Function test_projection

tests/test_basic.py:180–279

(
    test_data,
    fused: bool,
    calc_compensations: bool,
    camera_model: Literal["pinhole", "ortho", "fisheye"],
)

@pytest.mark.parametrize("fused", [False, True])
@pytest.mark.parametrize("calc_compensations", [True, False])
def test_projection(
    test_data,
    fused: bool,
    calc_compensations: bool,
    camera_model: Literal["pinhole", "ortho", "fisheye"],
):
    from gsplat.cuda._torch_impl import _fully_fused_projection
    from gsplat.cuda._wrapper import fully_fused_projection, quat_scale_to_covar_preci

    torch.manual_seed(42)

    Ks = test_data["Ks"]
    viewmats = test_data["viewmats"]
    height = test_data["height"]
    width = test_data["width"]
    quats = test_data["quats"]
    scales = test_data["scales"]
    means = test_data["means"]

    viewmats.requires_grad = True
    quats.requires_grad = True
    scales.requires_grad = True
    means.requires_grad = True

    # forward
    if fused:
        radii, means2d, depths, conics, compensations = fully_fused_projection(
            means,
            None,
            quats,
            scales,
            viewmats,
            Ks,
            width,
            height,
            calc_compensations=calc_compensations,
            camera_model=camera_model,
        )
    else:
        covars, _ = quat_scale_to_covar_preci(quats, scales, triu=True)  # [N, 6]
        radii, means2d, depths, conics, compensations = fully_fused_projection(
            means,
            covars,
            None,
            None,
            viewmats,
            Ks,
            width,
            height,
            calc_compensations=calc_compensations,
            camera_model=camera_model,
        )
    _covars, _ = quat_scale_to_covar_preci(quats, scales, triu=False)  # [N, 3, 3]
    _radii, _means2d, _depths, _conics, _compensations = _fully_fused_projection(
        means,
        _covars,
        viewmats,
        Ks,