def main(unused_argv):
tf.logging.set_verbosity(tf.logging.INFO)
style_files = _parse_style_files(os.path.expanduser(FLAGS.style_files))
with tf.python_io.TFRecordWriter(
os.path.expanduser(FLAGS.output_file)) as writer:
for style_label, style_file in enumerate(style_files):
tf.logging.info(
'Processing style file %s: %s' % (style_label, style_file))
feature = {'label': _int64_feature(style_label)}
style_image = image_utils.load_np_image(style_file)
buf = io.BytesIO()
skimage.io.imsave(buf, style_image, format='JPEG')
buf.seek(0)
feature['image_raw'] = _bytes_feature(buf.getvalue())
if FLAGS.compute_gram_matrices:
with tf.Graph().as_default():
style_end_points = learning.precompute_gram_matrices(
tf.expand_dims(tf.to_float(style_image), 0),
# We use 'pool5' instead of 'fc8' because a) fully-connected
# layers are already too deep in the network to be useful for
# style and b) they're quite expensive to store.
final_endpoint='pool5')
for name in style_end_points:
feature[name] = _float_feature(
style_end_points[name].flatten().tolist())
example = tf.train.Example(features=tf.train.Features(feature=feature))
writer.write(example.SerializeToString())
tf.logging.info('Output TFRecord file is saved at %s' % os.path.expanduser(
FLAGS.output_file))
def main(unused_argv):
tf.logging.set_verbosity(tf.logging.INFO)
style_files = _parse_style_files(os.path.expanduser(FLAGS.style_files))
with tf.python_io.TFRecordWriter(
os.path.expanduser(FLAGS.output_file)) as writer:
for style_label, style_file in enumerate(style_files):
tf.logging.info(
'Processing style file %s: %s' % (style_label, style_file))
feature = {'label': _int64_feature(style_label)}
style_image = image_utils.load_np_image(style_file)
buf = io.BytesIO()
scipy.misc.imsave(buf, style_image, format='JPEG')
buf.seek(0)
feature['image_raw'] = _bytes_feature(buf.getvalue())
if FLAGS.compute_gram_matrices:
with tf.Graph().as_default():
style_end_points = learning.precompute_gram_matrices(
tf.expand_dims(tf.to_float(style_image), 0),
# We use 'pool5' instead of 'fc8' because a) fully-connected
# layers are already too deep in the network to be useful for
# style and b) they're quite expensive to store.
final_endpoint='pool5')
for name, matrix in style_end_points.iteritems():
feature[name] = _float_feature(matrix.flatten().tolist())
example = tf.train.Example(features=tf.train.Features(feature=feature))
writer.write(example.SerializeToString())
tf.logging.info('Output TFRecord file is saved at %s' % os.path.expanduser(
FLAGS.output_file))
