项目实训第二周（车道线检测）

本周工作

上一周对于LaneNet这一模型进行了了相关复现，训练以及测试，因为LaneNet算是比较经典的一个车道线检测模型，距今时间较长，而且其问题我也在上一周的博客中指明，这一周我继续对于近今年国际上SOTA水平的车道线预测模型进行了阅读，其中包括SCNN模型，RESA模型。RESA模型是SCNN模型的延伸，而因为SCNN的速度较慢，帧率很低的问题我决定并不复现SCNN，而RESA模型是在SCNN上的改进，速度方面有较大提升，准确率也是在SOTA水平上，我根据论文复现了RESA模型，并进行了相关的模型训练和测试。并自己写了一个demo来验证模型的合理性。我的相关工作也可以看我的个人博客网站个人博客。

SCNN介绍

传统的CNN模型是由卷积块堆叠构建，由上一层输入的张量进行卷积，激活池化等操作，而这种模型对于强先验形状的效果并不好，对于遮挡的部分识别效果并不好。而SCNN在CNN的基础上进行改进，在图片的行和列上进行切片进行卷积操作从而使得图片中行和列可以传递信息，扩大感受野。

RESA网络结构

这是我复现出RESA网络的结构：

RESANet((backbone): ResNetWrapper((model): ResNet((conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu): ReLU(inplace=True)(maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)(layer1): Sequential((0): BasicBlock((conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu): ReLU(inplace=True)(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True))(1): BasicBlock((conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu): ReLU(inplace=True)(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True))(2): BasicBlock((conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu): ReLU(inplace=True)(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)))(layer2): Sequential((0): BasicBlock((conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu): ReLU(inplace=True)(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(downsample): Sequential((0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)(1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)))(1): BasicBlock((conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu): ReLU(inplace=True)(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True))(2): BasicBlock((conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu): ReLU(inplace=True)(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True))(3): BasicBlock((conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu): ReLU(inplace=True)(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)))(layer3): Sequential((0): BasicBlock((conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu): ReLU(inplace=True)(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(downsample): Sequential((0): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)))(1): BasicBlock((conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu): ReLU(inplace=True)(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True))(2): BasicBlock((conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu): ReLU(inplace=True)(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True))(3): BasicBlock((conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu): ReLU(inplace=True)(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True))(4): BasicBlock((conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu): ReLU(inplace=True)(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True))(5): BasicBlock((conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu): ReLU(inplace=True)(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)))(layer4): Sequential((0): BasicBlock((conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu): ReLU(inplace=True)(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(downsample): Sequential((0): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)(1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)))(1): BasicBlock((conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(4, 4), dilation=(4, 4), bias=False)(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu): ReLU(inplace=True)(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(4, 4), dilation=(4, 4), bias=False)(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True))(2): BasicBlock((conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(4, 4), dilation=(4, 4), bias=False)(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu): ReLU(inplace=True)(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(4, 4), dilation=(4, 4), bias=False)(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True))))(out): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False))(resa): RESA((conv_d0): Conv2d(128, 128, kernel_size=(1, 9), stride=(1, 1), padding=(0, 4), bias=False)(conv_u0): Conv2d(128, 128, kernel_size=(1, 9), stride=(1, 1), padding=(0, 4), bias=False)(conv_r0): Conv2d(128, 128, kernel_size=(9, 1), stride=(1, 1), padding=(4, 0), bias=False)(conv_l0): Conv2d(128, 128, kernel_size=(9, 1), stride=(1, 1), padding=(4, 0), bias=False)(conv_d1): Conv2d(128, 128, kernel_size=(1, 9), stride=(1, 1), padding=(0, 4), bias=False)(conv_u1): Conv2d(128, 128, kernel_size=(1, 9), stride=(1, 1), padding=(0, 4), bias=False)(conv_r1): Conv2d(128, 128, kernel_size=(9, 1), stride=(1, 1), padding=(4, 0), bias=False)(conv_l1): Conv2d(128, 128, kernel_size=(9, 1), stride=(1, 1), padding=(4, 0), bias=False)(conv_d2): Conv2d(128, 128, kernel_size=(1, 9), stride=(1, 1), padding=(0, 4), bias=False)(conv_u2): Conv2d(128, 128, kernel_size=(1, 9), stride=(1, 1), padding=(0, 4), bias=False)(conv_r2): Conv2d(128, 128, kernel_size=(9, 1), stride=(1, 1), padding=(4, 0), bias=False)(conv_l2): Conv2d(128, 128, kernel_size=(9, 1), stride=(1, 1), padding=(4, 0), bias=False)(conv_d3): Conv2d(128, 128, kernel_size=(1, 9), stride=(1, 1), padding=(0, 4), bias=False)(conv_u3): Conv2d(128, 128, kernel_size=(1, 9), stride=(1, 1), padding=(0, 4), bias=False)(conv_r3): Conv2d(128, 128, kernel_size=(9, 1), stride=(1, 1), padding=(4, 0), bias=False)(conv_l3): Conv2d(128, 128, kernel_size=(9, 1), stride=(1, 1), padding=(4, 0), bias=False)(conv_d4): Conv2d(128, 128, kernel_size=(1, 9), stride=(1, 1), padding=(0, 4), bias=False)(conv_u4): Conv2d(128, 128, kernel_size=(1, 9), stride=(1, 1), padding=(0, 4), bias=False)(conv_r4): Conv2d(128, 128, kernel_size=(9, 1), stride=(1, 1), padding=(4, 0), bias=False)(conv_l4): Conv2d(128, 128, kernel_size=(9, 1), stride=(1, 1), padding=(4, 0), bias=False))(decoder): BUSD((layers): ModuleList((0): UpsamplerBlock((conv): ConvTranspose2d(128, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))(bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)(follows): ModuleList((0): non_bottleneck_1d((conv3x1_1): Conv2d(64, 64, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0))(conv1x3_1): Conv2d(64, 64, kernel_size=(1, 3), stride=(1, 1), padding=(0, 1))(bn1): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)(conv3x1_2): Conv2d(64, 64, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0))(conv1x3_2): Conv2d(64, 64, kernel_size=(1, 3), stride=(1, 1), padding=(0, 1))(bn2): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)(dropout): Dropout2d(p=0, inplace=False))(1): non_bottleneck_1d((conv3x1_1): Conv2d(64, 64, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0))(conv1x3_1): Conv2d(64, 64, kernel_size=(1, 3), stride=(1, 1), padding=(0, 1))(bn1): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)(conv3x1_2): Conv2d(64, 64, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0))(conv1x3_2): Conv2d(64, 64, kernel_size=(1, 3), stride=(1, 1), padding=(0, 1))(bn2): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)(dropout): Dropout2d(p=0, inplace=False)))(interpolate_conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)(interpolate_bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(1): UpsamplerBlock((conv): ConvTranspose2d(64, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))(bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)(follows): ModuleList((0): non_bottleneck_1d((conv3x1_1): Conv2d(32, 32, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0))(conv1x3_1): Conv2d(32, 32, kernel_size=(1, 3), stride=(1, 1), padding=(0, 1))(bn1): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)(conv3x1_2): Conv2d(32, 32, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0))(conv1x3_2): Conv2d(32, 32, kernel_size=(1, 3), stride=(1, 1), padding=(0, 1))(bn2): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)(dropout): Dropout2d(p=0, inplace=False))(1): non_bottleneck_1d((conv3x1_1): Conv2d(32, 32, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0))(conv1x3_1): Conv2d(32, 32, kernel_size=(1, 3), stride=(1, 1), padding=(0, 1))(bn1): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)(conv3x1_2): Conv2d(32, 32, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0))(conv1x3_2): Conv2d(32, 32, kernel_size=(1, 3), stride=(1, 1), padding=(0, 1))(bn2): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)(dropout): Dropout2d(p=0, inplace=False)))(interpolate_conv): Conv2d(64, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)(interpolate_bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(2): UpsamplerBlock((conv): ConvTranspose2d(32, 16, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))(bn): BatchNorm2d(16, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)(follows): ModuleList((0): non_bottleneck_1d((conv3x1_1): Conv2d(16, 16, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0))(conv1x3_1): Conv2d(16, 16, kernel_size=(1, 3), stride=(1, 1), padding=(0, 1))(bn1): BatchNorm2d(16, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)(conv3x1_2): Conv2d(16, 16, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0))(conv1x3_2): Conv2d(16, 16, kernel_size=(1, 3), stride=(1, 1), padding=(0, 1))(bn2): BatchNorm2d(16, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)(dropout): Dropout2d(p=0, inplace=False))(1): non_bottleneck_1d((conv3x1_1): Conv2d(16, 16, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0))(conv1x3_1): Conv2d(16, 16, kernel_size=(1, 3), stride=(1, 1), padding=(0, 1))(bn1): BatchNorm2d(16, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)(conv3x1_2): Conv2d(16, 16, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0))(conv1x3_2): Conv2d(16, 16, kernel_size=(1, 3), stride=(1, 1), padding=(0, 1))(bn2): BatchNorm2d(16, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)(dropout): Dropout2d(p=0, inplace=False)))(interpolate_conv): Conv2d(32, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)(interpolate_bn): BatchNorm2d(16, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)))(output_conv): Conv2d(16, 7, kernel_size=(1, 1), stride=(1, 1), bias=False))(heads): ExistHead((dropout): Dropout2d(p=0.1, inplace=False)(conv8): Conv2d(128, 7, kernel_size=(1, 1), stride=(1, 1))(fc9): Linear(in_features=6440, out_features=128, bias=True)(fc10): Linear(in_features=128, out_features=6, bias=True))
)

它的网络结构如下图所示：
根据我的分析和查看相关代码，将RESA的模型网络结构总结如下：

1.编码器

用来提取图片的主要特征，可以使用resnet，vgg等网络，通过我的观察和研究，这一部分属于每一个车道线模型的基本步骤。

它的主要创新点为下面两个网络

2.RESA网络

代码主要如下所示：

class RESA(nn.Module):def __init__(self, cfg):super(RESA, self).__init__()self.iter = cfg.resa.iterchan = cfg.resa.input_channelfea_stride = cfg.backbone.fea_strideself.height = cfg.img_height // fea_strideself.width = cfg.img_width // fea_strideself.alpha = cfg.resa.alphaconv_stride = cfg.resa.conv_stride## 在垂直和水平方向上进行卷积，设置步长，实现了并行的传递信息for i in range(self.iter):conv_vert1 = nn.Conv2d(chan, chan, (1, conv_stride),padding=(0, conv_stride//2), groups=1, bias=False)conv_vert2 = nn.Conv2d(chan, chan, (1, conv_stride),padding=(0, conv_stride//2), groups=1, bias=False)setattr(self, 'conv_d'+str(i), conv_vert1)setattr(self, 'conv_u'+str(i), conv_vert2)conv_hori1 = nn.Conv2d(chan, chan, (conv_stride, 1),padding=(conv_stride//2, 0), groups=1, bias=False)conv_hori2 = nn.Conv2d(chan, chan, (conv_stride, 1),padding=(conv_stride//2, 0), groups=1, bias=False)setattr(self, 'conv_r'+str(i), conv_hori1)setattr(self, 'conv_l'+str(i), conv_hori2)idx_d = (torch.arange(self.height) + self.height //2**(self.iter - i)) % self.heightsetattr(self, 'idx_d'+str(i), idx_d)idx_u = (torch.arange(self.height) - self.height //2**(self.iter - i)) % self.heightsetattr(self, 'idx_u'+str(i), idx_u)idx_r = (torch.arange(self.width) + self.width //2**(self.iter - i)) % self.widthsetattr(self, 'idx_r'+str(i), idx_r)idx_l = (torch.arange(self.width) - self.width //2**(self.iter - i)) % self.widthsetattr(self, 'idx_l'+str(i), idx_l)def forward(self, x):x = x.clone()for direction in ['d', 'u']:for i in range(self.iter):conv = getattr(self, 'conv_' + direction + str(i))idx = getattr(self, 'idx_' + direction + str(i))x.add_(self.alpha * F.relu(conv(x[..., idx, :])))for direction in ['r', 'l']:for i in range(self.iter):conv = getattr(self, 'conv_' + direction + str(i))idx = getattr(self, 'idx_' + direction + str(i))x.add_(self.alpha * F.relu(conv(x[..., idx])))return x

对于SCNN的信息传递网络

  def message_passing_once(self, x, conv, vertical=True, reverse=False):"""Argument:----------x: input tensorvertical: vertical message passing or horizontalreverse: False for up-down or left-right, True for down-up or right-left"""nB, C, H, W = x.shape##对于输入的张量的H,W，迭代传递信息，时间会比resa慢很多if vertical:slices = [x[:, :, i:(i + 1), :] for i in range(H)]dim = 2else:slices = [x[:, :, :, i:(i + 1)] for i in range(W)]dim = 3if reverse:slices = slices[::-1]out = [slices[0]]for i in range(1, len(slices)):out.append(slices[i] + F.relu(conv(out[i - 1])))if reverse:out = out[::-1]return torch.cat(out, dim=dim)

比较可以看出resa时间上的优势十分明显。

3.BUSD

RESA的解码网络也加入了自己的元素，创新了双向上采样的解码器，解码器由两个分支组成，一个分支是恢复粗粒度特征，另一个是修复细微的损耗。输入将通过两个分支，并且将产生通道数量减半的2x上采样输出。通过这些堆叠的解码器块后，RESA生成的1/8特征图将恢复为与输入图像相同的大小。粗
粗粒度分支将从最后一层快速输出粗略向上采样的特征，这可能会忽略细节。设计了一条简单而浅的路径。我们首先应用1×1卷积来减少
通道数乘以输入特征图的2倍，然后是BN。双线性插值直接用于对输入特征图进行上采样。最后，执行ReLU。
细粒度分支用于微调来自粗粒度分支的信息丢失，并且路径比另一个更深。使用步幅为2的转置卷积对特征图进行上采样，并同时将通道数减少2倍。与粗粒度分支中使用的类似设计一样，对ReLU进行上采样。 Non-bottleneck块由具有BN和ReLU的四个3×1和1×3卷积组成，可以保持特征图的形状并以分解的方式有效地提取信息。在上采样操作之后，堆叠了两个non-bottleneck。

4.loss

该网络使用的是交叉熵损失，而具体过程就是使用真正的seg与通过模型前向计算后的seg并softmax后进行求交叉熵损失操作，并进行梯度计算和反向传播。