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ultralytics 8.2.60 refactor process_mask_upsample (#14474)

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Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Laughing 2024-07-19 01:25:38 +08:00 committed by GitHub
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4 changed files with 2 additions and 27 deletions
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4
docs/en/reference/utils/ops.md
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@ -99,10 +99,6 @@ keywords: Ultralytics, utility operations, non-max suppression, bounding box tra
<br><br>
## ::: ultralytics.utils.ops.process_mask_upsample
<br><br>
## ::: ultralytics.utils.ops.process_mask
<br><br>

2
ultralytics/__init__.py
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@ -1,6 +1,6 @@
# Ultralytics YOLO 🚀, AGPL-3.0 license
__version__ = "8.2.59"
__version__ = "8.2.60"
import os

2
ultralytics/models/yolo/segment/val.py
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@ -49,7 +49,7 @@ class SegmentationValidator(DetectionValidator):
if self.args.save_json:
check_requirements("pycocotools>=2.0.6")
# more accurate vs faster
self.process = ops.process_mask_upsample if self.args.save_json or self.args.save_txt else ops.process_mask
self.process = ops.process_mask_native if self.args.save_json or self.args.save_txt else ops.process_mask
self.stats = dict(tp_m=[], tp=[], conf=[], pred_cls=[], target_cls=[], target_img=[])
def get_desc(self):

21
ultralytics/utils/ops.py
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@ -652,27 +652,6 @@ def crop_mask(masks, boxes):
return masks * ((r >= x1) * (r < x2) * (c >= y1) * (c < y2))
def process_mask_upsample(protos, masks_in, bboxes, shape):
"""
Takes the output of the mask head, and applies the mask to the bounding boxes. This produces masks of higher quality
but is slower.
Args:
protos (torch.Tensor): [mask_dim, mask_h, mask_w]
masks_in (torch.Tensor): [n, mask_dim], n is number of masks after nms
bboxes (torch.Tensor): [n, 4], n is number of masks after nms
shape (tuple): the size of the input image (h,w)
Returns:
(torch.Tensor): The upsampled masks.
"""
c, mh, mw = protos.shape # CHW
masks = (masks_in @ protos.float().view(c, -1)).view(-1, mh, mw)
masks = F.interpolate(masks[None], shape, mode="bilinear", align_corners=False)[0] # CHW
masks = crop_mask(masks, bboxes) # CHW
return masks.gt_(0.0)
def process_mask(protos, masks_in, bboxes, shape, upsample=False):
"""
Apply masks to bounding boxes using the output of the mask head.