Package: bayesian-torch
A library for Bayesian neural network layers and uncertainty estimation in Deep LearningGet Started|Example usage|Documentation|CitingBayesian-Torch is a library of neural network layers and utilities extending the core of PyTorch to enable Bayesian inference in deep learning models to quantify principled uncertainty estimates in model predictions.OverviewBayesian-Torch is designed to be flexible and enables seamless extension of deterministic deep neural network model to corresponding Bayesian form by simply replacing the deterministic layers with Bayesian layers. It enables user to perform stochastic variational inference in deep neural networks.Bayesian layers:Variational layers with reparameterized Monte Carlo estimators[Blundell et al. 2015]LinearReparameterization 
Conv1dReparameterization, Conv2dReparameterization, Conv3dReparameterization, ConvTranspose1dReparameterization, ConvTranspose2dReparameterization, ConvTranspose3dReparameterization
LSTMReparameterizationVariational layers with Flipout Monte Carlo estimators[Wen et al. 2018]LinearFlipout 
Conv1dFlipout, Conv2dFlipout, Conv3dFlipout, ConvTranspose1dFlipout, ConvTranspose2dFlipout, ConvTranspose3dFlipout
LSTMFlipoutKey features:dnn_to_bnn(): Seamless conversion of model to be Uncertainty-aware with single line of code. An API to convert deterministic deep neural network (dnn) model of any architecture to Bayesian deep neural network (bnn) model, simplifying the model definition i.e. drop-in replacements  of Convolutional, Linear and LSTM layers to corresponding Bayesian layers. This will enable seamless conversion of existing topology of larger models to Bayesian deep neural network models for extending towards uncertainty-aware applications.MOPED: Scale Bayesian inference to large scale models by specifying weight priors and variational posteriors in Bayesian neural networks with Empirical Bayes [Krishnan et al. 2020]Quantization: Post Training Quantization of Bayesian deep neural network models for INT8 inference with simple API's [Lin et al. 2023]AvUC: Accuracy versus Uncertainty Calibration loss [Krishnan and Tickoo 2020]Installing Bayesian-TorchTo install core library usingpip:pip install bayesian-torchTo install latest development version from source:gitclonehttps://github.com/IntelLabs/bayesian-torchcdbayesian-torch
pipinstall.UsageThere are two ways to build Bayesian deep neural networks using Bayesian-Torch:Convert an existing deterministic deep neural network (dnn) model to Bayesian deep neural network (bnn) model with dnn_to_bnn() APIDefine your custom model using the Bayesian layers (ReparameterizationorFlipout)(1) For instance, building Bayesian-ResNet18 from torchvision deterministic ResNet18 model is as simple as:import torch
import torchvision
from bayesian_torch.models.dnn_to_bnn import dnn_to_bnn, get_kl_loss

const_bnn_prior_parameters = {
        "prior_mu": 0.0,
        "prior_sigma": 1.0,
        "posterior_mu_init": 0.0,
        "posterior_rho_init": -3.0,
        "type": "Reparameterization",  # Flipout or Reparameterization
        "moped_enable": False,  # True to initialize mu/sigma from the pretrained dnn weights
        "moped_delta": 0.5,
}
    
model = torchvision.models.resnet18()
dnn_to_bnn(model, const_bnn_prior_parameters)To use MOPED method i.e. setting the prior and initializing variational parameters from a pretrained deterministic model (helps training convergence of larger models):const_bnn_prior_parameters = {
        "prior_mu": 0.0,
        "prior_sigma": 1.0,
        "posterior_mu_init": 0.0,
        "posterior_rho_init": -3.0,
        "type": "Reparameterization",  # Flipout or Reparameterization
        "moped_enable": True,  # True to initialize mu/sigma from the pretrained dnn weights
        "moped_delta": 0.5,
}
    
model = torchvision.models.resnet18(pretrained=True)
dnn_to_bnn(model, const_bnn_prior_parameters)Training snippet:criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), args.learning_rate)

output = model(x_train)
kl = get_kl_loss(model)
ce_loss = criterion(output, y_train)
loss = ce_loss + kl / args.batch_size 

loss.backward()
optimizer.step()Testing snippet:model.eval()
with torch.no_grad():
    output_mc = []
    for mc_run in range(args.num_monte_carlo):
        logits = model(x_test)
        probs = torch.nn.functional.softmax(logits, dim=-1)
        output_mc.append(probs)
    output = torch.stack(output_mc)  
    pred_mean = output.mean(dim=0)
    y_pred = torch.argmax(pred_mean, axis=-1)
    test_acc = (y_pred.data.cpu().numpy() == y_test.data.cpu().numpy()).mean()Uncertainty Quantification:from utils.util import predictive_entropy, mutual_information

predictive_uncertainty = predictive_entropy(output.data.cpu().numpy())
model_uncertainty = mutual_information(output.data.cpu().numpy())(2) For building custom models, we have providedexample model implementationsusing the Bayesian layers.Example usage (training and evaluation of models)We have providedexample usagesandscriptsto train/evaluate the models. The instructions for CIFAR10 examples is provided below, similar scripts for ImageNet and MNIST are available.cd bayesian_torchTrainingTo train Bayesian ResNet on CIFAR10, run this command:Mean-field variational inference (Reparameterized Monte Carlo estimator)sh scripts/train_bayesian_cifar.shMean-field variational inference (Flipout Monte Carlo estimator)sh scripts/train_bayesian_flipout_cifar.shTo train deterministic ResNet on CIFAR10, run this command:Vanillash scripts/train_deterministic_cifar.shEvaluationTo evaluate Bayesian ResNet on CIFAR10, run this command:Mean-field variational inference (Reparameterized Monte Carlo estimator)sh scripts/test_bayesian_cifar.shMean-field variational inference (Flipout Monte Carlo estimator)sh scripts/test_bayesian_flipout_cifar.shTo evaluate deterministic ResNet on CIFAR10, run this command:Vanillash scripts/test_deterministic_cifar.shPost Training Quantization (PTQ)To quantize Bayesian ResNet (convert to INT8) and evaluate on CIFAR10, run this command:sh scripts/quantize_bayesian_cifar.shCitingIf you use this code, please cite as:@software{krishnan2022bayesiantorch,author={RanganathKrishnanandPiEspositoandMaheshSubedar},title={Bayesian-Torch:Bayesianneuralnetworklayersforuncertaintyestimation},month=jan,year=2022,doi={10.5281/zenodo.5908307},url={https://doi.org/10.5281/zenodo.5908307}howpublished={\url{https://github.com/IntelLabs/bayesian-torch}}}Accuracy versus Uncertainty Calibration (AvUC) loss@inproceedings{NEURIPS2020_d3d94468,title={Improvingmodelcalibrationwithaccuracyversusuncertaintyoptimization},author={Krishnan,RanganathandTickoo,Omesh},booktitle={AdvancesinNeuralInformationProcessingSystems},volume={33},pages={18237--18248},year={2020},url={https://proceedings.neurips.cc/paper/2020/file/d3d9446802a44259755d38e6d163e820-Paper.pdf}}Quantization framework for Bayesian deep learning@inproceedings{lin2023quantization,title={QuantizationforBayesianDeepLearning:Low-PrecisionCharacterizationandRobustness},author={Lin,Jun-LiangandKrishnan,RanganathandRanipa,KeyurRuganathbhaiandSubedar,MaheshandSanghavi,VrushabhandArunachalam,MeenaandTickoo,OmeshandIyer,RavishankarandKandemir,MahmutTaylan},booktitle={2023IEEEInternationalSymposiumonWorkloadCharacterization(IISWC)},pages={180--192},year={2023},organization={IEEE}}Model Priors with Empirical Bayes using DNN (MOPED)@inproceedings{krishnan2020specifying,title={Specifyingweightpriorsinbayesiandeepneuralnetworkswithempiricalbayes},author={Krishnan,RanganathandSubedar,MaheshandTickoo,Omesh},booktitle={ProceedingsoftheAAAIConferenceonArtificialIntelligence},volume={34},number={04},pages={4477--4484},year={2020},url={https://ojs.aaai.org/index.php/AAAI/article/view/5875}}This library and code is intended for researchers and developers, enables to quantify principled uncertainty estimates in deep learning models to develop uncertainty-aware AI models.
Feedbacks, issues and contributions are welcome.