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model_nokeh_base.py
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model_nokeh_base.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
import pyblur.pyblur
from my_pytorch_mssim import pytorch_msssim
class FE(nn.Module):
def __init__(self):
super(FE, self).__init__()
# Conv1
self.layer1 = nn.Conv2d(3, 32, kernel_size=3, padding=1)
self.layer2 = nn.Sequential(
nn.Conv2d(32, 32, kernel_size=3, padding=1),
nn.ReLU(),
nn.Conv2d(32, 32, kernel_size=3, padding=1)
)
self.layer3 = nn.Sequential(
nn.Conv2d(32, 32, kernel_size=3, padding=1),
nn.ReLU(),
nn.Conv2d(32, 32, kernel_size=3, padding=1)
)
# Conv2
self.layer5 = nn.Conv2d(32, 64, kernel_size=3, stride=2, padding=1)
self.layer6 = nn.Sequential(
nn.Conv2d(64, 64, kernel_size=3, padding=1),
nn.ReLU(),
nn.Conv2d(64, 64, kernel_size=3, padding=1)
)
self.layer7 = nn.Sequential(
nn.Conv2d(64, 64, kernel_size=3, padding=1),
nn.ReLU(),
nn.Conv2d(64, 64, kernel_size=3, padding=1)
)
# Conv3
self.layer9 = nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1)
self.layer10 = nn.Sequential(
nn.Conv2d(128, 128, kernel_size=3, padding=1),
nn.ReLU(),
nn.Conv2d(128, 128, kernel_size=3, padding=1)
)
self.layer11 = nn.Sequential(
nn.Conv2d(128, 128, kernel_size=3, padding=1),
nn.ReLU(),
nn.Conv2d(128, 128, kernel_size=3, padding=1)
)
def forward(self, x):
# Conv1
x = self.layer1(x)
x = self.layer2(x) + x
x = self.layer3(x) + x
# Conv2
x = self.layer5(x)
x = self.layer6(x) + x
x = self.layer7(x) + x
# Conv3
x = self.layer9(x)
x = self.layer10(x) + x
x = self.layer11(x) + x
return x
class Gen(nn.Module):
def __init__(self):
super(Gen, self).__init__()
# Deconv3
self.layer13 = nn.Sequential(
nn.Conv2d(128, 128, kernel_size=3, padding=1),
nn.ReLU(),
nn.Conv2d(128, 128, kernel_size=3, padding=1)
)
self.layer14 = nn.Sequential(
nn.Conv2d(128, 128, kernel_size=3, padding=1),
nn.ReLU(),
nn.Conv2d(128, 128, kernel_size=3, padding=1)
)
self.layer16 = nn.ConvTranspose2d(128, 64, kernel_size=4, stride=2, padding=1)
# Deconv2
self.layer17 = nn.Sequential(
nn.Conv2d(64, 64, kernel_size=3, padding=1),
nn.ReLU(),
nn.Conv2d(64, 64, kernel_size=3, padding=1)
)
self.layer18 = nn.Sequential(
nn.Conv2d(64, 64, kernel_size=3, padding=1),
nn.ReLU(),
nn.Conv2d(64, 64, kernel_size=3, padding=1)
)
self.layer20 = nn.ConvTranspose2d(64, 32, kernel_size=4, stride=2, padding=1)
# Deconv1
self.layer21 = nn.Sequential(
nn.Conv2d(32, 32, kernel_size=3, padding=1),
nn.ReLU(),
nn.Conv2d(32, 32, kernel_size=3, padding=1)
)
self.layer22 = nn.Sequential(
nn.Conv2d(32, 32, kernel_size=3, padding=1),
nn.ReLU(),
nn.Conv2d(32, 32, kernel_size=3, padding=1)
)
self.layer24 = nn.Conv2d(32, 3, kernel_size=3, padding=1)
def forward(self, x):
# Deconv3
x = self.layer13(x) + x
x = self.layer14(x) + x
x = self.layer16(x)
# Deconv2
x = self.layer17(x) + x
x = self.layer18(x) + x
x = self.layer20(x)
# Deconv1
x = self.layer21(x) + x
x = self.layer22(x) + x
x = self.layer24(x)
return x
class multi_bokeh(nn.Module):
def __init__(self):
super(multi_bokeh, self).__init__()
self.fe_lv1 = FE()
self.fe_lv2 = FE()
self.fe_lv3 = FE()
self.gen_lv1 = Gen()
self.gen_lv2 = Gen()
self.gen_lv3 = Gen()
self.rough_pre1 = pyblur.pyblur.DefocusBlur()
self.rough_pre2 = pyblur.pyblur.DefocusBlur()
self.rough_pre3 = pyblur.pyblur.DefocusBlur()
def forward(self, orig_lv3):
H = orig_lv3.size(2)
W = orig_lv3.size(3)
orig_lv2 = F.interpolate(orig_lv3, scale_factor=0.5, mode='bilinear')
orig_lv1 = F.interpolate(orig_lv2, scale_factor=0.5, mode='bilinear')
feature_lv2 = self.fe_lv1(orig_lv2)
feature_lv1 = self.fe_lv1(orig_lv1)+F.interpolate(feature_lv2,scale_factor=0.5, mode='bilinear')
# gen_lv1 = self.gen_lv1(feature_lv1)
# rough_pre1=self.rough_pre1(orig_lv1,3)
#
# out_lv1 = orig_lv1 + residual_lv1
# residual_lv1 = F.interpolate(residual_lv1, scale_factor=2, mode='bilinear')
feature_lv12 = F.interpolate(feature_lv1, scale_factor=2, mode='bilinear')
# feature_lv2 = self.fe_lv2(orig_lv2 + residual_lv1)
gen_lv12 = self.gen_lv2(feature_lv12 + feature_lv2)
gen_lv12 = self.gen_lv1(feature_lv12)
# out_lv2 = orig_lv2 + residual_lv2
feature_lv3 = self.fe_lv3(orig_lv3)
feature_lv23 = self.fe_lv2(orig_lv2) + F.interpolate(feature_lv3, scale_factor=0.5, mode='bilinear')
feature_lv3=F.interpolate(feature_lv23, scale_factor=2, mode='bilinear')
gen_lv1 = self.gen_lv1(feature_lv1)
gen_lv12 = self.gen_lv1(feature_lv12)
gen_lv23 = self.gen_lv1(feature_lv23)
gen_lv3 = self.gen_lv1(feature_lv3)
rough_pre1 = self.rough_pre1(orig_lv3,3)
rough_pre2 = self.rough_pre1(orig_lv2, 5)
rough_pre3 = self.rough_pre1(orig_lv1, 7)
ssim1 = pytorch_msssim.SSIM(gen_lv1,rough_pre1)
ssim12=pytorch_msssim.SSIM(gen_lv12,rough_pre2)
ssim23=pytorch_msssim.SSIM(gen_lv23,rough_pre2)
ssim3=pytorch_msssim.SSIM(gen_lv3,rough_pre3)
overall=((1-ssim1)/2+(1-ssim12)/2+(1-ssim23)/2+(1-ssim3)/2)*0.75
w1=ssim1/overall
w12=ssim12/overall
w23=ssim23/overall
w3=ssim3/overall
bokeh_image=w1*gen_lv1+w12*gen_lv12+w23+gen_lv23+w3*gen_lv3
return bokeh_image