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Discrete-Continuous-VLN

Code and Data of the CVPR 2022 paper: <br>Bridging the Gap Between Learning in Discrete and Continuous Environments for Vision-and-Language Navigation<br> Yicong Hong, Zun Wang, Qi Wu, Stephen Gould<br>

[Paper & Appendices] [CVPR2022 Video] [GitHub]

The method presented in this paper is also the base method for winning the: <br>1st Place in the Room-Across-Room (RxR) Habitat Challenge in CVPR 2022<br> Dong An, Zun Wang, Yangguang Li, Yi Wang, Yicong Hong, Yan Huang, Liang Wang, Jing Shao<br>

[Habitat-RxR Challenge Report] [Habitat-RxR Challenge Certificate]

"Interlinked. Interlinked. What's it like to hold the hand of someone you love? Interlinked. Interlinked. Did they teach you how to feel finger to finger? Interlinked. Interlinked. Do you long for having your heart interlinked? Interlinked. Do you dream about being interlinked? Interlinked." --- Blade Runner 2049 (2017).

TODOs

Update (18Sep2023): We sincerely apologize that our code has a bug in Policy_ViewSelection_CMA.py-Line#198-199 and Policy_ViewSelection_VLNBERT.py-Line#141-142; two important lines to control the visual encoders in training self.rgb_encoder.cnn.eval() and self.depth_encoder.eval() were accidentally deleted in the published version, which will cause around 3% absolute drop in results compared to the reported numbers in our paper. We have fixed this issue, and again, we want to deeply apologize for the inconvenience brought to all researchers.

Update: Thanks ZunWang for releasing the code for collecting the data and training the Candidate Waypoint Predictor.

Update: Thanks ZunWang for contributing the depth-only Candidate Waypoint Prediction model for FoV 90 (R2R-CE) and FoV 79 (RxR-CE), the architecture remains the same but the input reduces to the DD-PPO depth encoder features. The model produces more accurate waypoint prediction results than the one used in our paper. Weights are uploaded in the section below.

• VLN-CE Installation Guide
• Submitted version R2R-CE code of CMA and Recurrent-VLN-BERT with the CWP
• Running guide
• Pre-trained weights of the navigator networks and the CWP
• [ ] RxR-CE code
• Graph construction code
• Candidate Waypoint Predictor training code
• Connectivity graphs in continuous environments
• [ ] Graph-walk in continuous environments code
• Test all code for single-node multi-GPU-processing

Prerequisites

Installation

Follow the Habitat Installation Guide to install habitat-lab and habitat-sim. We use version v0.1.7 in our experiments, same as in the VLN-CE, please refer to the VLN-CE page for more details. In brief:

1. Create a virtual environment. We developed this project with Python 3.6.

   conda create -n dcvln python=3.6
   conda activate dcvln
   

2. Install habitat-sim for a machine with multiple GPUs or without an attached display (i.e. a cluster):

   conda install -c aihabitat -c conda-forge habitat-sim=0.1.7 headless
   

3. Clone this repository and install all requirements for habitat-lab, VLN-CE and our experiments. Note that we specify gym==0.21.0 because its latest version is not compatible with habitat-lab-v0.1.7.

   git clone git@github.com:YicongHong/Discrete-Continuous-VLN.git
   cd Discrete-Continuous-VLN
   python -m pip install -r requirements.txt
   pip install torch==1.8.1+cu111 torchvision==0.9.1+cu111 -f https://download.pytorch.org/whl/torch_stable.html
   

4. Clone a stable habitat-lab version from the github repository and install. The command below will install the core of Habitat Lab as well as the habitat_baselines.

   git clone --branch v0.1.7 git@github.com:facebookresearch/habitat-lab.git
   cd habitat-lab
   python setup.py develop --all # install habitat and habitat_baselines
   

Scenes: Matterport3D

Instructions copied from VLN-CE:

Matterport3D (MP3D) scene reconstructions are used. The official Matterport3D download script (download_mp.py) can be accessed by following the instructions on their project webpage. The scene data can then be downloaded:

# requires running with python 2.7
python download_mp.py --task habitat -o data/scene_datasets/mp3d/

Extract such that it has the form scene_datasets/mp3d/{scene}/{scene}.glb. There should be 90 scenes. Place the scene_datasets folder in data/.

Adapted MP3D Connectivity Graphs in Continuous Environments

We adapt the MP3D connectivity graphs defined for the discrete environments to the continuous Habitat-MP3D environments, such that all nodes are positioned in open space and all edges on the graph are fully traversable by an agent (with VLN-CE configursations). Please refer to Section 4.2 and Appendices A.1 in our paper for more details.

Link to download the adapted connectivity graphs.

Each file for a specific MP3D scene contains the positions of a set of nodes and edges connecting two adjacent nodes. From the node ids, you will find nodes inherited from the original graph, as well as new nodes added by us to complete the graph.

Trained Network Weights

• Candidate Waypoint Predictor: waypoint_prediction/checkpoints/check_val_best_avg_wayscore

  • The pre-trained weights used in our paper (FoV 90 RGB-D).
  • The pre-trained depth-only weights (FoV 90 for R2R-CE).
  • The pre-trained depth-only weights (FoV 79 for RxR-CE).

• ResNet-50 Depth Encoder: data/pretrained_models/ddppo-models/gibson-2plus-resnet50.pth

  • Trained for Point-Goal navigation in Gibson with DD-PPO.

• Recurrent VLN-BERT Initialization: data/pretrained_models/rec_vln_bert-models/vlnbert_prevalent_model.bin

  • From the pre-trained Transformers PREVALENT.

• Trained CMA agent: logs/checkpoints/cont-cwp-cma-ori/cma_ckpt_best.pth

  • Paper of the Cross-Modal Matching Agent

• Trained Recurrent VLN-BERT agent: logs/checkpoints/cont-cwp-vlnbert-ori/vlnbert_ckpt_best.pth

  • Paper of the Recurrent VLN-BERT

Running

Please refer to Peter Anderson's VLN paper for the R2R Navigation task, and Jacob Krantz's VLN-CE for R2R in continuous environments (R2R-CE).

Training and Evaluation

We apply two popular navigator models, CMA and Recurrent VLN-BERT in our experiments.

Use run_CMA.bash and run_VLNBERT.bash for Training with a single GPU, Training on a single node with multiple GPUs, Evaluation or Inference. Simply uncomment the corresponding lines in the files and do

bash run_CMA.bash

or

bash run_VLNBERT.bash

By running Evaluation, you should obtain very similar results as in logs/eval_results/. Running Inference generates the trajectories for submission to the R2R-CE Test Server.

Hardware

The training of networks are performed on a single NVIDIA RTX 3090 GPU, which takes about 3.5 days to complete.

Related Works

If you are interested in this research direction for VLN, below are some closely related works.

Waypoint Models for Instruction-guided Navigation in Continuous Environments (ICCV2021) by Jacob Krantz, Aaron Gokaslan, Dhruv Batra, Stefan Lee and Oleksandr Maksymets.

Sim-2-Sim Transfer for Vision-and-Language Navigation in Continuous Environments (2022) by Jacob Krantz and Stefan Lee.

Sim-to-Real Transfer for Vision-and-Language Navigation (CoRL2021) by Peter Anderson, Ayush Shrivastava, Joanne Truong, Arjun Majumdar, Devi Parikh, Dhruv Batra and Stefan Lee.

Citation

Please cite our paper:

@InProceedings{Hong_2022_CVPR,
    author    = {Hong, Yicong and Wang, Zun and Wu, Qi and Gould, Stephen},
    title     = {Bridging the Gap Between Learning in Discrete and Continuous Environments for Vision-and-Language Navigation},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2022}
}