# From https://raw.githubusercontent.com/milesial/Pytorch-UNet/master/unet/unet_model.py

import torch

import torch.nn as nn

import torch.nn.functional as F

class double_conv(nn.Module):

    '''(conv => BN => ReLU) * 2'''

    def __init__(self, in_ch, out_ch):

        super(double_conv, self).__init__()

        self.conv = nn.Sequential(

            nn.Conv2d(in_ch, out_ch, 3, padding=1),

            nn.BatchNorm2d(out_ch),

            nn.ReLU(inplace=True),

            nn.Conv2d(out_ch, out_ch, 3, padding=1),

            nn.BatchNorm2d(out_ch),

            nn.ReLU(inplace=True)

        )

    def forward(self, x):

        x = self.conv(x)

        return x

class inconv(nn.Module):

    def __init__(self, in_ch, out_ch):

        super(inconv, self).__init__()

        self.conv = double_conv(in_ch, out_ch)

    def forward(self, x):

        x = self.conv(x)

        return x

class down(nn.Module):

    def __init__(self, in_ch, out_ch):

        super(down, self).__init__()

        self.mpconv = nn.Sequential(

            nn.MaxPool2d(2),

            double_conv(in_ch, out_ch)

        )

    def forward(self, x):

        x = self.mpconv(x)

        return x

class up(nn.Module):

    def __init__(self, in_ch, out_ch, bilinear=True):

        super(up, self).__init__()

        #  would be a nice idea if the upsampling could be learned too,

        #  but my machine do not have enough memory to handle all those weights

        if bilinear:

            self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)

        else:

            self.up = nn.ConvTranspose2d(in_ch//2, in_ch//2, 2, stride=2)

        self.conv = double_conv(in_ch, out_ch)

    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))

        # for 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)

        x = self.conv(x)

        return x

class outconv(nn.Module):

    def __init__(self, in_ch, out_ch):

        super(outconv, self).__init__()

        self.conv = nn.Conv2d(in_ch, out_ch, 1)

    def forward(self, x):

        x = self.conv(x)

        return x

class UNet(nn.Module):

    def __init__(self, n_channels, n_classes, use_sigmoid=False, use_softmax=False):

        super(UNet, self).__init__()

        self.inc = inconv(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(1024, 256)

        self.up2 = up(512, 128)

        self.up3 = up(256, 64)

        self.up4 = up(128, 64)

        self.outc = outconv(64, n_classes)

        self.sigmoid = nn.Sequential(nn.Sigmoid() if use_sigmoid else nn.Dropout(p=0.),nn.LogSoftmax(dim=1) if use_softmax else nn.Dropout(p=0.))

    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)

        x = self.up2(x, x3)

        x = self.up3(x, x2)

        x = self.up4(x, x1)

        x = self.outc(x)

        return self.sigmoid(x)