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- '''DenseNet in PyTorch.'''
- import math
-
- import torch
- import torch.nn as nn
- import torch.nn.functional as F
-
-
- class Bottleneck(nn.Module):
- def __init__(self, in_planes, growth_rate):
- super(Bottleneck, self).__init__()
- self.bn1 = nn.BatchNorm2d(in_planes)
- self.conv1 = nn.Conv2d(in_planes, 4*growth_rate, kernel_size=1, bias=False)
- self.bn2 = nn.BatchNorm2d(4*growth_rate)
- self.conv2 = nn.Conv2d(4*growth_rate, growth_rate, kernel_size=3, padding=1, bias=False)
-
- def forward(self, x):
- out = self.conv1(F.relu(self.bn1(x)))
- out = self.conv2(F.relu(self.bn2(out)))
- out = torch.cat([out,x], 1)
- return out
-
-
- class Transition(nn.Module):
- def __init__(self, in_planes, out_planes):
- super(Transition, self).__init__()
- self.bn = nn.BatchNorm2d(in_planes)
- self.conv = nn.Conv2d(in_planes, out_planes, kernel_size=1, bias=False)
-
- def forward(self, x):
- out = self.conv(F.relu(self.bn(x)))
- out = F.avg_pool2d(out, 2)
- return out
-
-
- class DenseNet(nn.Module):
- def __init__(self, block, nblocks, growth_rate=12, reduction=0.5, num_classes=10):
- super(DenseNet, self).__init__()
- self.growth_rate = growth_rate
-
- num_planes = 2*growth_rate
- self.conv1 = nn.Conv2d(3, num_planes, kernel_size=3, padding=1, bias=False)
-
- self.dense1 = self._make_dense_layers(block, num_planes, nblocks[0])
- num_planes += nblocks[0]*growth_rate
- out_planes = int(math.floor(num_planes*reduction))
- self.trans1 = Transition(num_planes, out_planes)
- num_planes = out_planes
-
- self.dense2 = self._make_dense_layers(block, num_planes, nblocks[1])
- num_planes += nblocks[1]*growth_rate
- out_planes = int(math.floor(num_planes*reduction))
- self.trans2 = Transition(num_planes, out_planes)
- num_planes = out_planes
-
- self.dense3 = self._make_dense_layers(block, num_planes, nblocks[2])
- num_planes += nblocks[2]*growth_rate
- out_planes = int(math.floor(num_planes*reduction))
- self.trans3 = Transition(num_planes, out_planes)
- num_planes = out_planes
-
- self.dense4 = self._make_dense_layers(block, num_planes, nblocks[3])
- num_planes += nblocks[3]*growth_rate
-
- self.bn = nn.BatchNorm2d(num_planes)
- self.linear = nn.Linear(num_planes, num_classes)
-
- def _make_dense_layers(self, block, in_planes, nblock):
- layers = []
- for i in range(nblock):
- layers.append(block(in_planes, self.growth_rate))
- in_planes += self.growth_rate
- return nn.Sequential(*layers)
-
- def forward(self, x):
- out = self.conv1(x)
- out = self.trans1(self.dense1(out))
- out = self.trans2(self.dense2(out))
- out = self.trans3(self.dense3(out))
- out = self.dense4(out)
- out = F.avg_pool2d(F.relu(self.bn(out)), 4)
- out = out.view(out.size(0), -1)
- out = self.linear(out)
- return out
-
- def DenseNet121():
- return DenseNet(Bottleneck, [6,12,24,16], growth_rate=32)
-
- def DenseNet169():
- return DenseNet(Bottleneck, [6,12,32,32], growth_rate=32)
-
- def DenseNet201():
- return DenseNet(Bottleneck, [6,12,48,32], growth_rate=32)
-
- def DenseNet161():
- return DenseNet(Bottleneck, [6,12,36,24], growth_rate=48)
-
- def densenet_cifar():
- return DenseNet(Bottleneck, [6,12,24,16], growth_rate=12)
-
- def test():
- net = densenet_cifar()
- x = torch.randn(1,3,32,32)
- y = net(x)
- print(y)
-
- # test()
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