def setUp(self):
p = cl.get_platforms()[0]
devs = p.get_devices()
self.ctx = cl.Context(devices=devs)
self.queue = cl.CommandQueue(self.ctx, device=devs[0])
self.k = CLKernels(self.ctx)
self.k = CLKernels(self.ctx)
self.s_linear = cl.Sampler(self.ctx, False, cl.addressing_mode.CLAMP,
cl.filter_mode.LINEAR)
self.s_nearest = cl.Sampler(self.ctx, False, cl.addressing_mode.CLAMP,
cl.filter_mode.NEAREST)
def setUpClass(self):
self.queue = get_queue()
self.shape = (4, 5, 6)
self.size = 4 * 5 * 6
self.values = {
'shape_x': self.shape[2],
'shape_y': self.shape[1],
'shape_z': self.shape[0],
'llength': 2,
}
self.k = CLKernels(self.queue.context, self.values)
self.grid = np.zeros(self.shape, dtype=np.float64)
self.grid[0, 0, 0] = 1
self.grid[0, 0, 1] = 1
self.grid[0, 1, 1] = 1
self.grid[0, 0, 2] = 1
self.grid[0, 0, -1] = 1
self.grid[-1, 0, 0] = 1
self.cl_image = cl.image_from_array(self.queue.context,
self.grid.astype(np.float32))
self.sampler_linear = cl.Sampler(self.queue.context, True,
cl.addressing_mode.REPEAT,
cl.filter_mode.LINEAR)
self.sampler_nearest = cl.Sampler(self.queue.context, False,
cl.addressing_mode.CLAMP,
cl.filter_mode.NEAREST)
self.out = np.zeros(self.shape, dtype=np.float64)
self.cl_out = cl_array.zeros(self.queue, self.shape, dtype=np.float32)
def setUp(self):
p = cl.get_platforms()[0]
devs = p.get_devices()
self.ctx = cl.Context(devices=devs)
self.queue = cl.CommandQueue(self.ctx, device=devs[0])
self.k = CLKernels(self.ctx)
self.k = CLKernels(self.ctx)
self.s_linear = cl.Sampler(self.ctx, False, cl.addressing_mode.CLAMP,
cl.filter_mode.LINEAR)
self.s_nearest = cl.Sampler(self.ctx, False, cl.addressing_mode.CLAMP,
cl.filter_mode.NEAREST)
class TestCLKernels(unittest.TestCase):
"""Tests for the OpenCL kernels"""
#@classmethod
#def setUpClass(cls):
# p = cl.get_platforms()[0]
# devs = p.get_devices()
# cls.ctx = cl.Context(devices=devs)
# cls.queue = cl.CommandQueue(cls.ctx, device=devs[0])
# cls.k = CLKernels(cls.ctx)
# cls.s_linear = cl.Sampler(cls.ctx, False, cl.addressing_mode.CLAMP,
# cl.filter_mode.LINEAR)
# cls.s_nearest = cl.Sampler(cls.ctx, False, cl.addressing_mode.CLAMP,
# cl.filter_mode.NEAREST)
def setUp(self):
p = cl.get_platforms()[0]
devs = p.get_devices()
self.ctx = cl.Context(devices=devs)
self.queue = cl.CommandQueue(self.ctx, device=devs[0])
self.k = CLKernels(self.ctx)
self.k = CLKernels(self.ctx)
self.s_linear = cl.Sampler(self.ctx, False, cl.addressing_mode.CLAMP,
cl.filter_mode.LINEAR)
self.s_nearest = cl.Sampler(self.ctx, False, cl.addressing_mode.CLAMP,
cl.filter_mode.NEAREST)
def test_multiply(self):
np_in1 = np.arange(10, dtype=np.float32)
np_in2 = np.arange(10, dtype=np.float32)
np_out = np_in1 * np_in2
cl_in1 = cl_array.to_device(self.queue, np_in1)
cl_out = cl_array.to_device(self.queue, np.zeros(10, dtype=np.float32))
cl_in2 = cl_array.to_device(self.queue, np_in2)
self.k.multiply(cl_in1, cl_in2, cl_out)
self.assertTrue(np.allclose(np_out, cl_out.get()))
def test_conj_multiply(self):
np_in1 = np.zeros(10, dtype=np.complex64)
np_in2 = np.zeros(10, dtype=np.complex64)
np_in1.real = np.random.rand(10)
np_in1.imag = np.random.rand(10)
np_in2.real = np.random.rand(10)
np_in2.imag = np.random.rand(10)
np_out = np_in1.conj() * np_in2
cl_in1 = cl_array.to_device(self.queue, np_in1)
cl_in2 = cl_array.to_device(self.queue, np_in2)
cl_out = cl_array.to_device(self.queue, np.zeros(10,
dtype=np.complex64))
self.k.conj_multiply(cl_in1, cl_in2, cl_out)
self.assertTrue(np.allclose(np_out, cl_out.get()))
# TODO
#def test_calc_lcc(self):
# pass
#def test_take_best(self):
# pass
def test_rotate_template_mask(self):
shape = (5, 5, 5)
template = np.zeros(shape, dtype=np.float32)
template[2, 2, 1:4] = 1
template[2, 1:4, 2] = 1
rotmat = np.asarray([1, 0, 0, 0, 1, 0, 0, 0, 1] + [0] * 7,
dtype=np.float32)
self.queue.finish()
cl_template = cl.image_from_array(self.queue.context, template)
cl_out = cl_array.to_device(self.queue,
np.zeros(shape, dtype=np.float32))
center = np.asarray([2, 2, 2, 0], dtype=np.float32)
shape = np.asarray([5, 5, 5, 125], dtype=np.int32)
self.k.rotate_template(self.queue, (125, ), None, self.s_linear,
cl_template, rotmat, cl_out.data, center, shape)
answer = np.zeros((5, 5, 5), dtype=np.float32)
answer[0, 0, :2] = 1
answer[0, 0, -1] = 1
answer[0, :2, 0] = 1
answer[0, -1, 0] = 1
self.assertTrue(np.allclose(cl_out.get(), answer))
def test_rotate_grids_and_multiply(self):
shape = (5, 5, 5)
template = np.zeros(shape, dtype=np.float32)
template[2, 2, 1:4] = 1
template[2, 1:4, 2] = 1
mask = template * 2
np_out_template = np.zeros(shape, dtype=np.float32)
np_out_template[0, 0, :2] = 1
np_out_template[0, 0, -1] = 1
np_out_template[0, :2, 0] = 1
np_out_template[0, -1, 0] = 1
np_out_mask = np_out_template * 2
np_out_mask2 = np_out_mask**2
cl_template = cl.image_from_array(self.ctx, template)
cl_mask = cl.image_from_array(self.ctx, mask)
cl_rotmat = np.asarray([1, 0, 0, 0, 1, 0, 0, 0, 1] + [0] * 7,
dtype=np.float32)
cl_center = np.asarray([2, 2, 2, 0], dtype=np.float32)
cl_shape = np.asarray([5, 5, 5, 125], dtype=np.int32)
cl_radius = np.int32(2)
cl_out_template = cl_array.to_device(self.queue,
np.zeros(shape, dtype=np.float32))
cl_out_mask = cl_array.to_device(self.queue,
np.zeros(shape, dtype=np.float32))
cl_out_mask2 = cl_array.to_device(self.queue,
np.zeros(shape, dtype=np.float32))
gws = tuple([int(2 * cl_radius + 1)] * 3)
args = (cl_template, cl_mask, cl_rotmat, self.s_linear, self.s_nearest,
cl_center, cl_shape, cl_radius, cl_out_template.data,
cl_out_mask.data, cl_out_mask2.data)
self.k.rotate_grids_and_multiply(self.queue, gws, None, *args)
self.queue.finish()
self.assertTrue(np.allclose(np_out_template, cl_out_template.get()))
self.assertTrue(np.allclose(np_out_mask, cl_out_mask.get()))
self.assertTrue(np.allclose(np_out_mask2, cl_out_mask2.get()))
class TestCLKernels(unittest.TestCase):
"""Tests for the OpenCL kernels"""
#@classmethod
#def setUpClass(cls):
# p = cl.get_platforms()[0]
# devs = p.get_devices()
# cls.ctx = cl.Context(devices=devs)
# cls.queue = cl.CommandQueue(cls.ctx, device=devs[0])
# cls.k = CLKernels(cls.ctx)
# cls.s_linear = cl.Sampler(cls.ctx, False, cl.addressing_mode.CLAMP,
# cl.filter_mode.LINEAR)
# cls.s_nearest = cl.Sampler(cls.ctx, False, cl.addressing_mode.CLAMP,
# cl.filter_mode.NEAREST)
def setUp(self):
p = cl.get_platforms()[0]
devs = p.get_devices()
self.ctx = cl.Context(devices=devs)
self.queue = cl.CommandQueue(self.ctx, device=devs[0])
self.k = CLKernels(self.ctx)
self.k = CLKernels(self.ctx)
self.s_linear = cl.Sampler(self.ctx, False, cl.addressing_mode.CLAMP,
cl.filter_mode.LINEAR)
self.s_nearest = cl.Sampler(self.ctx, False, cl.addressing_mode.CLAMP,
cl.filter_mode.NEAREST)
def test_multiply(self):
np_in1 = np.arange(10, dtype=np.float32)
np_in2 = np.arange(10, dtype=np.float32)
np_out = np_in1 * np_in2
cl_in1 = cl_array.to_device(self.queue, np_in1)
cl_out = cl_array.to_device(self.queue, np.zeros(10, dtype=np.float32))
cl_in2 = cl_array.to_device(self.queue, np_in2)
self.k.multiply(cl_in1, cl_in2, cl_out)
self.assertTrue(np.allclose(np_out, cl_out.get()))
def test_conj_multiply(self):
np_in1 = np.zeros(10, dtype=np.complex64)
np_in2 = np.zeros(10, dtype=np.complex64)
np_in1.real = np.random.rand(10)
np_in1.imag = np.random.rand(10)
np_in2.real = np.random.rand(10)
np_in2.imag = np.random.rand(10)
np_out = np_in1.conj() * np_in2
cl_in1 = cl_array.to_device(self.queue, np_in1)
cl_in2 = cl_array.to_device(self.queue, np_in2)
cl_out = cl_array.to_device(self.queue, np.zeros(10, dtype=np.complex64))
self.k.conj_multiply(cl_in1, cl_in2, cl_out)
self.assertTrue(np.allclose(np_out, cl_out.get()))
# TODO
#def test_calc_lcc(self):
# pass
#def test_take_best(self):
# pass
def test_rotate_template_mask(self):
shape = (5, 5, 5)
template = np.zeros(shape, dtype=np.float32)
template[2, 2, 1:4] = 1
template[2, 1:4, 2] = 1
rotmat = np.asarray([1, 0, 0, 0, 1, 0, 0, 0, 1] + [0] * 7, dtype=np.float32)
self.queue.finish()
cl_template = cl.image_from_array(self.queue.context, template)
cl_out = cl_array.to_device(self.queue, np.zeros(shape, dtype=np.float32))
center = np.asarray([2, 2, 2, 0], dtype=np.float32)
shape = np.asarray([5, 5, 5, 125], dtype=np.int32)
self.k.rotate_template(self.queue, (125,), None, self.s_linear,
cl_template, rotmat, cl_out.data, center, shape)
answer = np.zeros((5, 5, 5), dtype=np.float32)
answer[0, 0, :2] = 1
answer[0, 0, -1] = 1
answer[0, :2, 0] = 1
answer[0, -1, 0] = 1
self.assertTrue(np.allclose(cl_out.get(), answer))
def test_rotate_grids_and_multiply(self):
shape = (5, 5, 5)
template = np.zeros(shape, dtype=np.float32)
template[2, 2, 1:4] = 1
template[2, 1:4, 2] = 1
mask = template * 2
np_out_template = np.zeros(shape, dtype=np.float32)
np_out_template[0, 0, :2] = 1
np_out_template[0, 0, -1] = 1
np_out_template[0, :2, 0] = 1
np_out_template[0, -1, 0] = 1
np_out_mask = np_out_template * 2
np_out_mask2 = np_out_mask ** 2
cl_template = cl.image_from_array(self.ctx, template)
cl_mask = cl.image_from_array(self.ctx, mask)
cl_rotmat = np.asarray([1, 0, 0, 0, 1, 0, 0, 0, 1] + [0] * 7, dtype=np.float32)
cl_center = np.asarray([2, 2, 2, 0], dtype=np.float32)
cl_shape = np.asarray([5, 5, 5, 125], dtype=np.int32)
cl_radius = np.int32(2)
cl_out_template = cl_array.to_device(self.queue, np.zeros(shape, dtype=np.float32))
cl_out_mask = cl_array.to_device(self.queue, np.zeros(shape, dtype=np.float32))
cl_out_mask2 = cl_array.to_device(self.queue, np.zeros(shape, dtype=np.float32))
gws = tuple([int(2 * cl_radius + 1)] * 3)
args = (cl_template, cl_mask, cl_rotmat, self.s_linear, self.s_nearest,
cl_center, cl_shape, cl_radius, cl_out_template.data, cl_out_mask.data,
cl_out_mask2.data)
self.k.rotate_grids_and_multiply(self.queue, gws, None, *args)
self.queue.finish()
self.assertTrue(np.allclose(np_out_template, cl_out_template.get()))
self.assertTrue(np.allclose(np_out_mask, cl_out_mask.get()))
self.assertTrue(np.allclose(np_out_mask2, cl_out_mask2.get()))
