This repository contains the code for Multimeasurement Generative Models published at ICLR 2022, implemented in PyTorch 1.9.0.
System pre-requisites:
- Anaconda or Miniconda
- CUDA drivers that support CUDA 11.1 (optional, but probably necessary for experiments other than MNIST)
# Clone this repository
git clone https://github.com/nnaisense/mems.git
cd mems
# Create and activate a conda environment
conda env create -f env.yml && conda activate mems
# Install the project in editable mode.
pip install -e .Notes:
- The required dataset will be downloaded when the first experiment starts, into the location configured in the config file (
data.data_dir), unless it already exists. - To log training to neptune,
pip install neptune-client, set the API token and then set thetrainer.neptuneconfig to a neptune project. Otherwise, CSVLogger will be used as fallback. - The code will generate images using MCMC during training and save them using the logger, according to the config in
model.training.sampling. To disable this, you can setmodel.training.sampling.mode=None.
# Train a 1x16 MDAE on MNIST
CUDA_VISIBLE_DEVICES=0 python run/train.py with configs/mdae_mnist_1x16.yaml -f
# Train a 1x8 MDAE on CIFAR10
CUDA_VISIBLE_DEVICES=0,1,2,3 python run/train.py with configs/mdae_cifar10_1x8.yaml -f
# Train a 4x8 MDAE on FFHQ256
CUDA_VISIBLE_DEVICES=0,1,2,3 python run/train.py with configs/mdae_ffhq256_4x8.yaml -f
# Train a 1x4 MUVB on MNIST
CUDA_VISIBLE_DEVICES=0 python run/train.py with configs/muvb_mnist_1x4.yaml -f
# You can modify any config entry from the command line, e.g.,
# Train a 0.5x4 MDAE on MNIST
python run/train.py with configs/mdae_mnist_1x16.yaml model.sigma=[0.5] model.M=[4] -fSaremi, S., & Srivastava, R. K. (2022). Multimeasurement Generative Models. International Conference on Learning Representations. https://openreview.net/forum?id=QRX0nCX_gk
@inproceedings{saremi2022multimeasurement,
title = {Multimeasurement Generative Models},
author = {Saeed Saremi and Rupesh Kumar Srivastava},
year = 2022,
booktitle = {International Conference on Learning Representations},
url = {https://openreview.net/forum?id=QRX0nCX_gk}
}