80 lines
2.3 KiB
Python
80 lines
2.3 KiB
Python
"""
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格式：直接cid为自带的index位；aid放不下了，通过字典来查，反正就5w个
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"""
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import os
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import traceback
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import logging
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logger = logging.getLogger(__name__)
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from multiprocessing import cpu_count
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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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# ###########如果是原始特征要先写save
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n_cpu = 0
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if n_cpu == 0:
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n_cpu = cpu_count()
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inp_root = r"./logs/anz/3_feature768"
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npys = []
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listdir_res = list(os.listdir(inp_root))
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for name in sorted(listdir_res):
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phone = np.load("%s/%s" % (inp_root, name))
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npys.append(phone)
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big_npy = np.concatenate(npys, 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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logger.debug(big_npy.shape) # (6196072, 192)#fp32#4.43G
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if big_npy.shape[0] > 2e5:
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# if(1):
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info = "Trying doing kmeans %s shape to 10k centers." % big_npy.shape[0]
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logger.info(info)
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try:
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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 * n_cpu,
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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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except:
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info = traceback.format_exc()
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logger.warning(info)
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np.save("tools/infer/big_src_feature_mi.npy", big_npy)
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##################train+add
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# big_npy=np.load("/bili-coeus/jupyter/jupyterhub-liujing04/vits_ch/inference_f0/big_src_feature_mi.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 = faiss.index_factory(768, "IVF%s,Flat" % n_ivf) # mi
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logger.info("Training...")
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index_ivf = faiss.extract_index_ivf(index) #
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index_ivf.nprobe = 1
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index.train(big_npy)
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faiss.write_index(
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index, "tools/infer/trained_IVF%s_Flat_baseline_src_feat_v2.index" % (n_ivf)
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)
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logger.info("Adding...")
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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.add(big_npy[i : i + batch_size_add])
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faiss.write_index(
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index, "tools/infer/added_IVF%s_Flat_mi_baseline_src_feat.index" % (n_ivf)
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)
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"""
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大小（都是FP32）
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big_src_feature 2.95G
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(3098036, 256)
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big_emb 4.43G
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(6196072, 192)
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big_emb双倍是因为求特征要repeat后再加pitch
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"""
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