Fix interpolate()

[RyannDaGreat]

Hi! I found an error in interpolate(). It will break when fed numpy arrays, because the variable inputs_are_torch will not have been defined when we reach the line with if inputs_are_torch:

I would make a pull request, but I'm not sure how (I've already forked this repo for another purpose, and won't be merging that because I've made changes that are irrelevant to this repo in it. Is there a way to fork twice?)

def interpolate(t, v0, v1, DOT_THRESHOLD=0.9995):
    """Helper function to (spherically) interpolate two arrays v1 v2.
    
    Taken from: https://gist.github.com/karpathy/00103b0037c5aaea32fe1da1af553355
    """

    if not isinstance(v0, np.ndarray):
        inputs_are_torch = True
        input_device = v0.device
        v0 = v0.cpu().numpy()
        v1 = v1.cpu().numpy()

    dot = np.sum(v0 * v1 / (np.linalg.norm(v0) * np.linalg.norm(v1)))
    if np.abs(dot) > DOT_THRESHOLD:
        v2 = (1 - t) * v0 + t * v1
    else:
        theta_0 = np.arccos(dot)
        sin_theta_0 = np.sin(theta_0)
        theta_t = theta_0 * t
        sin_theta_t = np.sin(theta_t)
        s0 = np.sin(theta_0 - theta_t) / sin_theta_0
        s1 = sin_theta_t / sin_theta_0
        v2 = s0 * v0 + s1 * v1

    if inputs_are_torch:
        v2 = torch.from_numpy(v2).to(input_device)

    return v2

Should become

def interpolate(t, v0, v1, DOT_THRESHOLD=0.9995):
    """Helper function to (spherically) interpolate two arrays v1 v2.
    
    Taken from: https://gist.github.com/karpathy/00103b0037c5aaea32fe1da1af553355
    """

    inputs_are_torch = False
    if not isinstance(v0, np.ndarray):
        inputs_are_torch = True
        input_device = v0.device
        v0 = v0.cpu().numpy()
        v1 = v1.cpu().numpy()

    dot = np.sum(v0 * v1 / (np.linalg.norm(v0) * np.linalg.norm(v1)))
    if np.abs(dot) > DOT_THRESHOLD:
        v2 = (1 - t) * v0 + t * v1
    else:
        theta_0 = np.arccos(dot)
        sin_theta_0 = np.sin(theta_0)
        theta_t = theta_0 * t
        sin_theta_t = np.sin(theta_t)
        s0 = np.sin(theta_0 - theta_t) / sin_theta_0
        s1 = sin_theta_t / sin_theta_0
        v2 = s0 * v0 + s1 * v1

    if inputs_are_torch:
        v2 = torch.from_numpy(v2).to(input_device)

    return v2