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cifar_densenet.py
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cifar_densenet.py
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import torch as torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import numpy as np
import torchvision
import torchvision.transforms as transforms
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.utils import shuffle
import torch.nn.init as init
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
import torchvision
import torchvision.transforms as transforms
'''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)