Data Loading and Management

class SubjectsLoader(torch.utils.data.DataLoader):
    """
    PyTorch DataLoader wrapper optimized for medical image subjects.
    
    Provides custom collate function that handles the complex nested structure
    of Subject instances containing multiple images and metadata.
    """
    def __init__(
        self,
        dataset: SubjectsDataset,
        batch_size: int = 1,
        shuffle: bool = False,
        num_workers: int = 0,
        **kwargs
    ): ...

import torchio as tio
import torch

# Create dataset
subjects = [...]  # List of subjects
dataset = tio.SubjectsDataset(subjects)

# Create optimized loader for subjects
loader = tio.SubjectsLoader(
    dataset,
    batch_size=4,
    shuffle=True,
    num_workers=4
)

for batch in loader:
    # batch is a dict containing batched subject data
    t1_batch = batch['t1'][tio.DATA]  # Shape: (4, 1, D, H, W)
    ages = batch['age']  # List of ages

class Queue(torch.utils.data.Dataset):
    """
    Queue for patch-based training with data augmentation.
    
    Maintains a buffer of patches extracted from subjects, enabling
    efficient training on large 3D volumes through patch-based sampling.
    
    Parameters:
    - subjects_dataset: SubjectsDataset containing subjects
    - max_length: Maximum number of patches to keep in queue
    - samples_per_volume: Number of patches to extract per subject
    - sampler: PatchSampler for patch extraction strategy
    - num_workers: Number of workers for parallel patch extraction
    - shuffle_subjects: Whether to shuffle subjects
    - shuffle_patches: Whether to shuffle patches
    """
    def __init__(
        self,
        subjects_dataset: SubjectsDataset,
        max_length: int,
        samples_per_volume: int,
        sampler: 'PatchSampler',
        num_workers: int = 0,
        shuffle_subjects: bool = True,
        shuffle_patches: bool = True,
        **kwargs
    ): ...
    
    def __len__(self) -> int:
        """Return current number of patches in queue"""
    
    def __getitem__(self, index: int) -> dict:
        """Get patch at index"""
    
    def set_max_length(self, max_length: int):
        """Update maximum queue length"""

# Create subjects dataset
subjects = [...]  # List of subjects
dataset = tio.SubjectsDataset(subjects, transform=preprocessing_transform)

# Define patch sampling
patch_size = 64
sampler = tio.data.UniformSampler(patch_size)

# Create queue for patch-based training
patches_queue = tio.Queue(
    subjects_dataset=dataset,
    max_length=300,  # Keep 300 patches in queue
    samples_per_volume=10,  # Extract 10 patches per subject
    sampler=sampler,
    num_workers=4,
    shuffle_subjects=True,
    shuffle_patches=True,
)

# Use with PyTorch DataLoader
patches_loader = torch.utils.data.DataLoader(
    patches_queue,
    batch_size=16,
    num_workers=0  # Queue already handles parallelization
)

# Training loop
for batch in patches_loader:
    inputs = batch['t1'][tio.DATA]  # Shape: (16, 1, 64, 64, 64)
    targets = batch['seg'][tio.DATA]  # Shape: (16, 1, 64, 64, 64)
    # Train model with patches

def history_collate(batch: Sequence, collate_transforms=True) -> dict:
    """
    Custom collate function that preserves transform history.
    
    Parameters:
    - batch: Sequence of Subject instances
    - collate_transforms: Whether to collate transform histories
    
    Returns:
    Collated batch dictionary
    """

def get_first_item(data_loader: torch.utils.data.DataLoader):
    """Get first item from data loader for inspection"""

def get_batch_images_and_size(batch: dict) -> tuple[list[str], int]:
    """
    Extract image names and batch size from batch.
    
    Returns:
    Tuple of (list of image names, batch size)
    """

def get_subjects_from_batch(batch: dict) -> list:
    """Extract individual subjects from batched data"""

def add_images_from_batch(
    images_dict: dict,
    batch: dict,
    batch_idx: int
):
    """Add images from batch to images dictionary"""

def get_torchio_cache_dir() -> Path:
    """Get TorchIO cache directory for temporary files"""

def create_dummy_dataset(
    num_subjects: int,
    size_range: tuple[int, int] = (10, 20),
    directory: Path = None,
    **kwargs
) -> SubjectsDataset:
    """
    Create dummy dataset for testing and development.
    
    Parameters:
    - num_subjects: Number of subjects to create
    - size_range: Range of image sizes
    - directory: Directory to save dummy images
    
    Returns:
    SubjectsDataset with dummy subjects
    """

# Create dummy dataset for testing
dummy_dataset = tio.utils.create_dummy_dataset(
    num_subjects=10,
    size_range=(20, 30),
    directory=Path('/tmp/dummy_medical_data')
)

# Use dummy dataset for testing transforms or training loops
dummy_loader = tio.SubjectsLoader(dummy_dataset, batch_size=2)

for batch in dummy_loader:
    # Test your code with dummy data
    pass