#!/usr/bin/python # -*- encoding: utf-8 -*- import torch import torch.nn as nn import torch.nn.functional as F import torchvision from bisenet_resnet import Resnet18 # from modules.bn import InPlaceABNSync as BatchNorm2d class ConvBNReLU(nn.Module): def __init__(self, in_chan, out_chan, ks=3, stride=1, padding=1, *args, **kwargs): 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) self.init_weight() def forward(self, x): x = self.conv(x) x = F.relu(self.bn(x)) return x def init_weight(self): for ly in self.children(): if isinstance(ly, nn.Conv2d): nn.init.kaiming_normal_(ly.weight, a=1) if not ly.bias is None: nn.init.constant_(ly.bias, 0) class BiSeNetOutput(nn.Module): def __init__(self, in_chan, mid_chan, n_classes, *args, **kwargs): 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) self.init_weight() def forward(self, x): x = self.conv(x) x = self.conv_out(x) return x def init_weight(self): for ly in self.children(): if isinstance(ly, nn.Conv2d): nn.init.kaiming_normal_(ly.weight, a=1) if not ly.bias is None: nn.init.constant_(ly.bias, 0) def get_params(self): wd_params, nowd_params = [], [] for name, module in self.named_modules(): if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d): wd_params.append(module.weight) if not module.bias is None: nowd_params.append(module.bias) elif isinstance(module, nn.BatchNorm2d): nowd_params += list(module.parameters()) return wd_params, nowd_params class AttentionRefinementModule(nn.Module): def __init__(self, in_chan, out_chan, *args, **kwargs): 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() self.init_weight() 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 def init_weight(self): for ly in self.children(): if isinstance(ly, nn.Conv2d): nn.init.kaiming_normal_(ly.weight, a=1) if not ly.bias is None: nn.init.constant_(ly.bias, 0) class ContextPath(nn.Module): def __init__(self, *args, **kwargs): 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) self.init_weight() def forward(self, x): H0, W0 = x.size()[2:] 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 def init_weight(self): for ly in self.children(): if isinstance(ly, nn.Conv2d): nn.init.kaiming_normal_(ly.weight, a=1) if not ly.bias is None: nn.init.constant_(ly.bias, 0) def get_params(self): wd_params, nowd_params = [], [] for name, module in self.named_modules(): if isinstance(module, (nn.Linear, nn.Conv2d)): wd_params.append(module.weight) if not module.bias is None: nowd_params.append(module.bias) elif isinstance(module, nn.BatchNorm2d): nowd_params += list(module.parameters()) return wd_params, nowd_params ### This is not used, since I replace this with the resnet feature with the same size class SpatialPath(nn.Module): def __init__(self, *args, **kwargs): super(SpatialPath, self).__init__() self.conv1 = ConvBNReLU(3, 64, ks=7, stride=2, padding=3) self.conv2 = ConvBNReLU(64, 64, ks=3, stride=2, padding=1) self.conv3 = ConvBNReLU(64, 64, ks=3, stride=2, padding=1) self.conv_out = ConvBNReLU(64, 128, ks=1, stride=1, padding=0) self.init_weight() def forward(self, x): feat = self.conv1(x) feat = self.conv2(feat) feat = self.conv3(feat) feat = self.conv_out(feat) return feat def init_weight(self): for ly in self.children(): if isinstance(ly, nn.Conv2d): nn.init.kaiming_normal_(ly.weight, a=1) if not ly.bias is None: nn.init.constant_(ly.bias, 0) def get_params(self): wd_params, nowd_params = [], [] for name, module in self.named_modules(): if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d): wd_params.append(module.weight) if not module.bias is None: nowd_params.append(module.bias) elif isinstance(module, nn.BatchNorm2d): nowd_params += list(module.parameters()) return wd_params, nowd_params class FeatureFusionModule(nn.Module): def __init__(self, in_chan, out_chan, *args, **kwargs): 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() self.init_weight() 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 def init_weight(self): for ly in self.children(): if isinstance(ly, nn.Conv2d): nn.init.kaiming_normal_(ly.weight, a=1) if not ly.bias is None: nn.init.constant_(ly.bias, 0) def get_params(self): wd_params, nowd_params = [], [] for name, module in self.named_modules(): if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d): wd_params.append(module.weight) if not module.bias is None: nowd_params.append(module.bias) elif isinstance(module, nn.BatchNorm2d): nowd_params += list(module.parameters()) return wd_params, nowd_params class BiSeNet(nn.Module): def __init__(self, n_classes, *args, **kwargs): super(BiSeNet, self).__init__() self.cp = ContextPath() ## here self.sp is deleted 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) self.init_weight() def forward(self, x): H, W = x.size()[2:] feat_res8, feat_cp8, feat_cp16 = self.cp(x) # here return res3b1 feature feat_sp = feat_res8 # use res3b1 feature to replace spatial path feature feat_fuse = self.ffm(feat_sp, 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 def init_weight(self): for ly in self.children(): if isinstance(ly, nn.Conv2d): nn.init.kaiming_normal_(ly.weight, a=1) if not ly.bias is None: nn.init.constant_(ly.bias, 0) def get_params(self): wd_params, nowd_params, lr_mul_wd_params, lr_mul_nowd_params = [], [], [], [] for name, child in self.named_children(): child_wd_params, child_nowd_params = child.get_params() if isinstance(child, FeatureFusionModule) or isinstance(child, BiSeNetOutput): lr_mul_wd_params += child_wd_params lr_mul_nowd_params += child_nowd_params else: wd_params += child_wd_params nowd_params += child_nowd_params return wd_params, nowd_params, lr_mul_wd_params, lr_mul_nowd_params if __name__ == "__main__": net = BiSeNet(19) net.cuda() net.eval() in_ten = torch.randn(16, 3, 640, 480).cuda() out, out16, out32 = net(in_ten) print(out.shape) net.get_params()