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Runtime error
| 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 |