Update inference.py

inference.py

@@ -287,17 +287,17 @@ def do_confidence_boost(inference, safe_preds, gp=None, batch_size=BATCH_SIZE, b
                     elif idx >= len(infer) and next_batch is not None and idx - len(infer) < len(next_batch):
                         temp_frame = next_batch[idx - len(infer)]
                     
+
                     if temp_frame is not None:
-                        boost_frame(safe_frame, temp_frame, dt, power=boost_scale, decay=boost_decay)
+                        with torch.no_grad():
+                            boost_frame(safe_frame, temp_frame, dt, power=boost_scale, decay=boost_decay)
 
             pbar.update(1*batch_size)
           
 
 def boost_frame(safe_frame, base_frame, dt, power=1, decay=1):
     safe_boxes = safe_frame[:, :4]
-    boxes = xywh2xyxy(base_frame[:, :4])  # center_x, center_y, width, height) to (x1, y1, x2, y2)
-    print(type(boxes))
-    print(type(safe_boxes))
+    boxes = xywh2xyxy(base_frame[:, :4])  # center_x, center_y, width, height) to (x1, y1, x2, y2)≈
     ious = box_iou(boxes, safe_boxes)
     score = torch.matmul(ious, safe_frame[:, 4])
     # score = iou(safe_box, base_box) * confidence(safe_box)