def is_primary_worker(scope='global'):
"""Check whether is the primary worker of all nodes (global) or the current node (local).
Args:
* scope: check scope ('global' OR 'local')
Returns:
* flag: whether is the primary worker
"""
if scope == 'global':
return True if not FLAGS.enbl_multi_gpu else mgw.rank() == 0
elif scope == 'local':
return True if not FLAGS.enbl_multi_gpu else mgw.local_rank() == 0
else:
raise ValueError('unrecognized worker scope: ' + scope)
def build(self, enbl_trn_val_split=False):
'''Build iterator(s) for tf.data.Dataset() object.
Args:
* enbl_trn_val_split: whether to split into training & validation subsets
Returns:
* iterator_trn: iterator for the training subset
* iterator_val: iterator for the validation subset
OR
* iterator: iterator for the chosen subset (training OR testing)
Example:
# build iterator(s)
dataset = xxxxDataset(is_train=True) # TF operations are not created
iterator = dataset.build() # TF operations are created
OR
iterator_trn, iterator_val = dataset.build(enbl_trn_val_split=True) # for dataset-train only
# use the iterator to obtain a mini-batch of images & labels
images, labels = iterator.get_next()
'''
# obtain list of data files' names
filenames = tf.data.Dataset.list_files(self.file_pattern, shuffle=True)
if self.enbl_shard:
filenames = filenames.shard(mgw.size(), mgw.rank())
# create a tf.data.Dataset from list of files
dataset = filenames.apply(
tf.contrib.data.parallel_interleave(
self.dataset_fn, cycle_length=FLAGS.cycle_length))
dataset = dataset.map(self.parse_fn,
num_parallel_calls=FLAGS.nb_threads)
# create iterators for training & validation subsets separately
if self.is_train and enbl_trn_val_split:
iterator_val = self.__make_iterator(
dataset.take(FLAGS.nb_smpls_val))
iterator_trn = self.__make_iterator(
dataset.skip(FLAGS.nb_smpls_val))
return iterator_trn, iterator_val
return self.__make_iterator(dataset)
def __is_primary_worker(cls): return not FLAGS.enbl_multi_gpu or mgw.rank() == 0
def __is_primary_worker(cls): """Weather it is the primary worker""" return not FLAGS.enbl_multi_gpu or mgw.rank() == 0
