def solve(self, wt_n, y_nd, bend_coef, rot_coef,f_res):
assert y_nd.shape == (self.n, self.d)
assert bend_coef in self.bend_coefs
assert np.allclose(rot_coef, self.rot_coef)
assert self.valid
self.initialize_solver(bend_coef, wt_n)
gemm(self.QN_gpu, self.WQN_gpu, self.NHN_gpu,
transa='T', alpha=1, beta=1)
lhs = self.NHN_gpu.get()
wy_nd = wt_n[:, None] * y_nd
rhs = self.NR + self.QN.T.dot(wy_nd)
z = scipy.linalg.solve(lhs, rhs)
theta = self.N.dot(z)
set_ThinPlateSpline(f_res, self.x_nd, theta)
def solve(self, wt_n, y_nd, bend_coef, f_res):
if y_nd.shape[0] != self.n or y_nd.shape[1] != self.d:
raise RuntimeError(
"The dimensions of y_nd doesn't match the dimensions of x_nd")
if not y_nd.flags.c_contiguous:
raise RuntimeError("Expected y_nd to be c-contiguous but it isn't")
self.sqrtWQN_gpu.set_async(np.sqrt(wt_n)[:, None] * self.QN)
geam(self.NKN_gpu, self.NRN_gpu, self.lhs_gpu, alpha=bend_coef, beta=1)
gemm(self.sqrtWQN_gpu,
self.sqrtWQN_gpu,
self.lhs_gpu,
transa='T',
alpha=1,
beta=1)
drv.memcpy_dtod_async(self.rhs_gpu.gpudata, self.NR_gpu.gpudata,
self.rhs_gpu.nbytes)
self.y_dnW_gpu.set_async(
y_nd.T * wt_n) # use transpose so that it is f_contiguous
gemm(self.QN_gpu,
self.y_dnW_gpu,
self.rhs_gpu,
transa='T',
transb='T',
alpha=1,
beta=1)
if lfd.registration._has_cula:
culinalg.cho_solve(self.lhs_gpu, self.rhs_gpu)
z = self.rhs_gpu.get()
culinalg.dot(self.N_gpu, self.rhs_gpu, out=self.theta_gpu)
theta = self.theta_gpu.get()
else: # if cula is not install perform the last two computations in the CPU
z = np.linalg.solve(self.lhs_gpu.get(), self.rhs_gpu.get())
theta = self.N.dot(z)
f_res.update(self.x_nd,
y_nd,
bend_coef,
self.rot_coef,
wt_n,
theta,
N=self.N,
z=z)
def solve(self, wt_n, y_nd, bend_coef, f_res):
if y_nd.shape[0] != self.n or y_nd.shape[1] != self.d:
raise RuntimeError("The dimensions of y_nd doesn't match the dimensions of x_nd")
if not y_nd.flags.c_contiguous:
raise RuntimeError("Expected y_nd to be c-contiguous but it isn't")
self.sqrtWQN_gpu.set_async(np.sqrt(wt_n)[:,None] * self.QN)
geam(self.NKN_gpu, self.NRN_gpu, self.lhs_gpu, alpha=bend_coef, beta=1)
gemm(self.sqrtWQN_gpu, self.sqrtWQN_gpu, self.lhs_gpu, transa='T', alpha=1, beta=1)
drv.memcpy_dtod_async(self.rhs_gpu.gpudata, self.NR_gpu.gpudata, self.rhs_gpu.nbytes)
self.y_dnW_gpu.set_async(y_nd.T * wt_n) # use transpose so that it is f_contiguous
gemm(self.QN_gpu, self.y_dnW_gpu, self.rhs_gpu, transa='T', transb='T', alpha=1, beta=1)
if lfd.registration._has_cula:
culinalg.cho_solve(self.lhs_gpu, self.rhs_gpu)
culinalg.dot(self.N_gpu, self.rhs_gpu, out=self.theta_gpu)
theta = self.theta_gpu.get()
else: # if cula is not install perform the last two computations in the CPU
z = np.linalg.solve(self.lhs_gpu.get(), self.rhs_gpu.get())
theta = self.N.dot(z)
f_res.set_ThinPlateSpline(self.x_nd, y_nd, bend_coef, self.rot_coef, wt_n, theta=theta)
