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ultralytics 8.1.40 search in Python sets {} for speed (#9450)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Glenn Jocher 2024-04-01 00:16:52 +02:00 • committed by GitHub
parent 30484d5925
commit ea527507fe
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41 changed files with 97 additions and 93 deletions
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10
ultralytics/nn/autobackend.py
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@ -374,9 +374,9 @@ class AutoBackend(nn.Module):
metadata = yaml_load(metadata)
if metadata:
for k, v in metadata.items():
if k in ("stride", "batch"):
if k in {"stride", "batch"}:
metadata[k] = int(v)
elif k in ("imgsz", "names", "kpt_shape") and isinstance(v, str):
elif k in {"imgsz", "names", "kpt_shape"} and isinstance(v, str):
metadata[k] = eval(v)
stride = metadata["stride"]
task = metadata["task"]
@ -531,8 +531,8 @@ class AutoBackend(nn.Module):
self.names = {i: f"class{i}" for i in range(nc)}
else: # Lite or Edge TPU
details = self.input_details[0]
integer = details["dtype"] in (np.int8, np.int16) # is TFLite quantized int8 or int16 model
if integer:
is_int = details["dtype"] in {np.int8, np.int16} # is TFLite quantized int8 or int16 model
if is_int:
scale, zero_point = details["quantization"]
im = (im / scale + zero_point).astype(details["dtype"]) # de-scale
self.interpreter.set_tensor(details["index"], im)
@ -540,7 +540,7 @@ class AutoBackend(nn.Module):
y = []
for output in self.output_details:
x = self.interpreter.get_tensor(output["index"])
if integer:
if is_int:
scale, zero_point = output["quantization"]
x = (x.astype(np.float32) - zero_point) * scale # re-scale
if x.ndim == 3: # if task is not classification, excluding masks (ndim=4) as well