@echo off
@rem for windows
start cmd /c ^
"cd /D [code dir]  ^
& [python.exe dir]\python.exe rank.py ^
--arch [model arch name] ^
--resume [pre-trained model dir] ^
--num_workers [worker numbers] ^
--image_num [batch numbers] ^
--batch_size [batch size] ^
--dataset [CIFAR10 or ImageNet] ^
--data_dir [data dir] ^
--calc_dis_mtx True ^
& pause"

# for linux
python rank.py \
--arch [model arch name] \
--resume [pre-trained model dir] \
--num_workers [worker numbers] \
--image_num [batch numbers] \
--batch_size [batch size] \
--dataset [CIFAR10 or ImageNet] \
--data_dir [data dir] \
--calc_dis_mtx True

python main_win.py \
--arch vgg_16_bn \
--resume [pre-trained model dir] \
--compress_rate [0.3]*2+[0.45]*3+[0.6]*3+[0.85]*4 \
--num_workers [worker numbers] \
--epochs 30 \
--lr 0.001 \
--lr_decay_step 5 \
--save_id 1 \
--weight_decay 0.005 \
--data_dir [dataset dir] \
--dataset CIFAR10 

python main_win.py \
--arch resnet_56 \
--resume [pre-trained model dir] \
--compress_rate [0.]+[0.2,0.]*2+[0.6,0.]*7+[0.7,0.]*9+[0.8,0.]*9 \
--num_workers [worker numbers] \
--epochs 30 \
--lr 0.001 \
--lr_decay_step 5 \
--save_id 1 \
--weight_decay 0.005 \
--data_dir [dataset dir] \
--dataset CIFAR10 

# for linux
python main_win.py \
--arch densenet_40 \
--resume [pre-trained model dir] \
--compress_rate [0.]+[0.5]*12+[0.3]+[0.4]*12+[0.3]+[0.4]*9+[0.]*3 \
--num_workers [worker numbers] \
--epochs 30 \
--lr 0.001 \
--lr_decay_step 5 \
--save_id 1 \
--weight_decay 0.005 \
--data_dir [dataset dir] \
--dataset CIFAR10 

python main_win.py \
--arch googlenet \
--resume [pre-trained model dir] \
--compress_rate [0.2]+[0.9]*24+[0.,0.4,0.] \
--num_workers [worker numbers] \
--epochs 1 \
--lr 0.001 \
--save_id 1 \
--weight_decay 0. \
--data_dir [dataset dir] \
--dataset CIFAR10

python main_win.py \
--arch googlenet \
--from_scratch True \
--resume finally_pruned_model/googlenet_1.pt \
--num_workers 2 \
--epochs 30 \
--lr 0.01 \
--lr_decay_step 5,15 \
--save_id 1 \
--weight_decay 0.005 \
--data_dir [dataset dir] \
--dataset CIFAR10

python main_win.py \
--arch resnet_50 \
--resume [pre-trained model dir] \
--data_dir [dataset dir] \
--dataset ImageNet \
--compress_rate [0.]+[0.1,0.1,0.4]*1+[0.7,0.7,0.4]*2+[0.2,0.2,0.4]*1+[0.7,0.7,0.4]*3+[0.2,0.2,0.3]*1+[0.7,0.7,0.3]*5+[0.1,0.1,0.1]+[0.2,0.3,0.1]*2 \
--num_workers [worker numbers] \
--batch_size 64 \
--epochs 2 \
--lr_decay_step 1 \
--lr 0.001 \
--save_id 1 \
--weight_decay 0. \
--input_size 224 \
--start_cov 0

python main_win.py \
--arch resnet_50 \
--from_scratch True \
--resume finally_pruned_model/resnet_50_1.pt \
--num_workers 8 \
--epochs 40 \
--lr 0.001 \
--lr_decay_step 5,20 \
--save_id 2 \
--batch_size 64 \
--weight_decay 0.0005 \
--input_size 224 \
--data_dir [dataset dir] \
--dataset ImageNet