Restored metric logging to third-party loggers

src/anomalib/cli/pipelines.py

@@ -6,13 +6,13 @@
 import logging
 
 from jsonargparse import Namespace
-from lightning_utilities.core.imports import package_available
+from lightning_utilities.core.imports import module_available
 
 from anomalib.cli.utils.help_formatter import get_short_docstring
 
 logger = logging.getLogger(__name__)
 
-if package_available("anomalib.pipelines"):
+if module_available("anomalib.pipelines"):
     from anomalib.pipelines import Benchmark
     from anomalib.pipelines.components.base import Pipeline
 

src/anomalib/cli/utils/openvino.py

@@ -6,12 +6,12 @@
 import logging
 
 from jsonargparse import ArgumentParser
-from lightning_utilities.core.imports import package_available
+from lightning_utilities.core.imports import module_available
 
 logger = logging.getLogger(__name__)
 
 
-if package_available("openvino"):
+if module_available("openvino"):
     from openvino.tools.ovc.cli_parser import get_common_cli_parser
 else:
     get_common_cli_parser = None

src/anomalib/deploy/inferencers/openvino_inferencer.py

@@ -9,7 +9,7 @@
 
 import cv2
 import numpy as np
-from lightning_utilities.core.imports import package_available
+from lightning_utilities.core.imports import module_available
 from omegaconf import DictConfig
 from PIL import Image
 
@@ -94,7 +94,7 @@ def __init__(
         task: str | None = None,
         config: dict | None = None,
     ) -> None:
-        if not package_available("openvino"):
+        if not module_available("openvino"):
             msg = "OpenVINO is not installed. Please install OpenVINO to use OpenVINOInferencer."
             raise ImportError(msg)
 

src/anomalib/loggers/wandb.py

@@ -9,12 +9,12 @@
 from lightning.fabric.utilities.types import _PATH
 from lightning.pytorch.loggers.wandb import WandbLogger
 from lightning.pytorch.utilities import rank_zero_only
-from lightning_utilities.core.imports import package_available
+from lightning_utilities.core.imports import module_available
 from matplotlib.figure import Figure
 
 from .base import ImageLoggerBase
 
-if package_available("wandb"):
+if module_available("wandb"):
     import wandb
 
 if TYPE_CHECKING:

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

@@ -12,7 +12,7 @@
 
 import numpy as np
 import torch
-from lightning_utilities.core.imports import package_available
+from lightning_utilities.core.imports import module_available
 from torch import nn
 from torchmetrics import Metric
 from torchvision.transforms.v2 import Transform
@@ -245,7 +245,7 @@ def to_openvino(
             ...     task="segmentation",
             ... )
         """
-        if not package_available("openvino"):
+        if not module_available("openvino"):
             logger.exception("Could not find OpenVINO. Please check OpenVINO installation.")
             raise ModuleNotFoundError
 
@@ -294,7 +294,7 @@ def _compress_ov_model(
         Returns:
             model (CompiledModel): Model in the OpenVINO format compressed with NNCF quantization.
         """
-        if not package_available("nncf"):
+        if not module_available("nncf"):
             logger.exception("Could not find NCCF. Please check NNCF installation.")
             raise ModuleNotFoundError
 

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

@@ -10,13 +10,13 @@
 from typing import TYPE_CHECKING
 
 from dotenv import load_dotenv
-from lightning_utilities.core.imports import package_available
+from lightning_utilities.core.imports import module_available
 
 from anomalib.models.image.vlm_ad.utils import Prompt
 
 from .base import Backend
 
-if package_available("openai"):
+if module_available("openai"):
     from openai import OpenAI
 else:
     OpenAI = None

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

@@ -7,7 +7,7 @@
 from pathlib import Path
 from typing import TYPE_CHECKING
 
-from lightning_utilities.core.imports import package_available
+from lightning_utilities.core.imports import module_available
 from PIL import Image
 
 from anomalib.models.image.vlm_ad.utils import Prompt
@@ -18,7 +18,7 @@
     from transformers.modeling_utils import PreTrainedModel
     from transformers.processing_utils import ProcessorMixin
 
-if package_available("transformers"):
+if module_available("transformers"):
     import transformers
 else:
     transformers = None

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

@@ -12,13 +12,13 @@
 import logging
 from pathlib import Path
 
-from lightning_utilities.core.imports import package_available
+from lightning_utilities.core.imports import module_available
 
 from anomalib.models.image.vlm_ad.utils import Prompt
 
 from .base import Backend
 
-if package_available("ollama"):
+if module_available("ollama"):
     from ollama import chat
     from ollama._client import _encode_image
 else:

src/anomalib/pipelines/benchmark/job.py

@@ -23,6 +23,21 @@
 
 logger = logging.getLogger(__name__)
 
+# Import external loggers
+try:
+    from anomalib.loggers import (
+        AnomalibCometLogger,
+        AnomalibMLFlowLogger,
+        AnomalibTensorBoardLogger,
+        AnomalibWandbLogger,
+    )
+
+    LOGGERS_AVAILABLE = True
+    logger.info("Successfully imported logger modules.")
+except ImportError:
+    LOGGERS_AVAILABLE = False
+    logger.warning("To use external loggers, install required packages using `anomalib install -v`")
+
 
 class BenchmarkJob(Job):
     """Benchmarking job.
@@ -69,6 +84,7 @@ def run(
                 accelerator=self.accelerator,
                 devices=devices,
                 default_root_dir=temp_dir,
+                logger=self._initialize_loggers(self.flat_cfg or {}) if LOGGERS_AVAILABLE else None,
             )
             fit_start_time = time.time()
             engine.fit(self.model, self.datamodule)
@@ -89,8 +105,49 @@ def run(
             **test_results[0],
         }
         logger.info(f"Completed with result {output}")
+
+        # Logging metrics to External Loggers
+        trainer = engine.trainer()
+        for logger_instance in trainer.loggers:
+            if isinstance(logger_instance, AnomalibCometLogger | AnomalibWandbLogger | AnomalibMLFlowLogger):
+                logger_instance.log_metrics(test_results[0])
+                logger.debug(f"Successfully logged metrics to {logger_instance.__class__.__name__}")
         return output
 
+    @staticmethod
+    def _initialize_loggers(logger_configs: dict[str, dict[str, Any]]) -> list[Any]:
+        """Initialize configured external loggers.
+
+        Args:
+            logger_configs: Dictionary mapping logger names to their configurations.
+
+        Returns:
+            Dictionary of initialized loggers.
+        """
+        logger_mapping = {
+            "tensorboard": AnomalibTensorBoardLogger,
+            "comet": AnomalibCometLogger,
+            "wandb": AnomalibWandbLogger,
+            "mlflow": AnomalibMLFlowLogger,
+        }
+
+        active_loggers = []
+        default_configs = {
+            "tensorboard": {"save_dir": "logs/benchmarks"},
+            "comet": {"project_name": "anomalib"},
+            "wandb": {"project": "anomalib"},
+            "mlflow": {"experiment_name": "anomalib"},
+        }
+
+        for logger_name, logger_class in logger_mapping.items():
+            # Use provided config or fall back to defaults
+            config = logger_configs.get(logger_name, default_configs.get(logger_name, {}))
+            logger_instance = logger_class(**config)
+            active_loggers.append(logger_instance)
+            logger.info(f"Successfully initialized {logger_name} logger")
+
+        return active_loggers
+
     @staticmethod
     def collect(results: list[dict[str, Any]]) -> pd.DataFrame:
         """Gather the results returned from run."""

src/anomalib/utils/exceptions/imports.py

@@ -22,7 +22,7 @@ def try_import(import_path: str) -> bool:
 
     warnings.warn(
         "The 'try_import' function is deprecated and will be removed in v2.0.0. "
-        "Use 'package_available' from lightning-utilities instead.",
+        "Use 'module_available' from lightning-utilities instead.",
         DeprecationWarning,
         stacklevel=2,
     )