本文整理匯總了Python中faiss.IndexIVFFlat方法的典型用法代碼示例。如果您正苦於以下問題：Python faiss.IndexIVFFlat方法的具體用法？Python faiss.IndexIVFFlat怎麽用？Python faiss.IndexIVFFlat使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類faiss的用法示例。
在下文中一共展示了faiss.IndexIVFFlat方法的4個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: _build_approximate_index
# 需要導入模塊: import faiss [as 別名]
# 或者: from faiss import IndexIVFFlat [as 別名]
def _build_approximate_index(self,
data: np.ndarray):
dimensionality = data.shape[1]
nlist = 100 if data.shape[0] > 100 else 2
if self.kernel_name in {'rbf'}:
quantizer = faiss.IndexFlatL2(dimensionality)
cpu_index_flat = faiss.IndexIVFFlat(quantizer, dimensionality, nlist, faiss.METRIC_L2)
else:
quantizer = faiss.IndexFlatIP(dimensionality)
cpu_index_flat = faiss.IndexIVFFlat(quantizer, dimensionality, nlist)
gpu_index_ivf = faiss.index_cpu_to_gpu(self.resource, 0, cpu_index_flat)
gpu_index_ivf.train(data)
gpu_index_ivf.add(data)
self.index = gpu_index_ivf 示例2: fit
# 需要導入模塊: import faiss [as 別名]
# 或者: from faiss import IndexIVFFlat [as 別名]
def fit(self, X):
if self._metric == 'angular':
X = sklearn.preprocessing.normalize(X, axis=1, norm='l2')
if X.dtype != numpy.float32:
X = X.astype(numpy.float32)
self.quantizer = faiss.IndexFlatL2(X.shape[1])
index = faiss.IndexIVFFlat(
self.quantizer, X.shape[1], self._n_list, faiss.METRIC_L2)
index.train(X)
index.add(X)
self.index = index 示例3: fit
# 需要導入模塊: import faiss [as 別名]
# 或者: from faiss import IndexIVFFlat [as 別名]
def fit(self, Ciu, show_progress=True):
import faiss
# train the model
super(FaissAlternatingLeastSquares, self).fit(Ciu, show_progress)
self.quantizer = faiss.IndexFlat(self.factors)
if self.use_gpu:
self.gpu_resources = faiss.StandardGpuResources()
item_factors = self.item_factors.astype('float32')
if self.approximate_recommend:
log.debug("Building faiss recommendation index")
# build up a inner product index here
if self.use_gpu:
index = faiss.GpuIndexIVFFlat(self.gpu_resources, self.factors, self.nlist,
faiss.METRIC_INNER_PRODUCT)
else:
index = faiss.IndexIVFFlat(self.quantizer, self.factors, self.nlist,
faiss.METRIC_INNER_PRODUCT)
index.train(item_factors)
index.add(item_factors)
index.nprobe = self.nprobe
self.recommend_index = index
if self.approximate_similar_items:
log.debug("Building faiss similar items index")
# likewise build up cosine index for similar_items, using an inner product
# index on normalized vectors`
norms = numpy.linalg.norm(item_factors, axis=1)
norms[norms == 0] = 1e-10
normalized = (item_factors.T / norms).T.astype('float32')
if self.use_gpu:
index = faiss.GpuIndexIVFFlat(self.gpu_resources, self.factors, self.nlist,
faiss.METRIC_INNER_PRODUCT)
else:
index = faiss.IndexIVFFlat(self.quantizer, self.factors, self.nlist,
faiss.METRIC_INNER_PRODUCT)
index.train(normalized)
index.add(normalized)
index.nprobe = self.nprobe
self.similar_items_index = index 示例4: __init__
# 需要導入模塊: import faiss [as 別名]
# 或者: from faiss import IndexIVFFlat [as 別名]
def __init__(self,
feats,
k,
index_path='',
index_key='',
nprobe=128,
omp_num_threads=None,
rebuild_index=True,
verbose=True,
**kwargs):
import faiss
if omp_num_threads is not None:
faiss.omp_set_num_threads(omp_num_threads)
self.verbose = verbose
with Timer('[faiss] build index', verbose):
if index_path != '' and not rebuild_index and os.path.exists(
index_path):
print('[faiss] read index from {}'.format(index_path))
index = faiss.read_index(index_path)
else:
feats = feats.astype('float32')
size, dim = feats.shape
index = faiss.IndexFlatIP(dim)
if index_key != '':
assert index_key.find(
'HNSW') < 0, 'HNSW returns distances insted of sims'
metric = faiss.METRIC_INNER_PRODUCT
nlist = min(4096, 8 * round(math.sqrt(size)))
if index_key == 'IVF':
quantizer = index
index = faiss.IndexIVFFlat(quantizer, dim, nlist,
metric)
else:
index = faiss.index_factory(dim, index_key, metric)
if index_key.find('Flat') < 0:
assert not index.is_trained
index.train(feats)
index.nprobe = min(nprobe, nlist)
assert index.is_trained
print('nlist: {}, nprobe: {}'.format(nlist, nprobe))
index.add(feats)
if index_path != '':
print('[faiss] save index to {}'.format(index_path))
mkdir_if_no_exists(index_path)
faiss.write_index(index, index_path)
with Timer('[faiss] query topk {}'.format(k), verbose):
knn_ofn = index_path + '.npz'
if os.path.exists(knn_ofn):
print('[faiss] read knns from {}'.format(knn_ofn))
self.knns = np.load(knn_ofn)['data']
else:
sims, nbrs = index.search(feats, k=k)
self.knns = [(np.array(nbr, dtype=np.int32),
1 - np.array(sim, dtype=np.float32))
for nbr, sim in zip(nbrs, sims)]