feat: add auto export metrics to CSV after training with config switch

nerfstudio/engine/trainer.py

@@ -342,9 +342,74 @@
         for callback in self.callbacks:
             callback.run_callback_at_location(step=self.step, location=TrainingCallbackLocation.AFTER_TRAIN)
 
+        # export metrics to CSV
+        self._export_metrics_to_csv()
+
         if not self.config.viewer.quit_on_train_completion:
             self._train_complete_viewer()
 
+    def _export_metrics_to_csv(self) -> None:
+        """Export final training metrics to CSV file.
+
+        This method is called at the end of training to export evaluation metrics
+        to a CSV file located at base_dir/final_metrics.csv.
+        """
+        import csv
+        from pathlib import Path
+
+        # Define output path
+        output_path = Path(self.base_dir) / "final_metrics.csv"
+
+        # Check if we have an eval dataset
+        if not self.pipeline.datamanager.eval_dataset:
+            CONSOLE.log("[yellow]No evaluation dataset found, skipping metrics export.[/yellow]")
+            return
+
+        try:
+            # Get average evaluation metrics across all images
+            CONSOLE.log(f"[cyan]Calculating final evaluation metrics for export...[/cyan]")
+            metrics_dict = self.pipeline.get_average_eval_image_metrics(step=self.step)
+
+            # Convert metrics to a format suitable for CSV
+            # Handle nested dictionaries if any
+            flattened_metrics = {}
+            for key, value in metrics_dict.items():
+                if isinstance(value, dict):
+                    for sub_key, sub_value in value.items():
+                        flattened_metrics[f"{key}/{sub_key}"] = sub_value
+                else:
+                    flattened_metrics[key] = value
+
+            # Write to CSV file
+            with open(output_path, 'w', newline='') as csvfile:
+                csv_writer = csv.writer(csvfile)
+
+                # Write header
+                csv_writer.writerow(['Metric', 'Value'])
+
+                # Write metrics
+                for metric_name, metric_value in flattened_metrics.items():
+                    # Convert to float/int if possible, otherwise keep as string
+                    try:
+                        if isinstance(metric_value, (int, float)):
+                            csv_writer.writerow([metric_name, metric_value])
+                        elif hasattr(metric_value, 'item'):
+                            # Handle torch.Tensor
+                            csv_writer.writerow([metric_name, metric_value.item()])
+                        else:
+                            # Try to convert to float
+                            csv_writer.writerow([metric_name, float(metric_value)])
+                    except (ValueError, TypeError):
+                        # If conversion fails, write as string
+                        csv_writer.writerow([metric_name, str(metric_value)])
+
+            CONSOLE.log(f"[bold][green]Successfully exported metrics to: {output_path}[/bold][/green]")
+
+        except Exception as e:
+            CONSOLE.log(f"[bold][red]Failed to export metrics to CSV: {e}[/bold][/red]")
+            import traceback
+            traceback.print_exc()
+
     @check_main_thread
     def _check_viewer_warnings(self) -> None:
         """Helper to print out any warnings regarding the way the viewer/loggers are enabled"""