🗑️ Remove numba

pyproject.toml

@@ -84,8 +84,7 @@ test = [
     "coverage[toml]",
     "tox",
 ]
-extra = ["numba>=0.58.1"]
-full = ["anomalib[core,openvino,loggers,notebooks,extra]"]
+full = ["anomalib[core,openvino,loggers,notebooks]"]
 dev = ["anomalib[full,docs,test]"]
 
 [project.scripts]

src/anomalib/metrics/per_image/__init__.py

@@ -8,7 +8,7 @@
 # SPDX-License-Identifier: Apache-2.0
 
 from .binclf_curve import per_image_binclf_curve, per_image_fpr, per_image_tpr
-from .binclf_curve_numpy import BinclfAlgorithm, BinclfThreshsChoice
+from .binclf_curve_numpy import BinclfThreshsChoice
 from .pimo import AUPIMO, PIMO, AUPIMOResult, PIMOResult, aupimo_scores, pimo_curves
 from .utils import (
     compare_models_pairwise_ttest_rel,
@@ -20,7 +20,6 @@
 
 __all__ = [
     # constants
-    "BinclfAlgorithm",
     "BinclfThreshsChoice",
     "StatsOutliersPolicy",
     "StatsRepeatedPolicy",

src/anomalib/metrics/per_image/_validate.py

@@ -171,7 +171,7 @@ def is_masks(masks: ndarray) -> None:
     if masks.dtype.kind == "b":
         pass
 
-    elif masks.dtype.kind in ("i", "u"):
+    elif masks.dtype.kind in {"i", "u"}:
         masks_unique_vals = np.unique(masks)
         if np.any((masks_unique_vals != 0) & (masks_unique_vals != 1)):
             msg = (
@@ -243,7 +243,7 @@ def is_images_classes(images_classes: ndarray) -> None:
 
     if images_classes.dtype.kind == "b":
         pass
-    elif images_classes.dtype.kind in ("i", "u"):
+    elif images_classes.dtype.kind in {"i", "u"}:
         unique_vals = np.unique(images_classes)
         if np.any((unique_vals != 0) & (unique_vals != 1)):
             msg = (

src/anomalib/metrics/per_image/binclf_curve.py

@@ -21,7 +21,7 @@
 from torch import Tensor
 
 from . import _validate, binclf_curve_numpy
-from .binclf_curve_numpy import BinclfAlgorithm, BinclfThreshsChoice
+from .binclf_curve_numpy import BinclfThreshsChoice
 
 # =========================================== ARGS VALIDATION ===========================================
 
@@ -47,7 +47,6 @@ def _validate_is_binclf_curves(binclf_curves: Tensor, valid_threshs: Tensor | No
 def per_image_binclf_curve(
     anomaly_maps: Tensor,
     masks: Tensor,
-    algorithm: BinclfAlgorithm | str = BinclfAlgorithm.NUMBA,
     threshs_choice: BinclfThreshsChoice | str = BinclfThreshsChoice.MINMAX_LINSPACE,
     threshs_given: Tensor | None = None,
     num_threshs: int | None = None,
@@ -59,7 +58,6 @@ def per_image_binclf_curve(
     Args:
         anomaly_maps (Tensor): Anomaly score maps of shape (N, H, W [, D, ...])
         masks (Tensor): Binary ground truth masks of shape (N, H, W [, D, ...])
-        algorithm (str, optional): Algorithm to use. Defaults to ALGORITHM_NUMBA.
         threshs_choice (str, optional): Sequence of thresholds to use. Defaults to THRESH_SEQUENCE_MINMAX_LINSPACE.
         return_result_object (bool, optional): Whether to return a `PerImageBinClfCurveResult` object. Defaults to True.
 
@@ -115,7 +113,6 @@ def per_image_binclf_curve(
     threshs_array, binclf_curves_array = binclf_curve_numpy.per_image_binclf_curve(
         anomaly_maps=anomaly_maps_array,
         masks=masks_array,
-        algorithm=algorithm,
         threshs_choice=threshs_choice,
         threshs_given=threshs_given_array,
         num_threshs=num_threshs,

src/anomalib/metrics/per_image/binclf_curve_numpy.py

@@ -20,31 +20,13 @@
 import numpy as np
 from numpy import ndarray
 
-try:
-    import numba  # noqa: F401
-except ImportError:
-    HAS_NUMBA = False
-else:
-    HAS_NUMBA = True
-
-
-if HAS_NUMBA:
-    from . import _binclf_curve_numba
-
 from . import _validate
 
 logger = logging.getLogger(__name__)
 
 # =========================================== CONSTANTS ===========================================
 
 
-class BinclfAlgorithm(Enum):
-    """Algorithm to use (relates to the low-level implementation)."""
-
-    PYTHON: str = "python"
-    NUMBA: str = "numba"
-
-
 class BinclfThreshsChoice(Enum):
     """Sequence of thresholds to use."""
 
@@ -95,8 +77,8 @@ def _validate_is_gts_batch(gts_batch: ndarray) -> None:
 # =========================================== PYTHON VERSION ===========================================
 
 
-def _binclf_one_curve_python(scores: ndarray, gts: ndarray, threshs: ndarray) -> ndarray:
-    """One binary classification matrix at each threshold (PYTHON implementation).
+def _binclf_one_curve(scores: ndarray, gts: ndarray, threshs: ndarray) -> ndarray:
+    """One binary classification matrix at each threshold.
 
     In the case where the thresholds are given (i.e. not considering all possible thresholds based on the scores),
     this weird-looking function is faster than the two options in `torchmetrics` on the CPU:
@@ -167,10 +149,10 @@ def score_less_than_thresh(score: float, thresh: float) -> bool:
     ).transpose(0, 2, 1)
 
 
-_binclf_multiple_curves_python = np.vectorize(_binclf_one_curve_python, signature="(n),(n),(k)->(k,2,2)")
-_binclf_multiple_curves_python.__doc__ = """
-Multiple binary classification matrix at each threshold (PYTHON implementation).
-vectorized version of `_binclf_one_curve_python` (see above)
+_binclf_multiple_curves = np.vectorize(_binclf_one_curve, signature="(n),(n),(k)->(k,2,2)")
+_binclf_multiple_curves.__doc__ = """
+Multiple binary classification matrix at each threshold.
+vectorized version of `_binclf_one_curve` (see above)
 """
 
 # =========================================== INTERFACE ===========================================
@@ -180,7 +162,6 @@ def binclf_multiple_curves(
     scores_batch: ndarray,
     gts_batch: ndarray,
     threshs: ndarray,
-    algorithm: BinclfAlgorithm | str = BinclfAlgorithm.NUMBA.value,
 ) -> ndarray:
     """Multiple binary classification matrix (per-instance scope) at each threshold (shared).
 
@@ -220,23 +201,12 @@ def binclf_multiple_curves(
 
         Thresholds are sorted in ascending order.
     """
-    algorithm = BinclfAlgorithm(algorithm)
     _validate_is_scores_batch(scores_batch)
     _validate_is_gts_batch(gts_batch)
     _validate.is_same_shape(scores_batch, gts_batch)
     _validate.is_threshs(threshs)
 
-    if algorithm == BinclfAlgorithm.NUMBA:
-        if HAS_NUMBA:
-            return _binclf_curve_numba.binclf_multiple_curves_numba(scores_batch, gts_batch, threshs)
-
-        logger.warning(
-            f"Algorithm '{BinclfAlgorithm.NUMBA.value}' was selected, but Numba is not installed. "
-            f"Falling back to '{BinclfAlgorithm.PYTHON.value}' implementation.",
-            "Notice that the performance will be slower. Consider installing Numba for faster computation.",
-        )
-
-    return _binclf_multiple_curves_python(scores_batch, gts_batch, threshs)
+    return _binclf_multiple_curves(scores_batch, gts_batch, threshs)
 
 
 # ========================================= PER-IMAGE BINCLF CURVE =========================================
@@ -258,7 +228,6 @@ def _get_threshs_minmax_linspace(anomaly_maps: ndarray, num_threshs: int) -> nda
 def per_image_binclf_curve(
     anomaly_maps: ndarray,
     masks: ndarray,
-    algorithm: BinclfAlgorithm | str = BinclfAlgorithm.NUMBA.value,
     threshs_choice: BinclfThreshsChoice | str = BinclfThreshsChoice.MINMAX_LINSPACE.value,
     threshs_given: ndarray | None = None,
     num_threshs: int | None = None,
@@ -268,7 +237,6 @@ def per_image_binclf_curve(
     Args:
         anomaly_maps (ndarray): Anomaly score maps of shape (N, H, W)
         masks (ndarray): Binary ground truth masks of shape (N, H, W)
-        algorithm (str, optional): Algorithm to use. Defaults to ALGORITHM_NUMBA.
         threshs_choice (str, optional): Sequence of thresholds to use. Defaults to THRESH_SEQUENCE_MINMAX_LINSPACE.
         #
         # `threshs_choice`-dependent arguments
@@ -308,7 +276,6 @@ def per_image_binclf_curve(
 
             Thresholds are sorted in ascending order.
     """
-    BinclfAlgorithm(algorithm)
     threshs_choice = BinclfThreshsChoice(threshs_choice)
     _validate.is_anomaly_maps(anomaly_maps)
     _validate.is_masks(masks)
@@ -350,7 +317,7 @@ def per_image_binclf_curve(
     scores_batch = anomaly_maps.reshape(anomaly_maps.shape[0], -1)
     gts_batch = masks.reshape(masks.shape[0], -1).astype(bool)  # make sure it is boolean
 
-    binclf_curves = binclf_multiple_curves(scores_batch, gts_batch, threshs, algorithm=algorithm)
+    binclf_curves = binclf_multiple_curves(scores_batch, gts_batch, threshs)
 
     num_images = anomaly_maps.shape[0]