We present the new scheme to compute Monte Carlo estimator in Bayesian VI settings with almost no memory cost in GPU, regardles of the number of samples. Our method is described in the paper (UAI2021): “Graph Reparameterizations for Enabling 1000+ Monte Carlo Iterations in Bayesian Deep Neural Networks”.

In addition, we provide an implementation framework to make your deterministic network Bayesian in PyTorch.

If you like our work, please click on a star. If you use our code in your research projects, please cite our paper above.

Bayesify your Neural Network

There are 3 main files which help you to Bayesify your deterministic network:

  1. bayes_layers.py – file contains a bayesian implementation of convolution(1d, 2d, 3d, transpose) and linear layers, according to approx posterior from Location-Scale family, i.e. which has 2 parameters mu and sigma. This file contains general definition, independent of specific distribution, as long as distribution contains 2 parameters mu and sigma. It uses forward method defined in vi_posteriors.py file. One of the main arguments for redefined classes is approx_post, which defined which posterior class to use from vi_posteriors.py. Please, specify this name same way as defined class in vi_posteriors.py. For example, if vi_posteriors.py contains class Gaus, then approx_post='Gaus'.

  2. vi_posteriors.py – file describes forward method, including kl term, for different approximate posterior distributions. Current implementation contains following disutributions:

  • Radial
  • Gaus

If you would like to implement your own class of distrubtions, in vi_posteriors.py copy one of defined classes and redefine following functions: forward(obj, x, fun=""), get_kl(obj, n_mc_iter, device).

It also contains usefull Utils class which provides

  • definition of loss functions:
    • get_loss_categorical
    • get_loss_normal,
  • different beta coefficients: get_beta for KL term and
  • allows to turn on/off computing the KL term, with function set_compute_kl. this is useful, when you perform testing/evaluation, and kl term is not required to be computed. In that case it accelerates computations.

Below is an example to bayesify your own network. Note the forward method, which handles situations if a layer is not of a Bayesian type, and thus, does not return kl term, e.g. ReLU(x).

import bayes_layers as bl # important for defining bayesian layers
class YourBayesNet(nn.Module):
    def __init__(self, num_classes, in_channels, 
        super(YourBayesNet, self).__init__()
        self.conv1 = bl.Conv2d(in_channels, 64,
                               kernel_size=11, stride=4,
        self.classifier = bl.Linear(1*1*128,
        self.layers = [self.conv1, nn.ReLU(), self.classifier]
    def forward(self, x):
        kl = 0
        for layer in self.layers:
            tmp = layer(x)
            if isinstance(tmp, tuple):
                x, kl_ = tmp
                kl += kl_
                x = tmp

        x = x.view(x.size(0), -1)
        logits, _kl = self.classifier.forward(x)
        kl += _kl
        return logits, kl

Then later in the main file during training, you can either use one of the loss functions, defined in utils as following:

output, kl = model(inputs)
kl = kl.mean()  # if several gpus are used to split minibatch

loss, _ = vi.Utils.get_loss_categorical(kl, output, targets, beta=beta) 
#loss, _ = vi.Utils.get_loss_normal(kl, output, targets, beta=beta) 

or design your own, e.g.

loss = kl_coef*kl - loglikelihood
  1. uncertainty_estimate.py – file describes set of functions to perform uncertainty estimation, e.g.
  • get_prediction_class – function which return the most common class in iterations
  • summary_class – function creates a summary file with statistics

Current implementation of networks for different problems


Script bayesian_dnn_class/main.py is the main executable code and all standard DNN models are located in bayesian_dnn_class/models, and are:

  • AlexNet
  • Fully Connected
  • DenseNet
  • ResNet
  • VGG