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Fix conversion ops using clone and copy (#4438)

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Laughing 2023-08-20 19:15:37 +08:00 • committed by GitHub
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commit 8f79ce45c1
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3 changed files with 29 additions and 48 deletions
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38
ultralytics/utils/ops.py
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@ -344,7 +344,8 @@ def xyxy2xywh(x):
Returns:
y (np.ndarray | torch.Tensor): The bounding box coordinates in (x, y, width, height) format.
"""
y = torch.empty_like(x) if isinstance(x, torch.Tensor) else np.empty_like(x)
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[..., 0] = (x[..., 0] + x[..., 2]) / 2 # x center
y[..., 1] = (x[..., 1] + x[..., 3]) / 2 # y center
y[..., 2] = x[..., 2] - x[..., 0] # width
@ -362,7 +363,8 @@ def xywh2xyxy(x):
Returns:
y (np.ndarray | torch.Tensor): The bounding box coordinates in (x1, y1, x2, y2) format.
"""
y = torch.empty_like(x) if isinstance(x, torch.Tensor) else np.empty_like(x)
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
dw = x[..., 2] / 2 # half-width
dh = x[..., 3] / 2 # half-height
y[..., 0] = x[..., 0] - dw # top left x
@ -386,7 +388,8 @@ def xywhn2xyxy(x, w=640, h=640, padw=0, padh=0):
y (np.ndarray | torch.Tensor): The coordinates of the bounding box in the format [x1, y1, x2, y2] where
x1,y1 is the top-left corner, x2,y2 is the bottom-right corner of the bounding box.
"""
y = torch.empty_like(x) if isinstance(x, torch.Tensor) else np.empty_like(x)
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[..., 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
@ -410,7 +413,8 @@ def xyxy2xywhn(x, w=640, h=640, clip=False, eps=0.0):
"""
if clip:
clip_boxes(x, (h - eps, w - eps)) # warning: inplace clip
y = torch.empty_like(x) if isinstance(x, torch.Tensor) else np.empty_like(x)
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[..., 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
@ -431,7 +435,7 @@ def xyn2xy(x, w=640, h=640, padw=0, padh=0):
Returns:
y (np.ndarray | torch.Tensor): The x and y coordinates of the top left corner of the bounding box
"""
y = torch.empty_like(x) if isinstance(x, torch.Tensor) else np.empty_like(x)
y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
y[..., 0] = w * x[..., 0] + padw # top left x
y[..., 1] = h * x[..., 1] + padh # top left y
return y
@ -446,9 +450,9 @@ def xywh2ltwh(x):
Returns:
y (np.ndarray | torch.Tensor): The bounding box coordinates in the xyltwh format
"""
y = torch.empty_like(x) if isinstance(x, torch.Tensor) else np.empty_like(x)
y[:, 0] = x[:, 0] - x[:, 2] / 2 # top left x
y[:, 1] = x[:, 1] - x[:, 3] / 2 # top left y
y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
y[..., 0] = x[..., 0] - x[..., 2] / 2 # top left x
y[..., 1] = x[..., 1] - x[..., 3] / 2 # top left y
return y
@ -461,9 +465,9 @@ def xyxy2ltwh(x):
Returns:
y (np.ndarray | torch.Tensor): The bounding box coordinates in the xyltwh format.
"""
y = torch.empty_like(x) if isinstance(x, torch.Tensor) else np.empty_like(x)
y[:, 2] = x[:, 2] - x[:, 0] # width
y[:, 3] = x[:, 3] - x[:, 1] # height
y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
y[..., 2] = x[..., 2] - x[..., 0] # width
y[..., 3] = x[..., 3] - x[..., 1] # height
return y
@ -474,9 +478,9 @@ def ltwh2xywh(x):
Args:
x (torch.Tensor): the input tensor
"""
y = torch.empty_like(x) if isinstance(x, torch.Tensor) else np.empty_like(x)
y[:, 0] = x[:, 0] + x[:, 2] / 2 # center x
y[:, 1] = x[:, 1] + x[:, 3] / 2 # center y
y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
y[..., 0] = x[..., 0] + x[..., 2] / 2 # center x
y[..., 1] = x[..., 1] + x[..., 3] / 2 # center y
return y
@ -569,9 +573,9 @@ def ltwh2xyxy(x):
Returns:
y (np.ndarray | torch.Tensor): the xyxy coordinates of the bounding boxes.
"""
y = torch.empty_like(x) if isinstance(x, torch.Tensor) else np.empty_like(x)
y[:, 2] = x[:, 2] + x[:, 0] # width
y[:, 3] = x[:, 3] + x[:, 1] # height
y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
y[..., 2] = x[..., 2] + x[..., 0] # width
y[..., 3] = x[..., 3] + x[..., 1] # height
return y