def get_features(split,
total_batch_size,
num_gpus,
data_dir,
num_targets,
dataset,
validate=False):
"""Reads the input data and distributes it over num_gpus GPUs.
Each tower of data has 1/FLAGS.num_gpus of the total_batch_size.
Args:
split: 'train' or 'test', split of the data to read.
total_batch_size: total number of data entries over all towers.
num_gpus: Number of GPUs to distribute the data on.
data_dir: Directory containing the input data.
num_targets: Number of objects present in the image.
dataset: The name of the dataset, either norb or mnist.
validate: If set, subset training data into training and test.
Returns:
A list of batched feature dictionaries.
Raises:
ValueError: If dataset is not mnist or norb.
"""
batch_size = total_batch_size // max(1, num_gpus)
features = []
for i in range(num_gpus):
with tf.device('/gpu:%d' % i):
if dataset == 'mnist':
features.append(
mnist_input_record.inputs(
data_dir=data_dir,
batch_size=batch_size,
split=split,
num_targets=num_targets,
validate=validate,
))
elif dataset == 'norb':
features.append(
norb_input_record.inputs(
data_dir=data_dir,
batch_size=batch_size,
split=split,
))
elif dataset == 'cifar10':
data_dir = os.path.join(data_dir, 'cifar-10-batches-bin')
features.append(
cifar10_input.inputs(split=split,
data_dir=data_dir,
batch_size=batch_size))
else:
raise ValueError(
'Unexpected dataset {!r}, must be mnist, norb, or cifar10.'
.format(dataset))
return features
def get_features(split, total_batch_size, num_gpus, data_dir, num_targets,
dataset, validate=False):
"""Reads the input data and distributes it over num_gpus GPUs.
Each tower of data has 1/FLAGS.num_gpus of the total_batch_size.
Args:
split: 'train' or 'test', split of the data to read.
total_batch_size: total number of data entries over all towers.
num_gpus: Number of GPUs to distribute the data on.
data_dir: Directory containing the input data.
num_targets: Number of objects present in the image.
dataset: The name of the dataset, either norb or mnist.
validate: If set, subset training data into training and test.
Returns:
A list of batched feature dictionaries.
Raises:
ValueError: If dataset is not mnist or norb.
"""
batch_size = total_batch_size // max(1, num_gpus)
features = []
for i in range(num_gpus):
with tf.device('/gpu:%d' % i):
if dataset == 'mnist':
features.append(
mnist_input_record.inputs(
data_dir=data_dir,
batch_size=batch_size,
split=split,
num_targets=num_targets,
validate=validate,
))
elif dataset == 'norb':
features.append(
norb_input_record.inputs(
data_dir=data_dir, batch_size=batch_size, split=split,
))
elif dataset == 'cifar10':
data_dir = os.path.join(data_dir, 'cifar-10-batches-bin')
features.append(
cifar10_input.inputs(
split=split, data_dir=data_dir, batch_size=batch_size))
else:
raise ValueError(
'Unexpected dataset {!r}, must be mnist, norb, or cifar10.'.format(
dataset))
return features
def get_features(split,
total_batch_size,
num_gpus,
data_dir,
num_targets,
dataset,
validate=False,
evaluate=False,
seed=None,
shuffled=False,
shift=2,
pad=0,
eval_shard=None):
"""Reads the input data and distributes it over num_gpus GPUs.
Each tower of data has 1/FLAGS.num_gpus of the total_batch_size.
Args:
split: 'train' or 'test', split of the data to read.
total_batch_size: total number of data entries over all towers.
num_gpus: Number of GPUs to distribute the data on.
data_dir: Directory containing the input data.
num_targets: Number of objects present in the image.
dataset: The name of the dataset, either norb or mnist.
validate: If set, subset training data into training and test.
evaluate: If set, prepare features for test time (e.g. no shuffling).
seed: If set, specify the seed for shuffling training batches.
shuffled: If set, use the shuffled version of .tfrecords dataset.
Returns:
A list of batched feature dictionaries.
Raises:
ValueError: If dataset is not mnist or norb.
"""
batch_size = total_batch_size // max(1, num_gpus)
features = []
for i in range(num_gpus):
with tf.device('/gpu:%d' % i):
if dataset == 'mnist':
features.append(
mnist_input_record.inputs(data_dir=data_dir,
batch_size=batch_size,
split=split,
shift=shift,
pad=pad,
shuffled=shuffled,
num_targets=num_targets,
validate=validate,
evaluate=evaluate,
seed=seed))
elif dataset == 'norb':
features.append(
norb_input_record.inputs(
data_dir=data_dir,
batch_size=batch_size,
split=split,
))
elif dataset == 'cifar10':
data_dir = os.path.join(data_dir, 'cifar-10-batches-bin')
features.append(
cifar10_input.inputs(split=split,
data_dir=data_dir,
batch_size=batch_size))
elif dataset == 'affnist':
features.append(
affnist_input_record.inputs(data_dir=data_dir,
batch_size=batch_size,
split=split,
shift=shift,
validate=validate,
evaluate=evaluate,
seed=seed,
eval_shard=eval_shard))
else:
raise ValueError('Unexpected dataset {!r}, must be mnist, '
'norb, cifar10, or affnist.'.format(dataset))
return features
