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[Doc]Add English version of documents in examples (#1070)
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This commit is contained in:
Hu Chuqi
@@ -1,31 +1,32 @@
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# MODNet Python部署示例
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English | [简体中文](README_CN.md)
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# MODNet Python Deployment Example
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在部署前，需确认以下两个步骤
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Before deployment, two steps require confirmation
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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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- 1. Software and hardware should meet the requirements. Please refer to [FastDeploy Environment Requirements](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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- 2. Install FastDeploy Python whl package. Refer to [FastDeploy Python Installation](../../../../../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MODNet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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This directory provides examples that `infer.py` fast finishes the deployment of MODNet on CPU/GPU and GPU accelerated by TensorRT. The script is as follows
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```bash
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#下载部署示例代码
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# Download the example code for deployment
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git clone https://github.com/PaddlePaddle/FastDeploy.git
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cd examples/vision/matting/modnet/python/
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#下载modnet模型文件和测试图片
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# Download modnet model files and test images
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wget https://bj.bcebos.com/paddlehub/fastdeploy/modnet_photographic_portrait_matting.onnx
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wget https://bj.bcebos.com/paddlehub/fastdeploy/matting_input.jpg
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wget https://bj.bcebos.com/paddlehub/fastdeploy/matting_bgr.jpg
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# CPU推理
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# CPU inference
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python infer.py --model modnet_photographic_portrait_matting.onnx --image matting_input.jpg --bg matting_bgr.jpg --device cpu
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# GPU推理
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# GPU inference
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python infer.py --model modnet_photographic_portrait_matting.onnx --image matting_input.jpg --bg matting_bgr.jpg --device gpu
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# GPU上使用TensorRT推理
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# TensorRT inference on GPU
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python infer.py --model modnet_photographic_portrait_matting.onnx --image matting_input.jpg --bg matting_bgr.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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The visualized result after running is as follows
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<div width="840">
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<img width="200" height="200" float="left" src="https://user-images.githubusercontent.com/67993288/186852040-759da522-fca4-4786-9205-88c622cd4a39.jpg">
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@@ -34,52 +35,51 @@ python infer.py --model modnet_photographic_portrait_matting.onnx --image mattin
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<img width="200" height="200" float="left" src="https://user-images.githubusercontent.com/67993288/186851964-4c9086b9-3490-4fcb-82f9-2106c63aa4f3.jpg">
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</div>
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## MODNet Python接口
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## MODNet Python Interface
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```python
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fastdeploy.vision.matting.MODNet(model_file, params_file=None, runtime_option=None, model_format=ModelFormat.ONNX)
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```
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MODNet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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MODNet model loading and initialization, among which model_file is the exported ONNX model format
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**参数**
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**Parameter**
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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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> * **model_file**(str): Model file path
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> * **params_file**(str): Parameter file path. No need to set when the model is in ONNX format
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> * **runtime_option**(RuntimeOption): Backend inference configuration. None by default, which is the default configuration
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> * **model_format**(ModelFormat): Model format. ONNX format by default
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### predict函数
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### predict function
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> ```python
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> MODNet.predict(image_data)
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> ```
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>
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> 模型预测结口，输入图像直接输出抠图结果。
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> Model prediction interface. Input images and output matting results.
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>
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> **参数**
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> **Parameter**
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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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> > * **image_data**(np.ndarray): Input data in HWC or BGR format
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> **返回**
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> **Return**
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>
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> > 返回`fastdeploy.vision.MattingResult`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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> > Return `fastdeploy.vision.MattingResult` structure. Refer to [Vision Model Prediction Results](../../../../../docs/api/vision_results/) for its description.
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### 类成员属性
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#### 预处理参数
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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
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### Class Member Property
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#### Pre-processing Parameter
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Users can modify the following pre-processing parameters to their needs, which affects the final inference and deployment results
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> > * **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[256, 256]
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> > * **alpha**(list[float]): 预处理归一化的alpha值，计算公式为`x'=x*alpha+beta`，alpha默认为[1. / 127.5, 1.f / 127.5, 1. / 127.5]
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> > * **beta**(list[float]): 预处理归一化的beta值，计算公式为`x'=x*alpha+beta`，beta默认为[-1.f, -1.f, -1.f]
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> > * **swap_rb**(bool): 预处理是否将BGR转换成RGB，默认True
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> > * **size**(list[int]): This parameter changes the size of the resize during preprocessing, containing two integer elements for [width, height] with default value [256, 256]
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> > * **alpha**(list[float]): Preprocess normalized alpha, and calculated as `x'=x*alpha+beta`. alpha defaults to [1. / 127.5, 1.f / 127.5, 1. / 127.5]
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> > * **beta**(list[float]): Preprocess normalized beta, and calculated as `x'=x*alpha+beta`. beta defaults to [-1.f, -1.f, -1.f]
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> > * **swap_rb**(bool): Whether to convert BGR to RGB in pre-processing. Default True
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## 其它文档
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## Other Documents
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- [MODNet 模型介绍](..)
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- [MODNet 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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- [MODNet Model Description](..)
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- [MODNet C++ Deployment](../cpp)
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- [Model Prediction Results](../../../../../docs/api/vision_results/)
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- [How to switch the model inference backend engine](../../../../../docs/cn/faq/how_to_change_backend.md)
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86
examples/vision/matting/modnet/python/README_CN.md
Normal file
86
examples/vision/matting/modnet/python/README_CN.md
Normal file
@@ -0,0 +1,86 @@
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[English](README.md) | 简体中文
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# MODNet 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MODNet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/matting/modnet/python/
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#下载modnet模型文件和测试图片
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wget https://bj.bcebos.com/paddlehub/fastdeploy/modnet_photographic_portrait_matting.onnx
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wget https://bj.bcebos.com/paddlehub/fastdeploy/matting_input.jpg
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wget https://bj.bcebos.com/paddlehub/fastdeploy/matting_bgr.jpg
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# CPU推理
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python infer.py --model modnet_photographic_portrait_matting.onnx --image matting_input.jpg --bg matting_bgr.jpg --device cpu
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# GPU推理
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python infer.py --model modnet_photographic_portrait_matting.onnx --image matting_input.jpg --bg matting_bgr.jpg --device gpu
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# GPU上使用TensorRT推理
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python infer.py --model modnet_photographic_portrait_matting.onnx --image matting_input.jpg --bg matting_bgr.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 width="840">
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<img width="200" height="200" float="left" src="https://user-images.githubusercontent.com/67993288/186852040-759da522-fca4-4786-9205-88c622cd4a39.jpg">
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<img width="200" height="200" float="left" src="https://user-images.githubusercontent.com/67993288/186851995-fe9f509f-97d4-4967-a3b0-ce2b3c2f5dca.jpg">
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<img width="200" height="200" float="left" src="https://user-images.githubusercontent.com/67993288/186852116-cf91445b-3a67-45d9-a675-c69fe77c383a.jpg">
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<img width="200" height="200" float="left" src="https://user-images.githubusercontent.com/67993288/186851964-4c9086b9-3490-4fcb-82f9-2106c63aa4f3.jpg">
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</div>
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## MODNet Python接口
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```python
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fastdeploy.vision.matting.MODNet(model_file, params_file=None, runtime_option=None, model_format=ModelFormat.ONNX)
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```
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MODNet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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> MODNet.predict(image_data)
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> ```
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>
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> 模型预测结口，输入图像直接输出抠图结果。
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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.MattingResult`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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### 类成员属性
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#### 预处理参数
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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
|
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> > * **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[256, 256]
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> > * **alpha**(list[float]): 预处理归一化的alpha值，计算公式为`x'=x*alpha+beta`，alpha默认为[1. / 127.5, 1.f / 127.5, 1. / 127.5]
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> > * **beta**(list[float]): 预处理归一化的beta值，计算公式为`x'=x*alpha+beta`，beta默认为[-1.f, -1.f, -1.f]
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> > * **swap_rb**(bool): 预处理是否将BGR转换成RGB，默认True
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## 其它文档
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- [MODNet 模型介绍](..)
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- [MODNet 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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