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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: |
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out._spatial_cache = x._spatial_cache |
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return out |
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