Source code for mmagic.models.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