| from collections import OrderedDict
|
|
|
| import torch
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| import torch.nn as nn
|
|
|
| from .bn import ABN
|
|
|
|
|
| class DenseModule(nn.Module):
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| def __init__(self, in_channels, growth, layers, bottleneck_factor=4, norm_act=ABN, dilation=1):
|
| super(DenseModule, self).__init__()
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| self.in_channels = in_channels
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| self.growth = growth
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| self.layers = layers
|
|
|
| self.convs1 = nn.ModuleList()
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| self.convs3 = nn.ModuleList()
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| for i in range(self.layers):
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| self.convs1.append(nn.Sequential(OrderedDict([
|
| ("bn", norm_act(in_channels)),
|
| ("conv", nn.Conv2d(in_channels, self.growth * bottleneck_factor, 1, bias=False))
|
| ])))
|
| self.convs3.append(nn.Sequential(OrderedDict([
|
| ("bn", norm_act(self.growth * bottleneck_factor)),
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| ("conv", nn.Conv2d(self.growth * bottleneck_factor, self.growth, 3, padding=dilation, bias=False,
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| dilation=dilation))
|
| ])))
|
| in_channels += self.growth
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|
|
| @property
|
| def out_channels(self):
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| return self.in_channels + self.growth * self.layers
|
|
|
| def forward(self, x):
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| inputs = [x]
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| for i in range(self.layers):
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| x = torch.cat(inputs, dim=1)
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| x = self.convs1[i](x)
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| x = self.convs3[i](x)
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| inputs += [x]
|
|
|
| return torch.cat(inputs, dim=1)
|
|
|
