""" Name: BASNet lib Description: The main module of the neural network. Source url: https://github.com/NathanUA/BASNet Modified by Nikita Selin (OPHoperHPO)[https://github.com/OPHoperHPO]. License: MIT License License: Copyright (c) 2019 Xuebin Qin Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. """ import torch import torch.nn as nn from torchvision import models ## code from: https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py # __all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101', # 'resnet152', 'ResNet34P','ResNet50S','ResNet50P','ResNet101P'] # # resnet18_dir = '/local/sda4/yqian3/RoadNets/resnet_model/resnet18-5c106cde.pth' # resnet34_dir = '/local/sda4/yqian3/RoadNets/resnet_model/resnet34-333f7ec4.pth' # resnet50_dir = '/local/sda4/yqian3/RoadNets/resnet_model/resnet50-19c8e357.pth' # resnet101_dir = '/local/sda4/yqian3/RoadNets/resnet_model/resnet101-5d3b4d8f.pth' # # model_urls = { # 'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth', # 'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth', # 'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth', # 'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth', # 'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth', # } 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=True) # -------------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)