mirror of
https://github.com/facebookresearch/faiss.git
synced 2025-06-03 21:54:02 +08:00
a8118acbc5
Changelog: - changed license: BSD+Patents -> MIT - propagates exceptions raised in sub-indexes of IndexShards and IndexReplicas - support for searching several inverted lists in parallel (parallel_mode != 0) - better support for PQ codes where nbit != 8 or 16 - IVFSpectralHash implementation: spectral hash codes inside an IVF - 6-bit per component scalar quantizer (4 and 8 bit were already supported) - combinations of inverted lists: HStackInvertedLists and VStackInvertedLists - configurable number of threads for OnDiskInvertedLists prefetching (including 0=no prefetch) - more test and demo code compatible with Python 3 (print with parentheses) - refactored benchmark code: data loading is now in a single file
95 lines
2.0 KiB
Python
95 lines
2.0 KiB
Python
# Copyright (c) Facebook, Inc. and its affiliates.
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#
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# This source code is licensed under the MIT license found in the
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# LICENSE file in the root directory of this source tree.
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#!/usr/bin/env python2
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from __future__ import print_function
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import os
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import time
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import numpy as np
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import pdb
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import faiss
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from datasets import load_sift1M, evaluate
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print("load data")
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xb, xq, xt, gt = load_sift1M()
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nq, d = xq.shape
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# we need only a StandardGpuResources per GPU
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res = faiss.StandardGpuResources()
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#################################################################
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# Exact search experiment
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#################################################################
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print("============ Exact search")
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flat_config = faiss.GpuIndexFlatConfig()
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flat_config.device = 0
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index = faiss.GpuIndexFlatL2(res, d, flat_config)
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print("add vectors to index")
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index.add(xb)
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print("warmup")
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index.search(xq, 123)
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print("benchmark")
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for lk in range(11):
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k = 1 << lk
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t, r = evaluate(index, xq, gt, k)
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# the recall should be 1 at all times
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print("k=%d %.3f ms, R@1 %.4f" % (k, t, r[1]))
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#################################################################
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# Approximate search experiment
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#################################################################
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print("============ Approximate search")
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index = faiss.index_factory(d, "IVF4096,PQ64")
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# faster, uses more memory
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# index = faiss.index_factory(d, "IVF16384,Flat")
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co = faiss.GpuClonerOptions()
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# here we are using a 64-byte PQ, so we must set the lookup tables to
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# 16 bit float (this is due to the limited temporary memory).
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co.useFloat16 = True
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index = faiss.index_cpu_to_gpu(res, 0, index, co)
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print("train")
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index.train(xt)
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print("add vectors to index")
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index.add(xb)
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print("warmup")
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index.search(xq, 123)
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print("benchmark")
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for lnprobe in range(10):
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nprobe = 1 << lnprobe
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index.setNumProbes(nprobe)
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t, r = evaluate(index, xq, gt, 100)
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print("nprobe=%4d %.3f ms recalls= %.4f %.4f %.4f" % (nprobe, t, r[1], r[10], r[100]))
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