| """Contains an implementation of image normalizers for perceptual loss. |
| |
| For licensing see accompanying LICENSE file. |
| Copyright (C) 2025 Apple Inc. All Rights Reserved. |
| """ |
|
|
| from __future__ import annotations |
|
|
| from typing import Sequence, Union |
|
|
| import torch |
| from torch import nn |
|
|
|
|
| class MeanStdNormalizer(nn.Module): |
| """Normalizing image input by mean and std.""" |
|
|
| mean: torch.Tensor |
| std_inv: torch.Tensor |
|
|
| def __init__( |
| self, |
| mean: Union[Sequence[float], torch.Tensor], |
| std: Union[Sequence[float], torch.Tensor], |
| ): |
| """Initialize MeanStdNormalizer.""" |
| super(MeanStdNormalizer, self).__init__() |
| if not isinstance(mean, torch.Tensor): |
| mean = torch.as_tensor(mean).view(-1, 1, 1) |
| if not isinstance(std, torch.Tensor): |
| std = torch.as_tensor(std).view(-1, 1, 1) |
| self.register_buffer("mean", mean) |
| |
| self.register_buffer("std_inv", 1.0 / std) |
|
|
| def forward(self, image: torch.Tensor) -> torch.Tensor: |
| """Apply mean and std normalization over input image.""" |
| return (image - self.mean) * self.std_inv |
|
|
|
|
| class AffineRangeNormalizer(nn.Module): |
| """Perform linear mapping to map input_range to output_range. |
| |
| Output_range defaults to (0, 1). |
| """ |
|
|
| def __init__( |
| self, |
| input_range: tuple[float, float], |
| output_range: tuple[float, float] = (0, 1), |
| ): |
| """Initialize AffineRangeNormalizer.""" |
| super().__init__() |
| input_min, input_max = input_range |
| output_min, output_max = output_range |
| if input_max <= input_min: |
| raise ValueError(f"Invalid input_range: {input_range}") |
| if output_max <= output_min: |
| raise ValueError(f"Invalid output_range: {output_range}") |
|
|
| self.scale = (output_max - output_min) / (input_max - input_min) |
| self.bias = output_min - input_min * self.scale |
|
|
| def forward(self, x: torch.Tensor) -> torch.Tensor: |
| """Apply affine range normalization over input image.""" |
| if self.scale != 1.0: |
| x = x * self.scale |
|
|
| if self.bias != 0.0: |
| x = x + self.bias |
|
|
| return x |
|
|
|
|
| class MobileNetNormalizer(AffineRangeNormalizer): |
| """Image normalization in mobilenet.""" |
|
|
| def __init__(self, input_range: tuple[float, float] = (0, 1)): |
| """Initialize MobileNetNormalizer.""" |
| super().__init__(input_range=input_range, output_range=(-1, 1)) |
|
|
