diagnostic

orchard.data_handler.diagnostic

Diagnostic Utilities for Health Checks and Smoke Tests.

This private submodule provides lightweight data utilities used exclusively for pipeline validation (health checks, smoke tests, CI). These are not part of the production training pipeline.

create_synthetic_dataset(num_classes=8, samples=100, resolution=28, channels=3, name='syntheticmnist')

Create a synthetic NPZ-compatible dataset for testing.

This function generates random image data and labels, saves them to a temporary .npz file, and returns a DatasetData object that can be used with the existing data pipeline.

Parameters:

Name	Type	Description	Default
num_classes	int	Number of classification categories (default: 8)	8
samples	int	Number of training samples (default: 100)	100
resolution	int	Image resolution (HxW) (default: 28)	28
channels	int	Number of color channels (default: 3 for RGB)	3
name	str	Dataset name for identification (default: "syntheticmnist")	'syntheticmnist'

Returns:

Name	Type	Description
DatasetData	DatasetData	A data object compatible with the existing pipeline

Example

data = create_synthetic_dataset(num_classes=8, samples=100) train_loader, val_loader, test_loader = get_dataloaders( ... data, cfg.dataset, cfg.training, cfg.augmentation, cfg.num_workers ... )

Source code in orchard/data_handler/diagnostic/synthetic.py

def create_synthetic_dataset(
    num_classes: int = 8,
    samples: int = 100,
    resolution: int = 28,
    channels: int = 3,
    name: str = "syntheticmnist",
) -> DatasetData:
    """
    Create a synthetic NPZ-compatible dataset for testing.

    This function generates random image data and labels, saves them to a
    temporary .npz file, and returns a DatasetData object that can be used
    with the existing data pipeline.

    Args:
        num_classes: Number of classification categories (default: 8)
        samples: Number of training samples (default: 100)
        resolution: Image resolution (HxW) (default: 28)
        channels: Number of color channels (default: 3 for RGB)
        name: Dataset name for identification (default: "syntheticmnist")

    Returns:
        DatasetData: A data object compatible with the existing pipeline

    Example:
        >>> data = create_synthetic_dataset(num_classes=8, samples=100)
        >>> train_loader, val_loader, test_loader = get_dataloaders(
        ...     data, cfg.dataset, cfg.training, cfg.augmentation, cfg.num_workers
        ... )
    """
    rng = np.random.default_rng(_SYNTHETIC_SEED)

    # Generate synthetic image data
    train_images = rng.integers(
        0, _SYNTHETIC_PIXEL_RANGE, (samples, resolution, resolution, channels), dtype=np.uint8
    )
    train_labels = rng.integers(0, num_classes, (samples, 1), dtype=np.uint8)

    # Validation and test sets are smaller (10% of training size each)
    val_samples = max(_MIN_SPLIT_SAMPLES, samples // 10)
    test_samples = max(_MIN_SPLIT_SAMPLES, samples // 10)

    val_images = rng.integers(
        0, _SYNTHETIC_PIXEL_RANGE, (val_samples, resolution, resolution, channels), dtype=np.uint8
    )
    val_labels = rng.integers(0, num_classes, (val_samples, 1), dtype=np.uint8)

    test_images = rng.integers(
        0, _SYNTHETIC_PIXEL_RANGE, (test_samples, resolution, resolution, channels), dtype=np.uint8
    )
    test_labels = rng.integers(0, num_classes, (test_samples, 1), dtype=np.uint8)

    # Create a temporary .npz file with standard format
    temp_file = tempfile.NamedTemporaryFile(
        suffix=".npz", delete=False, prefix="synthetic_dataset_"
    )
    temp_path = Path(temp_file.name)
    temp_file.close()

    # Save in NPZ format with correct key names
    np.savez(
        temp_path,
        train_images=train_images,
        train_labels=train_labels,
        val_images=val_images,
        val_labels=val_labels,
        test_images=test_images,
        test_labels=test_labels,
    )

    # Return a DatasetData object with all required parameters
    is_rgb = channels == 3

    return DatasetData(
        path=temp_path,
        name=name,
        is_rgb=is_rgb,
        num_classes=num_classes,
    )

create_synthetic_grayscale_dataset(num_classes=8, samples=100, resolution=28)

Create a synthetic grayscale NPZ dataset for testing.

Convenience function for creating single-channel (grayscale) synthetic data.

Parameters:

Name	Type	Description	Default
num_classes	int	Number of classification categories (default: 8)	8
samples	int	Number of training samples (default: 100)	100
resolution	int	Image resolution (HxW) (default: 28)	28

Returns:

Name	Type	Description
DatasetData	DatasetData	A grayscale data object compatible with the pipeline

Source code in orchard/data_handler/diagnostic/synthetic.py

def create_synthetic_grayscale_dataset(
    num_classes: int = 8,
    samples: int = 100,
    resolution: int = 28,
) -> DatasetData:
    """
    Create a synthetic grayscale NPZ dataset for testing.

    Convenience function for creating single-channel (grayscale) synthetic data.

    Args:
        num_classes: Number of classification categories (default: 8)
        samples: Number of training samples (default: 100)
        resolution: Image resolution (HxW) (default: 28)

    Returns:
        DatasetData: A grayscale data object compatible with the pipeline
    """
    return create_synthetic_dataset(
        num_classes=num_classes,
        samples=samples,
        resolution=resolution,
        channels=1,
        name="syntheticmnist_gray",
    )

create_temp_loader(dataset_path, batch_size=_DEFAULT_HEALTHCHECK_BATCH_SIZE)

Load a NPZ dataset lazily and return a DataLoader for health checks.

This avoids loading the entire dataset into RAM at once, which is critical for large datasets (e.g., 224x224 images).

Source code in orchard/data_handler/diagnostic/temp_loader.py

def create_temp_loader(
    dataset_path: Path, batch_size: int = _DEFAULT_HEALTHCHECK_BATCH_SIZE
) -> DataLoader[Any]:
    """
    Load a NPZ dataset lazily and return a DataLoader for health checks.

    This avoids loading the entire dataset into RAM at once, which is critical
    for large datasets (e.g., 224x224 images).
    """
    dataset = VisionDataset.lazy(dataset_path)
    loader = DataLoader(dataset, batch_size=batch_size, shuffle=True, num_workers=0)
    return loader