Fix reference info path for zero-shot learning (#3354)

* Apply #3265

* Updates for current develop

* Fix types, docstring, and comments

* Fix unit tests

src/otx/algo/visual_prompting/zero_shot_segment_anything.py

@@ -6,7 +6,7 @@
 from __future__ import annotations
 
 import logging as log
-import os
+import pickle
 from collections import defaultdict
 from copy import deepcopy
 from itertools import product
@@ -625,15 +625,16 @@ def _decide_cascade_results(
 class OTXZeroShotSegmentAnything(OTXZeroShotVisualPromptingModel):
     """Zero-Shot Visual Prompting model."""
 
-    def __init__(
+    def __init__(  # noqa: PLR0913
         self,
         backbone: Literal["tiny_vit", "vit_b"],
         label_info: LabelInfoTypes = NullLabelInfo(),
         optimizer: OptimizerCallable = DefaultOptimizerCallable,
         scheduler: LRSchedulerCallable | LRSchedulerListCallable = DefaultSchedulerCallable,
         metric: MetricCallable = VisualPromptingMetricCallable,
         torch_compile: bool = False,
-        root_reference_info: Path | str = "vpm_zsl_reference_infos",
+        reference_info_dir: Path | str = "reference_infos",
+        infer_reference_info_root: Path | str = "../.latest/train",
         save_outputs: bool = True,
         pixel_mean: list[float] | None = [123.675, 116.28, 103.53],  # noqa: B006
         pixel_std: list[float] | None = [58.395, 57.12, 57.375],  # noqa: B006
@@ -669,7 +670,8 @@ def __init__(
         )
 
         self.save_outputs = save_outputs
-        self.root_reference_info: Path = Path(root_reference_info)
+        self.reference_info_dir: Path = Path(reference_info_dir)
+        self.infer_reference_info_root: Path = Path(infer_reference_info_root)
 
         self.register_buffer("pixel_mean", Tensor(pixel_mean).view(-1, 1, 1), False)
         self.register_buffer("pixel_std", Tensor(pixel_std).view(-1, 1, 1), False)
@@ -877,21 +879,37 @@ def initialize_reference_info(self) -> None:
         self.register_buffer("reference_feats", torch.zeros(0, 1, self.model.embed_dim), False)
         self.register_buffer("used_indices", torch.tensor([], dtype=torch.int64), False)
 
-    def _find_latest_reference_info(self, root: Path) -> str | None:
-        """Find latest reference info to be used."""
-        if not Path.is_dir(root):
-            return None
-        if len(stamps := sorted(os.listdir(root), reverse=True)) > 0:
-            return stamps[0]
-        return None
+    def save_reference_info(self, default_root_dir: Path | str) -> None:
+        """Save reference info."""
+        reference_info = {
+            "reference_feats": self.reference_feats,
+            "used_indices": self.used_indices,
+        }
+        # save reference info
+        path_reference_info: Path = Path(default_root_dir) / self.reference_info_dir / "reference_info.pt"
+        path_reference_info.parent.mkdir(parents=True, exist_ok=True)
+        # TODO (sungchul): ticket no. 139210
+        torch.save(reference_info, path_reference_info)
+        pickle.dump(
+            {k: v.numpy() for k, v in reference_info.items()},
+            path_reference_info.with_suffix(".pickle").open("wb"),
+        )
+        log.info(f"Saved reference info at {path_reference_info}.")
 
-    def load_latest_reference_info(self, device: str | torch.device = "cpu") -> bool:
+    def load_reference_info(self, default_root_dir: Path | str, device: str | torch.device = "cpu") -> bool:
         """Load latest reference info to be used."""
-        if (latest_stamp := self._find_latest_reference_info(self.root_reference_info)) is not None:
-            latest_reference_info = self.root_reference_info / latest_stamp / "reference_info.pt"
-            reference_info = torch.load(latest_reference_info)
+        _infer_reference_info_root: Path = (
+            self.infer_reference_info_root
+            if self.infer_reference_info_root == self.infer_reference_info_root.absolute()
+            else Path(default_root_dir) / self.infer_reference_info_root
+        )
+
+        if (
+            path_reference_info := _infer_reference_info_root / self.reference_info_dir / "reference_info.pt"
+        ).is_file():
+            reference_info = torch.load(path_reference_info)
             retval = True
-            log.info(f"reference info saved at {latest_reference_info} was successfully loaded.")
+            log.info(f"reference info saved at {path_reference_info} was successfully loaded.")
         else:
             reference_info = {}
             retval = False