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# https://github.com/milesial/Pytorch-UNet
""" Full assembly of the parts to form the complete network """
import torch
import torch.nn as nn
import torch.nn.functional as F
class DoubleConv(nn.Module):
"""(convolution => [BN] => ReLU) * 2"""
def __init__(self, in_channels, out_channels,kernel_size=7):
super().__init__()
padding = int((kernel_size - 1) / 2)
self.double_conv = nn.Sequential(
nn.Conv1d(in_channels, out_channels, kernel_size=kernel_size, padding=padding),
nn.BatchNorm1d(out_channels),
nn.Sigmoid(),
#nn.ReLU(inplace=True),
nn.Conv1d(out_channels, out_channels, kernel_size=kernel_size, padding=padding),
nn.BatchNorm1d(out_channels),
#nn.ReLU(inplace=True)
nn.Sigmoid()
)
def forward(self, x):
return self.double_conv(x)
class Down(nn.Module):
"""Downscaling with maxpool then double conv"""
def __init__(self, in_channels, out_channels, kernel_size):
super().__init__()
self.maxpool_conv = nn.Sequential(
nn.MaxPool1d(2),
DoubleConv(in_channels, out_channels,kernel_size)
)
def forward(self, x):
return self.maxpool_conv(x)
class Up(nn.Module):
"""Upscaling then double conv"""
def __init__(self, in_channels, out_channels, kernel_size, bilinear=True):
super().__init__()
# if bilinear, use the normal convolutions to reduce the number of channels
if bilinear:
# self.up = F.interpolate()
self.up = nn.Upsample(scale_factor=2, mode='linear', align_corners=False)
else:
self.up = nn.ConvTranspose1d(in_channels // 2, in_channels // 2, kernel_size=2, stride=2)
self.conv = DoubleConv(in_channels, out_channels, kernel_size)
def forward(self, x1, x2):
x = self.up(x1)
# input is CHW
#diffY = torch.tensor([x2.size()[2] - x1.size()[2]])
#diffX = torch.tensor([x2.size()[3] - x1.size()[3]])
#x1 = F.pad(x1, [diffX // 2, diffX - diffX // 2,
# diffY // 2, diffY - diffY // 2])
# if you have padding issues, see
# https://github.com/HaiyongJiang/U-Net-Pytorch-Unstructured-Buggy/commit/0e854509c2cea854e247a9c615f175f76fbb2e3a
# https://github.com/xiaopeng-liao/Pytorch-UNet/commit/8ebac70e633bac59fc22bb5195e513d5832fb3bd
#x = torch.cat([x2, x1], dim=1)
return self.conv(x)
class OutConv(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size):
super(OutConv, self).__init__()
padding = int((kernel_size - 1) / 2)
self.conv = nn.Conv1d(in_channels, out_channels, kernel_size=kernel_size, padding=padding, bias=True)
def forward(self, x):
return self.conv(x)
class UNet0(nn.Module):
def __init__(self, n_channels, n_classes, bilinear=True):
super(UNet0, self).__init__()
self.n_channels = n_channels
self.n_classes = n_classes
self.bilinear = bilinear
self.inc = DoubleConv(n_channels, 64, kernel_size=7)
self.down1 = Down(64, 128, kernel_size=7)
self.down2 = Down(128, 256,kernel_size=5)
#self.down3 = Down(256, 512,kernel_size=3)
#self.up1 = Up(512, 256, kernel_size=3)
self.up2 = Up(256, 128, kernel_size=3)
self.up3 = Up(128, 64, kernel_size=3)
self.outc = OutConv(64, n_classes,kernel_size=1)
def forward(self, x):
x1 = self.inc(x)
x2 = self.down1(x1)
x3 = self.down2(x2)
#x4 = self.down3(x3)
#x = self.up1(x4, x3)
x = self.up2(x3, x2)
x = self.up3(x, x1)
logits = self.outc(x)
return logits
class UNet1(nn.Module):
def __init__(self, n_channels, n_classes, bilinear=True):
super(UNet1, self).__init__()
self.n_channels = n_channels
self.n_classes = n_classes
self.bilinear = bilinear
self.inc = DoubleConv(n_channels, 64, kernel_size=7)
self.down1 = Down(64, 128, kernel_size=7)
self.down2 = Down(128, 256,kernel_size=5)
self.down3 = Down(256, 512,kernel_size=3)
self.up1 = Up(512, 256, kernel_size=3)
self.up2 = Up(256, 128, kernel_size=3)
self.up3 = Up(128, 64, kernel_size=3)
self.outc = OutConv(64, n_classes,kernel_size=1)
def forward(self, x):
x1 = self.inc(x)
x2 = self.down1(x1)
x3 = self.down2(x2)
x4 = self.down3(x3)
x = self.up1(x4, x3)
x = self.up2(x, x2)
x = self.up3(x, x1)
logits = self.outc(x)
return logits
class UNet2(nn.Module):
def __init__(self, n_channels, n_classes, bilinear=True):
super(UNet2, self).__init__()
self.n_channels = n_channels
self.n_classes = n_classes
self.bilinear = bilinear
self.inc = DoubleConv(n_channels, 64, kernel_size=7)
self.down1 = Down(64, 128, kernel_size=7)
self.down2 = Down(128, 256,kernel_size=5)
self.down3 = Down(256, 512,kernel_size=3)
self.down4 = Down(512, 1024, kernel_size=3)
self.up = Up(1024, 512, kernel_size=3)
self.up1 = Up(512, 256, kernel_size=3)
self.up2 = Up(256, 128, kernel_size=3)
self.up3 = Up(128, 64, kernel_size=3)
self.outc = OutConv(64, n_classes,kernel_size=1)
def forward(self, x):
x1 = self.inc(x)
x2 = self.down1(x1)
x3 = self.down2(x2)
x4 = self.down3(x3)
x5 = self.down4(x4)
x = self.up(x5, x4)
x = self.up1(x, x3)
x = self.up2(x, x2)
x = self.up3(x, x1)
logits = self.outc(x)
return logits |