torch_geometric.nn.conv.GATConv
- class GATConv(in_channels: Union[int, Tuple[int, int]], out_channels: int, heads: int = 1, concat: bool = True, negative_slope: float = 0.2, dropout: float = 0.0, add_self_loops: bool = True, edge_dim: Optional[int] = None, fill_value: Union[float, Tensor, str] = 'mean', bias: bool = True, residual: bool = False, **kwargs)[source]
Bases:
MessagePassingThe graph attentional operator from the “Graph Attention Networks” paper.
\[\mathbf{x}^{\prime}_i = \sum_{j \in \mathcal{N}(i) \cup \{ i \}} \alpha_{i,j}\mathbf{\Theta}_t\mathbf{x}_{j},\]where the attention coefficients \(\alpha_{i,j}\) are computed as
\[\alpha_{i,j} = \frac{ \exp\left(\mathrm{LeakyReLU}\left( \mathbf{a}^{\top}_{s} \mathbf{\Theta}_{s}\mathbf{x}_i + \mathbf{a}^{\top}_{t} \mathbf{\Theta}_{t}\mathbf{x}_j \right)\right)} {\sum_{k \in \mathcal{N}(i) \cup \{ i \}} \exp\left(\mathrm{LeakyReLU}\left( \mathbf{a}^{\top}_{s} \mathbf{\Theta}_{s}\mathbf{x}_i + \mathbf{a}^{\top}_{t}\mathbf{\Theta}_{t}\mathbf{x}_k \right)\right)}.\]If the graph has multi-dimensional edge features \(\mathbf{e}_{i,j}\), the attention coefficients \(\alpha_{i,j}\) are computed as
\[\alpha_{i,j} = \frac{ \exp\left(\mathrm{LeakyReLU}\left( \mathbf{a}^{\top}_{s} \mathbf{\Theta}_{s}\mathbf{x}_i + \mathbf{a}^{\top}_{t} \mathbf{\Theta}_{t}\mathbf{x}_j + \mathbf{a}^{\top}_{e} \mathbf{\Theta}_{e} \mathbf{e}_{i,j} \right)\right)} {\sum_{k \in \mathcal{N}(i) \cup \{ i \}} \exp\left(\mathrm{LeakyReLU}\left( \mathbf{a}^{\top}_{s} \mathbf{\Theta}_{s}\mathbf{x}_i + \mathbf{a}^{\top}_{t} \mathbf{\Theta}_{t}\mathbf{x}_k + \mathbf{a}^{\top}_{e} \mathbf{\Theta}_{e} \mathbf{e}_{i,k} \right)\right)}.\]如果图不是二分图，\(\mathbf{\Theta}_{s} = \mathbf{\Theta}_{t}\)。
- Parameters:
in_channels (int or tuple) – Size of each input sample, or
-1to derive the size from the first input(s) to the forward method. A tuple corresponds to the sizes of source and target dimensionalities in case of a bipartite graph.out_channels (int) – Size of each output sample.
heads (int, optional) – Number of multi-head-attentions. (default:
1)concat (bool, optional) – If set to
False, the multi-head attentions are averaged instead of concatenated. (default:True)negative_slope (float, optional) – LeakyReLU angle of the negative slope. (default:
0.2)dropout (float, optional) – Dropout probability of the normalized attention coefficients which exposes each node to a stochastically sampled neighborhood during training. (default:
0)add_self_loops (bool, optional) – If set to
False, will not add self-loops to the input graph. (default:True)edge_dim (int, optional) – Edge feature dimensionality (in case there are any). (default:
None)fill_value (float 或 torch.Tensor 或 str, 可选) – 生成自环边特征的方式（在
edge_dim != None的情况下）。 如果给定为float或torch.Tensor，自环的边特征将直接由fill_value给出。 如果给定为str，自环的边特征将通过聚合指向特定节点的所有边的特征来计算， 根据一个归约操作。（"add","mean","min","max","mul"）。（默认："mean"）bias (bool, optional) – If set to
False, the layer will not learn an additive bias. (default:True)residual (bool, optional) – If set to
True, the layer will add a learnable skip-connection. (default:False)**kwargs (optional) – Additional arguments of
torch_geometric.nn.conv.MessagePassing.
- Shapes:
input: node features \((|\mathcal{V}|, F_{in})\) or \(((|\mathcal{V_s}|, F_{s}), (|\mathcal{V_t}|, F_{t}))\) if bipartite, edge indices \((2, |\mathcal{E}|)\), edge features \((|\mathcal{E}|, D)\) (optional)
output: node features \((|\mathcal{V}|, H * F_{out})\) or \(((|\mathcal{V}_t|, H * F_{out})\) if bipartite. If
return_attention_weights=True, then \(((|\mathcal{V}|, H * F_{out}), ((2, |\mathcal{E}|), (|\mathcal{E}|, H)))\) or \(((|\mathcal{V_t}|, H * F_{out}), ((2, |\mathcal{E}|), (|\mathcal{E}|, H)))\) if bipartite
- forward(x: Union[Tensor, Tuple[Tensor, Optional[Tensor]]], edge_index: Union[Tensor, SparseTensor], edge_attr: Optional[Tensor] = None, size: Optional[Tuple[int, int]] = None, return_attention_weights: Optional[Tensor] = None) Tensor[source]
- forward(x: Union[Tensor, Tuple[Tensor, Optional[Tensor]]], edge_index: Tensor, edge_attr: Optional[Tensor] = None, size: Optional[Tuple[int, int]] = None, return_attention_weights: bool = None) Tuple[Tensor, Tuple[Tensor, Tensor]]
- forward(x: Union[Tensor, Tuple[Tensor, Optional[Tensor]]], edge_index: SparseTensor, edge_attr: Optional[Tensor] = None, size: Optional[Tuple[int, int]] = None, return_attention_weights: bool = None) Tuple[Tensor, SparseTensor]
运行模块的前向传播。
- Parameters:
x (torch.Tensor or (torch.Tensor, torch.Tensor)) – The input node features.
edge_index (torch.Tensor or SparseTensor) – The edge indices.
edge_attr (torch.Tensor, optional) – The edge features. (default:
None)size ((int, int), optional) – The shape of the adjacency matrix. (default:
None)return_attention_weights (bool, optional) – If set to
True, will additionally return the tuple(edge_index, attention_weights), holding the computed attention weights for each edge. (default:None)
- Return type:
Union[Tensor,Tuple[Tensor,Tuple[Tensor,Tensor]],Tuple[Tensor,SparseTensor]]