machin Machin is a reinforcement library designed for pytorch. Reinforcement learning library(framework) designed for PyTorch, implements DQN, DDPG, A2C, PPO, SAC, MADDPG, A3C, APEX, IMPALA … Features 1. Automatic Starting from version 0.4.0, Machin now supports automatic config generation, you can get a configurationthrough: python -m machin.auto generate –algo DQN –env openai_gym –output config.json And automatically launch the experiment with pytorch lightning: python -m machin.auto launch –config config.json 2. Readable Compared to other reinforcement learning libraries such as the famous […]

BYOL for Audio This is a demo implementation of BYOL for Audio (BYOL-A), a self-supervised learning method for general-purpose audio representation, includes: Training code that can train models with arbitrary audio files. Evaluation code that can evaluate trained models with downstream tasks. Pretrained weights. If you find BYOL-A useful in your research, please use the following BibTeX entry for citation. @misc{niizumi2021byol-a, title={BYOL for Audio: Self-Supervised Learning for General-Purpose Audio Representation}, author={Daisuke Niizumi and Daiki Takeuchi and Yasunori Ohishi and Noboru […]

EigenGAN TensorFlow implementation of EigenGAN: Layer-Wise Eigen-Learning for GANs EigenGAN Usage Environment Python 3.6 TensorFlow 1.15 OpenCV, scikit-image, tqdm, oyaml we recommend Anaconda or Miniconda, then you can create the environment with commands below conda create -n EigenGAN python=3.6 source activate EigenGAN conda install opencv scikit-image tqdm tensorflow-gpu=1.15 conda install -c conda-forge oyaml NOTICE: if you create a new conda environment, remember to activate it before any other command source activate EigenGAN Data Preparation CelebA-unaligned (10.2GB, higher quality than the […]

TCMR_RELEASE This repository is the official Pytorch implementation of Beyond Static Features for Temporally Consistent 3D Human Pose and Shape from a Video. The base codes are largely borrowed from VIBE. Qualtitative result Paper teaser video Installation TCMR is tested on Ubuntu 16.04 with Pytorch 1.4 and Python 3.7.10. You may need sudo privilege for the installation. source scripts/install_pip.sh Quick demo Download the pre-trained demo TCMR and required data by below command and download SMPL layers from here (male&female) and […]

TorchShard TorchShard is a lightweight engine for slicing a PyTorch tensor into parallel shards. It can reduce GPU memory and scale up the training when the model has massive linear layers (e.g., ViT, BERT and GPT) or huge classes (millions). It has the same API design as PyTorch. Installation pip install torchshard More options in INSTALL.md. Usage import torchshard as ts ts.init_process_group(group_size=2) # init parallel groups m = torch.nn.Sequential( torch.nn.Linear(20, 30, bias=True), ts.nn.ParallelLinear(30, 30, bias=True, dim=None), # equal to nn.Linear() […]

LED2-Net This is PyTorch implementation of our CVPR 2021 Oral paper “LED2-Net: Monocular 360˚ Layout Estimation via Differentiable Depth Rendering”. You can visit our project website and upload your own panorama to see the 3D results! Prerequisite This repo is primarily based on PyTorch. You can use the follwoing command to intall the dependencies. pip install -r requirements.txt Preparing Training Data Under LED2Net/Dataset, we provide the dataloader of Matterport3D and Realtor360. The annotation formats of the two datasets follows PanoAnnotator. […]

MICOM MICOM is a Python package for metabolic modeling of microbial communities currently developed in the Gibbons Lab at the Institute for Systems Biology and the Human Systems Biology Group of Prof. Osbaldo Resendis Antonio at the National Institute of Genomic Medicine Mexico. MICOM allows you to construct a community model from a list on input COBRA models and manages exchange fluxes between individuals and individuals with the environment. It explicitly accounts for different abundances of individuals in the community […]

ReSim This repository provides the PyTorch implementation of Region Similarity Representation Learning (ReSim) described in this paper: @Article{xiao2021region, author = {Tete Xiao and Colorado J Reed and Xiaolong Wang and Kurt Keutzer and Trevor Darrell}, title = {Region Similarity Representation Learning}, journal = {arXiv preprint arXiv:2103.12902}, year = {2021}, } tldr; ReSim maintains spatial relationships in the convolutional feature maps when performing instance contrastive pre-training, which is useful for region-related tasks such as object detection, segmentation, and dense pose estimation. […]

AntroPy AntroPy is a Python 3 package providing several time-efficient algorithms for computing the complexity of time-series. It can be used for example to extract features from EEG signals. Installation pip install antropy Functions Entropy import numpy as np import antropy as ant np.random.seed(1234567) x = np.random.normal(size=3000) # Permutation entropy print(ant.perm_entropy(x, normalize=True)) # Spectral entropy print(ant.spectral_entropy(x, sf=100, method=’welch’, normalize=True)) # Singular value decomposition entropy print(ant.svd_entropy(x, normalize=True)) # Approximate entropy print(ant.app_entropy(x)) # Sample entropy print(ant.sample_entropy(x)) # Hjorth mobility and complexity print(ant.hjorth_params(x)) […]