Implemented mAR metric
This commit is contained in:
LinasKo
parent
b9b802e9ea
commit
83d4386fa2
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@ -8,6 +8,10 @@ from supervision.metrics.mean_average_precision import (
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MeanAveragePrecision,
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MeanAveragePrecisionResult,
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)
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from supervision.metrics.mean_average_recall import (
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MeanAverageRecall,
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MeanAverageRecallResult,
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)
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from supervision.metrics.precision import Precision, PrecisionResult
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from supervision.metrics.recall import Recall, RecallResult
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from supervision.metrics.utils.object_size import (
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@ -0,0 +1,512 @@
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from __future__ import annotations
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from copy import deepcopy
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from dataclasses import dataclass
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from typing import TYPE_CHECKING, List, Optional, Tuple, Union
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import numpy as np
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from matplotlib import pyplot as plt
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from supervision.config import ORIENTED_BOX_COORDINATES
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from supervision.detection.core import Detections
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from supervision.detection.utils import (
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box_iou_batch,
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mask_iou_batch,
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oriented_box_iou_batch,
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)
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from supervision.draw.color import LEGACY_COLOR_PALETTE
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from supervision.metrics.core import Metric, MetricTarget
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from supervision.metrics.utils.object_size import (
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ObjectSizeCategory,
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get_detection_size_category,
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)
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from supervision.metrics.utils.utils import ensure_pandas_installed
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if TYPE_CHECKING:
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import pandas as pd
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class MeanAverageRecall(Metric):
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def __init__(
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self,
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metric_target: MetricTarget = MetricTarget.BOXES,
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):
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self._metric_target = metric_target
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self._predictions_list: List[Detections] = []
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self._targets_list: List[Detections] = []
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self.max_detections = np.array([1, 10, 100])
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def reset(self) -> None:
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self._predictions_list = []
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self._targets_list = []
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def update(
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self,
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predictions: Union[Detections, List[Detections]],
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targets: Union[Detections, List[Detections]],
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) -> MeanAverageRecall:
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if not isinstance(predictions, list):
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predictions = [predictions]
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if not isinstance(targets, list):
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targets = [targets]
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if len(predictions) != len(targets):
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raise ValueError(
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f"The number of predictions ({len(predictions)}) and"
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f" targets ({len(targets)}) during the update must be the same."
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)
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self._predictions_list.extend(predictions)
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self._targets_list.extend(targets)
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return self
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def compute(self) -> MeanAverageRecallResult:
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result = self._compute(self._predictions_list, self._targets_list)
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small_predictions, small_targets = self._filter_predictions_and_targets_by_size(
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self._predictions_list, self._targets_list, ObjectSizeCategory.SMALL
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)
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result.small_objects = self._compute(small_predictions, small_targets)
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medium_predictions, medium_targets = (
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self._filter_predictions_and_targets_by_size(
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self._predictions_list, self._targets_list, ObjectSizeCategory.MEDIUM
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)
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)
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result.medium_objects = self._compute(medium_predictions, medium_targets)
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large_predictions, large_targets = self._filter_predictions_and_targets_by_size(
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self._predictions_list, self._targets_list, ObjectSizeCategory.LARGE
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)
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result.large_objects = self._compute(large_predictions, large_targets)
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return result
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def _compute(
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self, predictions_list: List[Detections], targets_list: List[Detections]
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) -> MeanAverageRecallResult:
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iou_thresholds = np.linspace(0.5, 0.95, 10)
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stats = []
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for predictions, targets in zip(predictions_list, targets_list):
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prediction_contents = self._detections_content(predictions)
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target_contents = self._detections_content(targets)
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if len(targets) > 0:
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if len(predictions) == 0:
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stats.append(
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(
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np.zeros((0, iou_thresholds.size), dtype=bool),
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np.zeros((0,), dtype=np.float32),
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np.zeros((0,), dtype=int),
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targets.class_id,
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)
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)
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else:
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if self._metric_target == MetricTarget.BOXES:
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iou = box_iou_batch(target_contents, prediction_contents)
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elif self._metric_target == MetricTarget.MASKS:
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iou = mask_iou_batch(target_contents, prediction_contents)
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elif self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:
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iou = oriented_box_iou_batch(
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target_contents, prediction_contents
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)
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else:
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raise ValueError(
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"Unsupported metric target for IoU calculation"
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)
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matches = self._match_detection_batch(
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predictions.class_id, targets.class_id, iou, iou_thresholds
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)
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stats.append(
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(
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matches,
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predictions.confidence,
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predictions.class_id,
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targets.class_id,
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)
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)
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if not stats:
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return MeanAverageRecallResult(
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metric_target=self._metric_target,
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recall_scores=np.zeros(iou_thresholds.shape[0]),
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recall_per_class=np.zeros((0, iou_thresholds.shape[0])),
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max_detections=self.max_detections,
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iou_thresholds=iou_thresholds,
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matched_classes=np.array([], dtype=int),
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small_objects=None,
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medium_objects=None,
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large_objects=None,
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)
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concatenated_stats = [np.concatenate(items, 0) for items in zip(*stats)]
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recall_scores_per_k, recall_per_class, unique_classes = (
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self._compute_average_recall_for_classes(*concatenated_stats)
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)
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return MeanAverageRecallResult(
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metric_target=self._metric_target,
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recall_scores=recall_scores_per_k,
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recall_per_class=recall_per_class,
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max_detections=self.max_detections,
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iou_thresholds=iou_thresholds,
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matched_classes=unique_classes,
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small_objects=None,
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medium_objects=None,
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large_objects=None,
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)
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def _compute_average_recall_for_classes(
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self,
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matches: np.ndarray,
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prediction_confidence: np.ndarray,
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prediction_class_ids: np.ndarray,
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true_class_ids: np.ndarray,
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) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
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sorted_indices = np.argsort(-prediction_confidence)
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matches = matches[sorted_indices]
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prediction_class_ids = prediction_class_ids[sorted_indices]
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unique_classes, class_counts = np.unique(true_class_ids, return_counts=True)
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recalls_at_k = []
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for max_detections in self.max_detections:
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# Shape: PxTh,P,C,C -> CxThx3
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confusion_matrix = self._compute_confusion_matrix(
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matches,
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prediction_class_ids,
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unique_classes,
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class_counts,
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max_detections=max_detections,
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)
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# Shape: CxThx3 -> CxTh
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recall_per_class = self._compute_recall(confusion_matrix)
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recalls_at_k.append(recall_per_class)
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# Shape: KxCxTh -> KxC
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recalls_at_k = np.array(recalls_at_k)
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average_recall_per_class = np.mean(recalls_at_k, axis=2)
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# Shape: KxC -> K
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recall_scores = np.mean(average_recall_per_class, axis=1)
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return recall_scores, recall_per_class, unique_classes
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@staticmethod
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def _match_detection_batch(
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predictions_classes: np.ndarray,
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target_classes: np.ndarray,
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iou: np.ndarray,
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iou_thresholds: np.ndarray,
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) -> np.ndarray:
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num_predictions, num_iou_levels = (
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predictions_classes.shape[0],
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iou_thresholds.shape[0],
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)
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correct = np.zeros((num_predictions, num_iou_levels), dtype=bool)
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correct_class = target_classes[:, None] == predictions_classes
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for i, iou_level in enumerate(iou_thresholds):
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matched_indices = np.where((iou >= iou_level) & correct_class)
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if matched_indices[0].shape[0]:
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combined_indices = np.stack(matched_indices, axis=1)
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iou_values = iou[matched_indices][:, None]
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matches = np.hstack([combined_indices, iou_values])
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if matched_indices[0].shape[0] > 1:
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matches = matches[matches[:, 2].argsort()[::-1]]
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matches = matches[np.unique(matches[:, 1], return_index=True)[1]]
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matches = matches[np.unique(matches[:, 0], return_index=True)[1]]
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correct[matches[:, 1].astype(int), i] = True
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return correct
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@staticmethod
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def _compute_confusion_matrix(
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sorted_matches: np.ndarray,
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sorted_prediction_class_ids: np.ndarray,
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unique_classes: np.ndarray,
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class_counts: np.ndarray,
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max_detections: Optional[int] = None,
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) -> np.ndarray:
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num_thresholds = sorted_matches.shape[1]
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num_classes = unique_classes.shape[0]
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confusion_matrix = np.zeros((num_classes, num_thresholds, 3))
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for class_idx, class_id in enumerate(unique_classes):
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is_class = sorted_prediction_class_ids == class_id
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num_true = class_counts[class_idx]
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num_predictions = is_class.sum()
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if num_predictions == 0:
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true_positives = np.zeros(num_thresholds)
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false_positives = np.zeros(num_thresholds)
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false_negatives = np.full(num_thresholds, num_true)
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elif num_true == 0:
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true_positives = np.zeros(num_thresholds)
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false_positives = np.full(num_thresholds, num_predictions)
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false_negatives = np.zeros(num_thresholds)
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else:
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limited_matches = sorted_matches[is_class][slice(max_detections)]
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true_positives = limited_matches.sum(0)
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false_positives = (1 - limited_matches).sum(0)
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false_negatives = num_true - true_positives
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false_negatives = num_true - true_positives
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confusion_matrix[class_idx] = np.stack(
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[true_positives, false_positives, false_negatives], axis=1
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)
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return confusion_matrix
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@staticmethod
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def _compute_recall(confusion_matrix: np.ndarray) -> np.ndarray:
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"""
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Broadcastable function, computing the recall from the confusion matrix.
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Arguments:
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confusion_matrix: np.ndarray, shape (N, ..., 3), where the last dimension
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contains the true positives, false positives, and false negatives.
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Returns:
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np.ndarray, shape (N, ...), containing the recall for each element.
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"""
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if not confusion_matrix.shape[-1] == 3:
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raise ValueError(
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f"Confusion matrix must have shape (..., 3), got "
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f"{confusion_matrix.shape}"
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)
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true_positives = confusion_matrix[..., 0]
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false_negatives = confusion_matrix[..., 2]
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denominator = true_positives + false_negatives
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recall = np.where(denominator == 0, 0, true_positives / denominator)
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return recall
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def _detections_content(self, detections: Detections) -> np.ndarray:
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"""Return boxes, masks or oriented bounding boxes from detections."""
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if self._metric_target == MetricTarget.BOXES:
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return detections.xyxy
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if self._metric_target == MetricTarget.MASKS:
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return (
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detections.mask
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if detections.mask is not None
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else self._make_empty_content()
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)
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if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:
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obb = detections.data.get(ORIENTED_BOX_COORDINATES)
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if obb is not None and len(obb) > 0:
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return np.array(obb, dtype=np.float32)
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return self._make_empty_content()
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raise ValueError(f"Invalid metric target: {self._metric_target}")
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def _make_empty_content(self) -> np.ndarray:
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if self._metric_target == MetricTarget.BOXES:
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return np.empty((0, 4), dtype=np.float32)
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if self._metric_target == MetricTarget.MASKS:
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return np.empty((0, 0, 0), dtype=bool)
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if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:
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return np.empty((0, 4, 2), dtype=np.float32)
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raise ValueError(f"Invalid metric target: {self._metric_target}")
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def _filter_detections_by_size(
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self, detections: Detections, size_category: ObjectSizeCategory
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) -> Detections:
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"""Return a copy of detections with contents filtered by object size."""
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new_detections = deepcopy(detections)
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if detections.is_empty() or size_category == ObjectSizeCategory.ANY:
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return new_detections
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sizes = get_detection_size_category(new_detections, self._metric_target)
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size_mask = sizes == size_category.value
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new_detections.xyxy = new_detections.xyxy[size_mask]
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if new_detections.mask is not None:
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new_detections.mask = new_detections.mask[size_mask]
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if new_detections.class_id is not None:
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new_detections.class_id = new_detections.class_id[size_mask]
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if new_detections.confidence is not None:
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new_detections.confidence = new_detections.confidence[size_mask]
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if new_detections.tracker_id is not None:
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new_detections.tracker_id = new_detections.tracker_id[size_mask]
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if new_detections.data is not None:
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for key, value in new_detections.data.items():
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new_detections.data[key] = np.array(value)[size_mask]
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return new_detections
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def _filter_predictions_and_targets_by_size(
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self,
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predictions_list: List[Detections],
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targets_list: List[Detections],
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size_category: ObjectSizeCategory,
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) -> Tuple[List[Detections], List[Detections]]:
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new_predictions_list = []
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new_targets_list = []
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for predictions, targets in zip(predictions_list, targets_list):
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new_predictions_list.append(
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self._filter_detections_by_size(predictions, size_category)
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)
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new_targets_list.append(
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self._filter_detections_by_size(targets, size_category)
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)
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return new_predictions_list, new_targets_list
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@dataclass
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class MeanAverageRecallResult:
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metric_target: MetricTarget
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@property
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def mAR_at_1(self) -> float:
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return self.recall_scores[0]
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@property
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def mAR_at_10(self) -> float:
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return self.recall_scores[1]
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@property
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def mAR_at_100(self) -> float:
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return self.recall_scores[2]
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recall_scores: np.ndarray
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recall_per_class: np.ndarray
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max_detections: np.ndarray
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iou_thresholds: np.ndarray
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matched_classes: np.ndarray
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small_objects: Optional[MeanAverageRecallResult]
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medium_objects: Optional[MeanAverageRecallResult]
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large_objects: Optional[MeanAverageRecallResult]
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def __str__(self) -> str:
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out_str = (
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f"{self.__class__.__name__}:\n"
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f"Metric target: {self.metric_target}\n"
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f"mAR @ 1: {self.mAR_at_1:.4f}\n"
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f"mAR @ 10: {self.mAR_at_10:.4f}\n"
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f"mAR @ 100: {self.mAR_at_100:.4f}\n"
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f"max detections: {self.max_detections}\n"
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f"IoU thresh: {self.iou_thresholds}\n"
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f"mAR per class:\n"
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)
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if self.recall_per_class.size == 0:
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out_str += " No results\n"
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for class_id, recall_of_class in zip(
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self.matched_classes, self.recall_per_class
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):
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out_str += f" {class_id}: {recall_of_class}\n"
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indent = " "
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if self.small_objects is not None:
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indented = indent + str(self.small_objects).replace("\n", f"\n{indent}")
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out_str += f"\nSmall objects:\n{indented}"
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if self.medium_objects is not None:
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indented = indent + str(self.medium_objects).replace("\n", f"\n{indent}")
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out_str += f"\nMedium objects:\n{indented}"
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if self.large_objects is not None:
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indented = indent + str(self.large_objects).replace("\n", f"\n{indent}")
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out_str += f"\nLarge objects:\n{indented}"
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return out_str
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def to_pandas(self) -> "pd.DataFrame":
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ensure_pandas_installed()
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import pandas as pd
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pandas_data = {
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"mAR @ 1": self.mAR_at_1,
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"mAR @ 10": self.mAR_at_10,
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"mAR @ 100": self.mAR_at_100,
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}
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if self.small_objects is not None:
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small_objects_df = self.small_objects.to_pandas()
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for key, value in small_objects_df.items():
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pandas_data[f"small_objects_{key}"] = value
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if self.medium_objects is not None:
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medium_objects_df = self.medium_objects.to_pandas()
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for key, value in medium_objects_df.items():
|
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pandas_data[f"medium_objects_{key}"] = value
|
||||
if self.large_objects is not None:
|
||||
large_objects_df = self.large_objects.to_pandas()
|
||||
for key, value in large_objects_df.items():
|
||||
pandas_data[f"large_objects_{key}"] = value
|
||||
|
||||
return pd.DataFrame(pandas_data, index=[0])
|
||||
|
||||
def plot(self):
|
||||
labels = ["mAR @ 1", "mAR @ 10", "mAR @ 100"]
|
||||
values = [self.mAR_at_1, self.mAR_at_10, self.mAR_at_100]
|
||||
colors = [LEGACY_COLOR_PALETTE[0]] * 3
|
||||
|
||||
if self.small_objects is not None:
|
||||
small_objects = self.small_objects
|
||||
labels += ["Small: mAR @ 1", "Small: mAR @ 10", "Small: mAR @ 100"]
|
||||
values += [
|
||||
small_objects.mAR_at_1,
|
||||
small_objects.mAR_at_10,
|
||||
small_objects.mAR_at_100,
|
||||
]
|
||||
colors += [LEGACY_COLOR_PALETTE[3]] * 3
|
||||
|
||||
if self.medium_objects is not None:
|
||||
medium_objects = self.medium_objects
|
||||
labels += ["Medium: mAR @ 1", "Medium: mAR @ 10", "Medium: mAR @ 100"]
|
||||
values += [
|
||||
medium_objects.mAR_at_1,
|
||||
medium_objects.mAR_at_10,
|
||||
medium_objects.mAR_at_100,
|
||||
]
|
||||
colors += [LEGACY_COLOR_PALETTE[2]] * 3
|
||||
|
||||
if self.large_objects is not None:
|
||||
large_objects = self.large_objects
|
||||
labels += ["Large: mAR @ 1", "Large: mAR @ 10", "Large: mAR @ 100"]
|
||||
values += [
|
||||
large_objects.mAR_at_1,
|
||||
large_objects.mAR_at_10,
|
||||
large_objects.mAR_at_100,
|
||||
]
|
||||
colors += [LEGACY_COLOR_PALETTE[4]] * 3
|
||||
|
||||
plt.rcParams["font.family"] = "monospace"
|
||||
|
||||
_, ax = plt.subplots(figsize=(10, 6))
|
||||
ax.set_ylim(0, 1)
|
||||
ax.set_ylabel("Value", fontweight="bold")
|
||||
title = (
|
||||
f"Mean Average Recall, by Object Size"
|
||||
f"\n(target: {self.metric_target.value}"
|
||||
)
|
||||
ax.set_title(title, fontweight="bold")
|
||||
|
||||
x_positions = range(len(labels))
|
||||
bars = ax.bar(x_positions, values, color=colors, align="center")
|
||||
|
||||
ax.set_xticks(x_positions)
|
||||
ax.set_xticklabels(labels, rotation=45, ha="right")
|
||||
|
||||
for bar in bars:
|
||||
y_value = bar.get_height()
|
||||
ax.text(
|
||||
bar.get_x() + bar.get_width() / 2,
|
||||
y_value + 0.02,
|
||||
f"{y_value:.2f}",
|
||||
ha="center",
|
||||
va="bottom",
|
||||
)
|
||||
|
||||
plt.rcParams["font.family"] = "sans-serif"
|
||||
|
||||
plt.tight_layout()
|
||||
plt.show()
|
||||
Loading…
Reference in New Issue