mtang724/gmc

Official Implementation of "Geometric Multimodal Contrastive Representation Learning" (https://arxiv.org/abs/2202.03390)

Geometric Multimodal Contrastive Representation Learning

Official Implementation of "Geometric Multimodal Contrastive Representation Learning", ICML 2022.

@article{poklukar2022gmc,
  title={Geometric Multimodal Contrastive Representation Learning},
  author={Poklukar, Petra and Vasco, Miguel and Yin, Hang and Melo, Francisco S and Paiva, Ana and Kragic, Danica},
  journal={arXiv preprint arXiv:2202.03390},
  year={2022}
}

Setup/Installation

conda env create -f gmc.yml
conda activate GMC
poetry install

Additionally, to set up the Delaunay Component Analysis evaluation framework by following the instructions on the official repository.

Download Datasets

cd gmc_code/
bash download_unsupervised_dataset.sh
bash download_supervised_dataset.sh
bash download_rl_dataset.sh

Experiments

This repository contains the code to replicate the experiments presented in the paper within the gmc_code folder. In every experiment, please set up the corresponding local machine path in ingredients/machine_ingredients.py file by copying the output of pwd to the ingredient file (e.g. for the unsupervised experiment):

cd unsupervised/
pwd

# Edit unsupervised/ingredients/machine_ingredients.py
@machine_ingredient.config
def machine_config():
    m_path = "copy-output-of-pwd-here"

To replicate the results, download the pretrained models:

cd gmc_code/
bash download_unsupervised_pretrain_models.sh
bash download_supervised_pretrain_models.sh
bash download_rl_pretrain_models.sh

1) Unsupervised Learning (MHD)

- Train Model

echo "** Train GMC"
python main_unsupervised.py -f with experiment.stage="train_model" 

echo "** Train classifier"
python main_unsupervised.py -f with experiment.stage="train_downstream_classfier"

- Evaluate/Replicate Results

echo "** Evaluate GMC - Classification"
python main_unsupervised.py -f with experiment.evaluation_mods=[0,1,2,3] experiment.stage="evaluate_downstream_classifier"

echo "** Evaluate GMC - DCA"
python main_unsupervised.py -f with experiment.stage="evaluate_dca"

• To evaluate with partial observations, select between [0], [1], [2], [3] in experiment.evaluation_mods;
• The DCA results are saved in the evaluation/gmc_mhd/log_0/results_dca_evaluation/ folder. For example, geometric alignement of complete and image representations are given in the joint_m1/DCA_results_version0.log file.

2) Supervised Learning (CMU-MOSI/CMU-MOSEI)

- Train Model

echo "** Train representation model"
python main_supervised.py -f with experiment.scenario="mosei" experiment.stage="train_model" 

- Evaluate/Replicate Results

echo "** Evaluate GMC - Classification"
python main_supervised.py -f with experiment.scenario="mosei" experiment.evaluation_mods=[0,1,2] experiment.stage="evaluate_downstream_classifier"

echo "** Evaluate GMC - DCA"
python main_supervised.py -f with experiment.scenario="mosei" experiment.stage="evaluate_dca"

• You can use CMU-MOSI dataset for both training and evaluation by setting experiment.scenario="mosi";
• To evaluate with partial observations, select between [0], [1], [2] in experiment.evaluation_mods;
• The DCA results are saved in the evaluation/gmc_mosei/log_0/results_dca_evaluation/ folder. For example, geometric alignement of complete and text representations are given in the joint_m1/DCA_results_version0.log file.

3) Reinforcement Learning (Multimodal Atari Games)

- Train Model

echo "** Train representation model"
python main_rl.py -f with experiment.stage="train_model" 

echo "** Train controller"
python main_rl.py -f with experiment.stage="train_downstream_controller" 

- Evaluate/Replicate Results

echo "** Evaluate GMC - RL Performance"
python main_rl.py -f with experiment.evaluation_mods=[0,1] experiment.stage="evaluate_downstream_controller"

echo "** Evaluate GMC - DCA"
python main_rl.py -f with experiment.stage="evaluate_dca"

• To evaluate with partial observations, select between [0], [1] in experiment.evaluation_mods;
• The DCA results are saved in the evaluation/gmc_pendulum/log_0/results_dca_evaluation/ folder. For example, geometric alignement of complete and text representations are given in the joint_m1/DCA_results_version0.log file.

FAQ

For any additional questions, feel free to email `miguel.vasco[at]tecnico.ulisboa.pt".