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model.py
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model.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
class lReLU(nn.Module):
def __init__(self):
super(lReLU, self).__init__()
def forward(self, x):
return torch.max(x * 0.2, x)
class Double_Conv2d(nn.Module):
def __init__(self, in_channel, out_channel):
super(Double_Conv2d, self).__init__()
self.double_conv2d = torch.nn.Sequential(
nn.Conv2d(
in_channels=in_channel,
out_channels=out_channel,
kernel_size=3,
padding=1,
),
lReLU(),
nn.Conv2d(
in_channels=out_channel,
out_channels=out_channel,
kernel_size=3,
padding=1,
),
lReLU()
)
def forward(self, x):
return self.double_conv2d(x)
class UNetSony(nn.Module):
def __init__(self):
super(UNetSony, self).__init__()
self.conv1 = Double_Conv2d(64, 64)
self.conv2 = Double_Conv2d(64, 128)
self.conv3 = Double_Conv2d(128, 256)
self.conv4 = Double_Conv2d(256, 512)
self.conv5 = Double_Conv2d(512, 1024)
self.up6 = nn.ConvTranspose2d(1024, 512, kernel_size=2, stride=2)
self.conv6 = Double_Conv2d(1024, 512)
self.up7 = nn.ConvTranspose2d(512, 256, kernel_size=2, stride=2)
self.conv7 = Double_Conv2d(512, 256)
self.up8 = nn.ConvTranspose2d(256, 128, kernel_size=2, stride=2)
self.conv8 = Double_Conv2d(256, 128)
self.up9 = nn.ConvTranspose2d(128, 64, kernel_size=2, stride=2)
self.conv9 = Double_Conv2d(128, 64)
def forward(self, x):
conv1 = self.conv1(x)
pool1 = F.max_pool2d(conv1, kernel_size=2)
conv2 = self.conv2(pool1)
pool2 = F.max_pool2d(conv2, kernel_size=2)
conv3 = self.conv3(pool2)
pool3 = F.max_pool2d(conv3, kernel_size=2)
conv4 = self.conv4(pool3)
pool4 = F.max_pool2d(conv4, kernel_size=2)
conv5 = self.conv5(pool4)
up6 = self.up6(conv5)
up6 = torch.cat([up6, conv4], 1)
conv6 = self.conv6(up6)
up7 = self.up7(conv6)
up7 = torch.cat([up7, conv3], 1)
conv7 = self.conv7(up7)
up8 = self.up8(conv7)
up8 = torch.cat([up8, conv2], 1)
conv8 = self.conv8(up8)
up9 = self.up9(conv8)
up9 = torch.cat([up9, conv1], 1)
conv9 = self.conv9(up9)
return conv9
class Encoder(nn.Module):
def __init__(self, in_channel, out_channel):
super(Encoder, self).__init__()
self.encoder = torch.nn.Sequential(
nn.Conv2d(
in_channels=in_channel, out_channels=16, kernel_size=3, padding=1
),
lReLU(),
nn.Conv2d(in_channels=16, out_channels=32, kernel_size=3, padding=1),
lReLU(),
nn.Conv2d(
in_channels=32, out_channels=out_channel, kernel_size=3, padding=1
),
lReLU(),
)
def forward(self, x):
return self.encoder(x)
class Decoder(nn.Module):
def __init__(self, in_channel, out_channel):
super(Decoder, self).__init__()
self.decoder = torch.nn.Sequential(
nn.Conv2d(
in_channels=in_channel, out_channels=32, kernel_size=3, padding=1
),
lReLU(),
nn.Conv2d(in_channels=32, out_channels=16, kernel_size=3, padding=1),
lReLU(),
nn.Conv2d(in_channels=16, out_channels=out_channel, kernel_size=1),
lReLU(),
)
def forward(self, x):
dc = self.decoder(x)
out = F.pixel_shuffle(dc, 2)
return out
class Task_filter(nn.Module):
def __init__(self):
super(Task_filter, self).__init__()
self.en_s = Encoder(4, 64)
self.en_t = Encoder(4, 64)
self.unet = UNetSony()
self.dc = Decoder(64, 12)
def forward(self, x, source):
if source:
for param in self.en_s.parameters():
param.requires_grad = True
for param in self.en_t.parameters():
param.requires_grad = False
for param in self.unet.parameters():
param.requires_grad = True
for param in self.dc.parameters():
param.requires_grad = True
en = self.en_s(x)
unet = self.unet(en)
dc = self.dc(unet)
return dc
else:
for param in self.en_s.parameters():
param.requires_grad = False
for param in self.en_t.parameters():
param.requires_grad = True
for param in self.unet.parameters():
param.requires_grad = True
for param in self.dc.parameters():
param.requires_grad = True
en = self.en_t(x)
unet = self.unet(en)
dc = self.dc(unet)
return dc
def _initialize_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
m.weight.data.normal_(0.0, 0.02)
if m.bias is not None:
m.bias.data.normal_(0.0, 0.02)
if isinstance(m, nn.ConvTranspose2d):
m.weight.data.normal_(0.0, 0.02)
class DoubleConv(nn.Module):
# Conv--> LReLU **2 (without BN)
def __init__(self, in_ch, out_ch):
super().__init__()
self.f = nn.Sequential(
nn.Conv2d(in_ch, out_ch, 3, padding=1),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(out_ch, out_ch, 3, padding=1),
nn.LeakyReLU(0.2, inplace=True),
)
def forward(self, x):
x = self.f(x)
return x
class Down(nn.Module):
def __init__(self, in_ch, out_ch):
super().__init__()
self.f = nn.Sequential(
nn.MaxPool2d(2),
DoubleConv(in_ch, out_ch),
)
def forward(self, x):
x = self.f(x)
return x
class Up(nn.Module):
# upsample and concat
def __init__(self, in_ch, out_ch):
super().__init__()
self.upsample = nn.ConvTranspose2d(in_ch, in_ch//2, 2, stride=2)
self.conv = DoubleConv(in_ch, out_ch)
def forward(self, x1, x2):
x1 = self.upsample(x1)
x = torch.cat([x2, x1], dim=1)
x = self.conv(x)
return x
class Unet(nn.Module):
def __init__(self):
super().__init__()
self.inc = DoubleConv(3, 32)
self.d1 = Down(32, 64)
self.d2 = Down(64, 128)
self.d3 = Down(128, 256)
self.d4 = Down(256, 512)
self.u1 = Up(512, 256)
self.u2 = Up(256, 128)
self.u3 = Up(128, 64)
self.u4 = Up(64, 32)
self.outc = OutConv(32, 3)
def forward(self, x):
x1 = self.inc(x)
x2 = self.d1(x1)
x3 = self.d2(x2)
x4 = self.d3(x3)
x5 = self.d4(x4)
x = self.u1(x5, x4)
x = self.u2(x, x3)
x = self.u3(x, x2)
x = self.u4(x, x1)
x = self.outc(x)
x = torch.sigmoid(x)
return x