├── .gitignore ├── .vscode ├── configurationCache.log ├── settings.json └── targets.log ├── README.md ├── demo ├── HRNet │ ├── .idea │ │ ├── .gitignore │ │ ├── HRNet.iml │ │ ├── inspectionProfiles │ │ │ ├── Project_Default.xml │ │ │ └── profiles_settings.xml │ │ ├── misc.xml │ │ └── modules.xml │ ├── HRNet.png │ ├── README.md │ ├── __pycache__ │ │ ├── draw_utils.cpython-39.pyc │ │ └── transforms.cpython-39.pyc │ ├── draw_utils.py │ ├── infer-onnxruntime.py │ ├── model │ │ ├── __init__.py │ │ ├── __pycache__ │ │ │ ├── __init__.cpython-39.pyc │ │ │ └── hrnet.cpython-39.pyc │ │ └── hrnet.py │ ├── my_dataset_coco.py │ ├── person.png │ ├── person1.jpeg │ ├── person2.jpeg │ ├── person_keypoints.json │ ├── plot_curve.py │ ├── predict.py │ ├── requirements.txt │ ├── test.py │ ├── test_result.jpg │ ├── train.py │ ├── train_multi_GPU.py │ ├── train_utils │ │ ├── __init__.py │ │ ├── coco_eval.py │ │ ├── coco_utils.py │ │ ├── distributed_utils.py │ │ ├── group_by_aspect_ratio.py │ │ ├── loss.py │ │ └── train_eval_utils.py │ ├── transforms.py │ └── validation.py ├── centernet │ ├── .gitignore │ ├── .idea │ │ ├── .gitignore │ │ ├── centernet-pytorch-main.iml │ │ ├── inspectionProfiles │ │ │ ├── Project_Default.xml │ │ │ └── profiles_settings.xml │ │ ├── misc.xml │ │ └── modules.xml │ ├── LICENSE │ ├── README.md │ ├── centernet.py │ ├── export_onnx.py │ ├── get_map.py │ ├── img │ │ └── street.jpg │ ├── infer-onnxruntime.py │ ├── info.md │ ├── mypredict.py │ ├── nets │ │ ├── centernet.py │ │ ├── centernet_training.py │ │ ├── hourglass.py │ │ └── resnet50.py │ ├── onnx_simplify.py │ ├── predict.py │ ├── requirements.txt │ ├── summary.py │ ├── train.py │ ├── utils │ │ ├── __init__.py │ │ ├── callbacks.py │ │ ├── dataloader.py │ │ ├── utils.py │ │ ├── utils_bbox.py │ │ ├── utils_fit.py │ │ └── utils_map.py │ ├── vision_for_centernet.py │ ├── voc_annotation.py │ └── 常见问题汇总.md ├── detr-main │ ├── .circleci │ │ └── config.yml │ ├── .github │ │ ├── CODE_OF_CONDUCT.md │ │ ├── CONTRIBUTING.md │ │ ├── DETR.png │ │ └── ISSUE_TEMPLATE │ │ │ ├── bugs.md │ │ │ ├── questions-help-support.md │ │ │ └── unexpected-problems-bugs.md │ ├── .gitignore │ ├── Dockerfile │ ├── LICENSE │ ├── README.md │ ├── d2 │ │ ├── README.md │ │ ├── configs │ │ │ ├── detr_256_6_6_torchvision.yaml │ │ │ └── detr_segm_256_6_6_torchvision.yaml │ │ ├── converter.py │ │ ├── detr │ │ │ ├── __init__.py │ │ │ ├── config.py │ │ │ ├── dataset_mapper.py │ │ │ └── detr.py │ │ └── train_net.py │ ├── datasets │ │ ├── __init__.py │ │ ├── coco.py │ │ ├── coco_eval.py │ │ ├── coco_panoptic.py │ │ ├── panoptic_eval.py │ │ └── transforms.py │ ├── demo.jpg │ ├── engine.py │ ├── hubconf.py │ ├── infer-onnxruntime.py │ ├── load_from_cpp.py │ ├── main.py │ ├── models │ │ ├── __init__.py │ │ ├── backbone.py │ │ ├── detr.py │ │ ├── detr_bak.py │ │ ├── matcher.py │ │ ├── position_encoding.py │ │ ├── segmentation.py │ │ └── transformer.py │ ├── mypredict.py │ ├── requirements.txt │ ├── run_with_submitit.py │ ├── street.jpg │ ├── test_all.py │ ├── tools.py │ ├── tox.ini │ └── util │ │ ├── __init__.py │ │ ├── box_ops.py │ │ ├── misc.py │ │ └── plot_utils.py ├── detr_demo │ ├── demo.jpg │ ├── export_onnx.py │ ├── infer-onnxruntime.py │ ├── model.py │ ├── onnx_simplify.py │ ├── predict.py │ └── tools.py ├── swin │ ├── __pycache__ │ │ ├── model.cpython-39.pyc │ │ ├── my_dataset.cpython-39.pyc │ │ └── utils.cpython-39.pyc │ ├── class_indices.json │ ├── create_confusion_matrix.py │ ├── export_onnx.py │ ├── infer-onnxruntime.py │ ├── model.py │ ├── my_dataset.py │ ├── onnx_simplify.py │ ├── predict.py │ ├── predict_bak.py │ ├── read_onnx.py │ ├── select_incorrect_samples.py │ ├── train.py │ └── utils.py ├── unet │ ├── #U5e38#U89c1#U95ee#U9898#U6c47#U603b.md │ ├── .gitignore │ ├── LICENSE │ ├── Medical_Datasets │ │ ├── ImageSets │ │ │ └── Segmentation │ │ │ │ ├── README.md │ │ │ │ ├── train.txt │ │ │ │ └── trainval.txt │ │ ├── Images │ │ │ ├── 0.png │ │ │ ├── 1.png │ │ │ ├── 10.png │ │ │ ├── 11.png │ │ │ ├── 12.png │ │ │ ├── 13.png │ │ │ ├── 14.png │ │ │ ├── 15.png │ │ │ ├── 16.png │ │ │ ├── 17.png │ │ │ ├── 18.png │ │ │ ├── 19.png │ │ │ ├── 2.png │ │ │ ├── 20.png │ │ │ ├── 21.png │ │ │ ├── 22.png │ │ │ ├── 23.png │ │ │ ├── 24.png │ │ │ ├── 25.png │ │ │ ├── 26.png │ │ │ ├── 27.png │ │ │ ├── 28.png │ │ │ ├── 29.png │ │ │ ├── 3.png │ │ │ ├── 4.png │ │ │ ├── 5.png │ │ │ ├── 6.png │ │ │ ├── 7.png │ │ │ ├── 8.png │ │ │ └── 9.png │ │ └── Labels │ │ │ ├── 0.png │ │ │ ├── 1.png │ │ │ ├── 10.png │ │ │ ├── 11.png │ │ │ ├── 12.png │ │ │ ├── 13.png │ │ │ ├── 14.png │ │ │ ├── 15.png │ │ │ ├── 16.png │ │ │ ├── 17.png │ │ │ ├── 18.png │ │ │ ├── 19.png │ │ │ ├── 2.png │ │ │ ├── 20.png │ │ │ ├── 21.png │ │ │ ├── 22.png │ │ │ ├── 23.png │ │ │ ├── 24.png │ │ │ ├── 25.png │ │ │ ├── 26.png │ │ │ ├── 27.png │ │ │ ├── 28.png │ │ │ ├── 29.png │ │ │ ├── 3.png │ │ │ ├── 4.png │ │ │ ├── 5.png │ │ │ ├── 6.png │ │ │ ├── 7.png │ │ │ ├── 8.png │ │ │ └── 9.png │ ├── README.md │ ├── VOCdevkit │ │ └── VOC2007 │ │ │ ├── ImageSets │ │ │ └── Segmentation │ │ │ │ └── README.md │ │ │ ├── JPEGImages │ │ │ └── README.md │ │ │ └── SegmentationClass │ │ │ └── README.md │ ├── datasets │ │ ├── JPEGImages │ │ │ └── 1.jpg │ │ ├── SegmentationClass │ │ │ └── 1.png │ │ └── before │ │ │ ├── 1.jpg │ │ │ └── 1.json │ ├── export.py │ ├── get_miou.py │ ├── img │ │ ├── cell.png │ │ └── street.jpg │ ├── json_to_dataset.py │ ├── logs │ │ └── README.MD │ ├── model_data │ │ └── README.md │ ├── nets │ │ ├── __init__.py │ │ ├── unet.py │ │ ├── unet_training.py │ │ └── vgg.py │ ├── predict.py │ ├── requirements.txt │ ├── summary.py │ ├── train.py │ ├── train_medical.py │ ├── unet.py │ ├── utils │ │ ├── __init__.py │ │ ├── callbacks.py │ │ ├── dataloader.py │ │ ├── dataloader_medical.py │ │ ├── utils.py │ │ ├── utils_fit.py │ │ └── utils_metrics.py │ ├── voc_annotation.py │ └── voc_annotation_medical.py ├── vit │ ├── class_indices.json │ ├── demo.jpg │ ├── export_onnx.py │ ├── flops.py │ ├── infer-onnxruntime.py │ ├── my_dataset.py │ ├── onnx_simplify.py │ ├── predict.py │ ├── train.py │ ├── utils.py │ └── vit_model.py ├── yolov5 │ ├── detect-for-yolov5-6.0.sh │ ├── export-yolov5-6.0.sh │ ├── yolov5-6.0 │ │ ├── .dockerignore │ │ ├── .gitattributes │ │ ├── .github │ │ │ ├── FUNDING.yml │ │ │ ├── ISSUE_TEMPLATE │ │ │ │ ├── bug-report.md │ │ │ │ ├── feature-request.md │ │ │ │ └── question.md │ │ │ ├── dependabot.yml │ │ │ └── workflows │ │ │ │ ├── ci-testing.yml │ │ │ │ ├── codeql-analysis.yml │ │ │ │ ├── greetings.yml │ │ │ │ ├── rebase.yml │ │ │ │ └── stale.yml │ │ ├── .gitignore │ │ ├── Arial.ttf │ │ ├── CONTRIBUTING.md │ │ ├── Dockerfile │ │ ├── LICENSE │ │ ├── README.md │ │ ├── detect.py │ │ ├── export.py │ │ ├── hubconf.py │ │ ├── models │ │ │ ├── __init__.py │ │ │ ├── common.py │ │ │ ├── experimental.py │ │ │ ├── hub │ │ │ │ ├── anchors.yaml │ │ │ │ ├── yolov3-spp.yaml │ │ │ │ ├── yolov3-tiny.yaml │ │ │ │ ├── yolov3.yaml │ │ │ │ ├── yolov5-bifpn.yaml │ │ │ │ ├── yolov5-fpn.yaml │ │ │ │ ├── yolov5-p2.yaml │ │ │ │ ├── yolov5-p6.yaml │ │ │ │ ├── yolov5-p7.yaml │ │ │ │ ├── yolov5-panet.yaml │ │ │ │ ├── yolov5l6.yaml │ │ │ │ ├── yolov5m6.yaml │ │ │ │ ├── yolov5n6.yaml │ │ │ │ ├── yolov5s-ghost.yaml │ │ │ │ ├── yolov5s-transformer.yaml │ │ │ │ ├── yolov5s6.yaml │ │ │ │ └── yolov5x6.yaml │ │ │ ├── tf.py │ │ │ ├── yolo.py │ │ │ ├── yolov5l.yaml │ │ │ ├── yolov5m.yaml │ │ │ ├── yolov5n.yaml │ │ │ ├── yolov5s.yaml │ │ │ └── yolov5x.yaml │ │ ├── requirements.txt │ │ ├── train.py │ │ ├── tutorial.ipynb │ │ ├── utils │ │ │ ├── __init__.py │ │ │ ├── activations.py │ │ │ ├── augmentations.py │ │ │ ├── autoanchor.py │ │ │ ├── aws │ │ │ │ ├── __init__.py │ │ │ │ ├── mime.sh │ │ │ │ ├── resume.py │ │ │ │ └── userdata.sh │ │ │ ├── callbacks.py │ │ │ ├── datasets.py │ │ │ ├── downloads.py │ │ │ ├── flask_rest_api │ │ │ │ ├── README.md │ │ │ │ ├── example_request.py │ │ │ │ └── restapi.py │ │ │ ├── general.py │ │ │ ├── google_app_engine │ │ │ │ ├── Dockerfile │ │ │ │ ├── additional_requirements.txt │ │ │ │ └── app.yaml │ │ │ ├── loggers │ │ │ │ ├── __init__.py │ │ │ │ └── wandb │ │ │ │ │ ├── README.md │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── log_dataset.py │ │ │ │ │ ├── sweep.py │ │ │ │ │ ├── sweep.yaml │ │ │ │ │ └── wandb_utils.py │ │ │ ├── loss.py │ │ │ ├── metrics.py │ │ │ ├── plots.py │ │ │ └── torch_utils.py │ │ └── val.py │ ├── yolov5.cpp │ └── yolov5.hpp └── yolov7 │ ├── README.md │ ├── cfg │ ├── baseline │ │ ├── r50-csp.yaml │ │ ├── x50-csp.yaml │ │ ├── yolor-csp-x.yaml │ │ ├── yolor-csp.yaml │ │ ├── yolor-d6.yaml │ │ ├── yolor-e6.yaml │ │ ├── yolor-p6.yaml │ │ ├── yolor-w6.yaml │ │ ├── yolov3-spp.yaml │ │ ├── yolov3.yaml │ │ └── yolov4-csp.yaml │ └── deploy │ │ ├── yolov7-d6.yaml │ │ ├── yolov7-e6.yaml │ │ ├── yolov7-e6e.yaml │ │ ├── yolov7-tiny-silu.yaml │ │ ├── yolov7-w6.yaml │ │ ├── yolov7.yaml │ │ └── yolov7x.yaml │ ├── data │ ├── coco.yaml │ ├── hyp.scratch.p5.yaml │ ├── hyp.scratch.p6.yaml │ └── hyp.scratch.tiny.yaml │ ├── detect.py │ ├── export_onnx.py │ ├── figure │ └── performance.png │ ├── hubconf.py │ ├── infer-onnxruntime.py │ ├── models │ ├── __init__.py │ ├── common.py │ ├── experimental.py │ ├── export.py │ └── yolo.py │ ├── onnx_simplify.py │ ├── scripts │ └── get_coco.sh │ ├── test.py │ ├── train.py │ ├── transforms.py │ └── utils │ ├── __init__.py │ ├── activations.py │ ├── autoanchor.py │ ├── aws │ ├── __init__.py │ ├── mime.sh │ ├── resume.py │ └── userdata.sh │ ├── datasets.py │ ├── general.py │ ├── google_app_engine │ ├── Dockerfile │ ├── additional_requirements.txt │ └── app.yaml │ ├── google_utils.py │ ├── loss.py │ ├── metrics.py │ ├── plots.py │ ├── torch_utils.py │ └── wandb_logging │ ├── __init__.py │ ├── log_dataset.py │ └── wandb_utils.py ├── sideline_learn ├── PixelShuffle │ └── PixelShuffle.py ├── cpp-move │ ├── forward.cpp │ ├── move.cpp │ └── test.cpp ├── multi-thread │ ├── 0624.cpp │ ├── 1.cpp │ ├── Makefile │ ├── async.cpp │ ├── fix_tow_con.cpp │ ├── no_used_future.cpp │ ├── one_con.cpp │ ├── readme.md │ ├── src │ │ └── package_task.cpp │ ├── tow_con.cpp │ ├── used_future.cpp │ ├── used_future_set.cpp │ ├── used_share_future_set.cpp │ └── workspace │ │ └── pro ├── ncnn_multi_thread │ ├── CMakeLists.txt │ ├── fastdet_test.cpp │ ├── include │ │ ├── fastdet.h │ │ └── infer.hpp │ ├── multi_thead_infer.cpp │ └── src │ │ ├── fastdet.cpp │ │ └── infer.cpp ├── paddle_code │ ├── swim-t │ │ ├── Swim-t.py │ │ ├── Swimblock.py │ │ ├── mask.py │ │ └── swim_t.md │ └── vi-t │ │ ├── attention.py │ │ ├── vit.py │ │ └── vit_model.md ├── tools │ ├── nms.py │ └── pic2video2.py ├── warpaffine-cuda │ ├── CMakeLists.txt │ ├── build │ │ ├── CMakeCache.txt │ │ ├── CMakeFiles │ │ │ ├── 3.16.3 │ │ │ │ ├── CMakeDetermineCompilerABI_C.bin │ │ │ │ ├── CMakeDetermineCompilerABI_CXX.bin │ │ │ │ ├── CompilerIdC │ │ │ │ │ ├── CMakeCCompilerId.c │ │ │ │ │ └── a.out │ │ │ │ └── CompilerIdCXX │ │ │ │ │ ├── CMakeCXXCompilerId.cpp │ │ │ │ │ └── a.out │ │ │ ├── CMakeRuleHashes.txt │ │ │ ├── Makefile2 │ │ │ ├── TargetDirectories.txt │ │ │ ├── cmake.check_cache │ │ │ ├── progress.marks │ │ │ ├── test.dir │ │ │ │ ├── CXX.includecache │ │ │ │ ├── build.make │ │ │ │ ├── depend.internal │ │ │ │ ├── depend.make │ │ │ │ ├── flags.make │ │ │ │ ├── link.txt │ │ │ │ └── progress.make │ │ │ └── warpaffine.dir │ │ │ │ ├── build.make │ │ │ │ ├── depend.internal │ │ │ │ ├── depend.make │ │ │ │ ├── flags.make │ │ │ │ ├── link.txt │ │ │ │ ├── progress.make │ │ │ │ └── src │ │ │ │ ├── warpaffine_generated_warpaffine.cu.o.cmake.pre-gen │ │ │ │ └── warpaffine_generated_warpaffine.cu.o.depend │ │ ├── Makefile │ │ ├── libwarpaffine.so │ │ └── test │ ├── include │ │ └── warpaffine.h │ ├── scripts │ │ └── warpaffine.py │ └── src │ │ ├── test.cpp │ │ └── warpaffine.cu └── yolo5-6.0-ros │ ├── .catkin_workspace │ ├── README.md │ ├── Screenshot from 2022-07-30 09-39-36.png │ └── src │ ├── CMakeLists.txt │ ├── base │ ├── CMakeLists.txt │ ├── code_info.txt │ ├── include │ │ └── base │ │ │ └── base.h │ ├── nodelet_plugins.xml │ ├── package.xml │ ├── src │ │ └── base.cpp │ └── srv │ │ └── RosImage.srv │ ├── client │ ├── CMakeLists.txt │ ├── include │ │ └── client_nodelet │ │ │ └── client_nodelet.h │ ├── launch │ │ └── client_nodelet.launch │ ├── nodelet_plugins.xml │ ├── package.xml │ └── src │ │ └── client_nodelet.cpp │ ├── yolov5-6.0 │ ├── CMakeLists.txt │ ├── include │ │ ├── basic_transform.h │ │ ├── calibrator.h │ │ ├── common.hpp │ │ ├── cuda_utils.h │ │ ├── logging.h │ │ ├── macros.h │ │ ├── preprocess.h │ │ ├── utils.h │ │ ├── yololayer.h │ │ └── yolov5-detect.h │ ├── src │ │ ├── basic_transform.cu │ │ ├── build_engine.cpp │ │ ├── calibrator.cpp │ │ ├── detect.cpp │ │ ├── preprocess.cu │ │ ├── yololayer.cu │ │ └── yolov5.cpp │ └── tools │ │ ├── gen_wts.py │ │ └── yolov5_trt.py │ ├── yolov5-infer │ ├── CMakeLists.txt │ ├── include │ │ └── yolov5_infer_nodelet │ │ │ └── yolov5_infer_nodelet.h │ ├── launch │ │ └── yolov5_infer_nodelet.launch │ ├── nodelet_plugins.xml │ ├── package.xml │ └── src │ │ └── yolov5_infer_nodelet.cpp │ └── yolov5-server │ ├── CMakeLists.txt │ ├── include │ └── yolov5_server_nodelet │ │ └── yolov5_server_nodelet.h │ ├── launch │ └── yolov5_server_nodelet.launch │ ├── nodelet_plugins.xml │ ├── package.xml │ └── src │ └── yolov5_server_nodelet.cpp ├── trt_cpp ├── CMakeLists.txt ├── Makefile ├── README.md ├── src │ ├── main.cpp │ └── trt │ │ ├── common │ │ ├── basic_tools.cu │ │ ├── basic_tools.hpp │ │ ├── cuda-tools.cpp │ │ ├── cuda-tools.hpp │ │ ├── matrix.cpp │ │ ├── matrix.hpp │ │ ├── mix-memory.cpp │ │ ├── mix-memory.hpp │ │ ├── simple-logger.cpp │ │ ├── simple-logger.hpp │ │ ├── trt-tensor.cpp │ │ └── trt-tensor.hpp │ │ ├── demo-infer │ │ ├── centernet │ │ │ ├── centernet.cu │ │ │ └── centernet.h │ │ ├── demo-infer.cu │ │ ├── demo-infer.hpp │ │ ├── detr │ │ │ ├── detr.cu │ │ │ └── detr.h │ │ ├── hrnet │ │ │ ├── hrnet.cu │ │ │ └── hrnet.h │ │ ├── unet │ │ │ ├── unet.cu │ │ │ └── unet.h │ │ ├── vit │ │ │ ├── vit.cu │ │ │ └── vit.h │ │ ├── yolov5 │ │ │ ├── yolov5.cpp │ │ │ └── yolov5.hpp │ │ ├── yolov5seg │ │ │ ├── README.md │ │ │ ├── yolact.png │ │ │ ├── yolov5seg.cu │ │ │ ├── yolov5seg.h │ │ │ └── yolov5seg.png │ │ └── yolov7 │ │ │ ├── yolov7.cu │ │ │ └── yolov7.h │ │ └── infer │ │ ├── trt-infer.cpp │ │ └── trt-infer.hpp ├── workspace │ ├── cat.jpg │ ├── centernet-gpu—pred.jpg │ ├── centernet-pred-street.jpg │ ├── centernet-pred.jpg │ ├── demo-infer.txt │ ├── detr-pred.jpg │ ├── flower.jpg │ ├── hrnet-cuda-pred.jpg │ ├── hrnet-pred.jpg │ ├── person.png │ ├── person1.jpeg │ ├── person2.jpeg │ ├── street.jpg │ └── warp-affine.jpg └── 模型权重.md └── trt_py ├── README.md ├── basic_infer ├── __pycache__ │ └── infer.cpython-39.pyc ├── _init__.py ├── infer.py ├── infer_bak.py ├── transforms.py └── warpaffine.py ├── build_engine ├── __pycache__ │ └── common.cpython-39.pyc ├── batch_1 │ ├── build_engine_b1.py │ └── common.py └── batch_dynamic │ └── build_engine_batch_image.py ├── centernet ├── CV学习笔记tensorrt之centernet加速.md ├── centernet_infer.py ├── centernet_infer_dynamci_batch.py └── images │ ├── cat.jpg │ └── street.jpg ├── detr ├── detr_infer.py ├── detr_infer_dynamic_batch.py └── images │ ├── cat.jpg │ └── street.jpg ├── hrnet ├── hrnet-pred.jpg ├── hrnet_inferece.py └── images │ ├── person.png │ ├── person1.png │ └── person2.png ├── util └── util.py ├── yolov5 ├── images │ ├── cat.jpg │ └── street.jpg └── yolov5_infer.py └── yolov7 ├── images └── street.jpg ├── infer-onnxruntime.py ├── transforms.py └── yolov7_infer.py /.gitignore: -------------------------------------------------------------------------------- 1 | trt_cpp/objs/ 2 | *.o 3 | *.mk 4 | trt_cpp/build/ 5 | trt_cpp/workspace/pro 6 | trt_cpp/workspace/demo_infer 7 | *.trtmodel 8 | *.pth 9 | *.onnx 10 | *.tensor 11 | *tensor 12 | *.idea 13 | *.__pycache__ 14 | *.pyengine 15 | *.vscode 16 | *.log 17 | *.cmake 18 | *.pyc 19 | *.vscode 20 | *.idea 21 | *.pt 22 | *.jpg 23 | sideline_learn/yolo5-6.0-ros/build_isolated 24 | sideline_learn/yolo5-6.0-ros/devel_isolated 25 | 26 | sideline_learn/ncnn_multi_thread/build 27 | sideline_learn/ncnn_multi_thread/data 28 | sideline_learn/ncnn_multi_thread/ncnn -------------------------------------------------------------------------------- /.vscode/configurationCache.log: -------------------------------------------------------------------------------- 1 | {"buildTargets":[],"launchTargets":[],"customConfigurationProvider":{"workspaceBrowse":{"browsePath":[],"compilerArgs":[]},"fileIndex":[]}} -------------------------------------------------------------------------------- /.vscode/settings.json: -------------------------------------------------------------------------------- 1 | { 2 | "makefile.extensionOutputFolder": "./.vscode", 3 | "files.associations": { 4 | "memory": "cpp", 5 | "iostream": "cpp", 6 | "array": "cpp", 7 | "atomic": "cpp", 8 | "bit": "cpp", 9 | "*.tcc": "cpp", 10 | "cctype": "cpp", 11 | "chrono": "cpp", 12 | "clocale": "cpp", 13 | "cmath": "cpp", 14 | "condition_variable": "cpp", 15 | "cstdarg": "cpp", 16 | "cstdint": "cpp", 17 | "cstdio": "cpp", 18 | "cstdlib": "cpp", 19 | "ctime": "cpp", 20 | "cwchar": "cpp", 21 | "cwctype": "cpp", 22 | "map": "cpp", 23 | "unordered_map": "cpp", 24 | "vector": "cpp", 25 | "exception": "cpp", 26 | "fstream": "cpp", 27 | "functional": "cpp", 28 | "future": "cpp", 29 | "initializer_list": "cpp", 30 | "iosfwd": "cpp", 31 | "istream": "cpp", 32 | "limits": "cpp", 33 | "mutex": "cpp", 34 | "new": "cpp", 35 | "optional": "cpp", 36 | "ostream": "cpp", 37 | "ratio": "cpp", 38 | "sstream": "cpp", 39 | "stdexcept": "cpp", 40 | "streambuf": "cpp", 41 | "string": "cpp", 42 | "string_view": "cpp", 43 | "system_error": "cpp", 44 | "thread": "cpp", 45 | "type_traits": "cpp", 46 | "tuple": "cpp", 47 | "typeinfo": "cpp", 48 | "utility": "cpp", 49 | "deque": "cpp", 50 | "iomanip": "cpp" 51 | } 52 | } -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | ### CV学习笔记 2 | 3 | #### 1、前言 4 | 5 | 本仓库为记录学习过程中，主要是在CV领域的一些学习经验，方便日后进行回顾，同时希望能够给有需要的同学提供一些帮助，希望遇到问题能够及时联系与探讨，如果有问题或者建议，可以+v:Rex1586662742,后面数字为qq 6 | 7 | TensoRT代码:https://github.com/shouxieai/learning-cuda-trt 8 | 9 | 项目代码: 10 | 11 | https://github.com/WZMIAOMIAO/deep-learning-for-image-processing 12 | 13 | https://www.bilibili.com/video/BV1yA411M7T4?spm_id_from=333.999.0.0 14 | 15 | 个人学习仓库地址:https://github.com/Rex-LK/tensorrt_learning 16 | 17 | 部分模型链接:https://pan.baidu.com/s/18yIsypWMg0sT_uAR_MhDSA 提取码: sh7c 18 | 19 | #### 2、内容 20 | 21 | 2.1、demo: 开源项目 22 | 23 | 2.2、sideline_learn: 日常学习的知识点，如ros、多线程等 24 | 25 | 2.3、trt_cpp: 使用cpp进行tensorRT 26 | 27 | 2.4、trt_py: 使用python进行tensorRT 28 | 29 | #### 3、更新日志 30 | 202401025:删除了onnx转engine的代码,便于复现，现在模型可以使用如下的方式进行转换: 31 | trtexec --onnx=yolov5s_mobv3s.onnx --saveEngine=test.engine 32 | 33 | 202211-27:更新了yolov5-7.0 的 实例分割代码 34 | 35 | 2022-07-30:增加了ros中使用yolov5&tensorRT的demo，包含两种启动方式，单节点启动以及client/server的方式，具体过程见sideline_learn/yolov5-6.0-ros/README.md 36 | 37 | 2022-07-09:增加了yolov7算法，支持cpp-tensorrt和python-tensorrt两种加速方式 38 | 39 | 40 | 2022-06-04:增加了hrnet人体关键点检测，使用cupy和torch两种后处理方式，同时在cpp中使用了gpu解码。 41 | 42 | 2022-06-1:优化了代码存放的文件夹，便于以后进行回溯和分类 43 | 44 | 2022-05-30:增加了cuda-python-tensorrt的推理方式 45 | 46 | onnx2trt:/build_engine/batch_1/build_engine_single_image.py 47 | 48 | 推理demo，目前只支持centernet以及detr、后续会进行其他模型的支持,例如: 49 | 50 | /centernet/centernet_infer.py. 51 | 52 | 原始项目都可以根目录下的demo文件夹找到，可以追溯到原项目地址。 53 | 54 | 2022-05-20:在onnx项目下增加了hrnet-tensorrt加速方式，预测效果与demo/HRnet作者里面的预测结果一致 55 | 56 | ...centernet、vit、unet、等均已经实现 57 | -------------------------------------------------------------------------------- /demo/HRNet/.idea/.gitignore: -------------------------------------------------------------------------------- 1 | # 默认忽略的文件 2 | /shelf/ 3 | /workspace.xml 4 | -------------------------------------------------------------------------------- /demo/HRNet/.idea/HRNet.iml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 12 | -------------------------------------------------------------------------------- /demo/HRNet/.idea/inspectionProfiles/profiles_settings.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 6 | -------------------------------------------------------------------------------- /demo/HRNet/.idea/misc.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | -------------------------------------------------------------------------------- /demo/HRNet/.idea/modules.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | -------------------------------------------------------------------------------- /demo/HRNet/HRNet.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Rex-LK/tensorrt_learning/45df1f33cab6a66a0d75c0bb26a3978d539dc888/demo/HRNet/HRNet.png -------------------------------------------------------------------------------- /demo/HRNet/__pycache__/draw_utils.cpython-39.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Rex-LK/tensorrt_learning/45df1f33cab6a66a0d75c0bb26a3978d539dc888/demo/HRNet/__pycache__/draw_utils.cpython-39.pyc -------------------------------------------------------------------------------- /demo/HRNet/__pycache__/transforms.cpython-39.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Rex-LK/tensorrt_learning/45df1f33cab6a66a0d75c0bb26a3978d539dc888/demo/HRNet/__pycache__/transforms.cpython-39.pyc -------------------------------------------------------------------------------- /demo/HRNet/infer-onnxruntime.py: -------------------------------------------------------------------------------- 1 | import transforms 2 | import onnxruntime 3 | import torch 4 | import cv2 5 | import numpy as np 6 | if __name__ == "__main__": 7 | 8 | resize_hw = (256, 192) 9 | img_path = "person.png" 10 | data_transform = transforms.Compose([ 11 | transforms.AffineTransform(scale=(1.25, 1.25), fixed_size=resize_hw), 12 | transforms.ToTensor(), 13 | transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) 14 | ]) 15 | 16 | # read single-person image 17 | img = cv2.imread(img_path) 18 | img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) 19 | img_tensor, target = data_transform(img, {"box": [0, 0, img.shape[1] - 1, img.shape[0] - 1]}) 20 | img_tensor = torch.unsqueeze(img_tensor, dim=0) 21 | session = onnxruntime.InferenceSession("/home/rex/Desktop/cv_demo/cv_model/hrnet/hrnet.onnx", providers=["CPUExecutionProvider"]) 22 | pred = session.run(["predict"], {"image": img_tensor.numpy()})[0] 23 | print(pred) 24 | 25 | -------------------------------------------------------------------------------- /demo/HRNet/model/__init__.py: -------------------------------------------------------------------------------- 1 | from .hrnet import HighResolutionNet 2 | -------------------------------------------------------------------------------- /demo/HRNet/model/__pycache__/__init__.cpython-39.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Rex-LK/tensorrt_learning/45df1f33cab6a66a0d75c0bb26a3978d539dc888/demo/HRNet/model/__pycache__/__init__.cpython-39.pyc -------------------------------------------------------------------------------- /demo/HRNet/model/__pycache__/hrnet.cpython-39.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Rex-LK/tensorrt_learning/45df1f33cab6a66a0d75c0bb26a3978d539dc888/demo/HRNet/model/__pycache__/hrnet.cpython-39.pyc -------------------------------------------------------------------------------- /demo/HRNet/person.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Rex-LK/tensorrt_learning/45df1f33cab6a66a0d75c0bb26a3978d539dc888/demo/HRNet/person.png -------------------------------------------------------------------------------- /demo/HRNet/person1.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Rex-LK/tensorrt_learning/45df1f33cab6a66a0d75c0bb26a3978d539dc888/demo/HRNet/person1.jpeg -------------------------------------------------------------------------------- /demo/HRNet/person2.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Rex-LK/tensorrt_learning/45df1f33cab6a66a0d75c0bb26a3978d539dc888/demo/HRNet/person2.jpeg -------------------------------------------------------------------------------- /demo/HRNet/person_keypoints.json: -------------------------------------------------------------------------------- 1 | { 2 | "keypoints": ["nose","left_eye","right_eye","left_ear","right_ear","left_shoulder","right_shoulder","left_elbow","right_elbow","left_wrist","right_wrist","left_hip","right_hip","left_knee","right_knee","left_ankle","right_ankle"], 3 | "skeleton": [[16,14],[14,12],[17,15],[15,13],[12,13],[6,12],[7,13],[6,7],[6,8],[7,9],[8,10],[9,11],[2,3],[1,2],[1,3],[2,4],[3,5],[4,6],[5,7]], 4 | "flip_pairs": [[1,2], [3,4], [5,6], [7,8], [9,10], [11,12], [13,14], [15,16]], 5 | "kps_weights": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.2, 1.2, 1.5, 1.5, 1.0, 1.0, 1.2, 1.2, 1.5, 1.5], 6 | "upper_body_ids": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10], 7 | "lower_body_ids": [11, 12, 13, 14, 15, 16] 8 | } -------------------------------------------------------------------------------- /demo/HRNet/plot_curve.py: -------------------------------------------------------------------------------- 1 | import datetime 2 | import matplotlib.pyplot as plt 3 | 4 | 5 | def plot_loss_and_lr(train_loss, learning_rate): 6 | try: 7 | x = list(range(len(train_loss))) 8 | fig, ax1 = plt.subplots(1, 1) 9 | ax1.plot(x, train_loss, 'r', label='loss') 10 | ax1.set_xlabel("step") 11 | ax1.set_ylabel("loss") 12 | ax1.set_title("Train Loss and lr") 13 | plt.legend(loc='best') 14 | 15 | ax2 = ax1.twinx() 16 | ax2.plot(x, learning_rate, label='lr') 17 | ax2.set_ylabel("learning rate") 18 | ax2.set_xlim(0, len(train_loss)) # 设置横坐标整数间隔 19 | plt.legend(loc='best') 20 | 21 | handles1, labels1 = ax1.get_legend_handles_labels() 22 | handles2, labels2 = ax2.get_legend_handles_labels() 23 | plt.legend(handles1 + handles2, labels1 + labels2, loc='upper right') 24 | 25 | fig.subplots_adjust(right=0.8) # 防止出现保存图片显示不全的情况 26 | fig.savefig('./loss_and_lr{}.png'.format(datetime.datetime.now().strftime("%Y%m%d-%H%M%S"))) 27 | plt.close() 28 | print("successful save loss curve! ") 29 | except Exception as e: 30 | print(e) 31 | 32 | 33 | def plot_map(mAP): 34 | try: 35 | x = list(range(len(mAP))) 36 | plt.plot(x, mAP, label='mAp') 37 | plt.xlabel('epoch') 38 | plt.ylabel('mAP') 39 | plt.title('Eval mAP') 40 | plt.xlim(0, len(mAP)) 41 | plt.legend(loc='best') 42 | plt.savefig('./mAP.png') 43 | plt.close() 44 | print("successful save mAP curve!") 45 | except Exception as e: 46 | print(e) 47 | -------------------------------------------------------------------------------- /demo/HRNet/requirements.txt: -------------------------------------------------------------------------------- 1 | numpy 2 | opencv_python==4.5.4.60 3 | lxml 4 | torch==1.10.1 5 | torchvision==0.11.1 6 | pycocotools 7 | matplotlib 8 | tqdm -------------------------------------------------------------------------------- /demo/HRNet/test.py: -------------------------------------------------------------------------------- 1 | import os 2 | import json 3 | 4 | import torch 5 | import numpy as np 6 | import cv2 7 | import matplotlib.pyplot as plt 8 | 9 | from model import HighResolutionNet 10 | from draw_utils import draw_keypoints 11 | import transforms 12 | 13 | 14 | 15 | def predict_single_person(): 16 | resize_hw = (256, 192) 17 | img_path = "person.png" 18 | data_transform = transforms.Compose([ 19 | transforms.AffineTransform(scale=(1, 1), fixed_size=resize_hw), 20 | ]) 21 | 22 | # read single-person image 23 | img = cv2.imread(img_path) 24 | img_tensor, target = data_transform(img, {"box": [0, 0, img.shape[1] - 1, img.shape[0] - 1]}) 25 | cv2.imshow("1",img_tensor) 26 | cv2.waitKey(0) 27 | 28 | 29 | predict_single_person() -------------------------------------------------------------------------------- /demo/HRNet/test_result.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Rex-LK/tensorrt_learning/45df1f33cab6a66a0d75c0bb26a3978d539dc888/demo/HRNet/test_result.jpg -------------------------------------------------------------------------------- /demo/HRNet/train_utils/__init__.py: -------------------------------------------------------------------------------- 1 | from .group_by_aspect_ratio import GroupedBatchSampler, create_aspect_ratio_groups 2 | from .distributed_utils import init_distributed_mode, save_on_master, mkdir 3 | from .coco_eval import EvalCOCOMetric 4 | from .coco_utils import coco_remove_images_without_annotations, convert_coco_poly_mask, convert_to_coco_api 5 | -------------------------------------------------------------------------------- /demo/HRNet/train_utils/loss.py: -------------------------------------------------------------------------------- 1 | import torch 2 | 3 | 4 | class KpLoss(object): 5 | def __init__(self): 6 | self.criterion = torch.nn.MSELoss(reduction='none') 7 | 8 | def __call__(self, logits, targets): 9 | assert len(logits.shape) == 4, 'logits should be 4-ndim' 10 | device = logits.device 11 | bs = logits.shape[0] 12 | # [num_kps, H, W] -> [B, num_kps, H, W] 13 | heatmaps = torch.stack([t["heatmap"].to(device) for t in targets]) 14 | # [num_kps] -> [B, num_kps] 15 | kps_weights = torch.stack([t["kps_weights"].to(device) for t in targets]) 16 | 17 | # [B, num_kps, H, W] -> [B, num_kps] 18 | loss = self.criterion(logits, heatmaps).mean(dim=[2, 3]) 19 | loss = torch.sum(loss * kps_weights) / bs 20 | return loss 21 | -------------------------------------------------------------------------------- /demo/centernet/.idea/.gitignore: -------------------------------------------------------------------------------- 1 | # 默认忽略的文件 2 | /shelf/ 3 | /workspace.xml 4 | -------------------------------------------------------------------------------- /demo/centernet/.idea/centernet-pytorch-main.iml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 12 | -------------------------------------------------------------------------------- /demo/centernet/.idea/inspectionProfiles/profiles_settings.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 6 | -------------------------------------------------------------------------------- /demo/centernet/.idea/misc.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | -------------------------------------------------------------------------------- /demo/centernet/.idea/modules.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | -------------------------------------------------------------------------------- /demo/centernet/LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2020 Bubbliiiing 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /demo/centernet/img/street.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Rex-LK/tensorrt_learning/45df1f33cab6a66a0d75c0bb26a3978d539dc888/demo/centernet/img/street.jpg -------------------------------------------------------------------------------- /demo/centernet/infer-onnxruntime.py: -------------------------------------------------------------------------------- 1 | from tkinter import image_names 2 | import onnxruntime 3 | import cv2 4 | import numpy as np 5 | from PIL import Image 6 | from utils.utils import (cvtColor, get_classes, preprocess_input, resize_image, 7 | show_config) 8 | import torch 9 | image = 'img/street.jpg' 10 | image = Image.open(image) 11 | image_shape = np.array(np.shape(image)[0:2]) 12 | image = cvtColor(image) 13 | image_data = resize_image(image, (512,512), None) 14 | image_data = np.expand_dims(np.transpose(preprocess_input(np.array(image_data, dtype='float32')), (2, 0, 1)), 0) 15 | images = np.asarray(image_data).astype(np.float32) 16 | session = onnxruntime.InferenceSession("centernet.onnx", providers=["CPUExecutionProvider"]) 17 | pred = session.run(["predict"], {"image": images})[0] 18 | pred = torch.from_numpy(pred) 19 | pred_hms = pred[0,:,0:20] 20 | pred_wh = pred[0,:,20:22] 21 | pred_xy = pred[0,:,22:] 22 | keep = pred_hms.max(-1).values > 0.3 23 | pred_hms = pred_hms[keep] 24 | pred_wh = pred_wh[keep] 25 | pred_xy = pred_xy[keep] 26 | print(pred_wh.shape) -------------------------------------------------------------------------------- /demo/centernet/info.md: -------------------------------------------------------------------------------- 1 | 1、预处理RGB 2 | 2、可以采用warpaffine 或者直接resize -------------------------------------------------------------------------------- /demo/centernet/onnx_simplify.py: -------------------------------------------------------------------------------- 1 | from onnxsim import simplify 2 | import onnx 3 | input_path="centernet.onnx" 4 | output_path="centernet_simp.onnx" 5 | onnx_model = onnx.load(input_path) 6 | model_simp, check = simplify(onnx_model,input_shapes={'image': [1, 3, 512, 512]}) 7 | assert check, "Simplified ONNX model could not be validated" 8 | onnx.save(model_simp, output_path) 9 | print('finished exporting onnx') -------------------------------------------------------------------------------- /demo/centernet/requirements.txt: -------------------------------------------------------------------------------- 1 | scipy==1.2.1 2 | numpy==1.17.0 3 | matplotlib==3.1.2 4 | opencv_python==4.1.2.30 5 | torch==1.2.0 6 | torchvision==0.4.0 7 | tqdm==4.60.0 8 | Pillow==8.2.0 9 | h5py==2.10.0 10 | -------------------------------------------------------------------------------- /demo/centernet/summary.py: -------------------------------------------------------------------------------- 1 | #--------------------------------------------# 2 | # 该部分代码用于看网络参数 3 | #--------------------------------------------# 4 | import torch 5 | from thop import clever_format, profile 6 | from torchsummary import summary 7 | 8 | from nets.centernet import CenterNet_HourglassNet, CenterNet_Resnet50 9 | 10 | if __name__ == "__main__": 11 | input_shape = [512, 512] 12 | num_classes = 20 13 | 14 | device = torch.device("cuda" if torch.cuda.is_available() else "cpu") 15 | model = CenterNet_Resnet50().to(device) 16 | summary(model, (3, input_shape[0], input_shape[1])) 17 | 18 | dummy_input = torch.randn(1, 3, input_shape[0], input_shape[1]).to(device) 19 | flops, params = profile(model.to(device), (dummy_input, ), verbose=False) 20 | #--------------------------------------------------------# 21 | # flops * 2是因为profile没有将卷积作为两个operations 22 | # 有些论文将卷积算乘法、加法两个operations。此时乘2 23 | # 有些论文只考虑乘法的运算次数，忽略加法。此时不乘2 24 | # 本代码选择乘2，参考YOLOX。 25 | #--------------------------------------------------------# 26 | flops = flops * 2 27 | flops, params = clever_format([flops, params], "%.3f") 28 | print('Total GFLOPS: %s' % (flops)) 29 | print('Total params: %s' % (params)) 30 | -------------------------------------------------------------------------------- /demo/centernet/utils/__init__.py: -------------------------------------------------------------------------------- 1 | # -------------------------------------------------------------------------------- /demo/detr-main/.circleci/config.yml: -------------------------------------------------------------------------------- 1 | version: 2.1 2 | 3 | jobs: 4 | python_lint: 5 | docker: 6 | - image: circleci/python:3.7 7 | steps: 8 | - checkout 9 | - run: 10 | command: | 11 | pip install --user --progress-bar off flake8 typing 12 | flake8 . 13 | 14 | test: 15 | docker: 16 | - image: circleci/python:3.7 17 | steps: 18 | - checkout 19 | - run: 20 | command: | 21 | pip install --user --progress-bar off scipy pytest 22 | pip install --user --progress-bar off --pre torch torchvision -f https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html 23 | pip install --user --progress-bar off onnx onnxruntime 24 | pytest . 25 | 26 | workflows: 27 | build: 28 | jobs: 29 | - python_lint 30 | - test 31 | -------------------------------------------------------------------------------- /demo/detr-main/.github/CODE_OF_CONDUCT.md: -------------------------------------------------------------------------------- 1 | # Code of Conduct 2 | 3 | Facebook has adopted a Code of Conduct that we expect project participants to adhere to. 4 | Please read the [full text](https://code.fb.com/codeofconduct/) 5 | so that you can understand what actions will and will not be tolerated. 6 | -------------------------------------------------------------------------------- /demo/detr-main/.github/CONTRIBUTING.md: -------------------------------------------------------------------------------- 1 | # Contributing to DETR 2 | We want to make contributing to this project as easy and transparent as 3 | possible. 4 | 5 | ## Our Development Process 6 | Minor changes and improvements will be released on an ongoing basis. Larger changes (e.g., changesets implementing a new paper) will be released on a more periodic basis. 7 | 8 | ## Pull Requests 9 | We actively welcome your pull requests. 10 | 11 | 1. Fork the repo and create your branch from `master`. 12 | 2. If you've added code that should be tested, add tests. 13 | 3. If you've changed APIs, update the documentation. 14 | 4. Ensure the test suite passes. 15 | 5. Make sure your code lints. 16 | 6. If you haven't already, complete the Contributor License Agreement ("CLA"). 17 | 18 | ## Contributor License Agreement ("CLA") 19 | In order to accept your pull request, we need you to submit a CLA. You only need 20 | to do this once to work on any of Facebook's open source projects. 21 | 22 | Complete your CLA here: 23 | 24 | ## Issues 25 | We use GitHub issues to track public bugs. Please ensure your description is 26 | clear and has sufficient instructions to be able to reproduce the issue. 27 | 28 | Facebook has a [bounty program](https://www.facebook.com/whitehat/) for the safe 29 | disclosure of security bugs. In those cases, please go through the process 30 | outlined on that page and do not file a public issue. 31 | 32 | ## Coding Style 33 | * 4 spaces for indentation rather than tabs 34 | * 80 character line length 35 | * PEP8 formatting following [Black](https://black.readthedocs.io/en/stable/) 36 | 37 | ## License 38 | By contributing to DETR, you agree that your contributions will be licensed 39 | under the LICENSE file in the root directory of this source tree. 40 | -------------------------------------------------------------------------------- /demo/detr-main/.github/DETR.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Rex-LK/tensorrt_learning/45df1f33cab6a66a0d75c0bb26a3978d539dc888/demo/detr-main/.github/DETR.png -------------------------------------------------------------------------------- /demo/detr-main/.github/ISSUE_TEMPLATE/bugs.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: "🐛 Bugs" 3 | about: Report bugs in DETR 4 | title: Please read & provide the following 5 | 6 | --- 7 | 8 | ## Instructions To Reproduce the 🐛 Bug: 9 | 10 | 1. what changes you made (`git diff`) or what code you wrote 11 | ``` 12 | 13 | ``` 14 | 2. what exact command you run: 15 | 3. what you observed (including __full logs__): 16 | ``` 17 | 18 | ``` 19 | 4. please simplify the steps as much as possible so they do not require additional resources to 20 | run, such as a private dataset. 21 | 22 | ## Expected behavior: 23 | 24 | If there are no obvious error in "what you observed" provided above, 25 | please tell us the expected behavior. 26 | 27 | ## Environment: 28 | 29 | Provide your environment information using the following command: 30 | ``` 31 | python -m torch.utils.collect_env 32 | ``` 33 | -------------------------------------------------------------------------------- /demo/detr-main/.github/ISSUE_TEMPLATE/questions-help-support.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: "How to do something❓" 3 | about: How to do something using DETR? 4 | 5 | --- 6 | 7 | ## ❓ How to do something using DETR 8 | 9 | Describe what you want to do, including: 10 | 1. what inputs you will provide, if any: 11 | 2. what outputs you are expecting: 12 | 13 | 14 | NOTE: 15 | 16 | 1. Only general answers are provided. 17 | If you want to ask about "why X did not work", please use the 18 | [Unexpected behaviors](https://github.com/facebookresearch/detr/issues/new/choose) issue template. 19 | 20 | 2. About how to implement new models / new dataloader / new training logic, etc., check documentation first. 21 | 22 | 3. We do not answer general machine learning / computer vision questions that are not specific to DETR, such as how a model works, how to improve your training/make it converge, or what algorithm/methods can be used to achieve X. 23 | -------------------------------------------------------------------------------- /demo/detr-main/.github/ISSUE_TEMPLATE/unexpected-problems-bugs.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: "Unexpected behaviors" 3 | about: Run into unexpected behaviors when using DETR 4 | title: Please read & provide the following 5 | 6 | --- 7 | 8 | If you do not know the root cause of the problem, and wish someone to help you, please 9 | post according to this template: 10 | 11 | ## Instructions To Reproduce the Issue: 12 | 13 | 1. what changes you made (`git diff`) or what code you wrote 14 | ``` 15 | 16 | ``` 17 | 2. what exact command you run: 18 | 3. what you observed (including __full logs__): 19 | ``` 20 | 21 | ``` 22 | 4. please simplify the steps as much as possible so they do not require additional resources to 23 | run, such as a private dataset. 24 | 25 | ## Expected behavior: 26 | 27 | If there are no obvious error in "what you observed" provided above, 28 | please tell us the expected behavior. 29 | 30 | If you expect the model to converge / work better, note that we do not give suggestions 31 | on how to train a new model. 32 | Only in one of the two conditions we will help with it: 33 | (1) You're unable to reproduce the results in DETR model zoo. 34 | (2) It indicates a DETR bug. 35 | 36 | ## Environment: 37 | 38 | Provide your environment information using the following command: 39 | ``` 40 | python -m torch.utils.collect_env 41 | ``` 42 | -------------------------------------------------------------------------------- /demo/detr-main/.gitignore: -------------------------------------------------------------------------------- 1 | .nfs* 2 | *.ipynb 3 | *.pyc 4 | .dumbo.json 5 | .DS_Store 6 | .*.swp 7 | *.pth 8 | **/__pycache__/** 9 | .ipynb_checkpoints/ 10 | datasets/data/ 11 | experiment-* 12 | *.tmp 13 | *.pkl 14 | **/.mypy_cache/* 15 | .mypy_cache/* 16 | not_tracked_dir/ 17 | .vscode 18 | -------------------------------------------------------------------------------- /demo/detr-main/Dockerfile: -------------------------------------------------------------------------------- 1 | FROM pytorch/pytorch:1.5-cuda10.1-cudnn7-runtime 2 | 3 | ENV DEBIAN_FRONTEND=noninteractive 4 | 5 | RUN apt-get update -qq && \ 6 | apt-get install -y git vim libgtk2.0-dev && \ 7 | rm -rf /var/cache/apk/* 8 | 9 | RUN pip --no-cache-dir install Cython 10 | 11 | COPY requirements.txt /workspace 12 | 13 | RUN pip --no-cache-dir install -r /workspace/requirements.txt 14 | -------------------------------------------------------------------------------- /demo/detr-main/d2/configs/detr_256_6_6_torchvision.yaml: -------------------------------------------------------------------------------- 1 | MODEL: 2 | META_ARCHITECTURE: "Detr" 3 | WEIGHTS: "detectron2://ImageNetPretrained/torchvision/R-50.pkl" 4 | PIXEL_MEAN: [123.675, 116.280, 103.530] 5 | PIXEL_STD: [58.395, 57.120, 57.375] 6 | MASK_ON: False 7 | RESNETS: 8 | DEPTH: 50 9 | STRIDE_IN_1X1: False 10 | OUT_FEATURES: ["res2", "res3", "res4", "res5"] 11 | DETR: 12 | GIOU_WEIGHT: 2.0 13 | L1_WEIGHT: 5.0 14 | NUM_OBJECT_QUERIES: 100 15 | DATASETS: 16 | TRAIN: ("coco_2017_train",) 17 | TEST: ("coco_2017_val",) 18 | SOLVER: 19 | IMS_PER_BATCH: 64 20 | BASE_LR: 0.0001 21 | STEPS: (369600,) 22 | MAX_ITER: 554400 23 | WARMUP_FACTOR: 1.0 24 | WARMUP_ITERS: 10 25 | WEIGHT_DECAY: 0.0001 26 | OPTIMIZER: "ADAMW" 27 | BACKBONE_MULTIPLIER: 0.1 28 | CLIP_GRADIENTS: 29 | ENABLED: True 30 | CLIP_TYPE: "full_model" 31 | CLIP_VALUE: 0.01 32 | NORM_TYPE: 2.0 33 | INPUT: 34 | MIN_SIZE_TRAIN: (480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800) 35 | CROP: 36 | ENABLED: True 37 | TYPE: "absolute_range" 38 | SIZE: (384, 600) 39 | FORMAT: "RGB" 40 | TEST: 41 | EVAL_PERIOD: 4000 42 | DATALOADER: 43 | FILTER_EMPTY_ANNOTATIONS: False 44 | NUM_WORKERS: 4 45 | VERSION: 2 46 | -------------------------------------------------------------------------------- /demo/detr-main/d2/configs/detr_segm_256_6_6_torchvision.yaml: -------------------------------------------------------------------------------- 1 | MODEL: 2 | META_ARCHITECTURE: "Detr" 3 | # WEIGHTS: "detectron2://ImageNetPretrained/torchvision/R-50.pkl" 4 | PIXEL_MEAN: [123.675, 116.280, 103.530] 5 | PIXEL_STD: [58.395, 57.120, 57.375] 6 | MASK_ON: True 7 | RESNETS: 8 | DEPTH: 50 9 | STRIDE_IN_1X1: False 10 | OUT_FEATURES: ["res2", "res3", "res4", "res5"] 11 | DETR: 12 | GIOU_WEIGHT: 2.0 13 | L1_WEIGHT: 5.0 14 | NUM_OBJECT_QUERIES: 100 15 | FROZEN_WEIGHTS: '' 16 | DATASETS: 17 | TRAIN: ("coco_2017_train",) 18 | TEST: ("coco_2017_val",) 19 | SOLVER: 20 | IMS_PER_BATCH: 64 21 | BASE_LR: 0.0001 22 | STEPS: (55440,) 23 | MAX_ITER: 92400 24 | WARMUP_FACTOR: 1.0 25 | WARMUP_ITERS: 10 26 | WEIGHT_DECAY: 0.0001 27 | OPTIMIZER: "ADAMW" 28 | BACKBONE_MULTIPLIER: 0.1 29 | CLIP_GRADIENTS: 30 | ENABLED: True 31 | CLIP_TYPE: "full_model" 32 | CLIP_VALUE: 0.01 33 | NORM_TYPE: 2.0 34 | INPUT: 35 | MIN_SIZE_TRAIN: (480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800) 36 | CROP: 37 | ENABLED: True 38 | TYPE: "absolute_range" 39 | SIZE: (384, 600) 40 | FORMAT: "RGB" 41 | TEST: 42 | EVAL_PERIOD: 4000 43 | DATALOADER: 44 | FILTER_EMPTY_ANNOTATIONS: False 45 | NUM_WORKERS: 4 46 | VERSION: 2 47 | -------------------------------------------------------------------------------- /demo/detr-main/d2/detr/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved 2 | from .config import add_detr_config 3 | from .detr import Detr 4 | from .dataset_mapper import DetrDatasetMapper 5 | -------------------------------------------------------------------------------- /demo/detr-main/d2/detr/config.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved 3 | from detectron2.config import CfgNode as CN 4 | 5 | 6 | def add_detr_config(cfg): 7 | """ 8 | Add config for DETR. 9 | """ 10 | cfg.MODEL.DETR = CN() 11 | cfg.MODEL.DETR.NUM_CLASSES = 80 12 | 13 | # For Segmentation 14 | cfg.MODEL.DETR.FROZEN_WEIGHTS = '' 15 | 16 | # LOSS 17 | cfg.MODEL.DETR.GIOU_WEIGHT = 2.0 18 | cfg.MODEL.DETR.L1_WEIGHT = 5.0 19 | cfg.MODEL.DETR.DEEP_SUPERVISION = True 20 | cfg.MODEL.DETR.NO_OBJECT_WEIGHT = 0.1 21 | 22 | # TRANSFORMER 23 | cfg.MODEL.DETR.NHEADS = 8 24 | cfg.MODEL.DETR.DROPOUT = 0.1 25 | cfg.MODEL.DETR.DIM_FEEDFORWARD = 2048 26 | cfg.MODEL.DETR.ENC_LAYERS = 6 27 | cfg.MODEL.DETR.DEC_LAYERS = 6 28 | cfg.MODEL.DETR.PRE_NORM = False 29 | 30 | cfg.MODEL.DETR.HIDDEN_DIM = 256 31 | cfg.MODEL.DETR.NUM_OBJECT_QUERIES = 100 32 | 33 | cfg.SOLVER.OPTIMIZER = "ADAMW" 34 | cfg.SOLVER.BACKBONE_MULTIPLIER = 0.1 35 | -------------------------------------------------------------------------------- /demo/detr-main/datasets/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved 2 | import torch.utils.data 3 | import torchvision 4 | 5 | from .coco import build as build_coco 6 | 7 | 8 | def get_coco_api_from_dataset(dataset): 9 | for _ in range(10): 10 | # if isinstance(dataset, torchvision.datasets.CocoDetection): 11 | # break 12 | if isinstance(dataset, torch.utils.data.Subset): 13 | dataset = dataset.dataset 14 | if isinstance(dataset, torchvision.datasets.CocoDetection): 15 | return dataset.coco 16 | 17 | 18 | def build_dataset(image_set, args): 19 | if args.dataset_file == 'coco': 20 | return build_coco(image_set, args) 21 | if args.dataset_file == 'coco_panoptic': 22 | # to avoid making panopticapi required for coco 23 | from .coco_panoptic import build as build_coco_panoptic 24 | return build_coco_panoptic(image_set, args) 25 | raise ValueError(f'dataset {args.dataset_file} not supported') 26 | -------------------------------------------------------------------------------- /demo/detr-main/datasets/panoptic_eval.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved 2 | import json 3 | import os 4 | 5 | import util.misc as utils 6 | 7 | try: 8 | from panopticapi.evaluation import pq_compute 9 | except ImportError: 10 | pass 11 | 12 | 13 | class PanopticEvaluator(object): 14 | def __init__(self, ann_file, ann_folder, output_dir="panoptic_eval"): 15 | self.gt_json = ann_file 16 | self.gt_folder = ann_folder 17 | if utils.is_main_process(): 18 | if not os.path.exists(output_dir): 19 | os.mkdir(output_dir) 20 | self.output_dir = output_dir 21 | self.predictions = [] 22 | 23 | def update(self, predictions): 24 | for p in predictions: 25 | with open(os.path.join(self.output_dir, p["file_name"]), "wb") as f: 26 | f.write(p.pop("png_string")) 27 | 28 | self.predictions += predictions 29 | 30 | def synchronize_between_processes(self): 31 | all_predictions = utils.all_gather(self.predictions) 32 | merged_predictions = [] 33 | for p in all_predictions: 34 | merged_predictions += p 35 | self.predictions = merged_predictions 36 | 37 | def summarize(self): 38 | if utils.is_main_process(): 39 | json_data = {"annotations": self.predictions} 40 | predictions_json = os.path.join(self.output_dir, "predictions.json") 41 | with open(predictions_json, "w") as f: 42 | f.write(json.dumps(json_data)) 43 | return pq_compute(self.gt_json, predictions_json, gt_folder=self.gt_folder, pred_folder=self.output_dir) 44 | return None 45 | -------------------------------------------------------------------------------- /demo/detr-main/demo.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Rex-LK/tensorrt_learning/45df1f33cab6a66a0d75c0bb26a3978d539dc888/demo/detr-main/demo.jpg -------------------------------------------------------------------------------- /demo/detr-main/infer-onnxruntime.py: -------------------------------------------------------------------------------- 1 | 2 | import onnxruntime 3 | import cv2 4 | import numpy as np 5 | from torchvision import transforms 6 | from PIL import Image 7 | import torch 8 | from tools import * 9 | 10 | 11 | if __name__ == "__main__": 12 | 13 | data_transform = transforms.Compose([ 14 | transforms.Resize(800), 15 | transforms.ToTensor(), 16 | transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) 17 | ]) 18 | img_path = "demo.jpg" 19 | img_o = Image.open(img_path) 20 | img = data_transform(img_o).unsqueeze(0) 21 | 22 | image_input = img.numpy() 23 | 24 | 25 | session = onnxruntime.InferenceSession("detr_sim.onnx", providers=["CPUExecutionProvider"]) 26 | pred = session.run(["predict"], {"image": image_input})[0] 27 | scores = torch.from_numpy(pred[0][:,0:91]) 28 | bboxes = torch.from_numpy(pred[0][:,91:]) 29 | keep = scores.max(-1).values > 0.7 30 | scores = scores[keep] 31 | bboxes = bboxes[keep] 32 | print(bboxes) 33 | fin_bboxes = rescale_bboxes(bboxes, img_o.size) 34 | plot_results(img_o, scores, fin_bboxes) 35 | -------------------------------------------------------------------------------- /demo/detr-main/load_from_cpp.py: -------------------------------------------------------------------------------- 1 | 2 | import numpy as np 3 | 4 | #这里发现转化的tensorrt模型无法推理出结果，可以将cpp推理的结果保存下来，到python中查看 5 | 6 | def load_tensor(file): 7 | 8 | with open(file, "rb") as f: 9 | binary_data = f.read() 10 | 11 | magic_number, ndims, dtype = np.frombuffer(binary_data, np.uint32, count=3, offset=0) 12 | assert magic_number == 0xFCCFE2E2, f"{file} not a tensor file." 13 | 14 | dims = np.frombuffer(binary_data, np.uint32, count=ndims, offset=3 * 4) 15 | 16 | if dtype == 0: 17 | np_dtype = np.float32 18 | elif dtype == 1: 19 | np_dtype = np.float16 20 | else: 21 | assert False, f"Unsupport dtype = {dtype}, can not convert to numpy dtype" 22 | 23 | return np.frombuffer(binary_data, np_dtype, offset=(ndims + 3) * 4).reshape(*dims) 24 | 25 | 26 | a = load_tensor('/home/rex/Desktop/tensorrt_learning/trt_cpp/workspace/detr_cpp.tensor') 27 | print(a) -------------------------------------------------------------------------------- /demo/detr-main/models/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved 2 | from .detr import build 3 | 4 | 5 | def build_model(args): 6 | return build(args) 7 | -------------------------------------------------------------------------------- /demo/detr-main/requirements.txt: -------------------------------------------------------------------------------- 1 | cython 2 | git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI&egg=pycocotools 3 | submitit 4 | torch>=1.5.0 5 | torchvision>=0.6.0 6 | git+https://github.com/cocodataset/panopticapi.git#egg=panopticapi 7 | scipy 8 | onnx 9 | onnxruntime 10 | -------------------------------------------------------------------------------- /demo/detr-main/street.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Rex-LK/tensorrt_learning/45df1f33cab6a66a0d75c0bb26a3978d539dc888/demo/detr-main/street.jpg -------------------------------------------------------------------------------- /demo/detr-main/tox.ini: -------------------------------------------------------------------------------- 1 | [flake8] 2 | max-line-length = 120 3 | ignore = F401,E402,F403,W503,W504 4 | -------------------------------------------------------------------------------- /demo/detr-main/util/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved 2 | -------------------------------------------------------------------------------- /demo/detr_demo/demo.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Rex-LK/tensorrt_learning/45df1f33cab6a66a0d75c0bb26a3978d539dc888/demo/detr_demo/demo.jpg -------------------------------------------------------------------------------- /demo/detr_demo/export_onnx.py: -------------------------------------------------------------------------------- 1 | from json import tool 2 | import requests 3 | import matplotlib.pyplot as plt 4 | 5 | from torchvision.models import resnet50 6 | import torchvision.transforms as T 7 | 8 | from tools import * 9 | from model import * 10 | 11 | 12 | if __name__ == '__main__': 13 | detr = DETRdemo(num_classes=91) 14 | state_dict = torch.load('detr_demo.pth') 15 | detr.load_state_dict(state_dict) 16 | detr.eval() 17 | 18 | dummy = torch.zeros(1,3,640,640) 19 | torch.onnx.export( 20 | detr,(dummy,), 21 | "detr.onnx", 22 | input_names=["image"], 23 | output_names=["output"], 24 | opset_version=11 25 | ) 26 | -------------------------------------------------------------------------------- /demo/detr_demo/infer-onnxruntime.py: -------------------------------------------------------------------------------- 1 | 2 | import imghdr 3 | import onnxruntime 4 | import cv2 5 | import numpy as np 6 | from torchvision import transforms 7 | from PIL import Image 8 | import os 9 | import torch 10 | from model import * 11 | from tools import * 12 | 13 | 14 | if __name__ == "__main__": 15 | 16 | data_transform = transforms.Compose([ 17 | transforms.Resize(640), 18 | transforms.ToTensor(), 19 | transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) 20 | ]) 21 | img_path = "demo.jpg" 22 | img_o = Image.open(img_path) 23 | img = data_transform(img_o).unsqueeze(0) 24 | 25 | image_input = img.numpy() 26 | 27 | 28 | session = onnxruntime.InferenceSession("detr_sim.onnx", providers=["CPUExecutionProvider"]) 29 | pred = session.run(["output"], {"image": image_input})[0] 30 | print(pred) 31 | scores = torch.from_numpy(pred[0][:,0:91]) 32 | bboxes = torch.from_numpy(pred[0][:,91:]) 33 | keep = scores.max(-1).values > 0.7 34 | scores = scores[keep] 35 | bboxes = bboxes[keep] 36 | print(bboxes) 37 | fin_bboxes = rescale_bboxes(bboxes, img_o.size) 38 | plot_results(img_o, scores, fin_bboxes) 39 | -------------------------------------------------------------------------------- /demo/detr_demo/onnx_simplify.py: -------------------------------------------------------------------------------- 1 | from onnxsim import simplify 2 | import onnx 3 | input_path="detr.onnx" 4 | output_path="detr_sim.onnx" 5 | onnx_model = onnx.load(input_path) 6 | model_simp, check = simplify(onnx_model) 7 | assert check, "Simplified ONNX model could not be validated" 8 | onnx.save(model_simp, output_path) 9 | print('finished exporting onnx') -------------------------------------------------------------------------------- /demo/detr_demo/predict.py: -------------------------------------------------------------------------------- 1 | from json import tool 2 | import requests 3 | import matplotlib.pyplot as plt 4 | 5 | 6 | from tools import * 7 | from model import * 8 | if __name__ == "__main__": 9 | 10 | detr = DETRdemo(num_classes=91) 11 | state_dict = torch.load('detr_demo.pth') 12 | detr.load_state_dict(state_dict) 13 | detr.eval() 14 | 15 | im = Image.open("demo.jpg") 16 | 17 | #记录原图大小,方便进行后处理 18 | img = transform(im).unsqueeze(0) 19 | pred = detr(img) 20 | # 尽可能的将后后处理放在onnx 21 | scores = pred[0][:,0:91] 22 | # 需要恢复成原图大小 23 | bboxes = pred[0][:,91:] 24 | keep = scores.max(-1).values > 0.7 25 | scores = scores[keep] 26 | print(scores.shape) 27 | bboxes = bboxes[keep] 28 | fin_bboxes = rescale_bboxes(bboxes, im.size) 29 | plot_results(im, scores, fin_bboxes) 30 | -------------------------------------------------------------------------------- /demo/swin/__pycache__/model.cpython-39.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Rex-LK/tensorrt_learning/45df1f33cab6a66a0d75c0bb26a3978d539dc888/demo/swin/__pycache__/model.cpython-39.pyc -------------------------------------------------------------------------------- /demo/swin/__pycache__/my_dataset.cpython-39.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Rex-LK/tensorrt_learning/45df1f33cab6a66a0d75c0bb26a3978d539dc888/demo/swin/__pycache__/my_dataset.cpython-39.pyc -------------------------------------------------------------------------------- /demo/swin/__pycache__/utils.cpython-39.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Rex-LK/tensorrt_learning/45df1f33cab6a66a0d75c0bb26a3978d539dc888/demo/swin/__pycache__/utils.cpython-39.pyc -------------------------------------------------------------------------------- /demo/swin/class_indices.json: -------------------------------------------------------------------------------- 1 | { 2 | "0": "daisy", 3 | "1": "dandelion", 4 | "2": "roses", 5 | "3": "sunflowers", 6 | "4": "tulips" 7 | } -------------------------------------------------------------------------------- /demo/swin/export_onnx.py: -------------------------------------------------------------------------------- 1 | 2 | from multiprocessing import dummy 3 | import os 4 | import json 5 | import onnx 6 | from psutil import cpu_count 7 | import sys 8 | 9 | 10 | import torch 11 | from PIL import Image 12 | from torchvision import transforms 13 | import matplotlib.pyplot as plt 14 | 15 | from model import swin_tiny_patch4_window7_224 as create_model 16 | 17 | 18 | def main(): 19 | 20 | model = create_model(num_classes=5).to("cpu") 21 | # load model weights 22 | model_weight_path = "./weights/model-9.pth" 23 | model.load_state_dict(torch.load(model_weight_path, map_location=device)) 24 | model.eval() 25 | 26 | 27 | dummy = torch.zeros(1,3,224,224) 28 | torch.onnx.export( 29 | model,(dummy,), 30 | "swin.onnx", 31 | input_names=["image"], 32 | output_names=["predict"], 33 | opset_version=12, 34 | dynamic_axes={"image":{0:"batch"},"predict":{0:"batch"}} 35 | ) 36 | 37 | 38 | if __name__ == '__main__': 39 | main() 40 | -------------------------------------------------------------------------------- /demo/swin/infer-onnxruntime.py: -------------------------------------------------------------------------------- 1 | 2 | import onnxruntime 3 | import cv2 4 | import numpy as np 5 | from torchvision import transforms 6 | from PIL import Image 7 | import os 8 | import torch 9 | 10 | 11 | if __name__ == "__main__": 12 | 13 | img_size = 224 14 | data_transform = transforms.Compose( 15 | [transforms.Resize(int(img_size * 1.14)), 16 | transforms.CenterCrop(img_size), 17 | transforms.ToTensor(), 18 | transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]) 19 | 20 | # load image 21 | img_path = "flower_photos/test.jpg" 22 | img = Image.open(img_path) 23 | img = data_transform(img) 24 | # expand batch dimension 25 | img = torch.unsqueeze(img, dim=0) 26 | image_input = img.numpy() 27 | session = onnxruntime.InferenceSession("weights/swin.onnx", providers=["CPUExecutionProvider"]) 28 | pred = session.run(["predict"], {"image": image_input})[0] 29 | print(pred) 30 | 31 | #t0.51477224 0.28504044 0.05650776 0.08253327 0.06114639 -------------------------------------------------------------------------------- /demo/swin/my_dataset.py: -------------------------------------------------------------------------------- 1 | from PIL import Image 2 | import torch 3 | from torch.utils.data import Dataset 4 | 5 | 6 | class MyDataSet(Dataset): 7 | """自定义数据集""" 8 | 9 | def __init__(self, images_path: list, images_class: list, transform=None): 10 | self.images_path = images_path 11 | self.images_class = images_class 12 | self.transform = transform 13 | 14 | def __len__(self): 15 | return len(self.images_path) 16 | 17 | def __getitem__(self, item): 18 | img = Image.open(self.images_path[item]) 19 | # RGB为彩色图片，L为灰度图片 20 | if img.mode != 'RGB': 21 | raise ValueError("image: {} isn't RGB mode.".format(self.images_path[item])) 22 | label = self.images_class[item] 23 | 24 | if self.transform is not None: 25 | img = self.transform(img) 26 | 27 | return img, label 28 | 29 | @staticmethod 30 | def collate_fn(batch): 31 | # 官方实现的default_collate可以参考 32 | # https://github.com/pytorch/pytorch/blob/67b7e751e6b5931a9f45274653f4f653a4e6cdf6/torch/utils/data/_utils/collate.py 33 | images, labels = tuple(zip(*batch)) 34 | 35 | images = torch.stack(images, dim=0) 36 | labels = torch.as_tensor(labels) 37 | return images, labels 38 | -------------------------------------------------------------------------------- /demo/swin/onnx_simplify.py: -------------------------------------------------------------------------------- 1 | from onnxsim import simplify 2 | import onnx 3 | input_path="swin.onnx" 4 | output_path="swin_sim.onnx" 5 | onnx_model = onnx.load(input_path) 6 | model_simp, check = simplify(onnx_model,input_shapes={'image': [1, 3, 224, 224]},dynamic_input_shape==True) 7 | assert check, "Simplified ONNX model could not be validated" 8 | onnx.save(model_simp, output_path) 9 | print('finished exporting onnx') -------------------------------------------------------------------------------- /demo/swin/read_onnx.py: -------------------------------------------------------------------------------- 1 | import onnx 2 | import onnxruntime 3 | model = onnx.load("swin.onnx") 4 | 5 | model() -------------------------------------------------------------------------------- /demo/unet/.gitignore: -------------------------------------------------------------------------------- 1 | *.onnx 2 | *.pth 3 | __pycache__ 4 | -------------------------------------------------------------------------------- /demo/unet/LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2021 Bubbliiiing 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /demo/unet/Medical_Datasets/ImageSets/Segmentation/README.md: -------------------------------------------------------------------------------- 1 | 存放的是指向文件名称的txt 2 | 3 | -------------------------------------------------------------------------------- /demo/unet/Medical_Datasets/ImageSets/Segmentation/train.txt: -------------------------------------------------------------------------------- 1 | 0 2 | 1 3 | 10 4 | 11 5 | 12 6 | 13 7 | 14 8 | 15 9 | 16 10 | 17 11 | 18 12 | 19 13 | 2 14 | 20 15 | 21 16 | 22 17 | 23 18 | 24 19 | 25 20 | 26 21 | 27 22 | 28 23 | 29 24 | 3 25 | 4 26 | 5 27 | 6 28 | 7 29 | 8 30 | 9 31 | 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/demo/unet/model_data/README.md: -------------------------------------------------------------------------------- 1 | 这里面存放的是已经训练好的权重，可通过百度网盘下载。 2 | -------------------------------------------------------------------------------- /demo/unet/nets/__init__.py: -------------------------------------------------------------------------------- 1 | # -------------------------------------------------------------------------------- /demo/unet/requirements.txt: -------------------------------------------------------------------------------- 1 | scipy 2 | numpy 3 | matplotlib 4 | opencv_python 5 | torch 6 | torchvision 7 | tqdm 8 | Pillow 9 | h5py -------------------------------------------------------------------------------- /demo/unet/summary.py: -------------------------------------------------------------------------------- 1 | #--------------------------------------------# 2 | # 该部分代码只用于看网络结构，并非测试代码 3 | #--------------------------------------------# 4 | from torchsummary import summary 5 | 6 | from nets.unet import Unet 7 | 8 | if __name__ == "__main__": 9 | model = Unet(num_classes = 21).train().cuda() 10 | # print("model have {} paramerters in total".format(sum(x.numel() for x in model.parameters()))) 11 | summary(model, (3, 512, 512)) 12 | -------------------------------------------------------------------------------- /demo/unet/utils/__init__.py: -------------------------------------------------------------------------------- 1 | # -------------------------------------------------------------------------------- /demo/unet/utils/utils.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from PIL import Image 3 | 4 | #---------------------------------------------------------# 5 | # 将图像转换成RGB图像，防止灰度图在预测时报错。 6 | # 代码仅仅支持RGB图像的预测，所有其它类型的图像都会转化成RGB 7 | #---------------------------------------------------------# 8 | def cvtColor(image): 9 | if len(np.shape(image)) == 3 and np.shape(image)[2] == 3: 10 | return image 11 | else: 12 | image = image.convert('RGB') 13 | return image 14 | 15 | #---------------------------------------------------# 16 | # 对输入图像进行resize 17 | #---------------------------------------------------# 18 | def resize_image(image, size): 19 | iw, ih = image.size 20 | w, h = size 21 | 22 | scale = min(w/iw, h/ih) 23 | nw = int(iw*scale) 24 | nh = int(ih*scale) 25 | 26 | image = image.resize((nw,nh), Image.BICUBIC) 27 | new_image = Image.new('RGB', size, (128,128,128)) 28 | new_image.paste(image, ((w-nw)//2, (h-nh)//2)) 29 | 30 | return new_image, nw, nh 31 | 32 | #---------------------------------------------------# 33 | # 获得学习率 34 | #---------------------------------------------------# 35 | def get_lr(optimizer): 36 | for param_group in optimizer.param_groups: 37 | return param_group['lr'] 38 | 39 | def preprocess_input(image): 40 | image /= 255.0 41 | return image 42 | -------------------------------------------------------------------------------- /demo/vit/class_indices.json: -------------------------------------------------------------------------------- 1 | { 2 | "0": "daisy", 3 | "1": "dandelion", 4 | "2": "roses", 5 | "3": "sunflowers", 6 | "4": "tulips" 7 | } -------------------------------------------------------------------------------- /demo/vit/demo.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Rex-LK/tensorrt_learning/45df1f33cab6a66a0d75c0bb26a3978d539dc888/demo/vit/demo.jpg -------------------------------------------------------------------------------- /demo/vit/export_onnx.py: -------------------------------------------------------------------------------- 1 | 2 | from multiprocessing import dummy 3 | import os 4 | import json 5 | import onnx 6 | from psutil import cpu_count 7 | import sys 8 | 9 | 10 | import torch 11 | from PIL import Image 12 | from torchvision import transforms 13 | import matplotlib.pyplot as plt 14 | 15 | from vit_model import vit_base_patch16_224_in21k as create_model 16 | 17 | 18 | def main(): 19 | 20 | # device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") 21 | device = 'cpu' 22 | model = create_model(num_classes=5, has_logits=False).to(device) 23 | # load model weights 24 | model_weight_path = "vit-model-9.pth" 25 | model.load_state_dict(torch.load(model_weight_path, map_location=device)) 26 | model.eval() 27 | 28 | 29 | dummy = torch.zeros(1,3,224,224) 30 | torch.onnx.export( 31 | model,(dummy,), 32 | "vit.onnx", 33 | input_names=["image"], 34 | output_names=["predict"], 35 | opset_version=12, 36 | dynamic_axes={"image":{0:"batch"},"predict":{0:"batch"}} 37 | ) 38 | 39 | 40 | if __name__ == '__main__': 41 | main() 42 | -------------------------------------------------------------------------------- /demo/vit/flops.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from fvcore.nn import FlopCountAnalysis 3 | 4 | from vit_model import Attention 5 | 6 | 7 | def main(): 8 | # Self-Attention 9 | a1 = Attention(dim=512, num_heads=1) 10 | a1.proj = torch.nn.Identity() # remove Wo 11 | 12 | # Multi-Head Attention 13 | a2 = Attention(dim=512, num_heads=8) 14 | 15 | # [batch_size, num_tokens, total_embed_dim] 16 | t = (torch.rand(32, 1024, 512),) 17 | 18 | flops1 = FlopCountAnalysis(a1, t) 19 | print("Self-Attention FLOPs:", flops1.total()) 20 | 21 | flops2 = FlopCountAnalysis(a2, t) 22 | print("Multi-Head Attention FLOPs:", flops2.total()) 23 | 24 | 25 | if __name__ == '__main__': 26 | main() 27 | 28 | -------------------------------------------------------------------------------- /demo/vit/infer-onnxruntime.py: -------------------------------------------------------------------------------- 1 | 2 | import onnxruntime 3 | import cv2 4 | import numpy as np 5 | from torchvision import transforms 6 | from PIL import Image 7 | import os 8 | import torch 9 | 10 | 11 | if __name__ == "__main__": 12 | 13 | img_size = 224 14 | data_transform = transforms.Compose( 15 | [transforms.Resize(256), 16 | transforms.CenterCrop(224), 17 | transforms.ToTensor(), 18 | transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]) 19 | # load image 20 | img_path = "demo.jpg" 21 | img = Image.open(img_path) 22 | img = data_transform(img) 23 | # expand batch dimension 24 | img = torch.unsqueeze(img, dim=0) 25 | image_input = img.numpy() 26 | session = onnxruntime.InferenceSession("vit.onnx", providers=["CPUExecutionProvider"]) 27 | pred = session.run(["predict"], {"image": image_input})[0] 28 | print(pred) -------------------------------------------------------------------------------- /demo/vit/my_dataset.py: -------------------------------------------------------------------------------- 1 | from PIL import Image 2 | import torch 3 | from torch.utils.data import Dataset 4 | 5 | 6 | class MyDataSet(Dataset): 7 | """自定义数据集""" 8 | 9 | def __init__(self, images_path: list, images_class: list, transform=None): 10 | self.images_path = images_path 11 | self.images_class = images_class 12 | self.transform = transform 13 | 14 | def __len__(self): 15 | return len(self.images_path) 16 | 17 | def __getitem__(self, item): 18 | img = Image.open(self.images_path[item]) 19 | # RGB为彩色图片，L为灰度图片 20 | if img.mode != 'RGB': 21 | raise ValueError("image: {} isn't RGB mode.".format(self.images_path[item])) 22 | label = self.images_class[item] 23 | 24 | if self.transform is not None: 25 | img = self.transform(img) 26 | 27 | return img, label 28 | 29 | @staticmethod 30 | def collate_fn(batch): 31 | # 官方实现的default_collate可以参考 32 | # https://github.com/pytorch/pytorch/blob/67b7e751e6b5931a9f45274653f4f653a4e6cdf6/torch/utils/data/_utils/collate.py 33 | images, labels = tuple(zip(*batch)) 34 | 35 | images = torch.stack(images, dim=0) 36 | labels = torch.as_tensor(labels) 37 | return images, labels 38 | -------------------------------------------------------------------------------- /demo/vit/onnx_simplify.py: -------------------------------------------------------------------------------- 1 | from onnxsim import simplify 2 | import onnx 3 | input_path="vit.onnx" 4 | output_path="vit.onnx" 5 | onnx_model = onnx.load(input_path) 6 | model_simp, check = simplify(onnx_model,input_shapes={'image': [1, 3, 224, 224]}) 7 | assert check, "Simplified ONNX model could not be validated" 8 | onnx.save(model_simp, output_path) 9 | print('finished exporting onnx') -------------------------------------------------------------------------------- /demo/vit/predict.py: -------------------------------------------------------------------------------- 1 | import os 2 | import json 3 | 4 | import torch 5 | from PIL import Image 6 | from torchvision import transforms 7 | import matplotlib.pyplot as plt 8 | 9 | from vit_model import vit_base_patch16_224_in21k as create_model 10 | 11 | 12 | def main(): 13 | device = 'cpu' 14 | data_transform = transforms.Compose( 15 | [transforms.Resize(256), 16 | transforms.CenterCrop(224), 17 | transforms.ToTensor(), 18 | transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]) 19 | img_path = "./demo.jpg" 20 | img = Image.open(img_path) 21 | img = data_transform(img) 22 | img = torch.unsqueeze(img, dim=0) 23 | model = create_model(num_classes=5, has_logits=False).to(device) 24 | model_weight_path = "vit-model-9.pth" 25 | model.load_state_dict(torch.load(model_weight_path, map_location=device)) 26 | model.eval() 27 | with torch.no_grad(): 28 | # predict class 29 | # output = torch.squeeze(model(img.to(device))).cpu() 30 | output = model(img.to(device)) 31 | print(output) 32 | # predict = torch.softmax(output, dim=0) 33 | 34 | if __name__ == '__main__': 35 | main() 36 | -------------------------------------------------------------------------------- /demo/yolov5/detect-for-yolov5-6.0.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | cd yolov5-6.0 4 | 5 | python detect.py --weights=yolov5s.pt --source=../workspace/car.jpg --iou-thres=0.5 --conf-thres=0.25 --project=../workspace/ 6 | 7 | mv ../workspace/exp/car.jpg ../workspace/car-pytorch.jpg 8 | rm -rf ../workspace/exp -------------------------------------------------------------------------------- /demo/yolov5/export-yolov5-6.0.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | cd yolov5-6.0 4 | python export.py --weights=yolov5s.pt --dynamic --include=onnx --opset=11 5 | 6 | mv yolov5s.onnx ../workspace/ -------------------------------------------------------------------------------- /demo/yolov5/yolov5-6.0/.gitattributes: -------------------------------------------------------------------------------- 1 | # this drop notebooks from GitHub language stats 2 | *.ipynb linguist-vendored 3 | -------------------------------------------------------------------------------- /demo/yolov5/yolov5-6.0/.github/FUNDING.yml: -------------------------------------------------------------------------------- 1 | # These are supported funding model platforms 2 | 3 | github: glenn-jocher 4 | patreon: ultralytics 5 | open_collective: ultralytics 6 | -------------------------------------------------------------------------------- /demo/yolov5/yolov5-6.0/.github/ISSUE_TEMPLATE/feature-request.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: "🚀 Feature request" 3 | about: Suggest an idea for this project 4 | title: '' 5 | labels: enhancement 6 | assignees: '' 7 | 8 | --- 9 | 10 | ## 🚀 Feature 11 | 12 | 13 | 14 | ## Motivation 15 | 16 | 18 | 19 | ## Pitch 20 | 21 | 22 | 23 | ## Alternatives 24 | 25 | 26 | 27 | ## Additional context 28 | 29 | 30 | -------------------------------------------------------------------------------- /demo/yolov5/yolov5-6.0/.github/ISSUE_TEMPLATE/question.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: "❓Question" 3 | about: Ask a general question 4 | title: '' 5 | labels: question 6 | assignees: '' 7 | 8 | --- 9 | 10 | ## ❔Question 11 | 12 | ## Additional context 13 | -------------------------------------------------------------------------------- /demo/yolov5/yolov5-6.0/.github/dependabot.yml: -------------------------------------------------------------------------------- 1 | version: 2 2 | updates: 3 | - package-ecosystem: pip 4 | directory: "/" 5 | schedule: 6 | interval: weekly 7 | time: "04:00" 8 | open-pull-requests-limit: 10 9 | reviewers: 10 | - glenn-jocher 11 | labels: 12 | - dependencies 13 | -------------------------------------------------------------------------------- /demo/yolov5/yolov5-6.0/.github/workflows/rebase.yml: -------------------------------------------------------------------------------- 1 | name: Automatic Rebase 2 | # https://github.com/marketplace/actions/automatic-rebase 3 | 4 | on: 5 | issue_comment: 6 | types: [created] 7 | 8 | jobs: 9 | rebase: 10 | name: Rebase 11 | if: github.event.issue.pull_request != '' && contains(github.event.comment.body, '/rebase') 12 | runs-on: ubuntu-latest 13 | steps: 14 | - name: Checkout the latest code 15 | uses: actions/checkout@v2 16 | with: 17 | fetch-depth: 0 18 | - name: Automatic Rebase 19 | uses: cirrus-actions/rebase@1.3.1 20 | env: 21 | GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} 22 | -------------------------------------------------------------------------------- /demo/yolov5/yolov5-6.0/Arial.ttf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Rex-LK/tensorrt_learning/45df1f33cab6a66a0d75c0bb26a3978d539dc888/demo/yolov5/yolov5-6.0/Arial.ttf -------------------------------------------------------------------------------- /demo/yolov5/yolov5-6.0/models/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Rex-LK/tensorrt_learning/45df1f33cab6a66a0d75c0bb26a3978d539dc888/demo/yolov5/yolov5-6.0/models/__init__.py -------------------------------------------------------------------------------- /demo/yolov5/yolov5-6.0/models/hub/yolov3-tiny.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | 3 | # Parameters 4 | nc: 80 # number of classes 5 | depth_multiple: 1.0 # model depth multiple 6 | width_multiple: 1.0 # layer channel multiple 7 | anchors: 8 | - [10,14, 23,27, 37,58] # P4/16 9 | - [81,82, 135,169, 344,319] # P5/32 10 | 11 | # YOLOv3-tiny backbone 12 | backbone: 13 | # [from, number, module, args] 14 | [[-1, 1, Conv, [16, 3, 1]], # 0 15 | [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 1-P1/2 16 | [-1, 1, Conv, [32, 3, 1]], 17 | [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 3-P2/4 18 | [-1, 1, Conv, [64, 3, 1]], 19 | [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 5-P3/8 20 | [-1, 1, Conv, [128, 3, 1]], 21 | [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 7-P4/16 22 | [-1, 1, Conv, [256, 3, 1]], 23 | [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 9-P5/32 24 | [-1, 1, Conv, [512, 3, 1]], 25 | [-1, 1, nn.ZeroPad2d, [[0, 1, 0, 1]]], # 11 26 | [-1, 1, nn.MaxPool2d, [2, 1, 0]], # 12 27 | ] 28 | 29 | # YOLOv3-tiny head 30 | head: 31 | [[-1, 1, Conv, [1024, 3, 1]], 32 | [-1, 1, Conv, [256, 1, 1]], 33 | [-1, 1, Conv, [512, 3, 1]], # 15 (P5/32-large) 34 | 35 | [-2, 1, Conv, [128, 1, 1]], 36 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 37 | [[-1, 8], 1, Concat, [1]], # cat backbone P4 38 | [-1, 1, Conv, [256, 3, 1]], # 19 (P4/16-medium) 39 | 40 | [[19, 15], 1, Detect, [nc, anchors]], # Detect(P4, P5) 41 | ] 42 | -------------------------------------------------------------------------------- /demo/yolov5/yolov5-6.0/models/hub/yolov3.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | 3 | # Parameters 4 | nc: 80 # number of classes 5 | depth_multiple: 1.0 # model depth multiple 6 | width_multiple: 1.0 # layer channel multiple 7 | anchors: 8 | - [10,13, 16,30, 33,23] # P3/8 9 | - [30,61, 62,45, 59,119] # P4/16 10 | - [116,90, 156,198, 373,326] # P5/32 11 | 12 | # darknet53 backbone 13 | backbone: 14 | # [from, number, module, args] 15 | [[-1, 1, Conv, [32, 3, 1]], # 0 16 | [-1, 1, Conv, [64, 3, 2]], # 1-P1/2 17 | [-1, 1, Bottleneck, [64]], 18 | [-1, 1, Conv, [128, 3, 2]], # 3-P2/4 19 | [-1, 2, Bottleneck, [128]], 20 | [-1, 1, Conv, [256, 3, 2]], # 5-P3/8 21 | [-1, 8, Bottleneck, [256]], 22 | [-1, 1, Conv, [512, 3, 2]], # 7-P4/16 23 | [-1, 8, Bottleneck, [512]], 24 | [-1, 1, Conv, [1024, 3, 2]], # 9-P5/32 25 | [-1, 4, Bottleneck, [1024]], # 10 26 | ] 27 | 28 | # YOLOv3 head 29 | head: 30 | [[-1, 1, Bottleneck, [1024, False]], 31 | [-1, 1, Conv, [512, [1, 1]]], 32 | [-1, 1, Conv, [1024, 3, 1]], 33 | [-1, 1, Conv, [512, 1, 1]], 34 | [-1, 1, Conv, [1024, 3, 1]], # 15 (P5/32-large) 35 | 36 | [-2, 1, Conv, [256, 1, 1]], 37 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 38 | [[-1, 8], 1, Concat, [1]], # cat backbone P4 39 | [-1, 1, Bottleneck, [512, False]], 40 | [-1, 1, Bottleneck, [512, False]], 41 | [-1, 1, Conv, [256, 1, 1]], 42 | [-1, 1, Conv, [512, 3, 1]], # 22 (P4/16-medium) 43 | 44 | [-2, 1, Conv, [128, 1, 1]], 45 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 46 | [[-1, 6], 1, Concat, [1]], # cat backbone P3 47 | [-1, 1, Bottleneck, [256, False]], 48 | [-1, 2, Bottleneck, [256, False]], # 27 (P3/8-small) 49 | 50 | [[27, 22, 15], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) 51 | ] 52 | -------------------------------------------------------------------------------- /demo/yolov5/yolov5-6.0/models/hub/yolov5-bifpn.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | 3 | # Parameters 4 | nc: 80 # number of classes 5 | depth_multiple: 1.0 # model depth multiple 6 | width_multiple: 1.0 # layer channel multiple 7 | anchors: 8 | - [10,13, 16,30, 33,23] # P3/8 9 | - [30,61, 62,45, 59,119] # P4/16 10 | - [116,90, 156,198, 373,326] # P5/32 11 | 12 | # YOLOv5 backbone 13 | backbone: 14 | # [from, number, module, args] 15 | [[-1, 1, Focus, [64, 3]], # 0-P1/2 16 | [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 17 | [-1, 3, C3, [128]], 18 | [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 19 | [-1, 9, C3, [256]], 20 | [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 21 | [-1, 9, C3, [512]] 22 | [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 23 | [-1, 1, SPP, [1024, [5, 9, 13]]], 24 | [-1, 3, C3, [1024, False]], # 9 25 | ] 26 | 27 | # YOLOv5 BiFPN head 28 | head: 29 | [[-1, 1, Conv, [512, 1, 1]], 30 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 31 | [[-1, 6], 1, Concat, [1]], # cat backbone P4 32 | [-1, 3, C3, [512, False]], # 13 33 | 34 | [-1, 1, Conv, [256, 1, 1]], 35 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 36 | [[-1, 4], 1, Concat, [1]], # cat backbone P3 37 | [-1, 3, C3, [256, False]], # 17 (P3/8-small) 38 | 39 | [-1, 1, Conv, [256, 3, 2]], 40 | [[-1, 14, 6], 1, Concat, [1]], # cat P4 41 | [-1, 3, C3, [512, False]], # 20 (P4/16-medium) 42 | 43 | [-1, 1, Conv, [512, 3, 2]], 44 | [[-1, 10], 1, Concat, [1]], # cat head P5 45 | [-1, 3, C3, [1024, False]], # 23 (P5/32-large) 46 | 47 | [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) 48 | ] 49 | -------------------------------------------------------------------------------- /demo/yolov5/yolov5-6.0/models/hub/yolov5-fpn.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | 3 | # Parameters 4 | nc: 80 # number of classes 5 | depth_multiple: 1.0 # model depth multiple 6 | width_multiple: 1.0 # layer channel multiple 7 | anchors: 8 | - [10,13, 16,30, 33,23] # P3/8 9 | - [30,61, 62,45, 59,119] # P4/16 10 | - [116,90, 156,198, 373,326] # P5/32 11 | 12 | # YOLOv5 backbone 13 | backbone: 14 | # [from, number, module, args] 15 | [[-1, 1, Focus, [64, 3]], # 0-P1/2 16 | [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 17 | [-1, 3, Bottleneck, [128]], 18 | [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 19 | [-1, 9, BottleneckCSP, [256]], 20 | [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 21 | [-1, 9, BottleneckCSP, [512]], 22 | [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 23 | [-1, 1, SPP, [1024, [5, 9, 13]]], 24 | [-1, 6, BottleneckCSP, [1024]], # 9 25 | ] 26 | 27 | # YOLOv5 FPN head 28 | head: 29 | [[-1, 3, BottleneckCSP, [1024, False]], # 10 (P5/32-large) 30 | 31 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 32 | [[-1, 6], 1, Concat, [1]], # cat backbone P4 33 | [-1, 1, Conv, [512, 1, 1]], 34 | [-1, 3, BottleneckCSP, [512, False]], # 14 (P4/16-medium) 35 | 36 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 37 | [[-1, 4], 1, Concat, [1]], # cat backbone P3 38 | [-1, 1, Conv, [256, 1, 1]], 39 | [-1, 3, BottleneckCSP, [256, False]], # 18 (P3/8-small) 40 | 41 | [[18, 14, 10], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) 42 | ] 43 | -------------------------------------------------------------------------------- /demo/yolov5/yolov5-6.0/models/hub/yolov5-panet.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | 3 | # Parameters 4 | nc: 80 # number of classes 5 | depth_multiple: 1.0 # model depth multiple 6 | width_multiple: 1.0 # layer channel multiple 7 | anchors: 8 | - [10,13, 16,30, 33,23] # P3/8 9 | - [30,61, 62,45, 59,119] # P4/16 10 | - [116,90, 156,198, 373,326] # P5/32 11 | 12 | # YOLOv5 backbone 13 | backbone: 14 | # [from, number, module, args] 15 | [[-1, 1, Focus, [64, 3]], # 0-P1/2 16 | [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 17 | [-1, 3, BottleneckCSP, [128]], 18 | [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 19 | [-1, 9, BottleneckCSP, [256]], 20 | [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 21 | [-1, 9, BottleneckCSP, [512]], 22 | [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 23 | [-1, 1, SPP, [1024, [5, 9, 13]]], 24 | [-1, 3, BottleneckCSP, [1024, False]], # 9 25 | ] 26 | 27 | # YOLOv5 PANet head 28 | head: 29 | [[-1, 1, Conv, [512, 1, 1]], 30 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 31 | [[-1, 6], 1, Concat, [1]], # cat backbone P4 32 | [-1, 3, BottleneckCSP, [512, False]], # 13 33 | 34 | [-1, 1, Conv, [256, 1, 1]], 35 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 36 | [[-1, 4], 1, Concat, [1]], # cat backbone P3 37 | [-1, 3, BottleneckCSP, [256, False]], # 17 (P3/8-small) 38 | 39 | [-1, 1, Conv, [256, 3, 2]], 40 | [[-1, 14], 1, Concat, [1]], # cat head P4 41 | [-1, 3, BottleneckCSP, [512, False]], # 20 (P4/16-medium) 42 | 43 | [-1, 1, Conv, [512, 3, 2]], 44 | [[-1, 10], 1, Concat, [1]], # cat head P5 45 | [-1, 3, BottleneckCSP, [1024, False]], # 23 (P5/32-large) 46 | 47 | [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) 48 | ] 49 | -------------------------------------------------------------------------------- /demo/yolov5/yolov5-6.0/models/hub/yolov5s-ghost.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | 3 | # Parameters 4 | nc: 80 # number of classes 5 | depth_multiple: 0.33 # model depth multiple 6 | width_multiple: 0.50 # layer channel multiple 7 | anchors: 8 | - [10,13, 16,30, 33,23] # P3/8 9 | - [30,61, 62,45, 59,119] # P4/16 10 | - [116,90, 156,198, 373,326] # P5/32 11 | 12 | # YOLOv5 backbone 13 | backbone: 14 | # [from, number, module, args] 15 | [[-1, 1, Focus, [64, 3]], # 0-P1/2 16 | [-1, 1, GhostConv, [128, 3, 2]], # 1-P2/4 17 | [-1, 3, C3Ghost, [128]], 18 | [-1, 1, GhostConv, [256, 3, 2]], # 3-P3/8 19 | [-1, 9, C3Ghost, [256]], 20 | [-1, 1, GhostConv, [512, 3, 2]], # 5-P4/16 21 | [-1, 9, C3Ghost, [512]], 22 | [-1, 1, GhostConv, [1024, 3, 2]], # 7-P5/32 23 | [-1, 1, SPP, [1024, [5, 9, 13]]], 24 | [-1, 3, C3Ghost, [1024, False]], # 9 25 | ] 26 | 27 | # YOLOv5 head 28 | head: 29 | [[-1, 1, GhostConv, [512, 1, 1]], 30 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 31 | [[-1, 6], 1, Concat, [1]], # cat backbone P4 32 | [-1, 3, C3Ghost, [512, False]], # 13 33 | 34 | [-1, 1, GhostConv, [256, 1, 1]], 35 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 36 | [[-1, 4], 1, Concat, [1]], # cat backbone P3 37 | [-1, 3, C3Ghost, [256, False]], # 17 (P3/8-small) 38 | 39 | [-1, 1, GhostConv, [256, 3, 2]], 40 | [[-1, 14], 1, Concat, [1]], # cat head P4 41 | [-1, 3, C3Ghost, [512, False]], # 20 (P4/16-medium) 42 | 43 | [-1, 1, GhostConv, [512, 3, 2]], 44 | [[-1, 10], 1, Concat, [1]], # cat head P5 45 | [-1, 3, C3Ghost, [1024, False]], # 23 (P5/32-large) 46 | 47 | [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) 48 | ] 49 | -------------------------------------------------------------------------------- /demo/yolov5/yolov5-6.0/models/hub/yolov5s-transformer.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | 3 | # Parameters 4 | nc: 80 # number of classes 5 | depth_multiple: 0.33 # model depth multiple 6 | width_multiple: 0.50 # layer channel multiple 7 | anchors: 8 | - [10,13, 16,30, 33,23] # P3/8 9 | - [30,61, 62,45, 59,119] # P4/16 10 | - [116,90, 156,198, 373,326] # P5/32 11 | 12 | # YOLOv5 backbone 13 | backbone: 14 | # [from, number, module, args] 15 | [[-1, 1, Focus, [64, 3]], # 0-P1/2 16 | [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 17 | [-1, 3, C3, [128]], 18 | [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 19 | [-1, 9, C3, [256]], 20 | [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 21 | [-1, 9, C3, [512]], 22 | [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 23 | [-1, 1, SPP, [1024, [5, 9, 13]]], 24 | [-1, 3, C3TR, [1024, False]], # 9 <-------- C3TR() Transformer module 25 | ] 26 | 27 | # YOLOv5 head 28 | head: 29 | [[-1, 1, Conv, [512, 1, 1]], 30 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 31 | [[-1, 6], 1, Concat, [1]], # cat backbone P4 32 | [-1, 3, C3, [512, False]], # 13 33 | 34 | [-1, 1, Conv, [256, 1, 1]], 35 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 36 | [[-1, 4], 1, Concat, [1]], # cat backbone P3 37 | [-1, 3, C3, [256, False]], # 17 (P3/8-small) 38 | 39 | [-1, 1, Conv, [256, 3, 2]], 40 | [[-1, 14], 1, Concat, [1]], # cat head P4 41 | [-1, 3, C3, [512, False]], # 20 (P4/16-medium) 42 | 43 | [-1, 1, Conv, [512, 3, 2]], 44 | [[-1, 10], 1, Concat, [1]], # cat head P5 45 | [-1, 3, C3, [1024, False]], # 23 (P5/32-large) 46 | 47 | [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) 48 | ] 49 | -------------------------------------------------------------------------------- /demo/yolov5/yolov5-6.0/models/yolov5l.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | 3 | # Parameters 4 | nc: 80 # number of classes 5 | depth_multiple: 1.0 # model depth multiple 6 | width_multiple: 1.0 # layer channel multiple 7 | anchors: 8 | - [10,13, 16,30, 33,23] # P3/8 9 | - [30,61, 62,45, 59,119] # P4/16 10 | - [116,90, 156,198, 373,326] # P5/32 11 | 12 | # YOLOv5 v6.0 backbone 13 | backbone: 14 | # [from, number, module, args] 15 | [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 16 | [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 17 | [-1, 3, C3, [128]], 18 | [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 19 | [-1, 6, C3, [256]], 20 | [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 21 | [-1, 9, C3, [512]], 22 | [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 23 | [-1, 3, C3, [1024]], 24 | [-1, 1, SPPF, [1024, 5]], # 9 25 | ] 26 | 27 | # YOLOv5 v6.0 head 28 | head: 29 | [[-1, 1, Conv, [512, 1, 1]], 30 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 31 | [[-1, 6], 1, Concat, [1]], # cat backbone P4 32 | [-1, 3, C3, [512, False]], # 13 33 | 34 | [-1, 1, Conv, [256, 1, 1]], 35 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 36 | [[-1, 4], 1, Concat, [1]], # cat backbone P3 37 | [-1, 3, C3, [256, False]], # 17 (P3/8-small) 38 | 39 | [-1, 1, Conv, [256, 3, 2]], 40 | [[-1, 14], 1, Concat, [1]], # cat head P4 41 | [-1, 3, C3, [512, False]], # 20 (P4/16-medium) 42 | 43 | [-1, 1, Conv, [512, 3, 2]], 44 | [[-1, 10], 1, Concat, [1]], # cat head P5 45 | [-1, 3, C3, [1024, False]], # 23 (P5/32-large) 46 | 47 | [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) 48 | ] 49 | -------------------------------------------------------------------------------- /demo/yolov5/yolov5-6.0/models/yolov5m.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | 3 | # Parameters 4 | nc: 80 # number of classes 5 | depth_multiple: 0.67 # model depth multiple 6 | width_multiple: 0.75 # layer channel multiple 7 | anchors: 8 | - [10,13, 16,30, 33,23] # P3/8 9 | - [30,61, 62,45, 59,119] # P4/16 10 | - [116,90, 156,198, 373,326] # P5/32 11 | 12 | # YOLOv5 v6.0 backbone 13 | backbone: 14 | # [from, number, module, args] 15 | [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 16 | [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 17 | [-1, 3, C3, [128]], 18 | [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 19 | [-1, 6, C3, [256]], 20 | [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 21 | [-1, 9, C3, [512]], 22 | [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 23 | [-1, 3, C3, [1024]], 24 | [-1, 1, SPPF, [1024, 5]], # 9 25 | ] 26 | 27 | # YOLOv5 v6.0 head 28 | head: 29 | [[-1, 1, Conv, [512, 1, 1]], 30 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 31 | [[-1, 6], 1, Concat, [1]], # cat backbone P4 32 | [-1, 3, C3, [512, False]], # 13 33 | 34 | [-1, 1, Conv, [256, 1, 1]], 35 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 36 | [[-1, 4], 1, Concat, [1]], # cat backbone P3 37 | [-1, 3, C3, [256, False]], # 17 (P3/8-small) 38 | 39 | [-1, 1, Conv, [256, 3, 2]], 40 | [[-1, 14], 1, Concat, [1]], # cat head P4 41 | [-1, 3, C3, [512, False]], # 20 (P4/16-medium) 42 | 43 | [-1, 1, Conv, [512, 3, 2]], 44 | [[-1, 10], 1, Concat, [1]], # cat head P5 45 | [-1, 3, C3, [1024, False]], # 23 (P5/32-large) 46 | 47 | [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) 48 | ] 49 | -------------------------------------------------------------------------------- /demo/yolov5/yolov5-6.0/models/yolov5n.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | 3 | # Parameters 4 | nc: 80 # number of classes 5 | depth_multiple: 0.33 # model depth multiple 6 | width_multiple: 0.25 # layer channel multiple 7 | anchors: 8 | - [10,13, 16,30, 33,23] # P3/8 9 | - [30,61, 62,45, 59,119] # P4/16 10 | - [116,90, 156,198, 373,326] # P5/32 11 | 12 | # YOLOv5 v6.0 backbone 13 | backbone: 14 | # [from, number, module, args] 15 | [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 16 | [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 17 | [-1, 3, C3, [128]], 18 | [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 19 | [-1, 6, C3, [256]], 20 | [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 21 | [-1, 9, C3, [512]], 22 | [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 23 | [-1, 3, C3, [1024]], 24 | [-1, 1, SPPF, [1024, 5]], # 9 25 | ] 26 | 27 | # YOLOv5 v6.0 head 28 | head: 29 | [[-1, 1, Conv, [512, 1, 1]], 30 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 31 | [[-1, 6], 1, Concat, [1]], # cat backbone P4 32 | [-1, 3, C3, [512, False]], # 13 33 | 34 | [-1, 1, Conv, [256, 1, 1]], 35 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 36 | [[-1, 4], 1, Concat, [1]], # cat backbone P3 37 | [-1, 3, C3, [256, False]], # 17 (P3/8-small) 38 | 39 | [-1, 1, Conv, [256, 3, 2]], 40 | [[-1, 14], 1, Concat, [1]], # cat head P4 41 | [-1, 3, C3, [512, False]], # 20 (P4/16-medium) 42 | 43 | [-1, 1, Conv, [512, 3, 2]], 44 | [[-1, 10], 1, Concat, [1]], # cat head P5 45 | [-1, 3, C3, [1024, False]], # 23 (P5/32-large) 46 | 47 | [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) 48 | ] 49 | -------------------------------------------------------------------------------- /demo/yolov5/yolov5-6.0/models/yolov5s.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | 3 | # Parameters 4 | nc: 80 # number of classes 5 | depth_multiple: 0.33 # model depth multiple 6 | width_multiple: 0.50 # layer channel multiple 7 | anchors: 8 | - [10,13, 16,30, 33,23] # P3/8 9 | - [30,61, 62,45, 59,119] # P4/16 10 | - [116,90, 156,198, 373,326] # P5/32 11 | 12 | # YOLOv5 v6.0 backbone 13 | backbone: 14 | # [from, number, module, args] 15 | [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 16 | [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 17 | [-1, 3, C3, [128]], 18 | [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 19 | [-1, 6, C3, [256]], 20 | [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 21 | [-1, 9, C3, [512]], 22 | [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 23 | [-1, 3, C3, [1024]], 24 | [-1, 1, SPPF, [1024, 5]], # 9 25 | ] 26 | 27 | # YOLOv5 v6.0 head 28 | head: 29 | [[-1, 1, Conv, [512, 1, 1]], 30 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 31 | [[-1, 6], 1, Concat, [1]], # cat backbone P4 32 | [-1, 3, C3, [512, False]], # 13 33 | 34 | [-1, 1, Conv, [256, 1, 1]], 35 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 36 | [[-1, 4], 1, Concat, [1]], # cat backbone P3 37 | [-1, 3, C3, [256, False]], # 17 (P3/8-small) 38 | 39 | [-1, 1, Conv, [256, 3, 2]], 40 | [[-1, 14], 1, Concat, [1]], # cat head P4 41 | [-1, 3, C3, [512, False]], # 20 (P4/16-medium) 42 | 43 | [-1, 1, Conv, [512, 3, 2]], 44 | [[-1, 10], 1, Concat, [1]], # cat head P5 45 | [-1, 3, C3, [1024, False]], # 23 (P5/32-large) 46 | 47 | [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) 48 | ] 49 | -------------------------------------------------------------------------------- /demo/yolov5/yolov5-6.0/models/yolov5x.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | 3 | # Parameters 4 | nc: 80 # number of classes 5 | depth_multiple: 1.33 # model depth multiple 6 | width_multiple: 1.25 # layer channel multiple 7 | anchors: 8 | - [10,13, 16,30, 33,23] # P3/8 9 | - [30,61, 62,45, 59,119] # P4/16 10 | - [116,90, 156,198, 373,326] # P5/32 11 | 12 | # YOLOv5 v6.0 backbone 13 | backbone: 14 | # [from, number, module, args] 15 | [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 16 | [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 17 | [-1, 3, C3, [128]], 18 | [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 19 | [-1, 6, C3, [256]], 20 | [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 21 | [-1, 9, C3, [512]], 22 | [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 23 | [-1, 3, C3, [1024]], 24 | [-1, 1, SPPF, [1024, 5]], # 9 25 | ] 26 | 27 | # YOLOv5 v6.0 head 28 | head: 29 | [[-1, 1, Conv, [512, 1, 1]], 30 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 31 | [[-1, 6], 1, Concat, [1]], # cat backbone P4 32 | [-1, 3, C3, [512, False]], # 13 33 | 34 | [-1, 1, Conv, [256, 1, 1]], 35 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 36 | [[-1, 4], 1, Concat, [1]], # cat backbone P3 37 | [-1, 3, C3, [256, False]], # 17 (P3/8-small) 38 | 39 | [-1, 1, Conv, [256, 3, 2]], 40 | [[-1, 14], 1, Concat, [1]], # cat head P4 41 | [-1, 3, C3, [512, False]], # 20 (P4/16-medium) 42 | 43 | [-1, 1, Conv, [512, 3, 2]], 44 | [[-1, 10], 1, Concat, [1]], # cat head P5 45 | [-1, 3, C3, [1024, False]], # 23 (P5/32-large) 46 | 47 | [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) 48 | ] 49 | -------------------------------------------------------------------------------- /demo/yolov5/yolov5-6.0/requirements.txt: -------------------------------------------------------------------------------- 1 | # pip install -r requirements.txt 2 | 3 | # Base ---------------------------------------- 4 | matplotlib>=3.2.2 5 | numpy>=1.18.5 6 | opencv-python>=4.1.2 7 | Pillow>=7.1.2 8 | PyYAML>=5.3.1 9 | requests>=2.23.0 10 | scipy>=1.4.1 11 | torch>=1.7.0 12 | torchvision>=0.8.1 13 | tqdm>=4.41.0 14 | 15 | # Logging ------------------------------------- 16 | tensorboard>=2.4.1 17 | # wandb 18 | 19 | # Plotting ------------------------------------ 20 | pandas>=1.1.4 21 | seaborn>=0.11.0 22 | 23 | # Export -------------------------------------- 24 | # coremltools>=4.1 # CoreML export 25 | # onnx>=1.9.0 # ONNX export 26 | # onnx-simplifier>=0.3.6 # ONNX simplifier 27 | # scikit-learn==0.19.2 # CoreML quantization 28 | # tensorflow>=2.4.1 # TFLite export 29 | # tensorflowjs>=3.9.0 # TF.js export 30 | 31 | # Extras -------------------------------------- 32 | # albumentations>=1.0.3 33 | # Cython # for pycocotools https://github.com/cocodataset/cocoapi/issues/172 34 | # pycocotools>=2.0 # COCO mAP 35 | # roboflow 36 | thop # FLOPs computation 37 | -------------------------------------------------------------------------------- /demo/yolov5/yolov5-6.0/utils/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Rex-LK/tensorrt_learning/45df1f33cab6a66a0d75c0bb26a3978d539dc888/demo/yolov5/yolov5-6.0/utils/__init__.py -------------------------------------------------------------------------------- /demo/yolov5/yolov5-6.0/utils/aws/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Rex-LK/tensorrt_learning/45df1f33cab6a66a0d75c0bb26a3978d539dc888/demo/yolov5/yolov5-6.0/utils/aws/__init__.py -------------------------------------------------------------------------------- /demo/yolov5/yolov5-6.0/utils/aws/mime.sh: -------------------------------------------------------------------------------- 1 | # AWS EC2 instance startup 'MIME' script https://aws.amazon.com/premiumsupport/knowledge-center/execute-user-data-ec2/ 2 | # This script will run on every instance restart, not only on first start 3 | # --- DO NOT COPY ABOVE COMMENTS WHEN PASTING INTO USERDATA --- 4 | 5 | Content-Type: multipart/mixed; boundary="//" 6 | MIME-Version: 1.0 7 | 8 | --// 9 | Content-Type: text/cloud-config; charset="us-ascii" 10 | MIME-Version: 1.0 11 | Content-Transfer-Encoding: 7bit 12 | Content-Disposition: attachment; filename="cloud-config.txt" 13 | 14 | #cloud-config 15 | cloud_final_modules: 16 | - [scripts-user, always] 17 | 18 | --// 19 | Content-Type: text/x-shellscript; charset="us-ascii" 20 | MIME-Version: 1.0 21 | Content-Transfer-Encoding: 7bit 22 | Content-Disposition: attachment; filename="userdata.txt" 23 | 24 | #!/bin/bash 25 | # --- paste contents of userdata.sh here --- 26 | --// 27 | -------------------------------------------------------------------------------- /demo/yolov5/yolov5-6.0/utils/aws/resume.py: -------------------------------------------------------------------------------- 1 | # Resume all interrupted trainings in yolov5/ dir including DDP trainings 2 | # Usage: $ python utils/aws/resume.py 3 | 4 | import os 5 | import sys 6 | from pathlib import Path 7 | 8 | import torch 9 | import yaml 10 | 11 | FILE = Path(__file__).resolve() 12 | ROOT = FILE.parents[2] # YOLOv5 root directory 13 | if str(ROOT) not in sys.path: 14 | sys.path.append(str(ROOT)) # add ROOT to PATH 15 | 16 | port = 0 # --master_port 17 | path = Path('').resolve() 18 | for last in path.rglob('*/**/last.pt'): 19 | ckpt = torch.load(last) 20 | if ckpt['optimizer'] is None: 21 | continue 22 | 23 | # Load opt.yaml 24 | with open(last.parent.parent / 'opt.yaml', errors='ignore') as f: 25 | opt = yaml.safe_load(f) 26 | 27 | # Get device count 28 | d = opt['device'].split(',') # devices 29 | nd = len(d) # number of devices 30 | ddp = nd > 1 or (nd == 0 and torch.cuda.device_count() > 1) # distributed data parallel 31 | 32 | if ddp: # multi-GPU 33 | port += 1 34 | cmd = f'python -m torch.distributed.run --nproc_per_node {nd} --master_port {port} train.py --resume {last}' 35 | else: # single-GPU 36 | cmd = f'python train.py --resume {last}' 37 | 38 | cmd += ' > /dev/null 2>&1 &' # redirect output to dev/null and run in daemon thread 39 | print(cmd) 40 | os.system(cmd) 41 | -------------------------------------------------------------------------------- /demo/yolov5/yolov5-6.0/utils/aws/userdata.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | # AWS EC2 instance startup script https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/user-data.html 3 | # This script will run only once on first instance start (for a re-start script see mime.sh) 4 | # /home/ubuntu (ubuntu) or /home/ec2-user (amazon-linux) is working dir 5 | # Use >300 GB SSD 6 | 7 | cd home/ubuntu 8 | if [ ! -d yolov5 ]; then 9 | echo "Running first-time script." # install dependencies, download COCO, pull Docker 10 | git clone https://github.com/ultralytics/yolov5 -b master && sudo chmod -R 777 yolov5 11 | cd yolov5 12 | bash data/scripts/get_coco.sh && echo "COCO done." & 13 | sudo docker pull ultralytics/yolov5:latest && echo "Docker done." & 14 | python -m pip install --upgrade pip && pip install -r requirements.txt && python detect.py && echo "Requirements done." & 15 | wait && echo "All tasks done." # finish background tasks 16 | else 17 | echo "Running re-start script." # resume interrupted runs 18 | i=0 19 | list=$(sudo docker ps -qa) # container list i.e. $'one\ntwo\nthree\nfour' 20 | while IFS= read -r id; do 21 | ((i++)) 22 | echo "restarting container $i: $id" 23 | sudo docker start $id 24 | # sudo docker exec -it $id python train.py --resume # single-GPU 25 | sudo docker exec -d $id python utils/aws/resume.py # multi-scenario 26 | done <<<"$list" 27 | fi 28 | -------------------------------------------------------------------------------- /demo/yolov5/yolov5-6.0/utils/flask_rest_api/example_request.py: -------------------------------------------------------------------------------- 1 | """Perform test request""" 2 | import pprint 3 | 4 | import requests 5 | 6 | DETECTION_URL = "http://localhost:5000/v1/object-detection/yolov5s" 7 | TEST_IMAGE = "zidane.jpg" 8 | 9 | image_data = open(TEST_IMAGE, "rb").read() 10 | 11 | response = requests.post(DETECTION_URL, files={"image": image_data}).json() 12 | 13 | pprint.pprint(response) 14 | -------------------------------------------------------------------------------- /demo/yolov5/yolov5-6.0/utils/flask_rest_api/restapi.py: -------------------------------------------------------------------------------- 1 | """ 2 | Run a rest API exposing the yolov5s object detection model 3 | """ 4 | import argparse 5 | import io 6 | 7 | import torch 8 | from PIL import Image 9 | from flask import Flask, request 10 | 11 | app = Flask(__name__) 12 | 13 | DETECTION_URL = "/v1/object-detection/yolov5s" 14 | 15 | 16 | @app.route(DETECTION_URL, methods=["POST"]) 17 | def predict(): 18 | if not request.method == "POST": 19 | return 20 | 21 | if request.files.get("image"): 22 | image_file = request.files["image"] 23 | image_bytes = image_file.read() 24 | 25 | img = Image.open(io.BytesIO(image_bytes)) 26 | 27 | results = model(img, size=640) # reduce size=320 for faster inference 28 | return results.pandas().xyxy[0].to_json(orient="records") 29 | 30 | 31 | if __name__ == "__main__": 32 | parser = argparse.ArgumentParser(description="Flask API exposing YOLOv5 model") 33 | parser.add_argument("--port", default=5000, type=int, help="port number") 34 | args = parser.parse_args() 35 | 36 | model = torch.hub.load("ultralytics/yolov5", "yolov5s", force_reload=True) # force_reload to recache 37 | app.run(host="0.0.0.0", port=args.port) # debug=True causes Restarting with stat 38 | -------------------------------------------------------------------------------- /demo/yolov5/yolov5-6.0/utils/google_app_engine/Dockerfile: -------------------------------------------------------------------------------- 1 | FROM gcr.io/google-appengine/python 2 | 3 | # Create a virtualenv for dependencies. This isolates these packages from 4 | # system-level packages. 5 | # Use -p python3 or -p python3.7 to select python version. Default is version 2. 6 | RUN virtualenv /env -p python3 7 | 8 | # Setting these environment variables are the same as running 9 | # source /env/bin/activate. 10 | ENV VIRTUAL_ENV /env 11 | ENV PATH /env/bin:$PATH 12 | 13 | RUN apt-get update && apt-get install -y python-opencv 14 | 15 | # Copy the application's requirements.txt and run pip to install all 16 | # dependencies into the virtualenv. 17 | ADD requirements.txt /app/requirements.txt 18 | RUN pip install -r /app/requirements.txt 19 | 20 | # Add the application source code. 21 | ADD . /app 22 | 23 | # Run a WSGI server to serve the application. gunicorn must be declared as 24 | # a dependency in requirements.txt. 25 | CMD gunicorn -b :$PORT main:app 26 | -------------------------------------------------------------------------------- /demo/yolov5/yolov5-6.0/utils/google_app_engine/additional_requirements.txt: -------------------------------------------------------------------------------- 1 | # add these requirements in your app on top of the existing ones 2 | pip==19.2 3 | Flask==1.0.2 4 | gunicorn==19.9.0 5 | -------------------------------------------------------------------------------- /demo/yolov5/yolov5-6.0/utils/google_app_engine/app.yaml: -------------------------------------------------------------------------------- 1 | runtime: custom 2 | env: flex 3 | 4 | service: yolov5app 5 | 6 | liveness_check: 7 | initial_delay_sec: 600 8 | 9 | manual_scaling: 10 | instances: 1 11 | resources: 12 | cpu: 1 13 | memory_gb: 4 14 | disk_size_gb: 20 -------------------------------------------------------------------------------- /demo/yolov5/yolov5-6.0/utils/loggers/wandb/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Rex-LK/tensorrt_learning/45df1f33cab6a66a0d75c0bb26a3978d539dc888/demo/yolov5/yolov5-6.0/utils/loggers/wandb/__init__.py -------------------------------------------------------------------------------- /demo/yolov5/yolov5-6.0/utils/loggers/wandb/log_dataset.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | 3 | from wandb_utils import WandbLogger 4 | 5 | WANDB_ARTIFACT_PREFIX = 'wandb-artifact://' 6 | 7 | 8 | def create_dataset_artifact(opt): 9 | logger = WandbLogger(opt, None, job_type='Dataset Creation') # TODO: return value unused 10 | 11 | 12 | if __name__ == '__main__': 13 | parser = argparse.ArgumentParser() 14 | parser.add_argument('--data', type=str, default='data/coco128.yaml', help='data.yaml path') 15 | parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset') 16 | parser.add_argument('--project', type=str, default='YOLOv5', help='name of W&B Project') 17 | parser.add_argument('--entity', default=None, help='W&B entity') 18 | parser.add_argument('--name', type=str, default='log dataset', help='name of W&B run') 19 | 20 | opt = parser.parse_args() 21 | opt.resume = False # Explicitly disallow resume check for dataset upload job 22 | 23 | create_dataset_artifact(opt) 24 | -------------------------------------------------------------------------------- /demo/yolov5/yolov5-6.0/utils/loggers/wandb/sweep.py: -------------------------------------------------------------------------------- 1 | import sys 2 | from pathlib import Path 3 | 4 | import wandb 5 | 6 | FILE = Path(__file__).resolve() 7 | ROOT = FILE.parents[3] # YOLOv5 root directory 8 | if str(ROOT) not in sys.path: 9 | sys.path.append(str(ROOT)) # add ROOT to PATH 10 | 11 | from train import train, parse_opt 12 | from utils.general import increment_path 13 | from utils.torch_utils import select_device 14 | from utils.callbacks import Callbacks 15 | 16 | 17 | def sweep(): 18 | wandb.init() 19 | # Get hyp dict from sweep agent 20 | hyp_dict = vars(wandb.config).get("_items") 21 | 22 | # Workaround: get necessary opt args 23 | opt = parse_opt(known=True) 24 | opt.batch_size = hyp_dict.get("batch_size") 25 | opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok or opt.evolve)) 26 | opt.epochs = hyp_dict.get("epochs") 27 | opt.nosave = True 28 | opt.data = hyp_dict.get("data") 29 | device = select_device(opt.device, batch_size=opt.batch_size) 30 | 31 | # train 32 | train(hyp_dict, opt, device, callbacks=Callbacks()) 33 | 34 | 35 | if __name__ == "__main__": 36 | sweep() 37 | -------------------------------------------------------------------------------- /demo/yolov5/yolov5.hpp: -------------------------------------------------------------------------------- 1 | #ifndef YOLOV5_HPP 2 | #define YOLOV5_HPP 3 | 4 | #include 5 | #include 6 | #include 7 | #include 8 | 9 | ///////////////////////////////////////////////////////////////////////////////////////// 10 | // 封装接口类 11 | namespace YoloV5{ 12 | 13 | struct Box{ 14 | float left, top, right, bottom, confidence; 15 | int class_label; 16 | 17 | Box() = default; 18 | 19 | Box(float left, float top, float right, float bottom, float confidence, int class_label) 20 | :left(left), top(top), right(right), bottom(bottom), confidence(confidence), class_label(class_label){} 21 | }; 22 | typedef std::vector BoxArray; 23 | 24 | class Infer{ 25 | public: 26 | virtual std::shared_future commit(const cv::Mat& input) = 0; 27 | }; 28 | 29 | std::shared_ptr create_infer( 30 | const std::string& file, 31 | int gpuid=0, float confidence_threshold=0.25, float nms_threshold=0.45 32 | ); 33 | }; 34 | 35 | #endif // YOLOV5_HPP -------------------------------------------------------------------------------- /demo/yolov7/cfg/baseline/r50-csp.yaml: -------------------------------------------------------------------------------- 1 | # parameters 2 | nc: 80 # number of classes 3 | depth_multiple: 1.0 # model depth multiple 4 | width_multiple: 1.0 # layer channel multiple 5 | 6 | # anchors 7 | anchors: 8 | - [12,16, 19,36, 40,28] # P3/8 9 | - [36,75, 76,55, 72,146] # P4/16 10 | - [142,110, 192,243, 459,401] # P5/32 11 | 12 | # CSP-ResNet backbone 13 | backbone: 14 | # [from, number, module, args] 15 | [[-1, 1, Stem, [128]], # 0-P1/2 16 | [-1, 3, ResCSPC, [128]], 17 | [-1, 1, Conv, [256, 3, 2]], # 2-P3/8 18 | [-1, 4, ResCSPC, [256]], 19 | [-1, 1, Conv, [512, 3, 2]], # 4-P3/8 20 | [-1, 6, ResCSPC, [512]], 21 | [-1, 1, Conv, [1024, 3, 2]], # 6-P3/8 22 | [-1, 3, ResCSPC, [1024]], # 7 23 | ] 24 | 25 | # CSP-Res-PAN head 26 | head: 27 | [[-1, 1, SPPCSPC, [512]], # 8 28 | [-1, 1, Conv, [256, 1, 1]], 29 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 30 | [5, 1, Conv, [256, 1, 1]], # route backbone P4 31 | [[-1, -2], 1, Concat, [1]], 32 | [-1, 2, ResCSPB, [256]], # 13 33 | [-1, 1, Conv, [128, 1, 1]], 34 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 35 | [3, 1, Conv, [128, 1, 1]], # route backbone P3 36 | [[-1, -2], 1, Concat, [1]], 37 | [-1, 2, ResCSPB, [128]], # 18 38 | [-1, 1, Conv, [256, 3, 1]], 39 | [-2, 1, Conv, [256, 3, 2]], 40 | [[-1, 13], 1, Concat, [1]], # cat 41 | [-1, 2, ResCSPB, [256]], # 22 42 | [-1, 1, Conv, [512, 3, 1]], 43 | [-2, 1, Conv, [512, 3, 2]], 44 | [[-1, 8], 1, Concat, [1]], # cat 45 | [-1, 2, ResCSPB, [512]], # 26 46 | [-1, 1, Conv, [1024, 3, 1]], 47 | 48 | [[19,23,27], 1, IDetect, [nc, anchors]], # Detect(P3, P4, P5) 49 | ] 50 | -------------------------------------------------------------------------------- /demo/yolov7/cfg/baseline/x50-csp.yaml: -------------------------------------------------------------------------------- 1 | # parameters 2 | nc: 80 # number of classes 3 | depth_multiple: 1.0 # model depth multiple 4 | width_multiple: 1.0 # layer channel multiple 5 | 6 | # anchors 7 | anchors: 8 | - [12,16, 19,36, 40,28] # P3/8 9 | - [36,75, 76,55, 72,146] # P4/16 10 | - [142,110, 192,243, 459,401] # P5/32 11 | 12 | # CSP-ResNeXt backbone 13 | backbone: 14 | # [from, number, module, args] 15 | [[-1, 1, Stem, [128]], # 0-P1/2 16 | [-1, 3, ResXCSPC, [128]], 17 | [-1, 1, Conv, [256, 3, 2]], # 2-P3/8 18 | [-1, 4, ResXCSPC, [256]], 19 | [-1, 1, Conv, [512, 3, 2]], # 4-P3/8 20 | [-1, 6, ResXCSPC, [512]], 21 | [-1, 1, Conv, [1024, 3, 2]], # 6-P3/8 22 | [-1, 3, ResXCSPC, [1024]], # 7 23 | ] 24 | 25 | # CSP-ResX-PAN head 26 | head: 27 | [[-1, 1, SPPCSPC, [512]], # 8 28 | [-1, 1, Conv, [256, 1, 1]], 29 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 30 | [5, 1, Conv, [256, 1, 1]], # route backbone P4 31 | [[-1, -2], 1, Concat, [1]], 32 | [-1, 2, ResXCSPB, [256]], # 13 33 | [-1, 1, Conv, [128, 1, 1]], 34 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 35 | [3, 1, Conv, [128, 1, 1]], # route backbone P3 36 | [[-1, -2], 1, Concat, [1]], 37 | [-1, 2, ResXCSPB, [128]], # 18 38 | [-1, 1, Conv, [256, 3, 1]], 39 | [-2, 1, Conv, [256, 3, 2]], 40 | [[-1, 13], 1, Concat, [1]], # cat 41 | [-1, 2, ResXCSPB, [256]], # 22 42 | [-1, 1, Conv, [512, 3, 1]], 43 | [-2, 1, Conv, [512, 3, 2]], 44 | [[-1, 8], 1, Concat, [1]], # cat 45 | [-1, 2, ResXCSPB, [512]], # 26 46 | [-1, 1, Conv, [1024, 3, 1]], 47 | 48 | [[19,23,27], 1, IDetect, [nc, anchors]], # Detect(P3, P4, P5) 49 | ] 50 | -------------------------------------------------------------------------------- /demo/yolov7/cfg/baseline/yolov3-spp.yaml: -------------------------------------------------------------------------------- 1 | # parameters 2 | nc: 80 # number of classes 3 | depth_multiple: 1.0 # model depth multiple 4 | width_multiple: 1.0 # layer channel multiple 5 | 6 | # anchors 7 | anchors: 8 | - [10,13, 16,30, 33,23] # P3/8 9 | - [30,61, 62,45, 59,119] # P4/16 10 | - [116,90, 156,198, 373,326] # P5/32 11 | 12 | # darknet53 backbone 13 | backbone: 14 | # [from, number, module, args] 15 | [[-1, 1, Conv, [32, 3, 1]], # 0 16 | [-1, 1, Conv, [64, 3, 2]], # 1-P1/2 17 | [-1, 1, Bottleneck, [64]], 18 | [-1, 1, Conv, [128, 3, 2]], # 3-P2/4 19 | [-1, 2, Bottleneck, [128]], 20 | [-1, 1, Conv, [256, 3, 2]], # 5-P3/8 21 | [-1, 8, Bottleneck, [256]], 22 | [-1, 1, Conv, [512, 3, 2]], # 7-P4/16 23 | [-1, 8, Bottleneck, [512]], 24 | [-1, 1, Conv, [1024, 3, 2]], # 9-P5/32 25 | [-1, 4, Bottleneck, [1024]], # 10 26 | ] 27 | 28 | # YOLOv3-SPP head 29 | head: 30 | [[-1, 1, Bottleneck, [1024, False]], 31 | [-1, 1, SPP, [512, [5, 9, 13]]], 32 | [-1, 1, Conv, [1024, 3, 1]], 33 | [-1, 1, Conv, [512, 1, 1]], 34 | [-1, 1, Conv, [1024, 3, 1]], # 15 (P5/32-large) 35 | 36 | [-2, 1, Conv, [256, 1, 1]], 37 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 38 | [[-1, 8], 1, Concat, [1]], # cat backbone P4 39 | [-1, 1, Bottleneck, [512, False]], 40 | [-1, 1, Bottleneck, [512, False]], 41 | [-1, 1, Conv, [256, 1, 1]], 42 | [-1, 1, Conv, [512, 3, 1]], # 22 (P4/16-medium) 43 | 44 | [-2, 1, Conv, [128, 1, 1]], 45 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 46 | [[-1, 6], 1, Concat, [1]], # cat backbone P3 47 | [-1, 1, Bottleneck, [256, False]], 48 | [-1, 2, Bottleneck, [256, False]], # 27 (P3/8-small) 49 | 50 | [[27, 22, 15], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) 51 | ] 52 | -------------------------------------------------------------------------------- /demo/yolov7/cfg/baseline/yolov3.yaml: -------------------------------------------------------------------------------- 1 | # parameters 2 | nc: 80 # number of classes 3 | depth_multiple: 1.0 # model depth multiple 4 | width_multiple: 1.0 # layer channel multiple 5 | 6 | # anchors 7 | anchors: 8 | - [10,13, 16,30, 33,23] # P3/8 9 | - [30,61, 62,45, 59,119] # P4/16 10 | - [116,90, 156,198, 373,326] # P5/32 11 | 12 | # darknet53 backbone 13 | backbone: 14 | # [from, number, module, args] 15 | [[-1, 1, Conv, [32, 3, 1]], # 0 16 | [-1, 1, Conv, [64, 3, 2]], # 1-P1/2 17 | [-1, 1, Bottleneck, [64]], 18 | [-1, 1, Conv, [128, 3, 2]], # 3-P2/4 19 | [-1, 2, Bottleneck, [128]], 20 | [-1, 1, Conv, [256, 3, 2]], # 5-P3/8 21 | [-1, 8, Bottleneck, [256]], 22 | [-1, 1, Conv, [512, 3, 2]], # 7-P4/16 23 | [-1, 8, Bottleneck, [512]], 24 | [-1, 1, Conv, [1024, 3, 2]], # 9-P5/32 25 | [-1, 4, Bottleneck, [1024]], # 10 26 | ] 27 | 28 | # YOLOv3 head 29 | head: 30 | [[-1, 1, Bottleneck, [1024, False]], 31 | [-1, 1, Conv, [512, [1, 1]]], 32 | [-1, 1, Conv, [1024, 3, 1]], 33 | [-1, 1, Conv, [512, 1, 1]], 34 | [-1, 1, Conv, [1024, 3, 1]], # 15 (P5/32-large) 35 | 36 | [-2, 1, Conv, [256, 1, 1]], 37 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 38 | [[-1, 8], 1, Concat, [1]], # cat backbone P4 39 | [-1, 1, Bottleneck, [512, False]], 40 | [-1, 1, Bottleneck, [512, False]], 41 | [-1, 1, Conv, [256, 1, 1]], 42 | [-1, 1, Conv, [512, 3, 1]], # 22 (P4/16-medium) 43 | 44 | [-2, 1, Conv, [128, 1, 1]], 45 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 46 | [[-1, 6], 1, Concat, [1]], # cat backbone P3 47 | [-1, 1, Bottleneck, [256, False]], 48 | [-1, 2, Bottleneck, [256, False]], # 27 (P3/8-small) 49 | 50 | [[27, 22, 15], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) 51 | ] 52 | -------------------------------------------------------------------------------- /demo/yolov7/data/coco.yaml: -------------------------------------------------------------------------------- 1 | # COCO 2017 dataset http://cocodataset.org 2 | 3 | # download command/URL (optional) 4 | download: bash ./scripts/get_coco.sh 5 | 6 | # train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/] 7 | train: ./coco/train2017.txt # 118287 images 8 | val: ./coco/val2017.txt # 5000 images 9 | test: ./coco/test-dev2017.txt # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794 10 | 11 | # number of classes 12 | nc: 80 13 | 14 | # class names 15 | names: [ 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', 16 | 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 17 | 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 18 | 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 19 | 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 20 | 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 21 | 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 22 | 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 23 | 'hair drier', 'toothbrush' ] 24 | -------------------------------------------------------------------------------- /demo/yolov7/data/hyp.scratch.p5.yaml: -------------------------------------------------------------------------------- 1 | lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3) 2 | lrf: 0.1 # final OneCycleLR learning rate (lr0 * lrf) 3 | momentum: 0.937 # SGD momentum/Adam beta1 4 | weight_decay: 0.0005 # optimizer weight decay 5e-4 5 | warmup_epochs: 3.0 # warmup epochs (fractions ok) 6 | warmup_momentum: 0.8 # warmup initial momentum 7 | warmup_bias_lr: 0.1 # warmup initial bias lr 8 | box: 0.05 # box loss gain 9 | cls: 0.3 # cls loss gain 10 | cls_pw: 1.0 # cls BCELoss positive_weight 11 | obj: 0.7 # obj loss gain (scale with pixels) 12 | obj_pw: 1.0 # obj BCELoss positive_weight 13 | iou_t: 0.20 # IoU training threshold 14 | anchor_t: 4.0 # anchor-multiple threshold 15 | fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5) 16 | hsv_h: 0.015 # image HSV-Hue augmentation (fraction) 17 | hsv_s: 0.7 # image HSV-Saturation augmentation (fraction) 18 | hsv_v: 0.4 # image HSV-Value augmentation (fraction) 19 | degrees: 0.0 # image rotation (+/- deg) 20 | translate: 0.2 # image translation (+/- fraction) 21 | scale: 0.9 # image scale (+/- gain) 22 | shear: 0.0 # image shear (+/- deg) 23 | perspective: 0.0 # image perspective (+/- fraction), range 0-0.001 24 | flipud: 0.0 # image flip up-down (probability) 25 | fliplr: 0.5 # image flip left-right (probability) 26 | mosaic: 1.0 # image mosaic (probability) 27 | mixup: 0.15 # image mixup (probability) 28 | copy_paste: 0.0 # image copy paste (probability) 29 | paste_in: 0.15 # image copy paste (probability) 30 | -------------------------------------------------------------------------------- /demo/yolov7/data/hyp.scratch.p6.yaml: -------------------------------------------------------------------------------- 1 | lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3) 2 | lrf: 0.2 # final OneCycleLR learning rate (lr0 * lrf) 3 | momentum: 0.937 # SGD momentum/Adam beta1 4 | weight_decay: 0.0005 # optimizer weight decay 5e-4 5 | warmup_epochs: 3.0 # warmup epochs (fractions ok) 6 | warmup_momentum: 0.8 # warmup initial momentum 7 | warmup_bias_lr: 0.1 # warmup initial bias lr 8 | box: 0.05 # box loss gain 9 | cls: 0.3 # cls loss gain 10 | cls_pw: 1.0 # cls BCELoss positive_weight 11 | obj: 0.7 # obj loss gain (scale with pixels) 12 | obj_pw: 1.0 # obj BCELoss positive_weight 13 | iou_t: 0.20 # IoU training threshold 14 | anchor_t: 4.0 # anchor-multiple threshold 15 | fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5) 16 | hsv_h: 0.015 # image HSV-Hue augmentation (fraction) 17 | hsv_s: 0.7 # image HSV-Saturation augmentation (fraction) 18 | hsv_v: 0.4 # image HSV-Value augmentation (fraction) 19 | degrees: 0.0 # image rotation (+/- deg) 20 | translate: 0.2 # image translation (+/- fraction) 21 | scale: 0.9 # image scale (+/- gain) 22 | shear: 0.0 # image shear (+/- deg) 23 | perspective: 0.0 # image perspective (+/- fraction), range 0-0.001 24 | flipud: 0.0 # image flip up-down (probability) 25 | fliplr: 0.5 # image flip left-right (probability) 26 | mosaic: 1.0 # image mosaic (probability) 27 | mixup: 0.15 # image mixup (probability) 28 | copy_paste: 0.0 # image copy paste (probability) 29 | paste_in: 0.15 # image copy paste (probability) 30 | -------------------------------------------------------------------------------- /demo/yolov7/data/hyp.scratch.tiny.yaml: -------------------------------------------------------------------------------- 1 | lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3) 2 | lrf: 0.01 # final OneCycleLR learning rate (lr0 * lrf) 3 | momentum: 0.937 # SGD momentum/Adam beta1 4 | weight_decay: 0.0005 # optimizer weight decay 5e-4 5 | warmup_epochs: 3.0 # warmup epochs (fractions ok) 6 | warmup_momentum: 0.8 # warmup initial momentum 7 | warmup_bias_lr: 0.1 # warmup initial bias lr 8 | box: 0.05 # box loss gain 9 | cls: 0.5 # cls loss gain 10 | cls_pw: 1.0 # cls BCELoss positive_weight 11 | obj: 1.0 # obj loss gain (scale with pixels) 12 | obj_pw: 1.0 # obj BCELoss positive_weight 13 | iou_t: 0.20 # IoU training threshold 14 | anchor_t: 4.0 # anchor-multiple threshold 15 | fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5) 16 | hsv_h: 0.015 # image HSV-Hue augmentation (fraction) 17 | hsv_s: 0.7 # image HSV-Saturation augmentation (fraction) 18 | hsv_v: 0.4 # image HSV-Value augmentation (fraction) 19 | degrees: 0.0 # image rotation (+/- deg) 20 | translate: 0.1 # image translation (+/- fraction) 21 | scale: 0.5 # image scale (+/- gain) 22 | shear: 0.0 # image shear (+/- deg) 23 | perspective: 0.0 # image perspective (+/- fraction), range 0-0.001 24 | flipud: 0.0 # image flip up-down (probability) 25 | fliplr: 0.5 # image flip left-right (probability) 26 | mosaic: 1.0 # image mosaic (probability) 27 | mixup: 0.05 # image mixup (probability) 28 | copy_paste: 0.0 # image copy paste (probability) 29 | paste_in: 0.05 # image copy paste (probability) 30 | -------------------------------------------------------------------------------- /demo/yolov7/export_onnx.py: -------------------------------------------------------------------------------- 1 | # from json import tool 2 | # import requests 3 | # import matplotlib.pyplot as plt 4 | 5 | # from torchvision.models import resnet50 6 | # import torchvision.transforms as T 7 | 8 | # from tools import * 9 | # from model import * 10 | 11 | 12 | # if __name__ == '__main__': 13 | # detr = DETRdemo(num_classes=91) 14 | # state_dict = torch.load('detr_demo.pth') 15 | # detr.load_state_dict(state_dict) 16 | # detr.eval() 17 | 18 | # dummy = torch.zeros(1,3,800,1066) 19 | # torch.onnx.export( 20 | # detr,(dummy,), 21 | # "detr.onnx", 22 | # input_names=["image"], 23 | # output_names=["predict"], 24 | # opset_version=12, 25 | # dynamic_axes={"image":{0:"batch"},"predict":{0:"batch"},} 26 | # ) 27 | 28 | # 为了正确导出onnx需要将models/yolo.py文件中56行 29 | 30 | # ```python 31 | # # x = x if self.training else (torch.cat(z, 1),x) # 修改为 32 | # x = x if self.training else (torch.cat(z, 1)) 33 | # ``` 34 | 35 | # 在detect.py中采用如下方式导出 36 | 37 | # ``` 38 | # dummy = torch.zeros(1,3,640,640) 39 | # torch.onnx.export( 40 | # model,(dummy,), 41 | # "yolov7.onnx", 42 | # input_names=["image"], 43 | # output_names=["predict"], 44 | # opset_version=13, 45 | # dynamic_axes={"image":{0:"batch"},"predict":{0:"batch"},} 46 | # ) 47 | # ``` -------------------------------------------------------------------------------- /demo/yolov7/figure/performance.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Rex-LK/tensorrt_learning/45df1f33cab6a66a0d75c0bb26a3978d539dc888/demo/yolov7/figure/performance.png -------------------------------------------------------------------------------- /demo/yolov7/models/__init__.py: -------------------------------------------------------------------------------- 1 | # init -------------------------------------------------------------------------------- /demo/yolov7/onnx_simplify.py: -------------------------------------------------------------------------------- 1 | from onnxsim import simplify 2 | import onnx 3 | input_path="yolov7.onnx" 4 | output_path="yolov7_sim.onnx" 5 | onnx_model = onnx.load(input_path) 6 | model_simp, check = simplify(onnx_model,input_shapes={'image': [0, 3, 640, 640]},dynamic_input_shape=True) 7 | assert check, "Simplified ONNX model could not be validated" 8 | onnx.save(model_simp, output_path) 9 | print('finished exporting onnx') -------------------------------------------------------------------------------- /demo/yolov7/scripts/get_coco.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | # COCO 2017 dataset http://cocodataset.org 3 | # Download command: bash ./scripts/get_coco.sh 4 | 5 | # Download/unzip labels 6 | d='./' # unzip directory 7 | url=https://github.com/ultralytics/yolov5/releases/download/v1.0/ 8 | f='coco2017labels-segments.zip' # or 'coco2017labels.zip', 68 MB 9 | echo 'Downloading' $url$f ' ...' 10 | curl -L $url$f -o $f && unzip -q $f -d $d && rm $f & # download, unzip, remove in background 11 | 12 | # Download/unzip images 13 | d='./coco/images' # unzip directory 14 | url=http://images.cocodataset.org/zips/ 15 | f1='train2017.zip' # 19G, 118k images 16 | f2='val2017.zip' # 1G, 5k images 17 | f3='test2017.zip' # 7G, 41k images (optional) 18 | for f in $f1 $f2 $f3; do 19 | echo 'Downloading' $url$f '...' 20 | curl -L $url$f -o $f && unzip -q $f -d $d && rm $f & # download, unzip, remove in background 21 | done 22 | wait # finish background tasks 23 | -------------------------------------------------------------------------------- /demo/yolov7/utils/__init__.py: -------------------------------------------------------------------------------- 1 | # init -------------------------------------------------------------------------------- /demo/yolov7/utils/aws/__init__.py: -------------------------------------------------------------------------------- 1 | #init -------------------------------------------------------------------------------- /demo/yolov7/utils/aws/mime.sh: -------------------------------------------------------------------------------- 1 | # AWS EC2 instance startup 'MIME' script https://aws.amazon.com/premiumsupport/knowledge-center/execute-user-data-ec2/ 2 | # This script will run on every instance restart, not only on first start 3 | # --- DO NOT COPY ABOVE COMMENTS WHEN PASTING INTO USERDATA --- 4 | 5 | Content-Type: multipart/mixed; boundary="//" 6 | MIME-Version: 1.0 7 | 8 | --// 9 | Content-Type: text/cloud-config; charset="us-ascii" 10 | MIME-Version: 1.0 11 | Content-Transfer-Encoding: 7bit 12 | Content-Disposition: attachment; filename="cloud-config.txt" 13 | 14 | #cloud-config 15 | cloud_final_modules: 16 | - [scripts-user, always] 17 | 18 | --// 19 | Content-Type: text/x-shellscript; charset="us-ascii" 20 | MIME-Version: 1.0 21 | Content-Transfer-Encoding: 7bit 22 | Content-Disposition: attachment; filename="userdata.txt" 23 | 24 | #!/bin/bash 25 | # --- paste contents of userdata.sh here --- 26 | --// 27 | -------------------------------------------------------------------------------- /demo/yolov7/utils/aws/resume.py: -------------------------------------------------------------------------------- 1 | # Resume all interrupted trainings in yolor/ dir including DDP trainings 2 | # Usage: $ python utils/aws/resume.py 3 | 4 | import os 5 | import sys 6 | from pathlib import Path 7 | 8 | import torch 9 | import yaml 10 | 11 | sys.path.append('./') # to run '$ python *.py' files in subdirectories 12 | 13 | port = 0 # --master_port 14 | path = Path('').resolve() 15 | for last in path.rglob('*/**/last.pt'): 16 | ckpt = torch.load(last) 17 | if ckpt['optimizer'] is None: 18 | continue 19 | 20 | # Load opt.yaml 21 | with open(last.parent.parent / 'opt.yaml') as f: 22 | opt = yaml.load(f, Loader=yaml.SafeLoader) 23 | 24 | # Get device count 25 | d = opt['device'].split(',') # devices 26 | nd = len(d) # number of devices 27 | ddp = nd > 1 or (nd == 0 and torch.cuda.device_count() > 1) # distributed data parallel 28 | 29 | if ddp: # multi-GPU 30 | port += 1 31 | cmd = f'python -m torch.distributed.launch --nproc_per_node {nd} --master_port {port} train.py --resume {last}' 32 | else: # single-GPU 33 | cmd = f'python train.py --resume {last}' 34 | 35 | cmd += ' > /dev/null 2>&1 &' # redirect output to dev/null and run in daemon thread 36 | print(cmd) 37 | os.system(cmd) 38 | -------------------------------------------------------------------------------- /demo/yolov7/utils/aws/userdata.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | # AWS EC2 instance startup script https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/user-data.html 3 | # This script will run only once on first instance start (for a re-start script see mime.sh) 4 | # /home/ubuntu (ubuntu) or /home/ec2-user (amazon-linux) is working dir 5 | # Use >300 GB SSD 6 | 7 | cd home/ubuntu 8 | if [ ! -d yolor ]; then 9 | echo "Running first-time script." # install dependencies, download COCO, pull Docker 10 | git clone -b paper https://github.com/WongKinYiu/yolor && sudo chmod -R 777 yolor 11 | cd yolor 12 | bash data/scripts/get_coco.sh && echo "Data done." & 13 | sudo docker pull nvcr.io/nvidia/pytorch:21.08-py3 && echo "Docker done." & 14 | python -m pip install --upgrade pip && pip install -r requirements.txt && python detect.py && echo "Requirements done." & 15 | wait && echo "All tasks done." # finish background tasks 16 | else 17 | echo "Running re-start script." # resume interrupted runs 18 | i=0 19 | list=$(sudo docker ps -qa) # container list i.e. $'one\ntwo\nthree\nfour' 20 | while IFS= read -r id; do 21 | ((i++)) 22 | echo "restarting container $i: $id" 23 | sudo docker start $id 24 | # sudo docker exec -it $id python train.py --resume # single-GPU 25 | sudo docker exec -d $id python utils/aws/resume.py # multi-scenario 26 | done <<<"$list" 27 | fi 28 | -------------------------------------------------------------------------------- /demo/yolov7/utils/google_app_engine/Dockerfile: -------------------------------------------------------------------------------- 1 | FROM gcr.io/google-appengine/python 2 | 3 | # Create a virtualenv for dependencies. This isolates these packages from 4 | # system-level packages. 5 | # Use -p python3 or -p python3.7 to select python version. Default is version 2. 6 | RUN virtualenv /env -p python3 7 | 8 | # Setting these environment variables are the same as running 9 | # source /env/bin/activate. 10 | ENV VIRTUAL_ENV /env 11 | ENV PATH /env/bin:$PATH 12 | 13 | RUN apt-get update && apt-get install -y python-opencv 14 | 15 | # Copy the application's requirements.txt and run pip to install all 16 | # dependencies into the virtualenv. 17 | ADD requirements.txt /app/requirements.txt 18 | RUN pip install -r /app/requirements.txt 19 | 20 | # Add the application source code. 21 | ADD . /app 22 | 23 | # Run a WSGI server to serve the application. gunicorn must be declared as 24 | # a dependency in requirements.txt. 25 | CMD gunicorn -b :$PORT main:app 26 | -------------------------------------------------------------------------------- /demo/yolov7/utils/google_app_engine/additional_requirements.txt: -------------------------------------------------------------------------------- 1 | # add these requirements in your app on top of the existing ones 2 | pip==18.1 3 | Flask==1.0.2 4 | gunicorn==19.9.0 5 | -------------------------------------------------------------------------------- /demo/yolov7/utils/google_app_engine/app.yaml: -------------------------------------------------------------------------------- 1 | runtime: custom 2 | env: flex 3 | 4 | service: yolorapp 5 | 6 | liveness_check: 7 | initial_delay_sec: 600 8 | 9 | manual_scaling: 10 | instances: 1 11 | resources: 12 | cpu: 1 13 | memory_gb: 4 14 | disk_size_gb: 20 -------------------------------------------------------------------------------- /demo/yolov7/utils/wandb_logging/__init__.py: -------------------------------------------------------------------------------- 1 | # init -------------------------------------------------------------------------------- /demo/yolov7/utils/wandb_logging/log_dataset.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | 3 | import yaml 4 | 5 | from wandb_utils import WandbLogger 6 | 7 | WANDB_ARTIFACT_PREFIX = 'wandb-artifact://' 8 | 9 | 10 | def create_dataset_artifact(opt): 11 | with open(opt.data) as f: 12 | data = yaml.load(f, Loader=yaml.SafeLoader) # data dict 13 | logger = WandbLogger(opt, '', None, data, job_type='Dataset Creation') 14 | 15 | 16 | if __name__ == '__main__': 17 | parser = argparse.ArgumentParser() 18 | parser.add_argument('--data', type=str, default='data/coco.yaml', help='data.yaml path') 19 | parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset') 20 | parser.add_argument('--project', type=str, default='YOLOR', help='name of W&B Project') 21 | opt = parser.parse_args() 22 | opt.resume = False # Explicitly disallow resume check for dataset upload job 23 | 24 | create_dataset_artifact(opt) 25 | -------------------------------------------------------------------------------- /sideline_learn/cpp-move/forward.cpp: -------------------------------------------------------------------------------- 1 | #include 2 | #include 3 | 4 | 5 | void Print(int& val) { 6 | std::cout << "lvalue refrence: val=" << val << std::endl; 7 | } 8 | 9 | void Print(int&& val) { 10 | std::cout << "rvalue refrence: val=" << val << std::endl; 11 | } 12 | 13 | // template 14 | // void TPrint(T &&t) { 15 | // //传进来为左值时，t为左值引用 16 | // //传进来右值时，t为右值引用 引用折叠 17 | // //按道理来说 传进来的值为右值引用时，会调用 Print(int &&val) 但实际上调用了Print(int &&val) 18 | // //因为传进来的 右值引用，这个右值引用实际上是左值 19 | // return Print(t); 20 | // } 21 | template 22 | void TPrint(T&& t) { 23 | //这里会根据 传进来的值判断是左值还是右值 24 | //然后活使用forward保持原来的属性 25 | //如果t为左值引用，forward会返回一个左值然后传递给Print 26 | //如果t为右值引用，forward会返回一个右值然后传递给Print 27 | //从而实现完美转发 28 | return Print(std::forward(t)); 29 | } 30 | 31 | 32 | // template 33 | // constexpr _Tp&& 34 | // forward(typename std::remove_reference<_Tp>::type&& __t) noexcept 35 | // { 36 | // static_assert(!std::is_lvalue_reference<_Tp>::value, "template argument" 37 | // " substituting _Tp is an lvalue reference type"); 38 | //强制类型转化 39 | // return static_cast<_Tp&&>(__t); 40 | // } 41 | 42 | 43 | //左值和右值 44 | //左值引用和右值引用 45 | //左值引用和右值引用 全为左值 46 | 47 | int main() { 48 | int date = 1021; 49 | TPrint(date); // 传入左值引用 50 | TPrint(501); //传入右值引用 实际上 "右值引用" 为左值 51 | 52 | return 0; 53 | } 54 | -------------------------------------------------------------------------------- /sideline_learn/cpp-move/move.cpp: -------------------------------------------------------------------------------- 1 | #include 2 | #include 3 | #include 4 | #include 5 | using namespace std; 6 | class String 7 | { 8 | public: 9 | String(const char* buf,const char* buf2) 10 | { 11 | _buf = new char[strlen(buf) + 1]; 12 | strcpy(_buf, buf); // 内存拷贝 13 | _buf2 = new char(strlen(buf2) + 1); 14 | strcpy(_buf2, buf2); // 内存拷贝 15 | cout<<"使用普通构造函数" < 2 | using namespace std; 3 | 4 | template 5 | 6 | void func(T& val) 7 | { 8 | cout <<"right-value" << val << endl; 9 | } 10 | template 11 | void func(T&& val) 12 | { 13 | cout <<"right-value" << val << endl; 14 | } 15 | 16 | int main() 17 | { 18 | //demo1 19 | int year = 2020; 20 | func(year); //传入左值 引用折叠 21 | func(2020); //传入右值 引用折叠 22 | return 0; 23 | } 24 | -------------------------------------------------------------------------------- /sideline_learn/multi-thread/async.cpp: -------------------------------------------------------------------------------- 1 | #include 2 | #include 3 | #include 4 | #include 5 | #include 6 | #include 7 | #include 8 | #include 9 | #include 10 | 11 | using namespace std; 12 | 13 | 14 | int task(int a,int b){ 15 | int ret_a = a * a; 16 | int ret_b = b * 2; 17 | return ret_a+ret_b; 18 | } 19 | 20 | 21 | int main(){ 22 | 23 | 24 | //对线程进行包装,之后传递参数 25 | // packaged_task t(task); 26 | //启动线程 27 | // t(1,2); 28 | 29 | //包装时传递参数 30 | 31 | packaged_task t(bind(task,1,2)); 32 | t(); 33 | 34 | //获取返回值 35 | int res = t.get_future().get(); 36 | 37 | cout <<"res:"< 2 | #include 3 | #include 4 | #include 5 | #include 6 | #include 7 | #include 8 | #include 9 | #include 10 | 11 | using namespace std; 12 | 13 | mutex mtx; 14 | condition_variable cv; 15 | 16 | void task(int a,int b, promise& ret){ 17 | int ret_a = a * a; 18 | int ret_b = b * 2; 19 | ret = ret_a + ret_b; 20 | cv.notify_one(); 21 | } 22 | 23 | //这个版本导致代码太多,使用future可以优雅的解决 24 | 25 | int main(){ 26 | int ret = 0; 27 | //如果需要传递引用，则需要加ref 28 | 29 | //将p传递进去 30 | thread t(task,1,2,ref(ret)); 31 | 32 | unique_locklock(mtx); 33 | //等待线程通知，然后往下执行 34 | cv.wait(lock); 35 | cout<< "return value is" << ret< 2 | #include 3 | #include 4 | #include 5 | #include 6 | #include 7 | #include 8 | #include 9 | #include 10 | 11 | using namespace std; 12 | 13 | 14 | int task(int a,int b){ 15 | int ret_a = a * a; 16 | //等待主线程的p_in 设置号值之后再继续 17 | int ret_b = b * 2; 18 | return ret_a+ret_b; 19 | } 20 | 21 | //为了简化上述方法，可以使用async 22 | 23 | int main(){ 24 | 25 | //使用async可以在线程中获得返回值 26 | //async一点创建新的线程做计算，如果使用launch::async则会开启新的线程 27 | //launch::deferred 为延迟调用,当有fu.get()时，才会开启线程 28 | future fu = async(launch::async,task,1,2); 29 | cout <<"return ret is :" << fu.get() < 2 | #include 3 | #include 4 | #include 5 | #include 6 | #include 7 | #include 8 | #include 9 | #include 10 | 11 | using namespace std; 12 | 13 | 14 | void task(int a,int b, promise& ret){ 15 | int ret_a = a * a; 16 | int ret_b = b * 2; 17 | 18 | ret.set_value(ret_a+ret_b); 19 | 20 | } 21 | 22 | //这个版本导致代码太多,使用future可以优雅的解决 23 | 24 | int main(){ 25 | int ret = 0; 26 | 27 | promisep; 28 | futuref = p.get_future(); //将f与p关联起来 29 | 30 | thread t(task,1,2,ref(p)); 31 | 32 | //f.get()会阻塞在这里等待 p给f传递值，且f.get()只能用一次 33 | cout <<"return ret is :" << f.get() < 2 | #include 3 | #include 4 | #include 5 | #include 6 | #include 7 | #include 8 | #include 9 | #include 10 | 11 | using namespace std; 12 | 13 | 14 | void task(int a, future&b, promise& ret){ 15 | int ret_a = a * a; 16 | //等待主线程的p_in 设置号值之后再继续 17 | int ret_b = b.get()* 2; 18 | 19 | ret.set_value(ret_a+ret_b); 20 | 21 | } 22 | 23 | //有时候 在给线程传值是,可能不需要立马传递值,可能需要过一段时间传递值 24 | 25 | int main(){ 26 | int ret = 0; 27 | 28 | promisep_ret; 29 | futuref_ret = p_ret.get_future(); 30 | 31 | promisep_in; 32 | futuref_in = p_in.get_future(); 33 | 34 | thread t(task,1,ref(f_in),ref(p_ret)); 35 | 36 | //do 37 | 38 | p_in.set_value(8); 39 | 40 | // 41 | 42 | cout <<"return ret is :" << f_ret.get() < 3 | #include 4 | #include 5 | #include 6 | #include 7 | #include 8 | #include 9 | #include 10 | #include 11 | 12 | using namespace std; 13 | 14 | 15 | void task(int a,shared_future&b, promise& ret){ 16 | int ret_a = a * a; 17 | //等待主线程的p_in 设置号值之后再继续 18 | int ret_b = b.get()* 2; 19 | 20 | ret.set_value(ret_a+ret_b); 21 | 22 | } 23 | 24 | 25 | int main(){ 26 | int ret = 0; 27 | //p_ret是不能赋值的 28 | promisep_ret; 29 | //可以通过move实现 30 | promisep_ret2 = move(p_ret); 31 | 32 | promisep_ret; 33 | futuref_ret = p_ret.get_future(); 34 | 35 | promisep_in; 36 | futuref_in = p_in.get_future(); 37 | 38 | //当创建了很多线程时 39 | //由于线程中 b.get()只能用一次,这里会出现异常 40 | //解决办法 41 | shared_future s_f = f_in.share(); 42 | 43 | thread t(task,1(s_f,ref(p_ret)); 44 | thread t(task,1(s_f,ref(p_ret)); 45 | thread t(task,1(s_f,ref(p_ret)); 46 | thread t(task,1(s_f,ref(p_ret)); 47 | 48 | p_in.set_value(8); 49 | 50 | cout <<"return ret is :" << f_ret.get() < 4 | #include 5 | using namespace std; 6 | using namespace fastdet; 7 | 8 | int main() { 9 | string img_path = "/data/rex/ncnn_proj/ncnn_multi_thread/data/imgs/3.jpg"; 10 | cv::Mat img = cv::imread(img_path); 11 | int img_width = img.cols; 12 | int img_height = img.rows; 13 | string param_path = 14 | "/data/rex/ncnn_proj/ncnn_multi_thread/data/model/FastestDet.param"; 15 | string model_path = 16 | "/data/rex/ncnn_proj/ncnn_multi_thread/data/model/FastestDet.bin"; 17 | FastDet* pred = new FastDet(352, 352, param_path, model_path); 18 | int class_num = sizeof(pred->class_names) / sizeof(pred->class_names[0]); 19 | pred->prepare_input(img); 20 | pred->infrence("input.1", "758", 6); 21 | pred->postprocess(img_width, img_height, class_num, 0.65); 22 | for (size_t i = 0; i < pred->nms_boxes.size(); i++) { 23 | TargetBox box = pred->nms_boxes[i]; 24 | cv::rectangle(img, cv::Point(box.x1, box.y1), cv::Point(box.x2, box.y2), 25 | cv::Scalar(0, 0, 255), 2); 26 | cv::putText(img, pred->class_names[box.category], cv::Point(box.x1, box.y1), 27 | cv::FONT_HERSHEY_SIMPLEX, 0.75, cv::Scalar(0, 255, 0), 2); 28 | } 29 | cv::imwrite("result_test.jpg", img); 30 | return 0; 31 | } 32 | 33 | // 单模型 多视频 34 | // 多模型 单视频 35 | // 多模型 多视频 -------------------------------------------------------------------------------- /sideline_learn/ncnn_multi_thread/include/infer.hpp: -------------------------------------------------------------------------------- 1 | #ifndef INFER_HPP 2 | #define INFER_HPP 3 | 4 | #include 5 | #include 6 | #include 7 | #include 8 | 9 | #include "fastdet.h" 10 | // 封装接口类 11 | class Infer 12 | { 13 | public: 14 | virtual std::shared_future> commit( 15 | cv::Mat &input) = 0; 16 | }; 17 | 18 | std::shared_ptr create_infer(const std::string ¶m_path, 19 | const std::string &model_path); 20 | 21 | #endif // INFER_HPP -------------------------------------------------------------------------------- /sideline_learn/ncnn_multi_thread/multi_thead_infer.cpp: -------------------------------------------------------------------------------- 1 | #include 2 | #include 3 | 4 | #include "infer.hpp" 5 | 6 | using namespace cv; 7 | using namespace std; 8 | using namespace fastdet; 9 | 10 | int main() 11 | { 12 | string param_path = 13 | "/home/rex/Desktop/ncnn_multi_thread/data/model/FastestDet.param"; 14 | string model_path = 15 | "/home/rex/Desktop/ncnn_multi_thread/data/model/FastestDet.bin"; 16 | 17 | auto infer = create_infer( 18 | param_path, 19 | model_path); // 创建及初始化推理器 20 | 21 | if (infer == nullptr) 22 | { 23 | printf("Infer is nullptr.\n"); 24 | return 0; 25 | } 26 | 27 | string img_path = "/home/rex/Desktop/ncnn_multi_thread/data/imgs/3.jpg"; 28 | Mat img = cv::imread(img_path); 29 | auto fut = infer->commit(img); // 将任务提交给推理器（推理器执行commit) 30 | vector res = fut.get(); // 等待结果 31 | 32 | for (size_t i = 0; i < res.size(); i++) 33 | { 34 | TargetBox box = res[i]; 35 | rectangle(img, cv::Point(box.x1, box.y1), cv::Point(box.x2, box.y2), 36 | cv::Scalar(0, 0, 255), 2); 37 | // cv::putText(img, pred->class_names[box.category], cv::Point(box.x1, 38 | // box.y1), 39 | // cv::FONT_HERSHEY_SIMPLEX, 0.75, cv::Scalar(0, 255, 0), 2); 40 | } 41 | cv::imwrite("result_test.jpg", img); 42 | 43 | return 0; 44 | } 45 | -------------------------------------------------------------------------------- /sideline_learn/tools/pic2video2.py: -------------------------------------------------------------------------------- 1 | import cv2 2 | import os 3 | from PIL import Image 4 | 5 | 6 | def PicToVideo(imgPath, videoPath): 7 | images = os.listdir(imgPath) 8 | print(len(images)) 9 | fps = 10 # 帧率 10 | fourcc = cv2.VideoWriter_fourcc(*"MJPG") 11 | im = Image.open(imgPath + images[0]) 12 | videoWriter = cv2.VideoWriter(videoPath, fourcc, fps, im.size) 13 | for im_name in range(len(images)): 14 | frame = cv2.imread(imgPath + images[im_name]) 15 | for i in range(90): 16 | videoWriter.write(frame) 17 | videoWriter.release() 18 | 19 | 20 | if __name__ == "__main__": 21 | 22 | imgPath = "/home/rex/Pictures/Wallpapers/" 23 | videoPath = "/home/rex/Desktop/test.avi" 24 | PicToVideo(imgPath, videoPath) 25 | -------------------------------------------------------------------------------- /sideline_learn/warpaffine-cuda/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | cmake_minimum_required(VERSION 2.6) 2 | 3 | project(warpaffine-cuda-test) 4 | set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11 -Wall -Ofast -g -Wfatal-errors -D_MWAITXINTRIN_H_INCLUDED") 5 | add_definitions(-std=c++11) 6 | add_definitions(-DAPI_EXPORTS) 7 | option(CUDA_USE_STATIC_CUDA_RUNTIME OFF) 8 | set(CMAKE_CXX_STANDARD 11) 9 | set(CMAKE_BUILD_TYPE Debug) 10 | 11 | find_package(CUDA REQUIRED) 12 | find_package(OpenCV) 13 | include_directories(${OpenCV_INCLUDE_DIRS}) 14 | 15 | if(WIN32) 16 | enable_language(CUDA) 17 | endif(WIN32) 18 | 19 | include_directories(${PROJECT_SOURCE_DIR}/include) 20 | 21 | # cuda 22 | include_directories(/usr/local/cuda/include) 23 | link_directories(/usr/local/cuda/lib64) 24 | # tensorrt 25 | include_directories(/home/rex/TensorRT-8.2.0.6/include/) 26 | link_directories(/home/rex/TensorRT-8.2.0.6/targets/x86_64-linux-gnu/lib) 27 | 28 | cuda_add_library(warpaffine SHARED src/warpaffine.cu) 29 | target_link_libraries(warpaffine) 30 | 31 | cuda_add_executable(test src/test.cpp) 32 | target_link_libraries(test warpaffine ${OpenCV_LIBS}) 33 | 34 | if(UNIX) 35 | add_definitions(-O2 -pthread) 36 | endif(UNIX) 37 | 38 | 39 | -------------------------------------------------------------------------------- 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https://raw.githubusercontent.com/Rex-LK/tensorrt_learning/45df1f33cab6a66a0d75c0bb26a3978d539dc888/sideline_learn/warpaffine-cuda/build/test -------------------------------------------------------------------------------- /sideline_learn/warpaffine-cuda/include/warpaffine.h: -------------------------------------------------------------------------------- 1 | 2 | #include 3 | #include 4 | #include 5 | #include 6 | #define min(a, b) ((a) < (b) ? (a) : (b)) 7 | #define num_threads 512 8 | 9 | typedef unsigned char uint8_t; 10 | 11 | struct MySize{ 12 | int width = 0, height = 0; 13 | 14 | MySize() = default; 15 | MySize(int w, int h) 16 | :width(w), height(h){} 17 | }; 18 | 19 | 20 | struct AffineMatrix{ 21 | // i2d 正变换的M矩阵 22 | float i2d[6]; 23 | // d2i M矩阵的逆矩阵 24 | float d2i[6]; 25 | // 求逆矩阵 26 | void invertAffineTransform(float imat[6], float omat[6]); 27 | 28 | void compute(const MySize& from, const MySize& to); 29 | }; 30 | 31 | __device__ void affine_project(float* matrix, int x, int y, float* proj_x, float* proj_y); 32 | 33 | __global__ void warp_affine_bilinear_kernel( 34 | uint8_t* src, int src_line_size, int src_width, int src_height, 35 | uint8_t* dst, int dst_line_size, int dst_width, int dst_height, 36 | uint8_t fill_value, AffineMatrix matrix 37 | ); 38 | 39 | void warp_affine_bilinear( 40 | uint8_t* src, int src_line_size, int src_width, int src_height, 41 | uint8_t* dst, int dst_line_size, int dst_width, int dst_height, 42 | uint8_t fill_value 43 | ); 44 | 45 | cv::Mat warpaffine_to_center_align(const cv::Mat& image, const cv::Size& size); 46 | 47 | #define checkRuntime(op) __check_cuda_runtime((op), #op, __FILE__, __LINE__) 48 | bool __check_cuda_runtime(cudaError_t code, const char* op, const char* file, int line); -------------------------------------------------------------------------------- /sideline_learn/warpaffine-cuda/scripts/warpaffine.py: -------------------------------------------------------------------------------- 1 | import cv2 2 | import matplotlib.pyplot as plt 3 | import numpy as np 4 | def inv_mat(M): 5 | k = M[0,0], 6 | b1 = M[0,2] 7 | b2 = M[1,2], 8 | k = list(k)[0] 9 | return np.array([[1/k,0,-b1/k], 10 | [0,1/k,-b2[0]/k]]) 11 | 12 | def transform(image,dst_size): 13 | oh,ow = image.shape[:2] 14 | dh,dw = dst_size 15 | scale = min(dw/ow,dh/oh) 16 | 17 | M = np.array([ 18 | [scale,0,-scale * ow * 0.5 + dw * 0.5], 19 | [0,scale,-scale * oh * 0.5 + dh * 0.5] 20 | ]) 21 | return cv2.warpAffine(image,M,dst_size),M,inv_mat(M) 22 | 23 | img = cv2.imread("/home/rex/Desktop/notebook/warpaffine/keji2.jpeg") 24 | img_d,M,inv= transform(img,(640,640)) 25 | 26 | plt.subplot(1,2,1) 27 | plt.title("WarpAffine") 28 | plt.imshow(img_d[...,::-1]) 29 | 30 | img_s = cv2.warpAffine(img_d,inv,img.shape[:2][::-1]) 31 | print(img_s) 32 | plt.subplot(1,2,2) 33 | plt.title("or") 34 | plt.imshow(img_s[...,::-1]) -------------------------------------------------------------------------------- /sideline_learn/warpaffine-cuda/src/test.cpp: -------------------------------------------------------------------------------- 1 | #include 2 | using namespace cv; 3 | 4 | int main(){ 5 | Mat image = imread("/home/rex/Desktop/rex_extra/notebook/warpaffine/keji1.jpeg"); 6 | Mat test_image = warpaffine_to_center_align(image, Size(640, 640)); 7 | imwrite("test.jpg", test_image); 8 | imshow("1",test_image); 9 | waitKey(0); 10 | return 0; 11 | } -------------------------------------------------------------------------------- /sideline_learn/yolo5-6.0-ros/.catkin_workspace: -------------------------------------------------------------------------------- 1 | # This file currently only serves to mark the location of a catkin workspace for tool integration 2 | -------------------------------------------------------------------------------- /sideline_learn/yolo5-6.0-ros/Screenshot from 2022-07-30 09-39-36.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Rex-LK/tensorrt_learning/45df1f33cab6a66a0d75c0bb26a3978d539dc888/sideline_learn/yolo5-6.0-ros/Screenshot from 2022-07-30 09-39-36.png -------------------------------------------------------------------------------- /sideline_learn/yolo5-6.0-ros/src/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | /opt/ros/melodic/share/catkin/cmake/toplevel.cmake -------------------------------------------------------------------------------- /sideline_learn/yolo5-6.0-ros/src/base/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | cmake_minimum_required(VERSION 3.20) 2 | project(base) 3 | 4 | add_compile_options(-std=c++11) 5 | 6 | 7 | find_package(catkin REQUIRED COMPONENTS 8 | nodelet 9 | roscpp 10 | rospy 11 | sensor_msgs 12 | cv_bridge 13 | std_msgs 14 | message_generation 15 | 16 | ) 17 | 18 | find_package(CUDA REQUIRED) 19 | find_package(PkgConfig REQUIRED) 20 | pkg_check_modules(JSONCPP jsoncpp) 21 | link_libraries(${JSONCPP_LIBRARIES}) 22 | 23 | add_service_files( 24 | FILES 25 | RosImage.srv 26 | ) 27 | 28 | generate_messages( 29 | DEPENDENCIES 30 | std_msgs 31 | sensor_msgs 32 | ) 33 | 34 | 35 | catkin_package( 36 | INCLUDE_DIRS include 37 | LIBRARIES base 38 | CATKIN_DEPENDS nodelet roscpp rospy sensor_msgs cv_bridge std_msgs message_runtime 39 | 40 | ) 41 | 42 | include_directories( 43 | include 44 | ${catkin_INCLUDE_DIRS} 45 | ) 46 | 47 | cuda_add_library(base SHARED src/base.cpp) 48 | 49 | add_dependencies(base ${PROJECT_NAME}_gencpp) 50 | 51 | target_link_libraries(base 52 | ${catkin_LIBRARIES} 53 | -lpthread 54 | ) 55 | 56 | -------------------------------------------------------------------------------- /sideline_learn/yolo5-6.0-ros/src/base/code_info.txt: -------------------------------------------------------------------------------- 1 | warning: 2 | 001 active_safety 3 | 002 active_safety cps status 4 | 003 active_safety top lidar loading 5 | 004 active_safety path check 6 | 005 active_safety junction speed limit 7 | 006 coudl not get matched cps info 8 | 007 active_safety end check 9 | 008 active_safety slow down 10 | 009 active_safety top safety 11 | 010 GPS or Chassis info may delay 12 | 011 trailer angle init error, could not get correct path 13 | 012 control fail 14 | 013 fms suspend 15 | 014 emergency stop not tiggered in TS area 16 | 015 emergency stop not released in TS area 17 | 016 no pass area stop 18 | 017 guarding not open 19 | 018 active_safety lane change 20 | 019 could not get stop dis rtgc 21 | 020 could not get stop dis qc 22 | 021 active_safety stereo_check fail 23 | 022 active_safety lidar_check fail 24 | 023 precise stop over 1.5 limit 25 | 024 lane change blocked by light tower 26 | 025 max steer arrive 27 | 026 routing block in cutout 28 | 027 cps info delay 29 | 028 precise stop timeout 30 | 029 too close for routing 31 | 030 vehicle param not init 32 | 031 lock task get 33 | 032 task abort 34 | 033 overtake stop 35 | 36 | error 37 | 001 routing_fail 38 | 002 top lidar scenario not calibed, require calib 39 | 003 websocket lost connection 40 | 004 stop dis config file open fail 41 | 006 cps_fetch crash 42 | 007 fms server disconnect 43 | 008 motion obstacle abnormal 44 | 009 active safety abnormal 45 | 010 module crash -------------------------------------------------------------------------------- /sideline_learn/yolo5-6.0-ros/src/base/include/base/base.h: -------------------------------------------------------------------------------- 1 | #ifndef _BASE_H 2 | #define _BASE_H 3 | 4 | #include 5 | #include 6 | #include 7 | #include 8 | #include "boost/type_index.hpp" 9 | 10 | #include "ros/ros.h" 11 | #include 12 | #include 13 | #include 14 | #include "std_msgs/String.h" 15 | 16 | 17 | namespace base 18 | { 19 | 20 | class Base : public nodelet::Nodelet 21 | { 22 | protected: 23 | ros::NodeHandle nh_; 24 | ros::NodeHandle pnh_; 25 | std::unordered_map params_; 26 | 27 | 28 | public: 29 | Base(){}; 30 | ~Base(){}; 31 | 32 | void init() 33 | { 34 | nh_ = getNodeHandle(); 35 | pnh_ = getPrivateNodeHandle(); 36 | std::string node_name = getName(); 37 | 38 | std::vector param_names; 39 | if (pnh_.getParamNames(param_names)) 40 | { 41 | for (std::string name : param_names) 42 | { 43 | std::string param_name; 44 | bool valid = get_param(node_name, name, param_name); 45 | if (valid) 46 | { 47 | std::string param_value; 48 | pnh_.getParam(name, param_value); 49 | ROS_INFO("settings: %s,%s", param_name.c_str(), param_value.c_str()); 50 | params_[param_name] = param_value; 51 | } 52 | } 53 | } 54 | } 55 | 56 | bool get_param(std::string &node_name, std::string &name, 57 | std::string ¶m_name) 58 | { 59 | std::stringstream ss(name); 60 | bool valid = false; 61 | while (getline(ss, param_name, '/')) 62 | { 63 | if ("/" + param_name == node_name) 64 | { 65 | valid = true; 66 | } 67 | } 68 | return valid; 69 | } 70 | }; 71 | 72 | } 73 | 74 | #endif -------------------------------------------------------------------------------- /sideline_learn/yolo5-6.0-ros/src/base/nodelet_plugins.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | -------------------------------------------------------------------------------- /sideline_learn/yolo5-6.0-ros/src/base/src/base.cpp: -------------------------------------------------------------------------------- 1 | #include "base/base.h" 2 | 3 | namespace base 4 | { 5 | 6 | } -------------------------------------------------------------------------------- /sideline_learn/yolo5-6.0-ros/src/base/srv/RosImage.srv: -------------------------------------------------------------------------------- 1 | sensor_msgs/Image image 2 | --- 3 | string result -------------------------------------------------------------------------------- /sideline_learn/yolo5-6.0-ros/src/client/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | cmake_minimum_required(VERSION 3.0.2) 2 | project(client_nodelet) 3 | 4 | add_compile_options(-std=c++11) 5 | 6 | 7 | find_package(catkin REQUIRED COMPONENTS 8 | base 9 | nodelet 10 | roscpp 11 | rospy 12 | std_msgs 13 | ) 14 | 15 | 16 | find_package(OpenCV REQUIRED) 17 | find_package(CUDA REQUIRED) 18 | find_package(PkgConfig REQUIRED) 19 | pkg_check_modules(JSONCPP jsoncpp) 20 | link_libraries(${JSONCPP_LIBRARIES}) 21 | 22 | catkin_package( 23 | INCLUDE_DIRS include 24 | LIBRARIES client_nodelet 25 | CATKIN_DEPENDS base nodelet roscpp rospy std_msgs message_runtime 26 | ) 27 | 28 | include_directories( 29 | include 30 | ${catkin_INCLUDE_DIRS} 31 | ) 32 | 33 | 34 | add_library(client_nodelet SHARED src/client_nodelet.cpp) 35 | target_link_libraries(client_nodelet 36 | ${catkin_LIBRARIES} 37 | ${OpenCV_LIBS} 38 | -lpthread 39 | ${OpenCV_LIBS} 40 | ) 41 | -------------------------------------------------------------------------------- /sideline_learn/yolo5-6.0-ros/src/client/include/client_nodelet/client_nodelet.h: -------------------------------------------------------------------------------- 1 | #ifndef CLIENT_CLIENT_NODELET_H 2 | #define CLIENT_CLIENT_NODELET_H 3 | 4 | #include 5 | #include 6 | #include 7 | #include 8 | 9 | #include 10 | #include 11 | #include "base/base.h" 12 | #include "base/RosImage.h" 13 | using namespace std; 14 | 15 | namespace client_nodelet 16 | { 17 | class ClientNodelet : public base::Base 18 | { 19 | public: 20 | ~ClientNodelet(); 21 | 22 | virtual void onInit(); 23 | 24 | private: 25 | void run(); 26 | 27 | private: 28 | thread thread_; 29 | string image_path; 30 | string client_name; 31 | }; 32 | } 33 | 34 | #endif -------------------------------------------------------------------------------- /sideline_learn/yolo5-6.0-ros/src/client/launch/client_nodelet.launch: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | -------------------------------------------------------------------------------- /sideline_learn/yolo5-6.0-ros/src/client/nodelet_plugins.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | -------------------------------------------------------------------------------- /sideline_learn/yolo5-6.0-ros/src/client/src/client_nodelet.cpp: -------------------------------------------------------------------------------- 1 | #include 2 | 3 | #include "client_nodelet/client_nodelet.h" 4 | #include 5 | #include 6 | 7 | using namespace std; 8 | using namespace cv; 9 | 10 | namespace client_nodelet 11 | { 12 | ClientNodelet::~ClientNodelet() {} 13 | 14 | void ClientNodelet::onInit() 15 | { 16 | init(); 17 | image_path = params_["image_path"]; 18 | client_name = params_["client_name"]; 19 | thread_ = thread(boost::bind(&ClientNodelet::run, this)); 20 | } 21 | 22 | void ClientNodelet::run() 23 | { 24 | 25 | setlocale(LC_ALL, ""); 26 | ros::service::waitForService(client_name); 27 | ros::ServiceClient client = nh_.serviceClient(client_name); 28 | 29 | Mat image = cv::imread(image_path); 30 | sensor_msgs::ImagePtr msg = cv_bridge::CvImage(std_msgs::Header(), "bgr8", image).toImageMsg(); 31 | sensor_msgs::Image msg1 = *msg; 32 | 33 | base::RosImage ai; 34 | ai.request.image = *msg; 35 | bool flag = client.call(ai); 36 | if (flag) 37 | { 38 | ROS_INFO("detected target,result = %s", ai.response.result.c_str()); 39 | } 40 | else 41 | { 42 | ROS_INFO("failed,no target"); 43 | } 44 | ros::Duration(0.2).sleep(); 45 | 46 | } 47 | 48 | } // namespace server_nodelet 49 | PLUGINLIB_EXPORT_CLASS(client_nodelet::ClientNodelet, 50 | nodelet::Nodelet) 51 | -------------------------------------------------------------------------------- /sideline_learn/yolo5-6.0-ros/src/yolov5-6.0/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | cmake_minimum_required(VERSION 3.20) 2 | 3 | project(yolov5_trt) 4 | set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11 -Wall -Ofast -g -Wfatal-errors -D_MWAITXINTRIN_H_INCLUDED") 5 | add_definitions(-std=c++11) 6 | add_definitions(-DAPI_EXPORTS) 7 | option(CUDA_USE_STATIC_CUDA_RUNTIME OFF) 8 | set(CMAKE_CXX_STANDARD 11) 9 | set(CMAKE_BUILD_TYPE Debug) 10 | 11 | find_package(CUDA REQUIRED) 12 | find_package(OpenCV) 13 | include_directories(${OpenCV_INCLUDE_DIRS}) 14 | 15 | if(WIN32) 16 | enable_language(CUDA) 17 | endif(WIN32) 18 | 19 | include_directories(${PROJECT_SOURCE_DIR}/include) 20 | # include and link dirs of cuda and tensorrt, you need adapt them if yours are different 21 | # cuda 22 | include_directories(/usr/local/cuda/include) 23 | link_directories(/usr/local/cuda/lib64) 24 | # tensorrt 25 | include_directories(/home/rex/TensorRT-8.2.0.6/include/) 26 | link_directories(/home/rex/TensorRT-8.2.0.6/targets/x86_64-linux-gnu/lib) 27 | 28 | 29 | cuda_add_library(myplugins SHARED src/yololayer.cu src/basic_transform.cu) 30 | target_link_libraries(myplugins nvinfer cudart) 31 | 32 | 33 | 34 | cuda_add_executable(yolov5 src/calibrator.cpp src/yolov5.cpp src/preprocess.cu) 35 | target_link_libraries(yolov5 nvinfer cudart myplugins ${OpenCV_LIBS}) 36 | 37 | cuda_add_executable(detect src/detect.cpp src/preprocess.cu) 38 | target_link_libraries(detect nvinfer cudart myplugins ${OpenCV_LIBS}) 39 | 40 | if(UNIX) 41 | add_definitions(-O2 -pthread) 42 | endif(UNIX) 43 | 44 | 45 | -------------------------------------------------------------------------------- /sideline_learn/yolo5-6.0-ros/src/yolov5-6.0/include/basic_transform.h: -------------------------------------------------------------------------------- 1 | #ifndef BASIC_TRANSFORM_H 2 | #define BASIC_TRANSFORM_H 3 | #include 4 | void PicResize(uint8_t*, uint8_t*, uint32_t, uint32_t, uint32_t, uint32_t); 5 | void PicNormalize(uint8_t*, float*, uint32_t, uint32_t); 6 | #endif 7 | -------------------------------------------------------------------------------- /sideline_learn/yolo5-6.0-ros/src/yolov5-6.0/include/calibrator.h: -------------------------------------------------------------------------------- 1 | #ifndef ENTROPY_CALIBRATOR_H 2 | #define ENTROPY_CALIBRATOR_H 3 | 4 | #include 5 | #include 6 | #include 7 | #include "macros.h" 8 | 9 | //! \class Int8EntropyCalibrator2 10 | //! 11 | //! \brief Implements Entropy calibrator 2. 12 | //! CalibrationAlgoType is kENTROPY_CALIBRATION_2. 13 | //! 14 | class Int8EntropyCalibrator2 : public nvinfer1::IInt8EntropyCalibrator2 15 | { 16 | public: 17 | Int8EntropyCalibrator2(int batchsize, int input_w, int input_h, const char* img_dir, const char* calib_table_name, const char* input_blob_name, bool read_cache = true); 18 | 19 | virtual ~Int8EntropyCalibrator2(); 20 | int getBatchSize() const TRT_NOEXCEPT override; 21 | bool getBatch(void* bindings[], const char* names[], int nbBindings) TRT_NOEXCEPT override; 22 | const void* readCalibrationCache(size_t& length) TRT_NOEXCEPT override; 23 | void writeCalibrationCache(const void* cache, size_t length) TRT_NOEXCEPT override; 24 | 25 | private: 26 | int batchsize_; 27 | int input_w_; 28 | int input_h_; 29 | int img_idx_; 30 | std::string img_dir_; 31 | std::vector img_files_; 32 | size_t input_count_; 33 | std::string calib_table_name_; 34 | const char* input_blob_name_; 35 | bool read_cache_; 36 | void* device_input_; 37 | std::vector calib_cache_; 38 | }; 39 | 40 | #endif // ENTROPY_CALIBRATOR_H 41 | -------------------------------------------------------------------------------- /sideline_learn/yolo5-6.0-ros/src/yolov5-6.0/include/cuda_utils.h: -------------------------------------------------------------------------------- 1 | #ifndef TRTX_CUDA_UTILS_H_ 2 | #define TRTX_CUDA_UTILS_H_ 3 | 4 | #include 5 | 6 | #ifndef CUDA_CHECK 7 | #define CUDA_CHECK(callstr)\ 8 | {\ 9 | cudaError_t error_code = callstr;\ 10 | if (error_code != cudaSuccess) {\ 11 | std::cerr << "CUDA error " << error_code << " at " << __FILE__ << ":" << __LINE__;\ 12 | assert(0);\ 13 | }\ 14 | } 15 | #endif // CUDA_CHECK 16 | 17 | #endif // TRTX_CUDA_UTILS_H_ 18 | 19 | -------------------------------------------------------------------------------- /sideline_learn/yolo5-6.0-ros/src/yolov5-6.0/include/macros.h: -------------------------------------------------------------------------------- 1 | #ifndef __MACROS_H 2 | #define __MACROS_H 3 | 4 | #ifdef API_EXPORTS 5 | #if defined(_MSC_VER) 6 | #define API __declspec(dllexport) 7 | #else 8 | #define API __attribute__((visibility("default"))) 9 | #endif 10 | #else 11 | 12 | #if defined(_MSC_VER) 13 | #define API __declspec(dllimport) 14 | #else 15 | #define API 16 | #endif 17 | #endif // API_EXPORTS 18 | 19 | #if NV_TENSORRT_MAJOR >= 8 20 | #define TRT_NOEXCEPT noexcept 21 | #define TRT_CONST_ENQUEUE const 22 | #else 23 | #define TRT_NOEXCEPT 24 | #define TRT_CONST_ENQUEUE 25 | #endif 26 | 27 | #endif // __MACROS_H 28 | -------------------------------------------------------------------------------- /sideline_learn/yolo5-6.0-ros/src/yolov5-6.0/include/preprocess.h: -------------------------------------------------------------------------------- 1 | #ifndef __PREPROCESS_H 2 | #define __PREPROCESS_H 3 | 4 | #include 5 | #include 6 | 7 | 8 | struct AffineMatrix{ 9 | float value[6]; 10 | }; 11 | 12 | 13 | void preprocess_kernel_img(uint8_t* src, int src_width, int src_height, 14 | float* dst, int dst_width, int dst_height, 15 | cudaStream_t stream); 16 | #endif // __PREPROCESS_H 17 | -------------------------------------------------------------------------------- /sideline_learn/yolo5-6.0-ros/src/yolov5-6.0/include/utils.h: -------------------------------------------------------------------------------- 1 | #ifndef TRTX_YOLOV5_UTILS_H_ 2 | #define TRTX_YOLOV5_UTILS_H_ 3 | 4 | #include 5 | #include 6 | 7 | static inline cv::Mat preprocess_img(cv::Mat& img, int input_w, int input_h) { 8 | int w, h, x, y; 9 | float r_w = input_w / (img.cols*1.0); 10 | float r_h = input_h / (img.rows*1.0); 11 | if (r_h > r_w) { 12 | w = input_w; 13 | h = r_w * img.rows; 14 | x = 0; 15 | y = (input_h - h) / 2; 16 | } else { 17 | w = r_h * img.cols; 18 | h = input_h; 19 | x = (input_w - w) / 2; 20 | y = 0; 21 | } 22 | cv::Mat re(h, w, CV_8UC3); 23 | cv::resize(img, re, re.size(), 0, 0, cv::INTER_LINEAR); 24 | cv::Mat out(input_h, input_w, CV_8UC3, cv::Scalar(128, 128, 128)); 25 | re.copyTo(out(cv::Rect(x, y, re.cols, re.rows))); 26 | return out; 27 | } 28 | 29 | static inline int read_files_in_dir(const char *p_dir_name, std::vector &file_names) { 30 | DIR *p_dir = opendir(p_dir_name); 31 | if (p_dir == nullptr) { 32 | return -1; 33 | } 34 | 35 | struct dirent* p_file = nullptr; 36 | while ((p_file = readdir(p_dir)) != nullptr) { 37 | if (strcmp(p_file->d_name, ".") != 0 && 38 | strcmp(p_file->d_name, "..") != 0) { 39 | //std::string cur_file_name(p_dir_name); 40 | //cur_file_name += "/"; 41 | //cur_file_name += p_file->d_name; 42 | std::string cur_file_name(p_file->d_name); 43 | file_names.push_back(cur_file_name); 44 | } 45 | } 46 | 47 | closedir(p_dir); 48 | return 0; 49 | } 50 | 51 | #endif // TRTX_YOLOV5_UTILS_H_ 52 | 53 | -------------------------------------------------------------------------------- /sideline_learn/yolo5-6.0-ros/src/yolov5-6.0/src/detect.cpp: -------------------------------------------------------------------------------- 1 | #include 2 | #include 3 | #include 4 | #include "cuda_utils.h" 5 | #include "logging.h" 6 | #include "common.hpp" 7 | #include "utils.h" 8 | #include "preprocess.h" 9 | #include "yolov5-detect.h" 10 | 11 | int main(int argc, char **argv) 12 | { 13 | cudaSetDevice(0); 14 | 15 | std::string engine_name = "yolov5s.engine"; 16 | yolov5 *det = new yolov5(engine_name); 17 | 18 | cv::Mat img = cv::imread("1.jpg"); 19 | int w = img.cols; 20 | int h = img.rows; 21 | unsigned char *d_image; 22 | cudaMalloc((void **)&d_image, sizeof(unsigned char) * w * h * 3); 23 | cudaMemcpy(d_image, img.data, w * h * 3 * sizeof(unsigned char),cudaMemcpyHostToDevice); 24 | 25 | bool flag = det->detect(d_image, w, h,img); 26 | 27 | cudaFree(d_image); 28 | return 0; 29 | } 30 | -------------------------------------------------------------------------------- /sideline_learn/yolo5-6.0-ros/src/yolov5-infer/include/yolov5_infer_nodelet/yolov5_infer_nodelet.h: -------------------------------------------------------------------------------- 1 | #ifndef YOLOV5_INFER_NODELET_H 2 | #define YOLOV5_INFER_NODELET_H 3 | 4 | #include 5 | #include 6 | #include 7 | #include 8 | #include "base/base.h" 9 | #include 10 | #include "yolov5-detect.h" 11 | 12 | using namespace cv; 13 | using namespace std; 14 | 15 | namespace yolov5_infer_nodelet 16 | { 17 | class Yolov5InferNodelet : public base::Base 18 | { 19 | public: 20 | ~Yolov5InferNodelet(); 21 | 22 | virtual void onInit(); 23 | 24 | private: 25 | void run(); 26 | 27 | private: 28 | int gpu_id; 29 | string engine_path; 30 | string image_path; 31 | thread thread_; 32 | yolov5 *infer; 33 | }; 34 | } 35 | 36 | #endif // YOLOV5_INFER_NODELET_H -------------------------------------------------------------------------------- /sideline_learn/yolo5-6.0-ros/src/yolov5-infer/launch/yolov5_infer_nodelet.launch: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | -------------------------------------------------------------------------------- /sideline_learn/yolo5-6.0-ros/src/yolov5-infer/nodelet_plugins.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | -------------------------------------------------------------------------------- /sideline_learn/yolo5-6.0-ros/src/yolov5-infer/src/yolov5_infer_nodelet.cpp: -------------------------------------------------------------------------------- 1 | #include 2 | #include 3 | #include "yolov5_infer_nodelet/yolov5_infer_nodelet.h" 4 | #include "cuda_runtime.h" 5 | #include "dirent.h" 6 | 7 | using namespace std; 8 | using namespace cv; 9 | 10 | namespace yolov5_infer_nodelet 11 | { 12 | Yolov5InferNodelet::~Yolov5InferNodelet() {} 13 | 14 | void Yolov5InferNodelet::onInit() 15 | { 16 | init(); 17 | gpu_id = stoi(params_["gpu_id"]); 18 | engine_path = params_["engine_path"]; 19 | image_path = params_["image_path"]; 20 | thread_ = thread(boost::bind(&Yolov5InferNodelet::run, this)); 21 | } 22 | 23 | void Yolov5InferNodelet::run() 24 | { 25 | cudaSetDevice(gpu_id); 26 | infer = new yolov5(engine_path); 27 | cv::Mat img = cv::imread(image_path); 28 | int w = img.cols; 29 | int h = img.rows; 30 | unsigned char *d_image; 31 | cudaMalloc((void **)&d_image, sizeof(unsigned char) * w * h * 3); 32 | cudaMemcpy(d_image, img.data, w * h * 3 * sizeof(unsigned char),cudaMemcpyHostToDevice); 33 | infer->detect(d_image, w, h,img); 34 | cv::imshow("show_image", img); 35 | cv::waitKey(0); 36 | cudaFree(d_image); 37 | } 38 | } 39 | PLUGINLIB_EXPORT_CLASS(yolov5_infer_nodelet::Yolov5InferNodelet, 40 | nodelet::Nodelet) -------------------------------------------------------------------------------- /sideline_learn/yolo5-6.0-ros/src/yolov5-server/include/yolov5_server_nodelet/yolov5_server_nodelet.h: -------------------------------------------------------------------------------- 1 | #ifndef YOLOV5_SERVER_NODELET_H 2 | #define YOLOV5_SERVER_NODELET_H 3 | 4 | #include 5 | #include 6 | #include 7 | 8 | #include "base/base.h" 9 | #include "base/RosImage.h" 10 | #include "yolov5-detect.h" 11 | 12 | using namespace cv; 13 | using namespace std; 14 | 15 | namespace yolov5_server_nodelet 16 | { 17 | class Yolov5ServerNodelet : public base::Base 18 | { 19 | public: 20 | ~Yolov5ServerNodelet(); 21 | 22 | virtual void onInit(); 23 | void run(); 24 | 25 | private: 26 | bool inference(base::RosImage::Request &request, 27 | base::RosImage::Response &response); 28 | 29 | int gpu_id; 30 | unsigned char *d_image; 31 | yolov5 *infer; 32 | std::string engine_path; 33 | std::string server_name; 34 | }; 35 | } 36 | 37 | #endif // YOLOV5_SERVER_NODELET_H -------------------------------------------------------------------------------- /sideline_learn/yolo5-6.0-ros/src/yolov5-server/launch/yolov5_server_nodelet.launch: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | -------------------------------------------------------------------------------- /sideline_learn/yolo5-6.0-ros/src/yolov5-server/nodelet_plugins.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | -------------------------------------------------------------------------------- /trt_cpp/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | cmake_minimum_required(VERSION 2.6) 2 | 3 | project(demo_trt) 4 | 5 | option(CUDA_USE_STATIC_CUDA_RUNTIME OFF) 6 | set(CMAKE_CXX_STANDARD 17) 7 | set(CMAKE_BUILD_TYPE Debug) 8 | set(EXECUTABLE_OUTPUT_PATH ${PROJECT_SOURCE_DIR}/workspace) 9 | set(HAS_PYTHON OFF) 10 | 11 | set(TENSORRT_DIR "/data/app/TensorRT-8.4.3.1") 12 | 13 | 14 | find_package(CUDA REQUIRED) 15 | find_package(OpenCV) 16 | 17 | include_directories( 18 | ${PROJECT_SOURCE_DIR}/src 19 | ${PROJECT_SOURCE_DIR}/src/trt 20 | ${PROJECT_SOURCE_DIR}/src/trt/common 21 | ${OpenCV_INCLUDE_DIRS} 22 | ${CUDA_TOOLKIT_ROOT_DIR}/include 23 | ${TENSORRT_DIR}/include 24 | ${CUDNN_DIR}/include 25 | ) 26 | link_directories( 27 | ${TENSORRT_DIR}/lib 28 | ${CUDA_TOOLKIT_ROOT_DIR}/lib64 29 | ${CUDNN_DIR}/lib 30 | ) 31 | 32 | 33 | set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11 -Wall -O0 -Wfatal-errors -pthread -w -g") 34 | set(CUDA_NVCC_FLAGS "${CUDA_NVCC_FLAGS} -std=c++11 -O0 -Xcompiler -fPIC -g -w ${CUDA_GEN_CODE}") 35 | file(GLOB_RECURSE cpp_srcs ${PROJECT_SOURCE_DIR}/src/*.cpp) 36 | file(GLOB_RECURSE cuda_srcs ${PROJECT_SOURCE_DIR}/src/*.cu) 37 | cuda_add_library(plugin_list SHARED ${cuda_srcs}) 38 | target_link_libraries(plugin_list nvinfer nvinfer_plugin nvonnxparser) 39 | target_link_libraries(plugin_list cuda cublas cudart cudnn) 40 | target_link_libraries(plugin_list protobuf pthread) 41 | target_link_libraries(plugin_list ${OpenCV_LIBS}) 42 | 43 | add_executable(demo_infer ${cpp_srcs}) 44 | 45 | target_link_libraries(demo_infer plugin_list) 46 | 47 | 48 | -------------------------------------------------------------------------------- /trt_cpp/README.md: -------------------------------------------------------------------------------- 1 | 2 | 3 | #### 1、demo工程 4 | 5 | 可以先参考demo的里面的工程，下载相应的模型权重，或者直接从给出的百度网盘下载。 6 | 7 | #### 2、export_onnx 8 | 9 | 下载好模型之后，在onnx2trt/demo里面一般可以使用export_onnx.py或者predict.py产生相应的onnx模型，并可以使用infer-onnxruntime使用onnx进行推理，也可以使用onnx_simplify对onnx进行简化。 10 | 11 | #### 3、tensorrt 12 | 13 | 在workspace下面放入需要转化的onnx模型，如vit.onnx、centernet.onnx等 14 | 15 | 在Makefile中修改本机对应的环境，可以参考手写AI提供的自动环境配置进行，或者修改CMakeLists.txt中本机环境 16 | 17 | 在src/main.cpp中提供了build_engine 和 inference的代码。直接在onnx2trt文件夹下，进行执行 make run，即可进行推理。 18 | 19 | 或者在CMakeLists中修改本机的tensorrt、protobuf路径，==可以将模型路径和图片路径修改为绝对路径，执行吗，修改src/main.cpp中的demo名字== 20 | 21 | mkdir build && cd build 22 | 23 | cmake .. && make -j 24 | 25 | 在worksapce中执行可执行文件./demo_infer 即可运行相应的demo 26 | 27 | #### 4、总结 28 | 29 | 本次学习课程学习了torch2onnx2trt的方式，同时复习了深度学习中的比较经典的模型，同时对一些模型的后处理采用c++的处理方式，同时也提高了c++的代码能力。 30 | 31 | 32 | 33 | -------------------------------------------------------------------------------- /trt_cpp/src/main.cpp: -------------------------------------------------------------------------------- 1 | 2 | #include 3 | #include 4 | #include 5 | #include 6 | #include 7 | 8 | using namespace std; 9 | 10 | int main() 11 | { 12 | demoInfer demo; 13 | string demo_name = "yolov5seg"; 14 | demo.do_infer(demo_name); 15 | return 0; 16 | } 17 | -------------------------------------------------------------------------------- /trt_cpp/src/trt/common/cuda-tools.hpp: -------------------------------------------------------------------------------- 1 | #ifndef CUDA_TOOLS_HPP 2 | #define CUDA_TOOLS_HPP 3 | 4 | #include 5 | #include 6 | 7 | #define checkRuntime(call) CUDATools::check_runtime(call, #call, __LINE__, __FILE__) 8 | 9 | #define checkKernel(...) \ 10 | __VA_ARGS__; \ 11 | do{cudaError_t cudaStatus = cudaPeekAtLastError(); \ 12 | if (cudaStatus != cudaSuccess){ \ 13 | INFOE("launch failed: %s", cudaGetErrorString(cudaStatus)); \ 14 | }} while(0); 15 | 16 | namespace CUDATools{ 17 | 18 | bool check_runtime(cudaError_t e, const char* call, int iLine, const char *szFile); 19 | bool check_device_id(int device_id); 20 | int current_device_id(); 21 | std::string device_description(); 22 | 23 | // 自动切换当前的deviceid，并在析构的时候切换回去 24 | class AutoDevice{ 25 | public: 26 | AutoDevice(int device_id = 0); 27 | virtual ~AutoDevice(); 28 | 29 | private: 30 | int old_ = -1; 31 | }; 32 | } 33 | 34 | 35 | #endif // CUDA_TOOLS_HPP -------------------------------------------------------------------------------- /trt_cpp/src/trt/common/matrix.hpp: -------------------------------------------------------------------------------- 1 | #ifndef GEMM_HPP 2 | #define GEMM_HPP 3 | 4 | #include 5 | #include 6 | #include 7 | #include 8 | /* 实现一个自定义的matrix类 */ 9 | class Matrix{ 10 | public: 11 | Matrix(); 12 | Matrix(int rows, int cols, const std::initializer_list& pdata={}); 13 | Matrix(int rows, int cols, const std::vector&v); 14 | 15 | const float& operator()(int irow, int icol)const {return data_[irow * cols_ + icol];} 16 | float& operator()(int irow, int icol){return data_[irow * cols_ + icol];} 17 | Matrix element_wise(const std::function &func) const; 18 | Matrix operator*(const Matrix &value) const; 19 | Matrix operator*(float value) const; 20 | 21 | Matrix operator+(float value) const; 22 | 23 | Matrix operator-(float value) const; 24 | Matrix operator/(float value) const; 25 | 26 | int rows() const{return rows_;} 27 | int cols() const{return cols_;} 28 | Matrix view(int rows, int cols) const; 29 | Matrix power(float y) const; 30 | float reduce_sum() const; 31 | float* ptr() const{return (float*)data_.data();} 32 | Matrix exp(float value); 33 | 34 | int rows_ = 0; 35 | int cols_ = 0; 36 | std::vector data_; 37 | }; 38 | 39 | /* 全局操作符重载，使得能够被cout << m; */ 40 | std::ostream& operator << (std::ostream& out, const Matrix& m); 41 | 42 | #endif // GEMM_HPP -------------------------------------------------------------------------------- /trt_cpp/src/trt/common/simple-logger.hpp: -------------------------------------------------------------------------------- 1 | #ifndef SIMPLE_LOGGER_HPP 2 | #define SIMPLE_LOGGER_HPP 3 | 4 | #include 5 | 6 | #define INFOD(...) SimpleLogger::__log_func(__FILE__, __LINE__, SimpleLogger::LogLevel::Debug, __VA_ARGS__) 7 | #define INFOV(...) SimpleLogger::__log_func(__FILE__, __LINE__, SimpleLogger::LogLevel::Verbose, __VA_ARGS__) 8 | #define INFO(...) SimpleLogger::__log_func(__FILE__, __LINE__, SimpleLogger::LogLevel::Info, __VA_ARGS__) 9 | #define INFOW(...) SimpleLogger::__log_func(__FILE__, __LINE__, SimpleLogger::LogLevel::Warning, __VA_ARGS__) 10 | #define INFOE(...) SimpleLogger::__log_func(__FILE__, __LINE__, SimpleLogger::LogLevel::Error, __VA_ARGS__) 11 | #define INFOF(...) SimpleLogger::__log_func(__FILE__, __LINE__, SimpleLogger::LogLevel::Fatal, __VA_ARGS__) 12 | 13 | 14 | namespace SimpleLogger{ 15 | 16 | enum class LogLevel : int{ 17 | Debug = 5, 18 | Verbose = 4, 19 | Info = 3, 20 | Warning = 2, 21 | Error = 1, 22 | Fatal = 0 23 | }; 24 | 25 | void set_log_level(LogLevel level); 26 | LogLevel get_log_level(); 27 | void __log_func(const char* file, int line, LogLevel level, const char* fmt, ...); 28 | 29 | }; // SimpleLogger 30 | 31 | #endif // SIMPLE_LOGGER_HPP -------------------------------------------------------------------------------- /trt_cpp/src/trt/demo-infer/centernet/centernet.h: -------------------------------------------------------------------------------- 1 | #ifndef CENTERNET_HPP 2 | #define CENTERNET_HPP 3 | 4 | #include 5 | #include 6 | #include 7 | 8 | struct bbox{ 9 | float left, top, right, bottom, confidence; 10 | int label; 11 | 12 | bbox() = default; 13 | bbox(float left, float top, float right, float bottom, float confidence, int label): 14 | left(left), top(top), right(right), bottom(bottom), confidence(confidence), label(label){} 15 | }; 16 | 17 | namespace Centernet{ 18 | void centernet_inference(); 19 | void centernet_inference_gpu(); 20 | }; 21 | 22 | 23 | 24 | 25 | #endif // CENTERNET_HPP -------------------------------------------------------------------------------- /trt_cpp/src/trt/demo-infer/detr/detr.h: -------------------------------------------------------------------------------- 1 | #ifndef DETR_HPP 2 | #define DETR_HPP 3 | 4 | #include 5 | #include 6 | #include 7 | #include 8 | 9 | namespace Detr{ 10 | void detr_inference(); 11 | }; 12 | 13 | struct Pred 14 | { 15 | std::vector>bbox; 16 | std::vectorlabel; 17 | }; 18 | 19 | 20 | 21 | 22 | #endif // DETR_HPP -------------------------------------------------------------------------------- /trt_cpp/src/trt/demo-infer/hrnet/hrnet.h: -------------------------------------------------------------------------------- 1 | #ifndef HRNET_HPP 2 | #define HRNET_HPP 3 | 4 | #include 5 | #include 6 | #include 7 | 8 | namespace Hrnet{ 9 | void hrnet_inference(); 10 | void hrnet_inference_gpu(); 11 | }; 12 | 13 | 14 | 15 | #endif // HRNET_HPP -------------------------------------------------------------------------------- /trt_cpp/src/trt/demo-infer/unet/unet.h: -------------------------------------------------------------------------------- 1 | #ifndef UNET_HPP 2 | #define UNET_HPP 3 | #include 4 | #include 5 | 6 | namespace Unet{ 7 | void unet_inference(); 8 | void render(cv::Mat& image, const cv::Mat& prob, const cv::Mat& iclass); 9 | std::tuple post_process(float* output, int output_width, int output_height, int num_class, int ibatch); 10 | }; 11 | 12 | #endif // UNET_HPP -------------------------------------------------------------------------------- /trt_cpp/src/trt/demo-infer/vit/vit.h: -------------------------------------------------------------------------------- 1 | #ifndef VIT_HPP 2 | #define VIT_HPP 3 | 4 | namespace Vit{ 5 | void vit_inference(); 6 | }; 7 | 8 | #endif // Vit_HPP -------------------------------------------------------------------------------- /trt_cpp/src/trt/demo-infer/yolov5/yolov5.hpp: -------------------------------------------------------------------------------- 1 | #ifndef YOLOV5_HPP 2 | #define YOLOV5_HPP 3 | 4 | #include 5 | #include 6 | #include 7 | #include 8 | 9 | ///////////////////////////////////////////////////////////////////////////////////////// 10 | // 封装接口类 11 | namespace YoloV5{ 12 | 13 | struct Box{ 14 | float left, top, right, bottom, confidence; 15 | int class_label; 16 | 17 | Box() = default; 18 | 19 | Box(float left, float top, float right, float bottom, float confidence, int class_label) 20 | :left(left), top(top), right(right), bottom(bottom), confidence(confidence), class_label(class_label){} 21 | }; 22 | typedef std::vector BoxArray; 23 | 24 | class Infer{ 25 | public: 26 | virtual std::shared_future commit(const cv::Mat& input) = 0; 27 | }; 28 | 29 | std::shared_ptr create_infer( 30 | const std::string& file, 31 | int gpuid=0, float confidence_threshold=0.25, float nms_threshold=0.45 32 | ); 33 | }; 34 | 35 | #endif // YOLOV5_HPP -------------------------------------------------------------------------------- /trt_cpp/src/trt/demo-infer/yolov5seg/yolact.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Rex-LK/tensorrt_learning/45df1f33cab6a66a0d75c0bb26a3978d539dc888/trt_cpp/src/trt/demo-infer/yolov5seg/yolact.png -------------------------------------------------------------------------------- /trt_cpp/src/trt/demo-infer/yolov5seg/yolov5seg.h: -------------------------------------------------------------------------------- 1 | #ifndef YOLOV5SEG 2 | #define YOLOV5SEG 3 | 4 | #include 5 | #include 6 | #include 7 | 8 | namespace Yolov5Seg 9 | { 10 | void yolov5Seg_inference(); 11 | }; 12 | 13 | #endif 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/trt_py/basic_infer/_init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Rex-LK/tensorrt_learning/45df1f33cab6a66a0d75c0bb26a3978d539dc888/trt_py/basic_infer/_init__.py -------------------------------------------------------------------------------- /trt_py/basic_infer/warpaffine.py: -------------------------------------------------------------------------------- 1 | import cv2 2 | import numpy as np 3 | 4 | def inv_mat(M): 5 | k = M[0, 0], 6 | b1 = M[0, 2] 7 | b2 = M[1, 2], 8 | return np.array([[1 / k[0], 0, -b1 / k[0]], 9 | [0, 1 / k[0], -b2[0] / k[0]]]) 10 | 11 | 12 | def image_transfer(image, dst_size): 13 | oh, ow = image.shape[:2] 14 | dh, dw = dst_size 15 | scale = min(dw / ow, dh / oh) 16 | 17 | M = np.array([ 18 | [scale, 0, -scale * ow * 0.5 + dw * 0.5], 19 | [0, scale, -scale * oh * 0.5 + dh * 0.5] 20 | ]) 21 | return cv2.warpAffine(image, M, dst_size), M, inv_mat(M) 22 | 23 | 24 | if __name__ == '__main__': 25 | img = cv2.imread("/hrnet/images/person2.png") 26 | cv2.imshow("img_o", img) 27 | print(img.shape) 28 | img_d, M, inv = image_transfer(img, (192, 256)) 29 | print(img_d.shape) 30 | cv2.imshow("img_d",img_d) 31 | img_s = cv2.warpAffine(img_d, inv, img.shape[:2][::-1]) 32 | cv2.imshow("img_s",img_s) 33 | cv2.waitKey(0) -------------------------------------------------------------------------------- /trt_py/build_engine/__pycache__/common.cpython-39.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Rex-LK/tensorrt_learning/45df1f33cab6a66a0d75c0bb26a3978d539dc888/trt_py/build_engine/__pycache__/common.cpython-39.pyc -------------------------------------------------------------------------------- /trt_py/build_engine/batch_dynamic/build_engine_batch_image.py: -------------------------------------------------------------------------------- 1 | import tensorrt as trt 2 | 3 | # 构建logger,builder,network 4 | logger = trt.Logger(trt.Logger.WARNING) 5 | builder = trt.Builder(logger) 6 | builder.max_batch_size = 1 7 | 8 | network = builder.create_network(1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)) 9 | parser = trt.OnnxParser(network, logger) 10 | # 读入onnx查看有无错误 11 | success = parser.parse_from_file("/home/rex/Desktop/tensorrt_learning/trt_py/build_engine/batch_dynamic/detr_sim.onnx") 12 | for idx in range(parser.num_errors): 13 | print(parser.get_error(idx)) 14 | if success: 15 | print('Construction sucess!!!') 16 | pass # Error handling code here 17 | 18 | profile = builder.create_optimization_profile() 19 | # profile.set_shape("images", (1, 3, 640, 640), (8, 3, 640, 640), (16, 3, 640, 640)) 20 | profile.set_shape("images", (1, 3, 800, 1066),(8, 3, 800, 1066), (16, 3,800, 1066)) 21 | 22 | # profile = builder.create_optimization_profile() 23 | # profile.set_shape("foo", (1,3, 512, 512), (20,3,640, 640), (10,3,640, 640)) 24 | config = builder.create_builder_config() 25 | config.add_optimization_profile(profile) 26 | config.max_workspace_size = 1 << 30 # 27 | serialized_engine = builder.build_serialized_network(network, config) 28 | with open("/home/rex/Desktop/tensorrt_learning/trt_py/build_engine/batch_dynamic/hrnet.pyengine", "wb") as f: 29 | print('正在写入engine文件．．．') 30 | f.write(serialized_engine) 31 | print('构建引擎成功！！！') -------------------------------------------------------------------------------- /trt_py/centernet/images/cat.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Rex-LK/tensorrt_learning/45df1f33cab6a66a0d75c0bb26a3978d539dc888/trt_py/centernet/images/cat.jpg -------------------------------------------------------------------------------- /trt_py/centernet/images/street.jpg: -------------------------------------------------------------------------------- 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