def search_knn(xq, xb, k, distance_type=faiss.METRIC_L2):
""" wrapper around the faiss knn functions without index """
nq, d = xq.shape
nb, d2 = xb.shape
assert d == d2
I = np.empty((nq, k), dtype='int64')
D = np.empty((nq, k), dtype='float32')
if distance_type == faiss.METRIC_L2:
heaps = faiss.float_maxheap_array_t()
heaps.k = k
heaps.nh = nq
heaps.val = faiss.swig_ptr(D)
heaps.ids = faiss.swig_ptr(I)
faiss.knn_L2sqr(faiss.swig_ptr(xq), faiss.swig_ptr(xb), d, nq, nb,
heaps)
elif distance_type == faiss.METRIC_INNER_PRODUCT:
heaps = faiss.float_minheap_array_t()
heaps.k = k
heaps.nh = nq
heaps.val = faiss.swig_ptr(D)
heaps.ids = faiss.swig_ptr(I)
faiss.knn_inner_product(faiss.swig_ptr(xq), faiss.swig_ptr(xb), d, nq,
nb, heaps)
return D, I
def _knn_search(queries, data, k, return_neighbours=False, res=None):
num_queries, dim = queries.shape
if res is None:
dists, idxs = np.empty((num_queries, k), dtype=np.float32), np.empty(
(num_queries, k), dtype=np.int64)
heaps = faiss.float_maxheap_array_t()
heaps.k, heaps.nh = k, num_queries
heaps.val, heaps.ids = faiss.swig_ptr(dists), faiss.swig_ptr(idxs)
faiss.knn_L2sqr(faiss.swig_ptr(queries), faiss.swig_ptr(data), dim,
num_queries, data.shape[0], heaps)
else:
dists, idxs = torch.empty(num_queries,
k,
dtype=torch.float32,
device=queries.device), torch.empty(
num_queries,
k,
dtype=torch.int64,
device=queries.device)
faiss.bruteForceKnn(
res, faiss.METRIC_L2,
faiss.cast_integer_to_float_ptr(data.storage().data_ptr() +
data.storage_offset() * 4),
data.is_contiguous(), data.shape[0],
faiss.cast_integer_to_float_ptr(queries.storage().data_ptr() +
queries.storage_offset() * 4),
queries.is_contiguous(), num_queries, dim, k,
faiss.cast_integer_to_float_ptr(dists.storage().data_ptr() +
dists.storage_offset() * 4),
faiss.cast_integer_to_long_ptr(idxs.storage().data_ptr() +
idxs.storage_offset() * 8))
if return_neighbours:
neighbours = data[idxs.reshape(-1)].reshape(-1, k, dim)
return dists, idxs, neighbours
else:
return dists, idxs
