更换文档检测模型
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PROJECT(infer_demo C CXX)
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CMAKE_MINIMUM_REQUIRED (VERSION 3.10)
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option(FASTDEPLOY_INSTALL_DIR "Path of downloaded fastdeploy sdk.")
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include(${FASTDEPLOY_INSTALL_DIR}/FastDeploy.cmake)
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include_directories(${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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target_link_libraries(infer_demo ${FASTDEPLOY_LIBS})
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set(CMAKE_INSTALL_PREFIX ${CMAKE_SOURCE_DIR}/build/install)
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install(TARGETS infer_demo DESTINATION ./)
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install(DIRECTORY models DESTINATION ./)
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install(DIRECTORY images DESTINATION ./)
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file(GLOB_RECURSE FASTDEPLOY_LIBS ${FASTDEPLOY_INSTALL_DIR}/lib/lib*.so*)
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file(GLOB_RECURSE ALL_LIBS ${FASTDEPLOY_INSTALL_DIR}/third_libs/install/lib*.so*)
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list(APPEND ALL_LIBS ${FASTDEPLOY_LIBS})
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install(PROGRAMS ${ALL_LIBS} DESTINATION lib)
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file(GLOB ADB_TOOLS run_with_adb.sh)
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install(PROGRAMS ${ADB_TOOLS} DESTINATION ./)
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[English](README.md) | 简体中文
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# PaddleDetection A311D 量化模型 C++ 部署示例
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本目录下提供的 `infer.cc`，可以帮助用户快速完成 PP-YOLOE 量化模型在 A311D 上的部署推理加速。
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## 1. 部署环境准备
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软硬件环境满足要求，以及交叉编译环境的准备，请参考：[FastDeploy 晶晨 A311d 编译文档](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install#自行编译安装)
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## 2. 部署模型准备
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1. 用户可以直接使用由 FastDeploy 提供的量化模型进行部署。
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2. 用户可以先使用 PaddleDetection 自行导出 Float32 模型，注意导出模型模型时设置参数：use_shared_conv=False，更多细节请参考：[PP-YOLOE](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyoloe)
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3. 用户可以使用 FastDeploy 提供的[一键模型自动化压缩工具](https://github.com/PaddlePaddle/FastDeploy/blob/develop/tools/common_tools/auto_compression/)，自行进行模型量化, 并使用产出的量化模型进行部署。（注意: 推理量化后的检测模型仍然需要FP32模型文件夹下的 infer_cfg.yml 文件，自行量化的模型文件夹内不包含此 yaml 文件，用户从 FP32 模型文件夹下复制此yaml文件到量化后的模型文件夹内即可。）
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4. 模型需要异构计算，异构计算文件可以参考：[异构计算](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/faq/heterogeneous_computing_on_timvx_npu.md)，由于 FastDeploy 已经提供了模型，可以先测试我们提供的异构文件，验证精度是否符合要求。
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更多量化相关相关信息可查阅[模型量化](../../../quantize/README.md)
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## 3. 在 A311D 上部署量化后的 PP-YOLOE 检测模型
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请按照以下步骤完成在 A311D 上部署 PP-YOLOE 量化模型：
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1. 交叉编译编译 FastDeploy 库，具体请参考：[交叉编译 FastDeploy](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install/a311d.md)
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2. 将编译后的库拷贝到当前目录，可使用如下命令：
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```bash
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# 下载部署示例代码
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git clone https://github.com/PaddlePaddle/PaddleDetection.git
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cd PaddleDetection/deploy/fastdeploy/amlogic/a311d/cpp
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# 注意：如果当前分支找不到下面的fastdeploy测试代码，请切换到develop分支
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# git checkout develop
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cp -r FastDeploy/build/fastdeploy-timvx/ PaddleDetection/deploy/fastdeploy/amlogic/a311d/cpp
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```
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3. 在当前路径下载部署所需的模型和示例图片：
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```bash
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cd PaddleDetection/deploy/fastdeploy/amlogic/a311d/cpp
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mkdir models && mkdir images
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wget https://bj.bcebos.com/fastdeploy/models/ppyoloe_noshare_qat.tar.gz
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tar -xvf ppyoloe_noshare_qat.tar.gz
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cp -r ppyoloe_noshare_qat models
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wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
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cp -r 000000014439.jpg images
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```
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4. 编译部署示例，可使入如下命令：
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```bash
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cd PaddleDetection/deploy/fastdeploy/amlogic/a311d/cpp
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mkdir build && cd build
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cmake -DCMAKE_TOOLCHAIN_FILE=${PWD}/../fastdeploy-timvx/toolchain.cmake -DFASTDEPLOY_INSTALL_DIR=${PWD}/../fastdeploy-timvx -DTARGET_ABI=arm64 ..
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make -j8
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make install
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# 成功编译之后，会生成 install 文件夹，里面有一个运行 demo 和部署所需的库
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```
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5. 基于 adb 工具部署 PP-YOLOE 检测模型到晶晨 A311D
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```bash
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# 进入 install 目录
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cd PaddleDetection/deploy/fastdeploy/amlogic/a311d/cpp/build/install/
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# 如下命令表示：bash run_with_adb.sh 需要运行的demo 模型路径 图片路径 设备的DEVICE_ID
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bash run_with_adb.sh infer_demo ppyoloe_noshare_qat 000000014439.jpg $DEVICE_ID
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```
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部署成功后运行结果如下：
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<img width="640" src="https://user-images.githubusercontent.com/30516196/203708564-43c49485-9b48-4eb2-8fe7-0fa517979fff.png">
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需要特别注意的是，在 A311D 上部署的模型需要是量化后的模型，模型的量化请参考：[模型量化](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/quantize.md)
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## 4. 更多指南
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- [PaddleDetection C++ API文档](https://www.paddlepaddle.org.cn/fastdeploy-api-doc/cpp/html/namespacefastdeploy_1_1vision_1_1detection.html)
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- [FastDeploy部署PaddleDetection模型概览](../../)
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- [Python部署](../python)
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## 5. 常见问题
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- [如何切换模型推理后端引擎](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/faq/how_to_change_backend.md)
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- [Intel GPU(独立显卡/集成显卡)的使用](https://github.com/PaddlePaddle/FastDeploy/blob/develop/tutorials/intel_gpu/README.md)
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- [编译CPU部署库](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install/cpu.md)
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- [编译GPU部署库](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install/gpu.md)
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- [编译Jetson部署库](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install/jetson.md)
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// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include "fastdeploy/vision.h"
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#ifdef WIN32
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const char sep = '\\';
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#else
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const char sep = '/';
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#endif
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void InitAndInfer(const std::string& model_dir, const std::string& image_file) {
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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 config_file = model_dir + sep + "infer_cfg.yml";
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auto subgraph_file = model_dir + sep + "subgraph.txt";
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fastdeploy::vision::EnableFlyCV();
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fastdeploy::RuntimeOption option;
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option.UseTimVX();
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option.SetLiteSubgraphPartitionPath(subgraph_file);
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auto model = fastdeploy::vision::detection::PPYOLOE(model_file, params_file,
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config_file, option);
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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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auto vis_im = fastdeploy::vision::VisDetection(im, res, 0.5);
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cv::imwrite("vis_result.jpg", vis_im);
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std::cout << "Visualized result saved in ./vis_result.jpg" << 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/quant_model "
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"path/to/image "
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"e.g ./infer_demo ./PPYOLOE_L_quant ./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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#!/bin/bash
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HOST_SPACE=${PWD}
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echo ${HOST_SPACE}
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WORK_SPACE=/data/local/tmp/test
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# The first parameter represents the demo name
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DEMO_NAME=image_classification_demo
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if [ -n "$1" ]; then
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DEMO_NAME=$1
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fi
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# The second parameter represents the model name
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MODEL_NAME=mobilenet_v1_fp32_224
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if [ -n "$2" ]; then
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MODEL_NAME=$2
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fi
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# The third parameter indicates the name of the image to be tested
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IMAGE_NAME=0001.jpg
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if [ -n "$3" ]; then
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IMAGE_NAME=$3
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fi
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# The fourth parameter represents the ID of the device
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ADB_DEVICE_NAME=
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if [ -n "$4" ]; then
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ADB_DEVICE_NAME="-s $4"
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fi
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# Set the environment variables required during the running process
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EXPORT_ENVIRONMENT_VARIABLES="export GLOG_v=5; export SUBGRAPH_ONLINE_MODE=true; export RKNPU_LOGLEVEL=5; export RKNN_LOG_LEVEL=5; ulimit -c unlimited; export VIV_VX_ENABLE_GRAPH_TRANSFORM=-pcq:1; export VIV_VX_SET_PER_CHANNEL_ENTROPY=100; export TIMVX_BATCHNORM_FUSION_MAX_ALLOWED_QUANT_SCALE_DEVIATION=300000; export VSI_NN_LOG_LEVEL=5;"
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EXPORT_ENVIRONMENT_VARIABLES="${EXPORT_ENVIRONMENT_VARIABLES}export LD_LIBRARY_PATH=${WORK_SPACE}/lib:\$LD_LIBRARY_PATH;"
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# Please install adb, and DON'T run this in the docker.
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set -e
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adb $ADB_DEVICE_NAME shell "rm -rf $WORK_SPACE"
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adb $ADB_DEVICE_NAME shell "mkdir -p $WORK_SPACE"
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# Upload the demo, librarys, model and test images to the device
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adb $ADB_DEVICE_NAME push ${HOST_SPACE}/lib $WORK_SPACE
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adb $ADB_DEVICE_NAME push ${HOST_SPACE}/${DEMO_NAME} $WORK_SPACE
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adb $ADB_DEVICE_NAME push models $WORK_SPACE
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adb $ADB_DEVICE_NAME push images $WORK_SPACE
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# Execute the deployment demo
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adb $ADB_DEVICE_NAME shell "cd $WORK_SPACE; ${EXPORT_ENVIRONMENT_VARIABLES} chmod +x ./${DEMO_NAME}; ./${DEMO_NAME} ./models/${MODEL_NAME} ./images/$IMAGE_NAME"
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