def lighting_jitter(images, alphastd, eigval, eigvec):
    """
    Perform AlexNet-style PCA jitter on the given images.
    Args:
        images (tensor): images to perform lighting jitter. Dimension is
            `num frames` x `channel` x `height` x `width`.
        alphastd (float): jitter ratio for PCA jitter.
        eigval (list): eigenvalues for PCA jitter.
        eigvec (list[list]): eigenvectors for PCA jitter.
    Returns:
        out_images (tensor): the jittered images, the dimension is
            `num frames` x `channel` x `height` x `width`.
    """
    if alphastd == 0:
        return images
    # generate alpha1, alpha2, alpha3.
    alpha = np.random.normal(0, alphastd, size=(1, 3))
    eig_vec = np.array(eigvec)
    eig_val = np.reshape(eigval, (1, 3))
    rgb = np.sum(
        eig_vec * np.repeat(alpha, 3, axis=0) * np.repeat(eig_val, 3, axis=0),
        axis=1,
    )
    out_images = torch.zeros_like(images)
    if len(images.shape) == 3:
        # C H W
        channel_dim = 0
    elif len(images.shape) == 4:
        # T C H W
        channel_dim = 1
    else:
        raise NotImplementedError(f"Unsupported dimension {len(images.shape)}")

    for idx in range(images.shape[channel_dim]):
        # C H W
        if len(images.shape) == 3:
            out_images[idx] = images[idx] + rgb[2 - idx]
        # T C H W
        elif len(images.shape) == 4:
            out_images[:, idx] = images[:, idx] + rgb[2 - idx]
        else:
            raise NotImplementedError(
                f"Unsupported dimension {len(images.shape)}"
            )

    return out_images
Perform AlexNet-style PCA jitter on the given images.
Args:
images (tensor): images to perform lighting jitter. Dimension is
`num frames` x `channel` x `height` x `width`.
alphastd (float): jitter ratio for PCA jitter.
eigval (list): eigenvalues for PCA jitter.
eigvec (list[list]): eigenvectors for PCA jitter.
Returns:
out_images (tensor): the jittered images, the dimension is
`num frames` x `channel` x `height` x `width`.
videomamba/video_sm/datasets/video_transforms.py