GMAN: Generative Meta-Adversarial Network for Unseen Object Navigation

Setup

• Clone the repository git clone https://github.com/sx-zhang/GMAN.git and move into the top-level directory cd GMAN
• Create conda environment. conda env create -f environment.yml
• Activate the environment. conda activate ng
• We provide pre-trained model of our GMAN in the trained_models directory.
• Download our dataset.
• Then extract files in data by tar -zxvf ai2thor_GMAN.tar.gz
• The data folder should look like this

  data/ 
    └── ai2thor//
        ├── FloorPlan1/
        │   ├── resnet18_featuremap.hdf5
        │   ├── graph.json
        │   ├── visible_object_map.json
        │   ├── att_in_view_v2.hdf5
        │   ├── grid.json
        ├── FloorPlan2/
        └── ...

Training and Evaluation

Train the baseline model

python main.py --title Basemodel --model BaseModel --workers 12 -–gpu-ids 0

Train our GMAN model

python main.py --title GMAN --model GMAN --workers 12 -–gpu-ids 0 --num_steps 20

Evaluate the GMAN model for seen objects

python full_eval.py \
    --title GMAN \
    --model GMAN \
    --results-json GMAN_seen.json \
    --gpu-ids 0 \
    --num_steps 20 \
    --seen seen

Evaluate the GMAN model for unseen objects

python full_eval.py \
    --title GMAN \
    --model GMAN \
    --results-json GMAN_unseen.json \
    --gpu-ids 0 \
    --num_steps 20 \
    --seen unseen