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#!/usr/bin/env python3 -u
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import os
import os.path as osp
import numpy as np
import faiss
def get_parser():
parser = argparse.ArgumentParser(
description="compute a pca matrix given an array of numpy features"
)
# fmt: off
parser.add_argument('data', help='numpy file containing features')
parser.add_argument('--output', help='where to save the pca matrix', required=True)
parser.add_argument('--dim', type=int, help='dim for pca reduction', required=True)
parser.add_argument('--eigen-power', type=float, default=0, help='eigen power, -0.5 for whitening')
return parser
def main():
parser = get_parser()
args = parser.parse_args()
print("Reading features")
x = np.load(args.data, mmap_mode="r")
print("Computing PCA")
pca = faiss.PCAMatrix(x.shape[-1], args.dim, args.eigen_power)
pca.train(x)
b = faiss.vector_to_array(pca.b)
A = faiss.vector_to_array(pca.A).reshape(pca.d_out, pca.d_in)
os.makedirs(args.output, exist_ok=True)
prefix = str(args.dim)
if args.eigen_power != 0:
prefix += f"_{args.eigen_power}"
np.save(osp.join(args.output, f"{prefix}_pca_A"), A.T)
np.save(osp.join(args.output, f"{prefix}_pca_b"), b)
if __name__ == "__main__":
main()
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