run_all_architecture_error_bars.sh

#!/bin/sh

# Main dataset path
main_path="/mnt/disk1/mimarlik-multilabel/dataset"
num_epoch=200
backbone="resnet18"
dataset="architecture"
num_runs=5

# List of methods
methods=("gaussian_mlr" "clr" "lsep")
supervisions=("strong" "weak")
config_path="A"

# for all methods and supervisions and num_runs and configs
for method in "${methods[@]}"
do
    for supervision in "${supervisions[@]}"
    do
        for j in $(seq 0 $((num_runs-1)))
        do
            experiment_name="error_bar_architecture_"$backbone"_"$method"_"$supervision"_"$j
            echo "Running experiment $experiment_name"

            # If method is gaussian_mlr
            if [ $method == "gaussian_mlr" ]; then
                python gaussian_mlr_trainer.py --config_path $config_path --main_path $main_path --num_epoch $num_epoch --experiment_name $experiment_name --backbone $backbone --dataset $dataset --supervision $supervision --domain "ARC"
            fi

            if [ $method == "clr" ]; then
                python clr_trainer.py --config_path $config_path --main_path $main_path --num_epoch $num_epoch --experiment_name $experiment_name --backbone $backbone --dataset $dataset --supervision $supervision --domain "ARC"
            fi

            if [ $method == "lsep" ]; then
                python lsep_trainer.py --config_path $config_path --main_path $main_path --num_epoch $num_epoch --experiment_name $experiment_name --backbone $backbone --dataset $dataset --supervision $supervision --stage "ranking" --domain "ARC"
                python lsep_trainer.py --config_path $config_path --main_path $main_path --num_epoch $num_epoch --experiment_name $experiment_name --backbone $backbone --dataset $dataset --supervision $supervision --stage "threshold" --domain "ARC"
            fi

        done
    done
done