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