Measuring uncertainty using MC Dropout on pytorch
I am trying to implement Bayesian CNN using Mc Dropout on Pytorch,
the main idea is that by applying dropout at test time and running over many forward passes , you get predictions from a variety of different models.
I’ve found an application of the Mc Dropout and I really did not get how they applied this method and how exactly they did choose the correct prediction from the list of predictions
here is the code

 def mcdropout_test(model):
    model.train()
    test_loss = 0
    correct = 0
    T = 100
    for data, target in test_loader:
        if args.cuda:
            data, target = data.cuda(), target.cuda()
        data, target = Variable(data, volatile=True), Variable(target)
        output_list = []
        for i in xrange(T):
            output_list.append(torch.unsqueeze(model(data), 0))
        output_mean = torch.cat(output_list, 0).mean(0)
        test_loss += F.nll_loss(F.log_softmax(output_mean), target, size_average=False).data[0]  # sum up batch loss
        pred = output_mean.data.max(1, keepdim=True)[1]  # get the index of the max log-probability
        correct += pred.eq(target.data.view_as(pred)).cpu().sum()

    test_loss /= len(test_loader.dataset)
    print('\nMC Dropout Test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.2f}%)\n'.format(
        test_loss, correct, len(test_loader.dataset),
        100. * correct / len(test_loader.dataset)))


    train()
    mcdropout_test()

I have replaced

data, target = Variable(data, volatile=True), Variable(target)

by adding

with torch.no_grad():  at the beginning

And this is how I have defined my CNN
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 192, 5, padding=2)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(192, 192, 5, padding=2)
        self.fc1 = nn.Linear(192 * 8 * 8, 1024)
        self.fc2 = nn.Linear(1024, 256)
        self.fc3 = nn.Linear(256, 10)
        self.dropout = nn.Dropout(p=0.3)
        
        nn.init.xavier_uniform_(self.conv1.weight)
        nn.init.constant_(self.conv1.bias, 0.0)
        nn.init.xavier_uniform_(self.conv2.weight)
        nn.init.constant_(self.conv2.bias, 0.0)
        nn.init.xavier_uniform_(self.fc1.weight)
        nn.init.constant_(self.fc1.bias, 0.0)
        nn.init.xavier_uniform_(self.fc2.weight)
        nn.init.constant_(self.fc2.bias, 0.0)
        nn.init.xavier_uniform_(self.fc3.weight)
        nn.init.constant_(self.fc3.bias, 0.0)


    def forward(self, x):
        x = self.pool(F.relu(self.dropout(self.conv1(x))))  # recommended to add the relu
        x = self.pool(F.relu(self.dropout(self.conv2(x))))  # recommended to add the relu
        x = x.view(-1, 192 * 8 * 8)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(self.dropout(x)))
        x = self.fc3(self.dropout(x))  # no activation function needed for the last layer
        return x

Can anyone help me to get the right implementation of the Monte Carlo Dropout method on CNN?

Implementing MC Dropout in Pytorch is easy. All that is needed to be done is to set the dropout layers of your model to train mode. This allows for different dropout masks to be used during the different various forward passes. Below is an implementation of MC Dropout in Pytorch illustrating how multiple predictions from the various forward passes are stacked together and used for computing different uncertainty metrics.
import sys

import numpy as np

import torch
import torch.nn as nn


def enable_dropout(model):
    &quot;&quot;&quot; Function to enable the dropout layers during test-time &quot;&quot;&quot;
    for m in model.modules():
        if m.__class__.__name__.startswith('Dropout'):
            m.train()

def get_monte_carlo_predictions(data_loader,
                                forward_passes,
                                model,
                                n_classes,
                                n_samples):
    &quot;&quot;&quot; Function to get the monte-carlo samples and uncertainty estimates
    through multiple forward passes

    Parameters
    ----------
    data_loader : object
        data loader object from the data loader module
    forward_passes : int
        number of monte-carlo samples/forward passes
    model : object
        keras model
    n_classes : int
        number of classes in the dataset
    n_samples : int
        number of samples in the test set
    &quot;&quot;&quot;

    dropout_predictions = np.empty((0, n_samples, n_classes))
    softmax = nn.Softmax(dim=1)
    for i in range(forward_passes):
        predictions = np.empty((0, n_classes))
        model.eval()
        enable_dropout(model)
        for i, (image, label) in enumerate(data_loader):

            image = image.to(torch.device('cuda'))
            with torch.no_grad():
                output = model(image)
                output = softmax(output) # shape (n_samples, n_classes)
            predictions = np.vstack((predictions, output.cpu().numpy()))

        dropout_predictions = np.vstack((dropout_predictions,
                                         predictions[np.newaxis, :, :]))
        # dropout predictions - shape (forward_passes, n_samples, n_classes)
    
    # Calculating mean across multiple MCD forward passes 
    mean = np.mean(dropout_predictions, axis=0) # shape (n_samples, n_classes)

    # Calculating variance across multiple MCD forward passes 
    variance = np.var(dropout_predictions, axis=0) # shape (n_samples, n_classes)

    epsilon = sys.float_info.min
    # Calculating entropy across multiple MCD forward passes 
    entropy = -np.sum(mean*np.log(mean + epsilon), axis=-1) # shape (n_samples,)

    # Calculating mutual information across multiple MCD forward passes 
    mutual_info = entropy - np.mean(np.sum(-dropout_predictions*np.log(dropout_predictions + epsilon),
                                            axis=-1), axis=0) # shape (n_samples,)

Moving on to the implementation which is posted in the question above, multiple predictions from T different forward passes are obtained by first setting the model to train mode (model.train()). Note that this is not desirable because unwanted stochasticity will be introduced in the predictions if there are layers other than dropout such as batch-norm in the model. Hence the best way is to just set the dropout layers to train mode as shown in the snippet above.
