class TestKernels(unittest.TestCase):
def setUp(self):
self.queue = get_queue()
self.kernels = Kernels(self.queue.context)
def test_rotate_image3d_0(self):
shape = (8, 6, 5)
np_image = np.zeros(shape, dtype=np.float32)
np_image[0, 0, 0] = 1
np_image[0, 0, 1] = 1
np_image[0, 0, 2] = 1
rotmat = [[1, 0, 0], [0, 1, 0], [0, 0, 1]]
np_out = np.zeros_like(np_image)
cl_image = cl.image_from_array(self.queue.context, np_image)
cl_out = cl_array.to_device(self.queue, np_out)
cl_sampler = cl.Sampler(self.queue.context, False,
cl.addressing_mode.CLAMP,
cl.filter_mode.LINEAR)
self.kernels.rotate_image3d(self.queue, cl_sampler, cl_image, rotmat,
cl_out)
self.assertTrue(np.allclose(np_image, cl_out.get()))
def test_rotate_image3d_1(self):
shape = (8, 6, 5)
np_image = np.zeros(shape, dtype=np.float32)
np_image[0, 0, 0] = 1
np_image[0, 0, 1] = 1
np_image[0, 0, 2] = 1
# 90 degree rotation around z-axis
rotmat = [[0, 1, 0], [1, 0, 0], [0, 0, 1]]
np_out = np.zeros_like(np_image)
expected = np.zeros_like(np_image)
expected[0, 0, 0] = 1
expected[0, 1, 0] = 1
expected[0, 2, 0] = 1
cl_image = cl.image_from_array(self.queue.context, np_image)
cl_out = cl_array.to_device(self.queue, np_out)
cl_sampler = cl.Sampler(self.queue.context, False,
cl.addressing_mode.CLAMP,
cl.filter_mode.LINEAR)
self.kernels.rotate_image3d(self.queue, cl_sampler, cl_image, rotmat,
cl_out)
self.assertTrue(np.allclose(expected, cl_out.get()))
def test_dilate_points_add_0(self):
shape = (8, 7, 6)
np_constraints = np.asarray([[3, 3, 3, 0, 0, 0, 1, 0]],
dtype=np.float32)
rotmat = [[1, 0, 0], [0, 1, 0], [0, 0, 1]]
np_out = np.zeros(shape, dtype=np.int32)
cl_constraints = cl_array.to_device(self.queue, np_constraints)
cl_out = cl_array.to_device(self.queue, np_out)
self.kernels.dilate_points_add(self.queue, cl_constraints, rotmat,
cl_out)
expected = np.zeros_like(np_out)
expected[3, 3, 2:5] = 1
expected[3, 2:5, 3] = 1
expected[2:5, 3, 3] = 1
self.assertTrue(np.allclose(expected, cl_out.get()))
def test_dilate_points_add_1(self):
shape = (8, 7, 6)
np_constraints = np.asarray([[3, 3, 3, 1, 1, 1, 1, 0]],
dtype=np.float32)
rotmat = [[1, 0, 0], [0, 1, 0], [0, 0, 1]]
np_out = np.zeros(shape, dtype=np.int32)
cl_constraints = cl_array.to_device(self.queue, np_constraints)
cl_out = cl_array.to_device(self.queue, np_out)
self.kernels.dilate_points_add(self.queue, cl_constraints, rotmat,
cl_out)
expected = np.zeros_like(np_out)
expected[2, 2, 1:4] = 1
expected[2, 1:4, 2] = 1
expected[1:4, 2, 2] = 1
self.assertTrue(np.allclose(expected, cl_out.get()))
def test_dilate_points_add_2(self):
SHAPE = (8, 7, 6)
CONSTRAINTS = [[3, 3, 3, 1, 1, 1, 1, 0], [3, 3, 3, 1, 1, 1, 1, 0]]
np_constraints = np.asarray(CONSTRAINTS, dtype=np.float32)
rotmat = [[1, 0, 0], [0, 1, 0], [0, 0, 1]]
np_out = np.zeros(SHAPE, dtype=np.int32)
cl_constraints = cl_array.to_device(self.queue, np_constraints)
cl_out = cl_array.to_device(self.queue, np_out)
self.kernels.dilate_points_add(self.queue, cl_constraints, rotmat,
cl_out)
expected = np.zeros_like(np_out)
expected[2, 2, 1:4] = len(CONSTRAINTS)
expected[2, 1:4, 2] = len(CONSTRAINTS)
expected[1:4, 2, 2] = len(CONSTRAINTS)
self.assertTrue(np.allclose(expected, cl_out.get()))
def test_count_0(self):
nrepeats = 3
shape = [5, 5, 5]
np_interspace = randint(2, size=shape).astype(np.int32)
np_access_interspace = randint(nrepeats, size=shape).astype(np.int32)
np_count = np.zeros([nrepeats] + shape, dtype=np.float32)
weight = 0.5
expected = np.zeros_like(np_count)
tmp = expected[0]
tmp[np_interspace == 1] += weight
for i in range(1, nrepeats):
tmp = expected[i]
tmp[np_access_interspace == i] += weight
cl_interspace = cl_array.to_device(self.queue, np_interspace)
cl_access_interspace = cl_array.to_device(self.queue,
np_access_interspace)
cl_count = cl_array.to_device(self.queue, np_count)
self.kernels.count(self.queue, cl_interspace, cl_access_interspace,
weight, cl_count)
self.assertTrue(np.allclose(expected, cl_count.get()))
def test_count_1(self):
nrepeats = 3
shape = [5, 5, 5]
np_interspace = randint(2, size=shape).astype(np.int32)
np_access_interspace = randint(nrepeats, size=shape).astype(np.int32)
np_count = np.ones([nrepeats] + shape, dtype=np.float32)
weight = 0.5
expected = np.ones_like(np_count)
tmp = expected[0]
tmp[np_interspace == 1] += weight
for i in range(1, nrepeats):
tmp = expected[i]
tmp[np_access_interspace == i] += weight
cl_interspace = cl_array.to_device(self.queue, np_interspace)
cl_access_interspace = cl_array.to_device(self.queue,
np_access_interspace)
cl_count = cl_array.to_device(self.queue, np_count)
self.kernels.count(self.queue, cl_interspace, cl_access_interspace,
weight, cl_count)
self.assertTrue(np.allclose(expected, cl_count.get()))
class TestKernels(unittest.TestCase):
def setUp(self):
self.queue = get_queue()
self.kernels = Kernels(self.queue.context)
def test_rotate_image3d_0(self):
shape = (8, 6, 5)
np_image = np.zeros(shape, dtype=np.float32)
np_image[0, 0, 0] = 1
np_image[0, 0, 1] = 1
np_image[0, 0, 2] = 1
rotmat = [[1, 0, 0],
[0, 1, 0],
[0, 0, 1]]
np_out = np.zeros_like(np_image)
cl_image = cl.image_from_array(self.queue.context, np_image)
cl_out = cl_array.to_device(self.queue, np_out)
cl_sampler = cl.Sampler(self.queue.context, False, cl.addressing_mode.CLAMP, cl.filter_mode.LINEAR)
self.kernels.rotate_image3d(self.queue, cl_sampler, cl_image, rotmat, cl_out)
self.assertTrue(np.allclose(np_image, cl_out.get()))
def test_rotate_image3d_1(self):
shape = (8, 6, 5)
np_image = np.zeros(shape, dtype=np.float32)
np_image[0, 0, 0] = 1
np_image[0, 0, 1] = 1
np_image[0, 0, 2] = 1
# 90 degree rotation around z-axis
rotmat = [[0, 1, 0],
[1, 0, 0],
[0, 0, 1]]
np_out = np.zeros_like(np_image)
expected = np.zeros_like(np_image)
expected[0, 0, 0] = 1
expected[0, 1, 0] = 1
expected[0, 2, 0] = 1
cl_image = cl.image_from_array(self.queue.context, np_image)
cl_out = cl_array.to_device(self.queue, np_out)
cl_sampler = cl.Sampler(self.queue.context, False, cl.addressing_mode.CLAMP, cl.filter_mode.LINEAR)
self.kernels.rotate_image3d(self.queue, cl_sampler, cl_image, rotmat, cl_out)
self.assertTrue(np.allclose(expected, cl_out.get()))
def test_dilate_points_add_0(self):
shape = (8, 7, 6)
np_constraints = np.asarray([[3, 3, 3, 0, 0, 0, 1, 0]], dtype=np.float32)
rotmat = [[1, 0, 0],
[0, 1, 0],
[0, 0, 1]]
np_out = np.zeros(shape, dtype=np.int32)
cl_constraints = cl_array.to_device(self.queue, np_constraints)
cl_out = cl_array.to_device(self.queue, np_out)
self.kernels.dilate_points_add(self.queue, cl_constraints, rotmat, cl_out)
expected = np.zeros_like(np_out)
expected[3, 3, 2:5] = 1
expected[3, 2:5, 3] = 1
expected[2:5, 3, 3] = 1
self.assertTrue(np.allclose(expected, cl_out.get()))
def test_dilate_points_add_1(self):
shape = (8, 7, 6)
np_constraints = np.asarray([[3, 3, 3, 1, 1, 1, 1, 0]], dtype=np.float32)
rotmat = [[1, 0, 0],
[0, 1, 0],
[0, 0, 1]]
np_out = np.zeros(shape, dtype=np.int32)
cl_constraints = cl_array.to_device(self.queue, np_constraints)
cl_out = cl_array.to_device(self.queue, np_out)
self.kernels.dilate_points_add(self.queue, cl_constraints, rotmat, cl_out)
expected = np.zeros_like(np_out)
expected[2, 2, 1:4] = 1
expected[2, 1:4, 2] = 1
expected[1:4, 2, 2] = 1
self.assertTrue(np.allclose(expected, cl_out.get()))
def test_dilate_points_add_2(self):
SHAPE = (8, 7, 6)
CONSTRAINTS = [[3, 3, 3, 1, 1, 1, 1, 0],
[3, 3, 3, 1, 1, 1, 1, 0]]
np_constraints = np.asarray(CONSTRAINTS, dtype=np.float32)
rotmat = [[1, 0, 0],
[0, 1, 0],
[0, 0, 1]]
np_out = np.zeros(SHAPE, dtype=np.int32)
cl_constraints = cl_array.to_device(self.queue, np_constraints)
cl_out = cl_array.to_device(self.queue, np_out)
self.kernels.dilate_points_add(self.queue, cl_constraints, rotmat, cl_out)
expected = np.zeros_like(np_out)
expected[2, 2, 1:4] = len(CONSTRAINTS)
expected[2, 1:4, 2] = len(CONSTRAINTS)
expected[1:4, 2, 2] = len(CONSTRAINTS)
self.assertTrue(np.allclose(expected, cl_out.get()))
def test_count_0(self):
nrepeats = 3
shape = [5, 5, 5]
np_interspace = randint(2, size=shape).astype(np.int32)
np_access_interspace = randint(nrepeats, size=shape).astype(np.int32)
np_count = np.zeros([nrepeats] + shape, dtype=np.float32)
weight = 0.5
expected = np.zeros_like(np_count)
tmp = expected[0]
tmp[np_interspace == 1] += weight
for i in range(1, nrepeats):
tmp = expected[i]
tmp[np_access_interspace == i] += weight
cl_interspace = cl_array.to_device(self.queue, np_interspace)
cl_access_interspace = cl_array.to_device(self.queue, np_access_interspace)
cl_count = cl_array.to_device(self.queue, np_count)
self.kernels.count(self.queue, cl_interspace, cl_access_interspace, weight, cl_count)
self.assertTrue(np.allclose(expected, cl_count.get()))
def test_count_1(self):
nrepeats = 3
shape = [5, 5, 5]
np_interspace = randint(2, size=shape).astype(np.int32)
np_access_interspace = randint(nrepeats, size=shape).astype(np.int32)
np_count = np.ones([nrepeats] + shape, dtype=np.float32)
weight = 0.5
expected = np.ones_like(np_count)
tmp = expected[0]
tmp[np_interspace == 1] += weight
for i in range(1, nrepeats):
tmp = expected[i]
tmp[np_access_interspace == i] += weight
cl_interspace = cl_array.to_device(self.queue, np_interspace)
cl_access_interspace = cl_array.to_device(self.queue, np_access_interspace)
cl_count = cl_array.to_device(self.queue, np_count)
self.kernels.count(self.queue, cl_interspace, cl_access_interspace, weight, cl_count)
self.assertTrue(np.allclose(expected, cl_count.get()))
