FastDeploy/examples/vision/sr/edvr/cpp

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EDVR C++ Deployment Example

This directory provides examples that infer.cc fast finishes the deployment of EDVR 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 EDVR 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)

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 EDVR model files and test videos
wget https://bj.bcebos.com/paddlehub/fastdeploy/EDVR_M_wo_tsa_SRx4.tar
tar -xvf EDVR_M_wo_tsa_SRx4.tar
wget https://bj.bcebos.com/paddlehub/fastdeploy/vsr_src.mp4


# CPU inference
./infer_demo EDVR_M_wo_tsa_SRx4 vsr_src.mp4 0 5
# GPU inference
./infer_demo EDVR_M_wo_tsa_SRx4 vsr_src.mp4 1 5
# TensorRT Inference on GPU
./infer_demo EDVR_M_wo_tsa_SRx4 vsr_src.mp4 2 5

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

• How to use FastDeploy C++ SDK in Windows

EDVR C++ Interface

EDVR Class

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

EDVR 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

EDVR::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 sequence 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