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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 # x8, x8, x16
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) # return res3b1 feature
feat_sp = feat_res8 # replace spatial path feature with res3b1 feature
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
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