├── effnet.py
└── readme.md


/effnet.py:
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 1 | '''
 2 | EffNet: AN EFFICIENT STRUCTURE FOR CONVOLUTIONAL NEURAL NETWORKS
 3 | Implementation in Pytorch of Effnet.
 4 | https://arxiv.org/abs/1801.06434
 5 | 
 6 | '''
 7 | import torch.nn as nn
 8 | 
 9 | class Flatten(nn.Module):
10 |     def forward(self, x):
11 |         x = x.view(x.size()[0], -1)
12 |         return x
13 |      
14 | class EffNet(nn.Module):
15 | 
16 |     def __init__(self, nb_classes=10, include_top=True, weights=None):
17 |         super(EffNet, self).__init__()
18 |         
19 |         self.block1 = self.make_layers(32, 64)
20 |         self.block2 = self.make_layers(64, 128)
21 |         self.block3 = self.make_layers(128, 256)
22 |         self.flatten = Flatten()
23 |         self.linear = nn.Linear(4096, nb_classes)
24 |         self.include_top = include_top
25 |         self.weights = weights
26 | 
27 |     def make_layers(self, ch_in, ch_out):
28 |         layers = [
29 |             nn.Conv2d(3, ch_in, kernel_size=(1,1), stride=(1,1), bias=False, padding=0, dilation=(1,1)) if ch_in ==32 else nn.Conv2d(ch_in, ch_in, kernel_size=(1,1),stride=(1,1), bias=False, padding=0, dilation=(1,1)) ,
30 |             self.make_post(ch_in),
31 |             # DepthWiseConvolution2D
32 |             nn.Conv2d(ch_in, 1 * ch_in, groups=ch_in, kernel_size=(1, 3),stride=(1,1), padding=(0,1), bias=False, dilation=(1,1)),
33 |             self.make_post(ch_in),
34 |             nn.MaxPool2d(kernel_size=(2,1), stride=(2,1)),
35 |             # DepthWiseConvolution2D
36 |             nn.Conv2d(ch_in, 1 * ch_in, groups=ch_in, kernel_size=(3, 1), stride=(1,1), padding=(1,0), bias=False, dilation=(1,1)),
37 |             self.make_post(ch_in),
38 |             nn.Conv2d(ch_in, ch_out, kernel_size=(1, 2), stride=(1, 2), bias=False, padding=(0,0), dilation=(1,1)),
39 |             self.make_post(ch_out),
40 |         ]
41 |         return nn.Sequential(*layers)
42 | 
43 |     def make_post(self, ch_in):
44 |         layers = [
45 |             nn.LeakyReLU(0.3),
46 |             nn.BatchNorm2d(ch_in, momentum=0.99)
47 |         ]
48 |         return nn.Sequential(*layers)
49 | 
50 |     def forward(self, x):
51 |         x = self.block1(x)
52 |         x = self.block2(x)
53 |         x = self.block3(x)
54 |         if self.include_top:
55 |             x = self.flatten(x)
56 |             x = self.linear(x)
57 |         return x


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/readme.md:
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1 | # EffNet: AN EFFICIENT STRUCTURE FOR CONVOLUTIONAL NEURAL NETWORKS
2 | 
3 | Implementation in Pytorch of Effnet.
4 | 
5 | https://arxiv.org/abs/1801.06434


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