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https://github.com/facebookresearch/faiss.git
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3dd7ba8ff9
Summary: Added a few functions in contrib to: - run range searches by batches on the query or the database side - emulate range search on GPU: search on GPU with k=1024, if the farthest neighbor is still within range, re-perform search on CPU - as reference implementations for precision-recall on range search datasets - optimized code to plot precision-recall plots (ie. sweep over thresholds) The new functions are mainly in a new `evaluation.py` Reviewed By: wickedfoo Differential Revision: D25627619 fbshipit-source-id: 58f90654c32c925557d7bbf8083efbb710712e03
35 lines
1.1 KiB
Python
35 lines
1.1 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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import sys
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import faiss
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import unittest
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import platform
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class TestOOMException(unittest.TestCase):
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@unittest.skipIf(platform.system() != 'Linux',
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'Test is Linux only.')
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def test_outrageous_alloc(self):
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import resource
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# https://github.com/facebookresearch/faiss/issues/758
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soft_as, hard_as = resource.getrlimit(resource.RLIMIT_AS)
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# make sure that allocing more than 10G will fail
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resource.setrlimit(resource.RLIMIT_AS, (10 * 1024 * 1024, hard_as))
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try:
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x = faiss.IntVector()
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try:
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x.resize(10**11) # 400 G of RAM
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except MemoryError:
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pass # good, that's what we expect
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else:
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assert False, "should raise exception"
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finally:
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resource.setrlimit(resource.RLIMIT_AS, (soft_as, hard_as))
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if __name__ == '__main__':
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unittest.main()
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