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DeepLabV3+:Mobilenetv2的改进以及浅层特征和深层特征的融合

目录

Mobilenetv2的改进

浅层特征和深层特征的融合

完整代码

参考资料


Mobilenetv2的改进

在DeeplabV3当中,一般不会5次下采样,可选的有3次下采样和4次下采样。因为要进行五次下采样的话会损失较多的信息。

在这里mobilenetv2会从之前写好的模块中得到,但注意的是,我们在这里获得的特征是[-1],也就是最后的1x1卷积不取,只取循环完后的模型。

down_idx是InvertedResidual进行的次数。

# t, c, n, s
[1, 16, 1, 1], 
[6, 24, 2, 2],    2
[6, 32, 3, 2],    4
[6, 64, 4, 2],    7  
[6, 96, 3, 1],
[6, 160, 3, 2],   14
[6, 320, 1, 1], 

根据下采样的不同,当downsample_factor=8时,进行3次下采样,对倒数两次,步长为2的InvertedResidual进行参数的修改,让步长变为1,膨胀系数为2。

当downsample_factor=16时,进行4次下采样,只需对最后一次进行参数的修改。

import torch
import torch.nn as nn
import torch.nn.functional as F
from functools import partial

from net.mobilenetv2 import mobilenetv2
from net.ASPP import ASPP

class MobileNetV2(nn.Module):
    def __init__(self, downsample_factor=8, pretrained=True):
        super(MobileNetV2, self).__init__()
        
        model           = 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

forward当中,会输出两个特征层,一个是浅层特征层,具有浅层的语义信息;另一个是深层特征层,具有深层的语义信息。

浅层特征和深层特征的融合

 具有高语义信息的部分先进行上采样,低语义信息的特征层进行1x1卷积,二者进行特征融合,再进行3x3卷积进行特征提取

self.aspp = ASPP(dim_in=in_channels, dim_out=256, rate=16//downsample_factor)

这一步就是获得那个绿色的特征层;

low_level_features = self.shortcut_conv(low_level_features)

从这里将是对浅层特征的初步处理(1x1卷积);

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))

上采样后进行特征融合,这样我们输入和输出的大小才相同,每一个像素点才能进行预测;

完整代码

# deeplabv3plus.py

import torch
import torch.nn as nn
import torch.nn.functional as F
from functools import partial

from net.xception import xception
from net.mobilenetv2 import mobilenetv2
from net.ASPP import ASPP

class MobileNetV2(nn.Module):
    def __init__(self, downsample_factor=8, pretrained=True):
        super(MobileNetV2, self).__init__()
        
        model           = 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

class 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 主干部分    
            self.backbone = xception(downsample_factor=downsample_factor, pretrained=pretrained)
            in_channels = 2048
            low_level_channels = 256
        elif backbone=="mobilenet":

            #   获得两个特征层:浅层特征 主干部分
            self.backbone = MobileNetV2(downsample_factor=downsample_factor, pretrained=pretrained)
            in_channels = 320
            low_level_channels = 24
        else:
            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, kernel_size=(3,3), stride=(1,1), padding=1),
            nn.BatchNorm2d(256),
            nn.ReLU(inplace=True),
            nn.Dropout(0.5),

            nn.Conv2d(256, 256, kernel_size=(3,3), stride=(1,1), padding=1),
            nn.BatchNorm2d(256),
            nn.ReLU(inplace=True),

            nn.Dropout(0.1),
        )
        self.cls_conv = nn.Conv2d(256, num_classes, kernel_size=(1,1), stride=(1,1))

    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

参考资料

DeepLabV3-/论文精选 at main · Auorui/DeepLabV3- (github.com)

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