def _worker_batches_in_numpy_array(numpy_inputs, batch_size, shift_ratio):
"""Yields batches from a numpy array, for a single worker."""
numpy_inputs = cnn_util.roll_numpy_batches(numpy_inputs, batch_size,
shift_ratio)
i = 0
total_batches = numpy_inputs.shape[0]
assert total_batches % batch_size == 0
while True:
yield numpy_inputs[i:i + batch_size, ...]
i = (i + batch_size) % total_batches
def minibatch(self,
dataset,
subset,
use_datasets,
datasets_repeat_cached_sample,
shift_ratio=0):
"""Get test image batches."""
del dataset, use_datasets, datasets_repeat_cached_sample
if (not hasattr(self, 'fake_images') or
not hasattr(self, 'fake_labels')):
raise ValueError('Must call set_fake_data() before calling minibatch '
'on TestImagePreprocessor')
if self.expected_subset is not None:
assert subset == self.expected_subset
shift_ratio = shift_ratio or self.shift_ratio
fake_images = cnn_util.roll_numpy_batches(self.fake_images, self.batch_size,
shift_ratio)
fake_labels = cnn_util.roll_numpy_batches(self.fake_labels, self.batch_size,
shift_ratio)
with tf.name_scope('batch_processing'):
image_slice, label_slice = tf.train.slice_input_producer(
[fake_images, fake_labels],
shuffle=False,
name='image_slice')
raw_images, raw_labels = tf.train.batch(
[image_slice, label_slice], batch_size=self.batch_size,
name='image_batch')
images = [[] for _ in range(self.num_splits)]
labels = [[] for _ in range(self.num_splits)]
for i in xrange(self.batch_size):
split_index = i % self.num_splits
raw_image = tf.cast(raw_images[i], self.dtype)
images[split_index].append(raw_image)
labels[split_index].append(raw_labels[i])
for split_index in xrange(self.num_splits):
images[split_index] = tf.parallel_stack(images[split_index])
labels[split_index] = tf.parallel_stack(labels[split_index])
normalized = [normalized_image(part) for part in images]
return [[tf.cast(part, self.dtype) for part in normalized], labels]
