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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 |