Pdf2 model task (#33)

* add needed dependency

* add extract_model_and_implement pipeline

* add merge_file_to_model_dict_to_model_dict

* implement `rdagent\app\model_implementation\eval.py`

* Running benchmark

* refine import

---------

Co-authored-by: Young <afe.young@gmail.com>

rdagent/components/task_implementation/model_implementation/benchmark/gt_code/dirgnn.py

@@ -74,6 +74,9 @@ class DirGNNConv(torch.nn.Module):
         return f"{self.__class__.__name__}({self.conv_in}, alpha={self.alpha})"
 
 
+model_cls = DirGNNConv
+
+
 if __name__ == "__main__":
     node_features = torch.load("node_features.pt")
     edge_index = torch.load("edge_index.pt")

rdagent/components/task_implementation/model_implementation/benchmark/gt_code/gpsconv.py

@@ -183,6 +183,9 @@ class GPSConv(torch.nn.Module):
         )
 
 
+model_cls = GPSConv
+
+
 if __name__ == "__main__":
     node_features = torch.load("node_features.pt")
     edge_index = torch.load("edge_index.pt")

rdagent/components/task_implementation/model_implementation/benchmark/gt_code/linkx.py

@@ -166,6 +166,7 @@ class LINKX(torch.nn.Module):
             f"out_channels={self.out_channels})"
         )
 
+model_cls = LINKX
 
 if __name__ == "__main__":
     node_features = torch.load("node_features.pt")

rdagent/components/task_implementation/model_implementation/benchmark/gt_code/pmlp.py

@@ -99,17 +99,14 @@ class PMLP(torch.nn.Module):
         return f"{self.__class__.__name__}({self.in_channels}, " f"{self.out_channels}, num_layers={self.num_layers})"
 
 
+model_cls = PMLP
+
 if __name__ == "__main__":
     node_features = torch.load("node_features.pt")
     edge_index = torch.load("edge_index.pt")
 
     # Model instantiation and forward pass
-    model = PMLP(
-        in_channels=node_features.size(-1),
-        hidden_channels=node_features.size(-1),
-        out_channels=node_features.size(-1),
-        num_layers=1,
-    )
+    model = PMLP(in_channels=node_features.size(-1), hidden_channels=node_features.size(-1), out_channels=node_features.size(-1), num_layers=1)
     output = model(node_features, edge_index)
 
     # Save output to a file

rdagent/components/task_implementation/model_implementation/benchmark/gt_code/visnet.py

@@ -1176,6 +1176,9 @@ class ViSNet(torch.nn.Module):
         return y, None
 
 
+model_cls = ViSNet
+
+
 if __name__ == "__main__":
     node_features = torch.load("node_features.pt")
     edge_index = torch.load("edge_index.pt")