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+#!/usr/bin/python
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+# -*- encoding: utf-8 -*-
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+
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+
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+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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+import torchvision
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+
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+from parsing_model.resnet import Resnet18
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+# from modules.bn import InPlaceABNSync as BatchNorm2d
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+
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+
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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, *args, **kwargs):
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+ super(ConvBNReLU, self).__init__()
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+ self.conv = nn.Conv2d(in_chan,
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+ out_chan,
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+ kernel_size = ks,
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+ stride = stride,
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+ padding = padding,
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+ bias = False)
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+ self.bn = nn.BatchNorm2d(out_chan)
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+ self.init_weight()
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+
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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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+
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+ def init_weight(self):
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+ for ly in self.children():
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+ if isinstance(ly, nn.Conv2d):
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+ nn.init.kaiming_normal_(ly.weight, a=1)
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+ if not ly.bias is None: nn.init.constant_(ly.bias, 0)
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+
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+class BiSeNetOutput(nn.Module):
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+ def __init__(self, in_chan, mid_chan, n_classes, *args, **kwargs):
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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, n_classes, kernel_size=1, bias=False)
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+ self.init_weight()
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+
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+ def forward(self, x):
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+ x = self.conv(x)
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+ x = self.conv_out(x)
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+ return x
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+
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+ def init_weight(self):
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+ for ly in self.children():
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+ if isinstance(ly, nn.Conv2d):
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+ nn.init.kaiming_normal_(ly.weight, a=1)
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+ if not ly.bias is None: nn.init.constant_(ly.bias, 0)
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+
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+ def get_params(self):
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+ wd_params, nowd_params = [], []
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+ for name, module in self.named_modules():
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+ if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d):
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+ wd_params.append(module.weight)
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+ if not module.bias is None:
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+ nowd_params.append(module.bias)
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+ elif isinstance(module, nn.BatchNorm2d):
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+ nowd_params += list(module.parameters())
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+ return wd_params, nowd_params
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+
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+
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+class AttentionRefinementModule(nn.Module):
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+ def __init__(self, in_chan, out_chan, *args, **kwargs):
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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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+ self.init_weight()
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+
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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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+
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+ def init_weight(self):
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+ for ly in self.children():
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+ if isinstance(ly, nn.Conv2d):
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+ nn.init.kaiming_normal_(ly.weight, a=1)
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+ if not ly.bias is None: nn.init.constant_(ly.bias, 0)
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+
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+
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+class ContextPath(nn.Module):
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+ def __init__(self, *args, **kwargs):
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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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+
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+ self.init_weight()
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+
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+ def forward(self, x):
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+ H0, W0 = x.size()[2:]
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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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+
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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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+
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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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+
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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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+
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+ return feat8, feat16_up, feat32_up # x8, x8, x16
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+
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+ def init_weight(self):
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+ for ly in self.children():
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+ if isinstance(ly, nn.Conv2d):
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+ nn.init.kaiming_normal_(ly.weight, a=1)
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+ if not ly.bias is None: nn.init.constant_(ly.bias, 0)
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+
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+ def get_params(self):
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+ wd_params, nowd_params = [], []
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+ for name, module in self.named_modules():
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+ if isinstance(module, (nn.Linear, nn.Conv2d)):
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+ wd_params.append(module.weight)
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+ if not module.bias is None:
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+ nowd_params.append(module.bias)
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+ elif isinstance(module, nn.BatchNorm2d):
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+ nowd_params += list(module.parameters())
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+ return wd_params, nowd_params
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+
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+
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+### This is not used, since I replace this with the resnet feature with the same size
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+class SpatialPath(nn.Module):
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+ def __init__(self, *args, **kwargs):
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+ super(SpatialPath, self).__init__()
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+ self.conv1 = ConvBNReLU(3, 64, ks=7, stride=2, padding=3)
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+ self.conv2 = ConvBNReLU(64, 64, ks=3, stride=2, padding=1)
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+ self.conv3 = ConvBNReLU(64, 64, ks=3, stride=2, padding=1)
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+ self.conv_out = ConvBNReLU(64, 128, ks=1, stride=1, padding=0)
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+ self.init_weight()
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+
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+ def forward(self, x):
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+ feat = self.conv1(x)
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+ feat = self.conv2(feat)
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+ feat = self.conv3(feat)
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+ feat = self.conv_out(feat)
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+ return feat
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+
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+ def init_weight(self):
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+ for ly in self.children():
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+ if isinstance(ly, nn.Conv2d):
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+ nn.init.kaiming_normal_(ly.weight, a=1)
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+ if not ly.bias is None: nn.init.constant_(ly.bias, 0)
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+
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+ def get_params(self):
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+ wd_params, nowd_params = [], []
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+ for name, module in self.named_modules():
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+ if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d):
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+ wd_params.append(module.weight)
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+ if not module.bias is None:
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+ nowd_params.append(module.bias)
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+ elif isinstance(module, nn.BatchNorm2d):
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+ nowd_params += list(module.parameters())
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+ return wd_params, nowd_params
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+
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+
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+class FeatureFusionModule(nn.Module):
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+ def __init__(self, in_chan, out_chan, *args, **kwargs):
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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,
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+ out_chan//4,
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+ kernel_size = 1,
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+ stride = 1,
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+ padding = 0,
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+ bias = False)
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+ self.conv2 = nn.Conv2d(out_chan//4,
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+ out_chan,
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+ kernel_size = 1,
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+ stride = 1,
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+ padding = 0,
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+ bias = False)
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+ self.relu = nn.ReLU(inplace=True)
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+ self.sigmoid = nn.Sigmoid()
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+ self.init_weight()
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+
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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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+
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+ def init_weight(self):
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+ for ly in self.children():
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+ if isinstance(ly, nn.Conv2d):
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+ nn.init.kaiming_normal_(ly.weight, a=1)
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+ if not ly.bias is None: nn.init.constant_(ly.bias, 0)
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+
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+ def get_params(self):
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+ wd_params, nowd_params = [], []
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+ for name, module in self.named_modules():
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+ if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d):
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+ wd_params.append(module.weight)
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+ if not module.bias is None:
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+ nowd_params.append(module.bias)
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+ elif isinstance(module, nn.BatchNorm2d):
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+ nowd_params += list(module.parameters())
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+ return wd_params, nowd_params
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+
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+
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+class BiSeNet(nn.Module):
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+ def __init__(self, n_classes, *args, **kwargs):
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+ super(BiSeNet, self).__init__()
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+ self.cp = ContextPath()
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+ ## here self.sp is deleted
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+ self.ffm = FeatureFusionModule(256, 256)
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+ self.conv_out = BiSeNetOutput(256, 256, n_classes)
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+ self.conv_out16 = BiSeNetOutput(128, 64, n_classes)
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+ self.conv_out32 = BiSeNetOutput(128, 64, n_classes)
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+ self.init_weight()
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+
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+ def forward(self, x):
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+ H, W = x.size()[2:]
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+ feat_res8, feat_cp8, feat_cp16 = self.cp(x) # here return res3b1 feature
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+ feat_sp = feat_res8 # use res3b1 feature to replace spatial path feature
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+ feat_fuse = self.ffm(feat_sp, feat_cp8)
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+
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+ feat_out = self.conv_out(feat_fuse)
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+ feat_out16 = self.conv_out16(feat_cp8)
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+ feat_out32 = self.conv_out32(feat_cp16)
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+
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+ feat_out = F.interpolate(feat_out, (H, W), mode='bilinear', align_corners=True)
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+ feat_out16 = F.interpolate(feat_out16, (H, W), mode='bilinear', align_corners=True)
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+ feat_out32 = F.interpolate(feat_out32, (H, W), mode='bilinear', align_corners=True)
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+ return feat_out, feat_out16, feat_out32
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+
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+ def init_weight(self):
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+ for ly in self.children():
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+ if isinstance(ly, nn.Conv2d):
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+ nn.init.kaiming_normal_(ly.weight, a=1)
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+ if not ly.bias is None: nn.init.constant_(ly.bias, 0)
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+
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+ def get_params(self):
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+ wd_params, nowd_params, lr_mul_wd_params, lr_mul_nowd_params = [], [], [], []
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+ for name, child in self.named_children():
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+ child_wd_params, child_nowd_params = child.get_params()
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+ if isinstance(child, FeatureFusionModule) or isinstance(child, BiSeNetOutput):
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+ lr_mul_wd_params += child_wd_params
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+ lr_mul_nowd_params += child_nowd_params
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+ else:
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+ wd_params += child_wd_params
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+ nowd_params += child_nowd_params
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+ return wd_params, nowd_params, lr_mul_wd_params, lr_mul_nowd_params
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+
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+
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+if __name__ == "__main__":
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+ net = BiSeNet(19)
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+ net.cuda()
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+ net.eval()
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+ in_ten = torch.randn(16, 3, 640, 480).cuda()
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+ out, out16, out32 = net(in_ten)
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+ print(out.shape)
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+
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+ net.get_params()
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