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run_cifar10.sh
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#! /bin/bash
## CIFAR10 , these experiments run on 8 2080ti GPUs.
python main.py \
-cc configs/default_cifar.txt \
--root './' \
--mode train \
--n_gpus_per_node 8 \
--training_batch_size 16 \
--testing_batch_size 16 \
--sampling_batch_size 64 \
--independent_log_gamma dis \
--f_learning_times 20 \
--image_gamma use \
--skip_type quad \
--n_discrete_steps 11 \
--phi_learning_rate 0.000016 \
--f_learning_rate 0.00008 \
--n_train_iters 200000 \
--pretrained_model ./pretrained_model/target_epsilon_cifar10.pt \
--workdir ./work_dir/cifar10_10steps \
--master_address 127.0.0.10 \
--master_port 4372
python main.py \
-cc configs/default_cifar.txt \
--root './' \
--mode train \
--n_gpus_per_node 8 \
--training_batch_size 16 \
--testing_batch_size 32 \
--sampling_batch_size 64 \
--independent_log_gamma dis \
--f_learning_times 20 \
--image_gamma use \
--eta 0.2 \
--skip_type quad \
--phi_learning_rate 0.000016 \
--f_learning_rate 0.00008 \
--n_discrete_steps 16 \
--n_train_iters 200000 \
--num_accum 1\
--pretrained_model ./pretrained_model/target_epsilon_cifar10.pt \
--workdir ./work_dir/cifar_15steps\
--master_address 127.0.0.10 \
--master_port 6987
# #! Note that we adopt the former 10steps weights (best one for 5steps) as the pretrained weights for 5 steps model.
python main.py \
-cc configs/default_cifar.txt \
--root './' \
--mode train \
--n_gpus_per_node 8 \
--training_batch_size 16 \
--testing_batch_size 32 \
--sampling_batch_size 64 \
--independent_log_gamma dis \
--f_learning_times 20 \
--image_gamma use \
--eta 0.2 \
--skip_type quad \
--phi_learning_rate 0.0000016 \
--f_learning_rate 0.0000008 \
--n_discrete_steps 6 \
--pretrained_model ./pretrained_model/cifar_10steps.pth\
--n_train_iters 200000 \
--num_accum 1\
--workdir ./work_dir/cifar_5steps\
--master_address 127.0.0.10 \
--master_port 6987