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AVAST: Attentive VAriational State Tracker for Vision-and-Language Navigation

This is the PyTorch implementation for AVAST: Attentive VAriational State Tracker for Vision-and-Language Navigation from National Chiao Tung University, Taiwan.

Installation

Clone the AVAST_R2R repository:

git clone --recursive https://github.com/NCTUMLlab/Je-Wei-Jang-AVAST_Attentive_Variational_State_Tracker_for_Vision-and-Language-Navigation.git
mv Je-Wei-Jang-AVAST_Attentive_Variational_State_Tracker_for_Vision-and-Language-Navigation AVAST_R2R
cd AVAST_R2R

Downloading pre-trained embedding and R2R datasets

bash download.sh

Downloading Matterport3D Dataset (optional)

To use original Matterport3D simulator you must first download the Matterport3D Dataset which is available after requesting access here. The download script that will be provided allows for downloading of selected data types. At minimum you must download the matterport_skybox_images. If you wish to use depth outputs then also download undistorted_depth_images and undistorted_camera_parameters.

Set an environment variable to the location of the unzipped dataset, where is the full absolute path (not a relative path or symlink) to the directory containing the individual matterport scan directories (17DRP5sb8fy, 2t7WUuJeko7, etc):

export MATTERPORT_DATA_DIR=<PATH>

Building using Docker

docker build --rm -t r2r/avast:base .
docker run --name r2r -it --gpus all \
    -e DISPLAY -e="QT_X11_NO_MITSHM=1" -v /tmp/.X11-unix:/tmp/.X11-unix \
    --mount type=bind,source=$MATTERPORT_DATA_DIR,target=/root/mount/AVAST_R2R/data/v1/scans \
    --volume `pwd`:/root/mount/AVAST_R2R \
    --restart=unless-stopped --shm-size 32G \
    -p <ssh_port>:22 -p <tensorboard_port>:6006 r2r/avast:base

Buiding Matterport3D simulator

Now (from inside the docker container), build the simulator code:

cd /root/mount/AVAST_R2R
mkdir build && cd build
cmake -DEGL_RENDERING=ON ..
make
cd ../
echo export PYTHONPATH=$PYTHONPATH:/root/mount/AVAST_R2R/build >> ~/.bashrc
source ~/.bashrc

Buiding Lookup Table for Location Connectivity

Now (from inside the docker container), build lookup table for location connectvity:

python3 tasks/data/scripts/generate_adj_dict.py > ./tasks/env/adj_dict/total_adj_dict.json

Training and evaluation

cd tasks

Pre-training state trackers

  • Pre-training attentive state tracker: 
    python3 ast_pre_train.py --mode pre_train --state_tracker ast --agent seq2seq
  • pre-training attentive variational state tracker: 
    python3 avast_pre_train.py --mode pre_train --state_tracker avast --agent seq2seq

Fine-tuning an agent with RL algorithms

  • Fine-tuning an agent with AST+REINFORCE: 
    python3 reinforce_fine_tune.py --mode train --state_tracker ast --load_pre_trained_dir <ast_path> --agent reinforce
  • Fine-tuning an agent with AVAST+SACD+RECED: 
    python3 sacd_fine_tune.py --mode train --state_tracker avast --load_pre_trained_dir <avast_path> --agent sacd --demo_activate --curriculum

Evaluating AST+Seq2Seq

  • Evaluating AST+Seq2Seq: 
    python3 ast_pre_train.py --mode test --state_tracker ast --agent seq2seq --load_dir <model_path>
  • Evaluating AVAST+Seq2Seq: 
    python3 avast_pre_train.py --mode test --state_tracker avast --agent seq2seq --load_dir <model_path>
  • Evaluating AST+REINFORCE: 
    python3 reinforce_fine_tune.py --mode test --state_tracker ast --agent reinforce --load_dir <model_path>
  • Evaluating AVAST+SACD: 
    python3 sacd_fine_tune.py --mode test --state_tracker avast --agent sacd --load_dir <model_path>