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from typing import *
import torch
import torch.nn as nn
from . import BACKEND, DEBUG
SparseTensorData = None # Lazy import
__all__ = [
'SparseTensor',
'sparse_batch_broadcast',
'sparse_batch_op',
'sparse_cat',
'sparse_unbind',
]
class SparseTensor:
"""
Sparse tensor with support for both torchsparse and spconv backends.
Parameters:
- feats (torch.Tensor): Features of the sparse tensor.
- coords (torch.Tensor): Coordinates of the sparse tensor.
- shape (torch.Size): Shape of the sparse tensor.
- layout (List[slice]): Layout of the sparse tensor for each batch
- data (SparseTensorData): Sparse tensor data used for convolusion
NOTE:
- Data corresponding to a same batch should be contiguous.
- Coords should be in [0, 1023]
"""
@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):
# Lazy import of sparse tensor backend
global SparseTensorData
if SparseTensorData is None:
import importlib
if BACKEND == 'torchsparse':
SparseTensorData = importlib.import_module('torchsparse').SparseTensor
elif BACKEND == 'spconv':
SparseTensorData = importlib.import_module('spconv.pytorch').SparseConvTensor
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])
if BACKEND == 'torchsparse':
self.data = SparseTensorData(feats, coords, **kwargs)
elif BACKEND == 'spconv':
spatial_shape = list(coords.max(0)[0] + 1)[1:]
self.data = SparseTensorData(feats.reshape(feats.shape[0], -1), coords, spatial_shape, shape[0], **kwargs)
self.data._features = feats
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', {})
if DEBUG:
try:
assert self.feats.shape[0] == self.coords.shape[0], f"Invalid feats shape: {self.feats.shape}, coords shape: {self.coords.shape}"
assert self.shape == self.__cal_shape(self.feats, self.coords), f"Invalid shape: {self.shape}"
assert self.layout == self.__cal_layout(self.coords, self.shape[0]), f"Invalid layout: {self.layout}"
for i in range(self.shape[0]):
assert torch.all(self.coords[self.layout[i], 0] == i), f"The data of batch {i} is not contiguous"
except Exception as e:
print('Debugging information:')
print(f"- Shape: {self.shape}")
print(f"- Layout: {self.layout}")
print(f"- Scale: {self._scale}")
print(f"- Coords: {self.coords}")
raise e
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:
if BACKEND == 'torchsparse':
return self.data.F
elif BACKEND == 'spconv':
return self.data.features
@feats.setter
def feats(self, value: torch.Tensor):
if BACKEND == 'torchsparse':
self.data.F = value
elif BACKEND == 'spconv':
self.data.features = value
@property
def coords(self) -> torch.Tensor:
if BACKEND == 'torchsparse':
return self.data.C
elif BACKEND == 'spconv':
return self.data.indices
@coords.setter
def coords(self, value: torch.Tensor):
if BACKEND == 'torchsparse':
self.data.C = value
elif BACKEND == 'spconv':
self.data.indices = value
@property
def dtype(self):
return self.feats.dtype
@property
def device(self):
return self.feats.device
@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:
if BACKEND == 'torchsparse':
return self.data.dense()
elif BACKEND == 'spconv':
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:])
if BACKEND == 'torchsparse':
new_data = SparseTensorData(
feats=feats,
coords=self.data.coords if coords is None else coords,
stride=self.data.stride,
spatial_range=self.data.spatial_range,
)
new_data._caches = self.data._caches
elif BACKEND == 'spconv':
new_data = SparseTensorData(
self.data.features.reshape(self.data.features.shape[0], -1),
self.data.indices,
self.data.spatial_shape,
self.data.batch_size,
self.data.grid,
self.data.voxel_num,
self.data.indice_dict
)
new_data._features = feats
new_data.benchmark = self.data.benchmark
new_data.benchmark_record = self.data.benchmark_record
new_data.thrust_allocator = self.data.thrust_allocator
new_data._timer = self.data._timer
new_data.force_algo = self.data.force_algo
new_data.int8_scale = self.data.int8_scale
if coords is not None:
new_data.indices = coords
new_tensor = SparseTensor(new_data, shape=torch.Size(new_shape), layout=self.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)
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
if isinstance(other, SparseTensor):
other = other.feats
new_feats = op(self.feats, other)
new_tensor = self.replace(new_feats)
if isinstance(other, SparseTensor):
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:
"""
Register a spatial cache.
The spatial cache can be any thing you want to cache.
The registery and retrieval of the cache is based on current scale.
"""
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):
"""
Get a spatial cache.
"""
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:
"""
Broadcast a 1D tensor to a sparse tensor along the batch dimension then perform an operation.
Args:
input (torch.Tensor): 1D tensor to broadcast.
target (SparseTensor): Sparse tensor to broadcast to.
op (callable): Operation to perform after broadcasting. Defaults to torch.add.
"""
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:
"""
Broadcast a 1D tensor to a sparse tensor along the batch dimension then perform an operation.
Args:
input (torch.Tensor): 1D tensor to broadcast.
target (SparseTensor): Sparse tensor to broadcast to.
op (callable): Operation to perform after broadcasting. Defaults to torch.add.
"""
return input.replace(op(input.feats, sparse_batch_broadcast(input, other)))
def sparse_cat(inputs: List[SparseTensor], dim: int = 0) -> SparseTensor:
"""
Concatenate a list of sparse tensors.
Args:
inputs (List[SparseTensor]): List of sparse tensors to concatenate.
"""
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]:
"""
Unbind a sparse tensor along a dimension.
Args:
input (SparseTensor): Sparse tensor to unbind.
dim (int): Dimension to unbind.
"""
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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