rename whole model using post-fix -model

autogl/module/model/dgl/gcn.py

@@ -114,7 +114,7 @@ class GCN(torch.nn.Module):
         return (prob_adj > 0).nonzero(as_tuple=False).t()
 
 
-@register_model("gcn")
+@register_model("gcn-model")
 class AutoGCN(BaseAutoModel):
     r"""
     AutoGCN.

autogl/module/model/dgl/gin.py

@@ -243,7 +243,7 @@ class GIN(torch.nn.Module):
         # return score_over_layer
 
 
-@register_model("gin")
+@register_model("gin-model")
 class AutoGIN(BaseAutoModel):
     r"""
     AutoGIN. The model used in this automodel is GIN, i.e., the graph isomorphism network from the `"How Powerful are

autogl/module/model/dgl/graphsage.py

@@ -101,7 +101,7 @@ class GraphSAGE(torch.nn.Module):
 
 
 
-@register_model("sage")
+@register_model("sage-model")
 class AutoSAGE(BaseAutoModel):
     r"""
     AutoSAGE. The model used in this automodel is GraphSAGE, i.e., the GraphSAGE from the `"Inductive Representation Learning on

autogl/module/model/dgl/topkpool.py

@@ -193,7 +193,7 @@ class Topkpool(torch.nn.Module):
         return score_over_layer
 
 
-@register_model("topkpool")
+@register_model("topkpool-model")
 class AutoTopkpool(BaseAutoModel):
     r"""
     AutoTopkpool. The model used in this automodel is from https://arxiv.org/abs/1905.05178, https://arxiv.org/abs/1905.02850

autogl/module/model/pyg/gat.py

@@ -114,7 +114,7 @@ class GAT(torch.nn.Module):
         return (prob_adj > 0).nonzero(as_tuple=False).t()
 
 
-@register_model("gat")
+@register_model("gat-model")
 class AutoGAT(BaseAutoModel):
     r"""
     AutoGAT. The model used in this automodel is GAT, i.e., the graph attentional network from the `"Graph Attention Networks"

autogl/module/model/pyg/gcn.py

@@ -284,7 +284,7 @@ class GCN(ClassificationSupportedSequentialModel):
         return (prob_adj > 0).nonzero(as_tuple=False).t()
 
 
-@register_model("gcn")
+@register_model("gcn-model")
 class AutoGCN(BaseAutoModel):
     r"""
     AutoGCN.

autogl/module/model/pyg/gin.py

@@ -113,7 +113,7 @@ class GIN(torch.nn.Module):
         return F.log_softmax(x, dim=1)
 
 
-@register_model("gin")
+@register_model("gin-model")
 class AutoGIN(BaseAutoModel):
     r"""
     AutoGIN. The model used in this automodel is GIN, i.e., the graph isomorphism network from the `"How Powerful are

autogl/module/model/pyg/graphsage.py

@@ -202,7 +202,7 @@ class GraphSAGE(ClassificationSupportedSequentialModel):
         return (prob_adj > 0).nonzero(as_tuple=False).t()
 
 
-@register_model("sage")
+@register_model("sage-model")
 class AutoSAGE(BaseAutoModel):
     r"""
     AutoSAGE. The model used in this automodel is GraphSAGE, i.e., the GraphSAGE from the `"Inductive Representation Learning on

autogl/module/model/pyg/topkpool.py

@@ -84,7 +84,7 @@ class Topkpool(torch.nn.Module):
         return x
 
 
-@register_model("topkpool")
+@register_model("topkpool-model")
 class AutoTopkpool(BaseAutoModel):
     r"""
     AutoTopkpool. The model used in this automodel is from https://arxiv.org/abs/1905.05178, https://arxiv.org/abs/1905.02850