def __init__(self, num_classes):

assert num_classes in [2, 10]

self.num_classes = num_classes

def read_labels(self, path_to_labels):

with open(path_to_labels, 'rb') as f:

labels = np.fromfile(f, dtype=np.uint8)

return labels

def read_all_images(self, path_to_data):

with open(path_to_data, 'rb') as f:

# read whole file in uint8 chunks

everything = np.fromfile(f, dtype=np.uint8)

# We force the data into 3x96x96 chunks, since the

# images are stored in "column-major order", meaning

# that "the first 96*96 values are the red channel,

# the next 96*96 are green, and the last are blue."

# The -1 is since the size of the pictures depends

# on the input file, and this way numpy determines

# the size on its own.

images = np.reshape(everything, (-1, DEPTH, WIDTH, HEIGHT))

# Now transpose the images into a standard image format

# readable by, for example, matplotlib.imshow

# You might want to comment this line or reverse the shuffle

# if you will use a learning algorithm like CNN, since they like

# their channels separated.

images = np.transpose(images, (0, 3, 2, 1))

return images

def download_and_extract(self):

# if the dataset already exists locally, no need to download it again.

if all((

os.path.exists(TRAIN_DATA_PATH),

os.path.exists(TRAIN_LABELS_PATH),

os.path.exists(TEST_DATA_PATH),

os.path.exists(TEST_LABELS_PATH),

)):

return

dest_directory = DATA_DIR

if not os.path.exists(dest_directory):

os.makedirs(dest_directory)

filename = DATA_URL.split('/')[-1]

filepath = os.path.join(dest_directory, filename)

if not os.path.exists(filepath):

def _progress(count, block_size, total_size):

sys.stdout.write('\rDownloading %s %.2f%%' % (filename,

float(count * block_size) / float(total_size) * 100.0))

sys.stdout.flush()

filepath, _ = urllib.urlretrieve(DATA_URL, filepath, reporthook=_progress)

print('Downloaded', filename)

tarfile.open(filepath, 'r:gz').extractall(dest_directory)

def load_dataset(self):

# download the extract the dataset.

self.download_and_extract()

# load the train and test data and labels.

x_train = self.read_all_images(TRAIN_DATA_PATH)

y_train = self.read_labels(TRAIN_LABELS_PATH)

x_test = self.read_all_images(TEST_DATA_PATH)

y_test = self.read_labels(TEST_LABELS_PATH)

# convert all images to floats in the range [0, 1]

x_train = x_train.astype('float32')

# x_train = (x_train + 53 - 127.5) / 127.5

x_train = (x_train - 10) / 255.0

x_test = x_test.astype('float32')

x_test = (x_test - 10) / 255.0

# x_test = (x_test +53 - 127.5) / 127.5

# convert the labels to be zero based.

y_train -= 1

y_test -= 1

if self.num_classes == 2:

# filter only the first 2 cifar10 classes

x_train = x_train[np.isin(y_train, [0, 2])]

y_train = y_train[np.isin(y_train, [0, 2])]

x_test = x_test[np.isin(y_test, [0, 2])]

y_test = y_test[np.isin(y_test, [0, 2])]

# create cifar10 equivalent labels

y_train_cifar10 = np.copy(y_train)

y_test_cifar10 = np.copy(y_test)

# we fix classes bird(1) and car(2) which are swapped in the two datasets

y_train_cifar10[y_train_cifar10 == 2] = 1

y_test_cifar10[y_test_cifar10 == 2] = 1

else:

# create cifar10 equivalent labels

y_train_cifar10 = np.copy(y_train)

y_test_cifar10 = np.copy(y_test)

# we fix classes bird(1) and car(2) which are swapped in the two datasets

y_train_cifar10[y_train_cifar10 == 1] = 10

y_train_cifar10[y_train_cifar10 == 2] = 1

y_train_cifar10[y_train_cifar10 == 10] = 2

y_test_cifar10[y_test_cifar10 == 1] = 10

y_test_cifar10[y_test_cifar10 == 2] = 1

y_test_cifar10[y_test_cifar10 == 10] = 2

# we fix classes horse(6) and monkey(7) which are swapped in the two datasets

# in this case, the resulting class 6, monkey, differs from CIFAR, where it

# corresponds to frogs

y_train_cifar10[y_train_cifar10 == 6] = 10

y_train_cifar10[y_train_cifar10 == 7] = 6

y_train_cifar10[y_train_cifar10 == 10] = 7

y_test_cifar10[y_test_cifar10 == 6] = 10

y_test_cifar10[y_test_cifar10 == 7] = 6

y_test_cifar10[y_test_cifar10 == 10] = 7

# convert labels to hot-one vectors.

y_train_cat = to_categorical(y_train_cifar10, self.num_classes)

y_test_cat = to_categorical(y_test_cifar10, self.num_classes)

return (x_train, y_train, y_train_cifar10, y_train_cat), (x_test, y_test, y_test_cifar10, y_test_cat)

def load_raw_dataset(self):

# download the extract the dataset.

self.download_and_extract()

# load the train and test data and labels.

x_train = self.read_all_images(TRAIN_DATA_PATH)

y_train = self.read_labels(TRAIN_LABELS_PATH)

x_test = self.read_all_images(TEST_DATA_PATH)

y_test = self.read_labels(TEST_LABELS_PATH)

# convert all images to floats in the range [0, 1]

x_train = x_train.astype('float32')

x_train = (x_train - 10) / 255.0

x_test = x_test.astype('float32')

x_test = (x_test - 10) / 255.0

# convert the labels to be zero based.

y_train -= 1

y_test -= 1

# convert labels to hot-one vectors.

y_train_cat = to_categorical(y_train, self.num_classes)

y_test_cat = to_categorical(y_test, self.num_classes)

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

def load_raw_ood_dataset(self, num_training=500, num_test=500):

# download the extract the dataset.

self.download_and_extract()

# load the train and test data and labels.

unlabeled = self.read_all_images(UNLABELED_DATA_PATH)

# convert all images to floats in the range [0, 1]

unlabeled = unlabeled.astype('float32')

unlabeled = (unlabeled - 10) / 255.0

np.random.shuffle(unlabeled)