Tutorial

How to setup a multi-client ethereum Eth1-Eth2 merge testnet with python

Mergenet tutorial How to setup a multi-client ethereum Eth1-Eth2 merge testnet with python Preparing the setup environment In this tutorial, we use a series of scripts to generate configurationfiles, and these scripts have dependencies that we need toinstall. You can either install these dependencies on your host or youcan run those scripts inside a docker container. We call thisenvironment setupenv. Preparing the setup environment on your host: apt-get install python3-dev python3-pip python3-venv golang # Check that you have Go 1.16+ […]

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Command-line Tools

Like the unix tree command but for GCP Org Heirarchy

gcptree Like the unix tree command but for GCP Org Heirarchy. For a note on coloring, the org node is green, folders and blue, and projects that are not ACTIVE are dimmed. These styles won’t show up if you redirect output and Windows is supported by virtue of the colorama package. pip install gcptree First make sure you are authenticated to gcloud and have your application default credentials set using: gcloud auth login –update-adc You just need your org id, […]

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IRC

A bouncer-style Matrix IRC bridge in python

Heisenbridge a bouncer-style Matrix IRC bridge. Heisenbridge brings IRC to Matrix by creating an environment where every user connects to each network individually like they would with a traditional IRC bouncer. Simplicity is achieved by exposing IRC in the most straightforward way as possible where it makes sense so it feels familiar for long time IRC users. Please file an issue when you find something is missing or isn’t working that you’d like to see fixed. Pull requests are more […]

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Tool

A collection of Blender tools Build With Python

A collection of Blender tools I’ve written for myself over the years. I use these daily so they should be bug-free, mostly. Feel free to take and use any parts of this project. gret can be typed with one hand in the search bar. Blender 2.92 or later required. Download the latest release. In Blender, go to Edit → Preferences → Add-ons → Install. Find and select the downloaded zip file, then click Install Add-on. Enable the add-on by clicking […]

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JAX

A Mixed Precision library for JAX in python

Mixed precision training in JAX Mixed precision training [0] is a technique that mixes the use of full andhalf precision floating point numbers during training to reduce the memorybandwidth requirements and improve the computational efficiency of a givenmodel. This library implements support for mixed precision training in JAX by providingtwo key abstractions (mixed precision “policies” and loss scaling). Neuralnetwork libraries (such as Haiku) can integrate with jmp and provide“Automatic Mixed Precision (AMP)” support (automating or simplifying applyingpolicies to modules). All […]

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Network

Attention in Attention Network for Image Super-Resolution

A2N This repository is an PyTorch implementation of the paper “Attention in Attention Network for Image Super-Resolution” [arXiv] Visual results in the paper are availble at Google Drive or Baidu Netdisk (password: 7t74). Unofficial TensorFlow implementation: https://github.com/Anuj040/superres Test Dependecies: PyTorch==0.4.1 (Will be updated to support PyTorch>1.0 in the future) You can download the test sets from Google Drive. Put the test data in ../Data/benchmark/. python main.py –scale 4 –data_test Set5 –pre_train ./experiment/model/aan_x4.pt –chop –test_only If you use CPU, please add […]

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Machine Learning

Unsupervised Pre-training for Person Re-identification

LUPerson The repository is for our CVPR2021 paper Unsupervised Pre-training for Person Re-identification. LUPerson Dataset LUPerson is currently the largest unlabeled dataset for Person Re-identification, which is used for Unsupervised Pre-training. LUPerson consists of 4M images of over 200K identities and covers a much diverse range of capturing environments. Details can be found at ./LUP. Pre-trained Models Finetuned Results For MGN with ResNet50: Dataset mAP cmc1 path MSMT17 66.06/79.93 85.08/87.63 MSMT DukeMTMC 82.27/91.70 90.35/92.82 Duke Market1501 91.12/96.16 96.26/97.12 Market CUHK03-L […]

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Bot

Bot that automatically answers giga unitel questions

Gigabot+ Bot que responde automáticamente as perguntas do giga unitel ATT: Não compativel para Windows 7 Para instalar esta ferramenta é muito fácil pip install requests python gb.py Inicio Treinar Jogar Só vai poder jogar caso o cliente estiver subscrito Antes de escolher a opção “jogar” escola a opção “treinar”, sai do programa e depois abre de novo Para usar em Android instale o script no termux By: Joa Roque GitHub https://github.com/joaroque/gigabot-plus    

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Machine Learning

Boosting Co-teaching with Compression Regularization for Label Noise

Nested-Co-teaching ([email protected]) Pytorch implementation of paper “Boosting Co-teaching with Compression Regularization for Label Noise” [PDF] If our project is helpful for your research, please consider citing : @inproceedings{chen2021boosting, title={Boosting Co-teaching with Compression Regularization for Label Noise}, author={Chen, Yingyi and Shen, Xi and Hu, Shell Xu and Suykens, Johan AK}, booktitle={CVPR Learning from Limited and Imperfect Data (L2ID) workshop}, year={2021} } Our model can be learnt in a single GPU GeForce GTX 1080Ti (12G), this code has been tested with Pytorch […]

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Transformer

Image data augmentation scheduler for albumentations transforms

albu_scheduler Scheduler for albumentations transforms based on PyTorch schedulers interface TransformMultiStepScheduler import albumentations as A from albu_scheduler import TransformMultiStepScheduler transform_1 = A.Compose([ A.RandomCrop(width=256, height=256), A.HorizontalFlip(p=0.5), A.RandomBrightnessContrast(p=0.2), ]) transform_2 = A.Compose([ A.RandomCrop(width=128, height=128), A.VerticalFlip(p=0.5), ]) scheduled_transform = TransformMultiStepScheduler(transforms=[transform_1, transform_2], milestones=[0, 10]) dataset = Dataset(transform=scheduled_transform) for epoch in range(100): train(…) validate(…) scheduled_transform.step() TransformSchedulerOnPlateau from albu_scheduler import TransformSchedulerOnPlateau scheduled_transform = TransformSchedulerOnPlateau(transforms=[transform_1, transform_2], mode=”max”, patience=5) dataset = Dataset(transform=scheduled_transform) for epoch in range(100): train(…) score = validate(…) scheduled_transform.step(score) git clone https://github.com/KiriLev/albu_scheduler cd albu_scheduler make install […]

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