一、写在前面
fcn是首次使用cnn来实现语义分割的，论文地址：fully convolutional networks for semantic segmentation
实现代码地址：https://github.com/shelhamer/fcn.berkeleyvision.org
全卷积神经网络主要使用了三种技术：
1. 卷积化（Convolutional）
2. 上采样（Upsample）
3. 跳跃结构（Skip Layer）
为了便于理解，我拿最简单的结构voc-fcn-alexnet进行说明，该网络结构主要用到了前面两个技术，不包含跳跃结构。
二、voc-fcn-alexnet 的train.prototxt文件
layer { ?name: "data" ?type: "Python" ?top: "data" ?top: "label" ?python_param { ???module: "voc_layers" ???layer: "SBDDSegDataLayer" ???param_str: "{\‘sbdd_dir\‘: \‘../data/sbdd/dataset\‘, \‘seed\‘: 1337, \‘split\‘: \‘train\‘, \‘mean\‘: (104.00699, 116.66877, 122.67892)}" ?}}layer { ?name: "conv1" ?type: "Convolution" ?bottom: "data" ?top: "conv1" ?convolution_param { ???num_output: 96 ???pad: 100 ???kernel_size: 11 ???group: 1 ???stride: 4 ?}}layer { ?name: "relu1" ?type: "ReLU" ?bottom: "conv1" ?top: "conv1"}layer { ?name: "pool1" ?type: "Pooling" ?bottom: "conv1" ?top: "pool1" ?pooling_param { ???pool: MAX ???kernel_size: 3 ???stride: 2 ?}}layer { ?name: "norm1" ?type: "LRN" ?bottom: "pool1" ?top: "norm1" ?lrn_param { ???local_size: 5 ???alpha: 0.0001 ???beta: 0.75 ?}}layer { ?name: "conv2" ?type: "Convolution" ?bottom: "norm1" ?top: "conv2" ?convolution_param { ???num_output: 256 ???pad: 2 ???kernel_size: 5 ???group: 2 ???stride: 1 ?}}layer { ?name: "relu2" ?type: "ReLU" ?bottom: "conv2" ?top: "conv2"}layer { ?name: "pool2" ?type: "Pooling" ?bottom: "conv2" ?top: "pool2" ?pooling_param { ???pool: MAX ???kernel_size: 3 ???stride: 2 ?}}layer { ?name: "norm2" ?type: "LRN" ?bottom: "pool2" ?top: "norm2" ?lrn_param { ???local_size: 5 ???alpha: 0.0001 ???beta: 0.75 ?}}layer { ?name: "conv3" ?type: "Convolution" ?bottom: "norm2" ?top: "conv3" ?convolution_param { ???num_output: 384 ???pad: 1 ???kernel_size: 3 ???group: 1 ???stride: 1 ?}}layer { ?name: "relu3" ?type: "ReLU" ?bottom: "conv3" ?top: "conv3"}layer { ?name: "conv4" ?type: "Convolution" ?bottom: "conv3" ?top: "conv4" ?convolution_param { ???num_output: 384 ???pad: 1 ???kernel_size: 3 ???group: 2 ???stride: 1 ?}}layer { ?name: "relu4" ?type: "ReLU" ?bottom: "conv4" ?top: "conv4"}layer { ?name: "conv5" ?type: "Convolution" ?bottom: "conv4" ?top: "conv5" ?convolution_param { ???num_output: 256 ???pad: 1 ???kernel_size: 3 ???group: 2 ???stride: 1 ?}}layer { ?name: "relu5" ?type: "ReLU" ?bottom: "conv5" ?top: "conv5"}layer { ?name: "pool5" ?type: "Pooling" ?bottom: "conv5" ?top: "pool5" ?pooling_param { ???pool: MAX ???kernel_size: 3 ???stride: 2 ?}}layer { ?name: "fc6" ?type: "Convolution" ?bottom: "pool5" ?top: "fc6" ?convolution_param { ???num_output: 4096 ???pad: 0 ???kernel_size: 6 ???group: 1 ???stride: 1 ?}}layer { ?name: "relu6" ?type: "ReLU" ?bottom: "fc6" ?top: "fc6"}layer { ?name: "drop6" ?type: "Dropout" ?bottom: "fc6" ?top: "fc6" ?dropout_param { ???dropout_ratio: 0.5 ?}}layer { ?name: "fc7" ?type: "Convolution" ?bottom: "fc6" ?top: "fc7" ?convolution_param { ???num_output: 4096 ???pad: 0 ???kernel_size: 1 ???group: 1 ???stride: 1 ?}}layer { ?name: "relu7" ?type: "ReLU" ?bottom: "fc7" ?top: "fc7"}layer { ?name: "drop7" ?type: "Dropout" ?bottom: "fc7" ?top: "fc7" ?dropout_param { ???dropout_ratio: 0.5 ?}}layer { ?name: "score_fr" ?type: "Convolution" ?bottom: "fc7" ?top: "score_fr" ?param { ???lr_mult: 1 ???decay_mult: 1 ?} ?param { ???lr_mult: 2 ???decay_mult: 0 ?} ?convolution_param { ???num_output: 21 ???pad: 0 ???kernel_size: 1 ?}}layer { ?name: "upscore" ?type: "Deconvolution" ?bottom: "score_fr" ?top: "upscore" ?param { ???lr_mult: 0 ?} ?convolution_param { ???num_output: 21 ???bias_term: false ???kernel_size: 63 ???stride: 32 ?}}layer { ?name: "score" ?type: "Crop" ?bottom: "upscore" ?bottom: "data" ?top: "score" ?crop_param { ???axis: 2 ???offset: 18 ?}}layer { ?name: "loss" ?type: "SoftmaxWithLoss" ?bottom: "score" ?bottom: "label" ?top: "loss" ?loss_param { ???ignore_label: 255 ???normalize: true ?}}三、网络结构
假设输入的图片为500x500，
根据train.prototxt文件，可以得到上图的网络结构，该网络结构除了前五层的卷积层，也把后面的三层也该为了卷积层，score_fr是卷积层的最后一层，也叫heatmap热图，热图就是我们最重要的高维特诊图，得到高维特征的heatmap之后就是最重要的一步也是最后的一步对原图像进行upsampling（即反卷积），把图像进行放大，得到原图像的大小。
四、损失函数
该网络的损失函数为SoftmaxWithLoss。首先进行softmax求解，求出每个像素点属于不同类别的概率，因为总共是分为21类，所以输出的通道数为21，即每个像素点对应21个概率值。然后求解每个像素点所属实际类别概率的log值之和的平均，再取负数，可得到损失函数，参考如下：
end
voc-fcn-alexnet网络结构理解
原文地址：https://www.cnblogs.com/smbx-ztbz/p/10505289.html