Caltech101

fuel/converters/__init__.py

@@ -12,6 +12,7 @@
 """
 from fuel.converters import adult
 from fuel.converters import binarized_mnist
+from fuel.converters import caltech101
 from fuel.converters import caltech101_silhouettes
 from fuel.converters import cifar10
 from fuel.converters import cifar100
@@ -23,6 +24,7 @@
 all_converters = (
     ('adult', adult.fill_subparser),
     ('binarized_mnist', binarized_mnist.fill_subparser),
+    ('caltech101', caltech101.fill_subparser),
     ('caltech101_silhouettes', caltech101_silhouettes.fill_subparser),
     ('cifar10', cifar10.fill_subparser),
     ('cifar100', cifar100.fill_subparser),

fuel/converters/caltech101.py

@@ -0,0 +1,196 @@
+import os
+import h5py
+
+import numpy
+import scipy.misc
+
+from six.moves import range
+
+from fuel.converters.base import fill_hdf5_file, MissingInputFiles
+
+CATEGORIES = (
+    'Leopards',
+    'emu',
+    'hedgehog',
+    'binocular',
+    'cougar_body',
+    'buddha',
+    'Faces_easy',
+    'beaver',
+    'windsor_chair',
+    'yin_yang',
+    'anchor',
+    'pagoda',
+    'mayfly',
+    'flamingo_head',
+    'headphone',
+    'joshua_tree',
+    'wrench',
+    'platypus',
+    'dollar_bill',
+    'dalmatian',
+    'mandolin',
+    'llama',
+    'electric_guitar',
+    'panda',
+    'lamp',
+    'pyramid',
+    'kangaroo',
+    'strawberry',
+    'stop_sign',
+    'flamingo',
+    'gerenuk',
+    'crayfish',
+    'ketch',
+    'crocodile_head',
+    'chandelier',
+    'cellphone',
+    'brain',
+    'car_side',
+    'ferry',
+    'nautilus',
+    'BACKGROUND_Google',
+    'metronome',
+    'water_lilly',
+    'dolphin',
+    'euphonium',
+    'crocodile',
+    'Faces',
+    'sunflower',
+    'garfield',
+    'soccer_ball',
+    'stapler',
+    'scorpion',
+    'wheelchair',
+    'saxophone',
+    'starfish',
+    'lotus',
+    'okapi',
+    'octopus',
+    'hawksbill',
+    'chair',
+    'crab',
+    'menorah',
+    'helicopter',
+    'accordion',
+    'rhino',
+    'ant',
+    'bass',
+    'bonsai',
+    'butterfly',
+    'ewer',
+    'pizza',
+    'umbrella',
+    'revolver',
+    'airplanes',
+    'grand_piano',
+    'trilobite',
+    'cannon',
+    'wild_cat',
+    'inline_skate',
+    'watch',
+    'pigeon',
+    'rooster',
+    'cup',
+    'dragonfly',
+    'barrel',
+    'ceiling_fan',
+    'lobster',
+    'minaret',
+    'schooner',
+    'cougar_face',
+    'Motorbikes',
+    'laptop',
+    'elephant',
+    'sea_horse',
+    'snoopy',
+    'brontosaurus',
+    'gramophone',
+    'camera',
+    'stegosaurus',
+    'tick',
+    'scissors',
+    'ibis')
+
+NUM_TRAIN = 20
+NUM_TEST = 10
+
+
+def read_image(imfile):
+    im = scipy.misc.imresize(scipy.misc.imread(imfile), (256, 256))
+    if im.ndim == 2:
+        return im.reshape(1, 256, 256)
+    else:
+        return numpy.rollaxis(im, 2, 0)
+
+
+def convert_silhouettes(directory, output_directory,
+                        output_file=None):
+    """ Convert the CalTech 101 Datasets.
+
+    Parameters
+    ----------
+    directory : str
+        Directory in which the required input files reside.
+    output_file : str
+        Where to save the converted dataset.
+
+    """
+    if output_file is None:
+        output_file = 'caltech101.hdf5'
+    output_file = os.path.join(output_directory, output_file)
+
+    input_dir = '101_ObjectCategories'
+    input_dir = os.path.join(directory, input_dir)
+
+    if not os.path.isdir(input_dir):
+        raise MissingInputFiles('Required files missing', [input_dir])
+
+    with h5py.File(output_file, mode="w") as h5file:
+        train_features = numpy.empty(
+            (len(CATEGORIES) * NUM_TRAIN, 3, 256, 256), dtype='uint8')
+        test_features = numpy.empty((len(CATEGORIES) * NUM_TEST, 3, 256, 256),
+                                    dtype='uint8')
+
+        for i, c in enumerate(CATEGORIES):
+            for j in range(NUM_TRAIN):
+                imfile = os.path.join(input_dir, c,
+                                      'image_{:04d}.jpg'.format(j + 1))
+                train_features[i * NUM_TRAIN + j] = read_image(imfile)
+            for j in range(NUM_TEST):
+                imfile = os.path.join(
+                    input_dir, c, 'image_{:04d}.jpg'.format(j + NUM_TRAIN + 1))
+                test_features[i * NUM_TEST + j] = read_image(imfile)
+
+        train_targets = numpy.repeat(numpy.arange(len(CATEGORIES)), NUM_TRAIN)
+        train_targets = train_targets.reshape(-1, 1)
+        test_targets = numpy.repeat(numpy.arange(len(CATEGORIES)), NUM_TEST)
+        test_targets = test_targets.reshape(-1, 1)
+
+        data = (
+            ('train', 'features', train_features),
+            ('train', 'targets', train_targets),
+            ('test', 'features', test_features),
+            ('test', 'targets', test_targets),
+        )
+        fill_hdf5_file(h5file, data)
+
+        for i, label in enumerate(('batch', 'channel', 'height', 'width')):
+            h5file['features'].dims[i].label = label
+
+        for i, label in enumerate(('batch', 'index')):
+            h5file['targets'].dims[i].label = label
+
+    return (output_file,)
+
+
+def fill_subparser(subparser):
+    """Sets up a subparser to convert CalTech101 Silhouettes Database files.
+
+    Parameters
+    ----------
+    subparser : :class:`argparse.ArgumentParser`
+        Subparser handling the `caltech101_silhouettes` command.
+
+    """
+    return convert_silhouettes

fuel/datasets/__init__.py

@@ -7,6 +7,7 @@
 from fuel.datasets.binarized_mnist import BinarizedMNIST
 from fuel.datasets.cifar10 import CIFAR10
 from fuel.datasets.cifar100 import CIFAR100
+from fuel.datasets.caltech101 import CalTech101
 from fuel.datasets.caltech101_silhouettes import CalTech101Silhouettes
 from fuel.datasets.iris import Iris
 from fuel.datasets.mnist import MNIST

fuel/datasets/caltech101.py

@@ -0,0 +1,27 @@
+# -*- coding: utf-8 -*-
+from fuel.utils import find_in_data_path
+from fuel.datasets import H5PYDataset
+from fuel.transformers.defaults import uint8_pixels_to_floatX
+
+
+class CalTech101(H5PYDataset):
+    u"""CalTech 101 dataset.
+
+    Parameters
+    ----------
+    which_sets : tuple of str
+        Which split to load. Valid values are 'train' and 'test'.
+
+    """
+    filename = 'caltech101.hdf5'
+    default_transformers = uint8_pixels_to_floatX(('features'),)
+
+    def __init__(self, which_sets, load_in_memory=True, **kwargs):
+        super(CalTech101, self).__init__(file_or_path=self.data_path,
+                                         which_sets=which_sets,
+                                         load_in_memory=load_in_memory,
+                                         **kwargs)
+
+    @property
+    def data_path(self):
+        return find_in_data_path(self.filename)