import os from tqdm import tqdm import json meta_file = '/etc/ssd1/jiangzhongtao/baai_embedding_tune/data/task_meta/casual/all.json' with open(meta_file, 'r') as f: meta_info = json.load(f) task_file = "/etc/ssd1/jiangzhongtao/baai_embedding_tune/data/all_collect/task_files.txt" unsample_to_be_divisible_by = None with open(task_file, 'r') as f: for line in f: file, _ = line.strip().split('\t') try: assert os.path.exists(file) except Exception: print(file) tasktype2batchsize = { 'classification': 2048, 'clustering': 2048, 'duplication': 512, 'nli': 512, 'retrieval': 512, 'sts': 256, 'super-ni': 2048, 'unk': 2048 } cnt = 0 type2cnt = dict() steps = 0 with open(task_file, 'r') as f: for line in tqdm(f): file, train_size = line.strip().split('\t') _, task_name, _ = os.path.split(file)[-1].split('_') task_type = meta_info[task_name]['task_type'] if task_type != 'retrieval': total_size = 0 with open(file, 'r') as f_file: for line in f_file: total_size += 1 else: queries = set() with open(file, 'r') as f_file: for line in f_file: queries.add(json.loads(line)['query']) total_size = len(queries) train_size = int(train_size) if train_size > 0: if total_size < train_size: raise ValueError(file) else: train_size = total_size batch_size = tasktype2batchsize[task_type] train_size = train_size // batch_size * batch_size steps += train_size // batch_size # print(file, train_size) cnt += train_size type2cnt[task_type] = type2cnt.get(task_type, 0) + train_size print('steps', steps) print(type2cnt) # { # 'retrieval': 2489655, # 'sts': 140819, # 'nli': 324951, # 'super-ni': 180000, # 'classification': 677510, # 'clustering': 452224, # 'duplication': 31409 # } print(cnt) # 3825445