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utils: remove inplace opt-in from normalize
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@ -37,21 +37,18 @@ def interpolate(x: Tensor, factor: float | torch.Size, mode: str = "nearest") ->
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# Adapted from https://github.com/pytorch/vision/blob/main/torchvision/transforms/_functional_tensor.py
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def normalize(
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tensor: Float[Tensor, "*batch channels height width"], mean: list[float], std: list[float], inplace: bool = False
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tensor: Float[Tensor, "*batch channels height width"], mean: list[float], std: list[float]
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) -> Float[Tensor, "*batch channels height width"]:
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assert tensor.is_floating_point()
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assert tensor.ndim >= 3
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if not inplace:
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tensor = tensor.clone()
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dtype = tensor.dtype
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mean_tensor = torch.tensor(mean, dtype=dtype, device=tensor.device).view(-1, 1, 1)
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std_tensor = torch.tensor(std, dtype=dtype, device=tensor.device).view(-1, 1, 1)
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if (std_tensor == 0).any():
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pixel_mean = torch.tensor(mean, dtype=dtype, device=tensor.device).view(-1, 1, 1)
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pixel_std = torch.tensor(std, dtype=dtype, device=tensor.device).view(-1, 1, 1)
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if (pixel_std == 0).any():
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raise ValueError(f"std evaluated to zero after conversion to {dtype}, leading to division by zero.")
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return tensor.sub_(mean_tensor).div_(std_tensor)
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return (tensor - pixel_mean) / pixel_std
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def image_to_tensor(image: Image.Image, device: Device | str | None = None, dtype: DType | None = None) -> Tensor:
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