create_temp_tar() - Code Metrics - Inspection of "Merge pull request #316 from aam-at/feature/ilsvrc..." - mila-udem/fuel - Measure and Improve Code Quality continuously with Scrutinizer

Completed

Push — master ( 566e5f...bf577a )

by David

created 2016-10-12 23:30 UTC

create_temp_tar() A

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Importance

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cc	2
c	0
b	0
f	0
dl	0
loc	9
rs	9.6666

from __future__ import division
import logging
import os.path
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager

import h5py
import numpy
from scipy.io.matlab import loadmat
from six.moves import zip, xrange


from fuel import config
from fuel.converters.base import check_exists
from fuel.datasets import H5PYDataset
from fuel.utils.formats import tar_open
from .ilsvrc2010 import (process_train_set,
                         process_other_set)

log = logging.getLogger(__name__)


DEVKIT_ARCHIVE = 'ILSVRC2012_devkit_t12.tar.gz'
DEVKIT_META_PATH = 'ILSVRC2012_devkit_t12/data/meta.mat'
DEVKIT_VALID_GROUNDTRUTH_PATH = ('ILSVRC2012_devkit_t12/data/'
                                 'ILSVRC2012_validation_ground_truth.txt')
TRAIN_IMAGES_TAR = 'ILSVRC2012_img_train.tar'
VALID_IMAGES_TAR = 'ILSVRC2012_img_val.tar'
TEST_IMAGES_TAR = 'ILSVRC2012_img_test.tar'
IMAGE_TARS = (TRAIN_IMAGES_TAR, VALID_IMAGES_TAR, TEST_IMAGES_TAR)
ALL_FILES = (DEVKIT_ARCHIVE,) + IMAGE_TARS


@check_exists(required_files=ALL_FILES)

def convert_ilsvrc2012(directory, output_directory,
                       output_filename='ilsvrc2012.hdf5',
                       shuffle_seed=config.default_seed):
    """Converter for data from the ILSVRC 2012 competition.

    Source files for this dataset can be obtained by registering at
    [ILSVRC2012WEB].

    Parameters
    ----------
    input_directory : str
        Path from which to read raw data files.
    output_directory : str
        Path to which to save the HDF5 file.
    output_filename : str, optional
        The output filename for the HDF5 file. Default: 'ilsvrc2012.hdf5'.
    shuffle_seed : int or sequence, optional
        Seed for a random number generator used to shuffle the order
        of the training set on disk, so that sequential reads will not
        be ordered by class.

    .. [ILSVRC2012WEB] http://image-net.org/challenges/LSVRC/2012/index

    """
    devkit_path = os.path.join(directory, DEVKIT_ARCHIVE)
    train, valid, test = [os.path.join(directory, fn) for fn in IMAGE_TARS]
    n_train, valid_groundtruth, n_test, wnid_map = prepare_metadata(
        devkit_path)
    n_valid = len(valid_groundtruth)
    output_path = os.path.join(output_directory, output_filename)

    with h5py.File(output_path, 'w') as f, create_temp_tar() as patch:
        log.info('Creating HDF5 datasets...')
        prepare_hdf5_file(f, n_train, n_valid, n_test)
        log.info('Processing training set...')
        process_train_set(f, train, patch, n_train, wnid_map, shuffle_seed)
        log.info('Processing validation set...')
        process_other_set(f, 'valid', valid, patch, valid_groundtruth, n_train)
        log.info('Processing test set...')
        process_other_set(f, 'test', test, patch, (None,) * n_test,
                          n_train + n_valid)
        log.info('Done.')

    return (output_path,)


def fill_subparser(subparser):
    """Sets up a subparser to convert the ILSVRC2012 dataset files.

    Parameters
    ----------
    subparser : :class:`argparse.ArgumentParser`
        Subparser handling the `ilsvrc2012` command.

    """
    subparser.add_argument(
        "--shuffle-seed", help="Seed to use for randomizing order of the "
                               "training set on disk.",
        default=config.default_seed, type=int, required=False)
    return convert_ilsvrc2012


def prepare_metadata(devkit_archive):

    """Extract dataset metadata required for HDF5 file setup.

    Parameters
    ----------
    devkit_archive : str or file-like object
        The filename or file-handle for the gzipped TAR archive
        containing the ILSVRC2012 development kit.

    Returns
    -------
    n_train : int
        The number of examples in the training set.
    valid_groundtruth : ndarray, 1-dimensional
        An ndarray containing the validation set groundtruth in terms of
        0-based class indices.
    n_test : int
        The number of examples in the test set
    wnid_map : dict
        A dictionary that maps WordNet IDs to 0-based class indices.

    """
    # Read what's necessary from the development kit.
    synsets, raw_valid_groundtruth = read_devkit(devkit_archive)

    # Mapping to take WordNet IDs to our internal 0-999 encoding.
    wnid_map = dict(zip((s.decode('utf8') for s in synsets['WNID']),
                        xrange(1000)))

    # Map the 'ILSVRC2012 ID' to our zero-based ID.
    ilsvrc_id_to_zero_based = dict(zip(synsets['ILSVRC2012_ID'],
                                       xrange(len(synsets))))

    # Map the validation set groundtruth to 0-999 labels.
    valid_groundtruth = [ilsvrc_id_to_zero_based[id_]
                         for id_ in raw_valid_groundtruth]

    # Get number of test examples from the test archive
    with tar_open(TEST_IMAGES_TAR) as f:
        n_test = sum(1 for _ in f)

    # Ascertain the number of filenames to prepare appropriate sized
    # arrays.
    n_train = int(synsets['num_train_images'].sum())
    log.info('Training set: {} images'.format(n_train))
    log.info('Validation set: {} images'.format(len(valid_groundtruth)))
    log.info('Test set: {} images'.format(n_test))
    n_total = n_train + len(valid_groundtruth) + n_test
    log.info('Total (train/valid): {} images'.format(n_total))
    return n_train, valid_groundtruth, n_test, wnid_map


def create_splits(n_train, n_valid, n_test):

    n_total = n_train + n_valid + n_test
    tuples = {}
    tuples['train'] = (0, n_train)
    tuples['valid'] = (n_train, n_train + n_valid)
    tuples['test'] = (n_train + n_valid, n_total)
    sources = ['encoded_images', 'targets', 'filenames']
    return OrderedDict(
        (split, OrderedDict((source, tuples[split]) for source in sources
                            if source != 'targets' or split != 'test'))
        for split in ('train', 'valid', 'test')
    )


@contextmanager
def create_temp_tar():
    try:
        _, temp_tar = tempfile.mkstemp(suffix='.tar')
        with tarfile.open(temp_tar, mode='w') as tar:
            tar.addfile(tarfile.TarInfo())
        yield temp_tar
    finally:
        os.remove(temp_tar)


def prepare_hdf5_file(hdf5_file, n_train, n_valid, n_test):

    """Create datasets within a given HDF5 file.

    Parameters
    ----------
    hdf5_file : :class:`h5py.File` instance
        HDF5 file handle to which to write.
    n_train : int
        The number of training set examples.
    n_valid : int
        The number of validation set examples.
    n_test : int
        The number of test set examples.

    """
    n_total = n_train + n_valid + n_test
    n_labeled = n_train + n_valid
    splits = create_splits(n_train, n_valid, n_test)
    hdf5_file.attrs['split'] = H5PYDataset.create_split_array(splits)
    vlen_dtype = h5py.special_dtype(vlen=numpy.dtype('uint8'))
    hdf5_file.create_dataset('encoded_images', shape=(n_total,),
                             dtype=vlen_dtype)
    hdf5_file.create_dataset('targets', shape=(n_labeled, 1),
                             dtype=numpy.int16)
    hdf5_file.create_dataset('filenames', shape=(n_total, 1), dtype='S32')


def read_devkit(f):
    """Read relevant information from the development kit archive.

    Parameters
    ----------
    f : str or file-like object
        The filename or file-handle for the gzipped TAR archive
        containing the ILSVRC2012 development kit.

    Returns
    -------
    synsets : ndarray, 1-dimensional, compound dtype
        See :func:`read_metadata_mat_file` for details.
    raw_valid_groundtruth : ndarray, 1-dimensional, int16
        The labels for the ILSVRC2012 validation set,
        distributed with the development kit code.

    """
    with tar_open(f) as tar:
        # Metadata table containing class hierarchy, textual descriptions, etc.
        meta_mat = tar.extractfile(DEVKIT_META_PATH)
        synsets = read_metadata_mat_file(meta_mat)

        # Raw validation data groundtruth, ILSVRC2012 IDs. Confusingly
        # distributed inside the development kit archive.
        raw_valid_groundtruth = numpy.loadtxt(tar.extractfile(
            DEVKIT_VALID_GROUNDTRUTH_PATH), dtype=numpy.int16)
    return synsets, raw_valid_groundtruth


def read_metadata_mat_file(meta_mat):

    """Read ILSVRC2012 metadata from the distributed MAT file.

    Parameters
    ----------
    meta_mat : str or file-like object
        The filename or file-handle for `meta.mat` from the
        ILSVRC2012 development kit.

    Returns
    -------
    synsets : ndarray, 1-dimensional, compound dtype
        A table containing ILSVRC2012 metadata for the "synonym sets"
        or "synsets" that comprise the classes and superclasses,
        including the following fields:
         * `ILSVRC2012_ID`: the integer ID used in the original
           competition data.
         * `WNID`: A string identifier that uniquely identifies
           a synset in ImageNet and WordNet.
         * `wordnet_height`: The length of the longest path to
           a leaf node in the FULL ImageNet/WordNet hierarchy
           (leaf nodes in the FULL ImageNet/WordNet hierarchy
           have `wordnet_height` 0).
         * `gloss`: A string representation of an English
           textual description of the concept represented by
           this synset.
         * `num_children`: The number of children in the hierarchy
           for this synset.
         * `words`: A string representation, comma separated,
           of different synoym words or phrases for the concept
           represented by this synset.
         * `children`: A vector of `ILSVRC2012_ID`s of children
           of this synset, padded with -1. Note that these refer
           to `ILSVRC2012_ID`s from the original data and *not*
           the zero-based index in the table.
         * `num_train_images`: The number of training images for
           this synset.

    """
    mat = loadmat(meta_mat, squeeze_me=True)
    synsets = mat['synsets']
    new_dtype = numpy.dtype([
        ('ILSVRC2012_ID', numpy.int16),
        ('WNID', ('S', max(map(len, synsets['WNID'])))),
        ('wordnet_height', numpy.int8),
        ('gloss', ('S', max(map(len, synsets['gloss'])))),
        ('num_children', numpy.int8),
        ('words', ('S', max(map(len, synsets['words'])))),
        ('children', (numpy.int8, max(synsets['num_children']))),
        ('num_train_images', numpy.uint16)
    ])
    new_synsets = numpy.empty(synsets.shape, dtype=new_dtype)
    for attr in ['ILSVRC2012_ID', 'WNID', 'wordnet_height', 'gloss',
                 'num_children', 'words', 'num_train_images']:
        new_synsets[attr] = synsets[attr]
    children = [numpy.atleast_1d(ch) for ch in synsets['children']]
    padded_children = [
        numpy.concatenate((c,
                           -numpy.ones(new_dtype['children'].shape[0] - len(c),
                                       dtype=numpy.int16)))
        for c in children
    ]
    new_synsets['children'] = padded_children
    return new_synsets


1		from __future__ import division
2		import logging
3		import os.path
4		import tarfile
5		import tempfile
6		from collections import OrderedDict
7		from contextlib import contextmanager
8
9		import h5py
10		import numpy
11		from scipy.io.matlab import loadmat
12		from six.moves import zip, xrange
		0 ignored issues – show Bug Best Practice introduced 2016-02-24 06:49 UTC by Report Bug Copy Issue Report This seems to re-define the built-in `zip`. It is generally discouraged to redefine built-ins as this makes code very hard to read. Loading history...
13
14		from fuel import config
15		from fuel.converters.base import check_exists
16		from fuel.datasets import H5PYDataset
17		from fuel.utils.formats import tar_open
18		from .ilsvrc2010 import (process_train_set,
19		process_other_set)
20
21		log = logging.getLogger(__name__)
		0 ignored issues – show Coding Style Naming introduced 2016-02-24 06:49 UTC by Report Bug Copy Issue Report The name `log` does not conform to the constant naming conventions (`(([A-Z_][A-Z0-9_])\|(__.__))$`). This check looks for invalid names for a range of different identifiers. You can set regular expressions to which the identifiers must conform if the defaults do not match your requirements. If your project includes a Pylint configuration file, the settings contained in that file take precedence. To find out more about Pylint, please refer to their site. Loading history...
22
23		DEVKIT_ARCHIVE = 'ILSVRC2012_devkit_t12.tar.gz'
24		DEVKIT_META_PATH = 'ILSVRC2012_devkit_t12/data/meta.mat'
25		DEVKIT_VALID_GROUNDTRUTH_PATH = ('ILSVRC2012_devkit_t12/data/'
26		'ILSVRC2012_validation_ground_truth.txt')
27		TRAIN_IMAGES_TAR = 'ILSVRC2012_img_train.tar'
28		VALID_IMAGES_TAR = 'ILSVRC2012_img_val.tar'
29		TEST_IMAGES_TAR = 'ILSVRC2012_img_test.tar'
30		IMAGE_TARS = (TRAIN_IMAGES_TAR, VALID_IMAGES_TAR, TEST_IMAGES_TAR)
31		ALL_FILES = (DEVKIT_ARCHIVE,) + IMAGE_TARS
32
33
34	View Code Duplication	@check_exists(required_files=ALL_FILES)
		0 ignored issues – show Duplication introduced 2016-10-12 23:31 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
35		def convert_ilsvrc2012(directory, output_directory,
36		output_filename='ilsvrc2012.hdf5',
37		shuffle_seed=config.default_seed):
38		"""Converter for data from the ILSVRC 2012 competition.
39
40		Source files for this dataset can be obtained by registering at
41		[ILSVRC2012WEB].
42
43		Parameters
44		----------
45		input_directory : str
46		Path from which to read raw data files.
47		output_directory : str
48		Path to which to save the HDF5 file.
49		output_filename : str, optional
50		The output filename for the HDF5 file. Default: 'ilsvrc2012.hdf5'.
51		shuffle_seed : int or sequence, optional
52		Seed for a random number generator used to shuffle the order
53		of the training set on disk, so that sequential reads will not
54		be ordered by class.
55
56		.. [ILSVRC2012WEB] http://image-net.org/challenges/LSVRC/2012/index
57
58		"""
59		devkit_path = os.path.join(directory, DEVKIT_ARCHIVE)
60		train, valid, test = [os.path.join(directory, fn) for fn in IMAGE_TARS]
61		n_train, valid_groundtruth, n_test, wnid_map = prepare_metadata(
62		devkit_path)
63		n_valid = len(valid_groundtruth)
64		output_path = os.path.join(output_directory, output_filename)
65
66		with h5py.File(output_path, 'w') as f, create_temp_tar() as patch:
67		log.info('Creating HDF5 datasets...')
68		prepare_hdf5_file(f, n_train, n_valid, n_test)
69		log.info('Processing training set...')
70		process_train_set(f, train, patch, n_train, wnid_map, shuffle_seed)
71		log.info('Processing validation set...')
72		process_other_set(f, 'valid', valid, patch, valid_groundtruth, n_train)
73		log.info('Processing test set...')
74		process_other_set(f, 'test', test, patch, (None,) * n_test,
75		n_train + n_valid)
76		log.info('Done.')
77
78		return (output_path,)
79
80
81		def fill_subparser(subparser):
82		"""Sets up a subparser to convert the ILSVRC2012 dataset files.
83
84		Parameters
85		----------
86		subparser : :class:`argparse.ArgumentParser`
87		Subparser handling the `ilsvrc2012` command.
88
89		"""
90		subparser.add_argument(
91		"--shuffle-seed", help="Seed to use for randomizing order of the "
92		"training set on disk.",
93		default=config.default_seed, type=int, required=False)
94		return convert_ilsvrc2012
95
96
97	View Code Duplication	def prepare_metadata(devkit_archive):
		0 ignored issues – show Duplication introduced 2016-10-12 23:31 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
98		"""Extract dataset metadata required for HDF5 file setup.
99
100		Parameters
101		----------
102		devkit_archive : str or file-like object
103		The filename or file-handle for the gzipped TAR archive
104		containing the ILSVRC2012 development kit.
105
106		Returns
107		-------
108		n_train : int
109		The number of examples in the training set.
110		valid_groundtruth : ndarray, 1-dimensional
111		An ndarray containing the validation set groundtruth in terms of
112		0-based class indices.
113		n_test : int
114		The number of examples in the test set
115		wnid_map : dict
116		A dictionary that maps WordNet IDs to 0-based class indices.
117
118		"""
119		# Read what's necessary from the development kit.
120		synsets, raw_valid_groundtruth = read_devkit(devkit_archive)
121
122		# Mapping to take WordNet IDs to our internal 0-999 encoding.
123		wnid_map = dict(zip((s.decode('utf8') for s in synsets['WNID']),
124		xrange(1000)))
125
126		# Map the 'ILSVRC2012 ID' to our zero-based ID.
127		ilsvrc_id_to_zero_based = dict(zip(synsets['ILSVRC2012_ID'],
128		xrange(len(synsets))))
129
130		# Map the validation set groundtruth to 0-999 labels.
131		valid_groundtruth = [ilsvrc_id_to_zero_based[id_]
132		for id_ in raw_valid_groundtruth]
133
134		# Get number of test examples from the test archive
135		with tar_open(TEST_IMAGES_TAR) as f:
136		n_test = sum(1 for _ in f)
137
138		# Ascertain the number of filenames to prepare appropriate sized
139		# arrays.
140		n_train = int(synsets['num_train_images'].sum())
141		log.info('Training set: {} images'.format(n_train))
142		log.info('Validation set: {} images'.format(len(valid_groundtruth)))
143		log.info('Test set: {} images'.format(n_test))
144		n_total = n_train + len(valid_groundtruth) + n_test
145		log.info('Total (train/valid): {} images'.format(n_total))
146		return n_train, valid_groundtruth, n_test, wnid_map
147
148
149	View Code Duplication	def create_splits(n_train, n_valid, n_test):
		0 ignored issues – show Duplication introduced 2016-10-12 23:31 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
150		n_total = n_train + n_valid + n_test
151		tuples = {}
152		tuples['train'] = (0, n_train)
153		tuples['valid'] = (n_train, n_train + n_valid)
154		tuples['test'] = (n_train + n_valid, n_total)
155		sources = ['encoded_images', 'targets', 'filenames']
156		return OrderedDict(
157		(split, OrderedDict((source, tuples[split]) for source in sources
158		if source != 'targets' or split != 'test'))
159		for split in ('train', 'valid', 'test')
160		)
161
162
163		@contextmanager
164		def create_temp_tar():
165		try:
166		_, temp_tar = tempfile.mkstemp(suffix='.tar')
167		with tarfile.open(temp_tar, mode='w') as tar:
168		tar.addfile(tarfile.TarInfo())
169		yield temp_tar
170		finally:
171		os.remove(temp_tar)
172
173
174	View Code Duplication	def prepare_hdf5_file(hdf5_file, n_train, n_valid, n_test):
		0 ignored issues – show Duplication introduced 2016-10-12 23:31 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
175		"""Create datasets within a given HDF5 file.
176
177		Parameters
178		----------
179		hdf5_file : :class:`h5py.File` instance
180		HDF5 file handle to which to write.
181		n_train : int
182		The number of training set examples.
183		n_valid : int
184		The number of validation set examples.
185		n_test : int
186		The number of test set examples.
187
188		"""
189		n_total = n_train + n_valid + n_test
190		n_labeled = n_train + n_valid
191		splits = create_splits(n_train, n_valid, n_test)
192		hdf5_file.attrs['split'] = H5PYDataset.create_split_array(splits)
193		vlen_dtype = h5py.special_dtype(vlen=numpy.dtype('uint8'))
194		hdf5_file.create_dataset('encoded_images', shape=(n_total,),
195		dtype=vlen_dtype)
196		hdf5_file.create_dataset('targets', shape=(n_labeled, 1),
197		dtype=numpy.int16)
198		hdf5_file.create_dataset('filenames', shape=(n_total, 1), dtype='S32')
199
200
201		def read_devkit(f):
202		"""Read relevant information from the development kit archive.
203
204		Parameters
205		----------
206		f : str or file-like object
207		The filename or file-handle for the gzipped TAR archive
208		containing the ILSVRC2012 development kit.
209
210		Returns
211		-------
212		synsets : ndarray, 1-dimensional, compound dtype
213		See :func:`read_metadata_mat_file` for details.
214		raw_valid_groundtruth : ndarray, 1-dimensional, int16
215		The labels for the ILSVRC2012 validation set,
216		distributed with the development kit code.
217
218		"""
219		with tar_open(f) as tar:
220		# Metadata table containing class hierarchy, textual descriptions, etc.
221		meta_mat = tar.extractfile(DEVKIT_META_PATH)
222		synsets = read_metadata_mat_file(meta_mat)
223
224		# Raw validation data groundtruth, ILSVRC2012 IDs. Confusingly
225		# distributed inside the development kit archive.
226		raw_valid_groundtruth = numpy.loadtxt(tar.extractfile(
227		DEVKIT_VALID_GROUNDTRUTH_PATH), dtype=numpy.int16)
228		return synsets, raw_valid_groundtruth
229
230
231	View Code Duplication	def read_metadata_mat_file(meta_mat):
		0 ignored issues – show Duplication introduced 2016-10-12 23:31 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
232		"""Read ILSVRC2012 metadata from the distributed MAT file.
233
234		Parameters
235		----------
236		meta_mat : str or file-like object
237		The filename or file-handle for `meta.mat` from the
238		ILSVRC2012 development kit.
239
240		Returns
241		-------
242		synsets : ndarray, 1-dimensional, compound dtype
243		A table containing ILSVRC2012 metadata for the "synonym sets"
244		or "synsets" that comprise the classes and superclasses,
245		including the following fields:
246		* `ILSVRC2012_ID`: the integer ID used in the original
247		competition data.
248		* `WNID`: A string identifier that uniquely identifies
249		a synset in ImageNet and WordNet.
250		* `wordnet_height`: The length of the longest path to
251		a leaf node in the FULL ImageNet/WordNet hierarchy
252		(leaf nodes in the FULL ImageNet/WordNet hierarchy
253		have `wordnet_height` 0).
254		* `gloss`: A string representation of an English
255		textual description of the concept represented by
256		this synset.
257		* `num_children`: The number of children in the hierarchy
258		for this synset.
259		* `words`: A string representation, comma separated,
260		of different synoym words or phrases for the concept
261		represented by this synset.
262		* `children`: A vector of `ILSVRC2012_ID`s of children
263		of this synset, padded with -1. Note that these refer
264		to `ILSVRC2012_ID`s from the original data and not
265		the zero-based index in the table.
266		* `num_train_images`: The number of training images for
267		this synset.
268
269		"""
270		mat = loadmat(meta_mat, squeeze_me=True)
271		synsets = mat['synsets']
272		new_dtype = numpy.dtype([
273		('ILSVRC2012_ID', numpy.int16),
274		('WNID', ('S', max(map(len, synsets['WNID'])))),
275		('wordnet_height', numpy.int8),
276		('gloss', ('S', max(map(len, synsets['gloss'])))),
277		('num_children', numpy.int8),
278		('words', ('S', max(map(len, synsets['words'])))),
279		('children', (numpy.int8, max(synsets['num_children']))),
280		('num_train_images', numpy.uint16)
281		])
282		new_synsets = numpy.empty(synsets.shape, dtype=new_dtype)
283		for attr in ['ILSVRC2012_ID', 'WNID', 'wordnet_height', 'gloss',
284		'num_children', 'words', 'num_train_images']:
285		new_synsets[attr] = synsets[attr]
286		children = [numpy.atleast_1d(ch) for ch in synsets['children']]
287		padded_children = [
288		numpy.concatenate((c,
289		-numpy.ones(new_dtype['children'].shape[0] - len(c),
290		dtype=numpy.int16)))
291		for c in children
292		]
293		new_synsets['children'] = padded_children
294		return new_synsets
295

mila-udem / fuel

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create_temp_tar() A

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