

DATASET=miniimagenet
DATASET_DIR=./dataset
TRAIN_DIR=../results/
		
mkdir -p $TRAIN_DIR




args=(
    # Execution
    --name metatest_in \
    --seed 21 
    --path ${TRAIN_DIR} \
    --data_path ${DATASET_DIR} \
    --dataset $DATASET
    --hp_setting 'in_metanas' \
    --use_hp_setting 1 \
    --workers 0 \
    --gpus 0 \
    --test_adapt_steps 1.0 \
    --batch_size 64 \
    # few shot params
    # examples per class
    --n 5 \

    # number classes  
    --k 5 \
    # test examples per class
    --q 1 \

    --meta_model_prune_threshold 0.01 \
    --alpha_prune_threshold 0.01 \
    # Meta Learning
    --meta_model searchcnn\
    --meta_epochs 0 \
    --warm_up_epochs  0\
    --use_pairwise_input_alphas \
    --eval_freq 0 \
    --eval_epochs 100 \
    --normalizer softmax \
    --normalizer_temp_anneal_mode linear \
    --normalizer_t_min 0.1 \
    --normalizer_t_max 1.0 \
    --drop_path_prob 0.2 \
    --test_task_train_steps 10

    # Architectures
    --init_channels 28 \
    --layers 4 \
    --reduction_layers 1 3 \
    --use_first_order_darts \

    --use_torchmeta_loader \

    # experiments
    --exp_const 1
    --exp_cell 1
    --wandb 0
    --wandb_name d
    --const_mult 10
    --unittest 0
    --cell_const_flag 1
    --cell_const_mult 4
    --light_exp 1
    --opsample 1
    --sampleno 2
    --naivenaive 0
    --naivesample 0
    --cell_phase 3
    --residual_flag 0
    --beta_sampling 0
    --eval_switch 0
    --alpha_expect 1
    --split_num 5
    --exp_name yours
    
)

python3 main.py "${args[@]}" 

            
