ultralytics 8.2.59 use Results.save_txt for validation (#14496)

Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

ultralytics/__init__.py

@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-__version__ = "8.2.58"
+__version__ = "8.2.59"
 
 import os
 

ultralytics/models/yolo/detect/val.py

@@ -160,8 +160,12 @@ class DetectionValidator(BaseValidator):
             if self.args.save_json:
                 self.pred_to_json(predn, batch["im_file"][si])
             if self.args.save_txt:
-                file = self.save_dir / "labels" / f'{Path(batch["im_file"][si]).stem}.txt'
-                self.save_one_txt(predn, self.args.save_conf, pbatch["ori_shape"], file)
+                self.save_one_txt(
+                    predn,
+                    self.args.save_conf,
+                    pbatch["ori_shape"],
+                    self.save_dir / "labels" / f'{Path(batch["im_file"][si]).stem}.txt',
+                )
 
     def finalize_metrics(self, *args, **kwargs):
         """Set final values for metrics speed and confusion matrix."""
@@ -261,12 +265,14 @@ class DetectionValidator(BaseValidator):
 
     def save_one_txt(self, predn, save_conf, shape, file):
         """Save YOLO detections to a txt file in normalized coordinates in a specific format."""
-        gn = torch.tensor(shape)[[1, 0, 1, 0]]  # normalization gain whwh
-        for *xyxy, conf, cls in predn.tolist():
-            xywh = (ops.xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
-            line = (cls, *xywh, conf) if save_conf else (cls, *xywh)  # label format
-            with open(file, "a") as f:
-                f.write(("%g " * len(line)).rstrip() % line + "\n")
+        from ultralytics.engine.results import Results
+
+        Results(
+            np.zeros((shape[0], shape[1]), dtype=np.uint8),
+            path=None,
+            names=self.names,
+            boxes=predn[:, :6],
+        ).save_txt(file, save_conf=save_conf)
 
     def pred_to_json(self, predn, filename):
         """Serialize YOLO predictions to COCO json format."""

ultralytics/models/yolo/obb/val.py

@@ -130,13 +130,19 @@ class OBBValidator(DetectionValidator):
 
     def save_one_txt(self, predn, save_conf, shape, file):
         """Save YOLO detections to a txt file in normalized coordinates in a specific format."""
-        gn = torch.tensor(shape)[[1, 0]]  # normalization gain whwh
-        for *xywh, conf, cls, angle in predn.tolist():
-            xywha = torch.tensor([*xywh, angle]).view(1, 5)
-            xyxyxyxy = (ops.xywhr2xyxyxyxy(xywha) / gn).view(-1).tolist()  # normalized xywh
-            line = (cls, *xyxyxyxy, conf) if save_conf else (cls, *xyxyxyxy)  # label format
-            with open(file, "a") as f:
-                f.write(("%g " * len(line)).rstrip() % line + "\n")
+        import numpy as np
+
+        from ultralytics.engine.results import Results
+
+        rboxes = torch.cat([predn[:, :4], predn[:, -1:]], dim=-1)
+        # xywh, r, conf, cls
+        obb = torch.cat([rboxes, predn[:, 4:6]], dim=-1)
+        Results(
+            np.zeros((shape[0], shape[1]), dtype=np.uint8),
+            path=None,
+            names=self.names,
+            obb=obb,
+        ).save_txt(file, save_conf=save_conf)
 
     def eval_json(self, stats):
         """Evaluates YOLO output in JSON format and returns performance statistics."""

ultralytics/models/yolo/pose/val.py

@@ -147,8 +147,14 @@ class PoseValidator(DetectionValidator):
             # Save
             if self.args.save_json:
                 self.pred_to_json(predn, batch["im_file"][si])
-            # if self.args.save_txt:
-            #    save_one_txt(predn, save_conf, shape, file=save_dir / 'labels' / f'{path.stem}.txt')
+            if self.args.save_txt:
+                self.save_one_txt(
+                    predn,
+                    pred_kpts,
+                    self.args.save_conf,
+                    pbatch["ori_shape"],
+                    self.save_dir / "labels" / f'{Path(batch["im_file"][si]).stem}.txt',
+                )
 
     def _process_batch(self, detections, gt_bboxes, gt_cls, pred_kpts=None, gt_kpts=None):
         """
@@ -217,6 +223,18 @@ class PoseValidator(DetectionValidator):
             on_plot=self.on_plot,
         )  # pred
 
+    def save_one_txt(self, predn, pred_kpts, save_conf, shape, file):
+        """Save YOLO detections to a txt file in normalized coordinates in a specific format."""
+        from ultralytics.engine.results import Results
+
+        Results(
+            np.zeros((shape[0], shape[1]), dtype=np.uint8),
+            path=None,
+            names=self.names,
+            boxes=predn[:, :6],
+            keypoints=pred_kpts,
+        ).save_txt(file, save_conf=save_conf)
+
     def pred_to_json(self, predn, filename):
         """Converts YOLO predictions to COCO JSON format."""
         stem = Path(filename).stem

ultralytics/models/yolo/segment/val.py

@@ -48,9 +48,8 @@ class SegmentationValidator(DetectionValidator):
         self.plot_masks = []
         if self.args.save_json:
             check_requirements("pycocotools>=2.0.6")
-            self.process = ops.process_mask_upsample  # more accurate
-        else:
-            self.process = ops.process_mask  # faster
+        # more accurate vs faster
+        self.process = ops.process_mask_upsample if self.args.save_json or self.args.save_txt else ops.process_mask
         self.stats = dict(tp_m=[], tp=[], conf=[], pred_cls=[], target_cls=[], target_img=[])
 
     def get_desc(self):
@@ -148,14 +147,23 @@ class SegmentationValidator(DetectionValidator):
 
             # Save
             if self.args.save_json:
-                pred_masks = ops.scale_image(
-                    pred_masks.permute(1, 2, 0).contiguous().cpu().numpy(),
-                    pbatch["ori_shape"],
-                    ratio_pad=batch["ratio_pad"][si],
+                self.pred_to_json(
+                    predn,
+                    batch["im_file"][si],
+                    ops.scale_image(
+                        pred_masks.permute(1, 2, 0).contiguous().cpu().numpy(),
+                        pbatch["ori_shape"],
+                        ratio_pad=batch["ratio_pad"][si],
+                    ),
+                )
+            if self.args.save_txt:
+                self.save_one_txt(
+                    predn,
+                    pred_masks,
+                    self.args.save_conf,
+                    pbatch["ori_shape"],
+                    self.save_dir / "labels" / f'{Path(batch["im_file"][si]).stem}.txt',
                 )
-                self.pred_to_json(predn, batch["im_file"][si], pred_masks)
-            # if self.args.save_txt:
-            #    save_one_txt(predn, save_conf, shape, file=save_dir / 'labels' / f'{path.stem}.txt')
 
     def finalize_metrics(self, *args, **kwargs):
         """Sets speed and confusion matrix for evaluation metrics."""
@@ -235,6 +243,18 @@ class SegmentationValidator(DetectionValidator):
         )  # pred
         self.plot_masks.clear()
 
+    def save_one_txt(self, predn, pred_masks, save_conf, shape, file):
+        """Save YOLO detections to a txt file in normalized coordinates in a specific format."""
+        from ultralytics.engine.results import Results
+
+        Results(
+            np.zeros((shape[0], shape[1]), dtype=np.uint8),
+            path=None,
+            names=self.names,
+            boxes=predn[:, :6],
+            masks=pred_masks,
+        ).save_txt(file, save_conf=save_conf)
+
     def pred_to_json(self, predn, filename, pred_masks):
         """
         Save one JSON result.