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print_train_lottery_iterative_command.sh
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print_train_lottery_iterative_command.sh
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# Copyright (c) 2019 Uber Technologies, Inc.
# Licensed under the Uber Non-Commercial License (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at the root directory of this project.
# See the License for the specific language governing permissions and
# limitations under the License.
#!/bin/bash
network=$1
orig_dir_name=$2
seed=$3
method=$4
weight_ind=$5
model_type=$6
exp=$7
execute=$8
case "$weight_ind" in
"")
weight_ind=-1
esac
case "$network" in
fc)
filedir=iter_lot_fc_orig
num_epochs=55
arch=fc_lot
lr=0.0012
data=mnist
large_batch_size=5000
small_batch_size=0
prune_base=0.8,0.9
layer_cutoff=4,6
case "$weight_ind" in
-1)
save_special_iter=-1
;;
*)
let "save_special_iter=$weight_ind*100"
esac
;;
conv2)
filedir=iter_lot_conv2_orig
num_epochs=27
arch=conv2_lot
lr=0.0002
data=cifar10
large_batch_size=1000
small_batch_size=1000
prune_base=0.9,0.8,0.9
layer_cutoff=4,8,10
case "$weight_ind" in
-1)
save_special_iter=-1
;;
*)
let "save_special_iter=$weight_ind*100"
esac
;;
conv4)
filedir=iter_lot_conv4_orig
num_epochs=34
arch=conv4_lot
lr=0.0003
data=cifar10
large_batch_size=1000
small_batch_size=1000
prune_base=0.9,0.8,0.9
layer_cutoff=8,12,14
case "$weight_ind" in
-1)
save_special_iter=-1
;;
*)
let "save_special_iter=$weight_ind*100"
esac
;;
conv6)
filedir=iter_lot_conv6_orig
num_epochs=40
arch=conv6_lot
lr=0.0003
data=cifar10
large_batch_size=1000
small_batch_size=1000
prune_base=0.85,0.8,0.9
layer_cutoff=12,16,18
case "$weight_ind" in
-1)
save_special_iter=-1
;;
*)
let "save_special_iter=$weight_ind*100"
esac
;;
*)
echo error!!!
esac
case "$model_type" in
"")
model_type=mask
esac
case "$exp" in
"")
exp=none
esac
for pb in {1..24}
do
let "pb2=$pb-1"
case "$execute" in
t)
case $pb in
1)
resman -d "./results/${filedir}/${orig_dir_name}_seed_${seed}/${method}_weight_ind${weight_ind}_${model_type}_exp_${exp}" -r "pp${pb}" -t "{runname}" -- python train_lottery.py --train_h5 "./data/${data}_train.h5" --test_h5 "./data/${data}_test.h5" --val_h5 "./data/${data}_val.h5" --train_batch_size 60 --num_epochs $num_epochs --eval_every 100 --print_every 100 --save_weights --save_loss --arch $arch --seed $seed --opt adam --lr $lr --mode save_res --method $method --prune_base $prune_base --prune_power 1 --layer_cutoff $layer_cutoff --prev_weights "./results/${filedir}/${orig_dir_name}_seed_${seed}" --orig_weights "./results/${filedir}/${orig_dir_name}_seed_${seed}" --final_weights_ind $weight_ind --save_special_iter $save_special_iter --model_type $model_type --exp $exp --large_batch_size $large_batch_size --test_batch_size $small_batch_size --val_batch_size $small_batch_size
;;
*)
resman -d "./results/${filedir}/${orig_dir_name}_seed_${seed}/${method}_weight_ind${weight_ind}_${model_type}_exp_${exp}" -r "pp${pb}" -t "{runname}" -- python train_lottery.py --train_h5 "./data/${data}_train.h5" --test_h5 "./data/${data}_test.h5" --val_h5 "./data/${data}_val.h5" --train_batch_size 60 --num_epochs $num_epochs --eval_every 100 --print_every 100 --save_weights --save_loss --arch $arch --seed $seed --opt adam --lr $lr --mode save_res --method $method --prune_base $prune_base --prune_power 1 --layer_cutoff $layer_cutoff --prev_weights "./results/${filedir}/${orig_dir_name}_seed_${seed}/${method}_weight_ind${weight_ind}_${model_type}_exp_${exp}/pp${pb2}" --orig_weights "./results/${filedir}/${orig_dir_name}_seed_${seed}" --final_weights_ind 2 --save_special_iter $save_special_iter --model_type $model_type --exp $exp --large_batch_size $large_batch_size --test_batch_size $small_batch_size --val_batch_size $small_batch_size
esac
;;
*)
case $pb in
1)
echo resman -d "./results/${filedir}/${orig_dir_name}_seed_${seed}/${method}_weight_ind${weight_ind}_${model_type}_exp_${exp}" -r "pp${pb}" -t "{runname}" -- python train_lottery.py --train_h5 "./data/${data}_train.h5" --test_h5 "./data/${data}_test.h5" --val_h5 "./data/${data}_val.h5" --train_batch_size 60 --num_epochs $num_epochs --eval_every 100 --print_every 100 --save_weights --save_loss --arch $arch --seed $seed --opt adam --lr $lr --mode save_res --method $method --prune_base $prune_base --prune_power 1 --layer_cutoff $layer_cutoff --prev_weights "./results/${filedir}/${orig_dir_name}_seed_${seed}" --orig_weights "./results/${filedir}/${orig_dir_name}_seed_${seed}" --final_weights_ind $weight_ind --save_special_iter $save_special_iter --model_type $model_type --exp $exp --large_batch_size $large_batch_size --test_batch_size $small_batch_size --val_batch_size $small_batch_size
;;
*)
echo resman -d "./results/${filedir}/${orig_dir_name}_seed_${seed}/${method}_weight_ind${weight_ind}_${model_type}_exp_${exp}" -r "pp${pb}" -t "{runname}" -- python train_lottery.py --train_h5 "./data/${data}_train.h5" --test_h5 "./data/${data}_test.h5" --val_h5 "./data/${data}_val.h5" --train_batch_size 60 --num_epochs $num_epochs --eval_every 100 --print_every 100 --save_weights --save_loss --arch $arch --seed $seed --opt adam --lr $lr --mode save_res --method $method --prune_base $prune_base --prune_power 1 --layer_cutoff $layer_cutoff --prev_weights "./results/${filedir}/${orig_dir_name}_seed_${seed}/${method}_weight_ind${weight_ind}_${model_type}_exp_${exp}/pp${pb2}" --orig_weights "./results/${filedir}/${orig_dir_name}_seed_${seed}" --final_weights_ind 2 --save_special_iter $save_special_iter --model_type $model_type --exp $exp --large_batch_size $large_batch_size --test_batch_size $small_batch_size --val_batch_size $small_batch_size
esac
esac
done