def forward(ctx,
input_features,
kernel,
tensor_stride=1,
stride=1,
kernel_size=-1,
dilation=1,
region_type=0,
region_offset=None,
generate_new_coords=False,
in_coords_key=None,
out_coords_key=None,
coords_manager=None):
"""
region_type=0 HyperCube
"""
# Prep arguments
# Kernel shape (n_spatial_kernels, in_nfeat, out_nfeat)
assert input_features.shape[1] == kernel.shape[1], \
"The input shape " + str(list(input_features.shape)) + \
" does not match the kernel shape " + str(list(kernel.shape))
if out_coords_key is None:
out_coords_key = CoordsKey(in_coords_key.D)
assert in_coords_key.D == out_coords_key.D
assert input_features.type() == kernel.type(), \
f"Type mismatch input: {input_features.type()} != kernel: {kernel.type()}"
if not input_features.is_contiguous():
input_features = input_features.contiguous()
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
ctx.kernel = kernel
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
out_feat = input_features.new()
fw_fn = getattr(
MEB, 'ConvolutionTransposeForward' + get_postfix(input_features))
fw_fn(ctx.in_feat, out_feat, kernel,
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, generate_new_coords)
return out_feat
def get_kernel_map(self,
in_key_or_tensor_strides,
out_key_or_tensor_strides,
stride=1,
kernel_size=3,
dilation=1,
region_type=0,
region_offset=None,
is_transpose=False,
is_pool=False,
on_gpu=False):
r"""Get kernel in-out maps for the specified coords keys or tensor strides.
"""
# region type 1 iteration with kernel_size 1 is invalid
assert kernel_size > 0, "Invalid kernel size."
if kernel_size == 1:
region_type = 0
if isinstance(in_key_or_tensor_strides, CoordsKey):
in_tensor_strides = in_key_or_tensor_strides.getTensorStride()
else:
in_tensor_strides = in_key_or_tensor_strides
if region_offset is None:
region_offset = torch.IntTensor()
in_coords_key = self._get_coords_key(in_key_or_tensor_strides)
out_coords_key = self._get_coords_key(out_key_or_tensor_strides)
tensor_strides = convert_to_int_tensor(in_tensor_strides, self.D)
strides = convert_to_int_tensor(stride, self.D)
kernel_sizes = convert_to_int_tensor(kernel_size, self.D)
dilations = convert_to_int_tensor(dilation, self.D)
D = in_coords_key.D
tensor_strides, strides, kernel_sizes, dilations, region_type = prep_args(
tensor_strides, strides, kernel_sizes, dilations, region_type, D)
kernel_map_fn = self.CPPCoordsManager.getKernelMapGPU \
if on_gpu else self.CPPCoordsManager.getKernelMapGPU
kernel_map = kernel_map_fn(
convert_to_int_list(tensor_strides, D), #
convert_to_int_list(strides, D), #
convert_to_int_list(kernel_sizes, D), #
convert_to_int_list(dilations, D), #
region_type,
region_offset,
in_coords_key.CPPCoordsKey,
out_coords_key.CPPCoordsKey,
is_transpose,
is_pool)
return kernel_map
def forward(ctx,
input_features,
kernel,
tensor_stride=1,
stride=1,
kernel_size=-1,
dilation=1,
region_type=0,
region_offset=None,
in_coords_key=None,
out_coords_key=None,
coords_manager=None):
"""
region_type=0 HyperCube
"""
# Prep arguments
# Kernel shape (n_spatial_kernels, in_nfeat, out_nfeat)
assert input_features.shape[1] == kernel.shape[1]
if out_coords_key is None:
out_coords_key = CoordsKey(in_coords_key.D)
assert in_coords_key.D == out_coords_key.D
assert input_features.type() == kernel.type()
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
ctx.kernel = kernel
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
out_feat = input_features.new()
fw_fn = getattr(MEB,
'ConvolutionForward' + get_postfix(input_features))
fw_fn(D, ctx.in_feat, out_feat, kernel,
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,
tensor_stride=1,
stride=1,
kernel_size=-1,
dilation=1,
region_type=0,
region_offset=None,
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
if not input_features.is_contiguous():
input_features = input_features.contiguous()
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
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
out_feat = input_features.new()
max_index = input_features.new().int()
ctx.max_index = max_index
fw_fn = getattr(MEB, 'MaxPoolingForward' + get_postfix(input_features))
fw_fn(input_features, out_feat, max_index,
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,
tensor_stride=1,
stride=1,
kernel_size=-1,
dilation=1,
region_type=0,
region_offset=None,
average=True,
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
if not input_features.is_contiguous():
input_features = input_features.contiguous()
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
ctx = save_ctx(ctx, tensor_stride, stride, kernel_size, dilation,
region_type, in_coords_key, out_coords_key,
coords_manager)
ctx.use_avg = average
D = in_coords_key.D
out_feat = input_features.new()
ctx.num_nonzero = input_features.new()
fw_fn = get_minkowski_function('AvgPoolingForward', 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, ctx.use_avg)
return out_feat
def get_kernel_map(self,
in_key_or_tensor_strides,
out_key_or_tensor_strides,
stride=1,
kernel_size=3,
dilation=1,
region_type=0,
is_transpose=False,
is_pool=False):
r"""Get kernel in-out maps for the specified coords keys or tensor strides.
"""
if isinstance(in_key_or_tensor_strides, CoordsKey):
in_tensor_strides = in_key_or_tensor_strides.getTensorStride()
else:
in_tensor_strides = in_key_or_tensor_strides
in_coords_key = self._get_coords_key(in_key_or_tensor_strides)
out_coords_key = self._get_coords_key(out_key_or_tensor_strides)
tensor_strides = convert_to_int_tensor(in_tensor_strides, self.D)
strides = convert_to_int_tensor(stride, self.D)
kernel_sizes = convert_to_int_tensor(kernel_size, self.D)
dilations = convert_to_int_tensor(dilation, self.D)
D = in_coords_key.D
tensor_strides, strides, kernel_sizes, dilations, region_type = prep_args(
tensor_strides, strides, kernel_sizes, dilations, region_type, D)
kernel_map = self.CPPCoordsManager.getKernelMap(
convert_to_int_list(tensor_strides, D), #
convert_to_int_list(strides, D), #
convert_to_int_list(kernel_sizes, D), #
convert_to_int_list(dilations, D), #
region_type,
in_coords_key.CPPCoordsKey,
out_coords_key.CPPCoordsKey,
is_transpose,
is_pool)
return kernel_map
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 = getattr(
MEB, 'PoolingTransposeForward' + get_postfix(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 get_kernel_map(self,
in_key_or_tensor_strides,
out_key_or_tensor_strides,
stride=1,
kernel_size=3,
dilation=1,
region_type=0,
region_offset=None,
is_transpose=False,
is_pool=False,
on_gpu=False):
r"""Get kernel in-out maps for the specified coords keys or tensor strides.
"""
# region type 1 iteration with kernel_size 1 is invalid
if isinstance(kernel_size, torch.Tensor):
assert (kernel_size >
0).all(), f"Invalid kernel size: {kernel_size}"
if (kernel_size == 1).all() == 1:
region_type = 0
elif isinstance(kernel_size, int):
assert kernel_size > 0, f"Invalid kernel size: {kernel_size}"
if kernel_size == 1:
region_type = 0
if isinstance(in_key_or_tensor_strides, CoordsKey):
in_tensor_strides = in_key_or_tensor_strides.getTensorStride()
else:
in_tensor_strides = in_key_or_tensor_strides
if region_offset is None:
region_offset = torch.IntTensor()
in_coords_key = self._get_coords_key(in_key_or_tensor_strides)
out_coords_key = self._get_coords_key(out_key_or_tensor_strides)
tensor_strides = convert_to_int_tensor(in_tensor_strides, self.D)
strides = convert_to_int_tensor(stride, self.D)
kernel_sizes = convert_to_int_tensor(kernel_size, self.D)
dilations = convert_to_int_tensor(dilation, self.D)
D = in_coords_key.D
tensor_strides, strides, kernel_sizes, dilations, region_type = prep_args(
tensor_strides, strides, kernel_sizes, dilations, region_type, D)
if on_gpu:
assert hasattr(
self.CPPCoordsManager, 'getKernelMapGPU'
), f"Function getKernelMapGPU not available. Please compile MinkowskiEngine where `torch.cuda.is_available()` is `True`."
kernel_map_fn = getattr(self.CPPCoordsManager, 'getKernelMapGPU')
else:
kernel_map_fn = self.CPPCoordsManager.getKernelMap
kernel_map = kernel_map_fn(
convert_to_int_list(tensor_strides, D), #
convert_to_int_list(strides, D), #
convert_to_int_list(kernel_sizes, D), #
convert_to_int_list(dilations, D), #
region_type,
region_offset,
in_coords_key.CPPCoordsKey,
out_coords_key.CPPCoordsKey,
is_transpose,
is_pool)
return kernel_map
