Fix tile CLI (#3204)

* update cli

* fix tiling cli

* update import location

* reinitiate datamodule for OV if tile is enabled

* bring tile-cli-2.0.0 to develop branch

* update style

* fix unit test

src/otx/cli/cli.py

@@ -365,7 +365,7 @@ def instantiate_model(self, model_config: Namespace) -> OTXModel:
         Returns:
             tuple: The model and optimizer and scheduler.
         """
-        from otx.core.model.base import OTXModel
+        from otx.core.model.base import OTXModel, OVModel
 
         skip = set()
 
@@ -393,7 +393,7 @@ def instantiate_model(self, model_config: Namespace) -> OTXModel:
         self.config_init[self.subcommand]["model"] = model
 
         # Update tile config due to adaptive tiling
-        if self.datamodule.config.tile_config.enable_tiler:
+        if not isinstance(model, OVModel) and self.datamodule.config.tile_config.enable_tiler:
             if not hasattr(model, "tile_config"):
                 msg = "The model does not have a tile_config attribute. Please check if the model supports tiling."
                 raise AttributeError(msg)

src/otx/engine/engine.py

@@ -352,13 +352,16 @@ def test(
 
         is_ir_ckpt = Path(str(checkpoint)).suffix in [".xml", ".onnx"]
         if is_ir_ckpt and not isinstance(model, OVModel):
-            datamodule = self._auto_configurator.update_ov_subset_pipeline(datamodule=datamodule, subset="test")
             model = self._auto_configurator.get_ov_model(model_name=str(checkpoint), label_info=datamodule.label_info)
             if self.device.accelerator != "cpu":
                 msg = "IR model supports inference only on CPU device. The device is changed automatic."
                 warn(msg, stacklevel=1)
                 self.device = DeviceType.cpu  # type: ignore[assignment]
 
+        # NOTE: Re-initiate datamodule without tiling as model API supports its own tiling mechanism
+        if isinstance(model, OVModel):
+            datamodule = self._auto_configurator.update_ov_subset_pipeline(datamodule=datamodule, subset="test")
+
         # NOTE, trainer.test takes only lightning based checkpoint.
         # So, it can't take the OTX1.x checkpoint.
         if checkpoint is not None and not is_ir_ckpt:
@@ -440,9 +443,12 @@ def predict(
 
         is_ir_ckpt = checkpoint is not None and Path(checkpoint).suffix in [".xml", ".onnx"]
         if is_ir_ckpt and not isinstance(model, OVModel):
-            datamodule = self._auto_configurator.update_ov_subset_pipeline(datamodule=datamodule, subset="test")
             model = self._auto_configurator.get_ov_model(model_name=str(checkpoint), label_info=datamodule.label_info)
 
+        # NOTE: Re-initiate datamodule for OVModel as model API supports its own data pipeline.
+        if isinstance(model, OVModel):
+            datamodule = self._auto_configurator.update_ov_subset_pipeline(datamodule=datamodule, subset="test")
+
         if checkpoint is not None and not is_ir_ckpt:
             loaded_checkpoint = torch.load(checkpoint)
             model.load_state_dict(loaded_checkpoint)

src/otx/engine/utils/auto_configurator.py

@@ -364,12 +364,15 @@ def update_ov_subset_pipeline(self, datamodule: OTXDataModule, subset: str = "te
         data_configuration = datamodule.config
         ov_test_config = self._load_default_config(model_name="openvino_model")["data"]["config"][f"{subset}_subset"]
         subset_config = getattr(data_configuration, f"{subset}_subset")
+        subset_config.batch_size = ov_test_config["batch_size"]
         subset_config.transform_lib_type = ov_test_config["transform_lib_type"]
         subset_config.transforms = ov_test_config["transforms"]
         data_configuration.tile_config.enable_tiler = False
         msg = (
-            f"For OpenVINO IR models, Update the following {subset} transforms: {subset_config.transforms}"
-            f"and transform_lib_type: {subset_config.transform_lib_type}"
+            f"For OpenVINO IR models, Update the following {subset} \n"
+            f"\t transforms: {subset_config.transforms} \n"
+            f"\t transform_lib_type: {subset_config.transform_lib_type} \n"
+            f"\t batch_size: {subset_config.batch_size} \n"
             "And the tiler is disabled."
         )
         warn(msg, stacklevel=1)

tests/unit/engine/test_engine.py

@@ -2,11 +2,13 @@
 # SPDX-License-Identifier: Apache-2.0
 
 from pathlib import Path
+from unittest.mock import create_autospec
 
 import pytest
 from otx.algo.classification.efficientnet_b0 import EfficientNetB0ForMulticlassCls
 from otx.algo.classification.torchvision_model import OTXTVModel
 from otx.core.config.device import DeviceConfig
+from otx.core.model.base import OVModel
 from otx.core.types.export import OTXExportFormatType
 from otx.core.types.precision import OTXPrecisionType
 from otx.engine import Engine
@@ -123,6 +125,9 @@ def test_testing_with_ov_model(self, fxt_engine, mocker) -> None:
         mock_test.assert_called_once()
         mock_torch_load.assert_not_called()
 
+        fxt_engine.model = create_autospec(OVModel)
+        fxt_engine.test(checkpoint="path/to/model.xml")
+
     def test_prediction_after_training(self, fxt_engine, mocker) -> None:
         mocker.patch("otx.engine.engine.OTXModel.load_state_dict")
         mock_predict = mocker.patch("otx.engine.engine.Trainer.predict")
@@ -137,6 +142,9 @@ def test_prediction_after_training(self, fxt_engine, mocker) -> None:
         fxt_engine.predict(checkpoint="path/to/new/checkpoint")
         mock_torch_load.assert_called_with("path/to/new/checkpoint")
 
+        fxt_engine.model = create_autospec(OVModel)
+        fxt_engine.predict(checkpoint="path/to/model.xml")
+
     def test_prediction_with_ov_model(self, fxt_engine, mocker) -> None:
         mock_predict = mocker.patch("otx.engine.engine.Trainer.predict")
         mock_torch_load = mocker.patch("torch.load")