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RoboTHOR ObjectNav

Installation

We've included instructions for installing the full AllenAct library (modifiable) with conda for our branch, although you can also use the official AllenAct repo (v0.5.0) or perhaps newer.

git clone -b allenact --single-branch https://github.com/allenai/embodied-clip.git embclip-allenact
cd embclip-allenact

export EMBCLIP_ENV_NAME=embclip-allenact
export CONDA_BASE="$(dirname $(dirname "${CONDA_EXE}"))"
export PIP_SRC="${CONDA_BASE}/envs/${EMBCLIP_ENV_NAME}/pipsrc"
conda env create --file ./conda/environment-base.yml --name $EMBCLIP_ENV_NAME
conda activate $EMBCLIP_ENV_NAME

# Install the appropriate cudatoolkit
conda env update --file ./conda/environment-<CUDA_VERSION>.yml --name $EMBCLIP_ENV_NAME
# OR for cpu mode
conda env update --file ./conda/environment-cpu.yml --name $EMBCLIP_ENV_NAME

# Install RoboTHOR and CLIP plugins
conda env update --file allenact_plugins/robothor_plugin/extra_environment.yml --name $EMBCLIP_ENV_NAME
conda env update --file allenact_plugins/clip_plugin/extra_environment.yml --name $EMBCLIP_ENV_NAME

# Download RoboTHOR dataset
bash datasets/download_navigation_datasets.sh robothor-objectnav

# Download pretrained ImageNet and CLIP visual encoders
python -c "from torchvision import models; models.resnet50(pretrained=True)"
python -c "import clip; clip.load('RN50')"

Please refer to the official AllenAct installation instructions for more details.

Headless mode

If you would like to use AllenAct on a headless machine (i.e. without a monitor), you'll need to:

  1. Start an x-display (sudo python scripts/startx.py &).
  2. Update the THOR_COMMIT_ID to 91139c909576f3bf95a187c5b02c6fd455d06b48 (a headless build of AI2-THOR). Also, set THOR_IS_HEADLESS = True right below this line.

Training

# ImageNet
PYTHONPATH=. python allenact/main.py -o storage/objectnav-robothor-rgb-imagenet-rn50 -b projects/objectnav_baselines/experiments/robothor objectnav_robothor_rgb_resnet50gru_ddppo

# CLIP
PYTHONPATH=. python allenact/main.py -o storage/objectnav-robothor-rgb-clip-rn50 -b projects/objectnav_baselines/experiments/robothor/clip objectnav_robothor_rgb_clipresnet50gru_ddppo

Using pretrained models

# ImageNet
curl -o pretrained_model_ckpts/objectnav-robothor-imagenet-rn50.195M.pt https://prior-model-weights.s3.us-east-2.amazonaws.com/embodied-ai/navigation/exp_Objectnav-RoboTHOR-RGB-ResNet50GRU-DDPPO__stage_00__steps_000195242243.pt

# CLIP
curl -o pretrained_model_ckpts/objectnav-robothor-clip-rn50.130M.pt https://prior-model-weights.s3.us-east-2.amazonaws.com/embodied-ai/navigation/exp_Objectnav-RoboTHOR-RGB-ClipResNet50GRU-DDPPO__stage_00__steps_000130091717.pt

You can use these models with the python allenact/main.py arguments -c pretrained_model_ckpts/objectnav-robothor-imagenet-rn50.195M.pt or -c pretrained_model_ckpts/objectnav-robothor-clip-rn50.130M.pt.

Evaluating

Simply append the --eval argument to the above python allenact/main.py commands.