CSVLogger: load previous epochs on resume for continuous plots

On init, load existing train.csv/val.csv so that epoch-level metrics
and plots include the full training history after checkpoint resume.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

src/training/train_gtauav.py

@@ -242,13 +242,25 @@ class CSVLogger:
     def __init__(self, output_dir: Path) -> None:
         self.log_dir = output_dir / "logs"
         self.log_dir.mkdir(parents=True, exist_ok=True)
-        self.train_rows: list[dict] = []
-        self.val_rows: list[dict] = []
         self._current_epoch: int = -1
         self._batch_columns: list[str] | None = None
         self._cumulative_batch_path = self.log_dir / "train_batches.csv"
         self._epoch_batch_path: Path | None = None
 
+        # Load existing CSV data on resume (so plots show full history).
+        train_csv = self.log_dir / "train.csv"
+        val_csv = self.log_dir / "val.csv"
+        if train_csv.exists():
+            self.train_rows = pd.read_csv(train_csv).to_dict("records")
+            LOGGER.info("CSVLogger: loaded %d previous train epochs", len(self.train_rows))
+        else:
+            self.train_rows = []
+        if val_csv.exists():
+            self.val_rows = pd.read_csv(val_csv).to_dict("records")
+            LOGGER.info("CSVLogger: loaded %d previous val epochs", len(self.val_rows))
+        else:
+            self.val_rows = []
+
     def log_batch(self, epoch: int, batch_idx: int, global_step: int, metrics: dict) -> None:
         """Log metrics for a single training batch. Writes to disk immediately."""
         row = {"epoch": epoch, "batch": batch_idx, "global_step": global_step, **metrics}