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