import unittest import torch from numpy.testing import assert_equal, assert_almost_equal if torch.cuda.is_available(): from .compute_spline_basis import compute_spline_basis from .compute_spline_basis import get_basis_kernel class SplineQuadraticGPUTest(unittest.TestCase): @unittest.skipIf(not torch.cuda.is_available(), 'no GPU') def test_open_spline(self): input = torch.cuda.FloatTensor([0, 0.05, 0.25, 0.5, 0.75, 0.95, 1]) kernel_size = torch.cuda.LongTensor([7]) is_open_spline = torch.cuda.LongTensor([1]) k_max = 4 K = 7 dim = 1 basis_kernel = get_basis_kernel(k_max, K, dim, 3) a1, i1 = compute_spline_basis(input, kernel_size, is_open_spline, 7, basis_kernel) a2 = [ [0.1667, 0.6667, 0.1667, 0], [0.0853, 0.6307, 0.2827, 0.0013], [0.1667, 0.6667, 0.1667, 0], [0.1667, 0.6667, 0.1667, 0], [0.1667, 0.6667, 0.1667, 0], [0.0013, 0.2827, 0.6307, 0.0853], [0.1667, 0.6667, 0.1667, 0], ] i2 = [[0, 1, 2, 3], [0, 1, 2, 3], [1, 2, 3, 4], [2, 3, 4, 5], [3, 4, 5, 6], [3, 4, 5, 6], [4, 5, 6, 0]] assert_almost_equal(a1.cpu().numpy(), a2, 4) assert_equal(i1.cpu().numpy(), i2) @unittest.skipIf(not torch.cuda.is_available(), 'no GPU') def test_closed_spline(self): input = torch.cuda.FloatTensor([0, 0.05, 0.25, 0.5, 0.75, 0.95, 1]) kernel_size = torch.cuda.LongTensor([4]) is_open_spline = torch.cuda.LongTensor([0]) k_max = 4 K = 4 dim = 1 basis_kernel = get_basis_kernel(k_max, K, dim, 3) a1, i1 = compute_spline_basis(input, kernel_size, is_open_spline, 4, basis_kernel) a2 = [ [0.1667, 0.6667, 0.1667, 0], [0.0853, 0.6307, 0.2827, 0.0013], [0.1667, 0.6667, 0.1667, 0], [0.1667, 0.6667, 0.1667, 0], [0.1667, 0.6667, 0.1667, 0], [0.0013, 0.2827, 0.6307, 0.0853], [0.1667, 0.6667, 0.1667, 0], ] i2 = [[0, 1, 2, 3], [0, 1, 2, 3], [1, 2, 3, 0], [2, 3, 0, 1], [3, 0, 1, 2], [3, 0, 1, 2], [0, 1, 2, 3]] assert_almost_equal(a1.cpu().numpy(), a2, 4) assert_equal(i1.cpu().numpy(), i2)