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import os
import sys
import faiss
import numpy as np
from sklearn.cluster import MiniBatchKMeans
from multiprocessing import cpu_count
exp_dir = sys.argv[1]
version = sys.argv[2]
try:
if version == "v1":
feature_dir = os.path.join(exp_dir, "3_feature256")
elif version == "v2":
feature_dir = os.path.join(exp_dir, "3_feature768")
npys = []
listdir_res = sorted(os.listdir(feature_dir))
for name in listdir_res:
file_path = os.path.join(feature_dir, name)
phone = np.load(file_path)
npys.append(phone)
big_npy = np.concatenate(npys, axis=0)
big_npy_idx = np.arange(big_npy.shape[0])
np.random.shuffle(big_npy_idx)
big_npy = big_npy[big_npy_idx]
if big_npy.shape[0] > 2e5:
big_npy = (
MiniBatchKMeans(
n_clusters=10000,
verbose=True,
batch_size=256 * cpu_count(),
compute_labels=False,
init="random",
)
.fit(big_npy)
.cluster_centers_
)
np.save(os.path.join(exp_dir, "total_fea.npy"), big_npy)
n_ivf = min(int(16 * np.sqrt(big_npy.shape[0])), big_npy.shape[0] // 39)
# index_trained
index_trained = faiss.index_factory(
256 if version == "v1" else 768, f"IVF{n_ivf},Flat"
)
index_ivf_trained = faiss.extract_index_ivf(index_trained)
index_ivf_trained.nprobe = 1
index_trained.train(big_npy)
index_filename_trained = (
f"trained_IVF{n_ivf}_Flat_nprobe_{index_ivf_trained.nprobe}_{version}.index"
)
index_filepath_trained = os.path.join(exp_dir, index_filename_trained)
faiss.write_index(index_trained, index_filepath_trained)
# index_added
index_added = faiss.index_factory(
256 if version == "v1" else 768, f"IVF{n_ivf},Flat"
)
index_ivf_added = faiss.extract_index_ivf(index_added)
index_ivf_added.nprobe = 1
index_added.train(big_npy)
index_filename_added = (
f"added_IVF{n_ivf}_Flat_nprobe_{index_ivf_added.nprobe}_{version}.index"
)
index_filepath_added = os.path.join(exp_dir, index_filename_added)
batch_size_add = 8192
for i in range(0, big_npy.shape[0], batch_size_add):
index_added.add(big_npy[i : i + batch_size_add])
faiss.write_index(index_added, index_filepath_added)
except Exception as error:
print(f"Failed to train index: {error}")
print("Index training finished!")
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