models.py

# encoding:utf-8
import os
import sys
import numpy as np
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, mid_channels=None):
        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),
            nn.BatchNorm2d(mid_channels),
            nn.ReLU(inplace=True),
            nn.Conv2d(mid_channels, out_channels, kernel_size=3, padding=1, bias=False),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True)
        )
    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):
        super().__init__()
        self.maxpool_conv = nn.Sequential(
            nn.MaxPool2d(2),
            DoubleConv(in_channels, out_channels)
        )

    def forward(self, x):
        return self.maxpool_conv(x)
class Up(nn.Module):
    """Upscaling then double conv"""

    def __init__(self, in_channels, out_channels):
        super().__init__()

        self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
        self.conv = DoubleConv(in_channels *2, out_channels, in_channels )
    def forward(self, x1, x2, alpha):
        x1 = self.up(x1)
        
        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])
        
        x2 = alpha * x2 + (1-alpha)* x1
        x1_ = x1.detach()
        x = torch.cat([x2, x1_], dim=1)
        x = self.conv(x)
        return x
class OutConv(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(OutConv, self).__init__()
        # self.conv = nn.Sequential(
        #     nn.Conv2d(in_channels, out_channels, kernel_size=1),
        #     nn.Sigmoid()
        #     )
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1)
    def forward(self, x):
        return self.conv(x)

class UNet_cut(nn.Module):
    def __init__(self, n_channels=1, n_classes=1):
        super(UNet_cut, self).__init__()
        self.n_channels = n_channels
        self.n_classes = n_classes
        self.inc = DoubleConv(n_channels, 64)
        self.down1 = Down(64, 128)
        self.down2 = Down(128, 256)
        self.down3 = Down(256, 512)
        self.down4 = Down(512, 512)

        self.up1 = Up(512, 256)
        self.up2 = Up(256, 128)
        self.up3 = Up(128, 64)
        self.up4 = Up(64, 64)
        self.outc = OutConv(64, self.n_classes)

    def forward(self, x, alpha):
        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, alpha)
        x = self.up2(x, x3, alpha)
        x = self.up3(x, x2, alpha)
        x = self.up4(x, x1, alpha)
        logits = self.outc(x)
        return x5, logits