├── LBCNN.py
└── README.md


/LBCNN.py:
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 1 | import torch
 2 | from torch.nn import Module, Parameter, Conv2d
 3 | 
 4 | class LBCNN(Module):
 5 |     def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1):
 6 | 		super(LBCNN, self).__init__()
 7 | 		self.nInputPlane = in_channels
 8 | 		self.nOutputPlane = out_channels
 9 | 		self.kW = kernel_size
10 | 		self.LBCNN = Conv2d(in_channels, out_channels, kernel_size, stride, padding, dilation, groups, False)
11 | 		self.LBCNN.weight.requires_grad=False
12 |          #init weight
13 | 		numElements = self.nInputPlane*self.nOutputPlane*self.kW*self.kW
14 | 		index = torch.randperm(numElements)
15 | 		self.LBCNN.weight.copy = (torch.Tensor(self.nOutputPlane,self.nInputPlane,self.kW,self.kW).uniform_(0, 1))
16 | 		temp = (torch.bernoulli(self.LBCNN.weight.copy)*2-1).view(-1)
17 | 		for i in range(1,numElements/2):
18 | 		    temp[index[i]] =0;
19 | 		self.LBCNN.weight.copy = temp.view(self.nOutputPlane,self.nInputPlane,self.kW,self.kW)
20 | 		self.LBCNN.weight = Parameter(self.LBCNN.weight.copy)
21 | 		
22 |     def forward(self, input):
23 |          return self.LBCNN.forward(input)
24 | 


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/README.md:
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 1 | # pytorch-LBCNN
 2 | Local Binary Convolutional Neural Networks by pytorch
 3 | 
 4 | ## 1.Original torch project
 5 | 
 6 | https://github.com/juefeix/lbcnn.torch
 7 | 
 8 | ## 2.This project by pytorch
 9 | 
10 | Only including the Convolutional layer through Local Binary,it was named LBCNN.py
11 | 
12 | ## 3.Usage
13 | 
14 | Making Local Binary Convolutional layer instead of the original Convolutional layer
15 | 
16 | e.g:
17 | 
18 | from LBCNN import LBCNN #module
19 | 
20 | ....
21 | 
22 | ....
23 | 
24 | self.conv = LBCNN(in_channels,out_channels,3,stride,1) #define LBCNN in model file
25 | 
26 | ## 4.See blog
27 | 
28 | http://blog.csdn.net/yyqq7226741/article/details/78308036
29 | ## If I can help you, please give me a star :star2::star2::star2:
30 | 


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