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

The heterogeneous edge-enhanced graph attentional operator from the “Heterogeneous Edge-Enhanced Graph Attention Network For Multi-Agent Trajectory Prediction” paper, which enhances GATConv by:

  1. type-specific transformations of nodes of different types

  2. edge type and edge feature incorporation, in which edges are assumed to have different types but contain the same kind of attributes

  • in_channels (int) – Size of each input sample, or -1 to derive the size from the first input(s) to the forward method.

  • out_channels (int) – Size of each output sample.

  • num_node_types (int) – The number of node types.

  • num_edge_types (int) – The number of edge types.

  • edge_type_emb_dim (int) – The embedding size of edge types.

  • edge_dim (int) – Edge feature dimensionality.

  • edge_attr_emb_dim (int) – The embedding size of edge features.

  • 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)

  • root_weight (bool, optional) – If set to False, the layer will not add transformed root node features to the output. (default: 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.

  • input: node features \((|\mathcal{V}|, F_{in})\), edge indices \((2, |\mathcal{E}|)\), node types \((|\mathcal{V}|)\), edge types \((|\mathcal{E}|)\), edge features \((|\mathcal{E}|, D)\) (optional)

  • output: node features \((|\mathcal{V}|, F_{out})\)

forward(x: Tensor, edge_index: Union[Tensor, SparseTensor], node_type: Tensor, edge_type: Tensor, edge_attr: Optional[Tensor] = None) Tensor[source]

Runs the forward pass of the module.


Resets all learnable parameters of the module.