Awesome
Recurrent VLN-BERT
Code of the CVPR 2021 Oral paper:<br> A Recurrent Vision-and-Language BERT for Navigation<br> Yicong Hong, Qi Wu, Yuankai Qi, Cristian Rodriguez-Opazo, Stephen Gould<br>
"Neo : Are you saying I have to choose whether Trinity lives or dies? The Oracle : No, you've already made the choice. Now you have to understand it." --- The Matrix Reloaded (2003).
Prerequisites
Installation
Install the Matterport3D Simulator. Notice that this code uses the old version (v0.1) of the simulator, but you can easily change to the latest version which supports batches of agents and it is much more efficient.
Please find the versions of packages in our environment here.
Install the Pytorch-Transformers. In particular, we use this version (same as OSCAR) in our experiments.
Data Preparation
Please follow the instructions below to prepare the data in directories:
- MP3D navigability graphs:
connectivity
- Download the connectivity maps [23.8MB].
- R2R data:
data
- Download the R2R data [5.8MB].
- Augmented data:
data/prevalent
- Download the collected triplets in PREVALENT [1.5GB] (pre-processed for easy use).
- MP3D image features:
img_features
- Download the Scene features [4.2GB] (ResNet-152-Places365).
Initial OSCAR and PREVALENT weights
Please refer to vlnbert_init.py to set up the directories.
- Pre-trained OSCAR weights
- Download the
base-no-labels
following this guide.
- Download the
- Pre-trained PREVALENT weights
- Download the
pytorch_model.bin
from here.
- Download the
Trained Network Weights
- Recurrent-VLN-BERT:
snap
- Download the trained network weights [2.5GB] for our OSCAR-based and PREVALENT-based models.
R2R Navigation
Please read Peter Anderson's VLN paper for the R2R Navigation task.
Reproduce Testing Results
To replicate the performance reported in our paper, load the trained network weights and run validation:
bash run/test_agent.bash
You can simply switch between the OSCAR-based and the PREVALENT-based VLN models by changing the arguments vlnbert
(oscar or prevalent) and load
(trained model paths).
Training
Navigator
To train the network from scratch, simply run:
bash run/train_agent.bash
The trained Navigator will be saved under snap/
.
Citation
If you use or discuss our Recurrent VLN-BERT, please cite our paper:
@InProceedings{Hong_2021_CVPR,
author = {Hong, Yicong and Wu, Qi and Qi, Yuankai and Rodriguez-Opazo, Cristian and Gould, Stephen},
title = {A Recurrent Vision-and-Language BERT for Navigation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
pages = {1643-1653}
}