HongChuang/ultralytics-ascend
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions 3

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

Browse source 
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>
This commit is contained in:
Mohammed Yasin 2025-01-24 18:00:36 +08:00 • committed by GitHub
parent 0e48a00303
commit 9181ff62f5
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
17 changed files with 320 additions and 208 deletions
Download patch file Download diff file Expand all files Collapse all files

67
ultralytics/models/yolo/segment/predict.py
View file

@ -27,29 +27,48 @@ class SegmentationPredictor(DetectionPredictor):
def postprocess(self, preds, img, orig_imgs):
"""Applies non-max suppression and processes detections for each image in an input batch."""
p = ops.non_max_suppression(
preds[0],
self.args.conf,
self.args.iou,
agnostic=self.args.agnostic_nms,
max_det=self.args.max_det,
nc=len(self.model.names),
classes=self.args.classes,
)
# tuple if PyTorch model or array if exported
protos = preds[1][-1] if isinstance(preds[1], tuple) else preds[1]
return super().postprocess(preds[0], img, orig_imgs, protos=protos)
if not isinstance(orig_imgs, list): # input images are a torch.Tensor, not a list
orig_imgs = ops.convert_torch2numpy_batch(orig_imgs)
def construct_results(self, preds, img, orig_imgs, protos):
"""
Constructs a list of result objects from the predictions.
results = []
proto = preds[1][-1] if isinstance(preds[1], tuple) else preds[1] # tuple if PyTorch model or array if exported
for i, (pred, orig_img, img_path) in enumerate(zip(p, orig_imgs, self.batch[0])):
if not len(pred): # save empty boxes
masks = None
elif self.args.retina_masks:
pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape)
masks = ops.process_mask_native(proto[i], pred[:, 6:], pred[:, :4], orig_img.shape[:2]) # HWC
else:
masks = ops.process_mask(proto[i], pred[:, 6:], pred[:, :4], img.shape[2:], upsample=True) # HWC
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[:, :6], masks=masks))
return results
Args:
preds (List[torch.Tensor]): List of predicted bounding boxes, scores, and masks.
img (torch.Tensor): The image after preprocessing.
orig_imgs (List[np.ndarray]): List of original images before preprocessing.
protos (List[torch.Tensor]): List of prototype masks.
Returns:
(list): List of result objects containing the original images, image paths, class names, bounding boxes, and masks.
"""
return [
self.construct_result(pred, img, orig_img, img_path, proto)
for pred, orig_img, img_path, proto in zip(preds, orig_imgs, self.batch[0], protos)
]
def construct_result(self, pred, img, orig_img, img_path, proto):
"""
Constructs the result object from the prediction.
Args:
pred (np.ndarray): The predicted bounding boxes, scores, and masks.
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.
proto (torch.Tensor): The prototype masks.
Returns:
(Results): The result object containing the original image, image path, class names, bounding boxes, and masks.
"""
if not len(pred): # save empty boxes
masks = None
elif self.args.retina_masks:
pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape)
masks = ops.process_mask_native(proto, pred[:, 6:], pred[:, :4], orig_img.shape[:2]) # HWC
else:
masks = ops.process_mask(proto, pred[:, 6:], pred[:, :4], img.shape[2:], upsample=True) # HWC
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], masks=masks)