Remove runtime parameter type check (#1999)

* Remove check_input_parameters_type() decorators

* Remove params validation tests

* Fix pre-commit

otx/algorithms/action/adapters/mmaction/data/cls_dataset.py

@@ -25,10 +25,6 @@
 from otx.algorithms.action.adapters.mmaction.data.pipelines import RawFrameDecode
 from otx.api.entities.datasets import DatasetEntity
 from otx.api.entities.label import LabelEntity
-from otx.api.utils.argument_checks import (
-    DatasetParamTypeCheck,
-    check_input_parameters_type,
-)
 
 
 # pylint: disable=too-many-instance-attributes
@@ -96,7 +92,6 @@ def __getitem__(self, index: int) -> Dict[str, Any]:
             item = self.video_info[list(self.video_info.keys())[index]]
             return item
 
-    @check_input_parameters_type({"otx_dataset": DatasetParamTypeCheck})
     # pylint: disable=too-many-arguments, invalid-name, super-init-not-called
     # TODO Check need for additional params such as multi_class, with_offset
     def __init__(
@@ -123,7 +118,6 @@ def __len__(self) -> int:
         """Return length of dataset."""
         return len(self.video_infos)
 
-    @check_input_parameters_type()
     def prepare_train_frames(self, idx: int) -> Dict[str, Any]:
         """Get training data and annotations after pipeline.
 
@@ -133,7 +127,6 @@ def prepare_train_frames(self, idx: int) -> Dict[str, Any]:
         item = copy(self.video_infos[idx])  # Copying dict(), not contents
         return self.pipeline(item)
 
-    @check_input_parameters_type()
     def prepare_test_frames(self, idx: int) -> Dict[str, Any]:
         """Get testing data after pipeline.
 

otx/algorithms/action/adapters/mmaction/data/det_dataset.py

@@ -35,10 +35,6 @@
 from otx.api.entities.datasets import DatasetEntity
 from otx.api.entities.label import LabelEntity
 from otx.api.entities.metadata import VideoMetadata
-from otx.api.utils.argument_checks import (
-    DatasetParamTypeCheck,
-    check_input_parameters_type,
-)
 from otx.api.utils.shape_factory import ShapeFactory
 
 root_logger = get_root_logger()
@@ -209,7 +205,6 @@ def _update_annotations(self, metadata: VideoMetadata, anns: List[Annotation]):
                 metadata.update("proposals", proposals[:, :4])
                 metadata.update("scores", proposals[:, 4])
 
-    @check_input_parameters_type({"otx_dataset": DatasetParamTypeCheck})
     # TODO Remove duplicated codes with mmaction's AVADataset
     def __init__(
         self,
@@ -245,7 +240,6 @@ def __init__(
         # TODO. Handle exclude file for AVA dataset
         self.exclude_file = None
 
-    @check_input_parameters_type()
     def prepare_train_frames(self, idx: int) -> Dict[str, Any]:
         """Get training data and annotations after pipeline.
 
@@ -255,7 +249,6 @@ def prepare_train_frames(self, idx: int) -> Dict[str, Any]:
         item = copy(self.video_infos[idx])  # Copying dict(), not contents
         return self.pipeline(item)
 
-    @check_input_parameters_type()
     def prepare_test_frames(self, idx: int) -> Dict[str, Any]:
         """Get testing data after pipeline.
 

otx/algorithms/action/adapters/mmaction/utils/config_utils.py

@@ -26,13 +26,8 @@
 from otx.api.entities.datasets import DatasetEntity
 from otx.api.entities.label import LabelEntity
 from otx.api.entities.model_template import TaskType
-from otx.api.utils.argument_checks import (
-    DatasetParamTypeCheck,
-    check_input_parameters_type,
-)
 
 
-@check_input_parameters_type()
 def patch_config(config: Config, data_pipeline_path: str, work_dir: str, task_type: TaskType):
     """Patch recipe config suitable to mmaction."""
     # FIXME omnisource is hard coded
@@ -47,7 +42,6 @@ def patch_config(config: Config, data_pipeline_path: str, work_dir: str, task_ty
         raise NotImplementedError(f"{task_type} is not supported in action task")
 
 
-@check_input_parameters_type()
 def _patch_cls_datasets(config: Config):
     """Patch cls dataset config suitable to mmaction."""
 
@@ -61,7 +55,6 @@ def _patch_cls_datasets(config: Config):
         cfg.labels = None
 
 
-@check_input_parameters_type()
 def _patch_det_dataset(config: Config):
     """Patch det dataset config suitable to mmaction."""
     assert "data" in config
@@ -72,7 +65,6 @@ def _patch_det_dataset(config: Config):
         cfg.type = "OTXActionDetDataset"
 
 
-@check_input_parameters_type()
 def set_data_classes(config: Config, labels: List[LabelEntity], task_type: TaskType):
     """Setter data classes into config."""
     for subset in ("train", "val", "test"):
@@ -88,7 +80,6 @@ def set_data_classes(config: Config, labels: List[LabelEntity], task_type: TaskT
             config.model["roi_head"]["bbox_head"]["topk"] = len(labels) - 1
 
 
-@check_input_parameters_type({"train_dataset": DatasetParamTypeCheck, "val_dataset": DatasetParamTypeCheck})
 def prepare_for_training(
     config: Union[Config, ConfigDict],
     train_dataset: DatasetEntity,

otx/algorithms/action/adapters/openvino/model_wrappers/openvino_models.py

@@ -21,7 +21,6 @@
 import numpy as np
 
 from otx.api.entities.datasets import DatasetItemEntity
-from otx.api.utils.argument_checks import check_input_parameters_type
 
 try:
     from openvino.model_zoo.model_api.adapters import OpenvinoAdapter
@@ -37,15 +36,13 @@
     warnings.warn(f"{e}, ModelAPI was not found.")
 
 
-@check_input_parameters_type()
 def softmax_numpy(x: np.ndarray):
     """Softmax numpy."""
     x = np.exp(x - np.max(x))
     x /= np.sum(x)
     return x
 
 
-@check_input_parameters_type()
 def get_multiclass_predictions(logits: np.ndarray, activate: bool = True):
     """Get multiclass predictions."""
     index = np.argmax(logits)
@@ -93,7 +90,6 @@ def _get_outputs(self):
                 layer_name = name
         return layer_name
 
-    @check_input_parameters_type()
     def preprocess(self, inputs: List[DatasetItemEntity]):
         """Pre-process."""
         meta = {"original_shape": inputs[0].media.numpy.shape}
@@ -108,14 +104,12 @@ def preprocess(self, inputs: List[DatasetItemEntity]):
         return dict_inputs, meta
 
     @staticmethod
-    @check_input_parameters_type()
     def _reshape(inputs: List[np.ndarray]) -> np.ndarray:
         """Reshape(expand, transpose, permute) the input np.ndarray."""
         np_inputs = np.expand_dims(inputs, axis=(0, 1))  # [1, 1, T, H, W, C]
         np_inputs = np_inputs.transpose(0, 1, -1, 2, 3, 4)  # [1, 1, C, T, H, W]
         return np_inputs
 
-    @check_input_parameters_type()
     # pylint: disable=unused-argument
     def postprocess(self, outputs: Dict[str, np.ndarray], meta: Dict[str, Any]):
         """Post-process."""
@@ -162,7 +156,6 @@ def _get_outputs(self):
                 out_names["labels"] = name
         return out_names
 
-    @check_input_parameters_type()
     def preprocess(self, inputs: List[DatasetItemEntity]):
         """Pre-process."""
         meta = {"original_shape": inputs[0].media.numpy.shape}
@@ -177,14 +170,12 @@ def preprocess(self, inputs: List[DatasetItemEntity]):
         return dict_inputs, meta
 
     @staticmethod
-    @check_input_parameters_type()
     def reshape(inputs: List[np.ndarray]) -> np.ndarray:
         """Reshape(expand, transpose, permute) the input np.ndarray."""
         np_inputs = np.expand_dims(inputs, axis=0)  # [1, T, H, W, C]
         np_inputs = np_inputs.transpose(0, -1, 1, 2, 3)  # [1, C, T, H, W]
         return np_inputs
 
-    @check_input_parameters_type()
     def postprocess(self, outputs: Dict[str, np.ndarray], meta: Dict[str, Any]):
         """Post-process."""
         # TODO Support multi label classification

otx/algorithms/action/tasks/inference.py

@@ -61,10 +61,6 @@
 from otx.api.usecases.tasks.interfaces.export_interface import ExportType, IExportTask
 from otx.api.usecases.tasks.interfaces.inference_interface import IInferenceTask
 from otx.api.usecases.tasks.interfaces.unload_interface import IUnload
-from otx.api.utils.argument_checks import (
-    DatasetParamTypeCheck,
-    check_input_parameters_type,
-)
 from otx.api.utils.vis_utils import get_actmap
 
 logger = get_root_logger()
@@ -74,12 +70,10 @@
 class ActionInferenceTask(BaseTask, IInferenceTask, IExportTask, IEvaluationTask, IUnload):
     """Inference Task Implementation of OTX Action Task."""
 
-    @check_input_parameters_type()
     def __init__(self, task_environment: TaskEnvironment, **kwargs):
         super().__init__(ActionConfig, task_environment, **kwargs)
         self.deploy_cfg = None
 
-    @check_input_parameters_type({"dataset": DatasetParamTypeCheck})
     def infer(
         self,
         dataset: DatasetEntity,
@@ -287,11 +281,7 @@ def _create_model(config: Config, from_scratch: bool = False):
             model = build_model(model_cfg)
         return model
 
-    @check_input_parameters_type()
-    def evaluate(
-        self,
-        output_resultset: ResultSetEntity,
-    ):
+    def evaluate(self, output_resultset: ResultSetEntity, evaluation_metric: Optional[str] = None):
         """Evaluate function of OTX Action Task."""
         logger.info("called evaluate()")
         self._remove_empty_frames(output_resultset.ground_truth_dataset)
@@ -321,7 +311,6 @@ def unload(self):
         if self._work_dir_is_temp:
             self._delete_scratch_space()
 
-    @check_input_parameters_type()
     def export(
         self,
         export_type: ExportType,

otx/algorithms/action/tasks/openvino.py

@@ -69,10 +69,6 @@
     IOptimizationTask,
     OptimizationType,
 )
-from otx.api.utils.argument_checks import (
-    DatasetParamTypeCheck,
-    check_input_parameters_type,
-)
 
 try:
     from openvino.model_zoo.model_api.adapters import OpenvinoAdapter, create_core
@@ -89,7 +85,6 @@
 class ActionOpenVINOInferencer(BaseInferencer):
     """ActionOpenVINOInferencer class in OpenVINO task for action recognition."""
 
-    @check_input_parameters_type()
     def __init__(
         self,
         task_type: str,
@@ -126,12 +121,10 @@ def __init__(
         else:
             self.converter = DetectionBoxToAnnotationConverter(self.label_schema)
 
-    @check_input_parameters_type()
     def pre_process(self, image: List[DatasetItemEntity]) -> Tuple[Dict[str, np.ndarray], Dict[str, Any]]:
         """Pre-process function of OpenVINO Inferencer for Action Recognition."""
         return self.model.preprocess(image)
 
-    @check_input_parameters_type()
     def post_process(
         self, prediction: Dict[str, np.ndarray], metadata: Dict[str, Any]
     ) -> Optional[AnnotationSceneEntity]:
@@ -140,15 +133,13 @@ def post_process(
         prediction = self.model.postprocess(prediction, metadata)
         return self.converter.convert_to_annotation(prediction, metadata)
 
-    @check_input_parameters_type()
     def predict(self, image: List[DatasetItemEntity]) -> AnnotationSceneEntity:
         """Predict function of OpenVINO Action Inferencer for Action Recognition."""
         data, metadata = self.pre_process(image)
         raw_predictions = self.forward(data)
         predictions = self.post_process(raw_predictions, metadata)
         return predictions
 
-    # @check_input_parameters_type()
     def forward(self, image: Dict[str, np.ndarray]) -> Dict[str, np.ndarray]:
         """Forward function of OpenVINO Action Inferencer for Action Recognition."""
 
@@ -158,13 +149,11 @@ def forward(self, image: Dict[str, np.ndarray]) -> Dict[str, np.ndarray]:
 class DataLoaderWrapper(DataLoader):
     """DataLoader implementation for ActionOpenVINOTask."""
 
-    @check_input_parameters_type()
     def __init__(self, dataloader: DataLoader, inferencer: BaseInferencer):
         super().__init__(config=None)
         self.dataloader = dataloader
         self.inferencer = inferencer
 
-    @check_input_parameters_type()
     def __getitem__(self, index: int):
         """Get item from dataset."""
         item = self.dataloader[index]
@@ -180,7 +169,6 @@ def __len__(self):
 class ActionOpenVINOTask(IDeploymentTask, IInferenceTask, IEvaluationTask, IOptimizationTask):
     """Task implementation for OTX Action Recognition using OpenVINO backend."""
 
-    @check_input_parameters_type()
     def __init__(self, task_environment: TaskEnvironment):
         self.task_environment = task_environment
         self.hparams = self.task_environment.get_hyper_parameters(ActionConfig)
@@ -203,7 +191,6 @@ def load_inferencer(self) -> ActionOpenVINOInferencer:
         )
 
     # pylint: disable=no-value-for-parameter
-    @check_input_parameters_type({"dataset": DatasetParamTypeCheck})
     def infer(
         self, dataset: DatasetEntity, inference_parameters: Optional[InferenceParameters] = None
     ) -> DatasetEntity:
@@ -223,7 +210,6 @@ def infer(
             update_progress_callback(int(i / dataset_size * 100))
         return dataset
 
-    @check_input_parameters_type()
     def evaluate(self, output_resultset: ResultSetEntity, evaluation_metric: Optional[str] = None):
         """Evaluate function of OpenVINOTask."""
 
@@ -234,7 +220,6 @@ def evaluate(self, output_resultset: ResultSetEntity, evaluation_metric: Optiona
         elif self.task_type == "ACTION_DETECTION":
             output_resultset.performance = MetricsHelper.compute_f_measure(output_resultset).get_performance()
 
-    @check_input_parameters_type()
     def deploy(self, output_model: ModelEntity) -> None:
         """Deploy function of OpenVINOTask."""
 
@@ -271,7 +256,6 @@ def deploy(self, output_model: ModelEntity) -> None:
         output_model.exportable_code = zip_buffer.getvalue()
         logger.info("Deploying completed")
 
-    @check_input_parameters_type({"dataset": DatasetParamTypeCheck})
     def optimize(
         self,
         optimization_type: OptimizationType,

otx/algorithms/action/utils/data.py

@@ -32,10 +32,8 @@
 from otx.api.entities.scored_label import ScoredLabel
 from otx.api.entities.shapes.rectangle import Rectangle
 from otx.api.entities.subset import Subset
-from otx.api.utils.argument_checks import check_input_parameters_type
 
 
-@check_input_parameters_type()
 def find_label_by_name(labels: List[LabelEntity], name: str, domain: Domain):
     """Return label from name."""
     matching_labels = [label for label in labels if label.name == name]

otx/algorithms/classification/adapters/mmcls/datasets/otx_datasets.py

@@ -22,10 +22,6 @@
 from otx.api.entities.datasets import DatasetEntity
 from otx.api.entities.id import ID
 from otx.api.entities.label import LabelEntity
-from otx.api.utils.argument_checks import (
-    DatasetParamTypeCheck,
-    check_input_parameters_type,
-)
 
 logger = get_logger()
 
@@ -35,7 +31,6 @@
 class OTXClsDataset(BaseDataset):
     """Multi-class classification dataset class."""
 
-    @check_input_parameters_type({"otx_dataset": DatasetParamTypeCheck})
     def __init__(
         self, otx_dataset: DatasetEntity, labels: List[LabelEntity], empty_label=None, **kwargs
     ):  # pylint: disable=super-init-not-called
@@ -83,7 +78,6 @@ def load_annotations(self):
             self.gt_labels.append(class_indices)
         self.gt_labels = np.array(self.gt_labels)
 
-    @check_input_parameters_type()
     def __getitem__(self, index: int):
         """Get item from dataset."""
         dataset = self.otx_dataset
@@ -435,7 +429,6 @@ class SelfSLDataset(Dataset):
 
     CLASSES = None
 
-    @check_input_parameters_type({"otx_dataset": DatasetParamTypeCheck})
     def __init__(
         self, otx_dataset: DatasetEntity, pipeline: Dict[str, Any], **kwargs
     ):  # pylint: disable=unused-argument