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from collections import OrderedDict
import torch
import torch.nn as nn
import numpy as np
import torch.nn.functional as F
import torchvision.models as models
'''
# --------------------------------------------
# Advanced nn.Sequential
# https://github.com/xinntao/BasicSR
# --------------------------------------------
'''
def sequential(*args):
"""Advanced nn.Sequential.
Args:
nn.Sequential, nn.Module
Returns:
nn.Sequential
"""
if len(args) == 1:
if isinstance(args[0], OrderedDict):
raise NotImplementedError('sequential does not support OrderedDict input.')
return args[0] # No sequential is needed.
modules = []
for module in args:
if isinstance(module, nn.Sequential):
for submodule in module.children():
modules.append(submodule)
elif isinstance(module, nn.Module):
modules.append(module)
return nn.Sequential(*modules)
# --------------------------------------------
# return nn.Sequantial of (Conv + BN + ReLU)
# --------------------------------------------
def conv(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1, bias=True, mode='CBR', negative_slope=0.2):
L = []
for t in mode:
if t == 'C':
L.append(nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride, padding=padding, bias=bias))
elif t == 'T':
L.append(nn.ConvTranspose2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride, padding=padding, bias=bias))
elif t == 'B':
L.append(nn.BatchNorm2d(out_channels, momentum=0.9, eps=1e-04, affine=True))
elif t == 'I':
L.append(nn.InstanceNorm2d(out_channels, affine=True))
elif t == 'R':
L.append(nn.ReLU(inplace=True))
elif t == 'r':
L.append(nn.ReLU(inplace=False))
elif t == 'L':
L.append(nn.LeakyReLU(negative_slope=negative_slope, inplace=True))
elif t == 'l':
L.append(nn.LeakyReLU(negative_slope=negative_slope, inplace=False))
elif t == '2':
L.append(nn.PixelShuffle(upscale_factor=2))
elif t == '3':
L.append(nn.PixelShuffle(upscale_factor=3))
elif t == '4':
L.append(nn.PixelShuffle(upscale_factor=4))
elif t == 'U':
L.append(nn.Upsample(scale_factor=2, mode='nearest'))
elif t == 'u':
L.append(nn.Upsample(scale_factor=3, mode='nearest'))
elif t == 'v':
L.append(nn.Upsample(scale_factor=4, mode='nearest'))
elif t == 'M':
L.append(nn.MaxPool2d(kernel_size=kernel_size, stride=stride, padding=0))
elif t == 'A':
L.append(nn.AvgPool2d(kernel_size=kernel_size, stride=stride, padding=0))
else:
raise NotImplementedError('Undefined type: '.format(t))
return sequential(*L)
# --------------------------------------------
# Res Block: x + conv(relu(conv(x)))
# --------------------------------------------
class ResBlock(nn.Module):
def __init__(self, in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1, bias=True, mode='CRC', negative_slope=0.2):
super(ResBlock, self).__init__()
assert in_channels == out_channels, 'Only support in_channels==out_channels.'
if mode[0] in ['R', 'L']:
mode = mode[0].lower() + mode[1:]
self.res = conv(in_channels, out_channels, kernel_size, stride, padding, bias, mode, negative_slope)
def forward(self, x):
res = self.res(x)
return x + res
# --------------------------------------------
# conv + subp (+ relu)
# --------------------------------------------
def upsample_pixelshuffle(in_channels=64, out_channels=3, kernel_size=3, stride=1, padding=1, bias=True, mode='2R', negative_slope=0.2):
assert len(mode)<4 and mode[0] in ['2', '3', '4'], 'mode examples: 2, 2R, 2BR, 3, ..., 4BR.'
up1 = conv(in_channels, out_channels * (int(mode[0]) ** 2), kernel_size, stride, padding, bias, mode='C'+mode, negative_slope=negative_slope)
return up1
# --------------------------------------------
# nearest_upsample + conv (+ R)
# --------------------------------------------
def upsample_upconv(in_channels=64, out_channels=3, kernel_size=3, stride=1, padding=1, bias=True, mode='2R', negative_slope=0.2):
assert len(mode)<4 and mode[0] in ['2', '3', '4'], 'mode examples: 2, 2R, 2BR, 3, ..., 4BR'
if mode[0] == '2':
uc = 'UC'
elif mode[0] == '3':
uc = 'uC'
elif mode[0] == '4':
uc = 'vC'
mode = mode.replace(mode[0], uc)
up1 = conv(in_channels, out_channels, kernel_size, stride, padding, bias, mode=mode, negative_slope=negative_slope)
return up1
# --------------------------------------------
# convTranspose (+ relu)
# --------------------------------------------
def upsample_convtranspose(in_channels=64, out_channels=3, kernel_size=2, stride=2, padding=0, bias=True, mode='2R', negative_slope=0.2):
assert len(mode)<4 and mode[0] in ['2', '3', '4'], 'mode examples: 2, 2R, 2BR, 3, ..., 4BR.'
kernel_size = int(mode[0])
stride = int(mode[0])
mode = mode.replace(mode[0], 'T')
up1 = conv(in_channels, out_channels, kernel_size, stride, padding, bias, mode, negative_slope)
return up1
'''
# --------------------------------------------
# Downsampler
# Kai Zhang, https://github.com/cszn/KAIR
# --------------------------------------------
# downsample_strideconv
# downsample_maxpool
# downsample_avgpool
# --------------------------------------------
'''
# --------------------------------------------
# strideconv (+ relu)
# --------------------------------------------
def downsample_strideconv(in_channels=64, out_channels=64, kernel_size=2, stride=2, padding=0, bias=True, mode='2R', negative_slope=0.2):
assert len(mode)<4 and mode[0] in ['2', '3', '4'], 'mode examples: 2, 2R, 2BR, 3, ..., 4BR.'
kernel_size = int(mode[0])
stride = int(mode[0])
mode = mode.replace(mode[0], 'C')
down1 = conv(in_channels, out_channels, kernel_size, stride, padding, bias, mode, negative_slope)
return down1
# --------------------------------------------
# maxpooling + conv (+ relu)
# --------------------------------------------
def downsample_maxpool(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=0, bias=True, mode='2R', negative_slope=0.2):
assert len(mode)<4 and mode[0] in ['2', '3'], 'mode examples: 2, 2R, 2BR, 3, ..., 3BR.'
kernel_size_pool = int(mode[0])
stride_pool = int(mode[0])
mode = mode.replace(mode[0], 'MC')
pool = conv(kernel_size=kernel_size_pool, stride=stride_pool, mode=mode[0], negative_slope=negative_slope)
pool_tail = conv(in_channels, out_channels, kernel_size, stride, padding, bias, mode=mode[1:], negative_slope=negative_slope)
return sequential(pool, pool_tail)
# --------------------------------------------
# averagepooling + conv (+ relu)
# --------------------------------------------
def downsample_avgpool(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1, bias=True, mode='2R', negative_slope=0.2):
assert len(mode)<4 and mode[0] in ['2', '3'], 'mode examples: 2, 2R, 2BR, 3, ..., 3BR.'
kernel_size_pool = int(mode[0])
stride_pool = int(mode[0])
mode = mode.replace(mode[0], 'AC')
pool = conv(kernel_size=kernel_size_pool, stride=stride_pool, mode=mode[0], negative_slope=negative_slope)
pool_tail = conv(in_channels, out_channels, kernel_size, stride, padding, bias, mode=mode[1:], negative_slope=negative_slope)
return sequential(pool, pool_tail)
class QFAttention(nn.Module):
def __init__(self, in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1, bias=True, mode='CRC', negative_slope=0.2):
super(QFAttention, self).__init__()
assert in_channels == out_channels, 'Only support in_channels==out_channels.'
if mode[0] in ['R', 'L']:
mode = mode[0].lower() + mode[1:]
self.res = conv(in_channels, out_channels, kernel_size, stride, padding, bias, mode, negative_slope)
def forward(self, x, gamma, beta):
gamma = gamma.unsqueeze(-1).unsqueeze(-1)
beta = beta.unsqueeze(-1).unsqueeze(-1)
res = (gamma)*self.res(x) + beta
return x + res
class FBCNN(nn.Module):
def __init__(self, in_nc=3, out_nc=3, nc=[64, 128, 256, 512], nb=4, act_mode='R', downsample_mode='strideconv',
upsample_mode='convtranspose'):
super(FBCNN, self).__init__()
self.m_head = conv(in_nc, nc[0], bias=True, mode='C')
self.nb = nb
self.nc = nc
# downsample
if downsample_mode == 'avgpool':
downsample_block = downsample_avgpool
elif downsample_mode == 'maxpool':
downsample_block = downsample_maxpool
elif downsample_mode == 'strideconv':
downsample_block = downsample_strideconv
else:
raise NotImplementedError('downsample mode [{:s}] is not found'.format(downsample_mode))
self.m_down1 = sequential(
*[ResBlock(nc[0], nc[0], bias=True, mode='C' + act_mode + 'C') for _ in range(nb)],
downsample_block(nc[0], nc[1], bias=True, mode='2'))
self.m_down2 = sequential(
*[ResBlock(nc[1], nc[1], bias=True, mode='C' + act_mode + 'C') for _ in range(nb)],
downsample_block(nc[1], nc[2], bias=True, mode='2'))
self.m_down3 = sequential(
*[ResBlock(nc[2], nc[2], bias=True, mode='C' + act_mode + 'C') for _ in range(nb)],
downsample_block(nc[2], nc[3], bias=True, mode='2'))
self.m_body_encoder = sequential(
*[ResBlock(nc[3], nc[3], bias=True, mode='C' + act_mode + 'C') for _ in range(nb)])
self.m_body_decoder = sequential(
*[ResBlock(nc[3], nc[3], bias=True, mode='C' + act_mode + 'C') for _ in range(nb)])
# upsample
if upsample_mode == 'upconv':
upsample_block = upsample_upconv
elif upsample_mode == 'pixelshuffle':
upsample_block = upsample_pixelshuffle
elif upsample_mode == 'convtranspose':
upsample_block = upsample_convtranspose
else:
raise NotImplementedError('upsample mode [{:s}] is not found'.format(upsample_mode))
self.m_up3 = nn.ModuleList([upsample_block(nc[3], nc[2], bias=True, mode='2'),
*[QFAttention(nc[2], nc[2], bias=True, mode='C' + act_mode + 'C') for _ in range(nb)]])
self.m_up2 = nn.ModuleList([upsample_block(nc[2], nc[1], bias=True, mode='2'),
*[QFAttention(nc[1], nc[1], bias=True, mode='C' + act_mode + 'C') for _ in range(nb)]])
self.m_up1 = nn.ModuleList([upsample_block(nc[1], nc[0], bias=True, mode='2'),
*[QFAttention(nc[0], nc[0], bias=True, mode='C' + act_mode + 'C') for _ in range(nb)]])
self.m_tail = conv(nc[0], out_nc, bias=True, mode='C')
self.qf_pred = sequential(*[ResBlock(nc[3], nc[3], bias=True, mode='C' + act_mode + 'C') for _ in range(nb)],
torch.nn.AdaptiveAvgPool2d((1,1)),
torch.nn.Flatten(),
torch.nn.Linear(512, 512),
nn.ReLU(),
torch.nn.Linear(512, 512),
nn.ReLU(),
torch.nn.Linear(512, 1),
nn.Sigmoid()
)
self.qf_embed = sequential(torch.nn.Linear(1, 512),
nn.ReLU(),
torch.nn.Linear(512, 512),
nn.ReLU(),
torch.nn.Linear(512, 512),
nn.ReLU()
)
self.to_gamma_3 = sequential(torch.nn.Linear(512, nc[2]),nn.Sigmoid())
self.to_beta_3 = sequential(torch.nn.Linear(512, nc[2]),nn.Tanh())
self.to_gamma_2 = sequential(torch.nn.Linear(512, nc[1]),nn.Sigmoid())
self.to_beta_2 = sequential(torch.nn.Linear(512, nc[1]),nn.Tanh())
self.to_gamma_1 = sequential(torch.nn.Linear(512, nc[0]),nn.Sigmoid())
self.to_beta_1 = sequential(torch.nn.Linear(512, nc[0]),nn.Tanh())
def forward(self, x, qf_input=None):
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_encoder(x4)
qf = self.qf_pred(x)
x = self.m_body_decoder(x)
qf_embedding = self.qf_embed(qf_input) if qf_input is not None else self.qf_embed(qf)
gamma_3 = self.to_gamma_3(qf_embedding)
beta_3 = self.to_beta_3(qf_embedding)
gamma_2 = self.to_gamma_2(qf_embedding)
beta_2 = self.to_beta_2(qf_embedding)
gamma_1 = self.to_gamma_1(qf_embedding)
beta_1 = self.to_beta_1(qf_embedding)
x = x + x4
x = self.m_up3[0](x)
for i in range(self.nb):
x = self.m_up3[i+1](x, gamma_3,beta_3)
x = x + x3
x = self.m_up2[0](x)
for i in range(self.nb):
x = self.m_up2[i+1](x, gamma_2, beta_2)
x = x + x2
x = self.m_up1[0](x)
for i in range(self.nb):
x = self.m_up1[i+1](x, gamma_1, beta_1)
x = x + x1
x = self.m_tail(x)
x = x[..., :h, :w]
return x, qf
if __name__ == "__main__":
x = torch.randn(1, 3, 96, 96)#.cuda()#.to(torch.device('cuda'))
fbar=FBAR()
y,qf = fbar(x)
print(y.shape,qf.shape)
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