# Ultralytics YOLO 🚀, AGPL-3.0 license
from pathlib import Path
import numpy as np
import torch
from ultralytics.models.yolo.detect import DetectionValidator
from ultralytics.utils import LOGGER, ops
from ultralytics.utils.checks import check_requirements
from ultralytics.utils.metrics import OKS_SIGMA, PoseMetrics, box_iou, kpt_iou
from ultralytics.utils.plotting import output_to_target, plot_images
class PoseValidator(DetectionValidator):
"""
A class extending the DetectionValidator class for validation based on a pose model.
Example:
```python
from ultralytics.models.yolo.pose import PoseValidator
args = dict(model='yolov8n-pose.pt', data='coco8-pose.yaml')
validator = PoseValidator(args=args)
validator()
```
"""
def __init__(self, dataloader=None, save_dir=None, pbar=None, args=None, _callbacks=None):
"""Initialize a 'PoseValidator' object with custom parameters and assigned attributes."""
super().__init__(dataloader, save_dir, pbar, args, _callbacks)
self.sigma = None
self.kpt_shape = None
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."
)
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()
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)",
)
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,
)
def init_metrics(self, model):
"""Initiate pose estimation metrics for YOLO model."""
super().init_metrics(model)
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):
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:
for k in self.stats.keys():
self.stats[k].append(stat[k])
if self.args.plots:
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_kpts = self._prepare_pred(pred, pbatch)
stat["conf"] = predn[:, 4]
stat["pred_cls"] = predn[:, 5]
# Evaluate
if nl:
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, bbox, cls)
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])
# 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, gt_bboxes, gt_cls, pred_kpts=None, gt_kpts=None):
"""
Return correct prediction matrix.
Args:
detections (torch.Tensor): Tensor of shape [N, 6] representing detections.
Each detection is of the format: x1, y1, x2, y2, conf, class.
labels (torch.Tensor): Tensor of shape [M, 5] representing labels.
Each label is of the format: class, x1, y1, x2, y2.
pred_kpts (torch.Tensor, optional): Tensor of shape [N, 51] representing predicted keypoints.
51 corresponds to 17 keypoints each with 3 values.
gt_kpts (torch.Tensor, optional): Tensor of shape [N, 51] representing ground truth keypoints.
Returns:
torch.Tensor: Correct prediction matrix of shape [N, 10] for 10 IoU levels.
"""
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(gt_bboxes)[:, 2:].prod(1) * 0.53
iou = kpt_iou(gt_kpts, pred_kpts, sigma=self.sigma, area=area)
else: # boxes
iou = box_iou(gt_bboxes, detections[:, :4])
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,
)
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
def pred_to_json(self, predn, filename):
"""Converts YOLO predictions to COCO JSON format."""
stem = Path(filename).stem
image_id = int(stem) if stem.isnumeric() else stem
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),
}
)
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}...")
try: # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb
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"
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")]):
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
except Exception as e:
LOGGER.warning(f"pycocotools unable to run: {e}")
return stats