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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 BACKEND, DEBUG
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SparseTensorData = None
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__all__ = [
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'SparseTensor',
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'sparse_batch_broadcast',
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'sparse_batch_op',
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'sparse_cat',
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'sparse_unbind',
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]
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class SparseTensor:
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"""
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Sparse tensor with support for both torchsparse and spconv backends.
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Parameters:
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- feats (torch.Tensor): Features of the sparse tensor.
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- coords (torch.Tensor): Coordinates of the sparse tensor.
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- shape (torch.Size): Shape of the sparse tensor.
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- layout (List[slice]): Layout of the sparse tensor for each batch
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- data (SparseTensorData): Sparse tensor data used for convolusion
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NOTE:
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- Data corresponding to a same batch should be contiguous.
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- Coords should be in [0, 1023]
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"""
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@overload
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def __init__(self, feats: torch.Tensor, coords: torch.Tensor, shape: Optional[torch.Size] = None, layout: Optional[List[slice]] = None, **kwargs): ...
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@overload
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def __init__(self, data, shape: Optional[torch.Size] = None, layout: Optional[List[slice]] = None, **kwargs): ...
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def __init__(self, *args, **kwargs):
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global SparseTensorData
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if SparseTensorData is None:
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import importlib
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if BACKEND == 'torchsparse':
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SparseTensorData = importlib.import_module('torchsparse').SparseTensor
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elif BACKEND == 'spconv':
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SparseTensorData = importlib.import_module('spconv.pytorch').SparseConvTensor
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method_id = 0
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if len(args) != 0:
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method_id = 0 if isinstance(args[0], torch.Tensor) else 1
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else:
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method_id = 1 if 'data' in kwargs else 0
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if method_id == 0:
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feats, coords, shape, layout = args + (None,) * (4 - len(args))
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if 'feats' in kwargs:
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feats = kwargs['feats']
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del kwargs['feats']
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if 'coords' in kwargs:
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coords = kwargs['coords']
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del kwargs['coords']
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if 'shape' in kwargs:
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shape = kwargs['shape']
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del kwargs['shape']
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if 'layout' in kwargs:
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layout = kwargs['layout']
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del kwargs['layout']
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if shape is None:
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shape = self.__cal_shape(feats, coords)
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if layout is None:
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layout = self.__cal_layout(coords, shape[0])
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if BACKEND == 'torchsparse':
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self.data = SparseTensorData(feats, coords, **kwargs)
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elif BACKEND == 'spconv':
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spatial_shape = list(coords.max(0)[0] + 1)[1:]
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self.data = SparseTensorData(feats.reshape(feats.shape[0], -1), coords, spatial_shape, shape[0], **kwargs)
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self.data._features = feats
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elif method_id == 1:
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data, shape, layout = args + (None,) * (3 - len(args))
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if 'data' in kwargs:
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data = kwargs['data']
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del kwargs['data']
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if 'shape' in kwargs:
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shape = kwargs['shape']
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del kwargs['shape']
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if 'layout' in kwargs:
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layout = kwargs['layout']
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del kwargs['layout']
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self.data = data
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if shape is None:
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shape = self.__cal_shape(self.feats, self.coords)
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if layout is None:
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layout = self.__cal_layout(self.coords, shape[0])
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self._shape = shape
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self._layout = layout
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self._scale = kwargs.get('scale', (1, 1, 1))
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self._spatial_cache = kwargs.get('spatial_cache', {})
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if DEBUG:
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try:
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assert self.feats.shape[0] == self.coords.shape[0], f"Invalid feats shape: {self.feats.shape}, coords shape: {self.coords.shape}"
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assert self.shape == self.__cal_shape(self.feats, self.coords), f"Invalid shape: {self.shape}"
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assert self.layout == self.__cal_layout(self.coords, self.shape[0]), f"Invalid layout: {self.layout}"
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for i in range(self.shape[0]):
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assert torch.all(self.coords[self.layout[i], 0] == i), f"The data of batch {i} is not contiguous"
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except Exception as e:
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print('Debugging information:')
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print(f"- Shape: {self.shape}")
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print(f"- Layout: {self.layout}")
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print(f"- Scale: {self._scale}")
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print(f"- Coords: {self.coords}")
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raise e
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def __cal_shape(self, feats, coords):
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shape = []
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shape.append(coords[:, 0].max().item() + 1)
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shape.extend([*feats.shape[1:]])
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return torch.Size(shape)
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def __cal_layout(self, coords, batch_size):
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seq_len = torch.bincount(coords[:, 0], minlength=batch_size)
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offset = torch.cumsum(seq_len, dim=0)
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layout = [slice((offset[i] - seq_len[i]).item(), offset[i].item()) for i in range(batch_size)]
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return layout
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@property
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def shape(self) -> torch.Size:
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return self._shape
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def dim(self) -> int:
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return len(self.shape)
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@property
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def layout(self) -> List[slice]:
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return self._layout
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@property
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def feats(self) -> torch.Tensor:
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if BACKEND == 'torchsparse':
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return self.data.F
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elif BACKEND == 'spconv':
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return self.data.features
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@feats.setter
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def feats(self, value: torch.Tensor):
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if BACKEND == 'torchsparse':
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self.data.F = value
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elif BACKEND == 'spconv':
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self.data.features = value
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@property
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def coords(self) -> torch.Tensor:
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if BACKEND == 'torchsparse':
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return self.data.C
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elif BACKEND == 'spconv':
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return self.data.indices
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@coords.setter
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def coords(self, value: torch.Tensor):
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if BACKEND == 'torchsparse':
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self.data.C = value
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elif BACKEND == 'spconv':
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self.data.indices = value
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@property
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def dtype(self):
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return self.feats.dtype
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@property
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def device(self):
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return self.feats.device
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@overload
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def to(self, dtype: torch.dtype) -> 'SparseTensor': ...
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@overload
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def to(self, device: Optional[Union[str, torch.device]] = None, dtype: Optional[torch.dtype] = None) -> 'SparseTensor': ...
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def to(self, *args, **kwargs) -> 'SparseTensor':
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device = None
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dtype = None
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if len(args) == 2:
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device, dtype = args
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elif len(args) == 1:
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if isinstance(args[0], torch.dtype):
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dtype = args[0]
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else:
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device = args[0]
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if 'dtype' in kwargs:
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assert dtype is None, "to() received multiple values for argument 'dtype'"
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dtype = kwargs['dtype']
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if 'device' in kwargs:
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assert device is None, "to() received multiple values for argument 'device'"
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device = kwargs['device']
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new_feats = self.feats.to(device=device, dtype=dtype)
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new_coords = self.coords.to(device=device)
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return self.replace(new_feats, new_coords)
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def type(self, dtype):
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new_feats = self.feats.type(dtype)
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return self.replace(new_feats)
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def cpu(self) -> 'SparseTensor':
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new_feats = self.feats.cpu()
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new_coords = self.coords.cpu()
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return self.replace(new_feats, new_coords)
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def cuda(self) -> 'SparseTensor':
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new_feats = self.feats.cuda()
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new_coords = self.coords.cuda()
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return self.replace(new_feats, new_coords)
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def half(self) -> 'SparseTensor':
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new_feats = self.feats.half()
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return self.replace(new_feats)
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def float(self) -> 'SparseTensor':
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new_feats = self.feats.float()
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return self.replace(new_feats)
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def detach(self) -> 'SparseTensor':
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new_coords = self.coords.detach()
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new_feats = self.feats.detach()
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return self.replace(new_feats, new_coords)
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def dense(self) -> torch.Tensor:
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if BACKEND == 'torchsparse':
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return self.data.dense()
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elif BACKEND == 'spconv':
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return self.data.dense()
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def reshape(self, *shape) -> 'SparseTensor':
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new_feats = self.feats.reshape(self.feats.shape[0], *shape)
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return self.replace(new_feats)
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def unbind(self, dim: int) -> List['SparseTensor']:
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return sparse_unbind(self, dim)
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def replace(self, feats: torch.Tensor, coords: Optional[torch.Tensor] = None) -> 'SparseTensor':
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new_shape = [self.shape[0]]
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new_shape.extend(feats.shape[1:])
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if BACKEND == 'torchsparse':
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new_data = SparseTensorData(
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feats=feats,
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coords=self.data.coords if coords is None else coords,
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stride=self.data.stride,
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spatial_range=self.data.spatial_range,
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)
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new_data._caches = self.data._caches
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elif BACKEND == 'spconv':
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new_data = SparseTensorData(
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self.data.features.reshape(self.data.features.shape[0], -1),
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self.data.indices,
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self.data.spatial_shape,
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self.data.batch_size,
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self.data.grid,
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self.data.voxel_num,
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self.data.indice_dict
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)
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new_data._features = feats
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new_data.benchmark = self.data.benchmark
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new_data.benchmark_record = self.data.benchmark_record
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new_data.thrust_allocator = self.data.thrust_allocator
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new_data._timer = self.data._timer
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new_data.force_algo = self.data.force_algo
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new_data.int8_scale = self.data.int8_scale
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if coords is not None:
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new_data.indices = coords
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new_tensor = SparseTensor(new_data, shape=torch.Size(new_shape), layout=self.layout, scale=self._scale, spatial_cache=self._spatial_cache)
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return new_tensor
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@staticmethod
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def full(aabb, dim, value, dtype=torch.float32, device=None) -> 'SparseTensor':
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N, C = dim
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x = torch.arange(aabb[0], aabb[3] + 1)
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y = torch.arange(aabb[1], aabb[4] + 1)
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z = torch.arange(aabb[2], aabb[5] + 1)
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coords = torch.stack(torch.meshgrid(x, y, z, indexing='ij'), dim=-1).reshape(-1, 3)
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coords = torch.cat([
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torch.arange(N).view(-1, 1).repeat(1, coords.shape[0]).view(-1, 1),
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coords.repeat(N, 1),
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], dim=1).to(dtype=torch.int32, device=device)
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feats = torch.full((coords.shape[0], C), value, dtype=dtype, device=device)
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return SparseTensor(feats=feats, coords=coords)
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def __merge_sparse_cache(self, other: 'SparseTensor') -> dict:
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new_cache = {}
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for k in set(list(self._spatial_cache.keys()) + list(other._spatial_cache.keys())):
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if k in self._spatial_cache:
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new_cache[k] = self._spatial_cache[k]
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if k in other._spatial_cache:
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if k not in new_cache:
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new_cache[k] = other._spatial_cache[k]
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else:
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new_cache[k].update(other._spatial_cache[k])
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return new_cache
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def __neg__(self) -> 'SparseTensor':
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return self.replace(-self.feats)
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def __elemwise__(self, other: Union[torch.Tensor, 'SparseTensor'], op: callable) -> 'SparseTensor':
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if isinstance(other, torch.Tensor):
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try:
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other = torch.broadcast_to(other, self.shape)
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other = sparse_batch_broadcast(self, other)
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except:
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pass
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if isinstance(other, SparseTensor):
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other = other.feats
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new_feats = op(self.feats, other)
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new_tensor = self.replace(new_feats)
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if isinstance(other, SparseTensor):
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new_tensor._spatial_cache = self.__merge_sparse_cache(other)
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return new_tensor
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def __add__(self, other: Union[torch.Tensor, 'SparseTensor', float]) -> 'SparseTensor':
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return self.__elemwise__(other, torch.add)
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def __radd__(self, other: Union[torch.Tensor, 'SparseTensor', float]) -> 'SparseTensor':
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return self.__elemwise__(other, torch.add)
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def __sub__(self, other: Union[torch.Tensor, 'SparseTensor', float]) -> 'SparseTensor':
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return self.__elemwise__(other, torch.sub)
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def __rsub__(self, other: Union[torch.Tensor, 'SparseTensor', float]) -> 'SparseTensor':
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return self.__elemwise__(other, lambda x, y: torch.sub(y, x))
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def __mul__(self, other: Union[torch.Tensor, 'SparseTensor', float]) -> 'SparseTensor':
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return self.__elemwise__(other, torch.mul)
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def __rmul__(self, other: Union[torch.Tensor, 'SparseTensor', float]) -> 'SparseTensor':
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return self.__elemwise__(other, torch.mul)
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def __truediv__(self, other: Union[torch.Tensor, 'SparseTensor', float]) -> 'SparseTensor':
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return self.__elemwise__(other, torch.div)
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def __rtruediv__(self, other: Union[torch.Tensor, 'SparseTensor', float]) -> 'SparseTensor':
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return self.__elemwise__(other, lambda x, y: torch.div(y, x))
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def __getitem__(self, idx):
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if isinstance(idx, int):
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idx = [idx]
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elif isinstance(idx, slice):
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idx = range(*idx.indices(self.shape[0]))
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elif isinstance(idx, torch.Tensor):
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if idx.dtype == torch.bool:
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assert idx.shape == (self.shape[0],), f"Invalid index shape: {idx.shape}"
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idx = idx.nonzero().squeeze(1)
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elif idx.dtype in [torch.int32, torch.int64]:
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assert len(idx.shape) == 1, f"Invalid index shape: {idx.shape}"
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else:
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raise ValueError(f"Unknown index type: {idx.dtype}")
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else:
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raise ValueError(f"Unknown index type: {type(idx)}")
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coords = []
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feats = []
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for new_idx, old_idx in enumerate(idx):
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coords.append(self.coords[self.layout[old_idx]].clone())
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coords[-1][:, 0] = new_idx
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feats.append(self.feats[self.layout[old_idx]])
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coords = torch.cat(coords, dim=0).contiguous()
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feats = torch.cat(feats, dim=0).contiguous()
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return SparseTensor(feats=feats, coords=coords)
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def register_spatial_cache(self, key, value) -> None:
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"""
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Register a spatial cache.
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The spatial cache can be any thing you want to cache.
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The registery and retrieval of the cache is based on current scale.
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"""
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scale_key = str(self._scale)
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if scale_key not in self._spatial_cache:
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self._spatial_cache[scale_key] = {}
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self._spatial_cache[scale_key][key] = value
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def get_spatial_cache(self, key=None):
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"""
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Get a spatial cache.
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"""
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scale_key = str(self._scale)
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cur_scale_cache = self._spatial_cache.get(scale_key, {})
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if key is None:
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return cur_scale_cache
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return cur_scale_cache.get(key, None)
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def sparse_batch_broadcast(input: SparseTensor, other: torch.Tensor) -> torch.Tensor:
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"""
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Broadcast a 1D tensor to a sparse tensor along the batch dimension then perform an operation.
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Args:
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input (torch.Tensor): 1D tensor to broadcast.
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target (SparseTensor): Sparse tensor to broadcast to.
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op (callable): Operation to perform after broadcasting. Defaults to torch.add.
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"""
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coords, feats = input.coords, input.feats
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broadcasted = torch.zeros_like(feats)
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for k in range(input.shape[0]):
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broadcasted[input.layout[k]] = other[k]
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return broadcasted
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def sparse_batch_op(input: SparseTensor, other: torch.Tensor, op: callable = torch.add) -> SparseTensor:
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"""
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Broadcast a 1D tensor to a sparse tensor along the batch dimension then perform an operation.
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Args:
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input (torch.Tensor): 1D tensor to broadcast.
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target (SparseTensor): Sparse tensor to broadcast to.
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op (callable): Operation to perform after broadcasting. Defaults to torch.add.
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"""
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return input.replace(op(input.feats, sparse_batch_broadcast(input, other)))
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def sparse_cat(inputs: List[SparseTensor], dim: int = 0) -> SparseTensor:
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"""
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Concatenate a list of sparse tensors.
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Args:
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inputs (List[SparseTensor]): List of sparse tensors to concatenate.
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"""
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if dim == 0:
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start = 0
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coords = []
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for input in inputs:
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coords.append(input.coords.clone())
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coords[-1][:, 0] += start
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start += input.shape[0]
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coords = torch.cat(coords, dim=0)
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feats = torch.cat([input.feats for input in inputs], dim=0)
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output = SparseTensor(
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coords=coords,
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feats=feats,
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)
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else:
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feats = torch.cat([input.feats for input in inputs], dim=dim)
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output = inputs[0].replace(feats)
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return output
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def sparse_unbind(input: SparseTensor, dim: int) -> List[SparseTensor]:
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"""
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Unbind a sparse tensor along a dimension.
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Args:
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input (SparseTensor): Sparse tensor to unbind.
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dim (int): Dimension to unbind.
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"""
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if dim == 0:
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return [input[i] for i in range(input.shape[0])]
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else:
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feats = input.feats.unbind(dim)
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return [input.replace(f) for f in feats]
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