run_gen_pkl_v22_mtml.py

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import sys
sys_path = ['/workspace/smlab', '/workspace']
for path in sys_path:
    if path not in sys.path:
        sys.path.append(path)
 
from smdataset.gen_data.generate_pkl_data_mtml import run_generate_pkl_data_from_json_v22
from smutils.utils_os import create_directory
from smutils.utils_data import split_train_and_val_v22
 
import abc
 
if __name__ == "__main__":
    
    #카테고리별 학습에 사용되는 프레임 수
    category_k={'action_upper' : 60,
                'action_lower' : 60,
                'pose' : 60, 
                'hand' : 60,
                'foot' : 60}
 
    #카테고리별 클래스 수
    category_info={'action_upper' : 6,
                   'action_lower' : 12,
                   'pose' : 8, 
                   'hand' : 4,
                   'foot' : 2}
 
    #카테고리별 클래스 설정을 위한 구간
    category_margin_ratio={'action_upper' : 0.3,
                           'action_lower' : 0.3,
                           'pose' : 0.4, 
                           'hand' : 0.4,
                           'foot' : 0.4}
    
    #파라미터 설정
    args = abc.abstractproperty()
    args.category_k = category_k
    args.data_root = '/media/safemotion/HDD5/Inno_Test/dataset/action_mtml_1st'
    args.pkl_root = '/media/safemotion/HDD5/pjm_test/action_train_dataset_2023/action_kp_imf'
    args.train_category = list(category_info.keys())
    args.category_margin_ratio = category_margin_ratio
    args.image_feat_folder = '/media/safemotion/HDD5/pjm_test/action_train_dataset_2023/image_features'#None
 
    #학습 데이터 생성
    run_generate_pkl_data_from_json_v22(args)
 
    #학습/평가 데이터 분리
    #TODO : 분리하고 남는 데이터가 많음, 학습에 최대한 활용할 방안 탐색필요
    # split_data_folder = '/media/safemotion/HDD5/pjm_test/action_train_dataset_2023/action_mtml_1st_split'
    split_data_folder = '/media/safemotion/HDD5/pjm_test/action_train_dataset_2023/action_kp_imf_split'
    split_train_and_val_v22(args.pkl_root, split_data_folder, category_info, total_data_num=100, ratio=0.8)