perf(eval): reuse contiguous prepared IDs

src/trackers/eval/clear.py

@@ -157,6 +157,8 @@ def compute_clear_metrics(
             "CLR_Frames": num_frames,
         }
 
+    gt_contiguous = unique_gt_ids.dtype.kind in "iu" and unique_gt_ids[0] == 0 and unique_gt_ids[-1] == num_gt_ids - 1
+
     # Initialize counters
     clr_tp = 0
     clr_fn = 0
@@ -176,8 +178,8 @@ def compute_clear_metrics(
 
     # Process each timestep
     for t, (gt_ids_t, tracker_ids_t) in enumerate(zip(gt_ids, tracker_ids)):
-        # Map GT IDs to indices using searchsorted (vectorized)
-        gt_indices_t = np.atleast_1d(np.searchsorted(unique_gt_ids, gt_ids_t))
+        # Map GT IDs directly or use the searchsorted fallback.
+        gt_indices_t = np.atleast_1d(gt_ids_t if gt_contiguous else np.searchsorted(unique_gt_ids, gt_ids_t))
 
         # Handle empty frames
         if len(gt_ids_t) == 0:

src/trackers/eval/hota.py

@@ -130,10 +130,17 @@ def compute_hota_metrics(
     num_tracker_ids = len(unique_tracker_ids)
 
     # `unique_gt_ids` / `unique_tracker_ids` are sorted (np.unique returns sorted
-    # output), so an id's row/column index is simply its position found by binary
-    # search. This replaces per-frame Python dict lookups in the hot loops below.
+    # output). Prepared zero-based integer IDs can be used as indices directly.
+    # Other ID layouts keep the binary-search mapping used by public callers.
+    # The check is done once before both passes.
     # Precondition: all per-frame IDs are present in unique_*_ids (guaranteed —
     # unique arrays are built from concatenation of all frames).
+    gt_contiguous = unique_gt_ids.dtype.kind in "iu" and unique_gt_ids[0] == 0 and unique_gt_ids[-1] == num_gt_ids - 1
+    tracker_contiguous = (
+        unique_tracker_ids.dtype.kind in "iu"
+        and unique_tracker_ids[0] == 0
+        and unique_tracker_ids[-1] == num_tracker_ids - 1
+    )
 
     # Variables for global association (ref: hota.py:48-50)
     potential_matches_count: np.ndarray = np.zeros((num_gt_ids, num_tracker_ids), dtype=np.float64)
@@ -145,15 +152,15 @@ def compute_hota_metrics(
         if len(gt_ids_t) == 0 or len(tracker_ids_t) == 0:
             # Still count IDs even if no matches possible
             if len(gt_ids_t) > 0:
-                gt_indices = np.searchsorted(unique_gt_ids, gt_ids_t)
+                gt_indices = gt_ids_t if gt_contiguous else np.searchsorted(unique_gt_ids, gt_ids_t)
                 gt_id_count[gt_indices] += 1
             if len(tracker_ids_t) > 0:
-                tr_indices = np.searchsorted(unique_tracker_ids, tracker_ids_t)
+                tr_indices = tracker_ids_t if tracker_contiguous else np.searchsorted(unique_tracker_ids, tracker_ids_t)
                 tracker_id_count[0, tr_indices] += 1
             continue
 
-        gt_indices = np.searchsorted(unique_gt_ids, gt_ids_t)
-        tr_indices = np.searchsorted(unique_tracker_ids, tracker_ids_t)
+        gt_indices = gt_ids_t if gt_contiguous else np.searchsorted(unique_gt_ids, gt_ids_t)
+        tr_indices = tracker_ids_t if tracker_contiguous else np.searchsorted(unique_tracker_ids, tracker_ids_t)
 
         similarity = similarity_scores[t]
 
@@ -188,8 +195,8 @@ def compute_hota_metrics(
             hota_fn += len(gt_ids_t)
             continue
 
-        gt_indices = np.searchsorted(unique_gt_ids, gt_ids_t)
-        tr_indices = np.searchsorted(unique_tracker_ids, tracker_ids_t)
+        gt_indices = gt_ids_t if gt_contiguous else np.searchsorted(unique_gt_ids, gt_ids_t)
+        tr_indices = tracker_ids_t if tracker_contiguous else np.searchsorted(unique_tracker_ids, tracker_ids_t)
 
         similarity = similarity_scores[t]
 

src/trackers/eval/identity.py

@@ -109,7 +109,15 @@ def compute_identity_metrics(
     num_tracker_ids = len(unique_tracker_ids)
 
     # `np.unique` sorts the IDs, and every per-frame ID is included in those
-    # arrays, so searchsorted maps all IDs directly to their global indices.
+    # arrays. Prepared zero-based integer IDs can be used as indices directly.
+    # Other ID layouts retain searchsorted, preserving the public-call fallback.
+    # The check is performed once before entering the frame loop.
+    gt_contiguous = unique_gt_ids.dtype.kind in "iu" and unique_gt_ids[0] == 0 and unique_gt_ids[-1] == num_gt_ids - 1
+    tracker_contiguous = (
+        unique_tracker_ids.dtype.kind in "iu"
+        and unique_tracker_ids[0] == 0
+        and unique_tracker_ids[-1] == num_tracker_ids - 1
+    )
 
     # Variables for global association (ref: identity.py:48-50)
     potential_matches_count = np.zeros((num_gt_ids, num_tracker_ids))
@@ -121,15 +129,15 @@ def compute_identity_metrics(
         if len(gt_ids_t) == 0 or len(tracker_ids_t) == 0:
             # Still count IDs even if no matches possible
             if len(gt_ids_t) > 0:
-                gt_indices = np.searchsorted(unique_gt_ids, gt_ids_t)
+                gt_indices = gt_ids_t if gt_contiguous else np.searchsorted(unique_gt_ids, gt_ids_t)
                 gt_id_count[gt_indices] += 1
             if len(tracker_ids_t) > 0:
-                tr_indices = np.searchsorted(unique_tracker_ids, tracker_ids_t)
+                tr_indices = tracker_ids_t if tracker_contiguous else np.searchsorted(unique_tracker_ids, tracker_ids_t)
                 tracker_id_count[tr_indices] += 1
             continue
 
-        gt_indices = np.searchsorted(unique_gt_ids, gt_ids_t)
-        tr_indices = np.searchsorted(unique_tracker_ids, tracker_ids_t)
+        gt_indices = gt_ids_t if gt_contiguous else np.searchsorted(unique_gt_ids, gt_ids_t)
+        tr_indices = tracker_ids_t if tracker_contiguous else np.searchsorted(unique_tracker_ids, tracker_ids_t)
 
         similarity = similarity_scores[t]