退购1.1定位算法

ultralytics/vit/rtdetr/val.py

@@ -0,0 +1,114 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+
+from pathlib import Path
+
+import torch
+
+from ultralytics.yolo.data import YOLODataset
+from ultralytics.yolo.data.augment import Compose, Format, LetterBox
+from ultralytics.yolo.utils import colorstr, ops
+from ultralytics.yolo.v8.detect import DetectionValidator
+
+__all__ = ['RTDETRValidator']
+
+
+# TODO: Temporarily, RT-DETR does not need padding.
+class RTDETRDataset(YOLODataset):
+
+    def __init__(self, *args, data=None, **kwargs):
+        super().__init__(*args, data=data, use_segments=False, use_keypoints=False, **kwargs)
+
+    def build_transforms(self, hyp=None):
+        """Temporarily, only for evaluation."""
+        transforms = Compose([LetterBox(new_shape=(self.imgsz, self.imgsz), auto=False, scaleFill=True)])
+        transforms.append(
+            Format(bbox_format='xywh',
+                   normalize=True,
+                   return_mask=self.use_segments,
+                   return_keypoint=self.use_keypoints,
+                   batch_idx=True,
+                   mask_ratio=hyp.mask_ratio,
+                   mask_overlap=hyp.overlap_mask))
+        return transforms
+
+
+class RTDETRValidator(DetectionValidator):
+
+    def build_dataset(self, img_path, mode='val', batch=None):
+        """Build YOLO Dataset
+
+        Args:
+            img_path (str): Path to the folder containing images.
+            mode (str): `train` mode or `val` mode, users are able to customize different augmentations for each mode.
+            batch (int, optional): Size of batches, this is for `rect`. Defaults to None.
+        """
+        return RTDETRDataset(
+            img_path=img_path,
+            imgsz=self.args.imgsz,
+            batch_size=batch,
+            augment=False,  # no augmentation
+            hyp=self.args,
+            rect=False,  # no rect
+            cache=self.args.cache or None,
+            prefix=colorstr(f'{mode}: '),
+            data=self.data)
+
+    def postprocess(self, preds):
+        """Apply Non-maximum suppression to prediction outputs."""
+        bboxes, scores = preds[:2]  # (1, bs, 300, 4), (1, bs, 300, nc)
+        bboxes, scores = bboxes.squeeze_(0), scores.squeeze_(0)  # (bs, 300, 4)
+        bs = len(bboxes)
+        outputs = [torch.zeros((0, 6), device=bboxes.device)] * bs
+        for i, bbox in enumerate(bboxes):  # (300, 4)
+            bbox = ops.xywh2xyxy(bbox)
+            score, cls = scores[i].max(-1)  # (300, )
+            # Do not need threshold for evaluation as only got 300 boxes here.
+            # idx = score > self.args.conf
+            pred = torch.cat([bbox, score[..., None], cls[..., None]], dim=-1)  # filter
+            outputs[i] = pred  # [idx]
+
+        return outputs
+
+    def update_metrics(self, preds, batch):
+        """Metrics."""
+        for si, pred in enumerate(preds):
+            idx = batch['batch_idx'] == si
+            cls = batch['cls'][idx]
+            bbox = batch['bboxes'][idx]
+            nl, npr = cls.shape[0], pred.shape[0]  # number of labels, predictions
+            shape = batch['ori_shape'][si]
+            correct_bboxes = torch.zeros(npr, self.niou, dtype=torch.bool, device=self.device)  # init
+            self.seen += 1
+
+            if npr == 0:
+                if nl:
+                    self.stats.append((correct_bboxes, *torch.zeros((2, 0), device=self.device), cls.squeeze(-1)))
+                    if self.args.plots:
+                        self.confusion_matrix.process_batch(detections=None, labels=cls.squeeze(-1))
+                continue
+
+            # Predictions
+            if self.args.single_cls:
+                pred[:, 5] = 0
+            predn = pred.clone()
+            predn[..., [0, 2]] *= shape[1]  # native-space pred
+            predn[..., [1, 3]] *= shape[0]  # native-space pred
+
+            # Evaluate
+            if nl:
+                tbox = ops.xywh2xyxy(bbox)  # target boxes
+                tbox[..., [0, 2]] *= shape[1]  # native-space pred
+                tbox[..., [1, 3]] *= shape[0]  # native-space pred
+                labelsn = torch.cat((cls, tbox), 1)  # native-space labels
+                correct_bboxes = self._process_batch(predn, labelsn)
+                # TODO: maybe remove these `self.` arguments as they already are member variable
+                if self.args.plots:
+                    self.confusion_matrix.process_batch(predn, labelsn)
+            self.stats.append((correct_bboxes, pred[:, 4], pred[:, 5], cls.squeeze(-1)))  # (conf, pcls, tcls)
+
+            # Save
+            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, shape, file)