"""BiSeNet (face-parsing.PyTorch) 网络结构，vendored。 源自 zllrunning/face-parsing.PyTorch，结构与权重 `79999_iter.pth`（CelebAMask-HQ 19 类）严格对应，仅修改 resnet18 骨干加载为「优先本地权重」以适配内网离线。 hair 类别索引 = 17。 """ import os import torch import torch.nn as nn import torch.nn.functional as F import torch.utils.model_zoo as modelzoo resnet18_url = "https://download.pytorch.org/models/resnet18-5c106cde.pth" _LOCAL_RESNET18 = os.path.join(os.path.dirname(__file__), "weights", "resnet18-5c106cde.pth") def conv3x3(in_planes, out_planes, stride=1): return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False) class BasicBlock(nn.Module): def __init__(self, in_chan, out_chan, stride=1): super(BasicBlock, self).__init__() self.conv1 = conv3x3(in_chan, out_chan, stride) self.bn1 = nn.BatchNorm2d(out_chan) self.conv2 = conv3x3(out_chan, out_chan) self.bn2 = nn.BatchNorm2d(out_chan) self.relu = nn.ReLU(inplace=True) self.downsample = None if in_chan != out_chan or stride != 1: self.downsample = nn.Sequential( nn.Conv2d(in_chan, out_chan, kernel_size=1, stride=stride, bias=False), nn.BatchNorm2d(out_chan), ) def forward(self, x): residual = self.conv1(x) residual = F.relu(self.bn1(residual)) residual = self.conv2(residual) residual = self.bn2(residual) shortcut = x if self.downsample is not None: shortcut = self.downsample(x) out = shortcut + residual out = self.relu(out) return out def create_layer_basic(in_chan, out_chan, bnum, stride=1): layers = [BasicBlock(in_chan, out_chan, stride=stride)] for _ in range(bnum - 1): layers.append(BasicBlock(out_chan, out_chan, stride=1)) return nn.Sequential(*layers) class Resnet18(nn.Module): def __init__(self): super(Resnet18, self).__init__() self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False) self.bn1 = nn.BatchNorm2d(64) self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) self.layer1 = create_layer_basic(64, 64, bnum=2, stride=1) self.layer2 = create_layer_basic(64, 128, bnum=2, stride=2) self.layer3 = create_layer_basic(128, 256, bnum=2, stride=2) self.layer4 = create_layer_basic(256, 512, bnum=2, stride=2) self.init_weight() def forward(self, x): x = self.conv1(x) x = F.relu(self.bn1(x)) x = self.maxpool(x) x = self.layer1(x) feat8 = self.layer2(x) # 1/8 feat16 = self.layer3(feat8) # 1/16 feat32 = self.layer4(feat16) # 1/32 return feat8, feat16, feat32 def init_weight(self): # 优先本地骨干权重（内网离线），缺失才回退 torch model_zoo（会查缓存）。 if os.path.isfile(_LOCAL_RESNET18): state_dict = torch.load(_LOCAL_RESNET18, map_location="cpu", weights_only=False) else: state_dict = modelzoo.load_url(resnet18_url) self_state_dict = self.state_dict() for k, v in state_dict.items(): if "fc" in k: continue self_state_dict.update({k: v}) self.load_state_dict(self_state_dict) 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, n_classes): super(BiSeNetOutput, self).__init__() self.conv = ConvBNReLU(in_chan, mid_chan, ks=3, stride=1, padding=1) self.conv_out = nn.Conv2d(mid_chan, n_classes, kernel_size=1, bias=False) def forward(self, x): x = self.conv(x) x = self.conv_out(x) return x 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 # feat8 未用，保持与权重结构一致 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, n_classes): super(BiSeNet, self).__init__() # 该权重(79999_iter.pth)的变体无独立 SpatialPath： # 直接用 ContextPath 的 resnet feat8（128ch）作为空间路径特征。 self.cp = ContextPath() self.ffm = FeatureFusionModule(256, 256) self.conv_out = BiSeNetOutput(256, 256, n_classes) self.conv_out16 = BiSeNetOutput(128, 64, n_classes) self.conv_out32 = BiSeNetOutput(128, 64, n_classes) def forward(self, x): h, w = x.size()[2:] feat_res8, feat_cp8, feat_cp16 = self.cp(x) feat_fuse = self.ffm(feat_res8, feat_cp8) feat_out = self.conv_out(feat_fuse) feat_out16 = self.conv_out16(feat_cp8) feat_out32 = self.conv_out32(feat_cp16) feat_out = F.interpolate(feat_out, (h, w), mode="bilinear", align_corners=True) feat_out16 = F.interpolate(feat_out16, (h, w), mode="bilinear", align_corners=True) feat_out32 = F.interpolate(feat_out32, (h, w), mode="bilinear", align_corners=True) return feat_out, feat_out16, feat_out32