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2d380e992b
Summary: Pull Request resolved: https://github.com/facebookresearch/faiss/pull/1867 Merging code for the 1T photodna index seems to fail at https://www.internalfb.com/phabricator/paste/view/P412975011?lines=174 with ``` terminate called after throwing an instance of 'facebook::manifold::blobstore::StorageException' what(): [400] Begin offset and/or length were invalid -- Begin offset must be positive and length must be non-negative. Received: offset = 2642410612, length = 0 Aborted (core dumped) ``` traces back to https://www.internalfb.com/intern/diffusion/FBS/browsefile/master/fbcode/manifold/blobstore/BlobstoreThriftHandler.cpp?lines=671%2C700%2C732 There is a single case where we don't check if the read or write size is 0. So let's try this fix. In the process I realized that the Manifold tests were non functional due to a name collision on common.py. Also fix this in all dependent files. Differential Revision: D28231710 fbshipit-source-id: 700ffa6ca0c82c49e7d1eae9e76549ec5ff16332
129 lines
3.8 KiB
Python
129 lines
3.8 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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# a few common functions for the tests
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from __future__ import absolute_import, division, print_function, unicode_literals
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import numpy as np
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import faiss
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# reduce number of threads to avoid excessive nb of threads in opt
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# mode (recuces runtime from 100s to 4s!)
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faiss.omp_set_num_threads(4)
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def random_unitary(n, d, seed):
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x = faiss.randn(n * d, seed).reshape(n, d)
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faiss.normalize_L2(x)
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return x
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class Randu10k:
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def __init__(self):
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self.nb = 10000
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self.nq = 1000
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self.nt = 10000
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self.d = 128
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self.xb = random_unitary(self.nb, self.d, 1)
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self.xt = random_unitary(self.nt, self.d, 2)
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self.xq = random_unitary(self.nq, self.d, 3)
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dotprods = np.dot(self.xq, self.xb.T)
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self.gt = dotprods.argmax(1)
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self.k = 100
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def launch(self, name, index):
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if not index.is_trained:
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index.train(self.xt)
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index.add(self.xb)
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return index.search(self.xq, self.k)
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def evalres(self, DI):
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D, I = DI
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e = {}
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for rank in 1, 10, 100:
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e[rank] = ((I[:, :rank] == self.gt.reshape(-1, 1)).sum() /
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float(self.nq))
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print("1-recalls: %s" % e)
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return e
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class Randu10kUnbalanced(Randu10k):
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def __init__(self):
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Randu10k.__init__(self)
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weights = 0.95 ** np.arange(self.d)
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rs = np.random.RandomState(123)
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weights = weights[rs.permutation(self.d)]
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self.xb *= weights
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self.xb /= np.linalg.norm(self.xb, axis=1)[:, np.newaxis]
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self.xq *= weights
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self.xq /= np.linalg.norm(self.xq, axis=1)[:, np.newaxis]
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self.xt *= weights
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self.xt /= np.linalg.norm(self.xt, axis=1)[:, np.newaxis]
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dotprods = np.dot(self.xq, self.xb.T)
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self.gt = dotprods.argmax(1)
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self.k = 100
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def get_dataset(d, nb, nt, nq):
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rs = np.random.RandomState(123)
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xb = rs.rand(nb, d).astype('float32')
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xt = rs.rand(nt, d).astype('float32')
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xq = rs.rand(nq, d).astype('float32')
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return (xt, xb, xq)
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def get_dataset_2(d, nt, nb, nq):
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"""A dataset that is not completely random but still challenging to
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index
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"""
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d1 = 10 # intrinsic dimension (more or less)
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n = nb + nt + nq
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rs = np.random.RandomState(1338)
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x = rs.normal(size=(n, d1))
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x = np.dot(x, rs.rand(d1, d))
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# now we have a d1-dim ellipsoid in d-dimensional space
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# higher factor (>4) -> higher frequency -> less linear
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x = x * (rs.rand(d) * 4 + 0.1)
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x = np.sin(x)
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x = x.astype('float32')
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return x[:nt], x[nt:nt + nb], x[nt + nb:]
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def make_binary_dataset(d, nt, nb, nq):
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assert d % 8 == 0
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rs = np.random.RandomState(123)
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x = rs.randint(256, size=(nb + nq + nt, int(d / 8))).astype('uint8')
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return x[:nt], x[nt:-nq], x[-nq:]
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def compare_binary_result_lists(D1, I1, D2, I2):
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"""comparing result lists is difficult because there are many
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ties. Here we sort by (distance, index) pairs and ignore the largest
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distance of each result. Compatible result lists should pass this."""
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assert D1.shape == I1.shape == D2.shape == I2.shape
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n, k = D1.shape
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ndiff = (D1 != D2).sum()
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assert ndiff == 0, '%d differences in distance matrix %s' % (
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ndiff, D1.shape)
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def normalize_DI(D, I):
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norm = I.max() + 1.0
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Dr = D.astype('float64') + I / norm
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# ignore -1s and elements on last column
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Dr[I1 == -1] = 1e20
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Dr[D == D[:, -1:]] = 1e20
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Dr.sort(axis=1)
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return Dr
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ndiff = (normalize_DI(D1, I1) != normalize_DI(D2, I2)).sum()
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assert ndiff == 0, '%d differences in normalized D matrix' % ndiff
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