mirror of https://github.com/PyRetri/PyRetri.git
76 lines
2.4 KiB
Python
76 lines
2.4 KiB
Python
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# -*- coding: utf-8 -*-
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from typing import Dict, List
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def check_exist(now_res: Dict, exist_results: List) -> bool:
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"""
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Check if the config exists.
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Args:
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now_res (Dict): configuration to be checked.
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exist_results (List): a list of existing configurations.
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Returns:
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bool: if the config exists.
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"""
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for e_r in exist_results:
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totoal_equal = True
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for key in now_res:
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if now_res[key] != e_r[key]:
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totoal_equal = False
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break
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if totoal_equal:
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return True
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return False
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def get_dir(root_path: str, dir: str, dataset: Dict) -> (str, str, str):
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"""
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Get the feature directory path of gallery set, query set and feature set for training PCA/SVD.
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Args:
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root_path (str): the root path of all extracted features.
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dir (str): the path of one single extracted feature directory.
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dataset (Dict): a dict containing the information of gallery set, query set and training set.
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Returns:
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tuple(str, str, str): path of gallery set, query set and feature set for training PCA/SVD.
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"""
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template_dir = os.path.join(root_path, dir)
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target = dir.split('_')[0] + '_' + dir.split('_')[1]
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gallery_fea_dir = template_dir.replace(target, dataset["gallery"])
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query_fea_dir = template_dir.replace(target, dataset["query"])
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train_fea_dir = template_dir.replace(target, dataset["train"])
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return gallery_fea_dir, query_fea_dir, train_fea_dir
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def get_default_result_dict(dir, data_name, query_name, fea_name) -> Dict:
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"""
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Get the default result dict based on the experimental factors.
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Args:
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dir (str): the path of one single extracted feature directory.
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data_name (str): the name of the dataset.
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query_name (str): the name of query process.
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fea_name (str): the name of the features to be loaded.
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Returns:
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result_dict (Dict): a default configuration dict.
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"""
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result_dict = {
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"data_name": data_name.split("_")[0],
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"dataprocess": dir.split("_")[0],
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"model_name": "_".join(dir.split("_")[-2:]),
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"feature_map_name": fea_name.split("_")[0],
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"fea_process_name": query_name
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}
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if fea_name == "fc":
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result_dict["aggregator_name"] = "none"
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else:
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result_dict["aggregator_name"] = fea_name.split("_")[1]
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return result_dict
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