claude_refactor_v3: Updated main (entry point), trainer_new (last version of train_gtauav), check: is extracted evluate() from train to evaluator.py correct in new context

src/training/trainer_new.py

@@ -3,13 +3,13 @@ from __future__ import annotations
 """Trainer for CVGL caption test on GTA-UAV-LR.
 
 Decomposed from src/training/train_gtauav.py::train into a class with one
-orchestrating method `run()` plus dedicated `_setup_*` / `_build_*` /
+orchestrating method `train()` plus dedicated `_setup_*` / `_build_*` /
 `_train_*` / `_evaluate_*` methods.
 
 Lifecycle:
-    Trainer(...) → run() → done.
+    Trainer(...) → train() → done.
 
-`run()` calls _build_* in dependency order, then _train_loop, then
+`train()` calls _build_* in dependency order, then _train_loop, then
 _final_evaluation; cleanup is in a `finally` block.
 
 Currently supports DINOv3 and StripNet backbones only. SOFIA v1/v7.1 model
@@ -80,7 +80,7 @@ _SUPPORTED_BACKBONES: frozenset[str] = frozenset({"dinov3", "stripnet"})
 
 
 def _build_param_groups(
-    model: nn.Module,
+    model: AsymmetricEncoder,
     lr: float,
     text_lr_factor: float,
     stripnet_backbone_lr_factor: float = 0.1,
@@ -130,7 +130,7 @@ def _cosine_warmup_schedule(warmup_steps: int, total_steps: int):
 
 
 def _embed_drone_queries(
-    model: nn.Module,
+    model: AsymmetricEncoder,
     train_ds: GTAUAVDataset,
     device: str,
     batch_size: int,
@@ -155,7 +155,7 @@ def _embed_drone_queries(
     )
     all_embs: list[torch.Tensor] = []
     with torch.inference_mode():
-        for batch in tqdm(loader, desc="dss-embed", unit="batch", leave=False):
+        for batch in tqdm(loader, desc="  dss-embed-queries", unit="batch", leave=False):
             drone_img = batch["drone_img"].to(device, non_blocking=True)
             altitude = batch.get("altitude")
             if altitude is not None:
@@ -178,7 +178,7 @@ class Trainer:
 
     All gin parameters arrive as 6 config objects; runtime state (model,
     optimizer, loaders, ...) is built lazily by _build_* methods and lives
-    on `self`. `run()` calls them in dependency order.
+    on `self`. `train()` calls them in dependency order.
 
     Backbones supported: 'dinov3', 'stripnet'.
     """
@@ -232,7 +232,7 @@ class Trainer:
     # Public entry point
     # ===================================================================
 
-    def run(self) -> None:
+    def train(self) -> None:
         """Full pipeline: setup → build → train → evaluate → cleanup."""
         self._validate_backbone()
         clear_vram()
@@ -1052,4 +1052,3 @@ class Trainer:
         if self.tracker is not None:
             self.tracker.close()
 
-