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import os | |
import sys | |
import faiss | |
import numpy as np | |
from sklearn.cluster import MiniBatchKMeans | |
from multiprocessing import cpu_count | |
# Parse command line arguments | |
exp_dir = str(sys.argv[1]) | |
index_algorithm = str(sys.argv[2]) | |
try: | |
feature_dir = os.path.join(exp_dir, f"extracted") | |
model_name = os.path.basename(exp_dir) | |
index_filename_added = f"{model_name}.index" | |
index_filepath_added = os.path.join(exp_dir, index_filename_added) | |
if os.path.exists(index_filepath_added): | |
pass | |
else: | |
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 and ( | |
index_algorithm == "Auto" or index_algorithm == "KMeans" | |
): | |
big_npy = ( | |
MiniBatchKMeans( | |
n_clusters=10000, | |
verbose=True, | |
batch_size=256 * cpu_count(), | |
compute_labels=False, | |
init="random", | |
) | |
.fit(big_npy) | |
.cluster_centers_ | |
) | |
n_ivf = min(int(16 * np.sqrt(big_npy.shape[0])), big_npy.shape[0] // 39) | |
# index_added | |
index_added = faiss.index_factory(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) | |
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) | |
print(f"Saved index file '{index_filepath_added}'") | |
except Exception as error: | |
print(f"An error occurred extracting the index: {error}") | |
print( | |
"If you are running this code in a virtual environment, make sure you have enough GPU available to generate the Index file." | |
) | |