Unify FFN and TrajMixer model architectures

train_util.py

@@ -300,6 +300,20 @@ def build_optimizer(args: Any, model: DeepHealth) -> AdamW:
     )
 
 
+def get_model_parameter_counts(model: torch.nn.Module) -> Dict[str, int]:
+    """Return stable total and trainable parameter counts."""
+    return {
+        "model_parameter_count": sum(
+            parameter.numel() for parameter in model.parameters()
+        ),
+        "trainable_parameter_count": sum(
+            parameter.numel()
+            for parameter in model.parameters()
+            if parameter.requires_grad
+        ),
+    }
+
+
 def set_optimizer_lr(optimizer: AdamW, lr: float) -> None:
     for param_group in optimizer.param_groups:
         param_group["lr"] = lr