[Fix] normalize by dst if edge_weight is None (dmlc#3744)

* [Fix] normalize by dst if edge_weight is None * [Doc] fix math formula display issue
sanchit-misra · Feb 17, 2022 · 22272de · 22272de
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commit 22272de
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Showing 4 changed files with 7 additions and 8 deletions.
diff --git a/python/dgl/nn/pytorch/conv/appnpconv.py b/python/dgl/nn/pytorch/conv/appnpconv.py
@@ -86,7 +86,7 @@ def forward(self, graph, feat, edge_weight=None):
         edge_weight: torch.Tensor, optional
             edge_weight to use in the message passing process. This is equivalent to
             using weighted adjacency matrix in the equation above, and
-            :math:\tilde{D}^{-1/2}\tilde{A} \tilde{D}^{-1/2}
+            :math:`\tilde{D}^{-1/2}\tilde{A} \tilde{D}^{-1/2}`
             is based on :class:`dgl.nn.pytorch.conv.graphconv.EdgeWeightNorm`.
 
         Returns
@@ -114,10 +114,9 @@ def forward(self, graph, feat, edge_weight=None):
                 if edge_weight is None:
                     feat = feat * src_norm
                 graph.ndata['h'] = feat
-                if edge_weight is None:
-                    edge_weight = th.ones(graph.number_of_edges(), 1)
-                graph.edata['w'] = self.edge_drop(
-                    edge_weight).to(feat.device)
+                w = th.ones(graph.number_of_edges(),
+                            1) if edge_weight is None else edge_weight
+                graph.edata['w'] = self.edge_drop(w).to(feat.device)
                 graph.update_all(fn.u_mul_e('h', 'w', 'm'),
                                  fn.sum('m', 'h'))
                 feat = graph.ndata.pop('h')

diff --git a/python/dgl/nn/pytorch/conv/gcn2conv.py b/python/dgl/nn/pytorch/conv/gcn2conv.py
@@ -192,7 +192,7 @@ def forward(self, graph, feat, feat_0, edge_weight=None):
         edge_weight: torch.Tensor, optional
             edge_weight to use in the message passing process. This is equivalent to
             using weighted adjacency matrix in the equation above, and
-            :math:\tilde{D}^{-1/2}\tilde{A} \tilde{D}^{-1/2}
+            :math:`\tilde{D}^{-1/2}\tilde{A} \tilde{D}^{-1/2}`
             is based on :class:`dgl.nn.pytorch.conv.graphconv.EdgeWeightNorm`.
 
 

diff --git a/python/dgl/nn/pytorch/conv/sgconv.py b/python/dgl/nn/pytorch/conv/sgconv.py
@@ -150,7 +150,7 @@ def forward(self, graph, feat, edge_weight=None):
         edge_weight: torch.Tensor, optional
             edge_weight to use in the message passing process. This is equivalent to
             using weighted adjacency matrix in the equation above, and
-            :math:\tilde{D}^{-1/2}\tilde{A} \tilde{D}^{-1/2}
+            :math:`\tilde{D}^{-1/2}\tilde{A} \tilde{D}^{-1/2}`
             is based on :class:`dgl.nn.pytorch.conv.graphconv.EdgeWeightNorm`.
 
         Returns

diff --git a/python/dgl/nn/pytorch/conv/tagconv.py b/python/dgl/nn/pytorch/conv/tagconv.py
@@ -108,7 +108,7 @@ def forward(self, graph, feat, edge_weight=None):
         edge_weight: torch.Tensor, optional
             edge_weight to use in the message passing process. This is equivalent to
             using weighted adjacency matrix in the equation above, and
-            :math:\tilde{D}^{-1/2}\tilde{A} \tilde{D}^{-1/2}
+            :math:`\tilde{D}^{-1/2}\tilde{A} \tilde{D}^{-1/2}`
             is based on :class:`dgl.nn.pytorch.conv.graphconv.EdgeWeightNorm`.
 
         Returns