OpenVINO NNCF updates

CHANGELOG.md

@@ -10,6 +10,8 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/).
 
 ### Changed
 
+- Changed default inference device to AUTO in https://github.com/openvinotoolkit/anomalib/pull/1534
+
 ### Deprecated
 
 - Support only Python 3.10 and greater in https://github.com/openvinotoolkit/anomalib/pull/1299

requirements/openvino.txt

@@ -1,6 +1,3 @@
-defusedxml==0.7.1
-requests>=2.26.0
-networkx~=2.5
-nncf>=2.1.0
-onnx>=1.10.1
-openvino-dev>=2022.3.0
+openvino-dev>=2023.0
+nncf>=2.5.0
+onnx>=1.13.1

src/anomalib/cli/cli.py

@@ -303,7 +303,7 @@ def train(self) -> Callable[..., _EVALUATE_OUTPUT]:
         return self.engine.train
 
     @property
-    def export(self) -> Callable[..., None]:
+    def export(self) -> Callable[..., Path | None]:
         """Export the model using engine's export method."""
         return self.engine.export
 

src/anomalib/deploy/export.py

@@ -52,7 +52,7 @@ def export_to_torch(
     export_path: Path | str,
     transform: dict[str, Any] | AnomalibDataset | AnomalibDataModule | A.Compose,
     task: TaskType | None = None,
-) -> None:
+) -> Path:
     """Export AnomalibModel to torch.
 
     Args:
@@ -64,6 +64,9 @@ def export_to_torch(
         task (TaskType | None): Task type should be provided if transforms is of type dict or A.Compose object.
             Defaults to ``None``.
 
+    Returns:
+        Path: Path to the exported pytorch model.
+
     Examples:
         Assume that we have a model to train and we want to export it to torch format.
 
@@ -92,10 +95,12 @@ def export_to_torch(
     """
     export_path = _create_export_path(export_path, ExportMode.TORCH)
     metadata = get_metadata(task=task, transform=transform, model=model)
+    pt_model_path = export_path / "model.pt"
     torch.save(
         obj={"model": model.model, "metadata": metadata},
-        f=export_path / "model.pt",
+        f=pt_model_path,
     )
+    return pt_model_path
 
 
 def export_to_onnx(
@@ -177,9 +182,9 @@ def export_to_openvino(
     model: AnomalyModule,
     input_size: tuple[int, int],
     transform: dict[str, Any] | AnomalibDataset | AnomalibDataModule | A.Compose,
-    mo_args: dict[str, Any] | None = None,
+    ov_args: dict[str, Any] | None = None,
     task: TaskType | None = None,
-) -> None:
+) -> Path:
     """Convert onnx model to OpenVINO IR.
 
     Args:
@@ -189,11 +194,14 @@ def export_to_openvino(
         transform (dict[str, Any] | AnomalibDataset | AnomalibDataModule | A.Compose): Data transforms (augmentations)
             used for the model. When using dict, ensure that the transform dict is in the format required by
             Albumentations.
-        mo_args: Model optimizer arguments for OpenVINO model conversion.
+        ov_args: Model optimizer arguments for OpenVINO model conversion.
             Defaults to ``None``.
         task (TaskType | None): Task type should be provided if transforms is of type dict or A.Compose object.
             Defaults to ``None``.
 
+    Returns:
+        Path: Path to the exported onnx model.
+
     Raises:
         ModuleNotFoundError: If OpenVINO is not installed.
 
@@ -232,13 +240,15 @@ def export_to_openvino(
 
     """
     model_path = export_to_onnx(model, input_size, export_path, transform, task, ExportMode.OPENVINO)
-    mo_args = {} if mo_args is None else mo_args
+    ov_model_path = model_path.with_suffix(".xml")
+    ov_args = {} if ov_args is None else ov_args
     if convert_model is not None and serialize is not None:
-        model = convert_model(input_model=str(model_path), output_dir=str(model_path.parent), **mo_args)
-        serialize(model, model_path.with_suffix(".xml"))
-    else:
-        logger.exception("Could not find OpenVINO methods. Please check OpenVINO installation.")
-        raise ModuleNotFoundError
+        model = convert_model(model_path, **ov_args)
+        serialize(model, ov_model_path)
+        return ov_model_path
+
+    logger.exception("Could not find OpenVINO methods. Please check OpenVINO installation.")
+    raise ModuleNotFoundError
 
 
 def get_metadata(

src/anomalib/deploy/inferencers/openvino_inferencer.py

@@ -21,7 +21,7 @@
 logger = logging.getLogger("anomalib")
 
 if find_spec("openvino") is not None:
-    from openvino.runtime import Core
+    import openvino.runtime as ov
 
     if TYPE_CHECKING:
         from openvino.runtime import CompiledModel
@@ -37,11 +37,12 @@ class OpenVINOInferencer(Inferencer):
         metadata (str | Path | dict, optional): Path to metadata file or a dict object defining the
             metadata.
             Defaults to ``None``.
-        device (str | None, optional): Device to run the inference on.
-            Defaults to ``CPU``.
+        device (str | None, optional): Device to run the inference on (AUTO, CPU, GPU, NPU).
+            Defaults to ``AUTO``.
         task (TaskType | None, optional): Task type.
             Defaults to ``None``.
-
+        config (dict | None, optional): Configuration parameters for the inference
+            Defaults to ``None``.
 
     Examples:
         Assume that we have an OpenVINO IR model and metadata files in the following structure:
@@ -89,7 +90,7 @@ def __init__(
         self,
         path: str | Path | tuple[bytes, bytes],
         metadata: str | Path | dict | None = None,
-        device: str | None = "CPU",
+        device: str | None = "AUTO",
         task: str | None = None,
         config: dict | None = None,
     ) -> None:
@@ -112,38 +113,37 @@ def load_model(self, path: str | Path | tuple[bytes, bytes]) -> tuple[Any, Any,
             [tuple[str, str, ExecutableNetwork]]: Input and Output blob names
                 together with the Executable network.
         """
-        ie_core = Core()
+        core = ov.Core()
         # If tuple of bytes is passed
-
         if isinstance(path, tuple):
-            model = ie_core.read_model(model=path[0], weights=path[1], init_from_buffer=True)
+            model = core.read_model(model=path[0], weights=path[1])
         else:
             path = path if isinstance(path, Path) else Path(path)
             if path.suffix in (".bin", ".xml"):
                 if path.suffix == ".bin":
                     bin_path, xml_path = path, path.with_suffix(".xml")
                 elif path.suffix == ".xml":
                     xml_path, bin_path = path, path.with_suffix(".bin")
-                model = ie_core.read_model(xml_path, bin_path)
+                model = core.read_model(xml_path, bin_path)
             elif path.suffix == ".onnx":
-                model = ie_core.read_model(path)
+                model = core.read_model(path)
             else:
                 msg = f"Path must be .onnx, .bin or .xml file. Got {path.suffix}"
                 raise ValueError(msg)
         # Create cache folder
         cache_folder = Path("cache")
         cache_folder.mkdir(exist_ok=True)
-        ie_core.set_property({"CACHE_DIR": cache_folder})
+        core.set_property({"CACHE_DIR": cache_folder})
 
-        compile_model = ie_core.compile_model(model=model, device_name=self.device, config=self.config)
+        compile_model = core.compile_model(model=model, device_name=self.device, config=self.config)
 
         input_blob = compile_model.input(0)
         output_blob = compile_model.output(0)
 
         return input_blob, output_blob, compile_model
 
     def pre_process(self, image: np.ndarray) -> np.ndarray:
-        """Pre process the input image by applying transformations.
+        """Pre-process the input image by applying transformations.
 
         Args:
             image (np.ndarray): Input image.
@@ -178,8 +178,8 @@ def post_process(self, predictions: np.ndarray, metadata: dict | DictConfig | No
 
         Args:
             predictions (np.ndarray): Raw output predicted by the model.
-            metadata (Dict, optional): Meta data. Post-processing step sometimes requires
-                additional meta data such as image shape. This variable comprises such info.
+            metadata (Dict, optional): Metadata. Post-processing step sometimes requires
+                additional metadata such as image shape. This variable comprises such info.
                 Defaults to None.
 
         Returns:

src/anomalib/engine/engine.py

@@ -459,9 +459,9 @@ def export(
         datamodule: AnomalibDataModule | None = None,
         dataset: AnomalibDataset | None = None,
         input_size: tuple[int, int] | None = None,
-        mo_args: dict[str, Any] | None = None,
+        ov_args: dict[str, Any] | None = None,
         ckpt_path: str | None = None,
-    ) -> None:
+    ) -> Path | None:
         """Export the model in the specified format.
 
         Args:
@@ -479,10 +479,13 @@ def export(
                  is optional. Defaults to None.
             input_size (tuple[int, int] | None, optional): This is required only if the model is exported to ONNX and
                 OpenVINO format. Defaults to None.
-            mo_args (dict[str, Any] | None, optional): This is optional and used only for OpenVINO's model optimizer.
+            ov_args (dict[str, Any] | None, optional): This is optional and used only for OpenVINO's model optimizer.
                 Defaults to None.
             ckpt_path (str | None): Checkpoint path. If provided, the model will be loaded from this path.
 
+        Returns:
+            Path: Path to the exported model.
+
         Raises:
             ValueError: If Dataset, Datamodule, and transform are not provided.
             TypeError: If path to the transform file is not a string or Path.
@@ -509,28 +512,30 @@ def export(
             logger.exception(f"Unknown type {type(transform)} for transform.")
             raise TypeError
 
-        if export_mode in (ExportMode.OPENVINO, ExportMode.ONNX):
-            assert input_size is not None, "input_size must be provided for OpenVINO and ONNX export modes."
         if export_path is None:
             export_path = Path(self.trainer.default_root_dir)
+
         if export_mode == ExportMode.TORCH:
-            export_to_torch(model=model, export_path=export_path, transform=transform, task=self.task)
-        elif export_mode == ExportMode.ONNX:
+            return export_to_torch(model=model, export_path=export_path, transform=transform, task=self.task)
+        if export_mode == ExportMode.ONNX:
             assert input_size is not None, "input_size must be provided for ONNX export mode."
-            export_to_onnx(
+            return export_to_onnx(
                 model=model,
                 input_size=input_size,
                 export_path=export_path,
                 transform=transform,
                 task=self.task,
             )
-        else:
+        if export_mode == ExportMode.OPENVINO:
             assert input_size is not None, "input_size must be provided for OpenVINO export mode."
-            export_to_openvino(
+            return export_to_openvino(
                 model=model,
                 input_size=input_size,
                 export_path=export_path,
                 transform=transform,
                 task=self.task,
-                mo_args=mo_args,
+                ov_args=ov_args,
             )
+
+        logging.error(f"Export mode {export_mode} is not supported yet.")
+        return None

src/anomalib/pipelines/benchmarking/benchmark.py

@@ -143,7 +143,7 @@ def get_single_model_metrics(
                 model=model,
                 input_size=input_size,
                 transform=engine.trainer.datamodule.test_data.transform,
-                mo_args={},
+                ov_args={},
                 task=engine.trainer.datamodule.test_data.task,
             )
             openvino_throughput = get_openvino_throughput(model_path=project_path, test_dataset=datamodule.test_data)