big_npy should be shuffled (#218)
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@ -670,6 +670,9 @@ def train_index(exp_dir1):
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phone = np.load("%s/%s" % (feature_dir, name))
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phone = np.load("%s/%s" % (feature_dir, name))
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npys.append(phone)
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npys.append(phone)
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big_npy = np.concatenate(npys, 0)
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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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np.save("%s/total_fea.npy" % exp_dir, big_npy)
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np.save("%s/total_fea.npy" % exp_dir, big_npy)
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# n_ivf = big_npy.shape[0] // 39
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# n_ivf = big_npy.shape[0] // 39
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n_ivf = min(int(16 * np.sqrt(big_npy.shape[0])), big_npy.shape[0] // 39)
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n_ivf = min(int(16 * np.sqrt(big_npy.shape[0])), big_npy.shape[0] // 39)
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@ -892,6 +895,9 @@ def train1key(
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phone = np.load("%s/%s" % (feature_dir, name))
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phone = np.load("%s/%s" % (feature_dir, name))
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npys.append(phone)
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npys.append(phone)
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big_npy = np.concatenate(npys, 0)
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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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np.save("%s/total_fea.npy" % exp_dir, big_npy)
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np.save("%s/total_fea.npy" % exp_dir, big_npy)
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# n_ivf = big_npy.shape[0] // 39
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# n_ivf = big_npy.shape[0] // 39
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n_ivf = min(int(16 * np.sqrt(big_npy.shape[0])), big_npy.shape[0] // 39)
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n_ivf = min(int(16 * np.sqrt(big_npy.shape[0])), big_npy.shape[0] // 39)
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