small_object_detection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"3\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patheffects as PathEffects\n",
    "import matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import struct\n",
    "import numpy as np\n",
    "from utils import *\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import sklearn\n",
    "from sklearn.manifold import TSNE\n",
    "from time import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "import argparse\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "from torchvision import datasets, transforms\n",
    "from torch.autograd import Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from glow import thops\n",
    "from glow import modules\n",
    "from glow import models\n",
    "from glow.config import JsonConfig\n",
    "import cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'glow.modules' from 'glow/modules.pyc'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import importlib\n",
    "reload(models)\n",
    "reload(modules)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lable_dict = {'/home/hyshuai/workspace/dataset/images_background/Grantha/character32': 605, '/home/hyshuai/workspace/dataset/images_background/Korean/character29': 276, '/home/hyshuai/workspace/dataset/images_background/Asomtavruli_(Georgian)/character40': 786, '/home/hyshuai/workspace/dataset/images_background/Japanese_(katakana)/character32': 319, '/home/hyshuai/workspace/dataset/images_background/Ojibwe_(Canadian_Aboriginal_Syllabics)/character07': 419, '/home/hyshuai/workspace/dataset/images_background/Early_Aramaic/character12': 415, '/home/hyshuai/workspace/dataset/images_background/Early_Aramaic/character13': 940, '/home/hyshuai/workspace/dataset/images_background/Early_Aramaic/character10': 591, '/home/hyshuai/workspace/dataset/images_background/Early_Aramaic/character11': 357, '/home/hyshuai/workspace/dataset/images_background/Early_Aramaic/character16': 87, '/home/hyshuai/workspace/dataset/images_background/Early_Aramaic/character17': 67, '/home/hyshuai/workspace/dataset/images_background/Early_Aramaic/character14': 908, '/home/hyshuai/workspace/dataset/images_background/Early_Aramaic/character15': 682, '/home/hyshuai/workspace/dataset/images_evaluation/Mongolian/character14': 295, '/home/hyshuai/workspace/dataset/images_background/Blackfoot_(Canadian_Aboriginal_Syllabics)/character12': 465, '/home/hyshuai/workspace/dataset/images_background/Early_Aramaic/character18': 744, '/home/hyshuai/workspace/dataset/images_background/Early_Aramaic/character19': 690, '/home/hyshuai/workspace/dataset/images_background/Blackfoot_(Canadian_Aboriginal_Syllabics)/character13': 720, '/home/hyshuai/workspace/dataset/images_background/Futurama/character18': 41, '/home/hyshuai/workspace/dataset/images_background/Futurama/character19': 402, '/home/hyshuai/workspace/dataset/images_background/Greek/character14': 768, '/home/hyshuai/workspace/dataset/images_background/Blackfoot_(Canadian_Aboriginal_Syllabics)/character10': 590, '/home/hyshuai/workspace/dataset/images_background/Futurama/character10': 983, '/home/hyshuai/workspace/dataset/images_background/Futurama/character11': 502, '/home/hyshuai/workspace/dataset/images_background/Futurama/character12': 583, '/home/hyshuai/workspace/dataset/images_background/Blackfoot_(Canadian_Aboriginal_Syllabics)/character11': 849, '/home/hyshuai/workspace/dataset/images_background/Futurama/character14': 520, '/home/hyshuai/workspace/dataset/images_background/Futurama/character15': 719, '/home/hyshuai/workspace/dataset/images_background/Futurama/character16': 526, '/home/hyshuai/workspace/dataset/images_background/Futurama/character17': 615, '/home/hyshuai/workspace/dataset/images_background/Burmese_(Myanmar)/character25': 195, '/home/hyshuai/workspace/dataset/images_background/Burmese_(Myanmar)/character24': 608, '/home/hyshuai/workspace/dataset/images_background/Burmese_(Myanmar)/character27': 310, '/home/hyshuai/workspace/dataset/images_background/Burmese_(Myanmar)/character26': 355, '/home/hyshuai/workspace/dataset/images_background/Burmese_(Myanmar)/character21': 66, '/home/hyshuai/workspace/dataset/images_background/Burmese_(Myanmar)/character20': 398, '/home/hyshuai/workspace/dataset/images_background/Burmese_(Myanmar)/character23': 287, '/home/hyshuai/workspace/dataset/images_background/Burmese_(Myanmar)/character22': 664, '/home/hyshuai/workspace/dataset/images_background/Asomtavruli_(Georgian)/character25': 646, '/home/hyshuai/workspace/dataset/images_background/Asomtavruli_(Georgian)/character24': 507, '/home/hyshuai/workspace/dataset/images_background/Asomtavruli_(Georgian)/character27': 815, '/home/hyshuai/workspace/dataset/images_background/Asomtavruli_(Georgian)/character26': 342, '/home/hyshuai/workspace/dataset/images_background/Burmese_(Myanmar)/character29': 624, '/home/hyshuai/workspace/dataset/images_background/Burmese_(Myanmar)/character28': 982, '/home/hyshuai/workspace/dataset/images_background/Asomtavruli_(Georgian)/character23': 481, '/home/hyshuai/workspace/dataset/images_background/Asomtavruli_(Georgian)/character22': 941, '/home/hyshuai/workspace/dataset/images_background/Japanese_(katakana)/character28': 471, '/home/hyshuai/workspace/dataset/images_evaluation/Keble/character24': 687, '/home/hyshuai/workspace/dataset/images_background/Japanese_(katakana)/character29': 578, '/home/hyshuai/workspace/dataset/images_evaluation/Keble/character21': 909, '/home/hyshuai/workspace/dataset/images_background/Futurama/character13': 70, '/home/hyshuai/workspace/dataset/images_background/Japanese_(hiragana)/character45': 180, '/home/hyshuai/workspace/dataset/images_background/Japanese_(hiragana)/character44': 83, '/home/hyshuai/workspace/dataset/images_background/Japanese_(hiragana)/character47': 665, '/home/hyshuai/workspace/dataset/images_background/Japanese_(hiragana)/character46': 809, '/home/hyshuai/workspace/dataset/images_background/Japanese_(hiragana)/character41': 446, '/home/hyshuai/workspace/dataset/images_background/Japanese_(hiragana)/character40': 821, '/home/hyshuai/workspace/dataset/images_background/Japanese_(hiragana)/character43': 510, '/home/hyshuai/workspace/dataset/images_background/Japanese_(hiragana)/character42': 8, '/home/hyshuai/workspace/dataset/images_background/Syriac_(Estrangelo)/character22': 109, '/home/hyshuai/workspace/dataset/images_background/Syriac_(Estrangelo)/character23': 120, '/home/hyshuai/workspace/dataset/images_background/Syriac_(Estrangelo)/character20': 88, '/home/hyshuai/workspace/dataset/images_background/Syriac_(Estrangelo)/character21': 562, '/home/hyshuai/workspace/dataset/images_background/Japanese_(hiragana)/character49': 318, '/home/hyshuai/workspace/dataset/images_background/Japanese_(hiragana)/character48': 539, '/home/hyshuai/workspace/dataset/images_background/Japanese_(katakana)/character23': 543, '/home/hyshuai/workspace/dataset/images_background/Japanese_(katakana)/character20': 942, '/home/hyshuai/workspace/dataset/images_background/Japanese_(katakana)/character21': 767, '/home/hyshuai/workspace/dataset/images_background/Sanskrit/character34': 770, '/home/hyshuai/workspace/dataset/images_background/Sanskrit/character35': 132, '/home/hyshuai/workspace/dataset/images_background/Sanskrit/character36': 362, '/home/hyshuai/workspace/dataset/images_background/Sanskrit/character37': 61, '/home/hyshuai/workspace/dataset/images_background/Sanskrit/character30': 973, '/home/hyshuai/workspace/dataset/images_background/Sanskrit/character31': 474, '/home/hyshuai/workspace/dataset/images_background/Sanskrit/character32': 138, '/home/hyshuai/workspace/dataset/images_background/Sanskrit/character33': 598, '/home/hyshuai/workspace/dataset/images_background/Japanese_(hiragana)/character04': 607, '/home/hyshuai/workspace/dataset/images_background/Sanskrit/character38': 2, 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'/home/hyshuai/workspace/dataset/images_background/Latin/character20': 883, '/home/hyshuai/workspace/dataset/images_background/Latin/character23': 816, '/home/hyshuai/workspace/dataset/images_background/Latin/character22': 509, '/home/hyshuai/workspace/dataset/images_background/Latin/character25': 56, '/home/hyshuai/workspace/dataset/images_background/Latin/character24': 951, '/home/hyshuai/workspace/dataset/images_background/Latin/character26': 399, '/home/hyshuai/workspace/dataset/images_background/Greek/character19': 129, '/home/hyshuai/workspace/dataset/images_background/Alphabet_of_the_Magi/character01': 444, '/home/hyshuai/workspace/dataset/images_background/Alphabet_of_the_Magi/character02': 594, '/home/hyshuai/workspace/dataset/images_background/Alphabet_of_the_Magi/character03': 313, '/home/hyshuai/workspace/dataset/images_background/Alphabet_of_the_Magi/character04': 976, '/home/hyshuai/workspace/dataset/images_background/Alphabet_of_the_Magi/character05': 445, '/home/hyshuai/workspace/dataset/images_background/Alphabet_of_the_Magi/character06': 673, '/home/hyshuai/workspace/dataset/images_background/Alphabet_of_the_Magi/character07': 776, '/home/hyshuai/workspace/dataset/images_background/Alphabet_of_the_Magi/character08': 277, '/home/hyshuai/workspace/dataset/images_background/Alphabet_of_the_Magi/character09': 501, '/home/hyshuai/workspace/dataset/images_background/Greek/character07': 721, '/home/hyshuai/workspace/dataset/images_background/N_Ko/character23': 743, '/home/hyshuai/workspace/dataset/images_background/Armenian/character29': 238, '/home/hyshuai/workspace/dataset/images_background/Armenian/character28': 366, '/home/hyshuai/workspace/dataset/images_background/Greek/character23': 739, '/home/hyshuai/workspace/dataset/images_background/Greek/character22': 775, '/home/hyshuai/workspace/dataset/images_background/Greek/character21': 40, '/home/hyshuai/workspace/dataset/images_background/Greek/character20': 967, '/home/hyshuai/workspace/dataset/images_background/Armenian/character23': 921, '/home/hyshuai/workspace/dataset/images_background/Armenian/character22': 798, '/home/hyshuai/workspace/dataset/images_background/Armenian/character21': 147, '/home/hyshuai/workspace/dataset/images_background/Greek/character24': 184, '/home/hyshuai/workspace/dataset/images_background/Balinese/character01': 864, '/home/hyshuai/workspace/dataset/images_background/Balinese/character03': 546, '/home/hyshuai/workspace/dataset/images_background/Balinese/character02': 866, '/home/hyshuai/workspace/dataset/images_background/Balinese/character05': 931, '/home/hyshuai/workspace/dataset/images_background/Balinese/character04': 916, '/home/hyshuai/workspace/dataset/images_background/Balinese/character07': 964, '/home/hyshuai/workspace/dataset/images_background/Balinese/character06': 617, '/home/hyshuai/workspace/dataset/images_background/Balinese/character09': 237, '/home/hyshuai/workspace/dataset/images_background/Balinese/character08': 580, '/home/hyshuai/workspace/dataset/images_background/N_Ko/character22': 841, '/home/hyshuai/workspace/dataset/images_background/Asomtavruli_(Georgian)/character14': 58, '/home/hyshuai/workspace/dataset/images_background/Asomtavruli_(Georgian)/character15': 491, '/home/hyshuai/workspace/dataset/images_background/Asomtavruli_(Georgian)/character16': 443, '/home/hyshuai/workspace/dataset/images_background/Asomtavruli_(Georgian)/character17': 306, '/home/hyshuai/workspace/dataset/images_background/Asomtavruli_(Georgian)/character10': 737, '/home/hyshuai/workspace/dataset/images_background/Asomtavruli_(Georgian)/character11': 796, '/home/hyshuai/workspace/dataset/images_background/Asomtavruli_(Georgian)/character12': 616, '/home/hyshuai/workspace/dataset/images_background/Asomtavruli_(Georgian)/character13': 900, '/home/hyshuai/workspace/dataset/images_background/Asomtavruli_(Georgian)/character18': 243, '/home/hyshuai/workspace/dataset/images_background/Asomtavruli_(Georgian)/character19': 956, '/home/hyshuai/workspace/dataset/images_background/Gujarati/character23': 82, '/home/hyshuai/workspace/dataset/images_evaluation/Keble/character16': 307, '/home/hyshuai/workspace/dataset/images_background/Cyrillic/character09': 301, '/home/hyshuai/workspace/dataset/images_background/Cyrillic/character08': 272, '/home/hyshuai/workspace/dataset/images_background/Cyrillic/character07': 112, '/home/hyshuai/workspace/dataset/images_background/Cyrillic/character06': 829, '/home/hyshuai/workspace/dataset/images_background/Cyrillic/character05': 701, '/home/hyshuai/workspace/dataset/images_background/Cyrillic/character04': 226, '/home/hyshuai/workspace/dataset/images_background/Cyrillic/character03': 765, '/home/hyshuai/workspace/dataset/images_background/Cyrillic/character02': 654, '/home/hyshuai/workspace/dataset/images_background/Cyrillic/character01': 832, '/home/hyshuai/workspace/dataset/images_background/Korean/character27': 412, '/home/hyshuai/workspace/dataset/images_background/Malay_(Jawi_-_Arabic)/character40': 894, '/home/hyshuai/workspace/dataset/images_background/Alphabet_of_the_Magi/character16': 285, '/home/hyshuai/workspace/dataset/images_background/Tifinagh/character22': 454, '/home/hyshuai/workspace/dataset/images_background/Tifinagh/character23': 10, '/home/hyshuai/workspace/dataset/images_background/Tifinagh/character20': 521, '/home/hyshuai/workspace/dataset/images_background/Tifinagh/character21': 442, '/home/hyshuai/workspace/dataset/images_background/Tifinagh/character26': 974, '/home/hyshuai/workspace/dataset/images_background/Tifinagh/character27': 302, '/home/hyshuai/workspace/dataset/images_background/Tifinagh/character24': 534, '/home/hyshuai/workspace/dataset/images_background/Tifinagh/character25': 351, '/home/hyshuai/workspace/dataset/images_background/N_Ko/character24': 359, '/home/hyshuai/workspace/dataset/images_background/Malay_(Jawi_-_Arabic)/character37': 934, '/home/hyshuai/workspace/dataset/images_background/Tifinagh/character28': 568, '/home/hyshuai/workspace/dataset/images_background/Tifinagh/character29': 300, '/home/hyshuai/workspace/dataset/images_background/Malay_(Jawi_-_Arabic)/character32': 34, '/home/hyshuai/workspace/dataset/images_background/Malay_(Jawi_-_Arabic)/character33': 979, '/home/hyshuai/workspace/dataset/images_background/Malay_(Jawi_-_Arabic)/character30': 804, '/home/hyshuai/workspace/dataset/images_background/Malay_(Jawi_-_Arabic)/character31': 560, '/home/hyshuai/workspace/dataset/images_background/Sanskrit/character12': 876, '/home/hyshuai/workspace/dataset/images_background/Sanskrit/character13': 980, '/home/hyshuai/workspace/dataset/images_background/Futurama/character03': 39, '/home/hyshuai/workspace/dataset/images_background/Sanskrit/character10': 337, '/home/hyshuai/workspace/dataset/images_background/Futurama/character02': 189, '/home/hyshuai/workspace/dataset/images_background/Mkhedruli_(Georgian)/character36': 338, '/home/hyshuai/workspace/dataset/images_background/Mkhedruli_(Georgian)/character37': 755, '/home/hyshuai/workspace/dataset/images_background/Mkhedruli_(Georgian)/character34': 376, '/home/hyshuai/workspace/dataset/images_background/Mkhedruli_(Georgian)/character35': 423, '/home/hyshuai/workspace/dataset/images_background/Mkhedruli_(Georgian)/character32': 35, '/home/hyshuai/workspace/dataset/images_background/Mkhedruli_(Georgian)/character33': 201, '/home/hyshuai/workspace/dataset/images_background/Mkhedruli_(Georgian)/character30': 886, '/home/hyshuai/workspace/dataset/images_background/Mkhedruli_(Georgian)/character31': 284, '/home/hyshuai/workspace/dataset/images_background/Japanese_(katakana)/character26': 211, '/home/hyshuai/workspace/dataset/images_background/Japanese_(katakana)/character27': 224, '/home/hyshuai/workspace/dataset/images_background/Japanese_(katakana)/character24': 124, '/home/hyshuai/workspace/dataset/images_background/Japanese_(katakana)/character25': 600, '/home/hyshuai/workspace/dataset/images_background/Japanese_(katakana)/character22': 749, '/home/hyshuai/workspace/dataset/images_background/Asomtavruli_(Georgian)/character30': 250, '/home/hyshuai/workspace/dataset/images_background/Mkhedruli_(Georgian)/character38': 924, '/home/hyshuai/workspace/dataset/images_background/Mkhedruli_(Georgian)/character39': 303, '/home/hyshuai/workspace/dataset/images_background/Asomtavruli_(Georgian)/character31': 925, '/home/hyshuai/workspace/dataset/images_background/Sanskrit/character14': 59, '/home/hyshuai/workspace/dataset/images_background/Gujarati/character20': 927, '/home/hyshuai/workspace/dataset/images_background/Asomtavruli_(Georgian)/character36': 257, '/home/hyshuai/workspace/dataset/images_background/Sanskrit/character15': 209, '/home/hyshuai/workspace/dataset/images_background/Asomtavruli_(Georgian)/character37': 889, '/home/hyshuai/workspace/dataset/images_evaluation/Mongolian/character22': 20, '/home/hyshuai/workspace/dataset/images_background/N_Ko/character26': 136, '/home/hyshuai/workspace/dataset/images_background/Asomtavruli_(Georgian)/character34': 552, '/home/hyshuai/workspace/dataset/images_evaluation/Glagolitic/character35': 159, '/home/hyshuai/workspace/dataset/images_background/Asomtavruli_(Georgian)/character35': 652, '/home/hyshuai/workspace/dataset/images_background/Gujarati/character45': 937, '/home/hyshuai/workspace/dataset/images_background/Asomtavruli_(Georgian)/character38': 278, '/home/hyshuai/workspace/dataset/images_background/Korean/character37': 199, '/home/hyshuai/workspace/dataset/images_background/Asomtavruli_(Georgian)/character39': 264, '/home/hyshuai/workspace/dataset/images_background/Korean/character34': 935, '/home/hyshuai/workspace/dataset/images_background/Korean/character40': 130, '/home/hyshuai/workspace/dataset/images_background/Korean/character36': 450, '/home/hyshuai/workspace/dataset/images_background/Gujarati/character48': 25, '/home/hyshuai/workspace/dataset/images_background/Gujarati/character44': 236, '/home/hyshuai/workspace/dataset/images_background/Armenian/character17': 73, '/home/hyshuai/workspace/dataset/images_background/Armenian/character14': 177, '/home/hyshuai/workspace/dataset/images_background/Armenian/character15': 13, '/home/hyshuai/workspace/dataset/images_background/Armenian/character12': 385, '/home/hyshuai/workspace/dataset/images_background/Armenian/character13': 292, '/home/hyshuai/workspace/dataset/images_background/Armenian/character10': 280, '/home/hyshuai/workspace/dataset/images_background/Armenian/character11': 619, '/home/hyshuai/workspace/dataset/images_background/Armenian/character18': 489, '/home/hyshuai/workspace/dataset/images_background/Armenian/character19': 144, '/home/hyshuai/workspace/dataset/images_background/N_Ko/character25': 448, '/home/hyshuai/workspace/dataset/images_background/Grantha/character13': 364, '/home/hyshuai/workspace/dataset/images_background/Grantha/character12': 212, '/home/hyshuai/workspace/dataset/images_background/Grantha/character11': 936, '/home/hyshuai/workspace/dataset/images_background/Grantha/character10': 312, '/home/hyshuai/workspace/dataset/images_background/Grantha/character17': 811, '/home/hyshuai/workspace/dataset/images_background/Grantha/character16': 28, '/home/hyshuai/workspace/dataset/images_background/Grantha/character15': 145, '/home/hyshuai/workspace/dataset/images_background/Grantha/character14': 384, '/home/hyshuai/workspace/dataset/images_background/Grantha/character19': 621, '/home/hyshuai/workspace/dataset/images_background/Grantha/character18': 874, 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'/home/hyshuai/workspace/dataset/images_background/Armenian/character37': 131, '/home/hyshuai/workspace/dataset/images_background/Armenian/character38': 414, '/home/hyshuai/workspace/dataset/images_background/Armenian/character39': 99, '/home/hyshuai/workspace/dataset/images_background/Alphabet_of_the_Magi/character10': 748, '/home/hyshuai/workspace/dataset/images_background/Sanskrit/character11': 169, '/home/hyshuai/workspace/dataset/images_evaluation/Aurek-Besh/character24': 843, '/home/hyshuai/workspace/dataset/images_background/Bengali/character22': 78, '/home/hyshuai/workspace/dataset/images_evaluation/Glagolitic/character29': 717, '/home/hyshuai/workspace/dataset/images_background/Early_Aramaic/character09': 658, '/home/hyshuai/workspace/dataset/images_background/Early_Aramaic/character08': 464, '/home/hyshuai/workspace/dataset/images_background/Early_Aramaic/character05': 179, '/home/hyshuai/workspace/dataset/images_background/Early_Aramaic/character04': 702, '/home/hyshuai/workspace/dataset/images_background/Early_Aramaic/character07': 655, '/home/hyshuai/workspace/dataset/images_background/Early_Aramaic/character06': 856, '/home/hyshuai/workspace/dataset/images_background/Early_Aramaic/character01': 923, '/home/hyshuai/workspace/dataset/images_background/Early_Aramaic/character03': 899, '/home/hyshuai/workspace/dataset/images_background/Early_Aramaic/character02': 208, '/home/hyshuai/workspace/dataset/images_background/Bengali/character38': 254, '/home/hyshuai/workspace/dataset/images_background/Bengali/character39': 734, '/home/hyshuai/workspace/dataset/images_background/Futurama/character25': 222, '/home/hyshuai/workspace/dataset/images_background/Futurama/character24': 421, '/home/hyshuai/workspace/dataset/images_background/Bengali/character36': 163, '/home/hyshuai/workspace/dataset/images_background/Futurama/character26': 789, '/home/hyshuai/workspace/dataset/images_background/Futurama/character21': 417, '/home/hyshuai/workspace/dataset/images_background/Futurama/character20': 933, '/home/hyshuai/workspace/dataset/images_background/Futurama/character23': 105, '/home/hyshuai/workspace/dataset/images_background/Futurama/character22': 146, '/home/hyshuai/workspace/dataset/images_background/Malay_(Jawi_-_Arabic)/character29': 731, '/home/hyshuai/workspace/dataset/images_background/Malay_(Jawi_-_Arabic)/character28': 747, '/home/hyshuai/workspace/dataset/images_background/Tifinagh/character55': 408, '/home/hyshuai/workspace/dataset/images_background/Tifinagh/character54': 341, '/home/hyshuai/workspace/dataset/images_background/Tifinagh/character53': 74, '/home/hyshuai/workspace/dataset/images_background/Tifinagh/character52': 769, '/home/hyshuai/workspace/dataset/images_background/Tifinagh/character51': 601, '/home/hyshuai/workspace/dataset/images_background/Tifinagh/character50': 807, '/home/hyshuai/workspace/dataset/images_background/Malay_(Jawi_-_Arabic)/character21': 433, '/home/hyshuai/workspace/dataset/images_background/Malay_(Jawi_-_Arabic)/character20': 279, '/home/hyshuai/workspace/dataset/images_background/Malay_(Jawi_-_Arabic)/character23': 558, '/home/hyshuai/workspace/dataset/images_background/Malay_(Jawi_-_Arabic)/character22': 902, '/home/hyshuai/workspace/dataset/images_background/Malay_(Jawi_-_Arabic)/character25': 393, '/home/hyshuai/workspace/dataset/images_background/Malay_(Jawi_-_Arabic)/character24': 358, '/home/hyshuai/workspace/dataset/images_background/Malay_(Jawi_-_Arabic)/character27': 111, '/home/hyshuai/workspace/dataset/images_background/Malay_(Jawi_-_Arabic)/character26': 897, '/home/hyshuai/workspace/dataset/images_background/Gujarati/character09': 914, '/home/hyshuai/workspace/dataset/images_background/Gujarati/character08': 283, '/home/hyshuai/workspace/dataset/images_background/Gujarati/character01': 18, '/home/hyshuai/workspace/dataset/images_background/Gujarati/character03': 657, '/home/hyshuai/workspace/dataset/images_background/Gujarati/character02': 955, '/home/hyshuai/workspace/dataset/images_background/Gujarati/character05': 424, '/home/hyshuai/workspace/dataset/images_background/Gujarati/character04': 674, '/home/hyshuai/workspace/dataset/images_background/Gujarati/character07': 348, '/home/hyshuai/workspace/dataset/images_background/Gujarati/character06': 387, '/home/hyshuai/workspace/dataset/images_background/Mkhedruli_(Georgian)/character41': 790, '/home/hyshuai/workspace/dataset/images_background/Mkhedruli_(Georgian)/character40': 215, '/home/hyshuai/workspace/dataset/images_background/Japanese_(katakana)/character39': 649, '/home/hyshuai/workspace/dataset/images_background/Japanese_(katakana)/character38': 602, '/home/hyshuai/workspace/dataset/images_background/Japanese_(katakana)/character35': 142, '/home/hyshuai/workspace/dataset/images_background/Japanese_(katakana)/character34': 466, '/home/hyshuai/workspace/dataset/images_background/Japanese_(katakana)/character37': 541, '/home/hyshuai/workspace/dataset/images_background/Japanese_(katakana)/character36': 245, '/home/hyshuai/workspace/dataset/images_background/Japanese_(katakana)/character31': 482, '/home/hyshuai/workspace/dataset/images_background/Japanese_(katakana)/character30': 890, '/home/hyshuai/workspace/dataset/images_background/Japanese_(katakana)/character33': 597, '/home/hyshuai/workspace/dataset/images_background/Armenian/character40': 202, '/home/hyshuai/workspace/dataset/images_background/Bengali/character05': 42, '/home/hyshuai/workspace/dataset/images_background/Japanese_(hiragana)/character30': 427, '/home/hyshuai/workspace/dataset/images_background/Japanese_(hiragana)/character31': 203, '/home/hyshuai/workspace/dataset/images_background/Japanese_(hiragana)/character32': 368, '/home/hyshuai/workspace/dataset/images_background/Japanese_(hiragana)/character33': 680, '/home/hyshuai/workspace/dataset/images_background/Japanese_(hiragana)/character34': 431, '/home/hyshuai/workspace/dataset/images_background/Japanese_(hiragana)/character35': 324, '/home/hyshuai/workspace/dataset/images_background/Japanese_(hiragana)/character36': 868, '/home/hyshuai/workspace/dataset/images_background/Japanese_(hiragana)/character37': 438, '/home/hyshuai/workspace/dataset/images_background/Japanese_(hiragana)/character38': 229, '/home/hyshuai/workspace/dataset/images_background/Japanese_(hiragana)/character39': 439, '/home/hyshuai/workspace/dataset/images_background/Hebrew/character03': 593, '/home/hyshuai/workspace/dataset/images_background/Hebrew/character02': 893, '/home/hyshuai/workspace/dataset/images_background/Hebrew/character01': 467, '/home/hyshuai/workspace/dataset/images_background/Hebrew/character07': 905, '/home/hyshuai/workspace/dataset/images_background/Hebrew/character06': 834, '/home/hyshuai/workspace/dataset/images_background/Hebrew/character05': 626, '/home/hyshuai/workspace/dataset/images_background/Hebrew/character04': 30, '/home/hyshuai/workspace/dataset/images_background/Hebrew/character09': 824, '/home/hyshuai/workspace/dataset/images_background/Hebrew/character08': 9, '/home/hyshuai/workspace/dataset/images_background/Bengali/character37': 556, '/home/hyshuai/workspace/dataset/images_background/Inuktitut_(Canadian_Aboriginal_Syllabics)/character12': 154, '/home/hyshuai/workspace/dataset/images_background/Bengali/character30': 766, '/home/hyshuai/workspace/dataset/images_background/Asomtavruli_(Georgian)/character05': 625, '/home/hyshuai/workspace/dataset/images_background/Bengali/character31': 378, '/home/hyshuai/workspace/dataset/images_background/Sanskrit/character09': 851, '/home/hyshuai/workspace/dataset/images_background/Sanskrit/character08': 336, '/home/hyshuai/workspace/dataset/images_background/Korean/character35': 799, '/home/hyshuai/workspace/dataset/images_background/Asomtavruli_(Georgian)/character04': 610, '/home/hyshuai/workspace/dataset/images_background/Korean/character33': 663, '/home/hyshuai/workspace/dataset/images_background/Japanese_(hiragana)/character14': 93, '/home/hyshuai/workspace/dataset/images_background/Korean/character31': 557, '/home/hyshuai/workspace/dataset/images_background/Korean/character30': 137, '/home/hyshuai/workspace/dataset/images_background/Sanskrit/character01': 795, '/home/hyshuai/workspace/dataset/images_background/Sanskrit/character03': 919, '/home/hyshuai/workspace/dataset/images_background/Sanskrit/character02': 563, '/home/hyshuai/workspace/dataset/images_background/Sanskrit/character05': 966, '/home/hyshuai/workspace/dataset/images_background/Sanskrit/character04': 286, '/home/hyshuai/workspace/dataset/images_background/Sanskrit/character07': 939, '/home/hyshuai/workspace/dataset/images_background/Sanskrit/character06': 915, '/home/hyshuai/workspace/dataset/images_evaluation/Tengwar/character23': 661, '/home/hyshuai/workspace/dataset/images_background/Grantha/character26': 627, '/home/hyshuai/workspace/dataset/images_background/Grantha/character27': 115, '/home/hyshuai/workspace/dataset/images_background/Grantha/character24': 223, '/home/hyshuai/workspace/dataset/images_background/Grantha/character25': 817, '/home/hyshuai/workspace/dataset/images_background/Grantha/character22': 891, '/home/hyshuai/workspace/dataset/images_background/Grantha/character23': 289, '/home/hyshuai/workspace/dataset/images_background/Grantha/character20': 197, '/home/hyshuai/workspace/dataset/images_background/Grantha/character21': 724, '/home/hyshuai/workspace/dataset/images_background/Japanese_(hiragana)/character52': 745, '/home/hyshuai/workspace/dataset/images_background/Grantha/character28': 298, '/home/hyshuai/workspace/dataset/images_background/Grantha/character29': 758, '/home/hyshuai/workspace/dataset/images_background/Braille/character13': 45, '/home/hyshuai/workspace/dataset/images_background/Braille/character12': 91, '/home/hyshuai/workspace/dataset/images_background/Braille/character11': 499, '/home/hyshuai/workspace/dataset/images_background/Braille/character10': 492, '/home/hyshuai/workspace/dataset/images_background/Braille/character17': 64, '/home/hyshuai/workspace/dataset/images_background/Braille/character16': 463, '/home/hyshuai/workspace/dataset/images_background/Braille/character15': 425, '/home/hyshuai/workspace/dataset/images_background/Braille/character14': 389, '/home/hyshuai/workspace/dataset/images_background/Braille/character19': 225, '/home/hyshuai/workspace/dataset/images_background/Braille/character18': 536, '/home/hyshuai/workspace/dataset/images_background/Asomtavruli_(Georgian)/character33': 232, '/home/hyshuai/workspace/dataset/images_background/Arcadian/character07': 540, '/home/hyshuai/workspace/dataset/images_background/Bengali/character07': 339, '/home/hyshuai/workspace/dataset/images_background/Bengali/character06': 722, '/home/hyshuai/workspace/dataset/images_background/Bengali/character01': 810, '/home/hyshuai/workspace/dataset/images_background/Asomtavruli_(Georgian)/character09': 678, '/home/hyshuai/workspace/dataset/images_background/Bengali/character03': 60, '/home/hyshuai/workspace/dataset/images_background/Bengali/character02': 802, '/home/hyshuai/workspace/dataset/images_background/Asomtavruli_(Georgian)/character08': 230, '/home/hyshuai/workspace/dataset/images_background/Bengali/character09': 262, '/home/hyshuai/workspace/dataset/images_background/Bengali/character08': 622, '/home/hyshuai/workspace/dataset/images_background/Balinese/character12': 943, '/home/hyshuai/workspace/dataset/images_background/Balinese/character13': 764, '/home/hyshuai/workspace/dataset/images_background/Balinese/character10': 675, '/home/hyshuai/workspace/dataset/images_background/Balinese/character11': 403, '/home/hyshuai/workspace/dataset/images_background/Balinese/character16': 461, '/home/hyshuai/workspace/dataset/images_background/Balinese/character17': 293, '/home/hyshuai/workspace/dataset/images_background/Balinese/character14': 343, '/home/hyshuai/workspace/dataset/images_background/Balinese/character15': 405, '/home/hyshuai/workspace/dataset/images_evaluation/Malayalam/character20': 122, '/home/hyshuai/workspace/dataset/images_background/Balinese/character18': 511, '/home/hyshuai/workspace/dataset/images_background/Balinese/character19': 519, '/home/hyshuai/workspace/dataset/images_background/Bengali/character04': 800, '/home/hyshuai/workspace/dataset/images_background/Korean/character23': 16, '/home/hyshuai/workspace/dataset/images_background/Cyrillic/character10': 55, '/home/hyshuai/workspace/dataset/images_background/Tagalog/character15': 6, '/home/hyshuai/workspace/dataset/images_background/Cyrillic/character12': 641, '/home/hyshuai/workspace/dataset/images_background/Cyrillic/character13': 0, '/home/hyshuai/workspace/dataset/images_background/Cyrillic/character14': 681, '/home/hyshuai/workspace/dataset/images_background/Cyrillic/character15': 503, '/home/hyshuai/workspace/dataset/images_background/Cyrillic/character16': 596, '/home/hyshuai/workspace/dataset/images_background/Ojibwe_(Canadian_Aboriginal_Syllabics)/character13': 22, '/home/hyshuai/workspace/dataset/images_background/Cyrillic/character18': 963, '/home/hyshuai/workspace/dataset/images_background/Cyrillic/character19': 396, '/home/hyshuai/workspace/dataset/images_background/Ojibwe_(Canadian_Aboriginal_Syllabics)/character10': 946, '/home/hyshuai/workspace/dataset/images_background/Ojibwe_(Canadian_Aboriginal_Syllabics)/character11': 971, '/home/hyshuai/workspace/dataset/images_background/Tagalog/character11': 875, '/home/hyshuai/workspace/dataset/images_background/Blackfoot_(Canadian_Aboriginal_Syllabics)/character04': 101, '/home/hyshuai/workspace/dataset/images_background/Arcadian/character09': 952, '/home/hyshuai/workspace/dataset/images_background/Arcadian/character08': 365, '/home/hyshuai/workspace/dataset/images_background/Tifinagh/character19': 435, '/home/hyshuai/workspace/dataset/images_background/Tifinagh/character18': 958, '/home/hyshuai/workspace/dataset/images_background/Ojibwe_(Canadian_Aboriginal_Syllabics)/character14': 771, '/home/hyshuai/workspace/dataset/images_background/Gujarati/character35': 220, '/home/hyshuai/workspace/dataset/images_background/Tifinagh/character13': 947, '/home/hyshuai/workspace/dataset/images_background/Tifinagh/character12': 47, '/home/hyshuai/workspace/dataset/images_background/Tifinagh/character11': 922, '/home/hyshuai/workspace/dataset/images_background/Tifinagh/character10': 949, '/home/hyshuai/workspace/dataset/images_background/Tifinagh/character17': 629, '/home/hyshuai/workspace/dataset/images_background/Tifinagh/character16': 697, '/home/hyshuai/workspace/dataset/images_background/Tifinagh/character15': 870, '/home/hyshuai/workspace/dataset/images_background/Tifinagh/character14': 917, '/home/hyshuai/workspace/dataset/images_background/Inuktitut_(Canadian_Aboriginal_Syllabics)/character09': 555, '/home/hyshuai/workspace/dataset/images_background/Gujarati/character37': 455, '/home/hyshuai/workspace/dataset/images_background/Korean/character26': 216, '/home/hyshuai/workspace/dataset/images_background/Inuktitut_(Canadian_Aboriginal_Syllabics)/character03': 725, '/home/hyshuai/workspace/dataset/images_background/Gujarati/character30': 831, '/home/hyshuai/workspace/dataset/images_background/Inuktitut_(Canadian_Aboriginal_Syllabics)/character01': 133, '/home/hyshuai/workspace/dataset/images_background/Inuktitut_(Canadian_Aboriginal_Syllabics)/character07': 326, '/home/hyshuai/workspace/dataset/images_background/Inuktitut_(Canadian_Aboriginal_Syllabics)/character06': 585, '/home/hyshuai/workspace/dataset/images_background/Inuktitut_(Canadian_Aboriginal_Syllabics)/character05': 4, '/home/hyshuai/workspace/dataset/images_background/Gujarati/character31': 630, '/home/hyshuai/workspace/dataset/images_background/Malay_(Jawi_-_Arabic)/character38': 391, '/home/hyshuai/workspace/dataset/images_background/Mkhedruli_(Georgian)/character09': 270, '/home/hyshuai/workspace/dataset/images_background/Mkhedruli_(Georgian)/character08': 803, '/home/hyshuai/workspace/dataset/images_background/Mkhedruli_(Georgian)/character07': 488, '/home/hyshuai/workspace/dataset/images_background/Mkhedruli_(Georgian)/character06': 772, '/home/hyshuai/workspace/dataset/images_background/Mkhedruli_(Georgian)/character05': 861, '/home/hyshuai/workspace/dataset/images_background/Mkhedruli_(Georgian)/character04': 881, '/home/hyshuai/workspace/dataset/images_background/Mkhedruli_(Georgian)/character03': 812, '/home/hyshuai/workspace/dataset/images_background/Mkhedruli_(Georgian)/character02': 171, '/home/hyshuai/workspace/dataset/images_background/Mkhedruli_(Georgian)/character01': 857, '/home/hyshuai/workspace/dataset/images_background/Japanese_(hiragana)/character09': 360, '/home/hyshuai/workspace/dataset/images_background/Malay_(Jawi_-_Arabic)/character36': 819, '/home/hyshuai/workspace/dataset/images_background/Japanese_(hiragana)/character01': 694, '/home/hyshuai/workspace/dataset/images_background/Japanese_(hiragana)/character03': 865, '/home/hyshuai/workspace/dataset/images_background/Japanese_(hiragana)/character02': 252, '/home/hyshuai/workspace/dataset/images_background/Japanese_(hiragana)/character05': 777, '/home/hyshuai/workspace/dataset/images_background/Malay_(Jawi_-_Arabic)/character34': 524, '/home/hyshuai/workspace/dataset/images_background/Japanese_(hiragana)/character07': 181, '/home/hyshuai/workspace/dataset/images_background/Japanese_(hiragana)/character06': 566, '/home/hyshuai/workspace/dataset/images_background/N_Ko/character05': 395, '/home/hyshuai/workspace/dataset/images_background/Malay_(Jawi_-_Arabic)/character35': 642, '/home/hyshuai/workspace/dataset/images_background/Korean/character10': 808, '/home/hyshuai/workspace/dataset/images_background/Gujarati/character38': 833, '/home/hyshuai/workspace/dataset/images_background/Gujarati/character39': 48, '/home/hyshuai/workspace/dataset/images_background/Bengali/character20': 544, '/home/hyshuai/workspace/dataset/images_background/N_Ko/character04': 880, '/home/hyshuai/workspace/dataset/images_background/Tagalog/character06': 437, '/home/hyshuai/workspace/dataset/images_background/Tagalog/character07': 299, '/home/hyshuai/workspace/dataset/images_background/Armenian/character41': 879, '/home/hyshuai/workspace/dataset/images_background/Tagalog/character04': 527, '/home/hyshuai/workspace/dataset/images_background/Korean/character28': 150, '/home/hyshuai/workspace/dataset/images_background/Tagalog/character05': 679, '/home/hyshuai/workspace/dataset/images_background/Ojibwe_(Canadian_Aboriginal_Syllabics)/character09': 549, '/home/hyshuai/workspace/dataset/images_background/Grantha/character30': 727}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "print(lable_dict['/home/hyshuai/workspace/dataset/images_evaluation/Mongolian/character22'])\n",
    "print(lable_dict['/home/hyshuai/workspace/dataset/images_background/Grantha/character19'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class_list_negative = ['/home/hyshuai/workspace/dataset/images_background/Grantha/character42', '/home/hyshuai/workspace/dataset/images_background/Grantha/character08', '/home/hyshuai/workspace/dataset/images_background/Grantha/character13', '/home/hyshuai/workspace/dataset/images_background/Grantha/character35', '/home/hyshuai/workspace/dataset/images_background/Grantha/character39', '/home/hyshuai/workspace/dataset/images_background/Grantha/character11', '/home/hyshuai/workspace/dataset/images_background/Grantha/character19', '/home/hyshuai/workspace/dataset/images_background/Grantha/character01', '/home/hyshuai/workspace/dataset/images_background/Grantha/character04', '/home/hyshuai/workspace/dataset/images_background/Grantha/character10', '/home/hyshuai/workspace/dataset/images_background/Grantha/character07', '/home/hyshuai/workspace/dataset/images_background/Grantha/character28', '/home/hyshuai/workspace/dataset/images_background/Grantha/character30', 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'/home/hyshuai/workspace/dataset/images_background/Korean/character28', '/home/hyshuai/workspace/dataset/images_background/Korean/character30', '/home/hyshuai/workspace/dataset/images_background/Korean/character15', '/home/hyshuai/workspace/dataset/images_background/Korean/character40', '/home/hyshuai/workspace/dataset/images_background/Korean/character22', '/home/hyshuai/workspace/dataset/images_background/Korean/character05', '/home/hyshuai/workspace/dataset/images_background/Korean/character03', '/home/hyshuai/workspace/dataset/images_background/Korean/character27', '/home/hyshuai/workspace/dataset/images_background/Korean/character12', '/home/hyshuai/workspace/dataset/images_background/Korean/character36', '/home/hyshuai/workspace/dataset/images_background/Korean/character21', '/home/hyshuai/workspace/dataset/images_background/Korean/character32', '/home/hyshuai/workspace/dataset/images_background/Korean/character02', '/home/hyshuai/workspace/dataset/images_background/Korean/character24', '/home/hyshuai/workspace/dataset/images_background/Korean/character17', '/home/hyshuai/workspace/dataset/images_background/Korean/character23', '/home/hyshuai/workspace/dataset/images_background/Korean/character14', '/home/hyshuai/workspace/dataset/images_background/Korean/character31', '/home/hyshuai/workspace/dataset/images_background/Korean/character37', '/home/hyshuai/workspace/dataset/images_background/Korean/character18', '/home/hyshuai/workspace/dataset/images_background/Korean/character38', '/home/hyshuai/workspace/dataset/images_background/Korean/character26', '/home/hyshuai/workspace/dataset/images_background/Korean/character25', '/home/hyshuai/workspace/dataset/images_background/Korean/character20', '/home/hyshuai/workspace/dataset/images_background/Korean/character09', '/home/hyshuai/workspace/dataset/images_background/Korean/character16', '/home/hyshuai/workspace/dataset/images_background/Korean/character06', '/home/hyshuai/workspace/dataset/images_background/Korean/character34', '/home/hyshuai/workspace/dataset/images_background/Korean/character33', '/home/hyshuai/workspace/dataset/images_background/Korean/character29', '/home/hyshuai/workspace/dataset/images_background/Anglo-Saxon_Futhorc/character08', '/home/hyshuai/workspace/dataset/images_background/Anglo-Saxon_Futhorc/character13', '/home/hyshuai/workspace/dataset/images_background/Anglo-Saxon_Futhorc/character11', '/home/hyshuai/workspace/dataset/images_background/Anglo-Saxon_Futhorc/character19', '/home/hyshuai/workspace/dataset/images_background/Anglo-Saxon_Futhorc/character01', '/home/hyshuai/workspace/dataset/images_background/Anglo-Saxon_Futhorc/character04', '/home/hyshuai/workspace/dataset/images_background/Anglo-Saxon_Futhorc/character10', '/home/hyshuai/workspace/dataset/images_background/Anglo-Saxon_Futhorc/character07', '/home/hyshuai/workspace/dataset/images_background/Anglo-Saxon_Futhorc/character28', '/home/hyshuai/workspace/dataset/images_background/Anglo-Saxon_Futhorc/character15', '/home/hyshuai/workspace/dataset/images_background/Anglo-Saxon_Futhorc/character22', '/home/hyshuai/workspace/dataset/images_background/Anglo-Saxon_Futhorc/character05', '/home/hyshuai/workspace/dataset/images_background/Anglo-Saxon_Futhorc/character03', '/home/hyshuai/workspace/dataset/images_background/Anglo-Saxon_Futhorc/character27', '/home/hyshuai/workspace/dataset/images_background/Anglo-Saxon_Futhorc/character12', '/home/hyshuai/workspace/dataset/images_background/Anglo-Saxon_Futhorc/character21', '/home/hyshuai/workspace/dataset/images_background/Anglo-Saxon_Futhorc/character02', '/home/hyshuai/workspace/dataset/images_background/Anglo-Saxon_Futhorc/character24', '/home/hyshuai/workspace/dataset/images_background/Anglo-Saxon_Futhorc/character17', '/home/hyshuai/workspace/dataset/images_background/Anglo-Saxon_Futhorc/character23', '/home/hyshuai/workspace/dataset/images_background/Anglo-Saxon_Futhorc/character14', '/home/hyshuai/workspace/dataset/images_background/Anglo-Saxon_Futhorc/character18', '/home/hyshuai/workspace/dataset/images_background/Anglo-Saxon_Futhorc/character26', '/home/hyshuai/workspace/dataset/images_background/Anglo-Saxon_Futhorc/character25', '/home/hyshuai/workspace/dataset/images_background/Anglo-Saxon_Futhorc/character20', '/home/hyshuai/workspace/dataset/images_background/Anglo-Saxon_Futhorc/character09', '/home/hyshuai/workspace/dataset/images_background/Anglo-Saxon_Futhorc/character16', '/home/hyshuai/workspace/dataset/images_background/Anglo-Saxon_Futhorc/character06', '/home/hyshuai/workspace/dataset/images_background/Anglo-Saxon_Futhorc/character29', '/home/hyshuai/workspace/dataset/images_background/Bengali/character42', '/home/hyshuai/workspace/dataset/images_background/Bengali/character08', '/home/hyshuai/workspace/dataset/images_background/Bengali/character13', '/home/hyshuai/workspace/dataset/images_background/Bengali/character35', '/home/hyshuai/workspace/dataset/images_background/Bengali/character39', '/home/hyshuai/workspace/dataset/images_background/Bengali/character11', '/home/hyshuai/workspace/dataset/images_background/Bengali/character19', '/home/hyshuai/workspace/dataset/images_background/Bengali/character01', '/home/hyshuai/workspace/dataset/images_background/Bengali/character04', '/home/hyshuai/workspace/dataset/images_background/Bengali/character10', '/home/hyshuai/workspace/dataset/images_background/Bengali/character07', '/home/hyshuai/workspace/dataset/images_background/Bengali/character28', '/home/hyshuai/workspace/dataset/images_background/Bengali/character30', '/home/hyshuai/workspace/dataset/images_background/Bengali/character15', '/home/hyshuai/workspace/dataset/images_background/Bengali/character40', '/home/hyshuai/workspace/dataset/images_background/Bengali/character44', '/home/hyshuai/workspace/dataset/images_background/Bengali/character22', '/home/hyshuai/workspace/dataset/images_background/Bengali/character05', '/home/hyshuai/workspace/dataset/images_background/Bengali/character03', '/home/hyshuai/workspace/dataset/images_background/Bengali/character27', '/home/hyshuai/workspace/dataset/images_background/Bengali/character12', '/home/hyshuai/workspace/dataset/images_background/Bengali/character36', '/home/hyshuai/workspace/dataset/images_background/Bengali/character43', '/home/hyshuai/workspace/dataset/images_background/Bengali/character21', '/home/hyshuai/workspace/dataset/images_background/Bengali/character32', '/home/hyshuai/workspace/dataset/images_background/Bengali/character02', '/home/hyshuai/workspace/dataset/images_background/Bengali/character24', '/home/hyshuai/workspace/dataset/images_background/Bengali/character17', '/home/hyshuai/workspace/dataset/images_background/Bengali/character23', '/home/hyshuai/workspace/dataset/images_background/Bengali/character14', '/home/hyshuai/workspace/dataset/images_background/Bengali/character31', '/home/hyshuai/workspace/dataset/images_background/Bengali/character37', '/home/hyshuai/workspace/dataset/images_background/Bengali/character18', '/home/hyshuai/workspace/dataset/images_background/Bengali/character38', '/home/hyshuai/workspace/dataset/images_background/Bengali/character26', '/home/hyshuai/workspace/dataset/images_background/Bengali/character45', '/home/hyshuai/workspace/dataset/images_background/Bengali/character25', '/home/hyshuai/workspace/dataset/images_background/Bengali/character46', '/home/hyshuai/workspace/dataset/images_background/Bengali/character20', '/home/hyshuai/workspace/dataset/images_background/Bengali/character09', '/home/hyshuai/workspace/dataset/images_background/Bengali/character16', '/home/hyshuai/workspace/dataset/images_background/Bengali/character41', '/home/hyshuai/workspace/dataset/images_background/Bengali/character06', '/home/hyshuai/workspace/dataset/images_background/Bengali/character34', '/home/hyshuai/workspace/dataset/images_background/Bengali/character33', '/home/hyshuai/workspace/dataset/images_background/Bengali/character29']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# negative\n",
    "\n",
    "dataset_negative = []\n",
    "for path_real in class_list_negative:\n",
    "    class_name = path_real\n",
    "    # image_paths = get_image_paths(path_real)\n",
    "    images = get_images_frompath_random_list_sort(path_real)\n",
    "#     print(class_name,\" : \",len(images))\n",
    "    dataset_negative.append(ImageObject(class_name,images))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class_positive = ['/home/hyshuai/workspace/dataset/images_evaluation/Glagolitic/character35',\n",
    " '/home/hyshuai/workspace/dataset/images_evaluation/Keble/character24',\n",
    " '/home/hyshuai/workspace/dataset/images_evaluation/Avesta/character15',\n",
    " '/home/hyshuai/workspace/dataset/images_evaluation/Aurek-Besh/character24',\n",
    " '/home/hyshuai/workspace/dataset/images_evaluation/Old_Church_Slavonic_(Cyrillic)/character14',\n",
    " '/home/hyshuai/workspace/dataset/images_evaluation/Ge_ez/character22',\n",
    " '/home/hyshuai/workspace/dataset/images_evaluation/Keble/character21',\n",
    " '/home/hyshuai/workspace/dataset/images_evaluation/Mongolian/character14',\n",
    " '/home/hyshuai/workspace/dataset/images_evaluation/Oriya/character16',\n",
    " '/home/hyshuai/workspace/dataset/images_evaluation/ULOG/character11',\n",
    " '/home/hyshuai/workspace/dataset/images_evaluation/Keble/character16',\n",
    " '/home/hyshuai/workspace/dataset/images_evaluation/Tengwar/character23',\n",
    " '/home/hyshuai/workspace/dataset/images_evaluation/Tibetan/character24',\n",
    " '/home/hyshuai/workspace/dataset/images_evaluation/Glagolitic/character07',\n",
    " '/home/hyshuai/workspace/dataset/images_evaluation/Glagolitic/character29',\n",
    " '/home/hyshuai/workspace/dataset/images_evaluation/Malayalam/character20',\n",
    " '/home/hyshuai/workspace/dataset/images_evaluation/Oriya/character17',\n",
    " '/home/hyshuai/workspace/dataset/images_evaluation/Angelic/character19',\n",
    " '/home/hyshuai/workspace/dataset/images_evaluation/Sylheti/character19',\n",
    " '/home/hyshuai/workspace/dataset/images_evaluation/Mongolian/character22']\n",
    "\n",
    "\n",
    "\n",
    "# print(class_positive)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "image_positive_paths=[['/home/hyshuai/workspace/dataset/images_evaluation/Glagolitic/character35/1149_17.png', '/home/hyshuai/workspace/dataset/images_evaluation/Glagolitic/character35/1149_07.png', '/home/hyshuai/workspace/dataset/images_evaluation/Glagolitic/character35/1149_10.png', '/home/hyshuai/workspace/dataset/images_evaluation/Glagolitic/character35/1149_15.png', '/home/hyshuai/workspace/dataset/images_evaluation/Glagolitic/character35/1149_14.png', '/home/hyshuai/workspace/dataset/images_evaluation/Glagolitic/character35/1149_09.png', '/home/hyshuai/workspace/dataset/images_evaluation/Glagolitic/character35/1149_13.png', '/home/hyshuai/workspace/dataset/images_evaluation/Glagolitic/character35/1149_01.png', '/home/hyshuai/workspace/dataset/images_evaluation/Glagolitic/character35/1149_04.png', '/home/hyshuai/workspace/dataset/images_evaluation/Glagolitic/character35/1149_16.png', '/home/hyshuai/workspace/dataset/images_evaluation/Glagolitic/character35/1149_06.png', '/home/hyshuai/workspace/dataset/images_evaluation/Glagolitic/character35/1149_12.png', '/home/hyshuai/workspace/dataset/images_evaluation/Glagolitic/character35/1149_03.png', '/home/hyshuai/workspace/dataset/images_evaluation/Glagolitic/character35/1149_11.png', '/home/hyshuai/workspace/dataset/images_evaluation/Glagolitic/character35/1149_02.png', '/home/hyshuai/workspace/dataset/images_evaluation/Glagolitic/character35/1149_19.png', '/home/hyshuai/workspace/dataset/images_evaluation/Glagolitic/character35/1149_20.png', '/home/hyshuai/workspace/dataset/images_evaluation/Glagolitic/character35/1149_08.png', '/home/hyshuai/workspace/dataset/images_evaluation/Glagolitic/character35/1149_05.png', '/home/hyshuai/workspace/dataset/images_evaluation/Glagolitic/character35/1149_18.png'],['/home/hyshuai/workspace/dataset/images_evaluation/Keble/character24/1269_11.png', '/home/hyshuai/workspace/dataset/images_evaluation/Keble/character24/1269_13.png', '/home/hyshuai/workspace/dataset/images_evaluation/Keble/character24/1269_14.png', '/home/hyshuai/workspace/dataset/images_evaluation/Keble/character24/1269_10.png', '/home/hyshuai/workspace/dataset/images_evaluation/Keble/character24/1269_17.png', '/home/hyshuai/workspace/dataset/images_evaluation/Keble/character24/1269_16.png', '/home/hyshuai/workspace/dataset/images_evaluation/Keble/character24/1269_19.png', '/home/hyshuai/workspace/dataset/images_evaluation/Keble/character24/1269_06.png', '/home/hyshuai/workspace/dataset/images_evaluation/Keble/character24/1269_08.png', '/home/hyshuai/workspace/dataset/images_evaluation/Keble/character24/1269_07.png', '/home/hyshuai/workspace/dataset/images_evaluation/Keble/character24/1269_03.png', '/home/hyshuai/workspace/dataset/images_evaluation/Keble/character24/1269_04.png', '/home/hyshuai/workspace/dataset/images_evaluation/Keble/character24/1269_02.png', '/home/hyshuai/workspace/dataset/images_evaluation/Keble/character24/1269_01.png', '/home/hyshuai/workspace/dataset/images_evaluation/Keble/character24/1269_18.png', '/home/hyshuai/workspace/dataset/images_evaluation/Keble/character24/1269_09.png', '/home/hyshuai/workspace/dataset/images_evaluation/Keble/character24/1269_15.png', '/home/hyshuai/workspace/dataset/images_evaluation/Keble/character24/1269_12.png', '/home/hyshuai/workspace/dataset/images_evaluation/Keble/character24/1269_05.png', '/home/hyshuai/workspace/dataset/images_evaluation/Keble/character24/1269_20.png'],['/home/hyshuai/workspace/dataset/images_evaluation/Avesta/character15/1077_20.png', '/home/hyshuai/workspace/dataset/images_evaluation/Avesta/character15/1077_16.png', '/home/hyshuai/workspace/dataset/images_evaluation/Avesta/character15/1077_13.png', '/home/hyshuai/workspace/dataset/images_evaluation/Avesta/character15/1077_04.png', '/home/hyshuai/workspace/dataset/images_evaluation/Avesta/character15/1077_01.png', '/home/hyshuai/workspace/dataset/images_evaluation/Avesta/character15/1077_10.png', '/home/hyshuai/workspace/dataset/images_evaluation/Avesta/character15/1077_06.png', '/home/hyshuai/workspace/dataset/images_evaluation/Avesta/character15/1077_11.png', '/home/hyshuai/workspace/dataset/images_evaluation/Avesta/character15/1077_07.png', '/home/hyshuai/workspace/dataset/images_evaluation/Avesta/character15/1077_02.png', '/home/hyshuai/workspace/dataset/images_evaluation/Avesta/character15/1077_09.png', '/home/hyshuai/workspace/dataset/images_evaluation/Avesta/character15/1077_03.png', '/home/hyshuai/workspace/dataset/images_evaluation/Avesta/character15/1077_19.png', '/home/hyshuai/workspace/dataset/images_evaluation/Avesta/character15/1077_17.png', '/home/hyshuai/workspace/dataset/images_evaluation/Avesta/character15/1077_15.png', '/home/hyshuai/workspace/dataset/images_evaluation/Avesta/character15/1077_18.png', '/home/hyshuai/workspace/dataset/images_evaluation/Avesta/character15/1077_05.png', '/home/hyshuai/workspace/dataset/images_evaluation/Avesta/character15/1077_14.png', '/home/hyshuai/workspace/dataset/images_evaluation/Avesta/character15/1077_08.png', '/home/hyshuai/workspace/dataset/images_evaluation/Avesta/character15/1077_12.png'],['/home/hyshuai/workspace/dataset/images_evaluation/Aurek-Besh/character24/1060_14.png', '/home/hyshuai/workspace/dataset/images_evaluation/Aurek-Besh/character24/1060_02.png', '/home/hyshuai/workspace/dataset/images_evaluation/Aurek-Besh/character24/1060_08.png', '/home/hyshuai/workspace/dataset/images_evaluation/Aurek-Besh/character24/1060_04.png', '/home/hyshuai/workspace/dataset/images_evaluation/Aurek-Besh/character24/1060_10.png', '/home/hyshuai/workspace/dataset/images_evaluation/Aurek-Besh/character24/1060_18.png', '/home/hyshuai/workspace/dataset/images_evaluation/Aurek-Besh/character24/1060_03.png', '/home/hyshuai/workspace/dataset/images_evaluation/Aurek-Besh/character24/1060_20.png', '/home/hyshuai/workspace/dataset/images_evaluation/Aurek-Besh/character24/1060_13.png', '/home/hyshuai/workspace/dataset/images_evaluation/Aurek-Besh/character24/1060_15.png', '/home/hyshuai/workspace/dataset/images_evaluation/Aurek-Besh/character24/1060_12.png', '/home/hyshuai/workspace/dataset/images_evaluation/Aurek-Besh/character24/1060_05.png', '/home/hyshuai/workspace/dataset/images_evaluation/Aurek-Besh/character24/1060_19.png', '/home/hyshuai/workspace/dataset/images_evaluation/Aurek-Besh/character24/1060_01.png', '/home/hyshuai/workspace/dataset/images_evaluation/Aurek-Besh/character24/1060_07.png', '/home/hyshuai/workspace/dataset/images_evaluation/Aurek-Besh/character24/1060_11.png', '/home/hyshuai/workspace/dataset/images_evaluation/Aurek-Besh/character24/1060_16.png', '/home/hyshuai/workspace/dataset/images_evaluation/Aurek-Besh/character24/1060_17.png', '/home/hyshuai/workspace/dataset/images_evaluation/Aurek-Besh/character24/1060_06.png', '/home/hyshuai/workspace/dataset/images_evaluation/Aurek-Besh/character24/1060_09.png'],['/home/hyshuai/workspace/dataset/images_evaluation/Old_Church_Slavonic_(Cyrillic)/character14/1402_10.png', '/home/hyshuai/workspace/dataset/images_evaluation/Old_Church_Slavonic_(Cyrillic)/character14/1402_01.png', '/home/hyshuai/workspace/dataset/images_evaluation/Old_Church_Slavonic_(Cyrillic)/character14/1402_06.png', '/home/hyshuai/workspace/dataset/images_evaluation/Old_Church_Slavonic_(Cyrillic)/character14/1402_15.png', '/home/hyshuai/workspace/dataset/images_evaluation/Old_Church_Slavonic_(Cyrillic)/character14/1402_03.png', '/home/hyshuai/workspace/dataset/images_evaluation/Old_Church_Slavonic_(Cyrillic)/character14/1402_11.png', '/home/hyshuai/workspace/dataset/images_evaluation/Old_Church_Slavonic_(Cyrillic)/character14/1402_04.png', '/home/hyshuai/workspace/dataset/images_evaluation/Old_Church_Slavonic_(Cyrillic)/character14/1402_07.png', '/home/hyshuai/workspace/dataset/images_evaluation/Old_Church_Slavonic_(Cyrillic)/character14/1402_14.png', '/home/hyshuai/workspace/dataset/images_evaluation/Old_Church_Slavonic_(Cyrillic)/character14/1402_12.png', '/home/hyshuai/workspace/dataset/images_evaluation/Old_Church_Slavonic_(Cyrillic)/character14/1402_19.png', '/home/hyshuai/workspace/dataset/images_evaluation/Old_Church_Slavonic_(Cyrillic)/character14/1402_20.png', '/home/hyshuai/workspace/dataset/images_evaluation/Old_Church_Slavonic_(Cyrillic)/character14/1402_05.png', '/home/hyshuai/workspace/dataset/images_evaluation/Old_Church_Slavonic_(Cyrillic)/character14/1402_08.png', '/home/hyshuai/workspace/dataset/images_evaluation/Old_Church_Slavonic_(Cyrillic)/character14/1402_02.png', '/home/hyshuai/workspace/dataset/images_evaluation/Old_Church_Slavonic_(Cyrillic)/character14/1402_17.png', '/home/hyshuai/workspace/dataset/images_evaluation/Old_Church_Slavonic_(Cyrillic)/character14/1402_09.png', '/home/hyshuai/workspace/dataset/images_evaluation/Old_Church_Slavonic_(Cyrillic)/character14/1402_13.png', '/home/hyshuai/workspace/dataset/images_evaluation/Old_Church_Slavonic_(Cyrillic)/character14/1402_16.png', '/home/hyshuai/workspace/dataset/images_evaluation/Old_Church_Slavonic_(Cyrillic)/character14/1402_18.png'],['/home/hyshuai/workspace/dataset/images_evaluation/Ge_ez/character22/1110_19.png', '/home/hyshuai/workspace/dataset/images_evaluation/Ge_ez/character22/1110_20.png', '/home/hyshuai/workspace/dataset/images_evaluation/Ge_ez/character22/1110_15.png', '/home/hyshuai/workspace/dataset/images_evaluation/Ge_ez/character22/1110_16.png', '/home/hyshuai/workspace/dataset/images_evaluation/Ge_ez/character22/1110_11.png', '/home/hyshuai/workspace/dataset/images_evaluation/Ge_ez/character22/1110_04.png', 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'/home/hyshuai/workspace/dataset/images_evaluation/Angelic/character19/0983_10.png', '/home/hyshuai/workspace/dataset/images_evaluation/Angelic/character19/0983_13.png', '/home/hyshuai/workspace/dataset/images_evaluation/Angelic/character19/0983_06.png', '/home/hyshuai/workspace/dataset/images_evaluation/Angelic/character19/0983_18.png', '/home/hyshuai/workspace/dataset/images_evaluation/Angelic/character19/0983_15.png'],['/home/hyshuai/workspace/dataset/images_evaluation/Sylheti/character19/1521_01.png', '/home/hyshuai/workspace/dataset/images_evaluation/Sylheti/character19/1521_14.png', '/home/hyshuai/workspace/dataset/images_evaluation/Sylheti/character19/1521_06.png', '/home/hyshuai/workspace/dataset/images_evaluation/Sylheti/character19/1521_18.png', '/home/hyshuai/workspace/dataset/images_evaluation/Sylheti/character19/1521_17.png', '/home/hyshuai/workspace/dataset/images_evaluation/Sylheti/character19/1521_09.png', '/home/hyshuai/workspace/dataset/images_evaluation/Sylheti/character19/1521_16.png', '/home/hyshuai/workspace/dataset/images_evaluation/Sylheti/character19/1521_15.png', '/home/hyshuai/workspace/dataset/images_evaluation/Sylheti/character19/1521_05.png', '/home/hyshuai/workspace/dataset/images_evaluation/Sylheti/character19/1521_03.png', '/home/hyshuai/workspace/dataset/images_evaluation/Sylheti/character19/1521_08.png', '/home/hyshuai/workspace/dataset/images_evaluation/Sylheti/character19/1521_11.png', '/home/hyshuai/workspace/dataset/images_evaluation/Sylheti/character19/1521_04.png', '/home/hyshuai/workspace/dataset/images_evaluation/Sylheti/character19/1521_10.png', '/home/hyshuai/workspace/dataset/images_evaluation/Sylheti/character19/1521_19.png', '/home/hyshuai/workspace/dataset/images_evaluation/Sylheti/character19/1521_02.png', '/home/hyshuai/workspace/dataset/images_evaluation/Sylheti/character19/1521_20.png', '/home/hyshuai/workspace/dataset/images_evaluation/Sylheti/character19/1521_13.png', '/home/hyshuai/workspace/dataset/images_evaluation/Sylheti/character19/1521_12.png', '/home/hyshuai/workspace/dataset/images_evaluation/Sylheti/character19/1521_07.png'],['/home/hyshuai/workspace/dataset/images_evaluation/Mongolian/character22/1380_15.png', '/home/hyshuai/workspace/dataset/images_evaluation/Mongolian/character22/1380_05.png', '/home/hyshuai/workspace/dataset/images_evaluation/Mongolian/character22/1380_18.png', '/home/hyshuai/workspace/dataset/images_evaluation/Mongolian/character22/1380_14.png', '/home/hyshuai/workspace/dataset/images_evaluation/Mongolian/character22/1380_08.png', '/home/hyshuai/workspace/dataset/images_evaluation/Mongolian/character22/1380_19.png', '/home/hyshuai/workspace/dataset/images_evaluation/Mongolian/character22/1380_06.png', '/home/hyshuai/workspace/dataset/images_evaluation/Mongolian/character22/1380_02.png', '/home/hyshuai/workspace/dataset/images_evaluation/Mongolian/character22/1380_10.png', '/home/hyshuai/workspace/dataset/images_evaluation/Mongolian/character22/1380_11.png', '/home/hyshuai/workspace/dataset/images_evaluation/Mongolian/character22/1380_07.png', '/home/hyshuai/workspace/dataset/images_evaluation/Mongolian/character22/1380_20.png', '/home/hyshuai/workspace/dataset/images_evaluation/Mongolian/character22/1380_12.png', '/home/hyshuai/workspace/dataset/images_evaluation/Mongolian/character22/1380_01.png', '/home/hyshuai/workspace/dataset/images_evaluation/Mongolian/character22/1380_16.png', '/home/hyshuai/workspace/dataset/images_evaluation/Mongolian/character22/1380_13.png', '/home/hyshuai/workspace/dataset/images_evaluation/Mongolian/character22/1380_04.png', '/home/hyshuai/workspace/dataset/images_evaluation/Mongolian/character22/1380_17.png', '/home/hyshuai/workspace/dataset/images_evaluation/Mongolian/character22/1380_09.png', '/home/hyshuai/workspace/dataset/images_evaluation/Mongolian/character22/1380_03.png']]\n",
    "# for path in image_positive_paths:\n",
    "#     print(path[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# positvie \n",
    "dataset_train_positive =[]\n",
    "dataset_test_positive = []\n",
    "for index , class_path in enumerate(class_positive):\n",
    "#     images = get_images_frompath_random_list_sort(class_path) # 固定随机抽样是否有问题\n",
    "    images = get_images_from_images_list(image_positive_paths[index])\n",
    "#     print(class_path, \" : \", len(images))\n",
    "    dataset_train_positive.append(ImageObject(class_path, images[:1])) # 1-shot\n",
    "    dataset_test_positive.append(ImageObject(class_path, images[1:]))\n",
    "\n",
    "\n",
    "\n",
    "print(len(dataset_train_positive[2].imgs))\n",
    "print(len(dataset_test_positive[2].imgs))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "# label:\n",
    "\n",
    "class_total_num = len(dataset_negative) + len(dataset_train_positive)\n",
    "print(\"class_total = \",class_total_num)\n",
    "np.random.seed(66) \n",
    "shufindex = np.random.permutation(class_total_num)\n",
    "for index, value in enumerate(dataset_negative):\n",
    "    value.label = shufindex[index]\n",
    "    print(value.name,  value.label)\n",
    "\n",
    "for index, value in enumerate(dataset_train_positive):\n",
    "    value.label = shufindex[index+len(dataset_negative)]\n",
    "    dataset_test_positive[index].label = shufindex[index+len(dataset_negative)]\n",
    "    print(value.name, value.label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# test_class_index = [159, 687, 486, 843, 27, 253, 909, 295, 912, 714, 307, 661, 858, 79, 717, 122, 51, 631, 828, 20]\n",
    "test_class_index = [data.label for data in dataset_train_positive]\n",
    "print(test_class_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# print(dataset_negative[0].imgs[2].shape)\n",
    "# # print(im[15])\n",
    "# # print(im.shape)\n",
    "# print(dataset_negative[0].imgs[0].shape)\n",
    "# x = [data.imgs for data in dataset_train_positive] \n",
    "# print(x[0][0].shape)\n",
    "# img_show(np.squeeze(x[0]))\n",
    "# img_show(np.squeeze(x[1]))\n",
    "# img_show(np.squeeze(x[2]))\n",
    "# img_show(np.squeeze(x[3]))\n",
    "# img_show(np.squeeze(x[4]))\n",
    "\n",
    "x_backgroud = np.array([data.imgs[0] for data in dataset_negative])\n",
    "label_backgroud = np.array([data.label for data in dataset_negative])\n",
    "x_positive = np.array([data.imgs[0] for data in dataset_train_positive])\n",
    "label_positive = np.array([data.label for data in dataset_train_positive])\n",
    "print(x_positive.shape)\n",
    "images_observe_train_total = np.concatenate((x_backgroud ,x_positive)) \n",
    "label_observe_total = np.concatenate((label_backgroud , label_positive))\n",
    "print(images_observe_train_total.shape)\n",
    "print(label_observe_total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_next_batch(dataset_train_positive,dataset_negative,batch_size,shuffle = True):\n",
    "    \n",
    "    total_leng = max(len(dataset_train_positive),len(dataset_negative))\n",
    "    \n",
    "    batch_total = total_leng // batch_size\n",
    "    \n",
    "    \n",
    "    for b in range(batch_total):\n",
    "        \n",
    "        x = np.stack(dataset_train_positive[b*batch_size  :(b+1)*batch_size],axis =0)\n",
    "        y = np.array([test_class_index.index(data.label)] * len(data.imgs))\n",
    "        yield x,y\n",
    "        \n",
    "        \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_next_single(dataset_train_positive,dataset_negative,p_size,n_size,shuffle = True):\n",
    "    \"\"\"\n",
    "    final batch_size = p_size + n_size \n",
    "    every dataset_negative should have same imgs leng\n",
    "    \"\"\"\n",
    "    #shuffle\n",
    "    if shuffle:\n",
    "        np.random.shuffle(dataset_train_positive)\n",
    "        np.random.shuffle(dataset_negative)\n",
    "        for data in dataset_negative:\n",
    "            np.random.shuffle(data.imgs)\n",
    "        for data in dataset_train_positive:\n",
    "            np.random.shuffle(data.imgs)\n",
    "    \n",
    "    \n",
    "    batch_negative = n_size \n",
    "#     print('batch_negative ',batch_negative)\n",
    "    len_n_class = len(dataset_negative)\n",
    "#     print('len_n_class ',len_n_class)\n",
    "    len_p_class = len(dataset_train_positive)\n",
    "#     print('len_p_class ',len_p_class)\n",
    "    len_n_imgs = len(dataset_negative[0].imgs)\n",
    "#     print('len_n_imgs ',len_n_imgs)\n",
    "    len_p_imgs = len(dataset_train_positive[0].imgs)\n",
    "    \n",
    "    negative_number = 0\n",
    "    \n",
    "    \n",
    "#     shuffindex = np.random.permutation(len_n)\n",
    "# #     print(shuffindex)\n",
    "#     np.random.shuffle(dataset_train_positive)\n",
    "#     np.random.shuffle(dataset_negative)\n",
    "    \n",
    "    for data in dataset_negative:\n",
    "        negative_number += len(data.imgs)\n",
    "#         np.random.shuffle(data.imgs)\n",
    "#     print('negative_number ',negative_number)\n",
    "    batch_total = negative_number  // batch_negative\n",
    "#     print('batch_total ',batch_total)\n",
    "    i_n_class = 0\n",
    "    i_n_img = 0\n",
    "    i_p_class = 0\n",
    "    i_p_img = 0\n",
    "    \n",
    "    \n",
    "    \n",
    "        \n",
    "    \n",
    "    for b in range(batch_total):\n",
    "        train_x =[]\n",
    "        train_y = []\n",
    "        create_negative_imgs = 0\n",
    "        while create_negative_imgs <= (batch_total * batch_negative) and create_negative_imgs < batch_negative:\n",
    "            \n",
    "            # while #如果一次循环没够，需要再次循环\n",
    "            if i_n_class >= len_n_class:\n",
    "                    i_n_class = 0\n",
    "                    i_n_img +=1\n",
    "                    if (i_n_img+1)  >len_n_imgs:\n",
    "                        i_n_img = 0\n",
    "#                     print('if i_n_class >= len_n_class i_n_class ',i_n_class)\n",
    "#                     print('if i_n_class >= len_n_class i_n_img ',i_n_img)\n",
    "            \n",
    "            for i in range(i_n_class,len_n_class):\n",
    "                    \n",
    "#                 print('for i in range(i_n_class i_n_class ',i_n_class)\n",
    "#                 print('for i in range(i_n_class i_n_img ',i_n_img)\n",
    "                train_x += dataset_negative[i_n_class].imgs[i_n_img  : (i_n_img+1) ]\n",
    "                train_y += [dataset_negative[i_n_class].label] \n",
    "                \n",
    "                i_n_class+=1\n",
    "                if i_n_class >= len_n_class:\n",
    "                    i_n_class = 0\n",
    "                    i_n_img +=1\n",
    "                    if (i_n_img+1)  > len_n_imgs:\n",
    "                        i_n_img = 0\n",
    "#                     print('if i_n_class >= len_n_class i_n_class ',i_n_class)\n",
    "#                     print('if i_n_class >= len_n_class i_n_img ',i_n_img)\n",
    "            \n",
    "                create_negative_imgs +=1\n",
    "                if create_negative_imgs >= batch_negative:\n",
    "#                     print(\"create_negative_imgs ok \",create_negative_imgs)\n",
    "                    break\n",
    "                    \n",
    "                \n",
    "        create_positive_imgs = 0    \n",
    "        while create_positive_imgs < p_size:\n",
    "            for i in range(i_p_class,len_p_class):\n",
    "#                 print('for i in range(i_p_class     ',i_p_class)\n",
    "#                 print('for i in range(i_p_img  ',i_p_img)\n",
    "                \n",
    "                train_x += dataset_train_positive[i_p_class].imgs[i_p_img:(i_p_img +1)]\n",
    "                train_y += [dataset_train_positive[i_p_class].label]\n",
    "                \n",
    "                i_p_class+=1\n",
    "                if i_p_class >= len_p_class:\n",
    "                    i_p_class = 0\n",
    "                    i_p_img +=1\n",
    "                    if (i_p_img +1) > len_p_imgs:\n",
    "                        i_p_img = 0\n",
    "#                     print('if i_p_class >= len_p_class i_p_class ',i_p_class)\n",
    "#                     print('if i_p_class >= len_p_class i_p_img ',i_p_img)\n",
    "                \n",
    "                create_positive_imgs+=1\n",
    "                if create_positive_imgs >= p_size:\n",
    "#                     print(\"create_positive_imgs ok \",create_positive_imgs)\n",
    "                    break\n",
    "        \n",
    "        shuffindex_out = np.random.permutation(len(train_y))\n",
    "#         print(train_x[0].shape)\n",
    "        train_x = np.stack(train_x,axis = 0)\n",
    "#         print('after concat : ',train_x[0].shape)\n",
    "        train_x = train_x[shuffindex_out]\n",
    "        train_y = np.array(train_y)[shuffindex_out]\n",
    "        \n",
    "        yield train_x,train_y  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "def get_next_perfect(dataset_train_positive,dataset_negative,p_size,n_size,shuffle = True):\n",
    "    \"\"\"\n",
    "    final batch_size = p_size + n_size * 2\n",
    "    every dataset_negative should have same imgs leng\n",
    "    \"\"\"\n",
    "    #shuffle\n",
    "    if shuffle:\n",
    "        np.random.shuffle(dataset_train_positive)\n",
    "        np.random.shuffle(dataset_negative)\n",
    "        for data in dataset_negative:\n",
    "            np.random.shuffle(data.imgs)\n",
    "        for data in dataset_train_positive:\n",
    "            np.random.shuffle(data.imgs)\n",
    "    \n",
    "    \n",
    "    batch_negative = n_size * 2\n",
    "#     print('batch_negative ',batch_negative)\n",
    "    len_n_class = len(dataset_negative)\n",
    "#     print('len_n_class ',len_n_class)\n",
    "    len_p_class = len(dataset_train_positive)\n",
    "#     print('len_p_class ',len_p_class)\n",
    "    len_n_imgs = len(dataset_negative[0].imgs)\n",
    "#     print('len_n_imgs ',len_n_imgs)\n",
    "    len_p_imgs = len(dataset_train_positive[0].imgs)\n",
    "    \n",
    "    negative_number = 0\n",
    "    \n",
    "    \n",
    "#     shuffindex = np.random.permutation(len_n)\n",
    "# #     print(shuffindex)\n",
    "#     np.random.shuffle(dataset_train_positive)\n",
    "#     np.random.shuffle(dataset_negative)\n",
    "    \n",
    "    for data in dataset_negative:\n",
    "        negative_number += len(data.imgs)\n",
    "#         np.random.shuffle(data.imgs)\n",
    "#     print('negative_number ',negative_number)\n",
    "    batch_total = negative_number  // batch_negative\n",
    "#     print('batch_total ',batch_total)\n",
    "    i_n_class = 0\n",
    "    i_n_img = 0\n",
    "    i_p_class = 0\n",
    "    i_p_img = 0\n",
    "    \n",
    "    \n",
    "    \n",
    "        \n",
    "    \n",
    "    for b in range(batch_total):\n",
    "        train_x =[]\n",
    "        train_y = []\n",
    "        create_negative_imgs = 0\n",
    "        while create_negative_imgs <= (batch_total * batch_negative) and create_negative_imgs < batch_negative:\n",
    "            \n",
    "            # while #如果一次循环没够，需要再次循环\n",
    "            if i_n_class >= len_n_class:\n",
    "                    i_n_class = 0\n",
    "                    i_n_img +=1\n",
    "                    if (i_n_img+1) *2 >len_n_imgs:\n",
    "                        i_n_img = 0\n",
    "#                     print('if i_n_class >= len_n_class i_n_class ',i_n_class)\n",
    "#                     print('if i_n_class >= len_n_class i_n_img ',i_n_img)\n",
    "            \n",
    "            for i in range(i_n_class,len_n_class):\n",
    "                    \n",
    "#                 print('for i in range(i_n_class i_n_class ',i_n_class)\n",
    "#                 print('for i in range(i_n_class i_n_img ',i_n_img)\n",
    "                train_x += dataset_negative[i_n_class].imgs[i_n_img * 2 : (i_n_img+1) *2]\n",
    "                train_y += [dataset_negative[i_n_class].label] * 2\n",
    "                \n",
    "                i_n_class+=1\n",
    "                if i_n_class >= len_n_class:\n",
    "                    i_n_class = 0\n",
    "                    i_n_img +=1\n",
    "                    if (i_n_img+1) *2 > len_n_imgs:\n",
    "                        i_n_img = 0\n",
    "#                     print('if i_n_class >= len_n_class i_n_class ',i_n_class)\n",
    "#                     print('if i_n_class >= len_n_class i_n_img ',i_n_img)\n",
    "            \n",
    "                create_negative_imgs +=2\n",
    "                if create_negative_imgs >= batch_negative:\n",
    "#                     print(\"create_negative_imgs ok \",create_negative_imgs)\n",
    "                    break\n",
    "                    \n",
    "                \n",
    "        create_positive_imgs = 0    \n",
    "        while create_positive_imgs < p_size:\n",
    "            for i in range(i_p_class,len_p_class):\n",
    "#                 print('for i in range(i_p_class     ',i_p_class)\n",
    "#                 print('for i in range(i_p_img  ',i_p_img)\n",
    "                \n",
    "                train_x += dataset_train_positive[i_p_class].imgs[i_p_img:(i_p_img +1)]\n",
    "                train_y += [dataset_train_positive[i_p_class].label]\n",
    "                \n",
    "                i_p_class+=1\n",
    "                if i_p_class >= len_p_class:\n",
    "                    i_p_class = 0\n",
    "                    i_p_img +=1\n",
    "                    if (i_p_img +1) > len_p_imgs:\n",
    "                        i_p_img = 0\n",
    "#                     print('if i_p_class >= len_p_class i_p_class ',i_p_class)\n",
    "#                     print('if i_p_class >= len_p_class i_p_img ',i_p_img)\n",
    "                \n",
    "                create_positive_imgs+=1\n",
    "                if create_positive_imgs >= p_size:\n",
    "#                     print(\"create_positive_imgs ok \",create_positive_imgs)\n",
    "                    break\n",
    "        \n",
    "        shuffindex_out = np.random.permutation(len(train_y))\n",
    "#         print(train_x[0].shape)\n",
    "        train_x = np.stack(train_x,axis = 0)\n",
    "#         print('after concat : ',train_x[0].shape)\n",
    "        train_x = train_x[shuffindex_out]\n",
    "        train_y = np.array(train_y)[shuffindex_out]\n",
    "        \n",
    "        yield train_x,train_y  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# dataaaaaaa = get_next_perfect(dataset_train_positive,dataset_negative,8,126)\n",
    "# for x ,y in dataaaaaaa:\n",
    "#     print(y)\n",
    "#     print('x o shape ' ,x.shape,y.shape)\n",
    "#     print(\"one batch is over\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def getnext_test(dataset_test_positive,test_class_index):\n",
    "    \n",
    "    for data in dataset_test_positive:\n",
    "        x = np.stack(data.imgs,axis =0)\n",
    "        y = np.array([test_class_index.index(data.label)] * len(data.imgs))\n",
    "        yield x,y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# dataaaaaaa = getnext_test(dataset_test_positive,test_class_index)\n",
    "# for x ,y in dataaaaaaa:\n",
    "#     print(y)\n",
    "#     print(x.shape,y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def imgs_show_norm(imgs,row,col):\n",
    "    fig,ax = plt.subplots(nrows=row,ncols=col,sharex=True,sharey=True)\n",
    "    ax = ax.flatten()\n",
    "    for i in range(row*col):\n",
    "        img = imgs[i]\n",
    "        min = np.min(img)\n",
    "#         print('min = ',min)\n",
    "        img = np.subtract(img,min) # 0->\n",
    "        max = np.max(img)\n",
    "#         print('max =',max)\n",
    "        img = np.divide(img,max)\n",
    "        \n",
    "        ax[i].imshow(img,cmap='Greys', interpolation='nearest')\n",
    "    \n",
    "    ax[0].set_xticks([])\n",
    "    ax[0].set_yticks([])\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def img_show_norm(img):\n",
    "    min_ = np.min(img)\n",
    "#     print('min = ',min_)\n",
    "    img = np.subtract(img,min_) # 0->\n",
    "    max_ = np.amax(a = img,keepdims=False)\n",
    "#     print('max =',max_)\n",
    "    img = np.divide(img,max_)  # 0->1\n",
    "#     print(img)\n",
    "    plt.figure(figsize=(1,1))\n",
    "    plt.imshow(img,cmap='Greys',interpolation='nearest')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def img_show(img):\n",
    "    plt.figure(figsize=(1,1))\n",
    "    plt.imshow(img,cmap='Greys',interpolation='nearest')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def imgs_show(imgs,row,col):\n",
    "    fig,ax = plt.subplots(nrows=row,ncols=col,sharex=True,sharey=True)\n",
    "    ax = ax.flatten()\n",
    "    for i in range(row*col):\n",
    "        img = imgs[i]\n",
    "        \n",
    "        ax[i].imshow(img,cmap='Greys', interpolation='nearest')\n",
    "    \n",
    "    ax[0].set_xticks([])\n",
    "    ax[0].set_yticks([])\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def compute_index_logical_equal(inputs,indexs):\n",
    "    logic_result = (inputs==indexs[0])\n",
    "    for value in indexs:\n",
    "        logic_result = np.logical_or(inputs == value,logic_result)\n",
    "    return logic_result\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# nb_digits = class_total_num\n",
    "net_auto = models.Glow(JsonConfig(\"hparams/omni_all_bg.json\"),test_class_index,is_mean=False,K=4,y_classes=class_total_num,is_split_2d=True,is_prior_wh=True)\n",
    "from CenterLoss import CenterLoss\n",
    "centerloss = CenterLoss(class_total_num, 16*4*4)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "net_auto.load_state_dict(torch.load('model/model_glow_ominst_all_bg_4k_20way_1shot_add_split_z_no_mean_center1_4000.pt'),strict=True)\n",
    "centerloss.load_state_dict(torch.load('model/model_center_ominst_all_bg_4k_20way_1shot_add_split_z_no_mean_center_weight1_4000.pt'),strict=True)\n",
    "\n",
    "for s in net_auto.flow.output_shapes:\n",
    "    print(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "use_cuda = torch.cuda.is_available()\n",
    "torch.manual_seed(666)\n",
    "device = torch.device(\"cuda\" if use_cuda else \"cpu\")\n",
    "\n",
    "\n",
    "\n",
    "criterion = nn.MSELoss()\n",
    "\n",
    "net_auto.to(device)\n",
    "net_auto.float()\n",
    "centerloss.to(device)\n",
    "centerloss.float()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "torch.cuda.empty_cache()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "center_loss_weight = 0.01\n",
    "optimizer = optim.Adam(net_auto.parameters(),lr=0.0001,weight_decay=5e-5) # decoder don't weight_decay\n",
    "optimzer4center = optim.SGD(centerloss.parameters(), lr =0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#X_train_nor\n",
    "net_auto.set_z_add_random(True)\n",
    "batch_size = 256\n",
    "net_auto.train()\n",
    "for epoch in range(30001):\n",
    "    #train:\n",
    "    \n",
    "    datas =  get_next_single(dataset_train_positive,dataset_negative,6,250,shuffle=True)\n",
    "#     datas = getnext(dataset_train_positive,dataset_negative)\n",
    "    \n",
    "    trainloss_g = 0\n",
    "    trainloss_c = 0\n",
    "    \n",
    "    for x,y in datas:\n",
    "        inputs ,lables = torch.from_numpy(x).float().to(device),\\\n",
    "            torch.from_numpy(y).long().to(device)\n",
    "#         print(torch.sum(lables)) # = 1167\n",
    "        \n",
    "        y_onehot = torch.FloatTensor(batch_size, class_total_num).to(device)\n",
    "        y_onehot.zero_()\n",
    "        y_onehot.scatter_(1, lables.view(-1,1), 1)\n",
    "#         print('y_onehot:', y_onehot)\n",
    "\n",
    "\n",
    "       \n",
    "\n",
    "        optimizer.zero_grad()\n",
    "        optimzer4center.zero_grad()\n",
    "        \n",
    "        z, det = net_auto(x=inputs, y_onehot=y_onehot)\n",
    "        y_logits = net_auto.class_flow(z)\n",
    "        \n",
    "        loss_g = models.Glow.loss_generative(det)\n",
    "        loss_c = models.Glow.loss_class(y_logits,lables)\n",
    "        loss_center = centerloss(lables, z.view(-1,16*4*4))\n",
    "        \n",
    "        print('%d  loss_g: %.4f, loss_c:%.4f ,loss_center: %.4f' % (epoch , loss_g.item(),loss_c.item(),loss_center.item()))\n",
    "        loss = loss_g + loss_c * 0.1 + center_loss_weight * loss_center\n",
    "        loss.backward()\n",
    "        torch.nn.utils.clip_grad_value_(net_auto.parameters(), 5)\n",
    "        torch.nn.utils.clip_grad_norm_(net_auto.parameters(), 100)\n",
    "        \n",
    "        \n",
    "        trainloss_g += loss_g.item()\n",
    "        trainloss_c += loss_c.item()\n",
    "        \n",
    "        optimizer.step()\n",
    "        optimzer4center.step()\n",
    "        \n",
    "    print('%d  gloss: %.3f, class loss:%.3f' % (epoch ,  trainloss_g,trainloss_c))\n",
    "    \n",
    "    if epoch % 200 == 0 and epoch >0:\n",
    "        torch.save(net_auto.state_dict(),'model_glow_ominst_all_bg_4k_20way_1shot_add_split_z_no_mean_center1_'+str(epoch)+'.pt')\n",
    "        torch.save(centerloss.state_dict(),'model_center_ominst_all_bg_4k_20way_1shot_add_split_z_no_mean_center_weight1_'+str(epoch)+'.pt')\n",
    "    \n",
    "# 0  oss_g: 2.7062, loss_c:0.1053 ,loss_center: 1.4971\n",
    "#2716  loss_g: 2.8283, loss_c:0.0685 ,loss_center: 1.0845\n",
    "#4453  loss_g: 2.7237, loss_c:0.0479 ,loss_center: 1.0123\n",
    "#6101  loss_g: 2.6684, loss_c:0.0472 ,loss_center: 0.9684\n",
    "#6345  loss_g: 2.6775, loss_c:0.0574 ,loss_center: 1.0082"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# net_auto.set_z_add_random(True)\n",
    "# net_auto.train()\n",
    "# torch.save(net_auto.state_dict(),'model_glow_ominst_all_bg_4k_20way_1shot_add_split_z_no_mean_center1_6345.pt')\n",
    "# torch.save(centerloss.state_dict(),'model_center_ominst_all_bg_4k_20way_1shot_add_split_z_no_mean_center_weight1_6345.pt')\n",
    "\n",
    "\n",
    "a = torch.rand((2,2,2))\n",
    "print(a)\n",
    "print(a.view(-1,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# net_auto.load_state_dict(torch.load('model_glow_ominst_all_bg_8k_20_way_5_shot_160.pt'))\n",
    "net_auto.set_z_add_random(False)\n",
    "use_cuda = torch.cuda.is_available()\n",
    "torch.manual_seed(666)\n",
    "device = torch.device(\"cuda\" if use_cuda else \"cpu\")\n",
    "net_auto.to(device)\n",
    "net_auto.float()\n",
    "net_auto.eval()\n",
    "\n",
    "test_loss = 0\n",
    "correct = 0\n",
    "test_datas = getnext_test(dataset_test_positive,test_class_index)\n",
    "for x ,y in test_datas:\n",
    "        inputs, labels = Variable(torch.from_numpy(x).float()).cuda(), Variable(torch.from_numpy(y).long()).cuda()\n",
    "#         print(labels)\n",
    "        \n",
    "        z, det = net_auto(x=inputs, y_onehot=None)\n",
    "        classify = net_auto.class_flow(z)\n",
    "        \n",
    "        \n",
    "        \n",
    "#         _,classify = net_auto(inputs)\n",
    "        test_loss += F.nll_loss(classify, labels, size_average=False).item() # sum up batch loss\n",
    "#         print(classify)\n",
    "        pred = classify.max(1,keepdim=True)[1]\n",
    "        print(pred)\n",
    "        \n",
    "        correct += pred.eq(labels.view_as(pred)).sum().item()\n",
    "#         print(correct)\n",
    "print(correct)\n",
    "print(correct * 1.0 / (19 * 20))\n",
    "print(test_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "# print(x_p.shape)\n",
    "print(label_positive)\n",
    "imgs_show(np.squeeze(x_positive),4,5)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "imgs_show(np.squeeze(images_observe_train_total[80:100]),4,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "imgs_show(np.squeeze(images_observe_train_total[0:20]),4,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_real_mean_var():\n",
    "    batch_size = 256\n",
    "    net_auto.set_z_add_random(False)\n",
    "    net_auto.eval()\n",
    "    mean_std_list =[MeanVarObject(i) for i in range(class_total_num)]\n",
    "    \n",
    "    datas =  get_next_perfect(dataset_train_positive,dataset_negative,6,125,shuffle=False)\n",
    "    \n",
    "    for x,y in datas:\n",
    "        inputs ,lables = torch.from_numpy(x).float().to(device),\\\n",
    "            torch.from_numpy(y).long().to(device)\n",
    "#         print(torch.sum(lables)) # = 1167\n",
    "        \n",
    "        y_onehot = torch.FloatTensor(batch_size, class_total_num).to(device)\n",
    "        y_onehot.zero_()\n",
    "        y_onehot.scatter_(1, lables.view(-1,1), 1)\n",
    "#         print('y_onehot:', y_onehot)\n",
    "        optimizer.zero_grad()\n",
    "        optimzer4center.zero_grad()\n",
    "        \n",
    "        z , _ = net_auto(x=inputs, y_onehot=y_onehot)\n",
    "        z_real = z.detach().cpu().numpy()\n",
    "        for i,label in enumerate(y) :\n",
    "            mean_std_list[label].add_z(z_real[i])\n",
    "    return mean_std_list\n",
    "\n",
    "mean_std_obj_list = get_real_mean_var()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# mean_std_list label : 0 ->984\n",
    "\n",
    "\n",
    "mean_list = []\n",
    "std_list =[]\n",
    "z_total_list = []\n",
    "\n",
    "for obj in mean_std_obj_list:\n",
    "#     print(obj.label)\n",
    "    mean_list.append(obj.get_mean())\n",
    "    std_list.append(obj.get_std())\n",
    "    z_total_list.append(obj.get_z_array())\n",
    "\n",
    "mean_list  = np.array(mean_list)\n",
    "std_list = np.array(std_list)\n",
    "z_total_list = np.concatenate(np.array(z_total_list))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "print(z_total_list.shape)\n",
    "mean_z = np.mean(z_total_list,axis=0)\n",
    "std_z = np.std(z_total_list,axis=0)\n",
    "color_z = z_total_list * 250\n",
    "# print(mean_std_obj_list[500].get_mean()[2,:,:])\n",
    "# print(mean_list.shape)\n",
    "# print(mean_list[500,2,:,:])\n",
    "# # print(mean_x[500,2,:,:])\n",
    "# # print(mean_x[20,2,:,:])\n",
    "# print(mean_std_obj_list[500].get_std()[2,:,:])\n",
    "# print(std_list[500,2,:,:])\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def compute_distance_u_total(z_list,mean_z):\n",
    "    # mean_std_obj_list[381].get_z_array()\n",
    "    divider = np.subtract(z_list,mean_z) ** 2\n",
    "#     print(divider.shape)\n",
    "    return np.mean(divider,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# plt.figure(figsize=(2000,1),dpi=30)\n",
    "# # plt.text(17527,100,'Pre')\n",
    "# # my_y_ticks = np.arange(-0.1, 0.1, 0.01)\n",
    "# # plt.yticks(my_y_ticks)\n",
    "# d = 2\n",
    "# w = 1\n",
    "# h = 0\n",
    "# print('std_z = ',std_z[d,w,h])\n",
    "# print('mean = ',u_total_real[d,w,h])\n",
    "\n",
    "# plt.scatter(x = z_total_list[:,d,w,h],y = np.ones_like(z_total_list[:,d,w,h]),s=75,c =color_z[:,d,w,h],  alpha=.5)\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "print('std_z = ',std_z[0:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "color = mean_list * 250\n",
    "u_total_real = np.mean(mean_list,axis=0)\n",
    "std_mean_real  = np.mean(np.square(std_list),axis=0)\n",
    "ui_squ_mean_real  = np.mean(np.square(mean_list),axis=0)\n",
    "u_total_sque_real = np.square(u_total_real)\n",
    "std_squ_total = std_mean_real + ui_squ_mean_real - u_total_sque_real\n",
    "std_total_real = np.sqrt(std_squ_total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# print('mean = ',u_total_real[2,:,:])\n",
    "print(\"  \",std_total_real[:,:,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# print('u_total =  ',u_total_real[2,:,:])\n",
    "# print('std_mean =  ',np.sqrt(std_mean_real[:,0,0]))\n",
    "# print('ui_squ_mean =  ',ui_squ_mean.shape)\n",
    "# print('u_total_sque =  ',u_total_sque.shape)\n",
    "# print('std_squ_total =  ',std_squ_total.shape)\n",
    "d = 2\n",
    "w = 1\n",
    "h = 3\n",
    "print('std_z = ',std_z[d,w,h])\n",
    "print('mean = ',u_total_real[d,w,h])\n",
    "print('std_list = ',std_list[100:200,d,w,h])\n",
    "# print('mean list                = \\n',mean_list[25:80,d,w,h])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.figure(figsize=(300,2))\n",
    "\n",
    "# my_y_ticks = np.arange(-0.1, 0.1, 0.01)\n",
    "# plt.yticks(my_y_ticks)\n",
    "\n",
    "\n",
    "print('std = ',std_total_real[d,w,h])\n",
    "print('mean = ',u_total_real[d,w,h])\n",
    "plt.scatter(x = mean_list[:,d,w,h],y = np.ones_like(mean_list[:,d,w,h]),s=75,c =color[:,d,w,h],  alpha=.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lables_obser_bias = [241 ,188, 635, 270, 774, 790, 772, 376, 201 , 26, 453, 785 ,875 ,634 ,527 ,718 ,299 ,  6 ,679, 373]\n",
    "for index ,value in enumerate(lables_obser_bias):\n",
    "    print(str(index+1),\" scale = \",np.sum(np.sort(np.reshape(std_list[value,:,:,:],64*4*4))[-50:]) / np.sum(std_list[value,:,:,:]) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lables_obser_bias = [543, 119 ,482 ,541, 806, 602, 211, 757, 600, 969, 942, 107, 752, 729, 698, 153 ,466 ,597,578 ,604]\n",
    "for index ,value in enumerate(lables_obser_bias):\n",
    "    print(str(index+1),\" scale = \",np.sum(np.sort(np.reshape(std_list[value,:,:,:],64*4*4))[-50:]) / np.sum(std_list[value,:,:,:]) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lables_obser_bias = [344 ,539, 446, 566 ,431, 680, 161, 164, 336 ,980, 132 , 38, 169 ,660, 795 ,286 ,337 ,939,710, 973]\n",
    "for index ,value in enumerate(lables_obser_bias):\n",
    "    print(str(index+1),\" scale = \",np.sum(np.sort(np.reshape(std_list[value,:,:,:],64*4*4))[-50:]) / np.sum(std_list[value,:,:,:]) )\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "print(\"---------------------------------\")\n",
    "print(\"847 scale = \",np.sum(np.sort(np.reshape(std_list[847,:,:,:],64*4*4))[-40:]) / np.sum(std_list[847,:,:,:]) )\n",
    "print(\"118 scale = \",np.sum(np.sort(np.reshape(std_list[118,:,:,:],64*4*4))[-40:]) / np.sum(std_list[118,:,:,:]) )\n",
    "print(\"364 scale = \",np.sum(np.sort(np.reshape(std_list[364,:,:,:],64*4*4))[-40:]) / np.sum(std_list[364,:,:,:]) )\n",
    "print(\"239 scale = \",np.sum(np.sort(np.reshape(std_list[239,:,:,:],64*4*4))[-40:]) / np.sum(std_list[239,:,:,:]) )\n",
    "print(\"76 scale = \",np.sum(np.sort(np.reshape(std_list[76,:,:,:],64*4*4))[-40:]) / np.sum(std_list[76,:,:,:]) )\n",
    "print(\"---------------------------------\")\n",
    "print(\"936 scale = \",np.sum(np.sort(np.reshape(std_list[936,:,:,:],64*4*4))[-40:]) / np.sum(std_list[936,:,:,:]) )\n",
    "print(\"847 scale = \",np.sum(np.sort(np.reshape(std_list[621,:,:,:],64*4*4))[-40:]) / np.sum(std_list[621,:,:,:]) )\n",
    "print(\"847 scale = \",np.sum(np.sort(np.reshape(std_list[275,:,:,:],64*4*4))[-40:]) / np.sum(std_list[275,:,:,:]) )\n",
    "print(\"847 scale = \",np.sum(np.sort(np.reshape(std_list[121,:,:,:],64*4*4))[-40:]) / np.sum(std_list[121,:,:,:]) )\n",
    "print(\"847 scale = \",np.sum(np.sort(np.reshape(std_list[312,:,:,:],64*4*4))[-40:]) / np.sum(std_list[312,:,:,:]) )\n",
    "print(\"---------------------------------\")\n",
    "print(\"---------------------------------\")\n",
    "print(\"---------------------------------\")\n",
    "print(\"847 scale = \",np.sum(np.sort(np.reshape(std_list[859,:,:,:],64*4*4))[-40:]) / np.sum(std_list[859,:,:,:]) )\n",
    "print(\"847 scale = \",np.sum(np.sort(np.reshape(std_list[389,:,:,:],64*4*4))[-40:]) / np.sum(std_list[389,:,:,:]) )\n",
    "print(\"847 scale = \",np.sum(np.sort(np.reshape(std_list[536,:,:,:],64*4*4))[-40:]) / np.sum(std_list[536,:,:,:]) )\n",
    "print(\"847 scale = \",np.sum(np.sort(np.reshape(std_list[730,:,:,:],64*4*4))[-40:]) / np.sum(std_list[730,:,:,:]) )\n",
    "print(\"847 scale = \",np.sum(np.sort(np.reshape(std_list[332,:,:,:],64*4*4))[-40:]) / np.sum(std_list[332,:,:,:]) )\n",
    "print(\"---------------------------------\")\n",
    "print(\"847 scale = \",np.sum(np.sort(np.reshape(std_list[760,:,:,:],64*4*4))[-40:]) / np.sum(std_list[760,:,:,:]) )\n",
    "print(\"847 scale = \",np.sum(np.sort(np.reshape(std_list[397,:,:,:],64*4*4))[-40:]) / np.sum(std_list[397,:,:,:]) )\n",
    "print(\"847 scale = \",np.sum(np.sort(np.reshape(std_list[463,:,:,:],64*4*4))[-40:]) / np.sum(std_list[463,:,:,:]) )\n",
    "print(\"847 scale = \",np.sum(np.sort(np.reshape(std_list[750,:,:,:],64*4*4))[-40:]) / np.sum(std_list[750,:,:,:]) )\n",
    "print(\"847 scale = \",np.sum(np.sort(np.reshape(std_list[803,:,:,:],64*4*4))[-40:]) / np.sum(std_list[803,:,:,:]) )\n",
    "\n",
    "print(\"---------------------------------\")\n",
    "print(\"---------------------------------\")\n",
    "print(\"---------------------------------\")\n",
    "print(\"847 scale = \",np.sum(np.sort(np.reshape(std_list[886,:,:,:],64*4*4))[-40:]) / np.sum(std_list[886,:,:,:]) )\n",
    "print(\"847 scale = \",np.sum(np.sort(np.reshape(std_list[882,:,:,:],64*4*4))[-40:]) / np.sum(std_list[882,:,:,:]) )\n",
    "print(\"847 scale = \",np.sum(np.sort(np.reshape(std_list[215,:,:,:],64*4*4))[-40:]) / np.sum(std_list[215,:,:,:]) )\n",
    "print(\"847 scale = \",np.sum(np.sort(np.reshape(std_list[430,:,:,:],64*4*4))[-40:]) / np.sum(std_list[430,:,:,:]) )\n",
    "print(\"847 scale = \",np.sum(np.sort(np.reshape(std_list[861,:,:,:],64*4*4))[-40:]) / np.sum(std_list[861,:,:,:]) )\n",
    "print(\"---------------------------------\")\n",
    "print(\"847 scale = \",np.sum(np.sort(np.reshape(std_list[812,:,:,:],64*4*4))[-40:]) / np.sum(std_list[812,:,:,:]) )\n",
    "print(\"847 scale = \",np.sum(np.sort(np.reshape(std_list[623,:,:,:],64*4*4))[-40:]) / np.sum(std_list[623,:,:,:]) )\n",
    "print(\"847 scale = \",np.sum(np.sort(np.reshape(std_list[381,:,:,:],64*4*4))[-40:]) / np.sum(std_list[381,:,:,:]) )\n",
    "print(\"847 scale = \",np.sum(np.sort(np.reshape(std_list[338,:,:,:],64*4*4))[-40:]) / np.sum(std_list[338,:,:,:]) )\n",
    "print(\"847 scale = \",np.sum(np.sort(np.reshape(std_list[840,:,:,:],64*4*4))[-40:]) / np.sum(std_list[840,:,:,:]) )\n",
    "\n",
    "\n",
    "\n",
    "# print(\"std fuza = \",np.sum(std_list[847,:,:,:]))\n",
    "# print(\"std fuza = \",np.sum(std_list[118,:,:,:]))\n",
    "# print(\"std fuza = \",np.sum(std_list[364,:,:,:]))\n",
    "# print(\"std fuza = \",np.sum(std_list[239,:,:,:]))\n",
    "# print(\"std fuza = \",np.sum(std_list[76,:,:,:]))\n",
    "# print(\"---------------------------------\")\n",
    "# print(\"std fuza = \",np.sum(std_list[936,:,:,:]))\n",
    "# print(\"std fuza = \",np.sum(std_list[621,:,:,:]))\n",
    "# print(\"std fuza = \",np.sum(std_list[275 ,:,:,:]))\n",
    "# print(\"std fuza = \",np.sum(std_list[121 ,:,:,:]))\n",
    "# print(\"std fuza = \",np.sum(std_list[312 ,:,:,:]))\n",
    "# print(\"---------------------------------\")\n",
    "# print(\"---------------------------------\")\n",
    "# print(\"---------------------------------\")\n",
    "# print(\"std fuza = \",np.sum(std_list[859,:,:,:]))\n",
    "# print(\"std fuza = \",np.sum(std_list[389,:,:,:]))\n",
    "# print(\"std fuza = \",np.sum(std_list[536 ,:,:,:]))\n",
    "# print(\"std fuza = \",np.sum(std_list[730 ,:,:,:]))\n",
    "# print(\"std fuza = \",np.sum(std_list[332 ,:,:,:]))\n",
    "# print(\"---------------------------------\")\n",
    "# print(\"std fuza = \",np.sum(std_list[760,:,:,:]))\n",
    "# print(\"std fuza = \",np.sum(std_list[397,:,:,:]))\n",
    "# print(\"std fuza = \",np.sum(std_list[463 ,:,:,:]))\n",
    "# print(\"std fuza = \",np.sum(std_list[750 ,:,:,:]))\n",
    "# print(\"std fuza = \",np.sum(std_list[803 ,:,:,:]))\n",
    "# print(\"---------------------------------\")\n",
    "# print(\"---------------------------------\")\n",
    "# print(\"---------------------------------\")\n",
    "# print(\"std fuza = \",np.sum(std_list[886,:,:,:]))\n",
    "# print(\"std fuza = \",np.sum(std_list[882,:,:,:]))\n",
    "# print(\"std fuza = \",np.sum(std_list[215 ,:,:,:]))\n",
    "# print(\"std fuza = \",np.sum(std_list[430 ,:,:,:]))\n",
    "# print(\"std fuza = \",np.sum(std_list[861 ,:,:,:]))\n",
    "# print(\"---------------------------------\")\n",
    "# print(\"std fuza = \",np.sum(std_list[812,:,:,:]))\n",
    "# print(\"std fuza = \",np.sum(std_list[623,:,:,:]))\n",
    "# print(\"std fuza = \",np.sum(std_list[381 ,:,:,:]))\n",
    "# print(\"std fuza = \",np.sum(std_list[338 ,:,:,:]))\n",
    "# print(\"std fuza = \",np.sum(std_list[840 ,:,:,:]))\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "print(\"---------------------------------\")\n",
    "print(\"std fuza = \",np.sum(std_list[849,:,:,:]))\n",
    "print(\"std fuza = \",np.sum(std_list[5,:,:,:]))\n",
    "print(\"std fuza = \",np.sum(std_list[101,:,:,:]))\n",
    "print(\"std fuza = \",np.sum(std_list[590,:,:,:]))\n",
    "print(\"std fuza = \",np.sum(std_list[235,:,:,:]))\n",
    "print(\"---------------------------------\")\n",
    "print(\"std fuza = \",np.sum(std_list[135,:,:,:]))\n",
    "print(\"std fuza = \",np.sum(std_list[214,:,:,:]))\n",
    "print(\"std fuza = \",np.sum(std_list[465 ,:,:,:]))\n",
    "print(\"std fuza = \",np.sum(std_list[411 ,:,:,:]))\n",
    "print(\"std fuza = \",np.sum(std_list[901 ,:,:,:]))\n",
    "print(\"---------------------------------\")\n",
    "print(\"std fuza = \",np.sum(std_list[54,:,:,:]))\n",
    "print(\"std fuza = \",np.sum(std_list[977,:,:,:]))\n",
    "print(\"std fuza = \",np.sum(std_list[580 ,:,:,:]))\n",
    "print(\"std fuza = \",np.sum(std_list[764 ,:,:,:]))\n",
    "print(\"std fuza = \",np.sum(std_list[403 ,:,:,:]))\n",
    "print(\"---------------------------------\")\n",
    "print(\"std fuza = \",np.sum(std_list[519,:,:,:]))\n",
    "print(\"std fuza = \",np.sum(std_list[864,:,:,:]))\n",
    "print(\"std fuza = \",np.sum(std_list[916 ,:,:,:]))\n",
    "print(\"std fuza = \",np.sum(std_list[675 ,:,:,:]))\n",
    "print(\"std fuza = \",np.sum(std_list[964 ,:,:,:]))\n",
    "\n",
    "\n",
    "print(np.sum(np.sort(np.reshape(std_list[977,:,:,:],64*4*4))[-40:]))\n",
    "print(np.sum(np.sort(np.reshape(std_list[580,:,:,:],64*4*4))[-40:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "print(\"---------------------------------\")\n",
    "print(\"std fuza = \",np.sum(std_list[220,:,:,:]))\n",
    "print(\"std fuza = \",np.sum(std_list[48,:,:,:]))\n",
    "print(\"std fuza = \",np.sum(std_list[379,:,:,:]))\n",
    "print(\"std fuza = \",np.sum(std_list[294,:,:,:]))\n",
    "print(\"std fuza = \",np.sum(std_list[18,:,:,:]))\n",
    "print(\"---------------------------------\")\n",
    "print(\"std fuza = \",np.sum(std_list[674,:,:,:]))\n",
    "print(\"std fuza = \",np.sum(std_list[97,:,:,:]))\n",
    "print(\"std fuza = \",np.sum(std_list[348 ,:,:,:]))\n",
    "print(\"std fuza = \",np.sum(std_list[65 ,:,:,:]))\n",
    "print(\"std fuza = \",np.sum(std_list[831 ,:,:,:]))\n",
    "print(\"---------------------------------\")\n",
    "print(\"std fuza = \",np.sum(std_list[547,:,:,:]))\n",
    "print(\"std fuza = \",np.sum(std_list[844,:,:,:]))\n",
    "print(\"std fuza = \",np.sum(std_list[236 ,:,:,:]))\n",
    "print(\"std fuza = \",np.sum(std_list[172 ,:,:,:]))\n",
    "print(\"std fuza = \",np.sum(std_list[424 ,:,:,:]))\n",
    "print(\"---------------------------------\")\n",
    "print(\"std fuza = \",np.sum(std_list[657,:,:,:]))\n",
    "print(\"std fuza = \",np.sum(std_list[46,:,:,:]))\n",
    "print(\"std fuza = \",np.sum(std_list[3 ,:,:,:]))\n",
    "print(\"std fuza = \",np.sum(std_list[255 ,:,:,:]))\n",
    "print(\"std fuza = \",np.sum(std_list[470 ,:,:,:]))\n",
    "\n",
    "\n",
    "print(np.sum(np.sort(np.reshape(std_list[46,:,:,:],64*4*4))[:-100]))\n",
    "print(np.sum(np.sort(np.reshape(std_list[3,:,:,:],64*4*4))[:-100]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "print('distance_u_simple = ',np.sum(compute_distance_u_total(mean_std_obj_list[220].get_z_array(),mean_z)))\n",
    "print('distance_u_simple = ',np.sum(compute_distance_u_total(mean_std_obj_list[48].get_z_array(),mean_z)))\n",
    "print('distance_u_simple = ',np.sum(compute_distance_u_total(mean_std_obj_list[379].get_z_array(),mean_z)))\n",
    "print('distance_u_simple = ',np.sum(compute_distance_u_total(mean_std_obj_list[294].get_z_array(),mean_z)))\n",
    "print('distance_u_simple = ',np.sum(compute_distance_u_total(mean_std_obj_list[18].get_z_array(),mean_z)))\n",
    "print(\"---------------------------------\")\n",
    "\n",
    "print('distance_u_simple  = ',np.sum(compute_distance_u_total(mean_std_obj_list[674].get_z_array(),mean_z)))\n",
    "print('distance_u_simple  = ',np.sum(compute_distance_u_total(mean_std_obj_list[97].get_z_array(),mean_z)))\n",
    "print('distance_u_simple = ',np.sum(compute_distance_u_total(mean_std_obj_list[348].get_z_array(),mean_z)))\n",
    "print('distance_u_simple = ',np.sum(compute_distance_u_total(mean_std_obj_list[65].get_z_array(),mean_z)))\n",
    "print('distance_u_simple = ',np.sum(compute_distance_u_total(mean_std_obj_list[831].get_z_array(),mean_z)))\n",
    "print(\"---------------------------------\")\n",
    "print('distance_u_simple = ',np.sum(compute_distance_u_total(mean_std_obj_list[547].get_z_array(),mean_z)))\n",
    "print('distance_u_simple = ',np.sum(compute_distance_u_total(mean_std_obj_list[844].get_z_array(),mean_z)))\n",
    "print('distance_u_simple = ',np.sum(compute_distance_u_total(mean_std_obj_list[236].get_z_array(),mean_z)))\n",
    "print('distance_u_simple = ',np.sum(compute_distance_u_total(mean_std_obj_list[172].get_z_array(),mean_z)))\n",
    "print('distance_u_simple = ',np.sum(compute_distance_u_total(mean_std_obj_list[424].get_z_array(),mean_z)))\n",
    "print(\"---------------------------------\")\n",
    "print('distance_u_simple = ',np.sum(compute_distance_u_total(mean_std_obj_list[657].get_z_array(),mean_z)))\n",
    "print('distance_u_simple = ',np.sum(compute_distance_u_total(mean_std_obj_list[46].get_z_array(),mean_z)))\n",
    "print('distance_u_simple = ',np.sum(compute_distance_u_total(mean_std_obj_list[3].get_z_array(),mean_z)))\n",
    "print('distance_u_simple = ',np.sum(compute_distance_u_total(mean_std_obj_list[255].get_z_array(),mean_z)))\n",
    "print('distance_u_simple = ',np.sum(compute_distance_u_total(mean_std_obj_list[470].get_z_array(),mean_z)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "# distance_u_simple = compute_distance_u_total(mean_std_obj_list[simple].get_z_array(),mean_z)\n",
    "\n",
    "print('distance_u_simple = ',np.sum(compute_distance_u_total(mean_std_obj_list[159].get_z_array(),mean_z)))\n",
    "print('distance_u_simple = ',np.sum(compute_distance_u_total(mean_std_obj_list[687].get_z_array(),mean_z)))\n",
    "print('distance_u_simple = ',np.sum(compute_distance_u_total(mean_std_obj_list[486].get_z_array(),mean_z)))\n",
    "print('distance_u_simple = ',np.sum(compute_distance_u_total(mean_std_obj_list[843].get_z_array(),mean_z)))\n",
    "print('distance_u_simple = ',np.sum(compute_distance_u_total(mean_std_obj_list[27].get_z_array(),mean_z)))\n",
    "print(\"---------------------------------\")\n",
    "\n",
    "print('distance_u_simple  = ',np.sum(compute_distance_u_total(mean_std_obj_list[253].get_z_array(),mean_z)))\n",
    "print('distance_u_simple  = ',np.sum(compute_distance_u_total(mean_std_obj_list[909].get_z_array(),mean_z)))\n",
    "print('distance_u_simple = ',np.sum(compute_distance_u_total(mean_std_obj_list[295].get_z_array(),mean_z)))\n",
    "print('distance_u_simple = ',np.sum(compute_distance_u_total(mean_std_obj_list[912].get_z_array(),mean_z)))\n",
    "print('distance_u_simple = ',np.sum(compute_distance_u_total(mean_std_obj_list[714].get_z_array(),mean_z)))\n",
    "print(\"---------------------------------\")\n",
    "print('distance_u_simple = ',np.sum(compute_distance_u_total(mean_std_obj_list[307].get_z_array(),mean_z)))\n",
    "print('distance_u_simple = ',np.sum(compute_distance_u_total(mean_std_obj_list[661].get_z_array(),mean_z)))\n",
    "print('distance_u_simple = ',np.sum(compute_distance_u_total(mean_std_obj_list[858].get_z_array(),mean_z)))\n",
    "print('distance_u_simple = ',np.sum(compute_distance_u_total(mean_std_obj_list[79].get_z_array(),mean_z)))\n",
    "print('distance_u_simple = ',np.sum(compute_distance_u_total(mean_std_obj_list[717].get_z_array(),mean_z)))\n",
    "print(\"---------------------------------\")\n",
    "print('distance_u_simple = ',np.sum(compute_distance_u_total(mean_std_obj_list[122].get_z_array(),mean_z)))\n",
    "print('distance_u_simple = ',np.sum(compute_distance_u_total(mean_std_obj_list[51].get_z_array(),mean_z)))\n",
    "print('distance_u_simple = ',np.sum(compute_distance_u_total(mean_std_obj_list[631].get_z_array(),mean_z)))\n",
    "print('distance_u_simple = ',np.sum(compute_distance_u_total(mean_std_obj_list[828].get_z_array(),mean_z)))\n",
    "print('distance_u_simple = ',np.sum(compute_distance_u_total(mean_std_obj_list[20].get_z_array(),mean_z)))\n",
    "\n",
    "\n",
    "# print(\"---------------------------------\")\n",
    "# print(\"std jiandan = \",np.sum(std_list[761,:,:,:]))\n",
    "# print(\"std complex = \",np.sum(std_list[761,:,:,:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "print(\"---------------------------------\")\n",
    "print(\"std fuza = \",np.sum(std_list[159,:,:,:]))\n",
    "print(\"std fuza = \",np.sum(std_list[687,:,:,:]))\n",
    "print(\"std fuza = \",np.sum(std_list[486,:,:,:]))\n",
    "print(\"std fuza = \",np.sum(std_list[843,:,:,:]))\n",
    "print(\"std fuza = \",np.sum(std_list[27,:,:,:]))\n",
    "print(\"---------------------------------\")\n",
    "print(\"std fuza = \",np.sum(std_list[253,:,:,:]))\n",
    "print(\"std fuza = \",np.sum(std_list[909,:,:,:]))\n",
    "print(\"std fuza = \",np.sum(std_list[295 ,:,:,:]))\n",
    "print(\"std fuza = \",np.sum(std_list[912 ,:,:,:]))\n",
    "print(\"std fuza = \",np.sum(std_list[714 ,:,:,:]))\n",
    "print(\"---------------------------------\")\n",
    "print(\"std fuza = \",np.sum(std_list[307,:,:,:]))\n",
    "print(\"std fuza = \",np.sum(std_list[661,:,:,:]))\n",
    "print(\"std fuza = \",np.sum(std_list[858 ,:,:,:]))\n",
    "print(\"std fuza = \",np.sum(std_list[79 ,:,:,:]))\n",
    "print(\"std fuza = \",np.sum(std_list[717 ,:,:,:]))\n",
    "print(\"---------------------------------\")\n",
    "print(\"std fuza = \",np.sum(std_list[122,:,:,:]))\n",
    "print(\"std fuza = \",np.sum(std_list[51,:,:,:]))\n",
    "print(\"std fuza = \",np.sum(std_list[631 ,:,:,:]))\n",
    "print(\"std fuza = \",np.sum(std_list[828 ,:,:,:]))\n",
    "print(\"std fuza = \",np.sum(std_list[20 ,:,:,:]))\n",
    "\n",
    "\n",
    "print(np.sum(np.sort(np.reshape(std_list[79,:,:,:],64*4*4))[-10:]))\n",
    "print(np.sum(np.sort(np.reshape(std_list[122,:,:,:],64*4*4))[-10:]))\n",
    "print(np.sum(np.sort(np.reshape(std_list[486,:,:,:],64*4*4))[-10:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "complexs = 239\n",
    "simple = 381\n",
    "print(\"u_total = \",u_total_real[2,:,:])\n",
    "print(\"std_real = \",std_total_real[2,:,:])\n",
    "print(\"---------------------------------\")\n",
    "# print(\"fuza = \",mean_list[239,2,:,:])\n",
    "# print(\"jiandan = \",mean_list[381,2,:,:])\n",
    "\n",
    "print(\"---------------------------------\")\n",
    "# distance_u_complex = compute_distance_u_total(mean_std_obj_list[complexs].get_z_array(),mean_z)\n",
    "print('distance_u_complex = ',np.sum(compute_distance_u_total(mean_std_obj_list[847].get_z_array(),mean_z)))\n",
    "print('distance_u_complex = ',np.sum(compute_distance_u_total(mean_std_obj_list[118].get_z_array(),mean_z)))\n",
    "print('distance_u_complex = ',np.sum(compute_distance_u_total(mean_std_obj_list[239].get_z_array(),mean_z)))\n",
    "print('distance_u_complex = ',np.sum(compute_distance_u_total(mean_std_obj_list[76].get_z_array(),mean_z)))\n",
    "print('distance_u_complex = ',np.sum(compute_distance_u_total(mean_std_obj_list[936].get_z_array(),mean_z)))\n",
    "print('distance_u_complex = ',np.sum(compute_distance_u_total(mean_std_obj_list[621].get_z_array(),mean_z)))\n",
    "print('distance_u_complex = ',np.sum(compute_distance_u_total(mean_std_obj_list[891].get_z_array(),mean_z)))\n",
    "print('distance_u_complex = ',np.sum(compute_distance_u_total(mean_std_obj_list[586].get_z_array(),mean_z)))\n",
    "print(\"---------------------------------\")\n",
    "# distance_u_simple = compute_distance_u_total(mean_std_obj_list[simple].get_z_array(),mean_z)\n",
    "print('distance_u_simple = ',np.sum(compute_distance_u_total(mean_std_obj_list[886].get_z_array(),mean_z)))\n",
    "print('distance_u_simple = ',np.sum(compute_distance_u_total(mean_std_obj_list[882].get_z_array(),mean_z)))\n",
    "print('distance_u_simple = ',np.sum(compute_distance_u_total(mean_std_obj_list[430].get_z_array(),mean_z)))\n",
    "print('distance_u_simple = ',np.sum(compute_distance_u_total(mean_std_obj_list[812].get_z_array(),mean_z)))\n",
    "print('distance_u_simple = ',np.sum(compute_distance_u_total(mean_std_obj_list[623].get_z_array(),mean_z)))\n",
    "print('distance_u_simple = ',np.sum(compute_distance_u_total(mean_std_obj_list[381].get_z_array(),mean_z)))\n",
    "print('distance_u_simple = ',np.sum(compute_distance_u_total(mean_std_obj_list[579].get_z_array(),mean_z)))\n",
    "print('distance_u_simple = ',np.sum(compute_distance_u_total(mean_std_obj_list[559].get_z_array(),mean_z)))\n",
    "# std_fuza = np.sort(np.reshape(std_list[239,:,:,:],16*4*4))\n",
    "# std_jiandan = np.sort(np.reshape(std_list[381,:,:,:],16*4*4))\n",
    "# print(\"std fuza = \",np.sum(std_fuza[100:150]) )\n",
    "# print(\"std jiandan = \",np.sum(std_jiandan[100:150]))\n",
    "print(\"---------------------------------\")\n",
    "print(\"std fuza = \",np.sum(std_list[847,:,:,:]))\n",
    "print(\"std fuza = \",np.sum(std_list[118,:,:,:]))\n",
    "print(\"std fuza = \",np.sum(std_list[239,:,:,:]))\n",
    "print(\"std fuza = \",np.sum(std_list[76,:,:,:]))\n",
    "print(\"std fuza = \",np.sum(std_list[936,:,:,:]))\n",
    "print(\"std fuza = \",np.sum(std_list[621,:,:,:]))\n",
    "print(\"std fuza = \",np.sum(std_list[621,:,:,:]))\n",
    "print(\"std fuza = \",np.sum(std_list[366 ,:,:,:]))\n",
    "print(\"std fuza = \",np.sum(std_list[292 ,:,:,:]))\n",
    "print(\"std fuza = \",np.sum(std_list[479 ,:,:,:]))\n",
    "print(\"std fuza = \",np.sum(std_list[955 ,:,:,:]))\n",
    "\n",
    "\n",
    "# print(\"---------------------------------\")\n",
    "# print(\"std fuza = \",np.sum(std_list[661,:,:,:]))\n",
    "# print(\"std fuza = \",np.sum(std_list[858,:,:,:]))\n",
    "# print(\"std fuza = \",np.sum(std_list[79,:,:,:]))\n",
    "# print(\"std fuza = \",np.sum(std_list[159,:,:,:]))\n",
    "# print(\"---------------------------------\")\n",
    "print(\"std jiandan = \",np.sum(std_list[886,:,:,:]))\n",
    "print(\"std jiandan = \",np.sum(std_list[882,:,:,:]))\n",
    "print(\"std jiandan = \",np.sum(std_list[430,:,:,:]))\n",
    "print(\"std jiandan = \",np.sum(std_list[812,:,:,:]))\n",
    "print(\"std jiandan = \",np.sum(std_list[623,:,:,:]))\n",
    "print(\"std jiandan = \",np.sum(std_list[381,:,:,:]))\n",
    "\n",
    "\n",
    "print(\"---------------------------------\")\n",
    "print(\"std jiandan = \",np.sum(std_list[761,:,:,:]))\n",
    "print(\"std jiandan = \",np.sum(std_list[589 ,:,:,:]))\n",
    "print(\"std jiandan = \",np.sum(std_list[572 ,:,:,:]))\n",
    "print(\"std jiandan = \",np.sum(std_list[129 ,:,:,:]))\n",
    "\n",
    "\n",
    "# print(\"---------------------------------\")\n",
    "# print(\"std jiandan = \",np.sum(std_list[979,:,:,:]))\n",
    "# print('distance_u_simple = ',np.sum(compute_distance_u_total(mean_std_obj_list[979].get_z_array(),mean_z)))\n",
    "\n",
    "# mean_std_obj_list[979].get_z_array()[12:14]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "batch_size = len(label_observe_total)\n",
    "lables = np.array([index for index in range(class_total_num)])\n",
    "lables = torch.from_numpy(lables).long().to(device)\n",
    "print(\"get_log_mean_var ,labels = \", lables)\n",
    "print(lables[-1])\n",
    "y_onehot = torch.FloatTensor(batch_size, class_total_num).cuda()\n",
    "y_onehot.zero_()\n",
    "y_onehot.scatter_(1, lables.view(-1, 1), 1)\n",
    "\n",
    "mean,var = net_auto.get_log_mean_var(batch_size,y_onehot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "center_mean = centerloss.centers.data.cpu().detach().numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "print(mean[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "print(mean[0,0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "print(center_mean.reshape(-1,16,4,4)[0,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "std = torch.exp(var.cpu()).detach().numpy()\n",
    "mean_x = mean.cpu().detach().numpy()\n",
    "# mean_y = np.ones_like(mean_x)\n",
    "color = (mean_x + 1 ) * 150\n",
    "\n",
    "u_total = np.mean(mean_x,axis=0)\n",
    "print('u_total =  ',u_total[:,0,0])\n",
    "std_mean = np.mean(np.square(std),axis=0)\n",
    "print('std_mean =  ',np.sqrt(std_mean[:,0,0]))\n",
    "ui_squ_mean = np.mean(np.square(mean_x),axis=0)\n",
    "print('ui_squ_mean =  ',ui_squ_mean.shape)\n",
    "u_total_sque = np.square(u_total)\n",
    "print('u_total_sque =  ',u_total_sque.shape)\n",
    "std_squ_total = std_mean + ui_squ_mean - u_total_sque\n",
    "std_total = np.sqrt(std_squ_total)\n",
    "print('std_squ_total =  ',std_squ_total.shape)\n",
    "print('std__total = ',std_total[2,:,:])\n",
    "print('std__total = ',std_total_real[2,:,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.figure(figsize=(200,5))\n",
    "\n",
    "# my_y_ticks = np.arange(-0.1, 0.1, 0.01)\n",
    "# plt.yticks(my_y_ticks)\n",
    "\n",
    "plt.scatter(x = mean_x[:,9,0,0],y = np.ones_like(mean_x[:,9,0,0]),s=75,c =color[:,9,0,0],  alpha=.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "criterion = nn.MSELoss(reduction='sum')\n",
    "ones_tensor = torch.ones((1, 32, 32)).float().cuda()\n",
    "std_total_gpu = torch.from_numpy(std_total_real).cuda()\n",
    "from scipy import misc\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "vec1 = mean_list[122]\n",
    "vec2 = mean_list[470]\n",
    "print(np.sqrt(np.sum(np.square( vec1 - vec2 ))))\n",
    "print(float(np.sum(vec1*vec2)) / np.linalg.norm(vec1)*np.linalg.norm(vec2))\n",
    "print(\"----------------------------------\")\n",
    "vec1 = mean_list[122]\n",
    "vec2 = mean_list[101]\n",
    "print(np.sqrt(np.sum(np.square( vec1 - vec2 ))))\n",
    "print(float(np.sum(vec1*vec2)) / np.linalg.norm(vec1)*np.linalg.norm(vec2))\n",
    "print(\"----------------------------------\")\n",
    "vec1 = mean_list[122]\n",
    "vec2 = mean_list[590]\n",
    "print(np.sqrt(np.sum(np.square( vec1 - vec2 ))))\n",
    "print(float(np.sum(vec1*vec2)) / np.linalg.norm(vec1)*np.linalg.norm(vec2))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "net_auto.set_z_add_random(False)\n",
    "\n",
    "# sample_p   sample_n 不是对应的label,labl才是\n",
    "\n",
    "sample_p =  5 #np.random.randint(20)  # 10 # \n",
    "sample_p_index = 0\n",
    "sample_n =   np.random.randint(964)       # 669 # 292 #   500 #    669 # \n",
    "sample_index = np.random.randint(18)    # 13  # 1 #    # 13   #\n",
    "print('sample_p = {},sample_n ={}, sample_index = {}'.format(sample_p,sample_n,sample_index))\n",
    "\n",
    "posi = np.concatenate(( np.array(dataset_train_positive[sample_p].imgs[sample_p_index:sample_p_index+1]) , np.array(dataset_negative[sample_n].imgs[sample_index:sample_index+2])))\n",
    "labl =  np.array([dataset_train_positive[sample_p].label, dataset_negative[sample_n].label,dataset_negative[sample_n].label])\n",
    "print(dataset_train_positive[sample_p].name)\n",
    "print(dataset_negative[sample_n].name)\n",
    "print(labl)\n",
    "inputs ,lables = torch.from_numpy(posi).float().to(device),\\\n",
    "            torch.from_numpy(labl).long().to(device)\n",
    "        \n",
    "y_onehot = torch.FloatTensor(3, class_total_num).to(device)\n",
    "y_onehot.zero_()\n",
    "y_onehot.scatter_(1, lables.view(-1,1), 1)\n",
    "# print('y_onehot:', y_onehot)\n",
    "# z, det, y_logits = net_auto(x=inputs, y_onehot=y_onehot)\n",
    "z, det = net_auto(x=inputs, y_onehot=y_onehot)\n",
    "y_logits = net_auto.class_flow(z)\n",
    "\n",
    "\n",
    "print('z.size() ' ,z.size())\n",
    "print('y_logits.size() ' ,y_logits.size())\n",
    "print(y_logits[1][1])\n",
    "\n",
    "print(np.sqrt(np.sum((mean_list[labl[0]] - mean_list[labl[1]]) ** 2)))\n",
    "vec1 = mean_list[labl[0]]\n",
    "vec2 = mean_list[labl[1]]\n",
    "vec3 = mean_list[labl[2]]\n",
    "\n",
    "# print(float(np.sum(vec1*vec2)) / np.linalg.norm(vec1)*np.linalg.norm(vec2))\n",
    "# print(\"----------------------------------\")\n",
    "\n",
    "# print(str(labl[1]),\" scale = \",np.sum(np.sort(np.reshape(std_list[labl[1],:,:,:],64*4*4))[-50:]) / np.sum(std_list[labl[1],:,:,:]) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "r =  np.random.uniform(0.5, 1.0)\n",
    "# print('random = ',r)\n",
    "divide = torch.div(torch.abs(torch.sub(z[1],z[0])), std_total_gpu) \n",
    "# scale = torch.cos(torch.clamp(divide,0,1.57))\n",
    "\n",
    "scale = torch.reciprocal(torch.exp(divide))\n",
    "# print(scale)\n",
    "# scale = torch.abs(torch.div(std_total_gpu * 0.3, torch.add(torch.sub(z[1],z[0]),1e-4)))\n",
    "# scale = torch.clamp(scale,0,1)\n",
    "# print(scale > 1e-1)\n",
    "# scale = 0.39\n",
    "divider_z = scale * (z[2] - z[1])\n",
    "obj = z[0] + divider_z\n",
    "\n",
    "\n",
    "# obj = z[0]\n",
    "# obj[0:30] = obj[0:30]+ divider_z[0:30]\n",
    "# d = 0\n",
    "# w = 0\n",
    "# h = 0\n",
    "# print('obj after divider = ' ,obj[d,w,h])\n",
    "# obj[d,w,h] = obj[d,w,h] + 50 * (z[2,d,w,h] - z[1,d,w,h])\n",
    "# print('obj after compute mean = ' ,obj[d,w,h])\n",
    "\n",
    "\n",
    "\n",
    "# print('obj mean = ',mean_x[-7,:,0,0])\n",
    "# print('std_squ_total = ',std_squ_total[:,0,0])\n",
    "\n",
    "# print(obj.size())\n",
    "obj = obj.view(-1,*obj.size())\n",
    "# print(obj.size())\n",
    "obj_oneshot = y_onehot[0].view(-1,class_total_num)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x_ = net_auto(z = obj,y_onehot = obj_oneshot,eps_std = 1e-8 ,reverse=True)\n",
    "\n",
    "x_ = torch.clamp(x_,-1,1)\n",
    "print(x_.size())\n",
    "\n",
    "print(\"abs sum = \",torch.sum(torch.abs(x_[0] - inputs[0])))\n",
    "# print(\"abs sum = \",torch.sum(x_[0]- inputs[0]))\n",
    "c = criterion(x_[0] , inputs[0])\n",
    "# print(\"criterion sum   = \",c)\n",
    "\n",
    "c_self = criterion(inputs[0],ones_tensor)\n",
    "\n",
    "print(\"l2  bi li   = \",c / c_self)\n",
    "# print(\"cos distance   = \",dd  / (m1 * m2) )\n",
    "print('zheng fu  mean distance = ',np.sqrt(np.sum((mean_list[labl[0]] - mean_list[labl[1]]) ** 2)))\n",
    "print('zheng fu  distance = ',np.sqrt(np.sum(np.square( vec1 - vec2 ))))\n",
    "print('fu distance = ',np.sqrt(np.sum(np.square( vec2 - vec3 ))))\n",
    "# print(str(sample_n),\" zhan bi = \",np.sum(np.sort(np.reshape(std_list[sample_n,:,:,:],64))[-50:]) / np.sum(std_list[sample_n,:,:,:]) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "img_show(np.squeeze(x_.data))\n",
    "img_show(np.squeeze(inputs.data[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "imgs_show(np.squeeze(np.concatenate((x_.data,inputs.data)) ),1,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from scipy import misc\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def sample_and_save(sample_p):\n",
    "    \n",
    "    sample_n = np.random.randint(964)\n",
    "#     sample_index = np.random.randint(18)\n",
    "    sample_index = np.random.permutation(20)[:2]\n",
    "    \n",
    "    print('sample_p = {},sample_n ={}, sample_index = {}'.format(sample_p,sample_n,sample_index))\n",
    "\n",
    "    posi = np.concatenate(( np.array(dataset_train_positive[sample_p].imgs)  ,  np.array(dataset_negative[sample_n].imgs)[sample_index]   ))\n",
    "    labl =  np.array([dataset_train_positive[sample_p].label, dataset_negative[sample_n].label,dataset_negative[sample_n].label])\n",
    "    print(dataset_train_positive[sample_p].name)\n",
    "    save_path = dataset_train_positive[sample_p].name.split('/home/hyshuai/workspace/dataset/images_evaluation/')[1]\n",
    "    save_path = './generate_img/' + str(save_path)\n",
    "    \n",
    "    if not os.path.isdir(save_path):\n",
    "        os.makedirs(save_path)\n",
    "    print(save_path)\n",
    "    \n",
    "    inputs ,lables = torch.from_numpy(posi).float().to(device),\\\n",
    "                torch.from_numpy(labl).long().to(device)\n",
    "\n",
    "    y_onehot = torch.FloatTensor(3, class_total_num).to(device)\n",
    "    y_onehot.zero_()\n",
    "    y_onehot.scatter_(1, lables.view(-1,1), 1)\n",
    "    # print('y_onehot:', y_onehot)\n",
    "    # z, det, y_logits = net_auto(x=inputs, y_onehot=y_onehot)\n",
    "    z, det = net_auto(x=inputs, y_onehot=y_onehot)\n",
    "#     y_logits = net_auto.class_flow(z)\n",
    "    \n",
    "    \n",
    "    divide = torch.div(torch.abs(torch.sub(z[1],z[0])), std_total_gpu) \n",
    "\n",
    "    scale = torch.reciprocal(torch.exp(divide))\n",
    "    divider_z = scale * (z[2] - z[1])\n",
    "    obj = z[0] + divider_z\n",
    "\n",
    "    obj = obj.view(-1,*obj.size())\n",
    "    obj_oneshot = y_onehot[0].view(-1,class_total_num)\n",
    "\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    x_ = net_auto(z = obj,y_onehot = obj_oneshot,eps_std = None ,reverse=True)\n",
    "\n",
    "    x_ = torch.clamp(x_,-1,1)\n",
    "    \n",
    "    diffrent_b = criterion(x_[0] , inputs[0]) / criterion(inputs[0],ones_tensor)\n",
    "    print(' diffrent_b = ',diffrent_b)\n",
    "\n",
    "    x_ = np.squeeze(x_.data.cpu().numpy())\n",
    "#     print(x_.shape)\n",
    "    tt = int(round(time.time() * 1000))\n",
    "    misc.imsave(save_path+\"/\"+str(tt)+\"_\"+str(diffrent_b.data.cpu())+ \".png\",x_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for i in range(0,20):\n",
    "\n",
    "    for _ in range(100):\n",
    "\n",
    "        sample_and_save(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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