quant_qgru_dpd_regr.sh

#!/bin/bash
source ./get_pt_file.sh

# Arguments
while getopts g: option
do
case "${option}"
in
g) gpu_device=${OPTARG};;
esac
done

# Global Settings
dataset_name=DPA_160MHz
accelerator=cpu
devices=0

# Hyperparameters
seed=0
n_epochs=100
frame_length=50
frame_stride=1
loss_type=l2
opt_type=adamw
batch_size=64
batch_size_eval=256
lr_schedule=1
lr=1e-3
lr_end=1e-6
decay_factor=0.5
patience=10

#########################
# Train
#########################
seed=(0)

# PA Model
PA_backbone=dgru
PA_hidden_size=8
PA_num_layers=1

# DPD Model
DPD_backbone=(qgru_amp1 qgru_amp1 qgru_amp1 qgru_amp1 qgru_amp1)
DPD_backbone=(qgru qgru qgru qgru qgru)
DPD_hidden_size=(6 9 13 20 30)
DPD_num_layers=(1 1 1 1 1)


# Quantization
quant_n_bits_w=16
quant_n_bits_a=16
# q_pretrain='--q_pretrain' enable to train a float model first
q_pretrain=''
pretrained_model='/Users/ali6/projects/OpenDPD/save/DPA_160MHz/train_dpd/amp2p_h'${DPD_hidden_size[0]}'_qgru_float'
quant_opts='--quant'
quant_dir_label='amp2p_h10_qgru_w'${quant_n_bits_w}'a'${quant_n_bits_a}

for i_seed in "${seed[@]}"; do
    for ((i=0; i<${#DPD_backbone[@]}; i++)); do
        quant_dir_label='amp2p_h'${DPD_hidden_size[$i]}'_qgru_w'${quant_n_bits_w}'a'${quant_n_bits_a}
        pretrained_model='/Users/ali6/projects/OpenDPD/save/DPA_160MHz/train_dpd/amp1p_h'${DPD_hidden_size[$i]}'_qgru_float'
        if [[ $q_pretrain == '--q_pretrain' ]]; then
            pretrained_model=''
        else
            pretrained_model=$(get_pt_file "$pretrained_model")
        fi
        # Train DPD
        step=train_dpd
        python main.py --dataset_name "$dataset_name" --seed "$i_seed" --step "$step"\
        --accelerator "$accelerator" --devices "$devices"\
        --PA_backbone "$PA_backbone" --PA_hidden_size "$PA_hidden_size" --PA_num_layers "$PA_num_layers"\
        --DPD_backbone "${DPD_backbone[$i]}" --DPD_hidden_size "${DPD_hidden_size[$i]}" --DPD_num_layers "${DPD_num_layers[$i]}"\
        --frame_length "$frame_length" --frame_stride "$frame_stride" --loss_type "$loss_type" --opt_type "$opt_type"\
        --batch_size "$batch_size" --batch_size_eval "$batch_size_eval" --n_epochs "$n_epochs" --lr_schedule "$lr_schedule"\
        --lr "$lr" --lr_end "$lr_end" --decay_factor "$decay_factor" --patience "$patience" \
        "$quant_opts" --n_bits_w "$quant_n_bits_w" --n_bits_a "$quant_n_bits_a" --pretrained_model "$pretrained_model" \
        --quant_dir_label "$quant_dir_label" --q_pretrain "$q_pretrain" \
        || exit 1;
        # Run DPD
        step=run_dpd
        python main.py --dataset_name "$dataset_name" --seed "$i_seed" --step "$step"\
          --accelerator "$accelerator" --devices "$devices"\
          --PA_backbone "$PA_backbone" --PA_hidden_size "$PA_hidden_size" --PA_num_layers "$PA_num_layers"\
          --DPD_backbone "${DPD_backbone[$i]}" --DPD_hidden_size "${DPD_hidden_size[$i]}" --DPD_num_layers "${DPD_num_layers[$i]}"\
          --frame_length "$frame_length" --frame_stride "$frame_stride" --loss_type "$loss_type" --opt_type "$opt_type"\
          --batch_size "$batch_size" --batch_size_eval "$batch_size_eval" --n_epochs "$n_epochs" --lr_schedule "$lr_schedule"\
          --lr "$lr" --lr_end "$lr_end" --decay_factor "$decay_factor" --patience "$patience" \
          "$quant_opts" --n_bits_w "$quant_n_bits_w" --n_bits_a "$quant_n_bits_a" --pretrained_model "$pretrained_model"  \
          --quant_dir_label "$quant_dir_label" --q_pretrain "$q_pretrain" \
          || exit 1;
    done
done