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model_relprop.py
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model_relprop.py
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'''
Author: Sai Aparna Aketi
'''
from __future__ import print_function
import torch.nn as nn
from compute_rscore import *
import torch.nn.functional as F
from blocks_resnet import *
###############################################################
class VGG16Net_cifar(nn.Module):
def __init__(self,vgg16):
super(VGG16Net_cifar, self).__init__()
self.layers = nn.Sequential(
Conv2d(vgg16.module.features[0]), #1
BatchNorm2d(vgg16.module.features[1]),
ReLU(),
Conv2d(vgg16.module.features[3]), #2
BatchNorm2d(vgg16.module.features[4]),
ReLU(),
MaxPool2d(vgg16.module.features[6]),
Conv2d(vgg16.module.features[7]), #3
BatchNorm2d(vgg16.module.features[8]),
ReLU(),
Conv2d(vgg16.module.features[10]), #4
BatchNorm2d(vgg16.module.features[11]),
ReLU(),
MaxPool2d(vgg16.module.features[13]),
Conv2d(vgg16.module.features[14]), #5
BatchNorm2d(vgg16.module.features[15]),
ReLU(),
Conv2d(vgg16.module.features[17]), #6
BatchNorm2d(vgg16.module.features[18]),
ReLU(),
Conv2d(vgg16.module.features[20]), #7
BatchNorm2d(vgg16.module.features[21]),
ReLU(),
MaxPool2d(vgg16.module.features[23]),
Conv2d(vgg16.module.features[24]), #8
BatchNorm2d(vgg16.module.features[25]),
ReLU(),
Conv2d(vgg16.module.features[27]), #9
BatchNorm2d(vgg16.module.features[28]),
ReLU(),
Conv2d(vgg16.module.features[30]), #10
BatchNorm2d(vgg16.module.features[31]),
ReLU(),
MaxPool2d(vgg16.module.features[33]),
Conv2d(vgg16.module.features[34]), #11
BatchNorm2d(vgg16.module.features[35]),
ReLU(),
Conv2d(vgg16.module.features[37]), #12
BatchNorm2d(vgg16.module.features[38]),
ReLU(),
Conv2d(vgg16.module.features[40]), #13
BatchNorm2d(vgg16.module.features[41]),
ReLU(),
MaxPool2d(vgg16.module.features[43]),
Reshape(f=512, n=1),
Linear(vgg16.module.classifier[0]),
ReLU(),
Linear(vgg16.module.classifier[3])
)
def forward(self, x):
out = self.layers(x)
return out
def relprop(self, R):
for l in range(len(self.layers), 0, -1):
R = self.layers[l-1].relprop(R)
self.layers[l-1].register_buffer('Rscore',R)
return R
##############################################################################
class RES56Net(nn.Module):
def __init__(self,res56):
super(RES56Net, self).__init__()
self.initial = nn.Sequential(Conv2d(res56.module.conv1),BatchNorm2d(res56.module.bn1),ReLU())
self.res_i = nn.Sequential(ResConnect_add())
self.layer1 = self._make_layer(block,res56.module.layer1, 9)
self.layer2_s = block_with_shortcut(res56.module.layer2[0])
self.layer2 = self._make_layer_s(block,res56.module.layer2, 9)
self.layer3_s = block_with_shortcut(res56.module.layer3[0])
self.layer3 = self._make_layer_s(block,res56.module.layer3, 8)
self.layer3_end = block_end(res56.module.layer3[8])
self.avgpool = AvgPool2d(res56.module.avgpool)
self.reshape = Reshape(f=64, n=1)
self.linear = Linear(res56.module.fc)
self.softmax = nn.Softmax(dim=1)
def _make_layer(self, block, layer, count):
layers = []
layers.append(block(layer[0]))
for i in range(1, count):
layers.append(block(layer[i]))
return nn.Sequential(*layers)
def _make_layer_s(self, block, layer, count):
layers = []
layers.append(block(layer[1]))
for i in range(2, count):
layers.append(block(layer[i]))
return nn.Sequential(*layers)
def forward(self, x):
x = self.initial(x)
x = self.res_i(x)
out = self.layer1(x)
out = self.layer2_s(out)
out = self.layer2(out)
out = self.layer3_s(out)
out = self.layer3(out)
out = self.layer3_end(out)
out = self.avgpool(out)
out = self.linear(self.reshape(out))
out = self.softmax(out)
return out
def relprop(self, R):
R = self.linear.relprop(R)
R = self.reshape.relprop(R)
R = self.avgpool.relprop(R)
R, Rprev = self.layer3_end.relprop(R)
for i in range(0,7):
R, Rprev = self.layer3[6-i].relprop(R, Rprev)
R, Rprev = self.layer3_s.relprop(R, Rprev)
for i in range(0,8):
R, Rprev = self.layer2[7-i].relprop(R, Rprev)
R, Rprev = self.layer2_s.relprop(R, Rprev)
for i in range(0,9):
R, Rprev = self.layer1[8-i].relprop(R, Rprev)
return R
################################################################################
class RES110Net(nn.Module):
def __init__(self,res110, n=1):
super(RES110Net, self).__init__()
self.initial = nn.Sequential(Conv2d(res110.module.conv1),BatchNorm2d(res110.module.bn1),ReLU())
self.res_i = nn.Sequential(ResConnect_add())
self.layer1 = self._make_layer(block,res110.module.layer1, 18)
self.layer2_s = block_with_shortcut(res110.module.layer2[0])
self.layer2 = self._make_layer_s(block,res110.module.layer2, 18)
self.layer3_s = block_with_shortcut(res110.module.layer3[0])
self.layer3 = self._make_layer_s(block,res110.module.layer3, 17)
self.layer3_end = block_end(res110.module.layer3[17])
self.avgpool = AvgPool2d(res110.module.avgpool)
self.reshape = Reshape(f=64,n=n)
self.linear = Linear(res110.module.fc)
self.softmax = nn.Softmax(dim=1)
def _make_layer(self, block, layer, count):
layers = []
layers.append(block(layer[0]))
for i in range(1, count):
layers.append(block(layer[i]))
return nn.Sequential(*layers)
def _make_layer_s(self, block, layer, count):
layers = []
layers.append(block(layer[1]))
for i in range(2, count):
layers.append(block(layer[i]))
return nn.Sequential(*layers)
def forward(self, x):
x = self.initial(x)
x = self.res_i(x)
out = self.layer1(x)
out = self.layer2_s(out)
out = self.layer2(out)
out = self.layer3_s(out)
out = self.layer3(out)
out = self.layer3_end(out)
out = self.avgpool(out)
out = self.linear(self.reshape(out))
out = self.softmax(out)
return out
def relprop(self, R):
R = self.linear.relprop(R)
R = self.reshape.relprop(R)
R = self.avgpool.relprop(R)
R, Rprev = self.layer3_end.relprop(R)
for i in range(0,16):
R, Rprev = self.layer3[15-i].relprop(R, Rprev)
R, Rprev = self.layer3_s.relprop(R, Rprev)
for i in range(0,17):
R, Rprev = self.layer2[16-i].relprop(R, Rprev)
R, Rprev = self.layer2_s.relprop(R, Rprev)
for i in range(0,18):
R, Rprev = self.layer1[17-i].relprop(R, Rprev)
return R
#####################################################################
class RES164Net(nn.Module):
def __init__(self,res164, n=1):
super(RES164Net, self).__init__()
self.initial = nn.Sequential(Conv2d(res164.module.conv1),BatchNorm2d(res164.module.bn1),ReLU())
self.res_i = nn.Sequential(ResConnect_add())
self.layer1_s = bottleneck_shortcut(res164.module.layer1[0])
self.layer1 = self._make_layer_s(bottleneck_block,res164.module.layer1, 18)
self.layer2_s = bottleneck_shortcut(res164.module.layer2[0])
self.layer2 = self._make_layer_s(bottleneck_block,res164.module.layer2, 18)
self.layer3_s = bottleneck_shortcut(res164.module.layer3[0])
self.layer3 = self._make_layer_s(bottleneck_block,res164.module.layer3, 17)
self.layer3_end = bottleneck_end(res164.module.layer3[17])
self.avgpool = AvgPool2d(res164.module.avgpool)
self.reshape = Reshape(f=256, n=n)
self.linear = Linear(res164.module.fc)
self.softmax = nn.Softmax(dim=1)
def _make_layer(self, bottleneck_block, layer, count):
layers = []
layers.append(bottleneck_block(layer[0]))
for i in range(1, count):
layers.append(bottleneck_block(layer[i]))
return nn.Sequential(*layers)
def _make_layer_s(self, bottleneck_block, layer, count):
layers = []
layers.append(bottleneck_block(layer[1]))
for i in range(2, count):
layers.append(bottleneck_block(layer[i]))
return nn.Sequential(*layers)
def forward(self, x):
x = self.initial(x)
x = self.res_i(x)
out = self.layer1_s(x)
out = self.layer1(out)
out = self.layer2_s(out)
out = self.layer2(out)
out = self.layer3_s(out)
out = self.layer3(out)
out = self.layer3_end(out)
out = self.avgpool(out)
out = self.linear(self.reshape(out))
out = self.softmax(out)
return out
def relprop(self, R):
R = self.linear.relprop(R)
R = self.reshape.relprop(R)
R = self.avgpool.relprop(R)
R, Rprev = self.layer3_end.relprop(R)
for i in range(0,16):
R, Rprev = self.layer3[15-i].relprop(R, Rprev)
R, Rprev = self.layer3_s.relprop(R, Rprev)
for i in range(0,17):
R, Rprev = self.layer2[16-i].relprop(R, Rprev)
R, Rprev = self.layer2_s.relprop(R, Rprev)
for i in range(0,17):
R, Rprev = self.layer1[16-i].relprop(R, Rprev)
R, Rprev = self.layer1_s.relprop(R, Rprev)
return R
#################################################################################
class RES34Net(nn.Module):
def __init__(self,res34):
super(RES34Net, self).__init__()
self.initial = nn.Sequential(Conv2d(res34.module.conv1),BatchNorm2d(res34.module.bn1),
ReLU(),MaxPool2d(res34.module.maxpool1))
self.res_i = nn.Sequential(ResConnect_add())
self.layer1 = self._make_layer(block,res34.module.layer1, 3)
self.layer2_s = block_with_shortcut(res34.module.layer2[0])
self.layer2 = self._make_layer_s(block,res34.module.layer2, 4)
self.layer3_s = block_with_shortcut(res34.module.layer3[0])
self.layer3 = self._make_layer_s(block,res34.module.layer3, 6)
self.layer4_s = block_with_shortcut(res34.module.layer4[0])
self.layer4 = self._make_layer_s(block,res34.module.layer4, 2)
self.layer4_end = block_end(res34.module.layer4[2])
self.avgpool = AvgPool2d(res34.module.avgpool)
self.reshape = Reshape(f=512, n=1)
self.linear = Linear(res34.module.fc)
self.softmax = nn.Softmax(dim=1)
def _make_layer(self, block, layer, count):
layers = []
layers.append(block(layer[0]))
for i in range(1, count):
layers.append(block(layer[i]))
return nn.Sequential(*layers)
def _make_layer_s(self, block, layer, count):
layers = []
layers.append(block(layer[1]))
for i in range(2, count):
layers.append(block(layer[i]))
return nn.Sequential(*layers)
def forward(self, x):
x = self.initial(x)
x = self.res_i(x)
out = self.layer1(x)
out = self.layer2_s(out)
out = self.layer2(out)
out = self.layer3_s(out)
out = self.layer3(out)
out = self.layer4_s(out)
out = self.layer4(out)
out = self.layer4_end(out)
out = self.avgpool(out)
out = self.linear(self.reshape(out))
out = self.softmax(out)
return out
def relprop(self, R):
R = self.linear.relprop(R)
R = self.reshape.relprop(R)
R = self.avgpool.relprop(R)
R, Rprev = self.layer4_end.relprop(R)
R, Rprev = self.layer4[0].relprop(R, Rprev)
R, Rprev = self.layer4_s.relprop(R, Rprev)
for i in range(0,5):
R, Rprev = self.layer3[4-i].relprop(R, Rprev)
R, Rprev = self.layer3_s.relprop(R, Rprev)
for i in range(0,3):
R, Rprev = self.layer2[2-i].relprop(R, Rprev)
R, Rprev = self.layer2_s.relprop(R, Rprev)
for i in range(0,3):
R, Rprev = self.layer1[2-i].relprop(R, Rprev)
return R