update api usecase and fix bug

docs/source/guide/explanation/additional_features/configurable_input_size.rst

@@ -16,7 +16,39 @@ Usage example:
 
     $ otx train \
         --config ... \
-        --data.input_size 512
+
+.. tab-set::
+
+    .. tab-item:: API 1
+
+        .. code-block:: python
+
+            from otx.algo.detection.yolox import YOLOXS
+            from otx.core.data.module import OTXDataModule
+            from otx.engine import Engine
+
+            input_size = (512, 512)
+            model = YOLOXS(label_info=5, input_size=input_size)  # should be tuple[int, int]
+            datamodule = OTXDataModule(..., input_size=input_size)
+            engine = Engine(model=model, datamodule=datamodule)
+            engine.train()
+
+    .. tab-item:: API 2
+
+        .. code-block:: python
+
+            from otx.core.data.module import OTXDataModule
+            from otx.engine import Engine
+
+            datamodule = OTXDataModule(..., input_size=(512, 512))
+            engine = Engine(model="yolox_s", datamodule=datamodule)  # model input size will be aligned with the datamodule input size
+            engine.train()
+
+    .. tab-item:: CLI
+
+        .. code-block:: bash
+
+            (otx) ...$ otx train ... --data.input_size 512
 
 .. _adaptive-input-size:
 
@@ -32,11 +64,30 @@ In "downscale" mode, the input size will either decrease or remain unchanged, en
 
 To activate this feature, use the following command with the desired mode:
 
-.. code-block::
+.. tab-set::
 
-    $ otx train \
-        --config ... \
-        --data.adaptive_input_size "auto | downscale"
+    .. tab-item:: API
+
+        .. code-block:: python
+
+            from otx.algo.detection.yolox import YOLOXS
+            from otx.core.data.module import OTXDataModule
+            from otx.engine import Engine
+
+            datamodule = OTXDataModule(
+                ...
+                adaptive_input_size="auto",  # auto or downscale
+                input_size_multiplier=YOLOXS.input_size_multiplier, # should set the input_size_multiplier of the model
+            )
+            model = YOLOXS(label_info=5, input_size=datamodule.input_size)
+            engine = Engine(model=model, datamodule=datamodule)
+            engine.train()
+
+    .. tab-item:: CLI
+
+        .. code-block:: bash
+
+            (otx) ...$ otx train ... --data.adaptive_input_size "auto | downscale"
 
 The adaptive process includes the following steps:
 

src/otx/engine/engine.py

@@ -143,7 +143,8 @@ def __init__(
         get_model_args: dict[str, Any] = {}
         if self._datamodule is not None:
             get_model_args["label_info"] = self._datamodule.label_info
-            get_model_args["input_size"] = self._datamodule.input_size
+            if (input_size := self._datamodule.input_size) is not None:
+                get_model_args["input_size"] = (input_size, input_size) if isinstance(input_size, int) else input_size
         self._model: OTXModel = (
             model if isinstance(model, OTXModel) else self._auto_configurator.get_model(**get_model_args)
         )

tests/unit/engine/test_engine.py

@@ -72,6 +72,14 @@ def test_model_init(self, tmp_path, mock_datamodule):
         assert engine._model.input_size == (1234, 1234)
         assert engine._model.label_info.num_classes == 4321
 
+    def test_model_init_datamodule_ipt_size_int(self, tmp_path, mock_datamodule):
+        mock_datamodule.input_size = 1234
+        data_root = "tests/assets/classification_dataset"
+        engine = Engine(work_dir=tmp_path, data_root=data_root)
+
+        assert engine._model.input_size == (1234, 1234)
+        assert engine._model.label_info.num_classes == 4321
+
     def test_model_setter(self, fxt_engine, mocker) -> None:
         assert isinstance(fxt_engine.model, TVModelForMulticlassCls)
         fxt_engine.model = "efficientnet_b0"