def profileNCSC(n_reps):
"""
The C reference version for comparison.
"""
numpy.random.seed(1)
data = numpy.random.uniform(low=10.0,
high=20.0,
size=(n_reps, n_pts,
n_pts)).astype(dtype=numpy.float32)
gamma = numpy.random.uniform(low=2.0, high=4.0,
size=(n_pts,
n_pts)).astype(dtype=numpy.float32)
otf_mask = numpy.fft.fftshift(pyRef.createOTFMask().reshape(16, 16))
ref_u = numpy.zeros_like(data)
ncs_sr = ncsC.NCSCSubRegion(r_size=n_pts)
start_time = time.time()
for i in range(n_reps):
ncs_sr.newRegion(data[i, :, :], gamma)
ncs_sr.setOTFMask(otf_mask)
ref_u[i, :, :] = ncs_sr.cSolve(alpha, verbose=False)
e_time = time.time() - start_time
ncs_sr.cleanup()
print("CNSC {0:.6f} seconds".format(e_time))
def test_sr_5():
"""
Test solvers.
"""
im_size = 16
ncs_sr = ncsC.NCSCSubRegion(im_size)
alpha = 0.02
verbose = False
for i in range(10):
gamma = numpy.random.uniform(low = 2.0, high = 4.0, size = (im_size, im_size))
image = numpy.random.uniform(low = 10.0, high = 20.0, size = (im_size, im_size))
otfmask = pyRef.randomOTFMask(im_size)
ncs_sr.newRegion(image, gamma)
ncs_sr.setOTFMask(otfmask)
im1 = pyRef.ncsSolve(image, gamma, otfmask, alpha, verbose = verbose)
im2 = ncs_sr.pySolve(alpha, verbose = verbose)
im3 = ncs_sr.pySolveGradient(alpha, verbose = verbose)
im4 = ncs_sr.cSolve(alpha, verbose = verbose)
if verbose:
print(numpy.max(numpy.abs(im1-im2)),
numpy.max(numpy.abs(im1-im3)),
numpy.max(numpy.abs(im1-im4)))
assert(numpy.allclose(im1,im2,atol = 1.0e-2))
assert(numpy.allclose(im1,im3,atol = 1.0e-2))
assert(numpy.allclose(im1,im4,atol = 1.0e-2))
ncs_sr.cleanup()
def test_ncs_noise_reduction_2():
# Setup
numpy.random.seed(1)
n_reps = 10
data = numpy.random.uniform(low = 10.0, high = 20.0, size = (n_reps, n_pts, n_pts)).astype(dtype = numpy.float32)
gamma = numpy.random.uniform(low = 2.0, high = 4.0, size = (n_reps, n_pts, n_pts)).astype(dtype = numpy.float32)
otf_mask_shift = pyRef.createOTFMask()
# OpenCL Setup.
u = numpy.zeros((n_reps, n_pts, n_pts), dtype = numpy.float32)
iters = numpy.zeros(n_reps, dtype = numpy.int32)
status = numpy.zeros(n_reps, dtype = numpy.int32)
data_buffer = cl.Buffer(context, cl.mem_flags.READ_ONLY | cl.mem_flags.COPY_HOST_PTR,
hostbuf = data)
gamma_buffer = cl.Buffer(context, cl.mem_flags.READ_ONLY | cl.mem_flags.COPY_HOST_PTR,
hostbuf = gamma)
otf_mask_buffer = cl.Buffer(context, cl.mem_flags.READ_ONLY | cl.mem_flags.COPY_HOST_PTR,
hostbuf = otf_mask_shift)
u_buffer = cl.Buffer(context, cl.mem_flags.WRITE_ONLY | cl.mem_flags.COPY_HOST_PTR,
hostbuf = u)
iters_buffer = cl.Buffer(context, cl.mem_flags.WRITE_ONLY | cl.mem_flags.COPY_HOST_PTR,
hostbuf = iters)
status_buffer = cl.Buffer(context, cl.mem_flags.WRITE_ONLY | cl.mem_flags.COPY_HOST_PTR,
hostbuf = status)
# OpenCL noise reduction.
program.ncsReduceNoise(queue, (n_reps*16,), (16,),
data_buffer,
gamma_buffer,
otf_mask_buffer,
u_buffer,
iters_buffer,
status_buffer,
numpy.float32(alpha))
cl.enqueue_copy(queue, u, u_buffer).wait()
cl.enqueue_copy(queue, iters, iters_buffer).wait()
cl.enqueue_copy(queue, status, status_buffer).wait()
queue.finish()
# NCSC noise reduction.
otf_mask = numpy.fft.fftshift(otf_mask_shift.reshape(16, 16))
ref_u = numpy.zeros_like(data)
ncs_sr = ncsC.NCSCSubRegion(r_size = n_pts)
for i in range(n_reps):
ncs_sr.newRegion(data[i,:,:], gamma[i,:,:])
ncs_sr.setOTFMask(otf_mask)
ref_u[i,:,:] = ncs_sr.cSolve(alpha, verbose = False)
ncs_sr.cleanup()
for i in range(n_reps):
norm_diff = numpy.max(numpy.abs(u[i,:,:] - ref_u[i,:,:]))/numpy.max(ref_u[i,:,:])
assert(norm_diff < 1.0e-2), "failed {0:d} {1:.3f}".format(i, norm_diff)
def test_calc_nc():
n_pts = 16
for i in range(100):
# OpenCL
u = numpy.random.uniform(low=1.0, high=10.0,
size=(n_pts,
n_pts)).astype(dtype=numpy.float32)
otf_mask_shift = pyRef.createOTFMask()
nc = numpy.zeros(1, dtype=numpy.float32)
u_buffer = cl.Buffer(context,
cl.mem_flags.READ_ONLY
| cl.mem_flags.COPY_HOST_PTR,
hostbuf=u)
otf_mask_buffer = cl.Buffer(context,
cl.mem_flags.READ_ONLY
| cl.mem_flags.COPY_HOST_PTR,
hostbuf=otf_mask_shift)
nc_buffer = cl.Buffer(context,
cl.mem_flags.WRITE_ONLY
| cl.mem_flags.COPY_HOST_PTR,
hostbuf=nc)
program.calc_nc_test(queue, (1, ), (1, ), u_buffer, otf_mask_buffer,
nc_buffer)
cl.enqueue_copy(queue, nc, nc_buffer).wait()
queue.finish()
# Reference 1
otf_mask = numpy.fft.fftshift(otf_mask_shift.reshape(16, 16))
ncs_sr = ncsC.NCSCSubRegion(r_size=n_pts)
ncs_sr.setOTFMask(otf_mask)
ncs_sr.setU(u)
ref1_nc = ncs_sr.calcNoiseContribution()
ncs_sr.cleanup()
norm_diff = abs(nc[0] - ref1_nc) / abs(ref1_nc)
assert (norm_diff <
1.0e-3), "Difference in results! {0:.6f}".format(norm_diff)
# Reference 2
u_r = numpy.copy(u).flatten()
u_c = numpy.zeros_like(u_r)
u_fft_r = numpy.zeros_like(u_r)
u_fft_c = numpy.zeros_like(u_c)
otf_mask_sqr = (otf_mask_shift * otf_mask_shift).flatten()
pyRef.fft_16x16(u_r, u_c, u_fft_r, u_fft_c)
ref2_nc = pyRef.calcNoiseContribution(u_fft_r, u_fft_c, otf_mask_sqr)
norm_diff = abs(nc[0] - ref2_nc) / abs(ref2_nc)
assert (norm_diff <
1.0e-3), "Difference in results! {0:.6f}".format(norm_diff)
def test_calc_ll():
n_pts = 16
for i in range(100):
# OpenCL
u = numpy.random.uniform(low=-2.0, high=10.0,
size=(n_pts,
n_pts)).astype(dtype=numpy.float32)
data = numpy.random.uniform(low=1.0, high=10.0,
size=(n_pts,
n_pts)).astype(dtype=numpy.float32)
gamma = numpy.random.uniform(low=1.0, high=2.0,
size=(n_pts,
n_pts)).astype(dtype=numpy.float32)
ll = numpy.zeros(1, dtype=numpy.float32)
u_buffer = cl.Buffer(context,
cl.mem_flags.READ_ONLY
| cl.mem_flags.COPY_HOST_PTR,
hostbuf=u)
data_buffer = cl.Buffer(context,
cl.mem_flags.READ_ONLY
| cl.mem_flags.COPY_HOST_PTR,
hostbuf=data)
gamma_buffer = cl.Buffer(context,
cl.mem_flags.READ_ONLY
| cl.mem_flags.COPY_HOST_PTR,
hostbuf=gamma)
ll_buffer = cl.Buffer(context,
cl.mem_flags.WRITE_ONLY
| cl.mem_flags.COPY_HOST_PTR,
hostbuf=ll)
program.cll_test(queue, (16, ), (16, ), u_buffer, data_buffer,
gamma_buffer, ll_buffer)
cl.enqueue_copy(queue, ll, ll_buffer).wait()
queue.finish()
# Reference 1
ncs_sr = ncsC.NCSCSubRegion(r_size=n_pts)
ncs_sr.newRegion(data, gamma)
ncs_sr.setU(u)
ref1_ll = ncs_sr.calcLogLikelihood()
ncs_sr.cleanup()
assert (abs(ll[0] - ref1_ll) <
1.0e-3), "Difference in results! {0:.6f} {1:.6f}".format(
ll[0], ref1_ll)
# Reference 2
ref2_ll = pyRef.calcLogLikelihood(u, data, gamma)
assert (abs(ll[0] - ref2_ll) <
1.0e-3), "Difference in results! {0:.6f} {1:.6f}".format(
ll[0], ref2_ll)
def test_calc_nc_grad_1():
n_pts = 16
for i in range(10):
# OpenCL gradient calculation.
u = numpy.random.uniform(low = 1.0, high = 10.0, size = (n_pts, n_pts)).astype(dtype = numpy.float32)
otf_mask_shift = pyRef.createOTFMask()
grad = numpy.zeros((n_pts, n_pts)).astype(numpy.float32)
u_buffer = cl.Buffer(context, cl.mem_flags.READ_ONLY | cl.mem_flags.COPY_HOST_PTR, hostbuf = u)
otf_mask_buffer = cl.Buffer(context, cl.mem_flags.READ_ONLY | cl.mem_flags.COPY_HOST_PTR, hostbuf = otf_mask_shift)
grad_buffer = cl.Buffer(context, cl.mem_flags.WRITE_ONLY | cl.mem_flags.COPY_HOST_PTR, hostbuf = grad)
program.calc_nc_grad_test(queue, (16,), (16,),
u_buffer,
otf_mask_buffer,
grad_buffer)
cl.enqueue_copy(queue, grad, grad_buffer).wait()
queue.finish()
# Reference 1
otf_mask = numpy.fft.fftshift(otf_mask_shift.reshape(16, 16))
ncs_sr = ncsC.NCSCSubRegion(r_size = n_pts)
ncs_sr.setOTFMask(otf_mask)
ncs_sr.setU(u)
ncs_sr.calcNoiseContribution()
ref1_grad = ncs_sr.calcNCGradient().reshape(grad.shape)
ncs_sr.cleanup()
ref_norm = numpy.abs(ref1_grad)
ref_norm[(ref_norm<1.0)] = 1.0
max_diff = numpy.max(numpy.abs(grad - ref1_grad)/ref_norm)
assert (max_diff < 1.0e-5), "Difference in results! {0:.8f}".format(max_diff)
# Reference 2
u_r = numpy.copy(u).flatten()
u_c = numpy.zeros_like(u_r)
u_fft_r = numpy.zeros_like(u_r)
u_fft_c = numpy.zeros_like(u_r)
ref2_grad = numpy.zeros_like(u_r)
otf_mask_sqr = otf_mask_shift * otf_mask_shift
pyRef.fft_16x16(u_r, u_c, u_fft_r, u_fft_c)
pyRef.calcNCGradientIFFT(u_fft_r, u_fft_c, otf_mask_sqr, ref2_grad)
ref_norm = numpy.abs(ref2_grad)
ref_norm[(ref_norm<1.0)] = 1.0
max_diff = numpy.max(numpy.abs(grad.flatten() - ref2_grad)/ref_norm)
assert (max_diff < 1.0e-5), "Difference in results! {0:.8f}".format(max_diff)
def test_ncs_noise_reduction_2():
# Setup
numpy.random.seed(1)
n_reps = 10
data = numpy.random.uniform(low=10.0,
high=20.0,
size=(n_reps, n_pts,
n_pts)).astype(dtype=numpy.float32)
gamma = numpy.random.uniform(low=2.0,
high=4.0,
size=(n_reps, n_pts,
n_pts)).astype(dtype=numpy.float32)
otf_mask_shift = pyRef.createOTFMask()
# CUDA Setup.
u = numpy.zeros((n_reps, n_pts, n_pts), dtype=numpy.float32)
iters = numpy.zeros(n_reps, dtype=numpy.int32)
status = numpy.zeros(n_reps, dtype=numpy.int32)
# CUDA noise reduction.
ncsReduceNoise(drv.In(data),
drv.In(gamma),
drv.In(otf_mask_shift),
drv.Out(u),
drv.Out(iters),
drv.Out(status),
numpy.float32(alpha),
block=(16, 1, 1),
grid=(n_reps, 1))
# NCSC noise reduction.
otf_mask = numpy.fft.fftshift(otf_mask_shift.reshape(16, 16))
ref_u = numpy.zeros_like(data)
ncs_sr = ncsC.NCSCSubRegion(r_size=n_pts)
for i in range(n_reps):
ncs_sr.newRegion(data[i, :, :], gamma[i, :, :])
ncs_sr.setOTFMask(otf_mask)
ref_u[i, :, :] = ncs_sr.cSolve(alpha, verbose=False)
ncs_sr.cleanup()
for i in range(n_reps):
norm_diff = numpy.max(
numpy.abs(u[i, :, :] - ref_u[i, :, :])) / numpy.max(ref_u[i, :, :])
assert (norm_diff < 1.0e-2), "failed {0:d} {1:.3f}".format(
i, norm_diff)
def test_sr_2():
"""
Test noise calculation.
"""
im_size = 16
ncs_sr = ncsC.NCSCSubRegion(im_size)
for i in range(10):
otfmask = pyRef.randomOTFMask(im_size)
u = numpy.random.uniform(low = 0.01, high = 10.0, size = (im_size, im_size))
ncs_sr.setOTFMask(otfmask)
ncs_sr.setU(u)
t1 = ncs_sr.calcNoiseContribution()
t2 = pyRef.calcNoiseContribution(u, otfmask)
assert(numpy.allclose(t1,t2))
ncs_sr.cleanup()
def test_sr_3():
"""
Test log likelihood gradient calculation.
"""
im_size = 16
ncs_sr = ncsC.NCSCSubRegion(im_size)
for i in range(10):
gamma = numpy.random.uniform(low = 2.0, high = 4.0, size = (im_size, im_size))
image = numpy.random.uniform(low = 0.01, high = 10.0, size = (im_size, im_size))
u = numpy.random.uniform(low = 0.01, high = 10.0, size = (im_size, im_size))
ncs_sr.newRegion(image, gamma)
ncs_sr.setU(u)
t1 = ncs_sr.calcLLGradient()
t2 = pyRef.calcLLGradient(u, image, gamma)
assert(numpy.allclose(t1,t2,atol = 1.0e-6))
ncs_sr.cleanup()
def test_calc_ll_grad():
n_pts = 16
for i in range(100):
# OpenCL
u = numpy.random.uniform(low=-2.0, high=10.0,
size=(n_pts,
n_pts)).astype(dtype=numpy.float32)
data = numpy.random.uniform(low=1.0, high=10.0,
size=(n_pts,
n_pts)).astype(dtype=numpy.float32)
gamma = numpy.random.uniform(low=1.0, high=2.0,
size=(n_pts,
n_pts)).astype(dtype=numpy.float32)
grad = numpy.zeros((n_pts, n_pts), dtype=numpy.float32)
u_buffer = cl.Buffer(context,
cl.mem_flags.READ_ONLY
| cl.mem_flags.COPY_HOST_PTR,
hostbuf=u)
data_buffer = cl.Buffer(context,
cl.mem_flags.READ_ONLY
| cl.mem_flags.COPY_HOST_PTR,
hostbuf=data)
gamma_buffer = cl.Buffer(context,
cl.mem_flags.READ_ONLY
| cl.mem_flags.COPY_HOST_PTR,
hostbuf=gamma)
grad_buffer = cl.Buffer(context,
cl.mem_flags.WRITE_ONLY
| cl.mem_flags.COPY_HOST_PTR,
hostbuf=grad)
program.cll_grad_test(queue, (16, ), (16, ), u_buffer, data_buffer,
gamma_buffer, grad_buffer)
cl.enqueue_copy(queue, grad, grad_buffer).wait()
queue.finish()
# Reference
ncs_sr = ncsC.NCSCSubRegion(r_size=n_pts)
ncs_sr.newRegion(data, gamma)
ncs_sr.setU(u)
ref1_grad = ncs_sr.calcLLGradient().reshape(grad.shape)
ncs_sr.cleanup()
ref1_norm = numpy.abs(ref1_grad)
ref1_norm[(ref1_norm < 1.0)] = 1.0
max_diff = numpy.max(numpy.abs(grad - ref1_grad) / ref1_norm)
assert (max_diff <
1.0e-5), "Difference in results! {0:.8f}".format(max_diff)
ref2_grad = numpy.zeros((n_pts, n_pts), dtype=numpy.float32)
pyRef.calcLLGradient(u, data, gamma, ref2_grad)
ref2_norm = numpy.abs(ref2_grad)
ref2_norm[(ref2_norm < 1.0)] = 1.0
max_diff = numpy.max(numpy.abs(grad - ref2_grad) / ref2_norm)
assert (max_diff <
1.0e-5), "Difference in results! {0:.8f}".format(max_diff)
