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unet_1d.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon May 16 08:00:02 2022
@author: rfablet
"""
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue May 3 09:33:28 2022
@author: rfablet
"""
""" Parts of the U-Net model """
import torch
import torch.nn as nn
import torch.nn.functional as F
rateDropout = 0.2
padding_mode = 'zeros'
class Bilin_layer(nn.Module):
def __init__(self, in_channels, out_channels, padding_mode='reflect'):
super().__init__()
self.conv1 = nn.Conv1d(in_channels, out_channels, kernel_size=3, padding=1, bias=False,padding_mode=padding_mode)
self.conv2 = nn.Conv1d(in_channels, out_channels, kernel_size=3, padding=1, bias=False,padding_mode=padding_mode)
self.conv3 = nn.Conv1d(in_channels, out_channels, kernel_size=3, padding=1, bias=False,padding_mode=padding_mode)
def forward(self,x):
return self.conv1(x) + self.conv2(x) * self.conv3(x)
class DoubleConv_1D(nn.Module):
"""(convolution => [BN] => ReLU) * 2"""
def __init__(self, in_channels, out_channels, mid_channels=None,padding_mode='reflect',activation='relu'):
super().__init__()
if not mid_channels:
mid_channels = out_channels
if activation == 'relu':
self.double_conv = nn.Sequential(
nn.Conv1d(in_channels, mid_channels, kernel_size=3, padding=1, bias=False,padding_mode=padding_mode),
nn.BatchNorm1d(mid_channels),
nn.ReLU(inplace=True),
nn.Dropout(rateDropout),
nn.Conv1d(mid_channels, out_channels, kernel_size=3, padding=1, bias=False,padding_mode=padding_mode),
nn.BatchNorm1d(out_channels),
nn.ReLU(inplace=True)
)
elif activation == 'tanh' :
self.double_conv = nn.Sequential(
nn.Conv1d(in_channels, mid_channels, kernel_size=3, padding=1, bias=False,padding_mode=padding_mode),
nn.BatchNorm1d(mid_channels),
nn.Tanh(),
nn.Dropout(rateDropout),
nn.Conv1d(mid_channels, out_channels, kernel_size=3, padding=1, bias=False,padding_mode=padding_mode),
nn.BatchNorm1d(out_channels),
nn.Tanh() )
elif activation == 'relu-bilin' :
self.double_conv = nn.Sequential(
nn.Conv1d(in_channels, mid_channels, kernel_size=3, padding=1, bias=False,padding_mode=padding_mode),
nn.BatchNorm1d(mid_channels),
nn.ReLU(inplace=True),
nn.Dropout(rateDropout),
Bilin_layer(mid_channels, out_channels, padding_mode=padding_mode),
nn.BatchNorm1d(out_channels) )
def forward(self, x):
return self.double_conv(x)
class DoubleConvNoBN(nn.Module):
"""(convolution => [BN] => ReLU) * 2"""
def __init__(self, in_channels, out_channels, mid_channels=None,padding_mode='reflect'):
super().__init__()
if not mid_channels:
mid_channels = out_channels
self.double_conv = nn.Sequential(
nn.Conv2d(in_channels, mid_channels, kernel_size=3, padding=1, bias=False,padding_mode=padding_mode),
#nn.BatchNorm2d(mid_channels),
nn.ReLU(inplace=True),
nn.Dropout(rateDropout),
nn.Conv2d(mid_channels, out_channels, kernel_size=3, padding=1, bias=False,padding_mode=padding_mode),
#nn.BatchNorm2d(out_channels),
nn.ReLU(inplace=True)
)
def forward(self, x):
return self.double_conv(x)
class DoubleConvLeakyReLu(nn.Module):
"""(convolution => [BN] => ReLU) * 2"""
def __init__(self, in_channels, out_channels, mid_channels=None,padding_mode='reflect'):
super().__init__()
if not mid_channels:
mid_channels = out_channels
self.double_conv = nn.Sequential(
nn.Conv2d(in_channels, mid_channels, kernel_size=3, padding=1, bias=False,padding_mode=padding_mode),
nn.BatchNorm2d(mid_channels),
nn.LeakyReLU(inplace=True),
nn.Dropout(rateDropout),
nn.Conv2d(mid_channels, out_channels, kernel_size=3, padding=1, bias=False,padding_mode=padding_mode),
nn.BatchNorm2d(out_channels),
nn.LeakyReLU(inplace=True)
)
def forward(self, x):
return self.double_conv(x)
class DoubleConvBILIN(nn.Module):
"""(convolution => [BN] => ReLU) * 2"""
def __init__(self, in_channels, out_channels, mid_channels=None,padding_mode='reflect'):
super().__init__()
if not mid_channels:
mid_channels = out_channels
self.conv1 = nn.Conv2d(in_channels, mid_channels, kernel_size=3, padding=1, bias=False,padding_mode=padding_mode)
self.conv21 = nn.Conv2d(mid_channels, mid_channels, kernel_size=3, padding=1, bias=False,padding_mode=padding_mode)
self.conv22 = nn.Conv2d(mid_channels, mid_channels, kernel_size=3, padding=1, bias=False,padding_mode=padding_mode)
self.conv23 = nn.Conv2d(mid_channels, mid_channels, kernel_size=3, padding=1, bias=False,padding_mode=padding_mode)
self.conv24 = nn.Conv2d(2*mid_channels, out_channels, kernel_size=3, padding=1, bias=False,padding_mode=padding_mode)
self.bn1 = nn.BatchNorm2d(mid_channels)
self.bn2 = nn.BatchNorm2d(out_channels)
def forward(self, x):
x1 = self.conv1(x)
x1 = F.relu( self.bn1(x1) )
x11 = self.conv21(x1)
x12 = self.conv22(x1)
x13 = self.conv23(x1)
x1 = self.conv24( torch.cat((x11,x12*x13),dim=1) )
x1 = self.bn2(x1)
return x1
class Down_1D(nn.Module):
"""Downscaling with maxpool then double conv"""
def __init__(self, in_channels, out_channels,padding_mode='reflect',activation='relu'):
super().__init__()
self.maxpool_conv = nn.Sequential(
nn.MaxPool1d(2),
#nn.AvgPool2d(2),
DoubleConv_1D(in_channels, out_channels,padding_mode=padding_mode,activation=activation)
)
def forward(self, x):
return self.maxpool_conv(x)
class Up_1D(nn.Module):
"""Upscaling then double conv"""
def __init__(self, in_channels, out_channels, bilinear=True,padding_mode='reflect',activation='relu'):
super().__init__()
# if bilinear, use the normal convolutions to reduce the number of channels
#self.up = nn.ConvTranspose1d(in_channels, in_channels // 2, kernel_size=2, stride=2)#,padding_mode=padding_mode)
self.up = nn.ConvTranspose1d(in_channels, out_channels, kernel_size=2, stride=2)#,padding_mode=padding_mode)
#self.conv = DoubleConv_1D(in_channels, out_channels,padding_mode=padding_mode,activation=activation)
self.conv = DoubleConv_1D(2*out_channels, out_channels,padding_mode=padding_mode,activation=activation)
def forward(self, x1, x2):
x1 = self.up(x1)
# input is CHW
diffY = x2.size()[2] - x1.size()[2]
#diffX = x2.size()[3] - x1.size()[3]
x1 = F.pad(x1, [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 Up_1D_2(nn.Module):
"""Upscaling then double conv"""
def __init__(self, in_channels, out_channels, bilinear=True,padding_mode='reflect',activation='relu'):
super().__init__()
# if bilinear, use the normal convolutions to reduce the number of channels
self.up = nn.ConvTranspose1d(in_channels, out_channels, kernel_size=2, stride=2)#,padding_mode=padding_mode)
self.conv = DoubleConv_1D(2*out_channels, out_channels,padding_mode=padding_mode,activation=activation)
def forward(self, x1, x2):
x1 = self.up(x1)
# input is CHW
diffY = x2.size()[2] - x1.size()[2]
#diffX = x2.size()[3] - x1.size()[3]
x1 = F.pad(x1, [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 Up2(nn.Module):
"""Upscaling then double conv"""
def __init__(self, in_channels1, in_channels2, out_channels, bilinear=True,activation='relu'):
super().__init__()
# if bilinear, use the normal convolutions to reduce the number of channels
if bilinear:
self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
self.conv = DoubleConv(in_channels1 + in_channels2, out_channels,activation=activation)
else:
self.up = nn.ConvTranspose2d(in_channels1, in_channels2, kernel_size=2, stride=2)
self.conv = DoubleConv(2*in_channels2, out_channels,activation=activation)
#self.conv = nn.Conv2d(2*in_channels2, out_channels, kernel_size=1, padding=0, bias=False)
def forward(self, x1, x2):
x1 = self.up(x1)
# input is CHW
diffY = x2.size()[2] - x1.size()[2]
diffX = 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 Up2Lin(nn.Module):
"""Upscaling then double conv"""
def __init__(self, in_channels1, in_channels2, out_channels, bilinear=True,activation='relu'):
super().__init__()
# if bilinear, use the normal convolutions to reduce the number of channels
if bilinear:
self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
self.conv = DoubleConv(in_channels1 + in_channels2, out_channels,activation='relu')
else:
self.up = nn.ConvTranspose2d(in_channels1, in_channels2, kernel_size=2, stride=2)
#self.conv = DoubleConv(2*in_channels2, out_channels)
self.conv = nn.Conv2d(2*in_channels2, out_channels, kernel_size=1, padding=0, bias=False)
def forward(self, x1, x2):
x1 = self.up(x1)
# input is CHW
diffY = x2.size()[2] - x1.size()[2]
diffX = 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 Up4(nn.Module):
"""Upscaling then double conv"""
def __init__(self, in_channels, out_channels, bilinear=True):
super().__init__()
# if bilinear, use the normal convolutions to reduce the number of channels
if bilinear:
self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
self.conv = DoubleConv(in_channels, out_channels, in_channels // 2)
else:
self.up = nn.ConvTranspose2d(in_channels, in_channels // 4, kernel_size=4, stride=4)
self.conv = DoubleConv(in_channels, out_channels)
def forward(self, x1, x2):
x1 = self.up(x1)
# input is CHW
diffY = x2.size()[2] - x1.size()[2]
diffX = x2.size()[3] - x1.size()[3]
x1 = F.pad(x1, [diffX // 4, diffX - diffX // 4,
diffY // 4, diffY - diffY // 4])
# 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):
super(OutConv, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1)
def forward(self, x):
return self.conv(x)
class OutConv_1D(nn.Module):
def __init__(self, in_channels, out_channels,padding_mode='reflect'):
super(OutConv_1D, self).__init__()
self.conv = nn.Conv1d(in_channels, out_channels, kernel_size=1,padding_mode=padding_mode)
def forward(self, x):
return self.conv(x)
class UNet(nn.Module):
def __init__(self, n_channels, n_classes, bilinear=False):
super(UNet, self).__init__()
self.n_channels = n_channels
self.n_classes = n_classes
self.bilinear = bilinear
self.inc = DoubleConv(n_channels, 64)
self.down1 = Down(64, 128)
self.down2 = Down(128, 256)
self.down3 = Down(256, 512)
factor = 2 if bilinear else 1
#self.down4 = Down(512, 1024 // factor)
#self.up1 = Up(1024, 512 // factor, bilinear)
self.up2 = Up(512, 256 // factor, bilinear)
self.up3 = Up(256, 128 // factor, bilinear)
self.up4 = Up(128, 64, bilinear)
self.outc = OutConv(64, n_classes)
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.up1(x5, x4)
#x5 = self.down4(x4)
#x = self.up1(x5, x4)
x = self.up2(x4, x3)
x = self.up3(x, x2)
x = self.up4(x, x1)
out = self.outc(x)
return out
class UNet_1D(nn.Module):
def __init__(self, n_channels, n_classes, bilinear=False,padding_mode='zeros',activation='relu'):
super(UNet_1D, self).__init__()
self.n_channels = n_channels
self.n_classes = n_classes
self.bilinear = bilinear
self.inc = DoubleConv_1D(n_channels, 32,padding_mode=padding_mode,activation=activation)
self.down1 = Down_1D(32, 64,padding_mode=padding_mode,activation=activation)
self.down2 = Down_1D(64, 128,padding_mode=padding_mode,activation=activation)
#self.down3 = Down(256, 512)
factor = 2 if bilinear else 1
#self.down4 = Down(512, 1024 // factor)
#self.up1 = Up(1024, 512 // factor, bilinear)
#self.up2 = Up(512, 256 // factor, bilinear)
self.up3 = Up_1D(128, 64 // factor, bilinear,padding_mode=padding_mode,activation=activation)
self.up4 = Up_1D(64, 32, bilinear,padding_mode=padding_mode,activation=activation)
self.outc = OutConv_1D(32, n_classes,padding_mode=padding_mode)
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.up1(x5, x4)
#x5 = self.down4(x4)
#x = self.up1(x5, x4)
#print(x3.shape)
#print(x4.shape)
#x = self.up2(x4, x3)
x = self.up3(x3, x2)
x = self.up4(x, x1)
out = self.outc(x)
return out
class UNet_1D_3scales(torch.nn.Module):
def __init__(self, n_channels, n_classes, bilinear=False,nfeat=32,padding_mode='zeros',activation='relu'):
super(UNet_1D_3scales, self).__init__()
self.n_channels = n_channels
self.n_classes = n_classes
self.bilinear = bilinear
self.nfeat = nfeat
self.inc = DoubleConv_1D(n_channels, self.nfeat,padding_mode=padding_mode,activation=activation)#,padding_mode=padding_mode,activation='relu')
self.down1 = Down_1D(self.nfeat, 2*self.nfeat,padding_mode=padding_mode,activation=activation)
self.down2 = Down_1D(2*self.nfeat, 4*self.nfeat,padding_mode=padding_mode,activation=activation)
#self.down3 = Down(256, 512)
factor = 2 if bilinear else 1
#self.down4 = Down(512, 1024 // factor)
#self.up1 = Up(1024, 512 // factor, bilinear)
#self.up2 = Up(512, 256 // factor, bilinear)
self.up3 = Up_1D(4*self.nfeat, 2*self.nfeat // factor, bilinear,padding_mode=padding_mode,activation=activation)
self.up4 = Up_1D(2*self.nfeat, self.nfeat, bilinear,padding_mode=padding_mode,activation=activation)
self.outc = OutConv_1D(self.nfeat, n_classes,padding_mode=padding_mode)
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.up1(x5, x4)
#x5 = self.down4(x4)
#x = self.up1(x5, x4)
#print(x3.shape)
#print(x4.shape)
#x = self.up2(x4, x3)
x = self.up3(x3, x2)
x = self.up4(x, x1)
out = self.outc(x)
return out
class UNet_1D_4scales(torch.nn.Module):
def __init__(self, n_channels, n_classes, bilinear=False,nfeat=32,padding_mode='zeros',activation='relu'):
super(UNet_1D_4scales, self).__init__()
self.n_channels = n_channels
self.n_classes = n_classes
self.bilinear = bilinear
self.nfeat = nfeat
self.inc = DoubleConv_1D(n_channels, self.nfeat,padding_mode=padding_mode,activation=activation)#,padding_mode=padding_mode,activation='relu')
self.down1 = Down_1D(self.nfeat, 2*self.nfeat,padding_mode=padding_mode,activation=activation)
self.down2 = Down_1D(2*self.nfeat, 4*self.nfeat,padding_mode=padding_mode,activation=activation)
self.down3 = Down_1D(4*self.nfeat, 8*self.nfeat,padding_mode=padding_mode,activation=activation)
factor = 2 if bilinear else 1
#self.down4 = Down(512, 1024 // factor)
#self.up1 = Up(1024, 512 // factor, bilinear)
self.up2 = Up_1D(8*self.nfeat, 4*self.nfeat // factor, bilinear,padding_mode=padding_mode,activation=activation)
self.up3 = Up_1D(4*self.nfeat, 2*self.nfeat // factor, bilinear,padding_mode=padding_mode,activation=activation)
self.up4 = Up_1D(2*self.nfeat, self.nfeat, bilinear,padding_mode=padding_mode,activation=activation)
self.outc = OutConv_1D(self.nfeat, n_classes,padding_mode=padding_mode)
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.up1(x5, x4)
#x5 = self.down4(x4)
#x = self.up1(x5, x4)
#print(x3.shape)
#print(x4.shape)
x = self.up2(x4, x3)
x = self.up3(x, x2)
x = self.up4(x, x1)
out = self.outc(x)
return out
class UNet_1D_5scales(torch.nn.Module):
def __init__(self, n_channels, n_classes, bilinear=False,nfeat=32,padding_mode='zeros',activation='relu'):
super(UNet_1D_5scales, self).__init__()
self.n_channels = n_channels
self.n_classes = n_classes
self.bilinear = bilinear
self.nfeat = nfeat
self.inc = DoubleConv_1D(n_channels, self.nfeat,padding_mode=padding_mode,activation=activation)#,padding_mode=padding_mode,activation='relu')
self.down1 = Down_1D(self.nfeat, 2*self.nfeat,padding_mode=padding_mode,activation=activation)
self.down2 = Down_1D(2*self.nfeat, 4*self.nfeat,padding_mode=padding_mode,activation=activation)
self.down3 = Down_1D(4*self.nfeat, 8*self.nfeat,padding_mode=padding_mode,activation=activation)
self.down4 = Down_1D(8*self.nfeat, 8*self.nfeat,padding_mode=padding_mode,activation=activation)
factor = 2 if bilinear else 1
#self.down4 = Down(512, 1024 // factor)
#self.up1 = Up(1024, 512 // factor, bilinear)
self.up1 = Up_1D(8*self.nfeat, 8*self.nfeat // factor, bilinear,padding_mode=padding_mode,activation=activation)
self.up2 = Up_1D(8*self.nfeat, 4*self.nfeat // factor, bilinear,padding_mode=padding_mode,activation=activation)
self.up3 = Up_1D(4*self.nfeat, 2*self.nfeat // factor, bilinear,padding_mode=padding_mode,activation=activation)
self.up4 = Up_1D(2*self.nfeat, self.nfeat, bilinear,padding_mode=padding_mode,activation=activation)
self.outc = OutConv_1D(self.nfeat, n_classes,padding_mode=padding_mode)
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.up1(x5, x4)
#x5 = self.down4(x4)
#x = self.up1(x5, x4)
#print(x3.shape)
#print(x4.shape)
x = self.up1(x4, x5)
x = self.up2(x, x3)
x = self.up3(x, x2)
x = self.up4(x, x1)
out = self.outc(x)
return out