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"""
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),
)
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