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save_example_images.py
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save_example_images.py
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# Copyright 2019 Deepmind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
r"""Loads images from the 80M@200K training set and saves them in PNG format.
Usage:
cd /path/to/deepmind_research
python -m unsupervised_adversarial_training.save_example_images \
--data_bin_path=/path/to/tiny_images.bin
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
from absl import app
from absl import flags
import numpy as np
from PIL import Image
DIR_NAME = os.path.dirname(__file__)
FLAGS = flags.FLAGS
flags.DEFINE_string('data_bin_path', None,
'path to 80M Tiny Images data binary')
flags.DEFINE_string('idxs_path', os.path.join(DIR_NAME, 'tiny_200K_idxs.txt'),
'path to file of indices indicating subset of 80M dataset')
flags.DEFINE_string('output_dir', os.path.join(DIR_NAME, 'images'),
'path to output directory for images')
flags.mark_flag_as_required('data_bin_path')
CIFAR_LABEL_IDX_TO_NAME = ['airplane', 'automobile', 'bird', 'cat', 'deer',
'dog', 'frog', 'horse', 'ship', 'truck']
DATASET_SIZE = 79302017
def _load_dataset_as_array(ds_path):
dataset = np.memmap(filename=ds_path, dtype=np.uint8, mode='r',
shape=(DATASET_SIZE, 3, 32, 32))
return dataset.transpose([0, 3, 2, 1])
def main(unused_argv):
dataset = _load_dataset_as_array(FLAGS.data_bin_path)
# Load the indices and labels of the 80M@200K training set
data_idxs, data_labels = np.loadtxt(
FLAGS.idxs_path,
delimiter=',',
dtype=[('index', np.uint64), ('label', np.uint8)],
unpack=True)
# Save images as PNG files
if not os.path.exists(FLAGS.output_dir):
os.makedirs(FLAGS.output_dir)
for i in range(100):
class_name = CIFAR_LABEL_IDX_TO_NAME[data_labels[i]]
file_name = 'im{}_{}.png'.format(i, class_name)
file_path = os.path.join(FLAGS.output_dir, file_name)
img = dataset[data_idxs[i]]
Image.fromarray(img).save(file_path)
if __name__ == '__main__':
app.run(main)