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Source code for mmedit.models.base_archs.img_normalize

# Copyright (c) OpenMMLab. All rights reserved.
from typing import Tuple

import torch
import torch.nn as nn


[docs]class ImgNormalize(nn.Conv2d): """Normalize images with the given mean and std value. Based on Conv2d layer, can work in GPU. Args: pixel_range (float): Pixel range of feature. img_mean (Tuple[float]): Image mean of each channel. img_std (Tuple[float]): Image std of each channel. sign (int): Sign of bias. Default -1. """ def __init__(self, pixel_range: float, img_mean: Tuple[float, float, float], img_std: Tuple[float, float, float], sign: int = -1): assert len(img_mean) == len(img_std) num_channels = len(img_mean) super().__init__(num_channels, num_channels, kernel_size=1) std = torch.Tensor(img_std) self.weight.data = torch.eye(num_channels).view( num_channels, num_channels, 1, 1) self.weight.data.div_(std.view(num_channels, 1, 1, 1)) self.bias.data = sign * pixel_range * torch.Tensor(img_mean) self.bias.data.div_(std) self.weight.requires_grad = False self.bias.requires_grad = False
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