Function compute_fid

skills/paper2code/worked/ddpm/src/evaluate.py:166–216

Compute FID score between generated samples and real data. §4 — "We report FID score... Our best results are FID: 3.17" Requires the pytorch-fid package: pip install pytorch-fid For CIFAR-10, you need pre-computed stats for the real training set, or provide a directory of real ima

(
    generated_dir: str,
    real_stats_path: Optional[str] = None,
    batch_size: int = 50,
    device: str = "cuda",
    dims: int = 2048,
)

def compute_fid(
    generated_dir: str,
    real_stats_path: Optional[str] = None,
    batch_size: int = 50,
    device: str = "cuda",
    dims: int = 2048,
) -> float:
    """Compute FID score between generated samples and real data.

    §4 — "We report FID score... Our best results are FID: 3.17"

    Requires the pytorch-fid package: pip install pytorch-fid

    For CIFAR-10, you need pre-computed stats for the real training set,
    or provide a directory of real images.

    Args:
        generated_dir: Directory containing generated .png images
        real_stats_path: Path to pre-computed .npz stats for real data,
                         OR directory containing real images
        batch_size: Batch size for Inception feature extraction
        device: Device for computation
        dims: Inception feature dimensionality (2048 = pool3)

    Returns:
        FID score (float). Lower is better.
    """
    try:
        from pytorch_fid import fid_score
    except ImportError:
        logger.error(
            "pytorch-fid not installed. Install with: pip install pytorch-fid\n"
            "Then re-run evaluation."
        )
        raise

    if real_stats_path is None:
        raise ValueError(
            "Must provide real_stats_path: either a .npz file with pre-computed "
            "Inception statistics, or a directory of real CIFAR-10 images."
        )

    fid = fid_score.calculate_fid_given_paths(
        [generated_dir, real_stats_path],
        batch_size=batch_size,
        device=torch.device(device),
        dims=dims,
    )

    logger.info(f"FID score: {fid:.2f}")
    return fid


if __name__ == "__main__":