conda create -n fast python=3.6
conda activate fast
conda install pytorch==1.2.0 torchvision==0.4.0 cudatoolkit=10.0 -c pytorch
conda install caffe-gpu
pip install -r requirements.txt
Download the checkpoints (link) and place them at experiments/smna/
and candidates/released/candidates_ranker_28D_0.6321
.
See R2R-EnvDrop Setup Matterport.
The following commands are used in our study:
bash run/test_agent_default.bash
# Command Format:
# bash [script] [cuda_device_id (default 0)] [setting] [repeat_time]
# -----Object---------
# mask
bash run/test_agent_mask_instr.bash 0 mask_object 1
# replace
bash run/test_agent_mask_instr.bash 0 replace_object 5
# controlled trial
bash run/test_agent_mask_instr.bash 0 random_mask_for_object 5
# ------Direction--------
# mask
bash run/test_agent_mask_instr.bash 0 mask_direction 1
# replace
bash run/test_agent_mask_instr.bash 0 replace_direction 5
# controlled trial
bash run/test_agent_mask_instr.bash 0 random_mask_for_direction 5
# ------Numeric--------
# numeric default
bash run/test_agent_mask_instr.bash 0 numeric_default 1
# mask
bash run/test_agent_mask_instr.bash 0 mask_numeric 1
# replace
bash run/test_agent_mask_instr.bash 0 replace_numeric 5
# controlled trial
bash run/test_agent_mask_instr.bash 0 random_mask_for_numeric 5
# mask only foreground objects
bash run/test_agent_mask_env.bash 0 foreground
# mask objects except for wall/floor/ceiling
bash run/test_agent_mask_env.bash 0 all_visible
# controlled trial
bash run/test_agent_mask_env.bash 0 foreground_controlled_trial
# flip
bash run/test_agent_mask_env.bash 0 flip
Please switch to the dynamic
branch for the dynamic masking experiments.
# dynamically mask environment object instances mentioned in the instructions
bash run/test_agent_dynamic_mask_env.bash 0 dynamic
# controlled trial
bash run/test_agent_dynamic_mask_env.bash 1 dynamic_controlled_trial