| | from typing import *
|
| | import torch
|
| | import torch.nn as nn
|
| | from . import SparseTensor
|
| |
|
| | __all__ = [
|
| | 'SparseDownsample',
|
| | 'SparseUpsample',
|
| | 'SparseSubdivide'
|
| | ]
|
| |
|
| |
|
| | class SparseDownsample(nn.Module):
|
| | """
|
| | Downsample a sparse tensor by a factor of `factor`.
|
| | Implemented as average pooling.
|
| | """
|
| | def __init__(self, factor: Union[int, Tuple[int, ...], List[int]]):
|
| | super(SparseDownsample, self).__init__()
|
| | self.factor = tuple(factor) if isinstance(factor, (list, tuple)) else factor
|
| |
|
| | def forward(self, input: SparseTensor) -> SparseTensor:
|
| | DIM = input.coords.shape[-1] - 1
|
| | factor = self.factor if isinstance(self.factor, tuple) else (self.factor,) * DIM
|
| | assert DIM == len(factor), 'Input coordinates must have the same dimension as the downsample factor.'
|
| |
|
| | coord = list(input.coords.unbind(dim=-1))
|
| | for i, f in enumerate(factor):
|
| | coord[i+1] = coord[i+1] // f
|
| |
|
| | MAX = [coord[i+1].max().item() + 1 for i in range(DIM)]
|
| | 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_feats = torch.scatter_reduce(
|
| | torch.zeros(code.shape[0], input.feats.shape[1], device=input.feats.device, dtype=input.feats.dtype),
|
| | dim=0,
|
| | index=idx.unsqueeze(1).expand(-1, input.feats.shape[1]),
|
| | src=input.feats,
|
| | reduce='mean'
|
| | )
|
| | new_coords = torch.stack(
|
| | [code // OFFSET[0]] +
|
| | [(code // OFFSET[i+1]) % MAX[i] for i in range(DIM)],
|
| | dim=-1
|
| | )
|
| | out = SparseTensor(new_feats, new_coords, input.shape,)
|
| | out._scale = tuple([s // f for s, f in zip(input._scale, factor)])
|
| | out._spatial_cache = input._spatial_cache
|
| |
|
| | out.register_spatial_cache(f'upsample_{factor}_coords', input.coords)
|
| | out.register_spatial_cache(f'upsample_{factor}_layout', input.layout)
|
| | out.register_spatial_cache(f'upsample_{factor}_idx', idx)
|
| |
|
| | return out
|
| |
|
| |
|
| | class SparseUpsample(nn.Module):
|
| | """
|
| | Upsample a sparse tensor by a factor of `factor`.
|
| | Implemented as nearest neighbor interpolation.
|
| | """
|
| | def __init__(self, factor: Union[int, Tuple[int, int, int], List[int]]):
|
| | super(SparseUpsample, self).__init__()
|
| | self.factor = tuple(factor) if isinstance(factor, (list, tuple)) else factor
|
| |
|
| | def forward(self, input: SparseTensor) -> SparseTensor:
|
| | DIM = input.coords.shape[-1] - 1
|
| | factor = self.factor if isinstance(self.factor, tuple) else (self.factor,) * DIM
|
| | assert DIM == len(factor), 'Input coordinates must have the same dimension as the upsample factor.'
|
| |
|
| | new_coords = input.get_spatial_cache(f'upsample_{factor}_coords')
|
| | new_layout = input.get_spatial_cache(f'upsample_{factor}_layout')
|
| | idx = input.get_spatial_cache(f'upsample_{factor}_idx')
|
| | if any([x is None for x in [new_coords, new_layout, idx]]):
|
| | raise ValueError('Upsample cache not found. SparseUpsample must be paired with SparseDownsample.')
|
| | new_feats = input.feats[idx]
|
| | out = SparseTensor(new_feats, new_coords, input.shape, new_layout)
|
| | out._scale = tuple([s * f for s, f in zip(input._scale, factor)])
|
| | out._spatial_cache = input._spatial_cache
|
| | return out
|
| |
|
| | class SparseSubdivide(nn.Module):
|
| | """
|
| | Upsample a sparse tensor by a factor of `factor`.
|
| | Implemented as nearest neighbor interpolation.
|
| | """
|
| | def __init__(self):
|
| | super(SparseSubdivide, self).__init__()
|
| |
|
| | def forward(self, input: SparseTensor) -> SparseTensor:
|
| | DIM = input.coords.shape[-1] - 1
|
| |
|
| | n_cube = torch.ones([2] * DIM, device=input.device, dtype=torch.int)
|
| | n_coords = torch.nonzero(n_cube)
|
| | n_coords = torch.cat([torch.zeros_like(n_coords[:, :1]), n_coords], dim=-1)
|
| | factor = n_coords.shape[0]
|
| | assert factor == 2 ** DIM
|
| |
|
| | new_coords = input.coords.clone()
|
| | new_coords[:, 1:] *= 2
|
| | new_coords = new_coords.unsqueeze(1) + n_coords.unsqueeze(0).to(new_coords.dtype)
|
| |
|
| | new_feats = input.feats.unsqueeze(1).expand(input.feats.shape[0], factor, *input.feats.shape[1:])
|
| | out = SparseTensor(new_feats.flatten(0, 1), new_coords.flatten(0, 1), input.shape)
|
| | out._scale = input._scale * 2
|
| | out._spatial_cache = input._spatial_cache
|
| | return out
|
| |
|
| |
|
