carvekit/ml/arch/basnet/basnet.py

"""
Source url: https://github.com/NathanUA/BASNet
Modified by Nikita Selin (OPHoperHPO)[https://github.com/OPHoperHPO].
License: MIT License
"""
import torch
import torch.nn as nn
from torchvision import models


def conv3x3(in_planes, out_planes, stride=1):
    """3x3 convolution with padding"""
    return nn.Conv2d(
        in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False
    )


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(BasicBlock, self).__init__()
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = nn.BatchNorm2d(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = nn.BatchNorm2d(planes)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out


class BasicBlockDe(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(BasicBlockDe, self).__init__()

        self.convRes = conv3x3(inplanes, planes, stride)
        self.bnRes = nn.BatchNorm2d(planes)
        self.reluRes = nn.ReLU(inplace=True)

        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = nn.BatchNorm2d(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = nn.BatchNorm2d(planes)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = self.convRes(x)
        residual = self.bnRes(residual)
        residual = self.reluRes(residual)

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(
            planes, planes, kernel_size=3, stride=stride, padding=1, bias=False
        )
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes * 4)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out


class RefUnet(nn.Module):
    def __init__(self, in_ch, inc_ch):
        super(RefUnet, self).__init__()

        self.conv0 = nn.Conv2d(in_ch, inc_ch, 3, padding=1)

        self.conv1 = nn.Conv2d(inc_ch, 64, 3, padding=1)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu1 = nn.ReLU(inplace=True)

        self.pool1 = nn.MaxPool2d(2, 2, ceil_mode=True)

        self.conv2 = nn.Conv2d(64, 64, 3, padding=1)
        self.bn2 = nn.BatchNorm2d(64)
        self.relu2 = nn.ReLU(inplace=True)

        self.pool2 = nn.MaxPool2d(2, 2, ceil_mode=True)

        self.conv3 = nn.Conv2d(64, 64, 3, padding=1)
        self.bn3 = nn.BatchNorm2d(64)
        self.relu3 = nn.ReLU(inplace=True)

        self.pool3 = nn.MaxPool2d(2, 2, ceil_mode=True)

        self.conv4 = nn.Conv2d(64, 64, 3, padding=1)
        self.bn4 = nn.BatchNorm2d(64)
        self.relu4 = nn.ReLU(inplace=True)

        self.pool4 = nn.MaxPool2d(2, 2, ceil_mode=True)

        self.conv5 = nn.Conv2d(64, 64, 3, padding=1)
        self.bn5 = nn.BatchNorm2d(64)
        self.relu5 = nn.ReLU(inplace=True)

        self.conv_d4 = nn.Conv2d(128, 64, 3, padding=1)
        self.bn_d4 = nn.BatchNorm2d(64)
        self.relu_d4 = nn.ReLU(inplace=True)

        self.conv_d3 = nn.Conv2d(128, 64, 3, padding=1)
        self.bn_d3 = nn.BatchNorm2d(64)
        self.relu_d3 = nn.ReLU(inplace=True)

        self.conv_d2 = nn.Conv2d(128, 64, 3, padding=1)
        self.bn_d2 = nn.BatchNorm2d(64)
        self.relu_d2 = nn.ReLU(inplace=True)

        self.conv_d1 = nn.Conv2d(128, 64, 3, padding=1)
        self.bn_d1 = nn.BatchNorm2d(64)
        self.relu_d1 = nn.ReLU(inplace=True)

        self.conv_d0 = nn.Conv2d(64, 1, 3, padding=1)

        self.upscore2 = nn.Upsample(
            scale_factor=2, mode="bilinear", align_corners=False
        )

    def forward(self, x):
        hx = x
        hx = self.conv0(hx)

        hx1 = self.relu1(self.bn1(self.conv1(hx)))
        hx = self.pool1(hx1)

        hx2 = self.relu2(self.bn2(self.conv2(hx)))
        hx = self.pool2(hx2)

        hx3 = self.relu3(self.bn3(self.conv3(hx)))
        hx = self.pool3(hx3)

        hx4 = self.relu4(self.bn4(self.conv4(hx)))
        hx = self.pool4(hx4)

        hx5 = self.relu5(self.bn5(self.conv5(hx)))

        hx = self.upscore2(hx5)

        d4 = self.relu_d4(self.bn_d4(self.conv_d4(torch.cat((hx, hx4), 1))))
        hx = self.upscore2(d4)

        d3 = self.relu_d3(self.bn_d3(self.conv_d3(torch.cat((hx, hx3), 1))))
        hx = self.upscore2(d3)

        d2 = self.relu_d2(self.bn_d2(self.conv_d2(torch.cat((hx, hx2), 1))))
        hx = self.upscore2(d2)

        d1 = self.relu_d1(self.bn_d1(self.conv_d1(torch.cat((hx, hx1), 1))))

        residual = self.conv_d0(d1)

        return x + residual


class BASNet(nn.Module):
    def __init__(self, n_channels, n_classes):
        super(BASNet, self).__init__()

        resnet = models.resnet34(pretrained=False)

        # -------------Encoder--------------

        self.inconv = nn.Conv2d(n_channels, 64, 3, padding=1)
        self.inbn = nn.BatchNorm2d(64)
        self.inrelu = nn.ReLU(inplace=True)

        # stage 1
        self.encoder1 = resnet.layer1  # 224
        # stage 2
        self.encoder2 = resnet.layer2  # 112
        # stage 3
        self.encoder3 = resnet.layer3  # 56
        # stage 4
        self.encoder4 = resnet.layer4  # 28

        self.pool4 = nn.MaxPool2d(2, 2, ceil_mode=True)

        # stage 5
        self.resb5_1 = BasicBlock(512, 512)
        self.resb5_2 = BasicBlock(512, 512)
        self.resb5_3 = BasicBlock(512, 512)  # 14

        self.pool5 = nn.MaxPool2d(2, 2, ceil_mode=True)

        # stage 6
        self.resb6_1 = BasicBlock(512, 512)
        self.resb6_2 = BasicBlock(512, 512)
        self.resb6_3 = BasicBlock(512, 512)  # 7

        # -------------Bridge--------------

        # stage Bridge
        self.convbg_1 = nn.Conv2d(512, 512, 3, dilation=2, padding=2)  # 7
        self.bnbg_1 = nn.BatchNorm2d(512)
        self.relubg_1 = nn.ReLU(inplace=True)
        self.convbg_m = nn.Conv2d(512, 512, 3, dilation=2, padding=2)
        self.bnbg_m = nn.BatchNorm2d(512)
        self.relubg_m = nn.ReLU(inplace=True)
        self.convbg_2 = nn.Conv2d(512, 512, 3, dilation=2, padding=2)
        self.bnbg_2 = nn.BatchNorm2d(512)
        self.relubg_2 = nn.ReLU(inplace=True)

        # -------------Decoder--------------

        # stage 6d
        self.conv6d_1 = nn.Conv2d(1024, 512, 3, padding=1)  # 16
        self.bn6d_1 = nn.BatchNorm2d(512)
        self.relu6d_1 = nn.ReLU(inplace=True)

        self.conv6d_m = nn.Conv2d(512, 512, 3, dilation=2, padding=2)
        self.bn6d_m = nn.BatchNorm2d(512)
        self.relu6d_m = nn.ReLU(inplace=True)

        self.conv6d_2 = nn.Conv2d(512, 512, 3, dilation=2, padding=2)
        self.bn6d_2 = nn.BatchNorm2d(512)
        self.relu6d_2 = nn.ReLU(inplace=True)

        # stage 5d
        self.conv5d_1 = nn.Conv2d(1024, 512, 3, padding=1)  # 16
        self.bn5d_1 = nn.BatchNorm2d(512)
        self.relu5d_1 = nn.ReLU(inplace=True)

        self.conv5d_m = nn.Conv2d(512, 512, 3, padding=1)
        self.bn5d_m = nn.BatchNorm2d(512)
        self.relu5d_m = nn.ReLU(inplace=True)

        self.conv5d_2 = nn.Conv2d(512, 512, 3, padding=1)
        self.bn5d_2 = nn.BatchNorm2d(512)
        self.relu5d_2 = nn.ReLU(inplace=True)

        # stage 4d
        self.conv4d_1 = nn.Conv2d(1024, 512, 3, padding=1)  # 32
        self.bn4d_1 = nn.BatchNorm2d(512)
        self.relu4d_1 = nn.ReLU(inplace=True)

        self.conv4d_m = nn.Conv2d(512, 512, 3, padding=1)
        self.bn4d_m = nn.BatchNorm2d(512)
        self.relu4d_m = nn.ReLU(inplace=True)

        self.conv4d_2 = nn.Conv2d(512, 256, 3, padding=1)
        self.bn4d_2 = nn.BatchNorm2d(256)
        self.relu4d_2 = nn.ReLU(inplace=True)

        # stage 3d
        self.conv3d_1 = nn.Conv2d(512, 256, 3, padding=1)  # 64
        self.bn3d_1 = nn.BatchNorm2d(256)
        self.relu3d_1 = nn.ReLU(inplace=True)

        self.conv3d_m = nn.Conv2d(256, 256, 3, padding=1)
        self.bn3d_m = nn.BatchNorm2d(256)
        self.relu3d_m = nn.ReLU(inplace=True)

        self.conv3d_2 = nn.Conv2d(256, 128, 3, padding=1)
        self.bn3d_2 = nn.BatchNorm2d(128)
        self.relu3d_2 = nn.ReLU(inplace=True)

        # stage 2d

        self.conv2d_1 = nn.Conv2d(256, 128, 3, padding=1)  # 128
        self.bn2d_1 = nn.BatchNorm2d(128)
        self.relu2d_1 = nn.ReLU(inplace=True)

        self.conv2d_m = nn.Conv2d(128, 128, 3, padding=1)
        self.bn2d_m = nn.BatchNorm2d(128)
        self.relu2d_m = nn.ReLU(inplace=True)

        self.conv2d_2 = nn.Conv2d(128, 64, 3, padding=1)
        self.bn2d_2 = nn.BatchNorm2d(64)
        self.relu2d_2 = nn.ReLU(inplace=True)

        # stage 1d
        self.conv1d_1 = nn.Conv2d(128, 64, 3, padding=1)  # 256
        self.bn1d_1 = nn.BatchNorm2d(64)
        self.relu1d_1 = nn.ReLU(inplace=True)

        self.conv1d_m = nn.Conv2d(64, 64, 3, padding=1)
        self.bn1d_m = nn.BatchNorm2d(64)
        self.relu1d_m = nn.ReLU(inplace=True)

        self.conv1d_2 = nn.Conv2d(64, 64, 3, padding=1)
        self.bn1d_2 = nn.BatchNorm2d(64)
        self.relu1d_2 = nn.ReLU(inplace=True)

        # -------------Bilinear Upsampling--------------
        self.upscore6 = nn.Upsample(
            scale_factor=32, mode="bilinear", align_corners=False
        )
        self.upscore5 = nn.Upsample(
            scale_factor=16, mode="bilinear", align_corners=False
        )
        self.upscore4 = nn.Upsample(
            scale_factor=8, mode="bilinear", align_corners=False
        )
        self.upscore3 = nn.Upsample(
            scale_factor=4, mode="bilinear", align_corners=False
        )
        self.upscore2 = nn.Upsample(
            scale_factor=2, mode="bilinear", align_corners=False
        )

        # -------------Side Output--------------
        self.outconvb = nn.Conv2d(512, 1, 3, padding=1)
        self.outconv6 = nn.Conv2d(512, 1, 3, padding=1)
        self.outconv5 = nn.Conv2d(512, 1, 3, padding=1)
        self.outconv4 = nn.Conv2d(256, 1, 3, padding=1)
        self.outconv3 = nn.Conv2d(128, 1, 3, padding=1)
        self.outconv2 = nn.Conv2d(64, 1, 3, padding=1)
        self.outconv1 = nn.Conv2d(64, 1, 3, padding=1)

        # -------------Refine Module-------------
        self.refunet = RefUnet(1, 64)

    def forward(self, x):
        hx = x

        # -------------Encoder-------------
        hx = self.inconv(hx)
        hx = self.inbn(hx)
        hx = self.inrelu(hx)

        h1 = self.encoder1(hx)  # 256
        h2 = self.encoder2(h1)  # 128
        h3 = self.encoder3(h2)  # 64
        h4 = self.encoder4(h3)  # 32

        hx = self.pool4(h4)  # 16

        hx = self.resb5_1(hx)
        hx = self.resb5_2(hx)
        h5 = self.resb5_3(hx)

        hx = self.pool5(h5)  # 8

        hx = self.resb6_1(hx)
        hx = self.resb6_2(hx)
        h6 = self.resb6_3(hx)

        # -------------Bridge-------------
        hx = self.relubg_1(self.bnbg_1(self.convbg_1(h6)))  # 8
        hx = self.relubg_m(self.bnbg_m(self.convbg_m(hx)))
        hbg = self.relubg_2(self.bnbg_2(self.convbg_2(hx)))

        # -------------Decoder-------------

        hx = self.relu6d_1(self.bn6d_1(self.conv6d_1(torch.cat((hbg, h6), 1))))
        hx = self.relu6d_m(self.bn6d_m(self.conv6d_m(hx)))
        hd6 = self.relu6d_2(self.bn6d_2(self.conv6d_2(hx)))

        hx = self.upscore2(hd6)  # 8 -> 16

        hx = self.relu5d_1(self.bn5d_1(self.conv5d_1(torch.cat((hx, h5), 1))))
        hx = self.relu5d_m(self.bn5d_m(self.conv5d_m(hx)))
        hd5 = self.relu5d_2(self.bn5d_2(self.conv5d_2(hx)))

        hx = self.upscore2(hd5)  # 16 -> 32

        hx = self.relu4d_1(self.bn4d_1(self.conv4d_1(torch.cat((hx, h4), 1))))
        hx = self.relu4d_m(self.bn4d_m(self.conv4d_m(hx)))
        hd4 = self.relu4d_2(self.bn4d_2(self.conv4d_2(hx)))

        hx = self.upscore2(hd4)  # 32 -> 64

        hx = self.relu3d_1(self.bn3d_1(self.conv3d_1(torch.cat((hx, h3), 1))))
        hx = self.relu3d_m(self.bn3d_m(self.conv3d_m(hx)))
        hd3 = self.relu3d_2(self.bn3d_2(self.conv3d_2(hx)))

        hx = self.upscore2(hd3)  # 64 -> 128

        hx = self.relu2d_1(self.bn2d_1(self.conv2d_1(torch.cat((hx, h2), 1))))
        hx = self.relu2d_m(self.bn2d_m(self.conv2d_m(hx)))
        hd2 = self.relu2d_2(self.bn2d_2(self.conv2d_2(hx)))

        hx = self.upscore2(hd2)  # 128 -> 256

        hx = self.relu1d_1(self.bn1d_1(self.conv1d_1(torch.cat((hx, h1), 1))))
        hx = self.relu1d_m(self.bn1d_m(self.conv1d_m(hx)))
        hd1 = self.relu1d_2(self.bn1d_2(self.conv1d_2(hx)))

        # -------------Side Output-------------
        db = self.outconvb(hbg)
        db = self.upscore6(db)  # 8->256

        d6 = self.outconv6(hd6)
        d6 = self.upscore6(d6)  # 8->256

        d5 = self.outconv5(hd5)
        d5 = self.upscore5(d5)  # 16->256

        d4 = self.outconv4(hd4)
        d4 = self.upscore4(d4)  # 32->256

        d3 = self.outconv3(hd3)
        d3 = self.upscore3(d3)  # 64->256

        d2 = self.outconv2(hd2)
        d2 = self.upscore2(d2)  # 128->256

        d1 = self.outconv1(hd1)  # 256

        # -------------Refine Module-------------
        dout = self.refunet(d1)  # 256

        return (
            torch.sigmoid(dout),
            torch.sigmoid(d1),
            torch.sigmoid(d2),
            torch.sigmoid(d3),
            torch.sigmoid(d4),
            torch.sigmoid(d5),
            torch.sigmoid(d6),
            torch.sigmoid(db),
        )