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