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Model-Transfer-Adaptability
Commit e133ba3a by Klin
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feat: Model Robustness, details in ALL/README.md

parent 51ce6c00
Showing with 1768 additions and 156 deletions
ykl/ALL/boundary_visualize.slurm 0 → 100644
#!/bin/bash
#- Job parameters
# (TODO)
# Please modify job name
#SBATCH -J Boundary # The job name
#SBATCH -o ret/ret-%j.out # Write the standard output to file named 'ret-<job_number>.out'
#SBATCH -e ret/ret-%j.err # Write the standard error to file named 'ret-<job_number>.err'
#- Resources
# (TODO)
# Please modify your requirements
#SBATCH -p nv-gpu # Submit to 'nv-gpu' Partitiion
#SBATCH -t 0-04:00:00 # Run for a maximum time of 0 days, 12 hours, 00 mins, 00 secs
#SBATCH --nodes=1 # Request N nodes
#SBATCH --gres=gpu:1 # Request M GPU per node
#SBATCH --gres-flags=enforce-binding # CPU-GPU Affinity
#SBATCH --qos=gpu-debug # Request QOS Type
###
### The system will alloc 8 or 16 cores per gpu by default.
### If you need more or less, use following:
### #SBATCH --cpus-per-task=K # Request K cores
###
###
### Without specifying the constraint, any available nodes that meet the requirement will be allocated
### You can specify the characteristics of the compute nodes, and even the names of the compute nodes
###
### #SBATCH --nodelist=gpu-v00 # Request a specific list of hosts
### #SBATCH --constraint="Volta|RTX8000" # Request GPU Type: Volta(V100 or V100S) or RTX8000
###
# set constraint for RTX8000 to meet my cuda
#SBATCH --constraint="Ampere|RTX8000"
#- Log information
echo "Job start at $(date "+%Y-%m-%d %H:%M:%S")"
echo "Job run at:"
echo "$(hostnamectl)"
#- Load environments
source /tools/module_env.sh
module list # list modules loaded
##- Tools
module load cluster-tools/v1.0
module load slurm-tools/v1.0
module load cmake/3.15.7
module load git/2.17.1
module load vim/8.1.2424
##- language
module load python3/3.8.16
##- CUDA
# module load cuda-cudnn/10.2-7.6.5
# module load cuda-cudnn/11.2-8.2.1
module load cuda-cudnn/11.1-8.2.1
##- virtualenv
# source xxxxx/activate
echo $(module list) # list modules loaded
echo $(which gcc)
echo $(which python)
echo $(which python3)
cluster-quota # nas quota
nvidia-smi --format=csv --query-gpu=name,driver_version,power.limit # gpu info
#- Warning! Please not change your CUDA_VISIBLE_DEVICES
#- in `.bashrc`, `env.sh`, or your job script
echo "Use GPU ${CUDA_VISIBLE_DEVICES}" # which gpus
#- The CUDA_VISIBLE_DEVICES variable is assigned and specified by SLURM
#- Job step
# [EDIT HERE(TODO)]
### FULL
python boundary_visualize.py --pca_gen --pca_mix --pca_path
# ### quant
python boundary_visualize.py --pca_gen --pca_mix --pca_path --quant --quant_type=INT --num_bits=4
python boundary_visualize.py --pca_gen --pca_mix --pca_path --quant --quant_type=INT --num_bits=5
# python boundary_visualize.py --pca_gen --pca_mix --pca_path --quant --quant_type=INT --num_bits=16
#- End
echo "Job end at $(date "+%Y-%m-%d %H:%M:%S")"
ykl/ALL/cfg.py
...@@ -9,7 +9,9 @@ ...@@ -9,7 +9,9 @@
# dropout: 'D' # dropout: 'D'
# MakeLayer: 'ML','BBLK'/'BTNK'/'IRES', ml_idx, blocks # MakeLayer: 'ML','BBLK'/'BTNK'/'IRES', ml_idx, blocks
# softmax: 'SM' # softmax: 'SM'
# class 100
# 在VIEW后输出特征（可选）
# class 100 模型部署时会根据数据集适配
ResNet_18_cfg_table = [ ResNet_18_cfg_table = [
['C','BRL',True,3,64,3,1,1,False], ['C','BRL',True,3,64,3,1,1,False],
['ML','BBLK',0,2], ['ML','BBLK',0,2],
......
ykl/ALL/gen_one.slurm
...@@ -11,11 +11,11 @@ ...@@ -11,11 +11,11 @@
# Please modify your requirements # Please modify your requirements
#SBATCH -p nv-gpu # Submit to 'nv-gpu' Partitiion #SBATCH -p nv-gpu # Submit to 'nv-gpu' Partitiion
#SBATCH -t 1-06:00:00 # Run for a maximum time of 0 days, 12 hours, 00 mins, 00 secs #SBATCH -t 0-08:00:00 # Run for a maximum time of 0 days, 12 hours, 00 mins, 00 secs
#SBATCH --nodes=1 # Request N nodes #SBATCH --nodes=1 # Request N nodes
#SBATCH --gres=gpu:1 # Request M GPU per node #SBATCH --gres=gpu:1 # Request M GPU per node
#SBATCH --gres-flags=enforce-binding # CPU-GPU Affinity #SBATCH --gres-flags=enforce-binding # CPU-GPU Affinity
#SBATCH --qos=gpu-normal # Request QOS Type #SBATCH --qos=gpu-short # Request QOS Type
### ###
### The system will alloc 8 or 16 cores per gpu by default. ### The system will alloc 8 or 16 cores per gpu by default.
...@@ -31,7 +31,7 @@ ...@@ -31,7 +31,7 @@
### ###
# set constraint for RTX8000 to meet my cuda # set constraint for RTX8000 to meet my cuda
#SBATCH --constraint="Ampere|RTX8000" #SBATCH --constraint="Ampere"
#- Log information #- Log information
...@@ -51,7 +51,7 @@ module load git/2.17.1 ...@@ -51,7 +51,7 @@ module load git/2.17.1
module load vim/8.1.2424 module load vim/8.1.2424
##- language ##- language
module load python3/3.6.8 module load python3/3.8.16
##- CUDA ##- CUDA
# module load cuda-cudnn/10.2-7.6.5 # module load cuda-cudnn/10.2-7.6.5
...@@ -86,10 +86,7 @@ else ...@@ -86,10 +86,7 @@ else
exit exit
fi fi
if [ $Quant = 'True' ]; then python gen_one.py --model $Model --dataset $Dataset --multi_label_prob 0.4 --multi_label_num $Label --adjust
python gen_one.py --model $Model --dataset $Dataset --quant --multi_label_prob 0.4 --multi_label_num $Label
else
python gen_one.py --model $Model --dataset $Dataset --multi_label_prob 0.4 --multi_label_num $Label
fi
#- End #- End
echo "Job end at $(date "+%Y-%m-%d %H:%M:%S")" echo "Job end at $(date "+%Y-%m-%d %H:%M:%S")"
ykl/ALL/gen_options.py deleted 100644 → 0
import os
import os.path as osp
class GenOption(object):
def __init__(self, args):
self.model = args.model
self.dataset = args.dataset
self.batchSize = 128
self.quant = args.quant
if self.dataset == "cifar10":
self.nClasses = 10
elif self.dataset == "cifar100":
self.nClasses = 100
else:
assert False, "invalid dataset"
# ----------Generator options ---------------------------------------------
# self.nEpochs = 100
self.nEpochs = 40
#每个epoch训练多少轮，和batchsize无关
self.iters = 200
#冻结embedding层权重
self.freeze = args.freeze
self.randemb = args.randemb
# 如果不为randomemb，需要根据weight_t调整
self.latent_dim = 64
# 针对imagenet等数据集需要调整
self.img_size = 32
self.channels = 3
self.lr_G = 0.001
# self.milestones_G = [40,60,80]
self.milestones_G = [20,30]
self.gamma_G = 0.1
self.b1 = 0.5
self.b2 = 0.999
# ----------More option ---------------------------------------------
self.multi_label_prob = args.multi_label_prob
self.multi_label_num = args.multi_label_num
self.no_DM = args.no_DM
self.noise_scale = args.noise_scale
self.intermediate_dim = 100
# if self.network == "resnet20":
# self.intermediate_dim = 64
def set(self,quant_type=None,num_bits=None,e_bits=None):
if self.quant:
self.quant_type = quant_type
self.num_bits = num_bits
self.e_bits = e_bits
if quant_type == 'FLOAT':
title = '%s_%d_E%d' % (quant_type, num_bits, e_bits)
else:
title = '%s_%d' % (quant_type, num_bits)
self.teacher_file = 'ckpt_quant/'+self.dataset+'/'+self.model+'/'+title+'.pt'
gen_path = 'ckpt_quant_gen/'+self.dataset+'/'+self.model
self.gen_file = gen_path + '/' + title + '.pt'
else:
self.teacher_file = 'ckpt_full/'+self.dataset+'/'+self.model+'.pt'
gen_path = 'ckpt_full_gen/'+self.dataset
self.gen_file = gen_path +'/'+ self.model+'.pt'
if not osp.exists(self.teacher_file):
assert False, "Empty teacher file"
if not osp.exists(gen_path):
os.makedirs(gen_path)
ykl/ALL/gen_result/cifar10/AlexNet.txt 0 → 100644
Gen: AlexNet cifar10
Namespace(dataset='cifar10', freeze=False, model='AlexNet', multi_label_num=2, multi_label_prob=0.4, no_DM=True, noise_scale=1.0, randemb=False)
Test Result:
Teacher Accuray: 88.640000
Train Result:
Gen acc: 99.824219
Time: 44.96s
Adjust Result:
Iters: 13
Gen acc: 98.945312
FGSM acc: 43.828125 -- 45.340000(Testloader)
PGD acc: 19.023438 -- 19.810000(Testloader)
Time: 19.30s
ykl/ALL/gen_result/cifar10/AlexNet_BN.txt 0 → 100644
Gen: AlexNet_BN cifar10
Namespace(dataset='cifar10', freeze=False, model='AlexNet_BN', multi_label_num=2, multi_label_prob=0.4, no_DM=True, noise_scale=1.0, randemb=False)
Test Result:
Teacher Accuray: 90.130000
Train Result:
Gen acc: 99.878906
Time: 59.58s
Adjust Result:
Iters: 3
Gen acc: 97.656250
FGSM acc: 51.015625 -- 48.620000(Testloader)
PGD acc: 21.601562 -- 19.170000(Testloader)
Time: 10.53s
ykl/ALL/gen_result/cifar10/Inception_BN.txt 0 → 100644
Gen: Inception_BN cifar10
Namespace(dataset='cifar10', freeze=False, model='Inception_BN', multi_label_num=2, multi_label_prob=0.4, no_DM=True, noise_scale=1.0, randemb=False)
Test Result:
Teacher Accuray: 94.830000
Train Result:
Gen acc: 99.976562
Time: 430.90s
Adjust Result:
Iters: 1
Gen acc: 94.375000
FGSM acc: 45.625000 -- 42.760000(Testloader)
PGD acc: 5.976562 -- 7.210000(Testloader)
Time: 102.60s
ykl/ALL/gen_result/cifar10/MobileNetV2.txt 0 → 100644
Gen: MobileNetV2 cifar10
Namespace(dataset='cifar10', freeze=False, model='MobileNetV2', multi_label_num=2, multi_label_prob=0.4, no_DM=True, noise_scale=1.0, randemb=False)
Test Result:
Teacher Accuray: 90.270000
Train Result:
Gen acc: 99.957031
Time: 246.35s
Adjust Result:
Iters: 2
Gen acc: 80.976562
FGSM acc: 37.890625 -- 40.190000(Testloader)
PGD acc: 3.320312 -- 5.590000(Testloader)
Time: 39.31s
ykl/ALL/gen_result/cifar10/ResNet_152.txt 0 → 100644
Gen: ResNet_152 cifar10
Namespace(dataset='cifar10', freeze=False, model='ResNet_152', multi_label_num=2, multi_label_prob=0.4, no_DM=True, noise_scale=1.0, randemb=False)
Test Result:
Teacher Accuray: 94.430000
Train Result:
Gen acc: 99.558594
Time: 967.98s
Adjust Result:
Iters: 5
Gen acc: 89.062500
FGSM acc: 42.421875 -- 44.370000(Testloader)
PGD acc: 12.343750 -- 10.190000(Testloader)
Time: 334.84s
ykl/ALL/gen_result/cifar10/ResNet_18.txt 0 → 100644
Gen: ResNet_18 cifar10
Namespace(dataset='cifar10', freeze=False, model='ResNet_18', multi_label_num=2, multi_label_prob=0.4, no_DM=True, noise_scale=1.0, randemb=False)
Test Result:
Teacher Accuray: 94.300000
Train Result:
Gen acc: 99.910156
Time: 143.98s
Adjust Result:
Iters: 14
Gen acc: 99.609375
FGSM acc: 47.968750 -- 47.410000(Testloader)
PGD acc: 10.585938 -- 11.240000(Testloader)
Time: 80.04s
ykl/ALL/gen_result/cifar10/ResNet_50.txt 0 → 100644
Gen: ResNet_50 cifar10
Namespace(dataset='cifar10', freeze=False, model='ResNet_50', multi_label_num=2, multi_label_prob=0.4, no_DM=True, noise_scale=1.0, randemb=False)
Test Result:
Teacher Accuray: 94.570000
Train Result:
Gen acc: 99.964844
Time: 394.87s
Adjust Result:
Iters: 18
Gen acc: 97.265625
FGSM acc: 46.015625 -- 44.400000(Testloader)
PGD acc: 11.250000 -- 8.920000(Testloader)
Time: 325.25s
ykl/ALL/gen_result/cifar10/VGG_16.txt 0 → 100644
Gen: VGG_16 cifar10
Namespace(dataset='cifar10', freeze=False, model='VGG_16', multi_label_num=2, multi_label_prob=0.4, no_DM=True, noise_scale=1.0, randemb=False)
Test Result:
Teacher Accuray: 93.130000
Train Result:
Gen acc: 99.863281
Time: 131.06s
Adjust Result:
Iters: 7
Gen acc: 99.726562
FGSM acc: 51.640625 -- 53.170000(Testloader)
PGD acc: 20.546875 -- 18.740000(Testloader)
Time: 35.02s
ykl/ALL/gen_result/cifar10/VGG_19.txt 0 → 100644
Gen: VGG_19 cifar10
Namespace(dataset='cifar10', freeze=False, model='VGG_19', multi_label_num=2, multi_label_prob=0.4, no_DM=True, noise_scale=1.0, randemb=False)
Test Result:
Teacher Accuray: 93.030000
Train Result:
Gen acc: 99.941406
Time: 147.04s
Adjust Result:
Iters: 21
Gen acc: 97.343750
FGSM acc: 49.375000 -- 50.580000(Testloader)
PGD acc: 14.687500 -- 16.890000(Testloader)
Time: 89.95s
ykl/ALL/gen_result/cifar100/AlexNet.txt 0 → 100644
Gen: AlexNet cifar100
Namespace(adjust=True, dataset='cifar100', freeze=False, model='AlexNet', multi_label_num=10, multi_label_prob=0.4, no_DM=True, noise_scale=1.0, randemb=False)
Test Result:
Teacher Accuray: 63.450000
Train Result:
Gen acc: 99.980469
Time: 46.65s
Adjust Result:
Iters: 49
Gen acc: 65.742188
FGSM acc: 55.117188 -- 21.870000(Testloader)
PGD acc: 6.992188 -- 7.740000(Testloader)
Time: 65.03s
ykl/ALL/gen_result/cifar100/AlexNet_BN.txt 0 → 100644
Gen: AlexNet_BN cifar100
Namespace(adjust=True, dataset='cifar100', freeze=False, model='AlexNet_BN', multi_label_num=10, multi_label_prob=0.4, no_DM=True, noise_scale=1.0, randemb=False)
Test Result:
Teacher Accuray: 66.310000
Train Result:
Gen acc: 99.984375
Time: 59.13s
Adjust Result:
Iters: 32
Gen acc: 68.007812
FGSM acc: 61.367188 -- 21.380000(Testloader)
PGD acc: 6.718750 -- 6.720000(Testloader)
Time: 51.86s
ykl/ALL/gen_result/cifar100/Inception_BN.txt 0 → 100644
Gen: Inception_BN cifar100
Namespace(adjust=True, dataset='cifar100', freeze=False, model='Inception_BN', multi_label_num=10, multi_label_prob=0.4, no_DM=True, noise_scale=1.0, randemb=False)
Test Result:
Teacher Accuray: 77.970000
Train Result:
Gen acc: 100.000000
Time: 430.51s
Adjust Result:
Iters: 484
Gen acc: 99.414062
FGSM acc: 38.085938 -- 18.840000(Testloader)
PGD acc: 4.843750 -- 3.130000(Testloader)
Time: 8508.16s
ykl/ALL/gen_result/cifar100/MobileNetV2.txt 0 → 100644
Gen: MobileNetV2 cifar100
Namespace(adjust=True, dataset='cifar100', freeze=False, model='MobileNetV2', multi_label_num=10, multi_label_prob=0.4, no_DM=True, noise_scale=1.0, randemb=False)
Test Result:
Teacher Accuray: 66.760000
Train Result:
Gen acc: 99.996094
Time: 251.41s
Adjust Result:
Iters: 25
Gen acc: 64.453125
FGSM acc: 29.218750 -- 17.310000(Testloader)
PGD acc: 3.007812 -- 2.980000(Testloader)
Time: 203.93s
ykl/ALL/gen_result/cifar100/ResNet_152.txt 0 → 100644
Gen: ResNet_152 cifar100
Namespace(adjust=True, dataset='cifar100', freeze=False, model='ResNet_152', multi_label_num=10, multi_label_prob=0.4, no_DM=True, noise_scale=1.0, randemb=False)
Test Result:
Teacher Accuray: 77.430000
Train Result:
Gen acc: 100.000000
Time: 973.32s
Adjust Result:
Iters: 46
Gen acc: 79.375000
FGSM acc: 33.164062 -- 22.460000(Testloader)
PGD acc: 5.859375 -- 5.310000(Testloader)
Time: 1796.65s
ykl/ALL/gen_result/cifar100/ResNet_18.txt 0 → 100644
Gen: ResNet_18 cifar100
Namespace(adjust=True, dataset='cifar100', freeze=False, model='ResNet_18', multi_label_num=10, multi_label_prob=0.4, no_DM=True, noise_scale=1.0, randemb=False)
Test Result:
Teacher Accuray: 75.720000
Train Result:
Gen acc: 100.000000
Time: 141.31s
Adjust Result:
Iters: 27
Gen acc: 93.515625
FGSM acc: 44.140625 -- 18.760000(Testloader)
PGD acc: 4.218750 -- 4.550000(Testloader)
Time: 150.11s
ykl/ALL/gen_result/cifar100/ResNet_50.txt 0 → 100644
Gen: ResNet_50 cifar100
Namespace(adjust=True, dataset='cifar100', freeze=False, model='ResNet_50', multi_label_num=10, multi_label_prob=0.4, no_DM=True, noise_scale=1.0, randemb=False)
Test Result:
Teacher Accuray: 77.220000
Train Result:
Gen acc: 100.000000
Time: 425.48s
Adjust Result:
Iters: 179
Gen acc: 99.218750
FGSM acc: 30.039062 -- 21.770000(Testloader)
PGD acc: 5.273438 -- 4.580000(Testloader)
Time: 2697.18s
ykl/ALL/gen_result/cifar100/VGG_16.txt 0 → 100644
Gen: VGG_16 cifar100
Namespace(adjust=True, dataset='cifar100', freeze=False, model='VGG_16', multi_label_num=10, multi_label_prob=0.4, no_DM=True, noise_scale=1.0, randemb=False)
Test Result:
Teacher Accuray: 71.120000
Train Result:
Gen acc: 99.992188
Time: 131.73s
Adjust Result:
Iters: 70
Gen acc: 74.375000
FGSM acc: 20.039062 -- 23.670000(Testloader)
PGD acc: 7.265625 -- 6.370000(Testloader)
Time: 228.97s
ykl/ALL/gen_result/cifar100/VGG_19.txt 0 → 100644
Gen: VGG_19 cifar100
Namespace(adjust=True, dataset='cifar100', freeze=False, model='VGG_19', multi_label_num=10, multi_label_prob=0.4, no_DM=True, noise_scale=1.0, randemb=False)
Test Result:
Teacher Accuray: 70.450000
Train Result:
Gen acc: 99.996094
Time: 146.33s
Adjust Result:
Iters: 43
Gen acc: 67.734375
FGSM acc: 33.125000 -- 26.100000(Testloader)
PGD acc: 5.312500 -- 6.180000(Testloader)
Time: 176.94s
ykl/ALL/generator.py
...@@ -6,6 +6,10 @@ from torch.nn import init ...@@ -6,6 +6,10 @@ from torch.nn import init
class Generator(nn.Module): class Generator(nn.Module):
def __init__(self, options=None, teacher_weight=None, freeze=True): def __init__(self, options=None, teacher_weight=None, freeze=True):
super(Generator, self).__init__() super(Generator, self).__init__()
# 记录额外信息，帮助模型边界测试的辅助
self.target_test_acc = None #训练目标在测试集上取得的acc
self.target_gen_acc = None #伪数据对训练目标边界的拟合效果
self.settings = options self.settings = options
# 注意这里有embedding层，两个分别是词典大小和向量长度 # 注意这里有embedding层，两个分别是词典大小和向量长度
# 用于将标签映射为向量 # 用于将标签映射为向量
......
ykl/ALL/get_param_flops.py
from model import * from model import Model
import sys import sys
import torch import torch
......
ykl/ALL/get_param_flops.slurm
...@@ -56,7 +56,7 @@ module load git/2.17.1 ...@@ -56,7 +56,7 @@ module load git/2.17.1
module load vim/8.1.2424 module load vim/8.1.2424
##- language ##- language
module load python3/3.6.8 module load python3/3.8.16
##- CUDA ##- CUDA
# module load cuda-cudnn/10.2-7.6.5 # module load cuda-cudnn/10.2-7.6.5
......
ykl/ALL/model.py
...@@ -11,28 +11,34 @@ class Model(nn.Module): ...@@ -11,28 +11,34 @@ class Model(nn.Module):
self.cfg_table = model_cfg_table[model_name] self.cfg_table = model_cfg_table[model_name]
adapt_dataset(self.cfg_table,dataset) adapt_dataset(self.cfg_table,dataset)
make_layers(self,self.cfg_table) make_layers(self,self.cfg_table)
self.model_state = None
def forward(self,x): def forward(self,x,out_feature=False):
x = model_forward(self,self.cfg_table,x) if self.model_state is None:
return x return model_forward(self,self.cfg_table,x,out_feature)
elif self.model_state == 'quantize':
return self.quantize_forward(x,out_feature)
elif self.model_state == 'freeze':
return self.quantize_inference(x,out_feature)
else:
assert False, "Illegal Model State"
def quantize(self, quant_type, num_bits=8, e_bits=3): def quantize(self, quant_type, num_bits=8, e_bits=3):
model_quantize(self,self.cfg_table,quant_type,num_bits,e_bits) model_quantize(self,self.cfg_table,quant_type,num_bits,e_bits)
self.model_state = 'quantize'
def quantize_forward(self,x): def quantize_forward(self,x,out_feature=False):
return model_utils(self,self.cfg_table,func='forward',x=x) return model_utils(self,self.cfg_table,func='forward',x=x,out_feature=out_feature)
def freeze(self): def freeze(self):
model_utils(self,self.cfg_table,func='freeze') model_utils(self,self.cfg_table,func='freeze')
self.model_state = 'freeze'
def quantize_inference(self,x): def quantize_inference(self,x,out_feature=False):
return model_utils(self,self.cfg_table,func='inference',x=x) return model_utils(self,self.cfg_table,func='inference',x=x,out_feature=out_feature)
def fakefreeze(self): def fakefreeze(self):
model_utils(self,self.cfg_table,func='fakefreeze') model_utils(self,self.cfg_table,func='fakefreeze')
def get_output_layer_weight(self): def get_output_layer_weight(self):
return get_output_layer_weight(self,self.cfg_table) return get_output_layer_weight(self,self.cfg_table)
\ No newline at end of file
def get_quant_output_layer_weight(self):
return get_quant_output_layer_weight(self,self.cfg_table)
\ No newline at end of file
ykl/ALL/model_deployment.py
...@@ -97,7 +97,7 @@ def make_layers(model,cfg_table): ...@@ -97,7 +97,7 @@ def make_layers(model,cfg_table):
model.add_module(name,layer) model.add_module(name,layer)
def model_forward(model,cfg_table,x): def model_forward(model,cfg_table,x,out_feature=False):
for i in range(len(cfg_table)): for i in range(len(cfg_table)):
cfg = cfg_table[i] cfg = cfg_table[i]
if cfg[0] == 'Inc': if cfg[0] == 'Inc':
...@@ -147,9 +147,15 @@ def model_forward(model,cfg_table,x): ...@@ -147,9 +147,15 @@ def model_forward(model,cfg_table,x):
elif cfg[0] == 'VW': elif cfg[0] == 'VW':
if len(cfg) == 1: #default if len(cfg) == 1: #default
x = x.view(x.size(0),-1) x = x.view(x.size(0),-1)
if out_feature:
feature = x
elif cfg[0] == 'SM': elif cfg[0] == 'SM':
x = F.softmax(x,dim=1) x = F.softmax(x,dim=1)
return x
if out_feature:
return x, feature
else:
return x
def model_quantize(model,cfg_table,quant_type,num_bits,e_bits): def model_quantize(model,cfg_table,quant_type,num_bits,e_bits):
...@@ -204,7 +210,7 @@ def model_quantize(model,cfg_table,quant_type,num_bits,e_bits): ...@@ -204,7 +210,7 @@ def model_quantize(model,cfg_table,quant_type,num_bits,e_bits):
# 增加了func='fakefreeze' # 增加了func='fakefreeze'
def model_utils(model,cfg_table,func,x=None): def model_utils(model,cfg_table,func,x=None,out_feature=False):
last_qo = None last_qo = None
# 表示已经经过反量化，用于区别反量化不再最后，而是在softmax前的情形 # 表示已经经过反量化，用于区别反量化不再最后，而是在softmax前的情形
done_flag = False done_flag = False
...@@ -287,6 +293,8 @@ def model_utils(model,cfg_table,func,x=None): ...@@ -287,6 +293,8 @@ def model_utils(model,cfg_table,func,x=None):
if func == 'inference' or func == 'forward': if func == 'inference' or func == 'forward':
if len(cfg) == 1: #default if len(cfg) == 1: #default
x = x.view(x.size(0),-1) x = x.view(x.size(0),-1)
if out_feature:
feature = x #只有伪量化的时候可直接输出，inference如需输出feature需要rescale
elif cfg[0] == 'SM': elif cfg[0] == 'SM':
if func == 'inference': if func == 'inference':
done_flag = True done_flag = True
...@@ -298,7 +306,10 @@ def model_utils(model,cfg_table,func,x=None): ...@@ -298,7 +306,10 @@ def model_utils(model,cfg_table,func,x=None):
if func == 'inference' and not done_flag: if func == 'inference' and not done_flag:
x = last_qo.dequantize_tensor(x) x = last_qo.dequantize_tensor(x)
return x if out_feature:
return x,feature
else:
return x
def make_inc_layers(model,inc_idx): def make_inc_layers(model,inc_idx):
......
ykl/ALL/noise_visualize.py 0 → 100644
from torch.utils.data import DataLoader
import torchvision
import torchvision.datasets as dsets
import torchvision.transforms as transforms
import os
import sys
import numpy as np
import torch
# CIFAR10提取的图像只有32x32像素，较为模糊，为更清晰显示，使用插值法进行了resize
if __name__ == "__main__":
noise_dir = 'noise_image/'
os.makedirs(noise_dir,exist_ok='True')
noise_scale_list = [1, 0.5, 0.1, 0.05, 0.01, 0.005, 0.001]
batchSize = 1
testloader = DataLoader(
dsets.CIFAR10(root='/lustre/datasets/CIFAR10',
train=False,
transform=transforms.ToTensor(),
download=False),
batch_size = batchSize,
shuffle = False
)
classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']
for data,target in testloader:
for image, label in zip(data,target):
print(classes[label])
torchvision.utils.save_image(image,noise_dir+'img_org.png')
for i, noise_scale in enumerate(noise_scale_list):
noise = np.random.normal(0., noise_scale, image.size())
noise = torch.tensor(noise).float()
noise_image = image + noise
noise_image.clamp_(0. , 255.)
torchvision.utils.save_image(noise_image,noise_dir+'img_noise'+str(noise_scale)+'.png')
sys.exit()
\ No newline at end of file
ykl/ALL/ptq_one.slurm
...@@ -51,7 +51,7 @@ module load git/2.17.1 ...@@ -51,7 +51,7 @@ module load git/2.17.1
module load vim/8.1.2424 module load vim/8.1.2424
##- language ##- language
module load python3/3.6.8 module load python3/3.8.16
##- CUDA ##- CUDA
# module load cuda-cudnn/10.2-7.6.5 # module load cuda-cudnn/10.2-7.6.5
......
ykl/ALL/robust_one.slurm 0 → 100644
#!/bin/bash
#- Job parameters
# (TODO)
# Please modify job name
#- Resources
# (TODO)
# Please modify your requirements
#SBATCH -p nv-gpu # Submit to 'nv-gpu' Partitiion
#SBATCH -t 1-06:00:00 # Run for a maximum time of 0 days, 12 hours, 00 mins, 00 secs
#SBATCH --nodes=1 # Request N nodes
#SBATCH --gres=gpu:1 # Request M GPU per node
#SBATCH --gres-flags=enforce-binding # CPU-GPU Affinity
#SBATCH --qos=gpu-normal # Request QOS Type
###
### The system will alloc 8 or 16 cores per gpu by default.
### If you need more or less, use following:
### #SBATCH --cpus-per-task=K # Request K cores
###
###
### Without specifying the constraint, any available nodes that meet the requirement will be allocated
### You can specify the characteristics of the compute nodes, and even the names of the compute nodes
###
### #SBATCH --nodelist=gpu-v00 # Request a specific list of hosts
### #SBATCH --constraint="Volta|RTX8000" # Request GPU Type: Volta(V100 or V100S) or RTX8000
###
# set constraint for RTX8000 to meet my cuda
#SBATCH --constraint="Ampere"
#- Log information
echo "Job start at $(date "+%Y-%m-%d %H:%M:%S")"
echo "Job run at:"
echo "$(hostnamectl)"
#- Load environments
source /tools/module_env.sh
module list # list modules loaded
##- Tools
module load cluster-tools/v1.0
module load slurm-tools/v1.0
module load cmake/3.15.7
module load git/2.17.1
module load vim/8.1.2424
##- language
module load python3/3.8.16
##- CUDA
# module load cuda-cudnn/10.2-7.6.5
# module load cuda-cudnn/11.2-8.2.1
module load cuda-cudnn/11.1-8.2.1
##- virtualenv
# source xxxxx/activate
echo $(module list) # list modules loaded
echo $(which gcc)
echo $(which python)
echo $(which python3)
cluster-quota # nas quota
nvidia-smi --format=csv --query-gpu=name,driver_version,power.limit # gpu info
#- Warning! Please not change your CUDA_VISIBLE_DEVICES
#- in `.bashrc`, `env.sh`, or your job script
echo "Use GPU ${CUDA_VISIBLE_DEVICES}" # which gpus
#- The CUDA_VISIBLE_DEVICES variable is assigned and specified by SLURM
#- Job step
# [EDIT HERE(TODO)]
if [ $Dataset = 'cifar10' ]; then
Label=2
elif [ $Dataset = 'cifar100' ]; then
Label=10
else
echo "Invalid Dataset $Dataset"
exit
fi
python robust_one.py --model $Model --dataset $Dataset --multi_label_num $Label --adjust
python robust_one.py --model $Model --dataset $Dataset --multi_label_num $Label
#- End
echo "Job end at $(date "+%Y-%m-%d %H:%M:%S")"
ykl/ALL/robust_result/cifar10/AlexNet.txt 0 → 100644
AlexNet cifar10
Title Org_acc Org_gen_acc FGSM_acc FGSM_gen_acc PGD_acc PGD_gen_acc Time
Full 88.640000 99.726562 45.340000 60.039062 19.860000 34.625000 12.48s
INT_2 10.000000 10.101562 10.000000 10.101562 10.000000 10.101562 27.51s
INT_3 10.120000 12.867188 10.000000 9.859375 10.000000 9.859375 28.69s
INT_4 55.640000 61.656250 13.900000 24.125000 11.280000 18.562500 24.35s
INT_5 82.880000 97.656250 37.790000 47.492188 29.120000 39.851562 29.08s
INT_6 87.300000 99.601562 44.420000 57.523438 30.590000 44.406250 28.29s
INT_7 88.230000 99.687500 44.040000 64.289062 25.940000 45.078125 26.63s
INT_8 88.450000 99.750000 45.230000 60.523438 23.230000 38.359375 29.04s
INT_9 88.630000 99.703125 45.200000 60.445312 21.140000 35.984375 26.95s
INT_10 88.700000 99.726562 45.230000 59.882812 20.510000 35.289062 26.87s
INT_11 88.660000 99.726562 45.250000 60.046875 20.100000 34.851562 25.40s
INT_12 88.660000 99.734375 45.260000 60.109375 19.830000 34.804688 26.59s
INT_13 88.630000 99.726562 45.250000 60.039062 19.860000 34.640625 28.14s
INT_14 88.630000 99.726562 45.230000 59.992188 19.820000 34.687500 28.48s
INT_15 88.620000 99.726562 45.390000 60.000000 19.920000 34.617188 26.30s
INT_16 88.630000 99.726562 45.350000 60.039062 19.840000 34.679688 26.49s
POT_2 10.000000 10.101562 10.000000 10.101562 10.000000 10.101562 37.71s
POT_3 16.660000 22.515625 10.010000 9.859375 10.000000 9.859375 38.29s
POT_4 69.810000 93.085938 28.190000 31.140625 17.220000 22.968750 44.90s
POT_5 69.760000 94.015625 27.550000 30.828125 15.820000 22.289062 53.51s
POT_6 70.680000 93.789062 27.980000 29.921875 15.690000 22.023438 74.98s
POT_7 70.320000 94.070312 28.060000 30.867188 15.880000 22.164062 118.00s
POT_8 69.810000 93.976562 27.690000 30.664062 15.670000 22.210938 201.64s
FLOAT_3_E1 23.220000 27.843750 14.160000 22.140625 11.230000 17.406250 38.73s
FLOAT_4_E1 68.120000 91.484375 23.850000 34.398438 17.930000 27.429688 44.20s
FLOAT_4_E2 66.360000 85.289062 24.400000 34.234375 20.050000 31.000000 43.02s
FLOAT_5_E1 79.430000 98.726562 29.100000 47.679688 18.930000 33.992188 54.85s
FLOAT_5_E2 84.020000 97.804688 37.570000 55.046875 24.200000 39.953125 54.37s
FLOAT_5_E3 85.030000 99.250000 39.370000 55.171875 19.650000 32.359375 54.10s
FLOAT_6_E1 83.350000 98.812500 32.690000 61.523438 18.640000 41.695312 75.39s
FLOAT_6_E2 87.150000 99.632812 40.570000 50.257812 22.020000 32.414062 75.66s
FLOAT_6_E3 87.980000 99.648438 44.120000 67.335938 20.330000 39.570312 75.61s
FLOAT_6_E4 85.250000 99.250000 39.030000 53.781250 19.680000 31.476562 75.94s
FLOAT_7_E1 85.030000 99.195312 35.290000 68.187500 17.640000 45.750000 118.53s
FLOAT_7_E2 87.490000 99.750000 41.860000 48.734375 20.750000 28.445312 118.74s
FLOAT_7_E3 88.610000 99.765625 44.980000 59.601562 19.960000 35.289062 118.59s
FLOAT_7_E4 88.090000 99.671875 43.810000 67.312500 20.060000 39.593750 118.47s
FLOAT_7_E5 85.270000 99.296875 39.270000 53.632812 19.510000 31.132812 118.94s
FLOAT_8_E1 86.080000 99.320312 36.530000 69.195312 17.620000 45.312500 202.51s
FLOAT_8_E2 87.870000 99.640625 41.830000 47.335938 19.320000 26.867188 202.51s
FLOAT_8_E3 88.730000 99.757812 44.970000 60.351562 19.860000 35.195312 202.19s
FLOAT_8_E4 88.630000 99.781250 44.840000 59.601562 19.950000 34.890625 202.12s
FLOAT_8_E5 88.150000 99.648438 44.280000 67.242188 20.160000 39.281250 202.64s
FLOAT_8_E6 85.200000 99.281250 38.980000 53.492188 19.830000 30.859375 202.72s
ykl/ALL/robust_result/cifar10/AlexNet_BN.txt 0 → 100644
AlexNet_BN cifar10
Title Org_acc Org_gen_acc FGSM_acc FGSM_gen_acc PGD_acc PGD_gen_acc Time
Full 90.130000 99.843750 48.620000 64.164062 19.220000 23.390625 14.21s
INT_2 10.000000 9.796875 10.000000 9.796875 10.000000 9.796875 28.30s
INT_3 14.040000 16.929688 10.000000 9.796875 10.000000 9.796875 31.25s
INT_4 48.760000 62.328125 15.280000 17.804688 13.540000 15.468750 35.92s
INT_5 76.310000 96.093750 23.750000 32.851562 16.270000 23.593750 28.12s
INT_6 88.910000 99.375000 43.970000 65.976562 24.860000 43.390625 31.49s
INT_7 89.790000 99.875000 47.430000 65.625000 24.060000 32.945312 33.04s
INT_8 90.240000 99.828125 47.930000 65.617188 21.380000 28.531250 34.08s
INT_9 90.070000 99.835938 48.230000 64.531250 20.100000 24.960938 31.51s
INT_10 90.100000 99.843750 48.380000 64.101562 19.560000 24.078125 31.64s
INT_11 90.120000 99.843750 48.360000 64.320312 19.390000 23.601562 30.91s
INT_12 90.160000 99.843750 48.490000 63.929688 19.300000 23.343750 33.59s
INT_13 90.140000 99.843750 48.490000 64.062500 19.200000 23.414062 37.41s
INT_14 90.130000 99.843750 48.550000 64.062500 19.200000 23.320312 34.77s
INT_15 90.140000 99.843750 48.650000 64.125000 19.310000 23.312500 34.93s
INT_16 90.130000 99.843750 48.610000 64.179688 19.170000 23.242188 27.59s
POT_2 10.000000 9.796875 10.000000 9.796875 10.000000 9.796875 40.82s
POT_3 17.480000 21.945312 10.930000 17.320312 10.470000 16.445312 39.56s
POT_4 74.750000 94.773438 28.570000 30.968750 16.050000 20.554688 46.32s
POT_5 74.280000 95.609375 30.290000 34.664062 16.780000 23.367188 56.10s
POT_6 74.770000 94.875000 30.550000 32.242188 17.040000 21.546875 77.70s
POT_7 74.510000 94.929688 30.430000 32.617188 16.910000 21.593750 120.50s
POT_8 74.440000 94.882812 30.590000 32.570312 17.060000 21.695312 203.68s
FLOAT_3_E1 23.080000 29.265625 10.980000 12.031250 10.450000 9.218750 40.04s
FLOAT_4_E1 68.200000 85.187500 20.790000 39.726562 14.550000 26.523438 46.18s
FLOAT_4_E2 72.840000 75.398438 24.180000 25.703125 18.250000 14.500000 46.47s
FLOAT_5_E1 82.640000 97.960938 33.660000 37.171875 19.340000 21.437500 56.24s
FLOAT_5_E2 85.490000 97.875000 39.450000 52.281250 22.870000 36.187500 56.42s
FLOAT_5_E3 87.690000 99.500000 44.720000 39.648438 22.760000 8.898438 55.78s
FLOAT_6_E1 85.790000 98.984375 40.120000 41.929688 20.980000 20.867188 77.81s
FLOAT_6_E2 87.740000 99.203125 42.470000 58.515625 21.300000 29.851562 77.15s
FLOAT_6_E3 89.570000 99.804688 48.220000 62.257812 21.390000 31.390625 77.14s
FLOAT_6_E4 87.980000 99.492188 45.690000 39.554688 22.790000 9.023438 77.11s
FLOAT_7_E1 86.840000 99.039062 41.350000 42.812500 19.330000 18.835938 120.79s
FLOAT_7_E2 88.630000 99.546875 44.440000 60.148438 21.050000 27.734375 120.65s
FLOAT_7_E3 89.950000 99.812500 48.060000 64.007812 19.780000 23.828125 120.79s
FLOAT_7_E4 89.280000 99.773438 48.030000 62.218750 21.390000 30.929688 120.54s
FLOAT_7_E5 87.790000 99.562500 45.490000 39.453125 22.620000 9.007812 120.39s
FLOAT_8_E1 86.990000 99.125000 42.250000 44.859375 19.130000 18.960938 203.77s
FLOAT_8_E2 88.850000 99.679688 44.820000 61.281250 20.120000 26.546875 203.59s
FLOAT_8_E3 90.120000 99.835938 48.090000 64.414062 19.210000 24.250000 203.75s
FLOAT_8_E4 89.810000 99.804688 48.030000 63.921875 19.600000 23.882812 203.55s
FLOAT_8_E5 89.560000 99.804688 48.230000 62.453125 21.530000 31.429688 203.70s
FLOAT_8_E6 87.870000 99.515625 45.530000 39.664062 22.880000 9.023438 204.57s
ykl/ALL/robust_result/cifar10/AlexNet_BN_adjust.txt 0 → 100644
AlexNet_BN cifar10
Title Org_acc Org_gen_acc FGSM_acc FGSM_gen_acc PGD_acc PGD_gen_acc Time
Full 90.130000 98.140625 48.620000 53.664062 19.240000 21.531250 14.11s
INT_2 10.000000 9.859375 10.000000 9.859375 10.000000 9.859375 35.01s
INT_3 14.040000 14.937500 10.000000 9.859375 10.000000 9.859375 32.19s
INT_4 48.760000 50.593750 15.270000 15.078125 13.680000 11.140625 33.45s
INT_5 76.310000 89.203125 23.770000 24.539062 16.410000 15.882812 35.46s
INT_6 88.910000 97.398438 44.340000 59.687500 25.030000 34.093750 34.29s
INT_7 89.790000 98.390625 47.460000 57.789062 24.340000 28.945312 34.86s
INT_8 90.240000 98.226562 48.240000 55.875000 21.340000 24.593750 28.40s
INT_9 90.070000 98.210938 48.250000 54.171875 20.160000 22.367188 35.74s
INT_10 90.100000 98.148438 48.370000 53.789062 19.640000 22.046875 27.08s
INT_11 90.120000 98.140625 48.360000 53.882812 19.380000 21.640625 28.42s
INT_12 90.160000 98.156250 48.430000 53.468750 19.290000 21.445312 35.95s
INT_13 90.140000 98.164062 48.510000 53.726562 19.230000 21.523438 33.68s
INT_14 90.130000 98.156250 48.610000 53.640625 19.320000 21.492188 35.24s
INT_15 90.140000 98.156250 48.630000 53.695312 19.190000 21.484375 32.02s
INT_16 90.130000 98.148438 48.590000 53.648438 19.190000 21.468750 30.39s
POT_2 10.000000 9.859375 10.000000 9.859375 10.000000 9.859375 36.74s
POT_3 17.480000 18.789062 10.110000 11.898438 10.070000 9.265625 39.12s
POT_4 74.750000 82.226562 28.580000 19.375000 16.420000 4.570312 44.06s
POT_5 74.280000 82.382812 30.280000 20.734375 16.600000 3.804688 54.91s
POT_6 74.770000 81.304688 30.540000 19.132812 16.790000 3.882812 75.97s
POT_7 74.510000 81.250000 30.410000 19.304688 17.110000 4.023438 120.26s
POT_8 74.440000 81.210938 30.580000 19.148438 16.620000 4.062500 202.14s
FLOAT_3_E1 23.080000 30.210938 11.580000 12.078125 10.740000 10.859375 38.64s
FLOAT_4_E1 68.200000 79.320312 20.760000 27.664062 14.320000 22.875000 43.86s
FLOAT_4_E2 72.840000 62.210938 24.170000 24.750000 18.080000 20.062500 44.07s
FLOAT_5_E1 82.640000 93.945312 34.060000 25.593750 19.360000 8.656250 54.97s
FLOAT_5_E2 85.490000 93.062500 39.460000 45.640625 22.900000 32.156250 55.10s
FLOAT_5_E3 87.690000 97.679688 44.720000 52.140625 22.650000 33.414062 56.69s
FLOAT_6_E1 85.790000 97.007812 40.050000 23.304688 20.800000 2.242188 78.21s
FLOAT_6_E2 87.740000 96.273438 42.320000 46.750000 21.030000 23.171875 78.11s
FLOAT_6_E3 89.570000 98.117188 48.210000 54.781250 21.460000 33.867188 77.11s
FLOAT_6_E4 87.980000 97.796875 45.490000 51.765625 22.620000 33.242188 77.57s
FLOAT_7_E1 86.840000 97.500000 40.980000 23.367188 19.280000 2.437500 120.82s
FLOAT_7_E2 88.630000 97.476562 44.790000 49.515625 21.340000 21.687500 120.77s
FLOAT_7_E3 89.950000 98.000000 47.930000 54.250000 19.720000 23.625000 120.26s
FLOAT_7_E4 89.280000 98.210938 48.330000 54.109375 21.410000 33.726562 121.09s
FLOAT_7_E5 87.790000 97.656250 44.930000 50.390625 22.760000 32.015625 120.11s
FLOAT_8_E1 86.990000 97.312500 42.130000 24.601562 18.890000 2.265625 204.38s
FLOAT_8_E2 88.850000 97.835938 44.780000 50.250000 20.240000 21.273438 203.89s
FLOAT_8_E3 90.120000 97.992188 47.990000 53.593750 19.320000 21.429688 203.68s
FLOAT_8_E4 89.810000 97.984375 48.180000 54.085938 19.630000 23.789062 203.57s
FLOAT_8_E5 89.560000 98.187500 48.020000 54.031250 21.520000 33.882812 203.36s
FLOAT_8_E6 87.870000 97.664062 45.340000 51.070312 22.850000 32.656250 203.67s
ykl/ALL/robust_result/cifar10/AlexNet_adjust.txt 0 → 100644
AlexNet cifar10
Title Org_acc Org_gen_acc FGSM_acc FGSM_gen_acc PGD_acc PGD_gen_acc Time
Full 88.640000 98.367188 45.350000 44.460938 19.840000 18.523438 12.74s
INT_2 10.000000 9.664062 10.000000 9.664062 10.000000 9.664062 25.70s
INT_3 10.120000 11.695312 10.000000 9.734375 10.000000 9.734375 26.12s
INT_4 55.630000 32.539062 14.140000 15.945312 11.180000 12.492188 26.08s
INT_5 82.870000 91.015625 38.720000 39.273438 29.440000 32.289062 27.52s
INT_6 87.290000 96.789062 43.940000 42.273438 30.270000 30.421875 26.53s
INT_7 88.210000 98.171875 44.080000 45.671875 25.950000 25.851562 27.96s
INT_8 88.450000 98.320312 45.320000 45.484375 23.240000 21.710938 26.62s
INT_9 88.620000 98.382812 45.110000 44.523438 21.210000 19.460938 28.96s
INT_10 88.700000 98.382812 45.330000 44.515625 20.510000 19.023438 27.46s
INT_11 88.660000 98.351562 45.370000 44.546875 20.060000 18.617188 28.68s
INT_12 88.650000 98.367188 45.280000 44.601562 19.970000 18.515625 28.58s
INT_13 88.610000 98.359375 45.300000 44.445312 19.930000 18.539062 28.37s
INT_14 88.630000 98.367188 45.280000 44.390625 19.850000 18.515625 28.09s
INT_15 88.620000 98.375000 45.340000 44.437500 19.920000 18.484375 27.94s
INT_16 88.640000 98.359375 45.360000 44.453125 19.860000 18.500000 29.81s
POT_2 10.000000 9.664062 10.000000 9.664062 10.000000 9.664062 38.80s
POT_3 16.660000 22.265625 10.000000 9.734375 10.000000 9.734375 39.44s
POT_4 69.820000 80.359375 28.350000 21.015625 17.140000 16.023438 44.81s
POT_5 69.760000 81.335938 27.910000 20.648438 15.550000 15.398438 54.06s
POT_6 70.700000 82.140625 27.860000 20.617188 15.480000 15.296875 75.89s
POT_7 70.330000 81.703125 27.980000 20.531250 15.410000 15.296875 118.40s
POT_8 69.780000 81.601562 27.390000 20.640625 15.190000 15.539062 202.54s
FLOAT_3_E1 23.290000 23.593750 14.870000 17.546875 12.380000 13.703125 38.63s
FLOAT_4_E1 68.090000 78.453125 23.870000 25.351562 17.530000 19.164062 45.01s
FLOAT_4_E2 66.380000 64.734375 24.690000 28.187500 20.490000 24.898438 44.96s
FLOAT_5_E1 79.440000 91.828125 28.910000 34.781250 18.920000 23.375000 54.94s
FLOAT_5_E2 84.040000 90.421875 38.090000 39.320312 23.950000 24.382812 54.86s
FLOAT_5_E3 85.010000 96.132812 39.170000 42.804688 19.610000 21.914062 54.40s
FLOAT_6_E1 83.300000 92.437500 33.340000 48.632812 18.750000 28.976562 76.28s
FLOAT_6_E2 87.160000 95.906250 40.520000 36.976562 21.800000 19.195312 76.35s
FLOAT_6_E3 88.000000 98.132812 44.240000 46.445312 20.020000 18.070312 75.01s
FLOAT_6_E4 85.260000 96.414062 39.190000 42.765625 19.890000 21.921875 76.13s
FLOAT_7_E1 85.010000 94.062500 35.230000 57.640625 17.780000 35.171875 118.51s
FLOAT_7_E2 87.530000 96.648438 41.790000 33.414062 20.990000 15.617188 119.42s
FLOAT_7_E3 88.620000 98.375000 44.910000 45.765625 19.990000 19.265625 118.86s
FLOAT_7_E4 88.100000 98.046875 44.130000 46.031250 19.990000 18.000000 118.58s
FLOAT_7_E5 85.250000 96.125000 39.210000 42.187500 19.480000 21.625000 119.04s
FLOAT_8_E1 86.050000 95.023438 36.430000 57.023438 17.670000 33.640625 202.68s
FLOAT_8_E2 87.840000 96.796875 42.310000 31.882812 19.590000 14.875000 202.56s
FLOAT_8_E3 88.720000 98.507812 44.790000 45.359375 19.850000 18.406250 203.11s
FLOAT_8_E4 88.620000 98.343750 44.770000 45.515625 20.090000 19.156250 203.37s
FLOAT_8_E5 88.150000 98.187500 44.180000 45.843750 20.120000 18.125000 203.58s
FLOAT_8_E6 85.170000 96.140625 38.670000 42.554688 19.550000 21.828125 203.49s
ykl/ALL/robust_result/cifar10/Inception_BN.txt 0 → 100644
Inception_BN cifar10
Title Org_acc Org_gen_acc FGSM_acc FGSM_gen_acc PGD_acc PGD_gen_acc Time
Full 94.830000 99.976562 42.840000 61.320312 7.240000 4.601562 158.12s
INT_2 10.000000 10.039062 10.000000 10.039062 10.000000 10.039062 289.91s
INT_3 10.680000 11.445312 10.090000 9.281250 10.320000 9.921875 286.58s
INT_4 29.840000 34.031250 13.340000 25.953125 11.530000 23.289062 309.70s
INT_5 80.350000 87.601562 31.260000 43.593750 16.150000 26.859375 310.38s
INT_6 93.480000 99.765625 41.320000 58.539062 12.690000 23.078125 314.44s
INT_7 94.540000 99.976562 42.930000 61.117188 9.610000 11.625000 313.02s
INT_8 94.830000 99.976562 43.680000 61.593750 8.100000 6.328125 311.08s
INT_9 94.840000 99.960938 43.210000 60.367188 7.430000 4.828125 309.57s
INT_10 94.790000 99.968750 42.950000 60.304688 7.220000 4.656250 305.39s
INT_11 94.840000 99.968750 42.830000 60.445312 7.210000 4.523438 308.33s
INT_12 94.850000 99.968750 42.900000 60.382812 7.230000 4.507812 313.92s
INT_13 94.820000 99.968750 42.740000 60.468750 7.250000 4.515625 292.75s
INT_14 94.830000 99.968750 42.790000 60.414062 7.060000 4.531250 309.78s
INT_15 94.810000 99.968750 42.850000 60.398438 7.250000 4.523438 309.29s
INT_16 94.830000 99.968750 42.800000 60.445312 7.200000 4.492188 307.34s
POT_2 10.000000 10.039062 10.000000 10.039062 10.000000 10.039062 449.54s
POT_3 11.500000 8.343750 9.780000 9.632812 9.630000 9.210938 502.14s
POT_4 38.810000 24.929688 14.590000 15.445312 10.510000 13.671875 639.36s
POT_5 40.190000 39.179688 16.720000 26.164062 13.180000 21.125000 909.17s
POT_6 23.960000 24.367188 13.880000 16.445312 9.070000 13.445312 1425.79s
POT_7 38.480000 48.031250 18.230000 26.125000 10.690000 18.312500 2487.01s
POT_8 40.730000 42.921875 19.370000 23.312500 12.090000 14.000000 4562.34s
FLOAT_3_E1 10.000000 9.890625 9.910000 9.218750 10.140000 9.398438 503.88s
FLOAT_4_E1 19.150000 30.679688 20.670000 15.750000 14.380000 12.265625 640.31s
FLOAT_4_E2 54.380000 44.984375 16.450000 25.640625 10.530000 16.679688 640.48s
FLOAT_5_E1 60.290000 76.406250 28.360000 38.593750 15.290000 22.578125 906.06s
FLOAT_5_E2 85.500000 98.218750 37.320000 39.484375 17.170000 17.351562 906.78s
FLOAT_5_E3 86.480000 99.593750 40.220000 59.359375 13.480000 26.515625 907.52s
FLOAT_6_E1 76.320000 95.289062 30.400000 43.734375 12.730000 19.054688 1420.68s
FLOAT_6_E2 92.260000 99.859375 40.440000 46.671875 14.370000 13.015625 1424.48s
FLOAT_6_E3 93.400000 99.929688 43.200000 62.179688 11.020000 16.570312 1426.48s
FLOAT_6_E4 86.480000 99.500000 38.810000 51.578125 14.470000 22.687500 1422.52s
FLOAT_7_E1 78.170000 97.359375 31.890000 42.812500 11.760000 14.968750 2480.58s
FLOAT_7_E2 93.010000 99.914062 42.120000 46.851562 13.230000 9.828125 2480.65s
FLOAT_7_E3 94.470000 99.976562 43.230000 57.070312 8.860000 6.117188 2485.42s
FLOAT_7_E4 93.430000 99.953125 42.890000 63.476562 11.410000 17.773438 2486.79s
FLOAT_7_E5 87.980000 98.765625 38.120000 51.718750 14.820000 25.601562 2484.98s
ykl/ALL/robust_result/cifar10/Inception_BN_adjust.txt 0 → 100644
Inception_BN cifar10
Title Org_acc Org_gen_acc FGSM_acc FGSM_gen_acc PGD_acc PGD_gen_acc Time
Full 94.830000 88.007812 42.810000 46.429688 7.230000 5.210938 157.45s
INT_2 10.000000 9.539062 10.000000 9.539062 10.000000 9.539062 312.83s
INT_3 10.750000 7.906250 9.430000 10.664062 9.670000 9.742188 273.20s
INT_4 29.690000 28.929688 14.540000 19.476562 10.040000 15.421875 327.39s
INT_5 80.670000 59.156250 30.230000 29.742188 16.490000 18.468750 326.41s
INT_6 93.540000 79.750000 42.680000 40.429688 13.360000 15.554688 313.06s
INT_7 94.510000 86.109375 42.520000 45.703125 9.560000 10.015625 321.22s
INT_8 94.860000 86.617188 43.170000 45.945312 8.010000 5.648438 317.96s
INT_9 94.810000 85.437500 43.200000 45.750000 7.580000 5.710938 326.22s
INT_10 94.840000 85.554688 42.920000 45.273438 7.240000 5.296875 308.54s
INT_11 94.840000 85.335938 42.870000 45.500000 7.360000 5.328125 308.32s
INT_12 94.850000 85.539062 42.750000 45.359375 7.230000 5.234375 319.09s
INT_13 94.820000 85.296875 42.870000 45.484375 7.120000 5.171875 313.66s
INT_14 94.840000 85.359375 42.820000 45.460938 7.210000 5.164062 308.37s
INT_15 94.810000 85.585938 42.770000 45.515625 7.200000 5.226562 309.83s
INT_16 94.830000 85.632812 42.780000 45.523438 7.270000 5.210938 312.72s
POT_2 10.000000 9.539062 10.000000 9.539062 10.000000 9.539062 442.95s
POT_3 11.540000 10.664062 10.350000 9.187500 10.290000 9.710938 500.26s
POT_4 38.340000 21.226562 26.370000 22.453125 16.150000 11.648438 639.03s
POT_5 39.980000 34.382812 17.060000 16.164062 11.520000 11.773438 908.58s
POT_6 24.030000 15.484375 13.480000 11.171875 9.170000 10.406250 1426.20s
POT_7 38.250000 40.664062 22.170000 20.437500 15.010000 9.546875 2483.35s
POT_8 41.360000 32.523438 23.510000 20.164062 14.980000 12.617188 4567.44s
FLOAT_3_E1 10.030000 9.968750 9.950000 9.984375 10.060000 10.132812 503.42s
FLOAT_4_E1 17.620000 28.054688 19.260000 16.750000 14.870000 11.382812 639.87s
FLOAT_4_E2 54.990000 26.539062 16.470000 19.804688 10.100000 9.843750 640.43s
FLOAT_5_E1 59.920000 48.804688 28.060000 29.125000 14.400000 19.906250 905.43s
FLOAT_5_E2 85.410000 71.265625 33.910000 26.796875 16.600000 12.984375 910.60s
FLOAT_5_E3 86.580000 72.437500 37.810000 41.031250 13.790000 18.593750 903.49s
FLOAT_6_E1 76.300000 58.875000 30.310000 32.015625 12.410000 15.710938 1420.05s
FLOAT_6_E2 92.190000 88.070312 41.010000 36.132812 14.840000 9.726562 1419.32s
FLOAT_6_E3 93.290000 86.539062 42.810000 42.671875 11.900000 12.796875 1422.39s
FLOAT_6_E4 86.740000 78.867188 39.560000 44.601562 14.680000 18.531250 1421.04s
FLOAT_7_E1 78.370000 58.156250 30.890000 33.570312 11.620000 14.000000 2480.21s
FLOAT_7_E2 93.030000 88.695312 42.090000 39.156250 13.060000 9.492188 2483.90s
FLOAT_7_E3 94.450000 88.250000 43.370000 43.000000 9.310000 6.257812 2482.01s
FLOAT_7_E4 93.570000 85.632812 43.480000 43.718750 11.540000 13.203125 2481.06s
FLOAT_7_E5 88.000000 68.929688 39.650000 41.398438 14.730000 17.562500 2482.61s
FLOAT_8_E1 82.240000 61.664062 31.650000 31.703125 10.610000 12.351562 4562.81s
FLOAT_8_E2 93.680000 86.945312 41.010000 35.921875 12.320000 8.031250 4563.76s
FLOAT_8_E3 94.760000 86.843750 42.640000 45.796875 7.780000 5.781250 4562.76s
FLOAT_8_E4 94.460000 88.773438 43.660000 41.367188 9.330000 5.921875 4575.23s
FLOAT_8_E5 93.290000 86.468750 41.740000 41.054688 11.830000 11.843750 4576.43s
FLOAT_8_E6 87.980000 77.648438 37.900000 42.898438 14.720000 18.507812 4572.51s
ykl/ALL/robust_result/cifar10/MobileNetV2.txt 0 → 100644
MobileNetV2 cifar10
Title Org_acc Org_gen_acc FGSM_acc FGSM_gen_acc PGD_acc PGD_gen_acc Time
Full 90.270000 99.960938 40.190000 76.007812 5.550000 16.015625 58.61s
INT_2 10.000000 9.453125 10.000000 9.453125 10.000000 9.453125 228.33s
INT_3 9.960000 9.453125 10.000000 9.453125 10.000000 9.453125 225.76s
INT_4 9.490000 10.398438 10.280000 9.835938 10.150000 9.960938 229.86s
INT_5 20.560000 38.156250 11.720000 18.320312 10.230000 14.906250 229.48s
INT_6 57.910000 94.695312 24.450000 39.335938 14.740000 25.492188 229.98s
INT_7 80.300000 99.617188 35.160000 64.382812 12.750000 33.750000 228.65s
INT_8 88.180000 99.898438 43.090000 76.250000 10.770000 28.632812 228.98s
INT_9 89.760000 99.937500 41.570000 74.953125 8.410000 21.726562 227.42s
INT_10 90.380000 99.968750 40.400000 75.234375 6.480000 18.093750 204.56s
INT_11 90.290000 99.945312 40.410000 76.359375 5.950000 17.062500 230.18s
INT_12 90.270000 99.960938 40.260000 75.882812 5.750000 16.234375 228.24s
INT_13 90.240000 99.960938 40.270000 76.000000 5.760000 16.195312 184.42s
INT_14 90.260000 99.960938 40.140000 75.937500 5.610000 16.101562 231.10s
INT_15 90.270000 99.960938 40.220000 76.000000 5.660000 16.085938 230.59s
INT_16 90.270000 99.960938 40.100000 76.117188 5.650000 16.085938 226.82s
POT_2 10.000000 9.453125 10.000000 9.453125 10.000000 9.453125 249.26s
POT_3 9.990000 9.343750 9.790000 9.484375 9.910000 9.453125 253.89s
POT_4 14.620000 21.656250 10.240000 14.851562 10.260000 14.046875 267.65s
POT_5 14.390000 20.992188 10.700000 12.523438 10.550000 9.789062 314.54s
POT_6 14.330000 20.406250 10.030000 11.757812 10.020000 11.039062 446.22s
POT_7 15.040000 22.085938 10.510000 15.671875 10.100000 12.734375 688.91s
POT_8 14.710000 20.492188 11.000000 12.359375 10.430000 11.156250 1190.58s
FLOAT_3_E1 9.660000 9.859375 9.960000 10.007812 9.920000 10.117188 222.21s
FLOAT_4_E1 11.660000 13.765625 10.170000 10.539062 11.100000 10.703125 269.14s
FLOAT_4_E2 13.230000 19.789062 10.430000 13.820312 10.080000 13.921875 269.10s
FLOAT_5_E1 17.160000 32.843750 11.100000 21.820312 10.590000 19.171875 320.41s
FLOAT_5_E2 26.760000 49.726562 14.300000 24.031250 11.460000 21.664062 316.64s
FLOAT_5_E3 46.320000 76.359375 28.230000 38.593750 15.810000 27.125000 325.47s
FLOAT_6_E1 28.010000 48.945312 11.350000 28.734375 10.320000 25.109375 445.64s
FLOAT_6_E2 45.820000 88.882812 21.670000 25.640625 13.820000 20.078125 445.42s
FLOAT_6_E3 69.910000 97.281250 37.480000 64.585938 13.410000 35.734375 444.29s
FLOAT_6_E4 46.590000 77.820312 27.800000 37.906250 15.840000 25.828125 444.06s
FLOAT_7_E1 33.850000 65.789062 12.760000 12.945312 9.460000 10.710938 695.39s
FLOAT_7_E2 59.400000 95.421875 21.080000 24.164062 9.980000 16.007812 675.21s
FLOAT_7_E3 85.620000 99.906250 38.860000 71.718750 11.040000 29.531250 682.41s
FLOAT_7_E4 68.900000 97.562500 36.920000 63.671875 13.480000 34.007812 676.20s
FLOAT_7_E5 47.350000 76.328125 23.630000 37.179688 13.530000 25.382812 689.76s
FLOAT_8_E1 41.420000 72.015625 11.360000 22.734375 10.010000 15.734375 1184.54s
FLOAT_8_E2 68.860000 97.851562 24.470000 32.976562 11.570000 21.460938 1189.14s
FLOAT_8_E3 89.220000 99.960938 40.040000 71.375000 8.530000 22.945312 1193.14s
FLOAT_8_E4 85.830000 99.867188 37.180000 68.492188 10.300000 27.601562 1187.11s
FLOAT_8_E5 68.490000 97.140625 36.850000 63.984375 13.690000 35.257812 1192.24s
FLOAT_8_E6 46.620000 76.835938 27.690000 38.179688 15.320000 25.703125 1191.62s
ykl/ALL/robust_result/cifar10/MobileNetV2_adjust.txt 0 → 100644
MobileNetV2 cifar10
Title Org_acc Org_gen_acc FGSM_acc FGSM_gen_acc PGD_acc PGD_gen_acc Time
Full 90.270000 75.890625 40.190000 36.140625 5.590000 3.234375 57.71s
INT_2 10.000000 9.960938 10.000000 9.960938 10.000000 9.960938 140.67s
INT_3 9.960000 9.960938 10.000000 9.960938 10.000000 9.960938 203.35s
INT_4 9.490000 8.921875 10.470000 9.171875 10.220000 9.117188 221.97s
INT_5 20.560000 21.453125 12.930000 13.609375 10.540000 13.062500 219.63s
INT_6 57.910000 59.773438 23.630000 22.429688 14.120000 12.093750 223.85s
INT_7 80.300000 70.710938 36.340000 31.156250 13.340000 12.796875 223.15s
INT_8 88.180000 72.945312 42.470000 36.648438 10.800000 7.296875 222.42s
INT_9 89.760000 76.039062 41.690000 35.085938 8.180000 4.953125 224.29s
INT_10 90.380000 76.070312 40.280000 35.804688 6.340000 3.812500 220.84s
INT_11 90.290000 75.656250 40.460000 36.070312 5.930000 3.484375 225.77s
INT_12 90.270000 75.679688 40.280000 36.039062 5.750000 3.281250 221.15s
INT_13 90.240000 75.804688 40.230000 36.039062 5.770000 3.203125 221.53s
INT_14 90.260000 75.851562 40.170000 36.195312 5.660000 3.187500 222.13s
INT_15 90.270000 75.882812 40.190000 36.039062 5.630000 3.218750 223.85s
INT_16 90.270000 75.875000 40.150000 36.078125 5.580000 3.203125 222.96s
POT_2 10.000000 9.960938 10.000000 9.960938 10.000000 9.960938 243.47s
POT_3 9.990000 9.882812 9.670000 9.281250 9.610000 9.593750 240.64s
POT_4 14.620000 12.492188 10.170000 16.406250 10.070000 15.171875 262.08s
POT_5 14.390000 11.203125 10.610000 12.218750 10.590000 10.539062 317.48s
POT_6 14.330000 12.171875 10.020000 12.226562 10.000000 10.890625 436.84s
POT_7 15.040000 14.234375 10.490000 16.687500 10.250000 14.820312 690.31s
POT_8 14.710000 12.039062 11.290000 12.656250 10.560000 11.804688 1186.04s
FLOAT_3_E1 9.660000 9.750000 10.420000 9.703125 10.340000 9.921875 238.90s
FLOAT_4_E1 11.660000 11.148438 11.340000 9.953125 10.950000 8.992188 261.89s
FLOAT_4_E2 13.230000 15.601562 11.040000 12.132812 10.780000 11.171875 264.80s
FLOAT_5_E1 17.160000 14.484375 10.780000 13.296875 10.350000 12.828125 319.40s
FLOAT_5_E2 26.760000 25.257812 14.530000 20.609375 11.370000 17.187500 322.91s
FLOAT_5_E3 46.320000 35.921875 26.910000 18.406250 14.750000 11.726562 323.57s
FLOAT_6_E1 28.010000 21.164062 11.670000 13.085938 10.670000 10.898438 441.74s
FLOAT_6_E2 45.820000 50.187500 21.360000 21.625000 13.380000 15.359375 441.60s
FLOAT_6_E3 69.910000 66.226562 37.160000 31.343750 13.500000 10.710938 419.23s
FLOAT_6_E4 46.590000 35.015625 27.670000 18.195312 15.450000 10.656250 438.30s
FLOAT_7_E1 33.850000 42.398438 12.750000 13.359375 9.600000 8.742188 672.35s
FLOAT_7_E2 59.400000 57.781250 21.050000 16.929688 10.500000 10.140625 679.77s
FLOAT_7_E3 85.620000 75.304688 39.370000 37.000000 10.910000 9.648438 679.33s
FLOAT_7_E4 68.900000 65.718750 36.710000 30.421875 13.590000 10.265625 690.20s
FLOAT_7_E5 47.350000 38.382812 24.230000 21.367188 13.760000 16.851562 668.22s
FLOAT_8_E1 41.420000 42.039062 11.290000 18.187500 9.980000 11.757812 1183.25s
FLOAT_8_E2 68.860000 58.406250 25.150000 18.640625 11.960000 9.539062 1182.87s
FLOAT_8_E3 89.220000 75.000000 39.870000 33.851562 8.730000 6.867188 1185.24s
FLOAT_8_E4 85.830000 75.945312 37.480000 36.562500 10.440000 8.906250 1187.76s
FLOAT_8_E5 68.490000 65.054688 36.680000 30.789062 13.730000 10.367188 1180.14s
FLOAT_8_E6 46.620000 36.007812 27.130000 17.242188 15.720000 10.234375 1187.43s
ykl/ALL/robust_result/cifar10/ResNet_152_adjust.txt 0 → 100644
ResNet_152 cifar10
Title Org_acc Org_gen_acc FGSM_acc FGSM_gen_acc PGD_acc PGD_gen_acc Time
Full 94.430000 82.945312 44.280000 40.617188 10.150000 11.281250 327.10s
INT_2 10.000000 10.179688 10.000000 10.179688 10.000000 10.179688 837.44s
INT_3 10.030000 15.242188 9.990000 10.992188 9.990000 10.914062 842.54s
INT_4 15.610000 15.093750 8.960000 11.781250 9.210000 11.539062 845.09s
INT_5 30.350000 11.750000 17.610000 10.328125 13.970000 10.304688 850.06s
INT_6 74.440000 24.976562 17.420000 52.289062 7.250000 24.015625 847.64s
INT_7 90.430000 58.632812 35.830000 51.773438 10.340000 14.453125 842.06s
INT_8 94.060000 80.796875 41.780000 38.859375 11.150000 11.289062 839.31s
INT_9 94.280000 82.921875 45.410000 39.656250 11.100000 11.148438 846.84s
INT_10 94.450000 83.195312 43.900000 40.585938 10.420000 11.867188 837.70s
INT_11 94.390000 83.273438 44.180000 40.460938 10.280000 11.273438 843.87s
INT_12 94.410000 83.070312 44.240000 40.648438 10.150000 11.406250 839.62s
INT_13 94.440000 82.968750 44.300000 40.492188 10.140000 11.210938 843.67s
INT_14 94.430000 82.875000 44.460000 40.781250 10.240000 11.265625 838.38s
INT_15 94.420000 82.914062 44.310000 40.679688 10.220000 11.312500 840.17s
INT_16 94.430000 82.906250 44.390000 40.632812 10.180000 11.281250 839.31s
POT_2 10.000000 10.179688 10.000000 10.179688 10.000000 10.179688 1220.44s
POT_3 10.010000 12.671875 10.390000 14.234375 10.420000 14.554688 1389.87s
POT_4 43.550000 32.453125 17.810000 21.554688 11.080000 16.914062 1775.54s
POT_5 43.000000 24.554688 17.400000 28.750000 11.100000 17.304688 2581.30s
POT_6 40.110000 34.664062 18.850000 22.343750 12.360000 17.015625 4165.48s
POT_7 34.390000 28.687500 19.560000 18.109375 13.220000 14.476562 7427.86s
POT_8 39.190000 39.695312 18.710000 18.664062 13.120000 16.187500 13804.55s
FLOAT_3_E1 9.970000 15.734375 10.000000 9.968750 10.000000 9.968750 1384.86s
FLOAT_4_E1 18.600000 10.773438 11.530000 9.539062 9.750000 8.382812 1780.54s
FLOAT_4_E2 19.530000 23.335938 12.460000 18.726562 10.830000 17.187500 1778.50s
FLOAT_5_E1 61.040000 23.507812 16.320000 25.726562 9.940000 11.859375 2579.43s
FLOAT_5_E2 73.510000 36.351562 26.920000 42.765625 14.370000 24.898438 2585.40s
FLOAT_5_E3 84.050000 45.218750 28.630000 41.265625 13.650000 26.765625 2581.44s
FLOAT_6_E1 87.580000 69.328125 36.360000 41.992188 14.300000 7.453125 4150.79s
FLOAT_6_E2 82.830000 49.992188 27.760000 35.648438 14.260000 22.250000 4152.38s
FLOAT_6_E3 91.170000 57.562500 37.240000 44.117188 12.300000 8.039062 4164.32s
FLOAT_6_E4 85.330000 46.023438 30.710000 40.304688 13.220000 26.726562 4160.66s
FLOAT_7_E1 90.300000 83.601562 35.850000 26.359375 15.280000 8.570312 7388.60s
FLOAT_7_E2 85.720000 48.398438 27.820000 32.976562 12.420000 19.898438 7393.53s
FLOAT_7_E3 93.620000 74.273438 43.020000 44.156250 12.260000 13.476562 7409.19s
FLOAT_7_E4 91.100000 59.023438 36.440000 43.937500 11.750000 8.085938 7428.80s
ykl/ALL/robust_result/cifar10/ResNet_18.txt 0 → 100644
ResNet_18 cifar10
Title Org_acc Org_gen_acc FGSM_acc FGSM_gen_acc PGD_acc PGD_gen_acc Time
Full 94.300000 99.984375 47.320000 82.757812 11.300000 60.851562 61.58s
INT_2 10.000000 9.882812 10.000000 9.882812 10.000000 9.882812 113.99s
INT_3 10.000000 9.625000 10.000000 9.625000 10.000000 9.625000 110.67s
INT_4 49.440000 55.828125 21.800000 34.109375 14.300000 27.742188 115.23s
INT_5 87.620000 98.531250 34.660000 52.773438 20.740000 42.156250 117.49s
INT_6 93.250000 99.945312 47.120000 82.835938 16.640000 66.960938 111.18s
INT_7 94.070000 99.953125 46.560000 81.468750 13.600000 62.968750 110.95s
INT_8 94.260000 99.976562 48.200000 83.757812 12.300000 62.914062 112.09s
INT_9 94.350000 99.976562 47.650000 83.375000 11.700000 62.328125 110.99s
INT_10 94.340000 99.976562 47.670000 82.531250 11.450000 61.117188 108.41s
INT_11 94.290000 99.976562 47.590000 82.851562 11.420000 61.273438 112.16s
INT_12 94.320000 99.984375 47.530000 82.875000 11.280000 61.054688 113.02s
INT_13 94.310000 99.984375 47.490000 82.789062 11.360000 61.078125 113.65s
INT_14 94.320000 99.976562 47.390000 82.820312 11.390000 61.085938 114.14s
INT_15 94.300000 99.976562 47.420000 82.796875 11.330000 61.125000 116.35s
INT_16 94.320000 99.976562 47.340000 82.750000 11.310000 61.164062 112.37s
POT_2 10.000000 9.882812 10.000000 9.882812 10.000000 9.882812 172.08s
POT_3 10.200000 14.468750 10.050000 13.390625 8.030000 11.812500 192.24s
POT_4 63.780000 75.414062 28.790000 18.781250 17.630000 14.562500 233.05s
POT_5 50.530000 51.570312 24.380000 10.195312 13.340000 9.984375 314.09s
POT_6 51.610000 57.195312 24.930000 10.351562 14.880000 10.078125 475.13s
POT_7 49.050000 53.460938 26.370000 11.148438 15.120000 10.148438 805.59s
POT_8 55.540000 59.414062 26.630000 10.859375 16.300000 10.265625 1438.61s
FLOAT_3_E1 12.740000 15.453125 11.000000 13.179688 11.810000 12.515625 190.74s
FLOAT_4_E1 47.470000 59.093750 22.790000 14.085938 17.650000 12.562500 232.41s
FLOAT_4_E2 52.510000 95.570312 17.470000 27.437500 10.200000 20.601562 233.23s
FLOAT_5_E1 81.840000 91.625000 36.050000 32.656250 18.880000 23.625000 315.02s
FLOAT_5_E2 85.740000 99.890625 35.820000 58.484375 14.070000 37.796875 315.16s
FLOAT_5_E3 89.480000 99.851562 44.030000 75.523438 17.910000 59.203125 315.12s
FLOAT_6_E1 87.390000 98.367188 40.690000 32.328125 16.650000 19.828125 476.72s
FLOAT_6_E2 91.650000 99.914062 42.820000 77.484375 13.280000 56.468750 476.46s
FLOAT_6_E3 93.410000 99.984375 46.900000 83.117188 14.290000 64.664062 476.15s
FLOAT_6_E4 89.530000 99.828125 44.860000 73.500000 18.060000 56.882812 476.13s
FLOAT_7_E1 90.160000 98.796875 42.160000 52.984375 15.160000 36.179688 807.53s
FLOAT_7_E2 92.610000 99.945312 45.390000 80.851562 12.460000 60.054688 807.65s
FLOAT_7_E3 94.080000 99.984375 48.150000 83.132812 13.180000 62.726562 807.36s
FLOAT_7_E4 93.120000 99.929688 47.440000 81.375000 15.060000 61.218750 807.40s
FLOAT_7_E5 89.320000 99.734375 41.960000 62.453125 18.720000 44.476562 807.31s
FLOAT_8_E1 90.340000 99.343750 41.100000 49.804688 14.850000 32.750000 1439.81s
FLOAT_8_E2 92.250000 99.953125 43.710000 72.687500 13.540000 49.375000 1439.54s
FLOAT_8_E3 94.240000 99.976562 47.240000 82.289062 11.960000 61.062500 1439.69s
FLOAT_8_E4 94.060000 99.976562 47.570000 82.531250 12.870000 62.132812 1439.74s
FLOAT_8_E5 93.310000 99.968750 46.900000 84.390625 14.510000 65.539062 1439.76s
FLOAT_8_E6 89.410000 99.781250 43.070000 67.773438 18.690000 50.945312 1440.24s
ykl/ALL/robust_result/cifar10/ResNet_18_adjust.txt 0 → 100644
ResNet_18 cifar10
Title Org_acc Org_gen_acc FGSM_acc FGSM_gen_acc PGD_acc PGD_gen_acc Time
Full 94.300000 99.414062 47.310000 47.835938 11.270000 10.867188 59.05s
INT_2 10.000000 9.843750 10.000000 9.843750 10.000000 9.843750 105.14s
INT_3 10.000000 10.007812 10.000000 10.007812 10.000000 10.007812 105.30s
INT_4 49.440000 28.398438 20.680000 24.406250 14.080000 17.132812 105.48s
INT_5 87.620000 87.671875 34.230000 39.539062 19.800000 19.164062 105.49s
INT_6 93.250000 98.453125 47.820000 52.632812 16.790000 18.226562 105.88s
INT_7 94.070000 98.898438 46.700000 47.726562 13.450000 13.914062 128.22s
INT_8 94.260000 99.398438 48.870000 48.085938 12.440000 12.304688 109.68s
INT_9 94.350000 99.437500 47.720000 49.085938 11.800000 11.414062 126.65s
INT_10 94.340000 99.406250 47.400000 47.968750 11.470000 11.085938 130.41s
INT_11 94.290000 99.414062 47.590000 48.164062 11.430000 11.085938 124.30s
INT_12 94.320000 99.421875 47.470000 48.023438 11.370000 10.812500 105.42s
INT_13 94.310000 99.414062 47.440000 48.015625 11.390000 10.882812 118.16s
INT_14 94.320000 99.414062 47.420000 48.093750 11.190000 10.921875 119.38s
INT_15 94.300000 99.414062 47.470000 48.031250 11.450000 10.921875 126.28s
INT_16 94.320000 99.414062 47.480000 48.093750 11.290000 10.953125 126.69s
POT_2 10.000000 9.843750 10.000000 9.843750 10.000000 9.843750 169.25s
POT_3 10.200000 14.890625 10.350000 10.046875 8.400000 7.742188 190.90s
POT_4 63.780000 61.000000 29.720000 10.218750 17.810000 5.429688 231.81s
POT_5 50.530000 68.671875 26.900000 10.445312 15.230000 4.335938 311.95s
POT_6 51.610000 72.226562 27.290000 10.101562 15.910000 9.226562 472.97s
POT_7 49.050000 71.421875 28.030000 10.531250 15.920000 5.140625 802.77s
POT_8 55.540000 71.281250 28.080000 10.250000 16.380000 8.296875 1439.04s
FLOAT_3_E1 12.740000 23.578125 11.370000 13.601562 11.430000 13.273438 189.11s
FLOAT_4_E1 47.470000 51.546875 24.570000 13.429688 17.960000 10.171875 228.67s
FLOAT_4_E2 52.510000 82.953125 16.440000 11.648438 9.930000 10.578125 229.72s
FLOAT_5_E1 81.840000 71.031250 37.930000 11.773438 20.110000 5.796875 311.31s
FLOAT_5_E2 85.740000 97.453125 36.790000 35.609375 12.920000 13.664062 313.17s
FLOAT_5_E3 89.480000 95.187500 43.070000 32.476562 17.940000 19.031250 312.07s
FLOAT_6_E1 87.390000 89.695312 41.370000 11.984375 16.970000 1.296875 473.94s
FLOAT_6_E2 91.650000 99.054688 42.760000 38.984375 13.620000 13.421875 473.40s
FLOAT_6_E3 93.410000 99.046875 46.530000 33.992188 14.550000 11.796875 473.30s
FLOAT_6_E4 89.530000 95.625000 44.260000 35.390625 17.820000 20.460938 472.14s
FLOAT_7_E1 90.160000 93.593750 41.590000 15.351562 15.700000 0.828125 801.96s
FLOAT_7_E2 92.610000 99.070312 44.680000 43.726562 12.340000 14.000000 802.51s
FLOAT_7_E3 94.080000 99.437500 48.210000 45.812500 13.040000 12.179688 801.48s
FLOAT_7_E4 93.120000 99.109375 47.720000 40.773438 14.930000 12.601562 801.43s
FLOAT_7_E5 89.320000 95.203125 42.210000 39.250000 17.570000 22.953125 801.24s
FLOAT_8_E1 90.340000 94.007812 41.470000 16.093750 14.760000 0.554688 1437.55s
FLOAT_8_E2 92.250000 99.171875 43.930000 30.164062 13.510000 12.054688 1439.68s
FLOAT_8_E3 94.240000 99.398438 47.440000 45.812500 12.370000 12.070312 1438.96s
FLOAT_8_E4 94.060000 99.382812 47.390000 45.953125 13.020000 12.484375 1438.85s
FLOAT_8_E5 93.310000 99.031250 46.520000 40.296875 14.590000 12.234375 1438.88s
FLOAT_8_E6 89.410000 91.976562 42.590000 26.476562 19.140000 15.687500 1439.03s
ykl/ALL/robust_result/cifar10/ResNet_50.txt 0 → 100644
ResNet_50 cifar10
Title Org_acc Org_gen_acc FGSM_acc FGSM_gen_acc PGD_acc PGD_gen_acc Time
Full 94.570000 99.945312 44.350000 70.015625 8.940000 13.328125 132.07s
INT_2 10.000000 9.820312 10.000000 9.820312 10.000000 9.820312 325.77s
INT_3 10.040000 14.148438 10.080000 12.039062 10.040000 12.109375 323.77s
INT_4 24.550000 21.554688 11.880000 9.898438 10.870000 9.882812 274.27s
INT_5 79.260000 83.382812 36.870000 39.500000 21.580000 22.937500 311.02s
INT_6 92.880000 99.015625 45.070000 55.203125 15.340000 19.492188 309.83s
INT_7 93.760000 99.921875 44.650000 64.335938 12.250000 14.515625 307.40s
INT_8 94.400000 99.921875 45.510000 71.429688 11.380000 16.343750 307.96s
INT_9 94.510000 99.945312 45.000000 70.609375 9.550000 14.187500 324.85s
INT_10 94.550000 99.945312 44.320000 69.859375 9.180000 13.507812 309.98s
INT_11 94.660000 99.945312 44.560000 70.039062 8.940000 13.304688 308.57s
INT_12 94.580000 99.945312 44.520000 69.953125 8.980000 13.421875 309.40s
INT_13 94.580000 99.945312 44.470000 69.945312 8.950000 13.195312 309.02s
INT_14 94.570000 99.945312 44.470000 69.929688 9.020000 13.171875 308.25s
INT_15 94.570000 99.945312 44.370000 70.031250 8.950000 13.242188 307.84s
INT_16 94.570000 99.945312 44.380000 69.937500 9.030000 13.304688 308.10s
POT_2 10.000000 9.820312 10.000000 9.820312 10.000000 9.820312 485.49s
POT_3 10.390000 12.835938 10.030000 9.585938 9.930000 9.476562 567.34s
POT_4 51.590000 74.953125 19.470000 30.929688 13.380000 19.828125 756.15s
POT_5 50.500000 73.953125 17.320000 36.148438 11.770000 31.187500 1123.11s
POT_6 55.340000 72.921875 22.490000 39.312500 14.490000 32.406250 1847.88s
POT_7 50.930000 72.921875 20.260000 36.695312 13.530000 28.648438 3331.46s
POT_8 44.430000 75.757812 18.900000 32.453125 14.180000 28.312500 6251.90s
FLOAT_3_E1 9.440000 15.210938 9.810000 3.289062 9.880000 2.976562 567.61s
FLOAT_4_E1 53.580000 32.593750 15.650000 21.671875 13.770000 18.554688 757.48s
FLOAT_4_E2 19.850000 58.656250 9.520000 37.015625 7.560000 35.359375 755.65s
ykl/ALL/robust_result/cifar10/ResNet_50_adjust.txt 0 → 100644
ResNet_50 cifar10
Title Org_acc Org_gen_acc FGSM_acc FGSM_gen_acc PGD_acc PGD_gen_acc Time
Full 94.570000 95.296875 44.360000 45.945312 8.980000 10.601562 135.24s
INT_2 10.000000 10.328125 10.000000 10.328125 10.000000 10.328125 313.70s
INT_3 10.040000 13.429688 10.050000 13.992188 10.070000 14.164062 318.85s
INT_4 24.480000 18.546875 10.970000 10.367188 10.650000 10.625000 316.06s
INT_5 79.260000 59.109375 37.500000 27.835938 19.430000 16.328125 296.41s
INT_6 92.840000 90.531250 46.820000 40.570312 15.700000 13.406250 323.31s
INT_7 93.730000 93.828125 43.610000 42.125000 11.800000 10.304688 305.24s
INT_8 94.450000 95.585938 45.520000 48.046875 10.950000 12.203125 303.49s
INT_9 94.550000 94.570312 44.480000 45.390625 9.540000 11.250000 303.94s
INT_10 94.580000 94.906250 44.460000 45.843750 9.100000 10.898438 305.67s
INT_11 94.640000 95.296875 44.510000 45.945312 9.030000 10.500000 308.67s
INT_12 94.580000 95.257812 44.410000 46.054688 8.940000 10.671875 303.62s
INT_13 94.590000 95.273438 44.450000 45.921875 8.950000 10.679688 305.91s
INT_14 94.600000 95.273438 44.470000 45.945312 8.960000 10.695312 272.73s
INT_15 94.570000 95.281250 44.520000 45.835938 8.930000 10.625000 289.87s
INT_16 94.570000 95.296875 44.380000 45.890625 8.950000 10.617188 272.32s
POT_2 10.000000 10.328125 10.000000 10.328125 10.000000 10.328125 485.14s
POT_3 10.260000 12.617188 10.000000 9.578125 10.000000 9.437500 564.90s
POT_4 51.730000 72.195312 19.410000 30.421875 13.800000 24.734375 757.13s
POT_5 53.030000 64.093750 21.140000 26.039062 14.780000 22.023438 1128.74s
POT_6 55.280000 66.914062 21.140000 33.882812 13.710000 28.804688 1857.15s
POT_7 51.220000 68.859375 18.990000 30.648438 12.140000 23.312500 3337.61s
POT_8 44.780000 58.265625 18.930000 25.929688 13.450000 21.671875 6253.15s
FLOAT_3_E1 9.440000 15.976562 9.010000 8.359375 8.890000 8.507812 567.10s
FLOAT_4_E1 52.520000 40.414062 16.870000 24.734375 12.980000 15.328125 757.10s
FLOAT_4_E2 19.800000 33.039062 10.030000 31.843750 8.310000 28.679688 755.31s
FLOAT_5_E1 84.380000 77.562500 40.060000 40.164062 21.950000 29.828125 1122.66s
FLOAT_5_E2 63.310000 59.867188 21.590000 31.250000 13.280000 24.078125 1127.25s
FLOAT_5_E3 88.820000 85.156250 42.390000 45.140625 20.040000 27.195312 1128.58s
FLOAT_6_E1 87.410000 84.593750 38.790000 45.656250 17.710000 28.890625 1851.13s
FLOAT_6_E2 88.610000 77.281250 35.700000 45.382812 15.570000 31.609375 1853.98s
FLOAT_6_E3 92.990000 90.609375 44.470000 49.640625 14.770000 17.515625 1857.08s
FLOAT_6_E4 88.470000 83.656250 42.930000 49.234375 19.750000 31.250000 1856.95s
FLOAT_7_E1 90.630000 88.554688 40.630000 48.429688 15.880000 28.632812 3327.18s
FLOAT_7_E2 91.940000 83.742188 38.530000 50.320312 14.890000 30.359375 3332.99s
FLOAT_7_E3 94.170000 93.312500 44.200000 46.835938 11.800000 12.992188 3340.01s
FLOAT_7_E4 92.950000 90.148438 43.750000 49.507812 14.640000 17.976562 3342.85s
FLOAT_7_E5 88.970000 84.078125 42.870000 47.156250 20.230000 29.546875 3342.01s
FLOAT_8_E1 90.840000 89.453125 39.530000 45.695312 14.220000 24.164062 6245.58s
FLOAT_8_E2 92.650000 85.210938 39.950000 52.820312 13.510000 27.437500 6251.00s
FLOAT_8_E3 94.470000 94.718750 44.390000 46.265625 10.180000 11.757812 6263.01s
FLOAT_8_E4 94.170000 92.882812 44.530000 48.015625 12.000000 14.109375 6269.03s
FLOAT_8_E5 92.950000 90.125000 44.670000 50.742188 14.740000 18.765625 6268.95s
FLOAT_8_E6 87.850000 83.859375 41.850000 43.132812 21.560000 28.687500 6267.85s
ykl/ALL/robust_result/cifar10/VGG_16.txt 0 → 100644
VGG_16 cifar10
Title Org_acc Org_gen_acc FGSM_acc FGSM_gen_acc PGD_acc PGD_gen_acc Time
Full 93.130000 99.031250 53.170000 51.195312 18.780000 25.054688 28.36s
INT_2 10.000000 10.070312 10.000000 10.070312 10.000000 10.070312 70.63s
INT_3 10.020000 9.812500 10.000000 10.070312 10.000000 10.070312 55.48s
INT_4 29.300000 10.828125 11.970000 11.328125 10.190000 10.593750 63.97s
INT_5 88.810000 92.210938 39.680000 52.656250 20.310000 38.015625 72.48s
INT_6 92.500000 98.500000 50.700000 54.453125 21.590000 33.304688 74.44s
INT_7 92.980000 98.812500 52.910000 52.335938 20.730000 29.117188 72.67s
INT_8 93.020000 98.992188 53.240000 51.328125 19.540000 26.250000 62.34s
INT_9 93.050000 99.070312 53.120000 51.601562 19.020000 25.835938 72.20s
INT_10 93.150000 99.000000 53.180000 51.078125 18.930000 25.390625 73.33s
INT_11 93.110000 99.023438 53.070000 51.085938 18.730000 25.179688 68.53s
INT_12 93.140000 99.031250 53.200000 51.062500 18.720000 25.125000 68.94s
INT_13 93.160000 99.023438 53.210000 51.210938 18.730000 25.125000 71.74s
INT_14 93.160000 99.031250 53.190000 51.265625 18.780000 25.101562 72.25s
INT_15 93.130000 99.031250 53.290000 51.148438 18.820000 25.101562 71.79s
INT_16 93.140000 99.031250 53.160000 51.187500 18.700000 25.132812 61.27s
POT_2 10.000000 10.070312 10.000000 10.070312 10.000000 10.070312 89.14s
POT_3 10.450000 13.343750 10.010000 10.148438 10.010000 10.046875 87.41s
POT_4 79.980000 64.750000 43.280000 32.359375 20.620000 22.156250 105.94s
POT_5 79.820000 64.664062 43.910000 31.320312 20.470000 21.453125 143.33s
POT_6 79.670000 65.164062 42.980000 31.812500 20.350000 22.554688 217.25s
POT_7 79.850000 64.421875 43.180000 31.375000 20.320000 21.734375 367.59s
POT_8 79.790000 64.773438 43.390000 31.101562 20.480000 21.281250 665.74s
FLOAT_3_E1 10.130000 10.070312 10.030000 10.085938 10.010000 10.062500 90.55s
FLOAT_4_E1 80.090000 53.960938 35.250000 32.960938 19.880000 24.148438 106.88s
FLOAT_4_E2 72.050000 83.281250 37.510000 34.007812 21.830000 25.718750 107.78s
FLOAT_5_E1 87.670000 76.468750 45.970000 35.593750 20.450000 23.593750 143.18s
FLOAT_5_E2 90.380000 97.765625 49.290000 43.804688 21.410000 28.507812 143.05s
FLOAT_5_E3 89.850000 95.109375 51.530000 42.296875 21.690000 24.343750 143.16s
FLOAT_6_E1 90.200000 85.960938 48.690000 40.906250 20.800000 25.617188 215.65s
FLOAT_6_E2 91.860000 98.812500 50.450000 45.195312 19.170000 26.992188 215.42s
FLOAT_6_E3 92.750000 98.593750 53.610000 50.125000 20.300000 26.750000 215.43s
FLOAT_6_E4 89.750000 95.078125 51.470000 42.039062 21.660000 24.109375 215.40s
FLOAT_7_E1 90.810000 90.343750 49.430000 41.398438 19.780000 25.335938 365.55s
FLOAT_7_E2 91.920000 98.968750 51.530000 45.648438 18.970000 26.421875 366.54s
FLOAT_7_E3 92.890000 99.023438 52.850000 51.203125 19.150000 25.984375 366.76s
FLOAT_7_E4 92.710000 98.578125 53.240000 50.257812 20.240000 27.101562 367.26s
FLOAT_7_E5 89.770000 95.179688 51.810000 42.375000 21.750000 24.351562 366.76s
FLOAT_8_E1 91.190000 91.710938 49.480000 42.101562 19.400000 25.476562 661.73s
FLOAT_8_E2 92.220000 99.132812 52.180000 47.023438 19.320000 26.281250 663.08s
FLOAT_8_E3 93.150000 99.023438 53.110000 50.234375 19.090000 25.382812 662.49s
FLOAT_8_E4 93.000000 99.007812 53.010000 51.062500 19.200000 25.875000 664.14s
FLOAT_8_E5 92.790000 98.632812 53.290000 49.976562 20.310000 26.875000 664.98s
FLOAT_8_E6 89.750000 95.062500 51.650000 42.023438 21.710000 24.554688 665.24s
ykl/ALL/robust_result/cifar10/VGG_16_adjust.txt 0 → 100644
VGG_16 cifar10
Title Org_acc Org_gen_acc FGSM_acc FGSM_gen_acc PGD_acc PGD_gen_acc Time
Full 93.140000 99.539062 53.180000 49.640625 18.800000 20.265625 28.55s
INT_2 10.000000 10.062500 10.000000 10.062500 10.000000 10.062500 70.01s
INT_3 10.020000 9.390625 10.000000 10.062500 10.000000 10.062500 55.97s
INT_4 29.300000 10.687500 11.750000 10.695312 9.820000 10.117188 69.62s
INT_5 88.550000 86.140625 39.580000 53.320312 19.850000 40.585938 65.74s
INT_6 92.310000 98.867188 50.520000 52.898438 21.360000 28.515625 65.59s
INT_7 92.980000 99.437500 52.860000 51.609375 20.450000 24.734375 72.48s
INT_8 93.130000 99.554688 53.380000 49.750000 19.640000 21.234375 75.11s
INT_9 93.080000 99.554688 53.190000 50.328125 19.190000 21.179688 63.08s
INT_10 93.140000 99.554688 53.380000 49.562500 18.820000 20.500000 72.66s
INT_11 93.120000 99.531250 53.100000 49.734375 18.720000 20.437500 57.33s
INT_12 93.160000 99.539062 53.180000 49.578125 18.690000 20.320312 72.70s
INT_13 93.170000 99.539062 53.140000 49.539062 18.720000 20.281250 55.47s
INT_14 93.150000 99.539062 53.220000 49.625000 18.640000 20.320312 72.86s
INT_15 93.140000 99.539062 53.190000 49.710938 18.670000 20.335938 60.35s
INT_16 93.140000 99.539062 53.170000 49.710938 18.740000 20.304688 74.73s
POT_2 10.000000 10.062500 10.000000 10.062500 10.000000 10.062500 88.77s
POT_3 10.450000 12.125000 10.140000 10.210938 9.960000 9.921875 91.41s
POT_4 79.980000 59.140625 43.350000 27.429688 20.610000 21.804688 108.44s
POT_5 79.780000 57.601562 43.470000 27.398438 20.450000 21.515625 143.89s
POT_6 80.060000 57.820312 43.140000 27.375000 20.040000 21.445312 216.30s
POT_7 79.890000 58.921875 43.630000 27.562500 20.730000 21.757812 366.70s
POT_8 79.900000 57.867188 43.730000 27.406250 20.540000 21.945312 664.48s
FLOAT_3_E1 10.130000 10.054688 10.000000 10.062500 10.010000 10.023438 90.10s
FLOAT_4_E1 80.220000 46.203125 35.740000 31.843750 19.140000 23.343750 104.98s
FLOAT_4_E2 72.180000 85.367188 35.890000 33.085938 21.340000 25.484375 109.12s
FLOAT_5_E1 87.580000 84.609375 46.180000 33.101562 20.860000 21.296875 144.63s
FLOAT_5_E2 90.320000 98.515625 48.540000 36.945312 20.950000 21.679688 145.13s
FLOAT_5_E3 89.790000 97.789062 51.460000 36.906250 21.600000 20.562500 144.24s
FLOAT_6_E1 90.310000 92.906250 48.590000 36.343750 20.680000 21.468750 215.38s
FLOAT_6_E2 91.670000 99.187500 50.740000 40.945312 19.230000 21.367188 216.65s
FLOAT_6_E3 92.800000 99.484375 53.400000 48.804688 20.180000 20.765625 215.16s
FLOAT_6_E4 89.540000 98.109375 51.650000 36.203125 21.940000 20.507812 215.94s
FLOAT_7_E1 90.740000 96.718750 49.670000 36.132812 19.640000 21.085938 365.69s
FLOAT_7_E2 92.000000 99.421875 51.320000 42.093750 19.180000 20.632812 365.91s
FLOAT_7_E3 93.010000 99.468750 52.940000 49.523438 19.250000 21.015625 365.89s
FLOAT_7_E4 92.650000 99.406250 53.010000 48.484375 20.330000 21.015625 366.05s
FLOAT_7_E5 89.570000 97.804688 51.280000 36.648438 21.830000 20.546875 366.57s
FLOAT_8_E1 91.060000 96.914062 49.670000 35.992188 19.690000 20.648438 661.56s
FLOAT_8_E2 92.060000 99.468750 52.110000 43.414062 18.690000 21.046875 661.99s
FLOAT_8_E3 93.190000 99.523438 53.030000 48.750000 19.070000 20.531250 663.80s
FLOAT_8_E4 92.970000 99.523438 53.000000 49.328125 19.200000 21.085938 664.10s
FLOAT_8_E5 92.760000 99.445312 53.290000 48.398438 20.350000 20.992188 664.13s
FLOAT_8_E6 89.740000 97.937500 51.530000 36.359375 21.750000 20.710938 664.38s
ykl/ALL/robust_result/cifar10/VGG_19.txt 0 → 100644
VGG_19 cifar10
Title Org_acc Org_gen_acc FGSM_acc FGSM_gen_acc PGD_acc PGD_gen_acc Time
Full 93.020000 99.914062 50.590000 60.492188 16.780000 26.593750 50.74s
INT_2 10.000000 9.679688 10.000000 9.679688 10.000000 9.679688 102.39s
INT_3 10.000000 9.679688 10.000000 9.679688 10.000000 9.679688 102.14s
INT_4 17.330000 22.578125 12.330000 15.132812 9.470000 14.117188 103.49s
INT_5 88.220000 98.562500 36.050000 61.734375 17.140000 41.718750 103.08s
INT_6 92.250000 99.867188 49.620000 69.320312 20.110000 40.828125 93.44s
INT_7 92.760000 99.914062 50.360000 63.726562 18.850000 32.343750 89.96s
INT_8 93.000000 99.921875 50.710000 61.210938 17.760000 28.468750 84.60s
INT_9 93.040000 99.898438 50.460000 60.617188 17.170000 27.273438 101.82s
INT_10 93.060000 99.914062 50.580000 60.523438 16.760000 26.765625 103.82s
INT_11 92.980000 99.921875 50.740000 60.351562 16.920000 26.546875 85.06s
INT_12 93.030000 99.914062 50.580000 60.468750 16.750000 26.625000 84.62s
INT_13 93.010000 99.914062 50.500000 60.523438 16.940000 26.554688 84.16s
INT_14 93.030000 99.914062 50.540000 60.515625 16.870000 26.554688 84.56s
INT_15 93.030000 99.914062 50.540000 60.476562 16.870000 26.562500 104.47s
INT_16 93.030000 99.914062 50.580000 60.437500 16.800000 26.531250 102.85s
POT_2 10.000000 9.679688 10.000000 9.679688 10.000000 9.679688 127.08s
POT_3 10.000000 9.679688 10.000000 9.679688 10.000000 9.679688 138.92s
POT_4 71.830000 82.578125 37.830000 24.765625 18.680000 17.468750 165.01s
POT_5 71.960000 81.296875 37.380000 24.375000 18.000000 17.312500 217.42s
POT_6 72.020000 82.164062 37.370000 24.554688 18.310000 17.453125 322.57s
POT_7 71.430000 82.203125 37.540000 24.234375 17.810000 17.039062 537.44s
POT_8 71.450000 81.195312 37.200000 24.437500 18.290000 17.109375 954.10s
FLOAT_3_E1 10.010000 9.679688 9.960000 9.687500 9.970000 9.679688 138.82s
FLOAT_4_E1 77.130000 77.609375 27.980000 43.015625 16.310000 30.054688 163.94s
FLOAT_4_E2 71.290000 94.898438 27.710000 33.773438 18.030000 25.085938 165.68s
FLOAT_5_E1 86.890000 95.406250 42.240000 38.531250 19.310000 23.898438 218.32s
FLOAT_5_E2 88.630000 99.554688 44.110000 47.046875 20.910000 26.898438 219.26s
FLOAT_5_E3 90.500000 99.679688 47.970000 48.367188 19.600000 24.515625 219.31s
FLOAT_6_E1 88.860000 97.664062 44.750000 35.500000 19.350000 20.781250 324.47s
FLOAT_6_E2 91.150000 99.820312 48.270000 52.203125 19.410000 26.687500 324.68s
FLOAT_6_E3 92.420000 99.898438 51.350000 60.164062 18.580000 26.484375 324.80s
FLOAT_6_E4 90.460000 99.710938 47.780000 48.328125 19.350000 24.445312 324.63s
FLOAT_7_E1 89.930000 98.476562 44.670000 35.250000 17.830000 20.281250 537.80s
FLOAT_7_E2 91.910000 99.859375 48.640000 55.484375 19.150000 26.453125 537.25s
FLOAT_7_E3 92.940000 99.914062 50.600000 59.750000 17.260000 27.234375 539.38s
FLOAT_7_E4 92.700000 99.898438 51.310000 60.328125 18.570000 26.500000 539.31s
FLOAT_7_E5 90.600000 99.703125 48.100000 48.070312 19.520000 24.328125 539.27s
FLOAT_8_E1 90.110000 98.507812 45.080000 35.031250 17.500000 19.875000 954.49s
FLOAT_8_E2 92.020000 99.914062 49.220000 55.992188 18.060000 25.960938 952.79s
FLOAT_8_E3 93.100000 99.898438 50.260000 59.765625 17.110000 26.656250 952.70s
FLOAT_8_E4 92.940000 99.906250 50.450000 59.320312 17.310000 27.242188 952.77s
FLOAT_8_E5 92.640000 99.921875 51.410000 60.554688 18.730000 26.437500 954.18s
FLOAT_8_E6 90.790000 99.734375 47.960000 48.148438 19.490000 24.445312 954.13s
ykl/ALL/robust_result/cifar10/VGG_19_adjust.txt 0 → 100644
VGG_19 cifar10
Title Org_acc Org_gen_acc FGSM_acc FGSM_gen_acc PGD_acc PGD_gen_acc Time
Full 93.030000 96.468750 50.580000 48.289062 16.840000 14.828125 52.77s
INT_2 10.000000 9.945312 10.000000 9.945312 10.000000 9.945312 96.00s
INT_3 10.000000 9.945312 10.000000 9.945312 10.000000 9.945312 94.59s
INT_4 17.340000 19.406250 12.000000 12.210938 10.190000 11.203125 86.37s
INT_5 88.200000 79.890625 35.510000 37.460938 17.080000 22.085938 95.28s
INT_6 92.270000 93.968750 49.290000 51.601562 20.150000 23.242188 96.17s
INT_7 92.800000 96.117188 50.060000 49.304688 18.910000 18.437500 90.90s
INT_8 92.980000 96.226562 50.590000 48.484375 17.920000 16.695312 90.05s
INT_9 93.040000 96.367188 50.320000 48.421875 17.180000 15.632812 92.23s
INT_10 93.050000 96.453125 50.610000 48.523438 16.900000 15.312500 90.34s
INT_11 93.000000 96.460938 50.580000 48.328125 16.930000 15.031250 90.91s
INT_12 93.030000 96.460938 50.600000 48.390625 16.840000 14.921875 90.62s
INT_13 93.020000 96.460938 50.560000 48.304688 16.840000 14.898438 92.62s
INT_14 93.020000 96.460938 50.510000 48.289062 16.850000 14.906250 91.85s
INT_15 93.030000 96.460938 50.550000 48.226562 16.820000 14.890625 90.73s
INT_16 93.020000 96.468750 50.640000 48.250000 16.730000 14.929688 93.51s
POT_2 10.000000 9.945312 10.000000 9.945312 10.000000 9.945312 131.40s
POT_3 10.000000 9.945312 10.000000 9.945312 10.000000 9.945312 142.94s
POT_4 71.750000 51.281250 37.890000 26.898438 18.670000 18.296875 169.33s
POT_5 71.750000 50.445312 36.830000 25.984375 18.010000 17.914062 221.88s
POT_6 71.900000 51.773438 37.120000 26.546875 18.570000 18.000000 325.98s
POT_7 71.420000 52.296875 37.100000 26.640625 18.220000 17.828125 540.99s
POT_8 71.330000 51.046875 37.440000 26.000000 18.410000 17.500000 955.80s
FLOAT_3_E1 10.000000 9.953125 10.070000 9.960938 10.010000 9.953125 139.87s
FLOAT_4_E1 77.180000 53.250000 26.920000 36.937500 15.590000 26.500000 167.38s
FLOAT_4_E2 71.290000 80.570312 28.350000 31.726562 17.350000 23.281250 166.30s
FLOAT_5_E1 86.940000 76.109375 42.550000 39.476562 19.490000 24.515625 219.69s
FLOAT_5_E2 88.600000 92.351562 44.490000 41.062500 21.190000 18.078125 219.69s
FLOAT_5_E3 90.470000 95.218750 47.770000 41.828125 19.290000 18.851562 220.72s
FLOAT_6_E1 88.850000 85.789062 44.830000 38.187500 19.830000 22.335938 325.89s
FLOAT_6_E2 91.160000 93.945312 48.750000 45.328125 19.660000 19.773438 325.96s
FLOAT_6_E3 92.440000 96.523438 51.350000 47.250000 18.790000 12.625000 325.88s
FLOAT_6_E4 90.540000 95.109375 48.140000 41.835938 19.490000 18.242188 325.96s
FLOAT_7_E1 89.910000 87.546875 44.600000 38.703125 17.840000 22.023438 540.81s
FLOAT_7_E2 91.930000 95.109375 49.150000 46.820312 19.320000 19.820312 540.80s
FLOAT_7_E3 92.920000 96.328125 50.620000 48.382812 17.310000 15.632812 540.87s
FLOAT_7_E4 92.700000 96.546875 51.260000 47.156250 18.720000 12.664062 540.89s
FLOAT_7_E5 90.580000 95.226562 47.660000 41.632812 19.500000 18.468750 540.80s
FLOAT_8_E1 90.100000 87.765625 45.000000 38.296875 17.510000 21.710938 955.80s
FLOAT_8_E2 92.040000 95.437500 49.610000 47.187500 18.310000 19.351562 955.09s
FLOAT_8_E3 93.090000 96.468750 50.520000 47.914062 16.990000 15.812500 954.98s
FLOAT_8_E4 92.920000 96.375000 50.560000 48.312500 17.400000 15.710938 955.04s
FLOAT_8_E5 92.620000 96.703125 51.200000 47.195312 18.730000 12.593750 955.57s
FLOAT_8_E6 90.770000 94.867188 47.870000 41.742188 19.570000 18.664062 954.81s
ykl/ALL/robust_result/cifar100/AlexNet.txt 0 → 100644
AlexNet cifar100
Title Org_acc Org_gen_acc FGSM_acc FGSM_gen_acc PGD_acc PGD_gen_acc Time
Full 63.440000 99.781250 21.870000 58.664062 7.790000 20.382812 8.91s
INT_2 1.000000 0.960938 1.000000 0.960938 1.000000 0.960938 27.25s
INT_3 0.990000 1.195312 1.000000 0.960938 1.000000 0.960938 27.09s
INT_4 15.960000 27.812500 3.650000 9.210938 2.870000 7.562500 26.44s
INT_5 46.760000 89.523438 14.230000 25.921875 11.530000 19.718750 26.94s
INT_6 58.120000 98.859375 20.030000 46.726562 14.010000 33.867188 27.15s
INT_7 61.680000 99.507812 21.820000 55.445312 11.780000 31.750000 27.73s
INT_8 62.860000 99.750000 22.370000 57.468750 9.870000 25.929688 24.49s
INT_9 63.150000 99.773438 22.030000 58.265625 8.570000 22.343750 25.93s
INT_10 63.360000 99.789062 22.140000 58.531250 8.090000 21.296875 20.20s
INT_11 63.360000 99.781250 22.100000 58.718750 7.920000 20.679688 19.94s
INT_12 63.400000 99.789062 21.940000 58.664062 7.740000 20.500000 19.83s
INT_13 63.440000 99.773438 21.980000 58.757812 7.780000 20.296875 19.74s
INT_14 63.430000 99.773438 21.870000 58.734375 7.740000 20.335938 22.01s
INT_15 63.450000 99.781250 21.950000 58.679688 7.750000 20.343750 28.26s
INT_16 63.440000 99.781250 21.890000 58.679688 7.720000 20.453125 27.28s
POT_2 1.000000 0.960938 1.000000 0.960938 1.000000 0.960938 31.73s
POT_3 1.330000 1.992188 1.030000 0.968750 1.020000 0.960938 34.02s
POT_4 24.460000 37.812500 6.950000 12.546875 4.390000 7.921875 36.31s
POT_5 25.240000 38.757812 6.860000 12.976562 4.070000 7.562500 44.06s
POT_6 24.910000 39.328125 7.130000 12.851562 4.310000 7.921875 59.85s
POT_7 24.810000 37.171875 7.270000 12.867188 4.160000 7.585938 92.18s
POT_8 25.050000 38.906250 7.120000 12.718750 4.130000 7.531250 161.44s
FLOAT_3_E1 1.960000 2.929688 1.450000 1.757812 1.190000 1.328125 30.23s
FLOAT_4_E1 15.090000 19.953125 7.860000 6.531250 5.730000 4.953125 35.10s
FLOAT_4_E2 18.310000 41.609375 4.630000 4.023438 3.960000 3.765625 33.30s
FLOAT_5_E1 38.590000 72.234375 14.170000 21.953125 9.610000 12.617188 44.45s
FLOAT_5_E2 44.250000 91.476562 13.070000 22.640625 8.780000 14.507812 43.65s
FLOAT_5_E3 50.410000 91.835938 17.620000 41.953125 8.930000 19.648438 45.00s
FLOAT_6_E1 48.020000 81.992188 17.060000 25.007812 9.070000 8.937500 59.66s
FLOAT_6_E2 55.160000 97.992188 16.790000 35.453125 8.730000 18.320312 58.73s
FLOAT_6_E3 59.950000 98.812500 21.100000 53.242188 9.260000 22.585938 57.76s
FLOAT_6_E4 49.880000 91.546875 17.620000 41.804688 8.830000 19.796875 58.66s
FLOAT_7_E1 51.440000 88.687500 18.360000 31.421875 8.520000 10.320312 93.81s
FLOAT_7_E2 58.390000 98.789062 17.930000 41.304688 8.260000 18.765625 93.89s
FLOAT_7_E3 62.220000 99.601562 21.890000 56.132812 8.620000 20.601562 93.65s
FLOAT_7_E4 60.150000 98.742188 21.200000 53.140625 8.920000 22.117188 92.42s
FLOAT_7_E5 50.190000 91.726562 17.770000 41.757812 8.690000 19.820312 93.67s
FLOAT_8_E1 53.620000 90.671875 18.810000 35.398438 7.840000 10.742188 160.80s
FLOAT_8_E2 58.680000 99.031250 18.510000 42.804688 8.330000 18.171875 160.99s
FLOAT_8_E3 62.860000 99.750000 21.930000 57.921875 8.130000 20.843750 159.48s
FLOAT_8_E4 62.490000 99.507812 21.980000 56.250000 8.680000 20.507812 159.30s
FLOAT_8_E5 60.100000 98.718750 20.930000 53.304688 9.170000 22.414062 159.52s
FLOAT_8_E6 50.440000 91.523438 17.520000 41.765625 8.950000 19.757812 161.18s
ykl/ALL/robust_result/cifar100/AlexNet_adjust.txt 0 → 100644
AlexNet cifar100
Title Org_acc Org_gen_acc FGSM_acc FGSM_gen_acc PGD_acc PGD_gen_acc Time
Full 63.440000 65.804688 21.880000 53.984375 7.660000 6.578125 8.41s
INT_2 1.000000 1.093750 1.000000 1.093750 1.000000 1.093750 23.27s
INT_3 0.990000 1.640625 1.000000 1.093750 1.000000 1.093750 21.28s
INT_4 15.960000 13.703125 2.340000 4.437500 1.950000 4.085938 21.70s
INT_5 46.760000 41.851562 12.940000 19.562500 10.280000 15.484375 29.08s
INT_6 58.120000 52.031250 19.850000 34.242188 14.180000 21.562500 23.98s
INT_7 61.680000 62.367188 21.370000 47.414062 11.940000 19.070312 26.71s
INT_8 62.860000 63.937500 22.190000 52.859375 9.820000 12.234375 25.46s
INT_9 63.160000 65.367188 22.220000 53.437500 8.680000 8.500000 19.93s
INT_10 63.360000 65.679688 21.890000 53.906250 8.190000 7.335938 28.45s
INT_11 63.360000 65.617188 22.180000 54.023438 7.850000 6.945312 26.87s
INT_12 63.380000 65.703125 21.910000 53.812500 7.840000 6.570312 22.55s
INT_13 63.440000 65.812500 21.880000 54.109375 7.790000 6.625000 23.92s
INT_14 63.430000 65.773438 21.990000 54.007812 7.690000 6.554688 22.24s
INT_15 63.450000 65.804688 21.930000 54.000000 7.730000 6.687500 23.07s
INT_16 63.440000 65.796875 21.920000 53.968750 7.790000 6.601562 19.49s
POT_2 1.000000 1.093750 1.000000 1.093750 1.000000 1.093750 33.10s
POT_3 1.330000 2.359375 1.000000 1.093750 1.000000 1.093750 33.87s
POT_4 24.460000 21.984375 7.210000 10.726562 4.430000 6.500000 35.21s
POT_5 25.240000 23.015625 7.100000 10.679688 3.860000 5.765625 43.64s
POT_6 24.910000 22.734375 7.130000 10.867188 4.040000 5.960938 59.41s
POT_7 24.810000 23.109375 7.120000 10.476562 4.020000 5.523438 93.92s
POT_8 25.050000 22.726562 7.380000 10.609375 3.810000 5.875000 160.87s
FLOAT_3_E1 1.960000 1.664062 1.050000 1.312500 1.070000 1.164062 37.02s
FLOAT_4_E1 15.090000 11.257812 6.890000 4.078125 5.000000 1.796875 37.46s
FLOAT_4_E2 18.310000 11.132812 5.500000 3.250000 4.670000 2.773438 36.25s
FLOAT_5_E1 38.590000 30.023438 14.710000 9.929688 10.150000 3.289062 43.81s
FLOAT_5_E2 44.250000 33.789062 13.560000 16.523438 9.300000 8.218750 45.09s
FLOAT_5_E3 50.410000 40.882812 16.380000 32.531250 8.120000 13.789062 45.03s
FLOAT_6_E1 48.020000 38.218750 16.760000 17.492188 9.100000 3.851562 59.94s
FLOAT_6_E2 55.150000 46.351562 17.260000 32.960938 8.870000 12.203125 59.64s
FLOAT_6_E3 59.950000 60.585938 21.310000 46.125000 9.260000 5.523438 59.46s
FLOAT_6_E4 49.880000 41.445312 16.450000 33.078125 7.880000 13.578125 59.79s
FLOAT_7_E1 51.440000 40.226562 17.770000 26.625000 8.190000 4.960938 94.36s
FLOAT_7_E2 58.410000 51.968750 18.080000 39.398438 8.650000 10.492188 93.51s
FLOAT_7_E3 62.230000 63.578125 21.860000 52.304688 8.610000 8.937500 93.97s
FLOAT_7_E4 60.140000 60.273438 21.240000 46.125000 9.240000 5.507812 93.16s
FLOAT_7_E5 50.190000 41.195312 16.190000 32.390625 7.940000 13.273438 93.84s
FLOAT_8_E1 53.620000 43.640625 18.290000 27.578125 7.720000 4.625000 161.06s
FLOAT_8_E2 58.660000 54.062500 18.560000 42.382812 8.340000 9.492188 160.72s
FLOAT_8_E3 62.860000 65.648438 21.950000 54.078125 8.060000 7.453125 160.37s
FLOAT_8_E4 62.490000 63.945312 21.700000 52.523438 8.410000 8.953125 160.88s
FLOAT_8_E5 60.040000 59.562500 21.440000 46.468750 9.160000 5.718750 161.28s
FLOAT_8_E6 50.440000 41.125000 16.410000 32.968750 8.110000 13.093750 161.59s
ykl/ALL/robust_result/cifar100/ResNet_18.txt 0 → 100644
ResNet_18 cifar100
Title Org_acc Org_gen_acc FGSM_acc FGSM_gen_acc PGD_acc PGD_gen_acc Time
Full 75.720000 100.000000 18.720000 72.023438 4.580000 9.796875 41.82s
INT_2 1.050000 0.937500 1.000000 1.046875 1.000000 1.046875 101.36s
INT_3 1.110000 1.164062 1.140000 0.890625 1.040000 0.976562 103.44s
INT_4 6.050000 18.429688 2.100000 4.140625 1.290000 2.445312 84.53s
INT_5 50.970000 95.859375 11.460000 32.476562 5.310000 14.367188 96.83s
INT_6 70.860000 100.000000 18.540000 68.101562 7.300000 23.320312 101.82s
INT_7 74.530000 99.992188 18.460000 66.476562 5.890000 15.171875 108.33s
INT_8 75.420000 100.000000 18.340000 70.250000 5.350000 14.757812 103.04s
INT_9 75.290000 100.000000 18.530000 71.007812 5.090000 14.656250 90.25s
INT_10 75.500000 100.000000 18.470000 71.429688 5.070000 14.210938 106.96s
INT_11 75.420000 100.000000 18.460000 71.023438 5.020000 14.171875 103.37s
INT_12 75.500000 100.000000 18.570000 71.500000 4.950000 13.500000 106.11s
INT_13 75.400000 100.000000 18.510000 71.289062 5.020000 14.000000 105.36s
INT_14 75.380000 100.000000 18.420000 71.210938 5.020000 14.218750 106.59s
INT_15 75.430000 100.000000 18.530000 71.335938 5.010000 13.796875 103.67s
INT_16 75.380000 100.000000 18.550000 71.273438 4.990000 14.125000 101.68s
POT_2 1.000000 1.132812 1.000000 1.046875 1.000000 1.046875 132.63s
POT_3 1.000000 0.882812 1.000000 0.992188 1.000000 0.992188 144.21s
POT_4 19.970000 37.257812 6.010000 3.460938 3.650000 1.351562 172.32s
POT_5 17.190000 46.421875 7.760000 3.218750 4.160000 1.492188 236.35s
POT_6 18.690000 37.445312 6.610000 4.101562 4.190000 1.710938 363.53s
POT_7 15.080000 30.570312 6.060000 3.289062 4.120000 1.453125 623.36s
POT_8 20.530000 46.226562 7.880000 3.085938 4.350000 1.320312 1136.85s
FLOAT_3_E1 0.960000 1.148438 1.040000 1.000000 1.120000 1.156250 145.13s
FLOAT_4_E1 25.630000 39.640625 5.910000 12.218750 5.320000 6.343750 172.46s
FLOAT_4_E2 15.960000 61.335938 3.200000 5.062500 1.720000 2.296875 171.38s
FLOAT_5_E1 53.420000 87.265625 12.640000 15.460938 6.110000 6.203125 234.96s
FLOAT_5_E2 53.880000 98.460938 8.820000 14.609375 4.340000 4.437500 236.93s
FLOAT_5_E3 58.640000 97.812500 15.390000 28.984375 6.130000 7.437500 237.52s
FLOAT_6_E1 59.440000 95.328125 14.160000 20.406250 6.050000 6.578125 363.66s
FLOAT_6_E2 64.600000 99.929688 12.210000 33.445312 5.090000 9.289062 362.85s
FLOAT_6_E3 71.680000 99.992188 18.200000 59.335938 6.320000 15.453125 364.36s
FLOAT_6_E4 58.550000 97.742188 14.810000 27.281250 5.960000 6.929688 363.45s
FLOAT_7_E1 64.430000 97.562500 14.970000 23.359375 6.290000 6.710938 623.47s
FLOAT_7_E2 68.690000 99.984375 14.460000 40.945312 6.280000 11.750000 624.23s
FLOAT_7_E3 74.380000 100.000000 18.630000 68.492188 5.570000 14.820312 624.86s
FLOAT_7_E4 71.490000 99.984375 18.110000 60.945312 6.200000 16.414062 624.94s
FLOAT_7_E5 57.370000 97.765625 15.000000 25.585938 6.150000 7.109375 624.37s
FLOAT_8_E1 65.180000 98.242188 15.500000 25.929688 6.210000 6.851562 1135.77s
FLOAT_8_E2 69.890000 100.000000 14.170000 45.382812 5.660000 12.070312 1136.86s
FLOAT_8_E3 75.280000 100.000000 18.200000 70.593750 5.310000 15.242188 1139.26s
FLOAT_8_E4 74.490000 99.992188 18.470000 68.578125 5.560000 14.664062 1139.00s
FLOAT_8_E5 71.590000 99.992188 18.130000 60.117188 6.080000 15.929688 1139.45s
FLOAT_8_E6 59.890000 98.406250 15.390000 28.210938 6.210000 7.296875 1137.74s
ykl/ALL/robust_result/cifar100/ResNet_18_adjust.txt 0 → 100644
ResNet_18 cifar100
Title Org_acc Org_gen_acc FGSM_acc FGSM_gen_acc PGD_acc PGD_gen_acc Time
Full 75.730000 93.804688 18.740000 45.265625 4.580000 4.468750 41.64s
INT_2 1.050000 0.960938 1.000000 0.960938 1.000000 0.960938 85.74s
INT_3 1.110000 1.054688 1.000000 1.093750 1.000000 1.093750 84.06s
INT_4 6.050000 11.140625 1.520000 3.367188 1.170000 2.390625 109.52s
INT_5 50.990000 67.367188 11.260000 27.296875 5.250000 12.703125 108.59s
INT_6 70.680000 90.703125 17.440000 36.882812 6.720000 10.109375 105.91s
INT_7 74.590000 92.890625 17.580000 40.734375 5.410000 7.945312 83.94s
INT_8 75.350000 93.460938 18.290000 43.820312 5.280000 6.593750 83.70s
INT_9 75.300000 93.265625 18.600000 44.914062 5.160000 6.023438 87.10s
INT_10 75.480000 93.523438 18.440000 45.007812 5.110000 5.562500 108.09s
INT_11 75.390000 93.476562 18.380000 44.750000 5.070000 5.585938 111.52s
INT_12 75.490000 93.640625 18.610000 45.195312 5.010000 5.390625 108.93s
INT_13 75.380000 93.476562 18.490000 45.015625 4.950000 5.570312 96.75s
INT_14 75.380000 93.476562 18.490000 45.039062 4.960000 5.562500 109.07s
INT_15 75.440000 93.546875 18.550000 45.156250 4.930000 5.507812 109.48s
INT_16 75.380000 93.429688 18.470000 44.898438 5.030000 5.687500 108.75s
POT_2 1.000000 0.960938 1.000000 0.960938 1.000000 0.960938 131.52s
POT_3 1.000000 1.765625 1.030000 1.242188 0.990000 1.125000 147.38s
POT_4 20.070000 25.234375 7.270000 4.390625 3.850000 3.218750 176.41s
POT_5 17.150000 23.843750 5.450000 4.718750 3.950000 3.031250 241.32s
POT_6 18.800000 25.750000 6.280000 6.242188 4.340000 4.000000 367.15s
POT_7 15.320000 23.148438 6.150000 4.093750 4.200000 3.007812 626.71s
POT_8 20.980000 23.523438 6.420000 5.703125 4.200000 3.703125 1138.57s
FLOAT_3_E1 0.960000 1.140625 1.080000 1.015625 0.810000 1.085938 145.22s
FLOAT_4_E1 25.430000 16.085938 6.060000 5.187500 3.970000 3.531250 176.01s
FLOAT_4_E2 16.120000 31.812500 1.930000 4.054688 1.170000 2.304688 174.85s
FLOAT_5_E1 52.850000 48.109375 11.740000 11.757812 6.430000 7.585938 241.36s
FLOAT_5_E2 54.290000 75.695312 9.530000 11.234375 4.940000 3.421875 240.09s
FLOAT_5_E3 58.550000 77.734375 15.420000 25.593750 6.730000 12.265625 239.32s
FLOAT_6_E1 59.440000 64.679688 14.050000 21.218750 6.450000 9.937500 366.27s
FLOAT_6_E2 64.250000 87.062500 12.510000 26.875000 5.470000 8.468750 367.72s
FLOAT_6_E3 71.590000 90.867188 18.400000 36.000000 6.360000 6.203125 368.44s
FLOAT_6_E4 58.480000 77.101562 15.430000 26.312500 6.730000 11.984375 367.47s
FLOAT_7_E1 64.330000 74.210938 14.460000 28.000000 6.290000 12.523438 626.82s
FLOAT_7_E2 68.740000 89.718750 15.260000 34.421875 6.390000 10.593750 628.45s
FLOAT_7_E3 74.330000 92.421875 18.450000 42.718750 5.490000 7.554688 627.38s
FLOAT_7_E4 71.460000 90.453125 18.310000 35.320312 6.390000 6.328125 627.53s
FLOAT_7_E5 57.240000 77.351562 15.610000 26.500000 6.520000 11.929688 627.83s
FLOAT_8_E1 65.190000 75.515625 14.840000 28.757812 6.210000 12.234375 1139.52s
FLOAT_8_E2 69.840000 90.054688 15.180000 33.796875 6.320000 9.906250 1139.23s
FLOAT_8_E3 75.230000 93.218750 18.350000 44.781250 5.180000 6.203125 1140.96s
FLOAT_8_E4 74.400000 92.492188 18.440000 42.757812 5.540000 7.664062 1137.38s
FLOAT_8_E5 71.530000 90.710938 18.720000 34.914062 6.320000 6.007812 1138.86s
FLOAT_8_E6 60.140000 79.359375 15.510000 30.039062 6.600000 12.195312 1140.52s
ykl/ALL/robust_result/cifar100_bkp/AlexNet.txt 0 → 100644
AlexNet cifar100
Title Org_acc Org_gen_acc FGSM_acc FGSM_gen_acc PGD_acc PGD_gen_acc Time
Full 63.450000 99.945312 21.860000 66.210938 7.770000 27.406250 12.27s
INT_2 1.000000 0.851562 1.000000 0.851562 1.000000 0.851562 26.09s
INT_3 0.990000 0.875000 1.000000 0.851562 1.000000 0.851562 25.51s
INT_4 15.970000 26.914062 2.270000 5.226562 2.010000 4.445312 25.78s
INT_5 46.720000 92.828125 13.120000 22.859375 10.730000 18.429688 26.86s
INT_6 58.070000 99.609375 20.240000 50.281250 14.080000 38.507812 26.59s
INT_7 61.670000 99.906250 21.460000 60.078125 12.010000 37.750000 23.32s
INT_8 62.890000 99.929688 22.090000 65.281250 9.910000 34.468750 25.05s
INT_9 63.180000 99.945312 22.290000 65.703125 8.690000 30.226562 25.91s
INT_10 63.330000 99.945312 22.000000 66.023438 8.160000 28.460938 23.97s
INT_11 63.310000 99.945312 22.100000 66.281250 7.850000 27.812500 22.86s
INT_12 63.400000 99.945312 21.920000 66.320312 7.780000 27.421875 26.03s
INT_13 63.460000 99.945312 21.890000 66.257812 7.750000 27.343750 27.17s
INT_14 63.420000 99.945312 21.890000 66.289062 7.790000 27.406250 25.76s
INT_15 63.440000 99.945312 21.940000 66.171875 7.740000 27.257812 28.05s
INT_16 63.440000 99.945312 21.980000 66.281250 7.760000 27.265625 25.70s
POT_2 1.000000 0.851562 1.000000 0.851562 1.000000 0.851562 34.37s
POT_3 1.330000 0.937500 1.000000 0.851562 1.000000 0.851562 33.10s
POT_4 24.470000 41.289062 7.430000 16.500000 4.990000 12.554688 36.69s
POT_5 25.270000 42.375000 7.760000 17.554688 4.150000 12.476562 43.05s
POT_6 24.940000 42.281250 7.360000 17.468750 4.220000 12.234375 60.00s
POT_7 24.790000 41.820312 7.580000 18.164062 4.060000 12.796875 93.74s
POT_8 25.060000 41.437500 7.210000 18.054688 4.280000 12.617188 160.86s
FLOAT_3_E1 1.930000 2.304688 1.440000 1.820312 1.260000 1.617188 32.22s
FLOAT_4_E1 15.120000 25.031250 7.050000 7.218750 5.240000 5.789062 36.64s
FLOAT_4_E2 18.330000 34.695312 5.740000 6.476562 4.730000 5.937500 35.29s
FLOAT_5_E1 38.560000 79.453125 14.710000 24.468750 10.340000 16.992188 44.30s
FLOAT_5_E2 44.220000 93.125000 14.580000 28.460938 10.160000 20.312500 43.24s
FLOAT_5_E3 50.550000 93.546875 16.390000 36.960938 8.120000 20.351562 43.29s
FLOAT_6_E1 47.990000 87.773438 16.470000 29.765625 9.370000 15.773438 59.58s
FLOAT_6_E2 55.120000 99.468750 17.160000 44.804688 9.420000 25.640625 60.68s
FLOAT_6_E3 59.990000 99.750000 21.540000 60.570312 9.420000 28.335938 59.20s
FLOAT_6_E4 49.970000 93.640625 16.600000 37.203125 8.050000 20.000000 60.02s
FLOAT_7_E1 51.540000 92.757812 17.860000 39.539062 8.510000 17.359375 93.95s
FLOAT_7_E2 58.320000 99.859375 18.170000 51.078125 8.430000 26.671875 93.07s
FLOAT_7_E3 62.260000 99.914062 21.910000 63.078125 8.440000 26.882812 93.84s
FLOAT_7_E4 60.040000 99.773438 21.320000 60.859375 9.240000 28.210938 94.14s
FLOAT_7_E5 50.240000 93.640625 16.580000 37.109375 8.030000 19.828125 93.02s
FLOAT_8_E1 53.610000 92.765625 18.240000 43.117188 7.660000 18.023438 160.95s
FLOAT_8_E2 58.720000 99.851562 18.200000 52.929688 8.290000 27.609375 160.38s
FLOAT_8_E3 62.820000 99.937500 22.070000 67.140625 8.160000 28.867188 160.66s
FLOAT_8_E4 62.420000 99.906250 21.750000 62.906250 8.420000 26.601562 160.51s
FLOAT_8_E5 60.120000 99.781250 21.140000 61.679688 9.260000 28.468750 161.37s
FLOAT_8_E6 50.310000 93.375000 16.300000 36.640625 7.850000 19.632812 161.20s
ykl/ALL/robust_result/cifar100_bkp/AlexNet_adjust.txt 0 → 100644
AlexNet cifar100
Title Org_acc Org_gen_acc FGSM_acc FGSM_gen_acc PGD_acc PGD_gen_acc Time
Full 63.440000 40.640625 21.870000 24.437500 7.720000 7.750000 10.40s
INT_2 1.000000 1.031250 1.000000 1.031250 1.000000 1.031250 27.93s
INT_3 0.990000 1.359375 1.000000 1.031250 1.000000 1.031250 28.42s
INT_4 15.960000 6.398438 3.190000 3.492188 2.510000 2.867188 25.97s
INT_5 46.760000 18.492188 14.760000 9.968750 11.850000 8.093750 24.20s
INT_6 58.120000 30.093750 20.090000 16.859375 14.060000 12.265625 27.94s
INT_7 61.680000 38.125000 21.950000 22.531250 11.510000 12.273438 27.70s
INT_8 62.860000 40.031250 21.980000 24.000000 9.650000 10.015625 27.06s
INT_9 63.160000 40.546875 22.110000 24.062500 8.480000 8.578125 22.35s
INT_10 63.360000 40.757812 22.030000 24.242188 8.070000 8.148438 28.25s
INT_11 63.360000 40.625000 22.040000 24.406250 7.890000 7.875000 24.18s
INT_12 63.380000 40.671875 21.960000 24.445312 7.780000 7.703125 25.65s
INT_13 63.440000 40.710938 21.900000 24.343750 7.780000 7.703125 26.41s
INT_14 63.430000 40.664062 21.900000 24.359375 7.750000 7.765625 28.50s
INT_15 63.450000 40.656250 21.910000 24.414062 7.770000 7.695312 29.40s
INT_16 63.440000 40.656250 21.920000 24.406250 7.700000 7.718750 28.96s
POT_2 1.000000 1.031250 1.000000 1.031250 1.000000 1.031250 34.26s
POT_3 1.330000 1.195312 1.020000 1.031250 1.030000 1.031250 32.69s
POT_4 24.460000 9.398438 6.620000 6.632812 4.390000 4.710938 34.70s
POT_5 25.240000 10.554688 7.100000 6.500000 4.050000 4.710938 42.45s
POT_6 24.910000 10.031250 7.150000 6.601562 4.140000 4.476562 60.39s
POT_7 24.810000 9.890625 6.970000 6.875000 4.010000 4.710938 92.57s
POT_8 25.050000 10.539062 7.430000 6.320312 4.310000 4.734375 162.23s
FLOAT_3_E1 1.960000 1.351562 1.390000 1.648438 1.220000 1.664062 33.76s
FLOAT_4_E1 15.090000 4.226562 7.150000 3.039062 5.430000 2.765625 35.21s
FLOAT_4_E2 18.310000 4.492188 4.130000 3.125000 3.650000 2.937500 35.69s
FLOAT_5_E1 38.590000 14.789062 15.370000 7.773438 9.790000 5.179688 44.28s
FLOAT_5_E2 44.250000 16.828125 13.450000 9.695312 8.910000 7.000000 44.45s
FLOAT_5_E3 50.410000 23.187500 16.520000 13.796875 8.270000 7.812500 44.88s
FLOAT_6_E1 48.020000 21.312500 16.270000 6.773438 8.780000 3.679688 61.10s
FLOAT_6_E2 55.150000 25.289062 17.050000 14.234375 9.010000 7.429688 61.39s
FLOAT_6_E3 59.950000 38.609375 21.440000 21.617188 9.480000 9.250000 59.84s
FLOAT_6_E4 49.880000 23.218750 16.680000 13.703125 8.140000 7.859375 60.05s
FLOAT_7_E1 51.440000 23.953125 17.860000 10.000000 7.960000 4.132812 94.77s
FLOAT_7_E2 58.410000 29.664062 18.260000 17.640625 8.860000 8.070312 94.29s
FLOAT_7_E3 62.230000 40.257812 21.940000 22.257812 8.560000 7.867188 94.02s
FLOAT_7_E4 60.140000 38.945312 21.520000 21.968750 9.160000 9.093750 94.04s
FLOAT_7_E5 50.190000 23.757812 16.300000 13.882812 7.970000 7.968750 94.16s
FLOAT_8_E1 53.620000 24.234375 18.030000 11.234375 7.520000 4.734375 161.72s
FLOAT_8_E2 58.660000 31.140625 18.620000 19.093750 8.720000 8.140625 161.20s
FLOAT_8_E3 62.860000 40.531250 22.000000 24.265625 8.140000 7.828125 160.96s
FLOAT_8_E4 62.490000 40.359375 22.000000 22.437500 8.470000 7.867188 161.25s
FLOAT_8_E5 60.040000 38.804688 21.310000 21.531250 9.210000 9.156250 161.08s
FLOAT_8_E6 50.440000 22.921875 16.300000 13.984375 7.900000 7.789062 160.57s
ykl/ALL/robust_result/cifar100_bkp/AlexNete.txt 0 → 100644
AlexNete cifar100
Title Org_acc Org_gen_acc FGSM_acc FGSM_gen_acc PGD_acc PGD_gen_acc Time
ykl/ALL/robust_result/cifar100_bkp/AlexNete_adjust.txt 0 → 100644
AlexNete cifar100
Title Org_acc Org_gen_acc FGSM_acc FGSM_gen_acc PGD_acc PGD_gen_acc Time
ykl/ALL/robust_result/cifar100_bkp/ResNet_18.txt 0 → 100644
ResNet_18 cifar100
Title Org_acc Org_gen_acc FGSM_acc FGSM_gen_acc PGD_acc PGD_gen_acc Time
Full 75.720000 48.468750 18.720000 51.976562 4.600000 36.257812 41.51s
INT_2 1.050000 1.062500 1.000000 1.062500 1.000000 1.062500 105.76s
INT_3 1.110000 0.937500 1.000000 0.992188 1.000000 0.992188 107.01s
INT_4 6.050000 5.179688 1.050000 2.148438 0.920000 1.468750 90.83s
INT_5 50.970000 30.781250 10.850000 33.718750 5.720000 23.820312 85.06s
INT_6 70.860000 44.273438 17.920000 48.929688 6.760000 36.203125 104.06s
INT_7 74.530000 49.281250 17.560000 52.179688 5.550000 38.210938 103.49s
INT_8 75.420000 48.562500 18.590000 52.656250 5.420000 36.968750 107.94s
INT_9 75.290000 48.656250 18.520000 52.882812 5.150000 36.921875 102.77s
INT_10 75.500000 48.429688 18.590000 52.679688 5.130000 36.890625 99.65s
INT_11 75.420000 48.929688 18.490000 52.796875 4.930000 36.820312 101.77s
INT_12 75.500000 48.671875 18.560000 52.546875 4.890000 36.789062 104.04s
INT_13 75.400000 48.750000 18.450000 52.781250 4.960000 36.718750 107.09s
INT_14 75.380000 48.812500 18.510000 52.757812 5.080000 36.734375 85.38s
INT_15 75.430000 48.625000 18.560000 52.664062 4.970000 36.804688 106.51s
INT_16 75.380000 48.828125 18.490000 52.812500 5.020000 36.789062 104.30s
POT_2 1.000000 1.062500 1.000000 1.062500 1.000000 1.062500 133.99s
POT_3 1.000000 1.632812 1.000000 0.992188 1.000000 0.992188 143.01s
POT_4 19.970000 23.000000 4.930000 5.875000 3.550000 3.562500 172.34s
POT_5 17.190000 20.859375 4.650000 5.976562 3.320000 3.960938 237.55s
POT_6 18.690000 21.367188 5.060000 3.843750 3.370000 2.828125 364.69s
POT_7 15.080000 24.492188 5.560000 6.539062 4.090000 4.414062 624.68s
POT_8 20.530000 20.875000 5.170000 6.101562 3.980000 3.890625 1139.09s
FLOAT_3_E1 0.960000 1.367188 0.830000 0.953125 1.190000 0.820312 144.80s
FLOAT_4_E1 25.630000 16.054688 5.740000 7.226562 3.630000 4.921875 172.17s
FLOAT_4_E2 15.960000 12.500000 1.980000 6.554688 1.360000 5.132812 171.87s
FLOAT_5_E1 53.420000 33.117188 13.590000 24.507812 7.510000 17.632812 237.25s
FLOAT_5_E2 53.880000 38.500000 7.500000 28.742188 4.260000 17.109375 237.45s
FLOAT_5_E3 58.640000 43.281250 14.920000 41.570312 6.500000 31.171875 237.14s
FLOAT_6_E1 59.440000 42.421875 14.460000 32.320312 6.570000 23.421875 363.70s
FLOAT_6_E2 64.600000 43.406250 11.560000 42.757812 5.270000 27.898438 364.25s
FLOAT_6_E3 71.680000 46.843750 18.480000 50.414062 6.330000 36.750000 364.37s
FLOAT_6_E4 58.550000 43.406250 14.920000 42.132812 6.250000 31.445312 365.01s
FLOAT_7_E1 64.430000 40.851562 15.280000 36.179688 6.430000 26.046875 625.27s
FLOAT_7_E2 68.690000 45.304688 14.340000 45.789062 6.220000 32.796875 625.91s
FLOAT_7_E3 74.380000 49.187500 18.570000 52.671875 5.480000 36.343750 627.07s
FLOAT_7_E4 71.490000 47.078125 18.710000 50.796875 6.470000 37.296875 627.11s
FLOAT_7_E5 57.370000 43.585938 14.750000 41.679688 6.410000 31.289062 626.12s
FLOAT_8_E1 65.180000 42.164062 15.020000 36.500000 6.260000 26.226562 1139.44s
FLOAT_8_E2 69.890000 46.632812 14.750000 47.617188 6.060000 34.531250 1140.83s
FLOAT_8_E3 75.280000 48.398438 18.450000 52.445312 5.190000 36.156250 1141.41s
FLOAT_8_E4 74.490000 49.156250 18.580000 52.726562 5.580000 36.234375 1141.37s
FLOAT_8_E5 71.590000 47.515625 18.680000 50.570312 6.420000 36.929688 1142.54s
FLOAT_8_E6 59.890000 42.718750 14.770000 40.554688 6.410000 30.367188 1142.01s
ykl/ALL/robust_utils.py 0 → 100644
import torch
import torch.nn as nn
import time
from autoattack import AutoAttack
from advertorch.attacks import GradientSignAttack, LinfBasicIterativeAttack,\
LinfPGDAttack, LinfMomentumIterativeAttack, \
CarliniWagnerL2Attack, JacobianSaliencyMapAttack, ElasticNetL1Attack
class MyDataset(torch.utils.data.Dataset):
def __init__(self, images, labels):
self.images = images
self.labels = labels
def __len__(self):
return len(self.images)
def __getitem__(self, index):
image = self.images[index]
label = self.labels[index]
return image, label
# 构建生成器的伪数据集
def build_gen_loader(generator, batchSize, iters, latent_dim, nClasses):
gen_images = []
gen_labels = []
for i in range(iters):
z = torch.randn(batchSize, latent_dim).cuda()
labels = torch.randint(0, nClasses, (batchSize,)).cuda()
z = z.contiguous()
labels = labels.contiguous()
images = generator(z, labels).detach()
gen_images.append(images)
gen_labels.append(labels)
gen_images = torch.cat(gen_images)
gen_labels = torch.cat(gen_labels)
gen_dataset = MyDataset(gen_images, gen_labels)
gen_loader = torch.utils.data.DataLoader(gen_dataset, batch_size=batchSize, shuffle=True)
return gen_loader
def test_autoattack(model, testloader, norm='Linf', eps=8/255, version='standard', verbose=True):
start_time = time.time()
adversary = AutoAttack(model, norm=norm, eps=eps, version=version, verbose=verbose)
if version == 'custom':
adversary.attacks_to_run = ['apgd-ce', 'apgd-t']
adversary.apgd.n_restarts = 1
adversary.apgd_targeted.n_restarts = 1
x_test = [x for (x,y) in testloader]
x_test = torch.cat(x_test, 0)
y_test = [y for (x,y) in testloader]
y_test = torch.cat(y_test, 0)
with torch.no_grad():
x_adv, y_adv = adversary.run_standard_evaluation(x_test, y_test, bs=testloader.batch_size, return_labels=True)
adv_correct = torch.sum(y_adv==y_test).data
total = y_test.shape[0]
rob_acc = adv_correct / total
timeinterval = time.time()-start_time
print('Attack Strength:%.4f \t AutoAttack Acc:%.3f (%d/%d)\t Time:%.2fs'%(eps, rob_acc, adv_correct, total, timeinterval))
def get_adversary(model, attack_type, c, num_classes, loss_fn=nn.CrossEntropyLoss()):
if (attack_type == "pgd"):
adversary = LinfPGDAttack(
model, loss_fn=loss_fn, eps=c,
nb_iter=10, eps_iter=c/4, rand_init=True, clip_min=0., clip_max=1.,
targeted=False)
elif (attack_type == "fgsm"):
adversary = GradientSignAttack(
model, loss_fn=loss_fn, eps=c,
clip_min=0., clip_max=1., targeted=False)
elif (attack_type == "mim"):
adversary = LinfMomentumIterativeAttack(
model, loss_fn=loss_fn, eps=c,
nb_iter=40, eps_iter=c/10, clip_min=0., clip_max=1.,
targeted=False)
elif (attack_type == "bim"):
adversary = LinfBasicIterativeAttack(
model, loss_fn=loss_fn, eps=c,
nb_iter=40, eps_iter=c/10, clip_min=0., clip_max=1.,
targeted=False)
elif (attack_type == "ela"):
adversary = ElasticNetL1Attack(
model, initial_const=c, confidence=0.1, max_iterations=100, clip_min=0., clip_max=1.,
targeted=False, num_classes=10)
elif (attack_type == "jsma"):
adversary = JacobianSaliencyMapAttack(
model, clip_min=0., clip_max=1., num_classes=10, gamma=c)
elif (attack_type == "cw"):
adversary = CarliniWagnerL2Attack(
model, confidence=0.01, max_iterations=1000, clip_min=0., clip_max=1., learning_rate=0.01,
targeted=False, num_classes=num_classes, binary_search_steps=1, initial_const=c)
elif (attack_type == None):
adversary = None
else:
raise NotImplementedError
return adversary
def test_robust(model, attack_type, c, num_classes, testloader, loss_fn=nn.CrossEntropyLoss(), is_return=True):
start_time = time.time()
adversary = get_adversary(model, attack_type, c, num_classes, loss_fn)
# ori_correct = 0
adv_correct = 0
total = 0
for batch_idx, (inputs, targets) in enumerate(testloader):
# if batch_idx < int(req_count/testloader.batch_size):
inputs, targets = inputs.cuda(), targets.cuda()
total += targets.size(0)
# ori_outputs = adversary.predict(inputs)
# ori_preds = ori_outputs.max(dim=1, keepdim=False)[1]
# ori_correct += ori_preds.eq(targets.data).cpu().sum()
# nat_acc = 100. * float(ori_correct) / total
if attack_type is None: #纯净样本
advs = inputs
with torch.no_grad():
adv_outputs = model(advs)
else:
advs = adversary.perturb(inputs, targets).detach()
with torch.no_grad():
adv_outputs = adversary.predict(advs)
adv_preds = adv_outputs.max(dim=1, keepdim=False)[1]
adv_correct += adv_preds.eq(targets.data).cpu().sum()
rob_acc = 100. * float(adv_correct) / total
timeinterval = time.time() - start_time
print('Attack Strength:%.4f Acc:%.3f (%d/%d) Time:%.2fs'%(c, rob_acc, adv_correct, total, timeinterval))
if is_return:
return rob_acc
# return nat_acc, rob_acc
\ No newline at end of file
ykl/ALL/scripts/gen_full_all_cifar10.sh → ykl/ALL/scripts/gen_all_cifar10.sh
#!/bin/bash #!/bin/bash
name_list="ResNet_152 ResNet_50 ResNet_18 MobileNetV2 Inception_BN VGG_19 VGG_16 AlexNet_BN AlexNet" name_list="ResNet_152 ResNet_50 ResNet_18 MobileNetV2 Inception_BN VGG_19 VGG_16 AlexNet_BN AlexNet"
if [ ! -d "ckpt_full_gen/cifar10" ]; then
mkdir -p "ckpt_full_gen/cifar10"
fi
for name in $name_list; do for name in $name_list; do
if [ ! -d "ret_one/$name" ]; then if [ ! -d "ret_one/$name" ]; then
mkdir -p "ret_one/$name" mkdir -p "ret_one/$name"
fi fi
sbatch --job-name=$name -o "ret_one/%x/%j.out" -e "ret_one/%x/%j.err" --export=Model=$name,Dataset=cifar10,Quant=False gen_one.slurm sbatch --job-name=$name -o "ret_one/%x/%j.out" -e "ret_one/%x/%j.err" --export=Model=$name,Dataset=cifar10 gen_one.slurm
done done
\ No newline at end of file
ykl/ALL/scripts/gen_full_all_cifar100.sh → ykl/ALL/scripts/gen_all_cifar100.sh
#!/bin/bash #!/bin/bash
name_list="ResNet_152 ResNet_50 ResNet_18 MobileNetV2 Inception_BN VGG_19 VGG_16 AlexNet_BN AlexNet" name_list="ResNet_152 ResNet_50 ResNet_18 MobileNetV2 Inception_BN VGG_19 VGG_16 AlexNet_BN AlexNet"
if [ ! -d "ckpt_full_gen/cifar100" ]; then
mkdir -p "ckpt_full_gen/cifar100"
fi
for name in $name_list; do for name in $name_list; do
if [ ! -d "ret_one/$name" ]; then if [ ! -d "ret_one/$name" ]; then
mkdir -p "ret_one/$name" mkdir -p "ret_one/$name"
fi fi
sbatch --job-name=$name -o "ret_one/%x/%j.out" -e "ret_one/%x/%j.err" --export=Model=$name,Dataset=cifar100,Quant=False gen_one.slurm sbatch --job-name=$name -o "ret_one/%x/%j.out" -e "ret_one/%x/%j.err" --export=Model=$name,Dataset=cifar100 gen_one.slurm
done done
\ No newline at end of file
ykl/ALL/scripts/gen_full_one_cifar10.sh → ykl/ALL/scripts/gen_one_cifar10.sh
...@@ -2,7 +2,4 @@ ...@@ -2,7 +2,4 @@
if [ ! -d "ret_one/$1" ]; then if [ ! -d "ret_one/$1" ]; then
mkdir -p "ret_one/$1" mkdir -p "ret_one/$1"
fi fi
if [ ! -d "ckpt_full_gen/cifar10/$1" ]; then sbatch --job-name=$1 -o "ret_one/%x/%j.out" -e "ret_one/%x/%j.err" --export=Model=$1,Dataset=cifar10 gen_one.slurm
mkdir -p "ckpt_full_gen/cifar10/$1"
fi
sbatch --job-name=$1 -o "ret_one/%x/%j.out" -e "ret_one/%x/%j.err" --export=Model=$1,Dataset=cifar10,Quant=False gen_one.slurm
ykl/ALL/scripts/gen_full_one_cifar100.sh → ykl/ALL/scripts/gen_one_cifar100.sh
...@@ -2,7 +2,4 @@ ...@@ -2,7 +2,4 @@
if [ ! -d "ret_one/$1" ]; then if [ ! -d "ret_one/$1" ]; then
mkdir -p "ret_one/$1" mkdir -p "ret_one/$1"
fi fi
if [ ! -d "ckpt_full_gen/cifar100/$1" ]; then sbatch --job-name=$1 -o "ret_one/%x/%j.out" -e "ret_one/%x/%j.err" --export=Model=$1,Dataset=cifar100 gen_one.slurm
mkdir -p "ckpt_full_gen/cifar100/$1"
fi
sbatch --job-name=$1 -o "ret_one/%x/%j.out" -e "ret_one/%x/%j.err" --export=Model=$1,Dataset=cifar100,Quant=False gen_one.slurm
ykl/ALL/scripts/gen_quant_one_cifar10.sh → ykl/ALL/scripts/loss_one_cifar10.sh
...@@ -2,7 +2,5 @@ ...@@ -2,7 +2,5 @@
if [ ! -d "ret_one/$1" ]; then if [ ! -d "ret_one/$1" ]; then
mkdir -p "ret_one/$1" mkdir -p "ret_one/$1"
fi fi
if [ ! -d "ckpt_quant_gen/cifar10/$1" ]; then
mkdir -p "ckpt_quant_gen/cifar10/$1" sbatch --job-name=$1 -o "ret_one/%x/%j.out" -e "ret_one/%x/%j.err" --export=Model=$1,Dataset=cifar10 loss_one.slurm
fi
sbatch --job-name=$1 -o "ret_one/%x/%j.out" -e "ret_one/%x/%j.err" --export=Model=$1,Dataset=cifar10,Quant=True gen_one.slurm
ykl/ALL/scripts/ptq_all_cifar10.sh
...@@ -4,8 +4,6 @@ for name in $name_list; do ...@@ -4,8 +4,6 @@ for name in $name_list; do
if [ ! -d "ret_one/$name" ]; then if [ ! -d "ret_one/$name" ]; then
mkdir -p "ret_one/$name" mkdir -p "ret_one/$name"
fi fi
if [ ! -d "ckpt_quant/cifar10/$name" ]; then
mkdir -p "ckpt_quant/cifar10/$name"
fi
sbatch --job-name=$name -o "ret_one/%x/%j.out" -e "ret_one/%x/%j.err" --export=Model=$name,Dataset=cifar10 ptq_one.slurm sbatch --job-name=$name -o "ret_one/%x/%j.out" -e "ret_one/%x/%j.err" --export=Model=$name,Dataset=cifar10 ptq_one.slurm
done done
\ No newline at end of file
ykl/ALL/scripts/ptq_all_cifar100.sh
...@@ -4,8 +4,6 @@ for name in $name_list; do ...@@ -4,8 +4,6 @@ for name in $name_list; do
if [ ! -d "ret_one/$name" ]; then if [ ! -d "ret_one/$name" ]; then
mkdir -p "ret_one/$name" mkdir -p "ret_one/$name"
fi fi
if [ ! -d "ckpt_quant/cifar100/$name" ]; then
mkdir -p "ckpt_quant/cifar100/$name"
fi
sbatch --job-name=$name -o "ret_one/%x/%j.out" -e "ret_one/%x/%j.err" --export=Model=$name,Dataset=cifar100 ptq_one.slurm sbatch --job-name=$name -o "ret_one/%x/%j.out" -e "ret_one/%x/%j.err" --export=Model=$name,Dataset=cifar100 ptq_one.slurm
done done
\ No newline at end of file
ykl/ALL/scripts/ptq_one_cifar10.sh
...@@ -2,7 +2,5 @@ ...@@ -2,7 +2,5 @@
if [ ! -d "ret_one/$1" ]; then if [ ! -d "ret_one/$1" ]; then
mkdir -p "ret_one/$1" mkdir -p "ret_one/$1"
fi fi
if [ ! -d "ckpt_quant/cifar10/$1" ]; then
mkdir -p "ckpt_quant/cifar10/$1"
fi
sbatch --job-name=$1 -o "ret_one/%x/%j.out" -e "ret_one/%x/%j.err" --export=Model=$1,Dataset=cifar10 ptq_one.slurm sbatch --job-name=$1 -o "ret_one/%x/%j.out" -e "ret_one/%x/%j.err" --export=Model=$1,Dataset=cifar10 ptq_one.slurm
ykl/ALL/scripts/ptq_one_cifar100.sh
...@@ -2,7 +2,5 @@ ...@@ -2,7 +2,5 @@
if [ ! -d "ret_one/$1" ]; then if [ ! -d "ret_one/$1" ]; then
mkdir -p "ret_one/$1" mkdir -p "ret_one/$1"
fi fi
if [ ! -d "ckpt_quant/cifar100/$1" ]; then
mkdir -p "ckpt_quant/cifar100/$1"
fi
sbatch --job-name=$1 -o "ret_one/%x/%j.out" -e "ret_one/%x/%j.err" --export=Model=$1,Dataset=cifar100 ptq_one.slurm sbatch --job-name=$1 -o "ret_one/%x/%j.out" -e "ret_one/%x/%j.err" --export=Model=$1,Dataset=cifar100 ptq_one.slurm
ykl/ALL/scripts/gen_quant_all_cifar10.sh → ykl/ALL/scripts/robust_all_cifar10.sh
#!/bin/bash #!/bin/bash
name_list="ResNet_152 ResNet_50 ResNet_18 MobileNetV2 Inception_BN VGG_19 VGG_16 AlexNet_BN AlexNet" name_list="ResNet_152 ResNet_50 ResNet_18 MobileNetV2 Inception_BN VGG_19 VGG_16 AlexNet_BN AlexNet"
if [ ! -d "ckpt_quant_gen/cifar10" ]; then
mkdir -p "ckpt_quant_gen/cifar10"
fi
for name in $name_list; do for name in $name_list; do
if [ ! -d "ret_one/$name" ]; then if [ ! -d "ret_one/$name" ]; then
mkdir -p "ret_one/$name" mkdir -p "ret_one/$name"
fi fi
sbatch --job-name=$name -o "ret_one/%x/%j.out" -e "ret_one/%x/%j.err" --export=Model=$name,Dataset=cifar10,Quant=True gen_one.slurm sbatch --job-name=$name -o "ret_one/%x/%j.out" -e "ret_one/%x/%j.err" --export=Model=$name,Dataset=cifar10 robust_one.slurm
done done
\ No newline at end of file
ykl/ALL/scripts/gen_quant_all_cifar100.sh → ykl/ALL/scripts/robust_all_cifar100.sh
#!/bin/bash #!/bin/bash
name_list="ResNet_152 ResNet_50 ResNet_18 MobileNetV2 Inception_BN VGG_19 VGG_16 AlexNet_BN AlexNet" name_list="ResNet_152 ResNet_50 ResNet_18 MobileNetV2 Inception_BN VGG_19 VGG_16 AlexNet_BN AlexNet"
if [ ! -d "ckpt_quant_gen/cifar100" ]; then
mkdir -p "ckpt_quant_gen/cifar100"
fi
for name in $name_list; do for name in $name_list; do
if [ ! -d "ret_one/$name" ]; then if [ ! -d "ret_one/$name" ]; then
mkdir -p "ret_one/$name" mkdir -p "ret_one/$name"
fi fi
sbatch --job-name=$name -o "ret_one/%x/%j.out" -e "ret_one/%x/%j.err" --export=Model=$name,Dataset=cifar100,Quant=True gen_one.slurm sbatch --job-name=$name -o "ret_one/%x/%j.out" -e "ret_one/%x/%j.err" --export=Model=$name,Dataset=cifar100 robust_one.slurm
done done
\ No newline at end of file
ykl/ALL/scripts/gen_quant_one_cifar100.sh → ykl/ALL/scripts/robust_one_cifar10.sh
...@@ -2,7 +2,5 @@ ...@@ -2,7 +2,5 @@
if [ ! -d "ret_one/$1" ]; then if [ ! -d "ret_one/$1" ]; then
mkdir -p "ret_one/$1" mkdir -p "ret_one/$1"
fi fi
if [ ! -d "ckpt_quant_gen/cifar100/$1" ]; then
mkdir -p "ckpt_quant_gen/cifar100/$1" sbatch --job-name=$1 -o "ret_one/%x/%j.out" -e "ret_one/%x/%j.err" --export=Model=$1,Dataset=cifar10 robust_one.slurm
fi
sbatch --job-name=$1 -o "ret_one/%x/%j.out" -e "ret_one/%x/%j.err" --export=Model=$1,Dataset=cifar100,Quant=True gen_one.slurm
ykl/ALL/scripts/robust_one_cifar100.sh 0 → 100644
#!/bin/bash
if [ ! -d "ret_one/$1" ]; then
mkdir -p "ret_one/$1"
fi
sbatch --job-name=$1 -o "ret_one/%x/%j.out" -e "ret_one/%x/%j.err" --export=Model=$1,Dataset=cifar100 robust_one.slurm
ykl/ALL/scripts/train_all_cifar10.sh
#!/bin/bash #!/bin/bash
name_list="ResNet_152 ResNet_50 ResNet_18 MobileNetV2 Inception_BN VGG_19 VGG_16 AlexNet_BN AlexNet" name_list="ResNet_152 ResNet_50 ResNet_18 MobileNetV2 Inception_BN VGG_19 VGG_16 AlexNet_BN AlexNet"
if [ ! -d "ckpt_full/cifar10" ]; then
mkdir -p "ckpt_full/cifar10"
fi
for name in $name_list; do for name in $name_list; do
if [ ! -d "ret_one/$name" ]; then if [ ! -d "ret_one/$name" ]; then
mkdir -p "ret_one/$name" mkdir -p "ret_one/$name"
......
ykl/ALL/scripts/train_all_cifar100.sh
#!/bin/bash #!/bin/bash
name_list="ResNet_152 ResNet_50 ResNet_18 MobileNetV2 Inception_BN VGG_19 VGG_16 AlexNet_BN AlexNet" name_list="ResNet_152 ResNet_50 ResNet_18 MobileNetV2 Inception_BN VGG_19 VGG_16 AlexNet_BN AlexNet"
if [ ! -d "ckpt_full/cifar100" ]; then
mkdir -p "ckpt_full/cifar100"
fi
for name in $name_list; do for name in $name_list; do
if [ ! -d "ret_one/$name" ]; then if [ ! -d "ret_one/$name" ]; then
mkdir -p "ret_one/$name" mkdir -p "ret_one/$name"
......
ykl/ALL/scripts/train_one_cifar10.sh
...@@ -2,7 +2,5 @@ ...@@ -2,7 +2,5 @@
if [ ! -d "ret_one/$1" ]; then if [ ! -d "ret_one/$1" ]; then
mkdir -p "ret_one/$1" mkdir -p "ret_one/$1"
fi fi
if [ ! -d "ckpt_full/cifar10" ]; then
mkdir -p "ckpt_full/cifar10"
fi
sbatch --job-name=$1 -o "ret_one/%x/%j.out" -e "ret_one/%x/%j.err" --export=Model=$1,Dataset=cifar10 train_one.slurm sbatch --job-name=$1 -o "ret_one/%x/%j.out" -e "ret_one/%x/%j.err" --export=Model=$1,Dataset=cifar10 train_one.slurm
\ No newline at end of file
ykl/ALL/scripts/train_one_cifar100.sh
...@@ -2,7 +2,5 @@ ...@@ -2,7 +2,5 @@
if [ ! -d "ckpt_full/cifar100" ]; then if [ ! -d "ckpt_full/cifar100" ]; then
mkdir -p "ckpt_full/cifar100" mkdir -p "ckpt_full/cifar100"
fi fi
if [ ! -d "ret_one/$1" ]; then
mkdir -p "ret_one/$1"
fi
sbatch --job-name=$1 -o "ret_one/%x/%j.out" -e "ret_one/%x/%j.err" --export=Model=$1,Dataset=cifar100 train_one.slurm sbatch --job-name=$1 -o "ret_one/%x/%j.out" -e "ret_one/%x/%j.err" --export=Model=$1,Dataset=cifar100 train_one.slurm
\ No newline at end of file
ykl/ALL/scripts/try_one.sh 0 → 100644
#!/bin/bash
if [ ! -d "ret_one/$1" ]; then
mkdir -p "ret_one/$1"
fi
sbatch --job-name=$1 -o "ret_one/%x/%j.out" -e "ret_one/%x/%j.err" --export=Model=$1,Dataset=cifar10 robust_one_try.slurm
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