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import numpy as np | |
import torch | |
from torch import nn | |
from torch.nn import init | |
from torch.nn import functional as F | |
class DoubleAttention(nn.Module): | |
def __init__(self, in_channels,c_m=128,c_n=128,reconstruct = True): | |
super().__init__() | |
self.in_channels=in_channels | |
self.reconstruct = reconstruct | |
self.c_m=c_m | |
self.c_n=c_n | |
self.convA=nn.Conv2d(in_channels,c_m,1) | |
self.convB=nn.Conv2d(in_channels,c_n,1) | |
self.convV=nn.Conv2d(in_channels,c_n,1) | |
if self.reconstruct: | |
self.conv_reconstruct = nn.Conv2d(c_m, in_channels, kernel_size = 1) | |
self.init_weights() | |
def init_weights(self): | |
for m in self.modules(): | |
if isinstance(m, nn.Conv2d): | |
init.kaiming_normal_(m.weight, mode='fan_out') | |
if m.bias is not None: | |
init.constant_(m.bias, 0) | |
elif isinstance(m, nn.BatchNorm2d): | |
init.constant_(m.weight, 1) | |
init.constant_(m.bias, 0) | |
elif isinstance(m, nn.Linear): | |
init.normal_(m.weight, std=0.001) | |
if m.bias is not None: | |
init.constant_(m.bias, 0) | |
def forward(self, x): | |
b, c, h,w=x.shape | |
assert c==self.in_channels | |
A=self.convA(x) #b,c_m,h,w | |
B=self.convB(x) #b,c_n,h,w | |
V=self.convV(x) #b,c_n,h,w | |
tmpA=A.view(b,self.c_m,-1) | |
attention_maps=F.softmax(B.view(b,self.c_n,-1)) | |
attention_vectors=F.softmax(V.view(b,self.c_n,-1)) | |
# step 1: feature gating | |
global_descriptors=torch.bmm(tmpA,attention_maps.permute(0,2,1)) #b.c_m,c_n | |
# step 2: feature distribution | |
tmpZ = global_descriptors.matmul(attention_vectors) #b,c_m,h*w | |
tmpZ=tmpZ.view(b,self.c_m,h,w) #b,c_m,h,w | |
if self.reconstruct: | |
tmpZ=self.conv_reconstruct(tmpZ) | |
return tmpZ |