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ultralytics 8.0.59 new MLFlow and feature updates (#1720)

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Glenn Jocher 2023-03-31 20:33:02 +02:00 • committed by GitHub
parent ccb6419835
commit e7876e1ba9
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29 changed files with 326 additions and 160 deletions
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26
ultralytics/yolo/engine/results.py
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@ -14,6 +14,7 @@ import torchvision.transforms.functional as F
from ultralytics.yolo.utils import LOGGER, SimpleClass, ops
from ultralytics.yolo.utils.plotting import Annotator, colors
from ultralytics.yolo.utils.torch_utils import TORCHVISION_0_10
class Results(SimpleClass):
@ -129,7 +130,10 @@ class Results(SimpleClass):
if masks is not None:
im = torch.as_tensor(annotator.im, dtype=torch.float16, device=masks.data.device).permute(2, 0, 1).flip(0)
im = F.resize(im.contiguous(), masks.data.shape[1:]) / 255
if TORCHVISION_0_10:
im = F.resize(im.contiguous(), masks.data.shape[1:], antialias=True) / 255
else:
im = F.resize(im.contiguous(), masks.data.shape[1:]) / 255
annotator.masks(masks.data, colors=[colors(x, True) for x in boxes.cls], im_gpu=im)
if probs is not None:
@ -259,7 +263,8 @@ class Masks(SimpleClass):
orig_shape (tuple): Original image size, in the format (height, width).
Properties:
segments (list): A list of segments which includes x, y, w, h, label, confidence, and mask of each detection.
xy (list): A list of segments (pixels) which includes x, y segments of each detection.
xyn (list): A list of segments (normalized) which includes x, y segments of each detection.
Methods:
cpu(): Returns a copy of the masks tensor on CPU memory.
@ -272,13 +277,28 @@ class Masks(SimpleClass):
self.masks = masks # N, h, w
self.orig_shape = orig_shape
def segments(self):
# Segments-deprecated (normalized)
LOGGER.warning("WARNING ⚠️ 'Masks.segments' is deprecated. Use 'Masks.xyn' for segments (normalized) and "
"'Masks.xy' for segments (pixels) instead.")
return self.xyn
@property
@lru_cache(maxsize=1)
def segments(self):
def xyn(self):
# Segments (normalized)
return [
ops.scale_segments(self.masks.shape[1:], x, self.orig_shape, normalize=True)
for x in ops.masks2segments(self.masks)]
@property
@lru_cache(maxsize=1)
def xy(self):
# Segments (pixels)
return [
ops.scale_segments(self.masks.shape[1:], x, self.orig_shape, normalize=False)
for x in ops.masks2segments(self.masks)]
@property
def shape(self):
return self.masks.shape