From f0c0ff8492783f0c9c6b0ba8ea77317f7ef57054 Mon Sep 17 00:00:00 2001
From: klemen1999 <klemenskrlj8@gmail.com>
Date: Mon, 16 Sep 2024 21:30:59 +0200
Subject: [PATCH] Corrected config valid sequence for predefined models

---
 luxonis_train/utils/config.py | 42 +++++++++++++++++------------------
 1 file changed, 21 insertions(+), 21 deletions(-)

diff --git a/luxonis_train/utils/config.py b/luxonis_train/utils/config.py
index bdcd13dc..31e4fe5b 100644
--- a/luxonis_train/utils/config.py
+++ b/luxonis_train/utils/config.py
@@ -77,27 +77,6 @@ class ModelConfig(BaseModelExtraForbid):
     visualizers: list[AttachedModuleConfig] = []
     outputs: list[str] = []
 
-    @model_validator(mode="after")
-    def check_main_metric(self) -> Self:
-        for metric in self.metrics:
-            if metric.is_main_metric:
-                logger.info(f"Main metric: `{metric.name}`")
-                return self
-
-        logger.warning("No main metric specified.")
-        if self.metrics:
-            metric = self.metrics[0]
-            metric.is_main_metric = True
-            name = metric.alias or metric.name
-            logger.info(f"Setting '{name}' as main metric.")
-        else:
-            logger.error(
-                "No metrics specified. "
-                "This is likely unintended unless "
-                "the configuration is not used for training."
-            )
-        return self
-
     @model_validator(mode="after")
     def check_predefined_model(self) -> Self:
         from luxonis_train.utils.registry import MODELS
@@ -120,6 +99,27 @@ def check_predefined_model(self) -> Self:
 
         return self
 
+    @model_validator(mode="after")
+    def check_main_metric(self) -> Self:
+        for metric in self.metrics:
+            if metric.is_main_metric:
+                logger.info(f"Main metric: `{metric.name}`")
+                return self
+
+        logger.warning("No main metric specified.")
+        if self.metrics:
+            metric = self.metrics[0]
+            metric.is_main_metric = True
+            name = metric.alias or metric.name
+            logger.info(f"Setting '{name}' as main metric.")
+        else:
+            logger.error(
+                "No metrics specified. "
+                "This is likely unintended unless "
+                "the configuration is not used for training."
+            )
+        return self
+
     @model_validator(mode="after")
     def check_graph(self) -> Self:
         from luxonis_train.utils.general import is_acyclic