From 1870a30a5d69332a701feca5565bb93cebdf7ca7 Mon Sep 17 00:00:00 2001 From: wam-v-tan Date: Thu, 4 Apr 2024 17:03:08 +0000 Subject: [PATCH] deploy: e0a37fe09dc2fb8c495d8bb5209f9c767bacb365 --- packages/index.html | 7 ++++++- search/search_index.json | 2 +- sitemap.xml.gz | Bin 319 -> 319 bytes 3 files changed, 7 insertions(+), 2 deletions(-) diff --git a/packages/index.html b/packages/index.html index 59707a65d..f8df63099 100644 --- a/packages/index.html +++ b/packages/index.html @@ -373,7 +373,7 @@

dashboardNot Found Not Found Not Found -nan +Not Found OUXT-Polaris/geographic_info @@ -576,6 +576,11 @@

dashboardOUXT-Polaris/geographic_conversion +Feature/publish covariance parameter +nan + + OUXT-Polaris/hermite_path_planner [Add] Use generate parameter library nan diff --git a/search/search_index.json b/search/search_index.json index 3463a560b..e9530b4ba 100644 --- a/search/search_index.json +++ b/search/search_index.json @@ -1 +1 @@ -{"config":{"lang":["en"],"separator":"[\\s\\-]+","pipeline":["stopWordFilter"]},"docs":[{"location":"","title":"OUXT Polaris Development Automation Tool","text":""},{"location":"#status-badge","title":"Status Badge","text":"Build Test Automation"},{"location":"#what-is-this","title":"What is this?","text":"

This tool supports to build our software and deploy them into local_machine and real robot. This tool works on ansible and github actions

"},{"location":"#contents-guide","title":"Contents Guide","text":"If you want to know how to setup development in your local machine.

See tutorials

If you want to see navigation demo in your local machine.

See tutorials

If you want to know how to use tools.

See tools

If you want to know hardware specification.

See hardware documenation

If you want to know how our CI/CD pipeline works.

See automation pipelines documentation

"},{"location":"automation_pipeline/","title":"Automation Pipeline","text":""},{"location":"automation_pipeline/#repository-architecture","title":"Repository Architecture","text":"

OUXT-Polaris softwares are separeated in many repositories in order to run continuous integration quickly. So, we developing integration pipeline for operating complex software stacks and deploy it.

"},{"location":"automation_pipeline/#integration-pipeline","title":"Integration Pipeline","text":"

Integration pipeline is deployed at github actions in each repositories.

"},{"location":"automation_pipeline/#actions-in-ouxt_automation-package","title":"Actions in ouxt_automation package","text":"

You can see the status of all Actions here.

ansible 

graph TB\n    pull_request --send --> ouxt_automation\n    developer --manual hook--> ouxt_automation\n    daily_hook --> ouxt_automation\n\n    linkStyle 0 stroke-width:2px,stroke:blue;\n    linkStyle 1 stroke-width:2px,stroke:blue;\n    linkStyle 2 stroke-width:2px,stroke:blue;\n\n    click ansible \"https://github.com/OUXT-Polaris/ouxt_automation\" \"ouxt_automation repository\"\n

run_ansible job runs ansible with setup-full playbook and check the setup tool works well. 

graph TB\n    pull_request --send --> ouxt_automation\n    developer --manual hook--> ouxt_automation\n    daily_hook --> ouxt_automation\n\n    linkStyle 0 stroke-width:2px,stroke:blue;\n    linkStyle 1 stroke-width:2px,stroke:blue;\n    linkStyle 2 stroke-width:2px,stroke:blue;\n\n    click ouxt_automation \"https://github.com/OUXT-Polaris/ouxt_automation\" \"ouxt_automation repository\"\n

run_ansible_with_docker job runs ansible with setup-docker playbook and check the setup tool works well with docker.

document 

graph TB\n    pull_request --send -->ouxt_automation\n    developer --manual hook-->ouxt_automation\n    daily_hook --> ouxt_automation\n    pull_request -- merged -->ouxt_automation\n    ouxt_automation -- deploy --> github_pages\n\n    linkStyle 0 stroke-width:2px,stroke:blue;\n    linkStyle 1 stroke-width:2px,stroke:red;\n    linkStyle 2 stroke-width:2px,stroke:red;\n    linkStyle 3 stroke-width:2px,stroke:red;\n    linkStyle 4 stroke-width:2px,stroke:red;\n\n    click ouxt_automation \"https://github.com/OUXT-Polaris/ouxt_automation\" \"ouxt_automation repository\"\n    click github_pages \"https://ouxt-polaris.github.io/ouxt_automation/\" \"github pages\"\n

documentation workflow generate this documentation site and deploy it into github pages.

deploy_workflow 

graph TB\n    developer --manual hook-->ouxt_automation\n    daily_hook --> ouxt_automation\n    ouxt_automation -- deploy workflow --> target_repository\n\n    linkStyle 0 stroke-width:2px,stroke:blue;\n    linkStyle 1 stroke-width:2px,stroke:blue;\n    linkStyle 2 stroke-width:2px,stroke:blue;\n\n    click ouxt_automation \"https://github.com/OUXT-Polaris/ouxt_automation\" \"ouxt_automation repository\"\n

deploy_workflow helps maintainers to deploy and maintain workflows for each pacakges. currently, over 25 packages are maintained by this workflow.

check_workflow_deployment 

graph TB\n    developer --manual hook-->ouxt_automation\n    daily_hook --> ouxt_automation\n    ouxt_automation -- check workflow exists --> target_repository\n    ouxt_automation -- if workflow does not exist --> slack\n    ouxt_automation -- request repos file --> artifact\n    artifact -- send repos file --> ouxt_automation\n\n    linkStyle 0 stroke-width:2px,stroke:blue;\n    linkStyle 1 stroke-width:2px,stroke:blue;\n    linkStyle 2 stroke-width:2px,stroke:blue;\n    linkStyle 3 stroke-width:2px,stroke:blue;\n    linkStyle 4 stroke-width:2px,stroke:blue;\n    linkStyle 5 stroke-width:2px,stroke:blue;\n\n    click ouxt_automation \"https://github.com/OUXT-Polaris/ouxt_automation\" \"ouxt_automation repository\"\n

check_workflow_deployment workflow checks github repositories in downloaded repos file and if the requred workflow does not exist in the target repository, notify this infomation to the team slack.

"},{"location":"automation_pipeline/#actions-in-each-repositories","title":"Actions in each repositories","text":"

In each repository, pull requests automatically runs build tests, unit tests and scenario test.

"},{"location":"license/","title":"LICENSE","text":"

This tool provides under Apache 2.0 license.

\n                                 Apache License\n                           Version 2.0, January 2004\n                        http://www.apache.org/licenses/\n\n   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION\n\n   1. Definitions.\n\n      \"License\" shall mean the terms and conditions for use, reproduction,\n      and distribution as defined by Sections 1 through 9 of this document.\n\n      \"Licensor\" shall mean the copyright owner or entity authorized by\n      the copyright owner that is granting the License.\n\n      \"Legal Entity\" shall mean the union of the acting entity and all\n      other entities that control, are controlled by, or are under common\n      control with that entity. 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You may reproduce and distribute copies of the\n      Work or Derivative Works thereof in any medium, with or without\n      modifications, and in Source or Object form, provided that You\n      meet the following conditions:\n\n      (a) You must give any other recipients of the Work or\n          Derivative Works a copy of this License; and\n\n      (b) You must cause any modified files to carry prominent notices\n          stating that You changed the files; and\n\n      (c) You must retain, in the Source form of any Derivative Works\n          that You distribute, all copyright, patent, trademark, and\n          attribution notices from the Source form of the Work,\n          excluding those notices that do not pertain to any part of\n          the Derivative Works; and\n\n      (d) If the Work includes a \"NOTICE\" text file as part of its\n          distribution, then any Derivative Works that You distribute must\n          include a readable copy of the attribution notices contained\n          within such NOTICE file, excluding those notices that do not\n          pertain to any part of the Derivative Works, in at least one\n          of the following places: within a NOTICE text file distributed\n          as part of the Derivative Works; within the Source form or\n          documentation, if provided along with the Derivative Works; or,\n          within a display generated by the Derivative Works, if and\n          wherever such third-party notices normally appear. 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Unless You explicitly state otherwise,\n      any Contribution intentionally submitted for inclusion in the Work\n      by You to the Licensor shall be under the terms and conditions of\n      this License, without any additional terms or conditions.\n      Notwithstanding the above, nothing herein shall supersede or modify\n      the terms of any separate license agreement you may have executed\n      with Licensor regarding such Contributions.\n\n   6. Trademarks. This License does not grant permission to use the trade\n      names, trademarks, service marks, or product names of the Licensor,\n      except as required for reasonable and customary use in describing the\n      origin of the Work and reproducing the content of the NOTICE file.\n\n   7. Disclaimer of Warranty. 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The text should be enclosed in the appropriate\n      comment syntax for the file format. We also recommend that a\n      file or class name and description of purpose be included on the\n      same \"printed page\" as the copyright notice for easier\n      identification within third-party archives.\n\n   Copyright [yyyy] [name of copyright owner]\n\n   Licensed under the Apache License, Version 2.0 (the \"License\");\n   you may not use this file except in compliance with the License.\n   You may obtain a copy of the License at\n\n       http://www.apache.org/licenses/LICENSE-2.0\n\n   Unless required by applicable law or agreed to in writing, software\n   distributed under the License is distributed on an \"AS IS\" BASIS,\n   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n   See the License for the specific language governing permissions and\n   limitations under the License.\n
"},{"location":"packages/","title":"Software Packages","text":""},{"location":"packages/#dashboard","title":"dashboard","text":"Release UpdateDashboard CodeReview BuildTest OUXT-Polaris/color_names nan OUXT-Polaris/data_buffer nan OUXT-Polaris/dynamixel_hardware_interface nan OUXT-Polaris/geographic_conversion nan OUXT-Polaris/geographic_info nan OUXT-Polaris/hermite_path_planner nan OUXT-Polaris/image_processing_utils OUXT-Polaris/joy_to_twist nan OUXT-Polaris/lua_vendor nan OUXT-Polaris/message_synchronizer nan OUXT-Polaris/miniv_control OUXT-Polaris/miniv_description nan OUXT-Polaris/navi_sim nan OUXT-Polaris/nmea_gps_driver nan OUXT-Polaris/nmea_hardware_interface nan OUXT-Polaris/nmea_to_geopose nan OUXT-Polaris/odom_frame_publisher OUXT-Polaris/ouxt_common nan OUXT-Polaris/pcl_apps nan OUXT-Polaris/perception_bringup OUXT-Polaris/playstation_controller_drivers nan OUXT-Polaris/quaternion_operation nan OUXT-Polaris/robotx_behavior_tree OUXT-Polaris/robotx_communication nan OUXT-Polaris/robotx_costmap_calculator nan OUXT-Polaris/robotx_ekf nan OUXT-Polaris/scan_segmentation nan OUXT-Polaris/sol_vendor nan OUXT-Polaris/tcp_sender nan OUXT-Polaris/usv_controller nan OUXT-Polaris/detic_onnx_ros2 nan 0 1 OUXT-Polaris/hermite_path_planner [Add] Use generate parameter library nan OUXT-Polaris/navi_sim fix rviz setting for grid_map calculator Feature/navi simulator OUXT-Polaris/nmea_hardware_interface Remove backwards compatibility with Galactic and Humble. nan OUXT-Polaris/pcl_apps updated the docs in pcd_loader nan OUXT-Polaris/perception_bringup launch\u30d5\u30a1\u30a4\u30eb\u3078\u306e\u8ffd\u8a18 update depends OUXT-Polaris/robotx_behavior_tree first commit for goaroundwaypoints nan OUXT-Polaris/detic_onnx_ros2 Feature/onnxruntime gpu nan"},{"location":"tools/docker_images/","title":"Docker Images","text":"

Some docker images are distributed on dockerhub. Please check the README on Dockerhub for detailed usage instructions. Click on the name of the Docker Image to jump to the Dockerhub details page.

Image Name Dockerfile Description wamvtan/vrx Dockerfile Docker Images for virtual RobotX wamvtan/dev_container Dockerfile Docker Image containing software development environment and ROS2 packages. wamvtan/l4t_opencv Dockerfile Docker Image for building opencv for Linux 4 Tegra wamvtan/yolox Dockerfile Docker Images for inferencing yolox. wamvtan/yolox_trainer Dockerfile Docker Images for training yolox model."},{"location":"tools/tools/","title":"Tools","text":""},{"location":"tools/tools/#automated-logger","title":"Automated logger","text":"

Plug & play automated rosbag logging tools for robots.

"},{"location":"tools/tools/#supported-platform","title":"Supported platform","text":""},{"location":"tools/tools/#how-it-works","title":"How it works","text":"

This tools is works on docker container, only you have to do is pull wamvtan/auto_logger image.

"},{"location":"tools/tools/#setup","title":"Setup","text":"Setup ASUSTOR FLASHSTOR 6 (FS6706T)

Setup ASUSTOR FLASHSTOR 6 (FS6706T) using asustor control center or AiMaster.

Setup Portainer in ASUSTOR FLASHSTOR 6 (FS6706T)

Please check these documentation. - Introducing Portainer(EN) - Introducing Portainer(JP)

"},{"location":"tools/tools/#train-yolox-model","title":"Train yolox model","text":""},{"location":"tools/tools/#requirement","title":"Requirement","text":" 
cd docker/train_yolox/checkpoints\nwget https://github.com/Megvii-BaseDetection/YOLOX/releases/download/0.1.1rc0/yolox_s.pth\ncd ../datasets\n

download datasets via this link.

docker-compose up --build\n

While training, you can see output like below.

100%|##########| 1/1 [00:00<00:00,  4.69it/s]\ntrain_yolox_container | Running per image evaluation...\ntrain_yolox_container | Evaluate annotation type *bbox*\ntrain_yolox_container | COCOeval_opt.evaluate() finished in 0.01 seconds.\ntrain_yolox_container | Accumulating evaluation results...\ntrain_yolox_container | 2022-06-03 04:51:11 | INFO     | pycocotools.coco:366 - index created!\ntrain_yolox_container | 2022-06-03 04:51:11 | INFO     | yolox.core.trainer:342 - \ntrain_yolox_container | Average forward time: 0.00 ms, Average NMS time: 0.00 ms, Average inference time: 0.00 ms\ntrain_yolox_container |  Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000\ntrain_yolox_container |  Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.000\ntrain_yolox_container |  Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000\ntrain_yolox_container |  Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\ntrain_yolox_container |  Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\ntrain_yolox_container |  Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\ntrain_yolox_container |  Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000\ntrain_yolox_container |  Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.000\ntrain_yolox_container |  Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000\ntrain_yolox_container |  Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\ntrain_yolox_container |  Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\ntrain_yolox_container |  Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\ntrain_yolox_container | \ntrain_yolox_container | 2022-06-03 04:51:11 | INFO     | yolox.core.trainer:352 - Save weights to ./YOLOX_outputs/yolox_s\ntrain_yolox_container | 2022-06-03 04:51:11 | INFO     | yolox.core.trainer:352 - Save weights to ./YOLOX_outputs/yolox_s\ntrain_yolox_container | 2022-06-03 04:51:12 | INFO     | yolox.core.trainer:203 - ---> start train epoch272\ntrain_yolox_container | 2022-06-03 04:51:15 | INFO     | yolox.core.trainer:352 - Save weights to ./YOLOX_outputs/yolox_s\ntrain_yolox_container | 2022-06-03 04:51:15 | INFO     | yolox.evaluators.coco_evaluator:235 - Evaluate in main process...\ntrain_yolox_container | 2022-06-03 04:51:15 | INFO     | yolox.evaluators.coco_evaluator:268 - Loading and preparing results...\ntrain_yolox_container | 2022-06-03 04:51:15 | INFO     | yolox.evaluators.coco_evaluator:268 - DONE (t=0.00s)\ntrain_yolox_container | 2022-06-03 04:51:15 | INFO     | pycocotools.coco:366 - creating index...\ntrain_yolox_container | 2022-06-03 04:51:15 | INFO     | pycocotools.coco:366 - index created!\ntrain_yolox_container | 2022-06-03 04:51:16 | INFO     | yolox.core.trainer:342 - \ntrain_yolox_container | Average forward time: 0.00 ms, Average NMS time: 0.00 ms, Average inference time: 0.00 ms\ntrain_yolox_container |  Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000\ntrain_yolox_container |  Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.000\ntrain_yolox_container |  Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000\ntrain_yolox_container |  Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\ntrain_yolox_container |  Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\ntrain_yolox_container |  Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\ntrain_yolox_container |  Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000\ntrain_yolox_container |  Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.000\ntrain_yolox_container |  Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000\ntrain_yolox_container |  Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\ntrain_yolox_container |  Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\ntrain_yolox_container |  Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n
"},{"location":"tools/tools/#convert-yolox-pytorch-model-into-tensorrt-model","title":"Convert yolox pytorch model into tensorrt model.","text":""},{"location":"tools/tools/#requirement_1","title":"Requirement","text":" 
cd docker/torch2trt\nsh convert.sh\n

Output should be like below.

docker-compose build\n\nBuilding torch2trt\nStep 1/12 : FROM nvcr.io/nvidia/pytorch:22.07-py3\n ---> 74d53f84c686\nStep 2/12 : RUN python3 -m pip install nvidia-pyindex packaging &&   python3 -m pip install --upgrade nvidia-tensorrt\n ---> Using cache\n ---> 8d655c414dd2\nStep 3/12 : RUN apt-get update &&   apt-get install -y git libgl1-mesa-dev &&   apt-get -y clean &&   rm -rf /var/lib/apt/lists/*\n ---> Using cache\n ---> 76bc393f04d5\nStep 4/12 : WORKDIR /\n ---> Using cache\n ---> 47b28fa00606\nStep 5/12 : RUN git clone https://github.com/NVIDIA-AI-IOT/torch2trt.git\n ---> Using cache\n ---> 1a4063050362\nStep 6/12 : WORKDIR /torch2trt\n ---> Using cache\n ---> 6e679ba630a9\nStep 7/12 : RUN python3 setup.py install --plugins\n ---> Using cache\n ---> f0f7dc347d9d\nStep 8/12 : WORKDIR /\n ---> Using cache\n ---> 1d4ffaaae9a0\nStep 9/12 : RUN git clone https://github.com/Megvii-BaseDetection/YOLOX.git\n ---> Using cache\n ---> 6cc15f5a7b36\nStep 10/12 : WORKDIR /YOLOX\n ---> Using cache\n ---> 3429678d2547\nStep 11/12 : RUN python3 -m pip install -r requirements.txt &&  python3 setup.py develop\n ---> Using cache\n ---> 377ef892e986\nStep 12/12 : RUN mkdir model\n ---> Using cache\n ---> 55caa4a2787d\nSuccessfully built 55caa4a2787d\nSuccessfully tagged torch2trt_torch2trt:latest\n\ndocker-compose up\n\nRecreating torch2trt_torch2trt_1 ... done\nAttaching to torch2trt_torch2trt_1\ntorch2trt_1  | \ntorch2trt_1  | =============\ntorch2trt_1  | == PyTorch ==\ntorch2trt_1  | =============\ntorch2trt_1  | \ntorch2trt_1  | NVIDIA Release 21.09 (build 26760254)\ntorch2trt_1  | PyTorch Version 1.10.0a0+3fd9dcf\ntorch2trt_1  | \ntorch2trt_1  | Container image Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.\ntorch2trt_1  | \ntorch2trt_1  | Copyright (c) 2014-2021 Facebook Inc.\ntorch2trt_1  | Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert)\ntorch2trt_1  | Copyright (c) 2012-2014 Deepmind Technologies    (Koray Kavukcuoglu)\ntorch2trt_1  | Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu)\ntorch2trt_1  | Copyright (c) 2011-2013 NYU                      (Clement Farabet)\ntorch2trt_1  | Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston)\ntorch2trt_1  | Copyright (c) 2006      Idiap Research Institute (Samy Bengio)\ntorch2trt_1  | Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz)\ntorch2trt_1  | Copyright (c) 2015      Google Inc.\ntorch2trt_1  | Copyright (c) 2015      Yangqing Jia\ntorch2trt_1  | Copyright (c) 2013-2016 The Caffe contributors\ntorch2trt_1  | All rights reserved.\ntorch2trt_1  | \ntorch2trt_1  | NVIDIA Deep Learning Profiler (dlprof) Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.\ntorch2trt_1  | \ntorch2trt_1  | Various files include modifications (c) NVIDIA CORPORATION.  All rights reserved.\ntorch2trt_1  | \ntorch2trt_1  | This container image and its contents are governed by the NVIDIA Deep Learning Container License.\ntorch2trt_1  | By pulling and using the container, you accept the terms and conditions of this license:\ntorch2trt_1  | https://developer.nvidia.com/ngc/nvidia-deep-learning-container-license\ntorch2trt_1  | \ntorch2trt_1  | NOTE: MOFED driver for multi-node communication was not detected.\ntorch2trt_1  |       Multi-node communication performance may be reduced.\ntorch2trt_1  | \ntorch2trt_1  | NOTE: The SHMEM allocation limit is set to the default of 64MB.  This may be\ntorch2trt_1  |    insufficient for PyTorch.  NVIDIA recommends the use of the following flags:\ntorch2trt_1  |    nvidia-docker run --ipc=host ...\ntorch2trt_1  | \ntorch2trt_1  | 2022-02-26 09:33:11.827 | INFO     | __main__:main:57 - loaded checkpoint done.\ntorch2trt_1  | [02/26/2022-09:33:13] [TRT] [I] [MemUsageChange] Init CUDA: CPU +188, GPU +0, now: CPU 1383, GPU 2123 (MiB)\ntorch2trt_1  | [02/26/2022-09:33:13] [TRT] [I] [MemUsageSnapshot] Begin constructing builder kernel library: CPU 1403 MiB, GPU 2123 MiB\ntorch2trt_1  | [02/26/2022-09:33:13] [TRT] [I] [MemUsageSnapshot] End constructing builder kernel library: CPU 1410 MiB, GPU 2123 MiB\ntorch2trt_1  | [02/26/2022-09:33:14] [TRT] [W] Tensor DataType is determined at build time for tensors not marked as input or output.\ntorch2trt_1  | [02/26/2022-09:33:14] [TRT] [W] FP16 support requested on hardware without native FP16 support, performance will be negatively affected.\ntorch2trt_1  | [02/26/2022-09:33:15] [TRT] [I] [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +0, GPU +8, now: CPU 2107, GPU 2409 (MiB)\ntorch2trt_1  | [02/26/2022-09:33:15] [TRT] [I] [MemUsageChange] Init cuDNN: CPU +0, GPU +8, now: CPU 2107, GPU 2417 (MiB)\ntorch2trt_1  | [02/26/2022-09:33:15] [TRT] [I] Local timing cache in use. Profiling results in this builder pass will not be stored.\ntorch2trt_1  | [02/26/2022-09:33:48] [TRT] [I] Detected 1 inputs and 1 output network tensors.\ntorch2trt_1  | [02/26/2022-09:33:48] [TRT] [I] Total Host Persistent Memory: 208144\ntorch2trt_1  | [02/26/2022-09:33:48] [TRT] [I] Total Device Persistent Memory: 640512\ntorch2trt_1  | [02/26/2022-09:33:48] [TRT] [I] Total Scratch Memory: 512\ntorch2trt_1  | [02/26/2022-09:33:48] [TRT] [I] [MemUsageStats] Peak memory usage of TRT CPU/GPU memory allocators: CPU 4 MiB, GPU 294 MiB\ntorch2trt_1  | [02/26/2022-09:33:48] [TRT] [I] [BlockAssignment] Algorithm ShiftNTopDown took 43.9912ms to assign 7 blocks to 214 nodes requiring 7181828 bytes.\ntorch2trt_1  | [02/26/2022-09:33:48] [TRT] [I] Total Activation Memory: 7181828\ntorch2trt_1  | [02/26/2022-09:33:48] [TRT] [I] [MemUsageChange] Init cuDNN: CPU +0, GPU +10, now: CPU 2129, GPU 2477 (MiB)\ntorch2trt_1  | [02/26/2022-09:33:48] [TRT] [I] [MemUsageChange] TensorRT-managed allocation in building engine: CPU +3, GPU +4, now: CPU 3, GPU 4 (MiB)\ntorch2trt_1  | [02/26/2022-09:33:48] [TRT] [I] [MemUsageChange] Init cuDNN: CPU +0, GPU +8, now: CPU 2128, GPU 2461 (MiB)\ntorch2trt_1  | [02/26/2022-09:33:48] [TRT] [I] [MemUsageChange] TensorRT-managed allocation in IExecutionContext creation: CPU +0, GPU +8, now: CPU 3, GPU 12 (MiB)\ntorch2trt_1  | 2022-02-26 09:33:48.565 | INFO     | __main__:main:71 - Converted TensorRT model done.\ntorch2trt_1  | 2022-02-26 09:33:48.581 | INFO     | __main__:main:79 - Converted TensorRT model engine file is saved for C++ inference.\ntorch2trt_torch2trt_1 exited with code 0\n
"},{"location":"tools/tools/#download-dataset","title":"Download dataset","text":"

you can download dataset via google drive by running this command.

ansible-playbook -i ansible/hosts/localhost.ini ansible/setup_dataset.yml --connection local --ask-become-pass\n
"},{"location":"tools/tools/#code-review-bot-by-chatgpt","title":"Code review bot by ChatGPT","text":"

We developed a code review bot using ChatGPT. When you request a review to the bot user named wam-v-tan, it analyzes the PR differences and automatically performs the code review.

The implementation of action can be found here.

Example of the code review can be seen here.

"},{"location":"tutorials/build_instraction/","title":"Build Instractions","text":"

If you want to know about supported platfroms, please see also this page.

"},{"location":"tutorials/build_instraction/#setup-develop-environment-full-package","title":"setup develop environment (full package)","text":"

first time

sh setup.sh\n

not first time (skip installing ros2)

sh update.sh\n
"},{"location":"tutorials/build_instraction/#troubleshooting","title":"Troubleshooting","text":"

Failed to connect to the host via ssh

Please execute command below to delete fingerprint of localhost:2022

ssh-keygen -f \"${HOME}/.ssh/known_hosts\" -R \"[localhost]:2022\"\n
"},{"location":"tutorials/build_instraction/#setup-real-robot","title":"setup real robot","text":"

Note

setting up real robot is fully automated via github actions, so using this setup-robot playbook manually is not recommended.

These operations should be run in robot.

  1. setup endpoint
sh ansible/setup_ansible.sh\nexport PERSONAL_ACCESS_TOKEN=$(ACCESS_TOKEN_OF_WAM_V_TAN_BOT)\nansible-playbook -i ansible/hosts/localhost.ini ansible/setup_endpoint.yml --connection local --ask-become-pass\n

If you want to know personal access token, please read this documentation.

"},{"location":"tutorials/build_instraction/#setup-firmware-development-environment","title":"setup firmware development environment","text":"

This playbook includes three steps. 1. Install Docker 1. Clone firmware package 1. Build firmware with docker

ansible-playbook -i ansible/hosts/localhost.ini ansible/setup_mbed_workspace.yml --connection local --ask-become-pass\n
"},{"location":"tutorials/navigation_demo/","title":"Navigation Demo","text":""},{"location":"tutorials/navigation_demo/#how-to-run-demo","title":"How to run demo","text":""},{"location":"tutorials/navigation_demo/#build-packages","title":"build packages","text":"

Please see also, this page.

"},{"location":"tutorials/navigation_demo/#run-simulator-and-planner","title":"run simulator and planner","text":"
ros2 launch navi_sim with_planner.launch.py behavior_config_filepath:=config/loop_demo.yaml\n
"},{"location":"tutorials/navigation_demo/#set-goal","title":"set goal","text":"

use 2d goal pose tool in rviz.

then, the navigation starts.

"},{"location":"tutorials/navigation_demo/#spawn-obstacle","title":"spawn obstacle","text":"

use clicked point tool in rviz

then, replan waypoints.

"},{"location":"tutorials/supported_platforms/","title":"Supported Platform","text":""},{"location":"tutorials/supported_platforms/#ubuntu-2204","title":"ubuntu 22.04","text":"

If your ubuntu version is older than 22.04, you can use docker environment.

ansible task install ROS2 foxy, so you do not necessary to setup ROS2

before you run ansible-script, you have to install ansible via apt

sudo apt install ansible\n
"},{"location":"tutorials/vrx_instruction/","title":"Run with VRX","text":"

Documents

"},{"location":"tutorials/vrx_instruction/#how-to-run-demo","title":"How to run demo","text":""},{"location":"tutorials/vrx_instruction/#confirm-dependencies","title":"Confirm dependencies","text":""},{"location":"tutorials/vrx_instruction/#run-the-vrx-simulator-and-lauch-vrx-simulation","title":"Run the vrx simulator and lauch vrx simulation","text":"
docker run --pull always -it wamvtan/vrx:latest --net=host --ipc=host --pid=host ros2 launch vrx_gz competition.launch.py world:=stationkeeping_task headless:=true urdf:=/home/config/wamv_target.urdf\n

you can change world:= for your purpose.

"},{"location":"tutorials/vrx_instruction/#run-vrx_bridgup-launch","title":"Run vrx_bridgup launch","text":" 
ros2 launch vrx_bridge vrx_bringup.launch.xml\n
"}]} \ No newline at end of file +{"config":{"lang":["en"],"separator":"[\\s\\-]+","pipeline":["stopWordFilter"]},"docs":[{"location":"","title":"OUXT Polaris Development Automation Tool","text":""},{"location":"#status-badge","title":"Status Badge","text":"Build Test Automation"},{"location":"#what-is-this","title":"What is this?","text":"

This tool supports to build our software and deploy them into local_machine and real robot. This tool works on ansible and github actions

"},{"location":"#contents-guide","title":"Contents Guide","text":"If you want to know how to setup development in your local machine.

See tutorials

If you want to see navigation demo in your local machine.

See tutorials

If you want to know how to use tools.

See tools

If you want to know hardware specification.

See hardware documenation

If you want to know how our CI/CD pipeline works.

See automation pipelines documentation

"},{"location":"automation_pipeline/","title":"Automation Pipeline","text":""},{"location":"automation_pipeline/#repository-architecture","title":"Repository Architecture","text":"

OUXT-Polaris softwares are separeated in many repositories in order to run continuous integration quickly. So, we developing integration pipeline for operating complex software stacks and deploy it.

"},{"location":"automation_pipeline/#integration-pipeline","title":"Integration Pipeline","text":"

Integration pipeline is deployed at github actions in each repositories.

"},{"location":"automation_pipeline/#actions-in-ouxt_automation-package","title":"Actions in ouxt_automation package","text":"

You can see the status of all Actions here.

ansible 

graph TB\n    pull_request --send --> ouxt_automation\n    developer --manual hook--> ouxt_automation\n    daily_hook --> ouxt_automation\n\n    linkStyle 0 stroke-width:2px,stroke:blue;\n    linkStyle 1 stroke-width:2px,stroke:blue;\n    linkStyle 2 stroke-width:2px,stroke:blue;\n\n    click ansible \"https://github.com/OUXT-Polaris/ouxt_automation\" \"ouxt_automation repository\"\n

run_ansible job runs ansible with setup-full playbook and check the setup tool works well. 

graph TB\n    pull_request --send --> ouxt_automation\n    developer --manual hook--> ouxt_automation\n    daily_hook --> ouxt_automation\n\n    linkStyle 0 stroke-width:2px,stroke:blue;\n    linkStyle 1 stroke-width:2px,stroke:blue;\n    linkStyle 2 stroke-width:2px,stroke:blue;\n\n    click ouxt_automation \"https://github.com/OUXT-Polaris/ouxt_automation\" \"ouxt_automation repository\"\n

run_ansible_with_docker job runs ansible with setup-docker playbook and check the setup tool works well with docker.

document 

graph TB\n    pull_request --send -->ouxt_automation\n    developer --manual hook-->ouxt_automation\n    daily_hook --> ouxt_automation\n    pull_request -- merged -->ouxt_automation\n    ouxt_automation -- deploy --> github_pages\n\n    linkStyle 0 stroke-width:2px,stroke:blue;\n    linkStyle 1 stroke-width:2px,stroke:red;\n    linkStyle 2 stroke-width:2px,stroke:red;\n    linkStyle 3 stroke-width:2px,stroke:red;\n    linkStyle 4 stroke-width:2px,stroke:red;\n\n    click ouxt_automation \"https://github.com/OUXT-Polaris/ouxt_automation\" \"ouxt_automation repository\"\n    click github_pages \"https://ouxt-polaris.github.io/ouxt_automation/\" \"github pages\"\n

documentation workflow generate this documentation site and deploy it into github pages.

deploy_workflow 

graph TB\n    developer --manual hook-->ouxt_automation\n    daily_hook --> ouxt_automation\n    ouxt_automation -- deploy workflow --> target_repository\n\n    linkStyle 0 stroke-width:2px,stroke:blue;\n    linkStyle 1 stroke-width:2px,stroke:blue;\n    linkStyle 2 stroke-width:2px,stroke:blue;\n\n    click ouxt_automation \"https://github.com/OUXT-Polaris/ouxt_automation\" \"ouxt_automation repository\"\n

deploy_workflow helps maintainers to deploy and maintain workflows for each pacakges. currently, over 25 packages are maintained by this workflow.

check_workflow_deployment 

graph TB\n    developer --manual hook-->ouxt_automation\n    daily_hook --> ouxt_automation\n    ouxt_automation -- check workflow exists --> target_repository\n    ouxt_automation -- if workflow does not exist --> slack\n    ouxt_automation -- request repos file --> artifact\n    artifact -- send repos file --> ouxt_automation\n\n    linkStyle 0 stroke-width:2px,stroke:blue;\n    linkStyle 1 stroke-width:2px,stroke:blue;\n    linkStyle 2 stroke-width:2px,stroke:blue;\n    linkStyle 3 stroke-width:2px,stroke:blue;\n    linkStyle 4 stroke-width:2px,stroke:blue;\n    linkStyle 5 stroke-width:2px,stroke:blue;\n\n    click ouxt_automation \"https://github.com/OUXT-Polaris/ouxt_automation\" \"ouxt_automation repository\"\n

check_workflow_deployment workflow checks github repositories in downloaded repos file and if the requred workflow does not exist in the target repository, notify this infomation to the team slack.

"},{"location":"automation_pipeline/#actions-in-each-repositories","title":"Actions in each repositories","text":"

In each repository, pull requests automatically runs build tests, unit tests and scenario test.

"},{"location":"license/","title":"LICENSE","text":"

This tool provides under Apache 2.0 license.

\n                                 Apache License\n                           Version 2.0, January 2004\n                        http://www.apache.org/licenses/\n\n   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION\n\n   1. Definitions.\n\n      \"License\" shall mean the terms and conditions for use, reproduction,\n      and distribution as defined by Sections 1 through 9 of this document.\n\n      \"Licensor\" shall mean the copyright owner or entity authorized by\n      the copyright owner that is granting the License.\n\n      \"Legal Entity\" shall mean the union of the acting entity and all\n      other entities that control, are controlled by, or are under common\n      control with that entity. For the purposes of this definition,\n      \"control\" means (i) the power, direct or indirect, to cause the\n      direction or management of such entity, whether by contract or\n      otherwise, or (ii) ownership of fifty percent (50%) or more of the\n      outstanding shares, or (iii) beneficial ownership of such entity.\n\n      \"You\" (or \"Your\") shall mean an individual or Legal Entity\n      exercising permissions granted by this License.\n\n      \"Source\" form shall mean the preferred form for making modifications,\n      including but not limited to software source code, documentation\n      source, and configuration files.\n\n      \"Object\" form shall mean any form resulting from mechanical\n      transformation or translation of a Source form, including but\n      not limited to compiled object code, generated documentation,\n      and conversions to other media types.\n\n      \"Work\" shall mean the work of authorship, whether in Source or\n      Object form, made available under the License, as indicated by a\n      copyright notice that is included in or attached to the work\n      (an example is provided in the Appendix below).\n\n      \"Derivative Works\" shall mean any work, whether in Source or Object\n      form, that is based on (or derived from) the Work and for which the\n      editorial revisions, annotations, elaborations, or other modifications\n      represent, as a whole, an original work of authorship. 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Unless You explicitly state otherwise,\n      any Contribution intentionally submitted for inclusion in the Work\n      by You to the Licensor shall be under the terms and conditions of\n      this License, without any additional terms or conditions.\n      Notwithstanding the above, nothing herein shall supersede or modify\n      the terms of any separate license agreement you may have executed\n      with Licensor regarding such Contributions.\n\n   6. Trademarks. This License does not grant permission to use the trade\n      names, trademarks, service marks, or product names of the Licensor,\n      except as required for reasonable and customary use in describing the\n      origin of the Work and reproducing the content of the NOTICE file.\n\n   7. Disclaimer of Warranty. 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In no event and under no legal theory,\n      whether in tort (including negligence), contract, or otherwise,\n      unless required by applicable law (such as deliberate and grossly\n      negligent acts) or agreed to in writing, shall any Contributor be\n      liable to You for damages, including any direct, indirect, special,\n      incidental, or consequential damages of any character arising as a\n      result of this License or out of the use or inability to use the\n      Work (including but not limited to damages for loss of goodwill,\n      work stoppage, computer failure or malfunction, or any and all\n      other commercial damages or losses), even if such Contributor\n      has been advised of the possibility of such damages.\n\n   9. Accepting Warranty or Additional Liability. While redistributing\n      the Work or Derivative Works thereof, You may choose to offer,\n      and charge a fee for, acceptance of support, warranty, indemnity,\n      or other liability obligations and/or rights consistent with this\n      License. However, in accepting such obligations, You may act only\n      on Your own behalf and on Your sole responsibility, not on behalf\n      of any other Contributor, and only if You agree to indemnify,\n      defend, and hold each Contributor harmless for any liability\n      incurred by, or claims asserted against, such Contributor by reason\n      of your accepting any such warranty or additional liability.\n\n   END OF TERMS AND CONDITIONS\n\n   APPENDIX: How to apply the Apache License to your work.\n\n      To apply the Apache License to your work, attach the following\n      boilerplate notice, with the fields enclosed by brackets \"[]\"\n      replaced with your own identifying information. (Don't include\n      the brackets!)  The text should be enclosed in the appropriate\n      comment syntax for the file format. We also recommend that a\n      file or class name and description of purpose be included on the\n      same \"printed page\" as the copyright notice for easier\n      identification within third-party archives.\n\n   Copyright [yyyy] [name of copyright owner]\n\n   Licensed under the Apache License, Version 2.0 (the \"License\");\n   you may not use this file except in compliance with the License.\n   You may obtain a copy of the License at\n\n       http://www.apache.org/licenses/LICENSE-2.0\n\n   Unless required by applicable law or agreed to in writing, software\n   distributed under the License is distributed on an \"AS IS\" BASIS,\n   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n   See the License for the specific language governing permissions and\n   limitations under the License.\n
"},{"location":"packages/","title":"Software Packages","text":""},{"location":"packages/#dashboard","title":"dashboard","text":"Release UpdateDashboard CodeReview BuildTest OUXT-Polaris/color_names nan OUXT-Polaris/data_buffer nan OUXT-Polaris/dynamixel_hardware_interface nan OUXT-Polaris/geographic_conversion OUXT-Polaris/geographic_info nan OUXT-Polaris/hermite_path_planner nan OUXT-Polaris/image_processing_utils OUXT-Polaris/joy_to_twist nan OUXT-Polaris/lua_vendor nan OUXT-Polaris/message_synchronizer nan OUXT-Polaris/miniv_control OUXT-Polaris/miniv_description nan OUXT-Polaris/navi_sim nan OUXT-Polaris/nmea_gps_driver nan OUXT-Polaris/nmea_hardware_interface nan OUXT-Polaris/nmea_to_geopose nan OUXT-Polaris/odom_frame_publisher OUXT-Polaris/ouxt_common nan OUXT-Polaris/pcl_apps nan OUXT-Polaris/perception_bringup OUXT-Polaris/playstation_controller_drivers nan OUXT-Polaris/quaternion_operation nan OUXT-Polaris/robotx_behavior_tree OUXT-Polaris/robotx_communication nan OUXT-Polaris/robotx_costmap_calculator nan OUXT-Polaris/robotx_ekf nan OUXT-Polaris/scan_segmentation nan OUXT-Polaris/sol_vendor nan OUXT-Polaris/tcp_sender nan OUXT-Polaris/usv_controller nan OUXT-Polaris/detic_onnx_ros2 nan 0 1 OUXT-Polaris/geographic_conversion Feature/publish covariance parameter nan OUXT-Polaris/hermite_path_planner [Add] Use generate parameter library nan OUXT-Polaris/navi_sim fix rviz setting for grid_map calculator Feature/navi simulator OUXT-Polaris/nmea_hardware_interface Remove backwards compatibility with Galactic and Humble. nan OUXT-Polaris/pcl_apps updated the docs in pcd_loader nan OUXT-Polaris/perception_bringup launch\u30d5\u30a1\u30a4\u30eb\u3078\u306e\u8ffd\u8a18 update depends OUXT-Polaris/robotx_behavior_tree first commit for goaroundwaypoints nan OUXT-Polaris/detic_onnx_ros2 Feature/onnxruntime gpu nan"},{"location":"tools/docker_images/","title":"Docker Images","text":"

Some docker images are distributed on dockerhub. Please check the README on Dockerhub for detailed usage instructions. Click on the name of the Docker Image to jump to the Dockerhub details page.

Image Name Dockerfile Description wamvtan/vrx Dockerfile Docker Images for virtual RobotX wamvtan/dev_container Dockerfile Docker Image containing software development environment and ROS2 packages. wamvtan/l4t_opencv Dockerfile Docker Image for building opencv for Linux 4 Tegra wamvtan/yolox Dockerfile Docker Images for inferencing yolox. wamvtan/yolox_trainer Dockerfile Docker Images for training yolox model."},{"location":"tools/tools/","title":"Tools","text":""},{"location":"tools/tools/#automated-logger","title":"Automated logger","text":"

Plug & play automated rosbag logging tools for robots.

"},{"location":"tools/tools/#supported-platform","title":"Supported platform","text":""},{"location":"tools/tools/#how-it-works","title":"How it works","text":"

This tools is works on docker container, only you have to do is pull wamvtan/auto_logger image.

"},{"location":"tools/tools/#setup","title":"Setup","text":"Setup ASUSTOR FLASHSTOR 6 (FS6706T)

Setup ASUSTOR FLASHSTOR 6 (FS6706T) using asustor control center or AiMaster.

Setup Portainer in ASUSTOR FLASHSTOR 6 (FS6706T)

Please check these documentation. - Introducing Portainer(EN) - Introducing Portainer(JP)

"},{"location":"tools/tools/#train-yolox-model","title":"Train yolox model","text":""},{"location":"tools/tools/#requirement","title":"Requirement","text":" 
cd docker/train_yolox/checkpoints\nwget https://github.com/Megvii-BaseDetection/YOLOX/releases/download/0.1.1rc0/yolox_s.pth\ncd ../datasets\n

download datasets via this link.

docker-compose up --build\n

While training, you can see output like below.

100%|##########| 1/1 [00:00<00:00,  4.69it/s]\ntrain_yolox_container | Running per image evaluation...\ntrain_yolox_container | Evaluate annotation type *bbox*\ntrain_yolox_container | COCOeval_opt.evaluate() finished in 0.01 seconds.\ntrain_yolox_container | Accumulating evaluation results...\ntrain_yolox_container | 2022-06-03 04:51:11 | INFO     | pycocotools.coco:366 - index created!\ntrain_yolox_container | 2022-06-03 04:51:11 | INFO     | yolox.core.trainer:342 - \ntrain_yolox_container | Average forward time: 0.00 ms, Average NMS time: 0.00 ms, Average inference time: 0.00 ms\ntrain_yolox_container |  Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000\ntrain_yolox_container |  Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.000\ntrain_yolox_container |  Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000\ntrain_yolox_container |  Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\ntrain_yolox_container |  Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\ntrain_yolox_container |  Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\ntrain_yolox_container |  Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000\ntrain_yolox_container |  Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.000\ntrain_yolox_container |  Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000\ntrain_yolox_container |  Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\ntrain_yolox_container |  Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\ntrain_yolox_container |  Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\ntrain_yolox_container | \ntrain_yolox_container | 2022-06-03 04:51:11 | INFO     | yolox.core.trainer:352 - Save weights to ./YOLOX_outputs/yolox_s\ntrain_yolox_container | 2022-06-03 04:51:11 | INFO     | yolox.core.trainer:352 - Save weights to ./YOLOX_outputs/yolox_s\ntrain_yolox_container | 2022-06-03 04:51:12 | INFO     | yolox.core.trainer:203 - ---> start train epoch272\ntrain_yolox_container | 2022-06-03 04:51:15 | INFO     | yolox.core.trainer:352 - Save weights to ./YOLOX_outputs/yolox_s\ntrain_yolox_container | 2022-06-03 04:51:15 | INFO     | yolox.evaluators.coco_evaluator:235 - Evaluate in main process...\ntrain_yolox_container | 2022-06-03 04:51:15 | INFO     | yolox.evaluators.coco_evaluator:268 - Loading and preparing results...\ntrain_yolox_container | 2022-06-03 04:51:15 | INFO     | yolox.evaluators.coco_evaluator:268 - DONE (t=0.00s)\ntrain_yolox_container | 2022-06-03 04:51:15 | INFO     | pycocotools.coco:366 - creating index...\ntrain_yolox_container | 2022-06-03 04:51:15 | INFO     | pycocotools.coco:366 - index created!\ntrain_yolox_container | 2022-06-03 04:51:16 | INFO     | yolox.core.trainer:342 - \ntrain_yolox_container | Average forward time: 0.00 ms, Average NMS time: 0.00 ms, Average inference time: 0.00 ms\ntrain_yolox_container |  Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000\ntrain_yolox_container |  Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.000\ntrain_yolox_container |  Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000\ntrain_yolox_container |  Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\ntrain_yolox_container |  Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\ntrain_yolox_container |  Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\ntrain_yolox_container |  Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000\ntrain_yolox_container |  Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.000\ntrain_yolox_container |  Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000\ntrain_yolox_container |  Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\ntrain_yolox_container |  Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\ntrain_yolox_container |  Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n
"},{"location":"tools/tools/#convert-yolox-pytorch-model-into-tensorrt-model","title":"Convert yolox pytorch model into tensorrt model.","text":""},{"location":"tools/tools/#requirement_1","title":"Requirement","text":" 
cd docker/torch2trt\nsh convert.sh\n

Output should be like below.

docker-compose build\n\nBuilding torch2trt\nStep 1/12 : FROM nvcr.io/nvidia/pytorch:22.07-py3\n ---> 74d53f84c686\nStep 2/12 : RUN python3 -m pip install nvidia-pyindex packaging &&   python3 -m pip install --upgrade nvidia-tensorrt\n ---> Using cache\n ---> 8d655c414dd2\nStep 3/12 : RUN apt-get update &&   apt-get install -y git libgl1-mesa-dev &&   apt-get -y clean &&   rm -rf /var/lib/apt/lists/*\n ---> Using cache\n ---> 76bc393f04d5\nStep 4/12 : WORKDIR /\n ---> Using cache\n ---> 47b28fa00606\nStep 5/12 : RUN git clone https://github.com/NVIDIA-AI-IOT/torch2trt.git\n ---> Using cache\n ---> 1a4063050362\nStep 6/12 : WORKDIR /torch2trt\n ---> Using cache\n ---> 6e679ba630a9\nStep 7/12 : RUN python3 setup.py install --plugins\n ---> Using cache\n ---> f0f7dc347d9d\nStep 8/12 : WORKDIR /\n ---> Using cache\n ---> 1d4ffaaae9a0\nStep 9/12 : RUN git clone https://github.com/Megvii-BaseDetection/YOLOX.git\n ---> Using cache\n ---> 6cc15f5a7b36\nStep 10/12 : WORKDIR /YOLOX\n ---> Using cache\n ---> 3429678d2547\nStep 11/12 : RUN python3 -m pip install -r requirements.txt &&  python3 setup.py develop\n ---> Using cache\n ---> 377ef892e986\nStep 12/12 : RUN mkdir model\n ---> Using cache\n ---> 55caa4a2787d\nSuccessfully built 55caa4a2787d\nSuccessfully tagged torch2trt_torch2trt:latest\n\ndocker-compose up\n\nRecreating torch2trt_torch2trt_1 ... done\nAttaching to torch2trt_torch2trt_1\ntorch2trt_1  | \ntorch2trt_1  | =============\ntorch2trt_1  | == PyTorch ==\ntorch2trt_1  | =============\ntorch2trt_1  | \ntorch2trt_1  | NVIDIA Release 21.09 (build 26760254)\ntorch2trt_1  | PyTorch Version 1.10.0a0+3fd9dcf\ntorch2trt_1  | \ntorch2trt_1  | Container image Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.\ntorch2trt_1  | \ntorch2trt_1  | Copyright (c) 2014-2021 Facebook Inc.\ntorch2trt_1  | Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert)\ntorch2trt_1  | Copyright (c) 2012-2014 Deepmind Technologies    (Koray Kavukcuoglu)\ntorch2trt_1  | Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu)\ntorch2trt_1  | Copyright (c) 2011-2013 NYU                      (Clement Farabet)\ntorch2trt_1  | Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston)\ntorch2trt_1  | Copyright (c) 2006      Idiap Research Institute (Samy Bengio)\ntorch2trt_1  | Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz)\ntorch2trt_1  | Copyright (c) 2015      Google Inc.\ntorch2trt_1  | Copyright (c) 2015      Yangqing Jia\ntorch2trt_1  | Copyright (c) 2013-2016 The Caffe contributors\ntorch2trt_1  | All rights reserved.\ntorch2trt_1  | \ntorch2trt_1  | NVIDIA Deep Learning Profiler (dlprof) Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.\ntorch2trt_1  | \ntorch2trt_1  | Various files include modifications (c) NVIDIA CORPORATION.  All rights reserved.\ntorch2trt_1  | \ntorch2trt_1  | This container image and its contents are governed by the NVIDIA Deep Learning Container License.\ntorch2trt_1  | By pulling and using the container, you accept the terms and conditions of this license:\ntorch2trt_1  | https://developer.nvidia.com/ngc/nvidia-deep-learning-container-license\ntorch2trt_1  | \ntorch2trt_1  | NOTE: MOFED driver for multi-node communication was not detected.\ntorch2trt_1  |       Multi-node communication performance may be reduced.\ntorch2trt_1  | \ntorch2trt_1  | NOTE: The SHMEM allocation limit is set to the default of 64MB.  This may be\ntorch2trt_1  |    insufficient for PyTorch.  NVIDIA recommends the use of the following flags:\ntorch2trt_1  |    nvidia-docker run --ipc=host ...\ntorch2trt_1  | \ntorch2trt_1  | 2022-02-26 09:33:11.827 | INFO     | __main__:main:57 - loaded checkpoint done.\ntorch2trt_1  | [02/26/2022-09:33:13] [TRT] [I] [MemUsageChange] Init CUDA: CPU +188, GPU +0, now: CPU 1383, GPU 2123 (MiB)\ntorch2trt_1  | [02/26/2022-09:33:13] [TRT] [I] [MemUsageSnapshot] Begin constructing builder kernel library: CPU 1403 MiB, GPU 2123 MiB\ntorch2trt_1  | [02/26/2022-09:33:13] [TRT] [I] [MemUsageSnapshot] End constructing builder kernel library: CPU 1410 MiB, GPU 2123 MiB\ntorch2trt_1  | [02/26/2022-09:33:14] [TRT] [W] Tensor DataType is determined at build time for tensors not marked as input or output.\ntorch2trt_1  | [02/26/2022-09:33:14] [TRT] [W] FP16 support requested on hardware without native FP16 support, performance will be negatively affected.\ntorch2trt_1  | [02/26/2022-09:33:15] [TRT] [I] [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +0, GPU +8, now: CPU 2107, GPU 2409 (MiB)\ntorch2trt_1  | [02/26/2022-09:33:15] [TRT] [I] [MemUsageChange] Init cuDNN: CPU +0, GPU +8, now: CPU 2107, GPU 2417 (MiB)\ntorch2trt_1  | [02/26/2022-09:33:15] [TRT] [I] Local timing cache in use. Profiling results in this builder pass will not be stored.\ntorch2trt_1  | [02/26/2022-09:33:48] [TRT] [I] Detected 1 inputs and 1 output network tensors.\ntorch2trt_1  | [02/26/2022-09:33:48] [TRT] [I] Total Host Persistent Memory: 208144\ntorch2trt_1  | [02/26/2022-09:33:48] [TRT] [I] Total Device Persistent Memory: 640512\ntorch2trt_1  | [02/26/2022-09:33:48] [TRT] [I] Total Scratch Memory: 512\ntorch2trt_1  | [02/26/2022-09:33:48] [TRT] [I] [MemUsageStats] Peak memory usage of TRT CPU/GPU memory allocators: CPU 4 MiB, GPU 294 MiB\ntorch2trt_1  | [02/26/2022-09:33:48] [TRT] [I] [BlockAssignment] Algorithm ShiftNTopDown took 43.9912ms to assign 7 blocks to 214 nodes requiring 7181828 bytes.\ntorch2trt_1  | [02/26/2022-09:33:48] [TRT] [I] Total Activation Memory: 7181828\ntorch2trt_1  | [02/26/2022-09:33:48] [TRT] [I] [MemUsageChange] Init cuDNN: CPU +0, GPU +10, now: CPU 2129, GPU 2477 (MiB)\ntorch2trt_1  | [02/26/2022-09:33:48] [TRT] [I] [MemUsageChange] TensorRT-managed allocation in building engine: CPU +3, GPU +4, now: CPU 3, GPU 4 (MiB)\ntorch2trt_1  | [02/26/2022-09:33:48] [TRT] [I] [MemUsageChange] Init cuDNN: CPU +0, GPU +8, now: CPU 2128, GPU 2461 (MiB)\ntorch2trt_1  | [02/26/2022-09:33:48] [TRT] [I] [MemUsageChange] TensorRT-managed allocation in IExecutionContext creation: CPU +0, GPU +8, now: CPU 3, GPU 12 (MiB)\ntorch2trt_1  | 2022-02-26 09:33:48.565 | INFO     | __main__:main:71 - Converted TensorRT model done.\ntorch2trt_1  | 2022-02-26 09:33:48.581 | INFO     | __main__:main:79 - Converted TensorRT model engine file is saved for C++ inference.\ntorch2trt_torch2trt_1 exited with code 0\n
"},{"location":"tools/tools/#download-dataset","title":"Download dataset","text":"

you can download dataset via google drive by running this command.

ansible-playbook -i ansible/hosts/localhost.ini ansible/setup_dataset.yml --connection local --ask-become-pass\n
"},{"location":"tools/tools/#code-review-bot-by-chatgpt","title":"Code review bot by ChatGPT","text":"

We developed a code review bot using ChatGPT. When you request a review to the bot user named wam-v-tan, it analyzes the PR differences and automatically performs the code review.

The implementation of action can be found here.

Example of the code review can be seen here.

"},{"location":"tutorials/build_instraction/","title":"Build Instractions","text":"

If you want to know about supported platfroms, please see also this page.

"},{"location":"tutorials/build_instraction/#setup-develop-environment-full-package","title":"setup develop environment (full package)","text":"

first time

sh setup.sh\n

not first time (skip installing ros2)

sh update.sh\n
"},{"location":"tutorials/build_instraction/#troubleshooting","title":"Troubleshooting","text":"

Failed to connect to the host via ssh

Please execute command below to delete fingerprint of localhost:2022

ssh-keygen -f \"${HOME}/.ssh/known_hosts\" -R \"[localhost]:2022\"\n
"},{"location":"tutorials/build_instraction/#setup-real-robot","title":"setup real robot","text":"

Note

setting up real robot is fully automated via github actions, so using this setup-robot playbook manually is not recommended.

These operations should be run in robot.

  1. setup endpoint
sh ansible/setup_ansible.sh\nexport PERSONAL_ACCESS_TOKEN=$(ACCESS_TOKEN_OF_WAM_V_TAN_BOT)\nansible-playbook -i ansible/hosts/localhost.ini ansible/setup_endpoint.yml --connection local --ask-become-pass\n

If you want to know personal access token, please read this documentation.

"},{"location":"tutorials/build_instraction/#setup-firmware-development-environment","title":"setup firmware development environment","text":"

This playbook includes three steps. 1. Install Docker 1. Clone firmware package 1. Build firmware with docker

ansible-playbook -i ansible/hosts/localhost.ini ansible/setup_mbed_workspace.yml --connection local --ask-become-pass\n
"},{"location":"tutorials/navigation_demo/","title":"Navigation Demo","text":""},{"location":"tutorials/navigation_demo/#how-to-run-demo","title":"How to run demo","text":""},{"location":"tutorials/navigation_demo/#build-packages","title":"build packages","text":"

Please see also, this page.

"},{"location":"tutorials/navigation_demo/#run-simulator-and-planner","title":"run simulator and planner","text":"
ros2 launch navi_sim with_planner.launch.py behavior_config_filepath:=config/loop_demo.yaml\n
"},{"location":"tutorials/navigation_demo/#set-goal","title":"set goal","text":"

use 2d goal pose tool in rviz.

then, the navigation starts.

"},{"location":"tutorials/navigation_demo/#spawn-obstacle","title":"spawn obstacle","text":"

use clicked point tool in rviz

then, replan waypoints.

"},{"location":"tutorials/supported_platforms/","title":"Supported Platform","text":""},{"location":"tutorials/supported_platforms/#ubuntu-2204","title":"ubuntu 22.04","text":"

If your ubuntu version is older than 22.04, you can use docker environment.

ansible task install ROS2 foxy, so you do not necessary to setup ROS2

before you run ansible-script, you have to install ansible via apt

sudo apt install ansible\n
"},{"location":"tutorials/vrx_instruction/","title":"Run with VRX","text":"

Documents

"},{"location":"tutorials/vrx_instruction/#how-to-run-demo","title":"How to run demo","text":""},{"location":"tutorials/vrx_instruction/#confirm-dependencies","title":"Confirm dependencies","text":""},{"location":"tutorials/vrx_instruction/#run-the-vrx-simulator-and-lauch-vrx-simulation","title":"Run the vrx simulator and lauch vrx simulation","text":"
docker run --pull always -it wamvtan/vrx:latest --net=host --ipc=host --pid=host ros2 launch vrx_gz competition.launch.py world:=stationkeeping_task headless:=true urdf:=/home/config/wamv_target.urdf\n

you can change world:= for your purpose.

"},{"location":"tutorials/vrx_instruction/#run-vrx_bridgup-launch","title":"Run vrx_bridgup launch","text":" 
ros2 launch vrx_bridge vrx_bringup.launch.xml\n
"}]} \ No newline at end of file diff --git a/sitemap.xml.gz b/sitemap.xml.gz index 7f658424f8c1b2498d82830ab378affa978ab973..a79fea54f86d4d49d8d5327c932c56c7d06c14d1 100644 GIT binary patch delta 15 Wcmdnbw4aGhzMF%?`|d_IOGW@CRs>uC delta 15 Wcmdnbw4aGhzMF&N%B78LmW%)>RRr1q