def generate_from_latent(self,
z,
c_first=None,
Rt=None,
K=None,
stats_dict={},
**kwargs):
''' Generates mesh from latent.
Args:
z (tensor): latent code z
c (tensor): latent conditioned code c
stats_dict (dict): stats dictionary
'''
threshold = np.log(self.threshold) - np.log(1. - self.threshold)
t0 = time.time()
# Compute bounding box size
box_size = 1 + self.padding
# Shortcut
if self.upsampling_steps == 0:
nx = self.resolution0
pointsf = box_size * make_3d_grid((-0.5, ) * 3, (0.5, ) * 3,
(nx, ) * 3)
values = self.eval_points(pointsf, z, c_first,
**kwargs).cpu().numpy()
value_grid = values.reshape(nx, nx, nx)
else:
mesh_extractor = MISE(self.resolution0, self.upsampling_steps,
threshold)
points = mesh_extractor.query()
while points.shape[0] != 0:
# Query points
pointsf = torch.FloatTensor(points).to(self.device)
# Normalize to bounding box
pointsf = pointsf / mesh_extractor.resolution
pointsf = box_size * (pointsf - 0.5)
# Evaluate model and update
values = self.eval_points(pointsf, z, c_first, Rt, K,
**kwargs).cpu().numpy()
values = values.astype(np.float64)
mesh_extractor.update(points, values)
points = mesh_extractor.query()
value_grid = mesh_extractor.to_dense()
# Extract mesh
stats_dict['time (eval points)'] = time.time() - t0
mesh = self.extract_mesh(value_grid,
z,
c_first,
Rt,
K,
stats_dict=stats_dict)
return mesh
def generate_from_latent(self,
z,
c=None,
stats_dict={},
data=None,
**kwargs):
''' Generates mesh from latent.
Args:
z (tensor): latent code z
c (tensor): latent conditioned code c
stats_dict (dict): stats dictionary
'''
assert data is not None
if self.is_explicit_mesh:
normal_faces = data.get('angles.normal_face').to(self.device)
normal_angles = data.get('angles.normal_angles').to(self.device)
if self.is_skip_surface_mask_generation_time:
t0 = time.time()
output = self.model.decode(None,
z,
c,
angles=normal_angles,
only_return_points=True,
**kwargs)
stats_dict['time (eval points)'] = time.time() - t0
t0 = time.time()
if not self.is_just_measuring_time:
output = self.model.decode(None,
z,
c,
angles=normal_angles,
**kwargs)
if not self.is_skip_surface_mask_generation_time:
stats_dict['time (eval points)'] = time.time() - t0
normal_vertices, normal_mask, _, _, _ = output
t0 = time.time()
B, N, P, D = normal_vertices.shape
normal_faces_all = torch.cat([(normal_faces + idx * P)
for idx in range(N)],
axis=1)
assert B == 1
mem_t = time.time()
verts = normal_vertices.view(
N * P, D).to('cpu').detach().numpy() / self.pnet_point_scale
faces = normal_faces_all.view(-1, 3).to('cpu').detach().numpy()
if self.is_just_measuring_time:
visbility = None
else:
visbility = (normal_mask > 0.5).view(
N * P).to('cpu').detach().numpy()
skip_t = time.time() - mem_t
mesh = trimesh.Trimesh(
verts,
faces,
process=False,
vertex_attributes={'vertex_visibility': visbility})
stats_dict['time (copy to trimesh)'] = time.time() - t0 - skip_t
else:
t0 = time.time()
threshold = 0.
#threshold = np.log(self.threshold) - np.log(1. - self.threshold)
# Compute bounding box size
box_size = 1 + self.padding
# Shortcut
if self.upsampling_steps == 0:
nx = self.resolution0
pointsf = box_size * make_3d_grid((-0.5, ) * 3, (0.5, ) * 3,
(nx, ) * 3)
values = self.eval_points(pointsf, z, c, data=data,
**kwargs).cpu().numpy()
value_grid = values.reshape(nx, nx, nx)
else:
mesh_extractor = MISE(self.resolution0, self.upsampling_steps,
threshold)
points = mesh_extractor.query()
while points.shape[0] != 0:
# Query points
pointsf = torch.FloatTensor(points).to(self.device)
# Normalize to bounding box
pointsf = pointsf / mesh_extractor.resolution
pointsf = box_size * (pointsf - 0.5)
# Evaluate model and update
values = self.eval_points(pointsf,
z,
c,
data=data,
**kwargs).cpu().numpy()
values = values.astype(np.float64)
mesh_extractor.update(points, values)
points = mesh_extractor.query()
value_grid = mesh_extractor.to_dense()
# Extract mesh
stats_dict['time (eval points)'] = time.time() - t0
mesh = self.extract_mesh(value_grid, z, c, stats_dict=stats_dict)
return mesh