def Finalize(src_V, param_id):
pyDeform.DenormalizeByTemplate(src_V, param_id.tolist())
print(
'iter=%d, loss1_forward=%.6f loss1_backward=%.6f loss2_forward=%.6f loss2_backward=%.6f'
% (it, np.sqrt(loss1_forward.item() / GV1.shape[0]),
np.sqrt(loss1_backward.item() / GV2.shape[0]),
np.sqrt(loss2_forward.item() / GV2.shape[0]),
np.sqrt(loss2_backward.item() / GV1.shape[0])))
current_loss = loss.item()
if save_path != '':
torch.save({'func': func, 'optim': optimizer}, save_path)
GV1_deformed = func.forward(GV1_device)
GV1_deformed = torch.from_numpy(GV1_deformed.data.cpu().numpy())
V1_copy = V1.clone()
#Finalize(V1_copy, F1, E1, V2G1, GV1_deformed, 1.0, param_id2)
pyDeform.NormalizeByTemplate(V1_copy, param_id1.tolist())
V1_origin = V1_copy.clone()
#V1_copy = V1_copy.to(device)
func.func = func.func.cpu()
V1_copy = func.forward(V1_copy)
V1_copy = torch.from_numpy(V1_copy.data.cpu().numpy())
src_to_src = torch.from_numpy(
np.array([i for i in range(V1_origin.shape[0])]).astype('int32'))
pyDeform.SolveLinear(V1_origin, F1, E1, src_to_src, V1_copy, 1, 1)
pyDeform.DenormalizeByTemplate(V1_origin, param_id2.tolist())
pyDeform.SaveMesh(output_path, V1_origin, F1)
def Finalize(src_V, src_F, src_E, src_to_graph, graph_V, rigidity, param_id): pyDeform.NormalizeByTemplate(src_V, param_id.tolist()) pyDeform.SolveLinear(src_V, src_F, src_E, src_to_graph, graph_V, rigidity) pyDeform.DenormalizeByTemplate(src_V, param_id.tolist())
V1_copy = V1_origin.clone().to(device)
if i != 0:
V1_copy = func.integrate(V1_copy, 0, i * 0.04, device)
V1_copy = torch.from_numpy(V1_copy.data.cpu().numpy())
src_to_src = torch.from_numpy(np.array([i for i in range(V1_origin.shape[0])]).astype('int32'))
E1 = np.zeros((F1.shape[0] * 3, 2), dtype='int32')
F1_numpy = F1.numpy()
E1[:F1.shape[0],:] = F1_numpy[:,0:2]
E1[F1.shape[0]:F1.shape[0]*2,:] = F1_numpy[:,1:3]
E1[F1.shape[0]*2:,0] = F1_numpy[:,2]
E1[F1.shape[0]*2:,1] = F1_numpy[:,0]
E1 = torch.from_numpy(E1)
pyDeform.SolveLinear(V1_deform, F1, E1, src_to_src, V1_copy, 1, 1)
pyDeform.DenormalizeByTemplate(V1_deform, param_id2.tolist())
pyDeform.SaveMesh('%s/src-%02d.obj'%(output_path,i), V1_deform, F1)
V2_copy = V2.clone()
pyDeform.NormalizeByTemplate(V2_copy, param_id2.tolist())
V2_origin = V2_copy.clone()
for i in range(26):
V2_deform = V2_origin.clone()
V2_copy = V2_origin.clone().to(device)
if i != 25:
V2_copy = func.integrate(V2_copy, 1, i * 0.04, device)
V2_copy = torch.from_numpy(V2_copy.data.cpu().numpy())
src_to_src = torch.from_numpy(np.array([i for i in range(V2_origin.shape[0])]).astype('int32'))