This is the paddle code for SeBoW(Self-Born wiring for neural trees), a kind of neural tree born form a large search space

Overview

SeBoW: Self-Born Wiring for neural trees(PaddlePaddle version)

methodology

This is the paddle code for SeBoW(Self-Born wiring for neural trees), a kind of neural decision tree born form a large search space, published as a conference paper on ICCV 2021, written by [Ying Chen](https://www.vipazoo.cn/people/chenying.html) et al. The code was written by [Haoling Li](https://github.com/HollyLee2000) and Ying Chen, and supported by Jie Song. This paddle implementation produces results comparable to the original PyTorch veision.

Prerequisites

  • Linux or Window
  • Python 3
  • CPU or NVIDIA GPU + CUDA CuDNN

Getting Started

Installation

  • Clone this repo:
git clone https://github.com/HollyLee2000/SeBoW-paddle
cd SeBoW-paddle

train/test introduction

  • Edit ForestModel.py to choose your dataset and Forest structure(the original large search space), by now only Cifar100 dataset has been tested on this paddle version. If you want to conduct on other datasets you need just do some adjustment and rewirte the dataloder

  • run train_cifar100_ForestModel.py to get the result of the forest model.

  • run sender_select.py to get the output of the sender(average router probability for each section of the Forest model), then retain the node in i-th section if its conditional probability obtained from previous senders is greater than the threshold C/(2 × Ci), in the original paper, C=2 and Ci means the number of the learners in i-th section.

  • run receiver_droupout.py, choose the only parent of each node with the largest weight in sampling vectors produced by the receiver, then you will get the final tree-structure.

  • Edit Forest_to_tree.py to apply your tree-structured model, during the retraining phase you will run train_tree.py to retrain the derived neural tree from scratch

  • The inference can be executed in two manners: multi-path inference and single-path inference. Multipath inference computes the weighted predictive distribution by running over all possible paths in the derived neural tree, such that all solvers in the tree will contribute to the final prediction(you have done this). However, in the single-path inference, only the most probable paths are executed based on the routing probability from routers, which enjoys less inference cost with some accuracy drop. You can run single-inference.py to apply this, but don't forget to adjust the structure of Tree_single_inference.py

Acknowledgments

The work is inspired by VIPA.

Owner
HollyLee
HollyLee
A deep learning based semantic search platform that computes similarity scores between provided query and documents

semanticsearch This is a deep learning based semantic search platform that computes similarity scores between provided query and documents. Documents

1 Nov 30, 2021
Implementing yolov4 target detection and tracking based on nao robot

Implementing yolov4 target detection and tracking based on nao robot

6 Apr 19, 2022
:fire: 2D and 3D Face alignment library build using pytorch

Face Recognition Detect facial landmarks from Python using the world's most accurate face alignment network, capable of detecting points in both 2D an

Adrian Bulat 6k Dec 31, 2022
Code to reproduce the results for Compositional Attention

Compositional-Attention This repository contains the official implementation for the paper Compositional Attention: Disentangling Search and Retrieval

Sarthak Mittal 58 Nov 30, 2022
This is an (re-)implementation of DeepLab-ResNet in TensorFlow for semantic image segmentation on the PASCAL VOC dataset.

DeepLab-ResNet-TensorFlow This is an (re-)implementation of DeepLab-ResNet in TensorFlow for semantic image segmentation on the PASCAL VOC dataset. Up

19 Jan 16, 2022
Robust Consistent Video Depth Estimation

[CVPR 2021] Robust Consistent Video Depth Estimation This repository contains Python and C++ implementation of Robust Consistent Video Depth, as descr

Facebook Research 213 Dec 17, 2022
MoveNetを用いたPythonでの姿勢推定のデモ

MoveNet-Python-Example MoveNetのPythonでの動作サンプルです。 ONNXに変換したモデルも同梱しています。変換自体を試したい方はMoveNet_tf2onnx.ipynbを使用ください。 2021/08/24時点でTensorFlow Hubで提供されている以下モデ

KazuhitoTakahashi 38 Dec 17, 2022
UAV-Networks-Routing is a Python simulator for experimenting routing algorithms and mac protocols on unmanned aerial vehicle networks.

UAV-Networks Simulator - Autonomous Networking - A.A. 20/21 UAV-Networks-Routing is a Python simulator for experimenting routing algorithms and mac pr

0 Nov 13, 2021
IndoNLI: A Natural Language Inference Dataset for Indonesian

IndoNLI: A Natural Language Inference Dataset for Indonesian This is a repository for data and code accompanying our EMNLP 2021 paper "IndoNLI: A Natu

15 Feb 10, 2022
Scheduling BilinearRewards

Scheduling_BilinearRewards Requirement Python 3 =3.5 Structure main.py This file includes the main function. For getting the results in Figure 1, ple

junghun.kim 0 Nov 25, 2021
给yolov5加个gui界面,使用pyqt5,yolov5是5.0版本

博文地址 https://xugaoxiang.com/2021/06/30/yolov5-pyqt5 代码执行 项目中使用YOLOv5的v5.0版本,界面文件是project.ui pip install -r requirements.txt python main.py 图片检测 视频检测

Xu GaoXiang 215 Dec 30, 2022
Code for EMNLP 2021 paper Contrastive Out-of-Distribution Detection for Pretrained Transformers.

Contra-OOD Code for EMNLP 2021 paper Contrastive Out-of-Distribution Detection for Pretrained Transformers. Requirements PyTorch Transformers datasets

Wenxuan Zhou 27 Oct 28, 2022
DeepLab resnet v2 model in pytorch

pytorch-deeplab-resnet DeepLab resnet v2 model implementation in pytorch. The architecture of deepLab-ResNet has been replicated exactly as it is from

Isht Dwivedi 601 Dec 22, 2022
CLUES: Few-Shot Learning Evaluation in Natural Language Understanding

CLUES: Few-Shot Learning Evaluation in Natural Language Understanding This repo contains the data and source code for baseline models in the NeurIPS 2

Microsoft 29 Dec 29, 2022
A simple image/video to Desmos graph converter run locally

Desmos Bezier Renderer A simple image/video to Desmos graph converter run locally Sample Result Setup Install dependencies apt update apt install git

Kevin JY Cui 339 Dec 23, 2022
PyTorch implementation of Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation.

ALiBi PyTorch implementation of Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation. Quickstart Clone this reposit

Jake Tae 4 Jul 27, 2022
High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features

CleanRL (Clean Implementation of RL Algorithms) CleanRL is a Deep Reinforcement Learning library that provides high-quality single-file implementation

Costa Huang 1.8k Jan 01, 2023
Adversarial Reweighting for Partial Domain Adaptation

Adversarial Reweighting for Partial Domain Adaptation Code for paper "Xiang Gu, Xi Yu, Yan Yang, Jian Sun, Zongben Xu, Adversarial Reweighting for Par

12 Dec 01, 2022
SweiNet is an uncertainty-quantifying shear wave speed (SWS) estimator for ultrasound shear wave elasticity (SWE) imaging.

SweiNet SweiNet is an uncertainty-quantifying shear wave speed (SWS) estimator for ultrasound shear wave elasticity (SWE) imaging. SweiNet takes as in

Felix Jin 3 Mar 31, 2022
Code for “ACE-HGNN: Adaptive Curvature ExplorationHyperbolic Graph Neural Network”

ACE-HGNN: Adaptive Curvature Exploration Hyperbolic Graph Neural Network This repository is the implementation of ACE-HGNN in PyTorch. Environment pyt

9 Nov 28, 2022