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"""
Data loading utilities for MNIST diffusion training.
"""
import torch
from torch.utils.data import Dataset, DataLoader, random_split
from torchvision import datasets, transforms
from models.autoencoder import AEModule
from .config import DEVICE, DIFFU_STEPS, NB_LABEL, DATA_DIR
from .diffusion import q_xt_x0
def get_mnist_dataset(train: bool = True) -> datasets.MNIST:
"""
Load MNIST dataset.
Args:
train: If True, load training set. Otherwise load test set.
Returns:
MNIST dataset
"""
return datasets.MNIST(
root=DATA_DIR,
train=train,
download=True,
transform=transforms.ToTensor(),
)
class DiffusionDataset(Dataset):
"""
Dataset wrapper for diffusion training.
Returns noisy image, timestep, label, and target noise.
Args:
dataset: Base image dataset (e.g., MNIST)
autoencoder: Optional autoencoder for latent diffusion
"""
def __init__(self, dataset: Dataset, autoencoder: AEModule | None = None):
super().__init__()
self.dataset = dataset
self.autoencoder = autoencoder
def __getitem__(self, index: int) -> tuple[torch.Tensor, ...]:
# Get image and label
img, label = self.dataset[index]
img = img.to(DEVICE)
# Optionally encode to latent space
if self.autoencoder is not None:
with torch.no_grad():
img = self.autoencoder.encode(img.unsqueeze(0)).squeeze(0)
# Normalize to [-1, 1]
img = img * 2 - 1
# Sample random timestep and add noise
t = torch.randint(1, DIFFU_STEPS + 1, (1,), device=DEVICE)
xt, eps = q_xt_x0(img, t)
# Convert label to one-hot vector
vec = torch.nn.functional.one_hot(
torch.tensor(min(label, NB_LABEL - 1)),
num_classes=NB_LABEL,
)
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, # Target: the noise that was added
)
def __len__(self) -> int:
return len(self.dataset)
class DiffusionDatasetX0(Dataset):
"""
Dataset wrapper for diffusion training where model predicts x0 instead of noise.
Returns noisy image, timestep, label, and target clean image.
Args:
dataset: Base image dataset (e.g., MNIST)
autoencoder: Optional autoencoder for latent diffusion
"""
def __init__(self, dataset: Dataset, autoencoder: AEModule | None = None):
super().__init__()
self.dataset = dataset
self.autoencoder = autoencoder
def __getitem__(self, index: int) -> tuple[torch.Tensor, ...]:
# Get image and label
img, label = self.dataset[index]
img = img.to(DEVICE)
# Optionally encode to latent space
if self.autoencoder is not None:
with torch.no_grad():
img = self.autoencoder.encode(img.unsqueeze(0)).squeeze(0)
# Normalize to [-1, 1]
img = img * 2 - 1
# Sample random timestep and add noise
t = torch.randint(1, DIFFU_STEPS + 1, (1,), device=DEVICE)
xt, _ = q_xt_x0(img, t)
# Convert label to one-hot vector
vec = torch.nn.functional.one_hot(
torch.tensor(min(label, NB_LABEL - 1)),
num_classes=NB_LABEL,
)
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),
img.clone().detach().to(dtype=torch.float32, device=DEVICE), # Target: clean image
)
def __len__(self) -> int:
return len(self.dataset)
class AutoencoderDataset(Dataset):
"""
Dataset wrapper for autoencoder training.
Returns image as both input and target.
Args:
dataset: Base image dataset
"""
def __init__(self, dataset: Dataset):
self.dataset = dataset
def __len__(self) -> int:
return len(self.dataset)
def __getitem__(self, idx: int) -> tuple[torch.Tensor, torch.Tensor]:
data = self.dataset[idx][0].to(DEVICE)
# data = data * 2 - 1 # Normalize to [-1, 1]
return data, data
def get_diffusion_dataloader(
predict_x0: bool = True,
batch_size: int = 64,
shuffle: bool = True,
num_workers: int = 4,
autoencoder: AEModule | None = None,
) -> DataLoader:
"""
Create a DataLoader for diffusion training.
Args:
predict_x0: If True, model predicts x0. Otherwise predicts noise.
batch_size: Batch size
shuffle: Whether to shuffle data
num_workers: Number of data loading workers
autoencoder: Optional autoencoder for latent diffusion
Returns:
DataLoader for training
"""
mnist = get_mnist_dataset(train=True)
if predict_x0:
dataset = DiffusionDatasetX0(mnist, autoencoder)
else:
dataset = DiffusionDataset(mnist, autoencoder)
return DataLoader(
dataset,
batch_size=batch_size,
shuffle=shuffle,
num_workers=num_workers,
persistent_workers=True if num_workers > 0 else False,
)
def get_diffusion_dataloaders(
predict_x0: bool = True,
batch_size: int = 64,
shuffle: bool = True,
num_workers: int = 4,
autoencoder: AEModule | None = None,
val_split: float = 0.1,
test_split: float = 0.1,
seed: int = 42,
) -> tuple[DataLoader, DataLoader, DataLoader]:
"""
Create DataLoaders for diffusion training, validation, and testing.
Args:
predict_x0: If True, model predicts x0. Otherwise predicts noise.
batch_size: Batch size
shuffle: Whether to shuffle training data
num_workers: Number of data loading workers
autoencoder: Optional autoencoder for latent diffusion
val_split: Fraction of data for validation
test_split: Fraction of data for testing
seed: Random seed for deterministic split
Returns:
Tuple of (train_loader, val_loader, test_loader)
"""
if val_split < 0 or test_split < 0 or (val_split + test_split) >= 1:
raise ValueError("val_split and test_split must be >= 0 and sum to < 1")
mnist = get_mnist_dataset(train=True)
if predict_x0:
dataset = DiffusionDatasetX0(mnist, autoencoder)
else:
dataset = DiffusionDataset(mnist, autoencoder)
total_len = len(dataset)
val_len = int(total_len * val_split)
test_len = int(total_len * test_split)
train_len = total_len - val_len - test_len
if train_len <= 0:
raise ValueError("Split sizes result in empty training set")
generator = torch.Generator().manual_seed(seed)
train_set, val_set, test_set = random_split(dataset, [train_len, val_len, test_len], generator=generator)
train_loader = DataLoader(
train_set,
batch_size=batch_size,
shuffle=shuffle,
num_workers=num_workers,
persistent_workers=True if num_workers > 0 else False,
)
val_loader = DataLoader(
val_set,
batch_size=batch_size,
shuffle=False,
num_workers=num_workers,
persistent_workers=True if num_workers > 0 else False,
)
test_loader = DataLoader(
test_set,
batch_size=batch_size,
shuffle=False,
num_workers=num_workers,
persistent_workers=True if num_workers > 0 else False,
)
return train_loader, val_loader, test_loader
def get_autoencoder_dataloader(
batch_size: int = 64,
shuffle: bool = True,
num_workers: int = 4,
) -> DataLoader:
"""
Create a DataLoader for autoencoder training.
Args:
batch_size: Batch size
shuffle: Whether to shuffle data
num_workers: Number of data loading workers
Returns:
DataLoader for training
"""
mnist = get_mnist_dataset(train=True)
dataset = AutoencoderDataset(mnist)
return DataLoader(
dataset,
batch_size=batch_size,
shuffle=shuffle,
num_workers=num_workers,
persistent_workers=True if num_workers > 0 else False,
)
def get_autoencoder_dataloaders(
batch_size: int = 64,
shuffle: bool = True,
num_workers: int = 4,
val_split: float = 0.1,
test_split: float = 0.1,
seed: int = 42,
) -> tuple[DataLoader, DataLoader, DataLoader]:
"""
Create DataLoaders for autoencoder training, validation, and testing.
Args:
batch_size: Batch size
shuffle: Whether to shuffle training data
num_workers: Number of data loading workers
val_split: Fraction of data for validation
test_split: Fraction of data for testing
seed: Random seed for deterministic split
Returns:
Tuple of (train_loader, val_loader, test_loader)
"""
if val_split < 0 or test_split < 0 or (val_split + test_split) >= 1:
raise ValueError("val_split and test_split must be >= 0 and sum to < 1")
mnist = get_mnist_dataset(train=True)
dataset = AutoencoderDataset(mnist)
total_len = len(dataset)
val_len = int(total_len * val_split)
test_len = int(total_len * test_split)
train_len = total_len - val_len - test_len
if train_len <= 0:
raise ValueError("Split sizes result in empty training set")
generator = torch.Generator().manual_seed(seed)
train_set, val_set, test_set = random_split(dataset, [train_len, val_len, test_len], generator=generator)
train_loader = DataLoader(
train_set,
batch_size=batch_size,
shuffle=shuffle,
num_workers=num_workers,
persistent_workers=True if num_workers > 0 else False,
)
val_loader = DataLoader(
val_set,
batch_size=batch_size,
shuffle=False,
num_workers=num_workers,
persistent_workers=True if num_workers > 0 else False,
)
test_loader = DataLoader(
test_set,
batch_size=batch_size,
shuffle=False,
num_workers=num_workers,
persistent_workers=True if num_workers > 0 else False,
)
return train_loader, val_loader, test_loader
|
