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ultralytics 8.0.235 YOLOv8 OBB train, val, predict and export (#4499)

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Glenn Jocher 2024-01-05 03:00:26 +01:00 • committed by GitHub
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ultralytics/models/yolo/pose/val.py
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@ -66,57 +66,63 @@ class PoseValidator(DetectionValidator):
is_pose = self.kpt_shape == [17, 3]
nkpt = self.kpt_shape[0]
self.sigma = OKS_SIGMA if is_pose else np.ones(nkpt) / nkpt
self.stats = dict(tp_p=[], tp=[], conf=[], pred_cls=[], target_cls=[])
def _prepare_batch(self, si, batch):
pbatch = super()._prepare_batch(si, batch)
kpts = batch['keypoints'][batch['batch_idx'] == si]
h, w = pbatch['imgsz']
kpts = kpts.clone()
kpts[..., 0] *= w
kpts[..., 1] *= h
kpts = ops.scale_coords(pbatch['imgsz'], kpts, pbatch['ori_shape'], ratio_pad=pbatch['ratio_pad'])
pbatch['kpts'] = kpts
return pbatch
def _prepare_pred(self, pred, pbatch):
predn = super()._prepare_pred(pred, pbatch)
nk = pbatch['kpts'].shape[1]
pred_kpts = predn[:, 6:].view(len(predn), nk, -1)
ops.scale_coords(pbatch['imgsz'], pred_kpts, pbatch['ori_shape'], ratio_pad=pbatch['ratio_pad'])
return predn, pred_kpts
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]
kpts = batch['keypoints'][idx]
nl, npr = cls.shape[0], pred.shape[0] # number of labels, predictions
nk = kpts.shape[1] # number of keypoints
shape = batch['ori_shape'][si]
correct_kpts = torch.zeros(npr, self.niou, dtype=torch.bool, device=self.device) # init
correct_bboxes = torch.zeros(npr, self.niou, dtype=torch.bool, device=self.device) # init
self.seen += 1
npr = len(pred)
stat = dict(conf=torch.zeros(0, device=self.device),
pred_cls=torch.zeros(0, device=self.device),
tp=torch.zeros(npr, self.niou, dtype=torch.bool, device=self.device),
tp_p=torch.zeros(npr, self.niou, dtype=torch.bool, device=self.device))
pbatch = self._prepare_batch(si, batch)
cls, bbox = pbatch.pop('cls'), pbatch.pop('bbox')
nl = len(cls)
stat['target_cls'] = cls
if npr == 0:
if nl:
self.stats.append((correct_bboxes, correct_kpts, *torch.zeros(
(2, 0), device=self.device), cls.squeeze(-1)))
for k in self.stats.keys():
self.stats[k].append(stat[k])
if self.args.plots:
self.confusion_matrix.process_batch(detections=None, labels=cls.squeeze(-1))
self.confusion_matrix.process_batch(detections=None, gt_bboxes=bbox, gt_cls=cls)
continue
# Predictions
if self.args.single_cls:
pred[:, 5] = 0
predn = pred.clone()
ops.scale_boxes(batch['img'][si].shape[1:], predn[:, :4], shape,
ratio_pad=batch['ratio_pad'][si]) # native-space pred
pred_kpts = predn[:, 6:].view(npr, nk, -1)
ops.scale_coords(batch['img'][si].shape[1:], pred_kpts, shape, ratio_pad=batch['ratio_pad'][si])
predn, pred_kpts = self._prepare_pred(pred, pbatch)
stat['conf'] = predn[:, 4]
stat['pred_cls'] = predn[:, 5]
# Evaluate
if nl:
height, width = batch['img'].shape[2:]
tbox = ops.xywh2xyxy(bbox) * torch.tensor(
(width, height, width, height), device=self.device) # target boxes
ops.scale_boxes(batch['img'][si].shape[1:], tbox, shape,
ratio_pad=batch['ratio_pad'][si]) # native-space labels
tkpts = kpts.clone()
tkpts[..., 0] *= width
tkpts[..., 1] *= height
tkpts = ops.scale_coords(batch['img'][si].shape[1:], tkpts, shape, ratio_pad=batch['ratio_pad'][si])
labelsn = torch.cat((cls, tbox), 1) # native-space labels
correct_bboxes = self._process_batch(predn[:, :6], labelsn)
correct_kpts = self._process_batch(predn[:, :6], labelsn, pred_kpts, tkpts)
stat['tp'] = self._process_batch(predn, bbox, cls)
stat['tp_p'] = self._process_batch(predn, bbox, cls, pred_kpts, pbatch['kpts'])
if self.args.plots:
self.confusion_matrix.process_batch(predn, labelsn)
self.confusion_matrix.process_batch(predn, bbox, cls)
# Append correct_masks, correct_boxes, pconf, pcls, tcls
self.stats.append((correct_bboxes, correct_kpts, pred[:, 4], pred[:, 5], cls.squeeze(-1)))
for k in self.stats.keys():
self.stats[k].append(stat[k])
# Save
if self.args.save_json:
@ -124,7 +130,7 @@ class PoseValidator(DetectionValidator):
# if self.args.save_txt:
# save_one_txt(predn, save_conf, shape, file=save_dir / 'labels' / f'{path.stem}.txt')
def _process_batch(self, detections, labels, pred_kpts=None, gt_kpts=None):
def _process_batch(self, detections, gt_bboxes, gt_cls, pred_kpts=None, gt_kpts=None):
"""
Return correct prediction matrix.
@ -142,12 +148,12 @@ class PoseValidator(DetectionValidator):
"""
if pred_kpts is not None and gt_kpts is not None:
# `0.53` is from https://github.com/jin-s13/xtcocoapi/blob/master/xtcocotools/cocoeval.py#L384
area = ops.xyxy2xywh(labels[:, 1:])[:, 2:].prod(1) * 0.53
area = ops.xyxy2xywh(gt_bboxes)[:, 2:].prod(1) * 0.53
iou = kpt_iou(gt_kpts, pred_kpts, sigma=self.sigma, area=area)
else: # boxes
iou = box_iou(labels[:, 1:], detections[:, :4])
iou = box_iou(gt_bboxes, detections[:, :4])
return self.match_predictions(detections[:, 5], labels[:, 0], iou)
return self.match_predictions(detections[:, 5], gt_cls, iou)
def plot_val_samples(self, batch, ni):
"""Plots and saves validation set samples with predicted bounding boxes and keypoints."""