Robot

Spider quadruped robot using NodeMUC-32S (ESP32) and MicroPython

Quadruped project using NodeMCU-32S and MicroPython. Brief introduction This project is about a spider quadruped. The robot can perform multiple gait in MOVE mode, and can rotate and translate with 6DOF in POSE mode. Both Remote control and Calibration are done via Wi-Fi on ESP32. Brian: NodeMCU-32S (ESP32). Actuator: 12 × 20Kg servo. Power supply: 3S 2200mAh Lipo battery. mini360 DC-DC bulk for powering ESP32, PCA9685 and MPU6050. 8A UBEC for 12 servos. Check the PCB file for details. The […]

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Filter

CFGAN: A Generic Collaborative Filtering Framework based on Generative Adversarial Networks

This repository provides a reference implementation of CFGAN as described in the following papers: CFGAN: A Generic Collaborative Filtering Framework based on Generative Adversarial Networks Dong-Kyu Chae, Jin-Soo Kang, Sang-Wook Kim, and Jung-Tae Lee 27th ACM Int’l Conf. on Information and Knowledge Management (CIKM 2018) Overview of CFGAN Authors Requirements The code has been tested running under Python 3.5. The required packages are as follows: Basic Usage Cite We encourage you to cite our paper if you have used the […]

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Tool

A grapheme to phoneme (G2P) tool for Spanish

A grapheme to phoneme (G2P) tool for Spanish. To use it, simply: Just for one time: cd g2p_spanish && chmod -R 744 ./* Create a UTF-8 file with one word per line: nano original.txt ./doPron.sh original.txt lexicon 1 The final file will be encoded in UTF-8 lexicon.pron.Note: the third parameter takes into account the differences in áéíóúÁÉÍÓÚ for the phonemes (value=1). Otherwise you can just keep value=0. Requires Python2, Linux Credits Transcriptor ortofonético: Andres Marzal, Maria Jose Castro, Salvador España […]

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Stream

Shared, streaming Python dict

Sychronized, streaming Python dictionary that uses shared memory as a backend Warning: This is an early hack. There are only few unit tests and so on. Maybe not stable! Features: Fast (compared to other shareing solutions) No running manager processes Works in spawn and fork context Safe locking between independent processes Tested with Python >= v3.9 on Linux and Windows Optional recursion for nested dicts General Concept UltraDict uses multiprocessing.shared_memory to synchronize a dict between multiple processes. It does so […]

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Language

Revitalize Region Feature for Democratizing Video-Language Pre-training

Revitalize Region Feature for Democratizing Video-Language Pre-training Guanyu Cai, Yixiao Ge, Alex Jinpeng Wang, Rui Yan, Xudong Lin, Ying Shan, Lianghua He, Xiaohu Qie, Jianping Wu, Mike Zheng Shou [Arxiv] Pytorch implementation of our method for video-language pre-training. Requirement conda create -n demovlp python=3.8 source activate demovlp pip install -r requirements Pre-trained weights Model Dataset Download DemoVLP WebVid+CC3M Model DemoVLP WebVid+CC3M+CC7M Model Data Download Pre-trained model mkdir pretrained cd pretrained wget -c https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_p16_224-80ecf9dd.pth cd    

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PyTorch

A PyTorch implementation of GraphSAGE

A PyTorch implementation of GraphSAGE This package contains a PyTorch implementation of GraphSAGE. Currently, only supervised versions of GraphSAGE-mean, GraphSAGE-GCN, GraphSAGE-maxpool and GraphSAGE-meanpool are implemented. Authors of this code package: Bin Yu Environment settings python>=3.6.8 pytorch>=1.0.0 Basic Usage Example Usage To run the supervised model on Cuda: GitHub View Github    

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PyTorch

Is Space-Time Attention All You Need for Video Understanding?

This is an official pytorch implementation of our ICML 2021 paper Is Space-Time Attention All You Need for Video Understanding?. In this repository, we provide PyTorch code for training and testing our proposed TimeSformer model. TimeSformer provides an efficient video classification framework that achieves state-of-the-art results on several video action recognition benchmarks such as Kinetics-400. If you find TimeSformer useful in your research, please use the following BibTeX entry for citation. @inproceedings{gberta_2021_ICML, author = {Gedas Bertasius    

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Server

Gathers all info from Cisco devices and creates/updates it in Netbox server

Script gathers all needed info from Cisco devices and creates entities in Netbox (devices, interfaces, prefixes, ip addresses, cdp connections etc) This script has two parts: What the main.py script does: Connects to ips of Cisco devices written in ip_list_all.txt or ip_list.txt files by netmiko or paramiko Gathers information according to templates Parses data and writes into api/”device_name”.csv files in json format Script’s main purpose is full integration between Cisco and Netbox (https://netbox.readthedocs.io/en/stable/) Script works fine with IOS, NX-OS, XR, […]

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Distributed

Exact Feature Distribution Matching for Arbitrary Style Transfer and Domain Generalization

The official codes of our CVPR2022 paper: Exact Feature Distribution Matching for Arbitrary Style Transfer and Domain Generalization One Sentence Summary: EFDM outperforms AdaIN, which only matches first and second order statistics, by implicitly matching high orders statistics in an efficient manner. Fig.1: AdaIN vs. histogram matching vs. EFDM via Sort-matching. A brief introduction:Many real-world tasks (e.g., Arbitrary Style Transfer and Domain Generalizaiton) can be cast as a feature distribution matching problem.With the assumption of Gaussian feature distribution, conventional feature […]

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