⚡️ Speed up function postprocess by 210%

codeflash/process/infer.py

@@ -2,18 +2,29 @@
 
 
 def sigmoid_stable(x):
-    return np.where(x >= 0, 1 / (1 + np.exp(-x)), np.exp(x) / (1 + np.exp(x)))
+    # Avoid repeated computation of exp(x)
+    ex = np.exp(-np.abs(x))
+    return np.where(x >= 0, 1 / (1 + ex), ex / (1 + ex))
 
 
 def postprocess(logits: np.array, max_detections: int = 8):
     batch_size, num_queries, num_classes = logits.shape
     logits_sigmoid = sigmoid_stable(logits)
-    processed_predictions = []
+    # Preallocate output as an array for efficiency
+    processed_predictions = [None] * batch_size
     for batch_idx in range(batch_size):
-        logits_flat = logits_sigmoid[batch_idx].reshape(-1)
-
-        sorted_indices = np.argsort(-logits_flat)[:max_detections]
-        processed_predictions.append(sorted_indices)
+        logits_flat = logits_sigmoid[batch_idx].ravel()
+        if logits_flat.size <= max_detections:
+            # If there are fewer elements than max_detections, just argsort all
+            sorted_indices = np.argsort(-logits_flat)
+        else:
+            # Partial sort for top max_detections
+            partition_indices = np.argpartition(-logits_flat, max_detections - 1)[:max_detections]
+            top_scores = logits_flat[partition_indices]
+            # Now sort these to get actual order
+            sorted_order = np.argsort(-top_scores)
+            sorted_indices = partition_indices[sorted_order]
+        processed_predictions[batch_idx] = sorted_indices
     return processed_predictions