update scripts

zlijingtao · Mar 16, 2022 · 98d80e7 · 98d80e7
1 parent 63e6008
commit 98d80e7
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diff --git a/reproduce/ace_run_table6.sh b/reproduce/ace_run_table6.sh
@@ -1,16 +1,16 @@
 #!/bin/bash
 cd "$(dirname "$0")"
-bash table6/ace_run_expert_vgg11_cut1.sh
-bash table6/ace_run_expert_vgg11_cut2.sh
-bash table6/ace_run_expert_vgg11_cut3.sh
+# bash table6/ace_run_expert_vgg11_cut1.sh
+# bash table6/ace_run_expert_vgg11_cut2.sh
+# bash table6/ace_run_expert_vgg11_cut3.sh
 bash table6/ace_run_finetune_lowLRlite_vgg.sh
 
-bash table6/ace_run_expert_resnet20_cut2.sh
-bash table6/ace_run_expert_resnet20_cut3.sh
-bash table6/ace_run_expert_resnet20_cut4.sh
-bash table6/ace_run_finetune_lowLRlite_resnet.sh
+# bash table6/ace_run_expert_resnet20_cut2.sh
+# bash table6/ace_run_expert_resnet20_cut3.sh
+# bash table6/ace_run_expert_resnet20_cut4.sh
+# bash table6/ace_run_finetune_lowLRlite_resnet.sh
 
-bash table6/ace_run_expert_mobilenet_cut2.sh
-bash table6/ace_run_expert_mobilenet_cut3.sh
-bash table6/ace_run_expert_mobilenet_cut4.sh
-bash table6/ace_run_finetune_lowLRlite_mobilenetv2.sh
+# bash table6/ace_run_expert_mobilenet_cut2.sh
+# bash table6/ace_run_expert_mobilenet_cut3.sh
+# bash table6/ace_run_expert_mobilenet_cut4.sh
+# bash table6/ace_run_finetune_lowLRlite_mobilenetv2.sh
diff --git a/reproduce/supplementary/MIA_optim/ace_run_MIAoptim_vgg11_cifar100.sh b/reproduce/supplementary/MIA_optim/ace_run_MIAoptim_vgg11_cifar100.sh
@@ -1,6 +1,6 @@
 #!/bin/bash
 cd "$(dirname "$0")"
-cd ../../
+cd ../../../
 GPU_id=0
 arch=vgg11_bn
 batch_size=128
@@ -19,29 +19,33 @@ ssim_threshold=0.5
 train_gan_AE_type=conv_normN0C16
 bc_list="8"
 gan_loss_type=SSIM
-folder_name="supple_saves/MIA_optim"
 
 
-for bc in $bc_list; do
-        bottleneck_option=noRELU_C${bc}S1
-        for dataset in $dataset_list; do
-                for random_seed in $random_seed_list; do
-                        for regularization_strength in $regularization_strength_list; do
-                                for cutlayer in $cutlayer_list; do
-                                        for num_client in $num_client_list; do
-                                                filename=ace_${scheme}_${arch}_cutlayer_${cutlayer}_client_${num_client}_seed${random_seed}_dataset_${dataset}_lr_${learning_rate}_${regularization}_both_${train_gan_AE_type}_${regularization_strength}_${num_epochs}epoch_bottleneck_${bottleneck_option}_ssim_${ssim_threshold}_advtrain
+
+folder_name="new_saves/finetune_lite"
+local_lr_list="0.005"
+bottleneck_option=norelu_C8S1
+interval=5
+transfer_source_task=cifar10
+for random_seed in $random_seed_list; do
+        for regularization_strength in $regularization_strength_list; do
+                for cutlayer in $cutlayer_list; do
+                        for num_client in $num_client_list; do
+                                for local_lr in $local_lr_list; do
+                                        for dataset in $dataset_list; do
+                                                filename=ace_${scheme}_${arch}_cutlayer_${cutlayer}_client_${num_client}_seed${random_seed}_dataset_${dataset}_lr_${learning_rate}_${regularization}_both_${train_gan_AE_type}_${regularization_strength}_${num_epochs}epoch_bottleneck_${bottleneck_option}_servertune_${local_lr}_loadserver_source_${transfer_source_task}
                                                 CUDA_VISIBLE_DEVICES=${GPU_id} python main_MIA.py --arch=${arch}  --cutlayer=$cutlayer --batch_size=${batch_size} \
                                                         --filename=$filename --num_client=$num_client --num_epochs=$num_epochs --save_more_checkpoints\
                                                         --dataset=$dataset --scheme=$scheme --regularization=${regularization} --regularization_strength=${regularization_strength}\
                                                         --random_seed=$random_seed --learning_rate=$learning_rate --gan_AE_type ${train_gan_AE_type} --gan_loss_type ${gan_loss_type}\
-                                                        --local_lr $local_lr --bottleneck_option ${bottleneck_option} --folder ${folder_name} --ssim_threshold ${ssim_threshold}
+                                                        --load_from_checkpoint --local_lr $local_lr --bottleneck_option ${bottleneck_option} --folder ${folder_name} --ssim_threshold ${ssim_threshold} \
+                                                        --load_from_checkpoint_server --transfer_source_task ${transfer_source_task} --optimize_computation ${interval}
 
                                                 target_client=0
                                                 attack_scheme=MIA_mf
                                                 attack_epochs=50
                                                 average_time=1
-
-
+
                                                 attack_from_later_layer_list="-1"
                                                 num_epochs_list="0 1 2 5 10 20 50 100 200"
                                                 for attack_from_later_layer in ${attack_from_later_layer_list}; do
@@ -65,29 +69,29 @@ for bc in $bc_list; do
         done
 done
 
-local_lr_list="0.005"
-bottleneck_option=norelu_C8S1
-interval=5
-transfer_source_task=cifar10
-for random_seed in $random_seed_list; do
-        for regularization_strength in $regularization_strength_list; do
-                for cutlayer in $cutlayer_list; do
-                        for num_client in $num_client_list; do
-                                for local_lr in $local_lr_list; do
-                                        for dataset in $dataset_list; do
-                                                filename=ace_${scheme}_${arch}_cutlayer_${cutlayer}_client_${num_client}_seed${random_seed}_dataset_${dataset}_lr_${learning_rate}_${regularization}_both_${train_gan_AE_type}_${regularization_strength}_${num_epochs}epoch_bottleneck_${bottleneck_option}_servertune_${local_lr}_loadserver_source_${transfer_source_task}
+folder_name="supple_saves/MIA_optim"
+
+
+for bc in $bc_list; do
+        bottleneck_option=noRELU_C${bc}S1
+        for dataset in $dataset_list; do
+                for random_seed in $random_seed_list; do
+                        for regularization_strength in $regularization_strength_list; do
+                                for cutlayer in $cutlayer_list; do
+                                        for num_client in $num_client_list; do
+                                                filename=ace_${scheme}_${arch}_cutlayer_${cutlayer}_client_${num_client}_seed${random_seed}_dataset_${dataset}_lr_${learning_rate}_${regularization}_both_${train_gan_AE_type}_${regularization_strength}_${num_epochs}epoch_bottleneck_${bottleneck_option}_ssim_${ssim_threshold}_advtrain
                                                 CUDA_VISIBLE_DEVICES=${GPU_id} python main_MIA.py --arch=${arch}  --cutlayer=$cutlayer --batch_size=${batch_size} \
                                                         --filename=$filename --num_client=$num_client --num_epochs=$num_epochs --save_more_checkpoints\
                                                         --dataset=$dataset --scheme=$scheme --regularization=${regularization} --regularization_strength=${regularization_strength}\
                                                         --random_seed=$random_seed --learning_rate=$learning_rate --gan_AE_type ${train_gan_AE_type} --gan_loss_type ${gan_loss_type}\
-                                                        --load_from_checkpoint --local_lr $local_lr --bottleneck_option ${bottleneck_option} --folder ${folder_name} --ssim_threshold ${ssim_threshold} \
-                                                        --load_from_checkpoint_server --transfer_source_task ${transfer_source_task} --optimize_computation ${interval}
+                                                        --local_lr $local_lr --bottleneck_option ${bottleneck_option} --folder ${folder_name} --ssim_threshold ${ssim_threshold}
 
                                                 target_client=0
                                                 attack_scheme=MIA_mf
                                                 attack_epochs=50
                                                 average_time=1
-
+
+
                                                 attack_from_later_layer_list="-1"
                                                 num_epochs_list="0 1 2 5 10 20 50 100 200"
                                                 for attack_from_later_layer in ${attack_from_later_layer_list}; do
@@ -110,5 +114,3 @@ for random_seed in $random_seed_list; do
                 done
         done
 done
-
-
diff --git a/reproduce/supplementary/other_evidence/ace_run_expert_vgg11_cut4.sh b/reproduce/supplementary/other_evidence/ace_run_expert_vgg11_cut4.sh
@@ -1,6 +1,6 @@
 #!/bin/bash
 cd "$(dirname "$0")"
-cd ../../
+cd ../../../
 GPU_id=0
 arch=vgg11_bn
 batch_size=128

diff --git a/reproduce/supplementary/other_evidence/ace_run_finetune_lowLRlite_source_svhn.sh b/reproduce/supplementary/other_evidence/ace_run_finetune_lowLRlite_source_svhn.sh
@@ -1,6 +1,6 @@
 #!/bin/bash
 cd "$(dirname "$0")"
-cd ../../
+cd ../../../
 GPU_id=0
 arch=vgg11_bn
 batch_size=128

diff --git a/reproduce/table6/ace_run_expert_vgg11_cut1.sh b/reproduce/table6/ace_run_expert_vgg11_cut1.sh
@@ -7,7 +7,7 @@ batch_size=128
 
 num_client=2
 num_epochs=200
-dataset_list="cifar10"
+dataset_list="cifar10 cifar100"
 scheme=V2_epoch
 random_seed_list="125"
 #Extra argement (store_true): --collude_use_public, --initialize_different  --collude_not_regularize  --collude_not_regularize --num_client_regularize ${num_client_regularize}

diff --git a/reproduce/table6/ace_run_expert_vgg11_cut3.sh b/reproduce/table6/ace_run_expert_vgg11_cut3.sh
@@ -7,7 +7,7 @@ batch_size=128
 
 num_client=2
 num_epochs=200
-dataset_list="cifar10"
+dataset_list="cifar10 cifar100"
 scheme=V2_epoch
 random_seed_list="125"
 #Extra argement (store_true): --collude_use_public, --initialize_different  --collude_not_regularize  --collude_not_regularize --num_client_regularize ${num_client_regularize}

diff --git a/reproduce/table6/ace_run_finetune_lowLRlite_vgg.sh b/reproduce/table6/ace_run_finetune_lowLRlite_vgg.sh
@@ -24,7 +24,7 @@ gan_loss_type=SSIM
 transfer_source_task=cifar10
 
 dataset_list="cifar100"
-learning_rate=0.02
+learning_rate=0.05
 local_lr_list="0.005"
 
 bottleneck_option=norelu_C1S1