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model6_dfe3s.py
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# -*- coding:utf-8 -*-
import torch
import torch.nn as nn
import numpy as np
import math
from modules import CARB,upsample_block,Nonlocal_CA
def calc_mean_std(feat, eps=1e-5):
# eps is a small value added to the variance to avoid divide-by-zero.
size = feat.size()
assert (len(size) == 4)
N, C = size[:2]
feat_var = feat.view(N, C, -1).var(dim=2) + eps
feat_std = feat_var.sqrt().view(N, C, 1, 1)
feat_mean = feat.view(N, C, -1).mean(dim=2).view(N, C, 1, 1)
return feat_mean, feat_std
def din(content_feat,encode_feat,eps=None):
size = content_feat.size()
content_mean, content_std = calc_mean_std(content_feat)
encode_mean, encode_std = calc_mean_std(encode_feat)
if eps==None:
normalized_feat = (content_feat - content_mean.expand(
size)) / content_std.expand(size)
else:
normalized_feat = (content_feat - content_mean.expand(
size)) / (content_std.expand(size)+eps)
return normalized_feat * encode_std.expand(size) + encode_mean.expand(size)
class Down2(nn.Module):
def __init__(self,c_in,c_out):
super(Down2, self).__init__()
self.conv_input = nn.Conv2d(in_channels=c_in, out_channels=32, kernel_size=3, stride=1, padding=1, bias=False)
self.relu = nn.PReLU()
self.convt_R1 = nn.Conv2d(in_channels=32, out_channels=c_out, kernel_size=3, stride=1, padding=1, bias=False)
self.down = nn.Conv2d(in_channels=32, out_channels=32, kernel_size=3, stride=2, 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):
out = self.relu(self.conv_input(x))
out = self.down(out)
LR_2x = self.convt_R1(out)
return LR_2x
class Branch1(nn.Module):
def __init__(self):
super(Branch1, self).__init__()
self.conv_input = nn.Conv2d(in_channels=3, out_channels=3, kernel_size=3, stride=1, padding=1, bias=False)
self.conv_input2 = nn.Conv2d(in_channels=3, out_channels=3, kernel_size=3, stride=1, padding=1, bias=False)
self.relu = nn.PReLU()
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):
out = self.relu(self.conv_input(x))
conv1 = self.conv_input2(out)
return conv1
class Branch2(nn.Module):
def __init__(self):
super(Branch2, self).__init__()
self.conv_input = nn.Conv2d(in_channels=64, 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.convt_F11 = CARB(64)
self.convt_F12 = CARB(64)
self.convt_F13 = CARB(64)
# style encode
self.s_conv1 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1, bias=False)
self.s_conv2 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=2, padding=1, bias=False)
self.s_conv3 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=2, padding=1, bias=False)
#-------------
self.u1 = upsample_block(64,256)
self.convt_shape1 = nn.Conv2d(in_channels=64, out_channels=3, 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):
out = self.relu(self.conv_input(x))
convt_F11 = self.convt_F11(out)
s1 = self.s_conv1(out)
convt_F11 = din(convt_F11,s1)
convt_F12 = self.convt_F12(convt_F11)
s2 =self.s_conv2(s1)
convt_F12 = din(convt_F12,s2)
convt_F13 = self.convt_F13(convt_F12)
s3 =self.s_conv3(s2)
convt_F13 = din(convt_F13,s3)
combine = out + convt_F13
up = self.u1(combine)
clean = self.convt_shape1(up)
return clean
class Branch3(nn.Module):
def __init__(self):
super(Branch3, self).__init__()
self.conv_input = nn.Conv2d(in_channels=64, 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.convt_F11 = CARB(64)
self.convt_F12 = CARB(64)
self.convt_F13 = CARB(64)
self.convt_F14 = CARB(64)
self.s_conv1 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1, bias=False)
self.s_conv2 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=2, padding=1, bias=False)
self.s_conv3 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=2, padding=1, bias=False)
self.s_conv3 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=2, padding=1, bias=False)
self.s_conv4 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=2, padding=1, bias=False)
self.u1 = upsample_block(64,256)
self.u2 = upsample_block(64,256)
self.convt_shape1 = nn.Conv2d(in_channels=64, out_channels=3, kernel_size=3, stride=1, padding=1, bias=False)
self.non_local = Nonlocal_CA(in_feat=64, inter_feat=64//8, reduction=8,sub_sample=False, bn_layer=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):
out = self.relu(self.conv_input(x))
convt_F11 = self.convt_F11(out)
s1 = self.s_conv1(out)
convt_F11 = din(convt_F11,s1)
convt_F12 = self.convt_F12(convt_F11)
s2 =self.s_conv2(s1)
convt_F12 = din(convt_F12,s2)
convt_F13 = self.convt_F13(convt_F12)
s3 =self.s_conv3(s2)
convt_F13 = din(convt_F13,s3)
# convt_F14 = self.convt_F14(convt_F13)
# s4 =self.s_conv4(s3)
# convt_F14 = din(convt_F14,s4)
# convt_F14 = self.non_local(convt_F14)
combine = out + convt_F13
up = self.u1(combine)
up = self.u2(up)
clean = self.convt_shape1(up)
return clean
class Branch4(nn.Module):
def __init__(self):
super(Branch4, self).__init__()
self.conv_input = nn.Conv2d(in_channels=64, 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.convt_F11 = CARB(64)
self.convt_F12 = CARB(64)
self.convt_F13 = CARB(64)
self.convt_F14 = CARB(64)
self.convt_F15 = CARB(64)
self.convt_F16 = CARB(64)
self.s_conv1 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1, bias=False)
self.s_conv2 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=2, padding=1, bias=False)
self.s_conv3 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=2, padding=1, bias=False)
self.s_conv4 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=2, padding=1, bias=False)
self.s_conv5 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=2, padding=1, bias=False)
self.s_conv6 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=2, padding=1, bias=False)
self.u1 = upsample_block(64,256)
self.u2 = upsample_block(64,256)
self.u3 = upsample_block(64,256)
self.convt_shape1 = nn.Conv2d(in_channels=64, out_channels=3, kernel_size=3, stride=1, padding=1, bias=False)
self.non_local = Nonlocal_CA(in_feat=64, inter_feat=64//8, reduction=8,sub_sample=False, bn_layer=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):
out = self.relu(self.conv_input(x))
convt_F11 = self.convt_F11(out)
s1 = self.s_conv1(out)
convt_F11 = din(convt_F11,s1)
convt_F12 = self.convt_F12(convt_F11)
s2 =self.s_conv2(s1)
convt_F12 = din(convt_F12,s2)
convt_F13 = self.convt_F13(convt_F12)
s3 =self.s_conv3(s2)
convt_F13 = din(convt_F13,s3)
convt_F14 = self.convt_F14(convt_F13)
s4 =self.s_conv4(s3)
convt_F14 = din(convt_F14,s4)
convt_F15 = self.convt_F15(convt_F14)
s5 =self.s_conv5(s4)
convt_F15 = din(convt_F15,s5)
# convt_F16 = self.convt_F16(convt_F15)
# s6 =self.s_conv6(s5)
# convt_F16 = din(convt_F16,s6)
# convt_F16 = self.non_local(convt_F16)
combine = out + convt_F15
up = self.u1(combine)
up = self.u2(up)
up = self.u3(up)
clean = self.convt_shape1(up)
return clean
class Branch5(nn.Module):
def __init__(self):
super(Branch5, self).__init__()
self.conv_input = nn.Conv2d(in_channels=64, 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.convt_F11 = CARB(64)
self.convt_F12 = CARB(64)
self.convt_F13 = CARB(64)
self.convt_F14 = CARB(64)
self.convt_F15 = CARB(64)
self.convt_F16 = CARB(64)
self.convt_F17 = CARB(64)
self.convt_F18 = CARB(64)
self.s_conv1 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1, bias=False)
self.s_conv2 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=2, padding=1, bias=False)
self.s_conv3 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1, bias=False)
self.s_conv4 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=2, padding=1, bias=False)
self.s_conv5 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1, bias=False)
self.s_conv6 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=2, padding=1, bias=False)
self.s_conv7 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1, bias=False)
self.s_conv8 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=2, padding=1, bias=False)
self.u1 = upsample_block(64,256)
self.u2 = upsample_block(64,256)
self.u3 = upsample_block(64,256)
self.u4 = upsample_block(64,256)
self.convt_shape1 = nn.Conv2d(in_channels=64, out_channels=3, kernel_size=3, stride=1, padding=1, bias=False)
self.non_local = Nonlocal_CA(in_feat=64, inter_feat=64//8, reduction=8,sub_sample=False, bn_layer=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):
out = self.relu(self.conv_input(x))
convt_F11 = self.convt_F11(out)
s1 = self.s_conv1(out)
convt_F11 = din(convt_F11,s1)
convt_F12 = self.convt_F12(convt_F11)
s2 =self.s_conv2(s1)
convt_F12 = din(convt_F12,s2)
convt_F13 = self.convt_F13(convt_F12)
s3 =self.s_conv3(s2)
convt_F13 = din(convt_F13,s3)
convt_F14 = self.convt_F14(convt_F13)
s4 =self.s_conv4(s3)
convt_F14 = din(convt_F14,s4)
convt_F15 = self.convt_F15(convt_F14)
s5 =self.s_conv5(s4)
convt_F15 = din(convt_F15,s5)
convt_F16 = self.convt_F16(convt_F15)
s6 =self.s_conv6(s5)
convt_F16 = din(convt_F16,s6)
convt_F17 = self.convt_F17(convt_F16)
s7 =self.s_conv7(s6)
convt_F17 = din(convt_F17,s7)
convt_F18 = self.convt_F18(convt_F17)
s8 =self.s_conv8(s7)
convt_F18 = din(convt_F18,s8)
convt_F18 = self.non_local(convt_F18)
combine = out + convt_F18
up = self.u1(combine)
up = self.u2(up)
up = self.u3(up)
up = self.u4(up)
clean = self.convt_shape1(up)
return clean
class Branch6(nn.Module):
def __init__(self):
super(Branch6, self).__init__()
self.conv_input = nn.Conv2d(in_channels=64, 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.convt_F11 = CARB(64)
self.convt_F12 = CARB(64)
self.convt_F13 = CARB(64)
self.convt_F14 = CARB(64)
self.convt_F15 = CARB(64)
self.convt_F16 = CARB(64)
self.convt_F17 = CARB(64)
self.convt_F18 = CARB(64)
self.s_conv1 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1, bias=False)
self.s_conv2 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=2, padding=1, bias=False)
self.s_conv3 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1, bias=False)
self.s_conv4 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=2, padding=1, bias=False)
self.s_conv5 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1, bias=False)
self.s_conv6 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=2, padding=1, bias=False)
self.s_conv7 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1, bias=False)
self.s_conv8 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=2, padding=1, bias=False)
self.u1 = upsample_block(64,256)
self.u2 = upsample_block(64,256)
self.u3 = upsample_block(64,256)
self.u4 = upsample_block(64,256)
self.u5 = upsample_block(64,256)
self.convt_shape1 = nn.Conv2d(in_channels=64, out_channels=3, kernel_size=3, stride=1, padding=1, bias=False)
self.non_local = Nonlocal_CA(in_feat=64, inter_feat=64//8, reduction=8,sub_sample=False, bn_layer=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):
out = self.relu(self.conv_input(x))
convt_F11 = self.convt_F11(out)
s1 = self.s_conv1(out)
convt_F11 = din(convt_F11,s1)
convt_F12 = self.convt_F12(convt_F11)
s2 =self.s_conv2(s1)
convt_F12 = din(convt_F12,s2)
convt_F13 = self.convt_F13(convt_F12)
s3 =self.s_conv3(s2)
convt_F13 = din(convt_F13,s3)
convt_F14 = self.convt_F14(convt_F13)
s4 =self.s_conv4(s3)
convt_F14 = din(convt_F14,s4)
convt_F15 = self.convt_F15(convt_F14)
s5 =self.s_conv5(s4)
convt_F15 = din(convt_F15,s5)
convt_F16 = self.convt_F16(convt_F15)
s6 =self.s_conv6(s5)
convt_F16 = din(convt_F16,s6)
convt_F17 = self.convt_F17(convt_F16)
s7 =self.s_conv7(s6)
convt_F17 = din(convt_F17,s7)
convt_F18 = self.convt_F18(convt_F17)
s8 =self.s_conv8(s7)
convt_F18 = din(convt_F18,s8)
convt_F18 = self.non_local(convt_F18)
combine = out + convt_F18
up = self.u1(combine)
up = self.u2(up)
up = self.u3(up)
up = self.u4(up)
up = self.u5(up)
clean = self.convt_shape1(up)
return clean
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.down2_1 = Down2(3,64)
self.down2_2 = Down2(64,64)
self.down2_3 = Down2(64,64)
self.down2_4 = Down2(64,64)
self.down2_5 = Down2(64,64)
# Branches
self.branch1 = Branch1()
self.branch2 = Branch2()
self.branch3 = Branch3()
self.branch4 = Branch4()
self.branch5 = Branch5()
self.branch6 = Branch6()
# scale
self.scale1 = ScaleLayer()
self.scale2 = ScaleLayer()
self.scale3 = ScaleLayer()
self.scale4 = ScaleLayer()
self.scale5 = ScaleLayer()
self.scale6 = ScaleLayer()
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):
# out = self.relu(self.conv_input(x))
# print('1')
b1 = self.branch1(x)
b1 = self.scale1(b1)
#---------
# print('2')
feat_down2 = self.down2_1(x)
b2 = self.branch2(feat_down2)
b2 = self.scale2(b2)
#---------
# print('3')
feat_down3 = self.down2_2(feat_down2)
b3 = self.branch3(feat_down3)
b3 = self.scale3(b3)
#---------\
# print('4')
feat_down4 = self.down2_3(feat_down3)
b4 = self.branch4(feat_down4)
b4 = self.scale4(b4)
#---------
# print('5')
feat_down5 = self.down2_4(feat_down4)
b5 = self.branch5(feat_down5)
b5 = self.scale5(b5)
#---------
feat_down6 = self.down2_5(feat_down5)
b6 = self.branch6(feat_down6)
b6 = self.scale6(b6)
# clean = x + b1 + b2 + b3 + b4
clean = b1 + b2 + b3 + b4 + b5 + b6
# clean = self.convt_shape1(combine)
return clean
class ScaleLayer(nn.Module):
def __init__(self, init_value=1.0):
super(ScaleLayer,self).__init__()
self.scale = nn.Parameter(torch.FloatTensor([init_value]))
def forward(self, x):
# print(self.scale)
return x * self.scale