(self,
init_cfg=None,
num_betas: int = 10,
mean_pose_path: str = '',
pose_param_conf: list = None,
input_feat_dim: int = 2048,
regressor_cfg: dict = None,
camera_cfg: dict = None)
| 381 | class ExPoseHandHead(ExPoseHead): |
| 382 | """Head for ExPose Hand Model.""" |
| 383 | def __init__(self, |
| 384 | init_cfg=None, |
| 385 | num_betas: int = 10, |
| 386 | mean_pose_path: str = '', |
| 387 | pose_param_conf: list = None, |
| 388 | input_feat_dim: int = 2048, |
| 389 | regressor_cfg: dict = None, |
| 390 | camera_cfg: dict = None): |
| 391 | super().__init__(init_cfg) |
| 392 | self.num_betas = num_betas |
| 393 | # poses |
| 394 | self.pose_param_conf = pose_param_conf |
| 395 | mean_poses_dict = {} |
| 396 | if os.path.exists(mean_pose_path): |
| 397 | with open(mean_pose_path, 'rb') as f: |
| 398 | mean_poses_dict = pickle.load(f) |
| 399 | start, mean_lst = self.load_param_decoder(mean_poses_dict) |
| 400 | |
| 401 | shape_mean = torch.zeros([num_betas], dtype=torch.float32) |
| 402 | shape_idxs = list(range(start, start + num_betas)) |
| 403 | self.register_buffer('shape_idxs', |
| 404 | torch.tensor(shape_idxs, dtype=torch.long)) |
| 405 | start += num_betas |
| 406 | mean_lst.append(shape_mean.view(-1)) |
| 407 | |
| 408 | # camera |
| 409 | mean, dim, scale_func = self.get_camera_param(camera_cfg) |
| 410 | self.camera_scale_func = scale_func |
| 411 | camera_idxs = list(range(start, start + dim)) |
| 412 | self.register_buffer('camera_idxs', |
| 413 | torch.tensor(camera_idxs, dtype=torch.long)) |
| 414 | start += dim |
| 415 | mean_lst.append(mean) |
| 416 | |
| 417 | param_mean = torch.cat(mean_lst).view(1, -1) |
| 418 | self.load_regressor(input_feat_dim, param_mean, regressor_cfg) |
| 419 | self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) |
| 420 | |
| 421 | def forward(self, features, cond=None): |
| 422 | """Forward function of ExPose Hand Head. |
nothing calls this directly
no test coverage detected