code: def main():
      parser = get_parser()
      args = parser.parse_args()
  
      faiss_specs = parse_faiss_specs(args.faiss_specs)
      print("Faiss Specs:", faiss_specs)
  
      feats_list = []
      with open(args.feats_scp, "r") as f_scp:
          for line in f_scp.readlines():
              _, feat_file = line.split()
              feat = np.load(feat_file)
              feats_list.append(feat)
  
          feats = np.concatenate(feats_list)
  
          os.makedirs(args.save_dir, exist_ok=True)
          # np.save(feat_path, feats)
  
          gc.collect()
  
      reload = False
      for spec in faiss_specs:
          print("Processing spec", spec)
  
          if reload:
              print("Reloading...")
              del feats
              gc.collect()
              feats = np.load(feat_path + ".npy")
  
          save_path = osp.join(args.save_dir, spec.spec_str)
          os.makedirs(save_path, exist_ok=True)
          d = feats.shape[-1]
          x = feats
          if spec.pca > 0:
              print("Computing PCA")
              pca = faiss.PCAMatrix(d, spec.pca)
              pca.train(x)
              d = spec.pca
              b = faiss.vector_to_array(pca.b)
              A = faiss.vector_to_array(pca.A).reshape(
                  pca.d_out, pca.d_in
              )
              np.save(osp.join(save_path, "pca_A"), A.T)
              np.save(osp.join(save_path, "pca_b"), b)
              print("Applying PCA")
              x = pca.apply_py(x)
  
          if spec.norm:
              reload = spec.pca <= 0
              print("Normalizing")
              faiss.normalize_L2(x)
  
          print("Computing kmeans")
          kmeans = faiss.Kmeans(
              d,
              spec.n_clus,
              niter=50,
              verbose=True,
              spherical=spec.sphere,
              max_points_per_centroid=feats.shape[0],
              gpu=True,
              nredo=3,
          )
          kmeans.train(x)
          np.save(osp.join(save_path, "centroids"), kmeans.centroids)
          del kmeans
          del x
          gc.collect()
  
file_path: espnet/egs2/ofuton_p_utagoe_db/svs1/pyscripts/feats/feats_cluster_faiss.py
imports: import argparse
  import gc
  import os
  import os.path
  import random
  from collections import namedtuple
  import faiss
  import numpy
  import soundfile
  import torch
  import tqdm
label_desc: Use image node_id 3 for calling a global function with example usage: main() without return types