modify some format problem

deploy/python/build_gallery.py

@@ -28,6 +28,7 @@ from python.predict_rec import RecPredictor
 from utils import logger
 from utils import config
 
+
 def split_datafile(data_file, image_root, delimiter="\t"):
     '''
         data_file: image path and info, which can be splitted by spacer
@@ -70,7 +71,6 @@ class GalleryBuilder(object):
         # when remove data in index, do not need extract fatures
         if operation_method != "remove":
             gallery_features = self._extract_features(gallery_images, config)
-        
         assert operation_method in [
             "new", "remove", "append"
         ], "Only append, remove and new operation are supported"
@@ -105,19 +105,20 @@ class GalleryBuilder(object):
                     min(int(len(gallery_images) // 8), 65536)) + ",Flat"
 
             # for binary index, add B at head of index_method
-            if  config["dist_type"]  == "hamming":
+            if config["dist_type"] == "hamming":
                 index_method = "B" + index_method
-            
+
             #dist_type
             dist_type = faiss.METRIC_INNER_PRODUCT if config[
                 "dist_type"] == "IP" else faiss.METRIC_L2
-            
+
             #build index
-            if config["dist_type"]  == "hamming":
-                index = faiss.index_binary_factory(config["embedding_size"], index_method)
+            if config["dist_type"] == "hamming":
+                index = faiss.index_binary_factory(config["embedding_size"],
+                                                   index_method)
             else:
-                index = faiss.index_factory(config["embedding_size"], index_method,
-                                            dist_type)
+                index = faiss.index_factory(config["embedding_size"],
+                                            index_method, dist_type)
                 index = faiss.IndexIDMap2(index)
             ids = {}
 
@@ -133,12 +134,12 @@ class GalleryBuilder(object):
 
             # only train when new index file
             if operation_method == "new":
-                if config["dist_type"]  == "hamming":
+                if config["dist_type"] == "hamming":
                     index.add(gallery_features)
                 else:
                     index.train(gallery_features)
-            
-            if not config["dist_type"]  == "hamming":
+
+            if not config["dist_type"] == "hamming":
                 index.add_with_ids(gallery_features, ids_now)
 
             for i, d in zip(list(ids_now), gallery_docs):
@@ -157,25 +158,26 @@ class GalleryBuilder(object):
                 del ids[k]
 
         # store faiss index file and id_map file
-        if  config["dist_type"]  == "hamming":
-            faiss.write_index_binary(index,
-                            os.path.join(config["index_dir"], "vector.index"))
+        if config["dist_type"] == "hamming":
+            faiss.write_index_binary(
+                index, os.path.join(config["index_dir"], "vector.index"))
         else:
-            faiss.write_index(index,
-                            os.path.join(config["index_dir"], "vector.index"))
+            faiss.write_index(
+                index, os.path.join(config["index_dir"], "vector.index"))
 
         with open(os.path.join(config["index_dir"], "id_map.pkl"), 'wb') as fd:
             pickle.dump(ids, fd)
 
     def _extract_features(self, gallery_images, config):
-
         # extract gallery features
         if config["dist_type"] == "hamming":
             gallery_features = np.zeros(
-                [len(gallery_images), config['embedding_size'] // 8], dtype=np.uint8)
+                [len(gallery_images), config['embedding_size'] // 8],
+                dtype=np.uint8)
         else:
             gallery_features = np.zeros(
-                [len(gallery_images), config['embedding_size']], dtype=np.float32)
+                [len(gallery_images), config['embedding_size']],
+                dtype=np.float32)
 
         #construct batch imgs and do inference
         batch_size = config.get("batch_size", 32)