BUILD

conda create -p ./venv python=3.6
source activate ./venv
sh ./build.sh && python -m gibson2.utils.assets_utils --download_assets

DATASET

• Gibson

1. get dataset here

2. copy URL of gibson_v2_4+.tar.gz

3. run command

python -m gibson2.utils.assets_utils --download_dataset {URL}

• Matterport3D

1. get dataset according to README

2. run command

python2 download_mp.py --task_data igibson -o . `

3. move each folder of scenes to Gibson Dataset path

You can check Gibson Dataset path by running

python -m gibson2.utils.assets_utils --download_assets

USAGE

• Train

python main.py --global_lr 5e-4 --exp_name 'AIM_del_mi' --critic_lr_coef 5e-2 --train_global 1 --dump_location train --scenes_file scenes/train.scenes

• Test (Example)

python main.py --exp_name 'eval_coscan_mp3dhq0f' --scenes_file scenes/mp3dhq0-f.scenes --dump_location std --num_episodes 10 --load_global best.global

• Analyze performance

python analyze.py --dir std --dataset gibson -ne 5 --bins 35,70
python analyze.py --dir std --dataset mp3d -ne 5 --bins 100

• Analyze performance

python scripts/easy_analyze.py rl --dataset hq --subset abcdef --dir std

• Specify GPU Index

export CUDA_VISIBLE_DEVICES=0
export GIBSON_DEVICE_ID=0

• Visualization

python main.py --exp_name 'eval_-72' --scenes_file scenes/mp3dhq0-f.scenes --dump_location ./temp --num_episodes 1 --load_global ./model_best.global  --vis_type 2
# dump at ./video/
python scripts/map2d.py  --exp_name 'eval_new-72' -ne 1 -ns 4