注意力机制论文:CCNet: Criss-Cross Attention for Semantic Segmentation及其PyTorch实现

CCNet: Criss-Cross Attention for Semantic Segmentation
PDF: https://arxiv.org/pdf/1811.11721.pdf
PyTorch代码: https://github.com/shanglianlm0525/CvPytorch
PyTorch代码: https://github.com/shanglianlm0525/PyTorch-Networks

DANet中，attention map计算的是所有像素与所有像素之间的相似性，空间复杂度为(HxW)x(HxW)，而本文采用了criss-cross思想，只计算每个像素与其同行同列即十字上的像素的相似性，通过进行循环(两次相同操作)，间接计算到每个像素与每个像素的相似性，将空间复杂度降为(HxW)x(H+W-1)

具有以下优点

1）较少的GPU内存使用;
2）计算效率高;
3）最先进的性能。

1 Criss-Cross Attention

2 Recurrent Criss-Cross Attention

在计算矩阵相乘时每个像素只抽取特征图中对应十字位置的像素进行点乘，计算相似度。和non-local的方法相比极大的降低了计算量，同时采用二阶注意力，能够从所有像素中获取全图像的上下文信息，以生成具有密集且丰富的上下文信息的新特征图。在计算矩阵相乘时，每个像素只抽取特征图中对应十字位置的像素进行点乘，计算相似度。

3 CCNet

4 实验结果

PyTorch代码:

import torch
import torch.nn as nndef INF(B, H, W):return -torch.diag(torch.tensor(float("inf")).repeat(H), 0).unsqueeze(0).repeat(B * W, 1, 1)class CrissCrossAttention(nn.Module):""" Criss-Cross Attention Module"""def __init__(self, in_dim):super(CrissCrossAttention, self).__init__()self.query_conv = nn.Conv2d(in_channels=in_dim, out_channels=in_dim // 8, kernel_size=1)self.key_conv = nn.Conv2d(in_channels=in_dim, out_channels=in_dim // 8, kernel_size=1)self.value_conv = nn.Conv2d(in_channels=in_dim, out_channels=in_dim, kernel_size=1)self.softmax = nn.Softmax(dim=3)self.INF = INFself.gamma = nn.Parameter(torch.zeros(1))def forward(self, x):m_batchsize, _, height, width = x.size()proj_query = self.query_conv(x)proj_query_H = proj_query.permute(0, 3, 1, 2).contiguous().view(m_batchsize * width, -1, height).permute(0, 2, 1)proj_query_W = proj_query.permute(0, 2, 1, 3).contiguous().view(m_batchsize * height, -1, width).permute(0, 2, 1)proj_key = self.key_conv(x)proj_key_H = proj_key.permute(0, 3, 1, 2).contiguous().view(m_batchsize * width, -1, height)proj_key_W = proj_key.permute(0, 2, 1, 3).contiguous().view(m_batchsize * height, -1, width)proj_value = self.value_conv(x)proj_value_H = proj_value.permute(0, 3, 1, 2).contiguous().view(m_batchsize * width, -1, height)proj_value_W = proj_value.permute(0, 2, 1, 3).contiguous().view(m_batchsize * height, -1, width)energy_H = (torch.bmm(proj_query_H, proj_key_H) + self.INF(m_batchsize, height, width)).view(m_batchsize, width,height, height).permute(0, 2, 1, 3)energy_W = torch.bmm(proj_query_W, proj_key_W).view(m_batchsize, height, width, width)concate = self.softmax(torch.cat([energy_H, energy_W], 3))att_H = concate[:, :, :, 0:height].permute(0, 2, 1, 3).contiguous().view(m_batchsize * width, height, height)# print(concate)# print(att_H)att_W = concate[:, :, :, height:height + width].contiguous().view(m_batchsize * height, width, width)out_H = torch.bmm(proj_value_H, att_H.permute(0, 2, 1)).view(m_batchsize, width, -1, height).permute(0, 2, 3, 1)out_W = torch.bmm(proj_value_W, att_W.permute(0, 2, 1)).view(m_batchsize, height, -1, width).permute(0, 2, 1, 3)# print(out_H.size(),out_W.size())return self.gamma * (out_H + out_W) + xif __name__=='__main__':model = CrissCrossAttention(16)print(model)input = torch.randn(1, 16, 64, 64)out = model(input)print(out.shape)