语义分割实战——基于DeepLabv3+神经网络头发分割系统源码

第一步：准备数据

头发分割数据，总共有1050张图片，里面的像素值为0和1，所以看起来全部是黑的，不影响使用

第二步：搭建模型

DeepLabV3+的网络结构如下图所示，主要为Encoder-Decoder结构。其中，Encoder为改进的DeepLabV3，Decoder为3+版本新提出的。

1.1、Encoder
在Encoder部分，主要包括了backbone（即：图1中的DCNN）、ASPP两大部分。

其中backbone有两种网络结构：将layer4改为空洞卷积的Resnet系列、改进的Xception。从backbone出来的feature map分两部分：一部分是最后一层卷积输出的feature maps，另一部分是中间的低级特征的feature maps；backbone输出的第一部分送入ASPP模块，第二部分则送入Decoder模块。
ASPP模块接受backbone的第一部分输出作为输入，使用了四种不同膨胀率的空洞卷积块（包括卷积、BN、激活层）和一个全局平均池化块（包括池化、卷积、BN、激活层）得到一共五组feature maps，将其concat起来之后，经过一个1*1卷积块（包括卷积、BN、激活、dropout层），最后送入Decoder模块。
1.2、Decoder
在Decoder部分，接收来自backbone中间层的低级feature maps和来自ASPP模块的输出作为输入。

首先，对低级feature maps使用1*1卷积进行通道降维，从256降到48（之所以需要降采样到48，是因为太多的通道会掩盖ASPP输出的feature maps的重要性，且实验验证48最佳）；
然后，对来自ASPP的feature maps进行插值上采样，得到与低级featuremaps尺寸相同的feature maps；
接着，将通道降维的低级feature maps和线性插值上采样得到的feature maps使用concat拼接起来，并送入一组3*3卷积块进行处理；
最后，再次进行线性插值上采样，得到与原图分辨率大小一样的预测图。

第三步：代码

1）损失函数为：交叉熵损失函数

2）网络代码：

import torch
import torch.nn as nn
import torch.nn.functional as F
from nets.xception import xception
from nets.mobilenetv2 import mobilenetv2class MobileNetV2(nn.Module):def __init__(self, downsample_factor=8, pretrained=True):super(MobileNetV2, self).__init__()from functools import partialmodel           = mobilenetv2(pretrained)self.features   = model.features[:-1]self.total_idx  = len(self.features)self.down_idx   = [2, 4, 7, 14]if downsample_factor == 8:for i in range(self.down_idx[-2], self.down_idx[-1]):self.features[i].apply(partial(self._nostride_dilate, dilate=2))for i in range(self.down_idx[-1], self.total_idx):self.features[i].apply(partial(self._nostride_dilate, dilate=4))elif downsample_factor == 16:for i in range(self.down_idx[-1], self.total_idx):self.features[i].apply(partial(self._nostride_dilate, dilate=2))def _nostride_dilate(self, m, dilate):classname = m.__class__.__name__if classname.find('Conv') != -1:if m.stride == (2, 2):m.stride = (1, 1)if m.kernel_size == (3, 3):m.dilation = (dilate//2, dilate//2)m.padding = (dilate//2, dilate//2)else:if m.kernel_size == (3, 3):m.dilation = (dilate, dilate)m.padding = (dilate, dilate)def forward(self, x):low_level_features = self.features[:4](x)x = self.features[4:](low_level_features)return low_level_features, x #-----------------------------------------#
#   ASPP特征提取模块
#   利用不同膨胀率的膨胀卷积进行特征提取
#-----------------------------------------#
class ASPP(nn.Module):def __init__(self, dim_in, dim_out, rate=1, bn_mom=0.1):super(ASPP, self).__init__()self.branch1 = nn.Sequential(nn.Conv2d(dim_in, dim_out, 1, 1, padding=0, dilation=rate,bias=True),nn.BatchNorm2d(dim_out, momentum=bn_mom),nn.ReLU(inplace=True),)self.branch2 = nn.Sequential(nn.Conv2d(dim_in, dim_out, 3, 1, padding=6*rate, dilation=6*rate, bias=True),nn.BatchNorm2d(dim_out, momentum=bn_mom),nn.ReLU(inplace=True),	)self.branch3 = nn.Sequential(nn.Conv2d(dim_in, dim_out, 3, 1, padding=12*rate, dilation=12*rate, bias=True),nn.BatchNorm2d(dim_out, momentum=bn_mom),nn.ReLU(inplace=True),	)self.branch4 = nn.Sequential(nn.Conv2d(dim_in, dim_out, 3, 1, padding=18*rate, dilation=18*rate, bias=True),nn.BatchNorm2d(dim_out, momentum=bn_mom),nn.ReLU(inplace=True),	)self.branch5_conv = nn.Conv2d(dim_in, dim_out, 1, 1, 0,bias=True)self.branch5_bn = nn.BatchNorm2d(dim_out, momentum=bn_mom)self.branch5_relu = nn.ReLU(inplace=True)self.conv_cat = nn.Sequential(nn.Conv2d(dim_out*5, dim_out, 1, 1, padding=0,bias=True),nn.BatchNorm2d(dim_out, momentum=bn_mom),nn.ReLU(inplace=True),		)def forward(self, x):[b, c, row, col] = x.size()#-----------------------------------------##   一共五个分支#-----------------------------------------#conv1x1 = self.branch1(x)conv3x3_1 = self.branch2(x)conv3x3_2 = self.branch3(x)conv3x3_3 = self.branch4(x)#-----------------------------------------##   第五个分支，全局平均池化+卷积#-----------------------------------------#global_feature = torch.mean(x,2,True)global_feature = torch.mean(global_feature,3,True)global_feature = self.branch5_conv(global_feature)global_feature = self.branch5_bn(global_feature)global_feature = self.branch5_relu(global_feature)global_feature = F.interpolate(global_feature, (row, col), None, 'bilinear', True)#-----------------------------------------##   将五个分支的内容堆叠起来#   然后1x1卷积整合特征。#-----------------------------------------#feature_cat = torch.cat([conv1x1, conv3x3_1, conv3x3_2, conv3x3_3, global_feature], dim=1)result = self.conv_cat(feature_cat)return resultclass DeepLab(nn.Module):def __init__(self, num_classes, backbone="mobilenet", pretrained=True, downsample_factor=16):super(DeepLab, self).__init__()if backbone=="xception":#----------------------------------##   获得两个特征层#   浅层特征    [128,128,256]#   主干部分    [30,30,2048]#----------------------------------#self.backbone = xception(downsample_factor=downsample_factor, pretrained=pretrained)in_channels = 2048low_level_channels = 256elif backbone=="mobilenet":#----------------------------------##   获得两个特征层#   浅层特征    [128,128,24]#   主干部分    [30,30,320]#----------------------------------#self.backbone = MobileNetV2(downsample_factor=downsample_factor, pretrained=pretrained)in_channels = 320low_level_channels = 24else:raise ValueError('Unsupported backbone - `{}`, Use mobilenet, xception.'.format(backbone))#-----------------------------------------##   ASPP特征提取模块#   利用不同膨胀率的膨胀卷积进行特征提取#-----------------------------------------#self.aspp = ASPP(dim_in=in_channels, dim_out=256, rate=16//downsample_factor)#----------------------------------##   浅层特征边#----------------------------------#self.shortcut_conv = nn.Sequential(nn.Conv2d(low_level_channels, 48, 1),nn.BatchNorm2d(48),nn.ReLU(inplace=True))		self.cat_conv = nn.Sequential(nn.Conv2d(48+256, 256, 3, stride=1, padding=1),nn.BatchNorm2d(256),nn.ReLU(inplace=True),nn.Dropout(0.5),nn.Conv2d(256, 256, 3, stride=1, padding=1),nn.BatchNorm2d(256),nn.ReLU(inplace=True),nn.Dropout(0.1),)self.cls_conv = nn.Conv2d(256, num_classes, 1, stride=1)def forward(self, x):H, W = x.size(2), x.size(3)#-----------------------------------------##   获得两个特征层#   low_level_features: 浅层特征-进行卷积处理#   x : 主干部分-利用ASPP结构进行加强特征提取#-----------------------------------------#low_level_features, x = self.backbone(x)x = self.aspp(x)low_level_features = self.shortcut_conv(low_level_features)#-----------------------------------------##   将加强特征边上采样#   与浅层特征堆叠后利用卷积进行特征提取#-----------------------------------------#x = F.interpolate(x, size=(low_level_features.size(2), low_level_features.size(3)), mode='bilinear', align_corners=True)x = self.cat_conv(torch.cat((x, low_level_features), dim=1))x = self.cls_conv(x)x = F.interpolate(x, size=(H, W), mode='bilinear', align_corners=True)return x

第四步：统计一些指标（训练过程中的loss和miou）