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