terryw9ucl / scod

Code accompanying "Sketching Curvature for Efficient Out-of-Distribution Detection for Deep Neural Networks"

SCOD: Sketching Curvature for Out-of-Distribution Detection

This repository contains a PyTorch implementation of the technique described in our paper:

Sharma, Apoorva, Navid Azizan, and Marco Pavone. "Sketching Curvature for Efficient Out-of-Distribution Detection for Deep Neural Networks." arXiv preprint arXiv:2102.12567 (2021).

Overview

This repository provides a framework for wrapping a pre-trained neural network with uncertainty estimates. It is designed to work with any pytorch model. We implement several such wrappers in a general framework. Given a pretrained DNN model : torch.nn.Module, the distribution that the network parameterizes dist_fam : nn_ood.distributions.DistFam, and a PyTorch dataset containing the training data dataset : torch.utils.data.Dataset, we can construct a uncertainty-equipped version of the network as follows:

unc_model = UncWrapper(model, dist_fam, **kwargs)
unc_model.process_dataset(dataset)

where wrapper specific hyperparameters are passed in as keyword arguments.

Then, we can use unc_model as we would use model, except the wrapped model now will produce an uncertainty score along with the normal model output:

output, unc = unc_model(input)

We implement several such uncertainty wrappers, available in nn_ood.posteriors:

• SCOD: Sketching Curvature for OoD Detection
• LocalEnsemble: Implements the algorithm described in (Madras et al., 2019)
• KFAC: Implements the algorithm described in (Ritter et al., 2019)
• Naive: Uses the model's output directly to compute an uncertainty score (e.g., entropy of output distribution)

We also compare to DeepEnsembles, which operate on a collection of models of identical architecture. Implementing Deep Ensembles in this framework is a similar process -- after having trained K models, we can intialized the wrapper with a list containing these models

models = [model1, model2, ..., modelK]
unc_model = Ensemble(models, dist_fam, **kwargs)

How to use

Downloading / installing dependencies

Clone this repo (including the submodules):

git clone --recurse-submodules [email protected]:StanfordASL/SCOD.git

Install the framework (this will autoinstall the required submodules)

pip install -e .

Download datasets (dataloaders expect data to be in ~/datasets). This script downloads the data for Wine and TinyImagenet. The other datasets used in these experiments are all downloaded automatically through pytorch.

./download_datasets.sh -t ~/datasets/

Make sure to update nn_ood/__init__.py to match the location of your dataset directory.

Running experiments

Each domain / experiment has a folder in experiments which contains a config.py file. This file defines all experiment specific details -- hyperparameters, which dataset to use, model architecture, functions to plot data, etc. The config file also defines which combinations of uncwrappers and hyperparameters to test.

The notebook experiments/run_experiments.ipynb has scripts which run experiments as defined by this config file. At the start of the script, replace EXP_FOLDER to point to the desired experiment folder so that the correct config.py file is used.

The notebook experiments/visualize.ipynb has code that generates plots from the results that are saved by the run_experiments.ipynb notebook. The notebook generates experiment-specific plots as defined in the appropriate EXP_FOLDER/config.py.