(
self,
num_classes: int, # 1
in_channels: Tuple[int],
out_channels: int,
voxel_size: float,
pts_prune_threshold: int,
train_cfg: Optional[dict] = None,
test_cfg: Optional[dict] = None,
init_cfg: Optional[dict] = None)
| 38 | """ |
| 39 | |
| 40 | def __init__( |
| 41 | self, |
| 42 | num_classes: int, # 1 |
| 43 | in_channels: Tuple[int], |
| 44 | out_channels: int, |
| 45 | voxel_size: float, |
| 46 | pts_prune_threshold: int, |
| 47 | train_cfg: Optional[dict] = None, |
| 48 | test_cfg: Optional[dict] = None, |
| 49 | init_cfg: Optional[dict] = None): |
| 50 | super(MinkNeck, self).__init__(init_cfg) |
| 51 | if ME is None: |
| 52 | raise ImportError( |
| 53 | 'Please follow `get_started.md` to install MinkowskiEngine.`') |
| 54 | self.voxel_size = voxel_size |
| 55 | self.pts_prune_threshold = pts_prune_threshold |
| 56 | self.train_cfg = train_cfg |
| 57 | self.test_cfg = test_cfg |
| 58 | self._init_layers(in_channels, out_channels, num_classes) |
| 59 | |
| 60 | @staticmethod |
| 61 | def _make_block(in_channels: int, out_channels: int) -> nn.Module: |
nothing calls this directly
no test coverage detected