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ultralytics 8.0.239 Ultralytics Actions and hub-sdk adoption (#7431)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Burhan <62214284+Burhan-Q@users.noreply.github.com>
Co-authored-by: Kayzwer <68285002+Kayzwer@users.noreply.github.com>
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Glenn Jocher 2024-01-10 03:16:08 +01:00 • committed by GitHub
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commit fe27db2f6e
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153
ultralytics/models/yolo/pose/val.py
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@ -31,38 +31,53 @@ 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
@ -71,21 +86,21 @@ class PoseValidator(DetectionValidator):
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 = 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
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]
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'])
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):
@ -93,14 +108,16 @@ class PoseValidator(DetectionValidator):
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))
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')
cls, bbox = pbatch.pop("cls"), pbatch.pop("bbox")
nl = len(cls)
stat['target_cls'] = cls
stat["target_cls"] = cls
if npr == 0:
if nl:
for k in self.stats.keys():
@ -113,13 +130,13 @@ class PoseValidator(DetectionValidator):
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]
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'])
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)
@ -128,7 +145,7 @@ class PoseValidator(DetectionValidator):
# 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')
@ -159,26 +176,30 @@ class PoseValidator(DetectionValidator):
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."""
@ -187,37 +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