add vector search to reg infer (#791)

* Update inference_rec.yaml
* Update predict.sh

deploy/configs/inference_rec.yaml

@@ -49,4 +49,21 @@ RecPreProcess:
         order: ''
     - ToCHWImage:
 
-RecPostProcess: null
+RecPostProcess: null
+
+
+# indexing engine config
+IndexProcess:
+  build:  
+    enable: True
+    index_path: "./logo_index/"
+    image_root: "dataset/LogoDet-3K-crop/train"
+    data_file: "dataset/LogoDet-3K-crop/LogoDet-3K+train.txt"
+    spacer: " "
+    dist_type: "IP"
+    pq_size: 100
+    embedding_size: 1000
+  infer:
+    index_path: "./logo_index/"
+    search_budget: 100
+    return_k: 10

deploy/python/predict_system.py

@@ -1,4 +1,4 @@
-# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -23,28 +23,78 @@ import numpy as np
 
 from python.predict_rec import RecPredictor
 from python.predict_det import DetPredictor
+from vector_search import Graph_Index
 
 from utils import logger
 from utils import config
 from utils.get_image_list import get_image_list
 
 
+def split_datafile(data_file, image_root):
+    gallery_images = []
+    gallery_docs = []
+    with open(datafile) as f:
+        lines = f.readlines()
+        for i, line in enumerate(lines):
+            line = line.strip().split("\t")
+            if line[0] == 'image_id':
+                 continue
+            image_file = os.path.join(image_root, line[3])
+            image_doc = line[1]
+            gallery_images.append(image_file)
+            gallery_docs.append(image_doc)
+    return gallery_images, gallery_docs
+
+
 class SystemPredictor(object):
     def __init__(self, config):
+
+        self.config = config
         self.rec_predictor = RecPredictor(config)
         self.det_predictor = DetPredictor(config)
 
+        assert 'IndexProcess' in config.keys(), "Index config not found ... "
+        self.indexer(config['IndexProcess'])
+        self.return_k = self.config['IndexProcess']['infer']['return_k']
+        self.search_budget = self.config['IndexProcess']['infer']['search_budget']
+    
+    def indexer(self, config):
+        if 'build' in config.keys() and config['build']['enable']:  # build the index from scratch    
+            with open(config['build']['datafile']) as f:
+                lines = f.readlines()
+            gallery_images, gallery_docs = split_datafile(config['build']['data_file'], config['build']['image_root'])
+            # extract gallery features
+            gallery_features = np.zeros([len(gallery_images), config['build']['embedding_size']], dtype=np.float32)
+            for i, image_file in enumerate(gallery_images):
+                img = cv2.imread(image_file)[:, :, ::-1]
+                rec_feat = self.rec_predictor.predict(img)
+                gallery_features[i,:] = rec_feat
+            # train index 
+            self.Searcher = Graph_Index(dist_type=config['build']['dist_type']) 
+            self.Searcher.build(gallery_vectors=gallery_features, gallery_docs=gallery_docs, 
+                pq_size=config['build']['pq_size'], index_path=config['build']['index_path'])
+            
+        else:   # load local index
+            self.Searcher = Graph_Index(dist_type=config['build']['dist_type']) 
+            self.Searcher.load(config['infer']['index_path'])
+
     def predict(self, img):
         output = []
         results = self.det_predictor.predict(img)
         for result in results:
-            print(result)
+            #print(result)
             xmin, xmax, ymin, ymax = result["bbox"].astype("int")
             crop_img = img[xmin:xmax, ymin:ymax, :].copy()
             rec_results = self.rec_predictor.predict(crop_img)
-            result["feature"] = rec_results
+            result["featrue"] = rec_results
+
+            scores, docs = self.Searcher.search(query=rec_results, return_k=self.return_k, search_budget=self.search_budget)
+            result["ret_docs"] = docs
+            result["ret_scores"] = scores
+
             output.append(result)
         return output
+            
 
 
 def main(config):
@@ -55,7 +105,7 @@ def main(config):
     for idx, image_file in enumerate(image_list):
         img = cv2.imread(image_file)[:, :, ::-1]
         output = system_predictor.predict(img)
-        print(output)
+        #print(output)
     return
 
 

deploy/shell/predict.sh

@@ -7,5 +7,5 @@ python3.7 python/predict_cls.py -c configs/inference_cls.yaml
 # detection
 # python3.7 python/predict_det.py -c configs/inference_rec.yaml
 
-# mainbody detection + feature extractor
-# python3.7 python/predict_system.py -c configs/inference_rec.yaml 
+# mainbody detection + feature extractor + retrieval
+# python3.7 python/predict_system.py -c configs/inference_rec.yaml 

deploy/vector_search/interface.py

@@ -22,7 +22,9 @@ import json
 from ctypes import *
 from numpy.ctypeslib import ndpointer
 
-lib = ctypes.cdll.LoadLibrary("./index.so")
+__dir__ = os.path.dirname(os.path.abspath(__file__))
+so_path = os.path.join(__dir__, "index.so")
+lib = ctypes.cdll.LoadLibrary(so_path)
 
 class IndexContext(Structure):
     _fields_=[("graph",c_void_p),