diff --git a/deploy/vector_search/Makefile b/deploy/vector_search/Makefile index 6a4ce215bec6ac5e799135f82d1938680641457f..ec2df598df631898a7a5d2ae6a62cc512b6958d1 100644 --- a/deploy/vector_search/Makefile +++ b/deploy/vector_search/Makefile @@ -1,6 +1,15 @@ -CXX=/usr/bin/g++-5 +CXX=g++ + +ifeq ($(OS),Windows_NT) + postfix=dll +else + postfix=so +endif all : index -index.so : src/config.h src/graph.h src/data.h interface.cc - $(CXX) -shared -fPIC interface.cc -o index.so -std=c++11 -Ofast -march=native -g -flto -funroll-loops -DOMP -fopenmp +index : src/config.h src/graph.h src/data.h interface.cc + ${CXX} -shared -fPIC interface.cc -o index.${postfix} -std=c++11 -Ofast -march=native -g -flto -funroll-loops -DOMP -fopenmp + +clean : + rm index.${postfix} \ No newline at end of file diff --git a/deploy/vector_search/README.md b/deploy/vector_search/README.md index 88d5c323c54ee9725463d9b2f20bf1d251f9b027..caa9582942218f625d8f4ff5886aac47a67122c5 100644 --- a/deploy/vector_search/README.md +++ b/deploy/vector_search/README.md @@ -1,8 +1,7 @@ # 向量检索 - -## 简介 +## 1. 简介 一些垂域识别任务（如车辆、商品等）需要识别的类别数较大，往往采用基于检索的方式，通过查询向量与底库向量进行快速的最近邻搜索，获得匹配的预测类别。向量检索模块提供基础的近似最近邻搜索算法，基于百度自研的Möbius算法，一种基于图的近似最近邻搜索算法，用于最大内积搜索 (MIPS)。 该模块提供python接口，支持numpy和 tensor类型向量,支持L2和Inner Product距离计算。 @@ -10,35 +9,67 @@ Mobius 算法细节详见论文 （[Möbius Transformation for Fast Inner Produc -## 安装 +## 2. 安装 + +### 2.1 直接使用提供的库文件 + +该文件夹下有已经编译好的`index.so`(gcc8.2.0下编译，用于Linux)以及`index.dll`(gcc10.3.0下编译，用于Windows)，可以跳过2.2与2.3节，直接使用。 + +如果因为gcc版本过低或者环境不兼容的问题，导致库文件无法使用，则需要在不同的平台下手动编译库文件。 + +**注意：** +请确保您的 C++ 编译器支持 C++11 标准。 + + +### 2.2 Linux上编译生成库文件 + +运行下面的命令，安装gcc与g++。 + +```shell +sudo apt-get update +sudo apt-get upgrade -y +sudo apt-get install build-essential gcc g++ +``` -若index.so不可用，在项目目录下运行以下命令生成新的index.so文件 +可以通过命令`gcc -v`查看gcc版本。 - make index.so +进入该文件夹，直接运行`make`即可，如果希望重新生成`index.so`文件，可以首先使用`make clean`清除已经生成的缓存，再使用`make`生成更新之后的库文件。 -编译环境: g++ 5.4.0 , 9.3.0. 其他版本也可能工作。 请确保您的 C++ 编译器支持 C++11 标准。 +### 2.3 Windows上编译生成库文件 +Windows上首先需要安装gcc编译工具，推荐使用[TDM-GCC](https://jmeubank.github.io/tdm-gcc/articles/2020-03/9.2.0-release)，进入官网之后，可以选择合适的版本进行下载。推荐下载[tdm64-gcc-10.3.0-2.exe](https://github.com/jmeubank/tdm-gcc/releases/download/v10.3.0-tdm64-2/tdm64-gcc-10.3.0-2.exe)。 -## 快速使用 +下载完成之后，按照默认的安装步骤进行安装即可。这里有3点需要注意： +1. 向量检索模块依赖于openmp，因此在安装到`choose components`步骤的时候，需要勾选上`openmp`的安装选项，否则之后编译的时候会报错`libgomp.spec: No such file or directory`，[参考链接](https://github.com/dmlc/xgboost/issues/1027) +2. 安装过程中会提示是否需要添加到系统的环境变量中，这里建议勾选上，否则之后使用的时候还需要手动添加系统环境变量。 +3. Linux上的编译命令为`make`，Windows上为`mingw32-make`，这里需要区分一下。 + + +安装完成后，可以打开一个命令行终端，通过命令`gcc -v`查看gcc版本。 + +在该文件夹下，运行命令`mingw32-make`，即可生成`index.dll`库文件。如果希望重新生成`index.dll`文件，可以首先使用`mingw32-make clean`清除已经生成的缓存，再使用`mingw32-make`生成更新之后的库文件。 + + +## 3. 快速使用 import numpy as np from interface import Graph_Index - + # 随机产生样本 - index_vectors = np.random.rand(100000,128).astype(np.float32) - query_vector = np.random.rand(128).astype(np.float32) + index_vectors = np.random.rand(100000,128).astype(np.float32) + query_vector = np.random.rand(128).astype(np.float32) index_docs = ["ID_"+str(i) for i in range(100000)] - + # 初始化索引结构 indexer = Graph_Index(dist_type="IP") #支持"IP"和"L2" indexer.build(gallery_vectors=index_vectors, gallery_docs=index_docs, pq_size=100, index_path='test') - + # 查询 scores, docs = indexer.search(query=query_vector, return_k=10, search_budget=100) print(scores) print(docs) - + # 保存与加载 - indexer.dump(index_path="test") - indexer.load(index_path="test") + indexer.dump(index_path="test") + indexer.load(index_path="test") diff --git a/deploy/vector_search/index.dll b/deploy/vector_search/index.dll new file mode 100644 index 0000000000000000000000000000000000000000..5d5475358d4318224062e8b6207ce8b036882282 Binary files /dev/null and b/deploy/vector_search/index.dll differ diff --git a/deploy/vector_search/index.so b/deploy/vector_search/index.so old mode 100644 new mode 100755 index 3ad2e3c8ca486473a5c01c953c678d842ddd1472..d58d4f74a54ee384bac8515b4821488fbd67e5a4 Binary files a/deploy/vector_search/index.so and b/deploy/vector_search/index.so differ diff --git a/deploy/vector_search/interface.py b/deploy/vector_search/interface.py index 51ef7bdd2aee567149d5c9bb6690a182791bceb9..218adbfe9826be2332f2262d671ab5566c2f2fb1 100644 --- a/deploy/vector_search/interface.py +++ b/deploy/vector_search/interface.py @@ -18,48 +18,77 @@ import numpy.ctypeslib as ctl import numpy as np import os import json +import platform from ctypes import * from numpy.ctypeslib import ndpointer __dir__ = os.path.dirname(os.path.abspath(__file__)) -so_path = os.path.join(__dir__, "index.so") +if platform.system() == "Windows": + lib_filename = "index.dll" +else: + lib_filename = "index.so" +so_path = os.path.join(__dir__, lib_filename) lib = ctypes.cdll.LoadLibrary(so_path) + class IndexContext(Structure): - _fields_=[("graph",c_void_p), - ("data",c_void_p)] + _fields_ = [("graph", c_void_p), ("data", c_void_p)] + # for mobius IP index build_mobius_index = lib.build_mobius_index build_mobius_index.restype = None -build_mobius_index.argtypes = [ctl.ndpointer(np.float32, flags='aligned, c_contiguous'), ctypes.c_int, ctypes.c_int, ctypes.c_int, ctypes.c_double, ctypes.c_char_p] +build_mobius_index.argtypes = [ + ctl.ndpointer( + np.float32, flags='aligned, c_contiguous'), ctypes.c_int, ctypes.c_int, + ctypes.c_int, ctypes.c_double, ctypes.c_char_p +] search_mobius_index = lib.search_mobius_index search_mobius_index.restype = None -search_mobius_index.argtypes = [ctl.ndpointer(np.float32, flags='aligned, c_contiguous'), ctypes.c_int, ctypes.c_int,ctypes.c_int,POINTER(IndexContext),ctl.ndpointer(np.uint64, flags='aligned, c_contiguous'),ctl.ndpointer(np.float64, flags='aligned, c_contiguous')] +search_mobius_index.argtypes = [ + ctl.ndpointer( + np.float32, flags='aligned, c_contiguous'), ctypes.c_int, ctypes.c_int, + ctypes.c_int, POINTER(IndexContext), ctl.ndpointer( + np.uint64, flags='aligned, c_contiguous'), ctl.ndpointer( + np.float64, flags='aligned, c_contiguous') +] load_mobius_index_prefix = lib.load_mobius_index_prefix load_mobius_index_prefix.restype = None -load_mobius_index_prefix.argtypes = [ctypes.c_int, ctypes.c_int, POINTER(IndexContext), ctypes.c_char_p] +load_mobius_index_prefix.argtypes = [ + ctypes.c_int, ctypes.c_int, POINTER(IndexContext), ctypes.c_char_p +] save_mobius_index_prefix = lib.save_mobius_index_prefix save_mobius_index_prefix.restype = None save_mobius_index_prefix.argtypes = [POINTER(IndexContext), ctypes.c_char_p] - # for L2 index build_l2_index = lib.build_l2_index build_l2_index.restype = None -build_l2_index.argtypes = [ctl.ndpointer(np.float32, flags='aligned, c_contiguous'), ctypes.c_int, ctypes.c_int, ctypes.c_int, ctypes.c_char_p] +build_l2_index.argtypes = [ + ctl.ndpointer( + np.float32, flags='aligned, c_contiguous'), ctypes.c_int, ctypes.c_int, + ctypes.c_int, ctypes.c_char_p +] search_l2_index = lib.search_l2_index search_l2_index.restype = None -search_l2_index.argtypes = [ctl.ndpointer(np.float32, flags='aligned, c_contiguous'), ctypes.c_int, ctypes.c_int,ctypes.c_int,POINTER(IndexContext),ctl.ndpointer(np.uint64, flags='aligned, c_contiguous'),ctl.ndpointer(np.float64, flags='aligned, c_contiguous')] +search_l2_index.argtypes = [ + ctl.ndpointer( + np.float32, flags='aligned, c_contiguous'), ctypes.c_int, ctypes.c_int, + ctypes.c_int, POINTER(IndexContext), ctl.ndpointer( + np.uint64, flags='aligned, c_contiguous'), ctl.ndpointer( + np.float64, flags='aligned, c_contiguous') +] load_l2_index_prefix = lib.load_l2_index_prefix load_l2_index_prefix.restype = None -load_l2_index_prefix.argtypes = [ctypes.c_int, ctypes.c_int, POINTER(IndexContext), ctypes.c_char_p] +load_l2_index_prefix.argtypes = [ + ctypes.c_int, ctypes.c_int, POINTER(IndexContext), ctypes.c_char_p +] save_l2_index_prefix = lib.save_l2_index_prefix save_l2_index_prefix.restype = None @@ -70,51 +99,68 @@ release_context.restype = None release_context.argtypes = [POINTER(IndexContext)] - class Graph_Index(object): """ graph index """ + def __init__(self, dist_type="IP"): self.dim = 0 self.total_num = 0 self.dist_type = dist_type self.mobius_pow = 2.0 - self.index_context = IndexContext(0,0) + self.index_context = IndexContext(0, 0) self.gallery_doc_dict = {} self.with_attr = False assert dist_type in ["IP", "L2"], "Only support IP and L2 distance ..." - - def build(self, gallery_vectors, gallery_docs=[], pq_size=100, index_path='graph_index/'): + + def build(self, + gallery_vectors, + gallery_docs=[], + pq_size=100, + index_path='graph_index/'): """ build index """ if paddle.is_tensor(gallery_vectors): - gallery_vectors = gallery_vectors.numpy() + gallery_vectors = gallery_vectors.numpy() assert gallery_vectors.ndim == 2, "Input vector must be 2D ..." - + self.total_num = gallery_vectors.shape[0] self.dim = gallery_vectors.shape[1] - assert (len(gallery_docs) == self.total_num if len(gallery_docs)>0 else True) - - print("training index -> num: {}, dim: {}, dist_type: {}".format(self.total_num, self.dim, self.dist_type)) + assert (len(gallery_docs) == self.total_num + if len(gallery_docs) > 0 else True) + + print("training index -> num: {}, dim: {}, dist_type: {}".format( + self.total_num, self.dim, self.dist_type)) if not os.path.exists(index_path): os.makedirs(index_path) - + if self.dist_type == "IP": - build_mobius_index(gallery_vectors,self.total_num,self.dim, pq_size, self.mobius_pow, create_string_buffer((index_path+"/index").encode('utf-8'))) - load_mobius_index_prefix(self.total_num, self.dim, ctypes.byref(self.index_context), create_string_buffer((index_path+"/index").encode('utf-8'))) + build_mobius_index( + gallery_vectors, self.total_num, self.dim, pq_size, + self.mobius_pow, + create_string_buffer((index_path + "/index").encode('utf-8'))) + load_mobius_index_prefix( + self.total_num, self.dim, + ctypes.byref(self.index_context), + create_string_buffer((index_path + "/index").encode('utf-8'))) else: - build_l2_index(gallery_vectors,self.total_num,self.dim, pq_size, create_string_buffer((index_path+"/index").encode('utf-8'))) - load_l2_index_prefix(self.total_num, self.dim, ctypes.byref(self.index_context), create_string_buffer((index_path+"/index").encode('utf-8'))) - - self.gallery_doc_dict = {} + build_l2_index( + gallery_vectors, self.total_num, self.dim, pq_size, + create_string_buffer((index_path + "/index").encode('utf-8'))) + load_l2_index_prefix( + self.total_num, self.dim, + ctypes.byref(self.index_context), + create_string_buffer((index_path + "/index").encode('utf-8'))) + + self.gallery_doc_dict = {} if len(gallery_docs) > 0: self.with_attr = True for i in range(gallery_vectors.shape[0]): - self.gallery_doc_dict[str(i)] = gallery_docs[i] + self.gallery_doc_dict[str(i)] = gallery_docs[i] self.gallery_doc_dict["total_num"] = self.total_num self.gallery_doc_dict["dim"] = self.dim @@ -134,15 +180,19 @@ class Graph_Index(object): ret_score = np.zeros(return_k, dtype=np.float64) if paddle.is_tensor(query): - query = query.numpy() + query = query.numpy() if self.dist_type == "IP": - search_mobius_index(query,self.dim,search_budget,return_k,ctypes.byref(self.index_context),ret_id,ret_score) + search_mobius_index(query, self.dim, search_budget, return_k, + ctypes.byref(self.index_context), ret_id, + ret_score) else: - search_l2_index(query,self.dim,search_budget,return_k,ctypes.byref(self.index_context),ret_id,ret_score) - + search_l2_index(query, self.dim, search_budget, return_k, + ctypes.byref(self.index_context), ret_id, + ret_score) + ret_id = ret_id.tolist() ret_doc = [] - if self.with_attr: + if self.with_attr: for i in range(return_k): ret_doc.append(self.gallery_doc_dict[str(ret_id[i])]) return ret_score, ret_doc @@ -155,28 +205,35 @@ class Graph_Index(object): os.makedirs(index_path) if self.dist_type == "IP": - save_mobius_index_prefix(ctypes.byref(self.index_context),create_string_buffer((index_path+"/index").encode('utf-8'))) + save_mobius_index_prefix( + ctypes.byref(self.index_context), + create_string_buffer((index_path + "/index").encode('utf-8'))) else: - save_l2_index_prefix(ctypes.byref(self.index_context), create_string_buffer((index_path+"/index").encode('utf-8'))) - + save_l2_index_prefix( + ctypes.byref(self.index_context), + create_string_buffer((index_path + "/index").encode('utf-8'))) + with open(index_path + "/info.json", "w") as f: json.dump(self.gallery_doc_dict, f) def load(self, index_path): self.gallery_doc_dict = {} - + with open(index_path + "/info.json", "r") as f: self.gallery_doc_dict = json.load(f) - + self.total_num = self.gallery_doc_dict["total_num"] self.dim = self.gallery_doc_dict["dim"] - self.dist_type = self.gallery_doc_dict["dist_type"] + self.dist_type = self.gallery_doc_dict["dist_type"] self.with_attr = self.gallery_doc_dict["with_attr"] if self.dist_type == "IP": - load_mobius_index_prefix(self.total_num,self.dim,ctypes.byref(self.index_context), create_string_buffer((index_path+"/index").encode('utf-8'))) + load_mobius_index_prefix( + self.total_num, self.dim, + ctypes.byref(self.index_context), + create_string_buffer((index_path + "/index").encode('utf-8'))) else: - load_l2_index_prefix(self.total_num,self.dim,ctypes.byref(self.index_context), create_string_buffer((index_path+"/index").encode('utf-8'))) - - - + load_l2_index_prefix( + self.total_num, self.dim, + ctypes.byref(self.index_context), + create_string_buffer((index_path + "/index").encode('utf-8'))) diff --git a/ppcls/configs/Logo/ResNet50_ReID.yaml b/ppcls/configs/Logo/ResNet50_ReID.yaml index 6e6a825df4c517265f350998792d2ff3e666b877..43798a209df5333ce4f14e3614265e5db46a57a4 100644 --- a/ppcls/configs/Logo/ResNet50_ReID.yaml +++ b/ppcls/configs/Logo/ResNet50_ReID.yaml @@ -96,7 +96,7 @@ DataLoader: dataset: name: LogoDataset image_root: "dataset/LogoDet-3K-crop/val/" - cls_label_path: "LogoDet-3K-crop/LogoDet-3K+query.txt" + cls_label_path: "dataset/LogoDet-3K-crop/LogoDet-3K+query.txt" transform_ops: - DecodeImage: to_rgb: True