def _create_validation_cache(archive, output, names, ground_truth, args):
images0 = sorted(names)
# Thinning
images = []
for i, image in enumerate(images0):
if i % args.thinning == 0:
images.append(image)
def _load_func(index):
y, name = ground_truth[index], images[index]
im = imread(archive.extractfile(name), num_channels=3)
x = _resize_image(im, args.width, args.height, args.mode == 'padding')
return x, np.array([y]).astype(np.int32)
from nnabla.utils.data_source import DataSourceWithFileCache
from nnabla.utils.data_source_implements import SimpleDataSource
from nnabla.logger import logger
logger.info('Num of data : {}'.format(len(images)))
shuffle = False
if args.shuffle == 'True':
shuffle = True
source = SimpleDataSource(_load_func, len(images), shuffle, rng=None)
DataSourceWithFileCache(source, cache_dir=output, shuffle=args.shuffle)
def _create_validation_cache(archive, output, names, ground_truth, args):
# ILSVRC2012_devkit_t12/readme.txt
# The ground truth of the validation images is in
# data/ILSVRC2012_validation_ground_truth.txt, where each line contains
# one ILSVRC2012_ID for one image, in the ascending alphabetical order
# of the image file names.
images0 = sorted(names)
# Thinning
images = []
for i, image in enumerate(images0):
if i % args.thinning == 0:
images.append(image)
def _load_func(index):
y, name = ground_truth[index], images[index]
im = imread(archive.extractfile(name))
x = _resize_image(im, args.width, args.height, args.mode == 'padding')
return x, np.array([y - 1]).astype(np.int32)
from nnabla.utils.data_source import DataSourceWithFileCache
from nnabla.utils.data_source_implements import SimpleDataSource
from nnabla.logger import logger
logger.info('Num of data : {}'.format(len(images)))
shuffle = False
if args.shuffle == 'True':
shuffle = True
source = SimpleDataSource(_load_func, len(images), shuffle, rng=None)
DataSourceWithFileCache(source, cache_dir=output, shuffle=args.shuffle)
def _convert(args, source):
_, ext = os.path.splitext(args.destination)
if ext.lower() == '.cache':
with DataSourceWithFileCache(source,
cache_dir=args.destination,
shuffle=args.shuffle) as ds:
print('Number of Data: {}'.format(ds.size))
print('Shuffle: {}'.format(args.shuffle))
print('Normalize: {}'.format(args.normalize))
pbar = tqdm(total=ds.size)
for i in range(ds.size):
ds._get_data(i)
pbar.update(1)
else:
print('Command `conv_dataset` only supports CACHE as destination.')
def _create_train_cache(archive, output, names, args):
# Read label and wordnet_id
wid2ind = np.loadtxt(fname=LABEL_WORDNETID, dtype=object, delimiter=",")
def _get_label(wordnet_id):
for item in wid2ind:
if item[1] == wordnet_id:
return item[0]
images0 = []
print("Count image in TAR")
pbar = tqdm.tqdm(total=len(names), unit='%')
for name in names:
category = os.path.splitext(name)[0]
marchive = tarfile.open(fileobj=archive.extractfile(name))
for mname in marchive.getnames():
if re.match(r'{}_[0-9]+\.JPEG'.format(category), mname):
images0.append((_get_label(name[:9]), name, marchive, mname))
else:
print('Invalid file {} includes in tar file'.format(mname))
exit(-1)
pbar.update(1)
pbar.close()
# Thinning
images = []
for i, image in enumerate(images0):
if i % args.thinning == 0:
images.append(image)
def _load_func(index):
y, name, marchive, mname = images[index]
im = imread(marchive.extractfile(mname), num_channels=3)
x = _resize_image(im, args.width, args.height, args.mode == 'padding')
return x, np.array([y]).astype(np.int32)
from nnabla.utils.data_source import DataSourceWithFileCache
from nnabla.utils.data_source_implements import SimpleDataSource
from nnabla.logger import logger
logger.info('Num of data : {}'.format(len(images)))
shuffle = True
if args.shuffle == 'False':
shuffle = False
source = SimpleDataSource(_load_func, len(images), shuffle, rng=None)
DataSourceWithFileCache(
source, cache_dir=output, shuffle=args.shuffle)
def _create_train_cache(archive, output, names, synsets_id, args):
images0 = []
print("Count image in TAR")
pbar = tqdm.tqdm(total=len(names), unit='%')
for name in names:
category = os.path.splitext(name)[0]
marchive = tarfile.open(fileobj=archive.extractfile(name))
for mname in marchive.getnames():
if re.match(r'{}_[0-9]+\.JPEG'.format(category), mname):
images0.append((synsets_id[category], name, marchive, mname))
else:
print('Invalid file {} includes in tar file'.format(mname))
exit(-1)
pbar.update(1)
pbar.close()
# Thinning
images = []
for i, image in enumerate(images0):
if i % args.thinning == 0:
images.append(image)
def _load_func(index):
y, name, marchive, mname = images[index]
im = scipy.misc.imread(marchive.extractfile(mname), mode='RGB')
x = _resize_image(im, args.width, args.height, args.mode == 'padding')
return x, np.array([y - 1]).astype(np.int32)
from nnabla.utils.data_source import DataSourceWithFileCache
from nnabla.utils.data_source_implements import SimpleDataSource
from nnabla.logger import logger
logger.info('Num of data : {}'.format(len(images)))
shuffle = True
if args.shuffle == 'False':
shuffle = False
source = SimpleDataSource(_load_func, len(images), shuffle, rng=None)
DataSourceWithFileCache(source, cache_dir=output, shuffle=args.shuffle)
nnabla_config.set('DATA_ITERATOR', 'data_source_file_cache_size',
'{}'.format(args.cache_size))
nnabla_config.set('DATA_ITERATOR', 'cache_file_format', '.h5')
HERE = os.path.dirname(__file__)
nnabla_examples_root = os.path.join(HERE, '../../../../nnabla-examples')
mnist_examples_root = os.path.realpath(
os.path.join(nnabla_examples_root, 'mnist-collection'))
sys.path.append(mnist_examples_root)
from mnist_data import MnistDataSource
mnist_training_cache = args.output + '/mnist_training.cache'
if not os.path.exists(mnist_training_cache):
os.makedirs(mnist_training_cache)
DataSourceWithFileCache(data_source=MnistDataSource(train=True,
shuffle=False,
rng=None),
cache_dir=mnist_training_cache,
shuffle=False,
rng=None)
mnist_test_cache = args.output + '/mnist_test.cache'
if not os.path.exists(mnist_test_cache):
os.makedirs(mnist_test_cache)
DataSourceWithFileCache(data_source=MnistDataSource(train=False,
shuffle=False,
rng=None),
cache_dir=mnist_test_cache,
shuffle=False,
rng=None)
def data_iterator(data_source,
batch_size,
rng=None,
with_memory_cache=True,
with_file_cache=False,
cache_dir=None,
epoch_begin_callbacks=[],
epoch_end_callbacks=[]):
'''data_iterator
Helper method to use :py:class:`DataSource <nnabla.utils.data_source.DataSource>`.
You can use :py:class:`DataIterator <nnabla.utils.data_iterator.DataIterator>` with your own :py:class:`DataSource <nnabla.utils.data_source.DataSource>`
for easy implementation of data sources.
For example,
.. code-block:: python
ds = YourOwnImplementationOfDataSource()
batch = data_iterator(ds, batch_size)
Args:
data_source (:py:class:`DataSource <nnabla.utils.data_source.DataSource>`):
Instance of DataSource class which provides data.
batch_size (int): Batch size.
rng (None or :obj:`numpy.random.RandomState`): Numpy random number
generator.
with_memory_cache (bool):
If ``True``, use :py:class:`.data_source.DataSourceWithMemoryCache`
to wrap ``data_source``. It is a good idea to set this as true unless
data_source provides on-memory data.
Default value is True.
with_file_cache (bool):
If ``True``, use :py:class:`.data_source.DataSourceWithFileCache`
to wrap ``data_source``.
If ``data_source`` is slow, enabling this option a is good idea.
Default value is False.
cache_dir (str):
Location of file_cache.
If this value is None, :py:class:`.data_source.DataSourceWithFileCache`
creates file caches implicitly on temporary directory and erases them all
when data_iterator is finished.
Otherwise, :py:class:`.data_source.DataSourceWithFileCache` keeps created cache.
Default is None.
epoch_begin_callbacks (list of functions): An item is a function
which takes an epoch index as an argument. These are called
at the beginning of an epoch.
epoch_end_callbacks (list of functions): An item is a function
which takes an epoch index as an argument. These are called
at the end of an epoch.
Returns:
:py:class:`DataIterator <nnabla.utils.data_iterator.DataIterator>`:
Instance of DataIterator.
'''
if with_file_cache:
ds = DataSourceWithFileCache(data_source=data_source,
cache_dir=cache_dir,
shuffle=data_source.shuffle,
rng=rng)
if with_memory_cache:
ds = DataSourceWithMemoryCache(ds,
shuffle=ds.shuffle,
rng=rng)
return DataIterator(ds,
batch_size,
epoch_begin_callbacks=epoch_begin_callbacks,
epoch_end_callbacks=epoch_end_callbacks)
else:
if with_memory_cache:
data_source = DataSourceWithMemoryCache(data_source,
shuffle=data_source.shuffle,
rng=rng)
return DataIterator(data_source, batch_size,
epoch_begin_callbacks=epoch_begin_callbacks,
epoch_end_callbacks=epoch_end_callbacks)
def slice(self, rng, num_of_slices=None, slice_pos=None,
slice_start=None, slice_end=None,
cache_dir=None):
'''
Slices the data iterator so that newly generated data iterator has access to limited portion of the original data.
Args:
rng (numpy.random.RandomState): Random generator for Initializer.
num_of_slices(int): Total number of slices to be made. Muts be used together with `slice_pos`.
slice_pos(int): Position of the slice to be assigned to the new data iterator. Must be used together with `num_of_slices`.
slice_start(int): Starting position of the range to be sliced into new data iterator. Must be used together with `slice_end`.
slice_end(int) : End position of the range to be sliced into new data iterator. Must be used together with `slice_start`.
cache_dir(str) : Directory to save cache files
Example:
.. code-block:: python
from nnabla.utils.data_iterator import data_iterator_simple
import numpy as np
def load_func1(index):
d = np.ones((2, 2)) * index
return d
di = data_iterator_simple(load_func1, 1000, batch_size=3)
di_s1 = di.slice(None, num_of_slices=10, slice_pos=0)
di_s2 = di.slice(None, num_of_slices=10, slice_pos=1)
di_s3 = di.slice(None, slice_start=100, slice_end=200)
di_s4 = di.slice(None, slice_start=300, slice_end=400)
'''
if num_of_slices is not None and slice_pos is not None and slice_start is None and slice_end is None:
size = self._size // num_of_slices
amount = self._size % num_of_slices
slice_start = slice_pos * size
if slice_pos < amount:
slice_start += slice_pos
else:
slice_start += amount
slice_end = slice_start + size
if slice_end > self._size:
slice_start -= (slice_end - self._size)
slice_end = self._size
elif num_of_slices is None and slice_pos is None and slice_start is not None and slice_end is not None:
pass
else:
logger.critical(
'You must specify position(num_of_slice and slice_pos) or range(slice_start and slice_end).')
return None
if cache_dir is None:
ds = self._data_source
while '_data_source' in dir(ds):
if '_cache_dir' in dir(ds):
cache_dir = ds._cache_dir
ds = ds._data_source
if cache_dir is None:
return DataIterator(
DataSourceWithMemoryCache(
SlicedDataSource(
self._data_source,
self._data_source.shuffle,
slice_start=slice_start,
slice_end=slice_end),
shuffle=self._shuffle,
rng=rng),
self._batch_size)
else:
return DataIterator(
DataSourceWithMemoryCache(
DataSourceWithFileCache(
SlicedDataSource(
self._data_source,
self._data_source.shuffle,
slice_start=slice_start,
slice_end=slice_end),
cache_dir=cache_dir,
cache_file_name_prefix='cache_sliced_{:08d}_{:08d}'.format(
slice_start,
slice_end),
shuffle=self._shuffle,
rng=rng),
shuffle=self._shuffle,
rng=rng),
self._batch_size)