Tools⚓︎

Automated logger⚓︎

Plug & play automated rosbag logging tools for robots.

Supported platform⚓︎

ASUSTOR FLASHSTOR 6 (FS6706T)

How it works⚓︎

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

Setup⚓︎

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)

Train yolox model⚓︎

Requirement⚓︎

docker
docker-compose
nvidia-docker2
nvidia-gpu (hardware)

cd docker/train_yolox/checkpoints
wget https://github.com/Megvii-BaseDetection/YOLOX/releases/download/0.1.1rc0/yolox_s.pth
cd ../datasets

download datasets via this link.

docker-compose up --build

While training, you can see output like below.

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

Convert yolox pytorch model into tensorrt model.⚓︎

Requirement⚓︎

docker
docker-compose
nvidia-docker2
nvidia-gpu (hardware)

cd docker/torch2trt
sh convert.sh

Output should be like below.

docker-compose build

Building torch2trt
Step 1/12 : FROM nvcr.io/nvidia/pytorch:22.07-py3
 ---> 74d53f84c686
Step 2/12 : RUN python3 -m pip install nvidia-pyindex packaging &&   python3 -m pip install --upgrade nvidia-tensorrt
 ---> Using cache
 ---> 8d655c414dd2
Step 3/12 : RUN apt-get update &&   apt-get install -y git libgl1-mesa-dev &&   apt-get -y clean &&   rm -rf /var/lib/apt/lists/*
 ---> Using cache
 ---> 76bc393f04d5
Step 4/12 : WORKDIR /
 ---> Using cache
 ---> 47b28fa00606
Step 5/12 : RUN git clone https://github.com/NVIDIA-AI-IOT/torch2trt.git
 ---> Using cache
 ---> 1a4063050362
Step 6/12 : WORKDIR /torch2trt
 ---> Using cache
 ---> 6e679ba630a9
Step 7/12 : RUN python3 setup.py install --plugins
 ---> Using cache
 ---> f0f7dc347d9d
Step 8/12 : WORKDIR /
 ---> Using cache
 ---> 1d4ffaaae9a0
Step 9/12 : RUN git clone https://github.com/Megvii-BaseDetection/YOLOX.git
 ---> Using cache
 ---> 6cc15f5a7b36
Step 10/12 : WORKDIR /YOLOX
 ---> Using cache
 ---> 3429678d2547
Step 11/12 : RUN python3 -m pip install -r requirements.txt &&  python3 setup.py develop
 ---> Using cache
 ---> 377ef892e986
Step 12/12 : RUN mkdir model
 ---> Using cache
 ---> 55caa4a2787d
Successfully built 55caa4a2787d
Successfully tagged torch2trt_torch2trt:latest

docker-compose up

Recreating torch2trt_torch2trt_1 ... done
Attaching to torch2trt_torch2trt_1
torch2trt_1  | 
torch2trt_1  | =============
torch2trt_1  | == PyTorch ==
torch2trt_1  | =============
torch2trt_1  | 
torch2trt_1  | NVIDIA Release 21.09 (build 26760254)
torch2trt_1  | PyTorch Version 1.10.0a0+3fd9dcf
torch2trt_1  | 
torch2trt_1  | Container image Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
torch2trt_1  | 
torch2trt_1  | Copyright (c) 2014-2021 Facebook Inc.
torch2trt_1  | Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert)
torch2trt_1  | Copyright (c) 2012-2014 Deepmind Technologies    (Koray Kavukcuoglu)
torch2trt_1  | Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu)
torch2trt_1  | Copyright (c) 2011-2013 NYU                      (Clement Farabet)
torch2trt_1  | Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston)
torch2trt_1  | Copyright (c) 2006      Idiap Research Institute (Samy Bengio)
torch2trt_1  | Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz)
torch2trt_1  | Copyright (c) 2015      Google Inc.
torch2trt_1  | Copyright (c) 2015      Yangqing Jia
torch2trt_1  | Copyright (c) 2013-2016 The Caffe contributors
torch2trt_1  | All rights reserved.
torch2trt_1  | 
torch2trt_1  | NVIDIA Deep Learning Profiler (dlprof) Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
torch2trt_1  | 
torch2trt_1  | Various files include modifications (c) NVIDIA CORPORATION.  All rights reserved.
torch2trt_1  | 
torch2trt_1  | This container image and its contents are governed by the NVIDIA Deep Learning Container License.
torch2trt_1  | By pulling and using the container, you accept the terms and conditions of this license:
torch2trt_1  | https://developer.nvidia.com/ngc/nvidia-deep-learning-container-license
torch2trt_1  | 
torch2trt_1  | NOTE: MOFED driver for multi-node communication was not detected.
torch2trt_1  |       Multi-node communication performance may be reduced.
torch2trt_1  | 
torch2trt_1  | NOTE: The SHMEM allocation limit is set to the default of 64MB.  This may be
torch2trt_1  |    insufficient for PyTorch.  NVIDIA recommends the use of the following flags:
torch2trt_1  |    nvidia-docker run --ipc=host ...
torch2trt_1  | 
torch2trt_1  | 2022-02-26 09:33:11.827 | INFO     | __main__:main:57 - loaded checkpoint done.
torch2trt_1  | [02/26/2022-09:33:13] [TRT] [I] [MemUsageChange] Init CUDA: CPU +188, GPU +0, now: CPU 1383, GPU 2123 (MiB)
torch2trt_1  | [02/26/2022-09:33:13] [TRT] [I] [MemUsageSnapshot] Begin constructing builder kernel library: CPU 1403 MiB, GPU 2123 MiB
torch2trt_1  | [02/26/2022-09:33:13] [TRT] [I] [MemUsageSnapshot] End constructing builder kernel library: CPU 1410 MiB, GPU 2123 MiB
torch2trt_1  | [02/26/2022-09:33:14] [TRT] [W] Tensor DataType is determined at build time for tensors not marked as input or output.
torch2trt_1  | [02/26/2022-09:33:14] [TRT] [W] FP16 support requested on hardware without native FP16 support, performance will be negatively affected.
torch2trt_1  | [02/26/2022-09:33:15] [TRT] [I] [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +0, GPU +8, now: CPU 2107, GPU 2409 (MiB)
torch2trt_1  | [02/26/2022-09:33:15] [TRT] [I] [MemUsageChange] Init cuDNN: CPU +0, GPU +8, now: CPU 2107, GPU 2417 (MiB)
torch2trt_1  | [02/26/2022-09:33:15] [TRT] [I] Local timing cache in use. Profiling results in this builder pass will not be stored.
torch2trt_1  | [02/26/2022-09:33:48] [TRT] [I] Detected 1 inputs and 1 output network tensors.
torch2trt_1  | [02/26/2022-09:33:48] [TRT] [I] Total Host Persistent Memory: 208144
torch2trt_1  | [02/26/2022-09:33:48] [TRT] [I] Total Device Persistent Memory: 640512
torch2trt_1  | [02/26/2022-09:33:48] [TRT] [I] Total Scratch Memory: 512
torch2trt_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
torch2trt_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.
torch2trt_1  | [02/26/2022-09:33:48] [TRT] [I] Total Activation Memory: 7181828
torch2trt_1  | [02/26/2022-09:33:48] [TRT] [I] [MemUsageChange] Init cuDNN: CPU +0, GPU +10, now: CPU 2129, GPU 2477 (MiB)
torch2trt_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)
torch2trt_1  | [02/26/2022-09:33:48] [TRT] [I] [MemUsageChange] Init cuDNN: CPU +0, GPU +8, now: CPU 2128, GPU 2461 (MiB)
torch2trt_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)
torch2trt_1  | 2022-02-26 09:33:48.565 | INFO     | __main__:main:71 - Converted TensorRT model done.
torch2trt_1  | 2022-02-26 09:33:48.581 | INFO     | __main__:main:79 - Converted TensorRT model engine file is saved for C++ inference.
torch2trt_torch2trt_1 exited with code 0

Download dataset⚓︎

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

Code review bot by ChatGPT⚓︎

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.

bot