mutual_attention.py

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

class _NonLocalBlockND(nn.Module):
    def __init__(self, in_channels, inter_channels=None, dimension=3, sub_sample=True, bn_layer=True):
        super(_NonLocalBlockND, self).__init__()

        assert dimension in [1, 2, 3]

        self.dimension = dimension
        self.sub_sample = sub_sample

        self.in_channels = in_channels
        self.inter_channels = inter_channels

        if self.inter_channels is None:
            self.inter_channels = in_channels // 2
            if self.inter_channels == 0:
                self.inter_channels = 1

        if dimension == 3:
            conv_nd = nn.Conv3d
            max_pool_layer = nn.MaxPool3d(kernel_size=(1, 2, 2))
            bn = nn.BatchNorm3d
        elif dimension == 2:
            conv_nd = nn.Conv2d
            max_pool_layer = nn.MaxPool2d(kernel_size=(2, 2))
            bn = nn.BatchNorm2d
        else:
            conv_nd = nn.Conv1d
            max_pool_layer = nn.MaxPool1d(kernel_size=(2))
            bn = nn.BatchNorm1d

        self.g = conv_nd(in_channels=self.in_channels, out_channels=self.inter_channels,
                         kernel_size=1, stride=1, padding=0)

        if bn_layer:
            self.W = nn.Sequential(
                conv_nd(in_channels=self.inter_channels, out_channels=self.in_channels,
                        kernel_size=1, stride=1, padding=0),
                bn(self.in_channels)
            )
            nn.init.constant_(self.W[1].weight, 0)
            nn.init.constant_(self.W[1].bias, 0)
        else:
            self.W = conv_nd(in_channels=self.inter_channels, out_channels=self.in_channels,
                             kernel_size=1, stride=1, padding=0)
            nn.init.constant_(self.W.weight, 0)
            nn.init.constant_(self.W.bias, 0)

        self.theta = conv_nd(in_channels=self.in_channels, out_channels=self.inter_channels,
                             kernel_size=1, stride=1, padding=0)
        self.phi = conv_nd(in_channels=self.in_channels, out_channels=self.inter_channels,
                           kernel_size=1, stride=1, padding=0)

        if sub_sample:
            self.g = nn.Sequential(self.g, max_pool_layer)
            self.phi = nn.Sequential(self.phi, max_pool_layer)

    def forward(self, x):
        '''
        :param x: (b, c, t, h, w)
        :return:
        '''

        batch_size = x.size(0)
     
        g_x = self.g(x).view(batch_size, self.inter_channels, -1)
        g_x = g_x.permute(0, 2, 1)

        theta_x = self.theta(x).view(batch_size, self.inter_channels, -1)
        theta_x = theta_x.permute(0, 2, 1)
        phi_x = self.phi(x).view(batch_size, self.inter_channels, -1)
        f = torch.matmul(theta_x, phi_x)
        f_div_C = F.softmax(f, dim=-1)
      
        y = torch.matmul(f_div_C, g_x)
        y = y.permute(0, 2, 1).contiguous()
        y = y.view(batch_size, self.inter_channels, *x.size()[2:])
        W_y = self.W(y)
        z = W_y + x

        return z
      
class NONLocalBlock1D_mutual(_NonLocalBlockND):
    def __init__(self, in_channels, inter_channels=None, sub_sample=False, bn_layer=True):
        super(NONLocalBlock1D_mutual, self).__init__(in_channels,
                                              inter_channels=inter_channels,
                                              dimension=1, sub_sample=sub_sample,
                                              bn_layer=bn_layer)

        if bn_layer:
            self.W = nn.Sequential(
                nn.Conv1d(in_channels=self.inter_channels*2, out_channels=self.in_channels,
                        kernel_size=1, stride=1, padding=0),
                nn.BatchNorm1d(self.in_channels)
            )
            nn.init.constant_(self.W[1].weight, 0)
            nn.init.constant_(self.W[1].bias, 0)
        else:
            self.W = nn.Conv1d(in_channels=self.inter_channels*2, out_channels=self.in_channels,
                             kernel_size=1, stride=1, padding=0)
            nn.init.constant_(self.W.weight, 0)
            nn.init.constant_(self.W.bias, 0)        
        
    def forward(self, x):
        '''
        :param x: (b, c, t, h, w)
        :return:
        '''

        batch_size = x.size(0)
        n_point = x.size(2)
        data_1 = x[0:1] # Serve as key

        g_x = self.g(x).view(batch_size, self.inter_channels, -1)
        g_x = g_x.permute(0, 2, 1)

        theta_x = self.theta(data_1).view(1, self.inter_channels, -1)
        theta_x = theta_x.permute(0, 2, 1)

        phi_x = self.phi(x).view(batch_size, self.inter_channels, -1)

        f = torch.matmul(theta_x, phi_x)
        f_div_C = F.softmax(f, dim=-1)
        
        fg_attention = torch.mean(f_div_C, dim=1)[:,:,None].repeat(1, 1, self.inter_channels)
        bg_attention = 1 - fg_attention

        fg_attention_features = torch.mul(fg_attention, g_x)
        bg_attention_features = torch.mul(bg_attention, g_x)

        y = torch.cat([fg_attention_features, bg_attention_features], dim=2)
        y = y.permute(0, 2, 1).contiguous()
        W_y = self.W(y)
        z = W_y + x  
             
        return z      
      
if __name__ == '__main__':
    img = torch.zeros(2, 3, 20)
    net = NONLocalBlock1D_mutual(3, sub_sample=False, bn_layer=True)
    out = net(img)
    print(out.size())