Recurrent Conditional Query Learning

Related tags

Deep Learning RCQL
Overview

Recurrent Conditional Query Learning (RCQL)

This repository contains the Pytorch implementation of

One Model Packs Thousands of Items with Recurrent Conditional Query Learning

Dongda Li, Zhaoquan Gu, Yuexuan Wang, Changwei Ren, Francis C.M. Lau

We propose a Recurrent Conditional Query Learning (RCQL) method to solve both 2D and 3D packing problems. We first embed states by a recurrent encoder, and then adopt attention with conditional queries from previous actions. The conditional query mechanism fills the information gap between learning steps, which shapes the problem as a Markov decision process. Benefiting from the recurrence, a single RCQL model is capable of handling different sizes of packing problems. Experiment results show that RCQL can effectively learn strong heuristics for offline and online strip packing problems (SPPs), out- performing a wide range of baselines in space utilization ratio. RCQL reduces the average bin gap ratio by 1.83% in offline 2D 40-box cases and 7.84% in 3D cases compared with state-of-the-art methods. Meanwhile, our method also achieves 5.64% higher space utilization ratio for SPPs with 1000 items than the state of the art.

Usage

Preparation

  1. Install conda
  2. Run conda env create -f environment.yml

Train

  1. Modify the config file in config.py as you need.
  2. Run python main.py.
You might also like...
the code of the paper: Recurrent Multi-view Alignment Network for Unsupervised Surface Registration (CVPR 2021)
the code of the paper: Recurrent Multi-view Alignment Network for Unsupervised Surface Registration (CVPR 2021)

RMA-Net This repo is the implementation of the paper: Recurrent Multi-view Alignment Network for Unsupervised Surface Registration (CVPR 2021). Paper

Wanquan Feng 205 Nov 9, 2022
PyTorch implementation DRO: Deep Recurrent Optimizer for Structure-from-Motion
PyTorch implementation DRO: Deep Recurrent Optimizer for Structure-from-Motion

DRO: Deep Recurrent Optimizer for Structure-from-Motion This is the official PyTorch implementation code for DRO-sfm. For technical details, please re

Alibaba Cloud 56 Dec 12, 2022
Stacked Recurrent Hourglass Network for Stereo Matching
Stacked Recurrent Hourglass Network for Stereo Matching

SRH-Net: Stacked Recurrent Hourglass Introduction This repository is supplementary material of our RA-L submission, which helps reviewers to understan

null 28 Jan 3, 2023
Official repository for the paper "Going Beyond Linear Transformers with Recurrent Fast Weight Programmers"

Recurrent Fast Weight Programmers This is the official repository containing the code we used to produce the experimental results reported in the pape

IDSIA 36 Nov 15, 2022
Official repository for the paper "Can You Learn an Algorithm? Generalizing from Easy to Hard Problems with Recurrent Networks"

Easy-To-Hard The official repository for the paper "Can You Learn an Algorithm? Generalizing from Easy to Hard Problems with Recurrent Networks". Gett

Avi Schwarzschild 52 Sep 8, 2022
An implementation of DeepMind's Relational Recurrent Neural Networks in PyTorch.
An implementation of DeepMind's Relational Recurrent Neural Networks in PyTorch.

relational-rnn-pytorch An implementation of DeepMind's Relational Recurrent Neural Networks (Santoro et al. 2018) in PyTorch. Relational Memory Core (

Sang-gil Lee 241 Nov 18, 2022
Pytorch implementation of the Variational Recurrent Neural Network (VRNN).
Pytorch implementation of the Variational Recurrent Neural Network (VRNN).

VariationalRecurrentNeuralNetwork Pytorch implementation of the Variational RNN (VRNN), from A Recurrent Latent Variable Model for Sequential Data. Th

emmanuel 251 Dec 17, 2022
PyTorch implementation of the Quasi-Recurrent Neural Network - up to 16 times faster than NVIDIA's cuDNN LSTM
PyTorch implementation of the Quasi-Recurrent Neural Network - up to 16 times faster than NVIDIA's cuDNN LSTM

Quasi-Recurrent Neural Network (QRNN) for PyTorch Updated to support multi-GPU environments via DataParallel - see the the multigpu_dataparallel.py ex

Salesforce 1.3k Dec 28, 2022
RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo Matching
RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo Matching

RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo Matching This repository contains the source code for our paper: RAFT-Stereo: Multilevel

Princeton Vision & Learning Lab 328 Jan 9, 2023
Comments
  • run error

    run error

    when I run the main.py, I got below error

    line 178, in update_rotate rotate_mask[i] = rotate.squeeze(-1).eq(i) NameError: name 'rotate_mask' is not defined

    opened by Xiong5Heng 3
  • runtimeerror

    runtimeerror

    您好，我在运行python main.py时遇到了这样的问题RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu! (when checking argument for argument mask in method wrapper__masked_select)

    opened by 13086628579 2
  • Simply run main.py with default params, avg_reward get smaller and smaller

    Simply run main.py with default params, avg_reward get smaller and smaller

    | actor_loss | 0.0489 | | alpha_loss | -29.1 | | avg_rewards | 272 | | entropy | 3.32 | | epoch | 0 | | explained_variance | -0.000163 | | gap_ratio | 0.85 | | value_loss | 41.3 | | var_gap_ratio | 8.53e-05 |

    ...

    | actor_loss | 0.00868 | | alpha_loss | -17.8 | | avg_rewards | -21.5 | | entropy | 0.358 | | epoch | 2.28e+03 | | explained_variance | 0.883 | | gap_ratio | 0.47 | | value_loss | 0.0347 | | var_gap_ratio | 0.000503 |

    opened by Jetcodery 2
Owner
Dongda
Dongda
GitHub
Code for ACL 21: Generating Query Focused Summaries from Query-Free Resources

marge This repository releases the code for Generating Query Focused Summaries from Query-Free Resources. Please cite the following paper [bib] if you

Yumo Xu 28 Nov 10, 2022
Official implementation for NIPS'17 paper: PredRNN: Recurrent Neural Networks for Predictive Learning Using Spatiotemporal LSTMs.

PredRNN: A Recurrent Neural Network for Spatiotemporal Predictive Learning The predictive learning of spatiotemporal sequences aims to generate future

THUML: Machine Learning Group @ THSS 243 Dec 26, 2022
VITS: Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech

VITS: Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech Jaehyeon Kim, Jungil Kong, and Juhee Son In our rece

Jaehyeon Kim 1.7k Jan 8, 2023
Official implementation of the paper DeFlow: Learning Complex Image Degradations from Unpaired Data with Conditional Flows

DeFlow: Learning Complex Image Degradations from Unpaired Data with Conditional Flows Official implementation of the paper DeFlow: Learning Complex Im

Valentin Wolf 86 Nov 16, 2022
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

null 1 Nov 30, 2021
Official implementation for "QS-Attn: Query-Selected Attention for Contrastive Learning in I2I Translation" (CVPR 2022)

QS-Attn: Query-Selected Attention for Contrastive Learning in I2I Translation (CVPR2022) https://arxiv.org/abs/2203.08483 Unpaired image-to-image (I2I

Xueqi Hu 50 Dec 16, 2022
Code for the ECCV2020 paper "A Differentiable Recurrent Surface for Asynchronous Event-Based Data"

A Differentiable Recurrent Surface for Asynchronous Event-Based Data Code for the ECCV2020 paper "A Differentiable Recurrent Surface for Asynchronous

Marco Cannici 21 Oct 5, 2022
Implementation of our paper 'RESA: Recurrent Feature-Shift Aggregator for Lane Detection' in AAAI2021.

RESA PyTorch implementation of the paper "RESA: Recurrent Feature-Shift Aggregator for Lane Detection". Our paper has been accepted by AAAI2021. Intro

null 137 Jan 2, 2023
OHLC Average Prediction of Apple Inc. Using LSTM Recurrent Neural Network

Stock Price Prediction of Apple Inc. Using Recurrent Neural Network OHLC Average Prediction of Apple Inc. Using LSTM Recurrent Neural Network Dataset:

Nouroz Rahman 410 Jan 5, 2023
Code and datasets for the paper "Combining Events and Frames using Recurrent Asynchronous Multimodal Networks for Monocular Depth Prediction" (RA-L, 2021)

Combining Events and Frames using Recurrent Asynchronous Multimodal Networks for Monocular Depth Prediction This is the code for the paper Combining E

Robotics and Perception Group 69 Dec 26, 2022