(WACV 2021)TripletAttention.py

# ---------------------------------------
# 论文: Rotate to Attend: Convolutional Triplet Attention Module (WACV 2021)
# Github地址: https://github.com/landskape-ai/triplet-attention
# ---------------------------------------

import torch
import torch.nn as nn


class BasicConv(nn.Module):
    def __init__(self, in_planes, out_planes, kernel_size, stride=1, padding=0, dilation=1, groups=1, relu=True,
                 bn=True, bias=False):
        super(BasicConv, self).__init__()
        self.out_channels = out_planes
        self.conv = nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding,
                              dilation=dilation, groups=groups, bias=bias)
        self.bn = nn.BatchNorm2d(out_planes, eps=1e-5, momentum=0.01, affine=True) if bn else None
        self.relu = nn.ReLU() if relu else None

    def forward(self, x):
        x = self.conv(x)
        if self.bn is not None:
            x = self.bn(x)
        if self.relu is not None:
            x = self.relu(x)
        return x


class ZPool(nn.Module):
    def forward(self, x):
        return torch.cat((torch.max(x, 1)[0].unsqueeze(1), torch.mean(x, 1).unsqueeze(1)), dim=1)


class AttentionGate(nn.Module):
    def __init__(self):
        super(AttentionGate, self).__init__()
        kernel_size = 7
        self.compress = ZPool()
        self.conv = BasicConv(2, 1, kernel_size, stride=1, padding=(kernel_size - 1) // 2, relu=False)

    def forward(self, x):
        x_compress = self.compress(x)
        x_out = self.conv(x_compress)
        scale = torch.sigmoid_(x_out)
        return x * scale


class TripletAttention(nn.Module):
    def __init__(self, no_spatial=False):
        super(TripletAttention, self).__init__()
        self.cw = AttentionGate()
        self.hc = AttentionGate()
        self.no_spatial = no_spatial
        if not no_spatial:
            self.hw = AttentionGate()

    def forward(self, x):
        x_perm1 = x.permute(0, 2, 1, 3).contiguous()
        x_out1 = self.cw(x_perm1)
        x_out11 = x_out1.permute(0, 2, 1, 3).contiguous()
        x_perm2 = x.permute(0, 3, 2, 1).contiguous()
        x_out2 = self.hc(x_perm2)
        x_out21 = x_out2.permute(0, 3, 2, 1).contiguous()
        if not self.no_spatial:
            x_out = self.hw(x)
            x_out = 1 / 3 * (x_out + x_out11 + x_out21)
        else:
            x_out = 1 / 2 * (x_out11 + x_out21)
        return x_out


# 输入 B C H W,  输出 B C H W
if __name__ == '__main__':
    input = torch.randn(3, 32, 64, 64)
    triplet = TripletAttention()
    output = triplet(input)
    print(output.shape)