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141 lines
5.1 KiB
Python
141 lines
5.1 KiB
Python
import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from .resnet import ResNet18
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class ConvBNReLU(nn.Module):
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def __init__(self, in_chan, out_chan, ks=3, stride=1, padding=1):
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super(ConvBNReLU, self).__init__()
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self.conv = nn.Conv2d(in_chan, out_chan, kernel_size=ks, stride=stride, padding=padding, bias=False)
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self.bn = nn.BatchNorm2d(out_chan)
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def forward(self, x):
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x = self.conv(x)
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x = F.relu(self.bn(x))
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return x
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class BiSeNetOutput(nn.Module):
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def __init__(self, in_chan, mid_chan, num_class):
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super(BiSeNetOutput, self).__init__()
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self.conv = ConvBNReLU(in_chan, mid_chan, ks=3, stride=1, padding=1)
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self.conv_out = nn.Conv2d(mid_chan, num_class, kernel_size=1, bias=False)
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def forward(self, x):
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feat = self.conv(x)
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out = self.conv_out(feat)
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return out, feat
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class AttentionRefinementModule(nn.Module):
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def __init__(self, in_chan, out_chan):
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super(AttentionRefinementModule, self).__init__()
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self.conv = ConvBNReLU(in_chan, out_chan, ks=3, stride=1, padding=1)
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self.conv_atten = nn.Conv2d(out_chan, out_chan, kernel_size=1, bias=False)
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self.bn_atten = nn.BatchNorm2d(out_chan)
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self.sigmoid_atten = nn.Sigmoid()
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def forward(self, x):
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feat = self.conv(x)
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atten = F.avg_pool2d(feat, feat.size()[2:])
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atten = self.conv_atten(atten)
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atten = self.bn_atten(atten)
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atten = self.sigmoid_atten(atten)
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out = torch.mul(feat, atten)
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return out
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class ContextPath(nn.Module):
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def __init__(self):
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super(ContextPath, self).__init__()
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self.resnet = ResNet18()
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self.arm16 = AttentionRefinementModule(256, 128)
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self.arm32 = AttentionRefinementModule(512, 128)
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self.conv_head32 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1)
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self.conv_head16 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1)
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self.conv_avg = ConvBNReLU(512, 128, ks=1, stride=1, padding=0)
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def forward(self, x):
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feat8, feat16, feat32 = self.resnet(x)
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h8, w8 = feat8.size()[2:]
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h16, w16 = feat16.size()[2:]
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h32, w32 = feat32.size()[2:]
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avg = F.avg_pool2d(feat32, feat32.size()[2:])
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avg = self.conv_avg(avg)
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avg_up = F.interpolate(avg, (h32, w32), mode='nearest')
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feat32_arm = self.arm32(feat32)
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feat32_sum = feat32_arm + avg_up
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feat32_up = F.interpolate(feat32_sum, (h16, w16), mode='nearest')
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feat32_up = self.conv_head32(feat32_up)
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feat16_arm = self.arm16(feat16)
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feat16_sum = feat16_arm + feat32_up
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feat16_up = F.interpolate(feat16_sum, (h8, w8), mode='nearest')
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feat16_up = self.conv_head16(feat16_up)
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return feat8, feat16_up, feat32_up # x8, x8, x16
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class FeatureFusionModule(nn.Module):
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def __init__(self, in_chan, out_chan):
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super(FeatureFusionModule, self).__init__()
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self.convblk = ConvBNReLU(in_chan, out_chan, ks=1, stride=1, padding=0)
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self.conv1 = nn.Conv2d(out_chan, out_chan // 4, kernel_size=1, stride=1, padding=0, bias=False)
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self.conv2 = nn.Conv2d(out_chan // 4, out_chan, kernel_size=1, stride=1, padding=0, bias=False)
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self.relu = nn.ReLU(inplace=True)
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self.sigmoid = nn.Sigmoid()
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def forward(self, fsp, fcp):
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fcat = torch.cat([fsp, fcp], dim=1)
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feat = self.convblk(fcat)
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atten = F.avg_pool2d(feat, feat.size()[2:])
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atten = self.conv1(atten)
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atten = self.relu(atten)
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atten = self.conv2(atten)
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atten = self.sigmoid(atten)
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feat_atten = torch.mul(feat, atten)
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feat_out = feat_atten + feat
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return feat_out
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class BiSeNet(nn.Module):
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def __init__(self, num_class):
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super(BiSeNet, self).__init__()
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self.cp = ContextPath()
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self.ffm = FeatureFusionModule(256, 256)
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self.conv_out = BiSeNetOutput(256, 256, num_class)
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self.conv_out16 = BiSeNetOutput(128, 64, num_class)
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self.conv_out32 = BiSeNetOutput(128, 64, num_class)
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def forward(self, x, return_feat=False):
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h, w = x.size()[2:]
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feat_res8, feat_cp8, feat_cp16 = self.cp(x) # return res3b1 feature
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feat_sp = feat_res8 # replace spatial path feature with res3b1 feature
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feat_fuse = self.ffm(feat_sp, feat_cp8)
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out, feat = self.conv_out(feat_fuse)
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out16, feat16 = self.conv_out16(feat_cp8)
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out32, feat32 = self.conv_out32(feat_cp16)
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out = F.interpolate(out, (h, w), mode='bilinear', align_corners=True)
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out16 = F.interpolate(out16, (h, w), mode='bilinear', align_corners=True)
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out32 = F.interpolate(out32, (h, w), mode='bilinear', align_corners=True)
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if return_feat:
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feat = F.interpolate(feat, (h, w), mode='bilinear', align_corners=True)
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feat16 = F.interpolate(feat16, (h, w), mode='bilinear', align_corners=True)
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feat32 = F.interpolate(feat32, (h, w), mode='bilinear', align_corners=True)
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return out, out16, out32, feat, feat16, feat32
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else:
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return out, out16, out32
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