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Conference Paper Python 3.6 Supports Habitat Lab

MultiON: Benchmarking Semantic Map Memory using Multi-Object Navigation

This is a PyTorch implementation of our NeurIPS 2020 paper, MultiON: Benchmarking Semantic Map Memory using Multi-Object Navigation.

Project Webpage: https://shivanshpatel35.github.io/multi-ON/

Architecture Overview

Installing dependencies:

This code is tested on python 3.6.10, pytorch v1.4.0 and CUDA V9.1.85.

Install pytorch from https://pytorch.org/ according to your machine configuration.

This code uses latest versions of habitat-sim and habitat-lab. Install them by following instructions at the respective web-sites.

Installing habitat-sim (via conda):

For headless machines with GPU
conda install habitat-sim headless -c conda-forge -c aihabitat
For machines with attached display
conda install habitat-sim -c conda-forge -c aihabitat

Installing habitat-lab:

git clone --branch stable https://github.com/facebookresearch/habitat-lab.git
cd habitat-lab
pip install -e .

We know that roadblocks can come up while installing Habitat, we are here to help! For installation issues in habitat, feel free to raise an issue in this repository, or in the corresponding habitat repository.

Setup

Clone the repository and install the requirements:

git clone https://github.com/saimwani/multiON
cd multiON
pip install -r requirements.txt

Downloading data and checkpoints

To evaluate pre-trained models and train new models, you will need to download the MultiON dataset, including objects inserted into the scenes, and model checkpoints. Running download_multion_data.sh from the root directory (multiON/) will download the data and extract it to appropriate directories. Note that you are still required to download Matterport3D scenes after you run the script (see section on Download Matterport3D scenes below). Running the script will download the OracleEgoMap (oracle-ego) pre-trained model by default. If you'd like to evaluate other pre-trained models, see this.

bash download_multion_data.sh

Download multiON dataset

You do not need to complete this step if you have successfully run the download_multion_data.sh script above.

Run the following to download multiON dataset and cached oracle occupancy maps:

mkdir data
cd data
mkdir datasets
cd datasets
wget -O multinav.zip "http://aspis.cmpt.sfu.ca/projects/multion/multinav.zip"
unzip multinav.zip && rm multinav.zip
cd ../
wget -O multiON_objects.zip "http://aspis.cmpt.sfu.ca/projects/multion/multiON_objects.zip"
unzip multiON_objects.zip && rm multiON_objects.zip
wget -O default.phys_scene_config.json "http://aspis.cmpt.sfu.ca/projects/multion/default.phys_scene_config.json"
cd ../
mkdir oracle_maps
cd oracle_maps
wget -O map300.pickle "http://aspis.cmpt.sfu.ca/projects/multion/map300.pickle"
cd ../

Download Matterport3D scenes

The Matterport scene dataset and multiON dataset should be placed in data folder under the root directory (multiON/) in the following format:

multiON/
  data/
    scene_datasets/
      mp3d/
        1LXtFkjw3qL/
          1LXtFkjw3qL.glb
          1LXtFkjw3qL.navmesh
          ...
    datasets/
      multinav/
        3_ON/
          train/
            ...
          val/
            val.json.gz
        2_ON
          ...
        1_ON
          ...

Download Matterport3D data for Habitat by following the instructions mentioned here.

Usage

Pre-trained models

You do not need to complete this step if you have successfully run the download_multion_data.sh script above.

mkdir model_checkpoints

Download a pre-trained agent model as shown below.

Agent Run
NoMap(RNN) wget -O model_checkpoints/ckpt.0.pth "http://aspis.cmpt.sfu.ca/projects/multion/model_checkpoints/ckpt.0.pth"
ProjNeural wget -O model_checkpoints/ckpt.1.pth "http://aspis.cmpt.sfu.ca/projects/multion/model_checkpoints/ckpt.1.pth"
ObjRecog wget -O model_checkpoints/ckpt.2.pth "http://aspis.cmpt.sfu.ca/projects/multion/model_checkpoints/ckpt.2.pth"
OracleEgoMap wget -O model_checkpoints/ckpt.3.pth "http://aspis.cmpt.sfu.ca/projects/multion/model_checkpoints/ckpt.3.pth"
OracleMap wget -O model_checkpoints/ckpt.4.pth "http://aspis.cmpt.sfu.ca/projects/multion/model_checkpoints/ckpt.4.pth"

Evaluation

Evaluation will run on the 3_ON test set by default. To change this, specify the dataset path here.

To evaluate a pretrained OracleEgoMap (oracle-ego) agent, run this from the root folder (multiON/):

python habitat_baselines/run.py --exp-config habitat_baselines/config/multinav/ppo_multinav.yaml --agent-type oracle-ego --run-type eval

For other agent types, the --agent-type argument should be changed according to this table:

Agent Agent type
NoMap(RNN) no-map
OracleMap oracle
OracleEgoMap oracle-ego
ProjNeuralmap proj-neural
ObjRecogMap obj-recog

Average evaluation metrics are printed on the console when evaluation ends. Detailed metrics are placed in eval/metrics directory.

Training

For training an OracleEgoMap (oracle-ego) agent, run this from the root directory:

python habitat_baselines/run.py --exp-config habitat_baselines/config/multinav/ppo_multinav.yaml --agent-type oracle-ego --run-type train

For other agent types, the --agent-type argument would change accordingly.

Citation

Saim Wani*, Shivansh Patel*, Unnat Jain*, Angel X. Chang, Manolis Savva, 2020. MultiON: Benchmarking Semantic Map Memory using Multi-Object Navigation in Neural Information Processing Systems (NeurIPS). PDF

Bibtex

  @inproceedings{wani2020multion,
  title={Multi-ON: Benchmarking Semantic Map Memory using Multi-Object Navigation},
  author={Saim Wani and Shivansh Patel and Unnat Jain and Angel X. Chang and Manolis Savva},
  booktitle={Neural Information Processing Systems (NeurIPS)},
  year={2020},
}

Acknowledgements

This repository is built upon Habitat Lab.