def __init__(self, opt: options.Options, device: D):
super(MeshGen, self).__init__(opt, device)
self.generator.eval()
template_name, template = load_template_mesh(opt, opt.start_level)
self.template = MeshInference(template_name, template, self.opt,
self.opt.start_level).to(self.device)
self.reconstruction_z = factory.NoiseMem(opt).load().to(device)
f'gen {self.opt.mesh_name} {i * num_samples + j +1:02d} / {len(self.generator.levels) * num_samples}'
)
def __call__(self, start_level: int):
with torch.no_grad():
if start_level < 0:
start_level = len(self)
start_level = min(len(self), start_level)
z = self.compose_z(start_level)
return self.generator(self.template.copy(), z, len(self) - 1)
if __name__ == '__main__':
opt_ = options.Options()
opt_.parse_cmdline()
device = CPU
with_noise = False
if opt_.gen_mode == 'generate':
mg = MeshGen(opt_, device)
mg.generate_all(opt_.num_gen_samples)
elif opt_.gen_mode == 'animate':
m2m = Mesh2Mesh(opt_, device)
in_mesh = MeshInference(
opt_.target, mesh_utils.load_real_mesh(opt_.target, 0, True), opt_,
0).to(device)
m2m.animate(in_mesh,
opt_.gen_levels[0],
opt_.gen_levels[1],
0, (12, 17),
zero_places=(0, 0, 1, 1, 1))