HongChuang/ultralytics-ascend
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Fix np.empty_like and torch.empty_like input type (#18062)

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Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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Laughing 2024-12-06 19:59:19 +08:00 • committed by GitHub
parent 7ba48cc078
commit 530e6b9342
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2 changed files with 15 additions and 4 deletions
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15
ultralytics/utils/ops.py
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@ -400,7 +400,7 @@ def xyxy2xywh(x):
y (np.ndarray | torch.Tensor): The bounding box coordinates in (x, y, width, height) format.
"""
assert x.shape[-1] == 4, f"input shape last dimension expected 4 but input shape is {x.shape}"
y = torch.empty_like(x) if isinstance(x, torch.Tensor) else np.empty_like(x) # faster than clone/copy
y = empty_like(x) # faster than clone/copy
y[..., 0] = (x[..., 0] + x[..., 2]) / 2 # x center
y[..., 1] = (x[..., 1] + x[..., 3]) / 2 # y center
y[..., 2] = x[..., 2] - x[..., 0] # width
@ -420,7 +420,7 @@ def xywh2xyxy(x):
y (np.ndarray | torch.Tensor): The bounding box coordinates in (x1, y1, x2, y2) format.
"""
assert x.shape[-1] == 4, f"input shape last dimension expected 4 but input shape is {x.shape}"
y = torch.empty_like(x) if isinstance(x, torch.Tensor) else np.empty_like(x) # faster than clone/copy
y = empty_like(x) # faster than clone/copy
xy = x[..., :2] # centers
wh = x[..., 2:] / 2 # half width-height
y[..., :2] = xy - wh # top left xy
@ -443,7 +443,7 @@ def xywhn2xyxy(x, w=640, h=640, padw=0, padh=0):
x1,y1 is the top-left corner, x2,y2 is the bottom-right corner of the bounding box.
"""
assert x.shape[-1] == 4, f"input shape last dimension expected 4 but input shape is {x.shape}"
y = torch.empty_like(x) if isinstance(x, torch.Tensor) else np.empty_like(x) # faster than clone/copy
y = empty_like(x) # faster than clone/copy
y[..., 0] = w * (x[..., 0] - x[..., 2] / 2) + padw # top left x
y[..., 1] = h * (x[..., 1] - x[..., 3] / 2) + padh # top left y
y[..., 2] = w * (x[..., 0] + x[..., 2] / 2) + padw # bottom right x
@ -469,7 +469,7 @@ def xyxy2xywhn(x, w=640, h=640, clip=False, eps=0.0):
if clip:
x = clip_boxes(x, (h - eps, w - eps))
assert x.shape[-1] == 4, f"input shape last dimension expected 4 but input shape is {x.shape}"
y = torch.empty_like(x) if isinstance(x, torch.Tensor) else np.empty_like(x, dtype=float) # faster than clone/copy
y = empty_like(x) # faster than clone/copy
y[..., 0] = ((x[..., 0] + x[..., 2]) / 2) / w # x center
y[..., 1] = ((x[..., 1] + x[..., 3]) / 2) / h # y center
y[..., 2] = (x[..., 2] - x[..., 0]) / w # width
@ -838,3 +838,10 @@ def clean_str(s):
(str): a string with special characters replaced by an underscore _
"""
return re.sub(pattern="[|@#!¡·$€%&()=?¿^*;:,¨´><+]", repl="_", string=s)
def empty_like(x):
"""Creates empty torch.Tensor or np.ndarray with same shape as input and float32 dtype."""
return (
torch.empty_like(x, dtype=torch.float32) if isinstance(x, torch.Tensor) else np.empty_like(x, dtype=np.float32)
)