Question about faiss searching

[whu-lyh]

Hi @ramdrop , thanks for your amazing works and the release of the code. I find some positions that quite confuse me, and looking forward to your answer.

1. here, why the number of search is len(dbFeat), in my opinion, the n_values should be used?

   faiss_index = faiss.IndexFlatL2(opt.output_dim)
   faiss_index.add(dbFeat)
   # dists, predictions = faiss_index.search(qFeat, max(n_values))   # the results is sorted
   dists, predictions = faiss_index.search(qFeat, len(dbFeat))

2. I wonder why the radius dramatically change the PR performance during getting the GT?

   def getPositives(self):
       # positives for evaluation are those within trivial threshold range
       # fit NN to find them, search by radius
       if self.positives is None:
           knn = NearestNeighbors(n_jobs=-1)
           knn.fit(self.dbStruct.utmDb)
           self.distances, self.positives = knn.radius_neighbors(self.dbStruct.utmQ, radius=self.dbStruct.nonTrivPosDistSqThr**0.5) 
       return self.positives

[whu-lyh]

Oops, I figured out the question1.