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add yolo v10 and modify pipeline

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王庆刚
2025-03-28 13:19:54 +08:00
parent 183299c06b
commit 798c596acc
471 changed files with 19109 additions and 7342 deletions
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ultralytics/models/yolo/pose/val.py
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@ -31,100 +31,125 @@ class PoseValidator(DetectionValidator):
super().__init__(dataloader, save_dir, pbar, args, _callbacks)
self.sigma = None
self.kpt_shape = None
self.args.task = 'pose'
self.args.task = "pose"
self.metrics = PoseMetrics(save_dir=self.save_dir, on_plot=self.on_plot)
if isinstance(self.args.device, str) and self.args.device.lower() == 'mps':
LOGGER.warning("WARNING ⚠️ Apple MPS known Pose bug. Recommend 'device=cpu' for Pose models. "
'See https://github.com/ultralytics/ultralytics/issues/4031.')
if isinstance(self.args.device, str) and self.args.device.lower() == "mps":
LOGGER.warning(
"WARNING ⚠️ Apple MPS known Pose bug. Recommend 'device=cpu' for Pose models. "
"See https://github.com/ultralytics/ultralytics/issues/4031."
)
def preprocess(self, batch):
"""Preprocesses the batch by converting the 'keypoints' data into a float and moving it to the device."""
batch = super().preprocess(batch)
batch['keypoints'] = batch['keypoints'].to(self.device).float()
batch["keypoints"] = batch["keypoints"].to(self.device).float()
return batch
def get_desc(self):
"""Returns description of evaluation metrics in string format."""
return ('%22s' + '%11s' * 10) % ('Class', 'Images', 'Instances', 'Box(P', 'R', 'mAP50', 'mAP50-95)', 'Pose(P',
'R', 'mAP50', 'mAP50-95)')
return ("%22s" + "%11s" * 10) % (
"Class",
"Images",
"Instances",
"Box(P",
"R",
"mAP50",
"mAP50-95)",
"Pose(P",
"R",
"mAP50",
"mAP50-95)",
)
def postprocess(self, preds):
"""Apply non-maximum suppression and return detections with high confidence scores."""
return ops.non_max_suppression(preds,
self.args.conf,
self.args.iou,
labels=self.lb,
multi_label=True,
agnostic=self.args.single_cls,
max_det=self.args.max_det,
nc=self.nc)
return ops.non_max_suppression(
preds,
self.args.conf,
self.args.iou,
labels=self.lb,
multi_label=True,
agnostic=self.args.single_cls,
max_det=self.args.max_det,
nc=self.nc,
)
def init_metrics(self, model):
"""Initiate pose estimation metrics for YOLO model."""
super().init_metrics(model)
self.kpt_shape = self.data['kpt_shape']
self.kpt_shape = self.data["kpt_shape"]
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):
"""Prepares a batch for processing by converting keypoints to float and moving to device."""
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):
"""Prepares and scales keypoints in a batch for pose processing."""
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:
self.pred_to_json(predn, batch['im_file'][si])
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')
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.
@ -142,35 +167,39 @@ 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."""
plot_images(batch['img'],
batch['batch_idx'],
batch['cls'].squeeze(-1),
batch['bboxes'],
kpts=batch['keypoints'],
paths=batch['im_file'],
fname=self.save_dir / f'val_batch{ni}_labels.jpg',
names=self.names,
on_plot=self.on_plot)
plot_images(
batch["img"],
batch["batch_idx"],
batch["cls"].squeeze(-1),
batch["bboxes"],
kpts=batch["keypoints"],
paths=batch["im_file"],
fname=self.save_dir / f"val_batch{ni}_labels.jpg",
names=self.names,
on_plot=self.on_plot,
)
def plot_predictions(self, batch, preds, ni):
"""Plots predictions for YOLO model."""
pred_kpts = torch.cat([p[:, 6:].view(-1, *self.kpt_shape) for p in preds], 0)
plot_images(batch['img'],
*output_to_target(preds, max_det=self.args.max_det),
kpts=pred_kpts,
paths=batch['im_file'],
fname=self.save_dir / f'val_batch{ni}_pred.jpg',
names=self.names,
on_plot=self.on_plot) # pred
plot_images(
batch["img"],
*output_to_target(preds, max_det=self.args.max_det),
kpts=pred_kpts,
paths=batch["im_file"],
fname=self.save_dir / f"val_batch{ni}_pred.jpg",
names=self.names,
on_plot=self.on_plot,
) # pred
def pred_to_json(self, predn, filename):
"""Converts YOLO predictions to COCO JSON format."""
@ -179,37 +208,41 @@ class PoseValidator(DetectionValidator):
box = ops.xyxy2xywh(predn[:, :4]) # xywh
box[:, :2] -= box[:, 2:] / 2 # xy center to top-left corner
for p, b in zip(predn.tolist(), box.tolist()):
self.jdict.append({
'image_id': image_id,
'category_id': self.class_map[int(p[5])],
'bbox': [round(x, 3) for x in b],
'keypoints': p[6:],
'score': round(p[4], 5)})
self.jdict.append(
{
"image_id": image_id,
"category_id": self.class_map[int(p[5])],
"bbox": [round(x, 3) for x in b],
"keypoints": p[6:],
"score": round(p[4], 5),
}
)
def eval_json(self, stats):
"""Evaluates object detection model using COCO JSON format."""
if self.args.save_json and self.is_coco and len(self.jdict):
anno_json = self.data['path'] / 'annotations/person_keypoints_val2017.json' # annotations
pred_json = self.save_dir / 'predictions.json' # predictions
LOGGER.info(f'\nEvaluating pycocotools mAP using {pred_json} and {anno_json}...')
anno_json = self.data["path"] / "annotations/person_keypoints_val2017.json" # annotations
pred_json = self.save_dir / "predictions.json" # predictions
LOGGER.info(f"\nEvaluating pycocotools mAP using {pred_json} and {anno_json}...")
try: # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb
check_requirements('pycocotools>=2.0.6')
check_requirements("pycocotools>=2.0.6")
from pycocotools.coco import COCO # noqa
from pycocotools.cocoeval import COCOeval # noqa
for x in anno_json, pred_json:
assert x.is_file(), f'{x} file not found'
assert x.is_file(), f"{x} file not found"
anno = COCO(str(anno_json)) # init annotations api
pred = anno.loadRes(str(pred_json)) # init predictions api (must pass string, not Path)
for i, eval in enumerate([COCOeval(anno, pred, 'bbox'), COCOeval(anno, pred, 'keypoints')]):
for i, eval in enumerate([COCOeval(anno, pred, "bbox"), COCOeval(anno, pred, "keypoints")]):
if self.is_coco:
eval.params.imgIds = [int(Path(x).stem) for x in self.dataloader.dataset.im_files] # im to eval
eval.evaluate()
eval.accumulate()
eval.summarize()
idx = i * 4 + 2
stats[self.metrics.keys[idx + 1]], stats[
self.metrics.keys[idx]] = eval.stats[:2] # update mAP50-95 and mAP50
stats[self.metrics.keys[idx + 1]], stats[self.metrics.keys[idx]] = eval.stats[
:2
] # update mAP50-95 and mAP50
except Exception as e:
LOGGER.warning(f'pycocotools unable to run: {e}')
LOGGER.warning(f"pycocotools unable to run: {e}")
return stats