fix so make in windows (#849)

* fix so make in windows

* add index.exe for win

* fix doc

* fix yaml

* fix exe to dll

deploy/vector_search/Makefile

-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

deploy/vector_search/README.md

 # 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")

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')))

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