ALFRED

A Benchmark for Interpreting Grounded Instructions for Everyday Tasks
Mohit Shridhar, Jesse Thomason, Daniel Gordon, Yonatan Bisk,
Winson Han, Roozbeh Mottaghi, Luke Zettlemoyer, Dieter Fox
CVPR 2020

ALFRED (Action Learning From Realistic Environments and Directives), is a new benchmark for learning a mapping from natural language instructions and egocentric vision to sequences of actions for household tasks. Long composition rollouts with non-reversible state changes are among the phenomena we include to shrink the gap between research benchmarks and real-world applications.

For the latest updates, see: askforalfred.com

Quickstart

Clone repo:

$ git clone https://github.com/askforalfred/alfred.git alfred
$ export ALFRED_ROOT=$(pwd)/alfred

Install requirements:

$ virtualenv -p $(which python3) --system-site-packages alfred_env # or whichever package manager you prefer
$ source alfred_env/bin/activate

$ cd $ALFRED_ROOT
$ pip install --upgrade pip
$ pip install -r requirements.txt

Download Trajectory JSONs and Resnet feats (~17GB):

$ cd $ALFRED_ROOT/data
$ sh download_data.sh json_feat

Train models:

$ cd $ALFRED_ROOT
$ python models/train/train_seq2seq.py --data data/json_feat_2.1.0 --model seq2seq_im_mask --dout exp/model:{model},name:pm_and_subgoals_01 --splits data/splits/oct21.json --gpu --batch 8 --pm_aux_loss_wt 0.1 --subgoal_aux_loss_wt 0.1

More Info

Dataset: Downloading full dataset, Folder structure, JSON structure.
Models: Training and Evaluation, File structure, Pre-trained models.
Data Generation: Generation, Replay Checks, Data Augmentation (high-res, depth, segementation masks etc).
FAQ: Frequently Asked Questions.

Prerequisites

Python 3
PyTorch 1.1.0
Torchvision 0.3.0
AI2THOR 2.1.0

See requirements.txt for all prerequisites

Hardware

Tested on:

GPU - GTX 1080 Ti (12GB)
CPU - Intel Xeon (Quad Core)
RAM - 16GB
OS - Ubuntu 16.04

Leaderboard

Run your model on test seen and unseen sets, and create an action-sequence dump of your agent:

$ cd $ALFRED_ROOT
$ python models/eval/leaderboard.py --model_path <model_path>/model.pth --model models.model.seq2seq_im_mask --data data/json_feat_2.1.0 --gpu --num_threads 5

This will create a JSON file, e.g. task_results_20191218_081448_662435.json, inside the <model_path> folder. Submit this JSON here: AI2 ALFRED Leaderboard. For rules and restrictions, see the getting started page.

Docker Setup

Install Docker and NVIDIA Docker.

Build

Build the image:

$ python scripts/docker_build.py

Modify docker_build.py and docker_run.py to your needs.

Run (Local)

For local machines:

$ python scripts/docker_run.py
 
  source ~/alfred_env/bin/activate
  cd $ALFRED_ROOT

Run (Headless)

For headless VMs and Cloud-Instances:

$ python scripts/docker_run.py --headless 

  # inside docker
  tmux new -s startx  # start a new tmux session

  # start nvidia-xconfig (might have to run this twice)
  sudo nvidia-xconfig -a --use-display-device=None --virtual=1280x1024
  sudo nvidia-xconfig -a --use-display-device=None --virtual=1280x1024

  # start X server on DISPLAY 0
  sudo python ~/alfred/scripts/startx.py 0  # if this throws errors e.g "(EE) Server terminated with error (1)" or "(EE) already running ..." try a display > 0

  # detach from tmux shell
  # Ctrl+b then d

  # source env
  source ~/alfred_env/bin/activate
  
  # set DISPLAY variable to match X server
  export DISPLAY=:0

  # check THOR
  cd $ALFRED_ROOT
  python scripts/check_thor.py

  ###############
  ## (300, 300, 3)
  ## Everything works!!!

You might have to modify X_DISPLAY in gen/constants.py depending on which display you use.

Cloud Instance

ALFRED can be setup on headless machines like AWS or GoogleCloud instances. The main requirement is that you have access to a GPU machine that supports OpenGL rendering. Run the startx.py script to examine the GPU devices on the host, generate a xorg.conf file, and then start X. You should be able to run AI2THOR normally for evaluation purposes. By default, the :0.0 display will be used, but if you are running on a machine with more than one GPU, you can address these by modifying the screen component of the display. So :0.0 refers to the first device, :0.1 the second and so on.

Also, checkout this guide: Setting up THOR on Google Cloud

Citation

If you find the dataset or code useful, please cite:

@inproceedings{ALFRED20,
  title ={{ALFRED: A Benchmark for Interpreting Grounded
           Instructions for Everyday Tasks}},
  author={Mohit Shridhar and Jesse Thomason and Daniel Gordon and Yonatan Bisk and
          Winson Han and Roozbeh Mottaghi and Luke Zettlemoyer and Dieter Fox},
  booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
  url  = {https://arxiv.org/abs/1912.01734}
}

License

MIT License

Change Log

07/04/2020:

Updated download links. Switched from Google Cloud to AWS. Old download links will be deactivated.

28/03/2020:

Updated the mask-interaction API to use IoU scores instead of max pixel count for selecting objects.
Results table in the paper will be updated with new numbers.

Contact

Questions or issues? Contact askforalfred@googlegroups.com

jiasenlu/alfred