Upload Quasar_axrvi_ranker.py

Quasar_axrvi_ranker.py

@@ -2254,7 +2254,8 @@ class MoETemporalEncoder(nn.Module):
         self.router = nn.Linear(input_dim, n_experts)
     
     def forward(self, x: torch.Tensor) -> torch.Tensor:
-        BN, T, F = x.shape
+       
+        BN, T, feat_dim = x.shape
         
         # Routing from first bar
         router_logits = self.router(x[:, 0, :])
@@ -2528,10 +2529,11 @@ class AXRVINet(nn.Module):
         Returns:
             Dictionary with all outputs
         """
-        B, N, T, F = sequences.shape
-        
+       
+        B, N, T, feat_dim = sequences.shape
+
         # Temporal encoding
-        h = self.temporal(sequences.view(B * N, T, F)).view(B, N, self.d_model)
+        h = self.temporal(sequences.view(B * N, T, feat_dim)).view(B, N, self.d_model)
         
         # Cross-asset processing
         total_gate_entropy = torch.tensor(0.0, device=h.device)