CFConv¶
-
class
dgl.nn.pytorch.conv.
CFConv
(node_in_feats, edge_in_feats, hidden_feats, out_feats)[source]¶ Bases:
torch.nn.modules.module.Module
CFConv from SchNet: A continuous-filter convolutional neural network for modeling quantum interactions
It combines node and edge features in message passing and updates node representations.
\[h_i^{(l+1)} = \sum_{j\in \mathcal{N}(i)} h_j^{l} \circ W^{(l)}e_ij\]where \(\circ\) represents element-wise multiplication and for \(\text{SPP}\) :
\[\text{SSP}(x) = \frac{1}{\beta} * \log(1 + \exp(\beta * x)) - \log(\text{shift})\]- Parameters
Example
>>> import dgl >>> import numpy as np >>> import torch as th >>> from dgl.nn import CFConv >>> g = dgl.graph(([0,1,2,3,2,5], [1,2,3,4,0,3])) >>> nfeat = th.ones(6, 10) >>> efeat = th.ones(6, 5) >>> conv = CFConv(10, 5, 3, 2) >>> res = conv(g, nfeat, efeat) >>> res tensor([[-0.1209, -0.2289], [-0.1209, -0.2289], [-0.1209, -0.2289], [-0.1135, -0.2338], [-0.1209, -0.2289], [-0.1283, -0.2240]], grad_fn=<SubBackward0>)
-
forward
(g, node_feats, edge_feats)[source]¶ Performs message passing and updates node representations.
- Parameters
g (DGLGraph) – The graph.
node_feats (torch.Tensor or pair of torch.Tensor) – The input node features. If a torch.Tensor is given, it represents the input node feature of shape \((N, D_{in})\) where \(D_{in}\) is size of input feature, \(N\) is the number of nodes. If a pair of torch.Tensor is given, which is the case for bipartite graph, the pair must contain two tensors of shape \((N_{src}, D_{in_{src}})\) and \((N_{dst}, D_{in_{dst}})\) separately for the source and destination nodes.
edge_feats (torch.Tensor) – The input edge feature of shape \((E, edge_in_feats)\) where \(E\) is the number of edges.
- Returns
The output node feature of shape \((N_{out}, out_feats)\) where \(N_{out}\) is the number of destination nodes.
- Return type
torch.Tensor