SD_branch.py

import torch
from torch.nn import Module, Sequential, Conv2d, ReLU, AvgPool2d, MaxPool2d, Parameter, Linear, Sigmoid, Softmax, Dropout, Embedding
torch_ver = torch.__version__[:3]

__all__ = ['PAM_Module']


class SD_Module(Module):
    def __init__(self, in_dim):
        super(SD_Module, self).__init__()
        self.chanel_in = in_dim
        self.query_conv = Conv2d(in_channels=in_dim, out_channels=in_dim//8, kernel_size=1)
        self.key_conv = Conv2d(in_channels=in_dim, out_channels=in_dim//8, kernel_size=1)
        self.value_conv = Conv2d(in_channels=in_dim, out_channels=in_dim, kernel_size=1)
        self.gamma = Parameter(torch.zeros(1))
        self.softmax = Softmax(dim=-1)
    def forward(self, x):
        m_batchsize, C, height, width = x.size()
        proj_query = self.query_conv(x).view(m_batchsize, -1, width*height).permute(0, 2, 1)
        proj_key = self.key_conv(x).view(m_batchsize, -1, width*height)
        energy = torch.bmm(proj_query, proj_key)
        attention = self.softmax(energy)
        proj_value = self.value_conv(x).view(m_batchsize, -1, width*height)
        out = torch.bmm(proj_value, attention.permute(0, 2, 1))
        out = out.view(m_batchsize, C, height, width)
        out = self.gamma*out + x
        return out