| | """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)) |
| |
|
