feat: Loss Trajectory Full precision Model

mzh/new_mzh/Loss_Trajectory_MIA/function.py

+from torch.autograd import Function
+
+
+class FakeQuantize(Function):
+
+    @staticmethod
+    def forward(ctx, x, qparam):
+        x = qparam.quantize_tensor(x)
+        x = qparam.dequantize_tensor(x)
+        return x
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        return grad_output, None

mzh/new_mzh/Loss_Trajectory_MIA/global_var.py

+class GlobalVariables:
+     SELF_INPLANES = 0

mzh/new_mzh/Loss_Trajectory_MIA/gol.py

+# -*- coding: utf-8 -*-
+
+# 用于多个module之间共享全局变量
+def _init():  # 初始化
+    global _global_dict
+    _global_dict = {}
+ 
+def set_value(value,is_bias=False):
+    # 定义一个全局变量
+    if is_bias:
+        _global_dict[0] = value
+    else:
+        _global_dict[1] = value
+ 
+ 
+def get_value(is_bias=False): # 给bias独立于各变量外的精度
+    if is_bias:
+        return _global_dict[0]
+    else:
+        return _global_dict[1]  
+

mzh/new_mzh/Loss_Trajectory_MIA/main.py

+import os
+import argparse
+import utils
+import normal
+import MIA
+
+def train_networks(args):
+    device = utils.get_pytorch_device()
+    utils.create_path('./outputs')
+    if 'distill' in args.mode:
+        model_path_tar = 'networks/{}/{}'.format(0, args.mode.split('_')[-1])
+        utils.create_path(model_path_tar)
+        model_path_dis = 'networks/{}/{}'.format(args.seed, args.mode)
+        utils.create_path(model_path_dis)
+    else:
+        model_path_tar = 'networks/{}/{}'.format(args.seed, args.mode)
+        utils.create_path(model_path_tar)
+        model_path_dis = None
+
+    utils.set_logger('outputs/train_models'.format(args.seed))
+    normal.train_models(args, model_path_tar, model_path_dis, device)
+
+def membership_inference_attack(args):
+    print(f'--------------{args.mia_type}-------------')
+
+    device = utils.get_pytorch_device()
+
+    if args.mia_type == 'build-dataset':
+        models_path = 'networks/{}'.format(0)
+        MIA.build_trajectory_membership_dataset(args, models_path, device)
+
+    if args.mia_type == 'black-box':
+        trained_models_path = 'networks/{}'.format(args.seed)
+        MIA.trajectory_black_box_membership_inference_attack(args, trained_models_path, device)
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='TrajectoryMIA')
+    parser.add_argument('--action', type=int, default=0, help=[0, 1])
+    parser.add_argument('--seed', type=int, default=0)
+    parser.add_argument('--mode', type=str, default='target', help=['target', 'shadow', 'distill_target', 'distill_shadow'])
+    parser.add_argument('--model', type=str, default='resnet18', help=['resnet18','resnet50','resnet152','mobilenetv2'])
+    parser.add_argument('--data', type=str, default='cifar10', help=['cinic10', 'cifar10', 'cifar100', 'gtsrb'])
+    parser.add_argument('--epochs', type=int, default=100)
+    parser.add_argument('--model_distill', type=str, default='resnet18',help=['resnet18','resnet50','resnet152','mobilenetv2'])
+    parser.add_argument('--epochs_distill', type=int, default=100)
+    parser.add_argument('--mia_type', type=str, help=['build-dataset', 'black-box'])
+
+    args = parser.parse_args()
+    utils.set_random_seeds(args.seed)
+    print('random seed:{}'.format(args.seed))
+
+    if args.action == 0:
+        train_networks(args)
+
+    elif args.action == 1:
+        membership_inference_attack(args)
+    

mzh/new_mzh/Loss_Trajectory_MIA/plot.py

+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn import metrics
+import argparse
+
+
+
+if __name__ == '__main__':
+ # 在 test_mia_attack_model存储的最优的数据
+    parser = argparse.ArgumentParser(description='PLOT_TrajectoryMIA')
+
+
+    parser.add_argument('--model', type=str, default='resnet18', help=['resnet18','resnet50','resnet152','mobilenetv2'])
+    parser.add_argument('--data', type=str, default='cifar10', help=['cinic10', 'cifar10', 'cifar100', 'gtsrb'])
+    parser.add_argument('--model_distill', type=str, default='resnet18',help=['resnet18','resnet50','resnet152','mobilenetv2'])
+
+    args = parser.parse_args()
+    data_auc = np.load(f'./outputs/{args.data}_{args.model}_{args.model_distill}_trajectory_auc.npy', allow_pickle=True).item()
+    for i in range(len(data_auc['fpr'])):
+        if data_auc['fpr'][i] > 0.001:
+            print('TPR at 0.1% FPR:  {:.1%}'.format(data_auc['tpr'][i-1]))
+            break
+    plt.plot(data_auc['fpr'], data_auc['tpr'], color='darkorange', lw=2, label='ROC curve')
+    plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')
+    plt.xlim([0.0, 1.0])
+    plt.ylim([0.0, 1.05])
+    plt.xlabel('False Positive Rate')
+    plt.ylabel('True Positive Rate')
+    plt.title('Receiver operating characteristic example')
+    plt.legend(loc="lower right")
+    # plt.show()
+    plt.savefig(f'./img/{args.data}_{args.model}_{args.model_distill}_roc_curve.png')
\ No newline at end of file

mzh/new_mzh/Loss_Trajectory_MIA/train_attack_152_10.sh

+#!/bin/bash
+
+#- Job parameters
+
+# (TODO)
+# Please modify job name
+
+#SBATCH -J Tra_152           # The job name
+#SBATCH -o ./info/ret-%j.out        # Write the standard output to file named 'ret-<job_number>.out'
+#SBATCH -e ./info/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 0-12:00:00                # 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
+###
+
+#- 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
+source ~/pyt1.5/bin/activate
+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 python3/3.6.8
+
+##- CUDA
+module load cuda-cudnn/11.1-8.1.1
+
+##- 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)]
+sleep 2s
+hostname
+
+echo "python main.py --mode target --model resnet152 --data cifar10"
+python main.py --mode target --model resnet152 --data cifar10
+
+echo "python main.py --mode shadow --model resnet152 --data cifar10"
+python main.py --mode shadow --model resnet152 --data cifar10
+
+echo "python main.py --mode distill_target --model resnet152 --data cifar10"
+python main.py --mode distill_target --model resnet152 --data cifar10
+
+echo "python main.py --mode distill_shadow --model resnet152 --data cifar10"
+python main.py --mode distill_shadow --model resnet152 --data cifar10
+
+echo "python main.py --action 1 --mode shadow --mia_type build-dataset --model resnet152  --model_distill resnet152 --data cifar10"
+python main.py --action 1 --mode shadow --mia_type build-dataset --model resnet152 --model_distill resnet152 --data cifar10
+
+echo "python main.py --action 1 --mode target --mia_type build-dataset --model resnet152 --model_distill resnet152 --data cifar10"
+python main.py --action 1 --mode target --mia_type build-dataset --model resnet152 --model_distill resnet152 --data cifar10
+
+echo "python main.py --action 1 --mia_type black-box --model resnet152 --model_distill resnet152 --data cifar10"
+python main.py --action 1 --mia_type black-box --model resnet152 --model_distill resnet152 --data cifar10 
+
+#- End
+echo "Job end at $(date "+%Y-%m-%d %H:%M:%S")"

mzh/new_mzh/Loss_Trajectory_MIA/train_attack_152_100.sh

+#!/bin/bash
+
+#- Job parameters
+
+# (TODO)
+# Please modify job name
+
+#SBATCH -J Tra_152_10           # The job name
+#SBATCH -o ./info/ret-%j.out        # Write the standard output to file named 'ret-<job_number>.out'
+#SBATCH -e ./info/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 0-12:00:00                # 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
+###
+
+#- 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
+source ~/pyt1.5/bin/activate
+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 python3/3.6.8
+
+##- CUDA
+module load cuda-cudnn/11.1-8.1.1
+
+##- 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)]
+sleep 2s
+hostname
+
+echo "python main.py --mode target --model resnet152 --data cifar100"
+python main.py --mode target --model resnet152 --data cifar100
+
+echo "python main.py --mode shadow --model resnet152 --data cifar100"
+python main.py --mode shadow --model resnet152 --data cifar100
+
+echo "python main.py --mode distill_target --model resnet152 --data cifar100"
+python main.py --mode distill_target --model resnet152 --data cifar100
+
+echo "python main.py --mode distill_shadow --model resnet152 --data cifar100"
+python main.py --mode distill_shadow --model resnet152 --data cifar100
+
+echo "python main.py --action 1 --mode shadow --mia_type build-dataset --model resnet152  --model_distill resnet152 --data cifar100"
+python main.py --action 1 --mode shadow --mia_type build-dataset --model resnet152 --model_distill resnet152 --data cifar100 
+
+echo "python main.py --action 1 --mode target --mia_type build-dataset --model resnet152 --model_distill resnet152 --data cifar100"
+python main.py --action 1 --mode target --mia_type build-dataset --model resnet152 --model_distill resnet152 --data cifar100 
+
+echo "python main.py --action 1 --mia_type black-box --model resnet152 --model_distill resnet152 --data cifar100"
+python main.py --action 1 --mia_type black-box --model resnet152 --model_distill resnet152 --data cifar100
+
+#- End
+echo "Job end at $(date "+%Y-%m-%d %H:%M:%S")"

mzh/new_mzh/Loss_Trajectory_MIA/train_attack_152_cinic.sh

+#!/bin/bash
+
+#- Job parameters
+
+# (TODO)
+# Please modify job name
+
+#SBATCH -J Tra_CIN_10           # The job name
+#SBATCH -o ./info/ret-%j.out        # Write the standard output to file named 'ret-<job_number>.out'
+#SBATCH -e ./info/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 3-00:00:00                # 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-long           # 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
+###
+
+#- 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
+source ~/pyt1.5/bin/activate
+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 python3/3.6.8
+
+##- CUDA
+module load cuda-cudnn/11.1-8.1.1
+
+##- 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)]
+sleep 2s
+hostname
+
+echo "python main.py --mode target --model resnet152 --data cinic10"
+python main.py --mode target --model resnet152 --data cinic10
+
+echo "python main.py --mode shadow --model resnet152 --data cinic10"
+python main.py --mode shadow --model resnet152 --data cinic10
+
+echo "python main.py --mode distill_target --model resnet152 --data cinic10"
+python main.py --mode distill_target --model resnet152 --data cinic10
+
+echo "python main.py --mode distill_shadow --model resnet152 --data cinic10"
+python main.py --mode distill_shadow --model resnet152 --data cinic10
+
+echo "python main.py --action 1 --mode shadow --mia_type build-dataset --model resnet152  --model_distill resnet152 --data cinic10"
+python main.py --action 1 --mode shadow --mia_type build-dataset --model resnet152 --model_distill resnet152 --data cinic10 
+
+echo "python main.py --action 1 --mode target --mia_type build-dataset --model resnet152 --model_distill resnet152 --data cinic10"
+python main.py --action 1 --mode target --mia_type build-dataset --model resnet152 --model_distill resnet152 --data cinic10 
+
+echo "python main.py --action 1 --mia_type black-box --model resnet152 --model_distill resnet152 --data cinic10"
+python main.py --action 1 --mia_type black-box --model resnet152 --model_distill resnet152 --data cinic10
+
+#- End
+echo "Job end at $(date "+%Y-%m-%d %H:%M:%S")"

mzh/new_mzh/Loss_Trajectory_MIA/train_attack_18_10.sh

+#!/bin/bash
+
+#- Job parameters
+
+# (TODO)
+# Please modify job name
+
+#SBATCH -J Tra_10_18           # The job name
+#SBATCH -o ./info/ret-%j.out        # Write the standard output to file named 'ret-<job_number>.out'
+#SBATCH -e ./info/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 1-00:00:00                # 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
+###
+
+#- 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
+source ~/pyt1.5/bin/activate
+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 python3/3.6.8
+
+##- CUDA
+module load cuda-cudnn/11.1-8.1.1
+
+##- 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)]
+sleep 2s
+hostname
+
+echo "python main.py --mode target --model resnet18 --data cifar10"
+python main.py --mode target --model resnet18 --data cifar10
+
+echo "python main.py --mode shadow --model resnet18 --data cifar10"
+python main.py --mode shadow --model resnet18 --data cifar10
+
+echo "python main.py --mode distill_target --model resnet18 --data cifar10"
+python main.py --mode distill_target --model resnet18 --data cifar10
+
+echo "python main.py --mode distill_shadow --model resnet18 --data cifar10"
+python main.py --mode distill_shadow --model resnet18 --data cifar10
+
+echo "python main.py --action 1 --mode shadow --mia_type build-dataset --model resnet18 --model_distill resnet18 --data cifar10"
+python main.py --action 1 --mode shadow --mia_type build-dataset --model resnet18 --model_distill resnet18 --data cifar10
+
+echo "python main.py --action 1 --mode target --mia_type build-dataset --model resnet18 --model_distill resnet18 --data cifar10"
+python main.py --action 1 --mode target --mia_type build-dataset --model resnet18 --model_distill resnet18 --data cifar10
+
+echo "python main.py --action 1 --mia_type black-box --model resnet18 --model_distill resnet18 --data cifar10"
+python main.py --action 1 --mia_type black-box --model resnet18  --model_distill resnet18 --data cifar10
+
+#- End
+echo "Job end at $(date "+%Y-%m-%d %H:%M:%S")"

mzh/new_mzh/Loss_Trajectory_MIA/train_attack_18_100.sh

+#!/bin/bash
+
+#- Job parameters
+
+# (TODO)
+# Please modify job name
+
+#SBATCH -J Tra_C100_18           # The job name
+#SBATCH -o ./info/ret-%j.out        # Write the standard output to file named 'ret-<job_number>.out'
+#SBATCH -e ./info/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 1-00:00:00                # 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
+###
+
+#- 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
+source ~/pyt1.5/bin/activate
+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 python3/3.6.8
+
+##- CUDA
+module load cuda-cudnn/11.1-8.1.1
+
+##- 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)]
+sleep 2s
+hostname
+
+echo "python main.py --mode target --model resnet18 --data cifar100"
+python main.py --mode target --model resnet18 --data cifar100
+
+echo "python main.py --mode shadow --model resnet18 --data cifar100"
+python main.py --mode shadow --model resnet18 --data cifar100
+
+echo "python main.py --mode distill_target --model resnet18 --data cifar100"
+python main.py --mode distill_target --model resnet18 --data cifar100
+
+echo "python main.py --mode distill_shadow --model resnet18 --data cifar100"
+python main.py --mode distill_shadow --model resnet18 --data cifar100
+
+echo "python main.py --action 1 --mode shadow --mia_type build-dataset --model resnet18 --model_distill resnet18 --data cifar100"
+python main.py --action 1 --mode shadow --mia_type build-dataset --model resnet18 --model_distill resnet18 --data cifar100
+
+echo "python main.py --action 1 --mode target --mia_type build-dataset --model resnet18 --model_distill resnet18 --data cifar100"
+python main.py --action 1 --mode target --mia_type build-dataset --model resnet18 --model_distill resnet18 --data cifar100
+
+echo "python main.py --action 1 --mia_type black-box --model resnet18 --model_distill resnet18 --data cifar100"
+python main.py --action 1 --mia_type black-box --model resnet18  --model_distill resnet18 --data cifar100
+
+#- End
+echo "Job end at $(date "+%Y-%m-%d %H:%M:%S")"

mzh/new_mzh/Loss_Trajectory_MIA/train_attack_18_cinic.sh

+#!/bin/bash
+
+#- Job parameters
+
+# (TODO)
+# Please modify job name
+
+#SBATCH -J Tra_CIN_18           # The job name
+#SBATCH -o ./info/ret-%j.out        # Write the standard output to file named 'ret-<job_number>.out'
+#SBATCH -e ./info/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 1-12:00:00                # 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-long           # 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
+###
+
+#- 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
+source ~/pyt1.5/bin/activate
+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 python3/3.6.8
+
+##- CUDA
+module load cuda-cudnn/11.1-8.1.1
+
+##- 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)]
+sleep 2s
+hostname
+
+echo "python main.py --mode target --model resnet18 --data cinic10"
+python main.py --mode target --model resnet18 --data cinic10
+
+echo "python main.py --mode shadow --model resnet18 --data cinic10"
+python main.py --mode shadow --model resnet18 --data cinic10
+
+echo "python main.py --mode distill_target --model resnet18 --data cinic10"
+python main.py --mode distill_target --model resnet18 --data cinic10
+
+echo "python main.py --mode distill_shadow --model resnet18 --data cinic10"
+python main.py --mode distill_shadow --model resnet18 --data cinic10
+
+echo "python main.py --action 1 --mode shadow --mia_type build-dataset --model resnet18 --model_distill resnet18 --data cinic10"
+python main.py --action 1 --mode shadow --mia_type build-dataset --model resnet18 --model_distill resnet18 --data cinic10
+
+echo "python main.py --action 1 --mode target --mia_type build-dataset --model resnet18 --model_distill resnet18 --data cinic10"
+python main.py --action 1 --mode target --mia_type build-dataset --model resnet18 --model_distill resnet18 --data cinic10
+
+echo "python main.py --action 1 --mia_type black-box --model resnet18 --model_distill resnet18 --data cinic10"
+python main.py --action 1 --mia_type black-box --model resnet18  --model_distill resnet18 --data cinic10
+
+#- End
+echo "Job end at $(date "+%Y-%m-%d %H:%M:%S")"

mzh/new_mzh/Loss_Trajectory_MIA/train_attack_50_10.sh

+#!/bin/bash
+
+#- Job parameters
+
+# (TODO)
+# Please modify job name
+
+#SBATCH -J Tra_50           # The job name
+#SBATCH -o ./info/ret-%j.out        # Write the standard output to file named 'ret-<job_number>.out'
+#SBATCH -e ./info/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 0-12:00:00                # 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
+###
+
+#- 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
+source ~/pyt1.5/bin/activate
+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 python3/3.6.8
+
+##- CUDA
+module load cuda-cudnn/11.1-8.1.1
+
+##- 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)]
+sleep 2s
+hostname
+
+echo "python main.py --mode target --model resnet50  "
+python main.py --mode target --model resnet50  
+
+echo "python main.py --mode shadow --model resnet50  "
+python main.py --mode shadow --model resnet50  
+
+echo "python main.py --mode distill_target --model resnet50  "
+python main.py --mode distill_target --model resnet50  
+
+echo "python main.py --mode distill_shadow --model resnet50  "
+python main.py --mode distill_shadow --model resnet50  
+
+echo "python main.py --action 1 --mode shadow --mia_type build-dataset --model resnet50 --model_distill resnet50  "
+python main.py --action 1 --mode shadow --mia_type build-dataset --model resnet50 --model_distill resnet50  
+
+echo "python main.py --action 1 --mode target --mia_type build-dataset --model resnet50 --model_distill resnet50  "
+python main.py --action 1 --mode target --mia_type build-dataset --model resnet50 --model_distill resnet50  
+
+echo "python main.py --action 1 --mia_type black-box --model resnet50 --model_distill resnet50  "
+python main.py --action 1 --mia_type black-box --model resnet50  --model_distill resnet50  
+
+#- End
+echo "Job end at $(date "+%Y-%m-%d %H:%M:%S")"

mzh/new_mzh/Loss_Trajectory_MIA/train_attack_50_100.sh

+#!/bin/bash
+
+#- Job parameters
+
+# (TODO)
+# Please modify job name
+
+#SBATCH -J Tra_50_10           # The job name
+#SBATCH -o ./info/ret-%j.out        # Write the standard output to file named 'ret-<job_number>.out'
+#SBATCH -e ./info/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 0-12:00:00                # 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
+###
+
+#- 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
+source ~/pyt1.5/bin/activate
+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 python3/3.6.8
+
+##- CUDA
+module load cuda-cudnn/11.1-8.1.1
+
+##- 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)]
+sleep 2s
+hostname
+
+echo "python main.py --mode target --model resnet50 --data cifar100"
+python main.py --mode target --model resnet50 --data cifar100
+
+echo "python main.py --mode shadow --model resnet50 --data cifar100"
+python main.py --mode shadow --model resnet50 --data cifar100
+
+echo "python main.py --mode distill_target --model resnet50 --data cifar100"
+python main.py --mode distill_target --model resnet50 --data cifar100
+
+echo "python main.py --mode distill_shadow --model resnet50 --data cifar100"
+python main.py --mode distill_shadow --model resnet50 --data cifar100
+
+echo "python main.py --action 1 --mode shadow --mia_type build-dataset --model resnet50 --model_distill resnet50 --data cifar100"
+python main.py --action 1 --mode shadow --mia_type build-dataset --model resnet50 --model_distill resnet50 --data cifar100
+
+echo "python main.py --action 1 --mode target --mia_type build-dataset --model resnet50 --model_distill resnet50 --data cifar100"
+python main.py --action 1 --mode target --mia_type build-dataset --model resnet50 --model_distill resnet50 --data cifar100
+
+echo "python main.py --action 1 --mia_type black-box --model resnet50 --model_distill resnet50 --data cifar100"
+python main.py --action 1 --mia_type black-box --model resnet50  --model_distill resnet50 --data cifar100
+
+#- End
+echo "Job end at $(date "+%Y-%m-%d %H:%M:%S")"

mzh/new_mzh/Loss_Trajectory_MIA/train_attack_50_cinic.sh

+#!/bin/bash
+
+#- Job parameters
+
+# (TODO)
+# Please modify job name
+
+#SBATCH -J Tra_CIN_50         # The job name
+#SBATCH -o ./info/ret-%j.out        # Write the standard output to file named 'ret-<job_number>.out'
+#SBATCH -e ./info/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 1-12:00:00                # 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-long           # 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
+###
+
+#- 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
+source ~/pyt1.5/bin/activate
+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 python3/3.6.8
+
+##- CUDA
+module load cuda-cudnn/11.1-8.1.1
+
+##- 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)]
+sleep 2s
+hostname
+
+echo "python main.py --mode target --model resnet50 --data cinic10"
+python main.py --mode target --model resnet50 --data cinic10
+
+echo "python main.py --mode shadow --model resnet50 --data cinic10"
+python main.py --mode shadow --model resnet50 --data cinic10
+
+echo "python main.py --mode distill_target --model resnet50 --data cinic10"
+python main.py --mode distill_target --model resnet50 --data cinic10
+
+echo "python main.py --mode distill_shadow --model resnet50 --data cinic10"
+python main.py --mode distill_shadow --model resnet50 --data cinic10
+
+echo "python main.py --action 1 --mode shadow --mia_type build-dataset --model resnet50 --model_distill resnet50 --data cinic10"
+python main.py --action 1 --mode shadow --mia_type build-dataset --model resnet50 --model_distill resnet50 --data cinic10
+
+echo "python main.py --action 1 --mode target --mia_type build-dataset --model resnet50 --model_distill resnet50 --data cinic10"
+python main.py --action 1 --mode target --mia_type build-dataset --model resnet50 --model_distill resnet50 --data cinic10
+
+echo "python main.py --action 1 --mia_type black-box --model resnet50 --model_distill resnet50 --data cinic10"
+python main.py --action 1 --mia_type black-box --model resnet50  --model_distill resnet50 --data cinic10
+
+#- End
+echo "Job end at $(date "+%Y-%m-%d %H:%M:%S")"

mzh/new_mzh/Loss_Trajectory_MIA/train_attack_mobile_10.sh

+#!/bin/bash
+
+#- Job parameters
+
+# (TODO)
+# Please modify job name
+
+#SBATCH -J Tra_Mobile          # The job name
+#SBATCH -o ./info/ret-%j.out        # Write the standard output to file named 'ret-<job_number>.out'
+#SBATCH -e ./info/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 0-12:00:00                # 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
+###
+
+#- 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
+source ~/pyt1.5/bin/activate
+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 python3/3.6.8
+
+##- CUDA
+module load cuda-cudnn/11.1-8.1.1
+
+##- 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)]
+sleep 2s
+hostname
+
+echo "python main.py --mode target --model mobilenetv2"
+python main.py --mode target --model mobilenetv2
+
+echo "python main.py --mode shadow --model mobilenetv2"
+python main.py --mode shadow --model mobilenetv2
+
+echo "python main.py --mode distill_target --model mobilenetv2"
+python main.py --mode distill_target --model mobilenetv2
+
+echo "python main.py --mode distill_shadow --model mobilenetv2"
+python main.py --mode distill_shadow --model mobilenetv2
+
+echo "python main.py --action 1 --mode shadow --mia_type build-dataset --model mobilenetv2 --model_distill mobilenetv2 "
+python main.py --action 1 --mode shadow --mia_type build-dataset --model mobilenetv2  --model_distill mobilenetv2
+
+echo "python main.py --action 1 --mode target --mia_type build-dataset --model mobilenetv2 --model_distill mobilenetv2"
+python main.py --action 1 --mode target --mia_type build-dataset --model mobilenetv2 --model_distill mobilenetv2
+
+echo "python main.py --action 1 --mia_type black-box --model mobilenetv2 --model_distill mobilenetv2"
+python main.py --action 1 --mia_type black-box --model mobilenetv2 --model_distill mobilenetv2
+
+#- End
+echo "Job end at $(date "+%Y-%m-%d %H:%M:%S")"

mzh/new_mzh/Loss_Trajectory_MIA/train_attack_mobile_100.sh

+#!/bin/bash
+
+#- Job parameters
+
+# (TODO)
+# Please modify job name
+
+#SBATCH -J Tra_100_Mobile          # The job name
+#SBATCH -o ./info/ret-%j.out        # Write the standard output to file named 'ret-<job_number>.out'
+#SBATCH -e ./info/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 0-12:00:00                # 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
+###
+
+#- 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
+source ~/pyt1.5/bin/activate
+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 python3/3.6.8
+
+##- CUDA
+module load cuda-cudnn/11.1-8.1.1
+
+##- 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)]
+sleep 2s
+hostname
+
+echo "python main.py --mode target --model mobilenetv2 --data cifar100"
+python main.py --mode target --model mobilenetv2 --data cifar100
+
+echo "python main.py --mode shadow --model mobilenetv2 --data cifar100"
+python main.py --mode shadow --model mobilenetv2 --data cifar100
+
+echo "python main.py --mode distill_target --model mobilenetv2 --data cifar100"
+python main.py --mode distill_target --model mobilenetv2 --data cifar100
+
+echo "python main.py --mode distill_shadow --model mobilenetv2 --data cifar100"
+python main.py --mode distill_shadow --model mobilenetv2 --data cifar100
+
+echo "python main.py --action 1 --mode shadow --mia_type build-dataset --model mobilenetv2 --data cifar100 --model_distill mobilenetv2"
+python main.py --action 1 --mode shadow --mia_type build-dataset --model mobilenetv2 --model_distill mobilenetv2 --data cifar100
+
+echo "python main.py --action 1 --mode target --mia_type build-dataset --model mobilenetv2 --data cifar100 --model_distill mobilenetv2"
+python main.py --action 1 --mode target --mia_type build-dataset --model mobilenetv2 --model_distill mobilenetv2 --data cifar100
+
+echo "python main.py --action 1 --mia_type black-box --model mobilenetv2  --model_distill mobilenetv2 --data cifar100"
+python main.py --action 1 --mia_type black-box --model mobilenetv2 --model_distill mobilenetv2 --data cifar100
+
+#- End
+echo "Job end at $(date "+%Y-%m-%d %H:%M:%S")"

mzh/new_mzh/Loss_Trajectory_MIA/train_attack_mobile_cinic.sh

+#!/bin/bash
+
+#- Job parameters
+
+# (TODO)
+# Please modify job name
+
+#SBATCH -J Tra_CIN_Mobile          # The job name
+#SBATCH -o ./info/ret-%j.out        # Write the standard output to file named 'ret-<job_number>.out'
+#SBATCH -e ./info/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 3-00:00:00                # 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-long           # 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
+###
+
+#- 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
+source ~/pyt1.5/bin/activate
+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 python3/3.6.8
+
+##- CUDA
+module load cuda-cudnn/11.1-8.1.1
+
+##- 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)]
+sleep 2s
+hostname
+
+echo "python main.py --mode target --model mobilenetv2 --data cinic10"
+python main.py --mode target --model mobilenetv2 --data cinic10
+
+echo "python main.py --mode shadow --model mobilenetv2 --data cinic10"
+python main.py --mode shadow --model mobilenetv2 --data cinic10
+
+echo "python main.py --mode distill_target --model mobilenetv2 --data cinic10"
+python main.py --mode distill_target --model mobilenetv2 --data cinic10
+
+echo "python main.py --mode distill_shadow --model mobilenetv2 --data cinic10"
+python main.py --mode distill_shadow --model mobilenetv2 --data cinic10
+
+echo "python main.py --action 1 --mode shadow --mia_type build-dataset --model mobilenetv2 --data cinic10 --model_distill mobilenetv2"
+python main.py --action 1 --mode shadow --mia_type build-dataset --model mobilenetv2 --model_distill mobilenetv2 --data cinic10
+
+echo "python main.py --action 1 --mode target --mia_type build-dataset --model mobilenetv2 --data cinic10 --model_distill mobilenetv2"
+python main.py --action 1 --mode target --mia_type build-dataset --model mobilenetv2 --model_distill mobilenetv2 --data cinic10
+
+echo "python main.py --action 1 --mia_type black-box --model mobilenetv2  --model_distill mobilenetv2 --data cinic10"
+python main.py --action 1 --mia_type black-box --model mobilenetv2  --model_distill mobilenetv2 --data cinic10
+
+#- End
+echo "Job end at $(date "+%Y-%m-%d %H:%M:%S")"

mzh/new_mzh/Loss_Trajectory_MIA/train_plot.sh

+#!/bin/bash
+
+#- Job parameters
+
+# (TODO)
+# Please modify job name
+
+#SBATCH -J PLOT          # The job name
+#SBATCH -o ./info/ret-%j.out        # Write the standard output to file named 'ret-<job_number>.out'
+#SBATCH -e ./info/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 0-12:00:00                # 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
+###
+
+#- 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
+source ~/pyt1.5/bin/activate
+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 python3/3.6.8
+
+##- CUDA
+module load cuda-cudnn/11.1-8.1.1
+
+##- 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)]
+sleep 2s
+hostname
+
+
+echo "python plot.py --model resnet50 --model_distill resnet50 --data cifar100"
+python plot.py --model resnet50 --model_distill resnet50 --data cifar100
+
+
+
+#- End
+echo "Job end at $(date "+%Y-%m-%d %H:%M:%S")"