)
- [FastestDet 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)