# 创建index对象index = faiss.IndexFlatIP(feature_dim)# 将数据集加载入index中index.add(np.ascontiguousarray(Datasets['feature']))# 获取index中向量的个数print(index.ntotal)# 获取与检索向量最邻近的topk的距离distance与索引值match_idxdistance, match_idx = index.search(feature.reshape(1,-1), top...
array(Datasets,dtype = datasets_type) return Datasets """调用faiss进行特征匹配 输入: Datasets:存储数据库 feature:待匹配的特征,shape=(128,) topk: 返回最近邻的topk个结果 输出: distance: topk的相似度 match_id: topk对应的位于Datasets的索引值 """ def featureSearch(Datasets, feature, to...
apt install libopenblas-dev 否则报错:CMake Error at /usr/share/cmake-3.18/Modules/FindPackageHandleStandardArgs.cmake:165 (message): Could NOT find BLAS (missing: BLAS_LIBRARIES) 安装swig: apt install swig PQM9S">否则报错:Could NOT find SWIG (missing: SWIG_EXECUTABLE SWIG_DIR python)。 2,...
目前,Faiss提供了一种临时的措施,可以直接读取Tensor底层的内存(Tensor必须是is_contiguous的),然后使用index的search_c() API来检索。关于在index中检索PyTorch Tensor的详细用法,你可以参考syszux_feature_vector.py中的NamesPathsClsFeatureVectorByFaissPytorch类: https:///DeepVAC/deepvac/blob/master/deepvac/syszu...
否则报错:Could NOT find SWIG (missing: SWIG_EXECUTABLE SWIG_DIR python)。 2,编译 在Faiss目录里,执行如下操作: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 make build cmake..makeVERBOSE=1make install 整个编译的最终产物有: libfaiss.a (C++库,也用于链接_swigfaiss.so) ...
xbt = np.ascontiguousarray(xb.T) D, I = knn_gpu(res, xbt.T, xq, k) np.testing.assert_array_equal(Iref, I) np.testing.assert_almost_equal(Dref, D, decimal=4) xqt = np.ascontiguousarray(xq.T) D, I = knn_gpu(res, xb, xqt.T, k) np.testing.assert_array_equal(Iref, I...
is_contiguous() assert xb.dtype == torch.float32 assert not xb.is_cuda, "use knn_gpu for GPU tensors" nq, d2 = xq.size() assert d2 == d assert xq.is_contiguous() @@ -543,7 +543,7 @@ def torch_replacement_knn(xq, xb, k, metric=faiss.METRIC_L2, metric_arg=0): xq_...
the array of codes */ void bincode_hist(sizet n, size_t nbits, const uint8_t *codes, int *hist); /// computea checksum on a table. size_t ivec_checksum (size_t n,const int *a); /** random subsamples a set of vectors if therearetoo many of them * *...
目前,Faiss提供了一种临时的措施,可以直接读取Tensor底层的内存(Tensor必须是is_contiguous的),然后使用index的search_c() API来检索。关于在index中检索PyTorch Tensor的详细用法,你可以参考syszux_feature_vector.py中的NamesPathsClsFeatureVectorByFaissPytorch类:...
not recognized"); } READ1 (vt->d_in); READ1 (vt->d_out); 1 (vt->is_trained); returnvt; static void read_ArrayInvertedListssizes ( IOReader *f, std::vector<size_t> & sizes) { size_tnlist = sizes.size(); uint...