add yolo v10 and modify pipeline

ultralytics/models/yolov10/predict.py

@@ -0,0 +1,38 @@
+from ultralytics.models.yolo.detect import DetectionPredictor
+import torch
+from ultralytics.utils import ops
+from ultralytics.engine.results import Results
+
+
+class YOLOv10DetectionPredictor(DetectionPredictor):
+    def postprocess(self, preds, img, orig_imgs):
+        if isinstance(preds, dict):
+            preds = preds["one2one"]
+
+        if isinstance(preds, (list, tuple)):
+            preds = preds[0]
+
+        if preds.shape[-1] == 6:
+            pass
+        else:
+            preds = preds.transpose(-1, -2)
+            bboxes, scores, labels = ops.v10postprocess(preds, self.args.max_det, preds.shape[-1]-4)
+            bboxes = ops.xywh2xyxy(bboxes)
+            preds = torch.cat([bboxes, scores.unsqueeze(-1), labels.unsqueeze(-1)], dim=-1)
+
+        mask = preds[..., 4] > self.args.conf
+        if self.args.classes is not None:
+            mask = mask & (preds[..., 5:6] == torch.tensor(self.args.classes, device=preds.device).unsqueeze(0)).any(2)
+        
+        preds = [p[mask[idx]] for idx, p in enumerate(preds)]
+
+        if not isinstance(orig_imgs, list):  # input images are a torch.Tensor, not a list
+            orig_imgs = ops.convert_torch2numpy_batch(orig_imgs)
+
+        results = []
+        for i, pred in enumerate(preds):
+            orig_img = orig_imgs[i]
+            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