ntheta = params.shape[0]
npts = us.shape[0]
seed = np.zeros((2 + ntheta, all_params.shape[0]), dtype=all_params.dtype)
for i in range(npts):
seed[0][2 * i] = 1.
seed[1][2 * i + 1] = 1.
for i in range(ntheta):
seed[i + 2][i + 2 * npts] = 1.
if nruns_J > 0:
tJ, J = utils.timer(
lambda all_params, *params: np.array(
[fjac(all_params, curr_seed, *params)
for curr_seed in seed]).transpose(),
(all_params, data.model.nbones, data.model.base_relatives,
data.model.parents, data.model.inverse_base_absolutes,
data.model.base_positions, data.model.weights, mirror_factor,
data.points, data.correspondences, data.model.triangles),
nruns=nruns_J,
limit=time_limit,
ret_val=True)
tJ += tf # !!!!!!!!! adding this because no function value is returned by fjac
print("J:")
# print(J)
hand_io.write_J(fn_out + "_J_" + name + ".txt", J)
else:
tJ = 0
utils.write_times(fn_out + "_times_" + name + ".txt", tf, tJ)
name = "PyTorch"
ntheta = params.shape[0]
npts = us.shape[0]
seed = torch.zeros((2 + ntheta, all_params.shape[0]), dtype=torch.float64)
for i in range(npts):
seed[0][2 * i] = 1.
seed[1][2 * i + 1] = 1.
for i in range(ntheta):
seed[i + 2][i + 2 * npts] = 1.
if nruns_J > 0:
tJ, res = utils.timer(
lambda *args: torch_seed_wrapper(seed, args),
(hand_objective_complicated, (all_params, ),
(data.model.nbones, data.model.base_relatives, data.model.parents,
data.model.inverse_base_absolutes, data.model.base_positions,
data.model.weights, mirror_factor, data.points,
data.correspondences, data.model.triangles), True),
nruns=nruns_J,
limit=time_limit,
ret_val=True)
tJ += tf # !!!!!!!!! adding this because no function value is returned by fjac
# print("J:")
# print(J)
hand_io.write_J(fn_out + "_J_" + name + ".txt", res[1])
else:
tJ = 0
utils.write_times(fn_out + "_times_" + name + ".txt", tf, tJ)