classGATConv(nn.Module): r""" Parameters ---------- in_feats : int, or pair of ints Input feature size; i.e, the number of dimensions of :math:`h_i^{(l)}`. ATConv can be applied on homogeneous graph and unidirectional `bipartite graph <https://docs.dgl.ai/generated/dgl.bipartite.html?highlight=bipartite>`__. If the layer is to be applied to a unidirectional bipartite graph, ``in_feats`` specifies the input feature size on both the source and destination nodes. If a scalar is given, the source and destination node feature size would take the same value. out_feats : int Output feature size; i.e, the number of dimensions of :math:`h_i^{(l+1)}`. num_heads : int Number of heads in Multi-Head Attention. feat_drop : float, optional Dropout rate on feature. Defaults: ``0``. attn_drop : float, optional Dropout rate on attention weight. Defaults: ``0``. negative_slope : float, optional LeakyReLU angle of negative slope. Defaults: ``0.2``. residual : bool, optional If True, use residual connection. Defaults: ``False``. activation : callable activation function/layer or None, optional. If not None, applies an activation function to the updated node features. Default: ``None``. allow_zero_in_degree : bool, optional If there are 0-in-degree nodes in the graph, output for those nodes will be invalid since no message will be passed to those nodes. This is harmful for some applications causing silent performance regression. This module will raise a DGLError if it detects 0-in-degree nodes in input graph. By setting ``True``, it will suppress the check and let the users handle it by themselves. Defaults: ``False``. Note ---- Zero in-degree nodes will lead to invalid output value. This is because no message will be passed to those nodes, the aggregation function will be appied on empty input. A common practice to avoid this is to add a self-loop for each node in the graph if it is homogeneous, which can be achieved by: >>> g = ... # a DGLGraph >>> g = dgl.add_self_loop(g) """
defforward(self, graph, feat, get_attention=False): r""" Description ----------- Compute graph attention network layer. Parameters ---------- graph : DGLGraph The graph. feat : torch.Tensor or pair of torch.Tensor If a torch.Tensor is given, the input feature of shape :math:`(N, D_{in})` where :math:`D_{in}` is size of input feature, :math:`N` is the number of nodes. If a pair of torch.Tensor is given, the pair must contain two tensors of shape :math:`(N_{in}, D_{in_{src}})` and :math:`(N_{out}, D_{in_{dst}})`. get_attention : bool, optional Whether to return the attention values. Default to False. Returns ------- torch.Tensor The output feature of shape :math:`(N, H, D_{out})` where :math:`H` is the number of heads, and :math:`D_{out}` is size of output feature. 这里将Heads直接返回，没有做拼接操作 torch.Tensor, optional The attention values of shape :math:`(E, H, 1)`, where :math:`E` is the number of edges. This is returned only when :attr:`get_attention` is ``True``. Raises ------ DGLError If there are 0-in-degree nodes in the input graph, it will raise DGLError since no message will be passed to those nodes. This will cause invalid output. The error can be ignored by setting ``allow_zero_in_degree`` parameter to ``True``. """
with graph.local_scope(): ifnot self._allow_zero_in_degree: # any() 判断给定的可迭代参数是否全部为 False，则返回 False，如果有一个为 True，则返回 True if (graph.in_degrees() == 0).any(): raise DGLError('There are 0-in-degree nodes in the graph, ' 'output for those nodes will be invalid. ' 'This is harmful for some applications, ' 'causing silent performance regression. ' 'Adding self-loop on the input graph by ' 'calling `g = dgl.add_self_loop(g)` will resolve ' 'the issue. Setting ``allow_zero_in_degree`` ' 'to be `True` when constructing this module will ' 'suppress the check and let the code run.')
