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resnet.py
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resnet.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.distributions.multivariate_normal import MultivariateNormal
import random
import numpy as np
import copy
__all__ = ['ResNet18', 'ResNet50']
import torch
import torch.nn as nn
from torch.nn import functional as F
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.distributions.multivariate_normal import MultivariateNormal
import random
import numpy as np
import copy
C_DIM=10
C_Start_Layer=4000
feature_store=[]
out_store=[]
feature_store1=[]
out_store1=[]
layer_number=0
out_store_tmp=[]
is_first_input=1
noise_threshold1=0.51
noise_threshold2=0.0001
noise_threshold3=0.0001
noise_ratio = 1.0
noise_increment=0.5
def perturb_inp(x):
return x
def perturb(x, y):
return x
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, in_planes, planes, stride=1):
super(BasicBlock, self).__init__()
self.bn1 = nn.BatchNorm2d(in_planes)
self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
if stride != 1 or in_planes != self.expansion*planes:
self.shortcut = nn.Sequential(
nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False)
)
def forward(self, x):
global feature_store
global layer_number
global out_store
global out_store1
global is_first_input
layer_number=layer_number+1
out = F.relu(self.bn1(x))
shortcut = self.shortcut(out) if hasattr(self, 'shortcut') else x
out = self.conv1(out)
out = self.conv2(F.relu(self.bn2(out)))
if is_first_input==0:
out = perturb(out, layer_number)
out += shortcut
return out
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, in_planes, planes, stride=1):
super(Bottleneck, self).__init__()
self.bn1 = nn.BatchNorm2d(in_planes)
self.conv1 = nn.Conv2d(in_planes, 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, self.expansion*planes, kernel_size=1, bias=False)
if stride != 1 or in_planes != self.expansion*planes:
self.shortcut = nn.Sequential(
nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False)
)
def forward(self, x):
global feature_store
global layer_number
global out_store
global out_store1
global is_first_input
layer_number=layer_number+1
out = F.relu(self.bn1(x))
shortcut = self.shortcut(out) if hasattr(self, 'shortcut') else x
out = self.conv1(out)
out = self.conv2(F.relu(self.bn2(out)))
out = self.conv3(F.relu(self.bn3(out)))
if is_first_input==0:
out = perturb(out, layer_number)
out += shortcut
return out
class ResNet(nn.Module):
def __init__(self, block, num_blocks, num_classes, n_threshold1, n_threshold2, n_threshold3, n_increment, n_ratio):
super(ResNet, self).__init__()
self.in_planes = 64
self.n_threshold1 = n_threshold1
self.n_threshold2 = n_threshold2
self.n_threshold3 = n_threshold3
self.n_increment = n_increment
self.n_ratio = n_ratio
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
self.linear = nn.Linear(512 * block.expansion, num_classes)
def _make_layer(self, block, planes, num_blocks, stride):
strides = [stride] + [1] * (num_blocks - 1)
layers = []
for stride in strides:
layers.append(block(self.in_planes, planes, stride))
self.in_planes = planes * block.expansion
return nn.Sequential(*layers)
def forward(self, x):
x = x.permute(0, 3, 1, 2)
global feature_store
global layer_number
global out_store
global feature_store1
global out_store1
global is_first_input
global noise_threshold1
global noise_threshold2
global noise_threshold3
global noise_ratio
global noise_increment
noise_threshold1 = self.n_threshold1
noise_threshold2 = self.n_threshold2
noise_threshold2 = self.n_threshold3
noise_increment = self.n_increment
noise_ratio = self.n_ratio
is_first_input=0
feature_store = []
out_store = []
feature_store1 = []
out_store1 = []
layer_number = 0
out = self.conv1(x)
out = self.layer1(out)
out = self.layer2(out)
out = self.layer3(out)
out = self.layer4(out)
out = F.avg_pool2d(out, 4)
out = out.view(out.size(0), -1)
outf = self.linear(out)
feature = outf
return outf
def ResNet18(classes=10, noise_threshold1=0, noise_threshold2=0, noise_threshold3=0, noise_increment=0, noise_ratio=0):
return ResNet(BasicBlock, [2, 2, 2, 2], num_classes=classes, n_threshold1=noise_threshold1, n_threshold2=noise_threshold2, n_threshold3=noise_threshold3, n_increment=noise_increment, n_ratio=noise_ratio)
def ResNet34():
return ResNet(BasicBlock, [3, 4, 6, 3])
def ResNet50(classes=10, noise_threshold1=0, noise_threshold2=0, noise_threshold3=0, noise_increment=0, noise_ratio=0):
return ResNet(Bottleneck, [3, 4, 6, 3], num_classes=classes, n_threshold1=noise_threshold1, n_threshold2=noise_threshold2, n_threshold3=noise_threshold3, n_increment=noise_increment, n_ratio=noise_ratio)
def ResNet101():
return ResNet(Bottleneck, [3, 4, 23, 3])
def ResNet152():
return ResNet(Bottleneck, [3, 8, 36, 3])
def ResNet1202():
return ResNet(BasicBlock, [50, 50, 50, 3])