FGPrompt 🤖

🙌 Official implementation of NeurIPS 2023 accepted paper "FGPrompt: Fine-grained Goal Prompting for Image-goal Navigation" <br>

<p> <a href="https://arxiv.org/abs/2310.07473"><img src="https://img.shields.io/badge/arxiv-2310.07473-A61E24?logo=arxiv&logoColor=red"></a> <a href="https://neurips.cc/virtual/2023/poster/70452"><img src="https://img.shields.io/badge/neurips-poster-8A5F99?logo=neutralinojs"></a> <a href="https://xinyusun.github.io/fgprompt-pages"><img src="https://img.shields.io/badge/project-page-0C7B34?logo=probot"></a> <a href="https://xinyusun.github.io/fgprompt-pages"><img src="https://img.shields.io/badge/click-8A2BE2"></a> <a href="https://www.easycounter.com/"><img src="https://www.easycounter.com/counter.php?xinyusun" border="0" alt="Free Hit Counters"></a> </p>

0. Overview

We aim to tackle the image-goal navigation task, in which the agent is required to reason the goal location from where a picture is shot. We leverage fine-grained and high-resolution feature maps in the goal image as prompts to perform conditioned embedding, which preserves detailed information in the goal image and guides the observation encoder to pay attention to goal-relevant regions. Compared with existing methods on the image-goal navigation benchmark, our method brings significant performance improvement on 3 benchmark datasets (i.e., Gibson, MP3D, and HM3D).

1. Install 🚀

1.1 Install habitat-lab 🛰️

# clone our repo
git clone https://github.com/XinyuSun/FGPrompt.git
cd FGPrompt

# clone habitat-lab code
git submodule init
git submodule update

# create conda env
conda create -n fgprompt python=3.8

# install habitat-sim
conda install habitat-sim=0.2.2 withbullet headless -c conda-forge -c aihabitat

# install pytorch1.11
pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113 -f https://download.pytorch.org/whl/torch_stable.html

# install habitat-lab and habitat-baselines
cd habitat-lab
git checkout 1f7cfbdd3debc825f1f2fd4b9e1a8d6d4bc9bfc7
pip install -e habitat-lab 
pip install -e habitat-baselines

1.2 Install other requirements 🍔

cd ..
pip install -r requirements.txt

We also provide a docker image for reproduce convenience

docker run -it --name "fgprompt-docker" -v `pwd`:/fgprompt --shm-size=32g --gpus=all csxinyusun/fgprompt:latest /bin/bash

2. Prepare dataset 🗂️

<!-- | ObjectNav | Gibson | train | [objectnav_gibson_train](https://utexas.box.com/s/7qtqqkxa37l969qrkwdn0lkwitmyropp) | `./data/datasets/zer/objectnav/gibson/v1/` | | ObjectNav | Gibson | val | [objectnav_gibson_val](https://utexas.box.com/s/wu28ms025o83ii4mwfljot1soj5dc7qo) | `./data/datasets/zer/objectnav/gibson/v1/` | -->

2.1 Download Datasets 📑

Download the dataset.zip file from google drive and unarchive it into data/dataset

For gibson dataset, we borrow the episodes generated from ZER to train our agent. We then follow the original imagenav paper to test our well-trained agent on the testing episodes. We organize all datasets as follows:

data/datasets/
└── imagenav
    ├── gibson
    │   └── v1
    │       ├── train
    │       │   └── content
    │       └── val
    ├── hm3d
    │   └── v1
    │       ├── train
    │       │   └── content
    │       ├── val_easy
    │       │   └── content
    │       ├── val_hard
    │       │   └── content
    │       └── val_medium
    │           └── content
    └── mp3d
        └── v1
            ├── test_easy
            ├── test_hard
            └── test_medium

2.2 Download Scene Datasets 🕌

Please read the official guidance to download Gibson, HM3D, and MP3D scene datasets, and put them in the data/scene_datasets directory using lower-case naming. Feel free to let me know if you having problem downloading these scene files, I will share you a copy on my cloud disk.

data/scene_datasets
├── gibson
├── hm3d
└── mp3d

3. Run ImageNav Training 🏂

3.1 Train a Mid-Fusion Agent 🥽

MAGNUM_LOG=quiet HABITAT_SIM_LOG=quiet python -m torch.distributed.launch \
--nproc_per_node=4 --master_port=15244 --nnodes=1 \
--node_rank=0 --master_addr=127.0.0.1 \
run.py --overwrite \
--exp-config exp_config/ddppo_imagenav_gibson.yaml,policy,reward,dataset,sensors,mid-fusion \
--run-type train --model-dir results/imagenav/mid-fusion

3.1 Train a Early-Fusion Agent 👒

MAGNUM_LOG=quiet HABITAT_SIM_LOG=quiet python -m torch.distributed.launch \
--nproc_per_node=4 --master_port=15244 --nnodes=1 \
--node_rank=0 --master_addr=127.0.0.1 \
run.py --overwrite \
--exp-config exp_config/ddppo_imagenav_gibson.yaml,policy,reward,dataset,sensors,early-fusion \
--run-type train --model-dir results/imagenav/early-fusion

4. Run Evaluation! 🏆

4.1 Download the Trained Model to Reproduce the Results 👯‍♀️

|Name|SR|SPL| |-|:-:|:-:| |Mid-fusion-r9|90.7%|62.1%| |Early-fusion-r9|90.4%|66.5%| |Early-fusion-r50|94.2%|68.5%|

Eval the midfusion model!

MAGNUM_LOG=quiet HABITAT_SIM_LOG=quiet python -m torch.distributed.launch \
--nproc_per_node=1 --master_port=15244 --nnodes=1 \
--node_rank=0 --master_addr=127.0.0.1 \
run.py \
--exp-config exp_config/ddppo_imagenav_gibson.yaml,policy,reward,dataset,sensors,mid-fusion,eval \
--run-type eval --model-dir results/imagenav/mid-fusion-r9 \
habitat_baselines.eval_ckpt_path_dir results/imagenav/mid-fusion-9/ckpts/mid-fusion-r9.pth

Eval the earlyfusion model!

# early-fusion-r9
MAGNUM_LOG=quiet HABITAT_SIM_LOG=quiet python -m torch.distributed.launch \
--nproc_per_node=1 --master_port=15244 --nnodes=1 \
--node_rank=0 --master_addr=127.0.0.1 \
run.py \
--exp-config exp_config/ddppo_imagenav_gibson.yaml,policy,reward,dataset,sensors,early-fusion,eval \
--run-type eval --model-dir results/imagenav/early-fusion-r9 \
habitat_baselines.eval_ckpt_path_dir results/imagenav/early-fusion-r9/ckpts/early-fusion-r9.pth

# early-fusion-r50
MAGNUM_LOG=quiet HABITAT_SIM_LOG=quiet python -m torch.distributed.launch \
--nproc_per_node=1 --master_port=15244 --nnodes=1 \
--node_rank=0 --master_addr=127.0.0.1 \
run.py \
--exp-config exp_config/ddppo_imagenav_gibson.yaml,policy,reward,dataset,sensors,early-fusion,eval \
--run-type eval --model-dir results/imagenav/early-fusion-r50 \
habitat_baselines.eval_ckpt_path_dir results/imagenav/early-fusion-r50/ckpts/early-fusion-r50.pth \
habitat_baselines.rl.ppo.backbone resnet50

4.2 Cross Domain Evaluation 🕺🏼

# train on gibson, eval on hm3d
# early-fusion-r50
MAGNUM_LOG=quiet HABITAT_SIM_LOG=quiet python -m torch.distributed.launch \
--nproc_per_node=1 --master_port=15244 --nnodes=1 \
--node_rank=0 --master_addr=127.0.0.1 \
run.py \
--exp-config exp_config/ddppo_imagenav_gibson.yaml,policy,reward,dataset-hm3d,sensors,early-fusion,eval \
--run-type eval --model-dir results/imagenav/early-fusion-r50 \
habitat_baselines.eval_ckpt_path_dir results/imagenav/early-fusion-r50/ckpts/early-fusion-r50.pth \
habitat_baselines.rl.ppo.backbone resnet50 \
habitat_baselines.eval.split val_easy # choose from [val_easy, val_hard, val_medium]

# train on gibson, eval on mp3d
# early-fusion-r50
MAGNUM_LOG=quiet HABITAT_SIM_LOG=quiet python -m torch.distributed.launch \
--nproc_per_node=1 --master_port=15244 --nnodes=1 \
--node_rank=0 --master_addr=127.0.0.1 \
run.py \
--exp-config exp_config/ddppo_imagenav_gibson.yaml,policy,reward,dataset-mp3d,sensors,early-fusion,eval \
--run-type eval --model-dir results/imagenav/early-fusion-r50 \
habitat_baselines.eval_ckpt_path_dir results/imagenav/early-fusion-r50/ckpts/early-fusion-r50.pth \
habitat_baselines.rl.ppo.backbone resnet50 \
habitat_baselines.eval.split test_easy # choose from [test_easy, test_hard, test_medium]

Cite This Paper! 🤗

@inproceedings{fgprompt2023,
 author = {Xinyu, Sun and Peihao, Chen and Jugang, Fan and Thomas, H. Li and Jian, Chen and Mingkui, Tan},
 title = {FGPrompt: Fine-grained Goal Prompting for Image-goal Navigation},
 booktitle = {37th Conference on Neural Information Processing Systems (NeurIPS 2023)},
 year = {2023},
}