Replace `enumerate` + index with `zip()` (#14574)

ultralytics/models/fastsam/predict.py

@@ -71,9 +71,7 @@ class FastSAMPredictor(DetectionPredictor):
 
         results = []
         proto = preds[1][-1] if len(preds[1]) == 3 else preds[1]  # second output is len 3 if pt, but only 1 if exported
-        for i, pred in enumerate(p):
-            orig_img = orig_imgs[i]
-            img_path = self.batch[0][i]
+        for i, (pred, orig_img, img_path) in enumerate(zip(p, orig_imgs, self.batch[0])):
             if not len(pred):  # save empty boxes
                 masks = None
             elif self.args.retina_masks:

ultralytics/models/nas/predict.py

@@ -52,9 +52,7 @@ class NASPredictor(BasePredictor):
             orig_imgs = ops.convert_torch2numpy_batch(orig_imgs)
 
         results = []
-        for i, pred in enumerate(preds):
-            orig_img = orig_imgs[i]
+        for pred, orig_img, img_path in zip(preds, orig_imgs, self.batch[0]):
             pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape)
-            img_path = self.batch[0][i]
             results.append(Results(orig_img, path=img_path, names=self.model.names, boxes=pred))
         return results

ultralytics/models/rtdetr/predict.py

@@ -56,18 +56,16 @@ class RTDETRPredictor(BasePredictor):
             orig_imgs = ops.convert_torch2numpy_batch(orig_imgs)
 
         results = []
-        for i, bbox in enumerate(bboxes):  # (300, 4)
+        for bbox, score, orig_img, img_path in zip(bboxes, scores, orig_imgs, self.batch[0]):  # (300, 4)
             bbox = ops.xywh2xyxy(bbox)
-            score, cls = scores[i].max(-1, keepdim=True)  # (300, 1)
-            idx = score.squeeze(-1) > self.args.conf  # (300, )
+            max_score, cls = score.max(-1, keepdim=True)  # (300, 1)
+            idx = max_score.squeeze(-1) > self.args.conf  # (300, )
             if self.args.classes is not None:
                 idx = (cls == torch.tensor(self.args.classes, device=cls.device)).any(1) & idx
-            pred = torch.cat([bbox, score, cls], dim=-1)[idx]  # filter
-            orig_img = orig_imgs[i]
+            pred = torch.cat([bbox, max_score, cls], dim=-1)[idx]  # filter
             oh, ow = orig_img.shape[:2]
             pred[..., [0, 2]] *= ow
             pred[..., [1, 3]] *= oh
-            img_path = self.batch[0][i]
             results.append(Results(orig_img, path=img_path, names=self.model.names, boxes=pred))
         return results
 

ultralytics/models/sam/predict.py

@@ -372,8 +372,7 @@ class Predictor(BasePredictor):
             orig_imgs = ops.convert_torch2numpy_batch(orig_imgs)
 
         results = []
-        for i, masks in enumerate([pred_masks]):
-            orig_img = orig_imgs[i]
+        for masks, orig_img, img_path in zip([pred_masks], orig_imgs, self.batch[0]):
             if pred_bboxes is not None:
                 pred_bboxes = ops.scale_boxes(img.shape[2:], pred_bboxes.float(), orig_img.shape, padding=False)
                 cls = torch.arange(len(pred_masks), dtype=torch.int32, device=pred_masks.device)
@@ -381,7 +380,6 @@ class Predictor(BasePredictor):
 
             masks = ops.scale_masks(masks[None].float(), orig_img.shape[:2], padding=False)[0]
             masks = masks > self.model.mask_threshold  # to bool
-            img_path = self.batch[0][i]
             results.append(Results(orig_img, path=img_path, names=names, masks=masks, boxes=pred_bboxes))
         # Reset segment-all mode.
         self.segment_all = False