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run.sh
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#accelerate launch training_general.py \
# --train_data_dir="~/MIA/MIA-Gen/VAEs/data/celeba64/total" \
# --resume_from_checkpoint "latest" \
# --resolution=64 --center_crop \
# --output_dir="ddpm-celeba-64-test" \
# --train_batch_size=16 \
# --num_epochs=500 \
# --gradient_accumulation_steps=1 \
# --learning_rate=1e-4 \
# --lr_warmup_steps=500 \
# --mixed_precision=no \
# --train_sta_idx=150000 \
# --train_end_idx=160000 \
# --eval_sta_idx=160000 \
# --eval_end_idx=170000
#accelerate launch training_general.py \
# --train_data_dir="~/MIA/MIA-Gen/VAEs/data/celeba64/total" \
# --resume_from_checkpoint "latest" \
# --resolution=64 --center_crop \
# --output_dir="ddpm-celeba-64-target2" \
# --train_batch_size=16 \
# --num_epochs=500 \
# --gradient_accumulation_steps=1 \
# --learning_rate=1e-4 \
# --lr_warmup_steps=500 \
# --mixed_precision=no \
# --train_sta_idx=0 \
# --train_end_idx=10000 \
# --eval_sta_idx=10000 \
# --eval_end_idx=11000
# for 100k training datasets
#accelerate launch training_general.py \
# --train_data_dir="~/MIA/MIA-Gen/VAEs/data/celeba64/total" \
# --resume_from_checkpoint "latest" \
# --resolution=64 --center_crop \
# --output_dir="ddpm-celeba-64-100k" \
# --train_batch_size=16 \
# --num_epochs=200 \
# --checkpointing_steps=1500 \
# --gradient_accumulation_steps=1 \
# --learning_rate=5e-11 \
# --lr_warmup_steps=500 \
# --mixed_precision=no \
# --train_sta_idx=0 \
# --train_end_idx=100000 \
# --eval_sta_idx=100000 \
# --eval_end_idx=110000
# for 50k training datasets target
accelerate launch training_general.py \
--train_data_dir="~/MIA/MIA-Gen/target_model/data/celeba64/total" \
--resume_from_checkpoint "latest" \
--resolution=64 --center_crop \
--output_dir="ddpm-celeba-64-50k" \
--train_batch_size=16 \
--num_epochs=400 \
--checkpointing_steps=1500 \
--gradient_accumulation_steps=1 \
--learning_rate=1e-4 \
--lr_warmup_steps=500 \
--mixed_precision=no \
--train_sta_idx=0 \
--train_end_idx=50000 \
--eval_sta_idx=50000 \
--eval_end_idx=60000
# for 50k training datasets shadow
accelerate launch training_general.py \
--train_data_dir="~/MIA/MIA-Gen/target_model/data/celeba64/total" \
--resume_from_checkpoint "latest" \
--resolution=64 --center_crop \
--output_dir="ddpm-celeba-64-50k-shadow" \
--train_batch_size=16 \
--num_epochs=400 \
--checkpointing_steps=1500 \
--gradient_accumulation_steps=1 \
--learning_rate=1e-4 \
--lr_warmup_steps=500 \
--mixed_precision=no \
--train_sta_idx=60000 \
--train_end_idx=110000 \
--eval_sta_idx=110000 \
--eval_end_idx=120000
# for 50k training datasets reference
accelerate launch training_general.py \
--train_data_dir="~/MIA/MIA-Gen/target_model/data/celeba64/total" \
--resume_from_checkpoint "latest" \
--resolution=64 --center_crop \
--output_dir="ddpm-celeba-64-50k-reference" \
--train_batch_size=16 \
--num_epochs=400 \
--checkpointing_steps=1500 \
--gradient_accumulation_steps=1 \
--learning_rate=1e-4 \
--lr_warmup_steps=500 \
--mixed_precision=no \
--train_sta_idx=120000 \
--train_end_idx=170000 \
--eval_sta_idx=170000 \
--eval_end_idx=180000
#### train ddpm for tiny-in dataset
accelerate launch training_general.py \
--train_data_dir="~/MIA/MIA-Gen/target_model/data/Tiny-IN" \
--resume_from_checkpoint "latest" \
--resolution=64 --center_crop \
--output_dir="ddpm-tinyin-64-30k" \
--train_batch_size=16 \
--num_epochs=400 \
--checkpointing_steps=1500 \
--gradient_accumulation_steps=1 \
--learning_rate=1e-4 \
--lr_warmup_steps=500 \
--mixed_precision=no \
--train_sta_idx=0 \
--train_end_idx=30000 \
--eval_sta_idx=30000 \
--eval_end_idx=35000
#### train ddpm for tiny-in dataset
accelerate launch training_general.py \
--train_data_dir="~/MIA/MIA-Gen/target_model/data/Tiny-IN" \
--resume_from_checkpoint "latest" \
--resolution=64 --center_crop \
--output_dir="ddpm-tinyin-64-30k-shadow" \
--train_batch_size=16 \
--num_epochs=400 \
--checkpointing_steps=1500 \
--gradient_accumulation_steps=1 \
--learning_rate=1e-4 \
--lr_warmup_steps=500 \
--mixed_precision=no \
--train_sta_idx=35000 \
--train_end_idx=65000 \
--eval_sta_idx=65000 \
--eval_end_idx=70000
#### train ddpm for tiny-in dataset
accelerate launch training_general.py \
--train_data_dir="~/MIA/MIA-Gen/target_model/data/Tiny-IN" \
--resume_from_checkpoint "latest" \
--resolution=64 --center_crop \
--output_dir="ddpm-tinyin-64-30k-reference" \
--train_batch_size=16 \
--num_epochs=400 \
--checkpointing_steps=1500 \
--gradient_accumulation_steps=1 \
--learning_rate=1e-4 \
--lr_warmup_steps=500 \
--mixed_precision=no \
--train_sta_idx=70000 \
--train_end_idx=100000 \
--eval_sta_idx=100000 \
--eval_end_idx=105000