From bc0ad0e227656a1f31117a33721e9431f11a81ca Mon Sep 17 00:00:00 2001
From: QueensGambit <curry-berry@freenet.de>
Date: Tue, 6 Aug 2024 21:04:20 +0200
Subject: [PATCH] Check for additional_loaders not None

---
 .../src/training/trainer_agent_pytorch.py     | 36 ++++++++++---------
 1 file changed, 19 insertions(+), 17 deletions(-)

diff --git a/DeepCrazyhouse/src/training/trainer_agent_pytorch.py b/DeepCrazyhouse/src/training/trainer_agent_pytorch.py
index 26dff358..eeb177fc 100644
--- a/DeepCrazyhouse/src/training/trainer_agent_pytorch.py
+++ b/DeepCrazyhouse/src/training/trainer_agent_pytorch.py
@@ -186,8 +186,9 @@ def train(self, cur_it=None):
                             # log the metric values to tensorboard
                             self._log_metrics(train_metric_values, global_step=self.k_steps, prefix="train_")
                             self._log_metrics(val_metric_values, global_step=self.k_steps, prefix="val_")
-                            for dataset_name, metric_values in additional_metric_values.items():
-                                self._log_metrics(metric_values, global_step=self.k_steps, prefix=f"{dataset_name}_")
+                            if self.additional_loaders is not None:
+                                for dataset_name, metric_values in additional_metric_values.items():
+                                    self._log_metrics(metric_values, global_step=self.k_steps, prefix=f"{dataset_name}_")
 
                             if self.tc.log_metrics_to_tensorboard and self.tc.export_grad_histograms:
                                 grads = []
@@ -332,21 +333,22 @@ def evaluate(self, train_loader):
 
         # do additional evaluations based on self.additional_loaders
         additional_metric_values = dict()
-        for dataset_name, dataloader in self.additional_loaders.items():
-            print(f"starting {dataset_name} eval")
-            metric_values = evaluate_metrics(
-                self.to.metrics,
-                dataloader,
-                self._model,
-                nb_batches=None,
-                ctx=self._ctx,
-                phase_weights={k: 1.0 for k, v in self.to.phase_weights.items()},  # use no weighting
-                sparse_policy_label=self.tc.sparse_policy_label,
-                apply_select_policy_from_plane=self.tc.select_policy_from_plane and not self.tc.is_policy_from_plane_data,
-                use_wdl=self.tc.use_wdl,
-                use_plys_to_end=self.tc.use_plys_to_end,
-            )
-            additional_metric_values[dataset_name] = metric_values
+        if self.additional_loaders is not None:
+            for dataset_name, dataloader in self.additional_loaders.items():
+                print(f"starting {dataset_name} eval")
+                metric_values = evaluate_metrics(
+                    self.to.metrics,
+                    dataloader,
+                    self._model,
+                    nb_batches=None,
+                    ctx=self._ctx,
+                    phase_weights={k: 1.0 for k, v in self.to.phase_weights.items()},  # use no weighting
+                    sparse_policy_label=self.tc.sparse_policy_label,
+                    apply_select_policy_from_plane=self.tc.select_policy_from_plane and not self.tc.is_policy_from_plane_data,
+                    use_wdl=self.tc.use_wdl,
+                    use_plys_to_end=self.tc.use_plys_to_end,
+                )
+                additional_metric_values[dataset_name] = metric_values
 
         self._model.train()  # return back to training mode
         return train_metric_values, val_metric_values, additional_metric_values