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
86 lines
2.1 KiB
Python
86 lines
2.1 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 numpy as np
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import faiss
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import time
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swig_ptr = faiss.swig_ptr
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if False:
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a = np.arange(10, 14).astype('float32')
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b = np.arange(20, 24).astype('float32')
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faiss.fvec_inner_product (swig_ptr(a), swig_ptr(b), 4)
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1/0
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xd = 100
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yd = 1000000
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np.random.seed(1234)
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faiss.omp_set_num_threads(1)
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print('xd=%d yd=%d' % (xd, yd))
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print('Running inner products test..')
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for d in 3, 4, 12, 36, 64:
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x = faiss.rand(xd * d).reshape(xd, d)
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y = faiss.rand(yd * d).reshape(yd, d)
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distances = np.empty((xd, yd), dtype='float32')
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t0 = time.time()
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for i in xrange(xd):
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faiss.fvec_inner_products_ny(swig_ptr(distances[i]),
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swig_ptr(x[i]),
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swig_ptr(y),
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d, yd)
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t1 = time.time()
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# sparse verification
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ntry = 100
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num, denom = 0, 0
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for t in range(ntry):
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xi = np.random.randint(xd)
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yi = np.random.randint(yd)
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num += abs(distances[xi, yi] - np.dot(x[xi], y[yi]))
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denom += abs(distances[xi, yi])
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print('d=%d t=%.3f s diff=%g' % (d, t1 - t0, num / denom))
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print('Running L2sqr test..')
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for d in 3, 4, 12, 36, 64:
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x = faiss.rand(xd * d).reshape(xd, d)
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y = faiss.rand(yd * d).reshape(yd, d)
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distances = np.empty((xd, yd), dtype='float32')
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t0 = time.time()
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for i in xrange(xd):
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faiss.fvec_L2sqr_ny(swig_ptr(distances[i]),
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swig_ptr(x[i]),
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swig_ptr(y),
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d, yd)
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t1 = time.time()
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# sparse verification
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ntry = 100
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num, denom = 0, 0
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for t in range(ntry):
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xi = np.random.randint(xd)
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yi = np.random.randint(yd)
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num += abs(distances[xi, yi] - np.sum((x[xi] - y[yi]) ** 2))
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denom += abs(distances[xi, yi])
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print('d=%d t=%.3f s diff=%g' % (d, t1 - t0, num / denom))
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