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