load_datasets.py

from keras.utils import to_categorical
import numpy as np

def load_cifar10():
    from keras.datasets import cifar10
    (x_train, y_train), (x_test, y_test) = cifar10.load_data()

    x_train = x_train.reshape(-1, 32, 32, 3).astype('float32') / 255.
    x_test = x_test.reshape(-1, 32, 32, 3).astype('float32') / 255.
    # mean = np.mean(x_train, axis=(0,1,2))
    # x_train -= mean
    # x_test -= mean
    
    y_train = to_categorical(y_train.astype('float32'))
    y_test = to_categorical(y_test.astype('float32'))
    
#     return (x_train[0:100], y_train[0:100]), (x_test[0:100], y_test[0:100])
    return (x_train, y_train), (x_test, y_test)

def load_cifar100():
    from keras.datasets import cifar100
    (x_train, y_train), (x_test, y_test) = cifar100.load_data()

    x_train = x_train.reshape(-1, 32, 32, 3).astype('float32') / 255.
    x_test = x_test.reshape(-1, 32, 32, 3).astype('float32') / 255.
    y_train = to_categorical(y_train.astype('float32'))
    y_test = to_categorical(y_test.astype('float32'))

    return (x_train, y_train), (x_test, y_test)

def load_mnist():
    # the data, shuffled and split between train and test sets
    from keras.datasets import mnist
    (x_train, y_train), (x_test, y_test) = mnist.load_data()

    x_train = x_train.reshape(-1, 28, 28, 1).astype('float32') / 255.
    x_test = x_test.reshape(-1, 28, 28, 1).astype('float32') / 255.
    y_train = to_categorical(y_train.astype('float32'))
    y_test = to_categorical(y_test.astype('float32'))
    return (x_train, y_train), (x_test, y_test)


def load_fmnist():
    # the data, shuffled and split between train and test sets
    from keras.datasets import fashion_mnist
    (x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()

    x_train = x_train.reshape(-1, 28, 28, 1).astype('float32') / 255.
    x_test = x_test.reshape(-1, 28, 28, 1).astype('float32') / 255.
    y_train = to_categorical(y_train.astype('float32'))
    y_test = to_categorical(y_test.astype('float32'))
    return (x_train, y_train), (x_test, y_test)


def load_svhn():
    from scipy import io as spio
    from keras.utils import to_categorical
    import numpy as np
    svhn = spio.loadmat("train_32x32.mat")
    x_train = np.einsum('ijkl->lijk', svhn["X"]).astype(np.float32) / 255.
    y_train = to_categorical((svhn["y"] - 1).astype('float32'))

    svhn_test = spio.loadmat("test_32x32.mat")
    x_test = np.einsum('ijkl->lijk', svhn_test["X"]).astype(np.float32) / 255.
    y_test = to_categorical((svhn_test["y"] - 1).astype('float32'))

    print(x_train.shape, y_train.shape, x_test.shape, y_test.shape)
    return (x_train, y_train), (x_test, y_test)


def get_annotations_map():
    valAnnotationsPath = 'tiny_imagenet/tiny-imagenet-200/val/val_annotations.txt'
    valAnnotationsFile = open(valAnnotationsPath, 'r')
    valAnnotationsContents = valAnnotationsFile.read()
    valAnnotations = {}

    for line in valAnnotationsContents.splitlines():
        pieces = line.strip().split()
        valAnnotations[pieces[0]] = pieces[1]

    return valAnnotations


def load_tiny_imagenet(path,num_classes):
    #Load images
    X_train=np.zeros([num_classes*500,64,64, 3],dtype='uint8')
    y_train=np.zeros([num_classes*500], dtype='uint8')

    trainPath=path+'/train'
    
    i=0
    j=0
    annotations={}
    for sChild in os.listdir(trainPath):
        sChildPath = os.path.join(os.path.join(trainPath,sChild),'images')
        annotations[sChild]=j
        for c in os.listdir(sChildPath):
            X=np.array(Image.open(os.path.join(sChildPath,c)))
            if len(np.shape(X))==2:
                X_train[i,:,:,0]=X/255.
                X_train[i,:,:,1]=X/255.
                X_train[i,:,:,2]=X/255.
            else:
                X_train[i]=X/255.
            y_train[i]=j
            i+=1
        j+=1
        if (j >= num_classes):
            break

    val_annotations_map = get_annotations_map()

    X_test = np.zeros([num_classes*50,64,64, 3],dtype='uint8')
    y_test = np.zeros([num_classes*50], dtype='uint8')

    i = 0
    testPath=path+'/val/images'
    for sChild in os.listdir(testPath):
        if val_annotations_map[sChild] in annotations.keys():
            sChildPath = os.path.join(testPath, sChild)
            X=np.array(Image.open(sChildPath))
            if len(np.shape(X))==2:
                X_train[i,:,:,0]=X/255.
                X_train[i,:,:,1]=X/255.
                X_train[i,:,:,2]=X/255.
            else:
                X_test[i]=X/255.
            y_test[i]=annotations[val_annotations_map[sChild]]
            i+=1
        else:
            pass
    
    y_train = to_categorical(y_train.astype('float32'))
    y_test = to_categorical(y_test.astype('float32'))
    return X_train,y_train,X_test,y_test

def resize(data_set, size):
    X_temp = []
    import scipy
    for i in range(data_set.shape[0]):
        resized = scipy.misc.imresize(data_set[i], (size, size))
        X_temp.append(resized)
    X_temp = np.array(X_temp, dtype=np.float32) / 255.
    return X_temp