"""Implementation of sample defense. This defense loads inception v3 checkpoint and classifies all images using loaded checkpoint. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import numpy as np from scipy.misc import imread import tensorflow as tf from tensorflow.contrib.slim.nets import inception slim = tf.contrib.slim tf.flags.DEFINE_string( 'master', '', 'The address of the TensorFlow master to use.') tf.flags.DEFINE_string( 'checkpoint_path', '', 'Path to checkpoint for inception network.') tf.flags.DEFINE_string( 'input_dir', '', 'Input directory with images.') tf.flags.DEFINE_string( 'output_file', '', 'Output file to save labels.') tf.flags.DEFINE_integer( 'image_width', 299, 'Width of each input images.') tf.flags.DEFINE_integer( 'image_height', 299, 'Height of each input images.') tf.flags.DEFINE_integer( 'batch_size', 16, 'How many images process at one time.') FLAGS = tf.flags.FLAGS def load_images(input_dir, batch_shape): """Read png images from input directory in batches. Args: input_dir: input directory batch_shape: shape of minibatch array, i.e. [batch_size, height, width, 3] Yields: filenames: list file names without path of each image Lenght of this list could be less than batch_size, in this case only first few images of the result are elements of the minibatch. images: array with all images from this batch """ images = np.zeros(batch_shape) filenames = [] idx = 0 batch_size = batch_shape[0] for filepath in tf.gfile.Glob(os.path.join(input_dir, '*.png')): with tf.gfile.Open(filepath) as f: image = imread(f, mode='RGB').astype(np.float) / 255.0 # Images for inception classifier are normalized to be in [-1, 1] interval. images[idx, :, :, :] = image * 2.0 - 1.0 filenames.append(os.path.basename(filepath)) idx += 1 if idx == batch_size: yield filenames, images filenames = [] images = np.zeros(batch_shape) idx = 0 if idx > 0: yield filenames, images def main(_): batch_shape = [FLAGS.batch_size, FLAGS.image_height, FLAGS.image_width, 3] num_classes = 1001 tf.logging.set_verbosity(tf.logging.INFO) with tf.Graph().as_default(): # Prepare graph x_input = tf.placeholder(tf.float32, shape=batch_shape) with slim.arg_scope(inception.inception_v3_arg_scope()): _, end_points = inception.inception_v3( x_input, num_classes=num_classes, is_training=False) predicted_labels = tf.argmax(end_points['Predictions'], 1) # Run computation saver = tf.train.Saver(slim.get_model_variables()) session_creator = tf.train.ChiefSessionCreator( scaffold=tf.train.Scaffold(saver=saver), checkpoint_filename_with_path=FLAGS.checkpoint_path, master=FLAGS.master) with tf.train.MonitoredSession(session_creator=session_creator) as sess: with tf.gfile.Open(FLAGS.output_file, 'w') as out_file: for filenames, images in load_images(FLAGS.input_dir, batch_shape): labels = sess.run(predicted_labels, feed_dict={x_input: images}) for filename, label in zip(filenames, labels): out_file.write('{0},{1}\n'.format(filename, label)) if __name__ == '__main__': tf.app.run()