Source code for torch_geometric.nn.norm.msg_norm

import torch
import torch.nn.functional as F
from torch import Tensor
from torch.nn import Parameter

[docs]class MessageNorm(torch.nn.Module): r"""Applies message normalization over the aggregated messages as described in the `"DeeperGCNs: All You Need to Train Deeper GCNs" <>`_ paper. .. math:: \mathbf{x}_i^{\prime} = \mathrm{MLP} \left( \mathbf{x}_{i} + s \cdot {\| \mathbf{x}_i \|}_2 \cdot \frac{\mathbf{m}_{i}}{{\|\mathbf{m}_i\|}_2} \right) Args: learn_scale (bool, optional): If set to :obj:`True`, will learn the scaling factor :math:`s` of message normalization. (default: :obj:`False`) """ def __init__(self, learn_scale: bool = False): super().__init__() self.scale = Parameter(torch.empty(1), requires_grad=learn_scale) self.reset_parameters()
[docs] def reset_parameters(self): r"""Resets all learnable parameters of the module."""
[docs] def forward(self, x: Tensor, msg: Tensor, p: float = 2.0) -> Tensor: r"""Forward pass. Args: x (torch.Tensor): The source tensor. msg (torch.Tensor): The message tensor :math:`\mathbf{M}`. p (float, optional): The norm :math:`p` to use for normalization. (default: :obj:`2.0`) """ msg = F.normalize(msg, p=p, dim=-1) x_norm = x.norm(p=p, dim=-1, keepdim=True) return msg * x_norm * self.scale
def __repr__(self) -> str: return (f'{self.__class__.__name__}' f'(learn_scale={self.scale.requires_grad})')