Netwalk is a Python library born out of a large remadiation project aimed at making network device discovery and management as fast and painless as possible. Installation Can be installed via pip with pip install git+ssh://[email protected]/icovada/netwalk.git A collection of scripts with extra features and examples is stored in the extras folder Code quality A lot of the code is covered by tests. More will be added in the future Fabric This object type defines an entire switched network and can […]

This repository is the official PyTorch implementation of Mutual Affine Network for Spatially Variant Kernel Estimation in Blind Image Super-Resolution(arxiv, supplementary). :rocket:  :rocket:  :rocket: News: Existing blind image super-resolution (SR) methods mostly assume blur kernels are spatially invariant across the whole image. However, such an assumption is rarely applicable for real images whose blur kernels are usually spatially variant due to factors such as object motion and out-of-focus. Hence, existing blind SR methods would inevitably give rise to poor performance […]

Code for the paper “On the Effects of Batch and Weight Normalization in Generative Adversarial Networks“. About the code Here two versions are provided, one for torch and one for PyTorch. The code used for the experiments in the paper was in torch and was a bit messy, with hand written backward pass of weight normalized layers and other staff used to test various ideas about GANs that are unrelated to the paper. So we decided to clean up the […]

Alias-Free GAN An unofficial version of Alias-Free Generative Adversarial Networks (https://arxiv.org/abs/2106.12423). This repository was heavily based on Kim Seonghyeon’s (rosinality) implementation. The goal of this version is to be maintainable, easy to use, and expand the features of existing implementations. This is built using pytorch and pytorch lightning (a framework that abstracts away much of the hardware specific code). See open issues unsupported features, planned features, and current bugs. Licence This project is officially licensed as an MIT project. However, […]

lecam-gan Regularizing Generative Adversarial Networks under Limited Data Implementation for our GAN regularization method. The proposed regularization 1) improves the performance of GANs under limited training data, and 2) complements the exisiting data augmentation approches. Please note that this is not an officially supported Google product. Paper Please cite our paper if you find the code or dataset useful for your research. Regularizing Generative Adversarial Networks under Limited Data Hung-Yu Tseng, Lu Jiang, Ce Liu, Ming-Hsuan Yang, Weilong Yang Computer […]

R²SQL The PyTorch implementation of paper Dynamic Hybrid Relation Network for Cross-Domain Context-Dependent Semantic Parsing. (AAAI 2021) Requirements The model is tested in python 3.6 with following requirements: torch==1.0.0 transformers==2.10.0 sqlparse pymysql progressbar nltk numpy six spacy All experiments on SParC and CoSQL datasets were run on NVIDIA V100 GPU with 32GB GPU memory. Tips: The 16GB GPU memory may appear out-of-memory error. Setup The SParC and CoSQL experiments in two different folders, you need to download different datasets from […]

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 […]

Visual-Interaction-Networks An implementation of Deepmind visual interaction networks in Pytorch. For the purpose of understanding the challenge of relational reasoning. they publised VIN that involves predicting the future in a physical scene. From just a glance, humans can infer not only what objects are where, but also what will happen to them over the upcoming seconds, minutes and even longer in some cases. For example, if you kick a football against a wall, your brain predicts what will happen when […]

i-RevNet: Deep Invertible Networks Pytorch implementation of i-RevNets. i-RevNets define a family of fully invertible deep networks, built from a succession of homeomorphic layers. Reference: Jörn-Henrik Jacobsen, Arnold Smeulders, Edouard Oyallon. i-RevNet: Deep Invertible Networks. International Conference on Learning Representations (ICLR), 2018. (https://iclr.cc/) The i-RevNet and its dual. The inverse can be obtained from the forward model with minimal adaption and is an i-RevNet as well. Read the paper for theoretical background and detailed analysis of the trained models. Pytorch […]

RMA-Net This repo is the implementation of the paper: Recurrent Multi-view Alignment Network for Unsupervised Surface Registration (CVPR 2021). Paper address: https://arxiv.org/abs/2011.12104 Project webpage: https://wanquanf.github.io/RMA-Net.html Prerequisite Installation The code has been tested with Python3.8, PyTorch 1.6 and Cuda 10.2: conda create –name rmanet conda activate rmanet conda install pytorch=1.6.0 torchvision=0.7.0 cudatoolkit=10.2 -c pytorch conda install -c conda-forge igl Other requirements include: eigen3, Openmesh and MeshlabServer. Build the cuda extension: python build_cuda.py Usage Pre-trained Models Download the pre-trained models and put […]