src/utils/postprocess.py

import torch
from torch.nn import functional as F


def calc_mean_std(feat, eps=1e-5):
	"""Calculate mean and std for adaptive_instance_normalization.
	Args:
		feat (Tensor): 4D tensor.
		eps (float): A small value added to the variance to avoid
			divide-by-zero. Default: 1e-5.
	"""
	size = feat.size()
	assert len(size) == 4, 'The input feature should be 4D tensor.'
	b, c = size[:2]
	feat_var = feat.view(b, c, -1).var(dim=2) + eps
	feat_std = feat_var.sqrt().view(b, c, 1, 1)
	feat_mean = feat.view(b, c, -1).mean(dim=2).view(b, c, 1, 1)
	return feat_mean, feat_std


def adaptive_instance_normalization(content_feat, style_feat):
	"""Adaptive instance normalization.
	Adjust the reference features to have the similar color and illuminations
	as those in the degradate features.
	Args:
		content_feat (Tensor): The reference feature.
		style_feat (Tensor): The degradate features.
	"""
	size = content_feat.size()
	style_mean, style_std = calc_mean_std(style_feat)
	content_mean, content_std = calc_mean_std(content_feat)
	normalized_feat = (content_feat - content_mean.expand(size)) / content_std.expand(size)
	return normalized_feat * style_std.expand(size) + style_mean.expand(size)


def wavelet_blur(image, radius: int):
    """
    Apply wavelet blur to the input tensor.
    """
    # input shape: (1, 3, H, W)
    # convolution kernel
    kernel_vals = [
        [0.0625, 0.125, 0.0625],
        [0.125, 0.25, 0.125],
        [0.0625, 0.125, 0.0625],
    ]
    kernel = torch.tensor(kernel_vals, dtype=image.dtype, device=image.device)
    # add channel dimensions to the kernel to make it a 4D tensor
    kernel = kernel[None, None]
    # repeat the kernel across all input channels
    kernel = kernel.repeat(3, 1, 1, 1)
    image = F.pad(image, (radius, radius, radius, radius), mode='replicate')
    # apply convolution
    output = F.conv2d(image, kernel, groups=3, dilation=radius)
    return output


def wavelet_decomposition(image, levels=5):
    """
    Apply wavelet decomposition to the input tensor.
    This function only returns the low frequency & the high frequency.
    """
    high_freq = torch.zeros_like(image)
    for i in range(levels):
        radius = 2 ** i
        low_freq = wavelet_blur(image, radius)
        high_freq += (image - low_freq)
        image = low_freq

    return high_freq, low_freq


def wavelet_reconstruction(content_feat, style_feat):
    """
    Apply wavelet decomposition, so that the content will have the same color as the style.
    """
    # calculate the wavelet decomposition of the content feature
    content_high_freq, content_low_freq = wavelet_decomposition(content_feat)
    del content_low_freq
    # calculate the wavelet decomposition of the style feature
    style_high_freq, style_low_freq = wavelet_decomposition(style_feat)
    del style_high_freq
    # reconstruct the content feature with the style's high frequency
    return content_high_freq + style_low_freq