| 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__()
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| self.factor = factor
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| self.mode = mode
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| assert self.mode in ['mean', 'max'], f'Invalid mode: {self.mode}'
|
|
|
| def forward(self, x: SparseTensor) -> SparseTensor:
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| cache = x.get_spatial_cache(f'downsample_{self.factor}')
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| if cache is None:
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| DIM = x.coords.shape[-1] - 1
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|
|
| coord = list(x.coords.unbind(dim=-1))
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| for i in range(DIM):
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| 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]
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| code = sum([c * o for c, o in zip(coord, OFFSET)])
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| code, idx = code.unique(return_inverse=True)
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|
|
| new_coords = torch.stack(
|
| [code // OFFSET[0]] +
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| [(code // OFFSET[i+1]) % MAX[i] for i in range(DIM)],
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| dim=-1
|
| )
|
| else:
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| 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),
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| dim=0,
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| index=idx.unsqueeze(1).expand(-1, x.feats.shape[1]),
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| src=x.feats,
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| reduce=self.mode,
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| include_self=False,
|
| )
|
| out = SparseTensor(new_feats, new_coords, x._shape)
|
| out._scale = tuple([s * self.factor for s in x._scale])
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| out._spatial_cache = x._spatial_cache
|
|
|
| if cache is None:
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| x.register_spatial_cache(f'downsample_{self.factor}', (new_coords, idx))
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| out.register_spatial_cache(f'upsample_{self.factor}', (x.coords, idx))
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| out.register_spatial_cache(f'shape', torch.Size(MAX))
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| if self.training:
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| subidx = x.coords[:, 1:] % self.factor
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| 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)
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| subdivision[idx, subidx] = True
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| 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()
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| 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):
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| 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
|
| |
