Merge pull request #106 from BUPT-GAMMA/docs

[Docs]fix docs bugs
BUPT-GAMMA · Jun 26, 2022 · ad77ad6 · ad77ad6
2 parents 78c8684 + 02639c8
commit ad77ad6
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Showing 4 changed files with 22 additions and 17 deletions.
diff --git a/openhgnn/layers/HeteroLinear.py b/openhgnn/layers/HeteroLinear.py
@@ -68,15 +68,15 @@ class HeteroLinearLayer(nn.Module):
     Examples
     ----------
 
-    >>>import torch as th
-    >>>linear_dict = {}
-    >>>linear_dict['author'] = [110, 64]
-    >>>linear_dict['paper'] = [128,64]
-    >>>h_dict = {}
-    >>>h_dict['author'] = th.tensor(10, 110)
-    >>>h_dict['paper'] = th.tensor(5, 128)
-    >>>layer = HeteroLinearLayer(linear_dict)
-    >>>out_dict = layer(h_dict)
+    >>> import torch as th
+    >>> linear_dict = {}
+    >>> linear_dict['author'] = [110, 64]
+    >>> linear_dict['paper'] = [128,64]
+    >>> h_dict = {}
+    >>> h_dict['author'] = th.tensor(10, 110)
+    >>> h_dict['paper'] = th.tensor(5, 128)
+    >>> layer = HeteroLinearLayer(linear_dict)
+    >>> out_dict = layer(h_dict)
 
     """
     def __init__(self, linear_dict, act=None, dropout=0.0, has_l2norm=True, has_bn=True, **kwargs):

diff --git a/openhgnn/layers/macro_layer/ATTConv.py b/openhgnn/layers/macro_layer/ATTConv.py
@@ -74,6 +74,8 @@ def forward(self, hg, h_neigh, h_center):
 
 class MacroConv(nn.Module):
     """
+    MacroConv
+    
     Parameters
     ----------
     in_feats : int

diff --git a/openhgnn/layers/micro_layer/HGConv.py b/openhgnn/layers/micro_layer/HGConv.py
@@ -49,14 +49,20 @@ def forward(self,
                 src_node_transformation_weight: nn.Parameter,
                 src_nodes_attention_weight: nn.Parameter):
         r"""Compute graph attention network layer.
+        
         Parameters
         ----------
-        graph : specific relational DGLHeteroGraph
-        feat : pair of torch.Tensor
+        graph: 
+            specific relational DGLHeteroGraph
+        feat: pair of torch.Tensor
             The pair contains two tensors of shape (N_{in}, D_{in_{src}})` and (N_{out}, D_{in_{dst}}).
-        dst_node_transformation_weight: Parameter (input_dst_dim, n_heads * hidden_dim)
-        src_node_transformation_weight: Parameter (input_src_dim, n_heads * hidden_dim)
-        src_nodes_attention_weight: Parameter (n_heads, 2 * hidden_dim)
+        dst_node_transformation_weight: 
+            Parameter (input_dst_dim, n_heads * hidden_dim)
+        src_node_transformation_weight: 
+            Parameter (input_src_dim, n_heads * hidden_dim)
+        src_nodes_attention_weight: 
+            Parameter (n_heads, 2 * hidden_dim)
+
         Returns
         -------
         torch.Tensor, shape (N, H, D_out)` where H is the number of heads, and D_out is size of output feature.

diff --git a/openhgnn/models/SLiCE.py b/openhgnn/models/SLiCE.py
@@ -17,9 +17,6 @@ def get_norm_id(id_map, some_id):
         id_map[some_id] = len(id_map)
     return id_map[some_id]
 
-"""
-从大图中根据一组边获得一个子图
-"""
 def norm_graph(node_id_map, edge_id_map, edge_list):
     norm_edge_list = []
     for e in edge_list: