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60 lines
2.2 KiB
60 lines
2.2 KiB
2 years ago
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from typing import List, Tuple, Union
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import torch
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import torch.nn as nn
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#from: https://github.com/kornia/kornia/blob/master/kornia/enhance/normalize.py
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def enhance_normalize(data: torch.Tensor, mean: torch.Tensor, std: torch.Tensor) -> torch.Tensor:
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r"""Normalize an image/video tensor with mean and standard deviation.
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.. math::
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\text{input[channel] = (input[channel] - mean[channel]) / std[channel]}
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Where `mean` is :math:`(M_1, ..., M_n)` and `std` :math:`(S_1, ..., S_n)` for `n` channels,
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Args:
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data: Image tensor of size :math:`(B, C, *)`.
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mean: Mean for each channel.
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std: Standard deviations for each channel.
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Return:
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Normalised tensor with same size as input :math:`(B, C, *)`.
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Examples:
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>>> x = torch.rand(1, 4, 3, 3)
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>>> out = normalize(x, torch.tensor([0.0]), torch.tensor([255.]))
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>>> out.shape
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torch.Size([1, 4, 3, 3])
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>>> x = torch.rand(1, 4, 3, 3)
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>>> mean = torch.zeros(4)
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>>> std = 255. * torch.ones(4)
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>>> out = normalize(x, mean, std)
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>>> out.shape
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torch.Size([1, 4, 3, 3])
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"""
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shape = data.shape
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if len(mean.shape) == 0 or mean.shape[0] == 1:
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mean = mean.expand(shape[1])
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if len(std.shape) == 0 or std.shape[0] == 1:
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std = std.expand(shape[1])
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# Allow broadcast on channel dimension
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if mean.shape and mean.shape[0] != 1:
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if mean.shape[0] != data.shape[1] and mean.shape[:2] != data.shape[:2]:
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raise ValueError(f"mean length and number of channels do not match. Got {mean.shape} and {data.shape}.")
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# Allow broadcast on channel dimension
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if std.shape and std.shape[0] != 1:
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if std.shape[0] != data.shape[1] and std.shape[:2] != data.shape[:2]:
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raise ValueError(f"std length and number of channels do not match. Got {std.shape} and {data.shape}.")
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mean = torch.as_tensor(mean, device=data.device, dtype=data.dtype)
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std = torch.as_tensor(std, device=data.device, dtype=data.dtype)
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if mean.shape:
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mean = mean[..., :, None]
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if std.shape:
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std = std[..., :, None]
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out: torch.Tensor = (data.view(shape[0], shape[1], -1) - mean) / std
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return out.view(shape)
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