This is the codebase for Out-of-Distribution Detection with a Single Unconditional Diffusion Model (DiffPath), implemented using PyTorch. The codebase is based on openai/improved-diffusion.
It is recommended to install dependencies in a conda environment:
conda create --name diffpath python=3.8
pip install -r requirements.txt
Download the ImageNet-64 diffusion model checkpoint trained with L-hybrid objective from the openai/improved-diffusion repo. The link is provided here as well:
https://openaipublic.blob.core.windows.net/diffusion/march-2021/imagenet64_uncond_100M_1500K.pt.
To perform OOD detection with DiffPath, first we calculate the diffusion path statistics for both train and test sets. We demonstrate the steps for the task of CIFAR10 (in-dist) vs SVHN (out-of-dist).
# calculate statistics for CIFAR10 training set on GPU ID 0
python save_train_statistics.py --data_dir /path/to/cifar10/dataset --dataset cifar10 --model_path /path/to/imagenet64/model/checkpoint
--batch_size 256 --n_ddim_steps 50 --device 0
# calculate statistics for CIFAR10 test set on GPU ID 0
python save_test_statistics.py --data_dir /path/to/cifar10/dataset --dataset cifar10 --model_path /path/to/imagenet64/model/checkpoint
--batch_size 256 --n_ddim_steps 50 --device 0
# calculate statistics for SVHN test set on GPU ID 0
python save_test_statistics.py --data_dir /path/to/svhn/dataset --dataset svhn --model_path /path/to/imagenet64/model/checkpoint
--batch_size 256 --n_ddim_steps 50 --device 0
The statistics will be saved as .npz files in train_statistics/ddim50 and test_statistics/ddim50 respectively.
Now perform OOD detection using DiffPath-6D:
python eval_6d.py --in_dist cifar10 --out_of_dist svhn --n_ddim_steps 50
The results will be printed to the screen.