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big_npy should be shuffled (#218)

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nadare 2023-05-04 23:03:52 +09:00 committed by GitHub
parent c4a18107dc
commit b18f921a50
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@ -670,6 +670,9 @@ def train_index(exp_dir1):
phone = np.load("%s/%s" % (feature_dir, name)) phone = np.load("%s/%s" % (feature_dir, name))
npys.append(phone) npys.append(phone)
big_npy = np.concatenate(npys, 0) big_npy = np.concatenate(npys, 0)
big_npy_idx = np.arange(big_npy.shape[0])
np.random.shuffle(big_npy_idx)
big_npy = big_npy[big_npy_idx]
np.save("%s/total_fea.npy" % exp_dir, big_npy) np.save("%s/total_fea.npy" % exp_dir, big_npy)
# n_ivf = big_npy.shape[0] // 39 # n_ivf = big_npy.shape[0] // 39
n_ivf = min(int(16 * np.sqrt(big_npy.shape[0])), big_npy.shape[0] // 39) n_ivf = min(int(16 * np.sqrt(big_npy.shape[0])), big_npy.shape[0] // 39)
@ -892,6 +895,9 @@ def train1key(
phone = np.load("%s/%s" % (feature_dir, name)) phone = np.load("%s/%s" % (feature_dir, name))
npys.append(phone) npys.append(phone)
big_npy = np.concatenate(npys, 0) big_npy = np.concatenate(npys, 0)
big_npy_idx = np.arange(big_npy.shape[0])
np.random.shuffle(big_npy_idx)
big_npy = big_npy[big_npy_idx]
np.save("%s/total_fea.npy" % exp_dir, big_npy) np.save("%s/total_fea.npy" % exp_dir, big_npy)
# n_ivf = big_npy.shape[0] // 39 # n_ivf = big_npy.shape[0] // 39
n_ivf = min(int(16 * np.sqrt(big_npy.shape[0])), big_npy.shape[0] // 39) n_ivf = min(int(16 * np.sqrt(big_npy.shape[0])), big_npy.shape[0] // 39)