LinkNet

LinkNet

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• LinkNet-Model(pytorch版本)

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• 1.一 论文导读
• 2.二 论文精读
• 3.三 代码实现
• 4.四 问题思索

《LinkNet:Exploiting Encoder Representations for Efficient Semantic Segmentation》
作者：Abhishek Chaurasia,etc;
单位：普渡大学
发表会议及时间：IEEE 2017

https://arxiv.org/abs/1707.03718

一 论文导读

二 论文精读

三 代码实现

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import torch.nn as nn
from torchvision import models
import torch

class BasicBlock(nn.Module):

    def __init__(self, in_planes, out_planes, stride=1, padding=0, bias=False):
        super(BasicBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_planes, out_planes, 3, stride, padding, bias=bias)
        self.bn1 = nn.BatchNorm2d(out_planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(out_planes, out_planes, 3, 1, padding, bias=bias)
        self.bn2 = nn.BatchNorm2d(out_planes)
        self.downsample = None
        if stride > 1:
            self.downsample = nn.Sequential(nn.Conv2d(in_planes, out_planes, 3, stride, bias=False),
                            nn.BatchNorm2d(out_planes),)

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out


class Encoder(nn.Module):

    def __init__(self, in_planes, out_planes, stride=1, padding=0, bias=False):
        super(Encoder, self).__init__()
        self.block1 = BasicBlock(in_planes, out_planes, stride, padding, bias)
        self.block2 = BasicBlock(out_planes, out_planes, 1, padding, bias)

    def forward(self, x):
        x = self.block1(x)
        x = self.block2(x)

        return x

class Decoder(nn.Module):

    def __init__(self, in_planes, out_planes, kernel_size, stride=1, padding=0, output_padding=0, bias=False):
        # TODO bias=True
        super(Decoder, self).__init__()
        self.conv1 = nn.Sequential(nn.Conv2d(in_planes, in_planes//4, 1, 1, 0, bias=bias),
                                nn.BatchNorm2d(in_planes//4),
                                nn.ReLU(inplace=True),)
        self.tp_conv = nn.Sequential(nn.ConvTranspose2d(in_planes//4, in_planes//4, kernel_size, stride, padding, output_padding, bias=bias),
                                nn.BatchNorm2d(in_planes//4),
                                nn.ReLU(inplace=True),)
        self.conv2 = nn.Sequential(nn.Conv2d(in_planes//4, out_planes, 1, 1, 0, bias=bias),
                                nn.BatchNorm2d(out_planes),
                                nn.ReLU(inplace=True),)

    def forward(self, x):
        x = self.conv1(x)
        x = self.tp_conv(x)
        x = self.conv2(x)

        return x

class linknet(nn.Module):
    def __init__(self, n_classes=12):
        super(linknet, self).__init__()
        
        base = models.resnet18(pretrained=False)


        self.in_block = nn.Sequential(
            base.conv1,
            base.bn1,
            base.relu,
            base.maxpool
        )

        self.encoder1 = base.layer1
        self.encoder2 = base.layer2
        self.encoder3 = base.layer3
        self.encoder4 = base.layer4

        self.decoder1 = Decoder(64, 64, 3, 1, 1, 0)
        self.decoder2 = Decoder(128, 64, 3, 2, 1, 1)
        self.decoder3 = Decoder(256, 128, 3, 2, 1, 1)
        self.decoder4 = Decoder(512, 256, 3, 2, 1, 1)

        # Classifier
        self.tp_conv1 = nn.Sequential(nn.ConvTranspose2d(64, 32, 3, 2, 1, 1),
                                      nn.BatchNorm2d(32),
                                      nn.ReLU(inplace=True),)
        self.conv2 = nn.Sequential(nn.Conv2d(32, 32, 3, 1, 1),
                                nn.BatchNorm2d(32),
                                nn.ReLU(inplace=True),)
        self.tp_conv2 = nn.ConvTranspose2d(32, n_classes, 2, 2, 0)

    def forward(self, x):
        # Initial block
        print('x:', x.size())
        x = self.in_block(x);print('x:', x.size())

        # Encoder blocks
        e1 = self.encoder1(x);print('e1:', e1.size())
        e2 = self.encoder2(e1);print('e2:', e2.size())
        e3 = self.encoder3(e2);print('e3:', e3.size())
        e4 = self.encoder4(e3);print('e4:', e4.size())

        # Decoder blocks
        d4 = e3 + self.decoder4(e4);print('d4:', d4.size())
        d3 = e2 + self.decoder3(d4);print('d3:', d3.size())
        d2 = e1 + self.decoder2(d3);print('d2:', d2.size())
        d1 = x + self.decoder1(d2);print('d1:', d1.size())

        # Classifier
        y = self.tp_conv1(d1);print('y:', y.size())
        y = self.conv2(y);print('y:', y.size())
        y = self.tp_conv2(y);print('y:', y.size())

        return y

# 随机生成输入数据
inputs = torch.randn((1, 3, 512, 512))
# 定义网络
net = linknet(n_classes=8)
# 前向传播
out = net(inputs)
# 打印输出大小
print('-----'*5)
print(out.size())
print('-----'*5)

四 问题思索