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⚡️ Speed up method Recall._compute_confusion_matrix by 51% #53

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⚡️ Speed up method Recall._compute_confusion_matrix by 51% #53

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30 changes: 17 additions & 13 deletions supervision/metrics/recall.py
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Original file line number Diff line number Diff line change
Expand Up @@ -334,27 +334,31 @@ class ids.
num_thresholds = sorted_matches.shape[1]
num_classes = unique_classes.shape[0]

# Initialize confusion matrix
confusion_matrix = np.zeros((num_classes, num_thresholds, 3))

# Vectorized approach to handle the class-based confusion matrix updates
for class_idx, class_id in enumerate(unique_classes):
is_class = sorted_prediction_class_ids == class_id
class_mask = sorted_prediction_class_ids == class_id
num_true = class_counts[class_idx]
num_predictions = is_class.sum()
num_predictions = class_mask.sum()

if num_predictions == 0:
true_positives = np.zeros(num_thresholds)
false_positives = np.zeros(num_thresholds)
false_negatives = np.full(num_thresholds, num_true)
confusion_matrix[class_idx, :, 0] = 0 # true_positives
confusion_matrix[class_idx, :, 1] = 0 # false_positives
confusion_matrix[class_idx, :, 2] = num_true # false_negatives
elif num_true == 0:
true_positives = np.zeros(num_thresholds)
false_positives = np.full(num_thresholds, num_predictions)
false_negatives = np.zeros(num_thresholds)
confusion_matrix[class_idx, :, 0] = 0 # true_positives
confusion_matrix[class_idx, :, 1] = num_predictions # false_positives
confusion_matrix[class_idx, :, 2] = 0 # false_negatives
else:
true_positives = sorted_matches[is_class].sum(0)
false_positives = (1 - sorted_matches[is_class]).sum(0)
true_positives = sorted_matches[class_mask].sum(axis=0)
false_positives = class_mask.sum() - true_positives
false_negatives = num_true - true_positives
confusion_matrix[class_idx] = np.stack(
[true_positives, false_positives, false_negatives], axis=1
)

confusion_matrix[class_idx, :, 0] = true_positives
confusion_matrix[class_idx, :, 1] = false_positives
confusion_matrix[class_idx, :, 2] = false_negatives

return confusion_matrix

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