FastDeploy/examples/vision/sr/ppmsvsr/cpp

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VSR C++ Deployment Example

This directory provides examples that infer.cc fast finishes the deployment of PP-MSVSR on CPU/GPU and GPU accelerated by TensorRT.

Before deployment, two steps require confirmation

• 1. Software and hardware should meet the requirements. Please refer to FastDeploy Environment Requirements
• 2. Download the precompiled deployment library and samples code according to your development environment. Refer to FastDeploy Precompiled Library

Taking the PP-MSVSR inference on Linux as an example, the compilation test can be completed by executing the following command in this directory. FastDeploy version 0.7.0 or above (x.x.x>=0.7.0) is required to support this model.

mkdir build
cd build
# Download the FastDeploy precompiled library. Users can choose your appropriate version in the `FastDeploy Precompiled Library` mentioned above 
wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
tar xvf fastdeploy-linux-x64-x.x.x.tgz
cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
make -j

# Download PP-MSVSR 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 inference
./infer_demo PP-MSVSR_reds_x4 vsr_src.mp4 0 2
# GPU inference
./infer_demo PP-MSVSR_reds_x4 vsr_src.mp4 1 2
# TensorRT Inference on GPU
./infer_demo PP-MSVSR_reds_x4 vsr_src.mp4 2 2

The above command works for Linux or MacOS. For SDK use-pattern in Windows, refer to:

• How to use FastDeploy C++ SDK in Windows

PP-MSVSR C++ Interface

PPMSVSR Class

fastdeploy::vision::sr::PPMSVSR(
        const string& model_file,
        const string& params_file = "",
        const RuntimeOption& runtime_option = RuntimeOption(),
        const ModelFormat& model_format = ModelFormat::PADDLE)

PP-MSVSR model loading and initialization, among which model_file is the exported Paddle model format.

Parameter

• 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 Function

PPMSVSR::Predict(std::vector<cv::Mat>& imgs, std::vector<cv::Mat>& results)

Model prediction interface. Input images and output detection results.

Parameter

• imgs: Input video frame sequences in HWC or BGR format
• results: Video SR results: video frame sequence after SR

• Model Description
• Python Deployment
• How to switch the model inference backend engine