Add causal person fusion and MuVAP stage-3 baselines

third_party/MuVAP/tasks/asd_metrics.py

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+import torch
+
+
+def ava_active_speaker_map(scores, labels):
+    scores = torch.as_tensor(scores, dtype=torch.float64).flatten()
+    labels = torch.as_tensor(labels, device=scores.device).bool().flatten()
+    if scores.shape != labels.shape:
+        raise ValueError("scores and labels must have the same shape")
+    if not torch.isfinite(scores).all():
+        raise ValueError("scores contain NaN or infinite values")
+
+    positives = labels.sum()
+    if not len(labels) or positives == 0:
+        return 0.0
+
+    order = torch.argsort(scores, descending=True, stable=True)
+    true_positives = labels[order].cumsum(0)
+    precision = true_positives / torch.arange(1, len(labels) + 1, dtype=torch.float64, device=scores.device)
+    recall = true_positives / positives
+    precision = torch.cat([precision.new_zeros(1), precision, precision.new_zeros(1)])
+    recall = torch.cat([recall.new_zeros(1), recall, recall.new_ones(1)])
+    precision = torch.flip(torch.cummax(torch.flip(precision, dims=[0]), dim=0).values, dims=[0])
+    indices = torch.where(recall[1:] != recall[:-1])[0] + 1
+    average_precision = ((recall[indices] - recall[indices - 1]) * precision[indices]).sum()
+    return 100 * average_precision.item()