def create_inputs_norb(is_train: bool, epochs: int):
import re
if is_train:
CHUNK_RE = re.compile(r"train\d+\.tfrecords")
else:
CHUNK_RE = re.compile(r"test\d+\.tfrecords")
processed_dir = './data'
chunk_files = [
os.path.join(processed_dir, fname)
for fname in os.listdir(processed_dir) if CHUNK_RE.match(fname)
]
image, label = norb.read_norb_tfrecord(chunk_files, epochs)
# TODO: is it the right order: add noise, resize, then corp?
image = tf.image.random_brightness(image, max_delta=32. / 255.)
image = tf.image.random_contrast(image, lower=0.5, upper=1.5)
image = tf.image.resize_images(image, [48, 48])
if is_train:
image = tf.random_crop(image, [32, 32, 1])
else:
image = tf.slice(image, [8, 8, 0], [32, 32, 1])
x, y = tf.train.shuffle_batch([image, label],
num_threads=cfg.num_threads,
batch_size=cfg.batch_size,
capacity=cfg.batch_size * 64,
min_after_dequeue=cfg.batch_size * 32,
allow_smaller_final_batch=False)
return x, y
def create_inputs_norb(is_train: bool, epochs: int):
import re
if is_train:
CHUNK_RE = re.compile(r"train\d+\.tfrecords")
else:
CHUNK_RE = re.compile(r"test\d+\.tfrecords")
processed_dir = './data'
chunk_files = [os.path.join(processed_dir, fname)
for fname in os.listdir(processed_dir)
if CHUNK_RE.match(fname)]
image, label = norb.read_norb_tfrecord(chunk_files, epochs)
if is_train:
# TODO: is it the right order: add noise, resize, then corp?
image = tf.image.random_brightness(image, max_delta=32. / 255.)
image = tf.image.random_contrast(image, lower=0.5, upper=1.5)
image = tf.image.resize_images(image, [48, 48])
image = tf.random_crop(image, [32, 32, 1])
else:
image = tf.image.resize_images(image, [48, 48])
image = tf.slice(image, [8, 8, 0], [32, 32, 1])
x, y = tf.train.shuffle_batch([image, label], num_threads=cfg.num_threads, batch_size=cfg.batch_size, capacity=cfg.batch_size * 64,
min_after_dequeue=cfg.batch_size * 32, allow_smaller_final_batch=False)
return x, y
