update interface

src/otx/algo/detection/atss.py

@@ -56,6 +56,7 @@ def __init__(
             torch_compile=torch_compile,
             tile_config=tile_config,
         )
+        breakpoint()
         self.tile_image_size = tile_image_size
 
     @property

src/otx/algo/instance_segmentation/heads/custom_roi_head.py

@@ -548,7 +548,6 @@ def bbox_loss(self, x: tuple[Tensor], sampling_results: list[SamplingResult], ba
 
 class CustomConvFCBBoxHead(Shared2FCBBoxHead, ClassIncrementalMixin):
     """CustomConvFCBBoxHead class for OTX."""
-    # checked
 
     def loss_and_target(
         self,

src/otx/cli/cli.py

@@ -331,18 +331,16 @@ def instantiate_classes(self, instantiate_engine: bool = True) -> None:
             # For num_classes update, Model and Metric are instantiated separately.
             model_config = self.config[self.subcommand].pop("model")
 
-            input_size = self.config["train"]["engine"].get("input_size")
-            if input_size is not None:
-                if isinstance(input_size, int):
-                    input_size = (input_size, input_size)
-                self.config["train"]["data"]["input_size"] = input_size
-                model_config["init_args"]["input_size"] = tuple(model_config["init_args"]["input_size"][:-2]) + input_size
-
             # Instantiate the things that don't need to special handling
             self.config_init = self.parser.instantiate_classes(self.config)
             self.workspace = self.get_config_value(self.config_init, "workspace")
             self.datamodule = self.get_config_value(self.config_init, "data")
 
+            if (input_size := self.datamodule.input_size) is not None:
+                if isinstance(input_size, int):
+                    input_size = (input_size, input_size)
+                model_config["init_args"]["input_size"] = tuple(model_config["init_args"]["input_size"][:-2]) + input_size
+
             # Instantiate the model and needed components
             self.model = self.instantiate_model(model_config=model_config)
 

src/otx/core/data/module.py

@@ -63,17 +63,22 @@ def __init__(
         auto_num_workers: bool = False,
         device: DeviceType = DeviceType.auto,
         input_size: int | tuple[int, int] | None = None,
+        adaptive_input_size: bool = False,
     ) -> None:
         """Constructor."""
         super().__init__()
         self.task = task
         self.data_format = data_format
         self.data_root = data_root
 
+        if adaptive_input_size:
+            print("adaptive_input_size works")
+
         if input_size is not None:
             for subset_cfg in [train_subset, val_subset, test_subset, unlabeled_subset]:
                 if subset_cfg.input_size is None:
                     subset_cfg.input_size = input_size
+        self.input_size = input_size
 
         self.train_subset = train_subset
         self.val_subset = val_subset

src/otx/engine/engine.py

@@ -122,7 +122,6 @@ def __init__(
         checkpoint: PathLike | None = None,
         device: DeviceType = DeviceType.auto,
         num_devices: int = 1,
-        input_size: Sequence[int] | int | None = None,
         **kwargs,
     ):
         """Initializes the OTX Engine.
@@ -147,17 +146,8 @@ def __init__(
             data_root=data_root,
             task=datamodule.task if datamodule is not None else task,
             model_name=None if isinstance(model, OTXModel) else model,
-            input_size=input_size,
         )
 
-        if input_size is not None:
-            if isinstance(datamodule, OTXDataModule) and datamodule.input_size != input_size:
-                msg = "Data module is already initialized. Input size will be ignored to data module."
-                logging.warning(msg)
-            if isinstance(model, OTXModel) and model.input_size != input_size:
-                msg = "Model is already initialized. Input size will be ignored to model."
-                logging.warning(msg)
-
         self._datamodule: OTXDataModule | None = (
             datamodule if datamodule is not None else self._auto_configurator.get_datamodule()
         )
@@ -169,6 +159,7 @@ def __init__(
             if isinstance(model, OTXModel)
             else self._auto_configurator.get_model(
                 label_info=self._datamodule.label_info if self._datamodule is not None else None,
+                input_size=self._datamodule.input_size,
             )
         )
 

src/otx/engine/utils/auto_configurator.py

@@ -65,7 +65,7 @@
     ],
     "common_semantic_segmentation_with_subset_dirs": [OTXTaskType.SEMANTIC_SEGMENTATION],
     "kinetics": [OTXTaskType.ACTION_CLASSIFICATION],
-    "mvtec_classification": [OTXTaskType.ANOMALY_CLASSIFICATION, OTXTaskType.ANOMALY_DETECTION, OTXTaskType.ANOMALY_SEGMENTATION],
+    "mvtec": [OTXTaskType.ANOMALY_CLASSIFICATION, OTXTaskType.ANOMALY_DETECTION, OTXTaskType.ANOMALY_SEGMENTATION],
 }
 
 OVMODEL_PER_TASK = {
@@ -245,7 +245,7 @@ def get_datamodule(self) -> OTXDataModule | None:
             **data_config,
         )
 
-    def get_model(self, model_name: str | None = None, label_info: LabelInfoTypes | None = None) -> OTXModel:
+    def get_model(self, model_name: str | None = None, label_info: LabelInfoTypes | None = None, input_size: Sequence[int] | None = None) -> OTXModel:
         """Retrieves the OTXModel instance based on the provided model name and meta information.
 
         Args:
@@ -278,6 +278,11 @@ def get_model(self, model_name: str | None = None, label_info: LabelInfoTypes |
 
         model_config = deepcopy(self.config["model"])
 
+        if input_size is not None:
+            if isinstance(input_size, int):
+                input_size = (input_size, input_size)
+            model_config["init_args"]["input_size"] = tuple(model_config["init_args"]["input_size"][:-2]) + input_size
+
         model_cls = get_model_cls_from_config(Namespace(model_config))
 
         if should_pass_label_info(model_cls):