mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-06 17:17:14 +08:00
[Model] add vsr serials models (#518)
* [Model] add vsr serials models Signed-off-by: ChaoII <849453582@qq.com> * [Model] add vsr serials models Signed-off-by: ChaoII <849453582@qq.com> * fix build problem Signed-off-by: ChaoII <849453582@qq.com> * fix code style Signed-off-by: ChaoII <849453582@qq.com> * modify according to review suggestions Signed-off-by: ChaoII <849453582@qq.com> * modify vsr trt example Signed-off-by: ChaoII <849453582@qq.com> * update sr directory * fix BindPPSR * add doxygen comment * add sr unit test * update model file url Signed-off-by: ChaoII <849453582@qq.com> Co-authored-by: Jason <jiangjiajun@baidu.com>
This commit is contained in:
ChaoII
14
examples/vision/sr/basicvsr/cpp/CMakeLists.txt
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14
examples/vision/sr/basicvsr/cpp/CMakeLists.txt
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PROJECT(infer_demo C CXX)
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CMAKE_MINIMUM_REQUIRED (VERSION 3.10)
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# 指定下载解压后的fastdeploy库路径
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option(FASTDEPLOY_INSTALL_DIR "Path of downloaded fastdeploy sdk.")
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include(${FASTDEPLOY_INSTALL_DIR}/FastDeploy.cmake)
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# 添加FastDeploy依赖头文件
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include_directories(${FASTDEPLOY_INCS})
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add_executable(infer_demo ${PROJECT_SOURCE_DIR}/infer.cc)
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# 添加FastDeploy库依赖
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target_link_libraries(infer_demo ${FASTDEPLOY_LIBS})
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74
examples/vision/sr/basicvsr/cpp/README.md
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74
examples/vision/sr/basicvsr/cpp/README.md
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# BasicVSR C++部署示例
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本目录下提供`infer.cc`快速完成BasicVSR在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。
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在部署前，需确认以下两个步骤
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- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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- 2. 根据开发环境，下载预编译部署库和samples代码，参考[FastDeploy预编译库](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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以Linux上BasicVSR推理为例，在本目录执行如下命令即可完成编译测试（如若只需在CPU上部署，可在[Fastdeploy C++预编译库](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md/CPP_prebuilt_libraries.md)下载CPU推理库）
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```bash
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#下载SDK，编译模型examples代码（SDK中包含了examples代码）
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# fastdeploy版本 >= 0.7.0
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wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-gpu-0.7.0.tgz
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tar xvf fastdeploy-linux-x64-gpu-0.7.0.tgz
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cd fastdeploy-linux-x64-gpu-0.7.0/examples/vision/sr/basicvsr/cpp/
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mkdir build && cd build
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cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/../../../../../../../fastdeploy-linux-x64-gpu-0.7.0
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make -j
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# 下载BasicVSR模型文件和测试视频
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wget https://bj.bcebos.com/paddlehub/fastdeploy/BasicVSR_reds_x4.tar
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tar -xvf BasicVSR_reds_x4.tar
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wget https://bj.bcebos.com/paddlehub/fastdeploy/vsr_src.mp4
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# CPU推理
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./infer_demo BasicVSR_reds_x4 vsr_src.mp4 0 2
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# GPU推理
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./infer_demo BasicVSR_reds_x4 vsr_src.mp4 1 2
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# GPU上TensorRT推理
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./infer_demo BasicVSR_reds_x4 vsr_src.mp4 2 2
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```
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以上命令只适用于Linux或MacOS, Windows下SDK的使用方式请参考:
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- [如何在Windows中使用FastDeploy C++ SDK](../../../../../docs/cn/faq/use_sdk_on_windows.md)
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## BasicVSR C++接口
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### BasicVSR类
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```c++
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fastdeploy::vision::sr::BasicVSR(
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const string& model_file,
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const string& params_file = "",
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const RuntimeOption& runtime_option = RuntimeOption(),
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const ModelFormat& model_format = ModelFormat::PADDLE)
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```
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BasicVSR模型加载和初始化，其中model_file为导出的Paddle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
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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Paddle格式
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#### Predict函数
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> ```c++
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> BasicVSR::Predict(std::vector<cv::Mat>& imgs, std::vector<cv::Mat>& results)
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> ```
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>
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> 模型预测接口，输入图像直接输出检测结果。
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>
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> **参数**
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>
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> > * **imgs**: 输入视频帧序列，注意需为HWC，BGR格式
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> > * **results**: 视频超分结果，超分后的视频帧序列
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- [模型介绍](../../)
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- [Python部署](../python)
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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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297
examples/vision/sr/basicvsr/cpp/infer.cc
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297
examples/vision/sr/basicvsr/cpp/infer.cc
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// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include "fastdeploy/vision.h"
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#ifdef WIN32
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const char sep = '\\';
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#else
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const char sep = '/';
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#endif
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void CpuInfer(const std::string& model_dir,
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const std::string& video_file, int frame_num) {
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auto model_file = model_dir + sep + "model.pdmodel";
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auto params_file = model_dir + sep + "model.pdiparams";
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auto model = fastdeploy::vision::sr::BasicVSR(model_file, params_file);
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if (!model.Initialized()) {
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std::cerr << "Failed to initialize." << std::endl;
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return;
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}
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// note: input/output shape is [b, n, c, h, w] (n = frame_nums; b=1(default))
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// b and n is dependent on export model shape
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// see https://github.com/PaddlePaddle/PaddleGAN/blob/develop/docs/zh_CN/tutorials/video_super_resolution.md
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cv::VideoCapture capture;
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// change your save video path
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std::string video_out_name = "output.mp4";
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capture.open(video_file);
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if (!capture.isOpened())
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{
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std::cout<<"can not open video "<<std::endl;
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return;
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}
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// Get Video info :fps, frame count
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// it used 4.x version of opencv below
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// notice your opencv version and method of api.
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int video_fps = static_cast<int>(capture.get(cv::CAP_PROP_FPS));
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int video_frame_count = static_cast<int>(capture.get(cv::CAP_PROP_FRAME_COUNT));
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// Set fixed size for output frame, only for msvsr model
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int out_width = 1280;
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int out_height = 720;
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std::cout << "fps: " << video_fps << "\tframe_count: " << video_frame_count << std::endl;
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// Create VideoWriter for output
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cv::VideoWriter video_out;
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std::string video_out_path("./");
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video_out_path += video_out_name;
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int fcc = cv::VideoWriter::fourcc('m', 'p', '4', 'v');
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video_out.open(video_out_path, fcc, video_fps, cv::Size(out_width, out_height), true);
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if (!video_out.isOpened())
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{
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std::cout << "create video writer failed!" << std::endl;
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return;
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}
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// Capture all frames and do inference
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cv::Mat frame;
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int frame_id = 0;
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bool reach_end = false;
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while (capture.isOpened())
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{
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std::vector<cv::Mat> imgs;
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for (int i = 0; i < frame_num; i++)
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{
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capture.read(frame);
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if (!frame.empty())
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{
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imgs.push_back(frame);
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}else{
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reach_end = true;
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}
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}
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if (reach_end)
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{
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break;
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}
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std::vector<cv::Mat> results;
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model.Predict(imgs, results);
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for (auto &item : results)
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{
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// cv::imshow("13",item);
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// cv::waitKey(30);
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video_out.write(item);
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std::cout << "Processing frame: "<< frame_id << std::endl;
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frame_id += 1;
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}
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}
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std::cout << "inference finished, output video saved at " << video_out_path << std::endl;
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capture.release();
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video_out.release();
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}
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void GpuInfer(const std::string& model_dir,
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const std::string& video_file, int frame_num) {
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auto model_file = model_dir + sep + "model.pdmodel";
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auto params_file = model_dir + sep + "model.pdiparams";
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auto option = fastdeploy::RuntimeOption();
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option.UseGpu();
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auto model = fastdeploy::vision::sr::BasicVSR(
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model_file, params_file, option);
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if (!model.Initialized()) {
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std::cerr << "Failed to initialize." << std::endl;
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return;
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}
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// note: input/output shape is [b, n, c, h, w] (n = frame_nums; b=1(default))
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// b and n is dependent on export model shape
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// see https://github.com/PaddlePaddle/PaddleGAN/blob/develop/docs/zh_CN/tutorials/video_super_resolution.md
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cv::VideoCapture capture;
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// change your save video path
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std::string video_out_name = "output.mp4";
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capture.open(video_file);
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if (!capture.isOpened())
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{
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std::cout<<"can not open video "<<std::endl;
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return;
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}
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// Get Video info :fps, frame count
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int video_fps = static_cast<int>(capture.get(cv::CAP_PROP_FPS));
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int video_frame_count = static_cast<int>(capture.get(cv::CAP_PROP_FRAME_COUNT));
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// Set fixed size for output frame, only for msvsr model
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int out_width = 1280;
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int out_height = 720;
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std::cout << "fps: " << video_fps << "\tframe_count: " << video_frame_count << std::endl;
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// Create VideoWriter for output
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cv::VideoWriter video_out;
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std::string video_out_path("./");
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video_out_path += video_out_name;
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int fcc = cv::VideoWriter::fourcc('m', 'p', '4', 'v');
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video_out.open(video_out_path, fcc, video_fps, cv::Size(out_width, out_height), true);
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if (!video_out.isOpened())
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{
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std::cout << "create video writer failed!" << std::endl;
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return;
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}
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// Capture all frames and do inference
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cv::Mat frame;
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int frame_id = 0;
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bool reach_end = false;
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while (capture.isOpened())
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{
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std::vector<cv::Mat> imgs;
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for (int i = 0; i < frame_num; i++)
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{
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capture.read(frame);
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if (!frame.empty())
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{
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imgs.push_back(frame);
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}else{
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reach_end = true;
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}
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}
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if (reach_end)
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{
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break;
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}
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std::vector<cv::Mat> results;
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model.Predict(imgs, results);
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for (auto &item : results)
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{
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// cv::imshow("13",item);
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// cv::waitKey(30);
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video_out.write(item);
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std::cout << "Processing frame: "<< frame_id << std::endl;
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frame_id += 1;
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}
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}
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std::cout << "inference finished, output video saved at " << video_out_path << std::endl;
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capture.release();
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video_out.release();
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}
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void TrtInfer(const std::string& model_dir,
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const std::string& video_file, int frame_num) {
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auto model_file = model_dir + sep + "model.pdmodel";
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auto params_file = model_dir + sep + "model.pdiparams";
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auto option = fastdeploy::RuntimeOption();
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option.UseGpu();
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option.UseTrtBackend();
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// use paddle-TRT
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option.EnablePaddleToTrt();
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auto model = fastdeploy::vision::sr::BasicVSR(
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model_file, params_file, option);
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if (!model.Initialized()) {
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std::cerr << "Failed to initialize." << std::endl;
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return;
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}
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// note: input/output shape is [b, n, c, h, w] (n = frame_nums; b=1(default))
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// b and n is dependent on export model shape
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// see https://github.com/PaddlePaddle/PaddleGAN/blob/develop/docs/zh_CN/tutorials/video_super_resolution.md
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cv::VideoCapture capture;
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// change your save video path
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std::string video_out_name = "output.mp4";
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capture.open(video_file);
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if (!capture.isOpened())
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{
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std::cout<<"can not open video "<<std::endl;
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return;
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}
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// Get Video info :fps, frame count
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int video_fps = static_cast<int>(capture.get(cv::CAP_PROP_FPS));
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int video_frame_count = static_cast<int>(capture.get(cv::CAP_PROP_FRAME_COUNT));
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// Set fixed size for output frame, only for msvsr model
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//Note that the resolution between the size and the original input is consistent when the model is exported,
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// for example: [1,2,3,180,320], after 4x super separation [1,2,3,720,1080].
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//Therefore, it is very important to derive the model
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int out_width = 1280;
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int out_height = 720;
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std::cout << "fps: " << video_fps << "\tframe_count: " << video_frame_count << std::endl;
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// Create VideoWriter for output
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cv::VideoWriter video_out;
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std::string video_out_path("./");
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video_out_path += video_out_name;
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int fcc = cv::VideoWriter::fourcc('m', 'p', '4', 'v');
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video_out.open(video_out_path, fcc, video_fps, cv::Size(out_width, out_height), true);
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if (!video_out.isOpened())
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{
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std::cout << "create video writer failed!" << std::endl;
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return;
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}
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// Capture all frames and do inference
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cv::Mat frame;
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int frame_id = 0;
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bool reach_end = false;
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while (capture.isOpened())
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{
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std::vector<cv::Mat> imgs;
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for (int i = 0; i < frame_num; i++)
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{
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capture.read(frame);
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if (!frame.empty())
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{
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imgs.push_back(frame);
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}else{
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reach_end = true;
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}
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}
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if (reach_end)
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{
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break;
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}
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std::vector<cv::Mat> results;
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model.Predict(imgs, results);
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for (auto &item : results)
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{
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// cv::imshow("13",item);
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// cv::waitKey(30);
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video_out.write(item);
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std::cout << "Processing frame: "<< frame_id << std::endl;
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frame_id += 1;
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}
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}
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std::cout << "inference finished, output video saved at " << video_out_path << std::endl;
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capture.release();
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video_out.release();
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}
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int main(int argc, char* argv[]) {
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if (argc < 4) {
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std::cout
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<< "Usage: infer_demo path/to/model_dir path/to/video frame number run_option, "
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"e.g ./infer_model ./vsr_model_dir ./person.mp4 0 2"
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<< std::endl;
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std::cout << "The data type of run_option is int, 0: run with cpu; 1: run "
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"with gpu; 2: run with gpu and use tensorrt backend."
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<< std::endl;
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return -1;
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}
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int frame_num = 2;
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if (argc == 5) {
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frame_num = std::atoi(argv[4]);
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}
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if (std::atoi(argv[3]) == 0) {
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CpuInfer(argv[1], argv[2], frame_num);
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} else if (std::atoi(argv[3]) == 1) {
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GpuInfer(argv[1], argv[2], frame_num);
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} else if (std::atoi(argv[3]) == 2) {
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TrtInfer(argv[1], argv[2], frame_num);
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}
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return 0;
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}
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