"""
  train_Dope
  Общая задача: оценка позиции объекта (Pose estimation)
  Реализуемая функция: обучение нейросетевой модели DOPE по заданному BOP-датасету

    python3 $PYTHON_EDUCATION --path /Users/idontsudo/webservice/server/build/public/7065d6b6-c8a3-48c5-9679-bb8f3a690296 \
        --name test1234 --datasetName 32123213

  25.04.2024 @shalenikol release 0.1
"""
import os

def train_Dope_i(path:str, wname:str, dname:str, outpath:str, epochs:int):
    results = f"torchrun --nproc_per_node=1 train.py --local_rank 0 --data {os.path.join(path,dname)} --object fork" \
        + f" -e {epochs} --batchsize 16 --exts jpg --imagesize 640 --pretrained" \
        + " --net_path /home/shalenikol/fork_work/dope_training/output/weights_2996/net_epoch_47.pth"
    print(results)

import argparse

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--path", required=True, help="Path for dataset")
    parser.add_argument("--name", required=True, help="String with result weights name")
    parser.add_argument("--datasetName", required=True, help="String with dataset name")
    parser.add_argument("--outpath", default="weights", help="Output path for weights")
    parser.add_argument("--epoch", default=3, help="How many training epochs")
    args = parser.parse_args()

    train_Dope_i(args.path, args.name, args.datasetName, args.outpath, args.epoch)