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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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print(f"Saved index file '{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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if "one array to concatenate" in str(error):
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print(
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"If you are running this code in a virtual environment, make sure you have enough GPU available to generate the Index file."
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)
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