🐞 fix(utils): revert OptionalImport class to handle missing optional dependencies (#2956)

* fix(data): enable pin_memory for DataLoader instances across the codebase

This commit updates various DataLoader instances in the project to enable the  option, enhancing performance for data loading on GPU. Changes were made in the following files:

- : Updated train and test DataLoader configurations.
- : Modified datamodule DataLoader to include .
- : Added  to evaluation DataLoader.
- : Updated DataLoader for datasets to utilize .
- : Enabled  for reference dataset DataLoader.
- : Adjusted inference DataLoader to include .

These changes aim to optimize memory usage and improve data transfer speeds during model training and inference.

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

* refactor(models): streamline decoder retrieval in  function

This commit refactors the  function in  to utilize a dictionary mapping for decoder architectures, improving readability and maintainability. The previous conditional checks have been replaced with a more efficient approach, enhancing the overall structure of the code.

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

* fix(model): update logit_scale initialization to use torch.log for consistency

This commit modifies the initialization of the logit_scale parameter in the CLIP model to utilize torch.log instead of np.log. This change ensures consistency in tensor operations and improves compatibility with PyTorch's computation graph.

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

* fix(model): update anomaly map generation to use torch tensors for calculations

This commit modifies the anomaly map generation logic to utilize PyTorch tensors instead of NumPy arrays for various calculations. This change enhances compatibility with the PyTorch computation graph and improves performance by leveraging GPU acceleration. Key updates include the conversion of statistical calculations and tensor operations to use PyTorch functions, ensuring consistency in tensor handling throughout the code.

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

* refactor(model): enhance anomaly map generation with PyTorch for statistical calculations

This commit refactors the anomaly map generation logic to replace NumPy-based statistical calculations with PyTorch equivalents, specifically using the  distribution for computing tau. Additionally, it improves precision handling by allowing the use of float64 in high precision mode. The changes streamline the computation process and maintain compatibility with the PyTorch computation graph.

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

* chore(license): update license year

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

* fix(download): enhance URL validation and update download logic

This commit improves the URL validation in the download function to ensure only http and https schemes are allowed. Additionally, it adds comments to clarify the safety of using  under these conditions, enhancing code readability and security awareness.

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

* 🗑️ chore(imports): remove OptionalImport class for optional dependency handling

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

* 🐛 fix(imports): Replace OptionalImport with dummy classes for missing dependencies

This commit removes the OptionalImport class and introduces dummy classes for various loggers and video utilities when their respective dependencies are not installed. The changes enhance error messaging by providing clear instructions for installation, improving user experience when encountering missing packages.

Affected files:
- video.py
- comet.py
- mlflow.py
- tensorboard.py
- wandb.py
- ollama.py

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

---------

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

Author: samet-akcay

src/anomalib/data/utils/video.py

@@ -33,16 +33,19 @@
 from lightning_utilities.core.imports import module_available
 
 from anomalib.data import VideoItem
-from anomalib.utils.imports import OptionalImport
 
 if TYPE_CHECKING or module_available("av"):
     from torchvision.datasets.video_utils import VideoClips
 else:
-    VideoClips = OptionalImport(
-        "av",
-        "uv pip install av",
-        "or `uv pip install anomalib[video]`",
-    )
+
+    class VideoClips:
+        """Dummy VideoClips class for when av is not installed."""
+
+        def __init__(self, *args, **kwargs) -> None:  # noqa: ARG002
+            msg = (
+                "av is not installed. Please install it using: `uv pip install av` or `uv pip install anomalib[video]`"
+            )
+            raise ImportError(msg)
 
 
 class ClipsIndexer(VideoClips, ABC):

src/anomalib/loggers/comet.py

@@ -30,18 +30,21 @@
 from lightning_utilities.core.imports import module_available
 from matplotlib.figure import Figure
 
-from anomalib.utils.imports import OptionalImport
-
 from .base import ImageLoggerBase
 
 if TYPE_CHECKING or module_available("comet_ml"):
     from lightning.pytorch.loggers.comet import CometLogger
 else:
-    CometLogger = OptionalImport(
-        "comet-ml",
-        "uv pip install comet-ml",
-        "or `uv pip install anomalib[loggers]`",
-    )
+
+    class CometLogger:
+        """Dummy CometLogger class for when comet-ml is not installed."""
+
+        def __init__(self, *args, **kwargs) -> None:  # noqa: ARG002
+            msg = (
+                "comet-ml is not installed. Please install it using: "
+                "`uv pip install comet-ml` or `uv pip install anomalib[loggers]`"
+            )
+            raise ImportError(msg)
 
 
 class AnomalibCometLogger(ImageLoggerBase, CometLogger):

src/anomalib/loggers/mlflow.py

@@ -30,18 +30,21 @@
 from lightning_utilities.core.imports import module_available
 from matplotlib.figure import Figure
 
-from anomalib.utils.imports import OptionalImport
-
 from .base import ImageLoggerBase
 
 if TYPE_CHECKING or module_available("mlflow"):
     from lightning.pytorch.loggers.mlflow import MLFlowLogger
 else:
-    MLFlowLogger = OptionalImport(
-        "mlflow",
-        "uv pip install mlflow",
-        "or `uv pip install anomalib[loggers]`",
-    )
+
+    class MLFlowLogger:
+        """Dummy MLFlowLogger class for when mlflow is not installed."""
+
+        def __init__(self, *args, **kwargs) -> None:  # noqa: ARG002
+            msg = (
+                "mlflow is not installed. Please install it using: "
+                "`uv pip install mlflow` or `uv pip install anomalib[loggers]`"
+            )
+            raise ImportError(msg)
 
 
 class AnomalibMLFlowLogger(ImageLoggerBase, MLFlowLogger):

src/anomalib/loggers/tensorboard.py

@@ -31,18 +31,21 @@
 from lightning_utilities.core.imports import module_available
 from matplotlib.figure import Figure
 
-from anomalib.utils.imports import OptionalImport
-
 from .base import ImageLoggerBase
 
 if TYPE_CHECKING or module_available("tensorboard"):
     from lightning.pytorch.loggers.tensorboard import TensorBoardLogger
 else:
-    TensorBoardLogger = OptionalImport(
-        "tensorboard",
-        "uv pip install tensorboard",
-        "or `uv pip install anomalib[loggers]`",
-    )
+
+    class TensorBoardLogger:
+        """Dummy TensorBoardLogger class for when tensorboard is not installed."""
+
+        def __init__(self, *args, **kwargs) -> None:  # noqa: ARG002
+            msg = (
+                "tensorboard is not installed. Please install it using: "
+                "`uv pip install tensorboard` or `uv pip install anomalib[loggers]`"
+            )
+            raise ImportError(msg)
 
 
 class AnomalibTensorBoardLogger(ImageLoggerBase, TensorBoardLogger):

src/anomalib/loggers/wandb.py

@@ -30,20 +30,23 @@
 from lightning_utilities.core.imports import module_available
 from matplotlib.figure import Figure
 
-from anomalib.utils.imports import OptionalImport
-
 from .base import ImageLoggerBase
 
 if TYPE_CHECKING or module_available("wandb"):
     import wandb
     from lightning.pytorch.loggers.wandb import WandbLogger
 else:
     wandb = None
-    WandbLogger = OptionalImport(
-        "wandb",
-        "uv pip install wandb",
-        "or `uv pip install anomalib[loggers]`",
-    )
+
+    class WandbLogger:
+        """Dummy WandbLogger class for when wandb is not installed."""
+
+        def __init__(self, *args, **kwargs) -> None:  # noqa: ARG002
+            msg = (
+                "wandb is not installed. Please install it using: "
+                "`uv pip install wandb` or `uv pip install anomalib[loggers]`"
+            )
+            raise ImportError(msg)
 
 
 if TYPE_CHECKING:

src/anomalib/models/image/vlm_ad/backends/ollama.py

@@ -41,18 +41,23 @@
 from lightning_utilities.core.imports import module_available
 
 from anomalib.models.image.vlm_ad.utils import Prompt
-from anomalib.utils.imports import OptionalImport
 
 from .base import Backend
 
 if TYPE_CHECKING or module_available("ollama"):
     from ollama import Image, chat
 else:
-    Image = OptionalImport(
-        "ollama",
-        "uv pip install ollama",
-        "or `uv pip install anomalib[vlm]`",
-    )
+
+    class Image:
+        """Dummy Image class for when ollama is not installed."""
+
+        def __init__(self, *args, **kwargs) -> None:  # noqa: ARG002
+            msg = (
+                "ollama is not installed. Please install it using: "
+                "`uv pip install ollama` or `uv pip install anomalib[vlm]`"
+            )
+            raise ImportError(msg)
+
     chat = None