통일된 DataScience 폴더 구조 제공 및 가상환경 작업의 부담감 해소

Related tags

Deep LearningLucas
Overview


Lucas

Hits


coded by linux shell

목차


Patch Note 📜


Team member

Contributors/People

ympark gbhwang cbchun
https://github.com/pym7857 https://github.com/gbhwang https://github.com/bermmie1000
  • You can see team member and github profile
  • You should probably find team member's lastest project



Requirements

  • python 3.xx



Mac버전 CookieCutter (autoenv)

🚫 주의
$> brew install autoenv 로 다운로드 받아서 실행시키면 터미널 고장납니다.
반드시 autoenv Github 에서 git clone 으로 다운받아 주세요. (현재 시점 21.3.24)

⚠️ mac버전만 소개합니다.

1. How to Install autoenv

$ git clone git://github.com/inishchith/autoenv.git ~/.autoenv

2.폴더 진입 시, activate 구현하기

$ echo 'source ~/.autoenv/activate.sh' >> ~/.zshrc
$ source ~/.zshrc

🔔 하단의.env파일은 현재 repo의 cookiecutter에서 자동으로 생성해줍니다. (스킵)

# .env 파일
echo "HELLO autoenv"
{
    source .dev-venv/bin/activate
    echo "virtual env is successfully activated!"
} ||
{
    echo "[virtual env start] is failed!"
}

.env파일 설정 후 첫 폴더 진입시 .env파일을 신뢰하고 실행할지 않을 지에 대한 동의가 나타납니다. autoenv 이 부분은 .env파일이 악의적으로 변경되었을때 사용자에게 알리기 위해서 있기 때문에 즐거운 마음으로 Y를 눌러줍시다.
이제 정상적으로 가상환경이 activate된 것을 확인할 수 있습니다.

3.폴더 탈출 시, deactivate 구현하기

$> vi ~/.zshrc

마지막줄에 다음의 명령어를 추가해줍니다.

export AUTOENV_ENABLE_LEAVE='"enabled"' 

🔔 하단의.env.leave파일은 현재 repo의 cookiecutter에서 자동으로 생성해줍니다. (스킵)

# .env.leave 파일
echo "BYEBYE"
{
    deactivate
    echo "virtual env is successfully deactivated!"
} ||
{
    echo "[virtual env quit] is failed!"
}

.env.leave파일 설정 후 해당 폴더에서 나가면
정상적으로 가상환경이 deactivate 되는 것을 확인할 수 있습니다.

4.Alias 설정하기

echo 'alias cookie="bash [각자 컴퓨터의 상대경로/cookie_cutter_project_dir.sh]"' >> ~/.zshrc
ex) echo 'alias cookie="bash /Users/gbhwang/Desktop/Project/Test/Lucas/mac/cookie_cutter_project_dir.sh"' >> ~/.zshrc

맥 파일경로 확인법을 참고하여
각자 mac폴더안의 cookie_cutter_project_dir.sh 파일의 경로를 확인하여 zshrc에 넣어주시면 됩니다.

이렇게 하면 cookie 명령어 만으로 간단하게 스크립트를 실행시킬 수 있게 됩니다.
위와 같이 설정하면 cookie [프로젝트 생성할 경로] [프로젝트 이름] 명령어로 프로젝트를 생성할 수 있게 됩니다.

5.How to Use

$> cd "where-you-want"
$> git clone https://github.com/LS-ELLO/Lucas.git
$> cd Lucas
$> cd mac

$> cookie [where-you-want] [your-project-name]
ex) $> cookie . test111



Windows버전 CookieCutter (ps-autoenv)

도움 주신 규본님 감사합니다.
ps-autoenv를 사용합니다.

1.How to install ps-autoenv

Powershell 실행 (관리자 권한 실행)

PS> Install-Module ps-autoenv
PS> Add-Content $PROFILE @("`n", "import-module ps-autoenv")

2.Alias 설정하기 (git-bash)

참조

  1. C:/Program Files/Git/etc/profile.d/aliases.sh 파일을 관리자 권한으로 Text Editor에 실행시킵니다.

  2. 다음의 명령어를 추가합니다.
    alias cookie='bash cookie_cutter_project_dir.sh의 상대경로'
    ex) alias cookie='bash D:/Lucas/windows/cookie_cutter_project_dir.sh'

    (aliases.sh)

    # Some good standards, which are not used if the user
    # creates his/her own .bashrc/.bash_profile
    
    # --show-control-chars: help showing Korean or accented characters
    alias ls='ls -F --color=auto --show-control-chars'
    alias ll='ls -l'
    alias cookie='bash [where-your-cookie_cutter_project_dir.sh]'
    
    case "$TERM" in
    ...

3.How to Use

Git Bash 실행

bash> cd "where-this-repo-downloaded"
bash> cd windows
bash> cookie [where-you-want] [your-project-name]
ex) cookie . 1bot

Powershell 실행

PS> Import-Module ps-autoenv
PS> cd "where-your-cookiecutter-project"
ex. PS> cd "C:\Users\ympark4\Documents\1bot"
PS> press 'Y'
🚫 PSSecurityException 오류 발생할때

https://extbrain.tistory.com/118 를 참조해서 해결주세요.



The resulting directory structure

The directory structure of your new project looks like this:

├── LICENSE
├── Makefile
├── README.md          ← The top-level README for developers using this project.
├── data
│   ├── external       ← Data from third party sources.
│   ├── interim        ← Intermediate data that has been transformed.
│   ├── processed      ← The final, canonical data sets for modeling.
│   └── raw            ← The original, immutable data dump.
├── docs               ← A default Sphinx project; see sphinx-doc.org for details
├── models             ← Trained and serialized models, model predictions, or model summaries
├── notebooks          ← Jupyter notebooks. Naming convention is a number (for ordering), the creator's initials, and a short `-` delimited description, e.g. `1.0-jqp-initial-data-exploration`.
├── references         ← Data dictionaries, manuals, and all other explanatory materials.
├── reports            ← Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures        ← Generated graphics and figures to be used in reporting
├── requirements.txt   ← The requirements file for reproducing the analysis environment, e.g. generated with `pip freeze > requirements.txt`
├── setup.py           ← makes project pip installable (pip install -e .) so src can be imported
├── src                ← Source code for use in this project.
│   ├── __init__.py  
│   ├── dataread      
│   │   └── __init__.py
│   │   └── example.py
│   │
│   ├── features       
│   │   └── __init__.py
│   │   └── example.py
│   │
│   ├── models     
│   │   └── __init__.py
│   │   └── example.py
│   │
│   ├── visualization    
│   │   └── __init__.py
│   │   └── example.py
├── App               
│   ├── android       
│   ├── ios           
│   ├── lib            
│   │   └── models
│   │   └── main.dart
│
└── .gitignore        



Owner
ello
ello
Code basis for the paper "Camera Condition Monitoring and Readjustment by means of Noise and Blur" (2021)

Camera Condition Monitoring and Readjustment by means of Noise and Blur This repository contains the source code of the paper: Wischow, M., Gallego, G

7 Dec 22, 2022
This is a code repository for the paper "Graph Auto-Encoders for Financial Clustering".

Repository for the paper "Graph Auto-Encoders for Financial Clustering" Requirements Python 3.6 torch torch_geometric Instructions This is a simple c

Edward Turner 1 Dec 02, 2021
Convolutional Neural Network for 3D meshes in PyTorch

MeshCNN in PyTorch SIGGRAPH 2019 [Paper] [Project Page] MeshCNN is a general-purpose deep neural network for 3D triangular meshes, which can be used f

Rana Hanocka 1.4k Jan 04, 2023
A program that uses computer vision to detect hand gestures, used for controlling movie players.

HandGestureDetection This program uses a Haar Cascade algorithm to detect the presence of your hand, and then passes it on to a self-created and self-

2 Nov 22, 2022
DLL: Direct Lidar Localization

DLL: Direct Lidar Localization Summary This package presents DLL, a direct map-based localization technique using 3D LIDAR for its application to aeri

Service Robotics Lab 127 Dec 16, 2022
Type4Py: Deep Similarity Learning-Based Type Inference for Python

Type4Py: Deep Similarity Learning-Based Type Inference for Python This repository contains the implementation of Type4Py and instructions for re-produ

Software Analytics Lab 45 Dec 15, 2022
Source code and dataset for ACL2021 paper: "ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models via Contrastive Learning".

ERICA Source code and dataset for ACL2021 paper: "ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models via Contrastive L

THUNLP 75 Nov 02, 2022
Unsupervised MRI Reconstruction via Zero-Shot Learned Adversarial Transformers

Official TensorFlow implementation of the unsupervised reconstruction model using zero-Shot Learned Adversarial TransformERs (SLATER). (https://arxiv.

ICON Lab 22 Dec 22, 2022
Using deep learning to predict gene structures of the coding genes in DNA sequences of Arabidopsis thaliana

DeepGeneAnnotator: A tool to annotate the gene in the genome The master thesis of the "Using deep learning to predict gene structures of the coding ge

Ching-Tien Wang 3 Sep 09, 2022
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models

PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models Code accompanying CVPR'20 paper of the same title. Paper lin

Alex Damian 7k Dec 30, 2022
Volumetric parameterization of the placenta to a flattened template

placenta-flattening A MATLAB algorithm for volumetric mesh parameterization. Developed for mapping a placenta segmentation derived from an MRI image t

Mazdak Abulnaga 12 Mar 14, 2022
This is an official pytorch implementation of Fast Fourier Convolution.

Fast Fourier Convolution (FFC) for Image Classification This is the official code of Fast Fourier Convolution for image classification on ImageNet. Ma

pkumi 199 Jan 03, 2023
YolactEdge: Real-time Instance Segmentation on the Edge

YolactEdge, the first competitive instance segmentation approach that runs on small edge devices at real-time speeds. Specifically, YolactEdge runs at up to 30.8 FPS on a Jetson AGX Xavier (and 172.7

Haotian Liu 1.1k Jan 06, 2023
Recursive Bayesian Networks

Recursive Bayesian Networks This repository contains the code to reproduce the results from the NeurIPS 2021 paper Lieck R, Rohrmeier M (2021) Recursi

Robert Lieck 11 Oct 18, 2022
EfficientNetv2 TensorRT int8

EfficientNetv2_TensorRT_int8 EfficientNetv2模型实现来自https://github.com/d-li14/efficientnetv2.pytorch 环境配置 ubuntu:18.04 cuda:11.0 cudnn:8.0 tensorrt:7

34 Apr 24, 2022
TensorFlow implementation of the paper "Hierarchical Attention Networks for Document Classification"

Hierarchical Attention Networks for Document Classification This is an implementation of the paper Hierarchical Attention Networks for Document Classi

Quoc-Tuan Truong 83 Dec 05, 2022
Human Pose Detection on EdgeTPU

Coral PoseNet Pose estimation refers to computer vision techniques that detect human figures in images and video, so that one could determine, for exa

google-coral 476 Dec 31, 2022
Anomaly Detection Based on Hierarchical Clustering of Mobile Robot Data

We proposed a new approach to detect anomalies of mobile robot data. We investigate each data seperately with two clustering method hierarchical and k-means. There are two sub-method that we used for

Zekeriyya Demirci 1 Jan 09, 2022
Code for the bachelors-thesis flaky fault localization

Flaky_Fault_Localization Scripts for the Bachelors-Thesis: "Flaky Fault Localization" by Christian Kasberger. The thesis examines the usefulness of sp

Christian Kasberger 1 Oct 26, 2021
PyTorch implementation of Value Iteration Networks (VIN): Clean, Simple and Modular. Visualization in Visdom.

VIN: Value Iteration Networks This is an implementation of Value Iteration Networks (VIN) in PyTorch to reproduce the results.(TensorFlow version) Key

Xingdong Zuo 215 Dec 07, 2022