mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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[Model] Add YOLOv5-seg (#988)
* add onnx_ort_runtime demo * rm in requirements * support batch eval * fixed MattingResults bug * move assignment for DetectionResult * integrated x2paddle * add model convert readme * update readme * re-lint * add processor api * Add MattingResult Free * change valid_cpu_backends order * add ppocr benchmark * mv bs from 64 to 32 * fixed quantize.md * fixed quantize bugs * Add Monitor for benchmark * update mem monitor * Set trt_max_batch_size default 1 * fixed ocr benchmark bug * support yolov5 in serving * Fixed yolov5 serving * Fixed postprocess * update yolov5 to 7.0 * add poros runtime demos * update readme * Support poros abi=1 * rm useless note * deal with comments * support pp_trt for ppseg * fixed symlink problem * Add is_mini_pad and stride for yolov5 * Add yolo series for paddle format * fixed bugs * fixed bug * support yolov5seg * fixed bug * refactor yolov5seg * fixed bug * mv Mask int32 to uint8 * add yolov5seg example * rm log info * fixed code style * add yolov5seg example in python * fixed dtype bug * update note * deal with comments * get sorted index * add yolov5seg test case * Add GPL-3.0 License * add round func * deal with comments * deal with commens Co-authored-by: Jason <jiangjiajun@baidu.com>
This commit is contained in:
WJJ1995
27
examples/vision/detection/yolov5seg/README.md
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27
examples/vision/detection/yolov5seg/README.md
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# YOLOv5Seg准备部署模型
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- YOLOv5Seg v7.0部署模型实现来自[YOLOv5](https://github.com/ultralytics/yolov5/tree/v7.0),和[基于COCO的预训练模型](https://github.com/ultralytics/yolov5/releases/tag/v7.0)
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- （1）[官方库](https://github.com/ultralytics/yolov5/releases/tag/v7.0)提供的*.onnx可直接进行部署；
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- （2）开发者基于自己数据训练的YOLOv5Seg v7.0模型，可使用[YOLOv5](https://github.com/ultralytics/yolov5)中的`export.py`导出ONNX文件后，完成部署。
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## 下载预训练ONNX模型
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为了方便开发者的测试，下面提供了YOLOv5Seg导出的各系列模型，开发者可直接下载使用。（下表中模型的精度来源于源官方库）
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| 模型 | 大小 | 精度 | 备注 |
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|:---------------------------------------------------------------- |:----- |:----- |:----- |
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| [YOLOv5n-seg](https://bj.bcebos.com/paddlehub/fastdeploy/yolov5n-seg.onnx) | 7.7MB | 27.6% | 此模型文件来源于[YOLOv5](https://github.com/ultralytics/yolov5)，GPL-3.0 License |
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| [YOLOv5s-seg](https://bj.bcebos.com/paddlehub/fastdeploy/yolov5s-seg.onnx) | 30MB | 37.6% | 此模型文件来源于[YOLOv5](https://github.com/ultralytics/yolov5)，GPL-3.0 License |
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| [YOLOv5m-seg](https://bj.bcebos.com/paddlehub/fastdeploy/yolov5m-seg.onnx) | 84MB | 45.0% | 此模型文件来源于[YOLOv5](https://github.com/ultralytics/yolov5)，GPL-3.0 License |
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| [YOLOv5l-seg](https://bj.bcebos.com/paddlehub/fastdeploy/yolov5l-seg.onnx) | 183MB | 49.0% | 此模型文件来源于[YOLOv5](https://github.com/ultralytics/yolov5)，GPL-3.0 License |
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| [YOLOv5x-seg](https://bj.bcebos.com/paddlehub/fastdeploy/yolov5x-seg.onnx) | 339MB | 50.7% | 此模型文件来源于[YOLOv5](https://github.com/ultralytics/yolov5)，GPL-3.0 License |
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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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## 版本说明
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- 本版本文档和代码基于[YOLOv5 v7.0](https://github.com/ultralytics/yolov5/tree/v7.0) 编写
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14
examples/vision/detection/yolov5seg/cpp/CMakeLists.txt
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14
examples/vision/detection/yolov5seg/cpp/CMakeLists.txt
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@@ -0,0 +1,14 @@
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PROJECT(infer_demo C CXX)
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CMAKE_MINIMUM_REQUIRED (VERSION 3.10)
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# Specify the fastdeploy library path after downloading and decompression
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option(FASTDEPLOY_INSTALL_DIR "Path of downloaded fastdeploy sdk.")
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include(${FASTDEPLOY_INSTALL_DIR}/FastDeploy.cmake)
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# Add FastDeploy dependent header files
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include_directories(${FASTDEPLOY_INCS})
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add_executable(infer_demo ${PROJECT_SOURCE_DIR}/infer.cc)
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# Add FastDeploy library dependencies
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target_link_libraries(infer_demo ${FASTDEPLOY_LIBS})
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74
examples/vision/detection/yolov5seg/cpp/README.md
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74
examples/vision/detection/yolov5seg/cpp/README.md
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# YOLOv5Seg C++部署示例
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本目录下提供`infer.cc`快速完成YOLOv5Seg在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。
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在部署前，需确认以下两个步骤
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|
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- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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- 2. 根据开发环境，下载预编译部署库和samples代码，参考[FastDeploy预编译库](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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以Linux上CPU推理为例，在本目录执行如下命令即可完成编译测试，支持此模型需保证FastDeploy版本1.0.3以上(x.x.x>=1.0.3)
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```bash
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mkdir build
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cd build
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# 下载 FastDeploy 预编译库，用户可在上文提到的`FastDeploy预编译库`中自行选择合适的版本使用
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wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
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tar xvf fastdeploy-linux-x64-x.x.x.tgz
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cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
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make -j
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# 1. 下载官方转换好的 YOLOv5Seg ONNX 模型文件和测试图片
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wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov5s-seg.onnx
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wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
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# CPU推理
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./infer_demo yolov5s-seg.onnx 000000014439.jpg 0
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# GPU推理
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./infer_demo yolov5s-seg.onnx 000000014439.jpg 1
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# GPU上TensorRT推理
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./infer_demo yolov5s-seg.onnx 000000014439.jpg 2
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```
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运行完成可视化结果如下图所示
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<img width="640" src="https://user-images.githubusercontent.com/19977378/209955620-657bdd1d-574c-40a2-b05d-42b9e5a15ae8.png">
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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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## YOLOv5Seg C++接口
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### YOLOv5Seg类
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```c++
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fastdeploy::vision::detection::YOLOv5Seg(
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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::ONNX)
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```
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YOLOv5Seg模型加载和初始化，其中model_file为导出的ONNX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，当模型格式为ONNX时，此参数传入空字符串即可
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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ONNX格式
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#### Predict函数
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```c++
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YOLOv5Seg::Predict(const cv::Mat& img, DetectionResult* result)
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```
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**参数**
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> > * **im**: 输入图像，注意需为HWC，BGR格式
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> > * **result**: 检测结果，包括检测框，各个框的置信度, DetectionResult说明参考[视觉模型预测结果](../../../../../docs/api/vision_results/)
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|
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- [模型介绍](../../)
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- [Python部署](../python)
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- [视觉模型预测结果](../../../../../docs/api/vision_results/)
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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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105
examples/vision/detection/yolov5seg/cpp/infer.cc
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105
examples/vision/detection/yolov5seg/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");
|
||||
// you may not use this file except in compliance with the License.
|
||||
// You may obtain a copy of the License at
|
||||
//
|
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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
|
||||
// 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.
|
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|
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#include "fastdeploy/vision.h"
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void CpuInfer(const std::string& model_file, const std::string& image_file) {
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auto model = fastdeploy::vision::detection::YOLOv5Seg(model_file);
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if (!model.Initialized()) {
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std::cerr << "Failed to initialize." << std::endl;
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return;
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}
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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);
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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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|
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void GpuInfer(const std::string& model_file, const std::string& image_file) {
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auto option = fastdeploy::RuntimeOption();
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option.UseGpu();
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auto model = fastdeploy::vision::detection::YOLOv5Seg(model_file, "", option);
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if (!model.Initialized()) {
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std::cerr << "Failed to initialize." << std::endl;
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return;
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}
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|
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auto im = cv::imread(image_file);
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|
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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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auto vis_im = fastdeploy::vision::VisDetection(im, res);
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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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|
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void TrtInfer(const std::string& model_file, const std::string& image_file) {
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auto option = fastdeploy::RuntimeOption();
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option.UseGpu();
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option.UseTrtBackend();
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option.SetTrtInputShape("images", {1, 3, 640, 640});
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auto model = fastdeploy::vision::detection::YOLOv5Seg(model_file, "", option);
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if (!model.Initialized()) {
|
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std::cerr << "Failed to initialize." << std::endl;
|
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return;
|
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}
|
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|
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auto im = cv::imread(image_file);
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|
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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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auto vis_im = fastdeploy::vision::VisDetection(im, res);
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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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|
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int main(int argc, char* argv[]) {
|
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if (argc < 4) {
|
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std::cout << "Usage: infer_demo path/to/model path/to/image run_option, "
|
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"e.g ./infer_model ./yolov5.onnx ./test.jpeg 0"
|
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<< std::endl;
|
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std::cout << "The data type of run_option is int, 0: run with cpu; 1: run "
|
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"with gpu; 2: run with gpu and use tensorrt backend."
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<< std::endl;
|
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return -1;
|
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}
|
||||
|
||||
if (std::atoi(argv[3]) == 0) {
|
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CpuInfer(argv[1], argv[2]);
|
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} else if (std::atoi(argv[3]) == 1) {
|
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GpuInfer(argv[1], argv[2]);
|
||||
} else if (std::atoi(argv[3]) == 2) {
|
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TrtInfer(argv[1], argv[2]);
|
||||
}
|
||||
return 0;
|
||||
}
|
||||
67
examples/vision/detection/yolov5seg/python/README.md
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67
examples/vision/detection/yolov5seg/python/README.md
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@@ -0,0 +1,67 @@
|
||||
# YOLOv5Seg 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YOLOv5Seg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 examples/vision/detection/yolov5seg/python/
|
||||
|
||||
#下载yolov5seg模型文件和测试图片
|
||||
wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov5s-seg.onnx
|
||||
wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
|
||||
|
||||
# CPU推理
|
||||
python infer.py --model yolov5s-seg.onnx --image 000000014439.jpg --device cpu
|
||||
# GPU推理
|
||||
python infer.py --model yolov5s-seg.onnx --image 000000014439.jpg --device gpu
|
||||
# GPU上使用TensorRT推理
|
||||
python infer.py --model yolov5s-seg.onnx --image 000000014439.jpg --device gpu --use_trt True
|
||||
```
|
||||
|
||||
运行完成可视化结果如下图所示
|
||||
|
||||
<img width="640" src="https://user-images.githubusercontent.com/19977378/209955620-657bdd1d-574c-40a2-b05d-42b9e5a15ae8.png">
|
||||
|
||||
## YOLOv5Seg Python接口
|
||||
|
||||
```python
|
||||
fastdeploy.vision.detection.YOLOv5Seg(model_file, params_file=None, runtime_option=None, model_format=ModelFormat.ONNX)
|
||||
```
|
||||
|
||||
YOLOv5Seg模型加载和初始化，其中model_file为导出的ONNX模型格式
|
||||
|
||||
**参数**
|
||||
|
||||
> * **model_file**(str): 模型文件路径
|
||||
> * **params_file**(str): 参数文件路径，当模型格式为ONNX格式时，此参数无需设定
|
||||
> * **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ONNX
|
||||
|
||||
### predict函数
|
||||
|
||||
```python
|
||||
YOLOv5Seg.predict(image_data)
|
||||
```
|
||||
|
||||
模型预测结口，输入图像直接输出检测结果。
|
||||
|
||||
**参数**
|
||||
|
||||
> > * **image_data**(np.ndarray): 输入数据，注意需为HWC，BGR格式
|
||||
|
||||
**返回**
|
||||
|
||||
> > 返回`fastdeploy.vision.DetectionResult`结构体，结构体说明参考文档[视觉模型预测结果](../../../../../docs/api/vision_results/)
|
||||
|
||||
## 其它文档
|
||||
|
||||
- [YOLOv5Seg 模型介绍](..)
|
||||
- [YOLOv5Seg C++部署](../cpp)
|
||||
- [模型预测结果说明](../../../../../docs/api/vision_results/)
|
||||
- [如何切换模型推理后端引擎](../../../../../docs/cn/faq/how_to_change_backend.md)
|
||||
56
examples/vision/detection/yolov5seg/python/infer.py
Normal file
56
examples/vision/detection/yolov5seg/python/infer.py
Normal file
@@ -0,0 +1,56 @@
|
||||
import fastdeploy as fd
|
||||
import cv2
|
||||
import os
|
||||
|
||||
|
||||
def parse_arguments():
|
||||
import argparse
|
||||
import ast
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument(
|
||||
"--model", default=None, help="Path of yolov5seg model.")
|
||||
parser.add_argument(
|
||||
"--image", default=None, help="Path of test image file.")
|
||||
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.set_trt_input_shape("images", [1, 3, 640, 640])
|
||||
return option
|
||||
|
||||
|
||||
args = parse_arguments()
|
||||
|
||||
# Configure runtime, load model
|
||||
runtime_option = build_option(args)
|
||||
model = fd.vision.detection.YOLOv5Seg(
|
||||
args.model, runtime_option=runtime_option)
|
||||
|
||||
# Predicting image
|
||||
if args.image is None:
|
||||
image = fd.utils.get_detection_test_image()
|
||||
else:
|
||||
image = args.image
|
||||
im = cv2.imread(image)
|
||||
result = model.predict(im)
|
||||
|
||||
# Visualization
|
||||
vis_im = fd.vision.vis_detection(im, result)
|
||||
cv2.imwrite("visualized_result.jpg", vis_im)
|
||||
print("Visualized result save in ./visualized_result.jpg")
|
||||
Reference in New Issue
Block a user