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
0
cmake/paddle_inference.cmake
Normal file → Executable file
0
cmake/paddle_inference.cmake
Normal file → Executable file
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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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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- [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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@@ -0,0 +1,105 @@
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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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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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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, "
|
||||
"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 "
|
||||
"with gpu; 2: run with gpu and use tensorrt backend."
|
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<< std::endl;
|
||||
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
Normal file
67
examples/vision/detection/yolov5seg/python/README.md
Normal file
@@ -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")
|
||||
0
fastdeploy/runtime/backends/paddle/paddle_backend.h
Normal file → Executable file
0
fastdeploy/runtime/backends/paddle/paddle_backend.h
Normal file → Executable file
1
fastdeploy/vision.h
Normal file → Executable file
1
fastdeploy/vision.h
Normal file → Executable file
@@ -22,6 +22,7 @@
|
||||
#include "fastdeploy/vision/detection/contrib/scaledyolov4.h"
|
||||
#include "fastdeploy/vision/detection/contrib/yolor.h"
|
||||
#include "fastdeploy/vision/detection/contrib/yolov5/yolov5.h"
|
||||
#include "fastdeploy/vision/detection/contrib/yolov5seg/yolov5seg.h"
|
||||
#include "fastdeploy/vision/detection/contrib/fastestdet/fastestdet.h"
|
||||
#include "fastdeploy/vision/detection/contrib/yolov5lite.h"
|
||||
#include "fastdeploy/vision/detection/contrib/yolov6.h"
|
||||
|
||||
@@ -48,7 +48,7 @@ void Mask::Reserve(int size) { data.reserve(size); }
|
||||
void Mask::Resize(int size) { data.resize(size); }
|
||||
|
||||
void Mask::Clear() {
|
||||
std::vector<int32_t>().swap(data);
|
||||
std::vector<uint8_t>().swap(data);
|
||||
std::vector<int64_t>().swap(shape);
|
||||
}
|
||||
|
||||
|
||||
@@ -67,7 +67,7 @@ struct FASTDEPLOY_DECL ClassifyResult : public BaseResult {
|
||||
*/
|
||||
struct FASTDEPLOY_DECL Mask : public BaseResult {
|
||||
/// Mask data buffer
|
||||
std::vector<int32_t> data;
|
||||
std::vector<uint8_t> data;
|
||||
/// Shape of mask
|
||||
std::vector<int64_t> shape; // (H,W) ...
|
||||
ResultType type = ResultType::MASK;
|
||||
@@ -107,7 +107,7 @@ struct FASTDEPLOY_DECL DetectionResult : public BaseResult {
|
||||
/** \brief For instance segmentation model, `masks` is the predict mask for all the deteced objects
|
||||
*/
|
||||
std::vector<Mask> masks;
|
||||
//// Shows if the DetectionResult has mask
|
||||
/// Shows if the DetectionResult has mask
|
||||
bool contain_masks = false;
|
||||
|
||||
ResultType type = ResultType::DETECTION;
|
||||
|
||||
217
fastdeploy/vision/detection/contrib/yolov5seg/postprocessor.cc
Executable file
217
fastdeploy/vision/detection/contrib/yolov5seg/postprocessor.cc
Executable file
@@ -0,0 +1,217 @@
|
||||
// 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/detection/contrib/yolov5seg/postprocessor.h"
|
||||
#include "fastdeploy/vision/utils/utils.h"
|
||||
|
||||
namespace fastdeploy {
|
||||
namespace vision {
|
||||
namespace detection {
|
||||
|
||||
YOLOv5SegPostprocessor::YOLOv5SegPostprocessor() {
|
||||
conf_threshold_ = 0.25;
|
||||
nms_threshold_ = 0.5;
|
||||
mask_threshold_ = 0.5;
|
||||
multi_label_ = true;
|
||||
max_wh_ = 7680.0;
|
||||
mask_nums_ = 32;
|
||||
}
|
||||
|
||||
bool YOLOv5SegPostprocessor::Run(
|
||||
const std::vector<FDTensor>& tensors, std::vector<DetectionResult>* results,
|
||||
const std::vector<std::map<std::string, std::array<float, 2>>>& ims_info) {
|
||||
int batch = tensors[0].shape[0];
|
||||
|
||||
results->resize(batch);
|
||||
|
||||
for (size_t bs = 0; bs < batch; ++bs) {
|
||||
// store mask information
|
||||
std::vector<std::vector<float>> mask_embeddings;
|
||||
(*results)[bs].Clear();
|
||||
if (multi_label_) {
|
||||
(*results)[bs].Reserve(tensors[0].shape[1] *
|
||||
(tensors[0].shape[2] - mask_nums_ - 5));
|
||||
} else {
|
||||
(*results)[bs].Reserve(tensors[0].shape[1]);
|
||||
}
|
||||
if (tensors[0].dtype != FDDataType::FP32) {
|
||||
FDERROR << "Only support post process with float32 data." << std::endl;
|
||||
return false;
|
||||
}
|
||||
const float* data = reinterpret_cast<const float*>(tensors[0].Data()) +
|
||||
bs * tensors[0].shape[1] * tensors[0].shape[2];
|
||||
for (size_t i = 0; i < tensors[0].shape[1]; ++i) {
|
||||
int s = i * tensors[0].shape[2];
|
||||
float cls_conf = data[s + 4];
|
||||
float confidence = data[s + 4];
|
||||
std::vector<float> mask_embedding(
|
||||
data + s + tensors[0].shape[2] - mask_nums_,
|
||||
data + s + tensors[0].shape[2]);
|
||||
for (size_t k = 0; k < mask_embedding.size(); ++k) {
|
||||
mask_embedding[k] *= cls_conf;
|
||||
}
|
||||
if (multi_label_) {
|
||||
for (size_t j = 5; j < tensors[0].shape[2] - mask_nums_; ++j) {
|
||||
confidence = data[s + 4];
|
||||
const float* class_score = data + s + j;
|
||||
confidence *= (*class_score);
|
||||
// filter boxes by conf_threshold
|
||||
if (confidence <= conf_threshold_) {
|
||||
continue;
|
||||
}
|
||||
int32_t label_id = std::distance(data + s + 5, class_score);
|
||||
|
||||
// convert from [x, y, w, h] to [x1, y1, x2, y2]
|
||||
(*results)[bs].boxes.emplace_back(std::array<float, 4>{
|
||||
data[s] - data[s + 2] / 2.0f + label_id * max_wh_,
|
||||
data[s + 1] - data[s + 3] / 2.0f + label_id * max_wh_,
|
||||
data[s + 0] + data[s + 2] / 2.0f + label_id * max_wh_,
|
||||
data[s + 1] + data[s + 3] / 2.0f + label_id * max_wh_});
|
||||
(*results)[bs].label_ids.push_back(label_id);
|
||||
(*results)[bs].scores.push_back(confidence);
|
||||
// TODO(wangjunjie06): No zero copy
|
||||
mask_embeddings.push_back(mask_embedding);
|
||||
}
|
||||
} else {
|
||||
const float* max_class_score = std::max_element(
|
||||
data + s + 5, data + s + tensors[0].shape[2] - mask_nums_);
|
||||
confidence *= (*max_class_score);
|
||||
// filter boxes by conf_threshold
|
||||
if (confidence <= conf_threshold_) {
|
||||
continue;
|
||||
}
|
||||
int32_t label_id = std::distance(data + s + 5, max_class_score);
|
||||
// convert from [x, y, w, h] to [x1, y1, x2, y2]
|
||||
(*results)[bs].boxes.emplace_back(std::array<float, 4>{
|
||||
data[s] - data[s + 2] / 2.0f + label_id * max_wh_,
|
||||
data[s + 1] - data[s + 3] / 2.0f + label_id * max_wh_,
|
||||
data[s + 0] + data[s + 2] / 2.0f + label_id * max_wh_,
|
||||
data[s + 1] + data[s + 3] / 2.0f + label_id * max_wh_});
|
||||
(*results)[bs].label_ids.push_back(label_id);
|
||||
(*results)[bs].scores.push_back(confidence);
|
||||
mask_embeddings.push_back(mask_embedding);
|
||||
}
|
||||
}
|
||||
|
||||
if ((*results)[bs].boxes.size() == 0) {
|
||||
return true;
|
||||
}
|
||||
// get box index after nms
|
||||
std::vector<int> index;
|
||||
utils::NMS(&((*results)[bs]), nms_threshold_, &index);
|
||||
|
||||
// deal with mask
|
||||
// step1: MatMul, (box_nums * 32) x (32 * 160 * 160) = box_nums * 160 * 160
|
||||
// step2: Sigmoid
|
||||
// step3: Resize to original image size
|
||||
// step4: Select pixels greater than threshold and crop
|
||||
(*results)[bs].contain_masks = true;
|
||||
(*results)[bs].masks.resize((*results)[bs].boxes.size());
|
||||
const float* data_mask =
|
||||
reinterpret_cast<const float*>(tensors[1].Data()) +
|
||||
bs * tensors[1].shape[1] * tensors[1].shape[2] * tensors[1].shape[3];
|
||||
cv::Mat mask_proto =
|
||||
cv::Mat(tensors[1].shape[1], tensors[1].shape[2] * tensors[1].shape[3],
|
||||
CV_32FC(1), const_cast<float*>(data_mask));
|
||||
// vector to cv::Mat for MatMul
|
||||
// after push_back, Mat of m*n becomes (m + 1) * n
|
||||
cv::Mat mask_proposals;
|
||||
for (size_t i = 0; i < index.size(); ++i) {
|
||||
mask_proposals.push_back(cv::Mat(mask_embeddings[index[i]]).t());
|
||||
}
|
||||
cv::Mat matmul_result = (mask_proposals * mask_proto).t();
|
||||
cv::Mat masks = matmul_result.reshape(
|
||||
(*results)[bs].boxes.size(), {static_cast<int>(tensors[1].shape[2]),
|
||||
static_cast<int>(tensors[1].shape[3])});
|
||||
// split for boxes nums
|
||||
std::vector<cv::Mat> mask_channels;
|
||||
cv::split(masks, mask_channels);
|
||||
|
||||
// scale the boxes to the origin image shape
|
||||
auto iter_out = ims_info[bs].find("output_shape");
|
||||
auto iter_ipt = ims_info[bs].find("input_shape");
|
||||
FDASSERT(iter_out != ims_info[bs].end() && iter_ipt != ims_info[bs].end(),
|
||||
"Cannot find input_shape or output_shape from im_info.");
|
||||
float out_h = iter_out->second[0];
|
||||
float out_w = iter_out->second[1];
|
||||
float ipt_h = iter_ipt->second[0];
|
||||
float ipt_w = iter_ipt->second[1];
|
||||
float scale = std::min(out_h / ipt_h, out_w / ipt_w);
|
||||
float pad_h = (out_h - ipt_h * scale) / 2;
|
||||
float pad_w = (out_w - ipt_w * scale) / 2;
|
||||
// for mask
|
||||
float pad_h_mask = (float)pad_h / out_h * tensors[1].shape[2];
|
||||
float pad_w_mask = (float)pad_w / out_w * tensors[1].shape[3];
|
||||
for (size_t i = 0; i < (*results)[bs].boxes.size(); ++i) {
|
||||
int32_t label_id = ((*results)[bs].label_ids)[i];
|
||||
// clip box
|
||||
(*results)[bs].boxes[i][0] =
|
||||
(*results)[bs].boxes[i][0] - max_wh_ * label_id;
|
||||
(*results)[bs].boxes[i][1] =
|
||||
(*results)[bs].boxes[i][1] - max_wh_ * label_id;
|
||||
(*results)[bs].boxes[i][2] =
|
||||
(*results)[bs].boxes[i][2] - max_wh_ * label_id;
|
||||
(*results)[bs].boxes[i][3] =
|
||||
(*results)[bs].boxes[i][3] - max_wh_ * label_id;
|
||||
(*results)[bs].boxes[i][0] =
|
||||
std::max(((*results)[bs].boxes[i][0] - pad_w) / scale, 0.0f);
|
||||
(*results)[bs].boxes[i][1] =
|
||||
std::max(((*results)[bs].boxes[i][1] - pad_h) / scale, 0.0f);
|
||||
(*results)[bs].boxes[i][2] =
|
||||
std::max(((*results)[bs].boxes[i][2] - pad_w) / scale, 0.0f);
|
||||
(*results)[bs].boxes[i][3] =
|
||||
std::max(((*results)[bs].boxes[i][3] - pad_h) / scale, 0.0f);
|
||||
(*results)[bs].boxes[i][0] = std::min((*results)[bs].boxes[i][0], ipt_w);
|
||||
(*results)[bs].boxes[i][1] = std::min((*results)[bs].boxes[i][1], ipt_h);
|
||||
(*results)[bs].boxes[i][2] = std::min((*results)[bs].boxes[i][2], ipt_w);
|
||||
(*results)[bs].boxes[i][3] = std::min((*results)[bs].boxes[i][3], ipt_h);
|
||||
// deal with mask
|
||||
cv::Mat dest, mask;
|
||||
// sigmoid
|
||||
cv::exp(-mask_channels[i], dest);
|
||||
dest = 1.0 / (1.0 + dest);
|
||||
// crop mask for feature map
|
||||
int x1 = static_cast<int>(pad_w_mask);
|
||||
int y1 = static_cast<int>(pad_h_mask);
|
||||
int x2 = static_cast<int>(tensors[1].shape[3] - pad_w_mask);
|
||||
int y2 = static_cast<int>(tensors[1].shape[2] - pad_h_mask);
|
||||
cv::Rect roi(x1, y1, x2 - x1, y2 - y1);
|
||||
dest = dest(roi);
|
||||
cv::resize(dest, mask, cv::Size(ipt_w, ipt_h), 0, 0, cv::INTER_LINEAR);
|
||||
// crop mask for source img
|
||||
int x1_src = static_cast<int>(round((*results)[bs].boxes[i][0]));
|
||||
int y1_src = static_cast<int>(round((*results)[bs].boxes[i][1]));
|
||||
int x2_src = static_cast<int>(round((*results)[bs].boxes[i][2]));
|
||||
int y2_src = static_cast<int>(round((*results)[bs].boxes[i][3]));
|
||||
cv::Rect roi_src(x1_src, y1_src, x2_src - x1_src, y2_src - y1_src);
|
||||
mask = mask(roi_src);
|
||||
mask = mask > mask_threshold_;
|
||||
// save mask in DetectionResult
|
||||
int keep_mask_h = y2_src - y1_src;
|
||||
int keep_mask_w = x2_src - x1_src;
|
||||
int keep_mask_numel = keep_mask_h * keep_mask_w;
|
||||
(*results)[bs].masks[i].Resize(keep_mask_numel);
|
||||
(*results)[bs].masks[i].shape = {keep_mask_h, keep_mask_w};
|
||||
uint8_t* keep_mask_ptr =
|
||||
reinterpret_cast<uint8_t*>((*results)[bs].masks[i].Data());
|
||||
std::memcpy(keep_mask_ptr, reinterpret_cast<uint8_t*>(mask.ptr()),
|
||||
keep_mask_numel * sizeof(uint8_t));
|
||||
}
|
||||
}
|
||||
return true;
|
||||
}
|
||||
|
||||
} // namespace detection
|
||||
} // namespace vision
|
||||
} // namespace fastdeploy
|
||||
79
fastdeploy/vision/detection/contrib/yolov5seg/postprocessor.h
Executable file
79
fastdeploy/vision/detection/contrib/yolov5seg/postprocessor.h
Executable file
@@ -0,0 +1,79 @@
|
||||
// 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/vision/common/result.h"
|
||||
|
||||
namespace fastdeploy {
|
||||
namespace vision {
|
||||
|
||||
namespace detection {
|
||||
/*! @brief Postprocessor object for YOLOv5Seg serials model.
|
||||
*/
|
||||
class FASTDEPLOY_DECL YOLOv5SegPostprocessor {
|
||||
public:
|
||||
/** \brief Create a postprocessor instance for YOLOv5Seg serials model
|
||||
*/
|
||||
YOLOv5SegPostprocessor();
|
||||
|
||||
/** \brief Process the result of runtime and fill to DetectionResult structure
|
||||
*
|
||||
* \param[in] tensors The inference result from runtime
|
||||
* \param[in] result The output result of detection
|
||||
* \param[in] ims_info The shape info list, record input_shape and output_shape
|
||||
* \return true if the postprocess successed, otherwise false
|
||||
*/
|
||||
bool Run(const std::vector<FDTensor>& tensors,
|
||||
std::vector<DetectionResult>* results,
|
||||
const std::vector<std::map<std::string, std::array<float, 2>>>& ims_info);
|
||||
|
||||
/// Set conf_threshold, default 0.25
|
||||
void SetConfThreshold(const float& conf_threshold) {
|
||||
conf_threshold_ = conf_threshold;
|
||||
}
|
||||
|
||||
/// Get conf_threshold, default 0.25
|
||||
float GetConfThreshold() const { return conf_threshold_; }
|
||||
|
||||
/// Set nms_threshold, default 0.5
|
||||
void SetNMSThreshold(const float& nms_threshold) {
|
||||
nms_threshold_ = nms_threshold;
|
||||
}
|
||||
|
||||
/// Get nms_threshold, default 0.5
|
||||
float GetNMSThreshold() const { return nms_threshold_; }
|
||||
|
||||
/// Set multi_label, set true for eval, default true
|
||||
void SetMultiLabel(bool multi_label) {
|
||||
multi_label_ = multi_label;
|
||||
}
|
||||
|
||||
/// Get multi_label, default true
|
||||
bool GetMultiLabel() const { return multi_label_; }
|
||||
|
||||
protected:
|
||||
float conf_threshold_;
|
||||
float nms_threshold_;
|
||||
bool multi_label_;
|
||||
float max_wh_;
|
||||
// channel nums of masks
|
||||
int mask_nums_;
|
||||
// mask threshold
|
||||
float mask_threshold_;
|
||||
};
|
||||
|
||||
} // namespace detection
|
||||
} // namespace vision
|
||||
} // namespace fastdeploy
|
||||
116
fastdeploy/vision/detection/contrib/yolov5seg/preprocessor.cc
Normal file
116
fastdeploy/vision/detection/contrib/yolov5seg/preprocessor.cc
Normal file
@@ -0,0 +1,116 @@
|
||||
// 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/detection/contrib/yolov5seg/preprocessor.h"
|
||||
#include "fastdeploy/function/concat.h"
|
||||
|
||||
namespace fastdeploy {
|
||||
namespace vision {
|
||||
namespace detection {
|
||||
|
||||
YOLOv5SegPreprocessor::YOLOv5SegPreprocessor() {
|
||||
size_ = {640, 640};
|
||||
padding_value_ = {114.0, 114.0, 114.0};
|
||||
is_mini_pad_ = false;
|
||||
is_no_pad_ = false;
|
||||
is_scale_up_ = true;
|
||||
stride_ = 32;
|
||||
max_wh_ = 7680.0;
|
||||
}
|
||||
|
||||
void YOLOv5SegPreprocessor::LetterBox(FDMat* mat) {
|
||||
float scale =
|
||||
std::min(size_[1] * 1.0 / mat->Height(), size_[0] * 1.0 / mat->Width());
|
||||
if (!is_scale_up_) {
|
||||
scale = std::min(scale, 1.0f);
|
||||
}
|
||||
|
||||
int resize_h = int(round(mat->Height() * scale));
|
||||
int resize_w = int(round(mat->Width() * scale));
|
||||
|
||||
int pad_w = size_[0] - resize_w;
|
||||
int pad_h = size_[1] - resize_h;
|
||||
if (is_mini_pad_) {
|
||||
pad_h = pad_h % stride_;
|
||||
pad_w = pad_w % stride_;
|
||||
} else if (is_no_pad_) {
|
||||
pad_h = 0;
|
||||
pad_w = 0;
|
||||
resize_h = size_[1];
|
||||
resize_w = size_[0];
|
||||
}
|
||||
if (std::fabs(scale - 1.0f) > 1e-06) {
|
||||
Resize::Run(mat, resize_w, resize_h);
|
||||
}
|
||||
if (pad_h > 0 || pad_w > 0) {
|
||||
float half_h = pad_h * 1.0 / 2;
|
||||
int top = int(round(half_h - 0.1));
|
||||
int bottom = int(round(half_h + 0.1));
|
||||
float half_w = pad_w * 1.0 / 2;
|
||||
int left = int(round(half_w - 0.1));
|
||||
int right = int(round(half_w + 0.1));
|
||||
Pad::Run(mat, top, bottom, left, right, padding_value_);
|
||||
}
|
||||
}
|
||||
|
||||
bool YOLOv5SegPreprocessor::Preprocess(FDMat* mat, FDTensor* output,
|
||||
std::map<std::string, std::array<float, 2>>* im_info) {
|
||||
// Record the shape of image and the shape of preprocessed image
|
||||
(*im_info)["input_shape"] = {static_cast<float>(mat->Height()),
|
||||
static_cast<float>(mat->Width())};
|
||||
// yolov5seg's preprocess steps
|
||||
// 1. letterbox
|
||||
// 2. convert_and_permute(swap_rb=true)
|
||||
LetterBox(mat);
|
||||
std::vector<float> alpha = {1.0f / 255.0f, 1.0f / 255.0f, 1.0f / 255.0f};
|
||||
std::vector<float> beta = {0.0f, 0.0f, 0.0f};
|
||||
ConvertAndPermute::Run(mat, alpha, beta, true);
|
||||
|
||||
// Record output shape of preprocessed image
|
||||
(*im_info)["output_shape"] = {static_cast<float>(mat->Height()),
|
||||
static_cast<float>(mat->Width())};
|
||||
|
||||
mat->ShareWithTensor(output);
|
||||
output->ExpandDim(0); // reshape to n, h, w, c
|
||||
return true;
|
||||
}
|
||||
|
||||
bool YOLOv5SegPreprocessor::Run(std::vector<FDMat>* images, std::vector<FDTensor>* outputs,
|
||||
std::vector<std::map<std::string, std::array<float, 2>>>* ims_info) {
|
||||
if (images->size() == 0) {
|
||||
FDERROR << "The size of input images should be greater than 0." << std::endl;
|
||||
return false;
|
||||
}
|
||||
ims_info->resize(images->size());
|
||||
outputs->resize(1);
|
||||
// Concat all the preprocessed data to a batch tensor
|
||||
std::vector<FDTensor> tensors(images->size());
|
||||
for (size_t i = 0; i < images->size(); ++i) {
|
||||
if (!Preprocess(&(*images)[i], &tensors[i], &(*ims_info)[i])) {
|
||||
FDERROR << "Failed to preprocess input image." << std::endl;
|
||||
return false;
|
||||
}
|
||||
}
|
||||
|
||||
if (tensors.size() == 1) {
|
||||
(*outputs)[0] = std::move(tensors[0]);
|
||||
} else {
|
||||
function::Concat(tensors, &((*outputs)[0]), 0);
|
||||
}
|
||||
return true;
|
||||
}
|
||||
|
||||
} // namespace detection
|
||||
} // namespace vision
|
||||
} // namespace fastdeploy
|
||||
113
fastdeploy/vision/detection/contrib/yolov5seg/preprocessor.h
Normal file
113
fastdeploy/vision/detection/contrib/yolov5seg/preprocessor.h
Normal file
@@ -0,0 +1,113 @@
|
||||
// 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/vision/common/result.h"
|
||||
|
||||
namespace fastdeploy {
|
||||
namespace vision {
|
||||
|
||||
namespace detection {
|
||||
/*! @brief Preprocessor object for YOLOv5Seg serials model.
|
||||
*/
|
||||
class FASTDEPLOY_DECL YOLOv5SegPreprocessor {
|
||||
public:
|
||||
/** \brief Create a preprocessor instance for YOLOv5Seg serials model
|
||||
*/
|
||||
YOLOv5SegPreprocessor();
|
||||
|
||||
/** \brief Process the input image and prepare input tensors for runtime
|
||||
*
|
||||
* \param[in] images The input image data list, all the elements are returned by cv::imread()
|
||||
* \param[in] outputs The output tensors which will feed in runtime
|
||||
* \param[in] ims_info The shape info list, record input_shape and output_shape
|
||||
* \return true if the preprocess successed, otherwise false
|
||||
*/
|
||||
bool Run(std::vector<FDMat>* images, std::vector<FDTensor>* outputs,
|
||||
std::vector<std::map<std::string, std::array<float, 2>>>* ims_info);
|
||||
|
||||
/// Set target size, tuple of (width, height), default size = {640, 640}
|
||||
void SetSize(const std::vector<int>& size) { size_ = size; }
|
||||
|
||||
/// Get target size, tuple of (width, height), default size = {640, 640}
|
||||
std::vector<int> GetSize() const { return size_; }
|
||||
|
||||
/// Set padding value, size should be the same as channels
|
||||
void SetPaddingValue(const std::vector<float>& padding_value) {
|
||||
padding_value_ = padding_value;
|
||||
}
|
||||
|
||||
/// Get padding value, size should be the same as channels
|
||||
std::vector<float> GetPaddingValue() const { return padding_value_; }
|
||||
|
||||
/// Set is_scale_up, if is_scale_up is false, the input image only
|
||||
/// can be zoom out, the maximum resize scale cannot exceed 1.0, default true
|
||||
void SetScaleUp(bool is_scale_up) {
|
||||
is_scale_up_ = is_scale_up;
|
||||
}
|
||||
|
||||
/// Get is_scale_up, default true
|
||||
bool GetScaleUp() const { return is_scale_up_; }
|
||||
|
||||
/// Set is_mini_pad, pad to the minimum rectange
|
||||
/// which height and width is times of stride
|
||||
void SetMiniPad(bool is_mini_pad) {
|
||||
is_mini_pad_ = is_mini_pad;
|
||||
}
|
||||
|
||||
/// Get is_mini_pad, default false
|
||||
bool GetMiniPad() const { return is_mini_pad_; }
|
||||
|
||||
/// Set padding stride, only for mini_pad mode
|
||||
void SetStride(int stride) {
|
||||
stride_ = stride;
|
||||
}
|
||||
|
||||
/// Get padding stride, default 32
|
||||
bool GetStride() const { return stride_; }
|
||||
|
||||
protected:
|
||||
bool Preprocess(FDMat* mat, FDTensor* output,
|
||||
std::map<std::string, std::array<float, 2>>* im_info);
|
||||
|
||||
void LetterBox(FDMat* mat);
|
||||
|
||||
// target size, tuple of (width, height), default size = {640, 640}
|
||||
std::vector<int> size_;
|
||||
|
||||
// padding value, size should be the same as channels
|
||||
std::vector<float> padding_value_;
|
||||
|
||||
// only pad to the minimum rectange which height and width is times of stride
|
||||
bool is_mini_pad_;
|
||||
|
||||
// while is_mini_pad = false and is_no_pad = true,
|
||||
// will resize the image to the set size
|
||||
bool is_no_pad_;
|
||||
|
||||
// if is_scale_up is false, the input image only can be zoom out,
|
||||
// the maximum resize scale cannot exceed 1.0
|
||||
bool is_scale_up_;
|
||||
|
||||
// padding stride, for is_mini_pad
|
||||
int stride_;
|
||||
|
||||
// for offseting the boxes by classes when using NMS
|
||||
float max_wh_;
|
||||
};
|
||||
|
||||
} // namespace detection
|
||||
} // namespace vision
|
||||
} // namespace fastdeploy
|
||||
80
fastdeploy/vision/detection/contrib/yolov5seg/yolov5seg.cc
Normal file
80
fastdeploy/vision/detection/contrib/yolov5seg/yolov5seg.cc
Normal file
@@ -0,0 +1,80 @@
|
||||
// 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/detection/contrib/yolov5seg/yolov5seg.h"
|
||||
|
||||
namespace fastdeploy {
|
||||
namespace vision {
|
||||
namespace detection {
|
||||
|
||||
YOLOv5Seg::YOLOv5Seg(const std::string& model_file, const std::string& params_file,
|
||||
const RuntimeOption& custom_option,
|
||||
const ModelFormat& model_format) {
|
||||
if (model_format == ModelFormat::ONNX) {
|
||||
valid_cpu_backends = {Backend::OPENVINO, Backend::ORT};
|
||||
valid_gpu_backends = {Backend::ORT, Backend::TRT};
|
||||
} else {
|
||||
valid_cpu_backends = {Backend::PDINFER, Backend::ORT, Backend::LITE};
|
||||
valid_gpu_backends = {Backend::PDINFER, Backend::ORT, Backend::TRT};
|
||||
}
|
||||
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 YOLOv5Seg::Initialize() {
|
||||
if (!InitRuntime()) {
|
||||
FDERROR << "Failed to initialize fastdeploy backend." << std::endl;
|
||||
return false;
|
||||
}
|
||||
return true;
|
||||
}
|
||||
|
||||
bool YOLOv5Seg::Predict(const cv::Mat& im, DetectionResult* result) {
|
||||
std::vector<DetectionResult> results;
|
||||
if (!BatchPredict({im}, &results)) {
|
||||
return false;
|
||||
}
|
||||
*result = std::move(results[0]);
|
||||
return true;
|
||||
}
|
||||
|
||||
bool YOLOv5Seg::BatchPredict(const std::vector<cv::Mat>& images, std::vector<DetectionResult>* results) {
|
||||
std::vector<std::map<std::string, std::array<float, 2>>> ims_info;
|
||||
std::vector<FDMat> fd_images = WrapMat(images);
|
||||
|
||||
if (!preprocessor_.Run(&fd_images, &reused_input_tensors_, &ims_info)) {
|
||||
FDERROR << "Failed to preprocess the input image." << std::endl;
|
||||
return false;
|
||||
}
|
||||
|
||||
reused_input_tensors_[0].name = InputInfoOfRuntime(0).name;
|
||||
if (!Infer(reused_input_tensors_, &reused_output_tensors_)) {
|
||||
FDERROR << "Failed to inference by runtime." << std::endl;
|
||||
return false;
|
||||
}
|
||||
|
||||
if (!postprocessor_.Run(reused_output_tensors_, results, ims_info)) {
|
||||
FDERROR << "Failed to postprocess the inference results by runtime." << std::endl;
|
||||
return false;
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
} // namespace detection
|
||||
} // namespace vision
|
||||
} // namespace fastdeploy
|
||||
76
fastdeploy/vision/detection/contrib/yolov5seg/yolov5seg.h
Executable file
76
fastdeploy/vision/detection/contrib/yolov5seg/yolov5seg.h
Executable file
@@ -0,0 +1,76 @@
|
||||
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. //NOLINT
|
||||
//
|
||||
// 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/fastdeploy_model.h"
|
||||
#include "fastdeploy/vision/detection/contrib/yolov5seg/preprocessor.h"
|
||||
#include "fastdeploy/vision/detection/contrib/yolov5seg/postprocessor.h"
|
||||
|
||||
namespace fastdeploy {
|
||||
namespace vision {
|
||||
namespace detection {
|
||||
/*! @brief YOLOv5Seg model object used when to load a YOLOv5Seg model exported by YOLOv5.
|
||||
*/
|
||||
class FASTDEPLOY_DECL YOLOv5Seg : public FastDeployModel {
|
||||
public:
|
||||
/** \brief Set path of model file and the configuration of runtime.
|
||||
*
|
||||
* \param[in] model_file Path of model file, e.g ./yolov5seg.onnx
|
||||
* \param[in] params_file Path of parameter file, e.g ppyoloe/model.pdiparams, if the model format is ONNX, this parameter will be ignored
|
||||
* \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 ONNX format
|
||||
*/
|
||||
YOLOv5Seg(const std::string& model_file, const std::string& params_file = "",
|
||||
const RuntimeOption& custom_option = RuntimeOption(),
|
||||
const ModelFormat& model_format = ModelFormat::ONNX);
|
||||
|
||||
std::string ModelName() const { return "yolov5seg"; }
|
||||
|
||||
/** \brief Predict the detection result for an input image
|
||||
*
|
||||
* \param[in] img The input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format
|
||||
* \param[in] result The output detection result will be writen to this structure
|
||||
* \return true if the prediction successed, otherwise false
|
||||
*/
|
||||
virtual bool Predict(const cv::Mat& img, DetectionResult* result);
|
||||
|
||||
/** \brief Predict the detection results for a batch of input images
|
||||
*
|
||||
* \param[in] imgs, The input image list, each element comes from cv::imread()
|
||||
* \param[in] results The output detection result list
|
||||
* \return true if the prediction successed, otherwise false
|
||||
*/
|
||||
virtual bool BatchPredict(const std::vector<cv::Mat>& imgs,
|
||||
std::vector<DetectionResult>* results);
|
||||
|
||||
/// Get preprocessor reference of YOLOv5Seg
|
||||
virtual YOLOv5SegPreprocessor& GetPreprocessor() {
|
||||
return preprocessor_;
|
||||
}
|
||||
|
||||
/// Get postprocessor reference of YOLOv5Seg
|
||||
virtual YOLOv5SegPostprocessor& GetPostprocessor() {
|
||||
return postprocessor_;
|
||||
}
|
||||
|
||||
protected:
|
||||
bool Initialize();
|
||||
YOLOv5SegPreprocessor preprocessor_;
|
||||
YOLOv5SegPostprocessor postprocessor_;
|
||||
};
|
||||
|
||||
} // namespace detection
|
||||
} // namespace vision
|
||||
} // namespace fastdeploy
|
||||
90
fastdeploy/vision/detection/contrib/yolov5seg/yolov5seg_pybind.cc
Executable file
90
fastdeploy/vision/detection/contrib/yolov5seg/yolov5seg_pybind.cc
Executable file
@@ -0,0 +1,90 @@
|
||||
// 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 BindYOLOv5Seg(pybind11::module& m) {
|
||||
pybind11::class_<vision::detection::YOLOv5SegPreprocessor>(
|
||||
m, "YOLOv5SegPreprocessor")
|
||||
.def(pybind11::init<>())
|
||||
.def("run", [](vision::detection::YOLOv5SegPreprocessor& self, std::vector<pybind11::array>& im_list) {
|
||||
std::vector<vision::FDMat> images;
|
||||
for (size_t i = 0; i < im_list.size(); ++i) {
|
||||
images.push_back(vision::WrapMat(PyArrayToCvMat(im_list[i])));
|
||||
}
|
||||
std::vector<FDTensor> outputs;
|
||||
std::vector<std::map<std::string, std::array<float, 2>>> ims_info;
|
||||
if (!self.Run(&images, &outputs, &ims_info)) {
|
||||
throw std::runtime_error("Failed to preprocess the input data in PaddleClasPreprocessor.");
|
||||
}
|
||||
for (size_t i = 0; i < outputs.size(); ++i) {
|
||||
outputs[i].StopSharing();
|
||||
}
|
||||
return make_pair(outputs, ims_info);
|
||||
})
|
||||
.def_property("size", &vision::detection::YOLOv5SegPreprocessor::GetSize, &vision::detection::YOLOv5SegPreprocessor::SetSize)
|
||||
.def_property("padding_value", &vision::detection::YOLOv5SegPreprocessor::GetPaddingValue, &vision::detection::YOLOv5SegPreprocessor::SetPaddingValue)
|
||||
.def_property("is_scale_up", &vision::detection::YOLOv5SegPreprocessor::GetScaleUp, &vision::detection::YOLOv5SegPreprocessor::SetScaleUp)
|
||||
.def_property("is_mini_pad", &vision::detection::YOLOv5SegPreprocessor::GetMiniPad, &vision::detection::YOLOv5SegPreprocessor::SetMiniPad)
|
||||
.def_property("stride", &vision::detection::YOLOv5SegPreprocessor::GetStride, &vision::detection::YOLOv5SegPreprocessor::SetStride);
|
||||
|
||||
pybind11::class_<vision::detection::YOLOv5SegPostprocessor>(
|
||||
m, "YOLOv5SegPostprocessor")
|
||||
.def(pybind11::init<>())
|
||||
.def("run", [](vision::detection::YOLOv5SegPostprocessor& self, std::vector<FDTensor>& inputs,
|
||||
const std::vector<std::map<std::string, std::array<float, 2>>>& ims_info) {
|
||||
std::vector<vision::DetectionResult> results;
|
||||
if (!self.Run(inputs, &results, ims_info)) {
|
||||
throw std::runtime_error("Failed to postprocess the runtime result in YOLOv5SegPostprocessor.");
|
||||
}
|
||||
return results;
|
||||
})
|
||||
.def("run", [](vision::detection::YOLOv5SegPostprocessor& self, std::vector<pybind11::array>& input_array,
|
||||
const std::vector<std::map<std::string, std::array<float, 2>>>& ims_info) {
|
||||
std::vector<vision::DetectionResult> results;
|
||||
std::vector<FDTensor> inputs;
|
||||
PyArrayToTensorList(input_array, &inputs, /*share_buffer=*/true);
|
||||
if (!self.Run(inputs, &results, ims_info)) {
|
||||
throw std::runtime_error("Failed to postprocess the runtime result in YOLOv5SegPostprocessor.");
|
||||
}
|
||||
return results;
|
||||
})
|
||||
.def_property("conf_threshold", &vision::detection::YOLOv5SegPostprocessor::GetConfThreshold, &vision::detection::YOLOv5SegPostprocessor::SetConfThreshold)
|
||||
.def_property("nms_threshold", &vision::detection::YOLOv5SegPostprocessor::GetNMSThreshold, &vision::detection::YOLOv5SegPostprocessor::SetNMSThreshold)
|
||||
.def_property("multi_label", &vision::detection::YOLOv5SegPostprocessor::GetMultiLabel, &vision::detection::YOLOv5SegPostprocessor::SetMultiLabel);
|
||||
|
||||
pybind11::class_<vision::detection::YOLOv5Seg, FastDeployModel>(m, "YOLOv5Seg")
|
||||
.def(pybind11::init<std::string, std::string, RuntimeOption,
|
||||
ModelFormat>())
|
||||
.def("predict",
|
||||
[](vision::detection::YOLOv5Seg& self, pybind11::array& data) {
|
||||
auto mat = PyArrayToCvMat(data);
|
||||
vision::DetectionResult res;
|
||||
self.Predict(mat, &res);
|
||||
return res;
|
||||
})
|
||||
.def("batch_predict", [](vision::detection::YOLOv5Seg& self, std::vector<pybind11::array>& data) {
|
||||
std::vector<cv::Mat> images;
|
||||
for (size_t i = 0; i < data.size(); ++i) {
|
||||
images.push_back(PyArrayToCvMat(data[i]));
|
||||
}
|
||||
std::vector<vision::DetectionResult> results;
|
||||
self.BatchPredict(images, &results);
|
||||
return results;
|
||||
})
|
||||
.def_property_readonly("preprocessor", &vision::detection::YOLOv5Seg::GetPreprocessor)
|
||||
.def_property_readonly("postprocessor", &vision::detection::YOLOv5Seg::GetPostprocessor);
|
||||
}
|
||||
} // namespace fastdeploy
|
||||
2
fastdeploy/vision/detection/detection_pybind.cc
Normal file → Executable file
2
fastdeploy/vision/detection/detection_pybind.cc
Normal file → Executable file
@@ -22,6 +22,7 @@ void BindYOLOR(pybind11::module& m);
|
||||
void BindYOLOv6(pybind11::module& m);
|
||||
void BindYOLOv5Lite(pybind11::module& m);
|
||||
void BindYOLOv5(pybind11::module& m);
|
||||
void BindYOLOv5Seg(pybind11::module& m);
|
||||
void BindFastestDet(pybind11::module& m);
|
||||
void BindYOLOX(pybind11::module& m);
|
||||
void BindNanoDetPlus(pybind11::module& m);
|
||||
@@ -40,6 +41,7 @@ void BindDetection(pybind11::module& m) {
|
||||
BindYOLOv6(detection_module);
|
||||
BindYOLOv5Lite(detection_module);
|
||||
BindYOLOv5(detection_module);
|
||||
BindYOLOv5Seg(detection_module);
|
||||
BindFastestDet(detection_module);
|
||||
BindYOLOX(detection_module);
|
||||
BindNanoDetPlus(detection_module);
|
||||
|
||||
22
fastdeploy/vision/detection/ppdet/postprocessor.cc
Normal file → Executable file
22
fastdeploy/vision/detection/ppdet/postprocessor.cc
Normal file → Executable file
@@ -32,30 +32,30 @@ bool PaddleDetPostprocessor::ProcessMask(
|
||||
int64_t out_mask_h = shape[1];
|
||||
int64_t out_mask_w = shape[2];
|
||||
int64_t out_mask_numel = shape[1] * shape[2];
|
||||
const int32_t* data = reinterpret_cast<const int32_t*>(tensor.CpuData());
|
||||
const uint8_t* data = reinterpret_cast<const uint8_t*>(tensor.CpuData());
|
||||
int index = 0;
|
||||
|
||||
for (int i = 0; i < results->size(); ++i) {
|
||||
(*results)[i].contain_masks = true;
|
||||
(*results)[i].masks.resize((*results)[i].boxes.size());
|
||||
for (int j = 0; j < (*results)[i].boxes.size(); ++j) {
|
||||
int x1 = static_cast<int>((*results)[i].boxes[j][0]);
|
||||
int y1 = static_cast<int>((*results)[i].boxes[j][1]);
|
||||
int x2 = static_cast<int>((*results)[i].boxes[j][2]);
|
||||
int y2 = static_cast<int>((*results)[i].boxes[j][3]);
|
||||
int x1 = static_cast<int>(round((*results)[i].boxes[j][0]));
|
||||
int y1 = static_cast<int>(round((*results)[i].boxes[j][1]));
|
||||
int x2 = static_cast<int>(round((*results)[i].boxes[j][2]));
|
||||
int y2 = static_cast<int>(round((*results)[i].boxes[j][3]));
|
||||
int keep_mask_h = y2 - y1;
|
||||
int keep_mask_w = x2 - x1;
|
||||
int keep_mask_numel = keep_mask_h * keep_mask_w;
|
||||
(*results)[i].masks[j].Resize(keep_mask_numel);
|
||||
(*results)[i].masks[j].shape = {keep_mask_h, keep_mask_w};
|
||||
const int32_t* current_ptr = data + index * out_mask_numel;
|
||||
const uint8_t* current_ptr = data + index * out_mask_numel;
|
||||
|
||||
int32_t* keep_mask_ptr =
|
||||
reinterpret_cast<int32_t*>((*results)[i].masks[j].Data());
|
||||
uint8_t* keep_mask_ptr =
|
||||
reinterpret_cast<uint8_t*>((*results)[i].masks[j].Data());
|
||||
for (int row = y1; row < y2; ++row) {
|
||||
size_t keep_nbytes_in_col = keep_mask_w * sizeof(int32_t);
|
||||
const int32_t* out_row_start_ptr = current_ptr + row * out_mask_w + x1;
|
||||
int32_t* keep_row_start_ptr = keep_mask_ptr + (row - y1) * keep_mask_w;
|
||||
size_t keep_nbytes_in_col = keep_mask_w * sizeof(uint8_t);
|
||||
const uint8_t* out_row_start_ptr = current_ptr + row * out_mask_w + x1;
|
||||
uint8_t* keep_row_start_ptr = keep_mask_ptr + (row - y1) * keep_mask_w;
|
||||
std::memcpy(keep_row_start_ptr, out_row_start_ptr, keep_nbytes_in_col);
|
||||
}
|
||||
index += 1;
|
||||
|
||||
@@ -21,7 +21,19 @@ namespace utils {
|
||||
|
||||
// The implementation refers to
|
||||
// https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.4/deploy/cpp/src/utils.cc
|
||||
void NMS(DetectionResult* result, float iou_threshold) {
|
||||
void NMS(DetectionResult* result, float iou_threshold,
|
||||
std::vector<int>* index) {
|
||||
// get sorted score indices
|
||||
std::vector<int> sorted_indices;
|
||||
if (index != nullptr) {
|
||||
std::map<float, int, std::greater<float>> score_map;
|
||||
for (size_t i = 0; i < result->scores.size(); ++i) {
|
||||
score_map.insert(std::pair<float, int>(result->scores[i], i));
|
||||
}
|
||||
for (auto iter : score_map) {
|
||||
sorted_indices.push_back(iter.second);
|
||||
}
|
||||
}
|
||||
utils::SortDetectionResult(result);
|
||||
|
||||
std::vector<float> area_of_boxes(result->boxes.size());
|
||||
@@ -63,6 +75,9 @@ void NMS(DetectionResult* result, float iou_threshold) {
|
||||
result->boxes.emplace_back(backup.boxes[i]);
|
||||
result->scores.push_back(backup.scores[i]);
|
||||
result->label_ids.push_back(backup.label_ids[i]);
|
||||
if (index != nullptr) {
|
||||
index->push_back(sorted_indices[i]);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
@@ -59,7 +59,8 @@ std::vector<int32_t> TopKIndices(const T* array, int array_size, int topk) {
|
||||
return res;
|
||||
}
|
||||
|
||||
void NMS(DetectionResult* output, float iou_threshold = 0.5);
|
||||
void NMS(DetectionResult* output, float iou_threshold = 0.5,
|
||||
std::vector<int>* index = nullptr);
|
||||
|
||||
void NMS(FaceDetectionResult* result, float iou_threshold = 0.5);
|
||||
|
||||
|
||||
2
fastdeploy/vision/vision_pybind.cc
Normal file → Executable file
2
fastdeploy/vision/vision_pybind.cc
Normal file → Executable file
@@ -46,7 +46,7 @@ void BindVision(pybind11::module& m) {
|
||||
"vision::Mask pickle with invalid state!");
|
||||
|
||||
vision::Mask m;
|
||||
m.data = t[0].cast<std::vector<int32_t>>();
|
||||
m.data = t[0].cast<std::vector<uint8_t>>();
|
||||
m.shape = t[1].cast<std::vector<int64_t>>();
|
||||
|
||||
return m;
|
||||
|
||||
@@ -39,10 +39,10 @@ cv::Mat VisDetection(const cv::Mat& im, const DetectionResult& result,
|
||||
if (result.scores[i] < score_threshold) {
|
||||
continue;
|
||||
}
|
||||
int x1 = static_cast<int>(result.boxes[i][0]);
|
||||
int y1 = static_cast<int>(result.boxes[i][1]);
|
||||
int x2 = static_cast<int>(result.boxes[i][2]);
|
||||
int y2 = static_cast<int>(result.boxes[i][3]);
|
||||
int x1 = static_cast<int>(round(result.boxes[i][0]));
|
||||
int y1 = static_cast<int>(round(result.boxes[i][1]));
|
||||
int x2 = static_cast<int>(round(result.boxes[i][2]));
|
||||
int y2 = static_cast<int>(round(result.boxes[i][3]));
|
||||
int box_h = y2 - y1;
|
||||
int box_w = x2 - x1;
|
||||
int c0 = color_map[3 * result.label_ids[i] + 0];
|
||||
@@ -68,10 +68,10 @@ cv::Mat VisDetection(const cv::Mat& im, const DetectionResult& result,
|
||||
int mask_h = static_cast<int>(result.masks[i].shape[0]);
|
||||
int mask_w = static_cast<int>(result.masks[i].shape[1]);
|
||||
// non-const pointer for cv:Mat constructor
|
||||
int32_t* mask_raw_data = const_cast<int32_t*>(
|
||||
static_cast<const int32_t*>(result.masks[i].Data()));
|
||||
uint8_t* mask_raw_data = const_cast<uint8_t*>(
|
||||
static_cast<const uint8_t*>(result.masks[i].Data()));
|
||||
// only reference to mask data (zero copy)
|
||||
cv::Mat mask(mask_h, mask_w, CV_32SC1, mask_raw_data);
|
||||
cv::Mat mask(mask_h, mask_w, CV_8UC1, mask_raw_data);
|
||||
if ((mask_h != box_h) || (mask_w != box_w)) {
|
||||
cv::resize(mask, mask, cv::Size(box_w, box_h));
|
||||
}
|
||||
@@ -79,7 +79,7 @@ cv::Mat VisDetection(const cv::Mat& im, const DetectionResult& result,
|
||||
int mc0 = 255 - c0 >= 127 ? 255 - c0 : 127;
|
||||
int mc1 = 255 - c1 >= 127 ? 255 - c1 : 127;
|
||||
int mc2 = 255 - c2 >= 127 ? 255 - c2 : 127;
|
||||
int32_t* mask_data = reinterpret_cast<int32_t*>(mask.data);
|
||||
uint8_t* mask_data = reinterpret_cast<uint8_t*>(mask.data);
|
||||
// inplace blending (zero copy)
|
||||
uchar* vis_im_data = static_cast<uchar*>(vis_im.data);
|
||||
for (size_t i = y1; i < y2; ++i) {
|
||||
|
||||
@@ -19,6 +19,7 @@ from .contrib.scaled_yolov4 import ScaledYOLOv4
|
||||
from .contrib.nanodet_plus import NanoDetPlus
|
||||
from .contrib.yolox import YOLOX
|
||||
from .contrib.yolov5 import *
|
||||
from .contrib.yolov5seg import *
|
||||
from .contrib.fastestdet import *
|
||||
from .contrib.yolov5lite import YOLOv5Lite
|
||||
from .contrib.yolov6 import YOLOv6
|
||||
|
||||
219
python/fastdeploy/vision/detection/contrib/yolov5seg.py
Normal file
219
python/fastdeploy/vision/detection/contrib/yolov5seg.py
Normal file
@@ -0,0 +1,219 @@
|
||||
# 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
|
||||
import logging
|
||||
from .... import FastDeployModel, ModelFormat
|
||||
from .... import c_lib_wrap as C
|
||||
|
||||
|
||||
class YOLOv5SegPreprocessor:
|
||||
def __init__(self):
|
||||
"""Create a preprocessor for YOLOv5Seg
|
||||
"""
|
||||
self._preprocessor = C.vision.detection.YOLOv5SegPreprocessor()
|
||||
|
||||
def run(self, input_ims):
|
||||
"""Preprocess input images for YOLOv5Seg
|
||||
|
||||
:param: input_ims: (list of numpy.ndarray)The input image
|
||||
:return: list of FDTensor
|
||||
"""
|
||||
return self._preprocessor.run(input_ims)
|
||||
|
||||
@property
|
||||
def size(self):
|
||||
"""
|
||||
Argument for image preprocessing step, the preprocess image size, tuple of (width, height), default size = [640, 640]
|
||||
"""
|
||||
return self._preprocessor.size
|
||||
|
||||
@property
|
||||
def padding_value(self):
|
||||
"""
|
||||
padding value for preprocessing, default [114.0, 114.0, 114.0]
|
||||
"""
|
||||
# padding value, size should be the same as channels
|
||||
return self._preprocessor.padding_value
|
||||
|
||||
@property
|
||||
def is_scale_up(self):
|
||||
"""
|
||||
is_scale_up for preprocessing, the input image only can be zoom out, the maximum resize scale cannot exceed 1.0, default true
|
||||
"""
|
||||
return self._preprocessor.is_scale_up
|
||||
|
||||
@property
|
||||
def is_mini_pad(self):
|
||||
"""
|
||||
is_mini_pad for preprocessing, pad to the minimum rectange which height and width is times of stride, default false
|
||||
"""
|
||||
return self._preprocessor.is_mini_pad
|
||||
|
||||
@property
|
||||
def stride(self):
|
||||
"""
|
||||
stride for preprocessing, only for mini_pad mode, default 32
|
||||
"""
|
||||
return self._preprocessor.stride
|
||||
|
||||
@size.setter
|
||||
def size(self, wh):
|
||||
assert isinstance(wh, (list, tuple)),\
|
||||
"The value to set `size` must be type of tuple or list."
|
||||
assert len(wh) == 2,\
|
||||
"The value to set `size` must contatins 2 elements means [width, height], but now it contains {} elements.".format(
|
||||
len(wh))
|
||||
self._preprocessor.size = wh
|
||||
|
||||
@padding_value.setter
|
||||
def padding_value(self, value):
|
||||
assert isinstance(
|
||||
value,
|
||||
list), "The value to set `padding_value` must be type of list."
|
||||
self._preprocessor.padding_value = value
|
||||
|
||||
@is_scale_up.setter
|
||||
def is_scale_up(self, value):
|
||||
assert isinstance(
|
||||
value,
|
||||
bool), "The value to set `is_scale_up` must be type of bool."
|
||||
self._preprocessor.is_scale_up = value
|
||||
|
||||
@is_mini_pad.setter
|
||||
def is_mini_pad(self, value):
|
||||
assert isinstance(
|
||||
value,
|
||||
bool), "The value to set `is_mini_pad` must be type of bool."
|
||||
self._preprocessor.is_mini_pad = value
|
||||
|
||||
@stride.setter
|
||||
def stride(self, value):
|
||||
assert isinstance(
|
||||
stride, int), "The value to set `stride` must be type of int."
|
||||
self._preprocessor.stride = value
|
||||
|
||||
|
||||
class YOLOv5SegPostprocessor:
|
||||
def __init__(self):
|
||||
"""Create a postprocessor for YOLOv5Seg
|
||||
"""
|
||||
self._postprocessor = C.vision.detection.YOLOv5SegPostprocessor()
|
||||
|
||||
def run(self, runtime_results, ims_info):
|
||||
"""Postprocess the runtime results for YOLOv5Seg
|
||||
|
||||
:param: runtime_results: (list of FDTensor)The output FDTensor results from runtime
|
||||
:param: ims_info: (list of dict)Record input_shape and output_shape
|
||||
:return: list of DetectionResult(If the runtime_results is predict by batched samples, the length of this list equals to the batch size)
|
||||
"""
|
||||
return self._postprocessor.run(runtime_results, ims_info)
|
||||
|
||||
@property
|
||||
def conf_threshold(self):
|
||||
"""
|
||||
confidence threshold for postprocessing, default is 0.25
|
||||
"""
|
||||
return self._postprocessor.conf_threshold
|
||||
|
||||
@property
|
||||
def nms_threshold(self):
|
||||
"""
|
||||
nms threshold for postprocessing, default is 0.5
|
||||
"""
|
||||
return self._postprocessor.nms_threshold
|
||||
|
||||
@property
|
||||
def multi_label(self):
|
||||
"""
|
||||
multi_label for postprocessing, set true for eval, default is True
|
||||
"""
|
||||
return self._postprocessor.multi_label
|
||||
|
||||
@conf_threshold.setter
|
||||
def conf_threshold(self, conf_threshold):
|
||||
assert isinstance(conf_threshold, float),\
|
||||
"The value to set `conf_threshold` must be type of float."
|
||||
self._postprocessor.conf_threshold = conf_threshold
|
||||
|
||||
@nms_threshold.setter
|
||||
def nms_threshold(self, nms_threshold):
|
||||
assert isinstance(nms_threshold, float),\
|
||||
"The value to set `nms_threshold` must be type of float."
|
||||
self._postprocessor.nms_threshold = nms_threshold
|
||||
|
||||
@multi_label.setter
|
||||
def multi_label(self, value):
|
||||
assert isinstance(
|
||||
value,
|
||||
bool), "The value to set `multi_label` must be type of bool."
|
||||
self._postprocessor.multi_label = value
|
||||
|
||||
|
||||
class YOLOv5Seg(FastDeployModel):
|
||||
def __init__(self,
|
||||
model_file,
|
||||
params_file="",
|
||||
runtime_option=None,
|
||||
model_format=ModelFormat.ONNX):
|
||||
"""Load a YOLOv5Seg model exported by YOLOv5.
|
||||
|
||||
:param model_file: (str)Path of model file, e.g ./yolov5s-seg.onnx
|
||||
:param params_file: (str)Path of parameters file, e.g yolox/model.pdiparams, if the model_fomat is ModelFormat.ONNX, this param will be ignored, can be set as empty string
|
||||
: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(YOLOv5Seg, self).__init__(runtime_option)
|
||||
|
||||
self._model = C.vision.detection.YOLOv5Seg(
|
||||
model_file, params_file, self._runtime_option, model_format)
|
||||
assert self.initialized, "YOLOv5Seg initialize failed."
|
||||
|
||||
def predict(self, input_image, conf_threshold=0.25, nms_iou_threshold=0.5):
|
||||
"""Detect an input image
|
||||
|
||||
:param input_image: (numpy.ndarray)The input image data, 3-D array with layout HWC, BGR format
|
||||
:param conf_threshold: confidence threshold for postprocessing, default is 0.25
|
||||
:param nms_iou_threshold: iou threshold for NMS, default is 0.5
|
||||
:return: DetectionResult
|
||||
"""
|
||||
|
||||
self.postprocessor.conf_threshold = conf_threshold
|
||||
self.postprocessor.nms_threshold = nms_iou_threshold
|
||||
return self._model.predict(input_image)
|
||||
|
||||
def batch_predict(self, images):
|
||||
"""Classify a batch of input image
|
||||
|
||||
:param im: (list of numpy.ndarray) The input image list, each element is a 3-D array with layout HWC, BGR format
|
||||
:return list of DetectionResult
|
||||
"""
|
||||
|
||||
return self._model.batch_predict(images)
|
||||
|
||||
@property
|
||||
def preprocessor(self):
|
||||
"""Get YOLOv5SegPreprocessor object of the loaded model
|
||||
|
||||
:return YOLOv5SegPreprocessor
|
||||
"""
|
||||
return self._model.preprocessor
|
||||
|
||||
@property
|
||||
def postprocessor(self):
|
||||
"""Get YOLOv5SegPostprocessor object of the loaded model
|
||||
|
||||
:return YOLOv5SegPostprocessor
|
||||
"""
|
||||
return self._model.postprocessor
|
||||
@@ -61,10 +61,6 @@ def test_detection_mask_rcnn():
|
||||
) < 1e-04, "There's diff in label_ids."
|
||||
|
||||
|
||||
# result = model.predict(im1)
|
||||
# with open("mask_rcnn_baseline.pkl", "wb") as f:
|
||||
# pickle.dump([np.array(result.boxes), np.array(result.scores), np.array(result.label_ids)], f)
|
||||
|
||||
def test_detection_mask_rcnn1():
|
||||
model_url = "https://bj.bcebos.com/paddlehub/fastdeploy/mask_rcnn_r50_1x_coco.tgz"
|
||||
input_url1 = "https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg"
|
||||
@@ -83,14 +79,18 @@ def test_detection_mask_rcnn1():
|
||||
option = rc.test_option
|
||||
option.set_model_path(model_file, params_file)
|
||||
option.use_paddle_infer_backend()
|
||||
runtime = fd.Runtime(option);
|
||||
runtime = fd.Runtime(option)
|
||||
|
||||
# compare diff
|
||||
im1 = cv2.imread("./resources/000000014439.jpg")
|
||||
for i in range(2):
|
||||
im1 = cv2.imread("./resources/000000014439.jpg")
|
||||
input_tensors = preprocessor.run([im1])
|
||||
output_tensors = runtime.infer({"image": input_tensors[0], "scale_factor": input_tensors[1], "im_shape": input_tensors[2]})
|
||||
output_tensors = runtime.infer({
|
||||
"image": input_tensors[0],
|
||||
"scale_factor": input_tensors[1],
|
||||
"im_shape": input_tensors[2]
|
||||
})
|
||||
results = postprocessor.run(output_tensors)
|
||||
result = results[0]
|
||||
|
||||
@@ -114,6 +114,7 @@ def test_detection_mask_rcnn1():
|
||||
assert diff_label_ids[scores > score_threshold].max(
|
||||
) < 1e-04, "There's diff in label_ids."
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
test_detection_mask_rcnn()
|
||||
test_detection_mask_rcnn1()
|
||||
|
||||
220
tests/models/test_yolov5seg.py
Normal file
220
tests/models/test_yolov5seg.py
Normal file
@@ -0,0 +1,220 @@
|
||||
# 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 fastdeploy import ModelFormat
|
||||
import fastdeploy as fd
|
||||
import cv2
|
||||
import os
|
||||
import pickle
|
||||
import numpy as np
|
||||
import runtime_config as rc
|
||||
|
||||
|
||||
def test_detection_yolov5seg():
|
||||
model_url = "https://bj.bcebos.com/paddlehub/fastdeploy/yolov5s-seg.onnx"
|
||||
input_url1 = "https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg"
|
||||
input_url2 = "https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000570688.jpg"
|
||||
result_url1 = "https://bj.bcebos.com/paddlehub/fastdeploy/yolov5seg_result1.pkl"
|
||||
result_url2 = "https://bj.bcebos.com/paddlehub/fastdeploy/yolov5seg_result2.pkl"
|
||||
fd.download(model_url, "resources")
|
||||
fd.download(input_url1, "resources")
|
||||
fd.download(input_url2, "resources")
|
||||
fd.download(result_url1, "resources")
|
||||
fd.download(result_url2, "resources")
|
||||
|
||||
model_file = "resources/yolov5s-seg.onnx"
|
||||
rc.test_option.use_ort_backend()
|
||||
model = fd.vision.detection.YOLOv5Seg(
|
||||
model_file, runtime_option=rc.test_option)
|
||||
|
||||
with open("resources/yolov5seg_result1.pkl", "rb") as f:
|
||||
expect1 = pickle.load(f)
|
||||
|
||||
with open("resources/yolov5seg_result2.pkl", "rb") as f:
|
||||
expect2 = pickle.load(f)
|
||||
|
||||
# compare diff
|
||||
im1 = cv2.imread("./resources/000000014439.jpg")
|
||||
im2 = cv2.imread("./resources/000000570688.jpg")
|
||||
|
||||
for i in range(3):
|
||||
# test single predict
|
||||
result1 = model.predict(im1)
|
||||
result2 = model.predict(im2)
|
||||
|
||||
diff_boxes_1 = np.fabs(
|
||||
np.array(result1.boxes) - np.array(expect1["boxes"]))
|
||||
diff_boxes_2 = np.fabs(
|
||||
np.array(result2.boxes) - np.array(expect2["boxes"]))
|
||||
|
||||
diff_label_1 = np.fabs(
|
||||
np.array(result1.label_ids) - np.array(expect1["label_ids"]))
|
||||
diff_label_2 = np.fabs(
|
||||
np.array(result2.label_ids) - np.array(expect2["label_ids"]))
|
||||
|
||||
diff_scores_1 = np.fabs(
|
||||
np.array(result1.scores) - np.array(expect1["scores"]))
|
||||
diff_scores_2 = np.fabs(
|
||||
np.array(result2.scores) - np.array(expect2["scores"]))
|
||||
|
||||
# for masks
|
||||
for j in range(np.array(result1.boxes).shape[0]):
|
||||
result_mask_1 = np.array(result1.masks[j].data).reshape(
|
||||
result1.masks[j].shape)
|
||||
diff_mask_1 = np.fabs(result_mask_1 - np.array(expect1["mask_" +
|
||||
str(j)]))
|
||||
nonzero_nums = np.count_nonzero(diff_mask_1)
|
||||
nonzero_count = nonzero_nums / (diff_mask_1.shape[0] *
|
||||
diff_mask_1.shape[1])
|
||||
assert nonzero_count < 1e-02, "The different pixel ratio of mask1 is greater than 1%."
|
||||
|
||||
for k in range(np.array(result2.boxes).shape[0]):
|
||||
result_mask_2 = np.array(result2.masks[k].data).reshape(
|
||||
result2.masks[k].shape)
|
||||
diff_mask_2 = np.fabs(result_mask_2 - np.array(expect2["mask_" +
|
||||
str(k)]))
|
||||
nonzero_nums = np.count_nonzero(diff_mask_2)
|
||||
nonzero_count = nonzero_nums / (diff_mask_2.shape[0] *
|
||||
diff_mask_2.shape[1])
|
||||
assert nonzero_count < 1e-02, "The different pixel ratio of mask2 is greater than 1%."
|
||||
|
||||
assert diff_boxes_1.max(
|
||||
) < 1e-01, "There's difference in detection boxes 1."
|
||||
assert diff_label_1.max(
|
||||
) < 1e-02, "There's difference in detection label 1."
|
||||
assert diff_scores_1.max(
|
||||
) < 1e-04, "There's difference in detection score 1."
|
||||
|
||||
assert diff_boxes_2.max(
|
||||
) < 1e-01, "There's difference in detection boxes 2."
|
||||
assert diff_label_2.max(
|
||||
) < 1e-02, "There's difference in detection label 2."
|
||||
assert diff_scores_2.max(
|
||||
) < 1e-04, "There's difference in detection score 2."
|
||||
|
||||
# test batch predict
|
||||
results = model.batch_predict([im1, im2])
|
||||
result1 = results[0]
|
||||
result2 = results[1]
|
||||
|
||||
diff_boxes_1 = np.fabs(
|
||||
np.array(result1.boxes) - np.array(expect1["boxes"]))
|
||||
diff_boxes_2 = np.fabs(
|
||||
np.array(result2.boxes) - np.array(expect2["boxes"]))
|
||||
|
||||
diff_label_1 = np.fabs(
|
||||
np.array(result1.label_ids) - np.array(expect1["label_ids"]))
|
||||
diff_label_2 = np.fabs(
|
||||
np.array(result2.label_ids) - np.array(expect2["label_ids"]))
|
||||
|
||||
diff_scores_1 = np.fabs(
|
||||
np.array(result1.scores) - np.array(expect1["scores"]))
|
||||
diff_scores_2 = np.fabs(
|
||||
np.array(result2.scores) - np.array(expect2["scores"]))
|
||||
|
||||
# for masks
|
||||
for j in range(np.array(result1.boxes).shape[0]):
|
||||
result_mask_1 = np.array(result1.masks[j].data).reshape(
|
||||
result1.masks[j].shape)
|
||||
diff_mask_1 = np.fabs(result_mask_1 - np.array(expect1["mask_" +
|
||||
str(j)]))
|
||||
nonzero_nums = np.count_nonzero(diff_mask_1)
|
||||
nonzero_count = nonzero_nums / (diff_mask_1.shape[0] *
|
||||
diff_mask_1.shape[1])
|
||||
assert nonzero_count < 1e-02, "The different pixel ratio of mask1 is greater than 1%."
|
||||
|
||||
for k in range(np.array(result2.boxes).shape[0]):
|
||||
result_mask_2 = np.array(result2.masks[k].data).reshape(
|
||||
result2.masks[k].shape)
|
||||
diff_mask_2 = np.fabs(result_mask_2 - np.array(expect2["mask_" +
|
||||
str(k)]))
|
||||
nonzero_nums = np.count_nonzero(diff_mask_2)
|
||||
nonzero_count = nonzero_nums / (diff_mask_2.shape[0] *
|
||||
diff_mask_2.shape[1])
|
||||
assert nonzero_count < 1e-02, "The different pixel ratio of mask2 is greater than 1%."
|
||||
|
||||
assert diff_boxes_1.max(
|
||||
) < 1e-01, "There's difference in detection boxes 1."
|
||||
assert diff_label_1.max(
|
||||
) < 1e-02, "There's difference in detection label 1."
|
||||
assert diff_scores_1.max(
|
||||
) < 1e-03, "There's difference in detection score 1."
|
||||
|
||||
assert diff_boxes_2.max(
|
||||
) < 1e-01, "There's difference in detection boxes 2."
|
||||
assert diff_label_2.max(
|
||||
) < 1e-02, "There's difference in detection label 2."
|
||||
assert diff_scores_2.max(
|
||||
) < 1e-04, "There's difference in detection score 2."
|
||||
|
||||
|
||||
def test_detection_yolov5seg_runtime():
|
||||
model_url = "https://bj.bcebos.com/paddlehub/fastdeploy/yolov5s-seg.onnx"
|
||||
input_url1 = "https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg"
|
||||
result_url1 = "https://bj.bcebos.com/paddlehub/fastdeploy/yolov5seg_result1.pkl"
|
||||
fd.download(model_url, "resources")
|
||||
fd.download(input_url1, "resources")
|
||||
fd.download(result_url1, "resources")
|
||||
|
||||
model_file = "resources/yolov5s-seg.onnx"
|
||||
|
||||
preprocessor = fd.vision.detection.YOLOv5SegPreprocessor()
|
||||
postprocessor = fd.vision.detection.YOLOv5SegPostprocessor()
|
||||
|
||||
rc.test_option.set_model_path(model_file, model_format=ModelFormat.ONNX)
|
||||
rc.test_option.use_ort_backend()
|
||||
runtime = fd.Runtime(rc.test_option)
|
||||
|
||||
with open("resources/yolov5seg_result1.pkl", "rb") as f:
|
||||
expect1 = pickle.load(f)
|
||||
|
||||
# compare diff
|
||||
im1 = cv2.imread("./resources/000000014439.jpg")
|
||||
|
||||
for i in range(3):
|
||||
# test runtime
|
||||
input_tensors, ims_info = preprocessor.run([im1.copy()])
|
||||
output_tensors = runtime.infer({"images": input_tensors[0]})
|
||||
results = postprocessor.run(output_tensors, ims_info)
|
||||
result1 = results[0]
|
||||
|
||||
diff_boxes_1 = np.fabs(
|
||||
np.array(result1.boxes) - np.array(expect1["boxes"]))
|
||||
diff_label_1 = np.fabs(
|
||||
np.array(result1.label_ids) - np.array(expect1["label_ids"]))
|
||||
diff_scores_1 = np.fabs(
|
||||
np.array(result1.scores) - np.array(expect1["scores"]))
|
||||
|
||||
# for masks
|
||||
for j in range(np.array(result1.boxes).shape[0]):
|
||||
result_mask_1 = np.array(result1.masks[j].data).reshape(
|
||||
result1.masks[j].shape)
|
||||
diff_mask_1 = np.fabs(result_mask_1 - np.array(expect1["mask_" +
|
||||
str(j)]))
|
||||
nonzero_nums = np.count_nonzero(diff_mask_1)
|
||||
nonzero_count = nonzero_nums / (diff_mask_1.shape[0] *
|
||||
diff_mask_1.shape[1])
|
||||
assert nonzero_count < 1e-02, "The different pixel ratio of mask1 is greater than 1%."
|
||||
|
||||
assert diff_boxes_1.max(
|
||||
) < 1e-01, "There's difference in detection boxes 1."
|
||||
assert diff_label_1.max(
|
||||
) < 1e-02, "There's difference in detection label 1."
|
||||
assert diff_scores_1.max(
|
||||
) < 1e-04, "There's difference in detection score 1."
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
test_detection_yolov5seg()
|
||||
test_detection_yolov5seg_runtime()
|
||||
Reference in New Issue
Block a user