Fix a bug that error is raised when train set size is greater than mi…

…nimumof batch size in HPO by exactly 1 (#2760)

deal with HPO edge case

src/otx/cli/utils/hpo.py

@@ -426,23 +426,30 @@ def _align_batch_size_search_space_to_dataset_size(self):
             if "range" in self._hpo_config["hp_space"][batch_size_name]:
                 max_val = self._hpo_config["hp_space"][batch_size_name]["range"][1]
                 min_val = self._hpo_config["hp_space"][batch_size_name]["range"][0]
+                step = 1
+                if self._hpo_config["hp_space"][batch_size_name]["param_type"] in ["quniform", "qloguniform"]:
+                    step = self._hpo_config["hp_space"][batch_size_name]["range"][2]
                 if max_val > self._train_dataset_size:
                     max_val = self._train_dataset_size
                     self._hpo_config["hp_space"][batch_size_name]["range"][1] = max_val
             else:
                 max_val = self._hpo_config["hp_space"][batch_size_name]["max"]
                 min_val = self._hpo_config["hp_space"][batch_size_name]["min"]
+                step = self._hpo_config["hp_space"][batch_size_name].get("step", 1)
 
                 if max_val > self._train_dataset_size:
                     max_val = self._train_dataset_size
                     self._hpo_config["hp_space"][batch_size_name]["max"] = max_val
 
             # If trainset size is lower than min batch size range,
             # fix batch size to trainset size
+            reason_to_fix_bs = ""
             if min_val >= max_val:
-                logger.info(
-                    "Train set size is equal or lower than batch size range. Batch size is fixed to train set size."
-                )
+                reason_to_fix_bs = "Train set size is equal or lower than batch size range."
+            elif max_val - min_val < step:
+                reason_to_fix_bs = "Difference between min and train set size is lesser than step."
+            if reason_to_fix_bs:
+                logger.info(f"{reason_to_fix_bs} Batch size is fixed to train set size.")
                 del self._hpo_config["hp_space"][batch_size_name]
                 self._fixed_hp[batch_size_name] = self._train_dataset_size
                 self._environment.set_hyper_parameter_using_str_key(self._fixed_hp)

tests/unit/cli/utils/test_hpo.py

@@ -1,4 +1,5 @@
 import json
+import yaml
 from copy import deepcopy
 from pathlib import Path
 from tempfile import TemporaryDirectory
@@ -450,6 +451,19 @@ def test_init_wrong_hpo_time_ratio(self, cls_task_env, hpo_time_ratio):
         with pytest.raises(ValueError):
             HpoRunner(cls_task_env, 100, 10, "fake_path", hpo_time_ratio)
 
+    @e2e_pytest_unit
+    @pytest.mark.parametrize("diff_from_min_bs", [0, 1])
+    def test_init_fix_batch_size(self, cls_task_env, diff_from_min_bs):
+        task_env = TaskEnvironmentManager(cls_task_env)
+        with (Path(task_env.get_model_template_path()).parent / "hpo_config.yaml").open() as f:
+            hpo_config = yaml.safe_load(f)
+        batch_size_name = task_env.get_batch_size_name()
+        min_bs = hpo_config["hp_space"][batch_size_name]["range"][0]
+        train_dataset_size = min_bs + diff_from_min_bs
+
+        hpo_runner = HpoRunner(cls_task_env, train_dataset_size, 10, "fake_path")
+        assert batch_size_name in hpo_runner._fixed_hp
+
     @e2e_pytest_unit
     def test_run_hpo(self, mocker, cls_task_env):
         cls_task_env.model = None