cifar10.py

########################################################################
#
# Functions for downloading the CIFAR-10 data-set from the internet
# and loading it into memory.
#
# Implemented in Python 3.5
#
# Usage:
# 1) Set the variable data_path with the desired storage path.
# 2) Call maybe_download_and_extract() to download the data-set
#    if it is not already located in the given data_path.
# 3) Call load_class_names() to get an array of the class-names.
# 4) Call load_training_data() and load_test_data() to get
#    the images, class-numbers and one-hot encoded class-labels
#    for the training-set and test-set.
# 5) Use the returned data in your own program.
#
# Format:
# The images for the training- and test-sets are returned as 4-dim numpy
# arrays each with the shape: [image_number, height, width, channel]
# where the individual pixels are floats between 0.0 and 1.0.
#
########################################################################
#
# This file is part of the TensorFlow Tutorials available at:
#
# https://github.com/Hvass-Labs/TensorFlow-Tutorials
#
# Published under the MIT License. See the file LICENSE for details.
#
# Copyright 2016 by Magnus Erik Hvass Pedersen
#
########################################################################

import numpy as np
import pickle
import os
import download
from dataset import one_hot_encoded

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# Directory where you want to download and save the data-set.
# Set this before you start calling any of the functions below.
data_path = "data/CIFAR-10/"

# URL for the data-set on the internet.
data_url = "https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz"

########################################################################
# Various constants for the size of the images.
# Use these constants in your own program.

# Width and height of each image.
img_size = 32

# Number of channels in each image, 3 channels: Red, Green, Blue.
num_channels = 3

# Length of an image when flattened to a 1-dim array.
img_size_flat = img_size * img_size * num_channels

# Number of classes.
num_classes = 10

########################################################################
# Various constants used to allocate arrays of the correct size.

# Number of files for the training-set.
_num_files_train = 5

# Number of images for each batch-file in the training-set.
_images_per_file = 10000

# Total number of images in the training-set.
# This is used to pre-allocate arrays for efficiency.
_num_images_train = _num_files_train * _images_per_file

########################################################################
# Private functions for downloading, unpacking and loading data-files.


def _get_file_path(filename=""):
    """
    Return the full path of a data-file for the data-set.

    If filename=="" then return the directory of the files.
    """

    return os.path.join(data_path, "cifar-10-batches-py/", filename)


def _unpickle(filename):
    """
    Unpickle the given file and return the data.

    Note that the appropriate dir-name is prepended the filename.
    """

    # Create full path for the file.
    file_path = _get_file_path(filename)

    print("Loading data: " + file_path)

    with open(file_path, mode='rb') as file:
        # In Python 3.X it is important to set the encoding,
        # otherwise an exception is raised here.
        data = pickle.load(file, encoding='bytes')

    return data


def _convert_images(raw):
    """
    Convert images from the CIFAR-10 format and
    return a 4-dim array with shape: [image_number, height, width, channel]
    where the pixels are floats between 0.0 and 1.0.
    """

    # Convert the raw images from the data-files to floating-points.
    raw_float = np.array(raw, dtype=float) / 255.0

    # Reshape the array to 4-dimensions.
    images = raw_float.reshape([-1, num_channels, img_size, img_size])

    # Reorder the indices of the array.
    images = images.transpose([0, 2, 3, 1])

    return images


def _load_data(filename):
    """
    Load a pickled data-file from the CIFAR-10 data-set
    and return the converted images (see above) and the class-number
    for each image.
    """

    # Load the pickled data-file.
    data = _unpickle(filename)

    # Get the raw images.
    raw_images = data[b'data']

    # Get the class-numbers for each image. Convert to numpy-array.
    cls = np.array(data[b'labels'])

    # Convert the images.
    images = _convert_images(raw_images)

    return images, cls


########################################################################
# Public functions that you may call to download the data-set from
# the internet and load the data into memory.


def maybe_download_and_extract():
    """
    Download and extract the CIFAR-10 data-set if it doesn't already exist
    in data_path (set this variable first to the desired path).
    """

    download.maybe_download_and_extract(url=data_url, download_dir=data_path)


def load_class_names():
    """
    Load the names for the classes in the CIFAR-10 data-set.

    Returns a list with the names. Example: names[3] is the name
    associated with class-number 3.
    """

    # Load the class-names from the pickled file.
    raw = _unpickle(filename="batches.meta")[b'label_names']

    # Convert from binary strings.
    names = [x.decode('utf-8') for x in raw]

    return names


def load_training_data():
    """
    Load all the training-data for the CIFAR-10 data-set.

    The data-set is split into 5 data-files which are merged here.

    Returns the images, class-numbers and one-hot encoded class-labels.
    """

    # Pre-allocate the arrays for the images and class-numbers for efficiency.
    images = np.zeros(shape=[_num_images_train, img_size, img_size, num_channels], dtype=float)
    cls = np.zeros(shape=[_num_images_train], dtype=int)

    # Begin-index for the current batch.
    begin = 0

    # For each data-file.
    for i in range(_num_files_train):
        # Load the images and class-numbers from the data-file.
        images_batch, cls_batch = _load_data(filename="data_batch_" + str(i + 1))

        # Number of images in this batch.
        num_images = len(images_batch)

        # End-index for the current batch.
        end = begin + num_images

        # Store the images into the array.
        images[begin:end, :] = images_batch

        # Store the class-numbers into the array.
        cls[begin:end] = cls_batch

        # The begin-index for the next batch is the current end-index.
        begin = end

    return images, cls, one_hot_encoded(class_numbers=cls, num_classes=num_classes)


def load_test_data():
    """
    Load all the test-data for the CIFAR-10 data-set.

    Returns the images, class-numbers and one-hot encoded class-labels.
    """

    images, cls = _load_data(filename="test_batch")

    return images, cls, one_hot_encoded(class_numbers=cls, num_classes=num_classes)

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