Refactor post-processor to match the pre-processor and evaluator pattern

Signed-off-by: Samet Akcay <samet.akcay@intel.com>

src/anomalib/models/components/base/anomaly_module.py

@@ -40,7 +40,7 @@ class AnomalibModule(ExportMixin, pl.LightningModule, ABC):
     def __init__(
         self,
         pre_processor: PreProcessor | bool = True,
-        post_processor: PostProcessor | None = None,
+        post_processor: PostProcessor | bool = True,
         evaluator: Evaluator | bool = True,
     ) -> None:
         super().__init__()
@@ -52,11 +52,11 @@ def __init__(
         self.callbacks: list[Callback]
 
         self.pre_processor = self._resolve_pre_processor(pre_processor)
-        self.post_processor = post_processor or self.default_post_processor()
+        self.post_processor = self._resolve_post_processor(post_processor)
         self.evaluator = self._resolve_evaluator(evaluator)
 
         self._input_size: tuple[int, int] | None = None
-        self._is_setup = False  # flag to track if setup has been called from the trainer
+        self._is_setup = False
 
     @property
     def name(self) -> str:
@@ -214,15 +214,53 @@ def configure_pre_processor(cls, image_size: tuple[int, int] | None = None) -> P
             ]),
         )
 
-    def default_post_processor(self) -> PostProcessor | None:
-        """Default post processor.
+    def _resolve_post_processor(self, post_processor: PostProcessor | bool) -> PostProcessor | None:
+        """Resolve and validate which post-processor to use.
 
-        Override in subclass for model-specific post-processing behaviour.
+        Args:
+            post_processor: Post-processor configuration
+                - True -> use default post-processor
+                - False -> no post-processor
+                - PostProcessor -> use the provided post-processor
+
+        Returns:
+            Configured post-processor
+        """
+        if isinstance(post_processor, PostProcessor):
+            return post_processor
+        if isinstance(post_processor, bool):
+            return self.configure_post_processor() if post_processor else None
+        msg = f"Invalid post-processor type: {type(post_processor)}"
+        raise TypeError(msg)
+
+    @classmethod
+    def configure_post_processor(cls) -> PostProcessor | None:
+        """Configure the default post-processor based on the learning type.
+
+        Returns:
+            PostProcessor: Configured post-processor instance.
+
+        Raises:
+            NotImplementedError: If no default post-processor is available for the model's learning type.
+
+        Examples:
+            Get default post-processor:
+
+            >>> post_processor = AnomalibModule.configure_post_processor()
+
+            Create model with custom post-processor:
+
+            >>> custom_post_processor = CustomPostProcessor()
+            >>> model = PatchCore(post_processor=custom_post_processor)
+
+            Disable post-processing:
+
+            >>> model = PatchCore(post_processor=False)
         """
-        if self.learning_type == LearningType.ONE_CLASS:
+        if cls.learning_type == LearningType.ONE_CLASS:
             return OneClassPostProcessor()
-        msg = f"No default post-processor available for model {self.__name__} with learning type {self.learning_type}. \
-              Please override the default_post_processor method in the model implementation."
+        msg = f"No default post-processor available for model with learning type {cls.learning_type}. \
+              Please override the configure_post_processor method in the model implementation."
         raise NotImplementedError(msg)
 
     def _resolve_evaluator(self, evaluator: Evaluator | bool) -> Evaluator | None:

src/anomalib/models/image/cfa/lightning_model.py

@@ -59,7 +59,7 @@ def __init__(
         num_hard_negative_features: int = 3,
         radius: float = 1e-5,
         pre_processor: PreProcessor | bool = True,
-        post_processor: PostProcessor | None = None,
+        post_processor: PostProcessor | bool = True,
         evaluator: Evaluator | bool = True,
     ) -> None:
         super().__init__(pre_processor=pre_processor, post_processor=post_processor, evaluator=evaluator)

src/anomalib/models/image/cflow/lightning_model.py

@@ -71,7 +71,7 @@ def __init__(
         permute_soft: bool = False,
         lr: float = 0.0001,
         pre_processor: PreProcessor | bool = True,
-        post_processor: PostProcessor | None = None,
+        post_processor: PostProcessor | bool = True,
         evaluator: Evaluator | bool = True,
     ) -> None:
         super().__init__(pre_processor=pre_processor, post_processor=post_processor, evaluator=evaluator)

src/anomalib/models/image/csflow/lightning_model.py

@@ -48,7 +48,7 @@ def __init__(
         clamp: int = 3,
         num_channels: int = 3,
         pre_processor: PreProcessor | bool = True,
-        post_processor: PostProcessor | None = None,
+        post_processor: PostProcessor | bool = True,
         evaluator: Evaluator | bool = True,
     ) -> None:
         super().__init__(pre_processor=pre_processor, post_processor=post_processor, evaluator=evaluator)

src/anomalib/models/image/dfkde/lightning_model.py

@@ -50,7 +50,7 @@ def __init__(
         feature_scaling_method: FeatureScalingMethod = FeatureScalingMethod.SCALE,
         max_training_points: int = 40000,
         pre_processor: PreProcessor | bool = True,
-        post_processor: PostProcessor | None = None,
+        post_processor: PostProcessor | bool = True,
         evaluator: Evaluator | bool = True,
     ) -> None:
         super().__init__(pre_processor=pre_processor, post_processor=post_processor, evaluator=evaluator)

src/anomalib/models/image/dfm/lightning_model.py

@@ -54,7 +54,7 @@ def __init__(
         pca_level: float = 0.97,
         score_type: str = "fre",
         pre_processor: PreProcessor | bool = True,
-        post_processor: PostProcessor | None = None,
+        post_processor: PostProcessor | bool = True,
         evaluator: Evaluator | bool = True,
     ) -> None:
         super().__init__(pre_processor=pre_processor, post_processor=post_processor, evaluator=evaluator)

src/anomalib/models/image/draem/lightning_model.py

@@ -51,7 +51,7 @@ def __init__(
         anomaly_source_path: str | None = None,
         beta: float | tuple[float, float] = (0.1, 1.0),
         pre_processor: PreProcessor | bool = True,
-        post_processor: PostProcessor | None = None,
+        post_processor: PostProcessor | bool = True,
         evaluator: Evaluator | bool = True,
     ) -> None:
         super().__init__(pre_processor=pre_processor, post_processor=post_processor, evaluator=evaluator)

src/anomalib/models/image/dsr/lightning_model.py

@@ -53,7 +53,7 @@ def __init__(
         latent_anomaly_strength: float = 0.2,
         upsampling_train_ratio: float = 0.7,
         pre_processor: PreProcessor | bool = True,
-        post_processor: PostProcessor | None = None,
+        post_processor: PostProcessor | bool = True,
         evaluator: Evaluator | bool = True,
     ) -> None:
         super().__init__(pre_processor=pre_processor, post_processor=post_processor, evaluator=evaluator)

src/anomalib/models/image/efficient_ad/lightning_model.py

@@ -76,7 +76,7 @@ def __init__(
         padding: bool = False,
         pad_maps: bool = True,
         pre_processor: PreProcessor | bool = True,
-        post_processor: PostProcessor | None = None,
+        post_processor: PostProcessor | bool = True,
         evaluator: Evaluator | bool = True,
     ) -> None:
         super().__init__(pre_processor=pre_processor, post_processor=post_processor, evaluator=evaluator)

src/anomalib/models/image/fastflow/lightning_model.py

@@ -50,7 +50,7 @@ def __init__(
         conv3x3_only: bool = False,
         hidden_ratio: float = 1.0,
         pre_processor: PreProcessor | bool = True,
-        post_processor: PostProcessor | None = None,
+        post_processor: PostProcessor | bool = True,
         evaluator: Evaluator | bool = True,
     ) -> None:
         super().__init__(pre_processor=pre_processor, post_processor=post_processor, evaluator=evaluator)

src/anomalib/models/image/fre/lightning_model.py

@@ -56,7 +56,7 @@ def __init__(
         input_dim: int = 65536,
         latent_dim: int = 220,
         pre_processor: PreProcessor | bool = True,
-        post_processor: PostProcessor | None = None,
+        post_processor: PostProcessor | bool = True,
         evaluator: Evaluator | bool = True,
     ) -> None:
         super().__init__(pre_processor=pre_processor, post_processor=post_processor, evaluator=evaluator)

src/anomalib/models/image/ganomaly/lightning_model.py

@@ -71,7 +71,7 @@ def __init__(
         beta1: float = 0.5,
         beta2: float = 0.999,
         pre_processor: PreProcessor | bool = True,
-        post_processor: PostProcessor | None = None,
+        post_processor: PostProcessor | bool = True,
         evaluator: Evaluator | bool = True,
     ) -> None:
         super().__init__(pre_processor=pre_processor, post_processor=post_processor, evaluator=evaluator)

src/anomalib/models/image/padim/lightning_model.py

@@ -50,7 +50,7 @@ def __init__(
         pre_trained: bool = True,
         n_features: int | None = None,
         pre_processor: PreProcessor | bool = True,
-        post_processor: PostProcessor | None = None,
+        post_processor: PostProcessor | bool = True,
         evaluator: Evaluator | bool = True,
     ) -> None:
         super().__init__(pre_processor=pre_processor, post_processor=post_processor, evaluator=evaluator)
@@ -132,6 +132,6 @@ def learning_type(self) -> LearningType:
         return LearningType.ONE_CLASS
 
     @staticmethod
-    def default_post_processor() -> OneClassPostProcessor:
+    def configure_post_processor() -> OneClassPostProcessor:
         """Return the default post-processor for PADIM."""
         return OneClassPostProcessor()

src/anomalib/models/image/patchcore/lightning_model.py

@@ -53,7 +53,7 @@ def __init__(
         coreset_sampling_ratio: float = 0.1,
         num_neighbors: int = 9,
         pre_processor: PreProcessor | bool = True,
-        post_processor: PostProcessor | None = None,
+        post_processor: PostProcessor | bool = True,
         evaluator: Evaluator | bool = True,
     ) -> None:
         super().__init__(pre_processor=pre_processor, post_processor=post_processor, evaluator=evaluator)
@@ -154,7 +154,7 @@ def learning_type(self) -> LearningType:
         return LearningType.ONE_CLASS
 
     @staticmethod
-    def default_post_processor() -> OneClassPostProcessor:
+    def configure_post_processor() -> OneClassPostProcessor:
         """Return the default post-processor for the model.
 
         Returns:

src/anomalib/models/image/reverse_distillation/lightning_model.py

@@ -48,7 +48,7 @@ def __init__(
         anomaly_map_mode: AnomalyMapGenerationMode = AnomalyMapGenerationMode.ADD,
         pre_trained: bool = True,
         pre_processor: PreProcessor | bool = True,
-        post_processor: PostProcessor | None = None,
+        post_processor: PostProcessor | bool = True,
         evaluator: Evaluator | bool = True,
     ) -> None:
         super().__init__(pre_processor=pre_processor, post_processor=post_processor, evaluator=evaluator)

src/anomalib/models/image/stfpm/lightning_model.py

@@ -44,7 +44,7 @@ def __init__(
         backbone: str = "resnet18",
         layers: Sequence[str] = ("layer1", "layer2", "layer3"),
         pre_processor: PreProcessor | bool = True,
-        post_processor: PostProcessor | None = None,
+        post_processor: PostProcessor | bool = True,
         evaluator: Evaluator | bool = True,
     ) -> None:
         super().__init__(pre_processor=pre_processor, post_processor=post_processor, evaluator=evaluator)

src/anomalib/models/image/uflow/lightning_model.py

@@ -49,7 +49,7 @@ def __init__(
         affine_subnet_channels_ratio: float = 1.0,
         permute_soft: bool = False,
         pre_processor: PreProcessor | bool = True,
-        post_processor: PostProcessor | None = None,
+        post_processor: PostProcessor | bool = True,
         evaluator: Evaluator | bool = True,
     ) -> None:
         """Uflow model.

src/anomalib/models/image/vlm_ad/lightning_model.py

@@ -102,7 +102,8 @@ def configure_transforms(image_size: tuple[int, int] | None = None) -> None:
         if image_size is not None:
             logger.warning("Ignoring image_size argument as each backend has its own transforms.")
 
-    def default_post_processor(self) -> PostProcessor | None:  # noqa: PLR6301
+    @classmethod
+    def configure_post_processor(cls) -> PostProcessor | None:
         """Post processing is not required for this model."""
         return None
 

src/anomalib/models/image/winclip/lightning_model.py

@@ -56,7 +56,7 @@ def __init__(
         scales: tuple = (2, 3),
         few_shot_source: Path | str | None = None,
         pre_processor: PreProcessor | bool = True,
-        post_processor: PostProcessor | None = None,
+        post_processor: PostProcessor | bool = True,
         evaluator: Evaluator | bool = True,
     ) -> None:
         super().__init__(pre_processor=pre_processor, post_processor=post_processor, evaluator=evaluator)
@@ -195,6 +195,6 @@ def configure_pre_processor(cls, image_size: tuple[int, int] | None = None) -> P
         return PreProcessor(val_transform=transform, test_transform=transform)
 
     @staticmethod
-    def default_post_processor() -> OneClassPostProcessor:
+    def configure_post_processor() -> OneClassPostProcessor:
         """Return the default post-processor for WinCLIP."""
         return OneClassPostProcessor()

src/anomalib/models/video/ai_vad/lightning_model.py

@@ -81,9 +81,10 @@ def __init__(
         n_neighbors_pose: int = 1,
         n_neighbors_deep: int = 1,
         pre_processor: PreProcessor | bool = True,
+        post_processor: PostProcessor | bool = True,
         **kwargs,
     ) -> None:
-        super().__init__(pre_processor=pre_processor, **kwargs)
+        super().__init__(pre_processor=pre_processor, post_processor=post_processor, **kwargs)
         self.model = AiVadModel(
             box_score_thresh=box_score_thresh,
             persons_only=persons_only,
@@ -179,6 +180,6 @@ def configure_pre_processor(cls, image_size: tuple[int, int] | None = None) -> P
         return PreProcessor()  # A pre-processor with no transforms.
 
     @staticmethod
-    def default_post_processor() -> PostProcessor:
+    def configure_post_processor() -> PostProcessor:
         """Return the default post-processor for AI-VAD."""
         return OneClassPostProcessor()

tests/unit/utils/callbacks/visualizer_callback/dummy_lightning_model.py

@@ -74,6 +74,6 @@ def learning_type(self) -> LearningType:
         return LearningType.ZERO_SHOT
 
     @staticmethod
-    def default_post_processor() -> PostProcessor:
+    def configure_post_processor() -> PostProcessor:
         """Returns a dummy post-processor."""
         return DummyPostProcessor()