adaptive_batchnorm.py

"""
Implementation of Adaptive BatchNorm

@author: ptrblck
"""

import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torchvision import datasets, transforms


# Globals
device = 'cuda' if torch.cuda.is_available() else 'cpu'
seed = 2809
batch_size = 10
lr = 0.01
log_interval = 10
epochs = 10
torch.manual_seed(seed)


class AdaptiveBatchNorm2d(nn.Module):
    '''
    Adaptive BN implementation using two additional parameters:
    out = a * x + b * bn(x)
    '''
    def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True):
        super(AdaptiveBatchNorm2d, self).__init__()
        self.bn = nn.BatchNorm2d(num_features, eps, momentum, affine)
        self.a = nn.Parameter(torch.FloatTensor(1, 1, 1, 1))
        self.b = nn.Parameter(torch.FloatTensor(1, 1, 1, 1))

    def forward(self, x):
        return self.a * x + self.b * self.bn(x)


class MyNet(nn.Module):
    def __init__(self):
        super(MyNet, self).__init__()
        self.conv1 = nn.Conv2d(in_channels=1,
                               out_channels=10,
                               kernel_size=5)
        self.conv1_bn = AdaptiveBatchNorm2d(10)
        self.conv2 = nn.Conv2d(in_channels=10,
                               out_channels=20,
                               kernel_size=5)
        self.conv2_bn = AdaptiveBatchNorm2d(20)
        self.fc1 = nn.Linear(320, 50)
        self.fc2 = nn.Linear(50, 10)

    def forward(self, x):
        x = F.relu(F.max_pool2d(self.conv1_bn(self.conv1(x)), 2))
        x = F.relu(F.max_pool2d(self.conv2_bn(self.conv2(x)), 2))
        x = x.view(-1, 320)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return F.log_softmax(x, dim=1)


def train(epoch):
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):
        data, target = data.to(device), target.to(device)
        optimizer.zero_grad()
        output = model(data)
        loss = F.nll_loss(output, target)
        loss.backward()
        optimizer.step()
        if batch_idx % log_interval == 0:
            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                epoch, batch_idx * len(data), len(train_loader.dataset),
                100. * batch_idx / len(train_loader), loss.item()))


def test():
    model.eval()
    test_loss = 0
    correct = 0
    with torch.no_grad():
        for data, target in test_loader:
            data, target = data.to(device), target.to(device)
            output = model(data)
            # sum up batch loss
            test_loss += F.nll_loss(output, target, size_average=False).item()
            # get the index of the max log-probability
            pred = output.data.max(1, keepdim=True)[1]
            correct += pred.eq(target.data.view_as(pred)).cpu().sum()

        test_loss /= len(test_loader.dataset)
        print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
            test_loss, correct, len(test_loader.dataset),
            100. * correct / len(test_loader.dataset)))


train_loader = torch.utils.data.DataLoader(
    datasets.MNIST('./data', train=True, download=True,
                   transform=transforms.Compose([
                       transforms.ToTensor(),
                       transforms.Normalize((0.1307,), (0.3081,))
                   ])),
    batch_size=batch_size,
    shuffle=True)

test_loader = torch.utils.data.DataLoader(
    datasets.MNIST('./data', train=False, transform=transforms.Compose([
                       transforms.ToTensor(),
                       transforms.Normalize((0.1307,), (0.3081,))
                   ])),
    batch_size=batch_size,
    shuffle=True)

model = MyNet().to(device)
optimizer = optim.SGD(model.parameters(), lr=lr, momentum=0.5)

for epoch in range(1, epochs + 1):
    train(epoch)
    test()