mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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[Model] add detection model : FastestDet (#842)
* model done, CLA fix * remove letter_box and ConvertAndPermute, use resize hwc2chw and convert in preprocess * remove useless values in preprocess * remove useless values in preprocess * fix reviewed problem * fix reviewed problem pybind * fix reviewed problem pybind * postprocess fix * add test_fastestdet.py, coco_val2017_500 fixed done, ready to review * fix reviewed problem * python/.../fastestdet.py * fix infer.cc, preprocess, python/fastestdet.py * fix examples/python/infer.py
This commit is contained in:
guxukai
14
examples/vision/detection/fastestdet/cpp/CMakeLists.txt
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14
examples/vision/detection/fastestdet/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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# Specifies the path to the fastdeploy library after you have downloaded it
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option(FASTDEPLOY_INSTALL_DIR "Path of downloaded fastdeploy sdk.")
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include(${FASTDEPLOY_INSTALL_DIR}/FastDeploy.cmake)
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# Include the FastDeploy dependency header file
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include_directories(${FASTDEPLOY_INCS})
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add_executable(infer_demo ${PROJECT_SOURCE_DIR}/infer.cc)
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# Add the FastDeploy library dependency
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target_link_libraries(infer_demo ${FASTDEPLOY_LIBS})
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87
examples/vision/detection/fastestdet/cpp/README.md
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87
examples/vision/detection/fastestdet/cpp/README.md
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# FastestDet C++部署示例
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本目录下提供`infer.cc`快速完成FastestDet在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。
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在部署前，需确认以下两个步骤
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- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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- 2. 根据开发环境，下载预编译部署库和samples代码，参考[FastDeploy预编译库](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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以Linux上CPU推理为例，在本目录执行如下命令即可完成编译测试
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```bash
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mkdir build
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cd build
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wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-1.0.3.tgz
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tar xvf fastdeploy-linux-x64-1.0.3.tgz
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cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-1.0.3
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make -j
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#下载官方转换好的FastestDet模型文件和测试图片
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wget https://bj.bcebos.com/paddlehub/fastdeploy/FastestDet.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 FastestDet.onnx 000000014439.jpg 0
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# GPU推理
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./infer_demo FastestDet.onnx 000000014439.jpg 1
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# GPU上TensorRT推理
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./infer_demo FastestDet.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/44280887/206176291-61eb118b-391b-4431-b79e-a393b9452138.jpg">
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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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## FastestDet C++接口
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### FastestDet类
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```c++
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fastdeploy::vision::detection::FastestDet(
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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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FastestDet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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> FastestDet::Predict(cv::Mat* im, DetectionResult* result,
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> float conf_threshold = 0.65,
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> float nms_iou_threshold = 0.45)
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> ```
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>
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> 模型预测接口，输入图像直接输出检测结果。
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>
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> **参数**
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>
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> > * **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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> > * **conf_threshold**: 检测框置信度过滤阈值
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> > * **nms_iou_threshold**: NMS处理过程中iou阈值
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### 类成员变量
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#### 预处理参数
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用户可按照自己的实际需求，修改下列预处理参数，从而影响最终的推理和部署效果
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> > * **size**(vector<int>): 通过此参数修改预处理过程中resize的大小，包含两个整型元素，表示[width, height], 默认值为[352, 352]
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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/fastestdet/cpp/infer.cc
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105
examples/vision/detection/fastestdet/cpp/infer.cc
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// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include "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::FastestDet(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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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::FastestDet(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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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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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, 352, 352});
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auto model = fastdeploy::vision::detection::FastestDet(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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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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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 ./FastestDet.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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}
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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]);
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} else if (std::atoi(argv[3]) == 2) {
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TrtInfer(argv[1], argv[2]);
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}
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return 0;
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}
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74
examples/vision/detection/fastestdet/python/README.md
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74
examples/vision/detection/fastestdet/python/README.md
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# FastestDet Python部署示例
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|
|
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在部署前，需确认以下两个步骤
|
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|
|
||||||
|
- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
|
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|
- 2. FastDeploy Python whl包安装，参考[FastDeploy Python安装](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
|
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|
|
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|
本目录下提供`infer.py`快速完成FastestDet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
|
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|
|
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|
```bash
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|
#下载部署示例代码
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||||||
|
git clone https://github.com/PaddlePaddle/FastDeploy.git
|
||||||
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cd examples/vision/detection/fastestdet/python/
|
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|
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#下载fastestdet模型文件和测试图片
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wget https://bj.bcebos.com/paddlehub/fastdeploy/FastestDet.onnx
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|
wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
|
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|
|
||||||
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# CPU推理
|
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python infer.py --model FastestDet.onnx --image 000000014439.jpg --device cpu
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# GPU推理
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python infer.py --model FastestDet.onnx --image 000000014439.jpg --device gpu
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# GPU上使用TensorRT推理
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python infer.py --model FastestDet.onnx --image 000000014439.jpg --device gpu --use_trt True
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|
```
|
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|
||||||
|
运行完成可视化结果如下图所示
|
||||||
|
|
||||||
|
<img width="640" src="https://user-images.githubusercontent.com/44280887/206176291-61eb118b-391b-4431-b79e-a393b9452138.jpg">
|
||||||
|
|
||||||
|
## FastestDet Python接口
|
||||||
|
|
||||||
|
```python
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fastdeploy.vision.detection.FastestDet(model_file, params_file=None, runtime_option=None, model_format=ModelFormat.ONNX)
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|
```
|
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|
|
||||||
|
FastestDet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
|
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|
> FastestDet.predict(image_data)
|
||||||
|
> ```
|
||||||
|
>
|
||||||
|
> 模型预测接口，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/)
|
||||||
|
|
||||||
|
### 类成员属性
|
||||||
|
#### 预处理参数
|
||||||
|
用户可按照自己的实际需求，修改下列预处理参数，从而影响最终的推理和部署效果
|
||||||
|
|
||||||
|
> > * **size**(list[int]): 通过此参数修改预处理过程中resize的大小，包含两个整型元素，表示[width, height], 默认值为[352, 352]
|
||||||
|
|
||||||
|
|
||||||
|
## 其它文档
|
||||||
|
|
||||||
|
- [FastestDet 模型介绍](..)
|
||||||
|
- [FastestDet 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)
|
||||||
51
examples/vision/detection/fastestdet/python/infer.py
Normal file
51
examples/vision/detection/fastestdet/python/infer.py
Normal file
@@ -0,0 +1,51 @@
|
|||||||
|
import fastdeploy as fd
|
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|
import cv2
|
||||||
|
|
||||||
|
|
||||||
|
def parse_arguments():
|
||||||
|
import argparse
|
||||||
|
import ast
|
||||||
|
parser = argparse.ArgumentParser()
|
||||||
|
parser.add_argument(
|
||||||
|
"--model", required=True, help="Path of FastestDet onnx model.")
|
||||||
|
parser.add_argument(
|
||||||
|
"--image", required=True, help="Path of test image file.")
|
||||||
|
parser.add_argument(
|
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|
"--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, 352, 352])
|
||||||
|
return option
|
||||||
|
|
||||||
|
|
||||||
|
args = parse_arguments()
|
||||||
|
|
||||||
|
# Configure runtime and load model
|
||||||
|
runtime_option = build_option(args)
|
||||||
|
model = fd.vision.detection.FastestDet(args.model, runtime_option=runtime_option)
|
||||||
|
|
||||||
|
# Predict picture detection results
|
||||||
|
im = cv2.imread(args.image)
|
||||||
|
result = model.predict(im)
|
||||||
|
|
||||||
|
# Visualization of prediction results
|
||||||
|
vis_im = fd.vision.vis_detection(im, result)
|
||||||
|
cv2.imwrite("visualized_result.jpg", vis_im)
|
||||||
|
print("Visualized result save in ./visualized_result.jpg")
|
||||||
@@ -22,6 +22,7 @@
|
|||||||
#include "fastdeploy/vision/detection/contrib/scaledyolov4.h"
|
#include "fastdeploy/vision/detection/contrib/scaledyolov4.h"
|
||||||
#include "fastdeploy/vision/detection/contrib/yolor.h"
|
#include "fastdeploy/vision/detection/contrib/yolor.h"
|
||||||
#include "fastdeploy/vision/detection/contrib/yolov5/yolov5.h"
|
#include "fastdeploy/vision/detection/contrib/yolov5/yolov5.h"
|
||||||
|
#include "fastdeploy/vision/detection/contrib/fastestdet/fastestdet.h"
|
||||||
#include "fastdeploy/vision/detection/contrib/yolov5lite.h"
|
#include "fastdeploy/vision/detection/contrib/yolov5lite.h"
|
||||||
#include "fastdeploy/vision/detection/contrib/yolov6.h"
|
#include "fastdeploy/vision/detection/contrib/yolov6.h"
|
||||||
#include "fastdeploy/vision/detection/contrib/yolov7/yolov7.h"
|
#include "fastdeploy/vision/detection/contrib/yolov7/yolov7.h"
|
||||||
|
|||||||
79
fastdeploy/vision/detection/contrib/fastestdet/fastestdet.cc
Normal file
79
fastdeploy/vision/detection/contrib/fastestdet/fastestdet.cc
Normal 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.
|
||||||
|
|
||||||
|
#include "fastdeploy/vision/detection/contrib/fastestdet/fastestdet.h"
|
||||||
|
|
||||||
|
namespace fastdeploy {
|
||||||
|
namespace vision {
|
||||||
|
namespace detection {
|
||||||
|
|
||||||
|
FastestDet::FastestDet(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 FastestDet::Initialize() {
|
||||||
|
if (!InitRuntime()) {
|
||||||
|
FDERROR << "Failed to initialize fastdeploy backend." << std::endl;
|
||||||
|
return false;
|
||||||
|
}
|
||||||
|
return true;
|
||||||
|
}
|
||||||
|
|
||||||
|
bool FastestDet::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 FastestDet::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/fastestdet/fastestdet.h
Normal file
76
fastdeploy/vision/detection/contrib/fastestdet/fastestdet.h
Normal 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/fastestdet/preprocessor.h"
|
||||||
|
#include "fastdeploy/vision/detection/contrib/fastestdet/postprocessor.h"
|
||||||
|
|
||||||
|
namespace fastdeploy {
|
||||||
|
namespace vision {
|
||||||
|
namespace detection {
|
||||||
|
/*! @brief FastestDet model object used when to load a FastestDet model exported by FastestDet.
|
||||||
|
*/
|
||||||
|
class FASTDEPLOY_DECL FastestDet : public FastDeployModel {
|
||||||
|
public:
|
||||||
|
/** \brief Set path of model file and the configuration of runtime.
|
||||||
|
*
|
||||||
|
* \param[in] model_file Path of model file, e.g ./fastestdet.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
|
||||||
|
*/
|
||||||
|
FastestDet(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 "fastestdet"; }
|
||||||
|
|
||||||
|
/** \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 FastestDet
|
||||||
|
virtual FastestDetPreprocessor& GetPreprocessor() {
|
||||||
|
return preprocessor_;
|
||||||
|
}
|
||||||
|
|
||||||
|
/// Get postprocessor reference of FastestDet
|
||||||
|
virtual FastestDetPostprocessor& GetPostprocessor() {
|
||||||
|
return postprocessor_;
|
||||||
|
}
|
||||||
|
|
||||||
|
protected:
|
||||||
|
bool Initialize();
|
||||||
|
FastestDetPreprocessor preprocessor_;
|
||||||
|
FastestDetPostprocessor postprocessor_;
|
||||||
|
};
|
||||||
|
|
||||||
|
} // namespace detection
|
||||||
|
} // namespace vision
|
||||||
|
} // namespace fastdeploy
|
||||||
@@ -0,0 +1,85 @@
|
|||||||
|
// 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 BindFastestDet(pybind11::module& m) {
|
||||||
|
pybind11::class_<vision::detection::FastestDetPreprocessor>(
|
||||||
|
m, "FastestDetPreprocessor")
|
||||||
|
.def(pybind11::init<>())
|
||||||
|
.def("run", [](vision::detection::FastestDetPreprocessor& 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("raise Exception('Failed to preprocess the input data in FastestDetPreprocessor.')");
|
||||||
|
}
|
||||||
|
for (size_t i = 0; i < outputs.size(); ++i) {
|
||||||
|
outputs[i].StopSharing();
|
||||||
|
}
|
||||||
|
return make_pair(outputs, ims_info);
|
||||||
|
})
|
||||||
|
.def_property("size", &vision::detection::FastestDetPreprocessor::GetSize, &vision::detection::FastestDetPreprocessor::SetSize);
|
||||||
|
|
||||||
|
pybind11::class_<vision::detection::FastestDetPostprocessor>(
|
||||||
|
m, "FastestDetPostprocessor")
|
||||||
|
.def(pybind11::init<>())
|
||||||
|
.def("run", [](vision::detection::FastestDetPostprocessor& 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("raise Exception('Failed to postprocess the runtime result in FastestDetPostprocessor.')");
|
||||||
|
}
|
||||||
|
return results;
|
||||||
|
})
|
||||||
|
.def("run", [](vision::detection::FastestDetPostprocessor& 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("raise Exception('Failed to postprocess the runtime result in FastestDetPostprocessor.')");
|
||||||
|
}
|
||||||
|
return results;
|
||||||
|
})
|
||||||
|
.def_property("conf_threshold", &vision::detection::FastestDetPostprocessor::GetConfThreshold, &vision::detection::FastestDetPostprocessor::SetConfThreshold)
|
||||||
|
.def_property("nms_threshold", &vision::detection::FastestDetPostprocessor::GetNMSThreshold, &vision::detection::FastestDetPostprocessor::SetNMSThreshold);
|
||||||
|
|
||||||
|
pybind11::class_<vision::detection::FastestDet, FastDeployModel>(m, "FastestDet")
|
||||||
|
.def(pybind11::init<std::string, std::string, RuntimeOption,
|
||||||
|
ModelFormat>())
|
||||||
|
.def("predict",
|
||||||
|
[](vision::detection::FastestDet& self, pybind11::array& data) {
|
||||||
|
auto mat = PyArrayToCvMat(data);
|
||||||
|
vision::DetectionResult res;
|
||||||
|
self.Predict(mat, &res);
|
||||||
|
return res;
|
||||||
|
})
|
||||||
|
.def("batch_predict", [](vision::detection::FastestDet& 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::FastestDet::GetPreprocessor)
|
||||||
|
.def_property_readonly("postprocessor", &vision::detection::FastestDet::GetPostprocessor);
|
||||||
|
}
|
||||||
|
} // namespace fastdeploy
|
||||||
132
fastdeploy/vision/detection/contrib/fastestdet/postprocessor.cc
Normal file
132
fastdeploy/vision/detection/contrib/fastestdet/postprocessor.cc
Normal file
@@ -0,0 +1,132 @@
|
|||||||
|
// 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/fastestdet/postprocessor.h"
|
||||||
|
#include "fastdeploy/vision/utils/utils.h"
|
||||||
|
|
||||||
|
namespace fastdeploy {
|
||||||
|
namespace vision {
|
||||||
|
namespace detection {
|
||||||
|
|
||||||
|
FastestDetPostprocessor::FastestDetPostprocessor() {
|
||||||
|
conf_threshold_ = 0.65;
|
||||||
|
nms_threshold_ = 0.45;
|
||||||
|
}
|
||||||
|
float FastestDetPostprocessor::Sigmoid(float x) {
|
||||||
|
return 1.0f / (1.0f + exp(-x));
|
||||||
|
}
|
||||||
|
|
||||||
|
float FastestDetPostprocessor::Tanh(float x) {
|
||||||
|
return 2.0f / (1.0f + exp(-2 * x)) - 1;
|
||||||
|
}
|
||||||
|
|
||||||
|
bool FastestDetPostprocessor::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 = 1;
|
||||||
|
|
||||||
|
results->resize(batch);
|
||||||
|
|
||||||
|
for (size_t bs = 0; bs < batch; ++bs) {
|
||||||
|
|
||||||
|
(*results)[bs].Clear();
|
||||||
|
// output (1,85,22,22) CHW
|
||||||
|
const float* output = reinterpret_cast<const float*>(tensors[0].Data()) + bs * tensors[0].shape[1] * tensors[0].shape[2] * tensors[0].shape[3];
|
||||||
|
int output_h = tensors[0].shape[2]; // out map height
|
||||||
|
int output_w = tensors[0].shape[3]; // out map weight
|
||||||
|
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 ipt_h = iter_ipt->second[0];
|
||||||
|
float ipt_w = iter_ipt->second[1];
|
||||||
|
|
||||||
|
// handle output boxes from out map
|
||||||
|
for (int h = 0; h < output_h; h++) {
|
||||||
|
for (int w = 0; w < output_w; w++) {
|
||||||
|
// object score
|
||||||
|
int obj_score_index = (h * output_w) + w;
|
||||||
|
float obj_score = output[obj_score_index];
|
||||||
|
|
||||||
|
// find max class
|
||||||
|
int category = 0;
|
||||||
|
float max_score = 0.0f;
|
||||||
|
int class_num = tensors[0].shape[1]-5;
|
||||||
|
for (size_t i = 0; i < class_num; i++) {
|
||||||
|
obj_score_index =((5 + i) * output_h * output_w) + (h * output_w) + w;
|
||||||
|
float cls_score = output[obj_score_index];
|
||||||
|
if (cls_score > max_score) {
|
||||||
|
max_score = cls_score;
|
||||||
|
category = i;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
float score = pow(max_score, 0.4) * pow(obj_score, 0.6);
|
||||||
|
|
||||||
|
// score threshold
|
||||||
|
if (score <= conf_threshold_) {
|
||||||
|
continue;
|
||||||
|
}
|
||||||
|
if (score > conf_threshold_) {
|
||||||
|
// handle box x y w h
|
||||||
|
int x_offset_index = (1 * output_h * output_w) + (h * output_w) + w;
|
||||||
|
int y_offset_index = (2 * output_h * output_w) + (h * output_w) + w;
|
||||||
|
int box_width_index = (3 * output_h * output_w) + (h * output_w) + w;
|
||||||
|
int box_height_index = (4 * output_h * output_w) + (h * output_w) + w;
|
||||||
|
|
||||||
|
float x_offset = Tanh(output[x_offset_index]);
|
||||||
|
float y_offset = Tanh(output[y_offset_index]);
|
||||||
|
float box_width = Sigmoid(output[box_width_index]);
|
||||||
|
float box_height = Sigmoid(output[box_height_index]);
|
||||||
|
|
||||||
|
float cx = (w + x_offset) / output_w;
|
||||||
|
float cy = (h + y_offset) / output_h;
|
||||||
|
|
||||||
|
// convert from [x, y, w, h] to [x1, y1, x2, y2]
|
||||||
|
(*results)[bs].boxes.emplace_back(std::array<float, 4>{
|
||||||
|
cx - box_width / 2.0f,
|
||||||
|
cy - box_height / 2.0f,
|
||||||
|
cx + box_width / 2.0f,
|
||||||
|
cy + box_height / 2.0f});
|
||||||
|
(*results)[bs].label_ids.push_back(category);
|
||||||
|
(*results)[bs].scores.push_back(score);
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
if ((*results)[bs].boxes.size() == 0) {
|
||||||
|
return true;
|
||||||
|
}
|
||||||
|
|
||||||
|
// scale boxes to origin shape
|
||||||
|
for (size_t i = 0; i < (*results)[bs].boxes.size(); ++i) {
|
||||||
|
(*results)[bs].boxes[i][0] = ((*results)[bs].boxes[i][0]) * ipt_w;
|
||||||
|
(*results)[bs].boxes[i][1] = ((*results)[bs].boxes[i][1]) * ipt_h;
|
||||||
|
(*results)[bs].boxes[i][2] = ((*results)[bs].boxes[i][2]) * ipt_w;
|
||||||
|
(*results)[bs].boxes[i][3] = ((*results)[bs].boxes[i][3]) * ipt_h;
|
||||||
|
}
|
||||||
|
//NMS
|
||||||
|
utils::NMS(&((*results)[bs]), nms_threshold_);
|
||||||
|
//clip box
|
||||||
|
for (size_t i = 0; i < (*results)[bs].boxes.size(); ++i) {
|
||||||
|
(*results)[bs].boxes[i][0] = std::max((*results)[bs].boxes[i][0], 0.0f);
|
||||||
|
(*results)[bs].boxes[i][1] = std::max((*results)[bs].boxes[i][1], 0.0f);
|
||||||
|
(*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);
|
||||||
|
}
|
||||||
|
}
|
||||||
|
return true;
|
||||||
|
}
|
||||||
|
|
||||||
|
} // namespace detection
|
||||||
|
} // namespace vision
|
||||||
|
} // namespace fastdeploy
|
||||||
@@ -0,0 +1,67 @@
|
|||||||
|
// 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 FastestDet serials model.
|
||||||
|
*/
|
||||||
|
class FASTDEPLOY_DECL FastestDetPostprocessor {
|
||||||
|
public:
|
||||||
|
/** \brief Create a postprocessor instance for FastestDet serials model
|
||||||
|
*/
|
||||||
|
FastestDetPostprocessor();
|
||||||
|
|
||||||
|
/** \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 0.65
|
||||||
|
void SetConfThreshold(const float& conf_threshold) {
|
||||||
|
conf_threshold_ = conf_threshold;
|
||||||
|
}
|
||||||
|
|
||||||
|
/// Get conf_threshold, default 0.65
|
||||||
|
float GetConfThreshold() const { return conf_threshold_; }
|
||||||
|
|
||||||
|
/// Set nms_threshold, default 0.45
|
||||||
|
void SetNMSThreshold(const float& nms_threshold) {
|
||||||
|
nms_threshold_ = nms_threshold;
|
||||||
|
}
|
||||||
|
|
||||||
|
/// Get nms_threshold, default 0.45
|
||||||
|
float GetNMSThreshold() const { return nms_threshold_; }
|
||||||
|
|
||||||
|
protected:
|
||||||
|
float conf_threshold_;
|
||||||
|
float nms_threshold_;
|
||||||
|
float Sigmoid(float x);
|
||||||
|
float Tanh(float x);
|
||||||
|
};
|
||||||
|
|
||||||
|
} // namespace detection
|
||||||
|
} // namespace vision
|
||||||
|
} // namespace fastdeploy
|
||||||
@@ -0,0 +1,81 @@
|
|||||||
|
// 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/fastestdet/preprocessor.h"
|
||||||
|
#include "fastdeploy/function/concat.h"
|
||||||
|
|
||||||
|
namespace fastdeploy {
|
||||||
|
namespace vision {
|
||||||
|
namespace detection {
|
||||||
|
|
||||||
|
FastestDetPreprocessor::FastestDetPreprocessor() {
|
||||||
|
size_ = {352, 352}; //{h,w}
|
||||||
|
}
|
||||||
|
|
||||||
|
bool FastestDetPreprocessor::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())};
|
||||||
|
|
||||||
|
// process after image load
|
||||||
|
double ratio = (size_[0] * 1.0) / std::max(static_cast<float>(mat->Height()),
|
||||||
|
static_cast<float>(mat->Width()));
|
||||||
|
|
||||||
|
// fastestdet's preprocess steps
|
||||||
|
// 1. resize
|
||||||
|
// 2. convert_and_permute(swap_rb=false)
|
||||||
|
Resize::Run(mat, size_[0], size_[1]); //resize
|
||||||
|
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};
|
||||||
|
//convert to float and HWC2CHW
|
||||||
|
ConvertAndPermute::Run(mat, alpha, beta, false);
|
||||||
|
|
||||||
|
// 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 FastestDetPreprocessor::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
|
||||||
@@ -0,0 +1,57 @@
|
|||||||
|
// 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 FastestDet serials model.
|
||||||
|
*/
|
||||||
|
class FASTDEPLOY_DECL FastestDetPreprocessor {
|
||||||
|
public:
|
||||||
|
/** \brief Create a preprocessor instance for FastestDet serials model
|
||||||
|
*/
|
||||||
|
FastestDetPreprocessor();
|
||||||
|
|
||||||
|
/** \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 = {352, 352}
|
||||||
|
void SetSize(const std::vector<int>& size) { size_ = size; }
|
||||||
|
|
||||||
|
/// Get target size, tuple of (width, height), default size = {352, 352}
|
||||||
|
std::vector<int> GetSize() const { return size_; }
|
||||||
|
|
||||||
|
protected:
|
||||||
|
bool Preprocess(FDMat* mat, FDTensor* output,
|
||||||
|
std::map<std::string, std::array<float, 2>>* im_info);
|
||||||
|
|
||||||
|
// target size, tuple of (width, height), default size = {352, 352}
|
||||||
|
std::vector<int> size_;
|
||||||
|
};
|
||||||
|
|
||||||
|
} // namespace detection
|
||||||
|
} // namespace vision
|
||||||
|
} // namespace fastdeploy
|
||||||
@@ -22,6 +22,7 @@ void BindYOLOR(pybind11::module& m);
|
|||||||
void BindYOLOv6(pybind11::module& m);
|
void BindYOLOv6(pybind11::module& m);
|
||||||
void BindYOLOv5Lite(pybind11::module& m);
|
void BindYOLOv5Lite(pybind11::module& m);
|
||||||
void BindYOLOv5(pybind11::module& m);
|
void BindYOLOv5(pybind11::module& m);
|
||||||
|
void BindFastestDet(pybind11::module& m);
|
||||||
void BindYOLOX(pybind11::module& m);
|
void BindYOLOX(pybind11::module& m);
|
||||||
void BindNanoDetPlus(pybind11::module& m);
|
void BindNanoDetPlus(pybind11::module& m);
|
||||||
void BindPPDet(pybind11::module& m);
|
void BindPPDet(pybind11::module& m);
|
||||||
@@ -39,6 +40,7 @@ void BindDetection(pybind11::module& m) {
|
|||||||
BindYOLOv6(detection_module);
|
BindYOLOv6(detection_module);
|
||||||
BindYOLOv5Lite(detection_module);
|
BindYOLOv5Lite(detection_module);
|
||||||
BindYOLOv5(detection_module);
|
BindYOLOv5(detection_module);
|
||||||
|
BindFastestDet(detection_module);
|
||||||
BindYOLOX(detection_module);
|
BindYOLOX(detection_module);
|
||||||
BindNanoDetPlus(detection_module);
|
BindNanoDetPlus(detection_module);
|
||||||
BindYOLOv7End2EndTRT(detection_module);
|
BindYOLOv7End2EndTRT(detection_module);
|
||||||
|
|||||||
@@ -19,6 +19,7 @@ from .contrib.scaled_yolov4 import ScaledYOLOv4
|
|||||||
from .contrib.nanodet_plus import NanoDetPlus
|
from .contrib.nanodet_plus import NanoDetPlus
|
||||||
from .contrib.yolox import YOLOX
|
from .contrib.yolox import YOLOX
|
||||||
from .contrib.yolov5 import *
|
from .contrib.yolov5 import *
|
||||||
|
from .contrib.fastestdet import *
|
||||||
from .contrib.yolov5lite import YOLOv5Lite
|
from .contrib.yolov5lite import YOLOv5Lite
|
||||||
from .contrib.yolov6 import YOLOv6
|
from .contrib.yolov6 import YOLOv6
|
||||||
from .contrib.yolov7end2end_trt import YOLOv7End2EndTRT
|
from .contrib.yolov7end2end_trt import YOLOv7End2EndTRT
|
||||||
|
|||||||
149
python/fastdeploy/vision/detection/contrib/fastestdet.py
Normal file
149
python/fastdeploy/vision/detection/contrib/fastestdet.py
Normal file
@@ -0,0 +1,149 @@
|
|||||||
|
# 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 FastestDetPreprocessor:
|
||||||
|
def __init__(self):
|
||||||
|
"""Create a preprocessor for FastestDet
|
||||||
|
"""
|
||||||
|
self._preprocessor = C.vision.detection.FastestDetPreprocessor()
|
||||||
|
|
||||||
|
def run(self, input_ims):
|
||||||
|
"""Preprocess input images for FastestDet
|
||||||
|
|
||||||
|
: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 = [352, 352]
|
||||||
|
"""
|
||||||
|
return self._preprocessor.size
|
||||||
|
|
||||||
|
@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
|
||||||
|
|
||||||
|
|
||||||
|
class FastestDetPostprocessor:
|
||||||
|
def __init__(self):
|
||||||
|
"""Create a postprocessor for FastestDet
|
||||||
|
"""
|
||||||
|
self._postprocessor = C.vision.detection.FastestDetPostprocessor()
|
||||||
|
|
||||||
|
def run(self, runtime_results, ims_info):
|
||||||
|
"""Postprocess the runtime results for FastestDet
|
||||||
|
|
||||||
|
: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 0.65
|
||||||
|
"""
|
||||||
|
return self._postprocessor.conf_threshold
|
||||||
|
|
||||||
|
@property
|
||||||
|
def nms_threshold(self):
|
||||||
|
"""
|
||||||
|
nms threshold for postprocessing, default is 0.45
|
||||||
|
"""
|
||||||
|
return self._postprocessor.nms_threshold
|
||||||
|
|
||||||
|
@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
|
||||||
|
|
||||||
|
|
||||||
|
class FastestDet(FastDeployModel):
|
||||||
|
def __init__(self,
|
||||||
|
model_file,
|
||||||
|
params_file="",
|
||||||
|
runtime_option=None,
|
||||||
|
model_format=ModelFormat.ONNX):
|
||||||
|
"""Load a FastestDet model exported by FastestDet.
|
||||||
|
|
||||||
|
:param model_file: (str)Path of model file, e.g ./FastestDet.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(FastestDet, self).__init__(runtime_option)
|
||||||
|
|
||||||
|
assert model_format == ModelFormat.ONNX, "FastestDet only support model format of ModelFormat.ONNX now."
|
||||||
|
self._model = C.vision.detection.FastestDet(
|
||||||
|
model_file, params_file, self._runtime_option, model_format)
|
||||||
|
|
||||||
|
assert self.initialized, "FastestDet initialize failed."
|
||||||
|
|
||||||
|
def predict(self, input_image):
|
||||||
|
"""Detect an input image
|
||||||
|
|
||||||
|
:param input_image: (numpy.ndarray)The input image data, 3-D array with layout HWC, BGR format
|
||||||
|
:return: DetectionResult
|
||||||
|
"""
|
||||||
|
assert input_image is not None, "Input image is None."
|
||||||
|
return self._model.predict(input_image)
|
||||||
|
|
||||||
|
def batch_predict(self, images):
|
||||||
|
assert len(images) == 1,"FastestDet is only support 1 image in batch_predict"
|
||||||
|
"""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 FastestDetPreprocessor object of the loaded model
|
||||||
|
|
||||||
|
:return FastestDetPreprocessor
|
||||||
|
"""
|
||||||
|
return self._model.preprocessor
|
||||||
|
|
||||||
|
@property
|
||||||
|
def postprocessor(self):
|
||||||
|
"""Get FastestDetPostprocessor object of the loaded model
|
||||||
|
|
||||||
|
:return FastestDetPostprocessor
|
||||||
|
"""
|
||||||
|
return self._model.postprocessor
|
||||||
111
tests/models/test_fastestdet.py
Normal file
111
tests/models/test_fastestdet.py
Normal file
@@ -0,0 +1,111 @@
|
|||||||
|
# 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_fastestdet():
|
||||||
|
model_url = "https://bj.bcebos.com/paddlehub/fastdeploy/FastestDet.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/fastestdet_result1.pkl"
|
||||||
|
fd.download(model_url, "resources")
|
||||||
|
fd.download(input_url1, "resources")
|
||||||
|
fd.download(input_url2, "resources")
|
||||||
|
fd.download(result_url1, "resources")
|
||||||
|
|
||||||
|
model_file = "resources/FastestDet.onnx"
|
||||||
|
model = fd.vision.detection.FastestDet(
|
||||||
|
model_file, runtime_option=rc.test_option)
|
||||||
|
|
||||||
|
with open("resources/fastestdet_result1.pkl", "rb") as f:
|
||||||
|
expect1 = pickle.load(f)
|
||||||
|
|
||||||
|
# compare diff
|
||||||
|
im1 = cv2.imread("./resources/000000014439.jpg")
|
||||||
|
print(expect1)
|
||||||
|
for i in range(3):
|
||||||
|
# test single predict
|
||||||
|
result1 = model.predict(im1)
|
||||||
|
|
||||||
|
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"]))
|
||||||
|
|
||||||
|
print(diff_boxes_1.max(), diff_boxes_1.mean())
|
||||||
|
assert diff_boxes_1.max(
|
||||||
|
) < 1e-04, "There's difference in detection boxes 1."
|
||||||
|
assert diff_label_1.max(
|
||||||
|
) < 1e-04, "There's difference in detection label 1."
|
||||||
|
assert diff_scores_1.max(
|
||||||
|
) < 1e-05, "There's difference in detection score 1."
|
||||||
|
|
||||||
|
def test_detection_fastestdet_runtime():
|
||||||
|
model_url = "https://bj.bcebos.com/paddlehub/fastdeploy/FastestDet.onnx"
|
||||||
|
input_url1 = "https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg"
|
||||||
|
result_url1 = "https://bj.bcebos.com/paddlehub/fastdeploy/fastestdet_result1.pkl"
|
||||||
|
fd.download(model_url, "resources")
|
||||||
|
fd.download(input_url1, "resources")
|
||||||
|
fd.download(result_url1, "resources")
|
||||||
|
|
||||||
|
model_file = "resources/FastestDet.onnx"
|
||||||
|
|
||||||
|
preprocessor = fd.vision.detection.FastestDetPreprocessor()
|
||||||
|
postprocessor = fd.vision.detection.FastestDetPostprocessor()
|
||||||
|
|
||||||
|
rc.test_option.set_model_path(model_file, model_format=ModelFormat.ONNX)
|
||||||
|
rc.test_option.use_openvino_backend()
|
||||||
|
runtime = fd.Runtime(rc.test_option)
|
||||||
|
|
||||||
|
with open("resources/fastestdet_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({"input.1": 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"]))
|
||||||
|
|
||||||
|
assert diff_boxes_1.max(
|
||||||
|
) < 1e-04, "There's difference in detection boxes 1."
|
||||||
|
assert diff_label_1.max(
|
||||||
|
) < 1e-04, "There's difference in detection label 1."
|
||||||
|
assert diff_scores_1.max(
|
||||||
|
) < 1e-05, "There's difference in detection score 1."
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
test_detection_fastestdet()
|
||||||
|
test_detection_fastestdet_runtime()
|
||||||
Reference in New Issue
Block a user