wehub-resource-sync
94 lines
4.3 KiB
Python
94 lines
4.3 KiB
Python
from typing import *
|
|
import torch
|
|
import torch.nn as nn
|
|
from .. import SparseTensor
|
|
|
|
|
|
class SparseSpatial2Channel(nn.Module):
|
|
"""
|
|
Downsample a sparse tensor by a factor of `factor`.
|
|
Implemented as rearranging its features from spatial to channel.
|
|
"""
|
|
def __init__(self, factor: int = 2):
|
|
super(SparseSpatial2Channel, self).__init__()
|
|
self.factor = factor
|
|
|
|
def forward(self, x: SparseTensor) -> SparseTensor:
|
|
DIM = x.coords.shape[-1] - 1
|
|
cache = x.get_spatial_cache(f'spatial2channel_{self.factor}')
|
|
if cache is None:
|
|
coord = list(x.coords.unbind(dim=-1))
|
|
for i in range(DIM):
|
|
coord[i+1] = coord[i+1] // self.factor
|
|
subidx = x.coords[:, 1:] % self.factor
|
|
subidx = sum([subidx[..., i] * self.factor ** i for i in range(DIM)])
|
|
|
|
MAX = [(s + self.factor - 1) // self.factor for s in x.spatial_shape]
|
|
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_coords = torch.stack(
|
|
[code // OFFSET[0]] +
|
|
[(code // OFFSET[i+1]) % MAX[i] for i in range(DIM)],
|
|
dim=-1
|
|
)
|
|
else:
|
|
new_coords, idx, subidx = cache
|
|
|
|
new_feats = torch.zeros(new_coords.shape[0] * self.factor ** DIM, x.feats.shape[1], device=x.feats.device, dtype=x.feats.dtype)
|
|
new_feats[idx * self.factor ** DIM + subidx] = x.feats
|
|
|
|
out = SparseTensor(new_feats.reshape(new_coords.shape[0], -1), new_coords, None if x._shape is None else torch.Size([x._shape[0], x._shape[1] * self.factor ** DIM]))
|
|
out._scale = tuple([s * self.factor for s in x._scale])
|
|
out._spatial_cache = x._spatial_cache
|
|
|
|
if cache is None:
|
|
x.register_spatial_cache(f'spatial2channel_{self.factor}', (new_coords, idx, subidx))
|
|
out.register_spatial_cache(f'channel2spatial_{self.factor}', (x.coords, idx, subidx))
|
|
out.register_spatial_cache(f'shape', torch.Size(MAX))
|
|
if self.training:
|
|
subdivision = torch.zeros((new_coords.shape[0], self.factor ** DIM), device=x.device, dtype=torch.bool)
|
|
subdivision[idx, subidx] = True
|
|
out.register_spatial_cache(f'subdivision', subdivision)
|
|
|
|
return out
|
|
|
|
|
|
class SparseChannel2Spatial(nn.Module):
|
|
"""
|
|
Upsample a sparse tensor by a factor of `factor`.
|
|
Implemented as rearranging its features from channel to spatial.
|
|
"""
|
|
def __init__(self, factor: int = 2):
|
|
super(SparseChannel2Spatial, 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'channel2spatial_{self.factor}')
|
|
if cache is None:
|
|
if subdivision is None:
|
|
raise ValueError('Cache not found. Provide subdivision tensor or pair SparseChannel2Spatial with SparseSpatial2Channel.')
|
|
else:
|
|
sub = subdivision.feats # [N, self.factor ** DIM]
|
|
N_leaf = sub.sum(dim=-1) # [N]
|
|
subidx = sub.nonzero()[:, -1]
|
|
new_coords = x.coords.clone().detach()
|
|
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):
|
|
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, subidx = cache
|
|
|
|
x_feats = x.feats.reshape(x.feats.shape[0] * self.factor ** DIM, -1)
|
|
new_feats = x_feats[idx * self.factor ** DIM + subidx]
|
|
out = SparseTensor(new_feats, new_coords, None if x._shape is None else torch.Size([x._shape[0], x._shape[1] // self.factor ** DIM]))
|
|
out._scale = tuple([s / self.factor for s in x._scale])
|
|
if cache is not None: # only keep cache when subdiv following it
|
|
out._spatial_cache = x._spatial_cache
|
|
return out
|