wehub-resource-sync
109 lines
4.2 KiB
Python
109 lines
4.2 KiB
Python
from typing import *
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import torch
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import torch.nn as nn
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from .. import SparseTensor
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__all__ = [
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'SparseDownsample',
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'SparseUpsample',
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]
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class SparseDownsample(nn.Module):
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"""
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Downsample a sparse tensor by a factor of `factor`.
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Implemented as average pooling.
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"""
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def __init__(self, factor: int, mode: Literal['mean', 'max'] = 'mean'):
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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}'
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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
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MAX = [(s + self.factor - 1) // self.factor for s in x.spatial_shape]
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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(
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[code // OFFSET[0]] +
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[(code // OFFSET[i+1]) % MAX[i] for i in range(DIM)],
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dim=-1
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)
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else:
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new_coords, idx = cache
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new_feats = torch.scatter_reduce(
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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,
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)
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out = SparseTensor(new_feats, new_coords, x._shape)
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out._scale = tuple([s * self.factor for s in x._scale])
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out._spatial_cache = x._spatial_cache
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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)])
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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)
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return out
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class SparseUpsample(nn.Module):
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"""
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Upsample a sparse tensor by a factor of `factor`.
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Implemented as nearest neighbor interpolation.
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"""
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def __init__(
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self, factor: int
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):
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super(SparseUpsample, self).__init__()
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self.factor = factor
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def forward(self, x: SparseTensor, subdivision: Optional[SparseTensor] = None) -> SparseTensor:
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DIM = x.coords.shape[-1] - 1
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cache = x.get_spatial_cache(f'upsample_{self.factor}')
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if cache is None:
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if subdivision is None:
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raise ValueError('Cache not found. Provide subdivision tensor or pair SparseUpsample with SparseDownsample.')
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else:
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sub = subdivision.feats
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N_leaf = sub.sum(dim=-1)
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subidx = sub.nonzero()[:, -1]
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new_coords = x.coords.clone().detach()
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new_coords[:, 1:] *= self.factor
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new_coords = torch.repeat_interleave(new_coords, N_leaf, dim=0, output_size=subidx.shape[0])
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for i in range(DIM):
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new_coords[:, i+1] += subidx // self.factor ** i % self.factor
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idx = torch.repeat_interleave(torch.arange(x.coords.shape[0], device=x.device), N_leaf, dim=0, output_size=subidx.shape[0])
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else:
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new_coords, idx = cache
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new_feats = x.feats[idx]
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out = SparseTensor(new_feats, new_coords, x._shape)
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out._scale = tuple([s / self.factor for s in x._scale])
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if cache is not None: # only keep cache when subdiv following it
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out._spatial_cache = x._spatial_cache
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return out
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