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from torch import nn
from torch.nn import init
import torch
import torch.nn.functional as F
class conv_block(nn.Module):
def __init__(self, ch_in, ch_out):
super(conv_block, self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(ch_in, ch_out, kernel_size=3, stride=1, padding=1, bias=True),
nn.BatchNorm2d(ch_out),
nn.ReLU(inplace=True),
nn.Conv2d(ch_out, ch_out, kernel_size=3, stride=1, padding=1, bias=True),
nn.BatchNorm2d(ch_out),
nn.ReLU(inplace=True),
)
def forward(self, x):
x = self.conv(x)
return x
class up_conv(nn.Module):
def __init__(self, ch_in, ch_out):
super(up_conv, self).__init__()
self.up = nn.Sequential(
nn.Upsample(scale_factor=2),
nn.Conv2d(ch_in, ch_out, kernel_size=3, stride=1, padding=1, bias=True),
nn.BatchNorm2d(ch_out),
nn.ReLU(inplace=True),
)
def forward(self, x):
x = self.up(x)
return x
class U_Net(nn.Module):
def __init__(self, img_ch=3, output_ch=1, multi_stage=False):
super(U_Net, self).__init__()
self.Maxpool = nn.MaxPool2d(kernel_size=2, stride=2)
self.Conv1 = conv_block(ch_in=img_ch, ch_out=64)
self.Conv2 = conv_block(ch_in=64, ch_out=128)
self.Conv3 = conv_block(ch_in=128, ch_out=256)
self.Conv4 = conv_block(ch_in=256, ch_out=512)
self.Conv5 = conv_block(ch_in=512, ch_out=1024)
self.Up5 = up_conv(ch_in=1024, ch_out=512)
self.Up_conv5 = conv_block(ch_in=1024, ch_out=512)
self.Up4 = up_conv(ch_in=512, ch_out=256)
self.Up_conv4 = conv_block(ch_in=512, ch_out=256)
self.Up3 = up_conv(ch_in=256, ch_out=128)
self.Up_conv3 = conv_block(ch_in=256, ch_out=128)
self.Up2 = up_conv(ch_in=128, ch_out=64)
self.Up_conv2 = conv_block(ch_in=128, ch_out=64)
self.Conv_1x1 = nn.Conv2d(64, output_ch, kernel_size=1, stride=1, padding=0)
self.activation = nn.Sequential(nn.Sigmoid())
# init_weights(self)
self.apply(self._init_weights)
def _init_weights(self, m):
init_type = "normal"
gain = 0.02
classname = m.__class__.__name__
if hasattr(m, "weight") and (
classname.find("Conv") != -1 or classname.find("Linear") != -1
):
if init_type == "normal":
init.normal_(m.weight.data, 0.0, gain)
elif init_type == "xavier":
init.xavier_normal_(m.weight.data, gain=gain)
elif init_type == "kaiming":
init.kaiming_normal_(m.weight.data, a=0, mode="fan_in")
elif init_type == "orthogonal":
init.orthogonal_(m.weight.data, gain=gain)
else:
raise NotImplementedError(
"initialization method [%s] is not implemented" % init_type
)
if hasattr(m, "bias") and m.bias is not None:
init.constant_(m.bias.data, 0.0)
elif classname.find("BatchNorm2d") != -1:
init.normal_(m.weight.data, 1.0, gain)
init.constant_(m.bias.data, 0.0)
def forward(self, x):
# encoding path
x1 = self.Conv1(x)
x2 = self.Maxpool(x1)
x2 = self.Conv2(x2)
x3 = self.Maxpool(x2)
x3 = self.Conv3(x3)
x4 = self.Maxpool(x3)
x4 = self.Conv4(x4)
x5 = self.Maxpool(x4)
x5 = self.Conv5(x5)
# decoding + concat path
d5 = self.Up5(x5)
d5 = torch.cat((x4, d5), dim=1)
d5 = self.Up_conv5(d5)
d4 = self.Up4(d5)
d4 = torch.cat((x3, d4), dim=1)
d4 = self.Up_conv4(d4)
d3 = self.Up3(d4)
d3 = torch.cat((x2, d3), dim=1)
d3 = self.Up_conv3(d3)
d2 = self.Up2(d3)
d2 = torch.cat((x1, d2), dim=1)
d2 = self.Up_conv2(d2)
d1 = self.Conv_1x1(d2)
d1 = self.activation(d1)
return d1
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