Add a large MJSynth dataset of cropped words images

fuel/converters/__init__.py

@@ -22,6 +22,7 @@
 from fuel.converters import svhn
 from fuel.converters import ilsvrc2010
 from fuel.converters import youtube_audio
+from fuel.converters import mjsynth
 
 __version__ = '0.2'
 all_converters = (
@@ -36,4 +37,5 @@
  ('mnist', mnist.fill_subparser),
  ('svhn', svhn.fill_subparser),
  ('ilsvrc2010', ilsvrc2010.fill_subparser),
- ('youtube_audio', youtube_audio.fill_subparser))
+ ('youtube_audio', youtube_audio.fill_subparser),
+ ('mjsynth', mjsynth.fill_subparser))

fuel/converters/mjsynth.py

@@ -0,0 +1,202 @@
+import io
+import os
+import tarfile
+import linecache
+import shutil
+
+import h5py
+import numpy
+from PIL import Image
+
+from fuel.converters.base import progress_bar, check_exists
+from fuel.datasets.hdf5 import H5PYDataset
+
+DISTRIBUTION_FILE = 'mjsynth.tar.gz'
+TAR_PATH = 'mnt/ramdisk/max/90kDICT32px'
+TRAIN_SIZE = 7224586
+TEST_SIZE = 891924
+VAL_SIZE = 802731
+BAD = {'train': ['./2194/2/334_EFFLORESCENT_24742.jpg',
+ './2128/2/369_REDACTED_63458.jpg',
+ './2069/4/192_whittier_86389.jpg',
+ './2025/2/364_SNORTERS_72304.jpg',
+ './2013/2/370_refract_63890.jpg',
+ './1881/4/225_Marbling_46673.jpg',
+ './1863/4/223_Diligently_21672.jpg',
+ './1817/2/363_actuating_904.jpg',
+ './1730/2/361_HEREON_35880.jpg',
+ './1696/4/211_Queened_61779.jpg',
+ './1650/2/355_stony_74902.jpg',
+ './1332/4/224_TETHERED_78397.jpg',
+ './936/2/375_LOCALITIES_44992.jpg',
+ './913/4/231_randoms_62372.jpg',
+ './905/4/234_Postscripts_59142.jpg',
+ './869/4/234_TRIASSIC_80582.jpg',
+ './627/6/83_PATRIARCHATE_55931.jpg',
+ './596/2/372_Ump_81662.jpg',
+ './554/2/366_Teleconferences_77948.jpg',
+ './495/6/81_MIDYEAR_48332.jpg',
+ './429/4/208_Mainmasts_46140.jpg',
+ './384/4/220_bolts_8596.jpg',
+ './368/4/232_friar_30876.jpg',
+ './275/6/96_hackle_34465.jpg',
+ './264/2/362_FORETASTE_30276.jpg',
+ './173/2/358_BURROWING_10395.jpg'],
+ 'test': ['./2911/6/77_heretical_35885.jpg',
+ './2852/6/60_TOILSOME_79481.jpg',
+ './2749/6/101_Chided_13155.jpg', ],
+ 'val': ['./2557/2/351_DOWN_23492.jpg',
+ './2540/4/246_SQUAMOUS_73902.jpg',
+ './2489/4/221_snored_72290.jpg']}
+
+
+@check_exists(required_files=[DISTRIBUTION_FILE])
+def convert_mjsynth(directory, output_directory,
+ output_filename='mjsynth.hdf5'):
+ """Converts the MJSynth dataset to HDF5.
+
+ Converts the MJSynth dataset to an HDF5 dataset compatible with
+ :class:`fuel.datasets.MJSynth`. The converted dataset is saved as
+ 'mjsynth.hdf5'.
+
+ It assumes the existence of the following file:
+
+ * `mjsynth.tar.gz`
+
+ Parameters
+ ----------
+ directory : str
+ Directory in which input files reside.
+ output_directory : str
+ Directory in which to save the converted dataset.
+ output_filename : str, optional
+ Name of the saved dataset. Defaults to 'mjsynth.hdf5'.
+
+ Returns
+ -------
+ output_paths : tuple of str
+ Single-element tuple containing the path to the converted dataset.
+
+ """
+ num_examples = TRAIN_SIZE + TEST_SIZE + VAL_SIZE
+ output_path = os.path.join(output_directory, output_filename)
+ h5file = h5py.File(output_path, mode='w')
+ dtype = h5py.special_dtype(vlen=numpy.dtype('uint8'))
+ hdf_features = h5file.create_dataset('features', (num_examples,),
+ dtype=dtype)
+ hdf_features_shapes = h5file.create_dataset('features_shapes',
+ (num_examples, 3),
+ dtype='int32')
+ dtype = h5py.special_dtype(vlen=numpy.dtype('S1'))
+ hdf_labels = h5file.create_dataset('targets', (num_examples,), dtype=dtype)
+ hdf_labels_shapes = h5file.create_dataset('targets_shapes',
+ (num_examples, 1),
+ dtype='int32')
+
+ # Attach shape annotations and scales
+ hdf_features.dims.create_scale(hdf_features_shapes, 'shapes')
+ hdf_features.dims[0].attach_scale(hdf_features_shapes)
+
+ hdf_labels.dims.create_scale(hdf_labels_shapes, 'shapes')
+ hdf_labels.dims[0].attach_scale(hdf_labels_shapes)
+
+ hdf_shapes_labels = h5file.create_dataset('features_shapes_labels',
+ (3,), dtype='S7')
+ hdf_shapes_labels[...] = ['channel'.encode('utf8'),
+ 'height'.encode('utf8'),
+ 'width'.encode('utf8')]
+ hdf_features.dims.create_scale(hdf_shapes_labels, 'shape_labels')
+ hdf_features.dims[0].attach_scale(hdf_shapes_labels)
+
+ hdf_shapes_labels = h5file.create_dataset('targets_shapes_labels',
+ (1,), dtype='S5')
+
+ hdf_shapes_labels[...] = ['index'.encode('utf8')]
+ hdf_labels.dims.create_scale(hdf_shapes_labels, 'shape_labels')
+ hdf_labels.dims[0].attach_scale(hdf_shapes_labels)
+
+ # Add axis annotations
+ hdf_features.dims[0].label = 'batch'
+ hdf_labels.dims[0].label = 'batch'
+
+ # Extract the temp files from TAR
+ if not os.path.exists(os.path.join(output_directory, TAR_PATH)):
+ input_file = os.path.join(directory, DISTRIBUTION_FILE)
+ with progress_bar('tar', os.path.getsize(input_file),
+ prefix='Extracting') as bar:
+ class ProgressFileObject(io.FileIO):
+ def read(self, size=-1):
+ bar.update(self.tell())
+ return io.FileIO.read(self, size)
+
+ tar_file = tarfile.open(fileobj=ProgressFileObject(input_file))
+ tar_file.extractall(path=output_directory)
+ tar_file.close()
+
+ # Convert
+ i = 0
+ for split, split_size in zip(['train', 'test', 'val'],
+ [TRAIN_SIZE, TEST_SIZE, VAL_SIZE]):
+ annotation_file = os.path.join(output_directory, TAR_PATH,
+ 'annotation_%s.txt' % split)
+ # Convert from JPEG to NumPy arrays
+ with progress_bar(split, split_size) as bar:
+ for line in open(annotation_file):
+ # Save image
+ filename, word_index = line.split()
+ if filename not in BAD[split]:
+ image = numpy.array(
+ Image.open(
+ os.path.join(output_directory, TAR_PATH,
+ filename[2:])))
+
+ image = image.transpose(2, 0, 1)
+ hdf_features[i] = image.flatten()
+ hdf_features_shapes[i] = image.shape
+
+ # Read word from lexicon without '\n'
+ word = list(linecache.getline(
+ os.path.join(output_directory, TAR_PATH,
+ 'lexicon.txt'),
+ int(word_index))[:-1])
+ hdf_labels[i] = numpy.array(word)
+ hdf_labels_shapes[i] = len(word)
+
+ if i % 1000 == 0:
+ h5file.flush()
+ # Update progress
+ i += 1
+ value = i if split == 'train' else \
+ i - TRAIN_SIZE if split == 'test' else \
+ i - TRAIN_SIZE - TEST_SIZE
+ bar.update(value)
+
+ # Add the labels
+ split_dict = {}
+ sources = ['features', 'targets']
+ split_dict['train'] = dict(zip(sources, [(0, TRAIN_SIZE)] * 2))
+ split_dict['test'] = dict(
+ zip(sources, [(TRAIN_SIZE, TRAIN_SIZE + TEST_SIZE)] * 2))
+ split_dict['val'] = dict(zip(sources, [
+ (TRAIN_SIZE + TEST_SIZE, TRAIN_SIZE + TEST_SIZE + VAL_SIZE)] * 2))
+ h5file.attrs['split'] = H5PYDataset.create_split_array(split_dict)
+
+ h5file.flush()
+ h5file.close()
+
+ # Remove the extracted temp files
+ shutil.rmtree(os.path.join(directory, TAR_PATH.split('/')[0]))
+
+ return (output_path,)
+
+
+def fill_subparser(subparser):
+ """Sets up a subparser to convert the MJSynth dataset files.
+
+ Parameters
+ ----------
+ subparser : :class:`argparse.ArgumentParser`
+ Subparser handling the `mjsynth` command.
+
+ """
+ return convert_mjsynth

fuel/datasets/__init__.py

@@ -15,3 +15,4 @@
 from fuel.datasets.svhn import SVHN
 from fuel.datasets.text import TextFile
 from fuel.datasets.billion import OneBillionWord
+from fuel.datasets.mjsynth import MJSynth

fuel/datasets/mjsynth.py

@@ -0,0 +1,23 @@
+from fuel.datasets import H5PYDataset
+from fuel.transformers import ScaleAndShift
+from fuel.utils import find_in_data_path
+
+
+class MJSynth(H5PYDataset):
+ """The MJSynth dataset of cropped words images.
+
+ Parameters
+ ----------
+ which_sets : tuple of str
+ Which split to load. Valid values are 'train', 'test' and 'val'.
+
+ """
+ filename = 'mjsynth.hdf5'
+
+ default_transformers = ((ScaleAndShift, [1 / 255.0, 0],
+ {'which_sources': ('features',)}),)
+
+ def __init__(self, which_sets, **kwargs):
+ super(MJSynth, self).__init__(
+ file_or_path=find_in_data_path(self.filename),
+ which_sets=which_sets, **kwargs)

fuel/downloaders/__init__.py

@@ -18,6 +18,7 @@
 from fuel.downloaders import svhn
 from fuel.downloaders import ilsvrc2010
 from fuel.downloaders import youtube_audio
+from fuel.downloaders import mjsynth
 
 all_downloaders = (
  ('adult', adult.fill_subparser),
@@ -31,4 +32,5 @@
  ('svhn', svhn.fill_subparser),
  ('ilsvrc2010', ilsvrc2010.fill_subparser),
  ('dogs_vs_cats', dogs_vs_cats.fill_subparser),
- ('youtube_audio', youtube_audio.fill_subparser))
+ ('youtube_audio', youtube_audio.fill_subparser),
+ ('mjsynth', mjsynth.fill_subparser))

fuel/downloaders/mjsynth.py

@@ -0,0 +1,19 @@
+from fuel.downloaders.base import default_downloader
+
+
+def fill_subparser(subparser):
+ """Set up a subparser to download the MJSynth dataset file.
+
+ The MJSynth dataset file `mjsynth.tar.gz` is downloaded from the VGG
+ Text Recognition Data webpage [VGG].
+
+ Parameters
+ ----------
+ subparser : :class:`argparse.ArgumentParser`
+ Subparser handling the `mjsynth` command.
+
+ """
+ subparser.set_defaults(
+ urls=['http://www.robots.ox.ac.uk/~vgg/data/text/mjsynth.tar.gz'],
+ filenames=['mjsynth.tar.gz'])
+ return default_downloader

tests/test_mjsynth.py

@@ -0,0 +1,50 @@
+import numpy
+
+from fuel.datasets import MJSynth
+from fuel.streams import DataStream
+from fuel.schemes import SequentialScheme
+from tests import skip_if_not_available
+
+
+def test_mjsynth():
+ skip_if_not_available(datasets=['mjsynth.hdf5'])
+
+ train = MJSynth(('train',), load_in_memory=False)
+ assert train.num_examples == 7224586
+ handle = train.open()
+ features, targets = train.get_data(handle, slice(7224576, 7224586))
+
+ assert features.shape[:1] == (10,)
+ assert targets.shape[:1] == (10,)
+ train.close(handle)
+
+ test = MJSynth(('test',), load_in_memory=False)
+ assert test.num_examples == 891924
+ handle = test.open()
+ features, targets = test.get_data(handle, slice(0, 15))
+
+ assert features.shape[:1] == (15,)
+ assert targets.shape[:1] == (15,)
+
+ assert features[0].dtype == numpy.uint8
+ assert targets[0].dtype == numpy.dtype('S1')
+
+ test.close(handle)
+
+ val = MJSynth(('val',), load_in_memory=False)
+ assert val.num_examples == 802731
+ handle = val.open()
+ features, targets = val.get_data(handle, slice(49990, 50000))
+
+ assert features.shape[:1] == (10,)
+ assert targets.shape[:1] == (10,)
+ val.close(handle)
+
+ stream = DataStream.default_stream(
+ test, iteration_scheme=SequentialScheme(10, 10))
+ features = next(stream.get_epoch_iterator())[0]
+
+ def test_feature(feature):
+ assert feature.min() >= 0.0 and feature.max() <= 1.0
+
+ [test_feature(feature) for feature in features]