def forward(
ctx,
input_features,
tensor_stride=1,
stride=1,
kernel_size=-1,
dilation=1,
region_type=-1,
region_offset=None,
average=False,
in_coords_key=None,
out_coords_key=None,
coords_manager=None,
):
assert isinstance(region_type, RegionType)
if out_coords_key is None:
out_coords_key = CoordsKey(in_coords_key.D)
assert in_coords_key.D == out_coords_key.D
tensor_stride, stride, kernel_size, dilation, region_type = prep_args(
tensor_stride, stride, kernel_size, dilation, region_type,
in_coords_key.D)
if region_offset is None:
region_offset = torch.IntTensor()
ctx.in_feat = input_features
out_feat = input_features.new()
ctx.num_nonzero = input_features.new()
ctx = save_ctx(
ctx,
tensor_stride,
stride,
kernel_size,
dilation,
region_type,
in_coords_key,
out_coords_key,
coords_manager,
)
D = in_coords_key.D
fw_fn = get_minkowski_function("PoolingTransposeForward",
input_features)
fw_fn(
ctx.in_feat,
out_feat,
ctx.num_nonzero,
convert_to_int_list(ctx.tensor_stride, D),
convert_to_int_list(ctx.stride, D),
convert_to_int_list(ctx.kernel_size, D),
convert_to_int_list(ctx.dilation, D),
region_type,
region_offset,
ctx.in_coords_key.CPPCoordsKey,
ctx.out_coords_key.CPPCoordsKey,
ctx.coords_man.CPPCoordsManager,
)
return out_feat
def forward(
ctx,
input_features: torch.Tensor,
pooling_mode: PoolingMode,
kernel_generator: KernelGenerator,
in_coordinate_map_key: CoordinateMapKey,
out_coordinate_map_key: CoordinateMapKey = None,
coordinate_manager: CoordinateManager = None,
):
if out_coordinate_map_key is None:
out_coordinate_map_key = CoordinateMapKey(
in_coordinate_map_key.get_coordinate_size())
input_features = input_features.contiguous()
ctx.input_features = input_features
ctx = save_ctx(
ctx,
kernel_generator,
in_coordinate_map_key,
out_coordinate_map_key,
coordinate_manager,
)
ctx.pooling_mode = pooling_mode
fw_fn = get_minkowski_function("LocalPoolingTransposeForward",
input_features)
out_feat, num_nonzero = fw_fn(
ctx.input_features,
kernel_generator.kernel_size,
kernel_generator.kernel_stride,
kernel_generator.kernel_dilation,
kernel_generator.region_type,
kernel_generator.region_offsets,
kernel_generator.expand_coordinates,
pooling_mode,
ctx.in_coordinate_map_key,
ctx.out_coordinate_map_key,
ctx.coordinate_manager._manager,
)
ctx.num_nonzero = num_nonzero
return out_feat
def forward(
ctx,
input_features: torch.Tensor,
kernel_weights: torch.Tensor,
kernel_generator: KernelGenerator,
in_coordinate_map_key: CoordinateMapKey,
out_coordinate_map_key: CoordinateMapKey = None,
coordinate_manager: CoordinateManager = None,
):
if out_coordinate_map_key is None:
out_coordinate_map_key = CoordinateMapKey(
in_coordinate_map_key.get_coordinate_size())
if not input_features.is_contiguous():
input_features = input_features.contiguous()
ctx.input_features = input_features
ctx.kernel_weights = kernel_weights
ctx = save_ctx(
ctx,
kernel_generator,
in_coordinate_map_key,
out_coordinate_map_key,
coordinate_manager,
)
fw_fn = get_minkowski_function("ConvolutionTransposeForward",
input_features)
return fw_fn(
ctx.input_features,
kernel_weights,
kernel_generator.kernel_size,
kernel_generator.kernel_stride,
kernel_generator.kernel_dilation,
kernel_generator.region_type,
kernel_generator.region_offsets,
kernel_generator.expand_coordinates,
ctx.in_coordinate_map_key,
ctx.out_coordinate_map_key,
ctx.coordinate_manager._manager,
)