This repo contains code both to train a lattice to curve encoder and curve to lattice policy network. All code is located in the src directory. The entry point for the forward model is main_forward.py. The entry point for the inverse model is main_inverse.py.
Please ensure you have the below dependencies (exact versions recommended but may not be necessary):
bidict==0.22.0
matplotlib==3.1.2
networkx==3.1
numpy==1.24.3
pandas==1.5.1
runstats==2.0.0
scikit-learn==1.2.2
scipy==1.10.1
seaborn==0.12.2
Shapely==1.8.2
tensorboard==2.5.0
tensorboardX==2.5.1
torch==1.12.1+[your_cuda_version]
torch-geometric==2.0.4
torch-scatter==2.1.1
torch-sparse==0.6.17
tqdm==4.64.0
PyYAML==6.0
dtaidistance==2.3.10
optuna==3.3.0
Please download the models and configurations from: https://drive.google.com/drive/folders/1YfSF-PbeHZ1IElxiN_wO-pMEfKGIWvZU
Place it in the following directory structure:
/path/to/GraphMetaMat/logs
Please download the model checkpoints from: https://drive.google.com/drive/folders/1VmB7y9wTI0vSrpx0g5lh4TVQgIC6UZr8
Place it in the following directory structure:
/path/to/GraphMetaMat/checkpoints
Please download the preprocessed datasets (e.g., stress_strain) datasets from: https://drive.google.com/drive/folders/13ga9DFFtsHcHGPEgc3eeJBqvX4KlEJK-
Place it in the following directory structure:
/path/to/GraphMetaMat/dataset/stress_strain
We specify all model and data configurations for metamaterial generation in 3 configuration files: config_general.py, config_dataset.py, and config_model.py. For the quick-run tutorial, we will run inference with a trained model on the stress-strain dataset. All generated otuputs and trained model files are saved to a log file. This is specified in the log_dir parameter of config_general.py. To run just inference, we need to specify log_dir that contains the trained model. We provide the trained models in the log files in Models and Configurations.
First, overwrite the config_general.py, config_dataset.py, and config_model.py from the /path/to/GraphMetaMat/logs/quick_run directory to the /path/to/GraphMetaMat directory.
Next, set the log_dir in config_general.yaml to the directory that contains the trained model:
load_model: /path/to/GraphMetaMat/logs/quick_run/trained_model # NOTE: this is different for non quick_run setups...
load_model_IL: /path/to/GraphMetaMat/logs/quick_run/trained_model # NOTE: this is different for non quick_run setups...
load_model_RL: /path/to/GraphMetaMat/logs/quick_run/trained_model # NOTE: this is different for non quick_run setups...
log_dir: /path/to/GraphMetaMat/logs/quick_run
Next, set the path to the datasets in config_dataset.yaml:
dataset_RL:
root_graph: /path/to/GraphMetaMat/dataset/stress_strain/standard
root_curve: /path/to/GraphMetaMat/dataset/stress_strain/standard
root_mapping: /path/to/GraphMetaMat/dataset/stress_strain/standard
dataset:
root_graph: /path/to/GraphMetaMat/dataset/stress_strain/standard
root_curve: /path/to/GraphMetaMat/dataset/stress_strain/standard
root_mapping: /path/to/GraphMetaMat/dataset/stress_strain/standard
Next, set the device flag (GPU or CPU) in /path/to/GraphMetaMat/src/config_general.yaml:
device: cpu # if using CPU
device: cuda # if using GPU
Run the model with:
$python3 main_inverse.py
You should see the following output, which reproduces our results from the stress-strain experiments:
Results:
mae: 0.0005315262824296951
mse: 2.246857320642448e-06
jaccard: 0.8847618663235556
Time taken: 2667.6254324913025s
If you see this output, congratulations! You have successfully ran the model.
Please first follow the steps in Quick Run to set up environment, download models, download data, and run inference.
For transmission datasets, see Transmission Curves.
See Run Training and Inference.
To run inference, follow the same steps as Quick Run but (1) obtain the configurations from a /path/to/GraphMetaMat/logs/*_inverse directory, (2) set dataset and dataset_RL in config_dataset.yaml accordingly, (3) set load_model_IL, load_model_RL and load_model in config_model.yaml following Trained Models, and (4) set num_epochs, num_imitation_epochs, and num_iters to be 0 in config_general.yaml.
All the preset configurations in the log files from Models and Configurations are by default for training and inference.
To run training and inference, follow the same steps from Quick Run but (1) obtain the configurations from a /path/to/GraphMetaMat/logs/*_forward directory, (2) set dataset in config_dataset.yaml accordingly, (3) set the load_model_IL, load_model_RL and load_model in config_model.yaml following Trained Models, and (4) set log_dir in config_general.yaml to be an empty directory, where the trained model and inference results will be saved.
Run the model with:
$python3 main_forward.py
All the preset configurations in the log files from Models and Configurations are by default for training.
To run training and inference, follow the same steps from Quick Run but (1) obtain the configurations from a /path/to/GraphMetaMat/logs/*_inverse directory, (2) set dataset and dataset_RL in config_dataset.yaml accordingly, where the trained model and inference results will be saved, and (3) set the load_model_IL, load_model_RL and load_model in config_model.yaml following Trained Models, and (4) set log_dir in config_general.yaml to be an empty directory, where the trained model and inference results will be saved.
Run the model with:
$python3 main_inverse.py
We store all the saved models under the following directory: logs/trained_models. Please set the load_model_IL, load_model_RL and directories in config_model.yaml accordingly.
load_model FLAG (Training and Inference): Pretrained forward models will have the suffix *_pt. Setting this FLAG will start training from the pretrained checkpoint.
load_model FLAG (Inference-Only): Finetuned ensemble of forward models will have the suffices *_ensemble. Setting this FLAG will bypass training by loading the trained ensemble.
load_model FLAG: Finetuned ensemble of forward models will have the suffices *_ensemble. This will be used as the surrogate model during RL training and inference.
load_model_IL FLAG (Optional): IL inverse models will have the suffix *_IL. Setting this FLAG will bypass just IL inverse model training by loading a trained IL inverse model.
load_model_RL FLAG (Inference-Only): RL inverse models will have the suffix *_RL. Setting this FLAG will bypass both IL and RL inverse model training by loading a trained RL inverse model.
For full training and inference, set both load_model_IL and load_model_RL to null.
Update the configuration in src/config.py as follows:
ETH_FULL_C_VECTOR = False
TASK = 'stress_strain'
Update the configuration in src/config.py as follows:
ETH_FULL_C_VECTOR = False
TASK = 'transmission'
To reproduce the benchmark results, please download the models and plotting scripts from https://drive.google.com/drive/folders/1takiWy7GFp5SNolhbSbNfM4siOQqOEXP, and the corresponding datasets from https://drive.google.com/drive/folders/1FlWRNBnQCLLWkXHp7CXuCT-A5HZPrxSk . To reproduce the benchmark results, please download models, plotting scripts, and datasets from: and