wehub-resource-sync
146 lines
5.0 KiB
Python
146 lines
5.0 KiB
Python
from typing import List, Optional, Tuple, Union
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import torch.nn.functional as F
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from torch import Tensor
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from torch_geometric.nn.aggr import Aggregation, MultiAggregation
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from torch_geometric.nn.conv import MessagePassing
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from torch_geometric.nn.dense.linear import Linear
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from torch_geometric.typing import Adj, OptPairTensor, Size, SparseTensor
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from torch_geometric.utils import spmm
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class SAGEConv(MessagePassing):
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r"""A variant of the GraphSAGE operator from the `"Inductive Representation
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Learning on Large Graphs" <https://arxiv.org/abs/1706.02216>`_ paper.
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.. math::
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\mathbf{x}^{\prime}_i = \mathbf{W}_1 \mathbf{x}_i + \mathbf{W}_2 \cdot
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\mathrm{mean}_{j \in \mathcal{N(i)}} \mathbf{x}_j
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If :obj:`project = True`, then :math:`\mathbf{x}_j` will first get
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projected via
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.. math::
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\mathbf{x}_j \leftarrow \sigma ( \mathbf{W}_3 \mathbf{x}_j +
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\mathbf{b})
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as described in Eq. (3) of the paper.
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Args:
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in_channels (int or tuple): Size of each input sample, or :obj:`-1` to
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derive the size from the first input(s) to the forward method.
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A tuple corresponds to the sizes of source and target
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dimensionalities.
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out_channels (int): Size of each output sample.
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aggr (str or Aggregation, optional): The aggregation scheme to use.
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Any aggregation of :obj:`torch_geometric.nn.aggr` can be used,
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*e.g.*, :obj:`"mean"`, :obj:`"max"`, or :obj:`"lstm"`.
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(default: :obj:`"mean"`)
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project (bool, optional): If set to :obj:`True`, the layer will apply a
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linear transformation followed by an activation function before
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aggregation (as described in Eq. (3) of the paper).
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(default: :obj:`True`)
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bias (bool, optional): If set to :obj:`False`, the layer will not learn
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an additive bias. (default: :obj:`True`)
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**kwargs (optional): Additional arguments of
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:class:`torch_geometric.nn.conv.MessagePassing`.
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Shapes:
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- **inputs:**
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node features :math:`(|\mathcal{V}|, F_{in})` or
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:math:`((|\mathcal{V_s}|, F_{s}), (|\mathcal{V_t}|, F_{t}))`
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if bipartite,
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edge indices :math:`(2, |\mathcal{E}|)`
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- **outputs:** node features :math:`(|\mathcal{V}|, F_{out})` or
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:math:`(|\mathcal{V_t}|, F_{out})` if bipartite
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"""
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def __init__(
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self,
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in_channels: Union[int, Tuple[int, int]],
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out_channels: int,
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aggr: Optional[Union[str, List[str], Aggregation]] = "mean",
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project: bool = True,
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bias: bool = True,
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**kwargs,
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):
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self.in_channels = in_channels
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self.out_channels = out_channels
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self.project = project
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if isinstance(in_channels, int):
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in_channels = (in_channels, in_channels)
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if aggr == "lstm":
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kwargs.setdefault("aggr_kwargs", {})
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kwargs["aggr_kwargs"].setdefault("in_channels", in_channels[0])
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kwargs["aggr_kwargs"].setdefault("out_channels", in_channels[0])
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super().__init__(aggr, **kwargs)
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if self.project:
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if in_channels[0] <= 0:
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raise ValueError(
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f"'{self.__class__.__name__}' does not "
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f"support lazy initialization with "
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f"`project=True`"
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)
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self.lin = Linear(in_channels[0], in_channels[0], bias=True)
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if isinstance(self.aggr_module, MultiAggregation):
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aggr_out_channels = self.aggr_module.get_out_channels(
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in_channels[0]
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)
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else:
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aggr_out_channels = in_channels[0]
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self.lin_l = Linear(aggr_out_channels, out_channels, bias=bias)
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self.lin_r = Linear(in_channels[1], out_channels, bias=False)
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self.reset_parameters()
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def reset_parameters(self):
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super().reset_parameters()
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if self.project:
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self.lin.reset_parameters()
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self.lin_l.reset_parameters()
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self.lin_r.reset_parameters()
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def forward(
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self,
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x: Union[Tensor, OptPairTensor],
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edge_index: Adj,
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size: Size = None,
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) -> Tensor:
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if isinstance(x, Tensor):
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x = (x, x)
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if self.project and hasattr(self, "lin"):
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x = (F.gelu(self.lin(x[0])), x[1])
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# propagate_type: (x: OptPairTensor)
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AX = self.propagate(edge_index, x=x, size=size)
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out = self.lin_l(AX)
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x_r = x[1]
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if x_r is not None:
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out = out + self.lin_r(x_r)
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return out
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def message(self, x_j: Tensor) -> Tensor:
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return x_j
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def message_and_aggregate(self, adj_t: Adj, x: OptPairTensor) -> Tensor:
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if isinstance(adj_t, SparseTensor):
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adj_t = adj_t.set_value(None, layout=None)
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return spmm(adj_t, x[0], reduce=self.aggr)
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def __repr__(self) -> str:
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return (
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f"{self.__class__.__name__}({self.in_channels}, "
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f"{self.out_channels}, aggr={self.aggr})"
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)
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