{
 "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(valGen, \\\n",
    "                    TEST_COVER_DIR, TEST_STEGO_DIR, TEST_COVER_BETA_DIR, TEST_STEGO_BETA_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
}解释一下