Machine Learning

SinNeRF: Training Neural Radiance Fields on Complex Scenes from a Single Image

pip install -r requirements.txt python eval.py –dataset_name llff –root_dir /dataset/nerf_llff_data/room –N_importance 64 –img_wh 504 378 –model nerf –ckpt_path room.ckpt –timestamp test Refactoring in progress. @InProceedings{Xu_2022_SinNeRF, author = {Xu, Dejia and Jiang, Yifan and Wang, Peihao and Fan, Zhiwen and Shi, Humphrey and Wang, Zhangyang}, title = {SinNeRF: Training Neural Radiance Fields on Complex Scenes from a Single Image}, journal={arXiv preprint arXiv:2204.00928},    

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Natural Language Processing

Accelerated NLP pipelines for fast inference on CPU and GPU. Built with Transformers, Optimum and ONNX Runtime

Accelerated NLP pipelines for fast inference 🚀 on CPU and GPU. Built with 🤗Transformers, Optimum and ONNX runtime. Installation: With PyPI: pip install optimum-transformers Or directly from GitHub: pip install git+https://github.com/AlekseyKorshuk/optimum-transformers Usage: The pipeline API is similar to transformers pipeline with just a few differences which are explained below. Just provide the path/url to the model, and it’ll download the model if needed from the hub and automatically create onnx graph and run inference.

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End-to-End

Nix-TTS: An Incredibly Lightweight End-to-End Text-to-Speech Model via Non End-to-End Distillation

An Incredibly Lightweight End-to-End Text-to-Speech Model via Non End-to-End Distillation Rendi Chevi, Radityo Eko Prasojo, Alham Fikri Aji This is a repository for our paper, 🐤 Nix-TTS (Submitted to INTERSPEECH 2022). We released the pretrained models, an interactive demo, and audio samples below. [📄 Paper Link] [🤗 Interactive Demo] [📢 Audio Samples] Abstract    We propose Nix-TTS, a lightweight neural TTS (Text-to-Speech) model achieved by applying knowledge distillation to a powerful yet large-sized generative TTS teacher model. Distilling a TTS model might […]

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Computer Vision

An all-in-one toolkit for computer vision

Introduction EasyCV is an all-in-one computer vision toolbox based on PyTorch, mainly focus on self-supervised learning, image classification, metric-learning, object detection and so on. Major features SOTA SSL Algorithms EasyCV provides state-of-the-art algorithms in self-supervised learning based on contrastive learning such as SimCLR, MoCO V2, Swav, DINO and also MAE based on masked image modeling. We also provides standard benchmark tools for ssl model evaluation. Vision Transformers EasyCV aims to provide plenty vision transformer models trained either using supervised learning […]

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Music

Music controller plugin for Steam OS

This is very WIP plugin for adding the native (MPRIS) media controls to the SteamOS Plugin Loader. Changes Required to the Plugin Loader To load the plugin, I had to do the following changes to the plugin_loader.service from the Plugin Loader in order to be able to connect to the User’s info instead of the Root’s. Hopefully this won’t be required for future versions [Service] User=deck Group=deck Environment=DBUS_SESSION_BUS_ADDRESS=unix:path=/run/user/1000/bus … GitHub View Github    

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metrics

MetricFlow allows you to define, build, and maintain metrics in code

Welcome to MetricFlow MetricFlow is a computational framework for building and maintaining consistent metric logic. The name comes from the approach taken to generate metrics. Using the user-defined semantic model, a query is first compiled into a metric dataflow plan. The plan is then converted to an abstract SQL object model, optimized, then rendered to engine-specific SQL. MetricFlow provides a set of abstractions that help you construct complicated logic and aggregate metrics to a range of granularities. As a developer […]

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PNG

Inject arbitrary data into PNGs

Inject arbitrary data into PNGs, without breaking the PNG spec. For technical details on how this works, and outdated Python 2 code, read this blogpost. original from 2016 How to use Listing PNG chunks ~ ./punk.py list source.png Chunk IHDR, 13 bytes Chunk IDAT, 226876 bytes Chunk IEND, 0 bytes Injecting data

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Python

Speeding up software with faster hardware: tradeoffs and alternatives

If you’re writing software to process data, you will often hit performance problems: batch jobs that run too slowly, or use too much memory. One potential solution is purchasing better hardware. With cloud computing, switching to a computer with more cores, or adding more RAM, can be done in a few minutes, or even just a few seconds. But as with any solution, there are tradeoffs involved. If your first solution to any performance problem is spending more money on […]

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Render

A modern pure-Python library for reading PDF files

A modern pure-Python library for reading PDF files. The goal is to have a modern interface to handle PDF files which is consistent with itself and typical Python syntax. The library should be Python-only (hence no C-extensions), but allow to change the backend. Similar in concept to matplotlib backends and Keras backends. The default backend could be PyPDF2. Possible other backends could be PyMuPDF (using MuPDF) and PikePDF (using QPDF). WARNING: This library is UNSTABLE at the moment! Expect many […]

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