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https://github.com/PaddlePaddle/FastDeploy.git
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[Backend] add sophgo backend (#1015)
* Add Sophgo Device add sophgo backend in fastdeploy add resnet50, yolov5s, liteseg examples. * replace sophgo lib with download links; fix model.cc bug * modify CodeStyle * remove unuseful files;change the names of sophgo device and sophgo backend * sophgo support python and add python examples * remove unuseful rows in cmake according pr Co-authored-by: Zilong Xing <zilong.xing@sophgo.com>
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examples/vision/detection/yolov5/sophgo/cpp/CMakeLists.txt
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examples/vision/detection/yolov5/sophgo/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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set(ENABLE_LITE_BACKEND OFF)
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#set(FDLIB ${FASTDEPLOY_INSTALL_DIR})
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include(${FASTDEPLOY_INSTALL_DIR}/FastDeploy.cmake)
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# 添加FastDeploy依赖头文件
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include_directories(${FASTDEPLOY_INCS})
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include_directories(${FastDeploy_INCLUDE_DIRS})
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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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examples/vision/detection/yolov5/sophgo/cpp/README.md
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examples/vision/detection/yolov5/sophgo/cpp/README.md
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# YOLOv5 C++部署示例
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本目录下提供`infer.cc`快速完成yolov5s模型在SOPHGO BM1684x板子上加速部署的示例。
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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
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2. 根据开发环境,从头编译FastDeploy仓库
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以上步骤请参考[SOPHGO部署库编译](../../../../../../docs/cn/build_and_install/sophgo.md)实现
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## 生成基本目录文件
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该例程由以下几个部分组成
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```text
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.
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├── CMakeLists.txt
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├── build # 编译文件夹
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├── image # 存放图片的文件夹
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├── infer.cc
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└── model # 存放模型文件的文件夹
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```
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## 编译
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### 编译并拷贝SDK到thirdpartys文件夹
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请参考[SOPHGO部署库编译](../../../../../../docs/cn/build_and_install/sophgo.md)仓库编译SDK,编译完成后,将在build目录下生成fastdeploy-0.0.3目录.
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### 拷贝模型文件,以及配置文件至model文件夹
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将Paddle模型转换为SOPHGO bmodel模型,转换步骤参考[文档](../README.md)
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将转换后的SOPHGO bmodel模型文件拷贝至model中
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### 准备测试图片至image文件夹
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```bash
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wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
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cp 000000014439.jpg ./images
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```
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### 编译example
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```bash
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cd build
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cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-0.0.3
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make
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```
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## 运行例程
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```bash
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./infer_demo model images/000000014439.jpg
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```
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- [模型介绍](../../)
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- [模型转换](../)
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examples/vision/detection/yolov5/sophgo/cpp/infer.cc
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examples/vision/detection/yolov5/sophgo/cpp/infer.cc
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// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include <string>
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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 InitAndInfer(const std::string& model_dir, const std::string& image_file) {
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auto model_file = model_dir + sep + "yolov5s_1684x_f32.bmodel";
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auto params_file = model_dir + sep + "";
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fastdeploy::RuntimeOption option;
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option.UseSophgo();
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auto model_format = fastdeploy::ModelFormat::SOPHGO;
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auto model = fastdeploy::vision::detection::YOLOv5(
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model_file, params_file, option, model_format);
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assert(model.Initialized());
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auto im = cv::imread(image_file);
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fastdeploy::vision::DetectionResult res;
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if (!model.Predict(im, &res)) {
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std::cerr << "Failed to predict." << std::endl;
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return;
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}
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std::cout << res.Str() << std::endl;
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}
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int main(int argc, char* argv[]) {
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if (argc < 3) {
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std::cout << "Usage: infer_demo path/to/model "
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"path/to/image "
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"run_option, "
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"e.g ./infer_demo ./model ./test.jpeg"
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<< std::endl;
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return -1;
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}
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std::string model_dir = argv[1];
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std::string test_image = argv[2];
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InitAndInfer(model_dir, test_image);
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return 0;
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}
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