def forward(
self,
input: SparseTensor,
coordinates: Union[torch.IntTensor, CoordinateMapKey,
SparseTensor] = None,
):
# Get a new coordinate map key or extract one from the coordinates
if input._manager.number_of_unique_batch_indices() == 1:
out_coordinate_map_key = input._manager.origin()
output, _ = input.F.max(0, True)
else:
out_coordinate_map_key = _get_coordinate_map_key(
input, coordinates)
output = self.pooling.apply(
input.F,
self.pooling_mode,
input.coordinate_map_key,
out_coordinate_map_key,
input._manager,
)
return SparseTensor(
output,
coordinate_map_key=out_coordinate_map_key,
coordinate_manager=input.coordinate_manager,
)
def forward(
self,
input: SparseTensor,
coordinates: Union[torch.IntTensor, CoordinateMapKey,
SparseTensor] = None,
):
r"""
:attr:`input` (`MinkowskiEngine.SparseTensor`): Input sparse tensor to apply a
convolution on.
:attr:`coordinates` ((`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
# Get a new coordinate map key or extract one from the coordinates
out_coordinate_map_key = _get_coordinate_map_key(input, coordinates)
outfeat = self.pooling.apply(
input.F,
self.pooling_mode,
self.kernel_generator,
input.coordinate_map_key,
out_coordinate_map_key,
input._manager,
)
return SparseTensor(
outfeat,
coordinate_map_key=out_coordinate_map_key,
coordinate_manager=input.coordinate_manager,
)
def forward(
self,
input,
coordinates: Union[torch.IntTensor, CoordinateMapKey,
SparseTensor] = None,
):
# Get a new coordinate map key or extract one from the coordinates
if isinstance(input, ME.TensorField):
in_coordinate_map_key = input.coordinate_field_map_key
out_coordinate_map_key = CoordinateMapKey(
input.coordinate_field_map_key.get_coordinate_size())
else:
in_coordinate_map_key = input.coordinate_map_key
out_coordinate_map_key = _get_coordinate_map_key(
input, coordinates)
output = self.pooling.apply(
input.F,
self.pooling_mode,
in_coordinate_map_key,
out_coordinate_map_key,
input._manager,
)
return SparseTensor(
output,
coordinate_map_key=out_coordinate_map_key,
coordinate_manager=input.coordinate_manager,
)
def forward(
self,
input: SparseTensor,
coordinates: Union[torch.Tensor, CoordinateMapKey,
SparseTensor] = None,
):
r"""
:attr:`input` (`MinkowskiEngine.SparseTensor`): Input sparse tensor to apply a
convolution on.
:attr:`coordinates` ((`torch.IntTensor`, `MinkowskiEngine.CoordinateMapKey`,
`MinkowskiEngine.SparseTensor`), optional): If provided, generate
results on the provided coordinates. None by default.
"""
assert isinstance(input, SparseTensor)
assert input.D == self.dimension
if self.use_mm:
# If the kernel_size == 1, the convolution is simply a matrix
# multiplication
out_coordinate_map_key = input.coordinate_map_key
outfeat = input.F.mm(self.kernel)
else:
# Get a new coordinate_map_key or extract one from the coords
out_coordinate_map_key = _get_coordinate_map_key(
input, coordinates, self.kernel_generator.expand_coordinates)
outfeat = self.conv.apply(
input.F,
self.kernel,
self.kernel_generator,
self.convolution_mode,
input.coordinate_map_key,
out_coordinate_map_key,
input._manager,
)
if self.bias is not None:
outfeat += self.bias
return SparseTensor(
outfeat,
coordinate_map_key=out_coordinate_map_key,
coordinate_manager=input._manager,
)
def forward(
self,
input: SparseTensor,
coordinates: Union[torch.IntTensor, CoordinateMapKey, SparseTensor] = None,
):
# Get a new coordinate map key or extract one from the coordinates
out_coordinate_map_key = _get_coordinate_map_key(input, coordinates)
output = self.pooling.apply(
input.F,
self.pooling_mode,
input.coordinate_map_key,
out_coordinate_map_key,
input._manager,
)
return SparseTensor(
output,
coordinate_map_key=out_coordinate_map_key,
coordinate_manager=input.coordinate_manager,
)
