def _iterate_reals(self, minibatch_size):
dataset_obj = self._get_dataset_obj()
while True:
images, _labels = dataset_obj.get_minibatch_np(minibatch_size)
if self._mirror_augment:
images = misc.apply_mirror_augment(images)
yield images
def _iterate_reals(self, minibatch_size):
dataset_obj = dataset.load_dataset(data_dir=config.data_dir, **self._dataset_args)
while True:
images, _labels = dataset_obj.get_minibatch_np(minibatch_size)
if self._mirror_augment:
images = misc.apply_mirror_augment(images)
yield images
def _iterate_reals(self,
minibatch_size,
return_label=False,
iterate_once=False,
num_samples=None,
mirror_augment=False):
dataset_obj = self._get_dataset_obj()
if iterate_once:
assert dataset_obj.num_samples
num_samples = dataset_obj.num_samples
begin = 0
while True:
end = begin + minibatch_size
if num_samples:
end = min(end, num_samples)
images, labels = dataset_obj.get_minibatch_np(end - begin)
if mirror_augment:
images = misc.apply_mirror_augment(images)
if return_label:
yield (images, labels)
else:
yield images
if end == num_samples:
break
begin = end
def _iterate_reals(self,
minibatch_size,
return_label=False,
num_samples=None,
mirror_augment=False):
dataset_obj = self._get_dataset_obj()
begin = 0
while True:
end = begin + minibatch_size
if num_samples:
end = min(end, num_samples)
try:
images, labels = dataset_obj.get_minibatch_np(end - begin)
except tf.errors.OutOfRangeError:
if num_samples is None:
break
else:
raise
if mirror_augment:
images = misc.apply_mirror_augment(images)
if return_label:
yield (images, labels)
else:
yield images
if num_samples and end == num_samples:
break
begin = end
def _iterate_reals(self, minibatch_size):
dataset_obj = self._get_dataset_obj()
while True:
images, _labels = dataset_obj.get_minibatch_np(minibatch_size)
#print('images.shape', images.shape)
if images.shape[1] == 1:
images = np.repeat(images, 3, axis=1)
#print('images.shape', images.shape)
if self._mirror_augment:
images = misc.apply_mirror_augment(images)
yield images
