add option to load metrics with kwargs

anomalib/utils/callbacks/__init__.py

@@ -84,12 +84,10 @@ def get_callbacks(config: Union[ListConfig, DictConfig]) -> List[Callback]:
     callbacks.append(post_processing_callback)
 
     # Add metric configuration to the model via MetricsConfigurationCallback
-    image_metric_names = config.metrics.image if "image" in config.metrics.keys() else None
-    pixel_metric_names = config.metrics.pixel if "pixel" in config.metrics.keys() else None
     metrics_callback = MetricsConfigurationCallback(
         config.dataset.task,
-        image_metric_names,
-        pixel_metric_names,
+        config.metrics.get("image", None),
+        config.metrics.get("pixel", None),
     )
     callbacks.append(metrics_callback)
 

anomalib/utils/callbacks/metrics_configuration.py

@@ -12,7 +12,7 @@
 from pytorch_lightning.utilities.cli import CALLBACK_REGISTRY
 
 from anomalib.models.components.base.anomaly_module import AnomalyModule
-from anomalib.utils.metrics import metric_collection_from_names
+from anomalib.utils.metrics import create_metric_collection
 
 logger = logging.getLogger(__name__)
 
@@ -74,8 +74,8 @@ def setup(
             pixel_metric_names = self.pixel_metric_names
 
         if isinstance(pl_module, AnomalyModule):
-            pl_module.image_metrics = metric_collection_from_names(image_metric_names, "image_")
-            pl_module.pixel_metrics = metric_collection_from_names(pixel_metric_names, "pixel_")
+            pl_module.image_metrics = create_metric_collection(image_metric_names, "image_")
+            pl_module.pixel_metrics = create_metric_collection(pixel_metric_names, "pixel_")
 
             pl_module.image_metrics.set_threshold(pl_module.image_threshold.value)
             pl_module.pixel_metrics.set_threshold(pl_module.pixel_threshold.value)

anomalib/utils/metrics/__init__.py

@@ -5,7 +5,7 @@
 
 import importlib
 import warnings
-from typing import List, Optional, Tuple, Union
+from typing import Any, Dict, List, Optional, Union
 
 import torchmetrics
 from omegaconf import DictConfig, ListConfig
@@ -23,23 +23,6 @@
 __all__ = ["AUROC", "AUPR", "AUPRO", "OptimalF1", "AnomalyScoreThreshold", "AnomalyScoreDistribution", "MinMax", "PRO"]
 
 
-def get_metrics(config: Union[ListConfig, DictConfig]) -> Tuple[AnomalibMetricCollection, AnomalibMetricCollection]:
-    """Create metric collections based on the config.
-
-    Args:
-        config (Union[DictConfig, ListConfig]): Config.yaml loaded using OmegaConf
-
-    Returns:
-        AnomalibMetricCollection: Image-level metric collection
-        AnomalibMetricCollection: Pixel-level metric collection
-    """
-    image_metric_names = config.metrics.image if "image" in config.metrics.keys() else []
-    pixel_metric_names = config.metrics.pixel if "pixel" in config.metrics.keys() else []
-    image_metrics = metric_collection_from_names(image_metric_names, "image_")
-    pixel_metrics = metric_collection_from_names(pixel_metric_names, "pixel_")
-    return image_metrics, pixel_metrics
-
-
 def metric_collection_from_names(metric_names: List[str], prefix: Optional[str]) -> AnomalibMetricCollection:
     """Create a metric collection from a list of metric names.
 
@@ -68,3 +51,125 @@ def metric_collection_from_names(metric_names: List[str], prefix: Optional[str])
         else:
             warnings.warn(f"No metric with name {metric_name} found in Anomalib metrics or TorchMetrics.")
     return metrics
+
+
+def _validate_metrics_dict(metrics: Dict[str, Dict[str, Any]]) -> None:
+    """Check the assumptions about metrics config dict.
+
+    - Keys are metric names
+    - Values are dictionaries.
+    - Internal dictionaries:
+        - have key "class_path" and its value is of type str
+        - have key init_args" and its value is of type dict).
+
+    """
+    assert all(
+        isinstance(metric, str) for metric in metrics.keys()
+    ), f"All keys (metric names) must be strings, found {sorted(metrics.keys())}"
+    assert all(
+        isinstance(metric, (dict, DictConfig)) for metric in metrics.values()
+    ), f"All values must be dictionaries, found {list(metrics.values())}"
+    assert all("class_path" in metric and isinstance(metric["class_path"], str) for metric in metrics.values()), (
+        "All internal dictionaries must have a 'class_path' key whose value is of type str, "
+        f"found {list(metrics.values())}"
+    )
+    assert all(
+        "init_args" in metric and isinstance(metric["init_args"], (dict, DictConfig)) for metric in metrics.values()
+    ), (
+        "All internal dictionaries must have a 'init_args' key whose value is of type dict, "
+        f"found {list(metrics.values())}"
+    )
+
+
+def _get_class_from_path(class_path: str) -> Any:
+    """Get a class from a module assuming the string format is `package.subpackage.module.ClassName`."""
+    module_name, class_name = class_path.rsplit(".", 1)
+    module = importlib.import_module(module_name)
+    assert hasattr(module, class_name), f"Class {class_name} not found in module {module_name}"
+    cls = getattr(module, class_name)
+    return cls
+
+
+def metric_collection_from_dicts(metrics: Dict[str, Dict[str, Any]], prefix: Optional[str]) -> AnomalibMetricCollection:
+    """Create a metric collection from a dict of "metric name" -> "metric specifications".
+
+    Example:
+
+    metrics = {
+        "PixelWiseF1Score": {
+            "class_path": "torchmetrics.F1Score",
+            "init_args": {},
+        },
+        "PixelWiseAUROC": {
+            "class_path": "anomalib.utils.metrics.AUROC",
+            "init_args": {
+                "compute_on_cpu": True,
+            },
+        },
+    }
+
+    In the config file, the same specifications (for pixel-wise metrics) look like:
+
+    ```yaml
+    metrics:
+        pixel:
+            PixelWiseF1Score:
+                class_path: torchmetrics.F1Score
+                init_args: {}
+            PixelWiseAUROC:
+                class_path: anomalib.utils.metrics.AUROC
+                init_args:
+                    compute_on_cpu: true
+    ```
+
+    Args:
+        metrics (Dict[str, Dict[str, Any]]): keys are metric names, values are dictionaries.
+            Internal Dict[str, Any] keys are "class_path" (value is string) and "init_args" (value is dict),
+            following the convention in Pytorch Lightning CLI.
+
+        prefix (Optional[str]): prefix to assign to the metrics in the collection.
+
+    Returns:
+        AnomalibMetricCollection: Collection of metrics.
+    """
+    _validate_metrics_dict(metrics)
+    metrics_collection = {}
+    for name, dict_ in metrics.items():
+        class_path = dict_["class_path"]
+        kwargs = dict_["init_args"]
+        cls = _get_class_from_path(class_path)
+        metrics_collection[name] = cls(**kwargs)
+    return AnomalibMetricCollection(metrics_collection, prefix=prefix)
+
+
+def create_metric_collection(
+    metrics: Union[List[str], Dict[str, Dict[str, Any]]], prefix: Optional[str]
+) -> AnomalibMetricCollection:
+    """Create a metric collection from a list of metric names or dictionaries.
+
+    This function will dispatch the actual creation to the appropriate function depending on the input type:
+
+        - if List[str] (names of metrics): see `metric_collection_from_names`
+        - if Dict[str, Dict[str, Any]] (path and init args of a class): see `metric_collection_from_dicts`
+
+    The function will first try to retrieve the metric from the metrics defined in Anomalib metrics module,
+    then in TorchMetrics package.
+
+    Args:
+        metrics (Union[List[str], Dict[str, Dict[str, Any]]]).
+        prefix (Optional[str]): prefix to assign to the metrics in the collection.
+
+    Returns:
+        AnomalibMetricCollection: Collection of metrics.
+    """
+    # fallback is using the names
+
+    if isinstance(metrics, (ListConfig, list)):
+        assert all(isinstance(metric, str) for metric in metrics), f"All metrics must be strings, found {metrics}"
+        return metric_collection_from_names(metrics, prefix)
+
+    if isinstance(metrics, (DictConfig, dict)):
+        _validate_metrics_dict(metrics)
+        return metric_collection_from_dicts(metrics, prefix)
+
+    raise ValueError(f"metrics must be a list or a dict, found {type(metrics)}")

docs/source/reference_guide/api/metrics.rst

@@ -1,6 +1,46 @@
 Metrics
 =======
 
+There are two ways of configuring metrics in the config file:
+
+1. a list of metric names, or
+2. a mapping of metric names to class path and init args.
+
+Each subsection in the section ``metrics`` of the config file can have a different style but inside each one it must be the same style.
+
+.. code-block:: yaml
+   :caption: Example of metrics configuration section in the config file.
+
+   metrics:
+      # imagewise metrics using the list of metric names style
+      image:
+         - F1Score
+         - AUROC
+      # pixelwise metrics using the mapping style
+      pixel:
+         F1Score:
+            class_path: torchmetrics.F1Score
+            init_args:
+            compute_on_cpu: true
+         AUROC:
+            class_path: anomalib.utils.metrics.AUROC
+            init_args:
+            compute_on_cpu: true
+
+List of metric names
+--------------------
+
+A list of strings that match the name of a class in ``anomalib.utils.metrics`` or ``torchmetrics`` (in this order of priority), which will be instantiated with default arguments.
+
+Mapping of metric names to class path and init args
+---------------------------------------------------
+
+A mapping of metric names (str) to a dictionary with two keys: "class_path" and "init_args".
+
+"class_path" is a string with the full path to a metric (from root package down to the class name, e.g.: "anomalib.utils.metrics.AUROC").
+
+"init_args" is a dictionary of arguments to be passed to the class constructor.
+
 .. automodule:: anomalib.utils.metrics
    :members:
    :undoc-members:

tests/helpers/dummy.py

@@ -0,0 +1,45 @@
+import shutil
+import tempfile
+from pathlib import Path
+
+import pytorch_lightning as pl
+import torch
+from torch import nn
+from torch.utils.data import DataLoader, Dataset
+
+from anomalib.utils.loggers.tensorboard import AnomalibTensorBoardLogger
+
+
+class DummyDataset(Dataset):
+    def __len__(self):
+        return 1
+
+    def __getitem__(self, idx):
+        return torch.ones(1)
+
+
+class DummyDataModule(pl.LightningDataModule):
+    def train_dataloader(self) -> DataLoader:
+        return DataLoader(DummyDataset())
+
+    def val_dataloader(self) -> DataLoader:
+        return DataLoader(DummyDataset())
+
+    def test_dataloader(self) -> DataLoader:
+        return DataLoader(DummyDataset())
+
+
+class DummyModel(nn.Module):
+    def __init__(self):
+        # pytlint: disable=useless-parent-delegation
+        super().__init__()
+
+
+class DummyLogger(AnomalibTensorBoardLogger):
+    def __init__(self):
+        self.tempdir = Path(tempfile.mkdtemp())
+        super().__init__(name="tensorboard_logs", save_dir=self.tempdir)
+
+    def __del__(self):
+        if self.tempdir.exists():
+            shutil.rmtree(self.tempdir)

tests/helpers/metrics.py

@@ -0,0 +1,27 @@
+"""Helpers for metrics tests."""
+
+from typing import Tuple, Union
+
+from omegaconf import DictConfig, ListConfig
+
+from anomalib.utils.metrics import (
+    AnomalibMetricCollection,
+    metric_collection_from_names,
+)
+
+
+def get_metrics(config: Union[ListConfig, DictConfig]) -> Tuple[AnomalibMetricCollection, AnomalibMetricCollection]:
+    """Create metric collections based on the config.
+
+    Args:
+        config (Union[DictConfig, ListConfig]): Config.yaml loaded using OmegaConf
+
+    Returns:
+        AnomalibMetricCollection: Image-level metric collection
+        AnomalibMetricCollection: Pixel-level metric collection
+    """
+    image_metric_names = config.metrics.image if "image" in config.metrics.keys() else []
+    pixel_metric_names = config.metrics.pixel if "pixel" in config.metrics.keys() else []
+    image_metrics = metric_collection_from_names(image_metric_names, "image_")
+    pixel_metrics = metric_collection_from_names(pixel_metric_names, "pixel_")
+    return image_metrics, pixel_metrics

tests/pre_merge/utils/callbacks/metrics_configuration_callback/data/config-good-00.yaml

@@ -0,0 +1,13 @@
+metrics:
+  pixel:
+    F1Score:
+      class_path: torchmetrics.F1Score
+      init_args:
+        compute_on_cpu: true
+    AUROC:
+      class_path: anomalib.utils.metrics.AUROC
+      init_args:
+        compute_on_cpu: true
+  image:
+    - F1Score
+    - AUROC

tests/pre_merge/utils/callbacks/metrics_configuration_callback/data/config-good-01.yaml

@@ -0,0 +1,13 @@
+metrics:
+  pixel:
+    - F1Score
+    - AUROC
+  image:
+    F1Score:
+      class_path: torchmetrics.F1Score
+      init_args:
+        compute_on_cpu: true
+    AUROC:
+      class_path: anomalib.utils.metrics.AUROC
+      init_args:
+        compute_on_cpu: true