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svhn.py
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svhn.py
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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import sys
from scipy.io import loadmat
import numpy as np
from scipy import linalg
import glob
import pickle
from six.moves import xrange # pylint: disable=redefined-builtin
from six.moves import urllib
import tensorflow as tf
from dataset_utils import *
DATA_URL_TRAIN = 'http://ufldl.stanford.edu/housenumbers/train_32x32.mat'
DATA_URL_TEST = 'http://ufldl.stanford.edu/housenumbers/test_32x32.mat'
FLAGS = tf.app.flags.FLAGS
tf.app.flags.DEFINE_string('data_dir', '/tmp/svhn', "")
tf.app.flags.DEFINE_integer('num_labeled_examples', 1000, "The number of labeled examples")
tf.app.flags.DEFINE_integer('num_valid_examples', 1000, "The number of validation examples")
tf.app.flags.DEFINE_integer('dataset_seed', 1, "dataset seed")
NUM_EXAMPLES_TRAIN = 73257
NUM_EXAMPLES_TEST = 26032
def maybe_download_and_extract():
if not os.path.exists(FLAGS.data_dir):
os.makedirs(FLAGS.data_dir)
filepath_train_mat = os.path.join(FLAGS.data_dir, 'train_32x32.mat')
filepath_test_mat = os.path.join(FLAGS.data_dir, 'test_32x32.mat')
if not os.path.exists(filepath_train_mat) or not os.path.exists(filepath_test_mat):
def _progress(count, block_size, total_size):
sys.stdout.write('\r>> Downloading %.1f%%' % (float(count * block_size) / float(total_size) * 100.0))
sys.stdout.flush()
urllib.request.urlretrieve(DATA_URL_TRAIN, filepath_train_mat, _progress)
urllib.request.urlretrieve(DATA_URL_TEST, filepath_test_mat, _progress)
# Training set
print("Loading training data...")
print("Preprocessing training data...")
train_data = loadmat(FLAGS.data_dir + '/train_32x32.mat')
train_x = (-127.5 + train_data['X']) / 255.
train_x = train_x.transpose((3, 0, 1, 2))
train_x = train_x.reshape([train_x.shape[0], -1])
train_y = train_data['y'].flatten().astype(np.int32)
train_y[train_y == 10] = 0
# Test set
print("Loading test data...")
test_data = loadmat(FLAGS.data_dir + '/test_32x32.mat')
test_x = (-127.5 + test_data['X']) / 255.
test_x = test_x.transpose((3, 0, 1, 2))
test_x = test_x.reshape((test_x.shape[0], -1))
test_y = test_data['y'].flatten().astype(np.int32)
test_y[test_y == 10] = 0
np.save('{}/train_images'.format(FLAGS.data_dir), train_x)
np.save('{}/train_labels'.format(FLAGS.data_dir), train_y)
np.save('{}/test_images'.format(FLAGS.data_dir), test_x)
np.save('{}/test_labels'.format(FLAGS.data_dir), test_y)
def load_svhn():
maybe_download_and_extract()
train_images = np.load('{}/train_images.npy'.format(FLAGS.data_dir)).astype(np.float32)
train_labels = np.load('{}/train_labels.npy'.format(FLAGS.data_dir)).astype(np.float32)
test_images = np.load('{}/test_images.npy'.format(FLAGS.data_dir)).astype(np.float32)
test_labels = np.load('{}/test_labels.npy'.format(FLAGS.data_dir)).astype(np.float32)
return (train_images, train_labels), (test_images, test_labels)
def prepare_dataset():
(train_images, train_labels), (test_images, test_labels) = load_svhn()
dirpath = os.path.join(FLAGS.data_dir, 'seed' + str(FLAGS.dataset_seed))
if not os.path.exists(dirpath):
os.makedirs(dirpath)
rng = np.random.RandomState(FLAGS.dataset_seed)
rand_ix = rng.permutation(NUM_EXAMPLES_TRAIN)
print(rand_ix)
_train_images, _train_labels = train_images[rand_ix], train_labels[rand_ix]
labeled_ind = np.arange(FLAGS.num_labeled_examples)
labeled_train_images, labeled_train_labels = _train_images[labeled_ind], _train_labels[labeled_ind]
_train_images = np.delete(_train_images, labeled_ind, 0)
_train_labels = np.delete(_train_labels, labeled_ind, 0)
convert_images_and_labels(labeled_train_images,
labeled_train_labels,
os.path.join(dirpath, 'labeled_train.tfrecords'))
convert_images_and_labels(train_images, train_labels,
os.path.join(dirpath, 'unlabeled_train.tfrecords'))
convert_images_and_labels(test_images,
test_labels,
os.path.join(dirpath, 'test.tfrecords'))
# Construct dataset for validation
train_images_valid, train_labels_valid = labeled_train_images, labeled_train_labels
test_images_valid, test_labels_valid = \
_train_images[:FLAGS.num_valid_examples], _train_labels[:FLAGS.num_valid_examples]
unlabeled_train_images_valid = np.concatenate(
(train_images_valid, _train_images[FLAGS.num_valid_examples:]), axis=0)
unlabeled_train_labels_valid = np.concatenate(
(train_labels_valid, _train_labels[FLAGS.num_valid_examples:]), axis=0)
convert_images_and_labels(train_images_valid,
train_labels_valid,
os.path.join(dirpath, 'labeled_train_val.tfrecords'))
convert_images_and_labels(unlabeled_train_images_valid,
unlabeled_train_labels_valid,
os.path.join(dirpath, 'unlabeled_train_val.tfrecords'))
convert_images_and_labels(test_images_valid,
test_labels_valid,
os.path.join(dirpath, 'test_val.tfrecords'))
def inputs(batch_size=100,
train=True, validation=False,
shuffle=True, num_epochs=None):
if validation:
if train:
filenames = ['labeled_train_val.tfrecords']
num_examples = FLAGS.num_labeled_examples
else:
filenames = ['test_val.tfrecords']
num_examples = FLAGS.num_valid_examples
else:
if train:
filenames = ['labeled_train.tfrecords']
num_examples = FLAGS.num_labeled_examples
else:
filenames = ['test.tfrecords']
num_examples = NUM_EXAMPLES_TEST
filenames = [os.path.join('seed' + str(FLAGS.dataset_seed), filename) for filename in filenames]
filename_queue = generate_filename_queue(filenames, FLAGS.data_dir, num_epochs)
image, label = read(filename_queue)
image = transform(tf.cast(image, tf.float32)) if train else image
return generate_batch([image, label], num_examples, batch_size, shuffle)
def unlabeled_inputs(batch_size=100,
validation=False,
shuffle=True):
if validation:
filenames = ['unlabeled_train_val.tfrecords']
num_examples = NUM_EXAMPLES_TRAIN - FLAGS.num_valid_examples
else:
filenames = ['unlabeled_train.tfrecords']
num_examples = NUM_EXAMPLES_TRAIN
filenames = [os.path.join('seed' + str(FLAGS.dataset_seed), filename) for filename in filenames]
filename_queue = generate_filename_queue(filenames, data_dir=FLAGS.data_dir)
image, label = read(filename_queue)
image = transform(tf.cast(image, tf.float32))
return generate_batch([image], num_examples, batch_size, shuffle)
def main(argv):
prepare_dataset()
if __name__ == "__main__":
tf.app.run()