In this notebook, we'll demonstrate the steps necessary to train a medium sized DenseNet (https://arxiv.org/abs/1608.06993) on either of two popularly used benchmark dataset in computer vision (CIFAR10 and CIFAR100). We'll be training the DKL model entirely end to end using the standard 300 Epoch training schedule and SGD.
This notebook is largely for tutorial purposes. If your goal is just to get (for example) a trained DKL + CIFAR100 model, we recommend that you move this code to a simple python script and run that, rather than training directly out of a python notebook. We find that training is just a bit faster out of a python notebook. We also of course recommend that you increase the size of the DenseNet used to a full sized model if you would like to achieve state of the art performance.
Furthermore, because this notebook involves training an actually reasonably large neural network, it is strongly recommended that you have a decent GPU available for this, as with all large deep learning models.
In [1]:
from torch.optim import SGD, Adam
from torch.optim.lr_scheduler import MultiStepLR
import torch.nn.functional as F
from torch import nn
import torch
import torchvision.datasets as dset
import torchvision.transforms as transforms
import gpytorch
import math
The first thing we'll do is set up some data augmentation transformations to use during training, as well as some basic normalization to use during both training and testing. We'll use random crops and flips to train the model, and do basic normalization at both training time and test time. To accomplish these transformations, we use standard torchvision transforms.
In [2]:
normalize = transforms.Normalize(mean=[0.5071, 0.4867, 0.4408], std=[0.2675, 0.2565, 0.2761])
aug_trans = [transforms.RandomCrop(32, padding=4), transforms.RandomHorizontalFlip()]
common_trans = [transforms.ToTensor(), normalize]
train_compose = transforms.Compose(aug_trans + common_trans)
test_compose = transforms.Compose(common_trans)
Next, we create dataloaders for the selected dataset using the built in torchvision datasets. The cell below will download either the cifar10 or cifar100 dataset, depending on which choice is made. The default here is cifar10, however training is just as fast on either dataset.
After downloading the datasets, we create standard torch.utils.data.DataLoaders for each dataset that we will be using to get minibatches of augmented data.
In [3]:
dataset = 'cifar10'
if dataset == 'cifar10':
d_func = dset.CIFAR10
train_set = dset.CIFAR10('data', train=True, transform=train_compose, download=True)
test_set = dset.CIFAR10('data', train=False, transform=test_compose)
train_loader = torch.utils.data.DataLoader(train_set, batch_size=256, shuffle=True)
test_loader = torch.utils.data.DataLoader(test_set, batch_size=256, shuffle=False)
num_classes = 10
elif dataset == 'cifar100':
d_func = dset.CIFAR100
train_set = dset.CIFAR100('data', train=True, transform=train_compose, download=True)
test_set = dset.CIFAR100('data', train=False, transform=test_compose)
train_loader = torch.utils.data.DataLoader(train_set, batch_size=256, shuffle=True)
test_loader = torch.utils.data.DataLoader(test_set, batch_size=256, shuffle=False)
num_classes = 100
else:
raise RuntimeError('dataset must be one of "cifar100" or "cifar10"')
With the data loaded, we can move on to defining our DKL model. A DKL model consists of three components: the neural network, the Gaussian process layer used after the neural network, and the Softmax likelihood.
The first step is defining the neural network architecture. To do this, we use a slightly modified version of the DenseNet available in the standard PyTorch package. Specifically, we modify it to remove the softmax layer, since we'll only be needing the final features extracted from the neural network.
In [4]:
from densenet import DenseNet
class DenseNetFeatureExtractor(DenseNet):
def forward(self, x):
features = self.features(x)
out = F.relu(features, inplace=True)
out = F.avg_pool2d(out, kernel_size=self.avgpool_size).view(features.size(0), -1)
return out
feature_extractor = DenseNetFeatureExtractor(block_config=(6, 6, 6), num_classes=num_classes).cuda()
num_features = feature_extractor.classifier.in_features
In [5]:
class GaussianProcessLayer(gpytorch.models.AdditiveGridInducingVariationalGP):
def __init__(self, num_dim, grid_bounds=(-10., 10.), grid_size=64):
super(GaussianProcessLayer, self).__init__(grid_size=grid_size, grid_bounds=[grid_bounds],
num_dim=num_dim, mixing_params=False, sum_output=False)
self.covar_module = gpytorch.kernels.ScaleKernel(
gpytorch.kernels.RBFKernel(
lengthscale_prior=gpytorch.priors.SmoothedBoxPrior(
math.exp(-1), math.exp(1), sigma=0.1, transform=torch.exp
)
)
)
self.mean_module = gpytorch.means.ConstantMean()
self.grid_bounds = grid_bounds
def forward(self, x):
mean = self.mean_module(x)
covar = self.covar_module(x)
return gpytorch.distributions.MultivariateNormal(mean, covar)
In [6]:
class DKLModel(gpytorch.Module):
def __init__(self, feature_extractor, num_dim, grid_bounds=(-10., 10.)):
super(DKLModel, self).__init__()
self.feature_extractor = feature_extractor
self.gp_layer = GaussianProcessLayer(num_dim=num_dim, grid_bounds=grid_bounds)
self.grid_bounds = grid_bounds
self.num_dim = num_dim
def forward(self, x):
features = self.feature_extractor(x)
features = gpytorch.utils.grid.scale_to_bounds(features, self.grid_bounds[0], self.grid_bounds[1])
res = self.gp_layer(features)
return res
model = DKLModel(feature_extractor, num_dim=num_features).cuda()
likelihood = gpytorch.likelihoods.SoftmaxLikelihood(num_features=model.num_dim, n_classes=num_classes).cuda()
Next, we define the basic optimization loop and testing code. This code is entirely analogous to the standard PyTorch training loop. We create a torch.optim.SGD optimizer with the parameters of the neural network on which we apply the standard amount of weight decay suggested from the paper, the parameters of the Gaussian process (from which we omit weight decay, as L2 regualrization on top of variational inference is not necessary), and the mixing parameters of the Softmax likelihood.
We use the standard learning rate schedule from the paper, where we decrease the learning rate by a factor of ten 50% of the way through training, and again at 75% of the way through training.
In [7]:
n_epochs = 300
lr = 0.1
optimizer = SGD([
{'params': model.feature_extractor.parameters()},
{'params': model.gp_layer.hyperparameters(), 'lr': lr * 0.01},
{'params': model.gp_layer.variational_parameters()},
{'params': likelihood.parameters()},
], lr=lr, momentum=0.9, nesterov=True, weight_decay=0)
scheduler = MultiStepLR(optimizer, milestones=[0.5 * n_epochs, 0.75 * n_epochs], gamma=0.1)
def train(epoch):
model.train()
likelihood.train()
mll = gpytorch.mlls.VariationalELBO(likelihood, model.gp_layer, num_data=len(train_loader.dataset))
train_loss = 0.
for batch_idx, (data, target) in enumerate(train_loader):
data, target = data.cuda(), target.cuda()
optimizer.zero_grad()
output = model(data)
loss = -mll(output, target)
loss.backward()
optimizer.step()
if (batch_idx + 1) % 25 == 0:
print('Train Epoch: %d [%03d/%03d], Loss: %.6f' % (epoch, batch_idx + 1, len(train_loader), loss.item()))
def test():
from torch.optim import SGD, Adam
from torch.optim.lr_scheduler import MultiStepLR
import torch.nn.functional as F
from torch import nn
import torch
model.eval()
likelihood.eval()
correct = 0
for data, target in test_loader:
data, target = data.cuda(), target.cuda()
with torch.no_grad():
output = likelihood(model(data))
pred = output.probs.argmax(1)
correct += pred.eq(target.view_as(pred)).cpu().sum()
print('Test set: Accuracy: {}/{} ({}%)'.format(
correct, len(test_loader.dataset), 100. * correct / float(len(test_loader.dataset))
))
In [8]:
for epoch in range(1, n_epochs + 1):
scheduler.step()
with gpytorch.settings.use_toeplitz(False), gpytorch.settings.max_preconditioner_size(0):
train(epoch)
test()
state_dict = model.state_dict()
likelihood_state_dict = likelihood.state_dict()
torch.save({'model': state_dict, 'likelihood': likelihood_state_dict}, 'dkl_cifar_checkpoint.dat')
In [9]:
%debug
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