90 lines
2.1 KiB
Python
90 lines
2.1 KiB
Python
#! /usr/bin/env python2
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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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from __future__ import print_function
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import numpy as np
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import time
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import faiss
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import sys
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# Get command-line arguments
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k = int(sys.argv[1])
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ngpu = int(sys.argv[2])
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# Load Leon's file format
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def load_mnist(fname):
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print("load", fname)
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f = open(fname)
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header = np.fromfile(f, dtype='int8', count=4*4)
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header = header.reshape(4, 4)[:, ::-1].copy().view('int32')
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print(header)
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nim, xd, yd = [int(x) for x in header[1:]]
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data = np.fromfile(f, count=nim * xd * yd,
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dtype='uint8')
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print(data.shape, nim, xd, yd)
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data = data.reshape(nim, xd, yd)
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return data
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basedir = "/path/to/mnist/data"
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x = load_mnist(basedir + 'mnist8m/mnist8m-patterns-idx3-ubyte')
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print("reshape")
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x = x.reshape(x.shape[0], -1).astype('float32')
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def train_kmeans(x, k, ngpu):
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"Runs kmeans on one or several GPUs"
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d = x.shape[1]
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clus = faiss.Clustering(d, k)
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clus.verbose = True
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clus.niter = 20
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# otherwise the kmeans implementation sub-samples the training set
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clus.max_points_per_centroid = 10000000
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res = [faiss.StandardGpuResources() for i in range(ngpu)]
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flat_config = []
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for i in range(ngpu):
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cfg = faiss.GpuIndexFlatConfig()
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cfg.useFloat16 = False
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cfg.device = i
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flat_config.append(cfg)
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if ngpu == 1:
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index = faiss.GpuIndexFlatL2(res[0], d, flat_config[0])
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else:
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indexes = [faiss.GpuIndexFlatL2(res[i], d, flat_config[i])
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for i in range(ngpu)]
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index = faiss.IndexReplicas()
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for sub_index in indexes:
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index.addIndex(sub_index)
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# perform the training
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clus.train(x, index)
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centroids = faiss.vector_float_to_array(clus.centroids)
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obj = faiss.vector_float_to_array(clus.obj)
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print("final objective: %.4g" % obj[-1])
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return centroids.reshape(k, d)
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print("run")
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t0 = time.time()
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train_kmeans(x, k, ngpu)
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t1 = time.time()
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print("total runtime: %.3f s" % (t1 - t0))
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