{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import os\n", "import sys\n", "os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'\n", "os.environ['CUDA_VISIBLE_DEVICES'] = '1' # set a GPU (with GPU Number)\n", "home = os.path.expanduser(\"~\")\n", "sys.path.append(home + '/tflib/') # path for 'tflib' folder\n", "import matplotlib.pyplot as plt\n", "from scipy.io import loadmat\n", "from SCA_SRNet_Spatial import * # use 'SCA_SRNet_JPEG' for JPEG domain" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def trnGen(cover_path, stego_path, cover_beta_path, stego_beta_path, thread_idx=0, n_threads=1):\n", " IL=os.listdir(cover_path)\n", " img_shape = plt.imread(cover_path +IL[0]).shape\n", " batch = np.empty((2, img_shape[0], img_shape[1], 2), dtype='float32')\n", " while True:\n", " indx = np.random.permutation(len(IL))\n", " for i in indx:\n", " batch[0,:,:,0] = plt.imread(cover_path + IL[i]) # use loadmat for loading JPEG decompressed images \n", " batch[0,:,:,1] = loadmat(cover_beta_path + IL[i].replace('pgm','mat'))['Beta'] # adjust for JPEG images\n", " batch[1,:,:,0] = plt.imread(stego_path + IL[i]) # use loadmat for loading JPEG decompressed images \n", " batch[1,:,:,1] = loadmat(stego_beta_path + IL[i].replace('pgm','mat'))['Beta'] # adjust for JPEG images\n", " rot = random.randint(0,3)\n", " if rand() < 0.5:\n", " yield [np.rot90(batch, rot, axes=[1,2]), np.array([0,1], dtype='uint8')]\n", " else:\n", " yield [np.flip(np.rot90(batch, rot, axes=[1,2]), axis=2), np.array([0,1], dtype='uint8')] " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def valGen(cover_path, stego_path, cover_beta_path, stego_beta_path, thread_idx=0, n_threads=1):\n", " IL=os.listdir(cover_path)\n", " img_shape = plt.imread(cover_path +IL[0]).shape\n", " batch = np.empty((2, img_shape[0], img_shape[1], 2), dtype='float32')\n", " while True:\n", " for i in range(len(IL)):\n", " batch[0,:,:,0] = plt.imread(cover_path + IL[i]) # use loadmat for loading JPEG decompressed images \n", " batch[0,:,:,1] = loadmat(cover_beta_path + IL[i].replace('pgm','mat'))['Beta'] # adjust for JPEG images\n", " batch[1,:,:,0] = plt.imread(stego_path + IL[i]) # use loadmat for loading JPEG decompressed images \n", " batch[1,:,:,1] = loadmat(stego_beta_path + IL[i].replace('pgm','mat'))['Beta'] # adjust for JPEG images\n", " yield [batch, np.array([0,1], dtype='uint8') ]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "train_batch_size = 32\n", "valid_batch_size = 40\n", "max_iter = 500000\n", "train_interval=100\n", "valid_interval=5000\n", "save_interval=5000\n", "num_runner_threads=10\n", "\n", "# save Betas as '.mat' files with variable name \"Beta\" and put them in thier corresponding directoroies. Make sure \n", "# all mat files in the directories can be loaded in Python without any errors.\n", "\n", "TRAIN_COVER_DIR = '/media/Cover_TRN/'\n", "TRAIN_STEGO_DIR = '/media/Stego_WOW_0.5_TRN/'\n", "TRAIN_COVER_BETA_DIR = '/media/Beta_Cover_WOW_0.5_TRN/'\n", "TRAIN_STEGO_BETA_DIR = '/media/Beta_Stego_WOW_0.5_TRN/'\n", "\n", "VALID_COVER_DIR = '/media/Cover_VAL/'\n", "VALID_STEGO_DIR = '/media/Stego_WOW_0.5_VAL/'\n", "VALID_COVER_BETA_DIR = '/media/Beta_Cover_WOW_0.5_VAL/'\n", "VALID_STEGO_BETA_DIR = '/media/Beta_Stego_WOW_0.5_VAL/'\n", "\n", "train_gen = partial(trnGen, \\\n", " TRAIN_COVER_DIR, TRAIN_STEGO_DIR, TRAIN_COVER_BETA_DIR, TRAIN_STEGO_BETA_DIR) \n", "valid_gen = partial(valGen, \\\n", " VALID_COVER_DIR, VALID_STEGO_DIR, VALID_COVER_BETA_DIR, VALID_STEGO_BETA_DIR)\n", "\n", "LOG_DIR= '/media/LogFiles/SCA_WOW_0.5' # path for a log direcotry\n", "# load_path= LOG_DIR + 'Model_460000.ckpt' # continue training from a specific checkpoint\n", "load_path=None # training from scratch\n", "\n", "if not os.path.exists(LOG_DIR):\n", " os.makedirs(LOG_DIR)\n", " \n", "train_ds_size = len(glob(TRAIN_COVER_DIR + '/*')) * 2\n", "valid_ds_size = len(glob(VALID_COVER_DIR +'/*')) * 2\n", "print 'train_ds_size: %i'%train_ds_size\n", "print 'valid_ds_size: %i'%valid_ds_size\n", "\n", "if valid_ds_size % valid_batch_size != 0:\n", " raise ValueError(\"change batch size for validation\")\n", "\n", "optimizer = AdamaxOptimizer\n", "boundaries = [400000] # learning rate adjustment at iteration 400K\n", "values = [0.001, 0.0001] # learning rates\n", "train(SCA_SRNet, train_gen, valid_gen , train_batch_size, valid_batch_size, valid_ds_size, \\\n", " optimizer, boundaries, values, train_interval, valid_interval, max_iter,\\\n", " save_interval, LOG_DIR,num_runner_threads, load_path)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Testing \n", "TEST_COVER_DIR = '/media/Cover_TST/'\n", "TEST_STEGO_DIR = '/media/Stego_WOW_0.5_TST/'\n", "TEST_COVER_BETA_DIR = '/media/Beta_Cover_WOW_0.5_TST/'\n", "TEST_STEGO_BETA_DIR = '/media/Beta_Stego_WOW_0.5_TST/'\n", "\n", "test_batch_size=40\n", "LOG_DIR = '/media/LogFiles/SCA_WOW_0.5/' \n", "LOAD_DIR = LOG_DIR + 'Model_435000.ckpt' # loading from a specific checkpoint\n", "\n", "test_gen = partial(gen_valid, \\\n", " TEST_COVER_DIR, TEST_STEGO_DIR)\n", "\n", "test_ds_size = len(glob(TEST_COVER_DIR + '/*')) * 2\n", "print 'test_ds_size: %i'%test_ds_size\n", "\n", "if test_ds_size % test_batch_size != 0:\n", " raise ValueError(\"change batch size for testing!\")\n", "\n", "test_dataset(SCA_SRNet, test_gen, test_batch_size, test_ds_size, LOAD_DIR)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.15rc1" } }, "nbformat": 4, "nbformat_minor": 2 }