chore: import upstream snapshot with attribution

kosmos-2/fairseq/examples/wav2vec/unsupervised/scripts/pca.py

@@ -0,0 +1,53 @@
+#!/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()