TensorFlow/squeezenet/src/generate_cifar_tfrecords.py [15:62]:
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r"""Downloads and converts cifar10 data to TFRecords of TF-Example protos.

This module downloads the cifar10 data, uncompresses it, reads the files
that make up the cifar10 data and creates two TFRecord datasets: one for train
and one for test. Each TFRecord dataset is comprised of a set of TF-Example
protocol buffers, each of which contain a single image and label.

The script should take several minutes to run.

"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os
import sys
import tarfile

import numpy as np
from six.moves import cPickle
from six.moves import urllib
import tensorflow.compat.v1 as tf
import argparse

from models.research.slim.datasets import dataset_utils

# The URL where the CIFAR data can be downloaded.
_DATA_URL = 'https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz'

# The number of training files.
_NUM_TRAIN_FILES = 5

# The height and width of each image.
_IMAGE_SIZE = 32

# The names of the classes.
_CLASS_NAMES = [
    'airplane',
    'automobile',
    'bird',
    'cat',
    'deer',
    'dog',
    'frog',
    'horse',
    'ship',
    'truck',
]
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TensorFlow/squeezenet/src/models/research/slim/datasets/download_and_convert_cifar10.py [15:61]:
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r"""Downloads and converts cifar10 data to TFRecords of TF-Example protos.

This module downloads the cifar10 data, uncompresses it, reads the files
that make up the cifar10 data and creates two TFRecord datasets: one for train
and one for test. Each TFRecord dataset is comprised of a set of TF-Example
protocol buffers, each of which contain a single image and label.

The script should take several minutes to run.

"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os
import sys
import tarfile

import numpy as np
from six.moves import cPickle
from six.moves import urllib
import tensorflow as tf

from datasets import dataset_utils

# The URL where the CIFAR data can be downloaded.
_DATA_URL = 'https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz'

# The number of training files.
_NUM_TRAIN_FILES = 5

# The height and width of each image.
_IMAGE_SIZE = 32

# The names of the classes.
_CLASS_NAMES = [
    'airplane',
    'automobile',
    'bird',
    'cat',
    'deer',
    'dog',
    'frog',
    'horse',
    'ship',
    'truck',
]
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