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examples/vision/sr/ppmsvsr/python/README.md
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# PP-MSVSR Python部署示例
English | [简体中文](README_CN.md)
# PP-MSVSR 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)
本目录下提供`infer.py`快速完成PP-MSVSR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
- 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)
This directory provides examples that `infer.py` fast finishes the deployment of PP-MSVSR on CPU/GPU and GPU accelerated by TensorRT. The script is as follows
```bash
#下载部署示例代码
# Download the deployment example code
git clone https://github.com/PaddlePaddle/FastDeploy.git
cd FastDeploy/examples/vision/sr/ppmsvsr/python
# 下载VSR模型文件和测试视频
# Download VSR model files and test videos
wget https://bj.bcebos.com/paddlehub/fastdeploy/PP-MSVSR_reds_x4.tar
tar -xvf PP-MSVSR_reds_x4.tar
wget https://bj.bcebos.com/paddlehub/fastdeploy/vsr_src.mp4
# CPU推理
# CPU inference
python infer.py --model PP-MSVSR_reds_x4 --video vsr_src.mp4 --frame_num 2 --device cpu
# GPU推理
# GPU inference
python infer.py --model PP-MSVSR_reds_x4 --video vsr_src.mp4 --frame_num 2 --device gpu
# 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）
# TensorRT inference on GPU （Attention: It is somewhat time-consuming for the operation of model serialization when running TensorRT inference for the first time. Please be patient.）
python infer.py --model PP-MSVSR_reds_x4 --video vsr_src.mp4 --frame_num 2 --device gpu --use_trt True
```
## VSR Python接口
## VSR Python Interface
```python
fd.vision.sr.PPMSVSR(model_file, params_file, runtime_option=None, model_format=ModelFormat.PADDLE)
```
PP-MSVSR模型加载和初始化，其中model_file和params_file为训练模型导出的Paddle inference文件，具体请参考其文档说明[模型导出](https://github.com/PaddlePaddle/PaddleGAN/blob/develop/docs/zh_CN/tutorials/video_super_resolution.md)
PP-MSVSR model loading and initialization, among which model_file and params_file are the Paddle inference files exported from the training model. Refer to [Model Export](https://github.com/PaddlePaddle/PaddleGAN/blob/develop/docs/zh_CN/tutorials/video_super_resolution.md) for more information
**参数**
**Parameter**
> * **model_file**(str): 模型文件路径
> * **params_file**(str): 参数文件路径
> * **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Paddle格式
> * **model_file**(str): Model file path
> * **params_file**(str): Parameter file path
> * **runtime_option**(RuntimeOption): Backend inference configuration. None by default, which is the default configuration
> * **model_format**(ModelFormat): Model format. Paddle format by default
### predict函数
### predict function
> ```python
> PPMSVSR.predict(frames)
> ```
>
> 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 detection results.
>
> **参数**
> **Parameter**
>
> > * **frames**(list[np.ndarray]): 输入数据，注意需为HWC，BGR格式, frames为视频帧序列
> > * **frames**(list[np.ndarray]): Input data in HWC or BGR format. Frames are the video frame sequences
> **返回** list[np.ndarray] 为超分后的视频帧序列
> **Return** list[np.ndarray] is the video frame sequence after SR
## 其它文档
## Other Documents
- [PP-MSVSR 模型介绍](..)
- [PP-MSVSR C++部署](../cpp)
- [如何切换模型推理后端引擎](../../../../../docs/cn/faq/how_to_change_backend.md)
- [PP-MSVSR Model Description](..)
- [PP-MSVSR C++ Deployment](../cpp)
- [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
# PP-MSVSR 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PP-MSVSR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 FastDeploy/examples/vision/sr/ppmsvsr/python
# 下载VSR模型文件和测试视频
wget https://bj.bcebos.com/paddlehub/fastdeploy/PP-MSVSR_reds_x4.tar
tar -xvf PP-MSVSR_reds_x4.tar
wget https://bj.bcebos.com/paddlehub/fastdeploy/vsr_src.mp4
# CPU推理
python infer.py --model PP-MSVSR_reds_x4 --video vsr_src.mp4 --frame_num 2 --device cpu
# GPU推理
python infer.py --model PP-MSVSR_reds_x4 --video vsr_src.mp4 --frame_num 2 --device gpu
# 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）
python infer.py --model PP-MSVSR_reds_x4 --video vsr_src.mp4 --frame_num 2 --device gpu --use_trt True
```
## VSR Python接口
```python
fd.vision.sr.PPMSVSR(model_file, params_file, runtime_option=None, model_format=ModelFormat.PADDLE)
```
PP-MSVSR模型加载和初始化，其中model_file和params_file为训练模型导出的Paddle inference文件，具体请参考其文档说明[模型导出](https://github.com/PaddlePaddle/PaddleGAN/blob/develop/docs/zh_CN/tutorials/video_super_resolution.md)
**参数**
> * **model_file**(str): 模型文件路径
> * **params_file**(str): 参数文件路径
> * **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Paddle格式
### predict函数
> ```python
> PPMSVSR.predict(frames)
> ```
>
> 模型预测接口，输入图像直接输出检测结果。
>
> **参数**
>
> > * **frames**(list[np.ndarray]): 输入数据，注意需为HWC，BGR格式, frames为视频帧序列
> **返回** list[np.ndarray] 为超分后的视频帧序列
## 其它文档
- [PP-MSVSR 模型介绍](..)
- [PP-MSVSR C++部署](../cpp)
- [如何切换模型推理后端引擎](../../../../../docs/cn/faq/how_to_change_backend.md)