faiss_test.py

# 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.

import numpy as np

d = 64  # dimension
nb = 100000  # database size
nq = 10000  # nb of queries
np.random.seed(1234)  # make reproducible
xb = np.random.random((nb, d)).astype("float32")
xb[:, 0] += np.arange(nb) / 1000.0
xq = np.random.random((nq, d)).astype("float32")
xq[:, 0] += np.arange(nq) / 1000.0

# import faiss  # make faiss available

# res = faiss.StandardGpuResources()  # use a single GPU

# ## Using a flat index

# index_flat = faiss.IndexFlatL2(d)  # build a flat (CPU) index

# # make it a flat GPU index
# gpu_index_flat = faiss.index_cpu_to_gpu(res, 0, index_flat)

# gpu_index_flat.add(xb)  # add vectors to the index
# print(gpu_index_flat.ntotal)

# k = 4  # we want to see 4 nearest neighbors
# D, I = gpu_index_flat.search(xq, k)  # actual search
# print(I[:5])  # neighbors of the 5 first queries
# print(I[-5:])  # neighbors of the 5 last queries


# ## Using an IVF index

# nlist = 100
# quantizer = faiss.IndexFlatL2(d)  # the other index
# index_ivf = faiss.IndexIVFFlat(quantizer, d, nlist, faiss.METRIC_L2)
# # here we specify METRIC_L2, by default it performs inner-product search

# # make it an IVF GPU index
# gpu_index_ivf = faiss.index_cpu_to_gpu(res, 0, index_ivf)

# assert not gpu_index_ivf.is_trained
# gpu_index_ivf.train(xb)  # add vectors to the index
# assert gpu_index_ivf.is_trained

# gpu_index_ivf.add(xb)  # add vectors to the index
# print(gpu_index_ivf.ntotal)

# k = 4  # we want to see 4 nearest neighbors
# D, I = gpu_index_ivf.search(xq, k)  # actual search
# print(I[:5])  # neighbors of the 5 first queries
# print(I[-5:])  # neighbors of the 5 last queries


# 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.

import numpy as np

d = 64  # dimension
nb = 100000  # database size
nq = 10000  # nb of queries
np.random.seed(1234)  # make reproducible
xb = np.random.random((nb, d)).astype("float32")
xb[:, 0] += np.arange(nb) / 1000.0
xq = np.random.random((nq, d)).astype("float32")
xq[:, 0] += np.arange(nq) / 1000.0

import faiss  # make faiss available

index = faiss.IndexFlatL2(d)  # build the index
print(index.is_trained)
index.add(xb)  # add vectors to the index
print(index.ntotal)

k = 4  # we want to see 4 nearest neighbors
D, I = index.search(xb[:5], k)  # sanity check
print(I)
# print(D)
D, I = index.search(xq, k)  # actual search
# print(I[:5])  # neighbors of the 5 first queries
# print(I[-5:])  # neighbors of the 5 last queries