def forward(self,
input: SparseTensor,
coords: Union[torch.IntTensor, CoordsKey,
SparseTensor] = None):
r"""
:attr:`input` (`MinkowskiEngine.SparseTensor`): Input sparse tensor to apply a
convolution on.
:attr:`coords` ((`torch.IntTensor`, `MinkowskiEngine.CoordsKey`,
`MinkowskiEngine.SparseTensor`), optional): If provided, generate
results on the provided coordinates. None by default.
"""
assert isinstance(input, SparseTensor)
assert input.D == self.dimension
# Create a region_offset
self.region_type_, self.region_offset_, _ = \
self.kernel_generator.get_kernel(input.tensor_stride, self.is_transpose)
# Get a new coords key or extract one from the coords
out_coords_key = _get_coords_key(input, coords)
output = self.pooling.apply(input.F, input.tensor_stride, self.stride,
self.kernel_size, self.dilation,
self.region_type_, self.region_offset_,
self.average, input.coords_key,
out_coords_key, input.coords_man)
return SparseTensor(output,
coords_key=out_coords_key,
coords_manager=input.coords_man)
def forward(self,
input: SparseTensor,
coords: Union[torch.IntTensor, CoordsKey,
SparseTensor] = None):
r"""
:attr:`input` (`MinkowskiEngine.SparseTensor`): Input sparse tensor to apply a
convolution on.
:attr:`coords` ((`torch.IntTensor`, `MinkowskiEngine.CoordsKey`,
`MinkowskiEngine.SparseTensor`), optional): If provided, generate
results on the provided coordinates. None by default.
"""
assert isinstance(input, SparseTensor)
assert input.D == self.dimension
# Create a region_offset
self.region_type_, self.region_offset_, _ = \
self.kernel_generator.get_kernel(input.tensor_stride, self.is_transpose)
if self.use_mm and coords is None:
# If the kernel_size == 1, the convolution is simply a matrix
# multiplication
outfeat = input.F.mm(self.kernel)
out_coords_key = input.coords_key
else:
if self.is_transpose:
conv = MinkowskiConvolutionTransposeFunction()
else:
conv = MinkowskiConvolutionFunction()
# Get a new coords key or extract one from the coords
out_coords_key = _get_coords_key(input, coords)
outfeat = conv.apply(input.F, self.kernel, input.tensor_stride,
self.stride, self.kernel_size, self.dilation,
self.region_type_, self.region_offset_,
input.coords_key, out_coords_key,
input.coords_man)
if self.has_bias:
outfeat += self.bias
return SparseTensor(outfeat,
coords_key=out_coords_key,
coords_manager=input.coords_man)
