import h5py import numpy as np import time from pathlib import Path from loguru import logger from pymilvus import connections, Collection def read_benchmark_hdf5(file_path): f = h5py.File(file_path, 'r') train = np.array(f["train"]) test = np.array(f["test"]) neighbors = np.array(f["neighbors"]) f.close() return train, test, neighbors dim = 128 TIMEOUT = 200 def search_test(host="127.0.0.1"): file_path = f"{str(Path(__file__).absolute().parent.parent.parent)}/assets/ann_hdf5/sift-128-euclidean.hdf5" train, test, neighbors = read_benchmark_hdf5(file_path) connections.connect(host=host, port="19530") collection = Collection(name="sift_128_euclidean") nq = 10000 topK = 100 search_params = {"metric_type": "L2", "params": {"nprobe": 10}} for i in range(3): t0 = time.time() logger.info(f"\nSearch...") # define output_fields of search result res = collection.search( test[:nq], "float_vector", search_params, topK, output_fields=["int64"], timeout=TIMEOUT ) t1 = time.time() logger.info(f"search cost {t1 - t0:.4f} seconds") result_ids = [] for hits in res: result_id = [] for hit in hits: result_id.append(hit.entity.get("int64")) result_ids.append(result_id) # calculate recall true_ids = neighbors[:nq, :topK] sum_radio = 0.0 for index, item in enumerate(result_ids): # tmp = set(item).intersection(set(flat_id_list[index])) assert len(item) == len(true_ids[index]), f"get {len(item)} but expect {len(true_ids[index])}" tmp = set(true_ids[index]).intersection(set(item)) sum_radio = sum_radio + len(tmp) / len(item) recall = round(sum_radio / len(result_ids), 3) logger.info(f"recall={recall}") assert 0.95 <= recall < 1.0, f"recall is {recall}, less than 0.95" if __name__ == "__main__": import argparse parser = argparse.ArgumentParser(description='config for recall test') parser.add_argument('--host', type=str, default="127.0.0.1", help='milvus server ip') args = parser.parse_args() host = args.host search_test(host)