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preresnet_sd_cifar.py
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preresnet_sd_cifar.py
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import torch
import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo
__all__ = ['resnet']
def conv3x3(in_planes, out_planes, stride=1):
"3x3 convolution with padding"
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None, death_rate=0.):
super(BasicBlock, self).__init__()
self.bn1 = nn.BatchNorm2d(inplanes)
self.relu = nn.ReLU(inplace=True)
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn2 = nn.BatchNorm2d(planes)
self.conv2 = conv3x3(planes, planes)
self.downsample = downsample
self.stride = stride
self.death_rate =death_rate
def forward(self, x):
residual = x
if self.downsample is not None:
residual = self.downsample(x)
if not self.training or torch.rand(1)[0] >= self.death_rate:
out = self.bn1(x)
out = self.relu(out)
out = self.conv1(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv2(out)
if self.training:
out /= (1. - self.death_rate)
out += residual
else:
out = residual
return out
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None, death_rate=0.):
super(Bottleneck, self).__init__()
self.bn1 = nn.BatchNorm2d(inplanes)
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
padding=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes)
self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
self.death_rate =death_rate
def forward(self, x):
residual = x
if self.downsample is not None:
residual = self.downsample(x)
if not self.training or torch.rand(1)[0] >= self.death_rate:
out = self.bn1(x)
out = self.relu(out)
out = self.conv1(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn3(out)
out = self.relu(out)
out = self.conv3(out)
if self.training:
out /= (1. - self.death_rate)
out += residual
else:
out = residual
return out
class ResNet(nn.Module):
def __init__(self, depth, num_classes=1000, death_mode='linear', death_rate=0.5):
super(ResNet, self).__init__()
# Model type specifies number of layers for CIFAR-10 model
assert (depth - 2) % 6 == 0, 'depth should be 6n+2'
n = (depth - 2) // 6
block = Bottleneck if depth >=44 else BasicBlock
nblocks = (depth - 2) // 2
if death_mode == 'uniform':
death_rates = [death_rate] * nblocks
print("Stochastic Depth: uniform mode")
elif death_mode == 'linear':
death_rates = [float(i + 1) * death_rate / float(nblocks)
for i in range(nblocks)]
print("Stochastic Depth: linear mode")
else:
death_rates = [0.] * (3 * n)
print("Stochastic Depth: none mode")
self.inplanes = 16
self.conv1 = nn.Conv2d(3, 16, kernel_size=3, padding=1,
bias=False)
self.layer1 = self._make_layer(block, 16, n, death_rates[:n])
self.layer2 = self._make_layer(block, 32, n, death_rates[n:2*n], stride=2)
self.layer3 = self._make_layer(block, 64, n, death_rates[2*n:], stride=2)
self.bn1 = nn.BatchNorm2d(64 * block.expansion)
self.relu = nn.ReLU(inplace=True)
self.avgpool = nn.AvgPool2d(8)
self.fc1 = nn.Linear(64 * block.expansion, 64 * block.expansion)
self.fc = nn.Linear(64 * block.expansion, num_classes)
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
def _make_layer(self, block, planes, blocks, death_rates, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes, planes * block.expansion,
kernel_size=1, stride=stride, bias=False),
#nn.BatchNorm2d(planes * block.expansion),
#nn.AvgPool2d((2,2), stride = (2, 2))
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample, death_rate=death_rates[0]))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes, death_rate=death_rates[i]))
return nn.Sequential(*layers)
def split2(self, x):
x1, x2 = torch.split(x,x.shape[1]/2,1)
return x1, x2
def reparameterize(self, mu, logvar):
if self.training:
std = logvar.mul(0.5).exp_()
eps = torch.autograd.Variable(std.data.new(std.size()).normal_())
return eps.mul(std).add_(mu)
else:
return mu
def forward(self, x):
x = self.conv1(x)
x = self.layer1(x) # 32x32
x = self.layer2(x) # 16x16
x = self.layer3(x) # 8x8
x = self.bn1(x)
x = self.relu(x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.fc1(x)
output = self.fc(x)
return output
def resnet(**kwargs):
"""
Constructs a ResNet model.
"""
return ResNet(**kwargs)