semvec/pypi-packages huggingface dataset:
MydataShield 0.0.5
Module to pseudonymize/anonymize Mydata
License: MIT
Requires Python: >=3.6

# MyData shield Project
<p>마이데이터 분석을 위하여 마아데이터에 포함한 개인정보 데이터를 가명/익명처리하는 프로젝트입니다.</p>

### 테스트 환경
  * Jupyter notebook

### 실행 환경
  * python 3.6 이상
  * pandas 1.4.1
  * SQLAlcemy 1.4.32
  * psycopg2-binary 2.9.3
  * numpy 1.21.5
  * pustil 5.9.0

### Json 형태의 금용 마이 데이터 표준 API에 포함한 개인 정보 데이터 '항목명' 가명 처리
  * 개인 정보로 생각되는 '항목명' 식별 및 설정
  * 해당 데이터 형식에 맞게 정규 표현식 및 문자열 치환으로 마스킹 처리  

### 형식이 일정하여 예측 가능한 개인 정보 데이터에 대한 가명 처리
  * 정규 표현식을 통한 가명 처리 기능 구현
  * 주민등록번호
  * 이메일
  * 전화번호
  * 추가 예정

 ### 익명 처리한 데이터 DB 연동 테스트
  * PostgreSQL 기반의 ExperDB
  * 대량의 데이터 처리 시 메모리에 대한 문제 해결을 위해 일정량의 데이터 처리 후 DB에 추가하는 형식으로 구현
stylegan2-torch 1.3.0
A simple, typed, commented Pytorch implementation of StyleGAN2.
License: MIT
Requires Python: >=3.8,<4.0

# StyleGAN2 Pytorch - Typed, Commented, Installable :)

A simple, typed, commented Pytorch implementation of StyleGAN2.

![action](https://img.shields.io/github/workflow/status/ppeetteerrs/stylegan2-torch/build?logo=githubactions&logoColor=white)
[![pypi](https://img.shields.io/pypi/v/stylegan2-torch.svg)](https://pypi.python.org/pypi/stylegan2-torch)
[![codecov](https://img.shields.io/codecov/c/github/ppeetteerrs/stylegan2-torch?label=codecov&logo=codecov)](https://app.codecov.io/gh/ppeetteerrs/stylegan2-torch)
[![docs](https://img.shields.io/github/deployments/ppeetteerrs/stylegan2-torch/github-pages?label=docs&logo=readthedocs)](https://ppeetteerrs.github.io/stylegan2-torch)

This implementation is adapted from [here](https://github.com/rosinality/stylegan2-pytorch). This implementation seems more stable and editable than the over-engineered official implementation.

The focus of this repository is simplicity and readability. If there are any bugs / issues, please kindly let me know or submit a pull request!

Refer to [my blog post](https://ppeetteerrsx.com/post/cuda/stylegan_cuda_kernels/) for an explanation on the custom CUDA kernels. The profiling code to optimize the custom operations is [here](https://github.com/ppeetteerrs/pytorch-cuda-kernels).

## Installation

```bash
pip install stylegan2-torch
```

## Training Tips

1. Use a multi-GPU setup. An RTX 3090 can handle batch size of up to 8 at 1024 resolution. Based on experience, batch size of 8 works but 16 or 32 should be safer.
2. Use LMDB dataset + SSD storage + multiple dataloader workers (and a big enough prefetch factor to cache at least one batch ahead). You never know how much time you waste on dataloading until you optimize it. For me, that shorted the training time by 30% (more time-saving than the custom CUDA kernels).

## Known Issues

Pytorch is known to cause random reboots when using non-deterministic algorithms. Set `torch.use_deterministic_algorithms(True)` if you encounter that.

## To Dos / Won't Dos
1. Tidy up `conv2d_gradfix.py` and `fused_act.py`. These were just copied over from the original repo so they are still ugly and untidy.
2. Provide pretrained model conversion method (not that hard tbh, just go map the state_dict keys).
3. Clean up util functions to aid training loop design.