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[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:
4
.gitignore
vendored
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.gitignore
vendored
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yalc.*
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yalc.*
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.yalc
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.yalc
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examples/vision/collect_quantize_cc.sh
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examples/vision/collect_quantize_cc.sh
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examples/vision/tests_quantize
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examples/vision/tests_quantize
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fastdeploy/LICENSE
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fastdeploy/ThirdPartyNotices.txt
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9
examples/vision/sr/README.md
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9
examples/vision/sr/README.md
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# sr 模型部署
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FastDeploy目前支持如下超分模型部署
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| 模型 | 说明 | 模型格式 | 版本 |
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|:-----------------------------------------|:----------------------|:-------|:----------------------------------------------------------------------------------|
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| [PaddleGAN/BasicVSR](./basicvsr) | BasicVSR 系列模型 | paddle | [develop](https://github.com/PaddlePaddle/PaddleGAN/blob/develop/docs/zh_CN/tutorials/video_super_resolution.md) |
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| [PaddleGAN/EDVR](./edvr) | EDVR 系列模型 | paddle | [develop](https://github.com/PaddlePaddle/PaddleGAN/blob/develop/docs/zh_CN/tutorials/video_super_resolution.md) |
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| [PaddleGAN/PP-MSVSR](./ppmsvsr) | PP-MSVSR 系列模型 | paddle | [develop](https://github.com/PaddlePaddle/PaddleGAN/blob/develop/docs/zh_CN/tutorials/video_super_resolution.md) |
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examples/vision/sr/basicvsr/README.md
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examples/vision/sr/basicvsr/README.md
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# BasicVSR模型部署
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## 模型版本说明
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- [PaddleGAN develop](https://github.com/PaddlePaddle/PaddleGAN)
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## 支持模型列表
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目前FastDeploy支持如下模型的部署
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- [BasicVSR](https://github.com/PaddlePaddle/PaddleGAN/blob/develop/docs/zh_CN/tutorials/video_super_resolution.md)。
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## 导出部署模型
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在部署前,需要先将训练好的BasicVSR导出成部署模型,导出BasicVSR导出模型步骤,参考文档[导出模型](https://github.com/PaddlePaddle/PaddleGAN/blob/develop/docs/zh_CN/tutorials/video_super_resolution.md)。
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| 模型 | 参数大小 | 精度 | 备注 |
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|:----------------------------------------------------------------------------|:-------|:----- | :------ |
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| [BasicVSR](https://bj.bcebos.com/paddlehub/fastdeploy/BasicVSR_reds_x4.tgz) | 30.1MB | - |
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**注意**:非常不建议在没有独立显卡的设备上运行该模型
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## 详细部署文档
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- [Python部署](python)
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- [C++部署](cpp)
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14
examples/vision/sr/basicvsr/cpp/CMakeLists.txt
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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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||||||
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// Unless required by applicable law or agreed to in writing, software
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||||||
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// distributed under the License is distributed on an "AS IS" BASIS,
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||||||
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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||||||
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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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|
}
|
||||||
|
// 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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|
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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);
|
||||||
|
if (!video_out.isOpened())
|
||||||
|
{
|
||||||
|
std::cout << "create video writer failed!" << std::endl;
|
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|
return;
|
||||||
|
}
|
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|
// Capture all frames and do inference
|
||||||
|
cv::Mat frame;
|
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|
int frame_id = 0;
|
||||||
|
bool reach_end = false;
|
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|
while (capture.isOpened())
|
||||||
|
{
|
||||||
|
std::vector<cv::Mat> imgs;
|
||||||
|
for (int i = 0; i < frame_num; i++)
|
||||||
|
{
|
||||||
|
capture.read(frame);
|
||||||
|
if (!frame.empty())
|
||||||
|
{
|
||||||
|
imgs.push_back(frame);
|
||||||
|
}else{
|
||||||
|
reach_end = true;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
if (reach_end)
|
||||||
|
{
|
||||||
|
break;
|
||||||
|
}
|
||||||
|
std::vector<cv::Mat> results;
|
||||||
|
model.Predict(imgs, results);
|
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|
for (auto &item : results)
|
||||||
|
{
|
||||||
|
// cv::imshow("13",item);
|
||||||
|
// cv::waitKey(30);
|
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|
video_out.write(item);
|
||||||
|
std::cout << "Processing frame: "<< frame_id << std::endl;
|
||||||
|
frame_id += 1;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
std::cout << "inference finished, output video saved at " << video_out_path << std::endl;
|
||||||
|
capture.release();
|
||||||
|
video_out.release();
|
||||||
|
}
|
||||||
|
|
||||||
|
void GpuInfer(const std::string& model_dir,
|
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|
const std::string& video_file, int frame_num) {
|
||||||
|
auto model_file = model_dir + sep + "model.pdmodel";
|
||||||
|
auto params_file = model_dir + sep + "model.pdiparams";
|
||||||
|
|
||||||
|
auto option = fastdeploy::RuntimeOption();
|
||||||
|
option.UseGpu();
|
||||||
|
auto model = fastdeploy::vision::sr::BasicVSR(
|
||||||
|
model_file, params_file, option);
|
||||||
|
|
||||||
|
if (!model.Initialized()) {
|
||||||
|
std::cerr << "Failed to initialize." << std::endl;
|
||||||
|
return;
|
||||||
|
}
|
||||||
|
// note: input/output shape is [b, n, c, h, w] (n = frame_nums; b=1(default))
|
||||||
|
// b and n is dependent on export model shape
|
||||||
|
// see https://github.com/PaddlePaddle/PaddleGAN/blob/develop/docs/zh_CN/tutorials/video_super_resolution.md
|
||||||
|
cv::VideoCapture capture;
|
||||||
|
// change your save video path
|
||||||
|
std::string video_out_name = "output.mp4";
|
||||||
|
capture.open(video_file);
|
||||||
|
if (!capture.isOpened())
|
||||||
|
{
|
||||||
|
std::cout<<"can not open video "<<std::endl;
|
||||||
|
return;
|
||||||
|
}
|
||||||
|
// Get Video info :fps, frame count
|
||||||
|
int video_fps = static_cast<int>(capture.get(cv::CAP_PROP_FPS));
|
||||||
|
int video_frame_count = static_cast<int>(capture.get(cv::CAP_PROP_FRAME_COUNT));
|
||||||
|
// Set fixed size for output frame, only for msvsr model
|
||||||
|
int out_width = 1280;
|
||||||
|
int out_height = 720;
|
||||||
|
std::cout << "fps: " << video_fps << "\tframe_count: " << video_frame_count << std::endl;
|
||||||
|
|
||||||
|
// Create VideoWriter for output
|
||||||
|
cv::VideoWriter video_out;
|
||||||
|
std::string video_out_path("./");
|
||||||
|
video_out_path += video_out_name;
|
||||||
|
int fcc = cv::VideoWriter::fourcc('m', 'p', '4', 'v');
|
||||||
|
video_out.open(video_out_path, fcc, video_fps, cv::Size(out_width, out_height), true);
|
||||||
|
if (!video_out.isOpened())
|
||||||
|
{
|
||||||
|
std::cout << "create video writer failed!" << std::endl;
|
||||||
|
return;
|
||||||
|
}
|
||||||
|
// Capture all frames and do inference
|
||||||
|
cv::Mat frame;
|
||||||
|
int frame_id = 0;
|
||||||
|
bool reach_end = false;
|
||||||
|
while (capture.isOpened())
|
||||||
|
{
|
||||||
|
std::vector<cv::Mat> imgs;
|
||||||
|
for (int i = 0; i < frame_num; i++)
|
||||||
|
{
|
||||||
|
capture.read(frame);
|
||||||
|
if (!frame.empty())
|
||||||
|
{
|
||||||
|
imgs.push_back(frame);
|
||||||
|
}else{
|
||||||
|
reach_end = true;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
if (reach_end)
|
||||||
|
{
|
||||||
|
break;
|
||||||
|
}
|
||||||
|
std::vector<cv::Mat> results;
|
||||||
|
model.Predict(imgs, results);
|
||||||
|
for (auto &item : results)
|
||||||
|
{
|
||||||
|
// cv::imshow("13",item);
|
||||||
|
// cv::waitKey(30);
|
||||||
|
video_out.write(item);
|
||||||
|
std::cout << "Processing frame: "<< frame_id << std::endl;
|
||||||
|
frame_id += 1;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
std::cout << "inference finished, output video saved at " << video_out_path << std::endl;
|
||||||
|
capture.release();
|
||||||
|
video_out.release();
|
||||||
|
}
|
||||||
|
|
||||||
|
void TrtInfer(const std::string& model_dir,
|
||||||
|
const std::string& video_file, int frame_num) {
|
||||||
|
auto model_file = model_dir + sep + "model.pdmodel";
|
||||||
|
auto params_file = model_dir + sep + "model.pdiparams";
|
||||||
|
auto option = fastdeploy::RuntimeOption();
|
||||||
|
option.UseGpu();
|
||||||
|
option.UseTrtBackend();
|
||||||
|
// use paddle-TRT
|
||||||
|
option.EnablePaddleToTrt();
|
||||||
|
auto model = fastdeploy::vision::sr::BasicVSR(
|
||||||
|
model_file, params_file, option);
|
||||||
|
|
||||||
|
if (!model.Initialized()) {
|
||||||
|
std::cerr << "Failed to initialize." << std::endl;
|
||||||
|
return;
|
||||||
|
}
|
||||||
|
|
||||||
|
// note: input/output shape is [b, n, c, h, w] (n = frame_nums; b=1(default))
|
||||||
|
// b and n is dependent on export model shape
|
||||||
|
// see https://github.com/PaddlePaddle/PaddleGAN/blob/develop/docs/zh_CN/tutorials/video_super_resolution.md
|
||||||
|
cv::VideoCapture capture;
|
||||||
|
// change your save video path
|
||||||
|
std::string video_out_name = "output.mp4";
|
||||||
|
capture.open(video_file);
|
||||||
|
if (!capture.isOpened())
|
||||||
|
{
|
||||||
|
std::cout<<"can not open video "<<std::endl;
|
||||||
|
return;
|
||||||
|
}
|
||||||
|
// Get Video info :fps, frame count
|
||||||
|
int video_fps = static_cast<int>(capture.get(cv::CAP_PROP_FPS));
|
||||||
|
int video_frame_count = static_cast<int>(capture.get(cv::CAP_PROP_FRAME_COUNT));
|
||||||
|
// Set fixed size for output frame, only for msvsr model
|
||||||
|
//Note that the resolution between the size and the original input is consistent when the model is exported,
|
||||||
|
// for example: [1,2,3,180,320], after 4x super separation [1,2,3,720,1080].
|
||||||
|
//Therefore, it is very important to derive the model
|
||||||
|
int out_width = 1280;
|
||||||
|
int out_height = 720;
|
||||||
|
std::cout << "fps: " << video_fps << "\tframe_count: " << video_frame_count << std::endl;
|
||||||
|
|
||||||
|
// Create VideoWriter for output
|
||||||
|
cv::VideoWriter video_out;
|
||||||
|
std::string video_out_path("./");
|
||||||
|
video_out_path += video_out_name;
|
||||||
|
int fcc = cv::VideoWriter::fourcc('m', 'p', '4', 'v');
|
||||||
|
video_out.open(video_out_path, fcc, video_fps, cv::Size(out_width, out_height), true);
|
||||||
|
if (!video_out.isOpened())
|
||||||
|
{
|
||||||
|
std::cout << "create video writer failed!" << std::endl;
|
||||||
|
return;
|
||||||
|
}
|
||||||
|
// Capture all frames and do inference
|
||||||
|
cv::Mat frame;
|
||||||
|
int frame_id = 0;
|
||||||
|
bool reach_end = false;
|
||||||
|
while (capture.isOpened())
|
||||||
|
{
|
||||||
|
std::vector<cv::Mat> imgs;
|
||||||
|
for (int i = 0; i < frame_num; i++)
|
||||||
|
{
|
||||||
|
capture.read(frame);
|
||||||
|
if (!frame.empty())
|
||||||
|
{
|
||||||
|
imgs.push_back(frame);
|
||||||
|
}else{
|
||||||
|
reach_end = true;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
if (reach_end)
|
||||||
|
{
|
||||||
|
break;
|
||||||
|
}
|
||||||
|
std::vector<cv::Mat> results;
|
||||||
|
model.Predict(imgs, results);
|
||||||
|
for (auto &item : results)
|
||||||
|
{
|
||||||
|
// cv::imshow("13",item);
|
||||||
|
// cv::waitKey(30);
|
||||||
|
video_out.write(item);
|
||||||
|
std::cout << "Processing frame: "<< frame_id << std::endl;
|
||||||
|
frame_id += 1;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
std::cout << "inference finished, output video saved at " << video_out_path << std::endl;
|
||||||
|
capture.release();
|
||||||
|
video_out.release();
|
||||||
|
}
|
||||||
|
|
||||||
|
int main(int argc, char* argv[]) {
|
||||||
|
if (argc < 4) {
|
||||||
|
std::cout
|
||||||
|
<< "Usage: infer_demo path/to/model_dir path/to/video frame number run_option, "
|
||||||
|
"e.g ./infer_model ./vsr_model_dir ./person.mp4 0 2"
|
||||||
|
<< std::endl;
|
||||||
|
std::cout << "The data type of run_option is int, 0: run with cpu; 1: run "
|
||||||
|
"with gpu; 2: run with gpu and use tensorrt backend."
|
||||||
|
<< std::endl;
|
||||||
|
return -1;
|
||||||
|
}
|
||||||
|
|
||||||
|
int frame_num = 2;
|
||||||
|
if (argc == 5) {
|
||||||
|
frame_num = std::atoi(argv[4]);
|
||||||
|
}
|
||||||
|
if (std::atoi(argv[3]) == 0) {
|
||||||
|
CpuInfer(argv[1], argv[2], frame_num);
|
||||||
|
} else if (std::atoi(argv[3]) == 1) {
|
||||||
|
GpuInfer(argv[1], argv[2], frame_num);
|
||||||
|
} else if (std::atoi(argv[3]) == 2) {
|
||||||
|
TrtInfer(argv[1], argv[2], frame_num);
|
||||||
|
}
|
||||||
|
return 0;
|
||||||
|
}
|
61
examples/vision/sr/basicvsr/python/README.md
Normal file
61
examples/vision/sr/basicvsr/python/README.md
Normal file
@@ -0,0 +1,61 @@
|
|||||||
|
# BasicVSR 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BasicVSR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/basicvsr/python
|
||||||
|
|
||||||
|
# 下载BasicVSR模型文件和测试视频
|
||||||
|
wget https://bj.bcebos.com/paddlehub/fastdeploy/BasicVSR_reds_x4.tar
|
||||||
|
tar -xvf BasicVSR_reds_x4.tar
|
||||||
|
wget https://bj.bcebos.com/paddlehub/fastdeploy/vsr_src.mp4
|
||||||
|
# CPU推理
|
||||||
|
python infer.py --model BasicVSR_reds_x4 --video person.mp4 --frame_num 2 --device cpu
|
||||||
|
# GPU推理
|
||||||
|
python infer.py --model BasicVSR_reds_x4 --video person.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 BasicVSR_reds_x4 --video person.mp4 --frame_num 2 --device gpu --use_trt True
|
||||||
|
```
|
||||||
|
|
||||||
|
## BasicVSR Python接口
|
||||||
|
|
||||||
|
```python
|
||||||
|
fd.vision.sr.BasicVSR(model_file, params_file, runtime_option=None, model_format=ModelFormat.PADDLE)
|
||||||
|
```
|
||||||
|
|
||||||
|
BasicVSR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
|
||||||
|
> BasicVSR.predict(frames)
|
||||||
|
> ```
|
||||||
|
>
|
||||||
|
> 模型预测结口,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
|
||||||
|
|
||||||
|
- [BasicVSR 模型介绍](..)
|
||||||
|
- [BasicVSR C++部署](../cpp)
|
||||||
|
- [如何切换模型推理后端引擎](../../../../../docs/cn/faq/how_to_change_backend.md)
|
86
examples/vision/sr/basicvsr/python/infer.py
Normal file
86
examples/vision/sr/basicvsr/python/infer.py
Normal file
@@ -0,0 +1,86 @@
|
|||||||
|
import cv2
|
||||||
|
import os
|
||||||
|
import fastdeploy as fd
|
||||||
|
|
||||||
|
|
||||||
|
def parse_arguments():
|
||||||
|
import argparse
|
||||||
|
import ast
|
||||||
|
parser = argparse.ArgumentParser()
|
||||||
|
parser.add_argument("--model", required=True, help="Path of model.")
|
||||||
|
parser.add_argument(
|
||||||
|
"--video", type=str, required=True, help="Path of test video file.")
|
||||||
|
parser.add_argument("--frame_num", type=int, default=2, help="frame num")
|
||||||
|
parser.add_argument(
|
||||||
|
"--device",
|
||||||
|
type=str,
|
||||||
|
default='cpu',
|
||||||
|
help="Type of inference device, support 'cpu' or 'gpu'.")
|
||||||
|
parser.add_argument(
|
||||||
|
"--use_trt",
|
||||||
|
type=ast.literal_eval,
|
||||||
|
default=False,
|
||||||
|
help="Wether to use tensorrt.")
|
||||||
|
return parser.parse_args()
|
||||||
|
|
||||||
|
|
||||||
|
def build_option(args):
|
||||||
|
option = fd.RuntimeOption()
|
||||||
|
if args.device.lower() == "gpu":
|
||||||
|
option.use_gpu()
|
||||||
|
if args.use_trt:
|
||||||
|
option.use_trt_backend()
|
||||||
|
option.enable_paddle_to_trt()
|
||||||
|
return option
|
||||||
|
|
||||||
|
|
||||||
|
args = parse_arguments()
|
||||||
|
|
||||||
|
# 配置runtime,加载模型
|
||||||
|
runtime_option = build_option(args)
|
||||||
|
model_file = os.path.join(args.model, "model.pdmodel")
|
||||||
|
params_file = os.path.join(args.model, "model.pdiparams")
|
||||||
|
model = fd.vision.sr.BasicVSR(
|
||||||
|
model_file, params_file, runtime_option=runtime_option)
|
||||||
|
|
||||||
|
# 该处应该与你导出模型的第二个维度一致模型输入shape=[b,n,c,h,w]
|
||||||
|
capture = cv2.VideoCapture(args.video)
|
||||||
|
video_out_name = "output.mp4"
|
||||||
|
video_fps = capture.get(cv2.CAP_PROP_FPS)
|
||||||
|
video_frame_count = capture.get(cv2.CAP_PROP_FRAME_COUNT)
|
||||||
|
# 注意导出模型时尺寸与原始输入的分辨一致比如:[1,2,3,180,320],经过4x超分后[1,2,3,720,1280]
|
||||||
|
# 所以导出模型相当重要(最关键的是根据netron查看网络输出shape)
|
||||||
|
out_width = 1280
|
||||||
|
out_height = 720
|
||||||
|
print(f"fps: {video_fps}\tframe_count: {video_frame_count}")
|
||||||
|
# Create VideoWriter for output
|
||||||
|
video_out_dir = "./"
|
||||||
|
video_out_path = os.path.join(video_out_dir, video_out_name)
|
||||||
|
fucc = cv2.VideoWriter_fourcc(*"mp4v")
|
||||||
|
video_out = cv2.VideoWriter(video_out_path, fucc, video_fps,
|
||||||
|
(out_width, out_height), True)
|
||||||
|
if not video_out.isOpened():
|
||||||
|
print("create video writer failed!")
|
||||||
|
# Capture all frames and do inference
|
||||||
|
frame_id = 0
|
||||||
|
reach_end = False
|
||||||
|
while capture.isOpened():
|
||||||
|
imgs = []
|
||||||
|
for i in range(args.frame_num):
|
||||||
|
_, frame = capture.read()
|
||||||
|
if frame is not None:
|
||||||
|
imgs.append(frame)
|
||||||
|
else:
|
||||||
|
reach_end = True
|
||||||
|
if reach_end:
|
||||||
|
break
|
||||||
|
results = model.predict(imgs)
|
||||||
|
for item in results:
|
||||||
|
# cv2.imshow("13", item)
|
||||||
|
# cv2.waitKey(30)
|
||||||
|
video_out.write(item)
|
||||||
|
print("Processing frame: ", frame_id)
|
||||||
|
frame_id += 1
|
||||||
|
print("inference finished, output video saved at: ", video_out_path)
|
||||||
|
capture.release()
|
||||||
|
video_out.release()
|
28
examples/vision/sr/edvr/README.md
Normal file
28
examples/vision/sr/edvr/README.md
Normal file
@@ -0,0 +1,28 @@
|
|||||||
|
# EDVR模型部署
|
||||||
|
|
||||||
|
## 模型版本说明
|
||||||
|
|
||||||
|
- [PaddleGAN develop](https://github.com/PaddlePaddle/PaddleGAN)
|
||||||
|
|
||||||
|
## 支持模型列表
|
||||||
|
|
||||||
|
目前FastDeploy支持如下模型的部署
|
||||||
|
|
||||||
|
- [EDVR](https://github.com/PaddlePaddle/PaddleGAN/blob/develop/docs/zh_CN/tutorials/video_super_resolution.md)。
|
||||||
|
|
||||||
|
|
||||||
|
## 导出部署模型
|
||||||
|
|
||||||
|
在部署前,需要先将训练好的EDVR导出成部署模型,导出EDVR导出模型步骤,参考文档[导出模型](https://github.com/PaddlePaddle/PaddleGAN/blob/develop/docs/zh_CN/tutorials/video_super_resolution.md)。
|
||||||
|
|
||||||
|
|
||||||
|
| 模型 | 参数大小 | 精度 | 备注 |
|
||||||
|
|:--------------------------------------------------------------------------------|:-------|:----- | :------ |
|
||||||
|
| [EDVR](https://bj.bcebos.com/paddlehub/fastdeploy/EDVR_M_wo_tsa_SRx4.tgz) | 14.9MB | - |
|
||||||
|
|
||||||
|
**注意**:非常不建议在没有独立显卡的设备上运行该模型
|
||||||
|
|
||||||
|
## 详细部署文档
|
||||||
|
|
||||||
|
- [Python部署](python)
|
||||||
|
- [C++部署](cpp)
|
14
examples/vision/sr/edvr/cpp/CMakeLists.txt
Normal file
14
examples/vision/sr/edvr/cpp/CMakeLists.txt
Normal file
@@ -0,0 +1,14 @@
|
|||||||
|
PROJECT(infer_demo C CXX)
|
||||||
|
CMAKE_MINIMUM_REQUIRED (VERSION 3.10)
|
||||||
|
|
||||||
|
# 指定下载解压后的fastdeploy库路径
|
||||||
|
option(FASTDEPLOY_INSTALL_DIR "Path of downloaded fastdeploy sdk.")
|
||||||
|
|
||||||
|
include(${FASTDEPLOY_INSTALL_DIR}/FastDeploy.cmake)
|
||||||
|
|
||||||
|
# 添加FastDeploy依赖头文件
|
||||||
|
include_directories(${FASTDEPLOY_INCS})
|
||||||
|
|
||||||
|
add_executable(infer_demo ${PROJECT_SOURCE_DIR}/infer.cc)
|
||||||
|
# 添加FastDeploy库依赖
|
||||||
|
target_link_libraries(infer_demo ${FASTDEPLOY_LIBS})
|
75
examples/vision/sr/edvr/cpp/README.md
Normal file
75
examples/vision/sr/edvr/cpp/README.md
Normal file
@@ -0,0 +1,75 @@
|
|||||||
|
# EDVR C++部署示例
|
||||||
|
|
||||||
|
本目录下提供`infer.cc`快速完成EDVR在CPU/GPU,以及GPU上通过TensorRT加速部署的示例。
|
||||||
|
|
||||||
|
在部署前,需确认以下两个步骤
|
||||||
|
|
||||||
|
- 1. 软硬件环境满足要求,参考[FastDeploy环境要求](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
|
||||||
|
- 2. 根据开发环境,下载预编译部署库和samples代码,参考[FastDeploy预编译库](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
|
||||||
|
|
||||||
|
以Linux上EDVR推理为例,在本目录执行如下命令即可完成编译测试(如若只需在CPU上部署,可在[Fastdeploy C++预编译库](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md/CPP_prebuilt_libraries.md)下载CPU推理库)
|
||||||
|
|
||||||
|
```bash
|
||||||
|
#下载SDK,编译模型examples代码(SDK中包含了examples代码)
|
||||||
|
# fastdeploy版本 >= 0.7.0
|
||||||
|
wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-gpu-0.7.0.tgz
|
||||||
|
tar xvf fastdeploy-linux-x64-gpu-0.7.0.tgz
|
||||||
|
cd fastdeploy-linux-x64-gpu-0.7.0/examples/vision/sr/edvr/cpp/
|
||||||
|
mkdir build && cd build
|
||||||
|
cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/../../../../../../../fastdeploy-linux-x64-gpu-0.7.0
|
||||||
|
make -j
|
||||||
|
|
||||||
|
# 下载EDVR模型文件和测试视频
|
||||||
|
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推理
|
||||||
|
./infer_demo EDVR_M_wo_tsa_SRx4 vsr_src.mp4 0 2
|
||||||
|
# GPU推理
|
||||||
|
./infer_demo EDVR_M_wo_tsa_SRx4 vsr_src.mp4 1 2
|
||||||
|
# GPU上TensorRT推理
|
||||||
|
./infer_demo EDVR_M_wo_tsa_SRx4 vsr_src.mp4 2 2
|
||||||
|
```
|
||||||
|
|
||||||
|
以上命令只适用于Linux或MacOS, Windows下SDK的使用方式请参考:
|
||||||
|
- [如何在Windows中使用FastDeploy C++ SDK](../../../../../docs/cn/faq/use_sdk_on_windows.md)
|
||||||
|
|
||||||
|
## EDVR C++接口
|
||||||
|
|
||||||
|
### EDVR类
|
||||||
|
|
||||||
|
```c++
|
||||||
|
fastdeploy::vision::sr::EDVR(
|
||||||
|
const string& model_file,
|
||||||
|
const string& params_file = "",
|
||||||
|
const RuntimeOption& runtime_option = RuntimeOption(),
|
||||||
|
const ModelFormat& model_format = ModelFormat::PADDLE)
|
||||||
|
```
|
||||||
|
|
||||||
|
EDVR模型加载和初始化,其中model_file为导出的Paddle模型格式。
|
||||||
|
|
||||||
|
**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
|
||||||
|
|
||||||
|
> ```c++
|
||||||
|
> EDVR::Predict(std::vector<cv::Mat>& imgs, std::vector<cv::Mat>& results)
|
||||||
|
> ```
|
||||||
|
>
|
||||||
|
> 模型预测接口,输入图像直接输出检测结果。
|
||||||
|
>
|
||||||
|
> **参数**
|
||||||
|
>
|
||||||
|
> > * **imgs**: 输入视频帧序列,注意需为HWC,BGR格式
|
||||||
|
> > * **results**: 视频超分结果,超分后的视频帧序列
|
||||||
|
|
||||||
|
- [模型介绍](../../)
|
||||||
|
- [Python部署](../python)
|
||||||
|
- [如何切换模型推理后端引擎](../../../../../docs/cn/faq/how_to_change_backend.md)
|
279
examples/vision/sr/edvr/cpp/infer.cc
Normal file
279
examples/vision/sr/edvr/cpp/infer.cc
Normal file
@@ -0,0 +1,279 @@
|
|||||||
|
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
|
||||||
|
//
|
||||||
|
// Licensed under the Apache License, Version 2.0 (the "License");
|
||||||
|
// you may not use this file except in compliance with the License.
|
||||||
|
// You may obtain a copy of the License at
|
||||||
|
//
|
||||||
|
// http://www.apache.org/licenses/LICENSE-2.0
|
||||||
|
//
|
||||||
|
// Unless required by applicable law or agreed to in writing, software
|
||||||
|
// distributed under the License is distributed on an "AS IS" BASIS,
|
||||||
|
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||||
|
// See the License for the specific language governing permissions and
|
||||||
|
// limitations under the License.
|
||||||
|
|
||||||
|
#include "fastdeploy/vision.h"
|
||||||
|
|
||||||
|
#ifdef WIN32
|
||||||
|
const char sep = '\\';
|
||||||
|
#else
|
||||||
|
const char sep = '/';
|
||||||
|
#endif
|
||||||
|
|
||||||
|
void CpuInfer(const std::string& model_dir,
|
||||||
|
const std::string& video_file, int frame_num) {
|
||||||
|
auto model_file = model_dir + sep + "model.pdmodel";
|
||||||
|
auto params_file = model_dir + sep + "model.pdiparams";
|
||||||
|
auto model = fastdeploy::vision::sr::EDVR(model_file, params_file);
|
||||||
|
|
||||||
|
if (!model.Initialized()) {
|
||||||
|
std::cerr << "Failed to initialize." << std::endl;
|
||||||
|
return;
|
||||||
|
}
|
||||||
|
// note: input/output shape is [b, n, c, h, w] (n = frame_nums; b=1(default))
|
||||||
|
// b and n is dependent on export model shape
|
||||||
|
// see https://github.com/PaddlePaddle/PaddleGAN/blob/develop/docs/zh_CN/tutorials/video_super_resolution.md
|
||||||
|
cv::VideoCapture capture;
|
||||||
|
// change your save video path
|
||||||
|
std::string video_out_name = "output.mp4";
|
||||||
|
capture.open(video_file);
|
||||||
|
if (!capture.isOpened())
|
||||||
|
{
|
||||||
|
std::cout<<"can not open video "<<std::endl;
|
||||||
|
return;
|
||||||
|
}
|
||||||
|
// Get Video info :fps, frame count
|
||||||
|
// it used 4.x version of opencv below
|
||||||
|
// notice your opencv version and method of api.
|
||||||
|
int video_fps = static_cast<int>(capture.get(cv::CAP_PROP_FPS));
|
||||||
|
int video_frame_count = static_cast<int>(capture.get(cv::CAP_PROP_FRAME_COUNT));
|
||||||
|
// Set fixed size for output frame, only for msvsr model
|
||||||
|
int out_width = 1280;
|
||||||
|
int out_height = 720;
|
||||||
|
std::cout << "fps: " << video_fps << "\tframe_count: " << video_frame_count << std::endl;
|
||||||
|
|
||||||
|
// Create VideoWriter for output
|
||||||
|
cv::VideoWriter video_out;
|
||||||
|
std::string video_out_path("./");
|
||||||
|
video_out_path += video_out_name;
|
||||||
|
int fcc = cv::VideoWriter::fourcc('m', 'p', '4', 'v');
|
||||||
|
video_out.open(video_out_path, fcc, video_fps, cv::Size(out_width, out_height), true);
|
||||||
|
if (!video_out.isOpened())
|
||||||
|
{
|
||||||
|
std::cout << "create video writer failed!" << std::endl;
|
||||||
|
return;
|
||||||
|
}
|
||||||
|
// Capture all frames and do inference
|
||||||
|
cv::Mat frame;
|
||||||
|
int frame_id = 0;
|
||||||
|
std::vector<cv::Mat> imgs;
|
||||||
|
while (capture.read(frame)){
|
||||||
|
if (!frame.empty())
|
||||||
|
{
|
||||||
|
if(frame_id < frame_num){
|
||||||
|
imgs.push_back(frame);
|
||||||
|
frame_id ++;
|
||||||
|
continue;
|
||||||
|
}
|
||||||
|
imgs.erase(imgs.begin());
|
||||||
|
imgs.push_back(frame);
|
||||||
|
}
|
||||||
|
frame_id ++;
|
||||||
|
std::vector<cv::Mat> results;
|
||||||
|
model.Predict(imgs, results);
|
||||||
|
for (auto &item : results)
|
||||||
|
{
|
||||||
|
// cv::imshow("13",item);
|
||||||
|
// cv::waitKey(30);
|
||||||
|
video_out.write(item);
|
||||||
|
std::cout << "Processing frame: "<< frame_id << std::endl;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
std::cout << "inference finished, output video saved at " << video_out_path << std::endl;
|
||||||
|
capture.release();
|
||||||
|
video_out.release();
|
||||||
|
}
|
||||||
|
|
||||||
|
void GpuInfer(const std::string& model_dir,
|
||||||
|
const std::string& video_file, int frame_num) {
|
||||||
|
auto model_file = model_dir + sep + "model.pdmodel";
|
||||||
|
auto params_file = model_dir + sep + "model.pdiparams";
|
||||||
|
|
||||||
|
auto option = fastdeploy::RuntimeOption();
|
||||||
|
option.UseGpu();
|
||||||
|
auto model = fastdeploy::vision::sr::EDVR(
|
||||||
|
model_file, params_file, option);
|
||||||
|
|
||||||
|
if (!model.Initialized()) {
|
||||||
|
std::cerr << "Failed to initialize." << std::endl;
|
||||||
|
return;
|
||||||
|
}
|
||||||
|
// note: input/output shape is [b, n, c, h, w] (n = frame_nums; b=1(default))
|
||||||
|
// b and n is dependent on export model shape
|
||||||
|
// see https://github.com/PaddlePaddle/PaddleGAN/blob/develop/docs/zh_CN/tutorials/video_super_resolution.md
|
||||||
|
cv::VideoCapture capture;
|
||||||
|
// change your save video path
|
||||||
|
std::string video_out_name = "output.mp4";
|
||||||
|
capture.open(video_file);
|
||||||
|
if (!capture.isOpened())
|
||||||
|
{
|
||||||
|
std::cout<<"can not open video "<<std::endl;
|
||||||
|
return;
|
||||||
|
}
|
||||||
|
// Get Video info :fps, frame count
|
||||||
|
int video_fps = static_cast<int>(capture.get(cv::CAP_PROP_FPS));
|
||||||
|
int video_frame_count = static_cast<int>(capture.get(cv::CAP_PROP_FRAME_COUNT));
|
||||||
|
// Set fixed size for output frame, only for msvsr model
|
||||||
|
int out_width = 1280;
|
||||||
|
int out_height = 720;
|
||||||
|
std::cout << "fps: " << video_fps << "\tframe_count: " << video_frame_count << std::endl;
|
||||||
|
|
||||||
|
// Create VideoWriter for output
|
||||||
|
cv::VideoWriter video_out;
|
||||||
|
std::string video_out_path("./");
|
||||||
|
video_out_path += video_out_name;
|
||||||
|
int fcc = cv::VideoWriter::fourcc('m', 'p', '4', 'v');
|
||||||
|
video_out.open(video_out_path, fcc, video_fps, cv::Size(out_width, out_height), true);
|
||||||
|
if (!video_out.isOpened())
|
||||||
|
{
|
||||||
|
std::cout << "create video writer failed!" << std::endl;
|
||||||
|
return;
|
||||||
|
}
|
||||||
|
// Capture all frames and do inference
|
||||||
|
cv::Mat frame;
|
||||||
|
int frame_id = 0;
|
||||||
|
std::vector<cv::Mat> imgs;
|
||||||
|
while (capture.read(frame)){
|
||||||
|
if (!frame.empty())
|
||||||
|
{
|
||||||
|
if(frame_id < frame_num){
|
||||||
|
imgs.push_back(frame);
|
||||||
|
frame_id ++;
|
||||||
|
continue;
|
||||||
|
}
|
||||||
|
imgs.erase(imgs.begin());
|
||||||
|
imgs.push_back(frame);
|
||||||
|
}
|
||||||
|
frame_id ++;
|
||||||
|
std::vector<cv::Mat> results;
|
||||||
|
model.Predict(imgs, results);
|
||||||
|
for (auto &item : results)
|
||||||
|
{
|
||||||
|
// cv::imshow("13",item);
|
||||||
|
// cv::waitKey(30);
|
||||||
|
video_out.write(item);
|
||||||
|
std::cout << "Processing frame: "<< frame_id << std::endl;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
std::cout << "inference finished, output video saved at " << video_out_path << std::endl;
|
||||||
|
capture.release();
|
||||||
|
video_out.release();
|
||||||
|
}
|
||||||
|
|
||||||
|
void TrtInfer(const std::string& model_dir,
|
||||||
|
const std::string& video_file, int frame_num) {
|
||||||
|
auto model_file = model_dir + sep + "model.pdmodel";
|
||||||
|
auto params_file = model_dir + sep + "model.pdiparams";
|
||||||
|
auto option = fastdeploy::RuntimeOption();
|
||||||
|
option.UseGpu();
|
||||||
|
option.UseTrtBackend();
|
||||||
|
// use paddle-TRT
|
||||||
|
option.EnablePaddleToTrt();
|
||||||
|
auto model = fastdeploy::vision::sr::EDVR(
|
||||||
|
model_file, params_file, option);
|
||||||
|
|
||||||
|
if (!model.Initialized()) {
|
||||||
|
std::cerr << "Failed to initialize." << std::endl;
|
||||||
|
return;
|
||||||
|
}
|
||||||
|
|
||||||
|
// note: input/output shape is [b, n, c, h, w] (n = frame_nums; b=1(default))
|
||||||
|
// b and n is dependent on export model shape
|
||||||
|
// see https://github.com/PaddlePaddle/PaddleGAN/blob/develop/docs/zh_CN/tutorials/video_super_resolution.md
|
||||||
|
cv::VideoCapture capture;
|
||||||
|
// change your save video path
|
||||||
|
std::string video_out_name = "output.mp4";
|
||||||
|
capture.open(video_file);
|
||||||
|
if (!capture.isOpened())
|
||||||
|
{
|
||||||
|
std::cout<<"can not open video "<<std::endl;
|
||||||
|
return;
|
||||||
|
}
|
||||||
|
// Get Video info :fps, frame count
|
||||||
|
int video_fps = static_cast<int>(capture.get(cv::CAP_PROP_FPS));
|
||||||
|
int video_frame_count = static_cast<int>(capture.get(cv::CAP_PROP_FRAME_COUNT));
|
||||||
|
// Set fixed size for output frame, only for msvsr model
|
||||||
|
//Note that the resolution between the size and the original input is consistent when the model is exported,
|
||||||
|
// for example: [1,2,3,180,320], after 4x super separation [1,2,3,720,1080].
|
||||||
|
//Therefore, it is very important to derive the model
|
||||||
|
int out_width = 1280;
|
||||||
|
int out_height = 720;
|
||||||
|
std::cout << "fps: " << video_fps << "\tframe_count: " << video_frame_count << std::endl;
|
||||||
|
|
||||||
|
// Create VideoWriter for output
|
||||||
|
cv::VideoWriter video_out;
|
||||||
|
std::string video_out_path("./");
|
||||||
|
video_out_path += video_out_name;
|
||||||
|
int fcc = cv::VideoWriter::fourcc('m', 'p', '4', 'v');
|
||||||
|
video_out.open(video_out_path, fcc, video_fps, cv::Size(out_width, out_height), true);
|
||||||
|
if (!video_out.isOpened())
|
||||||
|
{
|
||||||
|
std::cout << "create video writer failed!" << std::endl;
|
||||||
|
return;
|
||||||
|
}
|
||||||
|
// Capture all frames and do inference
|
||||||
|
cv::Mat frame;
|
||||||
|
int frame_id = 0;
|
||||||
|
std::vector<cv::Mat> imgs;
|
||||||
|
while (capture.read(frame)){
|
||||||
|
if (!frame.empty())
|
||||||
|
{
|
||||||
|
if(frame_id < frame_num){
|
||||||
|
imgs.push_back(frame);
|
||||||
|
frame_id ++;
|
||||||
|
continue;
|
||||||
|
}
|
||||||
|
imgs.erase(imgs.begin());
|
||||||
|
imgs.push_back(frame);
|
||||||
|
}
|
||||||
|
frame_id ++;
|
||||||
|
std::vector<cv::Mat> results;
|
||||||
|
model.Predict(imgs, results);
|
||||||
|
for (auto &item : results)
|
||||||
|
{
|
||||||
|
// cv::imshow("13",item);
|
||||||
|
// cv::waitKey(30);
|
||||||
|
video_out.write(item);
|
||||||
|
std::cout << "Processing frame: "<< frame_id << std::endl;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
std::cout << "inference finished, output video saved at " << video_out_path << std::endl;
|
||||||
|
capture.release();
|
||||||
|
video_out.release();
|
||||||
|
}
|
||||||
|
|
||||||
|
int main(int argc, char* argv[]) {
|
||||||
|
if (argc < 4) {
|
||||||
|
std::cout
|
||||||
|
<< "Usage: infer_demo path/to/model_dir path/to/video frame number run_option, "
|
||||||
|
"e.g ./infer_model ./vsr_model_dir ./person.mp4 0 2"
|
||||||
|
<< std::endl;
|
||||||
|
std::cout << "The data type of run_option is int, 0: run with cpu; 1: run "
|
||||||
|
"with gpu; 2: run with gpu and use tensorrt backend."
|
||||||
|
<< std::endl;
|
||||||
|
return -1;
|
||||||
|
}
|
||||||
|
|
||||||
|
int frame_num = 2;
|
||||||
|
if (argc == 5) {
|
||||||
|
frame_num = std::atoi(argv[4]);
|
||||||
|
}
|
||||||
|
if (std::atoi(argv[3]) == 0) {
|
||||||
|
CpuInfer(argv[1], argv[2], frame_num);
|
||||||
|
} else if (std::atoi(argv[3]) == 1) {
|
||||||
|
GpuInfer(argv[1], argv[2], frame_num);
|
||||||
|
} else if (std::atoi(argv[3]) == 2) {
|
||||||
|
TrtInfer(argv[1], argv[2], frame_num);
|
||||||
|
}
|
||||||
|
return 0;
|
||||||
|
}
|
61
examples/vision/sr/edvr/python/README.md
Normal file
61
examples/vision/sr/edvr/python/README.md
Normal file
@@ -0,0 +1,61 @@
|
|||||||
|
# EDVR 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EDVR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/edvr/python
|
||||||
|
|
||||||
|
# 下载VSR模型文件和测试视频
|
||||||
|
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推理
|
||||||
|
python infer.py --model EDVR_M_wo_tsa_SRx4 --video person.mp4 --frame_num 2 --device cpu
|
||||||
|
# GPU推理
|
||||||
|
python infer.py --model EDVR_M_wo_tsa_SRx4 --video person.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 EDVR_M_wo_tsa_SRx4 --video person.mp4 --frame_num 2 --device gpu --use_trt True
|
||||||
|
```
|
||||||
|
|
||||||
|
## EDVR Python接口
|
||||||
|
|
||||||
|
```python
|
||||||
|
fd.vision.sr.EDVR(model_file, params_file, runtime_option=None, model_format=ModelFormat.PADDLE)
|
||||||
|
```
|
||||||
|
|
||||||
|
EDVR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
|
||||||
|
> EDVR.predict(frames)
|
||||||
|
> ```
|
||||||
|
>
|
||||||
|
> 模型预测结口,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
|
||||||
|
|
||||||
|
- [EDVR 模型介绍](..)
|
||||||
|
- [EDVR C++部署](../cpp)
|
||||||
|
- [如何切换模型推理后端引擎](../../../../../docs/cn/faq/how_to_change_backend.md)
|
89
examples/vision/sr/edvr/python/infer.py
Normal file
89
examples/vision/sr/edvr/python/infer.py
Normal file
@@ -0,0 +1,89 @@
|
|||||||
|
import cv2
|
||||||
|
import os
|
||||||
|
import fastdeploy as fd
|
||||||
|
|
||||||
|
|
||||||
|
def parse_arguments():
|
||||||
|
import argparse
|
||||||
|
import ast
|
||||||
|
parser = argparse.ArgumentParser()
|
||||||
|
parser.add_argument("--model", required=True, help="Path of model.")
|
||||||
|
parser.add_argument(
|
||||||
|
"--video", type=str, required=True, help="Path of test video file.")
|
||||||
|
parser.add_argument("--frame_num", type=int, default=2, help="frame num")
|
||||||
|
parser.add_argument(
|
||||||
|
"--device",
|
||||||
|
type=str,
|
||||||
|
default='cpu',
|
||||||
|
help="Type of inference device, support 'cpu' or 'gpu'.")
|
||||||
|
parser.add_argument(
|
||||||
|
"--use_trt",
|
||||||
|
type=ast.literal_eval,
|
||||||
|
default=False,
|
||||||
|
help="Wether to use tensorrt.")
|
||||||
|
return parser.parse_args()
|
||||||
|
|
||||||
|
|
||||||
|
def build_option(args):
|
||||||
|
option = fd.RuntimeOption()
|
||||||
|
if args.device.lower() == "gpu":
|
||||||
|
option.use_gpu()
|
||||||
|
if args.use_trt:
|
||||||
|
option.use_trt_backend()
|
||||||
|
option.enable_paddle_to_trt()
|
||||||
|
return option
|
||||||
|
|
||||||
|
|
||||||
|
args = parse_arguments()
|
||||||
|
|
||||||
|
# 配置runtime,加载模型
|
||||||
|
runtime_option = build_option(args)
|
||||||
|
model_file = os.path.join(args.model, "model.pdmodel")
|
||||||
|
params_file = os.path.join(args.model, "model.pdiparams")
|
||||||
|
model = fd.vision.sr.EDVR(
|
||||||
|
model_file, params_file, runtime_option=runtime_option)
|
||||||
|
|
||||||
|
# 该处应该与你导出模型的第二个维度一致模型输入shape=[b,n,c,h,w]
|
||||||
|
capture = cv2.VideoCapture(args.video)
|
||||||
|
video_out_name = "output.mp4"
|
||||||
|
video_fps = capture.get(cv2.CAP_PROP_FPS)
|
||||||
|
video_frame_count = capture.get(cv2.CAP_PROP_FRAME_COUNT)
|
||||||
|
# 注意导出模型时尺寸与原始输入的分辨一致比如:[1,2,3,180,320],经过4x超分后[1,3,720,1280](注意此处与PP-MSVSR不同)
|
||||||
|
# 所以导出模型相当重要
|
||||||
|
out_width = 1280
|
||||||
|
out_height = 720
|
||||||
|
print(f"fps: {video_fps}\tframe_count: {video_frame_count}")
|
||||||
|
# Create VideoWriter for output
|
||||||
|
video_out_dir = "./"
|
||||||
|
video_out_path = os.path.join(video_out_dir, video_out_name)
|
||||||
|
fucc = cv2.VideoWriter_fourcc(*"mp4v")
|
||||||
|
video_out = cv2.VideoWriter(video_out_path, fucc, video_fps,
|
||||||
|
(out_width, out_height), True)
|
||||||
|
if not video_out.isOpened():
|
||||||
|
print("create video writer failed!")
|
||||||
|
# Capture all frames and do inference
|
||||||
|
frame_id = 0
|
||||||
|
imgs = []
|
||||||
|
while capture.isOpened():
|
||||||
|
ret, frame = capture.read()
|
||||||
|
if frame_id < args.frame_num and frame is not None:
|
||||||
|
imgs.append(frame)
|
||||||
|
frame_id += 1
|
||||||
|
continue
|
||||||
|
# 始终保持imgs队列中具有frame_num帧
|
||||||
|
imgs.pop(0)
|
||||||
|
imgs.append(frame)
|
||||||
|
frame_id += 1
|
||||||
|
# 视频读取完毕退出
|
||||||
|
if not ret:
|
||||||
|
break
|
||||||
|
results = model.predict(imgs)
|
||||||
|
for item in results:
|
||||||
|
# cv2.imshow("13", item)
|
||||||
|
# cv2.waitKey(30)
|
||||||
|
video_out.write(item)
|
||||||
|
print("Processing frame: ", frame_id)
|
||||||
|
frame_id += 1
|
||||||
|
print("inference finished, output video saved at: ", video_out_path)
|
||||||
|
capture.release()
|
||||||
|
video_out.release()
|
27
examples/vision/sr/ppmsvsr/README.md
Normal file
27
examples/vision/sr/ppmsvsr/README.md
Normal file
@@ -0,0 +1,27 @@
|
|||||||
|
# PP-MSVSR模型部署
|
||||||
|
|
||||||
|
## 模型版本说明
|
||||||
|
|
||||||
|
- [PaddleGAN develop](https://github.com/PaddlePaddle/PaddleGAN)
|
||||||
|
|
||||||
|
## 支持模型列表
|
||||||
|
|
||||||
|
目前FastDeploy支持如下模型的部署
|
||||||
|
|
||||||
|
- [PP-MSVSR](https://github.com/PaddlePaddle/PaddleGAN/blob/develop/docs/zh_CN/tutorials/video_super_resolution.md)。
|
||||||
|
|
||||||
|
|
||||||
|
## 导出部署模型
|
||||||
|
|
||||||
|
在部署前,需要先将训练好的PP-MSVSR导出成部署模型,导出PP-MSVSR导出模型步骤,参考文档[导出模型](https://github.com/PaddlePaddle/PaddleGAN/blob/develop/docs/zh_CN/tutorials/video_super_resolution.md)。
|
||||||
|
|
||||||
|
|
||||||
|
| 模型 | 参数大小 | 精度 | 备注 |
|
||||||
|
|:----------------------------------------------------------------------------|:------|:----- | :------ |
|
||||||
|
| [PP-MSVSR](https://bj.bcebos.com/paddlehub/fastdeploy/PP-MSVSR_reds_x4.tgz) | 8.8MB | - |
|
||||||
|
|
||||||
|
|
||||||
|
## 详细部署文档
|
||||||
|
|
||||||
|
- [Python部署](python)
|
||||||
|
- [C++部署](cpp)
|
14
examples/vision/sr/ppmsvsr/cpp/CMakeLists.txt
Normal file
14
examples/vision/sr/ppmsvsr/cpp/CMakeLists.txt
Normal file
@@ -0,0 +1,14 @@
|
|||||||
|
PROJECT(infer_demo C CXX)
|
||||||
|
CMAKE_MINIMUM_REQUIRED (VERSION 3.10)
|
||||||
|
|
||||||
|
# 指定下载解压后的fastdeploy库路径
|
||||||
|
option(FASTDEPLOY_INSTALL_DIR "Path of downloaded fastdeploy sdk.")
|
||||||
|
|
||||||
|
include(${FASTDEPLOY_INSTALL_DIR}/FastDeploy.cmake)
|
||||||
|
|
||||||
|
# 添加FastDeploy依赖头文件
|
||||||
|
include_directories(${FASTDEPLOY_INCS})
|
||||||
|
|
||||||
|
add_executable(infer_demo ${PROJECT_SOURCE_DIR}/infer.cc)
|
||||||
|
# 添加FastDeploy库依赖
|
||||||
|
target_link_libraries(infer_demo ${FASTDEPLOY_LIBS})
|
75
examples/vision/sr/ppmsvsr/cpp/README.md
Normal file
75
examples/vision/sr/ppmsvsr/cpp/README.md
Normal file
@@ -0,0 +1,75 @@
|
|||||||
|
# VSR C++部署示例
|
||||||
|
|
||||||
|
本目录下提供`infer.cc`快速完成PP-MSVSR在CPU/GPU,以及GPU上通过TensorRT加速部署的示例。
|
||||||
|
|
||||||
|
在部署前,需确认以下两个步骤
|
||||||
|
|
||||||
|
- 1. 软硬件环境满足要求,参考[FastDeploy环境要求](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
|
||||||
|
- 2. 根据开发环境,下载预编译部署库和samples代码,参考[FastDeploy预编译库](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
|
||||||
|
|
||||||
|
以Linux上 PP-MSVSR 推理为例,在本目录执行如下命令即可完成编译测试(如若只需在CPU上部署,可在[Fastdeploy C++预编译库](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md/CPP_prebuilt_libraries.md)下载CPU推理库)
|
||||||
|
|
||||||
|
```bash
|
||||||
|
#下载SDK,编译模型examples代码(SDK中包含了examples代码)
|
||||||
|
# fastdeploy版本 >= 0.7.0
|
||||||
|
wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-gpu-0.7.0.tgz
|
||||||
|
tar xvf fastdeploy-linux-x64-gpu-0.7.0.tgz
|
||||||
|
cd fastdeploy-linux-x64-gpu-0.7.0/examples/vision/sr/ppmsvsr/cpp/
|
||||||
|
mkdir build && cd build
|
||||||
|
cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/../../../../../../../fastdeploy-linux-x64-gpu-0.7.0
|
||||||
|
make -j
|
||||||
|
|
||||||
|
# 下载PP-MSVSR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
|
||||||
|
./infer_demo PP-MSVSR_reds_x4 vsr_src.mp4 0 2
|
||||||
|
# GPU推理
|
||||||
|
./infer_demo PP-MSVSR_reds_x4 vsr_src.mp4 1 2
|
||||||
|
# GPU上TensorRT推理
|
||||||
|
./infer_demo PP-MSVSR_reds_x4 vsr_src.mp4 2 2
|
||||||
|
```
|
||||||
|
|
||||||
|
以上命令只适用于Linux或MacOS, Windows下SDK的使用方式请参考:
|
||||||
|
- [如何在Windows中使用FastDeploy C++ SDK](../../../../../docs/cn/faq/use_sdk_on_windows.md)
|
||||||
|
|
||||||
|
## PP-MSVSR C++接口
|
||||||
|
|
||||||
|
### PPMSVSR类
|
||||||
|
|
||||||
|
```c++
|
||||||
|
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模型加载和初始化,其中model_file为导出的Paddle模型格式。
|
||||||
|
|
||||||
|
**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
|
||||||
|
|
||||||
|
> ```c++
|
||||||
|
> PPMSVSR::Predict(std::vector<cv::Mat>& imgs, std::vector<cv::Mat>& results)
|
||||||
|
> ```
|
||||||
|
>
|
||||||
|
> 模型预测接口,输入图像直接输出检测结果。
|
||||||
|
>
|
||||||
|
> **参数**
|
||||||
|
>
|
||||||
|
> > * **imgs**: 输入视频帧序列,注意需为HWC,BGR格式
|
||||||
|
> > * **results**: 视频超分结果,超分后的视频帧序列
|
||||||
|
|
||||||
|
- [模型介绍](../../)
|
||||||
|
- [Python部署](../python)
|
||||||
|
- [如何切换模型推理后端引擎](../../../../../docs/cn/faq/how_to_change_backend.md)
|
298
examples/vision/sr/ppmsvsr/cpp/infer.cc
Normal file
298
examples/vision/sr/ppmsvsr/cpp/infer.cc
Normal file
@@ -0,0 +1,298 @@
|
|||||||
|
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
|
||||||
|
//
|
||||||
|
// Licensed under the Apache License, Version 2.0 (the "License");
|
||||||
|
// you may not use this file except in compliance with the License.
|
||||||
|
// You may obtain a copy of the License at
|
||||||
|
//
|
||||||
|
// http://www.apache.org/licenses/LICENSE-2.0
|
||||||
|
//
|
||||||
|
// Unless required by applicable law or agreed to in writing, software
|
||||||
|
// distributed under the License is distributed on an "AS IS" BASIS,
|
||||||
|
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||||
|
// See the License for the specific language governing permissions and
|
||||||
|
// limitations under the License.
|
||||||
|
|
||||||
|
#include "fastdeploy/vision.h"
|
||||||
|
|
||||||
|
#ifdef WIN32
|
||||||
|
const char sep = '\\';
|
||||||
|
#else
|
||||||
|
const char sep = '/';
|
||||||
|
#endif
|
||||||
|
|
||||||
|
void CpuInfer(const std::string& model_dir,
|
||||||
|
const std::string& video_file, int frame_num) {
|
||||||
|
auto model_file = model_dir + sep + "model.pdmodel";
|
||||||
|
auto params_file = model_dir + sep + "model.pdiparams";
|
||||||
|
auto model = fastdeploy::vision::sr::PPMSVSR(model_file, params_file);
|
||||||
|
|
||||||
|
if (!model.Initialized()) {
|
||||||
|
std::cerr << "Failed to initialize." << std::endl;
|
||||||
|
return;
|
||||||
|
}
|
||||||
|
// note: input/output shape is [b, n, c, h, w] (n = frame_nums; b=1(default))
|
||||||
|
// b and n is dependent on export model shape
|
||||||
|
// see https://github.com/PaddlePaddle/PaddleGAN/blob/develop/docs/zh_CN/tutorials/video_super_resolution.md
|
||||||
|
cv::VideoCapture capture;
|
||||||
|
// change your save video path
|
||||||
|
std::string video_out_name = "output.mp4";
|
||||||
|
capture.open(video_file);
|
||||||
|
if (!capture.isOpened())
|
||||||
|
{
|
||||||
|
std::cout<<"can not open video "<<std::endl;
|
||||||
|
return;
|
||||||
|
}
|
||||||
|
// Get Video info :fps, frame count
|
||||||
|
// it used 4.x version of opencv below
|
||||||
|
// notice your opencv version and method of api.
|
||||||
|
int video_fps = static_cast<int>(capture.get(cv::CAP_PROP_FPS));
|
||||||
|
int video_frame_count = static_cast<int>(capture.get(cv::CAP_PROP_FRAME_COUNT));
|
||||||
|
// Set fixed size for output frame, only for msvsr model
|
||||||
|
int out_width = 1280;
|
||||||
|
int out_height = 720;
|
||||||
|
std::cout << "fps: " << video_fps << "\tframe_count: " << video_frame_count << std::endl;
|
||||||
|
|
||||||
|
// Create VideoWriter for output
|
||||||
|
cv::VideoWriter video_out;
|
||||||
|
std::string video_out_path("./");
|
||||||
|
video_out_path += video_out_name;
|
||||||
|
int fcc = cv::VideoWriter::fourcc('m', 'p', '4', 'v');
|
||||||
|
video_out.open(video_out_path, fcc, video_fps, cv::Size(out_width, out_height), true);
|
||||||
|
if (!video_out.isOpened())
|
||||||
|
{
|
||||||
|
std::cout << "create video writer failed!" << std::endl;
|
||||||
|
return;
|
||||||
|
}
|
||||||
|
// Capture all frames and do inference
|
||||||
|
cv::Mat frame;
|
||||||
|
int frame_id = 0;
|
||||||
|
bool reach_end = false;
|
||||||
|
while (capture.isOpened())
|
||||||
|
{
|
||||||
|
std::vector<cv::Mat> imgs;
|
||||||
|
for (int i = 0; i < frame_num; i++)
|
||||||
|
{
|
||||||
|
capture.read(frame);
|
||||||
|
if (!frame.empty())
|
||||||
|
{
|
||||||
|
imgs.push_back(frame);
|
||||||
|
}else{
|
||||||
|
reach_end = true;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
if (reach_end)
|
||||||
|
{
|
||||||
|
break;
|
||||||
|
}
|
||||||
|
std::vector<cv::Mat> results;
|
||||||
|
model.Predict(imgs, results);
|
||||||
|
for (auto &item : results)
|
||||||
|
{
|
||||||
|
// cv::imshow("13",item);
|
||||||
|
// cv::waitKey(30);
|
||||||
|
video_out.write(item);
|
||||||
|
std::cout << "Processing frame: "<< frame_id << std::endl;
|
||||||
|
frame_id += 1;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
std::cout << "inference finished, output video saved at " << video_out_path << std::endl;
|
||||||
|
capture.release();
|
||||||
|
video_out.release();
|
||||||
|
}
|
||||||
|
|
||||||
|
void GpuInfer(const std::string& model_dir,
|
||||||
|
const std::string& video_file, int frame_num) {
|
||||||
|
auto model_file = model_dir + sep + "model.pdmodel";
|
||||||
|
auto params_file = model_dir + sep + "model.pdiparams";
|
||||||
|
|
||||||
|
auto option = fastdeploy::RuntimeOption();
|
||||||
|
// use paddle-TRT
|
||||||
|
option.UseGpu();
|
||||||
|
option.UseTrtBackend();
|
||||||
|
option.EnablePaddleToTrt();
|
||||||
|
auto model = fastdeploy::vision::sr::PPMSVSR(
|
||||||
|
model_file, params_file, option);
|
||||||
|
|
||||||
|
if (!model.Initialized()) {
|
||||||
|
std::cerr << "Failed to initialize." << std::endl;
|
||||||
|
return;
|
||||||
|
}
|
||||||
|
// note: input/output shape is [b, n, c, h, w] (n = frame_nums; b=1(default))
|
||||||
|
// b and n is dependent on export model shape
|
||||||
|
// see https://github.com/PaddlePaddle/PaddleGAN/blob/develop/docs/zh_CN/tutorials/video_super_resolution.md
|
||||||
|
cv::VideoCapture capture;
|
||||||
|
// change your save video path
|
||||||
|
std::string video_out_name = "output.mp4";
|
||||||
|
capture.open(video_file);
|
||||||
|
if (!capture.isOpened())
|
||||||
|
{
|
||||||
|
std::cout<<"can not open video "<<std::endl;
|
||||||
|
return;
|
||||||
|
}
|
||||||
|
// Get Video info :fps, frame count
|
||||||
|
int video_fps = static_cast<int>(capture.get(cv::CAP_PROP_FPS));
|
||||||
|
int video_frame_count = static_cast<int>(capture.get(cv::CAP_PROP_FRAME_COUNT));
|
||||||
|
// Set fixed size for output frame, only for msvsr model
|
||||||
|
int out_width = 1280;
|
||||||
|
int out_height = 720;
|
||||||
|
std::cout << "fps: " << video_fps << "\tframe_count: " << video_frame_count << std::endl;
|
||||||
|
|
||||||
|
// Create VideoWriter for output
|
||||||
|
cv::VideoWriter video_out;
|
||||||
|
std::string video_out_path("./");
|
||||||
|
video_out_path += video_out_name;
|
||||||
|
int fcc = cv::VideoWriter::fourcc('m', 'p', '4', 'v');
|
||||||
|
video_out.open(video_out_path, fcc, video_fps, cv::Size(out_width, out_height), true);
|
||||||
|
if (!video_out.isOpened())
|
||||||
|
{
|
||||||
|
std::cout << "create video writer failed!" << std::endl;
|
||||||
|
return;
|
||||||
|
}
|
||||||
|
// Capture all frames and do inference
|
||||||
|
cv::Mat frame;
|
||||||
|
int frame_id = 0;
|
||||||
|
bool reach_end = false;
|
||||||
|
while (capture.isOpened())
|
||||||
|
{
|
||||||
|
std::vector<cv::Mat> imgs;
|
||||||
|
for (int i = 0; i < frame_num; i++)
|
||||||
|
{
|
||||||
|
capture.read(frame);
|
||||||
|
if (!frame.empty())
|
||||||
|
{
|
||||||
|
imgs.push_back(frame);
|
||||||
|
}else{
|
||||||
|
reach_end = true;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
if (reach_end)
|
||||||
|
{
|
||||||
|
break;
|
||||||
|
}
|
||||||
|
std::vector<cv::Mat> results;
|
||||||
|
model.Predict(imgs, results);
|
||||||
|
for (auto &item : results)
|
||||||
|
{
|
||||||
|
// cv::imshow("13",item);
|
||||||
|
// cv::waitKey(30);
|
||||||
|
video_out.write(item);
|
||||||
|
std::cout << "Processing frame: "<< frame_id << std::endl;
|
||||||
|
frame_id += 1;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
std::cout << "inference finished, output video saved at " << video_out_path << std::endl;
|
||||||
|
capture.release();
|
||||||
|
video_out.release();
|
||||||
|
}
|
||||||
|
|
||||||
|
void TrtInfer(const std::string& model_dir,
|
||||||
|
const std::string& video_file, int frame_num) {
|
||||||
|
auto model_file = model_dir + sep + "model.pdmodel";
|
||||||
|
auto params_file = model_dir + sep + "model.pdiparams";
|
||||||
|
auto option = fastdeploy::RuntimeOption();
|
||||||
|
option.UseGpu();
|
||||||
|
option.UseTrtBackend();
|
||||||
|
auto model = fastdeploy::vision::sr::PPMSVSR(
|
||||||
|
model_file, params_file, option);
|
||||||
|
|
||||||
|
if (!model.Initialized()) {
|
||||||
|
std::cerr << "Failed to initialize." << std::endl;
|
||||||
|
return;
|
||||||
|
}
|
||||||
|
|
||||||
|
// note: input/output shape is [b, n, c, h, w] (n = frame_nums; b=1(default))
|
||||||
|
// b and n is dependent on export model shape
|
||||||
|
// see https://github.com/PaddlePaddle/PaddleGAN/blob/develop/docs/zh_CN/tutorials/video_super_resolution.md
|
||||||
|
cv::VideoCapture capture;
|
||||||
|
// change your save video path
|
||||||
|
std::string video_out_name = "output.mp4";
|
||||||
|
capture.open(video_file);
|
||||||
|
if (!capture.isOpened())
|
||||||
|
{
|
||||||
|
std::cout<<"can not open video "<<std::endl;
|
||||||
|
return;
|
||||||
|
}
|
||||||
|
// Get Video info :fps, frame count
|
||||||
|
int video_fps = static_cast<int>(capture.get(cv::CAP_PROP_FPS));
|
||||||
|
int video_frame_count = static_cast<int>(capture.get(cv::CAP_PROP_FRAME_COUNT));
|
||||||
|
// Set fixed size for output frame, only for msvsr model
|
||||||
|
//Note that the resolution between the size and the original input is consistent when the model is exported,
|
||||||
|
// for example: [1,2,3,180,320], after 4x super separation [1,2,3,720,1080].
|
||||||
|
//Therefore, it is very important to derive the model
|
||||||
|
int out_width = 1280;
|
||||||
|
int out_height = 720;
|
||||||
|
std::cout << "fps: " << video_fps << "\tframe_count: " << video_frame_count << std::endl;
|
||||||
|
|
||||||
|
// Create VideoWriter for output
|
||||||
|
cv::VideoWriter video_out;
|
||||||
|
std::string video_out_path("./");
|
||||||
|
video_out_path += video_out_name;
|
||||||
|
int fcc = cv::VideoWriter::fourcc('m', 'p', '4', 'v');
|
||||||
|
video_out.open(video_out_path, fcc, video_fps, cv::Size(out_width, out_height), true);
|
||||||
|
if (!video_out.isOpened())
|
||||||
|
{
|
||||||
|
std::cout << "create video writer failed!" << std::endl;
|
||||||
|
return;
|
||||||
|
}
|
||||||
|
// Capture all frames and do inference
|
||||||
|
cv::Mat frame;
|
||||||
|
int frame_id = 0;
|
||||||
|
bool reach_end = false;
|
||||||
|
while (capture.isOpened())
|
||||||
|
{
|
||||||
|
std::vector<cv::Mat> imgs;
|
||||||
|
for (int i = 0; i < frame_num; i++)
|
||||||
|
{
|
||||||
|
capture.read(frame);
|
||||||
|
if (!frame.empty())
|
||||||
|
{
|
||||||
|
imgs.push_back(frame);
|
||||||
|
}else{
|
||||||
|
reach_end = true;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
if (reach_end)
|
||||||
|
{
|
||||||
|
break;
|
||||||
|
}
|
||||||
|
std::vector<cv::Mat> results;
|
||||||
|
model.Predict(imgs, results);
|
||||||
|
for (auto &item : results)
|
||||||
|
{
|
||||||
|
// cv::imshow("13",item);
|
||||||
|
// cv::waitKey(30);
|
||||||
|
video_out.write(item);
|
||||||
|
std::cout << "Processing frame: "<< frame_id << std::endl;
|
||||||
|
frame_id += 1;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
std::cout << "inference finished, output video saved at " << video_out_path << std::endl;
|
||||||
|
capture.release();
|
||||||
|
video_out.release();
|
||||||
|
}
|
||||||
|
|
||||||
|
int main(int argc, char* argv[]) {
|
||||||
|
if (argc < 4) {
|
||||||
|
std::cout
|
||||||
|
<< "Usage: infer_demo path/to/model_dir path/to/video frame number run_option, "
|
||||||
|
"e.g ./infer_model ./vsr_model_dir ./person.mp4 0 2"
|
||||||
|
<< std::endl;
|
||||||
|
std::cout << "The data type of run_option is int, 0: run with cpu; 1: run "
|
||||||
|
"with gpu; 2: run with gpu and use tensorrt backend."
|
||||||
|
<< std::endl;
|
||||||
|
return -1;
|
||||||
|
}
|
||||||
|
|
||||||
|
int frame_num = 2;
|
||||||
|
if (argc == 5) {
|
||||||
|
frame_num = std::atoi(argv[4]);
|
||||||
|
}
|
||||||
|
if (std::atoi(argv[3]) == 0) {
|
||||||
|
CpuInfer(argv[1], argv[2], frame_num);
|
||||||
|
} else if (std::atoi(argv[3]) == 1) {
|
||||||
|
GpuInfer(argv[1], argv[2], frame_num);
|
||||||
|
} else if (std::atoi(argv[3]) == 2) {
|
||||||
|
TrtInfer(argv[1], argv[2], frame_num);
|
||||||
|
}
|
||||||
|
return 0;
|
||||||
|
}
|
61
examples/vision/sr/ppmsvsr/python/README.md
Normal file
61
examples/vision/sr/ppmsvsr/python/README.md
Normal file
@@ -0,0 +1,61 @@
|
|||||||
|
# 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 person.mp4 --frame_num 2 --device cpu
|
||||||
|
# GPU推理
|
||||||
|
python infer.py --model PP-MSVSR_reds_x4 --video person.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 person.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)
|
||||||
|
> ```
|
||||||
|
>
|
||||||
|
> 模型预测结口,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)
|
86
examples/vision/sr/ppmsvsr/python/infer.py
Normal file
86
examples/vision/sr/ppmsvsr/python/infer.py
Normal file
@@ -0,0 +1,86 @@
|
|||||||
|
import cv2
|
||||||
|
import os
|
||||||
|
import fastdeploy as fd
|
||||||
|
|
||||||
|
|
||||||
|
def parse_arguments():
|
||||||
|
import argparse
|
||||||
|
import ast
|
||||||
|
parser = argparse.ArgumentParser()
|
||||||
|
parser.add_argument("--model", required=True, help="Path of model.")
|
||||||
|
parser.add_argument(
|
||||||
|
"--video", type=str, required=True, help="Path of test video file.")
|
||||||
|
parser.add_argument("--frame_num", type=int, default=2, help="frame num")
|
||||||
|
parser.add_argument(
|
||||||
|
"--device",
|
||||||
|
type=str,
|
||||||
|
default='cpu',
|
||||||
|
help="Type of inference device, support 'cpu' or 'gpu'.")
|
||||||
|
parser.add_argument(
|
||||||
|
"--use_trt",
|
||||||
|
type=ast.literal_eval,
|
||||||
|
default=False,
|
||||||
|
help="Wether to use tensorrt.")
|
||||||
|
return parser.parse_args()
|
||||||
|
|
||||||
|
|
||||||
|
def build_option(args):
|
||||||
|
option = fd.RuntimeOption()
|
||||||
|
if args.device.lower() == "gpu":
|
||||||
|
option.use_gpu()
|
||||||
|
if args.use_trt:
|
||||||
|
option.use_trt_backend()
|
||||||
|
option.enable_paddle_to_trt()
|
||||||
|
return option
|
||||||
|
|
||||||
|
|
||||||
|
args = parse_arguments()
|
||||||
|
|
||||||
|
# 配置runtime,加载模型
|
||||||
|
runtime_option = build_option(args)
|
||||||
|
model_file = os.path.join(args.model, "model.pdmodel")
|
||||||
|
params_file = os.path.join(args.model, "model.pdiparams")
|
||||||
|
model = fd.vision.sr.PPMSVSR(
|
||||||
|
model_file, params_file, runtime_option=runtime_option)
|
||||||
|
|
||||||
|
# 该处应该与你导出模型的第二个维度一致模型输入shape=[b,n,c,h,w]
|
||||||
|
capture = cv2.VideoCapture(args.video)
|
||||||
|
video_out_name = "output.mp4"
|
||||||
|
video_fps = capture.get(cv2.CAP_PROP_FPS)
|
||||||
|
video_frame_count = capture.get(cv2.CAP_PROP_FRAME_COUNT)
|
||||||
|
# 注意导出模型时尺寸与原始输入的分辨一致比如:[1,2,3,180,320],经过4x超分后[1,2,3,720,1280]
|
||||||
|
# 所以导出模型相当重要
|
||||||
|
out_width = 1280
|
||||||
|
out_height = 720
|
||||||
|
print(f"fps: {video_fps}\tframe_count: {video_frame_count}")
|
||||||
|
# Create VideoWriter for output
|
||||||
|
video_out_dir = "./"
|
||||||
|
video_out_path = os.path.join(video_out_dir, video_out_name)
|
||||||
|
fucc = cv2.VideoWriter_fourcc(*"mp4v")
|
||||||
|
video_out = cv2.VideoWriter(video_out_path, fucc, video_fps,
|
||||||
|
(out_width, out_height), True)
|
||||||
|
if not video_out.isOpened():
|
||||||
|
print("create video writer failed!")
|
||||||
|
# Capture all frames and do inference
|
||||||
|
frame_id = 0
|
||||||
|
reach_end = False
|
||||||
|
while capture.isOpened():
|
||||||
|
imgs = []
|
||||||
|
for i in range(args.frame_num):
|
||||||
|
_, frame = capture.read()
|
||||||
|
if frame is not None:
|
||||||
|
imgs.append(frame)
|
||||||
|
else:
|
||||||
|
reach_end = True
|
||||||
|
if reach_end:
|
||||||
|
break
|
||||||
|
results = model.predict(imgs)
|
||||||
|
for item in results:
|
||||||
|
# cv2.imshow("13", item)
|
||||||
|
# cv2.waitKey(30)
|
||||||
|
video_out.write(item)
|
||||||
|
print("Processing frame: ", frame_id)
|
||||||
|
frame_id += 1
|
||||||
|
print("inference finished, output video saved at: ", video_out_path)
|
||||||
|
capture.release()
|
||||||
|
video_out.release()
|
@@ -48,7 +48,7 @@ void CpuInfer(const std::string& model_dir, const std::string& video_file) {
|
|||||||
std::cerr << "Failed to predict." << std::endl;
|
std::cerr << "Failed to predict." << std::endl;
|
||||||
return;
|
return;
|
||||||
}
|
}
|
||||||
// such as adding this code can cancel trail datat bind
|
// such as adding this code can cancel trail data binding
|
||||||
// if(count++ == 10) model.UnbindRecorder();
|
// if(count++ == 10) model.UnbindRecorder();
|
||||||
// std::cout << result.Str() << std::endl;
|
// std::cout << result.Str() << std::endl;
|
||||||
cv::Mat out_img = fastdeploy::vision::VisMOT(frame, result, 0.0, &recorder);
|
cv::Mat out_img = fastdeploy::vision::VisMOT(frame, result, 0.0, &recorder);
|
||||||
@@ -91,7 +91,7 @@ void GpuInfer(const std::string& model_dir, const std::string& video_file) {
|
|||||||
std::cerr << "Failed to predict." << std::endl;
|
std::cerr << "Failed to predict." << std::endl;
|
||||||
return;
|
return;
|
||||||
}
|
}
|
||||||
// such as adding this code can cancel trail datat bind
|
// such as adding this code can cancel trail data binding
|
||||||
//if(count++ == 10) model.UnbindRecorder();
|
//if(count++ == 10) model.UnbindRecorder();
|
||||||
// std::cout << result.Str() << std::endl;
|
// std::cout << result.Str() << std::endl;
|
||||||
cv::Mat out_img = fastdeploy::vision::VisMOT(frame, result, 0.0, &trail_recorder);
|
cv::Mat out_img = fastdeploy::vision::VisMOT(frame, result, 0.0, &trail_recorder);
|
||||||
@@ -135,7 +135,7 @@ void TrtInfer(const std::string& model_dir, const std::string& video_file) {
|
|||||||
std::cerr << "Failed to predict." << std::endl;
|
std::cerr << "Failed to predict." << std::endl;
|
||||||
return;
|
return;
|
||||||
}
|
}
|
||||||
// such as adding this code can cancel trail datat bind
|
// such as adding this code can cancel trail data binding
|
||||||
// if(count++ == 10) model.UnbindRecorder();
|
// if(count++ == 10) model.UnbindRecorder();
|
||||||
// std::cout << result.Str() << std::endl;
|
// std::cout << result.Str() << std::endl;
|
||||||
cv::Mat out_img = fastdeploy::vision::VisMOT(frame, result, 0.0, &recorder);
|
cv::Mat out_img = fastdeploy::vision::VisMOT(frame, result, 0.0, &recorder);
|
||||||
|
@@ -30,7 +30,7 @@ python infer.py --model fairmot_hrnetv2_w18_dlafpn_30e_576x320 --video person.mp
|
|||||||
fd.vision.tracking.PPTracking(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
|
fd.vision.tracking.PPTracking(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
|
||||||
```
|
```
|
||||||
|
|
||||||
PP-Tracking模型加载和初始化,其中model_file, params_file以及config_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/PaddleSeg/tree/release/2.6/Matting)
|
PP-Tracking模型加载和初始化,其中model_file, params_file以及config_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/PaddleDetection/blob/release/2.5/deploy/pptracking/cpp/README.md)
|
||||||
|
|
||||||
**参数**
|
**参数**
|
||||||
|
|
||||||
@@ -42,7 +42,7 @@ PP-Tracking模型加载和初始化,其中model_file, params_file以及config_
|
|||||||
|
|
||||||
### predict函数
|
### predict函数
|
||||||
|
|
||||||
> ```python
|
> ```python
|
||||||
> PPTracking.predict(frame)
|
> PPTracking.predict(frame)
|
||||||
> ```
|
> ```
|
||||||
>
|
>
|
||||||
|
@@ -21,8 +21,7 @@ def parse_arguments():
|
|||||||
import argparse
|
import argparse
|
||||||
import ast
|
import ast
|
||||||
parser = argparse.ArgumentParser()
|
parser = argparse.ArgumentParser()
|
||||||
parser.add_argument(
|
parser.add_argument("--model", required=True, help="Path of model.")
|
||||||
"--model", required=True, help="Path of PaddleSeg model.")
|
|
||||||
parser.add_argument(
|
parser.add_argument(
|
||||||
"--video", type=str, required=True, help="Path of test video file.")
|
"--video", type=str, required=True, help="Path of test video file.")
|
||||||
parser.add_argument(
|
parser.add_argument(
|
||||||
|
@@ -55,6 +55,7 @@
|
|||||||
#include "fastdeploy/vision/segmentation/ppseg/model.h"
|
#include "fastdeploy/vision/segmentation/ppseg/model.h"
|
||||||
#include "fastdeploy/vision/tracking/pptracking/model.h"
|
#include "fastdeploy/vision/tracking/pptracking/model.h"
|
||||||
#include "fastdeploy/vision/headpose/contrib/fsanet.h"
|
#include "fastdeploy/vision/headpose/contrib/fsanet.h"
|
||||||
|
#include "fastdeploy/vision/sr/ppsr/model.h"
|
||||||
#endif
|
#endif
|
||||||
|
|
||||||
#include "fastdeploy/vision/visualize/visualize.h"
|
#include "fastdeploy/vision/visualize/visualize.h"
|
||||||
|
38
fastdeploy/vision/sr/ppsr/basicvsr.cc
Normal file
38
fastdeploy/vision/sr/ppsr/basicvsr.cc
Normal file
@@ -0,0 +1,38 @@
|
|||||||
|
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
|
||||||
|
//
|
||||||
|
// Licensed under the Apache License, Version 2.0 (the "License");
|
||||||
|
// you may not use this file except in compliance with the License.
|
||||||
|
// You may obtain a copy of the License at
|
||||||
|
//
|
||||||
|
// http://www.apache.org/licenses/LICENSE-2.0
|
||||||
|
//
|
||||||
|
// Unless required by applicable law or agreed to in writing, software
|
||||||
|
// distributed under the License is distributed on an "AS IS" BASIS,
|
||||||
|
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||||
|
// See the License for the specific language governing permissions and
|
||||||
|
// limitations under the License.
|
||||||
|
|
||||||
|
#include "fastdeploy/vision/sr/ppsr/basicvsr.h"
|
||||||
|
|
||||||
|
namespace fastdeploy {
|
||||||
|
namespace vision {
|
||||||
|
namespace sr {
|
||||||
|
|
||||||
|
BasicVSR::BasicVSR(const std::string& model_file,
|
||||||
|
const std::string& params_file,
|
||||||
|
const RuntimeOption& custom_option,
|
||||||
|
const ModelFormat& model_format){
|
||||||
|
// unsupported ORT backend
|
||||||
|
valid_cpu_backends = {Backend::PDINFER};
|
||||||
|
valid_gpu_backends = {Backend::PDINFER};
|
||||||
|
|
||||||
|
runtime_option = custom_option;
|
||||||
|
runtime_option.model_format = model_format;
|
||||||
|
runtime_option.model_file = model_file;
|
||||||
|
runtime_option.params_file = params_file;
|
||||||
|
|
||||||
|
initialized = Initialize();
|
||||||
|
}
|
||||||
|
} // namespace sr
|
||||||
|
} // namespace vision
|
||||||
|
} // namespace fastdeploy
|
41
fastdeploy/vision/sr/ppsr/basicvsr.h
Normal file
41
fastdeploy/vision/sr/ppsr/basicvsr.h
Normal file
@@ -0,0 +1,41 @@
|
|||||||
|
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
|
||||||
|
//
|
||||||
|
// Licensed under the Apache License, Version 2.0 (the "License");
|
||||||
|
// you may not use this file except in compliance with the License.
|
||||||
|
// You may obtain a copy of the License at
|
||||||
|
//
|
||||||
|
// http://www.apache.org/licenses/LICENSE-2.0
|
||||||
|
//
|
||||||
|
// Unless required by applicable law or agreed to in writing, software
|
||||||
|
// distributed under the License is distributed on an "AS IS" BASIS,
|
||||||
|
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||||
|
// See the License for the specific language governing permissions and
|
||||||
|
// limitations under the License.
|
||||||
|
|
||||||
|
#pragma once
|
||||||
|
#include "fastdeploy/vision/sr/ppsr/ppmsvsr.h"
|
||||||
|
|
||||||
|
namespace fastdeploy {
|
||||||
|
namespace vision {
|
||||||
|
namespace sr {
|
||||||
|
|
||||||
|
class FASTDEPLOY_DECL BasicVSR : public PPMSVSR{
|
||||||
|
public:
|
||||||
|
/**
|
||||||
|
* Set path of model file and configuration file, and the configuration of runtime
|
||||||
|
* @param[in] model_file Path of model file, e.g BasicVSR/model.pdmodel
|
||||||
|
* @param[in] params_file Path of parameter file, e.g BasicVSR/model.pdiparams
|
||||||
|
* @param[in] custom_option RuntimeOption for inference, the default will use cpu, and choose the backend defined in `valid_cpu_backends`
|
||||||
|
* @param[in] model_format Model format of the loaded model, default is Paddle format
|
||||||
|
*/
|
||||||
|
BasicVSR(const std::string& model_file,
|
||||||
|
const std::string& params_file,
|
||||||
|
const RuntimeOption& custom_option = RuntimeOption(),
|
||||||
|
const ModelFormat& model_format = ModelFormat::PADDLE);
|
||||||
|
/// model name contained BasicVSR
|
||||||
|
std::string ModelName() const override { return "BasicVSR"; }
|
||||||
|
};
|
||||||
|
|
||||||
|
} // namespace sr
|
||||||
|
} // namespace vision
|
||||||
|
} // namespace fastdeploy
|
71
fastdeploy/vision/sr/ppsr/edvr.cc
Normal file
71
fastdeploy/vision/sr/ppsr/edvr.cc
Normal file
@@ -0,0 +1,71 @@
|
|||||||
|
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
|
||||||
|
//
|
||||||
|
// Licensed under the Apache License, Version 2.0 (the "License");
|
||||||
|
// you may not use this file except in compliance with the License.
|
||||||
|
// You may obtain a copy of the License at
|
||||||
|
//
|
||||||
|
// http://www.apache.org/licenses/LICENSE-2.0
|
||||||
|
//
|
||||||
|
// Unless required by applicable law or agreed to in writing, software
|
||||||
|
// distributed under the License is distributed on an "AS IS" BASIS,
|
||||||
|
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||||
|
// See the License for the specific language governing permissions and
|
||||||
|
// limitations under the License.
|
||||||
|
|
||||||
|
#include "fastdeploy/vision/sr/ppsr/edvr.h"
|
||||||
|
|
||||||
|
namespace fastdeploy {
|
||||||
|
namespace vision {
|
||||||
|
namespace sr {
|
||||||
|
|
||||||
|
EDVR::EDVR(const std::string& model_file,
|
||||||
|
const std::string& params_file,
|
||||||
|
const RuntimeOption& custom_option,
|
||||||
|
const ModelFormat& model_format){
|
||||||
|
// unsupported ORT backend
|
||||||
|
valid_cpu_backends = {Backend::PDINFER};
|
||||||
|
valid_gpu_backends = {Backend::PDINFER};
|
||||||
|
|
||||||
|
runtime_option = custom_option;
|
||||||
|
runtime_option.model_format = model_format;
|
||||||
|
runtime_option.model_file = model_file;
|
||||||
|
runtime_option.params_file = params_file;
|
||||||
|
|
||||||
|
initialized = Initialize();
|
||||||
|
}
|
||||||
|
|
||||||
|
bool EDVR::Postprocess(std::vector<FDTensor>& infer_results, std::vector<cv::Mat>& results){
|
||||||
|
// group to image
|
||||||
|
// output_shape is [b, n, c, h, w] n = frame_nums b=1(default)
|
||||||
|
// b and n is dependence export model shape
|
||||||
|
// see https://github.com/PaddlePaddle/PaddleGAN/blob/develop/docs/zh_CN/tutorials/video_super_resolution.md
|
||||||
|
auto output_shape = infer_results[0].shape;
|
||||||
|
// EDVR
|
||||||
|
int h_ = output_shape[2];
|
||||||
|
int w_ = output_shape[3];
|
||||||
|
int c_ = output_shape[1];
|
||||||
|
int frame_num = 1;
|
||||||
|
float *out_data = static_cast<float *>(infer_results[0].Data());
|
||||||
|
cv::Mat temp = cv::Mat::zeros(h_, w_, CV_32FC3); // RGB image
|
||||||
|
int pix_num = h_ * w_;
|
||||||
|
int frame_pix_num = pix_num * c_;
|
||||||
|
for (int frame = 0; frame < frame_num; frame++) {
|
||||||
|
int index = 0;
|
||||||
|
for (int h = 0; h < h_; ++h) {
|
||||||
|
for (int w = 0; w < w_; ++w) {
|
||||||
|
temp.at<cv::Vec3f>(h, w) = {out_data[2 * pix_num + index + frame_pix_num * frame],
|
||||||
|
out_data[pix_num + index + frame_pix_num * frame],
|
||||||
|
out_data[index + frame_pix_num * frame]};
|
||||||
|
index += 1;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
// tmp data type is float[0-1.0],convert to uint type
|
||||||
|
cv::Mat res = cv::Mat::zeros(temp.size(), CV_8UC3);
|
||||||
|
temp.convertTo(res, CV_8UC3, 255);
|
||||||
|
results.push_back(res);
|
||||||
|
}
|
||||||
|
return true;
|
||||||
|
}
|
||||||
|
} // namespace sr
|
||||||
|
} // namespace vision
|
||||||
|
} // namespace fastdeploy
|
45
fastdeploy/vision/sr/ppsr/edvr.h
Normal file
45
fastdeploy/vision/sr/ppsr/edvr.h
Normal file
@@ -0,0 +1,45 @@
|
|||||||
|
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
|
||||||
|
//
|
||||||
|
// Licensed under the Apache License, Version 2.0 (the "License");
|
||||||
|
// you may not use this file except in compliance with the License.
|
||||||
|
// You may obtain a copy of the License at
|
||||||
|
//
|
||||||
|
// http://www.apache.org/licenses/LICENSE-2.0
|
||||||
|
//
|
||||||
|
// Unless required by applicable law or agreed to in writing, software
|
||||||
|
// distributed under the License is distributed on an "AS IS" BASIS,
|
||||||
|
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||||
|
// See the License for the specific language governing permissions and
|
||||||
|
// limitations under the License.
|
||||||
|
|
||||||
|
#pragma once
|
||||||
|
#include "fastdeploy/vision/sr/ppsr/ppmsvsr.h"
|
||||||
|
|
||||||
|
namespace fastdeploy {
|
||||||
|
namespace vision {
|
||||||
|
namespace sr {
|
||||||
|
|
||||||
|
|
||||||
|
class FASTDEPLOY_DECL EDVR : public PPMSVSR{
|
||||||
|
public:
|
||||||
|
/**
|
||||||
|
* Set path of model file and configuration file, and the configuration of runtime
|
||||||
|
* @param[in] model_file Path of model file, e.g EDVR/model.pdmodel
|
||||||
|
* @param[in] params_file Path of parameter file, e.g EDVR/model.pdiparams
|
||||||
|
* @param[in] custom_option RuntimeOption for inference, the default will use cpu, and choose the backend defined in `valid_cpu_backends`
|
||||||
|
* @param[in] model_format Model format of the loaded model, default is Paddle format
|
||||||
|
*/
|
||||||
|
EDVR(const std::string& model_file,
|
||||||
|
const std::string& params_file,
|
||||||
|
const RuntimeOption& custom_option = RuntimeOption(),
|
||||||
|
const ModelFormat& model_format = ModelFormat::PADDLE);
|
||||||
|
/// model name contained EDVR
|
||||||
|
std::string ModelName() const override { return "EDVR"; }
|
||||||
|
|
||||||
|
private:
|
||||||
|
bool Postprocess(std::vector<FDTensor>& infer_results,
|
||||||
|
std::vector<cv::Mat>& results) override;
|
||||||
|
};
|
||||||
|
} // namespace sr
|
||||||
|
} // namespace vision
|
||||||
|
} // namespace fastdeploy
|
18
fastdeploy/vision/sr/ppsr/model.h
Normal file
18
fastdeploy/vision/sr/ppsr/model.h
Normal file
@@ -0,0 +1,18 @@
|
|||||||
|
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
|
||||||
|
//
|
||||||
|
// Licensed under the Apache License, Version 2.0 (the "License");
|
||||||
|
// you may not use this file except in compliance with the License.
|
||||||
|
// You may obtain a copy of the License at
|
||||||
|
//
|
||||||
|
// http://www.apache.org/licenses/LICENSE-2.0
|
||||||
|
//
|
||||||
|
// Unless required by applicable law or agreed to in writing, software
|
||||||
|
// distributed under the License is distributed on an "AS IS" BASIS,
|
||||||
|
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||||
|
// See the License for the specific language governing permissions and
|
||||||
|
// limitations under the License.
|
||||||
|
|
||||||
|
#pragma once
|
||||||
|
#include "fastdeploy/vision/sr/ppsr/ppmsvsr.h"
|
||||||
|
#include "fastdeploy/vision/sr/ppsr/edvr.h"
|
||||||
|
#include "fastdeploy/vision/sr/ppsr/basicvsr.h"
|
128
fastdeploy/vision/sr/ppsr/ppmsvsr.cc
Normal file
128
fastdeploy/vision/sr/ppsr/ppmsvsr.cc
Normal file
@@ -0,0 +1,128 @@
|
|||||||
|
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
|
||||||
|
//
|
||||||
|
// Licensed under the Apache License, Version 2.0 (the "License");
|
||||||
|
// you may not use this file except in compliance with the License.
|
||||||
|
// You may obtain a copy of the License at
|
||||||
|
//
|
||||||
|
// http://www.apache.org/licenses/LICENSE-2.0
|
||||||
|
//
|
||||||
|
// Unless required by applicable law or agreed to in writing, software
|
||||||
|
// distributed under the License is distributed on an "AS IS" BASIS,
|
||||||
|
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||||
|
// See the License for the specific language governing permissions and
|
||||||
|
// limitations under the License.
|
||||||
|
|
||||||
|
#include "fastdeploy/vision/sr/ppsr/ppmsvsr.h"
|
||||||
|
|
||||||
|
namespace fastdeploy {
|
||||||
|
namespace vision {
|
||||||
|
namespace sr {
|
||||||
|
|
||||||
|
PPMSVSR::PPMSVSR(const std::string& model_file,
|
||||||
|
const std::string& params_file,
|
||||||
|
const RuntimeOption& custom_option,
|
||||||
|
const ModelFormat& model_format){
|
||||||
|
// unsupported ORT backend
|
||||||
|
valid_cpu_backends = {Backend::PDINFER};
|
||||||
|
valid_gpu_backends = {Backend::PDINFER};
|
||||||
|
|
||||||
|
runtime_option = custom_option;
|
||||||
|
runtime_option.model_format = model_format;
|
||||||
|
runtime_option.model_file = model_file;
|
||||||
|
runtime_option.params_file = params_file;
|
||||||
|
|
||||||
|
initialized = Initialize();
|
||||||
|
}
|
||||||
|
|
||||||
|
bool PPMSVSR::Initialize(){
|
||||||
|
if (!InitRuntime()) {
|
||||||
|
FDERROR << "Failed to initialize fastdeploy backend." << std::endl;
|
||||||
|
return false;
|
||||||
|
}
|
||||||
|
mean_ = {0., 0., 0.};
|
||||||
|
scale_ = {1., 1., 1.};
|
||||||
|
return true;
|
||||||
|
}
|
||||||
|
|
||||||
|
bool PPMSVSR::Preprocess(Mat* mat, std::vector<float>& output) {
|
||||||
|
|
||||||
|
BGR2RGB::Run(mat);
|
||||||
|
Normalize::Run(mat, mean_, scale_, true);
|
||||||
|
HWC2CHW::Run(mat);
|
||||||
|
// Csat float
|
||||||
|
float* ptr = static_cast<float *>(mat->Data());
|
||||||
|
size_t size = mat->Width() * mat->Height() * mat->Channels();
|
||||||
|
output = std::vector<float>(ptr, ptr + size);
|
||||||
|
return true;
|
||||||
|
}
|
||||||
|
|
||||||
|
bool PPMSVSR::Predict(std::vector<cv::Mat>& imgs, std::vector<cv::Mat>& results) {
|
||||||
|
|
||||||
|
// Theoretically, the more frame nums there are, the better the result will be,
|
||||||
|
// but it will lead to a significant increase in memory
|
||||||
|
int frame_num = imgs.size();
|
||||||
|
int rows = imgs[0].rows;
|
||||||
|
int cols = imgs[0].cols;
|
||||||
|
int channels = imgs[0].channels();
|
||||||
|
std::vector<FDTensor> input_tensors;
|
||||||
|
input_tensors.resize(1);
|
||||||
|
std::vector<float> all_data_temp;
|
||||||
|
for (int i = 0; i < frame_num; i++) {
|
||||||
|
Mat mat(imgs[i]);
|
||||||
|
std::vector<float> data_temp;
|
||||||
|
Preprocess(&mat, data_temp);
|
||||||
|
all_data_temp.insert(all_data_temp.end(), data_temp.begin(), data_temp.end());
|
||||||
|
}
|
||||||
|
// share memory in order to avoid memory copy, data type must be float32
|
||||||
|
input_tensors[0].SetExternalData({1 ,frame_num , channels, rows, cols}, FDDataType::FP32,
|
||||||
|
all_data_temp.data());
|
||||||
|
input_tensors[0].shape = {1, frame_num, channels, rows, cols};
|
||||||
|
input_tensors[0].name = InputInfoOfRuntime(0).name;
|
||||||
|
std::vector<FDTensor> output_tensors;
|
||||||
|
if (!Infer(input_tensors, &output_tensors)) {
|
||||||
|
FDERROR << "Failed to inference." << std::endl;
|
||||||
|
return false;
|
||||||
|
}
|
||||||
|
if (!Postprocess(output_tensors, results)) {
|
||||||
|
FDERROR << "Failed to post process." << std::endl;
|
||||||
|
return false;
|
||||||
|
}
|
||||||
|
return true;
|
||||||
|
}
|
||||||
|
|
||||||
|
bool PPMSVSR::Postprocess(std::vector<FDTensor>& infer_results, std::vector<cv::Mat>& results){
|
||||||
|
// group to image
|
||||||
|
// output_shape is [b, n, c, h, w] n = frame_nums b=1(default)
|
||||||
|
// b and n is dependence export model shape
|
||||||
|
// see https://github.com/PaddlePaddle/PaddleGAN/blob/develop/docs/zh_CN/tutorials/video_super_resolution.md
|
||||||
|
auto output_shape = infer_results[0].shape;
|
||||||
|
// PP-MSVSR
|
||||||
|
int h_ = output_shape[3];
|
||||||
|
int w_ = output_shape[4];
|
||||||
|
int c_ = output_shape[2];
|
||||||
|
int frame_num = output_shape[1];
|
||||||
|
|
||||||
|
float *out_data = static_cast<float *>(infer_results[0].Data());
|
||||||
|
cv::Mat temp = cv::Mat::zeros(h_, w_, CV_32FC3); // RGB image
|
||||||
|
int pix_num = h_ * w_;
|
||||||
|
int frame_pix_num = pix_num * c_;
|
||||||
|
for (int frame = 0; frame < frame_num; frame++) {
|
||||||
|
int index = 0;
|
||||||
|
for (int h = 0; h < h_; ++h) {
|
||||||
|
for (int w = 0; w < w_; ++w) {
|
||||||
|
temp.at<cv::Vec3f>(h, w) = {out_data[2 * pix_num + index + frame_pix_num * frame],
|
||||||
|
out_data[pix_num + index + frame_pix_num * frame],
|
||||||
|
out_data[index + frame_pix_num * frame]};
|
||||||
|
index += 1;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
// tmp data type is float[0-1.0],convert to uint type
|
||||||
|
cv::Mat res = cv::Mat::zeros(temp.size(), CV_8UC3);
|
||||||
|
temp.convertTo(res, CV_8UC3, 255);
|
||||||
|
results.push_back(res);
|
||||||
|
}
|
||||||
|
return true;
|
||||||
|
}
|
||||||
|
} // namespace sr
|
||||||
|
} // namespace vision
|
||||||
|
} // namespace fastdeploy
|
62
fastdeploy/vision/sr/ppsr/ppmsvsr.h
Normal file
62
fastdeploy/vision/sr/ppsr/ppmsvsr.h
Normal file
@@ -0,0 +1,62 @@
|
|||||||
|
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
|
||||||
|
//
|
||||||
|
// Licensed under the Apache License, Version 2.0 (the "License");
|
||||||
|
// you may not use this file except in compliance with the License.
|
||||||
|
// You may obtain a copy of the License at
|
||||||
|
//
|
||||||
|
// http://www.apache.org/licenses/LICENSE-2.0
|
||||||
|
//
|
||||||
|
// Unless required by applicable law or agreed to in writing, software
|
||||||
|
// distributed under the License is distributed on an "AS IS" BASIS,
|
||||||
|
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||||
|
// See the License for the specific language governing permissions and
|
||||||
|
// limitations under the License.
|
||||||
|
#pragma once
|
||||||
|
#include "fastdeploy/vision/common/processors/transform.h"
|
||||||
|
#include "fastdeploy/fastdeploy_model.h"
|
||||||
|
|
||||||
|
namespace fastdeploy {
|
||||||
|
namespace vision {
|
||||||
|
namespace sr {
|
||||||
|
|
||||||
|
|
||||||
|
class FASTDEPLOY_DECL PPMSVSR:public FastDeployModel{
|
||||||
|
public:
|
||||||
|
/**
|
||||||
|
* Set path of model file and configuration file, and the configuration of runtime
|
||||||
|
* @param[in] model_file Path of model file, e.g PPMSVSR/model.pdmodel
|
||||||
|
* @param[in] params_file Path of parameter file, e.g PPMSVSR/model.pdiparams
|
||||||
|
* @param[in] custom_option RuntimeOption for inference, the default will use cpu, and choose the backend defined in `valid_cpu_backends`
|
||||||
|
* @param[in] model_format Model format of the loaded model, default is Paddle format
|
||||||
|
*/
|
||||||
|
PPMSVSR(const std::string& model_file,
|
||||||
|
const std::string& params_file,
|
||||||
|
const RuntimeOption& custom_option = RuntimeOption(),
|
||||||
|
const ModelFormat& model_format = ModelFormat::PADDLE);
|
||||||
|
/// model name contained PP-MSVSR。
|
||||||
|
std::string ModelName() const override { return "PPMSVSR"; }
|
||||||
|
/**
|
||||||
|
* get super resolution frame sequence
|
||||||
|
* @param[in] imgs origin frame sequences
|
||||||
|
* @param[in] results super resolution frame sequence
|
||||||
|
* @return true if the prediction successed, otherwise false
|
||||||
|
*/
|
||||||
|
virtual bool Predict(std::vector<cv::Mat>& imgs,
|
||||||
|
std::vector<cv::Mat>& results);
|
||||||
|
|
||||||
|
protected:
|
||||||
|
PPMSVSR(){};
|
||||||
|
|
||||||
|
virtual bool Initialize();
|
||||||
|
|
||||||
|
virtual bool Preprocess(Mat* mat, std::vector<float>& output);
|
||||||
|
|
||||||
|
virtual bool Postprocess(std::vector<FDTensor>& infer_results,
|
||||||
|
std::vector<cv::Mat>& results);
|
||||||
|
|
||||||
|
std::vector<float> mean_;
|
||||||
|
std::vector<float> scale_;
|
||||||
|
};
|
||||||
|
} // namespace sr
|
||||||
|
} // namespace vision
|
||||||
|
} // namespace fastdeploy
|
70
fastdeploy/vision/sr/ppsr/ppsr_pybind.cc
Normal file
70
fastdeploy/vision/sr/ppsr/ppsr_pybind.cc
Normal file
@@ -0,0 +1,70 @@
|
|||||||
|
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
|
||||||
|
//
|
||||||
|
// Licensed under the Apache License, Version 2.0 (the "License");
|
||||||
|
// you may not use this file except in compliance with the License.
|
||||||
|
// You may obtain a copy of the License at
|
||||||
|
//
|
||||||
|
// http://www.apache.org/licenses/LICENSE-2.0
|
||||||
|
//
|
||||||
|
// Unless required by applicable law or agreed to in writing, software
|
||||||
|
// distributed under the License is distributed on an "AS IS" BASIS,
|
||||||
|
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||||
|
// See the License for the specific language governing permissions and
|
||||||
|
// limitations under the License.
|
||||||
|
#include "fastdeploy/pybind/main.h"
|
||||||
|
|
||||||
|
namespace fastdeploy{
|
||||||
|
void BindPPSR(pybind11::module &m) {
|
||||||
|
pybind11::class_<vision::sr::PPMSVSR, FastDeployModel>(m, "PPMSVSR")
|
||||||
|
.def(pybind11::init<std::string, std::string, RuntimeOption, ModelFormat>())
|
||||||
|
.def("predict", [](vision::sr::PPMSVSR& self, std::vector<pybind11::array>& datas){
|
||||||
|
std::vector<cv::Mat> inputs;
|
||||||
|
for (auto& data: datas){
|
||||||
|
auto mat = PyArrayToCvMat(data);
|
||||||
|
inputs.push_back(mat);
|
||||||
|
}
|
||||||
|
std::vector<cv::Mat> res;
|
||||||
|
std::vector<pybind11::array> res_pyarray;
|
||||||
|
self.Predict(inputs, res);
|
||||||
|
for (auto& img: res){
|
||||||
|
auto ret = pybind11::array_t<unsigned char>({img.rows, img.cols, img.channels()}, img.data);
|
||||||
|
res_pyarray.push_back(ret);
|
||||||
|
}
|
||||||
|
return res_pyarray;
|
||||||
|
});
|
||||||
|
pybind11::class_<vision::sr::EDVR, FastDeployModel>(m, "EDVR")
|
||||||
|
.def(pybind11::init<std::string, std::string, RuntimeOption, ModelFormat>())
|
||||||
|
.def("predict", [](vision::sr::EDVR& self, std::vector<pybind11::array>& datas){
|
||||||
|
std::vector<cv::Mat> inputs;
|
||||||
|
for (auto& data: datas){
|
||||||
|
auto mat = PyArrayToCvMat(data);
|
||||||
|
inputs.push_back(mat);
|
||||||
|
}
|
||||||
|
std::vector<cv::Mat> res;
|
||||||
|
std::vector<pybind11::array> res_pyarray;
|
||||||
|
self.Predict(inputs, res);
|
||||||
|
for (auto& img: res){
|
||||||
|
auto ret = pybind11::array_t<unsigned char>({img.rows, img.cols, img.channels()}, img.data);
|
||||||
|
res_pyarray.push_back(ret);
|
||||||
|
}
|
||||||
|
return res_pyarray;
|
||||||
|
});
|
||||||
|
pybind11::class_<vision::sr::BasicVSR, FastDeployModel>(m, "BasicVSR")
|
||||||
|
.def(pybind11::init<std::string, std::string, RuntimeOption, ModelFormat>())
|
||||||
|
.def("predict", [](vision::sr::BasicVSR& self, std::vector<pybind11::array>& datas){
|
||||||
|
std::vector<cv::Mat> inputs;
|
||||||
|
for (auto& data: datas){
|
||||||
|
auto mat = PyArrayToCvMat(data);
|
||||||
|
inputs.push_back(mat);
|
||||||
|
}
|
||||||
|
std::vector<cv::Mat> res;
|
||||||
|
std::vector<pybind11::array> res_pyarray;
|
||||||
|
self.Predict(inputs, res);
|
||||||
|
for (auto& img: res){
|
||||||
|
auto ret = pybind11::array_t<unsigned char>({img.rows, img.cols, img.channels()}, img.data);
|
||||||
|
res_pyarray.push_back(ret);
|
||||||
|
}
|
||||||
|
return res_pyarray;
|
||||||
|
});
|
||||||
|
}
|
||||||
|
} // namespace fastdeploy
|
25
fastdeploy/vision/sr/sr_pybind.cc
Normal file
25
fastdeploy/vision/sr/sr_pybind.cc
Normal file
@@ -0,0 +1,25 @@
|
|||||||
|
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
|
||||||
|
//
|
||||||
|
// Licensed under the Apache License, Version 2.0 (the "License");
|
||||||
|
// you may not use this file except in compliance with the License.
|
||||||
|
// You may obtain a copy of the License at
|
||||||
|
//
|
||||||
|
// http://www.apache.org/licenses/LICENSE-2.0
|
||||||
|
//
|
||||||
|
// Unless required by applicable law or agreed to in writing, software
|
||||||
|
// distributed under the License is distributed on an "AS IS" BASIS,
|
||||||
|
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||||
|
// See the License for the specific language governing permissions and
|
||||||
|
// limitations under the License.
|
||||||
|
|
||||||
|
#include "fastdeploy/pybind/main.h"
|
||||||
|
|
||||||
|
namespace fastdeploy {
|
||||||
|
|
||||||
|
void BindPPSR(pybind11::module& m);
|
||||||
|
|
||||||
|
void BindSR(pybind11::module& m) {
|
||||||
|
auto sr_module = m.def_submodule("sr", "sr(super resolution) submodule");
|
||||||
|
BindPPSR(sr_module);
|
||||||
|
}
|
||||||
|
} // namespace fastdeploy
|
@@ -27,6 +27,7 @@ void BindOcr(pybind11::module& m);
|
|||||||
void BindTracking(pybind11::module& m);
|
void BindTracking(pybind11::module& m);
|
||||||
void BindKeyPointDetection(pybind11::module& m);
|
void BindKeyPointDetection(pybind11::module& m);
|
||||||
void BindHeadPose(pybind11::module& m);
|
void BindHeadPose(pybind11::module& m);
|
||||||
|
void BindSR(pybind11::module& m);
|
||||||
#ifdef ENABLE_VISION_VISUALIZE
|
#ifdef ENABLE_VISION_VISUALIZE
|
||||||
void BindVisualize(pybind11::module& m);
|
void BindVisualize(pybind11::module& m);
|
||||||
#endif
|
#endif
|
||||||
@@ -142,6 +143,7 @@ void BindVision(pybind11::module& m) {
|
|||||||
BindTracking(m);
|
BindTracking(m);
|
||||||
BindKeyPointDetection(m);
|
BindKeyPointDetection(m);
|
||||||
BindHeadPose(m);
|
BindHeadPose(m);
|
||||||
|
BindSR(m);
|
||||||
#ifdef ENABLE_VISION_VISUALIZE
|
#ifdef ENABLE_VISION_VISUALIZE
|
||||||
BindVisualize(m);
|
BindVisualize(m);
|
||||||
#endif
|
#endif
|
||||||
|
@@ -24,6 +24,7 @@ from . import facealign
|
|||||||
from . import faceid
|
from . import faceid
|
||||||
from . import ocr
|
from . import ocr
|
||||||
from . import headpose
|
from . import headpose
|
||||||
|
from . import sr
|
||||||
from . import evaluation
|
from . import evaluation
|
||||||
from .utils import fd_result_to_json
|
from .utils import fd_result_to_json
|
||||||
from .visualize import *
|
from .visualize import *
|
||||||
|
15
python/fastdeploy/vision/sr/__init__.py
Normal file
15
python/fastdeploy/vision/sr/__init__.py
Normal file
@@ -0,0 +1,15 @@
|
|||||||
|
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
|
||||||
|
#
|
||||||
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||||
|
# you may not use this file except in compliance with the License.
|
||||||
|
# You may obtain a copy of the License at
|
||||||
|
#
|
||||||
|
# http://www.apache.org/licenses/LICENSE-2.0
|
||||||
|
#
|
||||||
|
# Unless required by applicable law or agreed to in writing, software
|
||||||
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||||
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||||
|
# See the License for the specific language governing permissions and
|
||||||
|
# limitations under the License.
|
||||||
|
from __future__ import absolute_import
|
||||||
|
from .ppsr import PPMSVSR, EDVR, BasicVSR
|
107
python/fastdeploy/vision/sr/ppsr/__init__.py
Normal file
107
python/fastdeploy/vision/sr/ppsr/__init__.py
Normal file
@@ -0,0 +1,107 @@
|
|||||||
|
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
|
||||||
|
#
|
||||||
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||||
|
# you may not use this file except in compliance with the License.
|
||||||
|
# You may obtain a copy of the License at
|
||||||
|
#
|
||||||
|
# http://www.apache.org/licenses/LICENSE-2.0
|
||||||
|
#
|
||||||
|
# Unless required by applicable law or agreed to in writing, software
|
||||||
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||||
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||||
|
# See the License for the specific language governing permissions and
|
||||||
|
# limitations under the License.
|
||||||
|
|
||||||
|
from __future__ import absolute_import
|
||||||
|
from .... import FastDeployModel, ModelFormat
|
||||||
|
from .... import c_lib_wrap as C
|
||||||
|
|
||||||
|
|
||||||
|
class PPMSVSR(FastDeployModel):
|
||||||
|
def __init__(self,
|
||||||
|
model_file,
|
||||||
|
params_file,
|
||||||
|
runtime_option=None,
|
||||||
|
model_format=ModelFormat.PADDLE):
|
||||||
|
"""Load a VSR model exported by PaddleGAN.
|
||||||
|
|
||||||
|
:param model_file: (str)Path of model file, e.g PPMSVSR/inference.pdmodel
|
||||||
|
:param params_file: (str)Path of parameters file, e.g PPMSVSR/inference.pdiparams
|
||||||
|
:param runtime_option: (fastdeploy.RuntimeOption)RuntimeOption for inference this model, if it's None, will use the default backend on CPU
|
||||||
|
:param model_format: (fastdeploy.ModelForamt)Model format of the loaded model
|
||||||
|
"""
|
||||||
|
super(PPMSVSR, self).__init__(runtime_option)
|
||||||
|
|
||||||
|
assert model_format == ModelFormat.PADDLE, "PPMSVSR model only support model format of ModelFormat.Paddle now."
|
||||||
|
self._model = C.vision.sr.PPMSVSR(model_file, params_file,
|
||||||
|
self._runtime_option, model_format)
|
||||||
|
assert self.initialized, "PPMSVSR model initialize failed."
|
||||||
|
|
||||||
|
def predict(self, input_images):
|
||||||
|
"""Predict the super resolution frame sequences for an input frame sequences
|
||||||
|
|
||||||
|
:param input_images: list[numpy.ndarray] The input image data, 3-D array with layout HWC, BGR format
|
||||||
|
:return: list[numpy.ndarray]
|
||||||
|
"""
|
||||||
|
assert input_images is not None, "The input image data is None."
|
||||||
|
return self._model.predict(input_images)
|
||||||
|
|
||||||
|
|
||||||
|
class EDVR(PPMSVSR):
|
||||||
|
def __init__(self,
|
||||||
|
model_file,
|
||||||
|
params_file,
|
||||||
|
runtime_option=None,
|
||||||
|
model_format=ModelFormat.PADDLE):
|
||||||
|
"""Load a EDVR model exported by PaddleGAN.
|
||||||
|
|
||||||
|
:param model_file: (str)Path of model file, e.g EDVR/inference.pdmodel
|
||||||
|
:param params_file: (str)Path of parameters file, e.g EDVR/inference.pdiparams
|
||||||
|
:param runtime_option: (fastdeploy.RuntimeOption)RuntimeOption for inference this model, if it's None, will use the default backend on CPU
|
||||||
|
:param model_format: (fastdeploy.ModelForamt)Model format of the loaded model
|
||||||
|
"""
|
||||||
|
super(PPMSVSR, self).__init__(runtime_option)
|
||||||
|
|
||||||
|
assert model_format == ModelFormat.PADDLE, "EDVR model only support model format of ModelFormat.Paddle now."
|
||||||
|
self._model = C.vision.sr.EDVR(model_file, params_file,
|
||||||
|
self._runtime_option, model_format)
|
||||||
|
assert self.initialized, "EDVR model initialize failed."
|
||||||
|
|
||||||
|
def predict(self, input_images):
|
||||||
|
"""Predict the super resolution frame sequences for an input frame sequences
|
||||||
|
|
||||||
|
:param input_images: list[numpy.ndarray] The input image data, 3-D array with layout HWC, BGR format
|
||||||
|
:return: list[numpy.ndarray]
|
||||||
|
"""
|
||||||
|
assert input_images is not None, "The input image data is None."
|
||||||
|
return self._model.predict(input_images)
|
||||||
|
|
||||||
|
|
||||||
|
class BasicVSR(PPMSVSR):
|
||||||
|
def __init__(self,
|
||||||
|
model_file,
|
||||||
|
params_file,
|
||||||
|
runtime_option=None,
|
||||||
|
model_format=ModelFormat.PADDLE):
|
||||||
|
"""Load a EDVR model exported by PaddleGAN.
|
||||||
|
|
||||||
|
:param model_file: (str)Path of model file, e.g BasicVSR/inference.pdmodel
|
||||||
|
:param params_file: (str)Path of parameters file, e.g BasicVSR/inference.pdiparams
|
||||||
|
:param runtime_option: (fastdeploy.RuntimeOption)RuntimeOption for inference this model, if it's None, will use the default backend on CPU
|
||||||
|
:param model_format: (fastdeploy.ModelForamt)Model format of the loaded model
|
||||||
|
"""
|
||||||
|
super(PPMSVSR, self).__init__(runtime_option)
|
||||||
|
|
||||||
|
assert model_format == ModelFormat.PADDLE, "BasicVSR model only support model format of ModelFormat.Paddle now."
|
||||||
|
self._model = C.vision.sr.BasicVSR(model_file, params_file,
|
||||||
|
self._runtime_option, model_format)
|
||||||
|
assert self.initialized, "BasicVSR model initialize failed."
|
||||||
|
|
||||||
|
def predict(self, input_images):
|
||||||
|
"""Predict the super resolution frame sequences for an input frame sequences
|
||||||
|
|
||||||
|
:param input_images: list[numpy.ndarray] The input image data, 3-D array with layout HWC, BGR format
|
||||||
|
:return: list[numpy.ndarray]
|
||||||
|
"""
|
||||||
|
assert input_images is not None, "The input image data is None."
|
||||||
|
return self._model.predict(input_images)
|
71
tests/models/test_basicvsr.py
Normal file
71
tests/models/test_basicvsr.py
Normal file
@@ -0,0 +1,71 @@
|
|||||||
|
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
|
||||||
|
#
|
||||||
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||||
|
# you may not use this file except in compliance with the License.
|
||||||
|
# You may obtain a copy of the License at
|
||||||
|
#
|
||||||
|
# http://www.apache.org/licenses/LICENSE-2.0
|
||||||
|
#
|
||||||
|
# Unless required by applicable law or agreed to in writing, software
|
||||||
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||||
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||||
|
# See the License for the specific language governing permissions and
|
||||||
|
# limitations under the License.
|
||||||
|
|
||||||
|
import fastdeploy as fd
|
||||||
|
import cv2
|
||||||
|
import os
|
||||||
|
import numpy as np
|
||||||
|
import pickle
|
||||||
|
import runtime_config as rc
|
||||||
|
|
||||||
|
|
||||||
|
def test_basicvsr():
|
||||||
|
model_url = "https://bj.bcebos.com/paddlehub/fastdeploy/basicvsr.tgz"
|
||||||
|
input_url = "https://bj.bcebos.com/paddlehub/fastdeploy/vsr_src.mp4"
|
||||||
|
fd.download_and_decompress(model_url, "resources")
|
||||||
|
fd.download(input_url, "resources")
|
||||||
|
model_path = "resources/basicvsr/BasicVSR_reds_x4"
|
||||||
|
# use default backend
|
||||||
|
runtime_option = fd.RuntimeOption()
|
||||||
|
model_file = os.path.join(model_path, "model.pdmodel")
|
||||||
|
params_file = os.path.join(model_path, "model.pdiparams")
|
||||||
|
model = fd.vision.sr.PPMSVSR(
|
||||||
|
model_file, params_file, runtime_option=rc.test_option)
|
||||||
|
# 该处应该与你导出模型的第二个维度一致模型输入shape=[b,n,c,h,w]
|
||||||
|
capture = cv2.VideoCapture("./resources/vsr_src.mp4")
|
||||||
|
frame_id = 0
|
||||||
|
reach_end = False
|
||||||
|
t = 0
|
||||||
|
while capture.isOpened():
|
||||||
|
imgs = []
|
||||||
|
for i in range(2):
|
||||||
|
_, frame = capture.read()
|
||||||
|
if frame is not None:
|
||||||
|
imgs.append(frame)
|
||||||
|
else:
|
||||||
|
reach_end = True
|
||||||
|
if reach_end:
|
||||||
|
break
|
||||||
|
results = model.predict(imgs)
|
||||||
|
for item in results:
|
||||||
|
if t < 10:
|
||||||
|
ret = pickle.load(
|
||||||
|
open("./resources/basicvsr/frame_" + str(t) + ".pkl",
|
||||||
|
"rb"))
|
||||||
|
mean_diff = np.fabs(ret["mean"] - item.mean())
|
||||||
|
std_diff = np.fabs(ret["std"] - item.std())
|
||||||
|
shape_diff = max(
|
||||||
|
np.fabs(np.array(ret["shape"]) - np.array(item.shape)))
|
||||||
|
thres = 1e-02
|
||||||
|
assert mean_diff < thres, "The mean diff is %f, which is bigger than %f" % (
|
||||||
|
mean_diff, thres)
|
||||||
|
assert std_diff < thres, "The std diff is %f, which is bigger than %f" % (
|
||||||
|
std_diff, thres)
|
||||||
|
assert shape_diff <= 0, "The shape diff is %f, which is bigger than %f" % (
|
||||||
|
shape_diff, 0)
|
||||||
|
t = t + 1
|
||||||
|
frame_id += 1
|
||||||
|
if t >= 10:
|
||||||
|
break
|
||||||
|
capture.release()
|
76
tests/models/test_edvr.py
Normal file
76
tests/models/test_edvr.py
Normal file
@@ -0,0 +1,76 @@
|
|||||||
|
test_pptracking.py # Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
|
||||||
|
#
|
||||||
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||||
|
# you may not use this file except in compliance with the License.
|
||||||
|
# You may obtain a copy of the License at
|
||||||
|
#
|
||||||
|
# http://www.apache.org/licenses/LICENSE-2.0
|
||||||
|
#
|
||||||
|
# Unless required by applicable law or agreed to in writing, software
|
||||||
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||||
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||||
|
# See the License for the specific language governing permissions and
|
||||||
|
# limitations under the License.
|
||||||
|
|
||||||
|
import fastdeploy as fd
|
||||||
|
import cv2
|
||||||
|
import os
|
||||||
|
import numpy as np
|
||||||
|
import pickle
|
||||||
|
import runtime_config as rc
|
||||||
|
|
||||||
|
|
||||||
|
def test_edvr():
|
||||||
|
model_url = "https://bj.bcebos.com/paddlehub/fastdeploy/edvr.tgz"
|
||||||
|
input_url = "https://bj.bcebos.com/paddlehub/fastdeploy/vsr_src.mp4"
|
||||||
|
fd.download_and_decompress(model_url, "resources")
|
||||||
|
fd.download(input_url, "resources")
|
||||||
|
model_path = "resources/edvr/EDVR_M_wo_tsa_SRx4"
|
||||||
|
# use default backend
|
||||||
|
runtime_option = fd.RuntimeOption()
|
||||||
|
model_file = os.path.join(model_path, "model.pdmodel")
|
||||||
|
params_file = os.path.join(model_path, "model.pdiparams")
|
||||||
|
model = fd.vision.sr.EDVR(
|
||||||
|
model_file, params_file, runtime_option=rc.test_option)
|
||||||
|
|
||||||
|
# 该处应该与你导出模型的第二个维度一致模型输入shape=[b,n,c,h,w]
|
||||||
|
capture = cv2.VideoCapture("./resources/vsr_src.mp4")
|
||||||
|
# Create VideoWriter for output
|
||||||
|
frame_id = 0
|
||||||
|
imgs = []
|
||||||
|
t = 0
|
||||||
|
while capture.isOpened():
|
||||||
|
ret, frame = capture.read()
|
||||||
|
if frame_id < 5 and frame is not None:
|
||||||
|
imgs.append(frame)
|
||||||
|
frame_id += 1
|
||||||
|
continue
|
||||||
|
# 始终保持imgs队列中具有frame_num帧
|
||||||
|
imgs.pop(0)
|
||||||
|
imgs.append(frame)
|
||||||
|
frame_id += 1
|
||||||
|
# 视频读取完毕退出
|
||||||
|
if not ret:
|
||||||
|
break
|
||||||
|
results = model.predict(imgs)
|
||||||
|
for item in results:
|
||||||
|
if frame_id <= 4:
|
||||||
|
continue
|
||||||
|
if t < 10:
|
||||||
|
ret = pickle.load(
|
||||||
|
open("./resources/edvr/frame_" + str(t) + ".pkl", "rb"))
|
||||||
|
mean_diff = np.fabs(ret["mean"] - item.mean())
|
||||||
|
std_diff = np.fabs(ret["std"] - item.std())
|
||||||
|
shape_diff = max(
|
||||||
|
np.fabs(np.array(ret["shape"]) - np.array(item.shape)))
|
||||||
|
thres = 1e-03
|
||||||
|
assert mean_diff < thres, "The mean diff is %f, which is bigger than %f" % (
|
||||||
|
mean_diff, thres)
|
||||||
|
assert std_diff < thres, "The std diff is %f, which is bigger than %f" % (
|
||||||
|
std_diff, thres)
|
||||||
|
assert shape_diff <= 0, "The shape diff is %f, which is bigger than %f" % (
|
||||||
|
shape_diff, 0)
|
||||||
|
t = t + 1
|
||||||
|
if t >= 10:
|
||||||
|
break
|
||||||
|
capture.release()
|
70
tests/models/test_ppmsvsr.py
Normal file
70
tests/models/test_ppmsvsr.py
Normal file
@@ -0,0 +1,70 @@
|
|||||||
|
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
|
||||||
|
#
|
||||||
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||||
|
# you may not use this file except in compliance with the License.
|
||||||
|
# You may obtain a copy of the License at
|
||||||
|
#
|
||||||
|
# http://www.apache.org/licenses/LICENSE-2.0
|
||||||
|
#
|
||||||
|
# Unless required by applicable law or agreed to in writing, software
|
||||||
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||||
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||||
|
# See the License for the specific language governing permissions and
|
||||||
|
# limitations under the License.
|
||||||
|
|
||||||
|
import fastdeploy as fd
|
||||||
|
import cv2
|
||||||
|
import os
|
||||||
|
import numpy as np
|
||||||
|
import pickle
|
||||||
|
import runtime_config as rc
|
||||||
|
|
||||||
|
|
||||||
|
def test_ppmsvsr():
|
||||||
|
model_url = "https://bj.bcebos.com/paddlehub/fastdeploy/ppmsvsr.tgz"
|
||||||
|
input_url = "https://bj.bcebos.com/paddlehub/fastdeploy/vsr_src.mp4"
|
||||||
|
fd.download_and_decompress(model_url, "resources")
|
||||||
|
fd.download(input_url, "resources")
|
||||||
|
model_path = "resources/ppmsvsr/MSVSR_reds_x4"
|
||||||
|
# use default backend
|
||||||
|
# runtime_option = fd.RuntimeOption()
|
||||||
|
model_file = os.path.join(model_path, "model.pdmodel")
|
||||||
|
params_file = os.path.join(model_path, "model.pdiparams")
|
||||||
|
model = fd.vision.sr.PPMSVSR(
|
||||||
|
model_file, params_file, runtime_option=rc.test_option)
|
||||||
|
# 该处应该与你导出模型的第二个维度一致模型输入shape=[b,n,c,h,w]
|
||||||
|
capture = cv2.VideoCapture("./resources/vsr_src.mp4")
|
||||||
|
frame_id = 0
|
||||||
|
reach_end = False
|
||||||
|
t = 0
|
||||||
|
while capture.isOpened():
|
||||||
|
imgs = []
|
||||||
|
for i in range(2):
|
||||||
|
_, frame = capture.read()
|
||||||
|
if frame is not None:
|
||||||
|
imgs.append(frame)
|
||||||
|
else:
|
||||||
|
reach_end = True
|
||||||
|
if reach_end:
|
||||||
|
break
|
||||||
|
results = model.predict(imgs)
|
||||||
|
for item in results:
|
||||||
|
if t < 10:
|
||||||
|
ret = pickle.load(
|
||||||
|
open("./resources/ppmsvsr/frame_" + str(t) + ".pkl", "rb"))
|
||||||
|
mean_diff = np.fabs(ret["mean"] - item.mean())
|
||||||
|
std_diff = np.fabs(ret["std"] - item.std())
|
||||||
|
shape_diff = max(
|
||||||
|
np.fabs(np.array(ret["shape"]) - np.array(item.shape)))
|
||||||
|
thres = 1e-03
|
||||||
|
assert mean_diff < thres, "The mean diff is %f, which is bigger than %f" % (
|
||||||
|
mean_diff, thres)
|
||||||
|
assert std_diff < thres, "The std diff is %f, which is bigger than %f" % (
|
||||||
|
std_diff, thres)
|
||||||
|
assert shape_diff <= 0, "The shape diff is %f, which is bigger than %f" % (
|
||||||
|
shape_diff, 0)
|
||||||
|
t = t + 1
|
||||||
|
frame_id += 1
|
||||||
|
if t >= 10:
|
||||||
|
break
|
||||||
|
capture.release()
|
Reference in New Issue
Block a user