Official source code to CVPR'20 paper, "When2com: Multi-Agent Perception via Communication Graph Grouping"

Last update: Nov 9, 2022

Related tags

Overview

When2com: Multi-Agent Perception via Communication Graph Grouping

This is the PyTorch implementation of our paper:
When2com: Multi-Agent Perception via Communication Graph Grouping
Yen-Cheng Liu, Junjiao Tian, Nathaniel Glaser, Zsolt Kira
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020

[Paper] [GitHub] [Project]

Prerequisites

Python 3.6
Pytorch 0.4.1
Other required packages in requirement.txt

Getting started

Download and install miniconda

wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
bash Miniconda3-latest-Linux-x86_64.sh

Create conda environment

conda create -n semseg python=3.6
source actviate semseg

Install the required packages

pip install -r requirements.txt

Download AirSim-MAP dataset and unzip it.

Download the zip file you would like to run

Move the datasets to the dataset path

mkdir dataset
mv (dataset folder name) dataset/

Training

# [Single-request multi-support] All norm  
python train.py --config configs/srms-allnorm.yml --gpu=0

# [Multi-request multi-support] when2com model  
python train.py --config configs/mrms-when2com.yml --gpu=0

Testing

# [Single-request multi-support] All norm  
python test.py --config configs/srms-allnorm.yml --model_path <your trained weights> --gpu=0

# [Multi-request multi-support] when2com model  
python test.py --config configs/mrms-when2com.yml --model_path <your trained weights> --gpu=0

Acknowledgments

This work was supported by ONR grant N00014-18-1-2829.
This code is built upon the implementation from Pytorch-semseg.

Citation

If you find this repository useful, please cite our paper:

@inproceedings{liu2020when2com,
    title={When2com: Multi-Agent Perception via Communication Graph Grouping},
    author={Yen-Cheng Liu and Junjiao Tian and Nathaniel Glaser and Zsolt Kira},
    booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
    year={2020}
}

Comments

Error about Dataset
Sorry to bother you! Here I unziped "airsim-mrms-noise-data.zip", then I run the model, it throw out the error below:

Loaded: selection label. Traceback (most recent call last): File "train.py", line 142, in <module> t_loader = data_loader( File "G:\TWang\MultiAgentPerception-master\ptsemseg\loader\airsim_loader.py", line 258, in __init__ raise Exception( Exception: No files for split=[train] found in dataset/airsim-mrms-noise-data

Actually, I split the dataset into the form, according to

Could you show me how you construct your dataset form? Or there lack the code of preprocess dataset to generate the split form?
opened by TianhangWang 1
Bump numpy from 1.21.0 to 1.22.0
Bumps numpy from 1.21.0 to 1.22.0.

Release notes

Sourced from numpy's releases.

v1.22.0

NumPy 1.22.0 Release Notes

NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. There have been many improvements, highlights are:

Annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release.

A preliminary version of the proposed Array-API is provided. This is a step in creating a standard collection of functions that can be used across application such as CuPy and JAX.

NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data.

New methods for quantile, percentile, and related functions. The new methods provide a complete set of the methods commonly found in the literature.

A new configurable allocator for use by downstream projects.

These are in addition to the ongoing work to provide SIMD support for commonly used functions, improvements to F2PY, and better documentation.

The Python versions supported in this release are 3.8-3.10, Python 3.7 has been dropped. Note that 32 bit wheels are only provided for Python 3.8 and 3.9 on Windows, all other wheels are 64 bits on account of Ubuntu, Fedora, and other Linux distributions dropping 32 bit support. All 64 bit wheels are also linked with 64 bit integer OpenBLAS, which should fix the occasional problems encountered by folks using truly huge arrays.

Expired deprecations

Deprecated numeric style dtype strings have been removed

Using the strings "Bytes0", "Datetime64", "Str0", "Uint32", and "Uint64" as a dtype will now raise a TypeError.

(gh-19539)

Expired deprecations for loads, ndfromtxt, and mafromtxt in npyio

numpy.loads was deprecated in v1.15, with the recommendation that users use pickle.loads instead. ndfromtxt and mafromtxt were both deprecated in v1.17 - users should use numpy.genfromtxt instead with the appropriate value for the usemask parameter.

(gh-19615)

... (truncated)

Commits

4adc87d Merge pull request #20685 from charris/prepare-for-1.22.0-release

fd66547 REL: Prepare for the NumPy 1.22.0 release.

125304b wip

c283859 Merge pull request #20682 from charris/backport-20416

5399c03 Merge pull request #20681 from charris/backport-20954

f9c45f8 Merge pull request #20680 from charris/backport-20663

794b36f Update armccompiler.py

d93b14e Update test_public_api.py

7662c07 Update init.py

311ab52 Update armccompiler.py

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dependencies
opened by dependabot[bot] 0
Bump numpy from 1.12.1 to 1.21.0
Bumps numpy from 1.12.1 to 1.21.0.

Release notes

Sourced from numpy's releases.

v1.21.0

NumPy 1.21.0 Release Notes

The NumPy 1.21.0 release highlights are

continued SIMD work covering more functions and platforms,

initial work on the new dtype infrastructure and casting,

universal2 wheels for Python 3.8 and Python 3.9 on Mac,

improved documentation,

improved annotations,

new PCG64DXSM bitgenerator for random numbers.

In addition there are the usual large number of bug fixes and other improvements.

The Python versions supported for this release are 3.7-3.9. Official support for Python 3.10 will be added when it is released.

:warning: Warning: there are unresolved problems compiling NumPy 1.21.0 with gcc-11.1 .

Optimization level -O3 results in many wrong warnings when running the tests.

On some hardware NumPy will hang in an infinite loop.

New functions

Add PCG64DXSM BitGenerator

Uses of the PCG64 BitGenerator in a massively-parallel context have been shown to have statistical weaknesses that were not apparent at the first release in numpy 1.17. Most users will never observe this weakness and are safe to continue to use PCG64. We have introduced a new PCG64DXSM BitGenerator that will eventually become the new default BitGenerator implementation used by default_rng in future releases. PCG64DXSM solves the statistical weakness while preserving the performance and the features of PCG64.

See upgrading-pcg64 for more details.

(gh-18906)

Expired deprecations

The shape argument numpy.unravel_index cannot be passed as dims keyword argument anymore. (Was deprecated in NumPy 1.16.)

... (truncated)

Commits

b235f9e Merge pull request #19283 from charris/prepare-1.21.0-release

34aebc2 MAINT: Update 1.21.0-notes.rst

493b64b MAINT: Update 1.21.0-changelog.rst

07d7e72 MAINT: Remove accidentally created directory.

032fca5 Merge pull request #19280 from charris/backport-19277

7d25b81 BUG: Fix refcount leak in ResultType

fa5754e BUG: Add missing DECREF in new path

61127bb Merge pull request #19268 from charris/backport-19264

143d45f Merge pull request #19269 from charris/backport-19228

d80e473 BUG: Removed typing for == and != in dtypes

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dependencies
opened by dependabot[bot] 0
Something about training

Sorry to bother you. Thanks for you work. When I enter the command 'python train.py --config configs/multi-request-multyi-support/mrms_when2com.yml --gpu=3' in the terminal to run the file, an error 'RuntimeError: CuDNN error: CUDNN_STATUS_MAPPING_ERROR' will be reported. Do you know how to solve this problem?

opened by lubin2022 0
The required version of the packages and python

Hello! Could you please tell me exactly the required version of the packages and python. In the tutorial, the required version of python is 3.6. In the requirements.txt, the required version of packages are matplotlib==2.0.0 numpy==1.22.0 scipy==0.19.0 torch==0.4.1 torchvision==0.2.0 tqdm==4.11.2 pydensecrf protobuf tensorboardX pyyaml pretrainedmodels opencv-python But there are some version mismatch issues. So I will appreciate it if you can tell me exactly the required version of the packages and python

opened by lubin2022 1

Official source code to CVPR'20 paper, "When2com: Multi-Agent Perception via Communication Graph Grouping"

Related tags

Overview

When2com: Multi-Agent Perception via Communication Graph Grouping

Prerequisites

Getting started

Download and install miniconda

Create conda environment

Install the required packages

Download AirSim-MAP dataset and unzip it.

Move the datasets to the dataset path

Training

Testing

Acknowledgments

Citation

Comments

Error about Dataset

Bump numpy from 1.21.0 to 1.22.0

v1.22.0

NumPy 1.22.0 Release Notes

Expired deprecations

Deprecated numeric style dtype strings have been removed

Expired deprecations for loads, ndfromtxt, and mafromtxt in npyio

Bump numpy from 1.12.1 to 1.21.0

v1.21.0

NumPy 1.21.0 Release Notes

New functions

Add PCG64DXSM BitGenerator

Expired deprecations

Something about training

The required version of the packages and python

Owner

An official source code for paper Deep Graph Clustering via Dual Correlation Reduction, accepted by AAAI 2022

Empirical Study of Transformers for Source Code & A Simple Approach for Handling Out-of-Vocabulary Identifiers in Deep Learning for Source Code

Code for the prototype tool in our paper "CoProtector: Protect Open-Source Code against Unauthorized Training Usage with Data Poisoning".

The LaTeX and Python code for generating the paper, experiments' results and visualizations reported in each paper is available (whenever possible) in the paper's directory

Official source code of Fast Point Transformer, CVPR 2022

Inference code for "StylePeople: A Generative Model of Fullbody Human Avatars" paper. This code is for the part of the paper describing video-based avatars.

Open source repository for the code accompanying the paper 'Non-Rigid Neural Radiance Fields Reconstruction and Novel View Synthesis of a Deforming Scene from Monocular Video'.

[CVPR2021] The source code for our paper 《Removing the Background by Adding the Background: Towards Background Robust Self-supervised Video Representation Learning》.

Open source code for Paper "A Co-Interactive Transformer for Joint Slot Filling and Intent Detection"

Source code, datasets and trained models for the paper Learning Advanced Mathematical Computations from Examples (ICLR 2021), by François Charton, Amaury Hayat (ENPC-Rutgers) and Guillaume Lample

Implementation of the paper "Language-agnostic representation learning of source code from structure and context".

source code and pre-trained/fine-tuned checkpoint for NAACL 2021 paper LightningDOT

Code to reproduce the experiments in the paper "Transformer Based Multi-Source Domain Adaptation" (EMNLP 2020)

Source code for the GPT-2 story generation models in the EMNLP 2020 paper "STORIUM: A Dataset and Evaluation Platform for Human-in-the-Loop Story Generation"

The source code for the Cutoff data augmentation approach proposed in this paper: "A Simple but Tough-to-Beat Data Augmentation Approach for Natural Language Understanding and Generation".

Source code for the Paper: CombOptNet: Fit the Right NP-Hard Problem by Learning Integer Programming Constraints}

source code the paper Fast and Robust Iterative Closet Point.

code for our paper "Source Data-absent Unsupervised Domain Adaptation through Hypothesis Transfer and Labeling Transfer"

Source code for paper "Document-Level Relation Extraction with Adaptive Thresholding and Localized Context Pooling", AAAI 2021

Expired deprecations for `loads`, `ndfromtxt`, and `mafromtxt` in npyio