This repository contains CVAE and CVQVAE implementations in PyTorch for MNIST dataset.
Run
pip install -r requirements.txt
python main.py --latent_size 2 --batch_size 64
The model will be evaluated after every epoch. You can check the images generated by the decoder in the images
folder.
When the training finishes, the model will be saved to model.pth
.
There are two types of encoders and decoders: mlp
and conv
. You can use one of them by passing the --encoder
and --decoder
options.
There are two types of CVAE: cvae
and cvqvae
. You can use one of them by passing the --model_type
option.
Run python main.py --help
for more help on options.
ONNX
You can export the decoder to the ONNX format and play with it in an interactive way using demo.html
# The onnx model will be saved as decoder.onnx
python main.py --export_onnx --latent_size 2 --encoder mlp --decoder mlp --model_path model.pth
python -m http.server
# Now open http://localhost:8000/demo.html
Note that because onnx.js
does not support the ConvTranspose
operator, you can only export the MLP decoder.
The web page only supports 2-dim latent representation. Feel free to hack around it!
Results
Below are some generated images. All models use 2-dim latent representations.