Zero-Shot Object Goal Visual Navigation

This implementation is modeified based on MJOLNIR and SAVN.

The code has been implemented and tested on Ubuntu 18.04, python 3.6, PyTorch 0.6 and CUDA 10.1

Setup

1. (Recommended) Create a virtual environment using virtualenv or conda:

virtualenv ZSON --python=python3.6
source ZSON/bin/activate

conda create -n ZSON python=3.6
conda activate ZSON

2. Clone the repository as:

git clone https://github.com/pioneer-innovation/Zero-Shot-Object-Navigation.git
cd Zero-Shot-Object-Navigation

3. For the rest of dependencies, please run

pip install -r requirements.txt --ignore-installed

Data

The offline data can be found here.

"data.zip" (~5 GB) contains everything needed for evalution. Please unzip it and put it into the Zero-Shot-Object-Navigation folder.

For training, please also download "train.zip" (~9 GB), and put all "Floorplan" folders into ./data/thor_v1_offline_data

Evaluation

Note: if you are not using gpu, you can remove the argument --gpu-ids 0

Evaluate our model under 18/4 class split

python main.py --eval \
    --test_or_val test \
    --episode_type TestValEpisode \
    --load_model pretrained_models/SelfAttention_test_18_4.dat \
    --model SelfAttention_test \
    --gpu-ids 0 \
    --zsd 1 \
    --split 18/4

Evaluate our model under 14/8 class split

python main.py --eval \
    --test_or_val test \
    --episode_type TestValEpisode \
    --load_model pretrained_models/SelfAttention_test_14_8.dat \
    --model SelfAttention_test \
    --gpu-ids 0 \
    --zsd 1 \
    --split 14/8

Training

Note: the folder to save trained model should be set up before training.

Train our model under 18/4 class split

python main.py \
    --title mjolnir_train \
    --model SelfAttention_test \
    --gpu-ids 0 \
    --workers 8 \
    --vis False \
    --save-model-dir trained_models/SA_18_4/ \
    --zsd 1 \
    --partial_reward 1 \
    --split 18/4

Train our model under 14/8 class split

python main.py \
    --title mjolnir_train \
    --model SelfAttention_test \
    --gpu-ids 0 \
    --workers 8 \
    --vis False \
    --save-model-dir trained_models/SA_14_8/ \
    --zsd 1 \
    --partial_reward 1 \
    --split 14/8