fix dataloader

federatedscope/trafficflow/dataloader/traffic_dataloader_v2.py

@@ -205,7 +205,7 @@ def split_into_mini_graphs(tensor, graph_size, dummy_value=0):
     graph_num = (node_num + graph_size - 1) // graph_size  # Round up division
 
     # Initialize output tensor with dummy values
-    output = np.full((timestep, horizon, graph_size, graph_num, dim), dummy_value, dtype=tensor.dtype)
+    output = np.full((timestep, horizon, graph_num, graph_size, dim), dummy_value, dtype=tensor.dtype)
 
     # Fill in the real data
     for i in range(graph_num):
@@ -213,8 +213,8 @@ def split_into_mini_graphs(tensor, graph_size, dummy_value=0):
         end_idx = min(start_idx + graph_size, node_num)  # Ensure we don't exceed the node number
         slice_size = end_idx - start_idx
 
-        # Assign the data to the corresponding mini-graph (adjusted indexing)
+        # Assign the data to the corresponding mini-graph
-        output[:, :, :slice_size, i, :] = tensor[:, :, start_idx:end_idx, :]
+        output[:, :, i, :slice_size, :] = tensor[:, :, start_idx:end_idx, :]
 
     return output
 

federatedscope/trafficflow/model/FedDGCNv2.py

@@ -1,6 +1,4 @@
 from torch.nn import ModuleList
-
-from federatedscope.register import register_model
 import torch
 import torch.nn as nn
 from federatedscope.trafficflow.model.DGCRUCell import DGCRUCell
@@ -71,7 +69,7 @@ class FedDGCN(nn.Module):
         self.end_conv2 = nn.Conv2d(1, args.horizon * self.output_dim, kernel_size=(1, self.hidden_dim), bias=True)
         self.end_conv3 = nn.Conv2d(1, args.horizon * self.output_dim, kernel_size=(1, self.hidden_dim), bias=True)
 
-    def forward(self, source, i=2):
+    def forward(self, source):
         node_embedding1 = self.node_embeddings1
         if self.use_D:
             t_i_d_data   = source[..., 1]
@@ -152,4 +150,27 @@ class FederatedFedDGCN(nn.Module):
         # Reshape the outputs into (batchsize, horizon, subgraph_num, subgraph_size, dims)
         output_tensor = torch.stack(subgraph_outputs, dim=2)  # (batchsize, horizon, subgraph_num, subgraph_size, dims)
 
+        self.update_main_model()
+
         return output_tensor
+
+    def update_main_model(self):
+        """
+        更新 main_model 的参数为 model_list 中所有模型参数的平均值。
+        """
+        # 遍历 main_model 的参数
+        with torch.no_grad():  # 确保更新时不会计算梯度
+            for name, main_param in self.main_model.named_parameters():
+                # 初始化平均值的容器
+                avg_param = torch.zeros_like(main_param)
+
+                # 遍历 model_list 中的所有模型
+                for model in self.model_list:
+                    # 加上当前模型的对应参数
+                    avg_param += model.state_dict()[name]
+
+                # 计算平均值
+                avg_param /= len(self.model_list)
+
+                # 更新 main_model 的参数
+                main_param.copy_(avg_param)

scripts/trafficflow_exp_scripts/D4.yaml

@@ -45,7 +45,7 @@ model:
   use_day: True
   use_week: True
   use_minigraph: True
-  minigraph_size: 3
+  minigraph_size: 10
 train:
   batch_or_epoch: 'epoch'
   local_update_steps: 1