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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
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commit 9181ff62f5
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48
ultralytics/models/yolo/detect/predict.py
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@ -20,22 +20,54 @@ class DetectionPredictor(BasePredictor):
```
"""
def postprocess(self, preds, img, orig_imgs):
def postprocess(self, preds, img, orig_imgs, **kwargs):
"""Post-processes predictions and returns a list of Results objects."""
preds = ops.non_max_suppression(
preds,
self.args.conf,
self.args.iou,
agnostic=self.args.agnostic_nms,
self.args.classes,
self.args.agnostic_nms,
max_det=self.args.max_det,
classes=self.args.classes,
nc=len(self.model.names),
end2end=getattr(self.model, "end2end", False),
rotated=self.args.task == "obb",
)
if not isinstance(orig_imgs, list): # input images are a torch.Tensor, not a list
orig_imgs = ops.convert_torch2numpy_batch(orig_imgs)
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)
results.append(Results(orig_img, path=img_path, names=self.model.names, boxes=pred))
return results
return self.construct_results(preds, img, orig_imgs, **kwargs)
def construct_results(self, preds, img, orig_imgs):
"""
Constructs a list of result objects from the predictions.
Args:
preds (List[torch.Tensor]): List of predicted bounding boxes and scores.
img (torch.Tensor): The image after preprocessing.
orig_imgs (List[np.ndarray]): List of original images before preprocessing.
Returns:
(list): List of result objects containing the original images, image paths, class names, and bounding boxes.
"""
return [
self.construct_result(pred, img, orig_img, img_path)
for pred, orig_img, img_path in zip(preds, orig_imgs, self.batch[0])
]
def construct_result(self, pred, img, orig_img, img_path):
"""
Constructs the result object from the prediction.
Args:
pred (torch.Tensor): The predicted bounding boxes and scores.
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.
Returns:
(Results): The result object containing the original image, image path, class names, and bounding boxes.
"""
pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape)
return Results(orig_img, path=img_path, names=self.model.names, boxes=pred[:, :6])