from typing import Optional, Callable, Union
from torch_geometric.typing import OptTensor, PairOptTensor, PairTensor, Adj
import torch
from torch import Tensor
from torch_sparse import SparseTensor, set_diag
from torch_geometric.utils import remove_self_loops, add_self_loops
from torch_geometric.nn.conv import MessagePassing
from ..inits import reset
def get_angle(v1: Tensor, v2: Tensor) -> Tensor:
return torch.atan2(
torch.cross(v1, v2, dim=1).norm(p=2, dim=1), (v1 * v2).sum(dim=1))
def point_pair_features(pos_i: Tensor, pos_j: Tensor, normal_i: Tensor,
normal_j: Tensor) -> Tensor:
pseudo = pos_j - pos_i
return torch.stack([
pseudo.norm(p=2, dim=1),
get_angle(normal_i, pseudo),
get_angle(normal_j, pseudo),
get_angle(normal_i, normal_j)
], dim=1)
[docs]class PPFConv(MessagePassing):
r"""The PPFNet operator from the `"PPFNet: Global Context Aware Local
Features for Robust 3D Point Matching" <https://arxiv.org/abs/1802.02669>`_
paper
.. math::
\mathbf{x}^{\prime}_i = \gamma_{\mathbf{\Theta}} \left( \max_{j \in
\mathcal{N}(i) \cup \{ i \}} h_{\mathbf{\Theta}} ( \mathbf{x}_j, \|
\mathbf{d_{j,i}} \|, \angle(\mathbf{n}_i, \mathbf{d_{j,i}}),
\angle(\mathbf{n}_j, \mathbf{d_{j,i}}), \angle(\mathbf{n}_i,
\mathbf{n}_j) \right)
where :math:`\gamma_{\mathbf{\Theta}}` and :math:`h_{\mathbf{\Theta}}`
denote neural networks, *.i.e.* MLPs, which takes in node features and
:class:`torch_geometric.transforms.PointPairFeatures`.
Args:
local_nn (torch.nn.Module, optional): A neural network
:math:`h_{\mathbf{\Theta}}` that maps node features :obj:`x` and
relative spatial coordinates :obj:`pos_j - pos_i` of shape
:obj:`[-1, in_channels + num_dimensions]` to shape
:obj:`[-1, out_channels]`, *e.g.*, defined by
:class:`torch.nn.Sequential`. (default: :obj:`None`)
global_nn (torch.nn.Module, optional): A neural network
:math:`\gamma_{\mathbf{\Theta}}` that maps aggregated node features
of shape :obj:`[-1, out_channels]` to shape :obj:`[-1,
final_out_channels]`, *e.g.*, defined by
:class:`torch.nn.Sequential`. (default: :obj:`None`)
add_self_loops (bool, optional): If set to :obj:`False`, will not add
self-loops to the input graph. (default: :obj:`True`)
**kwargs (optional): Additional arguments of
:class:`torch_geometric.nn.conv.MessagePassing`.
"""
def __init__(self, local_nn: Optional[Callable] = None,
global_nn: Optional[Callable] = None,
add_self_loops: bool = True, **kwargs):
kwargs.setdefault('aggr', 'max')
super(PPFConv, self).__init__(**kwargs)
self.local_nn = local_nn
self.global_nn = global_nn
self.add_self_loops = add_self_loops
self.reset_parameters()
[docs] def reset_parameters(self):
reset(self.local_nn)
reset(self.global_nn)
[docs] def forward(self, x: Union[OptTensor, PairOptTensor],
pos: Union[Tensor, PairTensor],
normal: Union[Tensor, PairTensor],
edge_index: Adj) -> Tensor: # yapf: disable
""""""
if not isinstance(x, tuple):
x: PairOptTensor = (x, None)
if isinstance(pos, Tensor):
pos: PairTensor = (pos, pos)
if isinstance(normal, Tensor):
normal: PairTensor = (normal, normal)
if self.add_self_loops:
if isinstance(edge_index, Tensor):
edge_index, _ = remove_self_loops(edge_index)
edge_index, _ = add_self_loops(edge_index,
num_nodes=pos[1].size(0))
elif isinstance(edge_index, SparseTensor):
edge_index = set_diag(edge_index)
# propagate_type: (x: PairOptTensor, pos: PairTensor, normal: PairTensor) # noqa
out = self.propagate(edge_index, x=x, pos=pos, normal=normal,
size=None)
if self.global_nn is not None:
out = self.global_nn(out)
return out
def message(self, x_j: OptTensor, pos_i: Tensor, pos_j: Tensor,
normal_i: Tensor, normal_j: Tensor) -> Tensor:
msg = point_pair_features(pos_i, pos_j, normal_i, normal_j)
if x_j is not None:
msg = torch.cat([x_j, msg], dim=1)
if self.local_nn is not None:
msg = self.local_nn(msg)
return msg
def __repr__(self):
return '{}(local_nn={}, global_nn={})'.format(self.__class__.__name__,
self.local_nn,
self.global_nn)