(self,
init_cfg=None,
num_betas: int = 10,
num_expression_coeffs: int = 10,
mean_pose_path: str = '',
shape_mean_path: str = '',
pose_param_conf: list = None,
input_feat_dim: int = 2048,
regressor_cfg: dict = None,
camera_cfg: dict = None)
| 296 | class ExPoseBodyHead(ExPoseHead): |
| 297 | """Head for ExPose Body Model.""" |
| 298 | def __init__(self, |
| 299 | init_cfg=None, |
| 300 | num_betas: int = 10, |
| 301 | num_expression_coeffs: int = 10, |
| 302 | mean_pose_path: str = '', |
| 303 | shape_mean_path: str = '', |
| 304 | pose_param_conf: list = None, |
| 305 | input_feat_dim: int = 2048, |
| 306 | regressor_cfg: dict = None, |
| 307 | camera_cfg: dict = None): |
| 308 | super().__init__(init_cfg) |
| 309 | self.num_betas = num_betas |
| 310 | self.num_expression_coeffs = num_expression_coeffs |
| 311 | # poses |
| 312 | self.pose_param_conf = pose_param_conf |
| 313 | mean_poses_dict = {} |
| 314 | if os.path.exists(mean_pose_path): |
| 315 | with open(mean_pose_path, 'rb') as f: |
| 316 | mean_poses_dict = pickle.load(f) |
| 317 | start, mean_lst = self.load_param_decoder(mean_poses_dict) |
| 318 | |
| 319 | # shape |
| 320 | if os.path.exists(shape_mean_path): |
| 321 | shape_mean = torch.from_numpy( |
| 322 | np.load(shape_mean_path, |
| 323 | allow_pickle=True)).to(dtype=torch.float32).reshape( |
| 324 | 1, -1)[:, :num_betas].reshape(-1) |
| 325 | else: |
| 326 | shape_mean = torch.zeros([num_betas], dtype=torch.float32) |
| 327 | shape_idxs = list(range(start, start + num_betas)) |
| 328 | self.register_buffer('shape_idxs', |
| 329 | torch.tensor(shape_idxs, dtype=torch.long)) |
| 330 | start += num_betas |
| 331 | mean_lst.append(shape_mean.view(-1)) |
| 332 | |
| 333 | # expression |
| 334 | expression_mean = torch.zeros([num_expression_coeffs], |
| 335 | dtype=torch.float32) |
| 336 | expression_idxs = list(range(start, start + num_expression_coeffs)) |
| 337 | self.register_buffer('expression_idxs', |
| 338 | torch.tensor(expression_idxs, dtype=torch.long)) |
| 339 | start += num_expression_coeffs |
| 340 | mean_lst.append(expression_mean.view(-1)) |
| 341 | |
| 342 | # camera |
| 343 | mean, dim, scale_func = self.get_camera_param(camera_cfg) |
| 344 | self.camera_scale_func = scale_func |
| 345 | camera_idxs = list(range(start, start + dim)) |
| 346 | self.register_buffer('camera_idxs', |
| 347 | torch.tensor(camera_idxs, dtype=torch.long)) |
| 348 | start += dim |
| 349 | mean_lst.append(mean) |
| 350 | |
| 351 | param_mean = torch.cat(mean_lst).view(1, -1) |
| 352 | self.load_regressor(input_feat_dim, param_mean, regressor_cfg) |
| 353 | |
| 354 | def forward(self, features): |
| 355 | """Forward function of ExPose Body Head. |
nothing calls this directly
no test coverage detected