ultralytics 8.2.28 improved NMS speeds (#13315)
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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3 changed files with 7 additions and 9 deletions
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@ -1,6 +1,6 @@
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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__version__ = "8.2.27"
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__version__ = "8.2.28"
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import os
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@ -184,7 +184,7 @@ class AutoBackend(nn.Module):
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LOGGER.info(f"Loading {w} for ONNX Runtime inference...")
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check_requirements(("onnx", "onnxruntime-gpu" if cuda else "onnxruntime"))
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if IS_RASPBERRYPI or IS_JETSON:
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# Fix error: module 'numpy.linalg._umath_linalg' has no attribute '_ilp64' when exporting to Tensorflow SavedModel on RPi and Jetson
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# Fix 'numpy.linalg._umath_linalg' has no attribute '_ilp64' for TF SavedModel on RPi and Jetson
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check_requirements("numpy==1.23.5")
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import onnxruntime
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@ -402,7 +402,7 @@ def xyxy2xywh(x):
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def xywh2xyxy(x):
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"""
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Convert bounding box coordinates from (x, y, width, height) format to (x1, y1, x2, y2) format where (x1, y1) is the
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top-left corner and (x2, y2) is the bottom-right corner.
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top-left corner and (x2, y2) is the bottom-right corner. Note: ops per 2 channels faster than per channel.
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Args:
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x (np.ndarray | torch.Tensor): The input bounding box coordinates in (x, y, width, height) format.
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@ -412,12 +412,10 @@ def xywh2xyxy(x):
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"""
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assert x.shape[-1] == 4, f"input shape last dimension expected 4 but input shape is {x.shape}"
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y = torch.empty_like(x) if isinstance(x, torch.Tensor) else np.empty_like(x) # faster than clone/copy
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dw = x[..., 2] / 2 # half-width
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dh = x[..., 3] / 2 # half-height
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y[..., 0] = x[..., 0] - dw # top left x
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y[..., 1] = x[..., 1] - dh # top left y
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y[..., 2] = x[..., 0] + dw # bottom right x
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y[..., 3] = x[..., 1] + dh # bottom right y
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xy = x[..., :2] # centers
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wh = x[..., 2:] / 2 # half width-height
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y[..., :2] = xy - wh # top left xy
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y[..., 2:] = xy + wh # bottom right xy
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return y
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