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import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader, Dataset
from torchvision import datasets, transforms
import matplotlib.pyplot as plt
import numpy as np
from trainer import Trainer
class UNet(nn.Module):
def __init__(self):
super().__init__()
## Inputs:
# xt: image at step t (1x28x28)
# t: step number (1)
# vec: one-hot vector of the label (10)
## Encoder for t
self.encodet = nn.Linear(1, 28*28)
## Encoder for vec
self.encodevec = nn.Linear(10, 28*28)
## UNet
self.conv1 = nn.Conv2d(3, 64, 3, padding=1)
self.conv2 = nn.Conv2d(64, 64, 3, padding=1)
self.maxpool1 = nn.MaxPool2d(2, 2) # 28x28 -> 14x14
self.conv3 = nn.Conv2d(64, 128, 3, padding=1)
self.conv4 = nn.Conv2d(128, 128, 3, padding=1)
self.maxpool2 = nn.MaxPool2d(2, 2) # 14x14 -> 7x7
self.conv5 = nn.Conv2d(128, 256, 3, padding=1)
self.conv6 = nn.Conv2d(256, 256, 3, padding=1)
self.upconv1 = nn.ConvTranspose2d(256, 128, 2, stride=2) # 7x7 -> 14x14
self.conv7 = nn.Conv2d(256, 128, 3, padding=1)
self.conv8 = nn.Conv2d(128, 128, 3, padding=1)
self.upconv2 = nn.ConvTranspose2d(128, 64, 2, stride=2) # 14x14 -> 28x28
self.conv9 = nn.Conv2d(128, 64, 3, padding=1)
self.conv10 = nn.Conv2d(64, 64, 3, padding=1)
self.conv11 = nn.Conv2d(64, 1, 3, padding=1)
def forward(self, xt, t, vec):
# Encode t and vec
t = F.relu(self.encodet(t))
t = t.view(-1, 1, 28, 28)
vec = F.relu(self.encodevec(vec))
vec = vec.view(-1, 1, 28, 28)
# Concat all 3
x = torch.cat((xt, t, vec), dim=1)
# UNet
x1 = F.relu(self.conv1(x))
x1 = F.relu(self.conv2(x1))
x2 = self.maxpool1(x1)
x2 = F.relu(self.conv3(x2))
x2 = F.relu(self.conv4(x2))
x3 = self.maxpool2(x2)
x3 = F.relu(self.conv5(x3))
x3 = F.relu(self.conv6(x3))
x4 = self.upconv1(x3)
x4 = torch.cat((x4, x2), dim=1)
x4 = F.relu(self.conv7(x4))
x4 = F.relu(self.conv8(x4))
x5 = self.upconv2(x4)
x5 = torch.cat((x5, x1), dim=1)
x5 = F.relu(self.conv9(x5))
x5 = F.relu(self.conv10(x5))
x5 = self.conv11(x5)
return x5
DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
DIFFU_STEPS = 20
BETA = torch.linspace(0.0001, 0.2, DIFFU_STEPS, device=DEVICE)
ALPHA = 1 - BETA
ALPHA_BAR = torch.cumprod(ALPHA, dim=0)
SIGMA2 = BETA
def q_xt_xt_1(xt_1, t):
t_ind = t.to(dtype=torch.long) if isinstance(t, torch.Tensor) else t
beta = BETA[t_ind]
mean = torch.sqrt(1 - beta) * xt_1
std = beta
return torch.distributions.Normal(mean, std)
def q_xt_x0(x0, t):
t_ind = t.to(dtype=torch.long) if isinstance(t, torch.Tensor) else t
alpha_bar = ALPHA_BAR[t_ind]
mean = torch.sqrt(alpha_bar) * x0
std = 1 - alpha_bar
return torch.distributions.Normal(mean, std)
def p_xt_1_xt(model, xt, t, vec):
t_ind = t.to(dtype=torch.long) if isinstance(t, torch.Tensor) else t
alpha_bar_t = ALPHA_BAR[t_ind]
alpha_bar_t_1 = ALPHA_BAR[t_ind-1]
alpha_t = ALPHA[t_ind]
beta_t = BETA[t_ind]
beta_tilde = (1 - alpha_bar_t_1) / (1 - alpha_bar_t) * beta_t
epsilon_theta = model(xt, t, vec)
# sigma_theta = torch.exp(nu * torch.log(beta_t) + (1 - nu) * torch.log(beta_tilde))
sigma_theta = beta_tilde
mu_theta = (xt - beta_t / torch.sqrt(1 - alpha_bar_t) * epsilon_theta) / torch.sqrt(alpha_t)
return torch.distributions.Normal(mu_theta, sigma_theta)
class MNISTDiffusionDataset(Dataset):
def __init__(self, train=True):
super().__init__()
self.mnist_data = datasets.MNIST(
root="./data",
train=train,
download=True,
transform=transforms.ToTensor(),
)
def __getitem__(self, index):
# Get the image and the label.
img, label = self.mnist_data[index]
img = img.to(DEVICE)
# Add noise to the image.
t = torch.randint(1, DIFFU_STEPS, (1,), device=DEVICE)
xt = q_xt_x0(img, t).sample()
eps = xt - img
# Convert the label to a one-hot vector.
vec = torch.nn.functional.one_hot(
torch.tensor(label),
num_classes=10,
)
return (
xt.clone().detach().to(dtype=torch.float32, device=DEVICE),
t.clone().detach().to(dtype=torch.float32, device=DEVICE),
vec.clone().detach().to(dtype=torch.float32, device=DEVICE),
eps,
)
def __len__(self):
return len(self.mnist_data)
def loss(y_pred, y_true):
return nn.MSELoss()(y_pred, y_true)
mnist_data = datasets.MNIST(
root="./data",
train=True,
download=True,
transform=transforms.ToTensor(),
)
img, label = mnist_data[0]
img = img.to(DEVICE)
fig = plt.figure(figsize=(DIFFU_STEPS, 2))
for t in range(1, DIFFU_STEPS):
xt_1 = q_xt_x0(img, t - 1).sample()
x_t = q_xt_xt_1(xt_1, t).sample()
ax = fig.add_subplot(2, DIFFU_STEPS, t)
ax.imshow(xt_1[0].cpu(), cmap="gray")
ax.axis("off")
ax = fig.add_subplot(2, DIFFU_STEPS, DIFFU_STEPS + t)
ax.imshow(x_t[0].cpu(), cmap="gray")
ax.axis("off")
fig.tight_layout()
fig.savefig("img.tmp.png")
############
# Training #
############
torch.multiprocessing.set_start_method("spawn")
# Load the model.
model = UNet().to(DEVICE)
model.load_state_dict(torch.load('model.pth'))
# Define the optimizer.
optimizer = torch.optim.Adam(model.parameters(), lr=5e-5)
# Define the training dataset.
train_dataset = MNISTDiffusionDataset(train=True)
train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True, num_workers=4)
trainer = Trainer()
criterion = loss
epochs = 5
# # Train the model.
# trainer.train(model, train_loader, epochs, optimizer, criterion)
# # Save the model.
# torch.save(model.state_dict(), "model.pth")
##############
# Evaluation #
##############
x = torch.randn(1, 1, 28, 28).to(DEVICE)
vec = torch.nn.functional.one_hot(torch.randint(0, 10, (1, 1)), num_classes=10).to(device=DEVICE, dtype=torch.float32)
print(vec)
fig = plt.figure(figsize=(DIFFU_STEPS, 2))
for ti in range(1, DIFFU_STEPS):
t = torch.tensor([[ti]], device=DEVICE, dtype=torch.float32)
x = p_xt_1_xt(model, x, t, vec).sample()
ax = fig.add_subplot(1, DIFFU_STEPS, ti)
ax.imshow(x[0, 0].detach().cpu(), cmap="gray")
ax.axis("off")
fig.tight_layout()
fig.savefig("diffused.tmp.png")
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