synthetic

`orchard.data_handler.diagnostic.synthetic` ¶

Synthetic Data Handler for Testing.

This module provides tiny synthetic NPZ datasets for unit tests without requiring any external downloads or network access. It generates random image data and labels that match the expected NPZ format specifications.

Note

mutmut's trampoline resolves default parameters in the wrapper before dispatching to the mutant function, making default-value mutations unkillable. Body mutations on internal pixel/label generation are also unobservable because tests verify the returned DatasetData metadata, not raw byte content. Lines are marked pragma: no mutate accordingly.

`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 target 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,  # pragma: no mutate
    samples: int = 100,  # pragma: no mutate
    resolution: int = 28,  # pragma: no mutate
    channels: int = 3,  # pragma: no mutate
    name: str = "syntheticmnist",  # pragma: no mutate
) -> 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 target 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
        ... )
    """
    # pragma: no mutate start
    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_"
    )
    # pragma: no mutate end
    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.