ps = faiss.ParameterSpace()
ps.initialize(index)
# make sure queries are in RAM
xq = xq.astype('float32').copy()
# a static C++ object that collects statistics about searches
ivfpq_stats = faiss.cvar.indexIVFPQ_stats
if parametersets == ['autotune'] or parametersets == ['autotuneMT']:
if parametersets == ['autotune']:
faiss.omp_set_num_threads(1)
# setup the Criterion object: optimize for 1-R@1
crit = faiss.OneRecallAtRCriterion(nq, 1)
# by default, the criterion will request only 1 NN
crit.nnn = 100
crit.set_groundtruth(None, gt.astype('int64'))
# then we let Faiss find the optimal parameters by itself
print("exploring operating points")
t0 = time.time()
op = ps.explore(index, xq, crit)
print("Done in %.3f s, available OPs:" % (time.time() - t0))
# opv is a C++ vector, so it cannot be accessed like a Python array
opv = op.optimal_pts
print("%-40s 1-R@1 time" % "Parameters")
for i in range(opv.size()):
xt = fvecs_read("sift1M/sift_learn.fvecs")
xb = fvecs_read("sift1M/sift_base.fvecs")
xq = fvecs_read("sift1M/sift_query.fvecs")
d = xt.shape[1]
print "load GT"
gt = ivecs_read("sift1M/sift_groundtruth.ivecs")
gt = gt.astype('int64')
k = gt.shape[1]
print "prepare criterion"
# criterion = 1-recall at 1
crit = faiss.OneRecallAtRCriterion(xq.shape[0], 1)
crit.set_groundtruth(None, gt)
crit.nnn = k
# indexes that are useful when there is no limitation on memory usage
unlimited_mem_keys = [
"IMI2x10,Flat", "IMI2x11,Flat", "IVF4096,Flat", "IVF16384,Flat",
"PCA64,IMI2x10,Flat"
]
# memory limited to 16 bytes / vector
keys_mem_16 = [
'IMI2x10,PQ16', 'IVF4096,PQ16', 'IMI2x10,PQ8+8', 'OPQ16_64,IMI2x10,PQ16'
]
# limited to 32 bytes / vector
