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utils: simplify normalize a bit
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@ -4,10 +4,11 @@ from numpy import array, float32
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from pathlib import Path
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from safetensors import safe_open as _safe_open # type: ignore
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from safetensors.torch import save_file as _save_file # type: ignore
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from torch import as_tensor, norm as _norm, manual_seed as _manual_seed # type: ignore
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from torch import norm as _norm, manual_seed as _manual_seed # type: ignore
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import torch
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from torch.nn.functional import pad as _pad, interpolate as _interpolate # type: ignore
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from torch import Tensor, device as Device, dtype as DType
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from jaxtyping import Float
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T = TypeVar("T")
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@ -35,7 +36,9 @@ 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(tensor: Tensor, mean: list[float], std: list[float], inplace: bool = False) -> Tensor:
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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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) -> 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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@ -43,19 +46,11 @@ def normalize(tensor: Tensor, mean: list[float], std: list[float], inplace: bool
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tensor = tensor.clone()
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dtype = tensor.dtype
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mean_tensor = as_tensor(mean, dtype=tensor.dtype, device=tensor.device)
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std_tensor = as_tensor(std, dtype=tensor.dtype, device=tensor.device)
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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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raise ValueError(f"std evaluated to zero after conversion to {dtype}, leading to division by zero.")
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if mean_tensor.ndim == 1:
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mean_tensor = mean_tensor.view(-1, 1, 1)
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if std_tensor.ndim == 1:
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std_tensor = std_tensor.view(-1, 1, 1)
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return tensor.sub_(mean_tensor).div_(std_tensor)
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