98 lines
4.1 KiB
Markdown
98 lines
4.1 KiB
Markdown
# Recurrent-VLN-BERT
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Code of the Recurrent-VLN-BERT paper:
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**A Recurrent Vision-and-Language BERT for Navigation**<br>
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[**Yicong Hong**](http://www.yiconghong.me/), [Qi Wu](http://www.qi-wu.me/), [Yuankai Qi](https://sites.google.com/site/yuankiqi/home), [Cristian Rodriguez-Opazo](https://crodriguezo.github.io/), [Stephen Gould](http://users.cecs.anu.edu.au/~sgould/)<br>
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[[Paper & Appendices](https://arxiv.org/abs/2011.13922) | [GitHub](https://github.com/YicongHong/Entity-Graph-VLN)]
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## Prerequisites
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### Installation
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Install the [Matterport3D Simulator](https://github.com/peteanderson80/Matterport3DSimulator).
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Please find the versions of packages in our environment [here](https://github.com/YicongHong/Recurrent-VLN-BERT/blob/main/recurrent-vln-bert.yml).
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Install the [Pytorch-Transformers](https://github.com/huggingface/transformers).
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In particular, we use [this version](https://github.com/huggingface/transformers/tree/067923d3267325f525f4e46f357360c191ba562e) (same as [OSCAR](https://github.com/microsoft/Oscar)) in our experiments.
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### Data Preparation
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Please follow the instructions below to prepare the data in directories:
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- MP3D navigability graphs: `connectivity`
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- Download the [connectivity maps [23.8MB]](https://github.com/peteanderson80/Matterport3DSimulator/tree/master/connectivity).
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- R2R data: `data`
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- Download the [R2R data [5.8MB]](https://github.com/peteanderson80/Matterport3DSimulator/tree/master/tasks/R2R/data).
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- Augmented data: `data/prevalent`
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- Download the [collected triplets in PREVALENT [1.5GB]](https://zenodo.org/record/4437864/files/prevalent_aug.json?download=1) (pre-processed for easy use).
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- MP3D image features: `img_features`
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- Download the [Scene features [4.2GB]](https://www.dropbox.com/s/85tpa6tc3enl5ud/ResNet-152-places365.zip?dl=1) (ResNet-152-Places365).
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### Initial OSCAR and PREVALENT weights
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Please refer to [vlnbert_init.py](https://github.com/YicongHong/Recurrent-VLN-BERT/blob/main/r2r_src/vlnbert/vlnbert_init.py) to set up the directories.
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- Pre-trained [OSCAR](https://github.com/microsoft/Oscar) weights
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- Download the `base-no-labels` following [this guide](https://github.com/microsoft/Oscar/blob/master/DOWNLOAD.md).
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- Pre-trained [PREVALENT](https://github.com/weituo12321/PREVALENT) weights
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- Download the `pytorch_model.bin` from [here](https://drive.google.com/drive/folders/1sW2xVaSaciZiQ7ViKzm_KbrLD_XvOq5y).
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### Trained Network Weights
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- Recurrent-VLN-BERT: `snap`
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- Download the [trained network weights [2.5GB]](https://zenodo.org/record/4437864/files/snap.zip?download=1) for our OSCAR-based and PREVALENT-based models.
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## R2R Navigation
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Please read Peter Anderson's VLN paper for the [R2R Navigation task](https://arxiv.org/abs/1711.07280).
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Our code is based on the code structure of the [EnvDrop](https://github.com/airsplay/R2R-EnvDrop).
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### Reproduce Testing Results
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To replicate the performance reported in our paper, load the trained network weights and run validation:
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```bash
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bash run/agent.bash
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```
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### Training
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#### Navigator
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To train the network from scratch, first train a Navigator on the R2R training split:
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Modify `run/agent.bash`, remove the argument for `--load` and set `--train listener`. Then,
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```bash
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bash run/agent.bash
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```
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The trained Navigator will be saved under `snap/`.
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#### Speaker
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You also need to train a [Speaker](https://github.com/airsplay/R2R-EnvDrop) for augmented training:
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```bash
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bash run/speak.bash
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```
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The trained Speaker will be saved under `snap/`.
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#### Augmented Navigator
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Finally, keep training the Navigator with the mixture of original data and [augmented data](http://www.cs.unc.edu/~airsplay/aug_paths.json):
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```bash
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bash run/bt_envdrop.bash
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```
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We apply a one-step learning rate decay to 1e-5 when training saturates.
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## Citation
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If you use or discuss our Entity Relationship Graph, please cite our paper:
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```
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@article{hong2020language,
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title={Language and Visual Entity Relationship Graph for Agent Navigation},
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author={Hong, Yicong and Rodriguez, Cristian and Qi, Yuankai and Wu, Qi and Gould, Stephen},
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journal={Advances in Neural Information Processing Systems},
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volume={33},
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year={2020}
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}
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```
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