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model8b8xt.py
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model8b8xt.py
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# -*- coding:utf-8 -*-
import torch
import torch.nn as nn
import numpy as np
import math
from torch.utils.checkpoint import checkpoint
class MeanShiftY(nn.Conv2d):
def __init__(self, y_mean, y_std, sign=-1):
super(MeanShiftY, self).__init__(1, 1, kernel_size=1)
std = torch.Tensor(y_std)
self.weight.data = torch.eye(1).view(1, 1, 1, 1)
self.weight.data.div_(std.view(1, 1, 1, 1))
self.bias.data = sign * torch.Tensor(y_mean)
self.bias.data.div_(std)
self.requires_grad = False
class upsample_block(nn.Module):
def __init__(self,in_channels,out_channels):
super(upsample_block,self).__init__()
self.conv = nn.Conv2d(in_channels,out_channels,3,stride=1,padding=1)
self.shuffler = nn.PixelShuffle(2)
self.prelu = nn.PReLU()
def forward(self,x):
return self.prelu(self.shuffler(self.conv(x)))
class make_mix(nn.Module):
def __init__(self, nChannels, growthRate, kernel_size=3):
super(make_mix, self).__init__()
# self.conv = nn.Conv2d(nChannels, growthRate, kernel_size=kernel_size, padding=(kernel_size-1)//2, bias=False)
self.conv1 = nn.Conv2d(nChannels, growthRate, kernel_size=3, padding=1, bias=False)
self.conv2 = nn.Conv2d(nChannels, growthRate, kernel_size=5, padding=2, bias=False)
self.relu1 = nn.PReLU()
self.relu2 = nn.PReLU()
self.frm = FRM(growthRate,growthRate)
self.channels = nChannels
# kernel attention
# self.ka = KAM(growthRate,growthRate)
self.krelu = nn.PReLU()
self.squeeze = nn.AdaptiveAvgPool2d(1)
self.conv_down = nn.Conv2d(growthRate, growthRate // 4, kernel_size=1, bias=False)
self.conv_up = nn.Conv2d(growthRate // 4, growthRate, kernel_size=1, bias=False)
self.sig = nn.Sigmoid()
def forward(self, x):
# original channel=64,growth rate=32
y1 = self.relu1(self.conv1(x))
y2 = self.relu2(self.conv2(x))
y = y1+y2
ks = self.squeeze(y)
cd = self.krelu(self.conv_down(ks))
cu = self.conv_up(cd)
w = self.sig(cu)
y1 = y1*w
y2 = y2*w
y = y1+y2
y = self.frm(y)
x_left = x[:,0:self.channels-32,:,:]
x_right = x[:,self.channels-32:self.channels,:,:]
y_left = y[:,0:32,:,:]
y_right = y[:,32:64,:,:]
iden = x_left
addi = x_right+y_left
tmp = torch.cat((iden,addi),1)
out = torch.cat((tmp,y_right),1)
return out
# Mixed Link Block architecture
class MLB(nn.Module):
def __init__(self, nChannels, nDenselayer, growthRate):
super(MLB, self).__init__()
nChannels_ = nChannels
modules = []
for i in range(nDenselayer):
modules.append(make_mix(nChannels_, growthRate))
nChannels_ += 32
self.dense_layers = nn.Sequential(*modules)
# reshape the channel size
self.conv_1x1 = nn.Conv2d(nChannels_, nChannels, kernel_size=1, padding=0, bias=False)
self.frm = FRM(nChannels,nChannels)
def forward(self, x):
out = self.dense_layers(x)
out = self.conv_1x1(out)
out = out + self.frm(x)
return out
class FRM(nn.Module):
'''The feature recalibration module'''
def __init__(self,inChannels,outChannels):
super(FRM, self).__init__()
self.swish = nn.Sigmoid()
self.channel_squeeze = nn.AdaptiveAvgPool2d(1)
self.conv_down = nn.Conv2d(inChannels * 4, inChannels // 4, kernel_size=1, bias=False)
self.conv_up = nn.Conv2d(inChannels // 4, inChannels * 4, kernel_size=1, bias=False)
self.sig = nn.Sigmoid()
self.trans1 = nn.Sequential(
nn.Conv2d(in_channels=inChannels, out_channels=inChannels * 4, kernel_size=1, stride=1, padding=0, bias=False),
nn.PReLU(),
)
self.trans2 = nn.Sequential(
nn.Conv2d(in_channels=inChannels * 4, out_channels=outChannels, kernel_size=1, stride=1, padding=0, bias=False),
nn.PReLU(),
)
def forward(self, x):
ex = self.trans1(x)
out1 = self.channel_squeeze(ex)
out1 = self.conv_down(out1)
# swish
out1 = out1*self.swish(out1)
out1 = self.conv_up(out1)
weight = self.sig(out1)
out=ex*weight
out=self.trans2(out)
return out
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.sub_mean = MeanShiftY([0.43806],[1.0])
self.add_mean = MeanShiftY([0.43806],[1.0],1)
self.conv_input = nn.Conv2d(in_channels=1, out_channels=64, kernel_size=3, stride=1, padding=1, bias=False)
self.conv_input2 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1, bias=False)
self.relu = nn.PReLU()
self.u1 = upsample_block(64,64*4)
self.u2 = upsample_block(64,64*4)
self.u3 = upsample_block(64,64*4)
self.ures1 = upsample_block(64,64*4)
self.ures2 = upsample_block(64,64*4)
self.ures3 = upsample_block(64,64*4)
self.sa = nn.Conv2d(in_channels=128, out_channels=1, kernel_size=1, stride=1, padding=0, bias=False)
self.conv_G = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=1, stride=1, padding=0, bias=False)
self.convt_R1 = nn.Conv2d(in_channels=64, out_channels=1, kernel_size=3, stride=1, padding=1, bias=False)
# add multi supervise
self.convt_F11 = MLB(64, 4, 64)
self.convt_F12 = MLB(64, 4, 64)
self.convt_F13 = MLB(64, 4, 64)
self.convt_F14 = MLB(64, 4, 64)
self.convt_F15 = MLB(64, 4, 64)
self.convt_F16 = MLB(64, 4, 64)
self.convt_F17 = MLB(64, 4, 64)
self.convt_F18 = MLB(64, 4, 64)
self.convt_shape1 = nn.Conv2d(in_channels=64, out_channels=1, kernel_size=3, stride=1, padding=1, bias=False)
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
if m.bias is not None:
m.bias.data.zero_()
def forward(self, x):
def pass1(*args):
x = args[0]
x = self.sub_mean(x)
out = self.relu(self.conv_input(x))
conv1 = self.conv_input2(out)
convt_F11 = self.convt_F11(conv1)
convt_F12 = self.convt_F12(convt_F11)
convt_F13 = self.convt_F13(convt_F12)
convt_F14 = self.convt_F14(convt_F13)
convt_F15 = self.convt_F15(convt_F14)
convt_F16 = self.convt_F16(convt_F15)
convt_F17 = self.convt_F17(convt_F16)
convt_F18 = self.convt_F18(convt_F17)
return convt_F16,convt_F17,convt_F18,out
convt_F16,convt_F17,convt_F18,out = checkpoint(pass1, x)
# x = self.sub_mean(x)
# out = self.relu(self.conv_input(x))
# conv1 = self.conv_input2(out)
# convt_F11 = self.convt_F11(conv1)
# convt_F12 = self.convt_F12(convt_F11)
# convt_F13 = self.convt_F13(convt_F12)
# convt_F14 = self.convt_F14(convt_F13)
# convt_F15 = self.convt_F15(convt_F14)
# convt_F16 = self.convt_F16(convt_F15)
# convt_F17 = self.convt_F17(convt_F16)
# convt_F18 = self.convt_F18(convt_F17)
# multi supervise
convt_F = [convt_F16,convt_F17,convt_F18]
u1 = self.u1(out)
u2 = self.u2(u1)
u3 = self.u3(u2)
u3 = self.convt_shape1(u3)
HR = []
HUR = []
for i in range(len(convt_F)):
# edge attention
res1 = self.ures1(convt_F[i])
res2 = self.ures2(res1)
res3 = self.ures3(res2)
G = self.conv_G(res3)
g1 = G[:,0:64,:,:]
g2 = G[:,64:128,:,:]
gu1 = self.sa(G)
ga = g1*gu1
# combine
gc = (ga+g2)/2
convt_R1 = self.convt_R1(gc)
tmp = u3 + convt_R1
tmp = self.add_mean(tmp)
HR.append(tmp)
HUR.append(convt_R1)
return HR,u2,HUR
class ScaleLayer(nn.Module):
def __init__(self, init_value=0.25):
super(ScaleLayer,self).__init__()
self.scale = nn.Parameter(torch.FloatTensor([init_value]))
def forward(self, input):
return input * self.scale
class L1_Charbonnier_loss(nn.Module):
"""L1 Charbonnierloss."""
def __init__(self):
super(L1_Charbonnier_loss, self).__init__()
self.eps = 1e-6
def forward(self, X, Y):
diff = torch.add(X, -Y)
error = torch.sqrt( diff * diff + self.eps )
loss = torch.sum(error)
return loss