fix(encoder): fix vlad to speed up centroids calculation

gnes/encoder/numeric/vlad.py

@@ -24,7 +24,7 @@ class VladEncoder(BaseNumericEncoder):
     batch_size = 2048
 
     def __init__(self, num_clusters: int,
-                 using_faiss_pred: bool=True,
+                 using_faiss_pred: bool = False,
                  *args, **kwargs):
         super().__init__(*args, **kwargs)
         self.num_clusters = num_clusters
@@ -37,6 +37,8 @@ def kmeans_train(self, vecs):
         kmeans = faiss.Kmeans(vecs.shape[1], self.num_clusters, niter=5, verbose=False)
         kmeans.train(vecs)
         self.centroids = kmeans.centroids
+        self.centroids_l2 = np.sum(self.centroids**2, axis=1).reshape([1, -1])
+        self.centroids_trans = np.transpose(self.centroids)
         if self.using_faiss_pred:
             self.faiss_index()
 
@@ -50,9 +52,9 @@ def kmeans_pred(self, vecs):
             _, pred = self.index_flat.search(vecs.astype(np.float32), 1)
             return np.reshape(pred, [-1])
         else:
-            vecs = np.reshape(vecs, [vecs.shape[0], 1, 1, vecs.shape[1]])
-            dist = np.sum(np.square(vecs - self.centroids), -1)
-            return np.argmax(-dist, axis=-1).reshape([-1]).astype(np.int32)
+            vecs_l2 = np.sum(vecs**2, axis=1).reshape([-1, 1])
+            dist = vecs_l2 + self.centroids_l2 - 2 * np.matmul(vecs, self.centroids_trans)
+            return np.argmax(dist, axis=-1).reshape([-1]).astype(np.int32)
 
     @batching
     def train(self, vecs: np.ndarray, *args, **kwargs):
@@ -79,6 +81,8 @@ def encode(self, vecs: np.ndarray, *args, **kwargs) -> np.ndarray:
     def _copy_from(self, x: 'VladEncoder') -> None:
         self.num_clusters = x.num_clusters
         self.centroids = x.centroids
+        self.centroids_l2 = x.centroids_l2
+        self.centroids_trans = np.transpose(self.centroids)
         self.using_faiss_pred = x.using_faiss_pred
         if self.using_faiss_pred:
             self.faiss_index()