mirror of
https://github.com/google-deepmind/deepmind-research.git
synced 2026-05-10 05:17:46 +08:00
Deepmind Team
78 lines
2.6 KiB
Python
78 lines
2.6 KiB
Python
# coding=utf-8
|
|
# 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)
|