退购1.1定位算法

ultralytics/vit/rtdetr/predict.py

@@ -0,0 +1,42 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+
+import torch
+
+from ultralytics.yolo.data.augment import LetterBox
+from ultralytics.yolo.engine.predictor import BasePredictor
+from ultralytics.yolo.engine.results import Results
+from ultralytics.yolo.utils import ops
+
+
+class RTDETRPredictor(BasePredictor):
+
+    def postprocess(self, preds, img, orig_imgs):
+        """Postprocess predictions and returns a list of Results objects."""
+        bboxes, scores = preds[:2]  # (1, bs, 300, 4), (1, bs, 300, nc)
+        bboxes, scores = bboxes.squeeze_(0), scores.squeeze_(0)
+        results = []
+        for i, bbox in enumerate(bboxes):  # (300, 4)
+            bbox = ops.xywh2xyxy(bbox)
+            score, cls = scores[i].max(-1)  # (300, )
+            idx = score > self.args.conf
+            pred = torch.cat([bbox, score[..., None], cls[..., None]], dim=-1)[idx]  # filter
+            orig_img = orig_imgs[i] if isinstance(orig_imgs, list) else orig_imgs
+            oh, ow = orig_img.shape[:2]
+            if not isinstance(orig_imgs, torch.Tensor):
+                pred[..., [0, 2]] *= ow
+                pred[..., [1, 3]] *= oh
+            path = self.batch[0]
+            img_path = path[i] if isinstance(path, list) else path
+            results.append(Results(orig_img=orig_img, path=img_path, names=self.model.names, boxes=pred))
+        return results
+
+    def pre_transform(self, im):
+        """Pre-transform input image before inference.
+
+        Args:
+            im (List(np.ndarray)): (N, 3, h, w) for tensor, [(h, w, 3) x N] for list.
+
+        Return: A list of transformed imgs.
+        """
+        # The size must be square(640) and scaleFilled.
+        return [LetterBox(self.imgsz, auto=False, scaleFill=True)(image=x) for x in im]