| | import torch |
| | import torch.nn as nn |
| | import torch.nn.functional as F |
| |
|
| | from .resnet import ResNet18 |
| |
|
| |
|
| | class ConvBNReLU(nn.Module): |
| |
|
| | def __init__(self, in_chan, out_chan, ks=3, stride=1, padding=1): |
| | super(ConvBNReLU, self).__init__() |
| | self.conv = nn.Conv2d(in_chan, out_chan, kernel_size=ks, stride=stride, padding=padding, bias=False) |
| | self.bn = nn.BatchNorm2d(out_chan) |
| |
|
| | def forward(self, x): |
| | x = self.conv(x) |
| | x = F.relu(self.bn(x)) |
| | return x |
| |
|
| |
|
| | class BiSeNetOutput(nn.Module): |
| |
|
| | def __init__(self, in_chan, mid_chan, num_class): |
| | super(BiSeNetOutput, self).__init__() |
| | self.conv = ConvBNReLU(in_chan, mid_chan, ks=3, stride=1, padding=1) |
| | self.conv_out = nn.Conv2d(mid_chan, num_class, kernel_size=1, bias=False) |
| |
|
| | def forward(self, x): |
| | feat = self.conv(x) |
| | out = self.conv_out(feat) |
| | return out, feat |
| |
|
| |
|
| | class AttentionRefinementModule(nn.Module): |
| |
|
| | def __init__(self, in_chan, out_chan): |
| | super(AttentionRefinementModule, self).__init__() |
| | self.conv = ConvBNReLU(in_chan, out_chan, ks=3, stride=1, padding=1) |
| | self.conv_atten = nn.Conv2d(out_chan, out_chan, kernel_size=1, bias=False) |
| | self.bn_atten = nn.BatchNorm2d(out_chan) |
| | self.sigmoid_atten = nn.Sigmoid() |
| |
|
| | def forward(self, x): |
| | feat = self.conv(x) |
| | atten = F.avg_pool2d(feat, feat.size()[2:]) |
| | atten = self.conv_atten(atten) |
| | atten = self.bn_atten(atten) |
| | atten = self.sigmoid_atten(atten) |
| | out = torch.mul(feat, atten) |
| | return out |
| |
|
| |
|
| | class ContextPath(nn.Module): |
| |
|
| | def __init__(self): |
| | super(ContextPath, self).__init__() |
| | self.resnet = ResNet18() |
| | self.arm16 = AttentionRefinementModule(256, 128) |
| | self.arm32 = AttentionRefinementModule(512, 128) |
| | self.conv_head32 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1) |
| | self.conv_head16 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1) |
| | self.conv_avg = ConvBNReLU(512, 128, ks=1, stride=1, padding=0) |
| |
|
| | def forward(self, x): |
| | feat8, feat16, feat32 = self.resnet(x) |
| | h8, w8 = feat8.size()[2:] |
| | h16, w16 = feat16.size()[2:] |
| | h32, w32 = feat32.size()[2:] |
| |
|
| | avg = F.avg_pool2d(feat32, feat32.size()[2:]) |
| | avg = self.conv_avg(avg) |
| | avg_up = F.interpolate(avg, (h32, w32), mode='nearest') |
| |
|
| | feat32_arm = self.arm32(feat32) |
| | feat32_sum = feat32_arm + avg_up |
| | feat32_up = F.interpolate(feat32_sum, (h16, w16), mode='nearest') |
| | feat32_up = self.conv_head32(feat32_up) |
| |
|
| | feat16_arm = self.arm16(feat16) |
| | feat16_sum = feat16_arm + feat32_up |
| | feat16_up = F.interpolate(feat16_sum, (h8, w8), mode='nearest') |
| | feat16_up = self.conv_head16(feat16_up) |
| |
|
| | return feat8, feat16_up, feat32_up |
| |
|
| |
|
| | class FeatureFusionModule(nn.Module): |
| |
|
| | def __init__(self, in_chan, out_chan): |
| | super(FeatureFusionModule, self).__init__() |
| | self.convblk = ConvBNReLU(in_chan, out_chan, ks=1, stride=1, padding=0) |
| | self.conv1 = nn.Conv2d(out_chan, out_chan // 4, kernel_size=1, stride=1, padding=0, bias=False) |
| | self.conv2 = nn.Conv2d(out_chan // 4, out_chan, kernel_size=1, stride=1, padding=0, bias=False) |
| | self.relu = nn.ReLU(inplace=True) |
| | self.sigmoid = nn.Sigmoid() |
| |
|
| | def forward(self, fsp, fcp): |
| | fcat = torch.cat([fsp, fcp], dim=1) |
| | feat = self.convblk(fcat) |
| | atten = F.avg_pool2d(feat, feat.size()[2:]) |
| | atten = self.conv1(atten) |
| | atten = self.relu(atten) |
| | atten = self.conv2(atten) |
| | atten = self.sigmoid(atten) |
| | feat_atten = torch.mul(feat, atten) |
| | feat_out = feat_atten + feat |
| | return feat_out |
| |
|
| |
|
| | class BiSeNet(nn.Module): |
| |
|
| | def __init__(self, num_class): |
| | super(BiSeNet, self).__init__() |
| | self.cp = ContextPath() |
| | self.ffm = FeatureFusionModule(256, 256) |
| | self.conv_out = BiSeNetOutput(256, 256, num_class) |
| | self.conv_out16 = BiSeNetOutput(128, 64, num_class) |
| | self.conv_out32 = BiSeNetOutput(128, 64, num_class) |
| |
|
| | def forward(self, x, return_feat=False): |
| | h, w = x.size()[2:] |
| | feat_res8, feat_cp8, feat_cp16 = self.cp(x) |
| | feat_sp = feat_res8 |
| | feat_fuse = self.ffm(feat_sp, feat_cp8) |
| |
|
| | out, feat = self.conv_out(feat_fuse) |
| | out16, feat16 = self.conv_out16(feat_cp8) |
| | out32, feat32 = self.conv_out32(feat_cp16) |
| |
|
| | out = F.interpolate(out, (h, w), mode='bilinear', align_corners=True) |
| | out16 = F.interpolate(out16, (h, w), mode='bilinear', align_corners=True) |
| | out32 = F.interpolate(out32, (h, w), mode='bilinear', align_corners=True) |
| |
|
| | if return_feat: |
| | feat = F.interpolate(feat, (h, w), mode='bilinear', align_corners=True) |
| | feat16 = F.interpolate(feat16, (h, w), mode='bilinear', align_corners=True) |
| | feat32 = F.interpolate(feat32, (h, w), mode='bilinear', align_corners=True) |
| | return out, out16, out32, feat, feat16, feat32 |
| | else: |
| | return out, out16, out32 |
| |
|
