torch_geometric.nn.conv.FeaStConv
- class FeaStConv(in_channels: int, out_channels: int, heads: int = 1, add_self_loops: bool = True, bias: bool = True, **kwargs)[source]
Bases:
MessagePassing来自“FeaStNet: Feature-Steered Graph Convolutions for 3D Shape Analysis”论文的（平移不变）特征导向卷积算子。
\[\mathbf{x}^{\prime}_i = \frac{1}{|\mathcal{N}(i)|} \sum_{j \in \mathcal{N}(i)} \sum_{h=1}^H q_h(\mathbf{x}_i, \mathbf{x}_j) \mathbf{W}_h \mathbf{x}_j\]使用 \(q_h(\mathbf{x}_i, \mathbf{x}_j) = \mathrm{softmax}_j (\mathbf{u}_h^{\top} (\mathbf{x}_j - \mathbf{x}_i) + c_h)\)，其中 \(H\) 表示注意力头的数量，\(\mathbf{W}_h\)、 \(\mathbf{u}_h\) 和 \(c_h\) 是可训练的参数。
- 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.
heads (int, optional) – 注意力头的数量 \(H\)。 (默认:
1)add_self_loops (bool, optional) – If set to
False, will not add self-loops to the input graph. (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.
- Shapes:
输入: 节点特征 \((|\mathcal{V}|, F_{in})\) 或 \(((|\mathcal{V_s}|, F_{in}), (|\mathcal{V_t}|, F_{in}))\) 如果是二分图， 边索引 \((2, |\mathcal{E}|)\)
output: node features \((|\mathcal{V}|, F_{out})\) or \((|\mathcal{V_t}|, F_{out})\) if bipartite