123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141142143144145146147148149150151152153154155156157158159160161162163164165166167168169170171172173174175176177178179180181182183184185186187188189190191192193194195196197198199200201202203204205206207208209210211212213214215216217218219220221222223224225226227228229230231232233234235236237238239240241242243244245246247248249250251252253254255256257258259260261262263264265266267268269270271272273274275276277278279280281282283 |
- #!/usr/bin/python
- # -*- encoding: utf-8 -*-
- import torch
- import torch.nn as nn
- import torch.nn.functional as F
- import torchvision
- from parsing_model.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()
|