SSD512_debug.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import cv2\n",
    "import keras\n",
    "from keras import backend as K\n",
    "from keras.applications.imagenet_utils import preprocess_input\n",
    "from keras.models import Model\n",
    "from keras.preprocessing import image\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "#from scipy.misc import imread\n",
    "from imageio import imread\n",
    "import tensorflow as tf\n",
    "import os\n",
    "\n",
    "from ssd_model import SSD512\n",
    "from ssd_utils import PriorUtil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "input_shape = (512, 512, 3)\n",
    "batch_size = 32\n",
    "\n",
    "image_size = input_shape[:2]\n",
    "from data_voc import GTUtility\n",
    "gt_util = GTUtility('data/VOC2007/')\n",
    "gt_util_train, gt_util_val = gt_util.split(gt_util, split=0.8)\n",
    "num_classes = gt_util.num_classes\n",
    "\n",
    "model = SSD512(input_shape, num_classes=num_classes)\n",
    "prior_util = PriorUtil(model)\n",
    "\n",
    "model.load_weights('./models/ssd512_voc_weights_fixed.hdf5', by_name=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "conv1_1                        [64, 512, 512]       [512, 512, 64]       \n",
      "conv1_2                        [64, 512, 512]       [512, 512, 64]       \n",
      "pool1                          [64, 256, 256]       [256, 256, 64]       \n",
      "conv2_1                        [128, 256, 256]      [256, 256, 128]      \n",
      "conv2_2                        [128, 256, 256]      [256, 256, 128]      \n",
      "pool2                          [128, 128, 128]      [128, 128, 128]      \n",
      "conv3_1                        [256, 128, 128]      [128, 128, 256]      \n",
      "conv3_2                        [256, 128, 128]      [128, 128, 256]      \n",
      "conv3_3                        [256, 128, 128]      [128, 128, 256]      \n",
      "pool3                          [256, 64, 64]        [64, 64, 256]        \n",
      "conv4_1                        [512, 64, 64]        [64, 64, 512]        \n",
      "conv4_2                        [512, 64, 64]        [64, 64, 512]        \n",
      "conv4_3                        [512, 64, 64]        [64, 64, 512]        \n",
      "pool4                          [512, 32, 32]        [32, 32, 512]        \n",
      "conv5_1                        [512, 32, 32]        [32, 32, 512]        \n",
      "conv5_2                        [512, 32, 32]        [32, 32, 512]        \n",
      "conv5_3                        [512, 32, 32]        [32, 32, 512]        \n",
      "pool5                          [512, 32, 32]        [32, 32, 512]        \n",
      "fc6                            [1024, 32, 32]       [32, 32, 1024]       \n",
      "fc7                            [1024, 32, 32]       [32, 32, 1024]       \n",
      "conv6_1                        [256, 32, 32]        [32, 32, 256]        \n",
      "conv6_2                        [512, 16, 16]        [16, 16, 512]        \n",
      "conv7_1                        [128, 16, 16]        [16, 16, 128]        \n",
      "conv7_2                        [256, 8, 8]          [8, 8, 256]          \n",
      "conv8_1                        [128, 8, 8]          [8, 8, 128]          \n",
      "conv8_2                        [256, 4, 4]          [4, 4, 256]          \n",
      "conv9_1                        [128, 4, 4]          [4, 4, 128]          \n",
      "conv9_2                        [256, 2, 2]          [2, 2, 256]          \n",
      "conv10_1                       [128, 2, 2]          [2, 2, 128]          \n",
      "conv4_3_norm                   [512, 64, 64]        [64, 64, 512]        \n",
      "conv10_2                       [256, 1, 1]          [1, 1, 256]          \n",
      "conv4_3_norm_mbox_conf         [84, 64, 64]         [64, 64, 84]         \n",
      "fc7_mbox_conf                  [126, 32, 32]        [32, 32, 126]        \n",
      "conv6_2_mbox_conf              [126, 16, 16]        [16, 16, 126]        \n",
      "conv7_2_mbox_conf              [126, 8, 8]          [8, 8, 126]          \n",
      "conv8_2_mbox_conf              [126, 4, 4]          [4, 4, 126]          \n",
      "conv9_2_mbox_conf              [84, 2, 2]           [2, 2, 84]           \n",
      "conv10_2_mbox_conf             [84, 1, 1]           [1, 1, 84]           \n",
      "conv4_3_norm_mbox_loc          [16, 64, 64]         [64, 64, 16]         \n",
      "fc7_mbox_loc                   [24, 32, 32]         [32, 32, 24]         \n",
      "conv6_2_mbox_loc               [24, 16, 16]         [16, 16, 24]         \n",
      "conv7_2_mbox_loc               [24, 8, 8]           [8, 8, 24]           \n",
      "conv8_2_mbox_loc               [24, 4, 4]           [4, 4, 24]           \n",
      "conv9_2_mbox_loc               [16, 2, 2]           [2, 2, 16]           \n",
      "conv10_2_mbox_loc              [16, 1, 1]           [1, 1, 16]           \n",
      "conv4_3_norm_mbox_conf_flat    [344064]             [344064]             \n",
      "fc7_mbox_conf_flat             [129024]             [129024]             \n",
      "conv6_2_mbox_conf_flat         [32256]              [32256]              \n",
      "conv7_2_mbox_conf_flat         [8064]               [8064]               \n",
      "conv8_2_mbox_conf_flat         [2016]               [2016]               \n",
      "conv9_2_mbox_conf_flat         [336]                [336]                \n",
      "conv10_2_mbox_conf_flat        [84]                 [84]                 \n",
      "conv4_3_norm_mbox_loc_flat     [65536]              [65536]              \n",
      "fc7_mbox_loc_flat              [24576]              [24576]              \n",
      "conv6_2_mbox_loc_flat          [6144]               [6144]               \n",
      "conv7_2_mbox_loc_flat          [1536]               [1536]               \n",
      "conv8_2_mbox_loc_flat          [384]                [384]                \n",
      "conv9_2_mbox_loc_flat          [64]                 [64]                 \n",
      "conv10_2_mbox_loc_flat         [16]                 [16]                 \n",
      "mbox_conf                      [515844]             [515844]             \n",
      "mbox_loc                       [98256]              [98256]              \n"
     ]
    }
   ],
   "source": [
    "from utils.caffe2keras import compare_output_shape\n",
    "compare_output_shape(model, './models/ssd512_voc_shape.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "input_1\n",
      "conv1_1\n",
      "conv1_2\n",
      "pool1\n",
      "conv2_1\n",
      "conv2_2\n",
      "pool2\n",
      "conv3_1\n",
      "conv3_2\n",
      "conv3_3\n",
      "pool3\n",
      "conv4_1\n",
      "conv4_2\n",
      "conv4_3\n",
      "pool4\n",
      "conv5_1\n",
      "conv5_2\n",
      "conv5_3\n",
      "pool5\n",
      "fc6\n",
      "fc7\n",
      "conv6_1\n",
      "conv6_2\n",
      "conv7_1\n",
      "conv7_2\n",
      "conv8_1\n",
      "conv8_2\n",
      "conv9_1\n",
      "conv9_2\n",
      "conv10_1\n",
      "conv4_3_norm\n",
      "conv10_2\n",
      "conv4_3_norm_mbox_conf\n",
      "fc7_mbox_conf\n",
      "conv6_2_mbox_conf\n",
      "conv7_2_mbox_conf\n",
      "conv8_2_mbox_conf\n",
      "conv9_2_mbox_conf\n",
      "conv10_2_mbox_conf\n",
      "conv4_3_norm_mbox_loc\n",
      "fc7_mbox_loc\n",
      "conv6_2_mbox_loc\n",
      "conv7_2_mbox_loc\n",
      "conv8_2_mbox_loc\n",
      "conv9_2_mbox_loc\n",
      "conv10_2_mbox_loc\n",
      "conv4_3_norm_mbox_conf_flat\n",
      "fc7_mbox_conf_flat\n",
      "conv6_2_mbox_conf_flat\n",
      "conv7_2_mbox_conf_flat\n",
      "conv8_2_mbox_conf_flat\n",
      "conv9_2_mbox_conf_flat\n",
      "conv10_2_mbox_conf_flat\n",
      "conv4_3_norm_mbox_loc_flat\n",
      "fc7_mbox_loc_flat\n",
      "conv6_2_mbox_loc_flat\n",
      "conv7_2_mbox_loc_flat\n",
      "conv8_2_mbox_loc_flat\n",
      "conv9_2_mbox_loc_flat\n",
      "conv10_2_mbox_loc_flat\n",
      "mbox_conf\n",
      "mbox_loc\n",
      "mbox_conf_logits\n",
      "mbox_loc_final\n",
      "mbox_conf_final\n",
      "predictions\n",
      "[[[-40.740494   -37.282875   -34.36676    -26.80413    -30.24263   ]\n",
      "  [-17.333527   -16.461388   -14.007469    -2.7578735   -5.108452  ]\n",
      "  [ -6.2072372   -9.237984    -7.886551     5.981949     6.4846497 ]\n",
      "  [ -7.4307632   -8.205444    -4.4833603   10.761452    15.6623    ]\n",
      "  [  1.2039642    4.5335693    8.479591    19.349243    24.746628  ]]\n",
      "\n",
      " [[-49.786774   -46.205734   -43.28962    -35.726982   -39.16549   ]\n",
      "  [-25.484894   -24.461388   -22.00747    -10.406166   -12.662163  ]\n",
      "  [-14.50824    -17.322136   -15.963699    -1.3679123   -0.6135254 ]\n",
      "  [-16.955284   -16.977753   -13.191368     2.283554     7.7613907 ]\n",
      "  [ -8.886848    -4.557251    -0.52040863  10.616287    16.81305   ]]\n",
      "\n",
      " [[-57.368256   -53.836594   -50.92047    -43.35784    -46.79634   ]\n",
      "  [-32.126015   -31.122513   -28.668602   -17.77073    -20.215874  ]\n",
      "  [-19.014816   -21.99253    -20.655106    -7.513893    -7.262886  ]\n",
      "  [-17.179535   -18.95282    -15.359337    -0.344635     3.9792328 ]\n",
      "  [ -5.157387    -3.2515945    0.51278687  11.115387    15.713425  ]]]\n"
     ]
    }
   ],
   "source": [
    "from ssd_data import preprocess_image\n",
    "po = np.get_printoptions()\n",
    "np.set_printoptions(formatter={'all': '{:+.6e}'.format})\n",
    "\n",
    "for layer in model.layers:\n",
    "    layer_name = layer.name\n",
    "    \n",
    "    n = 12; p = 5\n",
    "\n",
    "    file_name = os.path.join('ssd512_voc_activations_fish-bike', layer_name+'.npy')\n",
    "    try:\n",
    "        caffe_output = np.load(file_name)\n",
    "    except FileNotFoundError:\n",
    "        print(layer_name)\n",
    "        continue\n",
    "        \n",
    "    if len(caffe_output.shape) == 4:\n",
    "        caffe_output = caffe_output.transpose(0,2,3,1)\n",
    "\n",
    "    input_image = np.round(preprocess_image('pics/fish-bike.jpg', (512,512)), 4)\n",
    "    f = K.function(model.inputs, [model.get_layer(layer_name).output])\n",
    "    keras_output = f([[input_image]])[0]\n",
    "    \n",
    "    try:\n",
    "        diff = np.round(caffe_output[0]-keras_output[0], 9)\n",
    "    except ValueError:\n",
    "        print(\" %s %s\" % (caffe_output.shape, keras_output.shape))\n",
    "        print(layer_name)\n",
    "        continue\n",
    "    \n",
    "    min_diff = np.min(diff)\n",
    "    max_diff = np.max(diff)\n",
    "    mean_diff = np.mean(diff)\n",
    "    max_abs_diff = np.max(np.abs(diff))\n",
    "    diff_ratio = np.nonzero(np.round(diff,4))[0].shape[0] / np.prod(diff.shape)\n",
    "    \n",
    "    print('%-28s %10.6f %10.6f  %3i%%' % (layer_name, mean_diff, max_abs_diff, diff_ratio*100))\n",
    "\n",
    "np.set_printoptions(**po)\n",
    "img_path = './data/images/fish-bike.jpg'\n",
    "\n",
    "img = preprocess_image(img_path, size=image_size, lib='skimage')\n",
    "\n",
    "print(img.transpose((2, 0, 1))[:,:5,:5])"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "caffe preprocessing\n",
    "(3, 512, 512)\n",
    "[[[-40.7405 -37.2829 -34.3668 -26.8041 -30.2426]\n",
    "  [-17.3335 -16.4614 -14.0075  -2.7579  -5.1085]\n",
    "  [ -6.2072  -9.238   -7.8866   5.9819   6.4846]\n",
    "  [ -7.4308  -8.2054  -4.4834  10.7615  15.6623]\n",
    "  [  1.204    4.5336   8.4796  19.3492  24.7466]]\n",
    "\n",
    " [[-49.7868 -46.2057 -43.2896 -35.727  -39.1655]\n",
    "  [-25.4849 -24.4614 -22.0075 -10.4062 -12.6622]\n",
    "  [-14.5082 -17.3221 -15.9637  -1.3679  -0.6135]\n",
    "  [-16.9553 -16.9778 -13.1914   2.2836   7.7614]\n",
    "  [ -8.8868  -4.5573  -0.5204  10.6163  16.813 ]]\n",
    "\n",
    " [[-57.3683 -53.8366 -50.9205 -43.3578 -46.7963]\n",
    "  [-32.126  -31.1225 -28.6686 -17.7707 -20.2159]\n",
    "  [-19.0148 -21.9925 -20.6551  -7.5139  -7.2629]\n",
    "  [-17.1795 -18.9528 -15.3593  -0.3446   3.9792]\n",
    "  [ -5.1574  -3.2516   0.5128  11.1154  15.7134]]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "inputs = []\n",
    "images = []\n",
    "for img_path in ['./data/images/fish-bike.jpg',\n",
    "                 './data/images/cat.jpg',\n",
    "                 './data/images/boys.jpg',\n",
    "                 './data/images/car_cat.jpg',\n",
    "                 './data/images/car_cat2.jpg',\n",
    "                ]:\n",
    "    images.append(imread(img_path))\n",
    "    inputs.append(preprocess_image(img_path, image_size))\n",
    "\n",
    "inputs = np.array(inputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5/5 [==============================] - 0s 27ms/step\n"
     ]
    }
   ],
   "source": [
    "preds = model.predict(inputs, batch_size=1, verbose=1)\n",
    "\n",
    "results = [prior_util.decode(p) for p in preds]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW8AAAEACAYAAAB8nvebAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f514810a2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.32576913  0.          0.60566385  0.60846117  0.99035978 15.        ]\n",
      " [ 0.51347728  0.          1.          0.77319937  0.86698967 15.        ]\n",
      " [ 0.25105869  0.25083642  1.          1.          0.64433998 15.        ]\n",
      " ...\n",
      " [ 0.75996666  0.          1.          0.11296783  0.02257502  1.        ]\n",
      " [ 0.47727379  0.40722293  1.          0.82959997  0.02247155 11.        ]\n",
      " [ 0.88205355  0.          1.          0.2269821   0.02219578  1.        ]]\n"
     ]
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.imshow(images[0])\n",
    "res = prior_util.decode(preds[0])\n",
    "prior_util.plot_results(classes=gt_util.classes, confidence_threshold=0.5) # 0.1 !!!\n",
    "plt.show()\n",
    "print(res)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "caffe model\n",
    "array([[  2.     ,   0.63118,   0.05004,   0.2005 ,   0.94945,   0.98229],\n",
    "       [  7.     ,   0.10457,   0.45818,   0.07994,   0.49172,   0.11543],\n",
    "       [ 14.     ,   0.2004 ,   0.10655,   0.21848,   0.96107,   0.97251],\n",
    "       [ 15.     ,   0.99836,   0.41809,   0.02837,   0.69007,   0.61929]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                             max activation  mean diff   max diff  ratio\n",
      "input_1\n",
      "conv1_1                          518.164124   0.000000   0.000031    0%\n",
      "conv1_2                         1872.668335   0.000000   0.001343   35%\n",
      "pool1                           1872.668335   0.000000   0.001343   43%\n",
      "conv2_1                         2972.968994   0.000000   0.002686   54%\n",
      "conv2_2                         4747.130371   0.000000   0.004883   33%\n",
      "pool2                           4747.130371   0.000000   0.004883   52%\n",
      "conv3_1                         3989.785156   0.000000   0.003662   37%\n",
      "conv3_2                         3428.608643   0.000001   0.003174   36%\n",
      "conv3_3                         3365.445068   0.000000   0.003418   14%\n",
      "pool3                           3365.445068   0.000000   0.003418   24%\n",
      "conv4_1                         2772.205322   0.000000   0.001953   21%\n",
      "conv4_2                          787.670471   0.000000   0.000854   16%\n",
      "conv4_3                          559.113831   0.000000   0.000549    9%\n",
      "pool4                            559.113831   0.000000   0.000488   16%\n",
      "conv5_1                          312.488678   0.000000   0.000168   12%\n",
      "conv5_2                          162.484543   0.000000   0.000084    9%\n",
      "conv5_3                           62.775734   0.000000   0.000042    1%\n",
      "pool5                             62.775734   0.000000   0.000042    5%\n",
      "fc6                               17.861597   0.000000   0.000226    4%\n",
      "fc7                                4.549523   0.000000   0.000049    0%\n",
      "conv6_1                            4.778156   0.000000   0.000039    0%\n",
      "conv6_2                            5.578863   0.019012   2.956600   54%\n",
      "conv7_1                            5.894940   0.037630   3.489933   72%\n",
      "conv7_2                            7.663419   0.068775   6.755099   61%\n",
      "conv8_1                            9.691634   0.100497   5.262675   75%\n",
      "conv8_2                           11.592814   0.116731   7.740747   68%\n",
      "conv9_1                           18.754728   0.249361  14.748998   83%\n",
      "conv9_2                           12.894996   0.516999  12.219683   88%\n",
      "conv10_1                          15.583832   0.674318  15.024913   88%\n",
      "conv4_3_norm                       6.635412   0.000000   0.000008    0%\n",
      "conv10_2                           5.923618   1.021213   6.105431   73%\n",
      "conv4_3_norm_mbox_conf            11.731320   0.000000   0.000012    0%\n",
      "fc7_mbox_conf                     14.705492   0.000000   0.000030    3%\n",
      "conv6_2_mbox_conf                 15.509990   0.001996   4.523293  100%\n",
      "conv7_2_mbox_conf                 18.028910   0.002813   9.100971  100%\n",
      "conv8_2_mbox_conf                 16.830084   0.001444  10.163891  100%\n",
      "conv9_2_mbox_conf                 16.109234   0.039001  11.190186  100%\n",
      "conv10_2_mbox_conf                 6.172953   0.067181   4.013759  100%\n",
      "conv4_3_norm_mbox_loc              4.038123   0.000000   0.000007    0%\n",
      "fc7_mbox_loc                       2.898187   0.000000   0.000017    1%\n",
      "conv6_2_mbox_loc                   3.427546   0.060287   2.158798  100%\n",
      "conv7_2_mbox_loc                   2.633209   0.084234   2.493238  100%\n",
      "conv8_2_mbox_loc                   3.269521   0.021421   2.798764  100%\n",
      "conv9_2_mbox_loc                   0.857027   0.079836   2.533371  100%\n",
      "conv10_2_mbox_loc                  1.195606   0.017838   1.386647  100%\n",
      "conv4_3_norm_mbox_conf_flat       11.731320   0.000000   0.000012    0%\n",
      "fc7_mbox_conf_flat                14.705492   0.000000   0.000030    3%\n",
      "conv6_2_mbox_conf_flat            15.509990   0.001996   4.523293  100%\n",
      "conv7_2_mbox_conf_flat            18.028910   0.002813   9.100971  100%\n",
      "conv8_2_mbox_conf_flat            16.830084   0.001444  10.163891  100%\n",
      "conv9_2_mbox_conf_flat            16.109234   0.039001  11.190186  100%\n",
      "conv10_2_mbox_conf_flat            6.172953   0.067181   4.013759  100%\n",
      "conv4_3_norm_mbox_loc_flat         4.038123   0.000000   0.000007    0%\n",
      "fc7_mbox_loc_flat                  2.898187   0.000000   0.000017    1%\n",
      "conv6_2_mbox_loc_flat              3.427546   0.060287   2.158798  100%\n",
      "conv7_2_mbox_loc_flat              2.633209   0.084234   2.493238  100%\n",
      "conv8_2_mbox_loc_flat              3.269521   0.021421   2.798764  100%\n",
      "conv9_2_mbox_loc_flat              0.857027   0.079836   2.533371  100%\n",
      "conv10_2_mbox_loc_flat             1.195606   0.017838   1.386647  100%\n",
      "mbox_conf                         18.028910   0.000160  11.190186    9%\n",
      "mbox_loc                           4.038123   0.005219   2.798764    8%\n",
      "mbox_conf_logits\n",
      "mbox_loc_final\n",
      "mbox_conf_final\n",
      "predictions\n"
     ]
    }
   ],
   "source": [
    "# layer activation\n",
    "po = np.get_printoptions()\n",
    "#np.set_printoptions(formatter={'all': '{:+.6e}'.format})\n",
    "np.set_printoptions(formatter={'all': '{:+.8f}'.format})\n",
    "\n",
    "print('%-28s %14s %10s %10s  %4s' % ('', 'max activation', 'mean diff', 'max diff', 'ratio'))\n",
    "\n",
    "for layer in model.layers:\n",
    "    layer_name = layer.name\n",
    "    \n",
    "    file_name = os.path.join('./caffe_activation_dumps/ssd512_voc_activations_fish-bike', layer_name+'.npy')\n",
    "    try:\n",
    "        caffe_output = np.load(file_name)\n",
    "    except FileNotFoundError:\n",
    "        print(layer_name)\n",
    "        continue\n",
    "        \n",
    "    if len(caffe_output.shape) == 4:\n",
    "        caffe_output = caffe_output.transpose(0,2,3,1)\n",
    "\n",
    "    input_image = preprocess_image('./data/images/fish-bike.jpg', input_shape[:2])\n",
    "    f = K.function(model.inputs, [model.get_layer(layer_name).output])\n",
    "    keras_output = f([[input_image]])[0]\n",
    "\n",
    "    try:\n",
    "        diff = np.round(caffe_output[0]-keras_output[0], 9)\n",
    "    except ValueError:\n",
    "        print(layer_name)\n",
    "        continue\n",
    "    \n",
    "    max_value = np.max(keras_output)\n",
    "    min_diff = np.abs(np.min(diff))\n",
    "    max_diff = np.abs(np.max(diff))\n",
    "    mean_diff = np.abs(np.mean(diff))\n",
    "    \n",
    "    diff_ratio = np.nonzero(np.round(diff,5))[0].shape[0] / np.prod(diff.shape)\n",
    "    \n",
    "    print('%-28s %14.6f %10.6f %10.6f  %3i%%' % (layer_name, max_value, mean_diff, max_diff, diff_ratio*100))\n",
    "    \n",
    "    n = 12; p = 5\n",
    "    #print(np.round( diff[:n,:n,p], 8))\n",
    "\n",
    "np.set_printoptions(po)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                             input_shape          output_shape         img_size     min_size  max_size  aspect_ratios                variances              clip     flip     step    \n"
     ]
    }
   ],
   "source": [
    "fstr = '%-28s %-20s %-20s %-12s %-9s %-9s %-28s %-22s %-8s %-8s %-8s'\n",
    "print(fstr % ('', 'input_shape', 'output_shape', 'img_size', 'min_size', 'max_size', 'aspect_ratios', 'variances', 'clip', 'flip', 'step'))\n",
    "for l in model.layers:\n",
    "    if l.__class__.__name__ == 'PriorBox':\n",
    "        aspect_ratios = [ round(e, 2) for e in l.aspect_ratios ]\n",
    "        print(fstr % (l.name, l.input_shape, l.output_shape, l.img_size, l.min_size, l.max_size, aspect_ratios, l.variances, l.clip, l.flip, l.step))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "from utils.vis import plot_priorboxes\n",
    "from ssd_utils import calc_priorboxes\n",
    "        \n",
    "img_path = 'pics/fish-bike.jpg'\n",
    "image = imread(img_path)\n",
    "input_image = preprocess_image(img_path)\n",
    "\n",
    "for layer in model.layers:\n",
    "    if layer.__class__.__name__ == 'PriorBox':\n",
    "        prior_boxes = calc_priorboxes(model, layer.name)\n",
    "        nratios = len(layer.aspect_ratios)\n",
    "        nlocs = len(prior_boxes)/nratios\n",
    "        print('%-28s  locations: %-6i  aspect_ratios: %s' % (layer.name, nlocs, layer.aspect_ratios))\n",
    "        plot_priorboxes(prior_boxes, image, nratios)\n",
    "\n",
    "#np.save('ssd512_prior_boxes.npy', calc_priorboxes(model))\n",
    "#pb = np.load('ssd512_prior_boxes.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# compare the prior boxes\n",
    "\n",
    "mbox_priorbox = model.get_layer('mbox_priorbox')\n",
    "f = K.function(model.inputs, [mbox_priorbox.output])\n",
    "pb_keras = f([[input_image]])[0][0][:,:4]\n",
    "\n",
    "pb_caffe = np.load('ssd512_prior_boxes_caffe.npy')\n",
    "\n",
    "print(pb_keras[:10])\n",
    "print(np.array([np.min(pb_keras, 0), np.max(pb_keras, 0), np.mean(pb_keras, 0)]))\n",
    "print()\n",
    "print(pb_caffe[:10])\n",
    "print(np.array([np.min(pb_caffe, 0), np.max(pb_caffe, 0), np.mean(pb_caffe, 0)]))\n",
    "print()\n",
    "pb_diff = pb_keras - pb_caffe\n",
    "print(pb_diff[:10])\n",
    "print(np.mean(pb_diff, 0))\n",
    "print(np.nonzero(np.round(pb_diff, 6))[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# compare the positions\n",
    "\n",
    "pos_keras = np.array([pb_keras[:,0]+pb_keras[:,2], pb_keras[:,1]+pb_keras[:,3]]).T/2\n",
    "pos_caffe = np.array([pb_caffe[:,0]+pb_caffe[:,2], pb_caffe[:,1]+pb_caffe[:,3]]).T/2\n",
    "pos_diff = pos_keras - pos_caffe\n",
    "\n",
    "print(np.array([np.min(pos_keras, 0), np.max(pos_keras, 0), np.mean(pos_keras, 0)]))\n",
    "print()\n",
    "print(np.array([np.min(pos_caffe, 0), np.max(pos_caffe, 0), np.mean(pos_caffe, 0)]))\n",
    "print()\n",
    "print(pos_diff[:20])\n",
    "print(np.nonzero(np.round(pos_diff, 6))[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# compute positions\n",
    "\n",
    "steps = [8, 16, 32, 64, 128, 256, 512]\n",
    "img_width, img_height = 512, 512\n",
    "layer_sizes = (64, 32, 16, 8, 4, 2, 1)\n",
    "aspect_ratios = ([1.0, 1.0, 2, 0.5], [1.0, 1.0, 2, 0.5, 3, 0.3333333333333333], [1.0, 1.0, 2, 0.5, 3, 0.3333333333333333], [1.0, 1.0, 2, 0.5, 3, 0.3333333333333333], [1.0, 1.0, 2, 0.5, 3, 0.3333333333333333], [1.0, 1.0, 2, 0.5], [1.0, 1.0, 2, 0.5])\n",
    "\n",
    "idx = 0\n",
    "for i in range(len(layer_sizes)):\n",
    "    nboxes = len(aspect_ratios[i]) * layer_sizes[i]**2\n",
    "    print(\"%i  %s  %i\" % (layer_sizes[i], aspect_ratios[i], nboxes))\n",
    "    print(np.max(pos_keras[idx:idx+nboxes,0]))\n",
    "    print(np.max(pos_caffe[idx:idx+nboxes,0]))\n",
    "    #print(np.min(pos_keras[idx:idx+nboxes,0]))\n",
    "    #print(np.min(pos_caffe[idx:idx+nboxes,0]))\n",
    "    \n",
    "    layer_width = layer_sizes[i]\n",
    "    \n",
    "    # keras way\n",
    "    step_x = img_width / layer_width\n",
    "    centers_x = np.linspace(step_x / 2., img_width - step_x / 2., layer_width) / img_width\n",
    "    #centers_x = np.array([(0.5 + i) for i in range(layer_width)]) * step_x / img_width\n",
    "    print('%-20s %-20s' % (np.max(centers_x), step_x))\n",
    "    \n",
    "    # caffe way\n",
    "    step_x = steps[i]\n",
    "    centers_x = np.array([(0.5 + i) for i in range(layer_width)]) * step_x / img_width\n",
    "    print('%-20s %-20s' % (np.max(centers_x), step_x))\n",
    "    \n",
    "    print()\n",
    "    idx += nboxes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#calucalation of min and max_size\n",
    "\n",
    "min_dim = 512\n",
    "# conv4_3 ==> 64 x 64\n",
    "# fc7 ==> 32 x 32\n",
    "# conv6_2 ==> 16 x 16\n",
    "# conv7_2 ==> 8 x 8\n",
    "# conv8_2 ==> 4 x 4\n",
    "# conv9_2 ==> 2 x 2\n",
    "# conv10_2 ==> 1 x 1\n",
    "mbox_source_layers = ['conv4_3', 'fc7', 'conv6_2', 'conv7_2', 'conv8_2', 'conv9_2', 'conv10_2']\n",
    "# in percent %\n",
    "min_ratio = 15\n",
    "max_ratio = 90\n",
    "step = int(np.floor((max_ratio - min_ratio) / (len(mbox_source_layers) - 2)))\n",
    "min_sizes = []\n",
    "max_sizes = []\n",
    "for ratio in range(min_ratio, max_ratio + 1, step):\n",
    "    min_sizes.append(min_dim * ratio / 100.)\n",
    "    max_sizes.append(min_dim * (ratio + step) / 100.)\n",
    "min_sizes = [min_dim * 7 / 100.] + min_sizes\n",
    "max_sizes = [min_dim * 15 / 100.] + max_sizes\n",
    "\n",
    "print(min_sizes)\n",
    "print(max_sizes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.set_printoptions(precision=4)\n",
    "import math\n",
    "\n",
    "# calculation of min_sizes / max_sizes values\n",
    "# and why float in prototext and cat to int in PriorBox layer, confusing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[35.84, 76.8, 153.6, 230.4, 307.2, 384.0, 460.8]\n",
      "[76.8, 153.6, 230.4, 307.2, 384.0, 460.8, 537.6]\n"
     ]
    }
   ],
   "source": [
    "min_dim = 512\n",
    "mbox_source_layers = ['conv4_3', 'fc7', 'conv6_2', 'conv7_2', 'conv8_2', 'conv9_2', 'conv10_2']\n",
    "min_ratio = 15\n",
    "max_ratio = 90\n",
    "n = len(mbox_source_layers)\n",
    "\n",
    "step = int(math.floor((max_ratio - min_ratio) / (len(mbox_source_layers) - 2)))\n",
    "min_sizes = []\n",
    "max_sizes = []\n",
    "for ratio in range(min_ratio, max_ratio + 1, step):\n",
    "    min_sizes.append(min_dim * ratio / 100.)\n",
    "    max_sizes.append(min_dim * (ratio + step) / 100.)\n",
    "min_sizes = [min_dim * 7 / 100.] + min_sizes\n",
    "max_sizes = [min_dim * 15 / 100.] + max_sizes\n",
    "print(min_sizes)\n",
    "print(max_sizes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  76.8   ,  131.6571,  186.5143,  241.3714,  296.2286,  351.0857,  405.9429,  460.8   ])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# naiv approach according to the paper\n",
    "np.linspace(min_ratio, max_ratio, n+1) * min_dim / 100."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  35.84,   76.8 ,  153.6 ,  230.4 ,  307.2 ,  384.  ,  460.8 ,  537.6 ])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# why is it done in the following way?\n",
    "a = np.linspace(min_ratio, max_ratio, n-1)\n",
    "a = np.concatenate((np.array([7]), a ,np.array([a[-1]+min_ratio])))\n",
    "a * min_dim / 100."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  76.8,  131.7,  186.5,  241.4,  296.2,  351.1,  405.9,  460.8])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# as in pytb\n",
    "import numpy as np\n",
    "np.set_printoptions(precision=1)\n",
    "\n",
    "s_min = 0.15\n",
    "s_max = 0.90\n",
    "m = 8\n",
    "s = []\n",
    "for k in range(1,m+1):\n",
    "    s_k = s_min + (s_max - s_min) * (k - 1.0) / (m - 1.0) # equation 2\n",
    "    s.append(s_k)\n",
    "np.array(s) * 512"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# compute prior box sizes\n",
    "\n",
    "steps = [8, 16, 32, 64, 128, 256, 512]\n",
    "img_width, img_height = 512, 512\n",
    "layer_sizes = (64, 32, 16, 8, 4, 2, 1)\n",
    "aspect_ratios = ([1.0, 1.0, 2, 0.5], [1.0, 1.0, 2, 0.5, 3, 0.3333333333333333], [1.0, 1.0, 2, 0.5, 3, 0.3333333333333333], [1.0, 1.0, 2, 0.5, 3, 0.3333333333333333], [1.0, 1.0, 2, 0.5, 3, 0.3333333333333333], [1.0, 1.0, 2, 0.5], [1.0, 1.0, 2, 0.5])\n",
    "\n",
    "nboxes = []\n",
    "unique_size_values_keras = []\n",
    "for i in range(len(layer_sizes)):\n",
    "    layer_width = layer_height = layer_sizes[i]\n",
    "    nboxes.append( layer_width * layer_height * len(aspect_ratios[i]) )\n",
    "    min_size = int(min_sizes[i])\n",
    "    max_size = int(max_sizes[i])\n",
    "    box_widths = []\n",
    "    box_heights = []\n",
    "    for ar in aspect_ratios[i]:\n",
    "        if ar == 1 and len(box_widths) > 0:\n",
    "            box_widths.append(np.sqrt(min_size * max_size))\n",
    "            box_heights.append(np.sqrt(min_size * max_size))\n",
    "        else:\n",
    "            box_widths.append(min_size * np.sqrt(ar))\n",
    "            box_heights.append(min_size / np.sqrt(ar))\n",
    "    #print(aspect_ratios[i])\n",
    "    #print(np.array(box_widths)/img_width)\n",
    "    #print(np.array(box_heights)/img_height)\n",
    "    values = np.unique(np.round(np.array(box_widths)/img_width, 4))\n",
    "    print(values)\n",
    "    unique_size_values_keras.append(values)\n",
    "    #print()\n",
    "unique_size_values_keras = np.array(unique_size_values_keras)\n",
    "print()\n",
    "\n",
    "# caffe prior box sizes\n",
    "size_caffe = np.array([pb_caffe[:,2]-pb_caffe[:,0], pb_caffe[:,3]-pb_caffe[:,1]]).T\n",
    "offset = 0\n",
    "unique_size_values_caffe = []\n",
    "for n in nboxes:\n",
    "    values = np.unique(np.round(size_caffe[offset:offset+n],4))\n",
    "    print(values)\n",
    "    unique_size_values_caffe.append(values)\n",
    "    offset += n\n",
    "unique_size_values_caffe = np.array(unique_size_values_caffe)\n",
    "print()\n",
    "\n",
    "for i in range(len(nboxes)):\n",
    "    try:\n",
    "        print(unique_size_values_caffe[i] - unique_size_values_keras[i])\n",
    "    except:\n",
    "        pass"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "kaffe int min_size, max_size\n",
    "[ 0.  0. -0. -0.]\n",
    "[-0. -0. -0.  0.  0. -0.]\n",
    "[-0.  0. -0.  0.  0. -0.]\n",
    "[ 0.  0.  0. -0. -0.  0.]\n",
    "[-0. -0.  0. -0. -0. -0.]\n",
    "[ 0.  0.  0.  0.]\n",
    "[-0.  0.  0. -0.]\n",
    "keras int box_widths, box_heights\n",
    "[-0.0005  0.     -0.001  -0.0009]\n",
    "[ 0.0006  0.0004 -0.     -0.0008 -0.0012 -0.0019]\n",
    "[-0.0004  0.0012  0.     -0.0005  0.0005 -0.0012]\n",
    "[ 0.0005  0.0002  0.      0.0007  0.0003 -0.0005]\n",
    "[ 0.001   0.      0.0006  0.0002]\n",
    "[ 0.0005  0.      0.     -0.0009]\n",
    "keras float box_widths, box_heights\n",
    "[-0.0012 -0.0016 -0.0023 -0.0018]\n",
    "[-0.0007 -0.0008 -0.0012 -0.001  -0.0017 -0.002 ]\n",
    "[-0.0004 -0.0006 -0.0008 -0.0006 -0.0011 -0.0013]\n",
    "[-0.0002 -0.0003 -0.0004 -0.0002 -0.0005 -0.0006]\n",
    "[ 0.      0.     -0.0007  0.    ]\n",
    "[-0.0011 -0.0016 -0.0014 -0.0022]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                             input_shape          output_shape         img_size     min_size  max_size  aspect_ratios                variances              clip     flip     step    \n",
      "conv4_3_norm_mbox_priorbox   (None, 64, 64, 512)  (None, 16384, 8)     (512, 512)   35        76        [1.0, 1.0, 2, 0.5]           [ 0.1  0.1  0.2  0.2]  False    True     None    \n",
      "fc7_mbox_priorbox            (None, 32, 32, 1024) (None, 6144, 8)      (512, 512)   76        153       [1.0, 1.0, 2, 0.5, 3, 0.33]  [ 0.1  0.1  0.2  0.2]  False    True     None    \n",
      "conv6_2_mbox_priorbox        (None, 16, 16, 512)  (None, 1536, 8)      (512, 512)   153       230       [1.0, 1.0, 2, 0.5, 3, 0.33]  [ 0.1  0.1  0.2  0.2]  False    True     None    \n",
      "conv7_2_mbox_priorbox        (None, 8, 8, 256)    (None, 384, 8)       (512, 512)   230       307       [1.0, 1.0, 2, 0.5, 3, 0.33]  [ 0.1  0.1  0.2  0.2]  False    True     None    \n",
      "conv8_2_mbox_priorbox        (None, 4, 4, 256)    (None, 96, 8)        (512, 512)   307       384       [1.0, 1.0, 2, 0.5, 3, 0.33]  [ 0.1  0.1  0.2  0.2]  False    True     None    \n",
      "conv9_2_mbox_priorbox        (None, 2, 2, 256)    (None, 16, 8)        (512, 512)   384       460       [1.0, 1.0, 2, 0.5]           [ 0.1  0.1  0.2  0.2]  False    True     None    \n",
      "conv10_2_mbox_priorbox       (None, 1, 1, 256)    (None, 4, 8)         (512, 512)   460       537       [1.0, 1.0, 2, 0.5]           [ 0.1  0.1  0.2  0.2]  False    True     None    \n"
     ]
    }
   ],
   "source": [
    "fstr = '%-28s %-20s %-20s %-12s %-9s %-9s %-28s %-22s %-8s %-8s %-8s'\n",
    "print(fstr % ('', 'input_shape', 'output_shape', 'img_size', 'min_size', 'max_size', 'aspect_ratios', 'variances', 'clip', 'flip', 'step'))\n",
    "for l in model.layers:\n",
    "    if l.__class__.__name__ == 'PriorBox':\n",
    "        aspect_ratios = [ round(r, 2) for r in l.aspect_ratios ]\n",
    "        print(fstr % (l.name, l.input_shape, l.output_shape, l.img_size, l.min_size, l.max_size, aspect_ratios, l.variances, l.clip, l.flip, l.step))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "layer_widths = [64, 32, 16, 8, 4, 2, 1]\n",
    "\n",
    "layer_widths = []\n",
    "for l in model.layers:\n",
    "    if l.__class__.__name__ == 'PriorBox':\n",
    "        layer_widths.append(l.input_shape[1])\n",
    "\n",
    "gamma = 1.5\n",
    "image_width = image_size[0]\n",
    "a = image_width / np.array(layer_widths) * gamma\n"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}