UNet.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.InstanceNorm2d(out_ch),#
            nn.ReLU(inplace=True),
            nn.Conv2d(out_ch, out_ch, 3, padding=1),
            nn.BatchNorm2d(out_ch), #nn.InstanceNorm2d(out_ch),# 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 inconv_drop(nn.Module):
    def __init__(self, in_ch, out_ch):
        super(inconv_drop, self).__init__()
        self.conv = double_conv(in_ch, out_ch)

    def forward(self, x):
        x = F.dropout2d(self.conv(x), p=0.25, training=True)
        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 down_drop(nn.Module):
    def __init__(self, in_ch, out_ch):
        super(down_drop, self).__init__()
        self.mpconv = nn.Sequential(
            nn.MaxPool2d(2),
            double_conv(in_ch, out_ch)
        )

    def forward(self, x):
        x = F.dropout2d(self.mpconv(x), p=0.25, training=True)
        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))

        x = torch.cat([x2, x1], dim=1)
        x = self.conv(x)
        return x


class up_drop(nn.Module):
    def __init__(self, in_ch, out_ch, bilinear=True):
        super(up_drop, 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 = F.dropout2d(self.conv(x), p=0.25, training=True)
        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 UNet2D(nn.Module):
    def __init__(self, n_channels=3, n_classes=1, activation=False):
        super(UNet2D, 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.down1 = down(64, 128)
        # self.down2 = down(128, 256)
        # self.down3 = down(256, 256)
        # self.up1 = up(512, 128)
        # self.up2 = up(256, 64)
        # self.up3 = up(128, 64)
        self.outc = outconv(64, n_classes)
        self.activation = activation

    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)
        # 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)
        x = self.outc(x)
        if self.activation:
            x = torch.sigmoid(x)
        return x




from torch import nn
import torch
from torchvision import models
import torchvision
from torch.nn import functional as F


def conv3x3(in_, out):
    return nn.Conv2d(in_, out, 3, padding=1)


class ConvRelu(nn.Module):
    def __init__(self, in_: int, out: int):
        super(ConvRelu, self).__init__()
        self.conv = conv3x3(in_, out)
        self.activation = nn.ReLU(inplace=True)

    def forward(self, x):
        x = self.conv(x)
        x = self.activation(x)
        return x


class DecoderBlock(nn.Module):
    """
    Paramaters for Deconvolution were chosen to avoid artifacts, following
    link https://distill.pub/2016/deconv-checkerboard/
    """

    def __init__(self, in_channels, middle_channels, out_channels, is_deconv=True):
        super(DecoderBlock, self).__init__()
        self.in_channels = in_channels

        if is_deconv:
            self.block = nn.Sequential(
                ConvRelu(in_channels, middle_channels),
                nn.ConvTranspose2d(middle_channels, out_channels, kernel_size=4, stride=2,
                                   padding=1),
                nn.ReLU(inplace=True)
            )
        else:
            self.block = nn.Sequential(
                nn.Upsample(scale_factor=2, mode='bilinear'),
                ConvRelu(in_channels, middle_channels),
                ConvRelu(middle_channels, out_channels),
            )

    def forward(self, x):
        return self.block(x)


class UNet11(nn.Module):
    def __init__(self, num_classes=1, num_filters=32, pretrained=False):
        """
        :param num_classes:
        :param num_filters:
        :param pretrained:
            False - no pre-trained network used
            True - encoder pre-trained with VGG11
        """
        super().__init__()
        self.pool = nn.MaxPool2d(2, 2)

        self.num_classes = num_classes

        self.encoder = models.vgg11(pretrained=pretrained).features

        self.relu = nn.ReLU(inplace=True)
        self.conv1 = nn.Sequential(self.encoder[0],
                                   self.relu)

        self.conv2 = nn.Sequential(self.encoder[3],
                                   self.relu)

        self.conv3 = nn.Sequential(
            self.encoder[6],
            self.relu,
            self.encoder[8],
            self.relu,
        )
        self.conv4 = nn.Sequential(
            self.encoder[11],
            self.relu,
            self.encoder[13],
            self.relu,
        )

        self.conv5 = nn.Sequential(
            self.encoder[16],
            self.relu,
            self.encoder[18],
            self.relu,
        )

        self.center = DecoderBlock(256 + num_filters * 8, num_filters * 8 * 2, num_filters * 8, is_deconv=True)
        self.dec5 = DecoderBlock(512 + num_filters * 8, num_filters * 8 * 2, num_filters * 8, is_deconv=True)
        self.dec4 = DecoderBlock(512 + num_filters * 8, num_filters * 8 * 2, num_filters * 4, is_deconv=True)
        self.dec3 = DecoderBlock(256 + num_filters * 4, num_filters * 4 * 2, num_filters * 2, is_deconv=True)
        self.dec2 = DecoderBlock(128 + num_filters * 2, num_filters * 2 * 2, num_filters, is_deconv=True)
        self.dec1 = ConvRelu(64 + num_filters, num_filters)

        self.final = nn.Conv2d(num_filters, num_classes, kernel_size=1)

    def forward(self, x):
        conv1 = self.conv1(x)
        conv2 = self.conv2(self.pool(conv1))
        conv3 = self.conv3(self.pool(conv2))
        conv4 = self.conv4(self.pool(conv3))
        conv5 = self.conv5(self.pool(conv4))
        center = self.center(self.pool(conv5))

        dec5 = self.dec5(torch.cat([center, conv5], 1))
        dec4 = self.dec4(torch.cat([dec5, conv4], 1))
        dec3 = self.dec3(torch.cat([dec4, conv3], 1))
        dec2 = self.dec2(torch.cat([dec3, conv2], 1))
        dec1 = self.dec1(torch.cat([dec2, conv1], 1))

        if self.num_classes > 1:
            x_out = F.log_softmax(self.final(dec1), dim=1)
        else:
            x_out = self.final(dec1)

        return x_out


class UNet16(nn.Module):
    def __init__(self, num_classes=1, num_filters=32, pretrained=False):
        """
        :param num_classes:
        :param num_filters:
        :param pretrained:
            False - no pre-trained network used
            True - encoder pre-trained with VGG11
        """
        super().__init__()
        self.num_classes = num_classes

        self.pool = nn.MaxPool2d(2, 2)

        self.encoder = torchvision.models.vgg16(pretrained=pretrained).features

        self.relu = nn.ReLU(inplace=True)

        self.conv1 = nn.Sequential(self.encoder[0],
                                   self.relu,
                                   self.encoder[2],
                                   self.relu)

        self.conv2 = nn.Sequential(self.encoder[5],
                                   self.relu,
                                   self.encoder[7],
                                   self.relu)

        self.conv3 = nn.Sequential(self.encoder[10],
                                   self.relu,
                                   self.encoder[12],
                                   self.relu,
                                   self.encoder[14],
                                   self.relu)

        self.conv4 = nn.Sequential(self.encoder[17],
                                   self.relu,
                                   self.encoder[19],
                                   self.relu,
                                   self.encoder[21],
                                   self.relu)

        self.conv5 = nn.Sequential(self.encoder[24],
                                   self.relu,
                                   self.encoder[26],
                                   self.relu,
                                   self.encoder[28],
                                   self.relu)

        self.center = DecoderBlock(512, num_filters * 8 * 2, num_filters * 8)

        self.dec5 = DecoderBlock(512 + num_filters * 8, num_filters * 8 * 2, num_filters * 8)
        self.dec4 = DecoderBlock(512 + num_filters * 8, num_filters * 8 * 2, num_filters * 8)
        self.dec3 = DecoderBlock(256 + num_filters * 8, num_filters * 4 * 2, num_filters * 2)
        self.dec2 = DecoderBlock(128 + num_filters * 2, num_filters * 2 * 2, num_filters)
        self.dec1 = ConvRelu(64 + num_filters, num_filters)
        self.final = nn.Conv2d(num_filters, num_classes, kernel_size=1)

    def forward(self, x):
        conv1 = self.conv1(x)
        conv2 = self.conv2(self.pool(conv1))
        conv3 = self.conv3(self.pool(conv2))
        conv4 = self.conv4(self.pool(conv3))
        conv5 = self.conv5(self.pool(conv4))

        center = self.center(self.pool(conv5))

        dec5 = self.dec5(torch.cat([center, conv5], 1))

        dec4 = self.dec4(torch.cat([dec5, conv4], 1))
        dec3 = self.dec3(torch.cat([dec4, conv3], 1))
        dec2 = self.dec2(torch.cat([dec3, conv2], 1))
        dec1 = self.dec1(torch.cat([dec2, conv1], 1))

        if self.num_classes > 1:
            x_out = F.log_softmax(self.final(dec1), dim=1)
        else:
            x_out = self.final(dec1)

        return x_out


class DecoderBlockLinkNet(nn.Module):
    def __init__(self, in_channels, n_filters):
        super().__init__()

        self.relu = nn.ReLU(inplace=True)

        # B, C, H, W -> B, C/4, H, W
        self.conv1 = nn.Conv2d(in_channels, in_channels // 4, 1)
        self.norm1 = nn.BatchNorm2d(in_channels // 4)

        # B, C/4, H, W -> B, C/4, 2 * H, 2 * W
        self.deconv2 = nn.ConvTranspose2d(in_channels // 4, in_channels // 4, kernel_size=4,
                                          stride=2, padding=1, output_padding=0)
        self.norm2 = nn.BatchNorm2d(in_channels // 4)

        # B, C/4, H, W -> B, C, H, W
        self.conv3 = nn.Conv2d(in_channels // 4, n_filters, 1)
        self.norm3 = nn.BatchNorm2d(n_filters)

    def forward(self, x):
        x = self.conv1(x)
        x = self.norm1(x)
        x = self.relu(x)
        x = self.deconv2(x)
        x = self.norm2(x)
        x = self.relu(x)
        x = self.conv3(x)
        x = self.norm3(x)
        x = self.relu(x)
        return x


class LinkNet34(nn.Module):
    def __init__(self, num_classes=1, num_channels=3, pretrained=True):
        super().__init__()
        assert num_channels == 3
        self.num_classes = num_classes
        filters = [64, 128, 256, 512]
        resnet = models.resnet34(pretrained=pretrained)

        self.firstconv = resnet.conv1
        self.firstbn = resnet.bn1
        self.firstrelu = resnet.relu
        self.firstmaxpool = resnet.maxpool
        self.encoder1 = resnet.layer1
        self.encoder2 = resnet.layer2
        self.encoder3 = resnet.layer3
        self.encoder4 = resnet.layer4

        # Decoder
        self.decoder4 = DecoderBlockLinkNet(filters[3], filters[2])
        self.decoder3 = DecoderBlockLinkNet(filters[2], filters[1])
        self.decoder2 = DecoderBlockLinkNet(filters[1], filters[0])
        self.decoder1 = DecoderBlockLinkNet(filters[0], filters[0])

        # Final Classifier
        self.finaldeconv1 = nn.ConvTranspose2d(filters[0], 32, 3, stride=2)
        self.finalrelu1 = nn.ReLU(inplace=True)
        self.finalconv2 = nn.Conv2d(32, 32, 3)
        self.finalrelu2 = nn.ReLU(inplace=True)
        self.finalconv3 = nn.Conv2d(32, num_classes, 2, padding=1)

    # noinspection PyCallingNonCallable
    def forward(self, x):
        # Encoder
        x = self.firstconv(x)
        x = self.firstbn(x)
        x = self.firstrelu(x)
        x = self.firstmaxpool(x)
        e1 = self.encoder1(x)
        e2 = self.encoder2(e1)
        e3 = self.encoder3(e2)
        e4 = self.encoder4(e3)

        # Decoder with Skip Connections
        d4 = self.decoder4(e4) + e3
        d3 = self.decoder3(d4) + e2
        d2 = self.decoder2(d3) + e1
        d1 = self.decoder1(d2)

        # Final Classification
        f1 = self.finaldeconv1(d1)
        f2 = self.finalrelu1(f1)
        f3 = self.finalconv2(f2)
        f4 = self.finalrelu2(f3)
        f5 = self.finalconv3(f4)

        if self.num_classes > 1:
            x_out = F.log_softmax(f5, dim=1)
        else:
            x_out = f5
        return x_out


class Conv3BN(nn.Module):
    def __init__(self, in_: int, out: int, bn=False):
        super().__init__()
        self.conv = conv3x3(in_, out)
        self.bn = nn.BatchNorm2d(out) if bn else None
        self.activation = nn.ReLU(inplace=True)

    def forward(self, x):
        x = self.conv(x)
        if self.bn is not None:
            x = self.bn(x)
        x = self.activation(x)
        return x


class UNetModule(nn.Module):
    def __init__(self, in_: int, out: int):
        super().__init__()
        self.l1 = Conv3BN(in_, out)
        self.l2 = Conv3BN(out, out)

    def forward(self, x):
        x = self.l1(x)
        x = self.l2(x)
        return x


class UNet(nn.Module):
    """
    Vanilla UNet.
    Implementation from https://github.com/lopuhin/mapillary-vistas-2017/blob/master/unet_models.py
    """
    output_downscaled = 1
    module = UNetModule

    def __init__(self,
                 input_channels: int = 3,
                 filters_base: int = 32,
                 down_filter_factors=(1, 2, 4, 8, 16),
                 up_filter_factors=(1, 2, 4, 8, 16),
                 bottom_s=4,
                 num_classes=1,
                 add_output=True):
        super().__init__()
        self.num_classes = num_classes
        assert len(down_filter_factors) == len(up_filter_factors)
        assert down_filter_factors[-1] == up_filter_factors[-1]
        down_filter_sizes = [filters_base * s for s in down_filter_factors]
        up_filter_sizes = [filters_base * s for s in up_filter_factors]
        self.down, self.up = nn.ModuleList(), nn.ModuleList()
        self.down.append(self.module(input_channels, down_filter_sizes[0]))
        for prev_i, nf in enumerate(down_filter_sizes[1:]):
            self.down.append(self.module(down_filter_sizes[prev_i], nf))
        for prev_i, nf in enumerate(up_filter_sizes[1:]):
            self.up.append(self.module(
                down_filter_sizes[prev_i] + nf, up_filter_sizes[prev_i]))
        pool = nn.MaxPool2d(2, 2)
        pool_bottom = nn.MaxPool2d(bottom_s, bottom_s)
        upsample = nn.Upsample(scale_factor=2)
        upsample_bottom = nn.Upsample(scale_factor=bottom_s)
        self.downsamplers = [None] + [pool] * (len(self.down) - 1)
        self.downsamplers[-1] = pool_bottom
        self.upsamplers = [upsample] * len(self.up)
        self.upsamplers[-1] = upsample_bottom
        self.add_output = add_output
        if add_output:
            self.conv_final = nn.Conv2d(up_filter_sizes[0], num_classes, 1)

    def forward(self, x):
        xs = []
        for downsample, down in zip(self.downsamplers, self.down):
            x_in = x if downsample is None else downsample(xs[-1])
            x_out = down(x_in)
            xs.append(x_out)

        x_out = xs[-1]
        for x_skip, upsample, up in reversed(
                list(zip(xs[:-1], self.upsamplers, self.up))):
            x_out = upsample(x_out)
            x_out = up(torch.cat([x_out, x_skip], 1))

        if self.add_output:
            x_out = self.conv_final(x_out)
            if self.num_classes > 1:
                x_out = F.log_softmax(x_out, dim=1)
        return x_out


class AlbuNet(nn.Module):
    """
        UNet (https://arxiv.org/abs/1505.04597) with Resnet34(https://arxiv.org/abs/1512.03385) encoder
        Proposed by Alexander Buslaev: https://www.linkedin.com/in/al-buslaev/
        """

    def __init__(self, num_classes=1, num_filters=32, pretrained=False, is_deconv=False):
        """
        :param num_classes:
        :param num_filters:
        :param pretrained:
            False - no pre-trained network is used
            True  - encoder is pre-trained with resnet34
        :is_deconv:
            False: bilinear interpolation is used in decoder
            True: deconvolution is used in decoder
        """
        super().__init__()
        self.num_classes = num_classes

        self.pool = nn.MaxPool2d(2, 2)

        self.encoder = torchvision.models.resnet34(pretrained=pretrained)

        self.relu = nn.ReLU(inplace=True)

        self.conv1 = nn.Sequential(self.encoder.conv1,
                                   self.encoder.bn1,
                                   self.encoder.relu,
                                   self.pool)

        self.conv2 = self.encoder.layer1

        self.conv3 = self.encoder.layer2

        self.conv4 = self.encoder.layer3

        self.conv5 = self.encoder.layer4

        self.center = DecoderBlock(512, num_filters * 8 * 2, num_filters * 8, is_deconv)

        self.dec5 = DecoderBlock(512 + num_filters * 8, num_filters * 8 * 2, num_filters * 8, is_deconv)
        self.dec4 = DecoderBlock(256 + num_filters * 8, num_filters * 8 * 2, num_filters * 8, is_deconv)
        self.dec3 = DecoderBlock(128 + num_filters * 8, num_filters * 4 * 2, num_filters * 2, is_deconv)
        self.dec2 = DecoderBlock(64 + num_filters * 2, num_filters * 2 * 2, num_filters * 2 * 2, is_deconv)
        self.dec1 = DecoderBlock(num_filters * 2 * 2, num_filters * 2 * 2, num_filters, is_deconv)
        self.dec0 = ConvRelu(num_filters, num_filters)
        self.final = nn.Conv2d(num_filters, num_classes, kernel_size=1)

    def forward(self, x):
        conv1 = self.conv1(x)
        conv2 = self.conv2(conv1)
        conv3 = self.conv3(conv2)
        conv4 = self.conv4(conv3)
        conv5 = self.conv5(conv4)

        center = self.center(self.pool(conv5))

        dec5 = self.dec5(torch.cat([center, conv5], 1))

        dec4 = self.dec4(torch.cat([dec5, conv4], 1))
        dec3 = self.dec3(torch.cat([dec4, conv3], 1))
        dec2 = self.dec2(torch.cat([dec3, conv2], 1))
        dec1 = self.dec1(dec2)
        dec0 = self.dec0(dec1)

        if self.num_classes > 1:
            x_out = F.log_softmax(self.final(dec0), dim=1)
        else:
            x_out = self.final(dec0)

        return x_out