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