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ultralytics 8.3.67 NMS Export for Detect, Segment, Pose and OBB YOLO models (#18484)

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Signed-off-by: Mohammed Yasin <32206511+Y-T-G@users.noreply.github.com>
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com>
Co-authored-by: Laughing-q <1185102784@qq.com>
Co-authored-by: Ultralytics Assistant <135830346+UltralyticsAssistant@users.noreply.github.com>
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Mohammed Yasin 2025-01-24 18:00:36 +08:00 • committed by GitHub
parent 0e48a00303
commit 9181ff62f5
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17 changed files with 320 additions and 208 deletions
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39
ultralytics/models/yolo/pose/predict.py
View file

@ -1,6 +1,5 @@
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
from ultralytics.engine.results import Results
from ultralytics.models.yolo.detect.predict import DetectionPredictor
from ultralytics.utils import DEFAULT_CFG, LOGGER, ops
@ -30,27 +29,21 @@ class PosePredictor(DetectionPredictor):
"See https://github.com/ultralytics/ultralytics/issues/4031."
)
def postprocess(self, preds, img, orig_imgs):
"""Return detection results for a given input image or list of images."""
preds = ops.non_max_suppression(
preds,
self.args.conf,
self.args.iou,
agnostic=self.args.agnostic_nms,
max_det=self.args.max_det,
classes=self.args.classes,
nc=len(self.model.names),
)
def construct_result(self, pred, img, orig_img, img_path):
"""
Constructs the result object from the prediction.
if not isinstance(orig_imgs, list): # input images are a torch.Tensor, not a list
orig_imgs = ops.convert_torch2numpy_batch(orig_imgs)
Args:
pred (torch.Tensor): The predicted bounding boxes, scores, and keypoints.
img (torch.Tensor): The image after preprocessing.
orig_img (np.ndarray): The original image before preprocessing.
img_path (str): The path to the original image.
results = []
for pred, orig_img, img_path in zip(preds, orig_imgs, self.batch[0]):
pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape).round()
pred_kpts = pred[:, 6:].view(len(pred), *self.model.kpt_shape) if len(pred) else pred[:, 6:]
pred_kpts = ops.scale_coords(img.shape[2:], pred_kpts, orig_img.shape)
results.append(
Results(orig_img, path=img_path, names=self.model.names, boxes=pred[:, :6], keypoints=pred_kpts)
)
return results
Returns:
(Results): The result object containing the original image, image path, class names, bounding boxes, and keypoints.
"""
result = super().construct_result(pred, img, orig_img, img_path)
pred_kpts = pred[:, 6:].view(len(pred), *self.model.kpt_shape) if len(pred) else pred[:, 6:]
pred_kpts = ops.scale_coords(img.shape[2:], pred_kpts, orig_img.shape)
result.update(keypoints=pred_kpts)
return result

13
ultralytics/models/yolo/pose/val.py
View file

@ -61,19 +61,6 @@ class PoseValidator(DetectionValidator):
"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 or self.args.agnostic_nms,
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)