git clone git@github.com:bdvllrs/bimGW.git & cd bimGW
pip install -e .
pip install git+https://github.com/bdvllrs/bimGW.git
They can be found in config/.
It contains config/main.yaml which is the source configuration file. To use
different values,
create a new config/local.yaml with updated values.
You can also create a config/debug.yaml which will only be loaded if
the DEBUG environment
variable is set to 1.
When installing the project, copy the folder config to a folder of your
choosing, and set the environment
variable BIMGW_CONFIG_PATH to the path of the folder.
This repo contains the library containing the modules, dataset and dataloader, and the scripts used to train, evaluate and use the model.
The scripts are in the scripts folder.
The scripts use the configuration files, but they can be overridden using CLI
args:
python train.py "max_epochs=2" "global_workspace.batch_size=512"
Download link: https://zenodo.org/record/8112838.
This is the main dataset of the project. It can be
generated using the create_shape_dataset.py
script.
The dataset comprises 32x32 images. The images contain one shape (among a triangle, an "egg", and a "diamond") possessing different attributes:
- a size
- a location (x, y)
- a rotation
- a color
The model contains uni-modal modules which are pretrained:
- a VAE for the visual domain: available in
checkpoints/vae_v.ckpt, - the language model: available in
checkpoints/vae_t.ckpt.
To retrain each modality, use the corresponding training script:
train_vae.pyfor the VAE,train_lm.pyfor the language model.
Once the uni-modal modules have been train, one can save the latent vectors for
the dataset to speed up the training
of the full model using save_unimodal_latents.py script.
To train the global workspace, use the train.py script.