make_stl10_whitened.py

"""
This script makes a dataset of 32x32 approximately whitened STL-10 images.

It assumes that you have already run make_downsampled_stl10.py,
which downsamples the STL-10 images to 1/3 of their original resolution.

"""

from __future__ import print_function

from pylearn2.utils import serial
from pylearn2.datasets import preprocessing
from pylearn2.utils import string_utils as string
import numpy as np
import textwrap


def main():
    data_dir = string.preprocess('${PYLEARN2_DATA_PATH}/stl10')

    print('Loading STL-10 unlabeled and train datasets...')
    downsampled_dir = data_dir + '/stl10_32x32'

    data = serial.load(downsampled_dir + '/unlabeled.pkl')
    supplement = serial.load(downsampled_dir + '/train.pkl')

    print('Concatenating datasets...')
    data.set_design_matrix(np.concatenate((data.X, supplement.X), axis=0))

    print("Preparing output directory...")
    output_dir = data_dir + '/stl10_32x32_whitened'
    serial.mkdir(output_dir)
    README = open(output_dir + '/README', 'w')

    README.write(textwrap.dedent("""
    The .pkl files in this directory may be opened in python using
    cPickle, pickle, or pylearn2.serial.load.

    unsupervised.pkl, unlabeled.pkl, train.pkl, and test.pkl each contain
    a pylearn2 Dataset object defining an unlabeled
    dataset of a 32x32 approximately whitened version of the STL-10
    dataset. unlabeled.pkl contains unlabeled train examples. train.pkl
    contains labeled train examples. unsupervised.pkl contains the union
    of these (without any labels). test.pkl contains the labeled test
    examples.

    preprocessor.pkl contains a pylearn2 ZCA object that was used
    to approximately whiten the images. You may want to use this
    object later to preprocess other images.

    They were created with the pylearn2 script make_stl10_whitened.py.

    All other files in this directory, including this README, were
    created by the same script and are necessary for the other files
    to function correctly.
    """))

    README.close()

    print("Learning the preprocessor \
          and preprocessing the unsupervised train data...")
    preprocessor = preprocessing.ZCA()
    data.apply_preprocessor(preprocessor=preprocessor, can_fit=True)

    print('Saving the unsupervised data')
    data.use_design_loc(output_dir+'/unsupervised.npy')
    serial.save(output_dir + '/unsupervised.pkl', data)

    X = data.X
    unlabeled = X[0:100*1000, :]
    labeled = X[100*1000:, :]
    del X

    print("Saving the unlabeled data")
    data.X = unlabeled
    data.use_design_loc(output_dir + '/unlabeled.npy')
    serial.save(output_dir + '/unlabeled.pkl', data)
    del data
    del unlabeled

    print("Saving the labeled train data")
    supplement.X = labeled
    supplement.use_design_loc(output_dir+'/train.npy')
    serial.save(output_dir+'/train.pkl', supplement)
    del supplement
    del labeled

    print("Loading the test data")
    test = serial.load(downsampled_dir + '/test.pkl')

    print("Preprocessing the test data")
    test.apply_preprocessor(preprocessor=preprocessor, can_fit=False)

    print("Saving the test data")
    test.use_design_loc(output_dir+'/test.npy')
    serial.save(output_dir+'/test.pkl', test)

    serial.save(output_dir + '/preprocessor.pkl', preprocessor)

if __name__ == "__main__":
    main()