Two-Dimensional Examples

This folder contains the code implementation of the two-dimensional experiments in Section 3.2 of the paper On Investigating the Conservative Property of Score-Based Generative Models.

Usage

Train and evaluate models using the command with the following format.

python main.py --mode {$(1)} --workdir {$(2)} --config {$(3)}

• (1) mode: is set as train or eval for training or evaluation.
• (2) workdir: the directory created for saving the experimental results such as visualized examples and checkpoints.
• (3) config: the configuration file that specifies the hyper-parameters.

Examples of Training Commands

• Train a constrained score-based model (CSBM)

python3 main.py --workdir checkerboard_csbm --mode train --config configs/csbm/checkerboard_config.py

• Train an unconstrained score-based model (USBM)

python3 main.py --workdir checkerboard_usbm --mode train --config configs/usbm/checkerboard_config.py

• Train a quasi-conservative score-based model (QCSBM)

python3 main.py --workdir checkerboard_qcsbm --mode train --config configs/qcsbm/checkerboard_config.py

Examples of Evaluation Commands

• Evaluate the negative log likelihood (NLL)

python3 main.py --workdir checkerboard_qcsbm --mode eval --type nll --config configs/qcsbm/checkerboard_config.py --restore results/checkerboard_qcsbm/checkpoints/checkpoint_4000.pth

• Evaluate the score errors

python3 main.py --workdir checkerboard_qcsbm --mode eval --type score_err --config configs/qcsbm/checkerboard_config.py --restore results/checkerboard_qcsbm/checkpoints/checkpoint_4000.pth

• Evaluate the sampling performance

python3 main.py --workdir checkerboard_qcsbm --mode eval --type sampling --config configs/qcsbm/checkerboard_config.py --restore results/checkerboard_qcsbm/checkpoints/checkpoint_4000.pth