def get_solver(self, x_na, K_nn, bend_coefs, rot_coef):
n,d = x_na.shape
assert len(bend_coefs) <= len(self.bend_coefs)
assert n <= self.max_N
if not self.cur_solver is None:
self.cur_solver.valid = False
Q = np.c_[np.ones((n, 1)), x_na, K_nn]
A = np.r_[np.zeros((d+1, d+1)), np.c_[np.ones((n, 1)), x_na]].T
R = np.zeros((n+d+1, d))
R[1:d+1, :d] = np.diag(rot_coef)
n_cnts = A.shape[0]
_u,_s,_vh = np.linalg.svd(A.T)
N = _u[:,n_cnts:].copy()
N_gpu = self.N_gpu[:(n+d+1)*n].reshape(n+d+1, n)
N_gpu.set_async(N)
QN = Q.dot(N)
QN_gpu = self.QN_gpu[:n*n].reshape(n, n)
QN_gpu.set_async(QN)
WQN_gpu = self.WQN_gpu[:n*n].reshape(n, n)
NHN_gpu = self.NHN_gpu[:n*n].reshape(n, n)
NR = N.T.dot(R)
N_arr_gpu = []
O_gpu = []
ON_gpu = []
NON_gpu = []
for i, b in enumerate(bend_coefs):
O_b = np.zeros((n+d+1, n+d+1), np.float64)
O_b[d+1:, d+1:] += b * K_nn
O_b[1:d+1, 1:d+1] += np.diag(rot_coef)
offset = i * (n+d+1)*(n+d+1)
O_gpu.append(self.O_gpu[offset:offset + (n+d+1)*(n+d+1)].reshape(n+d+1, n+d+1))
O_gpu[-1].set(O_b)
offset = i * (n)*(n+d+1)
ON_gpu.append(self.ON_gpu[offset:offset + n*(n+d+1)].reshape(n+d+1, n))
offset = i * n * n
NON_gpu.append(self.NON_gpu[offset:offset + n*n].reshape(n, n))
N_arr_gpu.append(N_gpu)
O_ptrs = get_gpu_ptrs(O_gpu)
ON_ptrs = get_gpu_ptrs(ON_gpu)
NON_ptrs = get_gpu_ptrs(NON_gpu)
N_ptrs = get_gpu_ptrs(N_arr_gpu)
dot_batch_nocheck(O_gpu, N_arr_gpu, ON_gpu,
O_ptrs, N_ptrs, ON_ptrs,
b = 0)
dot_batch_nocheck(N_arr_gpu, ON_gpu, NON_gpu,
N_ptrs, ON_ptrs, NON_ptrs,
transa='T', b = 0)
NON_gpu = dict(zip(bend_coefs, NON_gpu))
NON = dict([(b, non.get_async()) for b, non in NON_gpu.iteritems()])
self.cur_solver = TPSSolver(bend_coefs, N, QN, NON, NR, x_na, K_nn, rot_coef,
QN_gpu, WQN_gpu, NON_gpu, NHN_gpu)
return self.cur_solver
def transform_points(self):
"""
computes the warp of self.pts under the current tps params
"""
fill_mat(self.pt_w_ptrs, self.trans_d_ptrs, self.dims_gpu, self.N)
dot_batch_nocheck(self.pts, self.lin_dd, self.pts_w, self.pt_ptrs, self.lin_dd_ptrs, self.pt_w_ptrs)
dot_batch_nocheck(self.kernels, self.w_nd, self.pts_w, self.kernel_ptrs, self.w_nd_ptrs, self.pt_w_ptrs)
sync()
def update_transform(self, b):
"""
computes the TPS associated with the current target pts
"""
self.set_tps_params(self.offset_mats[b])
dot_batch_nocheck(
self.proj_mats[b], self.pts_t, self.tps_params, self.proj_mat_ptrs[b], self.pt_t_ptrs, self.tps_param_ptrs
)
sync()
def bending_cost(self, b=DEFAULT_LAMBDA[1]):
## b * w_nd' * K * w_nd
## use pts_w as temporary storage
dot_batch_nocheck(self.kernels, self.w_nd, self.pts_w, self.kernel_ptrs, self.w_nd_ptrs, self.pt_w_ptrs, b=0)
dot_batch_nocheck(
self.pts_w, self.w_nd, self.bend_res, self.pt_w_ptrs, self.w_nd_ptrs, self.bend_res_ptrs, transa="T", b=0
)
bend_res = self.bend_res_mat.get()
return b * np.array([np.trace(bend_res[i * DATA_DIM : (i + 1) * DATA_DIM]) for i in range(self.N)])
def transform_trajs(self):
"""
computes the warp of l_traj and r_traj under current tps params
"""
fill_mat(self.l_traj_w_ptrs, self.trans_d_ptrs, self.l_traj_dims_gpu, self.N)
fill_mat(self.r_traj_w_ptrs, self.trans_d_ptrs, self.r_traj_dims_gpu, self.N)
dot_batch_nocheck(
self.l_traj, self.lin_dd, self.l_traj_w, self.l_traj_ptrs, self.lin_dd_ptrs, self.l_traj_w_ptrs
)
dot_batch_nocheck(
self.r_traj, self.lin_dd, self.r_traj_w, self.r_traj_ptrs, self.lin_dd_ptrs, self.r_traj_w_ptrs
)
dot_batch_nocheck(
self.l_traj_K, self.w_nd, self.l_traj_w, self.l_traj_K_ptrs, self.w_nd_ptrs, self.l_traj_w_ptrs
)
dot_batch_nocheck(
self.r_traj_K, self.w_nd, self.r_traj_w, self.r_traj_K_ptrs, self.w_nd_ptrs, self.r_traj_w_ptrs
)
sync()
def check_transform_pts(ctx, i=0):
import scikits.cuda.linalg as la
n = ctx.dims[i]
w_nd = ctx.w_nd[i].get()[:n]
lin_dd = ctx.lin_dd[i].get()
trans_d = ctx.trans_d[i].get()
k_nn = ctx.kernels[i].get()[:n, :n].reshape(n, n).copy()
x_nd = ctx.pts[i].get()[:n]
xw_nd = ctx.pts_w[i].get()[:n]
_k_gpu = gpuarray.to_gpu(k_nn)
_x_gpu = gpuarray.to_gpu(x_nd)
_lin_gpu = gpuarray.to_gpu(lin_dd)
_trans_gpu = gpuarray.to_gpu(trans_d)
_w_gpu = gpuarray.to_gpu(w_nd)
fill_mat(ctx.pt_w_ptrs, ctx.trans_d_ptrs, ctx.dims_gpu, ctx.N)
dot_batch_nocheck(ctx.pts, ctx.lin_dd, ctx.pts_w, ctx.pt_ptrs, ctx.lin_dd_ptrs, ctx.pt_w_ptrs)
xw_nd = ctx.pts_w[i].get()[:n]
cpu_xw_nd = np.dot(x_nd, lin_dd) + trans_d[None, :]
# assert np.allclose(xw_nd, cpu_xw_nd)
dot_batch_nocheck(ctx.kernels, ctx.w_nd, ctx.pts_w, ctx.kernel_ptrs, ctx.w_nd_ptrs, ctx.pt_w_ptrs)
xw_nd = ctx.pts_w[i].get()[:n]
cpu_xw_nd = cpu_xw_nd + np.dot(k_nn, w_nd)
# print "w_nd\n", w_nd[:3], np.max(w_nd)
# print "lin_dd\n", lin_dd[:3]
# print "trans_d\n", trans_d
# print "k_nn\n", k_nn[:3, :3]
# print "x_nd\n", x_nd[:3, :3]
# print cpu_xw_nd[:3]
if not (np.allclose(xw_nd, cpu_xw_nd)):
print "k dot w_nd is difference on cpu and gpu"
k_dot_w = np.dot(k_nn, w_nd)
k_gpu = [gpuarray.to_gpu(k_nn)]
w_gpu = [gpuarray.to_gpu(w_nd)]
res_gpu = [gpuarray.zeros((n, DATA_DIM), np.float32)]
k_ptrs = get_gpu_ptrs(k_gpu)
w_ptrs = get_gpu_ptrs(w_gpu)
res_ptrs = get_gpu_ptrs(res_gpu)
dot_batch_nocheck(k_gpu, w_gpu, res_gpu, k_ptrs, w_ptrs, res_ptrs)
res = res_gpu[0].get()
single_gpu = la.dot(_k_gpu, _w_gpu)
print "retry success {}".format(np.allclose(res, k_dot_w))
print "gpu success {}".format(np.allclose(single_gpu.get(), res))
assert np.allclose(single_gpu.get(), res)
raw_input("go?")
