Month: August 2021

COPASI

A simplified python interface to COPASI

BasiCO This project hosts a simplified python interface to COPASI. While all functionality from COPASI is exposed via automatically generated SWIG wrappers, this package aims to add a layer on top of that, to hide most of the complexity away when calling COPASI functions. Installation The package works with python 2.x and python 3.x, provided the following packages are installed: pandas and python-copasi are freely available on pypi. Once done, just have the basico directory in the PYTHONPATH or sys.path. […]

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Command-line Tools

A tracking logs processor to feed your LRS

Ralph Ralph is a command-line tool to fetch, extract, convert and push your tracking logs (aka learning events) from various storage backends to your LRS or any other compatible storage or database backend. Quick start guide Ralph is distributed as a standard python package; it can be installed via pip or any other python package manager (e.g Poetry, Pipenv, etc.): $ pip install ralph-malph Once installed, the ralph command should be available in your PATH. Try to invoke the program […]

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Command-line Tools

A cli Linux and Windows Nopaystation client made with python and wget

pyNPS pyNPS – A cli Linux and Windows Nopaystation client made with python 3 and wget PyNPS is a Nopaystation client writen in python 3 that, with the help of wget and pkg2zip, can search, download and decrypt/extract PSVita, PSP, PSX, PSM and PS3 games from Nopaystation database. It’s basically a command line version of NPSBrowser writen by a moron (aka me). There are tree methods you can install pyNPS: Using PIP This is for both 32 and 64bits systems. […]

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Python

Python’s deque: Implement Efficient Queues and Stacks

If you often work with lists in Python, then you probably know that they don’t perform fast enough when you need to pop and append items on their left end. Python’s collections module provides a class called deque that’s specially designed to provide fast and memory-efficient ways to append and pop item from both ends of the underlying data structure. Python’s deque is a low-level and highly optimized double-ended queue that’s useful for implementing elegant, efficient, and Pythonic queues and […]

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Segmentation

A Comprehensive Analysis of Weakly-Supervised Semantic Segmentation

The code of: A Comprehensive Analysis of Weakly-Supervised Semantic Segmentation in Different Image Domains, arXiv pre-print 2019 paper. Introduction We conduct the first comprehensive analysis of Weakly-Supervised Semantic Segmentation (WSSS) with image label supervision in different image domains. WSSS has been almost exclusively evaluated on PASCAL VOC2012 but little work has been done on applying to different image domains, such as histopathology and satellite images. The paper analyzes the compatibility of different methods for representative datasets and presents principles for […]

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Neural Network

Truly shift-invariant convolutional neural networks

Authors: Anadi Chaman and Ivan Dokmanić Convolutional neural networks were always assumed to be shift invariant, until recently when it was shown that the classification accuracy of a trained CNN can take a serious hit with merely a 1-pixel shift in input image. One of the primary reasons for this problem is the use of downsampling (popularly known as stride) layers in the networks. In this work, we present Adaptive Polyphase Sampling (APS), an easy-to-implement non-linear downsampling scheme that completely […]

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Graph

Rethinking Graph Neural Architecture Search from Message-passing

GNAS-MP Pytorch Implementation of Rethinking Graph Neural Architecture Search from Message-passing (CVPR2021) Getting Started 0. Prerequisites Linux NVIDIA GPU + CUDA CuDNN 1. Setup Python Environment # clone Github repo conda install git git clone https://github.com/phython96/GNAS-MP.git cd GNAS-MP # Install python environment conda env create -f environment_gpu.yml conda activate gnasmp 2. Download datasets The datasets are provided by project benchmarking-gnns, you can click here to download all the required datasets. 3. Search Architectures python scripts/search_molecules_zinc.sh [gpu_id] 4. Train & Test […]

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Configuration

NetBox plugin that stores configuration diffs and checks templates compliance

NetBox plugin that deals with Cisco device configuration (collects running config from Cisco devices, indicates config changes, and checks templates compliance). A plugin for NetBox to work with running-configuration of Cisco devices. Compatible with NetBox 2.9 and higher versions only. Collect actual information from Cisco devices (running_config, version, IP addresses, etc.) and shows it on a dedicated NetBox page. Save Cisco running configuration in a local directory and display all changes with git-like diffs. Set up configuration templates for distinct […]

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Automation

Duckiter will Automatically dockerize your Django projects

Duckiter Duckiter will Automatically dockerize your Django projects. Requirements : – python version : python version 3.6 or upper version – OS : – linux – MacOS – windows ( drop support ) – docker engine installed in your machine and already run through the usage of package Installation : clone project: pip install duckiter Usage : Duckiter has two individual steps: ​ 1- initialize Dockerfile ​ 2- build image from created Dockerfile To initialize Dockerfile (step 1) : in […]

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Transformer

Revamping Cross-Modal Recipe Retrieval with Hierarchical Transformers

image-to-recipe-transformers Code for CVPR 2021 paper: Revamping Cross-Modal Recipe Retrieval with Hierarchical Transformers and Self-supervised Learning This is the PyTorch companion code for the paper: Amaia Salvador, Erhan Gundogdu, Loris Bazzani, and Michael Donoser. Revamping Cross-Modal Recipe Retrieval with Hierarchical Transformers and Self-supervised Learning. CVPR 2021 If you find this code useful in your research, please consider citing using the following BibTeX entry: @inproceedings{salvador2021revamping, title={Revamping Cross-Modal Recipe Retrieval with Hierarchical Transformers and Self-supervised Learning}, author={Salvador, Amaia and Gundogdu, Erhan and […]

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