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from typing import List, Optional, Dict
import os
import torch
from utils.common.log import logger
import hashlib
def get_dataset_cache_path(root_dir: str,
classes: List[str], ignore_classes: List[str], idx_map: Optional[Dict[int, int]]):
def _hash(o):
if isinstance(o, list):
o = sorted(o)
elif isinstance(o, dict):
o = {k: o[k] for k in sorted(o)}
elif isinstance(o, set):
o = sorted(list(o))
# else:
# print(type(o))
obj = hashlib.md5()
obj.update(str(o).encode('utf-8'))
return obj.hexdigest()
cache_key = _hash(f'zql_data_{_hash(root_dir)}_{_hash(classes)}_{_hash(ignore_classes)}_{_hash(idx_map)}.cache')
# print(root_dir, classes, ignore_classes, idx_map)
# print('cache key', cache_key)
cache_file_path = os.path.join('/tmp', f'./zql_data_cache_{cache_key}.cache')
return cache_file_path
def cache_dataset_status(status, cache_file_path, dataset_name):
logger.info(f'cache dataset status: {dataset_name}')
torch.save(status, cache_file_path)
def read_cached_dataset_status(cache_file_path, dataset_name):
logger.info(f'read dataset cache: {dataset_name}')
return torch.load(cache_file_path)
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