def _SSIMForMultiScale(img1, img2, max_val=255, filter_size=11, filter_sigma=1.5, k1=0.01, k2=0.03):
    """Return the Structural Similarity Map between `img1` and `img2`.

    This function attempts to match the functionality of ssim_index_new.m by
    Zhou Wang: http://www.cns.nyu.edu/~lcv/ssim/msssim.zip

    Arguments:
        img1: Numpy array holding the first RGB image batch.
        img2: Numpy array holding the second RGB image batch.
        max_val: the dynamic range of the images (i.e., the difference between the
            maximum the and minimum allowed values).
        filter_size: Size of blur kernel to use (will be reduced for small images).
        filter_sigma: Standard deviation for Gaussian blur kernel (will be reduced
            for small images).
        k1: Constant used to maintain stability in the SSIM calculation (0.01 in
            the original paper).
        k2: Constant used to maintain stability in the SSIM calculation (0.03 in
            the original paper).

    Returns:
        Pair containing the mean SSIM and contrast sensitivity between `img1` and
        `img2`.

    Raises:
        RuntimeError: If input images don't have the same shape or don't have four
            dimensions: [batch_size, height, width, depth].
    """
    if img1.shape != img2.shape:
        raise RuntimeError('Input images must have the same shape (%s vs. %s).' % (img1.shape, img2.shape))
    if img1.ndim != 4:
        raise RuntimeError('Input images must have four dimensions, not %d' % img1.ndim)

    img1 = img1.astype(np.float32)
    img2 = img2.astype(np.float32)
    _, height, width, _ = img1.shape

    # Filter size can't be larger than height or width of images.
    size = min(filter_size, height, width)

    # Scale down sigma if a smaller filter size is used.
    sigma = size * filter_sigma / filter_size if filter_size else 0

    if filter_size:
        window = np.reshape(_FSpecialGauss(size, sigma), (1, size, size, 1))
        mu1 = signal.fftconvolve(img1, window, mode='valid')
        mu2 = signal.fftconvolve(img2, window, mode='valid')
        sigma11 = signal.fftconvolve(img1 * img1, window, mode='valid')
        sigma22 = signal.fftconvolve(img2 * img2, window, mode='valid')
        sigma12 = signal.fftconvolve(img1 * img2, window, mode='valid')
    else:
        # Empty blur kernel so no need to convolve.
        mu1, mu2 = img1, img2
        sigma11 = img1 * img1
        sigma22 = img2 * img2
        sigma12 = img1 * img2

    mu11 = mu1 * mu1
    mu22 = mu2 * mu2
    mu12 = mu1 * mu2
    sigma11 -= mu11
    sigma22 -= mu22
    sigma12 -= mu12

    # Calculate intermediate values used by both ssim and cs_map.
    c1 = (k1 * max_val) ** 2
    c2 = (k2 * max_val) ** 2
    v1 = 2.0 * sigma12 + c2
    v2 = sigma11 + sigma22 + c2
    ssim = np.mean((((2.0 * mu12 + c1) * v1) / ((mu11 + mu22 + c1) * v2)), axis=(1, 2, 3)) # Return for each image individually.
    cs = np.mean(v1 / v2, axis=(1, 2, 3))
    return ssim, cs
Return the Structural Similarity Map between `img1` and `img2`.

    This function attempts to match the functionality of ssim_index_new.m by
    Zhou Wang: http://www.cns.nyu.edu/~lcv/ssim/msssim.zip

    Arguments:
        img1: Numpy array holding the first RGB image batch.
        img2: Numpy array holding the second RGB image batch.
        max_val: the dynamic range of the images (i.e., the difference between the
            maximum the and minimum allowed values).
        filter_size: Size of blur kernel to use (will be reduced for small images).
        filter_sigma: Standard deviation for Gaussian blur kernel (will be reduced
            for small images).
        k1: Constant used to maintain stability in the SSIM calculation (0.01 in
            the original paper).
        k2: Constant used to maintain stability in the SSIM calculation (0.03 in
            the original paper).

    Returns:
        Pair containing the mean SSIM and contrast sensitivity between `img1` and
        `img2`.

    Raises:
        RuntimeError: If input images don't have the same shape or don't have four
            dimensions: [batch_size, height, width, depth].
    
converters/pggan_official/metrics/ms_ssim.py