语义分割-CVPR2018 Discriminative Feature Network（DFN）

这篇博客只是为了学习交流使用，来源知乎ycszen的文章。

论文：arxiv.org/abs/1804.09337

代码：https://github.com/ycszen/TorchSeg

详细解读请见：CVPR 2018 | 旷视科技Face++提出用于语义分割的判别特征网络DFN

目录

1. 介绍

2. 网络结构

3. 实验

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1. 介绍

现有语义分割方法仍然有待解决的两类挑战：类内不一致和类间无差别。

所以，本文从宏观角度出发重新思考语义分割任务，提出应该将同一类的像素考虑成一个整体，也就需要增强类内一致性，增大类间区分性。总结而言，我们需要更具有判别力的特征。

2. 网络结构

DFN主要包括两部分：Smooth Network 和Border Network。

Smooth Network主要解决类内不一致性问题。文中认为类内不一致性问题主要来自Context 的缺乏。需要引入Multi-scale Context 和Global Context；但是，不同阶段的特征虽然带来了Multi-scale Context，与此同时也带来了不同的判别能力；因此，需要对这些具有不同判别力的特征进行筛选，这就诞生了其中核心的设计 Channel Attention Block（CAB）

 CAB 利用相邻Stage 的特征计算Channel Attention 然后对低阶的特征进行筛选，如下图所示。文中认为，高阶的特征语义信息更强，更具有判别力。

通道注意块原理图。(a)中，黄色方块代表低阶特征，红色方块代表高阶特征。我们将相邻阶段的特征串接起来计算权重向量，从而对低阶段的特征映射进行权重调整。较红的颜色代表较高的权重值。(b)为阶段-4通道注意块的真实注意值向量。蓝色越深表示权重值越高。

在FCN架构中，卷积运算符输出一个分数映射，它给出每个像素处每个类的概率。

在公式1中，它隐含地表明不同通道的权值是相等的。然而，如之前所述，不同阶段的特征具有不同的重要性。为了获得类内一致的预测，我们需要提取类内一致的判别特征，抑制类内一致的判别特征。因此,在公式 3 ,特征图谱上的α值用于x,代表了CAB 特征选择。通过这种设计，可以使网络逐步获得判别特征，使类内预测保持一致。

Refinement residual block（RRB）优化残差模块:特征网络中每个阶段的特征图都经过RRB 。该模块的第一个组件是一个1×1卷积层，作者将通道数统一到512。同时，它可以整合所有通道的信息。接着是一个基本的残差块，可以优化特征图。此外，受ResNet的启发，这个block还可以增强每个阶段的识别能力。

Border Network 主要解决类间无差别的问题。特征网络有不同的阶段，低阶特征具有更详细的信息，高阶特征具有更高的语义信息。在我们的工作中，我们需要更多语义的语义边界。因此，我们设计了一个自底向上的边界网络。该网络能同时从低阶边缘获得准确的边缘信息和高阶边缘信息，消除了原有边缘缺乏语义信息的问题。这样，高阶语义信息可以逐阶段地优化低阶段地细节边界信息。利用传统的图像处理方法，如Canny[2]，从语义分割的ground truth中提取网络的监控信号。

DFN(
  (backbone): ResNet(
    (conv1): Sequential(
      (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
      (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU(inplace)
      (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (5): ReLU(inplace)
      (6): Conv2d(64, 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)
    (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
    (layer1): Sequential(
      (0): Bottleneck(
        (conv1): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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)
        (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
        (relu_inplace): ReLU(inplace)
        (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): Bottleneck(
        (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
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        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
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        (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
        (relu_inplace): ReLU(inplace)
      )
      (2): Bottleneck(
        (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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)
        (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
        (relu_inplace): ReLU(inplace)
      )
    )
    (layer2): Sequential(
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        (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
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        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
        (relu_inplace): ReLU(inplace)
        (downsample): Sequential(
          (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
          (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (1): Bottleneck(
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        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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)
        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
        (relu_inplace): ReLU(inplace)
      )
      (2): Bottleneck(
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        (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)
        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
        (relu_inplace): ReLU(inplace)
      )
      (3): Bottleneck(
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        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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)
        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
        (relu_inplace): ReLU(inplace)
      )
    )
    (layer3): Sequential(
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        (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
        (relu_inplace): ReLU(inplace)
        (downsample): Sequential(
          (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)
          (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (1): Bottleneck(
        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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)
        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
        (relu_inplace): ReLU(inplace)
      )
      (2): Bottleneck(
        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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)
        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
        (relu_inplace): ReLU(inplace)
      )
      (3): Bottleneck(
        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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)
        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
        (relu_inplace): ReLU(inplace)
      )
      (4): Bottleneck(
        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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)
        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
        (relu_inplace): ReLU(inplace)
      )
      (5): Bottleneck(
        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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)
        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
        (relu_inplace): ReLU(inplace)
      )
      (6): Bottleneck(
        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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)
        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
        (relu_inplace): ReLU(inplace)
      )
      (7): Bottleneck(
        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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)
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        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
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        (relu_inplace): ReLU(inplace)
      )
      (8): Bottleneck(
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        (relu_inplace): ReLU(inplace)
      )
      (9): Bottleneck(
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        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
        (relu_inplace): ReLU(inplace)
      )
      (10): Bottleneck(
        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
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        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
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        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
        (relu_inplace): ReLU(inplace)
      )
      (11): Bottleneck(
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        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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)
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        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
        (relu_inplace): ReLU(inplace)
      )
      (12): Bottleneck(
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        (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)
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        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
        (relu_inplace): ReLU(inplace)
      )
      (13): Bottleneck(
        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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)
        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
        (relu_inplace): ReLU(inplace)
      )
      (14): Bottleneck(
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        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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)
        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
        (relu_inplace): ReLU(inplace)
      )
      (15): Bottleneck(
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        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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)
        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
        (relu_inplace): ReLU(inplace)
      )
      (16): Bottleneck(
        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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)
        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
        (relu_inplace): ReLU(inplace)
      )
      (17): Bottleneck(
        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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)
        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
        (relu_inplace): ReLU(inplace)
      )
      (18): Bottleneck(
        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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)
        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
        (relu_inplace): ReLU(inplace)
      )
      (19): Bottleneck(
        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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)
        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
        (relu_inplace): ReLU(inplace)
      )
      (20): Bottleneck(
        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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)
        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
        (relu_inplace): ReLU(inplace)
      )
      (21): Bottleneck(
        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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)
        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
        (relu_inplace): ReLU(inplace)
      )
      (22): Bottleneck(
        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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)
        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
        (relu_inplace): ReLU(inplace)
      )
    )
    (layer4): Sequential(
      (0): Bottleneck(
        (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
        (relu_inplace): ReLU(inplace)
        (downsample): Sequential(
          (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)
          (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (1): Bottleneck(
        (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
        (relu_inplace): ReLU(inplace)
      )
      (2): Bottleneck(
        (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
        (relu_inplace): ReLU(inplace)
      )
    )
  )
  (global_context): Sequential(
    (0): AdaptiveAvgPool2d(output_size=1)
    (1): ConvBnRelu(
      (conv): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace)
    )
  )
  (smooth_pre_rrbs): ModuleList(
    (0): RefineResidual(
      (conv_1x1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (cbr): ConvBnRelu(
        (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
      (conv_refine): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (relu): ReLU()
    )
    (1): RefineResidual(
      (conv_1x1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (cbr): ConvBnRelu(
        (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
      (conv_refine): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (relu): ReLU()
    )
    (2): RefineResidual(
      (conv_1x1): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (cbr): ConvBnRelu(
        (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
      (conv_refine): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (relu): ReLU()
    )
    (3): RefineResidual(
      (conv_1x1): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (cbr): ConvBnRelu(
        (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
      (conv_refine): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (relu): ReLU()
    )
  )
  (cabs): ModuleList(
    (0): ChannelAttention(
      (channel_attention): SELayer(
        (avg_pool): AdaptiveAvgPool2d(output_size=1)
        (fc): Sequential(
          (0): Linear(in_features=1024, out_features=512, bias=True)
          (1): ReLU(inplace)
          (2): Linear(in_features=512, out_features=512, bias=True)
          (3): Sigmoid()
        )
      )
    )
    (1): ChannelAttention(
      (channel_attention): SELayer(
        (avg_pool): AdaptiveAvgPool2d(output_size=1)
        (fc): Sequential(
          (0): Linear(in_features=1024, out_features=512, bias=True)
          (1): ReLU(inplace)
          (2): Linear(in_features=512, out_features=512, bias=True)
          (3): Sigmoid()
        )
      )
    )
    (2): ChannelAttention(
      (channel_attention): SELayer(
        (avg_pool): AdaptiveAvgPool2d(output_size=1)
        (fc): Sequential(
          (0): Linear(in_features=1024, out_features=512, bias=True)
          (1): ReLU(inplace)
          (2): Linear(in_features=512, out_features=512, bias=True)
          (3): Sigmoid()
        )
      )
    )
    (3): ChannelAttention(
      (channel_attention): SELayer(
        (avg_pool): AdaptiveAvgPool2d(output_size=1)
        (fc): Sequential(
          (0): Linear(in_features=1024, out_features=512, bias=True)
          (1): ReLU(inplace)
          (2): Linear(in_features=512, out_features=512, bias=True)
          (3): Sigmoid()
        )
      )
    )
  )
  (smooth_aft_rrbs): ModuleList(
    (0): RefineResidual(
      (conv_1x1): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (cbr): ConvBnRelu(
        (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
      (conv_refine): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (relu): ReLU()
    )
    (1): RefineResidual(
      (conv_1x1): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (cbr): ConvBnRelu(
        (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
      (conv_refine): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (relu): ReLU()
    )
    (2): RefineResidual(
      (conv_1x1): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (cbr): ConvBnRelu(
        (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
      (conv_refine): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (relu): ReLU()
    )
    (3): RefineResidual(
      (conv_1x1): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (cbr): ConvBnRelu(
        (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
      (conv_refine): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (relu): ReLU()
    )
  )
  (smooth_heads): ModuleList(
    (0): DFNHead(
      (rrb): RefineResidual(
        (conv_1x1): Conv2d(512, 189, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (cbr): ConvBnRelu(
          (conv): Conv2d(189, 189, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (bn): BatchNorm2d(189, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace)
        )
        (conv_refine): Conv2d(189, 189, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (conv): Conv2d(189, 21, kernel_size=(1, 1), stride=(1, 1))
    )
    (1): DFNHead(
      (rrb): RefineResidual(
        (conv_1x1): Conv2d(512, 189, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (cbr): ConvBnRelu(
          (conv): Conv2d(189, 189, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (bn): BatchNorm2d(189, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace)
        )
        (conv_refine): Conv2d(189, 189, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (conv): Conv2d(189, 21, kernel_size=(1, 1), stride=(1, 1))
    )
    (2): DFNHead(
      (rrb): RefineResidual(
        (conv_1x1): Conv2d(512, 189, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (cbr): ConvBnRelu(
          (conv): Conv2d(189, 189, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (bn): BatchNorm2d(189, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace)
        )
        (conv_refine): Conv2d(189, 189, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (conv): Conv2d(189, 21, kernel_size=(1, 1), stride=(1, 1))
    )
    (3): DFNHead(
      (rrb): RefineResidual(
        (conv_1x1): Conv2d(512, 189, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (cbr): ConvBnRelu(
          (conv): Conv2d(189, 189, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (bn): BatchNorm2d(189, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace)
        )
        (conv_refine): Conv2d(189, 189, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (conv): Conv2d(189, 21, kernel_size=(1, 1), stride=(1, 1))
    )
  )
  (border_pre_rrbs): ModuleList(
    (0): RefineResidual(
      (conv_1x1): Conv2d(256, 21, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (cbr): ConvBnRelu(
        (conv): Conv2d(21, 21, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn): BatchNorm2d(21, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
      (conv_refine): Conv2d(21, 21, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (relu): ReLU()
    )
    (1): RefineResidual(
      (conv_1x1): Conv2d(512, 21, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (cbr): ConvBnRelu(
        (conv): Conv2d(21, 21, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn): BatchNorm2d(21, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
      (conv_refine): Conv2d(21, 21, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (relu): ReLU()
    )
    (2): RefineResidual(
      (conv_1x1): Conv2d(1024, 21, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (cbr): ConvBnRelu(
        (conv): Conv2d(21, 21, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn): BatchNorm2d(21, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
      (conv_refine): Conv2d(21, 21, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (relu): ReLU()
    )
    (3): RefineResidual(
      (conv_1x1): Conv2d(2048, 21, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (cbr): ConvBnRelu(
        (conv): Conv2d(21, 21, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn): BatchNorm2d(21, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
      (conv_refine): Conv2d(21, 21, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (relu): ReLU()
    )
  )
  (border_aft_rrbs): ModuleList(
    (0): RefineResidual(
      (conv_1x1): Conv2d(21, 21, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (cbr): ConvBnRelu(
        (conv): Conv2d(21, 21, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn): BatchNorm2d(21, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
      (conv_refine): Conv2d(21, 21, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (relu): ReLU()
    )
    (1): RefineResidual(
      (conv_1x1): Conv2d(21, 21, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (cbr): ConvBnRelu(
        (conv): Conv2d(21, 21, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn): BatchNorm2d(21, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
      (conv_refine): Conv2d(21, 21, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (relu): ReLU()
    )
    (2): RefineResidual(
      (conv_1x1): Conv2d(21, 21, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (cbr): ConvBnRelu(
        (conv): Conv2d(21, 21, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn): BatchNorm2d(21, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
      (conv_refine): Conv2d(21, 21, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (relu): ReLU()
    )
    (3): RefineResidual(
      (conv_1x1): Conv2d(21, 21, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (cbr): ConvBnRelu(
        (conv): Conv2d(21, 21, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn): BatchNorm2d(21, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
      (conv_refine): Conv2d(21, 21, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (relu): ReLU()
    )
  )
  (border_heads): ModuleList(
    (0): DFNHead(
      (rrb): RefineResidual(
        (conv_1x1): Conv2d(21, 9, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (cbr): ConvBnRelu(
          (conv): Conv2d(9, 9, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (bn): BatchNorm2d(9, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace)
        )
        (conv_refine): Conv2d(9, 9, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (conv): Conv2d(9, 1, kernel_size=(1, 1), stride=(1, 1))
    )
    (1): DFNHead(
      (rrb): RefineResidual(
        (conv_1x1): Conv2d(21, 9, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (cbr): ConvBnRelu(
          (conv): Conv2d(9, 9, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (bn): BatchNorm2d(9, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace)
        )
        (conv_refine): Conv2d(9, 9, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (conv): Conv2d(9, 1, kernel_size=(1, 1), stride=(1, 1))
    )
    (2): DFNHead(
      (rrb): RefineResidual(
        (conv_1x1): Conv2d(21, 9, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (cbr): ConvBnRelu(
          (conv): Conv2d(9, 9, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (bn): BatchNorm2d(9, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace)
        )
        (conv_refine): Conv2d(9, 9, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (conv): Conv2d(9, 1, kernel_size=(1, 1), stride=(1, 1))
    )
    (3): DFNHead(
      (rrb): RefineResidual(
        (conv_1x1): Conv2d(21, 9, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (cbr): ConvBnRelu(
          (conv): Conv2d(9, 9, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (bn): BatchNorm2d(9, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace)
        )
        (conv_refine): Conv2d(9, 9, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (conv): Conv2d(9, 1, kernel_size=(1, 1), stride=(1, 1))
    )
  )
)

3. 实验

首先是对Smooth Network 各部分的消融实验分析。下表详细比较了提出的平滑网络的性能。RRB:优化残差模块。CAB:通道注意区。DS：深监督。为了细化层次特征，我们在每个阶段添加 softmax损失，不包括全局平均池层。

通过可视化输出，可以看到Smooth Network确实可以将类内区域变得更加一致，如下图所示，在PASCAL VOC 2012数据集上平滑网络的结果。

同时，文中还对Smooth Network 和Border Network 进行了消融实验分析。

将边界网络与平滑网络相结合作为判别特征网络。SN:平滑网络。BN:边界网络。MS Flip:添加多尺度输入和左右翻转输入。

通过可视化Border Network的输出，可以看到Border Network确实可以很好地关注到Semantic Boundary区域，如下图所示。

基于PASCAL VOC 2012数据集的边界网络边界预测。第三列是Canny算子从Ground-Truth中提取的语义边界。最后一列是边界网络的预测结果。

最终，DFN 在PASCAL VOC 2012和Cityscapes上性能都达到了当时的最好值。