Merge branch 'ceccocats:master' into master
This commit is contained in:
Adam Cuellar
GitHub
+5
-2
@@ -3,10 +3,10 @@ cmake_minimum_required(VERSION 3.15)
|
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project (tkDNN)
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set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} ${CMAKE_CURRENT_SOURCE_DIR}/cmake)
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if(UNIX)
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set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11 -fPIC -Wno-deprecated-declarations -Wno-unused-variable ")
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set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++14 -fPIC -Wno-deprecated-declarations")
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endif()
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if(WIN32)
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set(CMAKE_CXX_STANDARD 11)
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set(CMAKE_CXX_STANDARD 14)
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set(CMAKE_CXX_FLAGS "/O2 /FS /EHsc")
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set(CMAKE_WINDOWS_EXPORT_ALL_SYMBOLS ON)
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endif(WIN32)
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@@ -140,6 +140,9 @@ target_link_libraries(test_shelfnet_berkeley tkDNN)
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add_executable(test_shelfnet_mapillary tests/shelfnet/shelfnet_mapillary.cpp)
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target_link_libraries(test_shelfnet_mapillary tkDNN)
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add_executable(test_shelfnet_coco tests/shelfnet/shelfnet_coco.cpp)
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target_link_libraries(test_shelfnet_coco tkDNN)
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# DEMOS
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add_executable(test_rtinference tests/test_rtinference/rtinference.cpp)
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target_link_libraries(test_rtinference tkDNN)
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@@ -17,10 +17,15 @@ If you use tkDNN in your research, please cite the [following paper](https://iee
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}
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```
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### What's new (20 July 2021)
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### What's new
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#### 20 July 2021
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- [x] Support to sematic segmentation [README](docs/README_seg.md)
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- [x] Support 2D/3D Object Detection and Tracking [README](docs/README_2d3dtracking.md)
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- [ ] Support to TensorRT8 (WIP)
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#### 24 November 2021
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- [x] Support to sematic segmentation on cuda 11
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- [x] Support to TensorRT8 (thanks to [Harshvardhan Chandirasekar](https://github.com/perseusdg)).
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TensorRT8 (and therefore Jetpack 4.6) is currently supported only on the branch tensorrt8 due to [performance issue with TensorRT8](https://docs.nvidia.com/deeplearning/tensorrt/release-notes/tensorrt-8.html)). We will merge it to the master as soon as those issues are fixed (probably in future minor releases).
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## FPS Results
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Inference FPS of yolov4 with tkDNN, average of 1200 images with the same dimension as the input size, on
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@@ -168,11 +173,10 @@ For specific details on how to run tkDNN on Windows 10 see [HERE](./docs/windows
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| yolo4_320 | Yolov4 <sup>8</sup> | [COCO 2017](http://cocodataset.org/) | 80 | 320x320 | [weights](https://cloud.hipert.unimore.it/s/d97CFzYqCPCp5Hg/download) |
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| yolo4_512 | Yolov4 <sup>8</sup> | [COCO 2017](http://cocodataset.org/) | 80 | 512x512 | [weights](https://cloud.hipert.unimore.it/s/d97CFzYqCPCp5Hg/download) |
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| yolo4_608 | Yolov4 <sup>8</sup> | [COCO 2017](http://cocodataset.org/) | 80 | 608x608 | [weights](https://cloud.hipert.unimore.it/s/d97CFzYqCPCp5Hg/download) |
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| yolo4_berkeley | Yolov4 <sup>8</sup> | [BDD100K ](https://bair.berkeley.edu/blog/2018/05/30/bdd/) | 10 | 540x320 | [weights](https://cloud.hipert.unimore.it/s/nkWFa5fgb4NTdnB/download) |
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| yolo4_berkeley | Yolov4 <sup>8</sup> | [BDD100K ](https://bair.berkeley.edu/blog/2018/05/30/bdd/) | 10 | 544x320 | [weights](https://cloud.hipert.unimore.it/s/nkWFa5fgb4NTdnB/download) |
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| yolo4tiny | Yolov4 tiny <sup>9</sup> | [COCO 2017](http://cocodataset.org/) | 80 | 416x416 | [weights](https://cloud.hipert.unimore.it/s/iRnc4pSqmx78gJs/download) |
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| yolo4x | Yolov4x-mish <sup>9</sup> | [COCO 2017](http://cocodataset.org/) |
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| yolo4x | Yolov4x-mish <sup>9</sup> | [COCO 2017](http://cocodataset.org/) | 80 | 640x640 | [weights](https://cloud.hipert.unimore.it/s/5MFjtNtgbDGdJEo/download) |
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| yolo4tiny_512 | Yolov4 tiny <sup>9</sup> | [COCO 2017](http://cocodataset.org/) | 80 | 512x512 | [weights](https://cloud.hipert.unimore.it/s/iRnc4pSqmx78gJs/download) |
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80 | 640x640 | [weights](https://cloud.hipert.unimore.it/s/5MFjtNtgbDGdJEo/download) |
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| yolo4x-cps | Scaled Yolov4 <sup>10</sup> | [COCO 2017](http://cocodataset.org/) | 80 | 512x512 | [weights](https://cloud.hipert.unimore.it/s/AfzHE4BfTeEm2gH/download) |
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| shelfnet | ShelfNet18_realtime<sup>11</sup> | [Cityscapes](https://www.cityscapes-dataset.com/) | 19 | 1024x1024 | [weights](https://cloud.hipert.unimore.it/s/mEDZMRJaGCFWSJF/download) |
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| shelfnet_berkeley | ShelfNet18_realtime<sup>11</sup> | [DeepDrive](https://bdd-data.berkeley.edu/) | 20 | 1024x1024 | [weights](https://cloud.hipert.unimore.it/s/m92e7QdD9gYMF7f/download) |
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+45
-36
@@ -9,7 +9,6 @@
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#include "Yolo3Detection.h"
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bool gRun;
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bool SAVE_RESULT = false;
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void sig_handler(int signo) {
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std::cout<<"request gateway stop\n";
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@@ -18,43 +17,53 @@ void sig_handler(int signo) {
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int main(int argc, char *argv[]) {
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std::cout<<"detection\n";
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signal(SIGINT, sig_handler);
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std::string net = "yolo4tiny_fp32.rt";
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if(argc > 1)
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net = argv[1];
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// get config file path and read it
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#ifdef __linux__
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std::string input = "../demo/yolo_test.mp4";
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std::string config_file = "../demo/demoConfig.yaml";
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#elif _WIN32
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std::string input = "..\\..\\..\\demo\\yolo_test.mp4";
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std::string config_file = "..\\..\\..\\demo\\demoConfig.yaml";
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#endif
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if(argc > 1)
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config_file = argv[1];
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YAML::Node conf = YAMLloadConf(config_file);
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if(!conf)
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FatalError("Problem with config file");
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if(argc > 2)
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input = argv[2];
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char ntype = 'y';
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if(argc > 3)
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ntype = argv[3][0];
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int n_classes = 80;
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if(argc > 4)
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n_classes = atoi(argv[4]);
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int n_batch = 1;
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if(argc > 5)
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n_batch = atoi(argv[5]);
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bool show = true;
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if(argc > 6)
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show = atoi(argv[6]);
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float conf_thresh=0.3;
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if(argc > 7)
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conf_thresh = atof(argv[7]);
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// read settings from config file
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std::string net = YAMLgetConf<std::string>(conf, "net", "yolo4tiny_fp32.rt");
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if(!fileExist(net.c_str()))
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FatalError("The given network does not exist. Create the rt first.");
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#ifdef __linux__
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std::string input = YAMLgetConf<std::string>(conf, "input", "../demo/yolo_test.mp4");
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#elif _WIN32
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std::string input = YAMLgetConf(conf, "win_input", "..\\..\\..\\demo\\yolo_test.mp4");
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#endif
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if(!fileExist(input.c_str()))
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FatalError("The given input video does not exist.");
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char ntype = YAMLgetConf<char>(conf, "ntype", 'y');
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int n_classes = YAMLgetConf<int>(conf, "n_classes", 80);
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int n_batch = YAMLgetConf<int>(conf, "n_batch", 1);
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if(n_batch < 1 || n_batch > 64)
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FatalError("Batch dim not supported");
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float conf_thresh = YAMLgetConf<float>(conf, "conf_thresh", 0.3);
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bool show = YAMLgetConf<bool>(conf, "show", true);
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bool save = YAMLgetConf<bool>(conf, "save", false);
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if(!show)
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SAVE_RESULT = true;
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std::cout <<"Net settings - net: "<< net
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<<", ntype: "<< ntype
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<<", n_classes: "<< n_classes
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<<", n_batch: "<< n_batch
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<<", conf_thresh: "<< conf_thresh<<"\n";
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std::cout <<"Demo settings - input: "<< input
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<<", show: "<< show
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<<", save: "<< save<<"\n\n";
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// create detection network
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tk::dnn::Yolo3Detection yolo;
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tk::dnn::CenternetDetection cnet;
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tk::dnn::MobilenetDetection mbnet;
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@@ -79,8 +88,7 @@ int main(int argc, char *argv[]) {
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detNN->init(net, n_classes, n_batch, conf_thresh);
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gRun = true;
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// open video stream
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cv::VideoCapture cap(input);
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if(!cap.isOpened())
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gRun = false;
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@@ -88,19 +96,21 @@ int main(int argc, char *argv[]) {
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std::cout<<"camera started\n";
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cv::VideoWriter resultVideo;
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if(SAVE_RESULT) {
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if(save) {
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int w = cap.get(cv::CAP_PROP_FRAME_WIDTH);
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int h = cap.get(cv::CAP_PROP_FRAME_HEIGHT);
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resultVideo.open("result.mp4", cv::VideoWriter::fourcc('M','P','4','V'), 30, cv::Size(w, h));
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}
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cv::Mat frame;
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if(show)
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cv::namedWindow("detection", cv::WINDOW_NORMAL);
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cv::Mat frame;
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std::vector<cv::Mat> batch_frame;
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std::vector<cv::Mat> batch_dnn_input;
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// start detection loop
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gRun = true;
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while(gRun) {
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batch_dnn_input.clear();
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batch_frame.clear();
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@@ -128,19 +138,18 @@ int main(int argc, char *argv[]) {
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cv::waitKey(1);
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}
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}
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if(n_batch == 1 && SAVE_RESULT)
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if(n_batch == 1 && save)
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resultVideo << frame;
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}
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std::cout<<"detection end\n";
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double mean = 0;
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double mean = 0;
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std::cout<<COL_GREENB<<"\n\nTime stats:\n";
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std::cout<<"Min: "<<*std::min_element(detNN->stats.begin(), detNN->stats.end())/n_batch<<" ms\n";
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std::cout<<"Min: "<<*std::min_element(detNN->stats.begin(), detNN->stats.end())/n_batch<<" ms\n";
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std::cout<<"Max: "<<*std::max_element(detNN->stats.begin(), detNN->stats.end())/n_batch<<" ms\n";
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for(int i=0; i<detNN->stats.size(); i++) mean += detNN->stats[i]; mean /= detNN->stats.size();
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std::cout<<"Avg: "<<mean/n_batch<<" ms\t"<<1000/(mean/n_batch)<<" FPS\n"<<COL_END;
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return 0;
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}
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@@ -0,0 +1,14 @@
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# video input
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input : "../demo/yolo_test.mp4"
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win_input : "..\\..\\..\\demo\\yolo_test.mp4"
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# network config
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net : "yolo4tiny_fp32.rt"
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ntype : 'y'
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n_classes : 80
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n_batch : 1
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conf_thresh : 0.3
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|
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# demo config
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show : true
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save : true
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+16
-15
@@ -32,21 +32,20 @@ make
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Once you have successfully created your rt file, run the demo:
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```
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./demo yolo4_fp32.rt ../demo/yolo_test.mp4 y
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./demo <path-to-config>
|
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```
|
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In general the demo program takes 7 parameters:
|
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```
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./demo <network-rt-file> <path-to-video> <kind-of-network> <number-of-classes> <n-batches> <show-flag> <conf-thresh>
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```
|
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where
|
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In general the demo program takes 1 parameter, the ```<path-to-config>``` that is the path to che configuration file. The parameter is optional and its default value is ```"../demo/demoConfig.yaml"```.
|
||||
|
||||
* ```<network-rt-file>``` is the rt file generated by a test
|
||||
* ```<<path-to-video>``` is the path to a video file or a camera input
|
||||
* ```<kind-of-network>``` is the type of network. Thee types are currently supported: ```y``` (YOLO family), ```c``` (CenterNet family) and ```m``` (MobileNet-SSD family)
|
||||
* ```<number-of-classes>```is the number of classes the network is trained on
|
||||
* ```<n-batches>``` number of batches to use in inference (N.B. you should first export TKDNN_BATCHSIZE to the required n_batches and create again the rt file for the network).
|
||||
* ```<show-flag>``` if set to 0 the demo will not show the visualization but save the video into result.mp4 (if n-batches ==1)
|
||||
* ```<conf-thresh>``` confidence threshold for the detector. Only bounding boxes with threshold greater than conf-thresh will be displayed.
|
||||
The config file is a yaml file with the following attributes:
|
||||
* ```net``` is the rt file generated by a test
|
||||
* ```input``` is the path to a video file or a camera input (on Linux)
|
||||
* ```win_input``` is the path to a video file or a camera input (on Windows)
|
||||
* ```ntype``` is the type of network. Thee types are currently supported: ```y``` (YOLO family), ```c``` (CenterNet family) and ```m``` (MobileNet-SSD family)
|
||||
* ```n_classes``` is the number of classes the network is trained on
|
||||
* ```n_batch``` number of batches to use in inference (N.B. you should first export TKDNN_BATCHSIZE to the required n_batches and create again the rt file for the network).
|
||||
* ```conf_thresh``` confidence threshold for the detector. Only bounding boxes with threshold greater than conf-thresh will be displayed.
|
||||
* ```show``` if set to 0 the demo will not show the visualization (if n-batches ==1)
|
||||
* ```save``` if set to 1 the demo will save the video of the demo into result.mp4 (if n-batches ==1)
|
||||
|
||||
N.B. By default it is used FP32 inference
|
||||
|
||||
@@ -61,7 +60,8 @@ To run the demo with FP16 inference follow these steps (example with yolov3):
|
||||
export TKDNN_MODE=FP16 # set the half floating point optimization
|
||||
rm yolo3_fp16.rt # be sure to delete(or move) old tensorRT files
|
||||
./test_yolo3 # run the yolo test (is slow)
|
||||
./demo yolo3_fp16.rt ../demo/yolo_test.mp4 y
|
||||
# set net: yolo3_fp16.rt in the config-file
|
||||
./demo
|
||||
```
|
||||
N.B. Using FP16 inference will lead to some errors in the results (first or second decimal).
|
||||
|
||||
@@ -86,7 +86,8 @@ export TKDNN_CALIB_LABEL_PATH=../demo/COCO_val2017/all_labels.txt
|
||||
export TKDNN_CALIB_IMG_PATH=../demo/COCO_val2017/all_images.txt
|
||||
rm yolo3_int8.rt # be sure to delete(or move) old tensorRT files
|
||||
./test_yolo3 # run the yolo test (is slow)
|
||||
./demo yolo3_int8.rt ../demo/yolo_test.mp4 y
|
||||
# set net: yolo3_int8.rt in the config-file
|
||||
./demo
|
||||
```
|
||||
N.B.
|
||||
|
||||
|
||||
@@ -182,6 +182,7 @@ class SegmentationNN {
|
||||
checkCuda(cudaMemcpyAsync(mean_d, mean.data(), mean.size() * sizeof(float), cudaMemcpyHostToDevice, netRT->stream));
|
||||
checkCuda(cudaMemcpyAsync(stddev_d, stddev.data(), stddev.size() * sizeof(float), cudaMemcpyHostToDevice, netRT->stream));
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
/**
|
||||
|
||||
@@ -21,6 +21,7 @@
|
||||
#include <ios>
|
||||
#include <chrono>
|
||||
|
||||
#include <yaml-cpp/yaml.h>
|
||||
|
||||
#define dnnType float
|
||||
|
||||
@@ -137,4 +138,19 @@ static inline bool isCudaPointer(void *data) {
|
||||
cudaPointerAttributes attr;
|
||||
return cudaPointerGetAttributes(&attr, data) == 0;
|
||||
}
|
||||
|
||||
inline YAML::Node YAMLloadConf(const std::string& conf_file) {
|
||||
std::cerr<<"Loading YAML: "<<conf_file<<"\n";
|
||||
return YAML::LoadFile(conf_file);
|
||||
}
|
||||
|
||||
template<typename T>
|
||||
inline T YAMLgetConf(YAML::Node conf, std::string key, T defaultVal) {
|
||||
T val = defaultVal;
|
||||
if(conf && conf[key]) {
|
||||
val = conf[key].as<T>();
|
||||
}
|
||||
return val;
|
||||
}
|
||||
|
||||
#endif //UTILS_H
|
||||
|
||||
@@ -0,0 +1,37 @@
|
||||
import sys
|
||||
import pandas as pd
|
||||
|
||||
if len(sys.argv) < 3:
|
||||
print("Error: two csv files are needed, old first new second")
|
||||
exit(1)
|
||||
|
||||
old_perf_file = str(sys.argv[1])
|
||||
new_perf_file = str(sys.argv[2])
|
||||
|
||||
verbose = False
|
||||
if len(sys.argv) == 4:
|
||||
verbose = bool(sys.argv[3])
|
||||
|
||||
print("Comparing {} vs {}".format(old_perf_file, new_perf_file))
|
||||
|
||||
df_old = pd.read_csv (old_perf_file, sep=';', header=None, index_col=0)
|
||||
df_new = pd.read_csv (new_perf_file, sep=';', header=None, index_col=0)
|
||||
|
||||
for index, row in df_new.iterrows():
|
||||
if index in df_old.index:
|
||||
if verbose:
|
||||
print("New: ",row[1], row[2], row[3])
|
||||
print("Old: ",df_old.loc[index][1], df_old.loc[index][2], df_old.loc[index][3])
|
||||
|
||||
print(index, end=': ')
|
||||
if abs(row[1] - df_old.loc[index][1]) < df_old.loc[index][1]*0.1:
|
||||
print("similar performance")
|
||||
elif (row[1] < df_old.loc[index][1]):
|
||||
print('\x1b[3;30;42m' + 'faster' + '\x1b[0m')
|
||||
elif (row[1] > df_old.loc[index][1]):
|
||||
if row[1] > df_old.loc[index][1] + df_old.loc[index][1] * 0.5 :
|
||||
print('\x1b[3;30;41m' + 'WAY SLOWER' + '\x1b[0m')
|
||||
else:
|
||||
print('\x1b[3;30;41m' + 'slower' + '\x1b[0m')
|
||||
|
||||
|
||||
+9
-1
@@ -17,6 +17,8 @@ bool CenterTrack::init(const std::string& tensor_path, const int n_classes, cons
|
||||
init_pre_inf();
|
||||
init_postprocessing();
|
||||
init_visualization(n_classes);
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
bool CenterTrack::init_preprocessing(){
|
||||
@@ -59,6 +61,8 @@ bool CenterTrack::init_preprocessing(){
|
||||
checkCuda( cudaMalloc(&input_d, sizeof(dnnType)*netRT->input_dim.tot() * nBatches));
|
||||
checkCuda( cudaMalloc(&input_pre_inf_d, sizeof(dnnType)*dim.tot()));
|
||||
checkCuda( cudaMalloc(&d_ptrs, dim.tot() * sizeof(float)) );
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
bool CenterTrack::init_pre_inf(){
|
||||
@@ -202,6 +206,8 @@ bool CenterTrack::init_postprocessing(){
|
||||
trRes.resize(nBatches);
|
||||
countTr.resize(nBatches, 0);
|
||||
trackId.resize(nBatches, 0);
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
bool CenterTrack::init_visualization(const int n_classes){
|
||||
@@ -274,6 +280,8 @@ bool CenterTrack::init_visualization(const int n_classes){
|
||||
faceId.push_back({3,0,4,7});
|
||||
faceId.push_back({2,3,7,6});
|
||||
// ([[0,1,5,4], [1,2,6, 5], [2,3,7,6], [3,0,4,7]]);
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
void CenterTrack::_get_additional_inputs(){
|
||||
@@ -308,7 +316,7 @@ void CenterTrack::preprocess(cv::Mat &frame, const int bi){
|
||||
}
|
||||
|
||||
float c[] = {new_width / 2.0f, new_height /2.0f};
|
||||
float s[] = {dim.w, dim.h};
|
||||
float s[] = {float(dim.w), float(dim.h)};
|
||||
// float s = new_width >= new_height ? new_width : new_height;
|
||||
// ----------- get_affine_transform
|
||||
// rot_rad = pi * 0 / 100 --> 0
|
||||
|
||||
@@ -119,6 +119,7 @@ bool CenternetDetection::init(const std::string& tensor_path, const int n_classe
|
||||
dst2.at<float>(2,0)=dst2.at<float>(1,0) + (-dst2.at<float>(0,1)+dst2.at<float>(1,1) );
|
||||
dst2.at<float>(2,1)=dst2.at<float>(1,1) + (dst2.at<float>(0,0)-dst2.at<float>(1,0) );
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
|
||||
|
||||
@@ -167,6 +167,8 @@ bool CenternetDetection3D::init(const std::string& tensor_path, const int n_clas
|
||||
faceId.push_back({2,3,7,6});
|
||||
faceId.push_back({3,0,4,7});
|
||||
// ([[0,1,5,4], [1,2,6, 5], [2,3,7,6], [3,0,4,7]]);
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
void CenternetDetection3D::preprocess(cv::Mat &frame, const int bi){
|
||||
|
||||
@@ -198,7 +198,7 @@ bool MobilenetDetection::init(const std::string& tensor_path, const int n_classe
|
||||
"bottle" , "wine glass" , "cup" , "fork" , "knife" , "spoon" , "bowl" , "banana" ,
|
||||
"apple" , "sandwich" , "orange" , "broccoli" , "carrot" , "hot dog" , "pizza" ,
|
||||
"donut" , "cake" , "chair" , "sofa" , "pottedplant" , "bed" , "diningtable" ,
|
||||
"toilet" , "tvmonitor" , "laptop" , "mouse" , "remote" , "keyboard" ,
|
||||
"toilet" , "tvmonitor" , "laptop" , "mouse" , "remote" , "keyboard" ,
|
||||
"cell phone" , "microwave" , "oven" , "toaster" , "sink" , "refrigerator" ,
|
||||
"book" , "clock" , "vase" , "scissors" , "teddy bear" , "hair drier" , "toothbrush"};
|
||||
classesNames = std::vector<std::string>(classes_names_, std::end(classes_names_));
|
||||
@@ -207,7 +207,7 @@ bool MobilenetDetection::init(const std::string& tensor_path, const int n_classe
|
||||
else{
|
||||
FatalError("Number of classes not supported for mobilenet");
|
||||
}
|
||||
return 1;
|
||||
return true;
|
||||
}
|
||||
|
||||
void MobilenetDetection::preprocess(cv::Mat &frame, const int bi){
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,2 @@
|
||||
person
|
||||
head
|
||||
@@ -0,0 +1,34 @@
|
||||
#include<iostream>
|
||||
#include<vector>
|
||||
#include "tkdnn.h"
|
||||
#include "test.h"
|
||||
#include "DarknetParser.h"
|
||||
|
||||
int main() {
|
||||
std::string bin_path = "yolo4-csp_crowd";
|
||||
std::vector<std::string> input_bins = {
|
||||
bin_path + "/layers/input.bin"
|
||||
};
|
||||
std::vector<std::string> output_bins = {
|
||||
bin_path + "/debug/layer144_out.bin",
|
||||
bin_path + "/debug/layer159_out.bin",
|
||||
bin_path + "/debug/layer174_out.bin"
|
||||
};
|
||||
std::string wgs_path = bin_path + "/layers";
|
||||
std::string cfg_path = std::string(TKDNN_PATH) + "/tests/darknet/cfg/yolo4-csp_crowd.cfg";
|
||||
std::string name_path = std::string(TKDNN_PATH) + "/tests/darknet/names/crowdhuman.names";
|
||||
downloadWeightsifDoNotExist(input_bins[0], bin_path, "https://cloud.hipert.unimore.it/s/RKWfWNmWXfJigsK/download");
|
||||
|
||||
// parse darknet network
|
||||
tk::dnn::Network *net = tk::dnn::darknetParser(cfg_path, wgs_path, name_path);
|
||||
net->print();
|
||||
|
||||
//convert network to tensorRT
|
||||
tk::dnn::NetworkRT *netRT = new tk::dnn::NetworkRT(net, net->getNetworkRTName(bin_path.c_str()));
|
||||
|
||||
int ret = testInference(input_bins, output_bins, net, netRT);
|
||||
net->releaseLayers();
|
||||
delete net;
|
||||
delete netRT;
|
||||
return ret;
|
||||
}
|
||||
@@ -0,0 +1,295 @@
|
||||
#include <iostream>
|
||||
#include <opencv2/highgui/highgui.hpp>
|
||||
#include <opencv2/imgproc/imgproc.hpp>
|
||||
|
||||
#include "tkdnn.h"
|
||||
#include "NetworkViz.h"
|
||||
|
||||
|
||||
const char *input_bin = "shelfnet_coco/debug/input.bin";
|
||||
|
||||
const char *backbone[] = {
|
||||
"shelfnet_coco/layers/backbone-conv1.bin",
|
||||
"shelfnet_coco/layers/backbone-layer1-0-conv1.bin",
|
||||
"shelfnet_coco/layers/backbone-layer1-0-conv2.bin",
|
||||
"shelfnet_coco/layers/backbone-layer1-1-conv1.bin",
|
||||
"shelfnet_coco/layers/backbone-layer1-1-conv2.bin",
|
||||
"shelfnet_coco/layers/backbone-layer2-0-conv1.bin",
|
||||
"shelfnet_coco/layers/backbone-layer2-0-conv2.bin",
|
||||
"shelfnet_coco/layers/backbone-layer2-0-downsample-0.bin",
|
||||
"shelfnet_coco/layers/backbone-layer2-1-conv1.bin",
|
||||
"shelfnet_coco/layers/backbone-layer2-1-conv2.bin",
|
||||
"shelfnet_coco/layers/backbone-layer3-0-conv1.bin",
|
||||
"shelfnet_coco/layers/backbone-layer3-0-conv2.bin",
|
||||
"shelfnet_coco/layers/backbone-layer3-0-downsample-0.bin",
|
||||
"shelfnet_coco/layers/backbone-layer3-1-conv1.bin",
|
||||
"shelfnet_coco/layers/backbone-layer3-1-conv2.bin",
|
||||
"shelfnet_coco/layers/backbone-layer4-0-conv1.bin",
|
||||
"shelfnet_coco/layers/backbone-layer4-0-conv2.bin",
|
||||
"shelfnet_coco/layers/backbone-layer4-0-downsample-0.bin",
|
||||
"shelfnet_coco/layers/backbone-layer4-1-conv1.bin",
|
||||
"shelfnet_coco/layers/backbone-layer4-1-conv2.bin"};
|
||||
|
||||
const char *conv_out[] = {
|
||||
"shelfnet_coco/layers/conv_out-conv-conv.bin",
|
||||
"shelfnet_coco/layers/conv_out-conv_out.bin",
|
||||
"shelfnet_coco/layers/conv_out16-conv-conv.bin",
|
||||
"shelfnet_coco/layers/conv_out16-conv_out.bin",
|
||||
"shelfnet_coco/layers/conv_out32-conv-conv.bin",
|
||||
"shelfnet_coco/layers/conv_out32-conv_out.bin"
|
||||
};
|
||||
|
||||
const char *decoder[] = {
|
||||
"shelfnet_coco/layers/decoder-bottom-conv1.bin",
|
||||
"shelfnet_coco/layers/decoder-bottom-conv12.bin",
|
||||
"shelfnet_coco/layers/decoder-up_conv_list-0-conv-conv.bin",
|
||||
"shelfnet_coco/layers/decoder-up_conv_list-0-conv_atten.bin",
|
||||
"shelfnet_coco/layers/decoder-up_dense_list-0-conv.bin",
|
||||
"shelfnet_coco/layers/decoder-up_conv_list-1-conv-conv.bin",
|
||||
"shelfnet_coco/layers/decoder-up_conv_list-1-conv_atten.bin",
|
||||
"shelfnet_coco/layers/decoder-up_dense_list-1-conv.bin"
|
||||
};
|
||||
|
||||
|
||||
const char *ladder[] = {
|
||||
"shelfnet_coco/layers/ladder-inconv-conv1.bin",
|
||||
"shelfnet_coco/layers/ladder-inconv-conv12.bin",
|
||||
"shelfnet_coco/layers/ladder-down_module_list-0-conv1.bin",
|
||||
"shelfnet_coco/layers/ladder-down_module_list-0-conv12.bin",
|
||||
"shelfnet_coco/layers/ladder-down_conv_list-0.bin",
|
||||
|
||||
"shelfnet_coco/layers/ladder-down_module_list-1-conv1.bin",
|
||||
"shelfnet_coco/layers/ladder-down_module_list-1-conv12.bin",
|
||||
"shelfnet_coco/layers/ladder-down_conv_list-1.bin",
|
||||
|
||||
"shelfnet_coco/layers/ladder-bottom-conv1.bin",
|
||||
"shelfnet_coco/layers/ladder-bottom-conv12.bin",
|
||||
|
||||
|
||||
|
||||
"shelfnet_coco/layers/ladder-up_conv_list-0-conv-conv.bin",
|
||||
"shelfnet_coco/layers/ladder-up_conv_list-0-conv_atten.bin",
|
||||
"shelfnet_coco/layers/ladder-up_dense_list-0-conv.bin",
|
||||
|
||||
|
||||
"shelfnet_coco/layers/ladder-up_conv_list-1-conv-conv.bin",
|
||||
"shelfnet_coco/layers/ladder-up_conv_list-1-conv_atten.bin",
|
||||
"shelfnet_coco/layers/ladder-up_dense_list-1-conv.bin"};
|
||||
|
||||
const char *trans[] = {
|
||||
"shelfnet_coco/layers/trans1-conv.bin",
|
||||
"shelfnet_coco/layers/trans2-conv.bin",
|
||||
"shelfnet_coco/layers/trans3-conv.bin"};
|
||||
int main()
|
||||
{
|
||||
|
||||
downloadWeightsifDoNotExist(input_bin, "shelfnet_coco", "https://cloud.hipert.unimore.it/s/KfQ9fGJQsgzNbiW/download");
|
||||
|
||||
int classes = 183;
|
||||
|
||||
// Network layout
|
||||
tk::dnn::dataDim_t dim(1, 3, 1024, 1024, 1);
|
||||
tk::dnn::Network net(dim);
|
||||
|
||||
int bi = 0, di = 0, li = 0, ci = 0;
|
||||
new tk::dnn::Conv2d(&net, 64, 7, 7, 2, 2, 3, 3, backbone[bi++], true);
|
||||
new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
|
||||
tk::dnn::Layer* last = new tk::dnn::Pooling (&net, 3, 3, 2, 2, 1, 1, tk::dnn::POOLING_MAX);
|
||||
|
||||
|
||||
|
||||
for(int i=0; i<2; ++i){
|
||||
new tk::dnn::Conv2d (&net, 64, 3, 3, 1, 1, 1, 1, backbone[bi++], true);
|
||||
new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
|
||||
new tk::dnn::Conv2d (&net, 64, 3, 3, 1, 1, 1, 1, backbone[bi++], true);
|
||||
new tk::dnn::Shortcut(&net, last);
|
||||
last = new tk::dnn::Activation (&net, CUDNN_ACTIVATION_RELU);
|
||||
}
|
||||
|
||||
std::vector<tk::dnn::Layer*> features;
|
||||
for(int i=0;i<3;++i){
|
||||
int out_channel = pow(2,7+i);
|
||||
std::cout<<out_channel<<std::endl;
|
||||
new tk::dnn::Conv2d (&net, out_channel, 3, 3, 2, 2, 1, 1, backbone[bi++], true);
|
||||
new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
|
||||
tk::dnn::Layer* bn2 = new tk::dnn::Conv2d (&net, out_channel, 3, 3, 1, 1, 1, 1, backbone[bi++], true);
|
||||
new tk::dnn::Route(&net, &last, 1);
|
||||
new tk::dnn::Conv2d (&net, out_channel, 1, 1, 2, 2, 0, 0, backbone[bi++], true);
|
||||
new tk::dnn::Shortcut(&net, bn2);
|
||||
last = new tk::dnn::Activation (&net, CUDNN_ACTIVATION_RELU);
|
||||
|
||||
new tk::dnn::Conv2d (&net, out_channel, 3, 3, 1, 1, 1, 1, backbone[bi++], true);
|
||||
new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
|
||||
new tk::dnn::Conv2d (&net, out_channel, 3, 3, 1, 1, 1, 1, backbone[bi++], true);
|
||||
|
||||
new tk::dnn::Shortcut(&net, last);
|
||||
last = new tk::dnn::Activation (&net, CUDNN_ACTIVATION_RELU);
|
||||
features.push_back(last);
|
||||
}
|
||||
|
||||
for(int i=0; i<features.size(); ++i){
|
||||
new tk::dnn::Route(&net, &features[i], 1);
|
||||
int out_channel = pow(2,6+i);
|
||||
new tk::dnn::Conv2d (&net, out_channel, 1, 1, 1, 1, 0, 0, trans[i], true);
|
||||
features[i] = new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
|
||||
}
|
||||
|
||||
//DECODER
|
||||
|
||||
last = features[2];
|
||||
std::vector<tk::dnn::Layer*> up_out;
|
||||
//bottom
|
||||
new tk::dnn::Conv2d (&net, 256, 3, 3, 1, 1, 1, 1, decoder[di++], true, false, 1, true);
|
||||
new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
|
||||
new tk::dnn::Conv2d (&net, 256, 3, 3, 1, 1, 1, 1, decoder[di++], true, false, 1, true);
|
||||
new tk::dnn::Shortcut(&net, last);
|
||||
last = new tk::dnn::Activation (&net, CUDNN_ACTIVATION_RELU);
|
||||
up_out.push_back(last);
|
||||
|
||||
for(int i=0; i<2; ++i){
|
||||
int out_channel = pow(2,7-i);
|
||||
//up-conv
|
||||
std::cout<<out_channel<<std::endl;
|
||||
new tk::dnn::Conv2d (&net, out_channel, 3, 3, 1, 1, 1, 1, decoder[di++], true);
|
||||
last = new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
|
||||
|
||||
new tk::dnn::Pooling(&net, last->output_dim.w, last->output_dim.h, last->output_dim.w, last->output_dim.h, 0, 0, tk::dnn::POOLING_AVERAGE);
|
||||
new tk::dnn::Conv2d (&net, out_channel, 1, 1, 1, 1, 0, 0, decoder[di++], true);
|
||||
|
||||
tk::dnn::Layer* act = new tk::dnn::Activation (&net, CUDNN_ACTIVATION_SIGMOID);
|
||||
new tk::dnn::Route(&net, &last, 1);
|
||||
new tk::dnn::Shortcut(&net, act, true);
|
||||
|
||||
//interpolate
|
||||
new tk::dnn::Resize(&net, 1,2,2);
|
||||
new tk::dnn::Shortcut(&net, features[1-i]);
|
||||
|
||||
//up-dense
|
||||
new tk::dnn::Conv2d (&net, out_channel, 3, 3, 1, 1, 1, 1, decoder[di++], true);
|
||||
last = new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
|
||||
up_out.push_back(last);
|
||||
}
|
||||
|
||||
//LADDER
|
||||
|
||||
std::vector<tk::dnn::Layer*> down_out;
|
||||
new tk::dnn::Conv2d (&net, 64, 3, 3, 1, 1, 1, 1, ladder[li++], true, false, 1, true);
|
||||
new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
|
||||
new tk::dnn::Conv2d (&net, 64, 3, 3, 1, 1, 1, 1, ladder[li++], true, false, 1, true);
|
||||
new tk::dnn::Shortcut(&net, last);
|
||||
new tk::dnn::Activation (&net, CUDNN_ACTIVATION_RELU);
|
||||
|
||||
for(int i=0; i<2;++i){
|
||||
int out_channel = pow(2,6+i);
|
||||
tk::dnn::Layer* l_last = new tk::dnn::Shortcut(&net, up_out[2-i]);
|
||||
|
||||
new tk::dnn::Conv2d (&net, out_channel, 3, 3, 1, 1, 1, 1, ladder[li++], true, false, 1, true);
|
||||
new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
|
||||
new tk::dnn::Conv2d (&net, out_channel, 3, 3, 1, 1, 1, 1, ladder[li++], true, false, 1, true);
|
||||
new tk::dnn::Shortcut(&net, l_last);
|
||||
l_last = new tk::dnn::Activation (&net, CUDNN_ACTIVATION_RELU);
|
||||
down_out.push_back(l_last);
|
||||
|
||||
new tk::dnn::Conv2d (&net, out_channel*2, 3, 3, 2, 2, 1, 1, ladder[li++], false);
|
||||
last = new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.0f); //should be ReLU
|
||||
}
|
||||
|
||||
new tk::dnn::Conv2d (&net, 256, 3, 3, 1, 1, 1, 1, ladder[li++], true, false, 1, true);
|
||||
new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
|
||||
new tk::dnn::Conv2d (&net, 256, 3, 3, 1, 1, 1, 1, ladder[li++], true, false, 1, true);
|
||||
new tk::dnn::Shortcut(&net, last);
|
||||
last = new tk::dnn::Activation (&net, CUDNN_ACTIVATION_RELU);
|
||||
up_out.clear();
|
||||
up_out.push_back(last);
|
||||
|
||||
for(int i=0; i<2; ++i){
|
||||
int out_channel = pow(2,7-i);
|
||||
//up-conv
|
||||
new tk::dnn::Conv2d (&net, out_channel, 3, 3, 1, 1, 1, 1, ladder[li++], true);
|
||||
last = new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
|
||||
|
||||
new tk::dnn::Pooling(&net, last->output_dim.w, last->output_dim.h, last->output_dim.w, last->output_dim.h, 0, 0, tk::dnn::POOLING_AVERAGE);
|
||||
new tk::dnn::Conv2d (&net, out_channel, 1, 1, 1, 1, 0, 0, ladder[li++], true);
|
||||
|
||||
tk::dnn::Layer* act = new tk::dnn::Activation (&net, CUDNN_ACTIVATION_SIGMOID);
|
||||
new tk::dnn::Route(&net, &last, 1);
|
||||
new tk::dnn::Shortcut(&net, act, true);
|
||||
|
||||
//interpolate
|
||||
new tk::dnn::Resize(&net, 1,2,2);
|
||||
new tk::dnn::Shortcut(&net, down_out[1-i]);
|
||||
|
||||
// //up-dense
|
||||
new tk::dnn::Conv2d (&net, out_channel, 3, 3, 1, 1, 1, 1, ladder[li++], true);
|
||||
last = new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
|
||||
up_out.push_back(last);
|
||||
}
|
||||
|
||||
|
||||
// for(int i=2;i>=0;--i){
|
||||
// new tk::dnn::Route(&net, &up_out[i], 1);
|
||||
new tk::dnn::Conv2d (&net, 64, 3, 3, 1, 1, 1, 1, conv_out[ci++], true);
|
||||
new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
|
||||
new tk::dnn::Conv2d (&net, classes, 3, 3, 1, 1, 1, 1, conv_out[ci++], false);
|
||||
/*up_out[i] =*/ new tk::dnn::Resize(&net, classes, net.input_dim.h, net.input_dim.w, true, tk::dnn::ResizeMode_t::LINEAR);
|
||||
// // }
|
||||
|
||||
new tk::dnn::Softmax(&net);
|
||||
|
||||
const char *output_bin = "shelfnet_coco/debug/softmax.bin";
|
||||
|
||||
// Load input
|
||||
dnnType *data;
|
||||
dnnType *input_h;
|
||||
readBinaryFile(input_bin, dim.tot(), &input_h, &data);
|
||||
std::cout<<"Input:"<<std::endl;
|
||||
|
||||
//print network model
|
||||
net.print();
|
||||
|
||||
// // convert network to tensorRT
|
||||
tk::dnn::NetworkRT netRT(&net, net.getNetworkRTName("shelfnet_coco"));
|
||||
|
||||
tk::dnn::dataDim_t dim1 = dim; //input dim
|
||||
dnnType *cudnn_out = nullptr;
|
||||
printCenteredTitle(" CUDNN inference ", '=', 30);
|
||||
{
|
||||
dim1.print();
|
||||
TKDNN_TSTART
|
||||
cudnn_out = net.infer(dim1, data);
|
||||
TKDNN_TSTOP
|
||||
dim1.print();
|
||||
}
|
||||
|
||||
tk::dnn::dataDim_t dim2 = dim;
|
||||
printCenteredTitle(" TENSORRT inference ", '=', 30);
|
||||
{
|
||||
dim2.print();
|
||||
TKDNN_TSTART
|
||||
netRT.infer(dim2, data);
|
||||
TKDNN_TSTOP
|
||||
dim2.print();
|
||||
}
|
||||
|
||||
dnnType *rt_out1 = (dnnType *)netRT.buffersRT[1];
|
||||
|
||||
printCenteredTitle(std::string(" CHECK RESULTS ").c_str(), '=', 30);
|
||||
dnnType *out1, *out1_h;
|
||||
int odim1 = dim1.tot();
|
||||
readBinaryFile(output_bin, odim1, &out1_h, &out1);
|
||||
|
||||
int ret_cudnn = 0, ret_tensorrt = 0, ret_cudnn_tensorrt = 0;
|
||||
// std::cout << "CUDNN vs correct" << std::endl;
|
||||
// ret_cudnn |= checkResult(odim1, cudnn_out, out1, true, 20) == 0 ? 0 : ERROR_CUDNN;
|
||||
|
||||
std::cout << "TRT vs correct" << std::endl;
|
||||
ret_tensorrt |=checkResult(odim1, rt_out1, out1) == 0 ? 0 : ERROR_TENSORRT;
|
||||
|
||||
std::cout << "CUDNN vs TRT " << std::endl;
|
||||
ret_cudnn_tensorrt |= checkResult(odim1, cudnn_out, rt_out1) == 0 ? 0 : ERROR_CUDNNvsTENSORRT;
|
||||
|
||||
cv::Mat viz = vizLayer2Mat(&net, net.num_layers-1);
|
||||
cv::imwrite("test.png", viz);
|
||||
|
||||
return ret_cudnn | ret_tensorrt | ret_cudnn_tensorrt;
|
||||
}
|
||||
Reference in New Issue
Block a user