| from typing import * |
| import torch |
| import torch.nn as nn |
| from .. import SparseTensor |
|
|
| __all__ = [ |
| 'SparseDownsample', |
| 'SparseUpsample', |
| ] |
|
|
|
|
| class SparseDownsample(nn.Module): |
| """ |
| Downsample a sparse tensor by a factor of `factor`. |
| Implemented as average pooling. |
| """ |
| def __init__(self, factor: int, mode: Literal['mean', 'max'] = 'mean'): |
| super(SparseDownsample, self).__init__() |
| self.factor = factor |
| self.mode = mode |
| assert self.mode in ['mean', 'max'], f'Invalid mode: {self.mode}' |
|
|
| def forward(self, x: SparseTensor) -> SparseTensor: |
| cache = x.get_spatial_cache(f'downsample_{self.factor}') |
| if cache is None: |
| DIM = x.coords.shape[-1] - 1 |
|
|
| coord = list(x.coords.unbind(dim=-1)) |
| for i in range(DIM): |
| coord[i+1] = coord[i+1] // self.factor |
|
|
| MAX = [(s + self.factor - 1) // self.factor for s in x.spatial_shape] |
| OFFSET = torch.cumprod(torch.tensor(MAX[::-1]), 0).tolist()[::-1] + [1] |
| code = sum([c * o for c, o in zip(coord, OFFSET)]) |
| code, idx = code.unique(return_inverse=True) |
|
|
| new_coords = torch.stack( |
| [code // OFFSET[0]] + |
| [(code // OFFSET[i+1]) % MAX[i] for i in range(DIM)], |
| dim=-1 |
| ) |
| else: |
| new_coords, idx = cache |
| |
| new_feats = torch.scatter_reduce( |
| torch.zeros(new_coords.shape[0], x.feats.shape[1], device=x.feats.device, dtype=x.feats.dtype), |
| dim=0, |
| index=idx.unsqueeze(1).expand(-1, x.feats.shape[1]), |
| src=x.feats, |
| reduce=self.mode, |
| include_self=False, |
| ) |
| out = SparseTensor(new_feats, new_coords, x._shape) |
| out._scale = tuple([s * self.factor for s in x._scale]) |
| out._spatial_cache = x._spatial_cache |
| |
| if cache is None: |
| x.register_spatial_cache(f'downsample_{self.factor}', (new_coords, idx)) |
| out.register_spatial_cache(f'upsample_{self.factor}', (x.coords, idx)) |
| out.register_spatial_cache(f'shape', torch.Size(MAX)) |
| if self.training: |
| subidx = x.coords[:, 1:] % self.factor |
| subidx = sum([subidx[..., i] * self.factor ** i for i in range(DIM)]) |
| subdivision = torch.zeros((new_coords.shape[0], self.factor ** DIM), device=x.device, dtype=torch.bool) |
| subdivision[idx, subidx] = True |
| out.register_spatial_cache(f'subdivision', subdivision) |
|
|
| return out |
|
|
|
|
| class SparseUpsample(nn.Module): |
| """ |
| Upsample a sparse tensor by a factor of `factor`. |
| Implemented as nearest neighbor interpolation. |
| """ |
| def __init__( |
| self, factor: int |
| ): |
| super(SparseUpsample, self).__init__() |
| self.factor = factor |
|
|
| def forward(self, x: SparseTensor, subdivision: Optional[SparseTensor] = None) -> SparseTensor: |
| DIM = x.coords.shape[-1] - 1 |
|
|
| cache = x.get_spatial_cache(f'upsample_{self.factor}') |
| if cache is None: |
| if subdivision is None: |
| raise ValueError('Cache not found. Provide subdivision tensor or pair SparseUpsample with SparseDownsample.') |
| else: |
| sub = subdivision.feats |
| N_leaf = sub.sum(dim=-1) |
| subidx = sub.nonzero()[:, -1] |
| new_coords = x.coords.clone().detach() |
| new_coords[:, 1:] *= self.factor |
| new_coords = torch.repeat_interleave(new_coords, N_leaf, dim=0, output_size=subidx.shape[0]) |
| for i in range(DIM): |
| new_coords[:, i+1] += subidx // self.factor ** i % self.factor |
| idx = torch.repeat_interleave(torch.arange(x.coords.shape[0], device=x.device), N_leaf, dim=0, output_size=subidx.shape[0]) |
| else: |
| new_coords, idx = cache |
| |
| new_feats = x.feats[idx] |
| out = SparseTensor(new_feats, new_coords, x._shape) |
| out._scale = tuple([s / self.factor for s in x._scale]) |
| if cache is not None: |
| out._spatial_cache = x._spatial_cache |
| |
| return out |
| |
