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import torch
import torch.nn as nn
import models.basicblock as B
import numpy as np
from utils import utils_image as util
import torch.fft
# for pytorch version >= 1.8.1
"""
# --------------------------------------------
# Kai Zhang ([email protected])
@inproceedings{zhang2020deep,
title={Deep unfolding network for image super-resolution},
author={Zhang, Kai and Van Gool, Luc and Timofte, Radu},
booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
pages={0--0},
year={2020}
}
# --------------------------------------------
"""
"""
# --------------------------------------------
# basic functions
# --------------------------------------------
"""
def splits(a, sf):
'''split a into sfxsf distinct blocks
Args:
a: NxCxWxH
sf: split factor
Returns:
b: NxCx(W/sf)x(H/sf)x(sf^2)
'''
b = torch.stack(torch.chunk(a, sf, dim=2), dim=4)
b = torch.cat(torch.chunk(b, sf, dim=3), dim=4)
return b
def p2o(psf, shape):
'''
Convert point-spread function to optical transfer function.
otf = p2o(psf) computes the Fast Fourier Transform (FFT) of the
point-spread function (PSF) array and creates the optical transfer
function (OTF) array that is not influenced by the PSF off-centering.
Args:
psf: NxCxhxw
shape: [H, W]
Returns:
otf: NxCxHxWx2
'''
otf = torch.zeros(psf.shape[:-2] + shape).type_as(psf)
otf[...,:psf.shape[2],:psf.shape[3]].copy_(psf)
for axis, axis_size in enumerate(psf.shape[2:]):
otf = torch.roll(otf, -int(axis_size / 2), dims=axis+2)
otf = torch.fft.fftn(otf, dim=(-2,-1))
#n_ops = torch.sum(torch.tensor(psf.shape).type_as(psf) * torch.log2(torch.tensor(psf.shape).type_as(psf)))
#otf[..., 1][torch.abs(otf[..., 1]) < n_ops*2.22e-16] = torch.tensor(0).type_as(psf)
return otf
def upsample(x, sf=3):
'''s-fold upsampler
Upsampling the spatial size by filling the new entries with zeros
x: tensor image, NxCxWxH
'''
st = 0
z = torch.zeros((x.shape[0], x.shape[1], x.shape[2]*sf, x.shape[3]*sf)).type_as(x)
z[..., st::sf, st::sf].copy_(x)
return z
def downsample(x, sf=3):
'''s-fold downsampler
Keeping the upper-left pixel for each distinct sfxsf patch and discarding the others
x: tensor image, NxCxWxH
'''
st = 0
return x[..., st::sf, st::sf]
def downsample_np(x, sf=3):
st = 0
return x[st::sf, st::sf, ...]
"""
# --------------------------------------------
# (1) Prior module; ResUNet: act as a non-blind denoiser
# x_k = P(z_k, beta_k)
# --------------------------------------------
"""
class ResUNet(nn.Module):
def __init__(self, in_nc=4, out_nc=3, nc=[64, 128, 256, 512], nb=2, act_mode='R', downsample_mode='strideconv', upsample_mode='convtranspose'):
super(ResUNet, self).__init__()
self.m_head = B.conv(in_nc, nc[0], bias=False, mode='C')
# downsample
if downsample_mode == 'avgpool':
downsample_block = B.downsample_avgpool
elif downsample_mode == 'maxpool':
downsample_block = B.downsample_maxpool
elif downsample_mode == 'strideconv':
downsample_block = B.downsample_strideconv
else:
raise NotImplementedError('downsample mode [{:s}] is not found'.format(downsample_mode))
self.m_down1 = B.sequential(*[B.ResBlock(nc[0], nc[0], bias=False, mode='C'+act_mode+'C') for _ in range(nb)], downsample_block(nc[0], nc[1], bias=False, mode='2'))
self.m_down2 = B.sequential(*[B.ResBlock(nc[1], nc[1], bias=False, mode='C'+act_mode+'C') for _ in range(nb)], downsample_block(nc[1], nc[2], bias=False, mode='2'))
self.m_down3 = B.sequential(*[B.ResBlock(nc[2], nc[2], bias=False, mode='C'+act_mode+'C') for _ in range(nb)], downsample_block(nc[2], nc[3], bias=False, mode='2'))
self.m_body = B.sequential(*[B.ResBlock(nc[3], nc[3], bias=False, mode='C'+act_mode+'C') for _ in range(nb)])
# upsample
if upsample_mode == 'upconv':
upsample_block = B.upsample_upconv
elif upsample_mode == 'pixelshuffle':
upsample_block = B.upsample_pixelshuffle
elif upsample_mode == 'convtranspose':
upsample_block = B.upsample_convtranspose
else:
raise NotImplementedError('upsample mode [{:s}] is not found'.format(upsample_mode))
self.m_up3 = B.sequential(upsample_block(nc[3], nc[2], bias=False, mode='2'), *[B.ResBlock(nc[2], nc[2], bias=False, mode='C'+act_mode+'C') for _ in range(nb)])
self.m_up2 = B.sequential(upsample_block(nc[2], nc[1], bias=False, mode='2'), *[B.ResBlock(nc[1], nc[1], bias=False, mode='C'+act_mode+'C') for _ in range(nb)])
self.m_up1 = B.sequential(upsample_block(nc[1], nc[0], bias=False, mode='2'), *[B.ResBlock(nc[0], nc[0], bias=False, mode='C'+act_mode+'C') for _ in range(nb)])
self.m_tail = B.conv(nc[0], out_nc, bias=False, mode='C')
def forward(self, x):
h, w = x.size()[-2:]
paddingBottom = int(np.ceil(h/8)*8-h)
paddingRight = int(np.ceil(w/8)*8-w)
x = nn.ReplicationPad2d((0, paddingRight, 0, paddingBottom))(x)
x1 = self.m_head(x)
x2 = self.m_down1(x1)
x3 = self.m_down2(x2)
x4 = self.m_down3(x3)
x = self.m_body(x4)
x = self.m_up3(x+x4)
x = self.m_up2(x+x3)
x = self.m_up1(x+x2)
x = self.m_tail(x+x1)
x = x[..., :h, :w]
return x
"""
# --------------------------------------------
# (2) Data module, closed-form solution
# It is a trainable-parameter-free module ^_^
# z_k = D(x_{k-1}, s, k, y, alpha_k)
# some can be pre-calculated
# --------------------------------------------
"""
class DataNet(nn.Module):
def __init__(self):
super(DataNet, self).__init__()
def forward(self, x, FB, FBC, F2B, FBFy, alpha, sf):
FR = FBFy + torch.fft.fftn(alpha*x, dim=(-2,-1))
x1 = FB.mul(FR)
FBR = torch.mean(splits(x1, sf), dim=-1, keepdim=False)
invW = torch.mean(splits(F2B, sf), dim=-1, keepdim=False)
invWBR = FBR.div(invW + alpha)
FCBinvWBR = FBC*invWBR.repeat(1, 1, sf, sf)
FX = (FR-FCBinvWBR)/alpha
Xest = torch.real(torch.fft.ifftn(FX, dim=(-2,-1)))
return Xest
"""
# --------------------------------------------
# (3) Hyper-parameter module
# --------------------------------------------
"""
class HyPaNet(nn.Module):
def __init__(self, in_nc=2, out_nc=8, channel=64):
super(HyPaNet, self).__init__()
self.mlp = nn.Sequential(
nn.Conv2d(in_nc, channel, 1, padding=0, bias=True),
nn.ReLU(inplace=True),
nn.Conv2d(channel, channel, 1, padding=0, bias=True),
nn.ReLU(inplace=True),
nn.Conv2d(channel, out_nc, 1, padding=0, bias=True),
nn.Softplus())
def forward(self, x):
x = self.mlp(x) + 1e-6
return x
"""
# --------------------------------------------
# main USRNet
# deep unfolding super-resolution network
# --------------------------------------------
"""
class USRNet(nn.Module):
def __init__(self, n_iter=8, h_nc=64, in_nc=4, out_nc=3, nc=[64, 128, 256, 512], nb=2, act_mode='R', downsample_mode='strideconv', upsample_mode='convtranspose'):
super(USRNet, self).__init__()
self.d = DataNet()
self.p = ResUNet(in_nc=in_nc, out_nc=out_nc, nc=nc, nb=nb, act_mode=act_mode, downsample_mode=downsample_mode, upsample_mode=upsample_mode)
self.h = HyPaNet(in_nc=2, out_nc=n_iter*2, channel=h_nc)
self.n = n_iter
def forward(self, x, k, sf, sigma):
'''
x: tensor, NxCxWxH
k: tensor, Nx(1,3)xwxh
sf: integer, 1
sigma: tensor, Nx1x1x1
'''
# initialization & pre-calculation
w, h = x.shape[-2:]
FB = p2o(k, (w*sf, h*sf))
FBC = torch.conj(FB)
F2B = torch.pow(torch.abs(FB), 2)
STy = upsample(x, sf=sf)
FBFy = FBC*torch.fft.fftn(STy, dim=(-2,-1))
x = nn.functional.interpolate(x, scale_factor=sf, mode='nearest')
# hyper-parameter, alpha & beta
ab = self.h(torch.cat((sigma, torch.tensor(sf).type_as(sigma).expand_as(sigma)), dim=1))
# unfolding
for i in range(self.n):
x = self.d(x, FB, FBC, F2B, FBFy, ab[:, i:i+1, ...], sf)
x = self.p(torch.cat((x, ab[:, i+self.n:i+self.n+1, ...].repeat(1, 1, x.size(2), x.size(3))), dim=1))
return x
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