mirror of
https://gitee.com/milvus-io/milvus.git
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3e11f2a700
Signed-off-by: zhuwenxing <wenxing.zhu@zilliz.com> Signed-off-by: zhuwenxing <wenxing.zhu@zilliz.com>
137 lines
4.2 KiB
Python
137 lines
4.2 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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import pymilvus
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from pymilvus import (
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connections,
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FieldSchema, CollectionSchema, DataType,
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Collection
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)
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pymilvus_version = pymilvus.__version__
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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 milvus_recall_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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default_fields = [
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FieldSchema(name="int64", dtype=DataType.INT64, is_primary=True),
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FieldSchema(name="float", dtype=DataType.FLOAT),
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FieldSchema(name="varchar", dtype=DataType.VARCHAR, max_length=65535),
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FieldSchema(name="float_vector", dtype=DataType.FLOAT_VECTOR, dim=dim)
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]
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default_schema = CollectionSchema(
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fields=default_fields, description="test collection")
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collection = Collection(name="sift_128_euclidean", schema=default_schema)
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nb = len(train)
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batch_size = 50000
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epoch = int(nb / batch_size)
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t0 = time.time()
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for i in range(epoch):
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print("epoch:", i)
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start = i * batch_size
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end = (i + 1) * batch_size
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if end > nb:
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end = nb
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data = [
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[i for i in range(start, end)],
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[np.float32(i) for i in range(start, end)],
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[str(i) for i in range(start, end)],
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train[start:end]
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]
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collection.insert(data)
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t1 = time.time()
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print(f"\nInsert {nb} vectors cost {t1 - t0:.4f} seconds")
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t0 = time.time()
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print(f"\nGet collection entities...")
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if pymilvus_version >= "2.2.0":
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collection.flush()
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else:
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collection.num_entities
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print(collection.num_entities)
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t1 = time.time()
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print(f"\nGet collection entities cost {t1 - t0:.4f} seconds")
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# create index
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default_index = {"index_type": "IVF_SQ8",
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"metric_type": "L2", "params": {"nlist": 64}}
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print(f"\nCreate index...")
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t0 = time.time()
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collection.create_index(field_name="float_vector",
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index_params=default_index)
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t1 = time.time()
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print(f"\nCreate index cost {t1 - t0:.4f} seconds")
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# load collection
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replica_number = 1
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print(f"\nload collection...")
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t0 = time.time()
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collection.load(replica_number=replica_number)
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t1 = time.time()
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print(f"\nload collection cost {t1 - t0:.4f} seconds")
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# search
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topK = 100
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nq = 10000
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search_params = {"metric_type": "L2", "params": {"nprobe": 10}}
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t0 = time.time()
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print(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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print(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])
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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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assert recall >= 0.95
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print(f"recall={recall}")
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# query
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expr = "int64 in [2,4,6,8]"
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output_fields = ["int64", "float"]
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res = collection.query(expr, output_fields, timeout=TIMEOUT)
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sorted_res = sorted(res, key=lambda k: k['int64'])
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for r in sorted_res:
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print(r)
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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,
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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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milvus_recall_test(host)
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