#!/usr/bin/env python
# Copyright 2019 Google LLC
#
# 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
#
# https://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.
"""Script to inspect a dataset, in particular label distribution.
"""
from absl import app
from absl import flags
from libml import data, utils
import numpy as np
import tensorflow as tf
from tqdm import trange
flags.DEFINE_integer('batch', 64, 'Batch size.')
flags.DEFINE_integer('samples', 1 << 16, 'Number of samples to load.')
FLAGS = flags.FLAGS
def main(argv):
del argv
utils.setup_tf()
nbatch = FLAGS.samples // FLAGS.batch
dataset = data.DATASETS[FLAGS.dataset]()
groups = [('labeled', dataset.train_labeled),
('unlabeled', dataset.train_unlabeled),
('test', dataset.test.repeat())]
groups = [(name, ds.batch(FLAGS.batch).prefetch(16).make_one_shot_iterator().get_next())
for name, ds in groups]
with tf.train.MonitoredSession() as sess:
for group, train_data in groups:
stats = np.zeros(dataset.nclass, np.int32)
minmax = [], []
for _ in trange(nbatch, leave=False, unit='img', unit_scale=FLAGS.batch, desc=group):
v = sess.run(train_data)['label']
for u in v:
stats[u] += 1
minmax[0].append(v.min())
minmax[1].append(v.max())
print(group)
print(' Label range', min(minmax[0]), max(minmax[1]))
print(' Stats', ' '.join(['%.2f' % (100 * x) for x in (stats / stats.max())]))
if __name__ == '__main__':
app.run(main)
