每日Attention学习24——Strip Convolution Block

模块出处

[TIP 21] [link] CoANet: Connectivity Attention Network for Road Extraction From Satellite Imagery

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模块名称

Strip Convolution Block (SCB)

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模块作用

多方向条形特征提取

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模块结构

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模块特点

• 类PSP设计，采用四个并行分支提取不同维度的信息
• 相比于经典的横向/纵向条形卷积，引入了两种斜方向的卷积来更好的学习斜向线条

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模块代码

import torch
import torch.nn as nn
import torch.nn.functional as F


class SCB(nn.Module):
    def __init__(self, in_channels, n_filters):
        super(SCB, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, in_channels // 4, 1)
        self.bn1 = nn.BatchNorm2d(in_channels // 4)
        self.relu1 = nn.ReLU()
        self.deconv1 = nn.Conv2d(
            in_channels // 4, in_channels // 8, (1, 9), padding=(0, 4)
        )
        self.deconv2 = nn.Conv2d(
            in_channels // 4, in_channels // 8, (9, 1), padding=(4, 0)
        )
        self.deconv3 = nn.Conv2d(
            in_channels // 4, in_channels // 8, (9, 1), padding=(4, 0)
        )
        self.deconv4 = nn.Conv2d(
            in_channels // 4, in_channels // 8, (1, 9), padding=(0, 4)
        )
        self.bn2 = nn.BatchNorm2d(in_channels // 4 + in_channels // 4)
        self.relu2 = nn.ReLU()
        self.conv3 = nn.Conv2d(
            in_channels // 4 + in_channels // 4, n_filters, 1)
        self.bn3 = nn.BatchNorm2d(n_filters)
        self.relu3 = nn.ReLU()

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu1(x)
        x1 = self.deconv1(x)
        x2 = self.deconv2(x)
        x3 = self.inv_h_transform(self.deconv3(self.h_transform(x)))
        x4 = self.inv_v_transform(self.deconv4(self.v_transform(x)))
        x = torch.cat((x1, x2, x3, x4), 1)
        x = self.bn2(x)
        x = self.relu2(x)
        x = self.conv3(x)
        x = self.bn3(x)
        x = self.relu3(x)
        return x

    def h_transform(self, x):
        shape = x.size()
        x = torch.nn.functional.pad(x, (0, shape[-1]))
        x = x.reshape(shape[0], shape[1], -1)[..., :-shape[-1]]
        x = x.reshape(shape[0], shape[1], shape[2], 2*shape[3]-1)
        return x

    def inv_h_transform(self, x):
        shape = x.size()
        x = x.reshape(shape[0], shape[1], -1).contiguous()
        x = torch.nn.functional.pad(x, (0, shape[-2]))
        x = x.reshape(shape[0], shape[1], shape[-2], 2*shape[-2])
        x = x[..., 0: shape[-2]]
        return x

    def v_transform(self, x):
        x = x.permute(0, 1, 3, 2)
        shape = x.size()
        x = torch.nn.functional.pad(x, (0, shape[-1]))
        x = x.reshape(shape[0], shape[1], -1)[..., :-shape[-1]]
        x = x.reshape(shape[0], shape[1], shape[2], 2*shape[3]-1)
        return x.permute(0, 1, 3, 2)

    def inv_v_transform(self, x):
        x = x.permute(0, 1, 3, 2)
        shape = x.size()
        x = x.reshape(shape[0], shape[1], -1)
        x = torch.nn.functional.pad(x, (0, shape[-2]))
        x = x.reshape(shape[0], shape[1], shape[-2], 2*shape[-2])
        x = x[..., 0: shape[-2]]
        return x.permute(0, 1, 3, 2)


if __name__ == '__main__':
    x = torch.randn([1, 64, 44, 44])
    scb = SCB(in_channels=64, n_filters=64)
    out = scb(x)
    print(out.shape)  # [1, 64, 44, 44]