def setUp(self):
self.queue = get_queue()
self.kernels = Kernels(self.queue.context)
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()))
def setUp(self):
self.queue = get_queue()
self.kernels = Kernels(self.queue.context)
def _gpu_init(self):
self.gpu_data = {}
g = self.gpu_data
d = self.data
q = self.queue
g['rcore'] = cl_array.to_device(q, float32array(d['rcore'].array))
g['rsurf'] = cl_array.to_device(q, float32array(d['rsurf'].array))
g['im_lsurf'] = cl.image_from_array(q.context, float32array(d['lsurf'].array))
g['sampler'] = cl.Sampler(q.context, False, cl.addressing_mode.CLAMP,
cl.filter_mode.LINEAR)
g['lsurf'] = cl_array.zeros_like(g['rcore'])
g['clashvol'] = cl_array.zeros_like(g['rcore'])
g['intervol'] = cl_array.zeros_like(g['rcore'])
g['interspace'] = cl_array.zeros(q, d['shape'], dtype=np.int32)
# complex arrays
g['ft_shape'] = list(d['shape'])
g['ft_shape'][0] = d['shape'][0]//2 + 1
g['ft_rcore'] = cl_array.zeros(q, g['ft_shape'], dtype=np.complex64)
g['ft_rsurf'] = cl_array.zeros_like(g['ft_rcore'])
g['ft_lsurf'] = cl_array.zeros_like(g['ft_rcore'])
g['ft_clashvol'] = cl_array.zeros_like(g['ft_rcore'])
g['ft_intervol'] = cl_array.zeros_like(g['ft_rcore'])
# allocate SAXS arrays
g['q'] = cl_array.to_device(q, float32array(d['q']))
g['targetIq'] = cl_array.to_device(q, float32array(d['targetIq']))
g['sq'] = cl_array.to_device(q, float32array(d['sq']))
g['base_Iq'] = cl_array.to_device(q, float32array(d['base_Iq']))
g['fifj'] = cl_array.to_device(q, float32array(d['fifj']))
g['rind'] = cl_array.to_device(q, d['rind'].astype(np.int32))
g['lind'] = cl_array.to_device(q, d['lind'].astype(np.int32))
g_rxyz = np.zeros((d['rxyz'].shape[0], 4), dtype=np.float32)
g_rxyz[:, :3] = d['rxyz'][:]
g_lxyz = np.zeros((d['lxyz'].shape[0], 4), dtype=np.float32)
g_lxyz[:, :3] = d['lxyz'][:]
g['rxyz'] = cl_array.to_device(q, g_rxyz)
g['lxyz'] = cl_array.to_device(q, g_lxyz)
g['rot_lxyz'] = cl_array.zeros_like(g['lxyz'])
g['chi2'] = cl_array.to_device(q, d['chi2'].astype(np.float32))
g['best_chi2'] = cl_array.to_device(q, d['best_chi2'].astype(np.float32))
g['rot_ind'] = cl_array.zeros(q, d['shape'], dtype=np.int32)
g['origin'] = np.zeros(4, dtype=np.float32)
g['origin'][:3] = d['origin'].astype(np.float32)
g['voxelspacing'] = np.float32(self.voxelspacing)
# kernels
g['k'] = Kernels(q.context)
g['saxs_k'] = saxs_Kernels(q.context)
g['k'].rfftn = pyclfft.RFFTn(q.context, d['shape'])
g['k'].irfftn = pyclfft.iRFFTn(q.context, d['shape'])
g['k'].rfftn(q, g['rcore'], g['ft_rcore'])
g['k'].rfftn(q, g['rsurf'], g['ft_rsurf'])
g['nrot'] = d['nrot']
g['max_clash'] = d['max_clash']
g['min_interaction'] = d['min_interaction']
