Merge branch 'develop' into kp/add_semisl

src/otx/algo/anomaly/padim.py

@@ -9,21 +9,21 @@
 
 from typing import TYPE_CHECKING, Literal
 
+from anomalib.callbacks.normalization.min_max_normalization import _MinMaxNormalizationCallback
+from anomalib.callbacks.post_processor import _PostProcessorCallback
 from anomalib.models.image import Padim as AnomalibPadim
 
 from otx.core.model.anomaly import OTXAnomaly
-from otx.core.model.base import OTXModel
-from otx.core.types.label import AnomalyLabelInfo
 from otx.core.types.task import OTXTaskType
 
 if TYPE_CHECKING:
     from lightning.pytorch.utilities.types import STEP_OUTPUT
     from torch.optim.optimizer import Optimizer
 
-    from otx.core.model.anomaly import AnomalyModelInputs
+    from otx.core.model.anomaly import AnomalyModelInputs, AnomalyModelOutputs
 
 
-class Padim(OTXAnomaly, OTXModel, AnomalibPadim):
+class Padim(OTXAnomaly, AnomalibPadim):
     """OTX Padim model.
 
     Args:
@@ -49,7 +49,6 @@ def __init__(
         ] = OTXTaskType.ANOMALY_CLASSIFICATION,
     ) -> None:
         OTXAnomaly.__init__(self)
-        OTXModel.__init__(self, label_info=AnomalyLabelInfo())
         AnomalibPadim.__init__(
             self,
             backbone=backbone,
@@ -132,3 +131,16 @@ def predict_step(
         if not isinstance(inputs, dict):
             inputs = self._customize_inputs(inputs)
         return AnomalibPadim.predict_step(self, inputs, batch_idx, **kwargs)  # type: ignore[misc]
+
+    def forward(
+        self,
+        inputs: AnomalyModelInputs,
+    ) -> AnomalyModelOutputs:
+        """Wrap forward method of the Anomalib model."""
+        outputs = self.validation_step(inputs)
+        # TODO(Ashwin): update forward implementation to comply with other OTX models
+        _PostProcessorCallback._post_process(outputs)  # noqa: SLF001
+        _PostProcessorCallback._compute_scores_and_labels(self, outputs)  # noqa: SLF001
+        _MinMaxNormalizationCallback._normalize_batch(outputs, self)  # noqa: SLF001
+
+        return self._customize_outputs(outputs=outputs, inputs=inputs)

src/otx/algo/anomaly/stfpm.py

@@ -9,21 +9,21 @@
 
 from typing import TYPE_CHECKING, Literal, Sequence
 
+from anomalib.callbacks.normalization.min_max_normalization import _MinMaxNormalizationCallback
+from anomalib.callbacks.post_processor import _PostProcessorCallback
 from anomalib.models.image.stfpm import Stfpm as AnomalibStfpm
 
 from otx.core.model.anomaly import OTXAnomaly
-from otx.core.model.base import OTXModel
-from otx.core.types.label import AnomalyLabelInfo
 from otx.core.types.task import OTXTaskType
 
 if TYPE_CHECKING:
     from lightning.pytorch.utilities.types import STEP_OUTPUT
     from torch.optim.optimizer import Optimizer
 
-    from otx.core.model.anomaly import AnomalyModelInputs
+    from otx.core.model.anomaly import AnomalyModelInputs, AnomalyModelOutputs
 
 
-class Stfpm(OTXAnomaly, OTXModel, AnomalibStfpm):
+class Stfpm(OTXAnomaly, AnomalibStfpm):
     """OTX STFPM model.
 
     Args:
@@ -46,7 +46,6 @@ def __init__(
         **kwargs,
     ) -> None:
         OTXAnomaly.__init__(self)
-        OTXModel.__init__(self, label_info=AnomalyLabelInfo())
         AnomalibStfpm.__init__(
             self,
             backbone=backbone,
@@ -124,3 +123,16 @@ def predict_step(
         if not isinstance(inputs, dict):
             inputs = self._customize_inputs(inputs)
         return AnomalibStfpm.predict_step(self, inputs, batch_idx, **kwargs)  # type: ignore[misc]
+
+    def forward(
+        self,
+        inputs: AnomalyModelInputs,
+    ) -> AnomalyModelOutputs:
+        """Wrap forward method of the Anomalib model."""
+        outputs = self.validation_step(inputs)
+        # TODO(Ashwin): update forward implementation to comply with other OTX models
+        _PostProcessorCallback._post_process(outputs)  # noqa: SLF001
+        _PostProcessorCallback._compute_scores_and_labels(self, outputs)  # noqa: SLF001
+        _MinMaxNormalizationCallback._normalize_batch(outputs, self)  # noqa: SLF001
+
+        return self._customize_outputs(outputs=outputs, inputs=inputs)

src/otx/algo/classification/backbones/efficientnet.py

@@ -679,7 +679,7 @@ def init_weights(self, pretrained: bool | str | None = None) -> None:
             checkpoint = torch.load(pretrained, None)
             load_checkpoint_to_model(self, checkpoint)
             print(f"init weight - {pretrained}")
-        elif pretrained is not None:
+        elif pretrained:
             cache_dir = Path.home() / ".cache" / "torch" / "hub" / "checkpoints"
             download_model(net=self, model_name=self.model_name, local_model_store_dir_path=str(cache_dir))
-            print(f"init weight - {pretrained_urls[self.model_name]}")
+            print(f"Download model weight in {cache_dir!s}")