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c83d2d6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 | """Sparse inference primitives extracted for the released SPAC VQ-VAE."""
from typing import *
import torch
__all__ = ['SparseTensor', 'sparse_batch_broadcast', 'sparse_batch_op', 'sparse_cat', 'sparse_unbind']
def _backend():
return 'torchsparse'
def _sparse_tensor_data_class():
import torchsparse
return (torchsparse.SparseTensor, 'torchsparse')
class SparseTensor:
@overload
def __init__(self, feats: torch.Tensor, coords: torch.Tensor, shape: Optional[torch.Size]=None, layout: Optional[List[slice]]=None, **kwargs):
...
@overload
def __init__(self, data, shape: Optional[torch.Size]=None, layout: Optional[List[slice]]=None, **kwargs):
...
def __init__(self, *args, **kwargs):
(SparseTensorData, backend) = _sparse_tensor_data_class()
method_id = 0
if len(args) != 0:
method_id = 0 if isinstance(args[0], torch.Tensor) else 1
else:
method_id = 1 if 'data' in kwargs else 0
if method_id == 0:
(feats, coords, shape, layout) = args + (None,) * (4 - len(args))
if 'feats' in kwargs:
feats = kwargs['feats']
del kwargs['feats']
if 'coords' in kwargs:
coords = kwargs['coords']
del kwargs['coords']
if 'shape' in kwargs:
shape = kwargs['shape']
del kwargs['shape']
if 'layout' in kwargs:
layout = kwargs['layout']
del kwargs['layout']
if shape is None:
shape = self.__cal_shape(feats, coords)
if layout is None:
layout = self.__cal_layout(coords, shape[0])
self.data = SparseTensorData(feats, coords, **kwargs)
elif method_id == 1:
(data, shape, layout) = args + (None,) * (3 - len(args))
if 'data' in kwargs:
data = kwargs['data']
del kwargs['data']
if 'shape' in kwargs:
shape = kwargs['shape']
del kwargs['shape']
if 'layout' in kwargs:
layout = kwargs['layout']
del kwargs['layout']
self.data = data
if shape is None:
shape = self.__cal_shape(self.feats, self.coords)
if layout is None:
layout = self.__cal_layout(self.coords, shape[0])
self._shape = shape
self._layout = layout
self._scale = kwargs.get('scale', (1, 1, 1))
self._spatial_cache = kwargs.get('spatial_cache', {})
def __cal_shape(self, feats, coords):
shape = []
shape.append(coords[:, 0].max().item() + 1)
shape.extend([*feats.shape[1:]])
return torch.Size(shape)
def __cal_layout(self, coords, batch_size):
seq_len = torch.bincount(coords[:, 0], minlength=batch_size)
offset = torch.cumsum(seq_len, dim=0)
layout = [slice((offset[i] - seq_len[i]).item(), offset[i].item()) for i in range(batch_size)]
return layout
@property
def shape(self) -> torch.Size:
return self._shape
def dim(self) -> int:
return len(self.shape)
@property
def layout(self) -> List[slice]:
return self._layout
@property
def feats(self) -> torch.Tensor:
backend = _backend()
return self.data.F
@feats.setter
def feats(self, value: torch.Tensor):
backend = _backend()
self.data.F = value
@property
def coords(self) -> torch.Tensor:
backend = _backend()
return self.data.C
@coords.setter
def coords(self, value: torch.Tensor):
backend = _backend()
self.data.C = value
@property
def dtype(self):
return self.feats.dtype
@property
def device(self):
return self.feats.device
@property
def is_quantized(self) -> bool:
feats = self.feats
return bool(getattr(feats, 'is_quantized', False))
@overload
def to(self, dtype: torch.dtype) -> 'SparseTensor':
...
@overload
def to(self, device: Optional[Union[str, torch.device]]=None, dtype: Optional[torch.dtype]=None) -> 'SparseTensor':
...
def to(self, *args, **kwargs) -> 'SparseTensor':
device = None
dtype = None
if len(args) == 2:
(device, dtype) = args
elif len(args) == 1:
if isinstance(args[0], torch.dtype):
dtype = args[0]
else:
device = args[0]
if 'dtype' in kwargs:
assert dtype is None, "to() received multiple values for argument 'dtype'"
dtype = kwargs['dtype']
if 'device' in kwargs:
assert device is None, "to() received multiple values for argument 'device'"
device = kwargs['device']
new_feats = self.feats.to(device=device, dtype=dtype)
new_coords = self.coords.to(device=device)
return self.replace(new_feats, new_coords)
def type(self, dtype):
new_feats = self.feats.type(dtype)
return self.replace(new_feats)
def cpu(self) -> 'SparseTensor':
new_feats = self.feats.cpu()
new_coords = self.coords.cpu()
return self.replace(new_feats, new_coords)
def cuda(self) -> 'SparseTensor':
new_feats = self.feats.cuda()
new_coords = self.coords.cuda()
return self.replace(new_feats, new_coords)
def half(self) -> 'SparseTensor':
new_feats = self.feats.half()
return self.replace(new_feats)
def float(self) -> 'SparseTensor':
new_feats = self.feats.float()
return self.replace(new_feats)
def detach(self) -> 'SparseTensor':
new_coords = self.coords.detach()
new_feats = self.feats.detach()
return self.replace(new_feats, new_coords)
def dense(self) -> torch.Tensor:
backend = _backend()
return self.data.dense()
def reshape(self, *shape) -> 'SparseTensor':
new_feats = self.feats.reshape(self.feats.shape[0], *shape)
return self.replace(new_feats)
def unbind(self, dim: int) -> List['SparseTensor']:
return sparse_unbind(self, dim)
def replace(self, feats: torch.Tensor, coords: Optional[torch.Tensor]=None) -> 'SparseTensor':
new_shape = [self.shape[0]]
new_shape.extend(feats.shape[1:])
(SparseTensorData, backend) = _sparse_tensor_data_class()
new_data = SparseTensorData(feats=feats, coords=self.data.coords if coords is None else coords, stride=self.data.stride)
if hasattr(self.data, '_caches'):
new_data._caches = self.data._caches
else:
new_data.cmaps = self.data.cmaps
new_data.kmaps = self.data.kmaps
eff_coords = new_data.C
new_layout = self.__cal_layout(eff_coords, int(new_shape[0]))
new_tensor = SparseTensor(new_data, shape=torch.Size(new_shape), layout=new_layout, scale=self._scale, spatial_cache=self._spatial_cache)
return new_tensor
@staticmethod
def full(aabb, dim, value, dtype=torch.float32, device=None) -> 'SparseTensor':
(N, C) = dim
x = torch.arange(aabb[0], aabb[3] + 1)
y = torch.arange(aabb[1], aabb[4] + 1)
z = torch.arange(aabb[2], aabb[5] + 1)
coords = torch.stack(torch.meshgrid(x, y, z, indexing='ij'), dim=-1).reshape(-1, 3)
coords = torch.cat([torch.arange(N).view(-1, 1).repeat(1, coords.shape[0]).view(-1, 1), coords.repeat(N, 1)], dim=1).to(dtype=torch.int32, device=device)
feats = torch.full((coords.shape[0], C), value, dtype=dtype, device=device)
return SparseTensor(feats=feats, coords=coords)
def __merge_sparse_cache(self, other: 'SparseTensor') -> dict:
new_cache = {}
for k in set(list(self._spatial_cache.keys()) + list(other._spatial_cache.keys())):
if k in self._spatial_cache:
new_cache[k] = self._spatial_cache[k]
if k in other._spatial_cache:
if k not in new_cache:
new_cache[k] = other._spatial_cache[k]
else:
new_cache[k].update(other._spatial_cache[k])
return new_cache
def __neg__(self) -> 'SparseTensor':
return self.replace(-self.feats)
@staticmethod
def __is_sparse_tensor_like(x) -> bool:
return isinstance(x, SparseTensor) or (not isinstance(x, torch.Tensor) and hasattr(x, 'feats') and hasattr(x, 'coords') and hasattr(x, 'replace') and hasattr(x, '_spatial_cache'))
def __elemwise__(self, other: Union[torch.Tensor, 'SparseTensor'], op: callable) -> 'SparseTensor':
if isinstance(other, torch.Tensor):
try:
other = torch.broadcast_to(other, self.shape)
other = sparse_batch_broadcast(self, other)
except:
pass
other_is_sparse = self.__is_sparse_tensor_like(other)
other_feats = other.feats if other_is_sparse else other
new_feats = op(self.feats, other_feats)
new_tensor = self.replace(new_feats)
if other_is_sparse:
new_tensor._spatial_cache = self.__merge_sparse_cache(other)
return new_tensor
def __add__(self, other: Union[torch.Tensor, 'SparseTensor', float]) -> 'SparseTensor':
return self.__elemwise__(other, torch.add)
def __radd__(self, other: Union[torch.Tensor, 'SparseTensor', float]) -> 'SparseTensor':
return self.__elemwise__(other, torch.add)
def __sub__(self, other: Union[torch.Tensor, 'SparseTensor', float]) -> 'SparseTensor':
return self.__elemwise__(other, torch.sub)
def __rsub__(self, other: Union[torch.Tensor, 'SparseTensor', float]) -> 'SparseTensor':
return self.__elemwise__(other, lambda x, y: torch.sub(y, x))
def __mul__(self, other: Union[torch.Tensor, 'SparseTensor', float]) -> 'SparseTensor':
return self.__elemwise__(other, torch.mul)
def __rmul__(self, other: Union[torch.Tensor, 'SparseTensor', float]) -> 'SparseTensor':
return self.__elemwise__(other, torch.mul)
def __truediv__(self, other: Union[torch.Tensor, 'SparseTensor', float]) -> 'SparseTensor':
return self.__elemwise__(other, torch.div)
def __rtruediv__(self, other: Union[torch.Tensor, 'SparseTensor', float]) -> 'SparseTensor':
return self.__elemwise__(other, lambda x, y: torch.div(y, x))
def __getitem__(self, idx):
if isinstance(idx, int):
idx = [idx]
elif isinstance(idx, slice):
idx = range(*idx.indices(self.shape[0]))
elif isinstance(idx, torch.Tensor):
if idx.dtype == torch.bool:
assert idx.shape == (self.shape[0],), f'Invalid index shape: {idx.shape}'
idx = idx.nonzero().squeeze(1)
elif idx.dtype in [torch.int32, torch.int64]:
assert len(idx.shape) == 1, f'Invalid index shape: {idx.shape}'
else:
raise ValueError(f'Unknown index type: {idx.dtype}')
else:
raise ValueError(f'Unknown index type: {type(idx)}')
coords = []
feats = []
for (new_idx, old_idx) in enumerate(idx):
coords.append(self.coords[self.layout[old_idx]].clone())
coords[-1][:, 0] = new_idx
feats.append(self.feats[self.layout[old_idx]])
coords = torch.cat(coords, dim=0).contiguous()
feats = torch.cat(feats, dim=0).contiguous()
return SparseTensor(feats=feats, coords=coords)
def register_spatial_cache(self, key, value) -> None:
scale_key = str(self._scale)
if scale_key not in self._spatial_cache:
self._spatial_cache[scale_key] = {}
self._spatial_cache[scale_key][key] = value
def get_spatial_cache(self, key=None):
scale_key = str(self._scale)
cur_scale_cache = self._spatial_cache.get(scale_key, {})
if key is None:
return cur_scale_cache
return cur_scale_cache.get(key, None)
def sparse_batch_broadcast(input: SparseTensor, other: torch.Tensor) -> torch.Tensor:
(coords, feats) = (input.coords, input.feats)
broadcasted = torch.zeros_like(feats)
for k in range(input.shape[0]):
broadcasted[input.layout[k]] = other[k]
return broadcasted
def sparse_batch_op(input: SparseTensor, other: torch.Tensor, op: callable=torch.add) -> SparseTensor:
return input.replace(op(input.feats, sparse_batch_broadcast(input, other)))
def sparse_cat(inputs: List[SparseTensor], dim: int=0) -> SparseTensor:
if dim == 0:
start = 0
coords = []
for input in inputs:
coords.append(input.coords.clone())
coords[-1][:, 0] += start
start += input.shape[0]
coords = torch.cat(coords, dim=0)
feats = torch.cat([input.feats for input in inputs], dim=0)
output = SparseTensor(coords=coords, feats=feats)
else:
feats = torch.cat([input.feats for input in inputs], dim=dim)
output = inputs[0].replace(feats)
return output
def sparse_unbind(input: SparseTensor, dim: int) -> List[SparseTensor]:
if dim == 0:
return [input[i] for i in range(input.shape[0])]
else:
feats = input.feats.unbind(dim)
return [input.replace(f) for f in feats]
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