import argparse import copy import logging import logging.handlers as handlers import pathlib import sys import faiss import numpy as np import vaex as vx import wandb sys.path.insert(0, str(pathlib.Path(__file__).parent.resolve())) from search.embeddings import Embeddings from search.faiss_search import FaissIndex from metrics import metrics from data.wikiart import WikiArt, MyTrain logger = logging.getLogger() def get_parser(): parser = argparse.ArgumentParser('dynamicDistances-NN Search Module') parser.add_argument('--dataset', default='wikiart', type=str, required=True) parser.add_argument('--topk', nargs='+', type=int, default=[5], help='Number of NN to consider while calculating recall') parser.add_argument('--mode', type=str, required=True, choices=['artist', 'label'], help='The type of matching to do') parser.add_argument('--method', type=str, default='IP', choices=['IP', 'L2'], help='The method to do NN search') parser.add_argument('--emb-dir', type=str, default=None, help='The directory where per image embeddings are stored (NOT USED when chunked)') parser.add_argument('--query_count', default=-1, type=int, help='Number of queries to consider. Works only for domainnet') parser.add_argument('--chunked', action='store_true', help='If I should read from chunked directory instead') parser.add_argument('--query-chunk-dir', type=str, required=True, help='The directory where chunked query embeddings should be saved/are already saved') parser.add_argument('--database-chunk-dir', type=str, required=True, help='The directory where chunked val embeddings should be saved/are already saved') parser.add_argument('--data-dir', type=str, default=None, help='The directory of concerned dataset. (HARD CODED LATER)') parser.add_argument('--multilabel', action='store_true', help='If the dataset is multilabel') return parser def get_log_handlers(args): # Create handlers c_handler = logging.StreamHandler() f_handler = handlers.RotatingFileHandler(f'search.log', maxBytes=int(1e6), backupCount=1000) c_handler.setLevel(logging.DEBUG) f_handler.setLevel(logging.DEBUG) # Create formatters and add it to handlers c_format = logging.Formatter('%(name)s - %(levelname)s - %(message)s') f_format = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') c_handler.setFormatter(c_format) f_handler.setFormatter(f_format) return c_handler, f_handler def main(): parser = get_parser() args = parser.parse_args() handlers = get_log_handlers(args) logger.addHandler(handlers[0]) logger.addHandler(handlers[1]) logger.setLevel(logging.DEBUG) if args.dataset == 'wikiart': dataset = WikiArt(args.data_dir) else: raise NotImplementedError query_embeddings = Embeddings(args.emb_dir, args.query_chunk_dir, files=None, chunked=args.chunked, file_ext='.pkl') val_embeddings = Embeddings(args.emb_dir, args.database_chunk_dir, files=None, chunked=args.chunked, file_ext='.pkl') query_embeddings.filenames = list(query_embeddings.filenames) val_embeddings.filenames = list(val_embeddings.filenames) # Filtering the dataset based on the files which actually exist. dataset.query_db = dataset.query_db[ dataset.query_db['name'].isin(query_embeddings.filenames)] dataset.val_db = dataset.val_db[ dataset.val_db['name'].isin(val_embeddings.filenames)] # Using only the embeddings corresponding to images in the datasets temp = vx.from_arrays(filename=query_embeddings.filenames, index=np.arange(len(query_embeddings.filenames))) print(temp) dataset.query_db = dataset.query_db.join(temp, left_on='name', right_on='filename', how='left') query_embeddings.embeddings = query_embeddings.embeddings[dataset.get_query_col('index')] try: b, h, w = query_embeddings.embeddings.shape query_embeddings.embeddings = query_embeddings.embeddings.reshape(b, 1, h * w) except ValueError: b, d = query_embeddings.embeddings.shape query_embeddings.embeddings = query_embeddings.embeddings.reshape(b, 1, d) query_embeddings.filenames = np.asarray(query_embeddings.filenames)[dataset.get_query_col('index')] temp = vx.from_arrays(filename=val_embeddings.filenames, index=np.arange(len(val_embeddings.filenames))) dataset.val_db = dataset.val_db.join(temp, left_on='name', right_on='filename', how='left') val_embeddings.embeddings = val_embeddings.embeddings[dataset.get_val_col('index')] try: b, h, w = val_embeddings.embeddings.shape val_embeddings.embeddings = val_embeddings.embeddings.reshape(b, 1, h * w) except ValueError: b, d = val_embeddings.embeddings.shape val_embeddings.embeddings = val_embeddings.embeddings.reshape(b, 1, d) val_embeddings.filenames = np.asarray(val_embeddings.filenames)[dataset.get_val_col('index')] # Building the faiss index embedding_size = query_embeddings.embeddings[0].shape[1] if args.method == 'IP': method = faiss.IndexFlatIP else: method = faiss.IndexFlatL2 search_module = FaissIndex(embedding_size=embedding_size, index_func=method) queries = np.asarray(query_embeddings.embeddings).reshape(len(query_embeddings.embeddings), embedding_size) database = np.asarray(val_embeddings.embeddings).reshape(len(val_embeddings.embeddings), embedding_size) search_module.build_index(database) _, nns_all = search_module.search_nns(queries, max(args.topk)) if args.multilabel: q_labels = dataset.query_db['multilabel'].values db_labels = dataset.val_db['multilabel'].values nns_all_pred = [q_labels[i] @ db_labels[nns_all[i]].T for i in range(len(nns_all))] # (n_queries, n_topk) nns_all_pred = np.array(nns_all_pred) else: nns_all_pred = nns_all classes = np.unique(dataset.get_val_col(args.mode)) mode_to_index = {classname: i for i, classname in enumerate(classes)} try: gts = np.asarray(list(map(lambda x: mode_to_index[x], dataset.get_query_col(args.mode).tolist()))) except KeyError: logger.error('Class not found in database. This query list cannot be evaluated') return evals = metrics.Metrics() for topk in args.topk: logger.info(f'Calculating recall@{topk}') nns_all_pred_topk = nns_all_pred[:, :topk] if args.multilabel: mode_recall = evals.get_recall_bin(copy.deepcopy(nns_all_pred_topk), topk) mode_mrr = evals.get_mrr_bin(copy.deepcopy(nns_all_pred_topk), topk) mode_map = evals.get_map_bin(copy.deepcopy(nns_all_pred_topk), topk) else: preds = dataset.get_val_col(args.mode)[nns_all_pred_topk.flatten()].reshape(len(queries), topk) preds = np.vectorize(mode_to_index.get)(preds) mode_recall = evals.get_recall(copy.deepcopy(preds), gts, topk) mode_mrr = evals.get_mrr(copy.deepcopy(preds), gts, topk) mode_map = evals.get_map(copy.deepcopy(preds), gts, topk) logger.info(f'Recall@{topk}: {mode_recall}') logger.info(f'MRR@{topk}: {mode_mrr}') logger.info(f'mAP@{topk}: {mode_map}') if __name__ == '__main__': main()