def eval_colllision(self):
coll_list = []
cont_list = []
for ii in range(8000):
filename = os.path.join(self.input_data_file, 'body_gen_{:06d}.pkl'.format(ii))
if not os.path.exists(filename):
continue
with open(filename, 'rb') as f:
body_param_input = pickle.load(f)
xh, self.cam_ext, self.cam_int= BodyParamParser.body_params_parse_fitting(body_param_input)
xhr = GeometryTransformer.convert_to_6D_rot(xh)
self.xhr_rec = xhr
T_mat = np.eye(4)
T_mat[1,:] = np.array([0,-1,0,0])
T_mat[2,:] = np.array([0,0,-1,0])
T_mat = torch.tensor(T_mat, dtype=torch.float32, device=self.device)
T_mat = T_mat.unsqueeze(0)
trans = torch.matmul(self.cam_ext[:1],T_mat)
loss_rec, loss_vposer, loss_contact, loss_collision = self.cal_loss(xhr, trans)
coll_list.append(loss_collision.item())
cont_list.append(loss_contact.item())
return coll_list, cont_list
def fitting(self, input_data_file):
with open(input_data_file, 'rb') as f:
body_param_input = pickle.load(f)
xh, self.cam_ext, self.cam_int = BodyParamParser.body_params_parse_fitting(
body_param_input)
xhr = GeometryTransformer.convert_to_6D_rot(xh)
self.xhr_rec.data = xhr.clone()
for ii in range(self.num_iter):
self.optimizer.zero_grad()
loss_rec, loss_vposer, loss_contact, loss_collision = self.cal_loss(
xhr, self.cam_ext)
loss = loss_rec + loss_vposer + loss_contact + loss_collision
if self.verbose:
print(
'[INFO][fitting] iter={:d}, l_rec={:f}, l_vposer={:f}, l_contact={:f}, l_collision={:f}'
.format(ii, loss_rec.item(), loss_vposer.item(),
loss_contact.item(), loss_collision.item()))
loss.backward(retain_graph=True)
self.optimizer.step()
print('[INFO][fitting] fitting finish, returning optimal value')
xh_rec = GeometryTransformer.convert_to_3D_rot(self.xhr_rec)
return xh_rec
def fitting(fittingconfig):
input_data_file = fittingconfig['input_data_file']
with open(input_data_file, 'rb') as f:
body_param_input = pickle.load(f)
xh, _, _ = BodyParamParser.body_params_parse_fitting(body_param_input)
return xh.detach().cpu().numpy()
def fitting(self, input_data_file):
with open(input_data_file, 'rb') as f:
body_param_input = pickle.load(f)
xh, self.cam_ext, self.cam_int= BodyParamParser.body_params_parse_fitting(body_param_input)
xhr = GeometryTransformer.convert_to_6D_rot(xh)
self.xhr_rec.data = xhr.clone()
T_mat = np.eye(4)
T_mat[1,:] = np.array([0,-1,0,0])
T_mat[2,:] = np.array([0,0,-1,0])
T_mat = torch.tensor(T_mat, dtype=torch.float32, device=self.device)
T_mat = T_mat.unsqueeze(0)
trans = torch.matmul(self.cam_ext[:1],T_mat)
for ii in range(self.num_iter):
self.optimizer.zero_grad()
loss_rec, loss_vposer, loss_contact, loss_collision = self.cal_loss(xhr, trans)
loss = loss_rec + loss_vposer + loss_contact + loss_collision
if self.verbose:
print('[INFO][fitting] iter={:d}, l_rec={:f}, l_vposer={:f}, l_contact={:f}, l_collision={:f}'.format(
ii, loss_rec.item(), loss_vposer.item(),
loss_contact.item(), loss_collision.item()) )
loss.backward(retain_graph=True)
self.optimizer.step()
print('[INFO][fitting] fitting finish, returning optimal value')
xh_rec = GeometryTransformer.convert_to_3D_rot(self.xhr_rec)
return xh_rec