Drop export tests

tests/integration/model/test_models.py

@@ -18,90 +18,11 @@
 from anomalib.utils.types import TaskType
 
 
-def models() -> set[str]:
+def models() -> list[str]:
     """Return all available models except ai_vad."""
     return get_available_models()
 
 
-def _get_objects(
-    model_name: str,
-    dataset_path: Path,
-    project_path: Path,
-) -> tuple[AnomalyModule, AnomalibDataModule, Engine]:
-    """Return model, dataset, and engine objects.
-
-    Args:
-        model_name (str): Name of the model to train
-        dataset_path (Path): Path to the root of dummy dataset
-        project_path (Path): path to the temporary project folder
-
-    Returns:
-        tuple[AnomalyModule, AnomalibDataModule, Engine]: Returns the created objects for model, dataset,
-            and engine
-    """
-    # select task type
-    if model_name in ("rkde", "ai_vad"):
-        task_type = TaskType.DETECTION
-    elif model_name in ("ganomaly", "dfkde"):
-        task_type = TaskType.CLASSIFICATION
-    else:
-        task_type = TaskType.SEGMENTATION
-
-    # set extra model args
-    # TODO(ashwinvaidya17): Fix these Edge cases
-    # https://github.com/openvinotoolkit/anomalib/issues/1478
-
-    extra_args = {}
-    if model_name == "patchcore":
-        extra_args["input_size"] = (256, 256)
-    elif model_name in ("rkde", "dfkde"):
-        extra_args["n_pca_components"] = 2
-
-    # select dataset
-    if model_name == "ai_vad":
-        # aivad expects UCSD dataset
-        dataset = UCSDped(
-            root=dataset_path / "ucsdped",
-            category="dummy",
-            task=task_type,
-            train_batch_size=1,
-            eval_batch_size=1,
-        )
-    else:
-        # EfficientAd requires that the batch size be lesser than the number of images in the dataset.
-        # This is so that the LR step size is not 0.
-        dataset = MVTec(
-            root=dataset_path / "mvtec",
-            category="dummy",
-            task=task_type,
-            train_batch_size=1,
-            eval_batch_size=1,
-        )
-
-    model = get_model(model_name, **extra_args)
-    engine = Engine(
-        logger=False,
-        default_root_dir=project_path,
-        max_epochs=1,
-        devices=1,
-        pixel_metrics=["F1Score", "AUROC"],
-        task=task_type,
-        callbacks=[
-            ModelCheckpoint(
-                dirpath=f"{project_path}/{model_name}/dummy/weights",
-                monitor=None,
-                filename="last",
-                save_last=True,
-                auto_insert_metric_name=False,
-            ),
-        ],
-        # TODO(ashwinvaidya17): Fix these Edge cases
-        # https://github.com/openvinotoolkit/anomalib/issues/1478
-        max_steps=70000 if model_name == "efficient_ad" else -1,
-    )
-    return model, dataset, engine
-
-
 class TestAPI:
     """Do sanity check on all models."""
 
@@ -114,7 +35,7 @@ def test_fit(self, model_name: str, dataset_path: Path, project_path: Path) -> N
             dataset_path (Path): Root to dataset from fixture.
             project_path (Path): Path to temporary project folder from fixture.
         """
-        model, dataset, engine = _get_objects(
+        model, dataset, engine = self._get_objects(
             model_name=model_name,
             dataset_path=dataset_path,
             project_path=project_path,
@@ -130,9 +51,76 @@ def test_test(self, model_name: str, dataset_path: Path, project_path: Path) ->
             dataset_path (Path): Root to dataset from fixture.
             project_path (Path): Path to temporary project folder from fixture.
         """
-        model, dataset, engine = _get_objects(
+        model, dataset, engine = self._get_objects(
             model_name=model_name,
             dataset_path=dataset_path,
             project_path=project_path,
         )
         engine.test(model=model, datamodule=dataset, ckpt_path=f"{project_path}/{model_name}/dummy/weights/last.ckpt")
+
+    def _get_objects(
+        self,
+        model_name: str,
+        dataset_path: Path,
+        project_path: Path,
+    ) -> tuple[AnomalyModule, AnomalibDataModule, Engine]:
+        """Return model, dataset, and engine objects.
+
+        Args:
+            model_name (str): Name of the model to train
+            dataset_path (Path): Path to the root of dummy dataset
+            project_path (Path): path to the temporary project folder
+
+        Returns:
+            tuple[AnomalyModule, AnomalibDataModule, Engine]: Returns the created objects for model, dataset,
+                and engine
+        """
+        # select task type
+        if model_name in ("rkde", "ai_vad"):
+            task_type = TaskType.DETECTION
+        elif model_name in ("ganomaly", "dfkde"):
+            task_type = TaskType.CLASSIFICATION
+        else:
+            task_type = TaskType.SEGMENTATION
+
+        # set extra model args
+        # TODO(ashwinvaidya17): Fix these Edge cases
+        # https://github.com/openvinotoolkit/anomalib/issues/1478
+
+        extra_args = {}
+        if model_name == "patchcore":
+            extra_args["input_size"] = (256, 256)
+        elif model_name in ("rkde", "dfkde"):
+            extra_args["n_pca_components"] = 2
+
+        # select dataset
+        if model_name == "ai_vad":
+            # aivad expects UCSD dataset
+            dataset = UCSDped(root=dataset_path / "ucsdped", category="dummy", task=task_type)
+        else:
+            # EfficientAd requires that the batch size be lesser than the number of images in the dataset.
+            # This is so that the LR step size is not 0.
+            dataset = MVTec(root=dataset_path / "mvtec", category="dummy", task=task_type, train_batch_size=2)
+
+        model = get_model(model_name, **extra_args)
+        engine = Engine(
+            logger=False,
+            default_root_dir=project_path,
+            max_epochs=1,
+            devices=1,
+            pixel_metrics=["F1Score", "AUROC"],
+            task=task_type,
+            callbacks=[
+                ModelCheckpoint(
+                    dirpath=f"{project_path}/{model_name}/dummy/weights",
+                    monitor=None,
+                    filename="last",
+                    save_last=True,
+                    auto_insert_metric_name=False,
+                ),
+            ],
+            # TODO(ashwinvaidya17): Fix these Edge cases
+            # https://github.com/openvinotoolkit/anomalib/issues/1478
+            max_steps=70000 if model_name == "efficient_ad" else -1,
+        )
+        return model, dataset, engine