import torch
from torch_geometric.nn.conv import MessagePassing
from torch_geometric.utils import remove_self_loops, add_self_loops
from ..inits import reset
def point_pair_features(pos_i, pos_j, norm_i, norm_j):
def get_angle(v1, v2):
return torch.atan2(
torch.cross(v1, v2, dim=1).norm(p=2, dim=1), (v1 * v2).sum(dim=1))
pseudo = pos_j - pos_i
return torch.stack([
pseudo.norm(p=2, dim=1),
get_angle(norm_i, pseudo),
get_angle(norm_j, pseudo),
get_angle(norm_i, norm_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`)
**kwargs (optional): Additional arguments of
:class:`torch_geometric.nn.conv.MessagePassing`.
"""
def __init__(self, local_nn=None, global_nn=None, **kwargs):
super(PPFConv, self).__init__(aggr='max', **kwargs)
self.local_nn = local_nn
self.global_nn = global_nn
self.reset_parameters()
[docs] def reset_parameters(self):
reset(self.local_nn)
reset(self.global_nn)
[docs] def forward(self, x, pos, norm, edge_index):
r"""
Args:
x (Tensor): The node feature matrix. Allowed to be :obj:`None`.
pos (Tensor or tuple): The node position matrix. Either given as
tensor for use in general message passing or as tuple for use
in message passing in bipartite graphs.
norm (Tensor or tuple): The normal vectors of each node. Either
given as tensor for use in general message passing or as tuple
for use in message passing in bipartite graphs.
edge_index (LongTensor): The edge indices.
"""
if torch.is_tensor(pos): # Add self-loops for symmetric adjacencies.
edge_index, _ = remove_self_loops(edge_index)
edge_index, _ = add_self_loops(edge_index, num_nodes=pos.size(0))
return self.propagate(edge_index, x=x, pos=pos, norm=norm)
def message(self, x_j, pos_i, pos_j, norm_i, norm_j):
msg = point_pair_features(pos_i, pos_j, norm_i, norm_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 update(self, aggr_out):
if self.global_nn is not None:
aggr_out = self.global_nn(aggr_out)
return aggr_out
def __repr__(self):
return '{}(local_nn={}, global_nn={})'.format(
self.__class__.__name__, self.local_nn, self.global_nn)