Normalizing-Flows-PyTorch

Implement Normalizing Flows with PyTorch.

Installation

Clone this repository:

git clone https://github.com/xyfJASON/normalizing-flows-pytorch
cd normalizing-flows-pytorch

Create and activate a conda environment:

conda create -n flow python=3.11
conda activate flow

Install requirements:

pip install -r requirements.txt

Dinh, Laurent, David Krueger, and Yoshua Bengio. "Nice: Non-linear independent components estimation." arXiv preprint arXiv:1410.8516 (2014).

To train on MNIST dataset:

accelerate-launch scripts/train_nice.py -c ./configs/nice_mnist.yaml -e ./runs/nice-mnist

To compute log-likelihood and bpd (bits per dim):

accelerate-launch scripts/test_nice.py eval_bpd -c CONFIG --weights WEIGHTS [--bspp BSPP]

-c: path to the configuration file
--weights: path to the model weights, e.g., ./runs/nice-mnist/ckpt/best/model.pt
--bspp: batch size per process

To sample from the model:

accelerate-launch scripts/test_nice.py sample -c CONFIG --weights WEIGHTS --save_dir SAVE_DIR --n_samples N_SAMPLES [--seed SEED] [--bspp BSPP]

-c: path to the configuration file
--weights: path to the model weights, e.g., ./runs/nice-mnist/ckpt/best/model.pt
--save_dir: directory to save the samples, e.g., ./samples/nice-mnist
--n_samples: number of samples to generate
--bspp: batch size per process

Dataset	Log-likelihood	BPD
MNIST (test split)	1932.6956	3.4435