Concat two values from two different HumanData. Args: key (Any): The common key of the two values. value_0 (Any): Value from 0. value_1 (Any): Value from 1. dim_0 (Union[None, int]):
(cls, key: Any, value_0: Any, value_1: Any,
dim_0: Union[None, int], dim_1: Union[None,
int])
| 1266 | |
| 1267 | @classmethod |
| 1268 | def __concat_value__(cls, key: Any, value_0: Any, value_1: Any, |
| 1269 | dim_0: Union[None, int], dim_1: Union[None, |
| 1270 | int]) -> dict: |
| 1271 | """Concat two values from two different HumanData. |
| 1272 | |
| 1273 | Args: |
| 1274 | key (Any): |
| 1275 | The common key of the two values. |
| 1276 | value_0 (Any): |
| 1277 | Value from 0. |
| 1278 | value_1 (Any): |
| 1279 | Value from 1. |
| 1280 | dim_0 (Union[None, int]): |
| 1281 | The dim for concat and slice. None for N/A. |
| 1282 | dim_1 (Union[None, int]): |
| 1283 | The dim for concat and slice. None for N/A. |
| 1284 | |
| 1285 | Returns: |
| 1286 | dict: |
| 1287 | Dict for concatenated result. |
| 1288 | """ |
| 1289 | ret_dict = {} |
| 1290 | if dim_0 is None or dim_1 is None: |
| 1291 | ret_dict[f'{key}_0'] = value_0 |
| 1292 | ret_dict[f'{key}_1'] = value_1 |
| 1293 | elif isinstance(value_0, list): |
| 1294 | ret_dict[key] = value_0 + value_1 |
| 1295 | # elif isinstance(value_0, np.ndarray): |
| 1296 | else: |
| 1297 | ret_dict[key] = np.concatenate((value_0, value_1), axis=dim_0) |
| 1298 | return ret_dict |
| 1299 | |
| 1300 | @classmethod |
| 1301 | def __add_zero_pad__(cls, compressed_array: np.ndarray, |
no test coverage detected