bring tile-cli-2.0.0 to develop branch

src/otx/engine/engine.py

@@ -349,15 +349,14 @@ 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) and isinstance(datamodule.subsets["test"], OTXTileDataset):
+        if isinstance(model, OVModel):
             datamodule = self._auto_configurator.update_ov_subset_pipeline(datamodule=datamodule, subset="test")
 
         # NOTE, trainer.test takes only lightning based checkpoint.
@@ -441,11 +440,10 @@ 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 without tiling as model API supports its own tiling mechanism
-        if isinstance(model, OVModel) and isinstance(datamodule.subsets["test"], OTXTileDataset):
+        # 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:

tests/unit/engine/test_engine.py

@@ -2,12 +2,14 @@
 # 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.types.export import OTXExportFormatType
+from otx.core.model.entity.base import OVModel
 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")