Spherical CNNs

Related tags

Deep Learnings2cnn
Overview

Spherical CNNs

Equivariant CNNs for the sphere and SO(3) implemented in PyTorch

Equivariance

Overview

This library contains a PyTorch implementation of the rotation equivariant CNNs for spherical signals (e.g. omnidirectional images, signals on the globe) as presented in [1]. Equivariant networks for the plane are available here.

Dependencies

(commands to install all the dependencies on a new conda environment)

conda create --name cuda9 python=3.6 
conda activate cuda9

# s2cnn deps
#conda install pytorch torchvision cuda90 -c pytorch # get correct command line at http://pytorch.org/
conda install -c anaconda cupy  
pip install pynvrtc joblib

# lie_learn deps
conda install -c anaconda cython  
conda install -c anaconda requests  

# shrec17 example dep
conda install -c anaconda scipy  
conda install -c conda-forge rtree shapely  
conda install -c conda-forge pyembree  
pip install "trimesh[easy]"  

Installation

To install, run

$ python setup.py install

Usage

Please have a look at the examples.

Please cite [1] in your work when using this library in your experiments.

Design choices for Spherical CNN Architectures

Spherical CNNs come with different choices of grids and grid hyperparameters which are on the first look not obviously related to those of conventional CNNs. The s2_near_identity_grid and so3_near_identity_grid are the preferred choices since they correspond to spatially localized kernels, defined at the north pole and rotated over the sphere via the action of SO(3). In contrast, s2_equatorial_grid and so3_equatorial_grid define line-like (or ring-like) kernels around the equator.

To clarify the possible parameter choices for s2_near_identity_grid:

max_beta:

Adapts the size of the kernel as angle measured from the north pole. Conventional CNNs on flat space usually use a fixed kernel size but pool the signal spatially. This spatial pooling gives the kernels in later layers an effectively increased field of view. One can emulate a pooling by a factor of 2 in spherical CNNs by decreasing the signal bandwidth by 2 and increasing max_beta by 2.

n_beta:

Number of rings of the kernel around the equator, equally spaced in [β=0, β=max_beta]. The choice n_beta=1 corresponds to a small 3x3 kernel in conv2d since in both cases the resulting kernel consists of one central pixel and one ring around the center.

n_alpha:

Gives the number of learned parameters of the rings around the pole. These values are per default equally spaced on the azimuth. A sensible number of values depends on the bandwidth and max_beta since a higher resolution or spatial extent allow to sample more fine kernels without producing aliased results. In practice this value is typically set to a constant, low value like 6 or 8. A reduced bandwidth of the signal is thereby counteracted by an increased max_beta to emulate spatial pooling.

The so3_near_identity_grid has two additional parameters max_gamma and n_gamma. SO(3) can be seen as a (principal) fiber bundle SO(3)→S² with the sphere S² as base space and fiber SO(2) attached to each point. The additional parameters control the grid on the fiber in the following way:

max_gamma:

The kernel spans over the fiber SO(2) between γ∈[0, max_gamma]. The fiber SO(2) encodes the kernel responses for every sampled orientation at a given position on the sphere. Setting max_gamma≨2π results in the kernel not seeing the responses of all kernel orientations simultaneously and is in general unfavored. Steerable CNNs [3] usually always use max_gamma=2π.

n_gamma:

Number of learned parameters on the fiber. Typically set equal to n_alpha, i.e. to a low value like 6 or 8.

See the deep model of the MNIST example for an example of how to adapt these parameters over layers.

Feedback

For questions and comments, feel free to contact us: geiger.mario (gmail), taco.cohen (gmail), jonas (argmin.xyz).

License

MIT

References

[1] Taco S. Cohen, Mario Geiger, Jonas Köhler, Max Welling, Spherical CNNs. International Conference on Learning Representations (ICLR), 2018.

[2] Taco S. Cohen, Mario Geiger, Jonas Köhler, Max Welling, Convolutional Networks for Spherical Signals. ICML Workshop on Principled Approaches to Deep Learning, 2017.

[3] Taco S. Cohen, Mario Geiger, Maurice Weiler, Intertwiners between Induced Representations (with applications to the theory of equivariant neural networks), ArXiv preprint 1803.10743, 2018.

Owner
Jonas Köhler
Jonas Köhler
Official implementation for (Show, Attend and Distill: Knowledge Distillation via Attention-based Feature Matching, AAAI-2021)

Show, Attend and Distill: Knowledge Distillation via Attention-based Feature Matching Official pytorch implementation of "Show, Attend and Distill: Kn

Clova AI Research 80 Dec 16, 2022
✨风纪委员会自动投票脚本,利用Github Action帮你进行裁决操作(为了让其他风纪委员有案件可判,本程序从中午12点才开始运行,有需要请自己修改运行时间)

风纪委员会自动投票 本脚本通过使用Github Action来实现B站风纪委员的自动投票功能,喜欢请给我点个STAR吧! 如果你不是风纪委员,在符合风纪委员申请条件的情况下,本脚本会自动帮你申请 投票时间是早上八点,如果有需要请自行修改.github/workflows/Judge.yml中的时间,

Pesy Wu 25 Feb 17, 2021
Code to generate datasets used in "How Useful is Self-Supervised Pretraining for Visual Tasks?"

Synthetic dataset rendering Framework for producing the synthetic datasets used in: How Useful is Self-Supervised Pretraining for Visual Tasks? Alejan

Princeton Vision & Learning Lab 21 Apr 29, 2022
(ImageNet pretrained models) The official pytorch implemention of the TPAMI paper "Res2Net: A New Multi-scale Backbone Architecture"

Res2Net The official pytorch implemention of the paper "Res2Net: A New Multi-scale Backbone Architecture" Our paper is accepted by IEEE Transactions o

Res2Net Applications 928 Dec 29, 2022
Official implementation of MSR-GCN (ICCV 2021 paper)

MSR-GCN Official implementation of MSR-GCN: Multi-Scale Residual Graph Convolution Networks for Human Motion Prediction (ICCV 2021 paper) [Paper] [Sup

LevonDang 42 Nov 07, 2022
Python implementation of NARS (Non-Axiomatic-Reasoning-System)

Python implementation of NARS (Non-Axiomatic-Reasoning-System)

Bowen XU 11 Dec 20, 2022
GARCH and Multivariate LSTM forecasting models for Bitcoin realized volatility with potential applications in crypto options trading, hedging, portfolio management, and risk management

Bitcoin Realized Volatility Forecasting with GARCH and Multivariate LSTM Author: Chi Bui This Repository Repository Directory ├── README.md

Chi Bui 113 Dec 29, 2022
The goal of the exercises below is to evaluate the candidate knowledge and problem solving expertise regarding the main development focuses for the iFood ML Platform team: MLOps and Feature Store development.

The goal of the exercises below is to evaluate the candidate knowledge and problem solving expertise regarding the main development focuses for the iFood ML Platform team: MLOps and Feature Store dev

George Rocha 0 Feb 03, 2022
Pytorch Implementation of Auto-Compressing Subset Pruning for Semantic Image Segmentation

Pytorch Implementation of Auto-Compressing Subset Pruning for Semantic Image Segmentation Introduction ACoSP is an online pruning algorithm that compr

Merantix 8 Dec 07, 2022
Adversarial Texture Optimization from RGB-D Scans (CVPR 2020).

AdversarialTexture Adversarial Texture Optimization from RGB-D Scans (CVPR 2020). Scanning Data Download Please refer to data directory for details. B

Jingwei Huang 153 Nov 28, 2022
[3DV 2021] A Dataset-Dispersion Perspective on Reconstruction Versus Recognition in Single-View 3D Reconstruction Networks

dispersion-score Official implementation of 3DV 2021 Paper A Dataset-dispersion Perspective on Reconstruction versus Recognition in Single-view 3D Rec

Yefan 7 May 28, 2022
Explaining in Style: Training a GAN to explain a classifier in StyleSpace

Explaining in Style: Official TensorFlow Colab Explaining in Style: Training a GAN to explain a classifier in StyleSpace Oran Lang, Yossi Gandelsman,

Google 197 Nov 08, 2022
Self-Supervised Monocular 3D Face Reconstruction by Occlusion-Aware Multi-view Geometry Consistency[ECCV 2020]

Self-Supervised Monocular 3D Face Reconstruction by Occlusion-Aware Multi-view Geometry Consistency(ECCV 2020) This is an official python implementati

304 Jan 03, 2023
gtfs2vec - Learning GTFS Embeddings for comparing PublicTransport Offer in Microregions

gtfs2vec This is a companion repository for a gtfs2vec - Learning GTFS Embeddings for comparing PublicTransport Offer in Microregions publication. Vis

Politechnika Wrocławska - repozytorium dla informatyków 5 Oct 10, 2022
Neural Contours: Learning to Draw Lines from 3D Shapes (CVPR2020)

Neural Contours: Learning to Draw Lines from 3D Shapes This repository contains the PyTorch implementation for CVPR 2020 Paper "Neural Contours: Learn

93 Dec 16, 2022
Class-Attentive Diffusion Network for Semi-Supervised Classification [AAAI'21] (official implementation)

Class-Attentive Diffusion Network for Semi-Supervised Classification Official Implementation of AAAI 2021 paper Class-Attentive Diffusion Network for

Jongin Lim 7 Sep 20, 2022
LieTransformer: Equivariant Self-Attention for Lie Groups

LieTransformer This repository contains the implementation of the LieTransformer used for experiments in the paper LieTransformer: Equivariant Self-At

OxCSML (Oxford Computational Statistics and Machine Learning) 50 Dec 28, 2022
GDSC-ML Team Interview Task

GDSC-ML-Team---Interview-Task Task 1 : Clean or Messy room In this task we have to classify the given test images as clean or messy. - Link for datase

Aayush. 1 Jan 19, 2022
Implementation of the paper NAST: Non-Autoregressive Spatial-Temporal Transformer for Time Series Forecasting.

Non-AR Spatial-Temporal Transformer Introduction Implementation of the paper NAST: Non-Autoregressive Spatial-Temporal Transformer for Time Series For

Chen Kai 66 Nov 28, 2022
AutoVideo: An Automated Video Action Recognition System

AutoVideo is a system for automated video analysis. It is developed based on D3M infrastructure, which describes machine learning with generic pipeline languages. Currently, it focuses on video actio

Data Analytics Lab at Texas A&M University 267 Dec 17, 2022