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faiss.py
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faiss.py
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# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
#@nolint
# not linting this file because it imports * form swigfaiss, which
# causes a ton of useless warnings.
import numpy as np
import sys
import inspect
import pdb
import platform
import subprocess
import logging
logger = logging.getLogger(__name__)
def instruction_set():
if platform.system() == "Darwin":
if subprocess.check_output(["/usr/sbin/sysctl", "hw.optional.avx2_0"])[-1] == '1':
return "AVX2"
else:
return "default"
elif platform.system() == "Linux":
import numpy.distutils.cpuinfo
if "avx2" in numpy.distutils.cpuinfo.cpu.info[0].get('flags', ""):
return "AVX2"
else:
return "default"
try:
instr_set = instruction_set()
if instr_set == "AVX2":
logger.info("Loading faiss with AVX2 support.")
from .swigfaiss_avx2 import *
else:
logger.info("Loading faiss.")
from .swigfaiss import *
except ImportError:
# we import * so that the symbol X can be accessed as faiss.X
logger.info("Loading faiss.")
from .swigfaiss import *
__version__ = "%d.%d.%d" % (FAISS_VERSION_MAJOR,
FAISS_VERSION_MINOR,
FAISS_VERSION_PATCH)
##################################################################
# The functions below add or replace some methods for classes
# this is to be able to pass in numpy arrays directly
# The C++ version of the classnames will be suffixed with _c
##################################################################
def replace_method(the_class, name, replacement, ignore_missing=False):
try:
orig_method = getattr(the_class, name)
except AttributeError:
if ignore_missing:
return
raise
if orig_method.__name__ == 'replacement_' + name:
# replacement was done in parent class
return
setattr(the_class, name + '_c', orig_method)
setattr(the_class, name, replacement)
def handle_Clustering():
def replacement_train(self, x, index, weights=None):
n, d = x.shape
assert d == self.d
if weights is not None:
assert weights.shape == (n, )
self.train_c(n, swig_ptr(x), index, swig_ptr(weights))
else:
self.train_c(n, swig_ptr(x), index)
def replacement_train_encoded(self, x, codec, index, weights=None):
n, d = x.shape
assert d == codec.sa_code_size()
assert codec.d == index.d
if weights is not None:
assert weights.shape == (n, )
self.train_encoded_c(n, swig_ptr(x), codec, index, swig_ptr(weights))
else:
self.train_encoded_c(n, swig_ptr(x), codec, index)
replace_method(Clustering, 'train', replacement_train)
replace_method(Clustering, 'train_encoded', replacement_train_encoded)
handle_Clustering()
def handle_Quantizer(the_class):
def replacement_train(self, x):
n, d = x.shape
assert d == self.d
self.train_c(n, swig_ptr(x))
def replacement_compute_codes(self, x):
n, d = x.shape
assert d == self.d
codes = np.empty((n, self.code_size), dtype='uint8')
self.compute_codes_c(swig_ptr(x), swig_ptr(codes), n)
return codes
def replacement_decode(self, codes):
n, cs = codes.shape
assert cs == self.code_size
x = np.empty((n, self.d), dtype='float32')
self.decode_c(swig_ptr(codes), swig_ptr(x), n)
return x
replace_method(the_class, 'train', replacement_train)
replace_method(the_class, 'compute_codes', replacement_compute_codes)
replace_method(the_class, 'decode', replacement_decode)
handle_Quantizer(ProductQuantizer)
handle_Quantizer(ScalarQuantizer)
def handle_Index(the_class):
def replacement_add(self, x):
assert x.flags.contiguous
n, d = x.shape
assert d == self.d
self.add_c(n, swig_ptr(x))
def replacement_add_with_ids(self, x, ids):
n, d = x.shape
assert d == self.d
assert ids.shape == (n, ), 'not same nb of vectors as ids'
self.add_with_ids_c(n, swig_ptr(x), swig_ptr(ids))
def replacement_assign(self, x, k):
n, d = x.shape
assert d == self.d
labels = np.empty((n, k), dtype=np.int64)
self.assign_c(n, swig_ptr(x), swig_ptr(labels), k)
return labels
def replacement_train(self, x):
assert x.flags.contiguous
n, d = x.shape
assert d == self.d
self.train_c(n, swig_ptr(x))
def replacement_search(self, x, k):
n, d = x.shape
assert d == self.d
distances = np.empty((n, k), dtype=np.float32)
labels = np.empty((n, k), dtype=np.int64)
self.search_c(n, swig_ptr(x),
k, swig_ptr(distances),
swig_ptr(labels))
return distances, labels
def replacement_search_and_reconstruct(self, x, k):
n, d = x.shape
assert d == self.d
distances = np.empty((n, k), dtype=np.float32)
labels = np.empty((n, k), dtype=np.int64)
recons = np.empty((n, k, d), dtype=np.float32)
self.search_and_reconstruct_c(n, swig_ptr(x),
k, swig_ptr(distances),
swig_ptr(labels),
swig_ptr(recons))
return distances, labels, recons
def replacement_remove_ids(self, x):
if isinstance(x, IDSelector):
sel = x
else:
assert x.ndim == 1
index_ivf = try_extract_index_ivf (self)
if index_ivf and index_ivf.direct_map.type == DirectMap.Hashtable:
sel = IDSelectorArray(x.size, swig_ptr(x))
else:
sel = IDSelectorBatch(x.size, swig_ptr(x))
return self.remove_ids_c(sel)
def replacement_reconstruct(self, key):
x = np.empty(self.d, dtype=np.float32)
self.reconstruct_c(key, swig_ptr(x))
return x
def replacement_reconstruct_n(self, n0, ni):
x = np.empty((ni, self.d), dtype=np.float32)
self.reconstruct_n_c(n0, ni, swig_ptr(x))
return x
def replacement_update_vectors(self, keys, x):
n = keys.size
assert keys.shape == (n, )
assert x.shape == (n, self.d)
self.update_vectors_c(n, swig_ptr(keys), swig_ptr(x))
def replacement_range_search(self, x, thresh):
n, d = x.shape
assert d == self.d
res = RangeSearchResult(n)
self.range_search_c(n, swig_ptr(x), thresh, res)
# get pointers and copy them
lims = rev_swig_ptr(res.lims, n + 1).copy()
nd = int(lims[-1])
D = rev_swig_ptr(res.distances, nd).copy()
I = rev_swig_ptr(res.labels, nd).copy()
return lims, D, I
def replacement_sa_encode(self, x):
n, d = x.shape
assert d == self.d
codes = np.empty((n, self.sa_code_size()), dtype='uint8')
self.sa_encode_c(n, swig_ptr(x), swig_ptr(codes))
return codes
def replacement_sa_decode(self, codes):
n, cs = codes.shape
assert cs == self.sa_code_size()
x = np.empty((n, self.d), dtype='float32')
self.sa_decode_c(n, swig_ptr(codes), swig_ptr(x))
return x
replace_method(the_class, 'add', replacement_add)
replace_method(the_class, 'add_with_ids', replacement_add_with_ids)
replace_method(the_class, 'assign', replacement_assign)
replace_method(the_class, 'train', replacement_train)
replace_method(the_class, 'search', replacement_search)
replace_method(the_class, 'remove_ids', replacement_remove_ids)
replace_method(the_class, 'reconstruct', replacement_reconstruct)
replace_method(the_class, 'reconstruct_n', replacement_reconstruct_n)
replace_method(the_class, 'range_search', replacement_range_search)
replace_method(the_class, 'update_vectors', replacement_update_vectors,
ignore_missing=True)
replace_method(the_class, 'search_and_reconstruct',
replacement_search_and_reconstruct, ignore_missing=True)
replace_method(the_class, 'sa_encode', replacement_sa_encode)
replace_method(the_class, 'sa_decode', replacement_sa_decode)
def handle_IndexBinary(the_class):
def replacement_add(self, x):
assert x.flags.contiguous
n, d = x.shape
assert d * 8 == self.d
self.add_c(n, swig_ptr(x))
def replacement_add_with_ids(self, x, ids):
n, d = x.shape
assert d * 8 == self.d
assert ids.shape == (n, ), 'not same nb of vectors as ids'
self.add_with_ids_c(n, swig_ptr(x), swig_ptr(ids))
def replacement_train(self, x):
assert x.flags.contiguous
n, d = x.shape
assert d * 8 == self.d
self.train_c(n, swig_ptr(x))
def replacement_reconstruct(self, key):
x = np.empty(self.d // 8, dtype=np.uint8)
self.reconstruct_c(key, swig_ptr(x))
return x
def replacement_search(self, x, k):
n, d = x.shape
assert d * 8 == self.d
distances = np.empty((n, k), dtype=np.int32)
labels = np.empty((n, k), dtype=np.int64)
self.search_c(n, swig_ptr(x),
k, swig_ptr(distances),
swig_ptr(labels))
return distances, labels
def replacement_range_search(self, x, thresh):
n, d = x.shape
assert d * 8 == self.d
res = RangeSearchResult(n)
self.range_search_c(n, swig_ptr(x), thresh, res)
# get pointers and copy them
lims = rev_swig_ptr(res.lims, n + 1).copy()
nd = int(lims[-1])
D = rev_swig_ptr(res.distances, nd).copy()
I = rev_swig_ptr(res.labels, nd).copy()
return lims, D, I
def replacement_remove_ids(self, x):
if isinstance(x, IDSelector):
sel = x
else:
assert x.ndim == 1
sel = IDSelectorBatch(x.size, swig_ptr(x))
return self.remove_ids_c(sel)
replace_method(the_class, 'add', replacement_add)
replace_method(the_class, 'add_with_ids', replacement_add_with_ids)
replace_method(the_class, 'train', replacement_train)
replace_method(the_class, 'search', replacement_search)
replace_method(the_class, 'range_search', replacement_range_search)
replace_method(the_class, 'reconstruct', replacement_reconstruct)
replace_method(the_class, 'remove_ids', replacement_remove_ids)
def handle_VectorTransform(the_class):
def apply_method(self, x):
assert x.flags.contiguous
n, d = x.shape
assert d == self.d_in
y = np.empty((n, self.d_out), dtype=np.float32)
self.apply_noalloc(n, swig_ptr(x), swig_ptr(y))
return y
def replacement_reverse_transform(self, x):
n, d = x.shape
assert d == self.d_out
y = np.empty((n, self.d_in), dtype=np.float32)
self.reverse_transform_c(n, swig_ptr(x), swig_ptr(y))
return y
def replacement_vt_train(self, x):
assert x.flags.contiguous
n, d = x.shape
assert d == self.d_in
self.train_c(n, swig_ptr(x))
replace_method(the_class, 'train', replacement_vt_train)
# apply is reserved in Pyton...
the_class.apply_py = apply_method
replace_method(the_class, 'reverse_transform',
replacement_reverse_transform)
def handle_AutoTuneCriterion(the_class):
def replacement_set_groundtruth(self, D, I):
if D:
assert I.shape == D.shape
self.nq, self.gt_nnn = I.shape
self.set_groundtruth_c(
self.gt_nnn, swig_ptr(D) if D else None, swig_ptr(I))
def replacement_evaluate(self, D, I):
assert I.shape == D.shape
assert I.shape == (self.nq, self.nnn)
return self.evaluate_c(swig_ptr(D), swig_ptr(I))
replace_method(the_class, 'set_groundtruth', replacement_set_groundtruth)
replace_method(the_class, 'evaluate', replacement_evaluate)
def handle_ParameterSpace(the_class):
def replacement_explore(self, index, xq, crit):
assert xq.shape == (crit.nq, index.d)
ops = OperatingPoints()
self.explore_c(index, crit.nq, swig_ptr(xq),
crit, ops)
return ops
replace_method(the_class, 'explore', replacement_explore)
def handle_MatrixStats(the_class):
original_init = the_class.__init__
def replacement_init(self, m):
assert len(m.shape) == 2
original_init(self, m.shape[0], m.shape[1], swig_ptr(m))
the_class.__init__ = replacement_init
handle_MatrixStats(MatrixStats)
this_module = sys.modules[__name__]
for symbol in dir(this_module):
obj = getattr(this_module, symbol)
# print symbol, isinstance(obj, (type, types.ClassType))
if inspect.isclass(obj):
the_class = obj
if issubclass(the_class, Index):
handle_Index(the_class)
if issubclass(the_class, IndexBinary):
handle_IndexBinary(the_class)
if issubclass(the_class, VectorTransform):
handle_VectorTransform(the_class)
if issubclass(the_class, AutoTuneCriterion):
handle_AutoTuneCriterion(the_class)
if issubclass(the_class, ParameterSpace):
handle_ParameterSpace(the_class)
###########################################
# Add Python references to objects
# we do this at the Python class wrapper level.
###########################################
def add_ref_in_constructor(the_class, parameter_no):
# adds a reference to parameter parameter_no in self
# so that that parameter does not get deallocated before self
original_init = the_class.__init__
def replacement_init(self, *args):
original_init(self, *args)
self.referenced_objects = [args[parameter_no]]
def replacement_init_multiple(self, *args):
original_init(self, *args)
pset = parameter_no[len(args)]
self.referenced_objects = [args[no] for no in pset]
if type(parameter_no) == dict:
# a list of parameters to keep, depending on the number of arguments
the_class.__init__ = replacement_init_multiple
else:
the_class.__init__ = replacement_init
def add_ref_in_method(the_class, method_name, parameter_no):
original_method = getattr(the_class, method_name)
def replacement_method(self, *args):
ref = args[parameter_no]
if not hasattr(self, 'referenced_objects'):
self.referenced_objects = [ref]
else:
self.referenced_objects.append(ref)
return original_method(self, *args)
setattr(the_class, method_name, replacement_method)
def add_ref_in_function(function_name, parameter_no):
# assumes the function returns an object
original_function = getattr(this_module, function_name)
def replacement_function(*args):
result = original_function(*args)
ref = args[parameter_no]
result.referenced_objects = [ref]
return result
setattr(this_module, function_name, replacement_function)
add_ref_in_constructor(IndexIVFFlat, 0)
add_ref_in_constructor(IndexIVFFlatDedup, 0)
add_ref_in_constructor(IndexPreTransform, {2: [0, 1], 1: [0]})
add_ref_in_method(IndexPreTransform, 'prepend_transform', 0)
add_ref_in_constructor(IndexIVFPQ, 0)
add_ref_in_constructor(IndexIVFPQR, 0)
add_ref_in_constructor(Index2Layer, 0)
add_ref_in_constructor(Level1Quantizer, 0)
add_ref_in_constructor(IndexIVFScalarQuantizer, 0)
add_ref_in_constructor(IndexIDMap, 0)
add_ref_in_constructor(IndexIDMap2, 0)
add_ref_in_constructor(IndexHNSW, 0)
add_ref_in_method(IndexShards, 'add_shard', 0)
add_ref_in_method(IndexBinaryShards, 'add_shard', 0)
add_ref_in_constructor(IndexRefineFlat, 0)
add_ref_in_constructor(IndexBinaryIVF, 0)
add_ref_in_constructor(IndexBinaryFromFloat, 0)
add_ref_in_constructor(IndexBinaryIDMap, 0)
add_ref_in_constructor(IndexBinaryIDMap2, 0)
add_ref_in_method(IndexReplicas, 'addIndex', 0)
add_ref_in_method(IndexBinaryReplicas, 'addIndex', 0)
add_ref_in_constructor(BufferedIOWriter, 0)
add_ref_in_constructor(BufferedIOReader, 0)
# seems really marginal...
# remove_ref_from_method(IndexReplicas, 'removeIndex', 0)
if hasattr(this_module, 'GpuIndexFlat'):
# handle all the GPUResources refs
add_ref_in_function('index_cpu_to_gpu', 0)
add_ref_in_constructor(GpuIndexFlat, 0)
add_ref_in_constructor(GpuIndexFlatIP, 0)
add_ref_in_constructor(GpuIndexFlatL2, 0)
add_ref_in_constructor(GpuIndexIVFFlat, 0)
add_ref_in_constructor(GpuIndexIVFScalarQuantizer, 0)
add_ref_in_constructor(GpuIndexIVFPQ, 0)
add_ref_in_constructor(GpuIndexBinaryFlat, 0)
###########################################
# GPU functions
###########################################
def index_cpu_to_gpu_multiple_py(resources, index, co=None, gpus=None):
""" builds the C++ vectors for the GPU indices and the
resources. Handles the case where the resources are assigned to
the list of GPUs """
if gpus is None:
gpus = range(len(resources))
vres = GpuResourcesVector()
vdev = IntVector()
for i, res in zip(gpus, resources):
vdev.push_back(i)
vres.push_back(res)
index = index_cpu_to_gpu_multiple(vres, vdev, index, co)
index.referenced_objects = resources
return index
def index_cpu_to_all_gpus(index, co=None, ngpu=-1):
index_gpu = index_cpu_to_gpus_list(index, co=co, gpus=None, ngpu=ngpu)
return index_gpu
def index_cpu_to_gpus_list(index, co=None, gpus=None, ngpu=-1):
""" Here we can pass list of GPU ids as a parameter or ngpu to
use first n GPU's. gpus mut be a list or None"""
if (gpus is None) and (ngpu == -1): # All blank
gpus = range(get_num_gpus())
elif (gpus is None) and (ngpu != -1): # Get number of GPU's only
gpus = range(ngpu)
res = [StandardGpuResources() for _ in gpus]
index_gpu = index_cpu_to_gpu_multiple_py(res, index, co, gpus)
return index_gpu
###########################################
# numpy array / std::vector conversions
###########################################
# mapping from vector names in swigfaiss.swig and the numpy dtype names
vector_name_map = {
'Float': 'float32',
'Byte': 'uint8',
'Char': 'int8',
'Uint64': 'uint64',
'Long': 'int64',
'Int': 'int32',
'Double': 'float64'
}
def vector_to_array(v):
""" convert a C++ vector to a numpy array """
classname = v.__class__.__name__
assert classname.endswith('Vector')
dtype = np.dtype(vector_name_map[classname[:-6]])
a = np.empty(v.size(), dtype=dtype)
if v.size() > 0:
memcpy(swig_ptr(a), v.data(), a.nbytes)
return a
def vector_float_to_array(v):
return vector_to_array(v)
def copy_array_to_vector(a, v):
""" copy a numpy array to a vector """
n, = a.shape
classname = v.__class__.__name__
assert classname.endswith('Vector')
dtype = np.dtype(vector_name_map[classname[:-6]])
assert dtype == a.dtype, (
'cannot copy a %s array to a %s (should be %s)' % (
a.dtype, classname, dtype))
v.resize(n)
if n > 0:
memcpy(v.data(), swig_ptr(a), a.nbytes)
###########################################
# Wrapper for a few functions
###########################################
def kmin(array, k):
"""return k smallest values (and their indices) of the lines of a
float32 array"""
m, n = array.shape
I = np.zeros((m, k), dtype='int64')
D = np.zeros((m, k), dtype='float32')
ha = float_maxheap_array_t()
ha.ids = swig_ptr(I)
ha.val = swig_ptr(D)
ha.nh = m
ha.k = k
ha.heapify()
ha.addn(n, swig_ptr(array))
ha.reorder()
return D, I
def kmax(array, k):
"""return k largest values (and their indices) of the lines of a
float32 array"""
m, n = array.shape
I = np.zeros((m, k), dtype='int64')
D = np.zeros((m, k), dtype='float32')
ha = float_minheap_array_t()
ha.ids = swig_ptr(I)
ha.val = swig_ptr(D)
ha.nh = m
ha.k = k
ha.heapify()
ha.addn(n, swig_ptr(array))
ha.reorder()
return D, I
def pairwise_distances(xq, xb, mt=METRIC_L2, metric_arg=0):
"""compute the whole pairwise distance matrix between two sets of
vectors"""
nq, d = xq.shape
nb, d2 = xb.shape
assert d == d2
dis = np.empty((nq, nb), dtype='float32')
if mt == METRIC_L2:
pairwise_L2sqr(
d, nq, swig_ptr(xq),
nb, swig_ptr(xb),
swig_ptr(dis))
else:
pairwise_extra_distances(
d, nq, swig_ptr(xq),
nb, swig_ptr(xb),
mt, metric_arg,
swig_ptr(dis))
return dis
def rand(n, seed=12345):
res = np.empty(n, dtype='float32')
float_rand(swig_ptr(res), res.size, seed)
return res
def randint(n, seed=12345, vmax=None):
res = np.empty(n, dtype='int64')
if vmax is None:
int64_rand(swig_ptr(res), res.size, seed)
else:
int64_rand_max(swig_ptr(res), res.size, vmax, seed)
return res
lrand = randint
def randn(n, seed=12345):
res = np.empty(n, dtype='float32')
float_randn(swig_ptr(res), res.size, seed)
return res
def eval_intersection(I1, I2):
""" size of intersection between each line of two result tables"""
n = I1.shape[0]
assert I2.shape[0] == n
k1, k2 = I1.shape[1], I2.shape[1]
ninter = 0
for i in range(n):
ninter += ranklist_intersection_size(
k1, swig_ptr(I1[i]), k2, swig_ptr(I2[i]))
return ninter
def normalize_L2(x):
fvec_renorm_L2(x.shape[1], x.shape[0], swig_ptr(x))
# MapLong2Long interface
def replacement_map_add(self, keys, vals):
n, = keys.shape
assert (n,) == keys.shape
self.add_c(n, swig_ptr(keys), swig_ptr(vals))
def replacement_map_search_multiple(self, keys):
n, = keys.shape
vals = np.empty(n, dtype='int64')
self.search_multiple_c(n, swig_ptr(keys), swig_ptr(vals))
return vals
replace_method(MapLong2Long, 'add', replacement_map_add)
replace_method(MapLong2Long, 'search_multiple', replacement_map_search_multiple)
###########################################
# Kmeans object
###########################################
class Kmeans:
"""shallow wrapper around the Clustering object. The important method
is train()."""
def __init__(self, d, k, **kwargs):
"""d: input dimension, k: nb of centroids. Additional
parameters are passed on the ClusteringParameters object,
including niter=25, verbose=False, spherical = False
"""
self.d = d
self.k = k
self.gpu = False
self.cp = ClusteringParameters()
for k, v in kwargs.items():
if k == 'gpu':
self.gpu = v
else:
# if this raises an exception, it means that it is a non-existent field
getattr(self.cp, k)
setattr(self.cp, k, v)
self.centroids = None
def train(self, x, weights=None):
n, d = x.shape
assert d == self.d
clus = Clustering(d, self.k, self.cp)
if self.cp.spherical:
self.index = IndexFlatIP(d)
else:
self.index = IndexFlatL2(d)
if self.gpu:
if self.gpu == True:
ngpu = -1
else:
ngpu = self.gpu
self.index = index_cpu_to_all_gpus(self.index, ngpu=ngpu)
clus.train(x, self.index, weights)
centroids = vector_float_to_array(clus.centroids)
self.centroids = centroids.reshape(self.k, d)
stats = clus.iteration_stats
self.obj = np.array([
stats.at(i).obj for i in range(stats.size())
])
return self.obj[-1] if self.obj.size > 0 else 0.0
def assign(self, x):
assert self.centroids is not None, "should train before assigning"
self.index.reset()
self.index.add(self.centroids)
D, I = self.index.search(x, 1)
return D.ravel(), I.ravel()
# IndexProxy was renamed to IndexReplicas, remap the old name for any old code
# people may have
IndexProxy = IndexReplicas
ConcatenatedInvertedLists = HStackInvertedLists
###########################################
# serialization of indexes to byte arrays
###########################################
def serialize_index(index):
""" convert an index to a numpy uint8 array """
writer = VectorIOWriter()
write_index(index, writer)
return vector_to_array(writer.data)
def deserialize_index(data):
reader = VectorIOReader()
copy_array_to_vector(data, reader.data)
return read_index(reader)
def serialize_index_binary(index):
""" convert an index to a numpy uint8 array """
writer = VectorIOWriter()
write_index_binary(index, writer)
return vector_to_array(writer.data)
def deserialize_index_binary(data):
reader = VectorIOReader()
copy_array_to_vector(data, reader.data)
return read_index_binary(reader)
###########################################
# ResultHeap
###########################################
class ResultHeap:
"""Accumulate query results from a sliced dataset. The final result will
be in self.D, self.I."""
def __init__(self, nq, k):
" nq: number of query vectors, k: number of results per query "
self.I = np.zeros((nq, k), dtype='int64')
self.D = np.zeros((nq, k), dtype='float32')
self.nq, self.k = nq, k
heaps = float_maxheap_array_t()
heaps.k = k
heaps.nh = nq
heaps.val = swig_ptr(self.D)
heaps.ids = swig_ptr(self.I)
heaps.heapify()
self.heaps = heaps
def add_result(self, D, I):
"""D, I do not need to be in a particular order (heap or sorted)"""
assert D.shape == (self.nq, self.k)
assert I.shape == (self.nq, self.k)
self.heaps.addn_with_ids(
self.k, faiss.swig_ptr(D),
faiss.swig_ptr(I), self.k)
def finalize(self):
self.heaps.reorder()