load_cifar_100.py

import numpy as np
import matplotlib.pyplot as plt
import pickle
import torchvision.transforms as transforms
def unpickle(file):
    with open(file, 'rb') as fo:
        data = pickle.load(fo, encoding='bytes')
    return data

def load_cifar_100_data(data_dir, negatives=False):

    transform = transforms.Compose([
        transforms.Resize(32),
        transforms.CenterCrop(32),
        transforms.ToTensor(),
        transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

    meta_data_dict = unpickle(data_dir + "/meta")
    cifar_label_names = meta_data_dict[b'fine_label_names']
    cifar_label_names = np.array(cifar_label_names)


    cifar_train_data_dict = unpickle(data_dir + "/train")
    cifar_train_data = cifar_train_data_dict[b'data']
    cifar_train_filenames = cifar_train_data_dict[b'filenames']
    cifar_train_labels = cifar_train_data_dict[b'fine_labels']
    cifar_train_data = cifar_train_data.reshape((len(cifar_train_data), 3, 32, 32))


    cifar_test_data_dict = unpickle(data_dir + "/test")
    cifar_test_data = cifar_test_data_dict[b'data']
    cifar_test_filenames = cifar_test_data_dict[b'filenames']
    cifar_test_labels = cifar_test_data_dict[b'fine_labels']
    cifar_test_data = cifar_test_data.reshape((len(cifar_test_data), 3, 32, 32))
    if negatives:
        cifar_test_data = cifar_test_data.transpose(0, 2, 3, 1).astype(np.float32)
    else:
        cifar_test_data = np.rollaxis(cifar_test_data, 1, 4)
    cifar_test_filenames = np.array(cifar_test_filenames)
    cifar_test_labels = np.array(cifar_test_labels)
    return cifar_train_data, cifar_train_filenames, cifar_train_labels, \
        cifar_test_data, cifar_test_filenames, cifar_test_labels, cifar_label_names
if __name__ == "__main__":
    """show it works"""
    cifar_100_dir = "./data/cifar100"#'cifar10'
    train_data, train_filenames, train_labels, test_data, test_filenames, test_labels, label_names = \
        load_cifar_100_data(cifar_100_dir)
    print("Train data: ", train_data.shape)
    print("Train filenames: ", train_filenames.shape)
    print("Train labels: ", train_labels.shape)
    print("Test data: ", test_data.shape)
    print("Test filenames: ", test_filenames.shape)
    print("Test labels: ", test_labels.shape)
    print("Label names: ", label_names.shape)
    # Don't forget that the label_names and filesnames are in binary and need conversion if used.
    # display some random training images in a 25x25 grid
    num_plot = 5
    f, ax = plt.subplots(num_plot, num_plot)
    for m in range(num_plot):
        for n in range(num_plot):
            idx = np.random.randint(0, train_data.shape[0])
            ax[m, n].imshow(train_data[idx])
            ax[m, n].get_xaxis().set_visible(False)
            ax[m, n].get_yaxis().set_visible(False)
    f.subplots_adjust(hspace=0.1)
    f.subplots_adjust(wspace=0)
    plt.show()