torch_geometric.nn.conv.HEATConv
- class HEATConv(in_channels: int, out_channels: int, num_node_types: int, num_edge_types: int, edge_type_emb_dim: int, edge_dim: int, edge_attr_emb_dim: int, heads: int = 1, concat: bool = True, negative_slope: float = 0.2, dropout: float = 0.0, root_weight: bool = True, bias: bool = True, **kwargs)[source]
Bases:
MessagePassing来自“Heterogeneous Edge-Enhanced Graph Attention Network For Multi-Agent Trajectory Prediction”论文的异构边缘增强图注意力操作符。
不同类型节点的特定类型转换
边类型和边特征结合，其中假设边具有不同的类型但包含相同类型的属性
- Parameters:
in_channels (int) – Size of each input sample, or
-1to derive the size from the first input(s) to the forward method.out_channels (int) – Size of each output sample.
num_node_types (int) – 节点类型的数量。
num_edge_types (int) – 边的类型数量。
edge_type_emb_dim (int) – 边类型的嵌入大小。
edge_dim (int) – Edge feature dimensionality.
edge_attr_emb_dim (int) – 边特征的嵌入大小。
heads (int, optional) – Number of multi-head-attentions. (default:
1)concat (bool, 可选) – 如果设置为
False，多头注意力机制将被平均而不是连接。 (默认: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)root_weight (bool, 可选) – 如果设置为
False，该层将 不会将转换后的根节点特征添加到输出中。 (默认:True)bias (bool, optional) – If set to
False, the layer will not learn an additive bias. (default:True)**kwargs (optional) – Additional arguments of
torch_geometric.nn.conv.MessagePassing.
- Shapes:
输入: 节点特征 \((|\mathcal{V}|, F_{in})\), 边索引 \((2, |\mathcal{E}|)\), 节点类型 \((|\mathcal{V}|)\), 边类型 \((|\mathcal{E}|)\), 边特征 \((|\mathcal{E}|, D)\) (可选)
output: node features \((|\mathcal{V}|, F_{out})\)