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T4
Running
on
T4
| 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 | |