A Pytorch Implementation for Compact Bilinear Pooling.

Overview

CompactBilinearPooling-Pytorch

A Pytorch Implementation for Compact Bilinear Pooling. Adapted from tensorflow_compact_bilinear_pooling

Prerequisites

Install pytorch_fft by

pip install pytorch_fft

Usage

from torch import nn
from torch.autograd import Variable
from CompactBilinearPooling import CompactBilinearPooling

bottom1 = Variable(torch.randn(128, 512, 14, 14)).cuda()
bottom2 = Variable(torch.randn(128, 512, 14, 14)).cuda()

layer = CompactBilinearPooling(512, 512, 8000)
layer.cuda()
layer.train()

out = layer(bottom1, bottom2)

Reference

Yang Gao, et al. "Compact Bilinear Pooling." in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2016).
Akira Fukui, et al. "Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding." arXiv preprint arXiv:1606.01847 (2016).
A PyTorch implementation of L-BFGS.

PyTorch-LBFGS: A PyTorch Implementation of L-BFGS Authors: Hao-Jun Michael Shi (Northwestern University) and Dheevatsa Mudigere (Facebook) What is it?

Hao-Jun Michael Shi 478 Dec 27, 2022
Differentiable ODE solvers with full GPU support and O(1)-memory backpropagation.

PyTorch Implementation of Differentiable ODE Solvers This library provides ordinary differential equation (ODE) solvers implemented in PyTorch. Backpr

Ricky Chen 4.4k Jan 04, 2023
Distiller is an open-source Python package for neural network compression research.

Wiki and tutorials | Documentation | Getting Started | Algorithms | Design | FAQ Distiller is an open-source Python package for neural network compres

Intel Labs 4.1k Dec 28, 2022
A tutorial on "Bayesian Compression for Deep Learning" published at NIPS (2017).

Code release for "Bayesian Compression for Deep Learning" In "Bayesian Compression for Deep Learning" we adopt a Bayesian view for the compression of

Karen Ullrich 190 Dec 30, 2022
Training PyTorch models with differential privacy

Opacus is a library that enables training PyTorch models with differential privacy. It supports training with minimal code changes required on the cli

1.3k Dec 29, 2022
Bunch of optimizer implementations in PyTorch

Bunch of optimizer implementations in PyTorch

Hyeongchan Kim 76 Jan 03, 2023
TorchShard is a lightweight engine for slicing a PyTorch tensor into parallel shards

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

Kaiyu Yue 275 Nov 22, 2022
PyTorch implementation of Glow, Generative Flow with Invertible 1x1 Convolutions

glow-pytorch PyTorch implementation of Glow, Generative Flow with Invertible 1x1 Convolutions

Kim Seonghyeon 433 Dec 27, 2022
An optimizer that trains as fast as Adam and as good as SGD.

AdaBound An optimizer that trains as fast as Adam and as good as SGD, for developing state-of-the-art deep learning models on a wide variety of popula

LoLo 2.9k Dec 27, 2022
A PyTorch repo for data loading and utilities to be shared by the PyTorch domain libraries.

A PyTorch repo for data loading and utilities to be shared by the PyTorch domain libraries.

878 Dec 30, 2022
Over9000 optimizer

Optimizers and tests Every result is avg of 20 runs. Dataset LR Schedule Imagenette size 128, 5 epoch Imagewoof size 128, 5 epoch Adam - baseline OneC

Mikhail Grankin 405 Nov 27, 2022
TorchSSL: A PyTorch-based Toolbox for Semi-Supervised Learning

TorchSSL: A PyTorch-based Toolbox for Semi-Supervised Learning

1k Dec 28, 2022
Implementation of LambdaNetworks, a new approach to image recognition that reaches SOTA with less compute

Lambda Networks - Pytorch Implementation of λ Networks, a new approach to image recognition that reaches SOTA on ImageNet. The new method utilizes λ l

Phil Wang 1.5k Jan 07, 2023
A collection of extensions and data-loaders for few-shot learning & meta-learning in PyTorch

Torchmeta A collection of extensions and data-loaders for few-shot learning & meta-learning in PyTorch. Torchmeta contains popular meta-learning bench

Tristan Deleu 1.7k Jan 06, 2023
Learning Sparse Neural Networks through L0 regularization

Example implementation of the L0 regularization method described at Learning Sparse Neural Networks through L0 regularization, Christos Louizos, Max W

AMLAB 202 Nov 10, 2022
PyTorch Extension Library of Optimized Autograd Sparse Matrix Operations

PyTorch Sparse This package consists of a small extension library of optimized sparse matrix operations with autograd support. This package currently

Matthias Fey 757 Jan 04, 2023
A tiny package to compare two neural networks in PyTorch

Compare neural networks by their feature similarity

Anand Krishnamoorthy 180 Dec 30, 2022
You like pytorch? You like micrograd? You love tinygrad! ❤️

For something in between a pytorch and a karpathy/micrograd This may not be the best deep learning framework, but it is a deep learning framework. Due

George Hotz 9.7k Jan 05, 2023
Differentiable SDE solvers with GPU support and efficient sensitivity analysis.

PyTorch Implementation of Differentiable SDE Solvers This library provides stochastic differential equation (SDE) solvers with GPU support and efficie

Google Research 1.2k Jan 04, 2023
PyTorch extensions for fast R&D prototyping and Kaggle farming

Pytorch-toolbelt A pytorch-toolbelt is a Python library with a set of bells and whistles for PyTorch for fast R&D prototyping and Kaggle farming: What

Eugene Khvedchenya 1.3k Jan 05, 2023