def _verify_read_up_to_out(self, shared_queue):
with self.test_session():
num_files = 3
num_records_per_file = 7
tfrecord_paths = test_utils.create_tfrecord_files(
self.get_temp_dir(),
num_files=num_files,
num_records_per_file=num_records_per_file)
p_reader = parallel_reader.ParallelReader(io_ops.TFRecordReader,
shared_queue,
num_readers=5)
data_files = parallel_reader.get_data_files(tfrecord_paths)
filename_queue = input_lib.string_input_producer(data_files,
num_epochs=1)
key, value = p_reader.read_up_to(filename_queue, 4)
count0 = 0
count1 = 0
count2 = 0
all_keys_count = 0
all_values_count = 0
sv = supervisor.Supervisor(logdir=self.get_temp_dir())
with sv.prepare_or_wait_for_session() as sess:
sv.start_queue_runners(sess)
while True:
try:
current_keys, current_values = sess.run([key, value])
self.assertEquals(len(current_keys), len(current_values))
all_keys_count += len(current_keys)
all_values_count += len(current_values)
for current_key in current_keys:
if '0-of-3' in str(current_key):
count0 += 1
if '1-of-3' in str(current_key):
count1 += 1
if '2-of-3' in str(current_key):
count2 += 1
except errors_impl.OutOfRangeError:
break
self.assertEquals(count0, num_records_per_file)
self.assertEquals(count1, num_records_per_file)
self.assertEquals(count2, num_records_per_file)
self.assertEquals(all_keys_count, num_files * num_records_per_file)
self.assertEquals(all_values_count, all_keys_count)
self.assertEquals(count0 + count1 + count2, all_keys_count)
def read_and_decode(filename_queue, tfrec_len):
batch_size = 5
IMAGE_WIDTH = 10
common_queue = tf.RandomShuffleQueue(capacity=10 + 3 * batch_size,
min_after_dequeue=10 + batch_size,
dtypes=[tf.string, tf.string])
p_reader = parallel_reader.ParallelReader(io_ops.TFRecordReader,
common_queue,
num_readers=tfrec_len)
_, serialized_example = p_reader.read(filename_queue)
features = tf.parse_single_example(
serialized_example,
# Defaults are not specified since both keys are required.
features={
'height': tf.FixedLenFeature([], tf.int64),
'image_raw': tf.FixedLenFeature([], tf.string)
})
bucket_boundaries = [7, 11, 18, 23, 33]
image = tf.decode_raw(features['image_raw'], tf.uint8)
height = tf.cast(features['height'], tf.int32)
image_shape = tf.stack([height, IMAGE_WIDTH])
print("*********image shape****", image_shape)
image = tf.reshape(image, image_shape)
(l, images) = tf.contrib.training.bucket_by_sequence_length(height, [image], batch_size,\
bucket_boundaries,capacity=1 * batch_size, dynamic_pad=True)
table_index = tf.train.range_input_producer(
batch_size, shuffle=True).dequeue_many(batch_size)
ret = tf.gather(images[0], table_index)
return (l, ret)
def _verify_all_data_sources_read(self, shared_queue):
with self.cached_session():
tfrecord_paths = test_utils.create_tfrecord_files(
self.get_temp_dir(), num_files=3)
num_readers = len(tfrecord_paths)
p_reader = parallel_reader.ParallelReader(io_ops.TFRecordReader,
shared_queue,
num_readers=num_readers)
data_files = parallel_reader.get_data_files(tfrecord_paths)
filename_queue = input_lib.string_input_producer(data_files)
key, value = p_reader.read(filename_queue)
count0 = 0
count1 = 0
count2 = 0
num_reads = 50
sv = supervisor.Supervisor(logdir=self.get_temp_dir())
with sv.prepare_or_wait_for_session() as sess:
sv.start_queue_runners(sess)
for _ in range(num_reads):
current_key, _ = sess.run([key, value])
if '0-of-3' in str(current_key):
count0 += 1
if '1-of-3' in str(current_key):
count1 += 1
if '2-of-3' in str(current_key):
count2 += 1
self.assertGreater(count0, 0)
self.assertGreater(count1, 0)
self.assertGreater(count2, 0)
self.assertEquals(count0 + count1 + count2, num_reads)