Source code for torch_geometric.nn.conv.gen_conv

from typing import List, Optional, Tuple, Union

from torch import Tensor
from torch.nn import (

from torch_geometric.nn.aggr import Aggregation, MultiAggregation
from torch_geometric.nn.conv import MessagePassing
from torch_geometric.nn.dense.linear import Linear
from torch_geometric.nn.inits import reset
from torch_geometric.nn.norm import MessageNorm
from torch_geometric.typing import Adj, OptPairTensor, OptTensor, Size

class MLP(Sequential):
    def __init__(self, channels: List[int], norm: Optional[str] = None,
                 bias: bool = True, dropout: float = 0.):
        m = []
        for i in range(1, len(channels)):
            m.append(Linear(channels[i - 1], channels[i], bias=bias))

            if i < len(channels) - 1:
                if norm and norm == 'batch':
                    m.append(BatchNorm1d(channels[i], affine=True))
                elif norm and norm == 'layer':
                    m.append(LayerNorm(channels[i], elementwise_affine=True))
                elif norm and norm == 'instance':
                    m.append(InstanceNorm1d(channels[i], affine=False))
                elif norm:
                    raise NotImplementedError(
                        f'Normalization layer "{norm}" not supported.')


[docs]class GENConv(MessagePassing): r"""The GENeralized Graph Convolution (GENConv) from the `"DeeperGCN: All You Need to Train Deeper GCNs" <>`_ paper. :class:`GENConv` supports both :math:`\textrm{softmax}` (see :class:`~torch_geometric.nn.aggr.SoftmaxAggregation`) and :math:`\textrm{powermean}` (see :class:`~torch_geometric.nn.aggr.PowerMeanAggregation`) aggregation. Its message construction is given by: .. math:: \mathbf{x}_i^{\prime} = \mathrm{MLP} \left( \mathbf{x}_i + \mathrm{AGG} \left( \left\{ \mathrm{ReLU} \left( \mathbf{x}_j + \mathbf{e_{ji}} \right) +\epsilon : j \in \mathcal{N}(i) \right\} \right) \right) .. note:: For an example of using :obj:`GENConv`, see `examples/ <>`_. Args: in_channels (int or tuple): Size of each input sample, or :obj:`-1` to derive the size from the first input(s) to the forward method. A tuple corresponds to the sizes of source and target dimensionalities. out_channels (int): Size of each output sample. aggr (str or Aggregation, optional): The aggregation scheme to use. Any aggregation of :obj:`torch_geometric.nn.aggr` can be used, (:obj:`"softmax"`, :obj:`"powermean"`, :obj:`"add"`, :obj:`"mean"`, :obj:`max`). (default: :obj:`"softmax"`) t (float, optional): Initial inverse temperature for softmax aggregation. (default: :obj:`1.0`) learn_t (bool, optional): If set to :obj:`True`, will learn the value :obj:`t` for softmax aggregation dynamically. (default: :obj:`False`) p (float, optional): Initial power for power mean aggregation. (default: :obj:`1.0`) learn_p (bool, optional): If set to :obj:`True`, will learn the value :obj:`p` for power mean aggregation dynamically. (default: :obj:`False`) msg_norm (bool, optional): If set to :obj:`True`, will use message normalization. (default: :obj:`False`) learn_msg_scale (bool, optional): If set to :obj:`True`, will learn the scaling factor of message normalization. (default: :obj:`False`) norm (str, optional): Norm layer of MLP layers (:obj:`"batch"`, :obj:`"layer"`, :obj:`"instance"`) (default: :obj:`batch`) num_layers (int, optional): The number of MLP layers. (default: :obj:`2`) expansion (int, optional): The expansion factor of hidden channels in MLP layers. (default: :obj:`2`) eps (float, optional): The epsilon value of the message construction function. (default: :obj:`1e-7`) bias (bool, optional): If set to :obj:`False`, the layer will not learn an additive bias. (default: :obj:`True`) edge_dim (int, optional): Edge feature dimensionality. If set to :obj:`None`, Edge feature dimensionality is expected to match the `out_channels`. Other-wise, edge features are linearly transformed to match `out_channels` of node feature dimensionality. (default: :obj:`None`) **kwargs (optional): Additional arguments of :class:`torch_geometric.nn.conv.GenMessagePassing`. Shapes: - **input:** node features :math:`(|\mathcal{V}|, F_{in})` or :math:`((|\mathcal{V_s}|, F_{s}), (|\mathcal{V_t}|, F_{t}))` if bipartite, edge indices :math:`(2, |\mathcal{E}|)`, edge attributes :math:`(|\mathcal{E}|, D)` *(optional)* - **output:** node features :math:`(|\mathcal{V}|, F_{out})` or :math:`(|\mathcal{V}_t|, F_{out})` if bipartite """ def __init__( self, in_channels: Union[int, Tuple[int, int]], out_channels: int, aggr: Optional[Union[str, List[str], Aggregation]] = 'softmax', t: float = 1.0, learn_t: bool = False, p: float = 1.0, learn_p: bool = False, msg_norm: bool = False, learn_msg_scale: bool = False, norm: str = 'batch', num_layers: int = 2, expansion: int = 2, eps: float = 1e-7, bias: bool = False, edge_dim: Optional[int] = None, **kwargs, ): # Backward compatibility: semi_grad = True if aggr == 'softmax_sg' else False aggr = 'softmax' if aggr == 'softmax_sg' else aggr aggr = 'powermean' if aggr == 'power' else aggr # Override args of aggregator if `aggr_kwargs` is specified if 'aggr_kwargs' not in kwargs: if aggr == 'softmax': kwargs['aggr_kwargs'] = dict(t=t, learn=learn_t, semi_grad=semi_grad) elif aggr == 'powermean': kwargs['aggr_kwargs'] = dict(p=p, learn=learn_p) super().__init__(aggr=aggr, **kwargs) self.in_channels = in_channels self.out_channels = out_channels self.eps = eps if isinstance(in_channels, int): in_channels = (in_channels, in_channels) if in_channels[0] != out_channels: self.lin_src = Linear(in_channels[0], out_channels, bias=bias) if edge_dim is not None and edge_dim != out_channels: self.lin_edge = Linear(edge_dim, out_channels, bias=bias) if isinstance(self.aggr_module, MultiAggregation): aggr_out_channels = self.aggr_module.get_out_channels(out_channels) else: aggr_out_channels = out_channels if aggr_out_channels != out_channels: self.lin_aggr_out = Linear(aggr_out_channels, out_channels, bias=bias) if in_channels[1] != out_channels: self.lin_dst = Linear(in_channels[1], out_channels, bias=bias) channels = [out_channels] for i in range(num_layers - 1): channels.append(out_channels * expansion) channels.append(out_channels) self.mlp = MLP(channels, norm=norm, bias=bias) if msg_norm: self.msg_norm = MessageNorm(learn_msg_scale)
[docs] def reset_parameters(self): super().reset_parameters() reset(self.mlp) if hasattr(self, 'msg_norm'): self.msg_norm.reset_parameters() if hasattr(self, 'lin_src'): self.lin_src.reset_parameters() if hasattr(self, 'lin_edge'): self.lin_edge.reset_parameters() if hasattr(self, 'lin_aggr_out'): self.lin_aggr_out.reset_parameters() if hasattr(self, 'lin_dst'): self.lin_dst.reset_parameters()
[docs] def forward(self, x: Union[Tensor, OptPairTensor], edge_index: Adj, edge_attr: OptTensor = None, size: Size = None) -> Tensor: if isinstance(x, Tensor): x = (x, x) if hasattr(self, 'lin_src'): x = (self.lin_src(x[0]), x[1]) # propagate_type: (x: OptPairTensor, edge_attr: OptTensor) out = self.propagate(edge_index, x=x, edge_attr=edge_attr, size=size) if hasattr(self, 'lin_aggr_out'): out = self.lin_aggr_out(out) if hasattr(self, 'msg_norm'): h = x[1] if x[1] is not None else x[0] assert h is not None out = self.msg_norm(h, out) x_dst = x[1] if x_dst is not None: if hasattr(self, 'lin_dst'): x_dst = self.lin_dst(x_dst) out = out + x_dst return self.mlp(out)
def message(self, x_j: Tensor, edge_attr: OptTensor) -> Tensor: if edge_attr is not None and hasattr(self, 'lin_edge'): edge_attr = self.lin_edge(edge_attr) if edge_attr is not None: assert x_j.size(-1) == edge_attr.size(-1) msg = x_j if edge_attr is None else x_j + edge_attr return msg.relu() + self.eps def __repr__(self) -> str: return (f'{self.__class__.__name__}({self.in_channels}, ' f'{self.out_channels}, aggr={self.aggr})')