In this tutorial, we use the MNIST dataset and some standard PyTorch examples to show a synthetic problem where the input to the objective function is a 28 x 28 image. The main idea is to train a variational auto-encoder (VAE) on the MNIST dataset and run Bayesian Optimization in the latent space. We also refer readers to this tutorial, which discusses the method of jointly training a VAE with a predictor (e.g., classifier), and shows a similar tutorial for the MNIST setting.
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets # transforms
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
dtype = torch.double
SMOKE_TEST = os.environ.get("SMOKE_TEST", False)
Let's first define our synthetic expensive-to-evaluate objective function. We assume that it takes the following form:
$$\text{image} \longrightarrow \text{image classifier} \longrightarrow \text{scoring function} \longrightarrow \text{score}.$$
The classifier is a convolutional neural network (CNN) trained using the architecture of the PyTorch CNN example.
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 20, 5, 1)
self.conv2 = nn.Conv2d(20, 50, 5, 1)
self.fc1 = nn.Linear(4 * 4 * 50, 500)
self.fc2 = nn.Linear(500, 10)
def forward(self, x):
x = F.relu(self.conv1(x))
x = F.max_pool2d(x, 2, 2)
x = F.relu(self.conv2(x))
x = F.max_pool2d(x, 2, 2)
x = x.view(-1, 4 * 4 * 50)
x = F.relu(self.fc1(x))
x = self.fc2(x)
return F.log_softmax(x, dim=1)
def get_pretrained_dir() -> str:
"""
Get the directory of pretrained models, which are in the BoTorch repo.
Returns the location specified by PRETRAINED_LOCATION if that env
var is set; otherwise checks if we are in a likely part of the BoTorch
repo (botorch/botorch or botorch/tutorials) and returns the right path.
"""
if "PRETRAINED_LOCATION" in os.environ.keys():
return os.environ["PRETRAINED_LOCATION"]
cwd = os.getcwd()
folder = os.path.basename(cwd)
# automated tests run from botorch folder
if folder == "botorch":
return os.path.join(cwd, "tutorials/pretrained_models/")
# typical case (running from tutorial folder)
elif folder == "tutorials":
return os.path.join(cwd, "pretrained_models/")
raise FileNotFoundError("Could not figure out location of pretrained models.")
cnn_weights_path = os.path.join(get_pretrained_dir(), "mnist_cnn.pt")
cnn_model = Net().to(dtype=dtype, device=device)
cnn_state_dict = torch.load(cnn_weights_path, map_location=device)
cnn_model.load_state_dict(cnn_state_dict);
Our VAE model follows the PyTorch VAE example, except that we use the same data transform from the CNN tutorial for consistency. We then instantiate the model and again load a pre-trained model. To train these models, we refer readers to the PyTorch Github repository.
class VAE(nn.Module):
def __init__(self):
super().__init__()
self.fc1 = nn.Linear(784, 400)
self.fc21 = nn.Linear(400, 20)
self.fc22 = nn.Linear(400, 20)
self.fc3 = nn.Linear(20, 400)
self.fc4 = nn.Linear(400, 784)
def encode(self, x):
h1 = F.relu(self.fc1(x))
return self.fc21(h1), self.fc22(h1)
def reparameterize(self, mu, logvar):
std = torch.exp(0.5 * logvar)
eps = torch.randn_like(std)
return mu + eps * std
def decode(self, z):
h3 = F.relu(self.fc3(z))
return torch.sigmoid(self.fc4(h3))
def forward(self, x):
mu, logvar = self.encode(x.view(-1, 784))
z = self.reparameterize(mu, logvar)
return self.decode(z), mu, logvar
vae_weights_path = os.path.join(get_pretrained_dir(), "mnist_vae.pt")
vae_model = VAE().to(dtype=dtype, device=device)
vae_state_dict = torch.load(vae_weights_path, map_location=device)
vae_model.load_state_dict(vae_state_dict);
We now define the scoring function that maps digits to scores. The function below prefers the digit '3'.
def score(y):
"""Returns a 'score' for each digit from 0 to 9. It is modeled as a squared exponential
centered at the digit '3'.
"""
return torch.exp(-2 * (y - 3) ** 2)
Given the scoring function, we can now write our overall objective, which as discussed above, starts with an image and outputs a score. Let's say the objective computes the expected score given the probabilities from the classifier.
def score_image(x):
"""The input x is an image and an expected score
based on the CNN classifier and the scoring
function is returned.
"""
with torch.no_grad():
probs = torch.exp(cnn_model(x)) # b x 10
scores = score(
torch.arange(10, device=device, dtype=dtype)
).expand(probs.shape)
return (probs * scores).sum(dim=1)
Finally, we define a helper function decode that takes as input the parameters mu and logvar of the variational distribution and performs reparameterization and the decoding. We use batched Bayesian optimization to search over the parameters mu and logvar
def decode(train_x):
with torch.no_grad():
decoded = vae_model.decode(train_x)
return decoded.view(train_x.shape[0], 1, 28, 28)
We use a SingleTaskGP to model the score of an image generated by a latent representation. The model is initialized with points drawn from $[-6, 6]^{20}$.
from botorch.models import SingleTaskGP
from gpytorch.mlls.exact_marginal_log_likelihood import ExactMarginalLogLikelihood
from botorch.utils.transforms import normalize, unnormalize
from botorch.models.transforms import Standardize, Normalize
d = 20
bounds = torch.tensor([[-6.0] * d, [6.0] * d], device=device, dtype=dtype)
def gen_initial_data(n=5):
# generate training data
train_x = unnormalize(
torch.rand(n, d, device=device, dtype=dtype),
bounds=bounds
)
train_obj = score_image(decode(train_x)).unsqueeze(-1)
best_observed_value = train_obj.max().item()
return train_x, train_obj, best_observed_value
def get_fitted_model(train_x, train_obj, state_dict=None):
# initialize and fit model
model = SingleTaskGP(
train_X=normalize(train_x, bounds),
train_Y=train_obj,
outcome_transform=Standardize(m=1)
)
if state_dict is not None:
model.load_state_dict(state_dict)
mll = ExactMarginalLogLikelihood(model.likelihood, model)
mll.to(train_x)
fit_gpytorch_mll(mll)
return model
The helper function below takes an acquisition function as an argument, optimizes it, and returns the batch $\{x_1, x_2, \ldots x_q\}$ along with the observed function values. For this example, we'll use a small batch of $q=3$.
from botorch.optim import optimize_acqf
BATCH_SIZE = 3 if not SMOKE_TEST else 2
NUM_RESTARTS = 10 if not SMOKE_TEST else 2
RAW_SAMPLES = 256 if not SMOKE_TEST else 4
def optimize_acqf_and_get_observation(acq_func):
"""Optimizes the acquisition function, and returns a
new candidate and a noisy observation"""
# optimize
candidates, _ = optimize_acqf(
acq_function=acq_func,
bounds=torch.stack(
[
torch.zeros(d, dtype=dtype, device=device),
torch.ones(d, dtype=dtype, device=device),
]
),
q=BATCH_SIZE,
num_restarts=NUM_RESTARTS,
raw_samples=RAW_SAMPLES,
)
# observe new values
new_x = unnormalize(candidates.detach(), bounds=bounds)
new_obj = score_image(decode(new_x)).unsqueeze(-1)
return new_x, new_obj
The Bayesian optimization "loop" for a batch size of $q$ simply iterates the following steps: (1) given a surrogate model, choose a batch of points $\{x_1, x_2, \ldots x_q\}$, (2) observe $f(x)$ for each $x$ in the batch, and (3) update the surrogate model. We run N_BATCH=75 iterations. The acquisition function is approximated using MC_SAMPLES=2048 samples. We also initialize the model with 5 randomly drawn points.
from botorch import fit_gpytorch_mll
from botorch.acquisition.monte_carlo import qExpectedImprovement
from botorch.sampling.normal import SobolQMCNormalSampler
seed = 1
torch.manual_seed(seed)
N_BATCH = 25 if not SMOKE_TEST else 3
best_observed = []
# call helper function to initialize model
train_x, train_obj, best_value = gen_initial_data(n=5)
best_observed.append(best_value)
We are now ready to run the BO loop (this make take a few minutes, depending on your machine).
import warnings
from matplotlib import pyplot as plt
warnings.filterwarnings("ignore")
print(f"\nRunning BO ", end="")
state_dict = None
# run N_BATCH rounds of BayesOpt after the initial random batch
for iteration in range(N_BATCH):
# fit the model
model = get_fitted_model(
train_x=train_x,
train_obj=train_obj,
state_dict=state_dict,
)
# define the qNEI acquisition function
qEI = qExpectedImprovement(
model=model, best_f=train_obj.max()
)
# optimize and get new observation
new_x, new_obj = optimize_acqf_and_get_observation(qEI)
# update training points
train_x = torch.cat((train_x, new_x))
train_obj = torch.cat((train_obj, new_obj))
# update progress
best_value = train_obj.max().item()
best_observed.append(best_value)
state_dict = model.state_dict()
print(".", end="")
EI recommends the best point observed so far. We can visualize what the images corresponding to recommended points would have been if the BO process ended at various times. Here, we show the progress of the algorithm by examining the images at 0%, 10%, 25%, 50%, 75%, and 100% completion. The first image is the best image found through the initial random batch.
import numpy as np
from matplotlib import pyplot as plt
%matplotlib inline
fig, ax = plt.subplots(1, 6, figsize=(14, 14))
percentages = np.array([0, 10, 25, 50, 75, 100], dtype=np.float32)
inds = (N_BATCH * BATCH_SIZE * percentages / 100 + 4).astype(int)
for i, ax in enumerate(ax.flat):
b = torch.argmax(score_image(decode(train_x[: inds[i], :])), dim=0)
img = decode(train_x[b].view(1, -1)).squeeze().cpu()
ax.imshow(img, alpha=0.8, cmap="gray")
