在pytorch上使用MC Dropout测量不确定性 [英] Measuring uncertainty using MC Dropout on pytorch

问题描述

我正在尝试在Pytorch上使用Mc Dropout实施贝叶斯CNN，主要思想是，通过在测试时应用辍学并遍历许多前向传递，您可以从各种不同的模型中获得预测.我找到了Mc Dropout的一个应用程序，但我真的没有得到他们如何应用此方法以及他们如何从预测列表中选择正确的预测

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

这是代码

 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()

我已替换

数据，目标=变量(数据，volatile = True)，变量(目标)

通过添加

开头为torch.no_grad():

这就是我定义CNN的方式

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

有人可以帮助我在CNN上正确实现Monte Carlo Dropout方法吗?

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

推荐答案

在Pytorch中实现MC Dropout很容易.所有需要做的就是将模型的辍学层设置为训练模式.这允许在不同的各种前向通过过程中使用不同的防漏罩.以下是Pytorch中MC Dropout的实现，说明了如何将来自各个正向传递的多个预测堆叠在一起并用于计算不同的不确定性指标.

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):
    """ Function to enable the dropout layers during test-time """
    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):
    """ 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
    """

    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,)

继续上面问题中发布的实现，首先将模型设置为训练模式( model.train())，可以从T个不同的前向通过获得多个预测.请注意，这是不希望的，因为如果模型中存在除辍学以外的其他层(例如批处理范数)，则在预测中会引入不必要的随机性.因此，最好的方法是将辍学层设置为训练模式，如上面的代码片段所示.

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.

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