Solution_7_U_net_ResNet34_Pretrained.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 作者原实现 80epoch 水平翻转测试加权(0.011) 多阶段LR BCELoss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "![](http://op4a94iq8.bkt.clouddn.com/18-9-2/22394851.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](http://op4a94iq8.bkt.clouddn.com/18-9-3/22875224.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/mnt/ml/anaconda35/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "import random\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('seaborn-white')\n",
    "import seaborn as sns\n",
    "sns.set_style(\"white\")\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "# import cv2\n",
    "from sklearn.model_selection import train_test_split  # 分层分割数据集超方便的\n",
    "\n",
    "from tqdm import tqdm_notebook, tnrange\n",
    "from itertools import chain\n",
    "from skimage.io import imread, imshow, concatenate_images\n",
    "from skimage.transform import resize\n",
    "from skimage.morphology import label\n",
    "\n",
    "from keras.preprocessing.image import load_img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training imgs path: TrainData/images/\n",
      "Masks of training imgs path: TrainData/masks/\n",
      "Test imgs path: TestData/images/\n"
     ]
    }
   ],
   "source": [
    "# Set some parameters\n",
    "im_width = 101\n",
    "im_height = 128\n",
    "im_chan = 3\n",
    "\n",
    "basicpath = ''\n",
    "path_train = basicpath + 'TrainData/'\n",
    "path_test = basicpath + 'TestData/'\n",
    "\n",
    "path_train_images = path_train + 'images/'\n",
    "path_train_masks = path_train + 'masks/'\n",
    "path_test_images = path_test + 'images/'\n",
    "\n",
    "print(\"Training imgs path:\",path_train_images)\n",
    "print(\"Masks of training imgs path:\",path_train_masks)\n",
    "print(\"Test imgs path:\",path_test_images)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "img_size_ori = 101\n",
    "img_size_target = 128\n",
    "\n",
    "def upsample(img):# not used\n",
    "    if img_size_ori == img_size_target:\n",
    "        return img\n",
    "    return resize(img, (img_size_target, img_size_target), mode='constant', preserve_range=True)\n",
    "    #res = np.zeros((img_size_target, img_size_target), dtype=img.dtype)\n",
    "    #res[:img_size_ori, :img_size_ori] = img\n",
    "    #return res\n",
    "    \n",
    "def downsample(img):# not used\n",
    "    if img_size_ori == img_size_target:\n",
    "        return img\n",
    "    return resize(img, (img_size_ori, img_size_ori), mode='constant', preserve_range=True)\n",
    "    #return img[:img_size_ori, :img_size_ori]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of train： 4000\n",
      "              z\n",
      "id             \n",
      "575d24d81d  843\n",
      "a266a2a9df  794\n",
      "75efad62c1  468\n",
      "34e51dba6a  727\n",
      "4875705fb0  797\n",
      "              z\n",
      "id             \n",
      "4ac19fb269  306\n",
      "1825fadf99  157\n",
      "f59821d067  305\n",
      "5b435fad9d  503\n",
      "e340e7bfca  783\n"
     ]
    },
    {
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       "              z\n",
       "id             \n",
       "353e010b7b  264\n",
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       "52551f7a80  610\n",
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Loading of training/testing ids and depths\n",
    "\n",
    "train_df = pd.read_csv(\"train.csv\", index_col=\"id\", usecols=[0])\n",
    "depths_df = pd.read_csv(\"depths.csv\", index_col=\"id\")\n",
    "train_df = train_df.join(depths_df) # 每个id链接上相应的深度\n",
    "test_df = depths_df[~depths_df.index.isin(train_df.index)]\n",
    "\n",
    "print(\"number of train：\",len(train_df))\n",
    "print(train_df.head(5))\n",
    "print(depths_df.head(5))\n",
    "test_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "A Jupyter Widget"
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     "text": [
      "/mnt/ml/anaconda35/lib/python3.6/site-packages/keras_preprocessing/image.py:489: UserWarning: grayscale is deprecated. Please use color_mode = \"grayscale\"\n",
      "  warnings.warn('grayscale is deprecated. Please use '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
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    {
     "data": {
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     "name": "stdout",
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     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "train_df[\"images\"] = [np.array(load_img(\"TrainData/images/{}.png\".format(idx), grayscale=True)) / 255\n",
    "                              for idx in tqdm_notebook(train_df.index)]\n",
    "\n",
    "train_df[\"masks\"] = [np.array(load_img(\"TrainData/masks/{}.png\".format(idx), grayscale=True))/ 255  \n",
    "                              for idx in tqdm_notebook(train_df.index)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>z</th>\n",
       "      <th>images</th>\n",
       "      <th>masks</th>\n",
       "      <th>coverage</th>\n",
       "      <th>coverage_class</th>\n",
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       "      <td>0.740908</td>\n",
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      "text/plain": [
       "              z                                             images  \\\n",
       "id                                                                   \n",
       "575d24d81d  843  [[0.5254901960784314, 0.5137254901960784, 0.52...   \n",
       "a266a2a9df  794  [[0.3411764705882353, 0.3764705882352941, 0.33...   \n",
       "75efad62c1  468  [[0.5686274509803921, 0.4666666666666667, 0.32...   \n",
       "34e51dba6a  727  [[0.5411764705882353, 0.4745098039215686, 0.39...   \n",
       "4875705fb0  797  [[0.06666666666666667, 0.0784313725490196, 0.0...   \n",
       "782ae9b7e7  677  [[0.6078431372549019, 0.6196078431372549, 0.61...   \n",
       "9842f69f8d  907  [[0.4392156862745098, 0.4117647058823529, 0.4,...   \n",
       "aa94cfb806  754  [[0.5058823529411764, 0.49019607843137253, 0.4...   \n",
       "50d3073821  810  [[0.4666666666666667, 0.5333333333333333, 0.41...   \n",
       "28f865caaa  147  [[0.43137254901960786, 0.4392156862745098, 0.4...   \n",
       "b5e1371b3b  325  [[0.4117647058823529, 0.4196078431372549, 0.43...   \n",
       "57e394bc67  765  [[0.4, 0.396078431372549, 0.4, 0.4431372549019...   \n",
       "b45ad3932e  613  [[0.43137254901960786, 0.4117647058823529, 0.4...   \n",
       "ef51bbcde7  712  [[0.4, 0.396078431372549, 0.38823529411764707,...   \n",
       "d4d34af4f7  114  [[0.3607843137254902, 0.3843137254901961, 0.42...   \n",
       "\n",
       "                                                        masks  coverage  \\\n",
       "id                                                                        \n",
       "575d24d81d  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  0.000000   \n",
       "a266a2a9df  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  0.504950   \n",
       "75efad62c1  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  0.993334   \n",
       "34e51dba6a  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  0.149201   \n",
       "4875705fb0  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  0.042839   \n",
       "782ae9b7e7  [[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,...  0.225370   \n",
       "9842f69f8d  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  0.000000   \n",
       "aa94cfb806  [[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,...  0.665817   \n",
       "50d3073821  [[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,...  0.297030   \n",
       "28f865caaa  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  0.000000   \n",
       "b5e1371b3b  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  0.740908   \n",
       "57e394bc67  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  0.000000   \n",
       "b45ad3932e  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  0.283698   \n",
       "ef51bbcde7  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  0.000000   \n",
       "d4d34af4f7  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  0.138614   \n",
       "\n",
       "            coverage_class  \n",
       "id                          \n",
       "575d24d81d               0  \n",
       "a266a2a9df               6  \n",
       "75efad62c1              10  \n",
       "34e51dba6a               2  \n",
       "4875705fb0               1  \n",
       "782ae9b7e7               3  \n",
       "9842f69f8d               0  \n",
       "aa94cfb806               7  \n",
       "50d3073821               3  \n",
       "28f865caaa               0  \n",
       "b5e1371b3b               8  \n",
       "57e394bc67               0  \n",
       "b45ad3932e               3  \n",
       "ef51bbcde7               0  \n",
       "d4d34af4f7               2  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[\"coverage\"] = train_df.masks.map(np.sum) / pow(img_size_ori, 2) # 统计图片中像素为1的个数占整个图片101x101=10201个像素的比例\n",
    "\n",
    "def cov_to_class(val):    \n",
    "    for i in range(0, 11):\n",
    "        if val * 10 <= i :\n",
    "            return i\n",
    "        \n",
    "train_df[\"coverage_class\"] = train_df.coverage.map(cov_to_class)  #按照10%递增一档的比例进行阶梯分类\n",
    "\n",
    "train_df.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/mnt/ml/anaconda35/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'Coverage class')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JNWGAkyRJkqSaGNfpBkiSVFcRMQs4FFiRmbuVss8DbwOeAe4Hjs/Mx8u+s4DpwFrg9My8\nqZQfBMwAxgKXZuYF7f4skqR6cAROkqSBuxw4qEfZzcBumflq4LfAWQARsQtwBLBrec1XImJsRIwF\nLgYOBnYBjix1JUlajwFOkqQByszbgJU9yr6XmWvK0wXAxLI9DZibmU9n5oPAImDv8liUmQ9k5jPA\n3FJXkqT1GOAkSRo6JwDfKds7AI807FtSyvoqlyRpPQY4SZKGQER8AlgDXNnptkiSRg4XMZEkqcUi\n4jiqxU32z8yuUrwUmNRQbWIpYwPlkiQ9R78Bro8Vts4BTgQeK9U+npnzyz5X2JIkjVqlv/so8LeZ\n+WTDrnnAVRFxIbA9MAW4AxgDTImInaiC2xHAUe1ttSSpLpoZgbsc+DIwu0f5RZn5hcaCHitsbQ98\nPyJ2LrsvBt5MNbf/zoiYl5n3DqLtkiR1VETMAfYDto2IJcDZVKtObgrcHBEACzLz5My8JyKuAe6l\nmlp5SmauLcc5FbiJ6iTnrMy8p+0fRpJUC/0GuMy8LSImN3m8dStsAQ9GRPcKW1BW2AKIiO4Vtgxw\nkqTayswjeym+bAP1zwfO76V8PjC/hU2TJI1Qg1nE5NSI+FVEzIqIrUqZK2xJkiRJ0hAZaICbCbwC\n2ANYBnyxZS2SJEmSJPVqQKtQZuaj3dsRcQnwrfLUFbYkSZIkaYgMKMBFxITMXFaevgP4Tdl2hS1J\nkiRJGiLN3EagtxW29ouIPYAuYDHwfgBX2JIkSZKkodPMKpSusCVJkiRJw8BgVqGUJEmSJLWRAU6S\nJEmSasIAJ0mSJEk1YYCTJEmSpJowwEmSJElSTRjgJEmSJKkmDHCSJEmSVBMGOEmSJEmqCQOcJEmS\nJNWEAU6SJEmSasIAJ0mSJEk1YYCTJEmSpJowwEmSJElSTRjgJEmSJKkmDHCSJEmSVBMGOEmSJEmq\nCQOcJEmSJNWEAU6SJEmSamJcpxsgSVJdRcQs4FBgRWbuVsq2Bq4GJgOLgcMzc1VEjAFmAIcATwLH\nZebC8ppjgU+Ww346M69o5+eQJNWHI3CSJA3c5cBBPcrOBG7JzCnALeU5wMHAlPI4CZgJ6wLf2cBU\nYG/g7IjYashbLkmqJQOcJEkDlJm3ASt7FE8DukfQrgAOayifnZldmbkA2DIiJgBvAW7OzJWZuQq4\nmfVDoSRJgAFOkqRWG5+Zy8r2cmB82d4BeKSh3pJS1le5JEnrMcBJkjREMrML6Op0OyRJI0e/i5j0\ncYH254G3Ac8A9wPHZ+bjETEZuA/I8vIFmXlyec2eVNcKvBCYD3ywdGySJI0kj0bEhMxcVqZIrijl\nS4FJDfUmlrKlwH49ym9tQzslSTXUzAjc5aw/F/9mYLfMfDXwW+Cshn33Z+Ye5XFyQ/lM4ESevYDb\n+f2SpJFoHnBs2T4WuLGh/JiIGBMR+wCry1TLm4ADI2KrsnjJgaVMkqT19BvgertAOzO/l5lrytMF\nVGcL+1TOQL4kMxeUUbfZPHtRtyRJtRQRc4CfVZuxJCKmAxcAb46I/wIOKM+hmn3yALAIuAT4AEBm\nrgTOA+4sj3NLmSRJ62nFfeBOoLrfTbedIuIXwB+AT2bmj6kuxl7SUMcLtCVJtZeZR/axa/9e6nYB\np/RxnFnArBY2TZI0Qg1qEZOI+ASwBriyFC0DdszM1wBnAFdFxEsG10RJkiRJEgxiBC4ijqNa3GT/\n7sVIMvNp4OmyfVdE3A/sTHWBduM0y+4LtyVJkiRJTRrQCFxEHAR8FHh7Zj7ZUP7SiBhbtl9OtVjJ\nA+Ui7T9ExD4RMQY4hmcv6pYkSZIkNaGZ2wjMoVreeNuIWAKcTbXq5KbAzREBz94u4A3AuRHxJ+DP\nwMkNF2J/gGdvI/Cd8pAkSZIkNanfANfHBdqX9VH3OuC6Pvb9HNhto1onSZIkSVpnUIuYSJIkSZLa\nxwAnSZIkSTVhgJMkSZKkmjDASZIkSVJNGOAkSZIkqSYMcJIkSZJUEwY4SZIkSaoJA5wkSZIk1YQB\nTpIkSZJqwgAnSZIkSTVhgJMkSZKkmjDASZIkSVJNGOAkSZIkqSYMcJIkSZJUEwY4SZIkSaoJA5wk\nSZIk1YQBTpIkSZJqwgAnSZIkSTVhgJMkSZKkmhjX6QZIkjQSRcSHgfcBXcCvgeOBCcBcYBvgLuC9\nmflMRGwKzAb2BH4PvDszF3ei3ZKk4c0ROEmSWiwidgBOB/bKzN2AscARwGeBizLzlcAqYHp5yXRg\nVSm/qNSTJGk9BjhJkobGOOCFETEOeBGwDHgTcG3ZfwVwWNmeVp5T9u8fEWPa2FZJUk0Y4CRJarHM\nXAp8AXiYKritppoy+XhmrinVlgA7lO0dgEfKa9eU+tu0s82SpHpo6hq4iJgFHAqsKFNBiIitgauB\nycBi4PDMXFXOGM4ADgGeBI7LzIXlNccCnyyH/XRmXoEkSSNMRGxFNaq2E/A48A3goI42SpI0IjQ7\nAnc563c8ZwK3ZOYU4JbyHOBgYEp5nATMhHWB72xgKrA3cHbp4CRJGmkOAB7MzMcy80/A9cC+wJZl\nSiXARGBp2V4KTAIo+7egWsxEkqTnaCrAZeZtwMoexY3z9XvO45+dmV2ZuYCqs5oAvAW4OTNXZuYq\n4GY8GylJGpkeBvaJiBeVmSn7A/cCPwTeWeocC9xYtueV55T9P8jMrja2V5JUE4O5Bm58Zi4r28uB\n8WV73Tz+onuOf1/lkiSNKJl5O9ViJAupbiGwCfBV4GPAGRGxiOoat8vKSy4DtinlZ/DsrBZJkp6j\nJfeBy8yuiPBMoSRJRWaeTXXpQKMHqC4j6Fn3KeBd7WiXJKneBjMC92iZGkn5uaKUr5vHX3TP8e+r\nXJIkSZLUhMEEuMb5+j3n8R8TEWMiYh9gdZlqeRNwYERsVRYvObCUSZIkSZKa0OxtBOYA+wHbRsQS\nqikhFwDXRMR04CHg8FJ9PtUtBBZR3UbgeIDMXBkR5wF3lnrnZmbPhVEkSZIkSX1oKsBl5pF97Nq/\nl7pdwCl9HGcWMKvp1kmSJEmS1hnMFEpJkiRJUhsZ4CRJkiSpJgxwkiRJklQTBjhJkiRJqgkDnCRJ\nkiTVhAFOkiRJkmrCACdJkiRJNWGAkyRJkqSaMMBJkiRJUk0Y4CRJkiSpJgxwkiRJklQTBjhJkiRJ\nqgkDnCRJkiTVhAFOkiRJkmrCACdJkiRJNWGAkyRJkqSaMMBJkiRJUk0Y4CRJkiSpJgxwkiRJklQT\nBjhJkiRJqgkDnCRJkiTVxLhON0CSpJEoIrYELgV2A7qAE4AErgYmA4uBwzNzVUSMAWYAhwBPAsdl\n5sIONFuSNMw5AidJ0tCYAXw3M18F7A7cB5wJ3JKZU4BbynOAg4Ep5XESMLP9zZUk1cGAR+AiIqjO\nInZ7OfApYEvgROCxUv7xzJxfXnMWMB1YC5yemTcN9P0lSRquImIL4A3AcQCZ+QzwTERMA/Yr1a4A\nbgU+BkwDZmdmF7AgIraMiAmZuazNTZckDXMDDnCZmcAeABExFlgK3AAcD1yUmV9orB8RuwBHALsC\n2wPfj4idM3PtQNsgSdIwtRPVicyvRcTuwF3AB4HxDaFsOTC+bO8APNLw+iWlzAAnSXqOVk2h3B+4\nPzMf2kCdacDczHw6Mx8EFgF7t+j9JUkaTsYBrwVmZuZrgD/y7HRJAMpoW1cH2iZJqrFWBbgjgDkN\nz0+NiF9FxKyI2KqU9XV2UZKkkWYJsCQzby/Pr6UKdI9GxASA8nNF2b8UmNTw+omlTJKk5xh0gIuI\n5wNvB75RimYCr6CaXrkM+OJg30OSpDrJzOXAI+V6cahmqtwLzAOOLWXHAjeW7XnAMRExJiL2AVZ7\n/ZskqTetuI3AwcDCzHwUoPsnQERcAnyrPPXsoiRpNDkNuLKc6HyA6hrxTYBrImI68BBweKk7n+oW\nAouobiNwfPub235X3f5wp5sAwFFTd+x0EySpaa0IcEfSMH2yx6pZ7wB+U7bnAVdFxIVUi5hMAe5o\nwftLkjTsZOYvgb162bV/L3W7gFOGvFGSpNobVICLiBcDbwbe31D8uYjYg+rC7MXd+zLznoi4hmoK\nyRrgFFeglCRJkqTmDSrAZeYfgW16lL13A/XPB84fzHtKkiRJ0mjVqlUoJUmSJElDzAAnSZIkSTVh\ngJMkSZKkmjDASZIkSVJNGOAkSZIkqSYMcJIkSZJUEwY4SZIkSaoJA5wkSZIk1YQBTpIkSZJqwgAn\nSZIkSTVhgJMkSZKkmjDASZIkSVJNGOAkSZIkqSYMcJIkSZJUEwY4SZIkSaoJA5wkSZIk1YQBTpIk\nSZJqwgAnSZIkSTVhgJMkSZKkmjDASZIkSVJNGOAkSZIkqSYMcJIkSZJUE+M63QBJkkaqiBgL/BxY\nmpmHRsROwFxgG+Au4L2Z+UxEbArMBvYEfg+8OzMXd6jZkqRhzBE4SZKGzgeB+xqefxa4KDNfCawC\nppfy6cCqUn5RqSdJ0noGPQIXEYuBJ4C1wJrM3CsitgauBiYDi4HDM3NVRIwBZgCHAE8Cx2XmwsG2\nQZKk4SYiJgJvBc4Hzih94JuAo0qVK4BzgJnAtLINcC3w5YgYk5ld7WyzJNXVVbc/3OkmrHPU1B2H\n9PitGoF7Y2bukZl7lednArdk5hTglvIc4GBgSnmcRNVpSZI0En0J+Cjw5/J8G+DxzFxTni8Bdijb\nOwCPAJT9q0t9SZKeY6imUE6jOrNI+XlYQ/nszOzKzAXAlhExYYjaIElSR0TEocCKzLyr022RJI0s\nrQhwXcD3IuKuiDiplI3PzGVlezkwvmyvO8NYNJ59lCRppNgXeHu5zGAu1dTJGVQnLrsvX5gILC3b\nS4FJAGX/FlSLmUiS9BytCHB/k5mvpZoeeUpEvKFxZ5m/7xx+SdKokZlnZebEzJwMHAH8IDOPBn4I\nvLNUOxa4sWzPK88p+3/g9W+SpN4MOsBl5tLycwVwA7A38Gj31Mjyc0Wpvu4MY9F49lGSpJHuY1QL\nmiyiusbtslJ+GbBNKT+DZ68dlyTpOQa1CmVEvBjYJDOfKNsHAufy7JnEC1j/DOOpETEXmAqsbphq\nKUnSiJOZtwK3lu0HqE509qzzFPCutjZMklRLg72NwHjghojoPtZVmfndiLgTuCYipgMPAYeX+vOp\nbiGwiOo2AscP8v0lSZIkadQYVIArZxJ376X898D+vZR3AacM5j0lSZIkabQaqtsISJIkSZJazAAn\nSZIkSTVhgJMkSZKkmjDASZIkSVJNGOAkSZIkqSYMcJIkSZJUEwY4SZIkSaoJA5wkSZIk1YQBTpIk\nSZJqYlynGyBJktRJV93+cKebsM5RU3fsdBMkDXOOwEmSJElSTRjgJEmSJKkmDHCSJEmSVBMGOEmS\nJEmqCQOcJEmSJNWEAU6SJEmSasIAJ0mSJEk1YYCTJEmSpJowwEmSJElSTRjgJEmSJKkmDHCSJEmS\nVBPjOt0ASZIkVa66/eFONwGAo6bu2OkmrON3Ij2XAU6SpBaLiEnAbGA80AV8NTNnRMTWwNXAZGAx\ncHhmroqIMcAM4BDgSeC4zFzYibZLkoa3AQe4DXRO5wAnAo+Vqh/PzPnlNWcB04G1wOmZedMg2i5J\n0nC1BvhIZi6MiM2BuyLiZuA44JbMvCAizgTOBD4GHAxMKY+pwMzyU5Kk5xjMCFxfnRPARZn5hcbK\nEbELcASwK7A98P2I2Dkz1w6iDZIkDTuZuQxYVrafiIj7gB2AacB+pdoVwK1UAW4aMDszu4AFEbFl\nREwox5E0DAyXqZzgdM7RbsCLmGTmsu7pHZn5BNDdOfVlGjA3M5/OzAeBRcDeA31/SZLqICImA68B\nbgfGN4Sy5VSzWKDqPx9peNkSNtynSpJGqZasQtmjcwI4NSJ+FRGzImKrUmbnJEkaVSJiM+A64EOZ\n+YfGfWW0rasjDZMk1dagFzHp2TlFxEzgPKpO6Tzgi8AJg30fSZLqJCKeR9U/XpmZ15fiR7unRkbE\nBGBFKV8KTGp4+cRSJnXEcJouKOm5BjUC11vnlJmPZubazPwzcAnPTpO0c5IkjQplVcnLgPsy88KG\nXfOAY8v2scCNDeXHRMSYiNgHWO31b5Kk3gxmFcpeO6ceF12/A/hN2Z4HXBURF1ItYjIFuGOg7y9J\n0jC2L/Be4NcR8ctS9nHgAuCaiJgOPAQcXvbNp7qFwCKq2wgc397mSqqT4TRC6oIq7TeYKZR9dU5H\nRsQeVFMoFwPvB8jMeyLiGuCduP4BAAAMhUlEQVReqhUsT3EFSknSSJSZPwHG9LF7/17qdwGnDGmj\nJEkjwoAD3AY6p/kbeM35wPkDfU9JkiRJGs1asgqlJEmSJGnoDXoVSg1Mq+cuO/9YkiRJGvkMcE0a\nTheLSpIkScOBfyO3nwFOkkYBR/0lSRoZvAZOkiRJkmrCACdJkiRJNWGAkyRJkqSaMMBJkiRJUk0Y\n4CRJkiSpJlyFUn0ajavWDfelcOvwHUqSJGnoOAInSZIkSTXhCNwIMdxHjmB0juhJkiRJrWSAU23V\nIbS22lB8ZoOwJElSfRjgpFGuDkHYkClJklQxwEkadeownbcOwVqSJLWfAU6SBsmwJUmS2sUAJ2nY\nMyBJkiRVvI2AJEmSJNWEAU6SJEmSasIAJ0mSJEk1YYCTJEmSpJowwEmSJElSTRjgJEmSJKkm2n4b\ngYg4CJgBjAUuzcwL2t0GSZKGG/tHSVIz2joCFxFjgYuBg4FdgCMjYpd2tkGSpOHG/lGS1Kx2j8Dt\nDSzKzAcAImIuMA24t+wfC7B8+fJBvcnjjw3u9ZKkDVuyZPDn/xp+148d9MHqr7/+EewjJakWBttH\n9tc/tjvA7QA80vB8CTC14fkEgKOPPrqdbZIkbaQZrT3cBOD+1h6ydvrrH8E+UpJqoYV9ZK/9Y9uv\ngevHncDrgWXA2g63RZI0tMZSdU53drohNWEfKUmjwwb7x3YHuKXApIbnE0sZAJn5NPCTNrdJktQ5\no33krdsG+0ewj5SkUabP/rHdAe5OYEpE7ETVMR0BHNXmNkiSNNzYP0qSmjKmq6urrW8YEYcAX6Ia\nGpyVmecP4lgbXHI5IjYFZgN7Ar8H3p2Ziwf6fnXQxHdyBvA+YA3wGHBCZj7U9oa2UbNLc0fE3wPX\nAq/LzJ+3sYkd0cz3EhGHA+cAXcDdmTmi/6Bs4v+fHYErgC1LnTMzc37bG9omETELOBRYkZm79bJ/\nDNX3dQjwJHBcZi5sbytHjlb2j30c39sU9BARk6j+ThhP9Xvuq5nZ4ks866msjPpzYGlmHtrp9nRa\nRGwJXArsRvVv5YTM/FlnW9VZEfFhqr8pu4BfA8dn5lOdbVX79dZXRsTWwNXAZGAxcHhmrmrVe7b9\nRt6ZOT8zd87MVwwyvDWz5PJ0YFVmvhK4CPjsQN+vDpr8Tn4B7JWZr6YKK59rbyvbq9mluSNic+CD\nwO3tbWFnNPO9RMQU4Cxg38zcFfhQ2xvaRk3+W/kkcE1mvoZqhOQr7W1l210OHLSB/QcDU8rjJGBm\nG9o0YrWqf+yNtyno0xrgI5m5C7APcIrfyzofBO7rdCOGkRnAdzPzVcDujPLvJiJ2AE6n+ptyN6oT\nQ0d0tlUdcznr95VnArdk5hTglvK8Zdoe4Fpo3ZLLmfkM0L3kcqNpVGfLoQor+5czxiNVv99JZv4w\nM58sTxdQXWcxkjXz7wTgPKqAP1rOHDXzvZwIXNx9xigzV7S5je3WzHfSBbykbG8B/K6N7Wu7zLwN\nWLmBKtOA2ZnZlZkLgC0jYkJ7WqeN1OzvwlElM5d1jxpn5hNUf5Tv0NlWdV5ETATeSjXiNOpFxBbA\nG4DLADLzmcx8vLOtGhbGAS+MiHHAixjhfWJf+ugrGzPIFcBhrXzPOge43pZc7vlLd12dzFwDrAa2\naUvrOqOZ76TRdOA7Q9qizuv3O4mI1wKTMvPb7WxYhzXzb2VnYOeI+GlELCjTr0ayZr6Tc4D3RMQS\nYD5wWnuaNmxt7O8cdY7/rfoREZOB1zBKZmL040vAR4E/d7ohw8ROVJedfC0ifhERl0bEizvdqE7K\nzKXAF4CHqVbGXZ2Z3+tsq4aV8Zm5rGwvp5qm3TJ1DnAahIh4D7AX8PlOt6WTImIT4ELgI51uyzA0\njmpq3H7AkcAl5RqA0exI4PLMnEh13dfXy78hSTUWEZsB1wEfysw/dLo9nRQR3dfy3NXptgwj44DX\nAjPLFPo/0uIpcXUTEVtRjTLtBGwPvLj8bakeMrOLagZPy9T5D49+l1xurFOGd7egWsxkpGrmOyEi\nDgA+Aby9LEs9kvX3nWxOdUHyrRGxmOoaiHkRsVe7GtghzfxbWQLMy8w/ZeaDwG+pAt1I1cx3Mh24\nBqBcvP4CYNu2tG54aup3joYF/1v1ISKeRxXerszM6zvdnmFgX+DtpU+cC7wpIv6joy3qvCXAkszs\nHp29lirQjWYHAA9m5mOZ+SfgeuCvO9ym4eTR7ksKys+WXoYy3G7kvTGaWXJ5HnAs8DPgncAPSgoe\nqfr9TiLiNcC/AweNgmuaoJ/vJDNX0/AHeETcCvzjKFiFspn/f75JNeL0tYjYlmpK5QNtbWV7NfOd\nPAzsD1weEX9JFeAea2srh5d5wKkRMReYSjWFZlk/r1FneJuCXpTr4i8D7svMCzvdnuEgM8+iWsCK\niNiPqk8c1SMrmbk8Ih6JiMjMpOoH7u10uzrsYWCfiHgR8L9U38lI/9tpY3RnkAvKzxtbefDajsCV\na9pOBW6iuuj4msy8JyLOjYi3l2qXAdtExCLgDEb4cHeT38nngc2Ab0TELyNiXoea2xZNfiejTpPf\ny03A7yPiXuCHwD9l5ogdwW7yO/kIcGJE3A3MoVo2f8SeFIqIOVQnwCIilkTE9Ig4OSJOLlXmU4X6\nRcAlwAc61FT1o69/351t1bCwL/BeqlGmX5bHIZ1ulIal04ArI+JXwB7AZzrcno4qo5HXAgupbiGw\nCfDVjjaqQ3rrK6mC25sj4r+oRitbetuWtt8HTpIkSZI0MLUdgZMkSZKk0cYAJ0mSJEk1YYCTJEmS\npJowwEmSJElSTRjgJEmSJKkm6nwfOKntImI74EvA64DHgUeBD2XmbzvaMEmSWmCk93Oj6H6vGsEc\ngZOaVG74egNwa2a+IjP3pLrZ6fgheC9PrkiS2qqd/Vx5P/s6aQD8H0dq3huBP2Xmv3UXZObdETEm\nIj4PHAx0AZ/OzKsjYi7w9cz8NkBEXA58i6pzvADYD9gUuDgz/z0i9gPOA1YBrwJ2johvApOAFwAz\nMvOr5VjTgY9RnR29G3g6M0+NiJcC/wbsWJr4ocz86VB9IZKkEaXXfg7WhbvPUZO+LiLGAp8FDgL+\nDFySmf/ao85MqpHGFwLXZubZpfwC4O3AGuB7mfmPEfEu4GxgLbA6M9+w8V+v1BqOwEnN2w24q5fy\nvwP2AHYHDgA+HxETgKuBwwEi4vnA/sC3gelUv/xfR9VxnBgRO5VjvRb4YGbuXJ6fUM6A7gWcHhHb\nRMT2wD8D+wD7UnWA3WYAF5Vj/z1waUs+uSRpNOirn4P69XUnAZOBPTLz1cCVvdT5RGbuBbwa+NuI\neHVEbAO8A9i1vO7Tpe6ngLdk5u5U4U7qGAOcNHh/A8zJzLWZ+SjwI6rO6jvAGyNiU6ozlrdl5v8C\nBwLHRMQvgduBbYAp5Vh3ZOaDDcc+PSLuBhZQnZ2cAuwN/CgzV2bmn4BvNNQ/APhyOfY84CURsdnQ\nfGxJ0ihSt77uAODfM3MNQGau7OUzHR4RC4FfALsCuwCrgaeAyyLi74AnS92fApdHxInA2Ca/M2lI\nOIVSat49wDubrZyZT5WLpd8CvBuYW3aNAU7LzJsa65dpJX/s8fwA4K8y88lyrBf087abAPtk5lPN\ntlOSpGKj+jmob19XRgP/EXhdZq4qUz9fkJlrImJvqpHEdwKnAm/KzJMjYirwVuCuiNgzM38/0PeX\nBsMROKl5PwA2jYiTugsi4tVUc/PfHRFjy7z8NwB3lCpXA8cDrwe+W8puAv4hIp5XjrFzRLy4l/fb\nAlhVOrRXUU0jAbiTaqrHVuUC8L9veM33gNMa2rfHoD6xJGk06bWfi4jXAz+mXn3dzcD7uxdKiYit\ne+x/CVWQXB0R46lGDykjeVtk5nzgw1RTRomIV2Tm7Zn5KeAxqpFCqSMcgZOalJldEfEO4EsR8TGq\nKRaLgQ8Bm1FdYN0FfDQzl5eXfQ/4OnBjZj5Tyi6lmpe/sFwU/hhwWC9v+V3g5Ii4D0iqqSVk5tKI\n+AxVx7kS+E+qKR8ApwMXR8SvqP7/vg04uSVfgCRpROunn/sJ8FfUp6+7FNgZ+FVE/Am4BPhyw2e9\nOyJ+UY77CNUUSYDNgRsj4gVUo4hnlPLPR8SUUnZL+R6kjhjT1dXV6TZI2kgRsVlm/k85s3gDMCsz\nb+h0uyRJahX7Oql3TqGU6umccvH2b4AHgW92uD2SJLWafZ3UC0fgJEmSJKkmHIGTJEmSpJowwEmS\nJElSTRjgJEmSJKkmDHCSJEmSVBMGOEmSJEmqCQOcJEmSJNXE/wcRvX/Qe7qUGAAAAABJRU5ErkJg\ngg==\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 2, figsize=(15,5))\n",
    "sns.distplot(train_df.coverage, kde=False, ax=axs[0])\n",
    "sns.distplot(train_df.coverage_class, bins=10, kde=False, ax=axs[1])\n",
    "plt.suptitle(\"Salt coverage\")\n",
    "axs[0].set_xlabel(\"Coverage\")\n",
    "axs[1].set_xlabel(\"Coverage class\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/mnt/ml/anaconda35/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Depth distribution')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TQrl9Qn8uHhaHj0UmNnZlu3btYtmyZcydOxcfHx+eeOIJvvjiC5KSkigvL+fzzz8HoKqq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\nxgRlScIYY0xQliSMMcYEZUnCGGNMUP8Pv9s2nSBmuZYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting the depth distributions¶\n",
    "\n",
    "sns.distplot(train_df.z, label=\"Train\")\n",
    "sns.distplot(test_df.z, label=\"Test\")\n",
    "plt.legend()\n",
    "plt.title(\"Depth distribution\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3200,)\n",
      "(3200, 128, 128, 1)\n",
      "(3200, 128, 128, 1)\n",
      "(3200,)\n",
      "(3200,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['492383a53b', '3577258d6b', '3c1d07fc67', '89a74723fd',\n",
       "       '4015200239', '38232d1fe3', 'fe85864f8f', '65a55b69b7',\n",
       "       'a266a2a9df', 'af296d1c0a', 'b8dea42e1a', '0ad38c8f25',\n",
       "       'b890b261a0', '96f8fd0d4b', '9c6f8e4676', 'd1857e0577',\n",
       "       '8e84b940e3', '331ec135dd', 'e1009fa370', '12c92c9f70'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create train/validation split stratified by salt coverage\n",
    "\n",
    "ids_train, ids_valid, x_train, x_valid, y_train, y_valid, cov_train, cov_test, depth_train, depth_test = train_test_split(\n",
    "    train_df.index.values,\n",
    "    np.array(train_df.images.map(upsample).tolist()).reshape(-1, img_size_target, img_size_target, 1), # [4000,101,101,1]\n",
    "    np.array(train_df.masks.map(upsample).tolist()).reshape(-1, img_size_target, img_size_target, 1), # [4000,101,101,1]\n",
    "    train_df.coverage.values,# 4000个值\n",
    "    train_df.z.values, # 4000个值\n",
    "    test_size=0.2, stratify=train_df.coverage_class, random_state= 1234)  \n",
    "# !!!! stratify 分层split的指标 覆盖比例的等级  后面还可以使用深度等级分层split\n",
    "\n",
    "print(ids_train.shape)\n",
    "print(x_train.shape)\n",
    "print(y_train.shape)\n",
    "print(cov_train.shape)\n",
    "print(depth_train.shape)\n",
    "type(x_train) # all are np.ndarray\n",
    "ids_valid[0:20]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](http://op4a94iq8.bkt.clouddn.com/18-9-5/70500295.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(800, 128, 128, 1)\n",
      "(800, 128, 128, 1)\n"
     ]
    }
   ],
   "source": [
    "print(x_valid.shape)\n",
    "print(y_valid.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3200, 128, 128, 3)\n",
      "(3200, 128, 128, 1)\n",
      "(800, 128, 128, 3)\n",
      "(800, 128, 128, 1)\n"
     ]
    }
   ],
   "source": [
    "mean= [0.485,0.456,0.406]\n",
    "std=  [0.229,0.224,0.225]\n",
    "\n",
    "x_train=np.concatenate((x_train-mean[2]/std[2],\n",
    "                  x_train-mean[1]/std[1],\n",
    "                  x_train-mean[0]/std[0]),axis=-1)\n",
    "\n",
    "x_valid=np.concatenate((x_valid-mean[2]/std[2],\n",
    "                  x_valid-mean[1]/std[1],\n",
    "                  x_valid-mean[0]/std[0]),axis=-1)\n",
    "\n",
    "print(x_train.shape)\n",
    "print(y_train.shape)\n",
    "print(x_valid.shape)\n",
    "print(y_valid.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 模型定义模块"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from torch import nn\n",
    "from torch.nn import functional as F\n",
    "from torch.autograd import Variable\n",
    "import torch\n",
    "from torchvision import models\n",
    "import torchvision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# # 反卷积计算示例\n",
    "# a_Deconv=nn.ConvTranspose2d(256,128,3,stride=2,\\\n",
    "#                                          padding=1,output_padding=1,bias=True, dilation=1)\n",
    "# x=torch.randn(2,256,6,6)\n",
    "# out=a_Deconv(x)\n",
    "# out.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "# class Conv2D_block(nn.Module):\n",
    "#     def __init__(self,C_in,C_out,filter_size=3,paddingType=\"SAME\",ActionType=\"Relu\"):\n",
    "#         super().__init__()\n",
    "        \n",
    "#         padding={}\n",
    "#         if filter_size==3:\n",
    "#             padding[\"SAME\"]=1\n",
    "#         if filter_size==1:\n",
    "#             padding[\"SAME\"]=0\n",
    "#         padding[\"VALID\"]=0\n",
    "        \n",
    "#         self.Conv=nn.Conv2d(C_in, C_out, filter_size, stride=1,padding=padding[paddingType])\n",
    "#         self.BN=nn.BatchNorm2d(C_out)\n",
    "#         self.ActionType=ActionType\n",
    "        \n",
    "#     def forward(self,x):\n",
    "#         out=self.Conv(x)\n",
    "#         out=self.BN(out)\n",
    "#         if self.ActionType==\"Relu\":\n",
    "#             out=F.relu6(out,inplace=True)\n",
    "#         return out\n",
    "            \n",
    "# class Residual_block(nn.Module):\n",
    "#     def __init__(self,C_in,C_out,filter_size=3,paddingType=\"SAME\",ActionType=\"Relu\"):\n",
    "#         super().__init__()\n",
    "        \n",
    "#         self.BN=nn.BatchNorm2d(C_in)\n",
    "#         self.Conv1=Conv2D_block(C_in,C_out,filter_size,paddingType,ActionType)\n",
    "#         self.Conv2=Conv2D_block(C_out,C_out,filter_size,paddingType,ActionType=None)\n",
    "        \n",
    "#     def forward(self,x):\n",
    "#         out=F.relu6(x,inplace=True)\n",
    "#         out=self.BN(out)\n",
    "#         out=self.Conv1(out)\n",
    "#         out=self.Conv2(out)\n",
    "#         out=out+x  # skip connection\n",
    "#         return out\n",
    "\n",
    "# class Eecoder_with_residual_block(nn.Module):\n",
    "    \n",
    "#     def __init__(self,C_in,C_out,DropoutRatio = 0.5,filter_size=3,paddingType=\"SAME\"):\n",
    "#         super().__init__()\n",
    "        \n",
    "#         padding={}\n",
    "#         if filter_size==3:\n",
    "#             padding[\"SAME\"]=1\n",
    "#         if filter_size==1:\n",
    "#             padding[\"SAME\"]=0\n",
    "#         padding[\"VALID\"]=0\n",
    "        \n",
    "#         self.Conv_1=nn.Conv2d(C_in,C_out,filter_size,stride=1,padding=padding[paddingType])\n",
    "#         self.Res_1=Residual_block(C_out,C_out)\n",
    "#         self.Res_2=Residual_block(C_out,C_out)\n",
    "    \n",
    "#     def forward(self,x):\n",
    "#         out=self.Conv_1(x)\n",
    "#         out=self.Res_1(out)\n",
    "#         out=self.Res_2(out)\n",
    "#         out=F.relu6(out,inplace=True)\n",
    "#         return out\n",
    "\n",
    "# class Decoder_with_residual_block(nn.Module):\n",
    "    \n",
    "#     def __init__(self,C_in,C_out,DropoutRatio = 0.5,filter_size=3,paddingType=\"SAME\"):\n",
    "#         super().__init__()\n",
    "        \n",
    "#         padding={}\n",
    "#         if filter_size==3:\n",
    "#             padding[\"SAME\"]=1\n",
    "#         if filter_size==1:\n",
    "#             padding[\"SAME\"]=0\n",
    "#         padding[\"VALID\"]=0  \n",
    "        \n",
    "#         if paddingType==\"VALID\":\n",
    "#             output_padding=0\n",
    "#         else:\n",
    "#             output_padding=1\n",
    "            \n",
    "#         self.Deconv_1=nn.ConvTranspose2d(C_in,C_out,filter_size, stride=2,\\\n",
    "#                                          padding=padding[paddingType],output_padding=output_padding,bias=True, dilation=1)\n",
    "#         self.Dropout=nn.Dropout(DropoutRatio)\n",
    "#         self.Conv_1=nn.Conv2d(C_in,C_out,filter_size,stride=1,padding=1)\n",
    "#         self.Res_1=Residual_block(C_out,C_out)\n",
    "#         self.Res_2=Residual_block(C_out,C_out)\n",
    "    \n",
    "#     def forward(self,x,skipx):\n",
    "#         out=self.Deconv_1(x)\n",
    "#         out=torch.cat([out,skipx],dim=1)\n",
    "#         out=self.Dropout(out)\n",
    "        \n",
    "#         out=self.Conv_1(out)\n",
    "#         out=self.Res_1(out)\n",
    "#         out=self.Res_2(out)\n",
    "#         out=F.relu6(out,inplace=True)\n",
    "#         return out\n",
    "        \n",
    "# class Middle_with_residual_block(nn.Module):\n",
    "    \n",
    "#     def __init__(self,C_in,C_out,DropoutRatio = 0.5,filter_size=3,paddingType=\"SAME\"):\n",
    "#         super().__init__()\n",
    "        \n",
    "#         padding={}\n",
    "#         if filter_size==3:\n",
    "#             padding[\"SAME\"]=1\n",
    "#         if filter_size==1:\n",
    "#             padding[\"SAME\"]=0\n",
    "#         padding[\"VALID\"]=0\n",
    "            \n",
    "#         self.Conv_1=nn.Conv2d(C_in,C_out,filter_size,stride=1,padding=padding[paddingType])\n",
    "#         self.Res_1=Residual_block(C_out,C_out)\n",
    "#         self.Res_2=Residual_block(C_out,C_out)\n",
    "       \n",
    "#     def forward(self,x):\n",
    "#         out=self.Conv_1(x)\n",
    "#         out=self.Res_1(out)\n",
    "#         out=self.Res_2(out)\n",
    "#         out=F.relu6(out,inplace=True)\n",
    "#         return out\n",
    "    \n",
    "# class U_net_with_residual(nn.Module):\n",
    "    \n",
    "#     def __init__(self,filter_num=16,DropoutRatio = 0.5,filter_size=3,paddingType=\"SAME\"):\n",
    "#         super().__init__()\n",
    "        \n",
    "#         self.Dropout_1=nn.Dropout(DropoutRatio/2)\n",
    "#         self.Dropout_2=nn.Dropout(DropoutRatio)\n",
    "        \n",
    "#         self.Encoder_L1=Eecoder_with_residual_block(im_chan,filter_num,DropoutRatio/2)\n",
    "#         self.Encoder_L2=Eecoder_with_residual_block(filter_num*1,filter_num*2,DropoutRatio)\n",
    "#         self.Encoder_L3=Eecoder_with_residual_block(filter_num*2,filter_num*4,DropoutRatio)\n",
    "#         self.Encoder_L4=Eecoder_with_residual_block(filter_num*4,filter_num*8,DropoutRatio)\n",
    "        \n",
    "#         self.Middle=Middle_with_residual_block(filter_num*8,filter_num*16,DropoutRatio)\n",
    "        \n",
    "#         self.Decoder_L4=Decoder_with_residual_block(filter_num*16,filter_num*8,DropoutRatio,paddingType=\"SAME\")\n",
    "#         self.Decoder_L3=Decoder_with_residual_block(filter_num*8,filter_num*4,DropoutRatio,paddingType=\"VALID\")\n",
    "#         self.Decoder_L2=Decoder_with_residual_block(filter_num*4,filter_num*2,DropoutRatio,paddingType=\"SAME\")\n",
    "#         self.Decoder_L1=Decoder_with_residual_block(filter_num*2,filter_num*1,DropoutRatio,paddingType=\"VALID\")\n",
    "        \n",
    "    \n",
    "#         self.Out=nn.Conv2d(filter_num,1,1,stride=1,padding=0)\n",
    "        \n",
    "#     def forward(self,x):\n",
    "#         Encoder_L1=self.Encoder_L1(x)\n",
    "#         Pool_L1=F.max_pool2d(Encoder_L1,2, stride=2)\n",
    "#         Pool_L1=self.Dropout_1(Pool_L1)\n",
    "        \n",
    "#         Encoder_L2=self.Encoder_L2(Pool_L1)\n",
    "#         Pool_L2=F.max_pool2d(Encoder_L2,2, stride=2)\n",
    "#         Pool_L2=self.Dropout_2(Pool_L2)\n",
    "        \n",
    "#         Encoder_L3=self.Encoder_L3(Pool_L2)\n",
    "#         Pool_L3=F.max_pool2d(Encoder_L3,2, stride=2)\n",
    "#         Pool_L3=self.Dropout_2(Pool_L3)\n",
    "        \n",
    "#         Encoder_L4=self.Encoder_L4(Pool_L3)\n",
    "#         Pool_L4=F.max_pool2d(Encoder_L4,2, stride=2)\n",
    "#         Pool_L4=self.Dropout_2(Pool_L4)\n",
    "        \n",
    "#         Middle=self.Middle(Pool_L4)\n",
    "        \n",
    "#         Summary=False\n",
    "#         if Summary:\n",
    "#             print(\"1:\",Encoder_L1.shape)\n",
    "#             print(\"2:\",Encoder_L2.shape)\n",
    "#             print(\"3:\",Encoder_L3.shape)\n",
    "#             print(\"4:\",Encoder_L4.shape)\n",
    "#             print(\"5:\",Middle.shape)\n",
    "        \n",
    "        \n",
    "#         Decoder_L4=self.Decoder_L4(Middle,Encoder_L4)\n",
    "#         Decoder_L3=self.Decoder_L3(Decoder_L4,Encoder_L3)\n",
    "#         Decoder_L2=self.Decoder_L2(Decoder_L3,Encoder_L2)\n",
    "#         Decoder_L1=self.Decoder_L1(Decoder_L2,Encoder_L1)\n",
    "        \n",
    "#         if Summary:\n",
    "#             print(\"6:\",Decoder_L4.shape)\n",
    "#             print(\"7:\",Decoder_L3.shape)\n",
    "#             print(\"8:\",Decoder_L2.shape)\n",
    "#             print(\"9:\",Decoder_L1.shape)\n",
    "            \n",
    "#         logits=self.Dropout_1(Decoder_L1)\n",
    "#         logits=self.Out(logits)\n",
    "#         #loits=F.sigmoid(logits)\n",
    "        \n",
    "#         if Summary:\n",
    "#             print(\"10:\",logits.shape)\n",
    "        \n",
    "#         return logits\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from torch import nn\n",
    "from torch.nn import functional as F\n",
    "import torch\n",
    "from torchvision import models\n",
    "import torchvision\n",
    "\n",
    "\"\"\"\n",
    "This script has been taken (and modified) from :\n",
    "https://github.com/ternaus/TernausNet\n",
    "@ARTICLE{arXiv:1801.05746,\n",
    "         author = {V. Iglovikov and A. Shvets},\n",
    "          title = {TernausNet: U-Net with VGG11 Encoder Pre-Trained on ImageNet for Image Segmentation},\n",
    "        journal = {ArXiv e-prints},\n",
    "         eprint = {1801.05746}, \n",
    "           year = 2018\n",
    "        }\n",
    "\"\"\"\n",
    "\n",
    "def conv3x3(in_, out):\n",
    "    return nn.Conv2d(in_, out, 3, padding=1)\n",
    "\n",
    "class ConvRelu(nn.Module):\n",
    "    def __init__(self, in_, out):\n",
    "        super().__init__()\n",
    "        self.conv = conv3x3(in_, out)\n",
    "        self.activation = nn.ReLU(inplace=True)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.conv(x)\n",
    "        x = self.activation(x)\n",
    "        return x\n",
    "    \n",
    "\n",
    "class DecoderBlockV2(nn.Module):\n",
    "    def __init__(self, in_channels, middle_channels, out_channels, is_deconv=True):\n",
    "        super(DecoderBlockV2, self).__init__()\n",
    "        self.in_channels = in_channels\n",
    "\n",
    "        if is_deconv:\n",
    "            \"\"\"\n",
    "                Paramaters for Deconvolution were chosen to avoid artifacts, following\n",
    "                link https://distill.pub/2016/deconv-checkerboard/\n",
    "            \"\"\"\n",
    "\n",
    "            self.block = nn.Sequential(\n",
    "                ConvRelu(in_channels, middle_channels),\n",
    "                nn.ConvTranspose2d(middle_channels, out_channels, kernel_size=4, stride=2,\n",
    "                                   padding=1),\n",
    "                nn.ReLU(inplace=True)\n",
    "            )\n",
    "        else:\n",
    "            self.block = nn.Sequential(\n",
    "                nn.Upsample(scale_factor=2, mode='bilinear'),\n",
    "                ConvRelu(in_channels, middle_channels),\n",
    "                ConvRelu(middle_channels, out_channels),\n",
    "            )\n",
    "\n",
    "    def forward(self, x):\n",
    "        return self.block(x)\n",
    "\n",
    "\n",
    "class UNetResNet(nn.Module):\n",
    "    \"\"\"PyTorch U-Net model using ResNet(34, 101 or 152) encoder.\n",
    "    UNet: https://arxiv.org/abs/1505.04597\n",
    "    ResNet: https://arxiv.org/abs/1512.03385\n",
    "    Proposed by Alexander Buslaev: https://www.linkedin.com/in/al-buslaev/\n",
    "    Args:\n",
    "            encoder_depth (int): Depth of a ResNet encoder (34, 101 or 152).\n",
    "            num_classes (int): Number of output classes.\n",
    "            num_filters (int, optional): Number of filters in the last layer of decoder. Defaults to 32.\n",
    "            dropout_2d (float, optional): Probability factor of dropout layer before output layer. Defaults to 0.2.\n",
    "            pretrained (bool, optional):\n",
    "                False - no pre-trained weights are being used.\n",
    "                True  - ResNet encoder is pre-trained on ImageNet.\n",
    "                Defaults to False.\n",
    "            is_deconv (bool, optional):\n",
    "                False: bilinear interpolation is used in decoder.\n",
    "                True: deconvolution is used in decoder.\n",
    "                Defaults to False.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, encoder_depth, num_classes, num_filters=32, dropout_2d_ration=0.5,\n",
    "                 pretrained=False, is_deconv=False):\n",
    "        super().__init__()\n",
    "        self.num_classes = num_classes\n",
    "        self.dropout_2d_ratio = dropout_2d_ration\n",
    "\n",
    "        if encoder_depth == 34:\n",
    "            self.encoder = torchvision.models.resnet34(pretrained=pretrained)\n",
    "            bottom_channel_nr = 512\n",
    "        elif encoder_depth == 101:\n",
    "            self.encoder = torchvision.models.resnet101(pretrained=pretrained)\n",
    "            bottom_channel_nr = 2048\n",
    "        elif encoder_depth == 152:\n",
    "            self.encoder = torchvision.models.resnet152(pretrained=pretrained)\n",
    "            bottom_channel_nr = 2048\n",
    "        else:\n",
    "            raise NotImplementedError('only 34, 101, 152 version of Resnet are implemented')\n",
    "\n",
    "        self.pool = nn.MaxPool2d(2, 2)\n",
    "\n",
    "        self.relu = nn.ReLU(inplace=True)\n",
    "\n",
    "        self.conv1 = nn.Sequential(self.encoder.conv1,\n",
    "                                   self.encoder.bn1,\n",
    "                                   self.encoder.relu)\n",
    "\n",
    "        self.conv2 = self.encoder.layer1\n",
    "\n",
    "        self.conv3 = self.encoder.layer2\n",
    "\n",
    "        self.conv4 = self.encoder.layer3\n",
    "\n",
    "        self.conv5 = self.encoder.layer4\n",
    "\n",
    "        self.center = DecoderBlockV2(bottom_channel_nr, num_filters * 8 * 2, num_filters * 8, is_deconv)\n",
    "        self.dec5 = DecoderBlockV2(bottom_channel_nr + num_filters * 8, num_filters * 8 * 2, num_filters * 8, is_deconv)\n",
    "        self.dec4 = DecoderBlockV2(bottom_channel_nr // 2 + num_filters * 8, num_filters * 8 * 2, num_filters * 8,\n",
    "                                   is_deconv)\n",
    "        self.dec3 = DecoderBlockV2(bottom_channel_nr // 4 + num_filters * 8, num_filters * 4 * 2, num_filters * 2,\n",
    "                                   is_deconv)\n",
    "        self.dec2 = DecoderBlockV2_NoDouble(bottom_channel_nr // 8 + num_filters * 2, num_filters * 2 * 2, num_filters * 2 * 2,\n",
    "                                   is_deconv)\n",
    "        self.dec1 = DecoderBlockV2(num_filters * 2 * 2, num_filters * 2 * 2, num_filters, is_deconv)\n",
    "        self.dec0 = ConvRelu(num_filters, num_filters)\n",
    "        self.final = nn.Conv2d(num_filters, num_classes, kernel_size=1)\n",
    "\n",
    "    def forward(self, x):\n",
    "        print(\"x:\",x.shape)\n",
    "        \n",
    "        conv1 = self.conv1(x)\n",
    "        conv1=F.dropout2d(conv1, p=self.dropout_2d_ratio/2)\n",
    "        print(\"conv1:\",conv1.shape)\n",
    "        \n",
    "        conv2 = self.conv2(conv1)\n",
    "        conv2=F.dropout2d(conv2, p=self.dropout_2d_ratio)\n",
    "        print(\"conv2:\",conv2.shape)\n",
    "        \n",
    "        conv3 = self.conv3(conv2)\n",
    "        conv3=F.dropout2d(conv3, p=self.dropout_2d_ratio)\n",
    "        print(\"conv3:\",conv3.shape)\n",
    "        \n",
    "        conv4 = self.conv4(conv3)\n",
    "        conv4=F.dropout2d(conv4, p=self.dropout_2d_ratio)\n",
    "        print(\"conv4:\",conv4.shape)\n",
    "        \n",
    "        conv5 = self.conv5(conv4)\n",
    "        conv5=F.dropout2d(conv5, p=self.dropout_2d_ratio)\n",
    "        print(\"conv5:\",conv5.shape)\n",
    "        \n",
    "        \n",
    "        pool = self.pool(conv5)\n",
    "        print(\"pool:\",pool.shape)\n",
    "        \n",
    "        center = self.center(pool)\n",
    "        print(\"center:\",center.shape)\n",
    "        \n",
    "        tmp=torch.cat([center, conv5],1)\n",
    "        tmp=F.dropout2d(tmp, p=self.dropout_2d_ratio)\n",
    "        dec5 = self.dec5(tmp)\n",
    "        print(\"dec5:\",dec5.shape)\n",
    "    \n",
    "        tmp=torch.cat([dec5, conv4],1)\n",
    "        tmp=F.dropout2d(tmp, p=self.dropout_2d_ratio)\n",
    "        dec4 = self.dec4(tmp)\n",
    "        print(\"dec4:\",dec4.shape)\n",
    "        \n",
    "        tmp=torch.cat([dec4, conv3], 1)\n",
    "        tmp=F.dropout2d(tmp, p=self.dropout_2d_ratio)\n",
    "        dec3 = self.dec3(tmp)\n",
    "        print(\"dec3:\",dec3.shape)\n",
    "        \n",
    "        tmp=torch.cat([dec3, conv2], 1)\n",
    "        tmp=F.dropout2d(tmp, p=self.dropout_2d_ratio)\n",
    "        dec2 = self.dec2(tmp)\n",
    "        print(\"dec2:\",dec2.shape)\n",
    "        \n",
    "        dec2=F.dropout2d(dec2, p=self.dropout_2d_ratio/2)\n",
    "        dec1 = self.dec1(dec2)\n",
    "        print(\"dec1:\",dec1.shape)\n",
    "        \n",
    "        dec1=F.dropout2d(dec1, p=self.dropout_2d_ratio/2)\n",
    "        dec0 = self.dec0(dec1)\n",
    "        print(\"dec0:\",dec0.shape)\n",
    "        \n",
    "        logits=self.final(F.dropout2d(dec0, p=self.dropout_2d_ratio/2))\n",
    "        print(\"logits:\",logits.shape)\n",
    "        return logits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x: torch.Size([32, 3, 128, 128])\n",
      "conv1: torch.Size([32, 64, 64, 64])\n",
      "conv2: torch.Size([32, 64, 64, 64])\n",
      "conv3: torch.Size([32, 128, 32, 32])\n",
      "conv4: torch.Size([32, 256, 16, 16])\n",
      "conv5: torch.Size([32, 512, 8, 8])\n",
      "pool: torch.Size([32, 512, 4, 4])\n",
      "center: torch.Size([32, 256, 8, 8])\n",
      "dec5: torch.Size([32, 256, 16, 16])\n",
      "dec4: torch.Size([32, 256, 32, 32])\n",
      "dec3: torch.Size([32, 64, 64, 64])\n",
      "dec2: torch.Size([32, 128, 128, 128])\n",
      "dec1: torch.Size([32, 32, 256, 256])\n",
      "dec0: torch.Size([32, 32, 256, 256])\n",
      "logits: torch.Size([32, 1, 256, 256])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/mnt/ml/anaconda35/lib/python3.6/site-packages/torch/nn/functional.py:1749: UserWarning: Default upsampling behavior when mode=bilinear is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details.\n",
      "  \"See the documentation of nn.Upsample for details.\".format(mode))\n"
     ]
    }
   ],
   "source": [
    "import time \n",
    "since=time.time()\n",
    "\n",
    "x=torch.randn(32,3,128,128).cuda()\n",
    "\n",
    "model=UNetResNet(encoder_depth=34,num_classes=1,num_filters=32, dropout_2d_ration=0.5,pretrained=True)\n",
    "\n",
    "model.cuda()\n",
    "\n",
    "out=model(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](http://op4a94iq8.bkt.clouddn.com/18-9-5/28793794.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 指标计算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Score the model and do a threshold optimization by the best IoU.\n",
    "\n",
    "# src: https://www.kaggle.com/aglotero/another-iou-metric\n",
    "def iou_metric(y_true_in, y_pred_in, print_table=False):\n",
    "    labels = y_true_in\n",
    "    y_pred = y_pred_in\n",
    "\n",
    "\n",
    "    true_objects = 2\n",
    "    pred_objects = 2\n",
    "\n",
    "    # Jiaxin fin that if all zeros, then, the background is treated as object\n",
    "    temp1 = np.histogram2d(labels.flatten(), y_pred.flatten(), bins=([0,0.5,1], [0,0.5, 1]))\n",
    "#     temp1 = np.histogram2d(labels.flatten(), y_pred.flatten(), bins=(true_objects, pred_objects))\n",
    "    #print(temp1)\n",
    "    intersection = temp1[0]\n",
    "    #print(\"temp2 = \",temp1[1])\n",
    "    #print(intersection.shape)\n",
    "   # print(intersection)\n",
    "    # Compute areas (needed for finding the union between all objects)\n",
    "    #print(np.histogram(labels, bins = true_objects))\n",
    "    area_true = np.histogram(labels,bins=[0,0.5,1])[0]\n",
    "    #print(\"area_true = \",area_true)\n",
    "    area_pred = np.histogram(y_pred, bins=[0,0.5,1])[0]\n",
    "    area_true = np.expand_dims(area_true, -1)\n",
    "    area_pred = np.expand_dims(area_pred, 0)\n",
    "\n",
    "    # Compute union\n",
    "    union = area_true + area_pred - intersection\n",
    "  \n",
    "    # Exclude background from the analysis\n",
    "    intersection = intersection[1:,1:]\n",
    "    intersection[intersection == 0] = 1e-9\n",
    "    \n",
    "    union = union[1:,1:]\n",
    "    union[union == 0] = 1e-9\n",
    "\n",
    "    # Compute the intersection over union\n",
    "    iou = intersection / union\n",
    "\n",
    "    # Precision helper function\n",
    "    def precision_at(threshold, iou):\n",
    "        matches = iou > threshold\n",
    "        true_positives = np.sum(matches, axis=1) == 1   # Correct objects\n",
    "        false_positives = np.sum(matches, axis=0) == 0  # Missed objects\n",
    "        false_negatives = np.sum(matches, axis=1) == 0  # Extra objects\n",
    "        tp, fp, fn = np.sum(true_positives), np.sum(false_positives), np.sum(false_negatives)\n",
    "        return tp, fp, fn\n",
    "\n",
    "    # Loop over IoU thresholds\n",
    "    prec = []\n",
    "    if print_table:\n",
    "        print(\"Thresh\\tTP\\tFP\\tFN\\tPrec.\")\n",
    "    for t in np.arange(0.5, 1.0, 0.05):\n",
    "        tp, fp, fn = precision_at(t, iou)\n",
    "        if (tp + fp + fn) > 0:\n",
    "            p = tp / (tp + fp + fn)\n",
    "        else:\n",
    "            p = 0\n",
    "        if print_table:\n",
    "            print(\"{:1.3f}\\t{}\\t{}\\t{}\\t{:1.3f}\".format(t, tp, fp, fn, p))\n",
    "        prec.append(p)\n",
    "    \n",
    "    if print_table:\n",
    "        print(\"AP\\t-\\t-\\t-\\t{:1.3f}\".format(np.mean(prec)))\n",
    "    return np.mean(prec)\n",
    "\n",
    "def iou_metric_batch(y_true_in, y_pred_in):\n",
    "    y_pred_in = y_pred_in > 0.5 # added by sgx 20180728\n",
    "    batch_size = y_true_in.shape[0]\n",
    "    metric = []\n",
    "    for batch in range(batch_size):\n",
    "        value = iou_metric(y_true_in[batch], y_pred_in[batch])\n",
    "        metric.append(value)\n",
    "    #print(\"this batch metric = \",metric)\n",
    "    return np.mean(metric)\n",
    "\n",
    "def my_iou_metric(label, pred):\n",
    "    '''\n",
    "    tf.py_func接收的是tensor，然后将其转化为numpy array送入func函数，最后再将func函数输出的numpy array转化为tensor返回。\n",
    "    ref: https://blog.csdn.net/tiankongtiankong01/article/details/80568311\n",
    "    '''\n",
    "    metric_value = tf.py_func(iou_metric_batch, [label, pred], tf.float64)\n",
    "    return metric_value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# 准备训练数据并训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6400, 3, 128, 128)\n",
      "(6400, 1, 128, 128)\n",
      "(800, 3, 128, 128)\n",
      "(800, 1, 128, 128)\n",
      "<class 'numpy.float32'>\n",
      "<class 'numpy.float32'>\n"
     ]
    }
   ],
   "source": [
    "#Data augmentation\n",
    "x_train2 = np.transpose(np.append(x_train, [np.fliplr(x) for x in x_train], axis=0),(0,3,1,2)).astype(np.float32)\n",
    "y_train2 = np.transpose(np.append(y_train, [np.fliplr(x) for x in y_train], axis=0),(0,3,1,2)).astype(np.float32)\n",
    "x_valid2 = np.transpose(x_valid,(0,3,1,2)).astype(np.float32)\n",
    "y_valid2 = np.transpose(y_valid,(0,3,1,2)).astype(np.float32)\n",
    "\n",
    "print(x_train2.shape)\n",
    "print(y_train2.shape)\n",
    "print(x_valid2.shape)\n",
    "print(y_valid2.shape)  # !!!不要对valid进行数据增强\n",
    "print(type(x_train2[0,0,0,0]))\n",
    "print(type(x_valid2[0,0,0,0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 自定义一个Pytorch的数据加载器,必须继承自torch.utils.data.Dataset\n",
    "# https://stackoverflow.com/questions/50052295/how-do-you-load-images-into-pytorch-dataloader\n",
    "\n",
    "class saltIDDataset(torch.utils.data.Dataset):\n",
    "\n",
    "    def __init__(self,preprocessed_images,train=True, preprocessed_masks=None):\n",
    "        \"\"\"\n",
    "        Args:\n",
    "            text_file(string): path to text file\n",
    "            root_dir(string): directory with all train images\n",
    "        \"\"\"\n",
    "        self.train = train\n",
    "        self.images = preprocessed_images\n",
    "        if self.train:\n",
    "            self.masks = preprocessed_masks\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.images)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        image = self.images[idx]\n",
    "        mask = None\n",
    "        if self.train:\n",
    "            mask = self.masks[idx]\n",
    "        return (image, mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trainng samples: 6400\n",
      "Validation samples: 800\n"
     ]
    }
   ],
   "source": [
    "salt_ID_dataset_train = saltIDDataset(x_train2, \n",
    "                                      train=True, \n",
    "                                      preprocessed_masks=y_train2)\n",
    "\n",
    "salt_ID_dataset_val = saltIDDataset(x_valid2, \n",
    "                                      train=True, \n",
    "                                      preprocessed_masks=y_valid2)\n",
    "\n",
    "batch_size = 32\n",
    "\n",
    "train_loader = torch.utils.data.DataLoader(dataset=salt_ID_dataset_train, \n",
    "                                           batch_size=batch_size, \n",
    "                                           shuffle=True)\n",
    "\n",
    "val_loader = torch.utils.data.DataLoader(dataset=salt_ID_dataset_val, \n",
    "                                           batch_size=batch_size, \n",
    "                                           shuffle=False)\n",
    "\n",
    "print(\"Trainng samples:\",train_loader.dataset.__len__())\n",
    "print(\"Validation samples:\",val_loader.dataset.__len__())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class ArgParser(object):\n",
    "    def __init__(self):\n",
    "        \n",
    "        self.MaxEpoch=60\n",
    "        self.LR_Policy=\"MultiStage\"\n",
    "        \n",
    "        self.n_gpus=1\n",
    "        \n",
    "        self.load_pretrained_model=False\n",
    "        self.pretarined_model_path=\"Results\"\n",
    "        \n",
    "        self.device=torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "        \n",
    "        self.start_save_epoch=30\n",
    "        self.save_step=2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class DiceLoss(nn.Module):\n",
    "    def __init__(self, smooth=0, eps=1e-7):\n",
    "        super(DiceLoss, self).__init__()\n",
    "        self.smooth = smooth\n",
    "        self.eps = eps\n",
    "\n",
    "    def forward(self, output, target):\n",
    "        return 1 - (2 * torch.sum(output * target) + self.smooth) / (\n",
    "                torch.sum(output) + torch.sum(target) + self.smooth + self.eps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def LearningRateAdjust(LR_Policy,optimizer,cur_epoch,history_metirc):\n",
    "    if LR_Policy==\"MultiSatge\":\n",
    "        if cur_epoch==1:\n",
    "            lr=0.002\n",
    "        elif cur_epoch==30:\n",
    "            lr=0.001\n",
    "        elif cur_epoch==45:\n",
    "            lr=0.0005\n",
    "        elif cur_epoch==50:\n",
    "            lr=0.0001\n",
    "        else:\n",
    "            return\n",
    "        \n",
    "        for param_group in optimizer.param_groups:\n",
    "            param_group[\"lr\"]=lr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import time\n",
    "import shutil\n",
    "        \n",
    "def custom_weights_init(m):\n",
    "    pass \n",
    "\n",
    "def save_checkpoint(state, is_best, path,args):\n",
    "    save_path=os.path.join(path,'saved_Gpu_model_state.pt')\n",
    "    torch.save(state, save_path)\n",
    "    if is_best:\n",
    "        shutil.copyfile(save_path, \n",
    "                        os.path.join(args.pretarined_model_path ,'best_Gpu_model_state.pt'))\n",
    "\n",
    "    \n",
    "def train(model,train_loader,optimizer,criterion,args,epoch,class_weights,Dice_loss):\n",
    "    since=time.time()\n",
    "    model.train()\n",
    "    train_losses = []\n",
    "    \n",
    "    for batch_idx, (images, masks) in enumerate(train_loader):\n",
    "        \n",
    "        images, masks = images.to(args.device), masks.to(args.device)\n",
    "        images, masks = Variable(images), Variable(masks)\n",
    "        \n",
    "        logits = model(images)\n",
    "        \n",
    "        loss_1 = criterion(logits, masks)\n",
    "        loss_2=Dice_loss(F.sigmoid(logits), masks)\n",
    "        \n",
    "        combine_cofficient=1\n",
    "        loss=combine_cofficient*loss_1+(1-combine_cofficient)*loss_2\n",
    "        \n",
    "        train_losses.append(loss.data)\n",
    "        \n",
    "        #dice_loss = bioloss.dice_error(output, target)\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        \n",
    "#         if batch_idx%20==0:\n",
    "#             now=time.time()\n",
    "#             print(\"Loss:{},escaped time:{}\".format(loss,now-since))\n",
    "#             since=now\n",
    "#             #break\n",
    "            \n",
    "    now=time.time()\n",
    "    mean_loss=np.mean(train_losses)\n",
    "    print(\"Mean Train Loss:{},escaped time:{}\".format(mean_loss,now-since))\n",
    "    return mean_loss\n",
    "\n",
    "def test(model,test_loader,criterion,args,epoch,class_weights,Dice_loss):\n",
    "    since=time.time()\n",
    "    model.eval()\n",
    "    test_losses = []\n",
    "    \n",
    "    for batch_idx, (images, masks) in enumerate(test_loader):\n",
    "        \n",
    "        images, masks = images.to(args.device), masks.to(args.device)\n",
    "        images, masks = Variable(images), Variable(masks)\n",
    "        \n",
    "        logits = model(images)\n",
    "        \n",
    "        loss_1 = criterion(logits, masks)\n",
    "        loss_2=Dice_loss(F.sigmoid(logits), masks)\n",
    "        \n",
    "        combine_cofficient=1\n",
    "        loss=combine_cofficient*loss_1+(1-combine_cofficient)*loss_2\n",
    "        \n",
    "        test_losses.append(loss.data)\n",
    "        #dice_loss = bioloss.dice_error(output, target)\n",
    "        \n",
    "    now=time.time()\n",
    "    mean_loss=np.mean(test_losses)\n",
    "    print(\"Mean Val Loss:{},escaped time:{}\".format(mean_loss,now-since))\n",
    "            \n",
    "    return mean_loss\n",
    "   \n",
    "train_losses_history=[]\n",
    "val_losses_history=[]\n",
    "\n",
    "def main():\n",
    "    \n",
    "    args=ArgParser()\n",
    "    \n",
    "    torch.cuda.manual_seed_all(4200)# 设置全局固定随机种子方便复现\n",
    "    np.random.seed(133700)\n",
    "    \n",
    "#     model=U_net_with_residual(16) # 16 is the basic filter_nums, for controlling the width of U-Net\n",
    "    model=UNetResNet(encoder_depth=34,num_classes=1,num_filters=16, dropout_2d_ration=0.8,pretrained=True)\n",
    "    \n",
    "#     gpu_ids=range(args.n_gpus)\n",
    "#     model=nn.parallel.DataParallel(model,device_ids=gpu_ids)\n",
    "    \n",
    "    if args.load_pretrained_model==True:\n",
    "        state=torch.load(args.pretarined_model_path)\n",
    "        model.load_state_dict(state)\n",
    "    else:\n",
    "        model.apply(custom_weights_init)    \n",
    "    \n",
    "    # deploy model to GPU or CPU\n",
    "    model.to(args.device)\n",
    "    \n",
    "    # -------------------------------------------------------------------------------- #\n",
    "    optimizer = torch.optim.Adam(model.parameters(), lr=0.001)\n",
    "    #optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.9, weight_decay=5e-4)\n",
    "    \n",
    "    #criterion = nn.CrossEntropyLoss()\n",
    "    criterion = nn.BCEWithLogitsLoss()\n",
    "    # -------------------------------------------------------------------------------- #\n",
    "    Dice_loss=DiceLoss(smooth=1)\n",
    "    \n",
    "    \n",
    "    class_weights=[0.5,0.5] \n",
    "    \n",
    "    best_metric=100  # 注意指标的大小与性能的关系\n",
    "    metirc_history=[]\n",
    "    for epoch in range(1,args.MaxEpoch):\n",
    "        LearningRateAdjust(args.LR_Policy,optimizer,epoch,metirc_history)\n",
    "        \n",
    "        mean_train_loss_=train(model,train_loader,optimizer,criterion,args,epoch,class_weights,Dice_loss)\n",
    "        train_losses_history.append(mean_train_loss_)\n",
    "        \n",
    "        mean_val_loss_=test(model,val_loader,criterion,args,epoch,class_weights,Dice_loss)\n",
    "        val_losses_history.append(mean_val_loss_)\n",
    "        \n",
    "        if  epoch>=args.start_save_epoch :\n",
    "            if mean_val_loss_<best_metric:\n",
    "                best_metric=mean_val_loss_\n",
    "                is_best=True\n",
    "                save_checkpoint(model.state_dict(), is_best, args.pretarined_model_path,args)\n",
    "            else:\n",
    "                is_best=False\n",
    "                if epoch % args.save_step ==0:\n",
    "#                     save_path=os.path.join(args.pretarined_model_path,str(epoch))\n",
    "#                     if not os.path.exists(save_path):\n",
    "#                         os.mkdir(save_path)\n",
    "#                     save_checkpoint(model.state_dict(), is_best, save_path, args)\n",
    "                    torch.save(model.state_dict(),\"Results/MM_{}.pt\".format(epoch))\n",
    "    torch.save(model.state_dict(),\"Results/MM.pt\")\n",
    "    return model\n",
    "\n",
    "   \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](http://op4a94iq8.bkt.clouddn.com/18-9-4/76928144.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading: \"https://download.pytorch.org/models/resnet34-333f7ec4.pth\" to /root/.torch/models/resnet34-333f7ec4.pth\n",
      "100%|██████████| 87306240/87306240 [00:10<00:00, 8354781.50it/s] \n",
      "/mnt/ml/anaconda35/lib/python3.6/site-packages/torch/nn/functional.py:1749: UserWarning: Default upsampling behavior when mode=bilinear is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details.\n",
      "  \"See the documentation of nn.Upsample for details.\".format(mode))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Train Loss:0.41191595792770386,escaped time:39.753209829330444\n",
      "Mean Val Loss:0.43105262517929077,escaped time:1.5953917503356934\n",
      "Mean Train Loss:0.2719200849533081,escaped time:39.72522854804993\n",
      "Mean Val Loss:0.2769571542739868,escaped time:1.6016871929168701\n",
      "Mean Train Loss:0.23014751076698303,escaped time:39.75101900100708\n",
      "Mean Val Loss:0.29405421018600464,escaped time:1.6042873859405518\n",
      "Mean Train Loss:0.19841910898685455,escaped time:39.78574538230896\n",
      "Mean Val Loss:0.1852065473794937,escaped time:1.597484827041626\n",
      "Mean Train Loss:0.17505007982254028,escaped time:39.794458627700806\n",
      "Mean Val Loss:0.19053076207637787,escaped time:1.6028656959533691\n",
      "Mean Train Loss:0.15623679757118225,escaped time:39.80159831047058\n",
      "Mean Val Loss:0.4508691132068634,escaped time:1.6111266613006592\n",
      "Mean Train Loss:0.15316087007522583,escaped time:39.80487871170044\n",
      "Mean Val Loss:0.19836683571338654,escaped time:1.6036310195922852\n",
      "Mean Train Loss:0.15582041442394257,escaped time:39.796340227127075\n",
      "Mean Val Loss:0.421157568693161,escaped time:1.6044938564300537\n",
      "Mean Train Loss:0.13395296037197113,escaped time:39.813560485839844\n",
      "Mean Val Loss:0.5622787475585938,escaped time:1.6083605289459229\n",
      "Mean Train Loss:0.12941086292266846,escaped time:39.82098317146301\n",
      "Mean Val Loss:0.34731486439704895,escaped time:1.6011269092559814\n",
      "Mean Train Loss:0.1417549103498459,escaped time:39.80044078826904\n",
      "Mean Val Loss:0.2125028669834137,escaped time:1.6078159809112549\n",
      "Mean Train Loss:0.12850847840309143,escaped time:39.797553300857544\n",
      "Mean Val Loss:0.15374259650707245,escaped time:1.606398582458496\n",
      "Mean Train Loss:0.11270585656166077,escaped time:39.79945182800293\n",
      "Mean Val Loss:0.3764951229095459,escaped time:1.6010959148406982\n",
      "Mean Train Loss:0.09891141951084137,escaped time:39.79319977760315\n",
      "Mean Val Loss:0.28309720754623413,escaped time:1.608774185180664\n",
      "Mean Train Loss:0.08449836820363998,escaped time:39.78901672363281\n",
      "Mean Val Loss:0.37060728669166565,escaped time:1.6021184921264648\n",
      "Mean Train Loss:0.09450000524520874,escaped time:39.80830407142639\n",
      "Mean Val Loss:0.19643636047840118,escaped time:1.6028943061828613\n",
      "Mean Train Loss:0.08206164091825485,escaped time:39.808560371398926\n",
      "Mean Val Loss:0.5986523628234863,escaped time:1.6010496616363525\n",
      "Mean Train Loss:0.07967595756053925,escaped time:39.816949129104614\n",
      "Mean Val Loss:0.2320536971092224,escaped time:1.6114559173583984\n",
      "Mean Train Loss:0.06650052964687347,escaped time:39.848822355270386\n",
      "Mean Val Loss:0.2982136607170105,escaped time:1.611945629119873\n",
      "Mean Train Loss:0.059030018746852875,escaped time:39.81988334655762\n",
      "Mean Val Loss:0.21517065167427063,escaped time:1.601505994796753\n",
      "Mean Train Loss:0.05242227017879486,escaped time:39.80306673049927\n",
      "Mean Val Loss:0.35276275873184204,escaped time:1.605423927307129\n",
      "Mean Train Loss:0.05777742713689804,escaped time:39.79058337211609\n",
      "Mean Val Loss:0.2709488272666931,escaped time:1.6010961532592773\n",
      "Mean Train Loss:0.06943061947822571,escaped time:39.786598205566406\n",
      "Mean Val Loss:0.17877042293548584,escaped time:1.6043272018432617\n",
      "Mean Train Loss:0.04899149015545845,escaped time:39.81573033332825\n",
      "Mean Val Loss:0.23599910736083984,escaped time:1.6083886623382568\n",
      "Mean Train Loss:0.04359345883131027,escaped time:39.8349187374115\n",
      "Mean Val Loss:0.4174797832965851,escaped time:1.6083505153656006\n",
      "Mean Train Loss:0.04650891199707985,escaped time:39.841718912124634\n",
      "Mean Val Loss:0.2405133694410324,escaped time:1.609736680984497\n",
      "Mean Train Loss:0.06952496618032455,escaped time:39.85050320625305\n",
      "Mean Val Loss:0.3151632249355316,escaped time:1.6109375953674316\n",
      "Mean Train Loss:0.05773549526929855,escaped time:39.84411811828613\n",
      "Mean Val Loss:0.747179388999939,escaped time:1.6070897579193115\n",
      "Mean Train Loss:0.04077130928635597,escaped time:39.885300159454346\n",
      "Mean Val Loss:0.21756605803966522,escaped time:1.6115989685058594\n",
      "Mean Train Loss:0.04674385115504265,escaped time:39.837507247924805\n",
      "Mean Val Loss:0.20994603633880615,escaped time:1.6081876754760742\n",
      "Mean Train Loss:0.05148710682988167,escaped time:39.81489562988281\n",
      "Mean Val Loss:0.2008667141199112,escaped time:1.6091930866241455\n",
      "Mean Train Loss:0.04890036955475807,escaped time:39.82956027984619\n",
      "Mean Val Loss:0.3416423797607422,escaped time:1.6119999885559082\n",
      "Mean Train Loss:0.03791563957929611,escaped time:39.83623480796814\n",
      "Mean Val Loss:0.22660842537879944,escaped time:1.6102850437164307\n",
      "Mean Train Loss:0.03179158642888069,escaped time:39.90564465522766\n",
      "Mean Val Loss:0.2634355425834656,escaped time:1.611107587814331\n",
      "Mean Train Loss:0.02744092047214508,escaped time:39.92857766151428\n",
      "Mean Val Loss:0.26414963603019714,escaped time:1.6120920181274414\n",
      "Mean Train Loss:0.025833791121840477,escaped time:39.83091592788696\n",
      "Mean Val Loss:0.35030049085617065,escaped time:1.6013023853302002\n",
      "Mean Train Loss:0.024882130324840546,escaped time:39.779492139816284\n",
      "Mean Val Loss:0.30154064297676086,escaped time:1.5953724384307861\n",
      "Mean Train Loss:0.02328743226826191,escaped time:39.706031799316406\n",
      "Mean Val Loss:0.3053440749645233,escaped time:1.5948717594146729\n",
      "Mean Train Loss:0.022107845172286034,escaped time:39.73504304885864\n",
      "Mean Val Loss:0.27445828914642334,escaped time:1.5971505641937256\n",
      "Mean Train Loss:0.021582383662462234,escaped time:39.724456787109375\n",
      "Mean Val Loss:0.35795915126800537,escaped time:1.5939505100250244\n",
      "Mean Train Loss:0.02175072580575943,escaped time:39.69500184059143\n",
      "Mean Val Loss:0.38537657260894775,escaped time:1.5911898612976074\n",
      "Mean Train Loss:0.09042883664369583,escaped time:39.67882466316223\n",
      "Mean Val Loss:0.4172808527946472,escaped time:1.5912096500396729\n",
      "Mean Train Loss:0.06759761273860931,escaped time:39.67892932891846\n",
      "Mean Val Loss:0.2390633374452591,escaped time:1.5925040245056152\n",
      "Mean Train Loss:0.04457965865731239,escaped time:39.68597364425659\n",
      "Mean Val Loss:0.2704519033432007,escaped time:1.5895051956176758\n",
      "Mean Train Loss:0.027912462130188942,escaped time:39.68212819099426\n",
      "Mean Val Loss:0.2796263098716736,escaped time:1.590632438659668\n",
      "Mean Train Loss:0.02441122941672802,escaped time:39.68874669075012\n",
      "Mean Val Loss:0.2738008201122284,escaped time:1.5931928157806396\n",
      "Mean Train Loss:0.02121865749359131,escaped time:39.68331527709961\n",
      "Mean Val Loss:0.3419932425022125,escaped time:1.591723918914795\n",
      "Mean Train Loss:0.0199125986546278,escaped time:39.73067331314087\n",
      "Mean Val Loss:0.3396623730659485,escaped time:1.5977489948272705\n",
      "Mean Train Loss:0.018950646743178368,escaped time:39.709250688552856\n",
      "Mean Val Loss:0.365174263715744,escaped time:1.5955002307891846\n",
      "Mean Train Loss:0.01900363527238369,escaped time:39.71806240081787\n",
      "Mean Val Loss:0.34455248713493347,escaped time:1.5953354835510254\n",
      "Mean Train Loss:0.01818184368312359,escaped time:39.723889112472534\n",
      "Mean Val Loss:0.44580161571502686,escaped time:1.6013615131378174\n",
      "Mean Train Loss:0.017653977498412132,escaped time:39.723448753356934\n",
      "Mean Val Loss:0.3787580132484436,escaped time:1.5928361415863037\n",
      "Mean Train Loss:0.017572840675711632,escaped time:39.71094107627869\n",
      "Mean Val Loss:0.3867081105709076,escaped time:1.596858024597168\n",
      "Mean Train Loss:0.0374143160879612,escaped time:39.69305992126465\n",
      "Mean Val Loss:1.523941993713379,escaped time:1.5900535583496094\n",
      "Mean Train Loss:0.06288500875234604,escaped time:39.672600507736206\n",
      "Mean Val Loss:0.1721247285604477,escaped time:1.5903491973876953\n",
      "Mean Train Loss:0.0385548435151577,escaped time:39.67739963531494\n",
      "Mean Val Loss:0.7284972667694092,escaped time:1.5910730361938477\n",
      "Mean Train Loss:0.033988021314144135,escaped time:39.6742525100708\n",
      "Mean Val Loss:0.40193966031074524,escaped time:1.5901153087615967\n",
      "Mean Train Loss:0.023887764662504196,escaped time:39.67731738090515\n",
      "Mean Val Loss:0.3285014033317566,escaped time:1.5903770923614502\n",
      "Mean Train Loss:0.01956820860505104,escaped time:39.734830379486084\n",
      "Mean Val Loss:0.33472752571105957,escaped time:1.5984742641448975\n"
     ]
    }
   ],
   "source": [
    "model=main()\n",
    "\n",
    "train_loss_series = pd.Series(train_losses_history)\n",
    "val_loss_series = pd.Series(val_losses_history)\n",
    "train_loss_series.plot(label=\"train\")\n",
    "val_loss_series.plot(label=\"validation\")\n",
    "plt.legend()"
   ]
  },
  {
   "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
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from torch import nn\n",
    "from torch.nn import functional as F\n",
    "from torch.autograd import Variable\n",
    "import torch\n",
    "from torchvision import models\n",
    "import torchvision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'M'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MODELS_NAME[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(800, 3, 128, 128)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/mnt/ml/anaconda35/lib/python3.6/site-packages/torch/nn/functional.py:1749: UserWarning: Default upsampling behavior when mode=bilinear is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details.\n",
      "  \"See the documentation of nn.Upsample for details.\".format(mode))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(800, 1, 128, 128)\n",
      "(800, 1, 128, 128)\n",
      "(800, 1, 128, 128)\n",
      "cost time: 1.7175564765930176\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e4d3094efa5d4bed8abd4dc9362bb1f1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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ZNW50J4DVqjrcz1ibW2V1DQvXfc38ldv4eNV25q/azuZd5Q1vGCU9JYlQCMqrGjNKZd2C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\nNfCFt9pqVR0eva0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hSE9PZ+rUqUybNo2bbrqJhx566JD2M2HChHoTwoGW1aeqyr/BuY0x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\nxhgA/j/RTqzGcnX9NgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(800, 3, 128, 128)\n",
      "(800, 1, 128, 128)\n",
      "(800, 1, 128, 128)\n",
      "(800, 1, 128, 128)\n",
      "cost time: 1.7182836532592773\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4a2437c8595544a5995a3517edfe2dc9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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rnwG8+34V981bwX3zV3DJfS/TMxHnowcN45N6IPduTclTRKQTDe5dzNfHj+Zr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\nJU8REZF2UvIUERFpJyVPERGRdvr/YapIKpr+ONIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(800, 3, 128, 128)\n",
      "(800, 1, 128, 128)\n",
      "(800, 1, 128, 128)\n",
      "(800, 1, 128, 128)\n",
      "cost time: 1.7248625755310059\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "62c45cce832047d782cfea2073cb829f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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tDcH6EIwxpp1LtSYjY4wxACnBmiaj+F2HEIzlxkVkPPAgEABmqerd9SwzCbgD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\nSwjGGGM8lhCMMcYA8P9l8mYSOvq6dgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(800, 3, 128, 128)\n",
      "(800, 1, 128, 128)\n",
      "(800, 1, 128, 128)\n",
      "(800, 1, 128, 128)\n",
      "cost time: 1.735971450805664\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d813c7fec067421292d683c0cbe5b7e0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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TpllCMGVedEQoYwYnExHmZci7v9id10yZZQnBGJyRRy/2b8363YfynPjQmNLK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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(800, 3, 128, 128)\n",
      "(800, 1, 128, 128)\n",
      "(800, 1, 128, 128)\n",
      "(800, 1, 128, 128)\n",
      "cost time: 1.7203166484832764\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3ab5329a3bff4220b8f4600d94dc6166",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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s8Few5sLVpEppmvQtTdLuQ7zx43o+WriFz5Yk0a91HHe1r07pYoUCHaIxecKf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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(800, 3, 128, 128)\n",
      "(800, 1, 128, 128)\n",
      "(800, 1, 128, 128)\n",
      "(800, 1, 128, 128)\n",
      "cost time: 1.734450340270996\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "15991636104d4183a60cd5384db488ab",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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pkxfkV2cP5ZOtJXyydV+ywwEsWRhjTEoqzAsCsL8yNe6vasnCGGNSUDDgB6Ci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\nJksWxhhjYvr//LH9PN7FR+gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(800, 3, 128, 128)\n",
      "(800, 1, 128, 128)\n",
      "(800, 1, 128, 128)\n",
      "(800, 1, 128, 128)\n",
      "cost time: 1.7312803268432617\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b77f2a35ec4a43d8afbfc2f366fc58a6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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Jkg93nG/HHccTgEtUtezS6t/iLm75p4js834Of9Yi8jevcBboFdyVhp8Br+OuTgNXcFsG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\nrrsxdYQlDGOOUYw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OQFIQ++4PrFHVdV5ymQmM+r4BG2NMfRHjj+K3w7uwZvs+Fq2O/CFD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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(800, 3, 128, 128)\n",
      "(800, 1, 128, 128)\n",
      "(800, 1, 128, 128)\n",
      "(800, 1, 128, 128)\n",
      "cost time: 1.710602045059204\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "76174401b82f47e39e000ecbf4c35681",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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CCdufz1yIOzFaRRRVLVXVN5tqJ1TVkDdfaWPzRekCvIDbP5bhToTO9qrnoJn7Ku7Er6/3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\nwxhjTEz+PwnlX3c9xfOJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(800, 3, 128, 128)\n",
      "(800, 1, 128, 128)\n",
      "(800, 1, 128, 128)\n",
      "(800, 1, 128, 128)\n",
      "cost time: 1.722402572631836\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "181187bacdd94e8d8cbebae8338798bb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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QlG0wxph4rd9WyUNzl/D0+8uJEOH8QQVccWofeqXYTWlLSkoYNmwYQC9VXZas\n90lmt9gQ4GtVLVbVKmAqMLKR8mOB57y/zwReV9UNXkJ5HRiexFiNMWafdGidw81nD2DOdacydkgP\n/veTUob9z1x++exHLFy5uaXDa3bJTC7dgRW+6RJv3i5E5GCgF/Dmnq5rjDGppHvbMJNGHs6864dy\n2cl9eEvX8YP753Hx3/7Dh8s3tHR4zSZVBvTHANNVtbalAzHGmETonBfihrMO5d0bvse1Z/RjQclm\nRj38Pn98ZTHVtXUtHV7SJTO5lAKFvukCb15DxrCjS2xP1zXGmJTVJhxkwvf6Mu/6oVxwXA8efauY\n0Y++z4oNZS0dWlIlM7kUAX1FpJeIZOMSyMzYQiJyKNAOeN83+zXgDBFpJyLtgDO8ecYYk5Zys7O4\n47wjeOCCQXy9Zhs/uP8dXv18dUuHlTRJSy6qWgNMwCWFxcA0VV0oIpNEZISv6BhgqqpGfOtuAG7H\nJagiYJI3zxhj0trZRx7EyxNPomfHVlzxzIfcOuNzKqr3vxGBpJ2KnGx2KrIxJp1V1dRx56tf8Nd5\nSznsoHz+PHYQvTu1Tvr77g+nIhtjjNmN7KxMbj57AFMuGkzppnLO/vM8pn9YQrpW+GNZcjHGmBZ0\n2oAuvHLVSRzRvQ3X/vNTrv7HJ2ytqG7psPaZJRdjjGlh3dqEefbnx3PN6f2Y9elKfnD/vLS/GaYl\nF2OMSQGBzAwmDuvLtMu/Q21dhFEPv8djbxe3dFh7zZKLMcakkME92zN74kmcKp34w+zFaXunZUsu\nxhiTYtrkBrn4u70A2FiWnuMvllyMMSYFhbPd4bm8Kj2vgbHkYowxKSgUDABQnqYXWFpyMcaYFBT2\nkku6Xr1vycUYY1JQONtruVi3mDHGmEQJW7eYMcaYRLMxF2OMMQmXk5VJRgZUWLeYMcaYRMnIyCAc\nDFjLxRhjTGKFLLkYY4xJtHAwQHlVXUuHsVcsuRhjTIoKBTPtOhdjjDGJFc4OpG1yyUrmi4vIcOA+\nIABMUdXJDZQZDdwGRIBPVfUCb34t8JlX7BtVHZHMWI0xJtWk84B+0pKLiASAB4HTgRKgSERmquoi\nX5m+wI3ACaq6UUQ6+16iXFUHJis+Y4xJdaFggG2VNS0dxl5JZrfYEOBrVS1W1SpgKjAypszPgQdV\ndSOAqq5NYjzGGJNW3IC+tVxidQdW+KZLgONiyvQDEJF3cV1nt6nqq96ykIjMB2qAyar6YhJjNcaY\nlJPOYy4tPaCfBfQFTgXGAo+JSFtv2cGqOhi4APiTiPRpmRCNMaZlpPOYSzKTSylQ6Jsu8Ob5lQAz\nVbVaVZcCX+KSDapa6v0uBua5E/WyAAAZDUlEQVQCg5IYqzHGpJxQGneLJTO5FAF9RaSXiGQDY4CZ\nMWVexLVaEJGOuG6yYhFpJyI5vvknAIswxpgDiOsWs4sod6KqNcAE4DVgMTBNVReKyCQRiZ5W/Bqw\nXkQWAXOA61R1PdAfmC8in3rzJ/vPMjPGmANBOBigqraOmtr0SzBJvc5FVWcDs2Pm3eL7OwJc4/34\ny7wHHJHM2IwxJtWFgq7+X1FTR+tASw+R75n0itYYYw4g9Q8MS8NxF0suxhiToqIPDEvH05EtuRhj\nTIoKZ6fv0ygtuRhjTIqybjFjjDEJF7ZuMWOMMYkWsm4xY4wxiWYtF2OMMQlXP+ZiycUYY0yi1J8t\nVpV+V+hbcjHGmBQVspaLMcaYRLMxF2OMMQkXDGQQyMyw61yMMcYkTkZGBqGsTOsWM8YYk1jh7PR8\nGqUlF2OMSWGhYIAK6xYzxhiTSOGgtVyMMcYkmHWLGWOMSbhQMJCWpyIn9THHIjIcuA8IAFNUdXID\nZUYDtwER4FNVvcCbPx64ySv2e1V9MpmxGmNMKgoHA2wqr27pMPZY0louIhIAHgTOAgYAY0VkQEyZ\nvsCNwAmqehhwtTe/PXArcBwwBLhVRNolK1ZjjElVYRvQ38UQ4GtVLVbVKmAqMDKmzM+BB1V1I4Cq\nrvXmnwm8rqobvGWvA8OTGKsxxqSkdB1zSWa3WHdghW+6BNcS8esHICLv4rrOblPVV3ezbvfkhWqM\nMakplKZniyV1zCXO9+8LnAoUAG+LyBEtGpExxqQQ6xbbVSlQ6Jsu8Ob5lQAzVbVaVZcCX+KSTTzr\nGmPMfi+cnZ63f0lmy6UI6CsivXCJYQxwQUyZF4GxwN9EpCOum6wYWALc4RvEPwM38G+MMQeUcDBA\nTV2E6to6goH0uXokaZGqag0wAXgNWAxMU9WFIjJJREZ4xV4D1ovIImAOcJ2qrlfVDcDtuARVBEzy\n5hljzAElXZ/pktQxF1WdDcyOmXeL7+8IcI33E7vu48DjyYzPGGNSXTS5VFTVkh8KtnA08UufNpYx\nxhyAwmnacrHkYowxKSycbcnFGGNMgtW3XNLsdORGx1xE5PyYWRHgW+ATVd2atKiMMcYAvjGX6roW\njmTPNDWgf04D89oDR4rIJar6ZhJiMsYY44l2i6XbnZEbTS6q+tOG5ovIwcA0dr2dizHGmAQ6oAb0\nVXU5kD7nxBljTJpK1zGXvUouIiJAZYJjMcYYEyOU7Q7T6dZyaWpAfxZuEN+vPdAN+EmygjLGGOOE\ng/vhmAvw3zHTEWA98JX3jBZjjDFJFErTbrGmBvTfiv4tIl2AY4F8YB2wdnfrGWOMSYxgIJNgICPt\nusXiGnPxnnP/H+BHwGjgAxH5YTIDM8YY44Sy0u+BYfHeuPL/AcdGH0MsIp2A/wOmJyswY4wxTig7\nkHZjLvGeLZbpe749uHEXu3WMMcY0g3AwsH+Nufi8KiKvAc950z8m5lb6xhhjkiMcTL9usbhaH6p6\nHfAX4Ejv5y+qen0yAzPGGOOEsgOU72f3Fqunqs8DzycxFmOMMQ0IBzOp2J+6xURkK7teRAmQAURU\nNT8pURljjKkXDgZYvz29Li1s6jqXvH15cREZDtwHBIApqjo5ZvnFwN1AqTfrAVWd4i2rBT7z5n+j\nqiP2JRZjjElX4ewA5Rv3o5bLvhCRAPAgcDpQAhSJyExVXRRT9B+qOqGBlyhX1YHJis8YY9JFaH8d\n0N9LQ4CvVbXYu1XMVGBkEt/PGGP2S+Fg+l3nkrSWC9AdWOGbLqHh57+MEpGTgS+BX6tqdJ2QiMwH\naoDJqvpiEmM1xpiUlY7XubT0hZCzgJ6qeiTwOvCkb9nBqjoYuAD4k4j0aYkAjTGmpYWzXbdYJNLQ\n+VWpKZnJpRQo9E0XsGPgHgBVXa+q0efCTAGO8S0r9X4XA3OBQUmM1RhjUlYoGKAuAlW16XOtSzKT\nSxHQV0R6iUg2MAaY6S8gIt18kyOAxd78diKS4/3dETgBiD0RwBhjDgjR2+5XVKVPcknamIuq1ojI\nBOA13KnIj6vqQhGZBMxX1ZnARBEZgRtX2QBc7K3eH3hUROpwCXByA2eZGWPMAaH+UcfVtbRJkyfM\nJ3NAH1WdTcw9yFT1Ft/fNwI3NrDee8ARyYzNGGPSRTgNH3Xc0gP6xhhjmhBOw6dRWnIxxpgUF/J1\ni6ULSy7GGJPioi2XdLqQ0pKLMcakuHC2JRdjjDEJFrZuMWOMMYkWsgF9Y4wxiWbdYsYYYxLOusWM\nMcYk3I5usfS5/YslF2OMSXGBzAyyszKt5WKMMSaxQlmZNuZijDEmscLZ6fXAMEsuxhiTBsLBgHWL\nGWOMSayQJRdjjDGJFs4O2JiLMcaYxAoHbczFGGNMgoWDASpqLLkYY4xJoFCanS2W1Mcci8hw4D4g\nAExR1ckxyy8G7gZKvVkPqOoUb9l44CZv/u9V9clkxmqMMaksHAxQUZ0+V+gnLbmISAB4EDgdKAGK\nRGSmqi6KKfoPVZ0Qs2574FZgMBABPvTW3ZiseI0xJpXZqcg7DAG+VtViVa0CpgIj41z3TOB1Vd3g\nJZTXgeFJitMYY1Jeul1Emcxuse7ACt90CXBcA+VGicjJwJfAr1V1xW7W7Z6sQI0xJtVFr3OJRCJk\nZGS0dDhNaukB/VlAT1U9Etc6sXEVY4xpQPS2+5U16THukszkUgoU+qYL2DFwD4CqrlfVSm9yCnBM\nvOsaY8yBJBR0h+t06RpLZnIpAvqKSC8RyQbGADP9BUSkm29yBLDY+/s14AwRaSci7YAzvHnGGHNA\nSrcHhiVtzEVVa0RkAi4pBIDHVXWhiEwC5qvqTGCiiIwAaoANwMXeuhtE5HZcggKYpKobkhWrMcak\nuuijjg/45AKgqrOB2THzbvH9fSNw427WfRx4PJnxGWNMutjxNMr0SC4tPaBvjDEmDtFusXS5eaUl\nF2OMSQPp1i1mycUYY9LAjpaLnYpsjDEmQUJpdraYJRdjjEkD0W6xChvQN8YYkyjpdp2LJRdjjEkD\nllyMMcYkXE6W3f7FGGNMgmVmZhAKZtp1LsYYYxIrlEYPDLPkYowxaSIcTJ8HhllyMcaYNJFOjzq2\n5GKMMWkiFAzYmIsxxpjECmdby8UYY0yC2ZiLMcaYhHNni9mNK40xxiRQODtAZZp0iyX1SZQiMhy4\nD/eY4ymqOnk35UYB04FjVXW+iPQEFgPqFfm3ql6RzFiNMSbVhYOZaTPmkrTkIiIB4EHgdKAEKBKR\nmaq6KKZcHnAV8EHMSyxR1YHJis8YY9KNnYrsDAG+VtViVa0CpgIjGyh3O3AnUJHEWIwxJu2Fsm1A\nH6A7sMI3XeLNqyciRwOFqvpyA+v3EpGPReQtETkpiXEaY0xaCAcDVNbUUVcXaelQmtRiA/oikgnc\nA/ymgcWrgB6qOgi4BnhWRPKbMz5jjEk10adRVtSkfuslmcmlFCj0TRd486LygMOBuSKyDDgemCki\ng1W1UlXXA6jqh8ASoF8SYzXGmJRX/0yXNOgaS+bZYkVAXxHphUsqY4ALogtVdTPQMTotInOBa72z\nxToBG1S1VkR6A32B4iTGaowxKS+dHhiWtJaLqtYAE4DXcKcVT1PVhSIySURGNLH6ycACEfkEd4ry\nFaq6IVmxGmNMOghle91iaZBcknqdi6rOBmbHzLtlN2VP9f39PPB8MmMzxph0s6NbLPWv0rcr9I0x\nJk1Yt5gxxpiEC2e7Q7YlF2OMMQkTSqOzxSy5GGNMmoh2i1Ue4Ne5GGOMSaBwtrVcjDHGJJgN6Btj\njEm4kCUXY4wxiZaTlUlGBlRYt5gxxphEycjIIJSVHs90seRijDFpJJxtycUYY0yChYMBu/2LMcaY\nxAoFM9PixpWWXIwxJo1Yt5gxxpiEc91illyMMcYkUChoLRdjjDEJFg4GbMzFGGNMYoWzLbkYY4xJ\nsHCadIsl9THHIjIcuA8IAFNUdfJuyo0CpgPHqup8b96NwCVALTBRVV9LZqzGGJMOQgf6gL6IBIAH\ngbOAAcBYERnQQLk84CrgA9+8AcAY4DBgOPCQ93ppoX///owcOZIRI0Zw3nnn8dFHH+3V6zzxxBOU\nl5fHtWzQoEF79R6NKSkp4eyzz96jdW644QZeffXVXeZ/8MEHXH755YkKzZgDlusWO7AvohwCfK2q\nxapaBUwFRjZQ7nbgTqDCN28kMFVVK1V1KfC193qJddddMGfOzvPmzHHz90EoFGLGjBnMnDmTa665\nhnvuuWevXuepp57abXJpbNnu1NTU7FUcxpjUEQ4GqKqto6Y2tRNMMrvFugMrfNMlwHH+AiJyNFCo\nqi+LyHUx6/47Zt3uCY/w2GNh9GiYNg2GDnWJJTqdINu2bSM/P79+esqUKbzyyitUVVVx+umnM3Hi\nRMrKyrj66qtZvXo1dXV1XHnllXz77besXbuW8ePH07ZtW55++un613jqqacaXHbvvfcyZ84cQqEQ\nDz30EB07duSGG24gOzubxYsXc/TRR3PVVVdx++2389VXX1FTU8OECRM47bTT+Oqrr7jxxhuprq6m\nrq6OP//5z2RlZVFbW8tNN93Exx9/TJcuXXjooYcIhUIsXryYW2+9lfLycnr06MEdd9xBmzZtdtr2\nt99+mzvuuINwOMwxxxyTsH1qzIEsFHRtgoqaOloHUnfYvMUiE5FM4B7gNy0VA0OHukQyejTccsvO\niWYfVFRUMHLkSIYPH85NN93ElVdeCcC8efNYvnw506dPZ8aMGSxcuJCioiLeeecdOnfuzMyZM3np\npZc46aSTuOiii+jcuTNPPvnkTokFaHBZWVkZRx11FDNnzmTw4MFM8yXINWvWMHXqVG688UYeeeQR\njj/+eKZPn85TTz3F3XffTVlZGVOnTuWiiy5ixowZPP/883Tt2hWA5cuXM27cOF5++WXy8vJ47TU3\n9PXb3/6Wa6+9llmzZtGvXz8eeOCBnWKsrKzk5ptv5pFHHuGFF15g3bp1+7RPjTFO/QPDUnzcJZnJ\npRQo9E0XePOi8oDDgbkisgw4HpgpIoPjWDdxhg6FX/wCbr/d/d7HxAI7usVeffVVpkyZwvXXX08k\nEuHdd9/l3Xff5dxzz+W8886juLiYZcuW0a9fP9577z3uvvtu5s+fT15e3h6/ZzAYZKgX++GHH05p\n6Y7dNXz4cAIB94GcN28ejz32GCNHjuTCCy+ksrKSVatWMXDgQB599FH+8pe/sHLlSkKhEAAFBQX0\n798fgMMOO4zS0lK2bt3K1q1bGTLE9VSed955zJ8/f6d4iouLKSgooGfPnmRkZDBixIg935HGmF1E\nHxiW6qcjJ7NbrAjoKyK9cIlhDHBBdKGqbgY6RqdFZC5wrarOF5Fy4FkRuQc4COgL/CcpUc6ZAw8/\nDDff7H4PHZqQBBM1aNAgNm7cyIYNG4hEIlx22WWMGTNml3IvvPACb731Fn/60584/vjjmTBhwh69\nTzAYJCMjA4DMzExqa3d88MLh8E5l77//fnr37r3TvD59+nDUUUcxd+5cLrvsMn73u99RWFhIdnZ2\nfZlAIEBlZeUexWWMSaxwdno8jTJpLRdVrQEmAK8Bi4FpqrpQRCaJSKPVWFVdCEwDFgGvAr9U1cTv\nSf8Yy6RJO7rIYgf598GSJUuora2lbdu2nHjiiTz//PNs374dcN1V69evZ82aNYTDYUaOHMkll1zC\nokWLAGjVqlV92ViNLWvMiSeeyDPPPEMkEgGof68VK1ZQWFjIRRddxLBhw1DV3b5GXl4e+fn59a2V\nGTNmcOyxx+5Upnfv3pSWlvLNN98A8PLLL+9xrMaYXaVLt1hSr3NR1dnA7Jh5t+ym7Kkx038A/pC0\n4ACKinYeY4mOwRQV7VPrJTrmAhCJRLjzzjsJBAKceOKJLFmypL7lkpuby913383y5cu56667yMzM\nJCsri9tuuw2A0aNHc+mll9K5c+ddxl0aW9aYK6+8kjvuuIMRI0ZQV1dHQUEBjz76KK+88gozZswg\nKyuLjh07cvnll7Nt27bdvs6dd95ZP6BfWFjIH//4x52W5+TkMGnSJC677LL6Af29SYbGmJ3VJ5cU\nb7lkRGuw6UZEegJL33jjDQoKClo6HGOMaRbLvt3OsHveYtaEExlwUH7TK8QoKSlh2LBhAL1UdVmi\n44tKasvFGGNMYvXs2IpPbjmdvFCwpUNpVOqeJG2MMaZBqZ5YwJKLMcaYJLDkYowxJuEsuRhjjEk4\nSy7GGGMSzpKLMcaYhLPkYowxJuHS+TqXAMDq1atbOg5jjEkbvmNmUh/AmM7JpRvAuHHjWjoOY4xJ\nR92AJcl68XROLkXAScAqILVvsmOMMakjgEssRcl8k7S9t5gxxpjUZQP6xhhjEi6du8XiIiLDgftw\nTcEpqjo5ZvkVwC9xXWvbgMtUdVGzB0rTsfrKjQKmA8eq6vyGyjSHOPbtxcDd7HiK6AOqOqVZg9wR\nS5P7VkRGA7cBEeBTVb0gtkxziWPf3gtEnwuRC3RW1bbNG2V9LE3F2gN4EmjrlbnBexxHi4gj3oOB\nx4FOwAbgJ6pa0uyBulgeB84G1qrq4Q0sz8Bty/eBMuBiVf2oeaNs2H7dchGRAPAgcBYwABgrIgNi\nij2rqkeo6kDgLuCeZg4TiDtWRCQPuAr4oHkj3CWOuOIF/qGqA72flkosTcYqIn2BG4ETVPUw4Opm\nD3RHLE3Gq6q/ju5X4M/AC80fadyfg5twDwschHsi7UPNG+UOccb738BTqnokMAn4Iy3nCWB4I8vP\nwj2pty9wGfBwM8QUl/06uQBDgK9VtVhVq4CpwEh/AVXd4ptshau1toQmY/XcDtwJVDRncA2IN95U\nEE+sPwceVNWNAKq6tplj9NvTfTsWeK5ZIttVPLFGgOiDR9oAK5sxvljxxDsAeNP7e04Dy5uNqr6N\naz3tzkhcIoyo6r+BtiLSrXmia9z+nly6Ayt80yXevJ2IyC9FZAmu5TKxmWKL1WSsInI0UKiqqfDM\n4Lj2LTBKRBaIyHQRKWye0HYRT6z9gH4i8q6I/NvrOmkp8e7baBdOL3YcDJtbPLHeBvxEREpwT6b9\nVfOE1qB44v0UON/7+zwgT0Q6NENseyPuz0pz29+TS1xU9UFV7QNcj2vCpxwRycR12f2mpWPZA7OA\nnl73wuu4fvdUlYXrWjgV1xJ4TERaZAxjD40BpqtqKp+OPxZ4QlULcGMDT3uf51R1LXCKiHwMnIIb\nM0zl/ZuSUvkfnAilgL+2XMCOweWGTAXOTWpEu9dUrHnA4cBcEVkGHA/MFJHBzRVgjCb3raquV9VK\nb3IKcEwzxRYrns9BCTBTVatVdSnwJS7ZtIQ9+dyOoeW6xCC+WC8BpgGo6vtACOjYLNHtKp7P7UpV\nPd8bI/p/3rxNzRfiHtnTY1yz2d/PFisC+opIL9wOHwPsdAaQiPRV1a+8yR8AX9EyGo1VVTfj+0KK\nyFzg2hY8WyyefdtNVVd5kyOAxc0bYr0mYwVexNWw/yYiHXHdZMXNGuUO8cSLiBwKtAPeb97wdhJP\nrN8Aw4AnRKQ/Lrmsa9Yod4jnc9sR2KCqdbiTPB5v9ijjNxOYICJTgeOAzb7vXIvar1suqloDTABe\nwx3YpqnqQhGZJCIjvGITRGShiHwCXAOMT+FYU0ac8U709u2nuLGsi1M41teA9SKyCDeIe52qrk/h\neMEdGKeqaotdCR1nrL8Bfu59Dp7DnS7bIjHHGe+pgIrIl0AX4A8tESuAiDyHqzyIiJSIyCUicoV3\nCQW4Maxi4GvgMeDKFgp1F3aFvjHGmITbr1suxhhjWoYlF2OMMQlnycUYY0zCWXIxxhiTcJZcjDHG\nJNz+fp2LMQ3ybufxhjfZFXcF9jqgJ7BSVRu6Cee+vN+puOuSzt6DdebSwLVM3t2mB6vqhETGaEwi\nWcvFHJC8uwdE7yr8CHCv9/dAoK6p9UXEKmbGNMK+IMbsKiAijwHfxV3FPVJVy72WxCfAicBzIvIU\nLjH18Na7WlXfFZFTcM/YAHdH4JO9v1uLyHTcbXw+xD0nJCIiw3C3ec/CXUH+C99tcwAQkZ/irhbf\nhLux4k7LjUk11nIxZld9cbffPwx3MB/lW5atqoNV9X9wCeReVT3WKxN9Xs21wC+9ltBJQLk3fxDu\nOTEDgN7ACSISwj2z48eqegQuwfzCH4x3C/XfASfgEltCu+yMSQZLLsbsaqmqfuL9/SFuHCbqH76/\nTwMe8G4dNBPIF5HWwLvAPSIyEWjr3XIE4D+qWuLds+oT73XFe78vvTJPsqOlE3UcMFdV13nPIPkH\nxqQ46xYzZlf+LqdaIOyb3u77OxM4XlVjH9w2WURext1e/l0ROXM3r2vfP7PfspaLMXvvX/gefCUi\nA73ffVT1M1W9EzeGcmgjr6FATxE5xJu+EHgrpswHuOeLdBCRIPCjRG2AMcliycWYvTcRGOw9aXMR\nEL1T7dUi8rmILACqgVd29wJeq+enwD9F5DPcmWqPxJRZhXua4/u4LreWenSBMXGzuyIbY4xJOGu5\nGGOMSThLLsYYYxLOkosxxpiEs+RijDEm4Sy5GGOMSThLLsYYYxLOkosxxpiEs+RijDEm4f4/gafX\nwZo36HwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(800, 3, 128, 128)\n",
      "(800, 1, 128, 128)\n",
      "(800, 1, 128, 128)\n",
      "(800, 1, 128, 128)\n",
      "cost time: 1.7127699851989746\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "439454a3626e407f95fabbfa6af0f7d8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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kpT0PGHVPRKQoCkwW7n5FXuVm1goYDxxdEkFJ0UUiEa46vi2dm9dj+IufMvjx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\nISIicSlZiIhIXEoWIiIS1/8DeCTap6/QfyIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(800, 3, 128, 128)\n",
      "(800, 1, 128, 128)\n",
      "(800, 1, 128, 128)\n",
      "(800, 1, 128, 128)\n",
      "cost time: 1.730250358581543\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "eda19aa98f0d499db736744fccd6590a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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ciksqRcCMSAezMcZ0JMcP7MYzV3yRgD+NqY/8u01vjZGwYaeJZsNOjTEdydrt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\njDEA/D/hgFXTcRnhgAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(800, 3, 128, 128)\n",
      "(800, 1, 128, 128)\n",
      "(800, 1, 128, 128)\n",
      "(800, 1, 128, 128)\n",
      "cost time: 1.720334768295288\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3ba0475f6e7f48d6af869f28a683db45",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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ffzyIt357Asf1y+FP//cto//8LjMW5DeJTnBLFsYY04h65biRUy/+8mjaZAW4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\nGGOMien/A/EEOCThzWT6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(800, 3, 128, 128)\n",
      "(800, 1, 128, 128)\n",
      "(800, 1, 128, 128)\n",
      "(800, 1, 128, 128)\n",
      "cost time: 1.7156085968017578\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3c35e35dbe8f42c2bf6b179206d11b8c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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3thm8+NNRXH5iH6Z/uJrL/voJO4srEh1W3MQyQWwAeoZN53rzopnIgc1Hh7Ku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\nuFba1gRprQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(800, 3, 128, 128)\n",
      "(800, 1, 128, 128)\n",
      "(800, 1, 128, 128)\n",
      "(800, 1, 128, 128)\n",
      "cost time: 1.710137128829956\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4e425153ffe849a6ae1c7337fa21e2a3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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ftovhsOnSST0JmK2qtakOxBhjdlennCC/O+Ngnv/Z0XTpEOKypz/hf54oavOd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\nmLgsQRhjjInLEoQxxpi4LEEYY4yJ6/8D4kDj0j52ISkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(800, 3, 128, 128)\n",
      "(800, 1, 128, 128)\n",
      "(800, 1, 128, 128)\n",
      "(800, 1, 128, 128)\n",
      "cost time: 1.7078707218170166\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1a65ce89ee564c96803bae6cde91e8c1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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GGg+fN4IH5y7nnte/YsWWPdzzP8Poa08RavYSOWkejEnADFWtjncgdamqrqFw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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "MODELS_NAME=os.listdir(\"Results\")\n",
    "\n",
    "for model_name in MODELS_NAME:\n",
    "    if model_name[0]!='M':\n",
    "        continue\n",
    "    #model=U_net_with_residual(16)\n",
    "    model=UNetResNet(encoder_depth=34,num_classes=1,num_filters=16, dropout_2d_ration=0.8,pretrained=False)\n",
    "\n",
    "\n",
    "    state = torch.load('Results/{}'.format(model_name))#,map_location={'cuda:0': 'cpu'})  #\n",
    "    model.load_state_dict(state) #,map_location='cpu'\n",
    "\n",
    "    if torch.cuda.is_available():\n",
    "        model=model.cuda()\n",
    "    \n",
    "    \n",
    "    # ------------------------------------\n",
    "    VAL_USE_SYM_FLAG=False\n",
    "\n",
    "    x_valid_Sym=np.transpose(np.array([np.fliplr(x) for x in x_valid]),(0,3,1,2)).astype(np.float32)\n",
    "    print(x_valid_Sym.shape)\n",
    "\n",
    "    y_valid_Sym=np.zeros_like(x_valid_Sym).astype(np.float32)\n",
    "\n",
    "    salt_ID_dataset_valid_Sym = saltIDDataset(x_valid_Sym, \n",
    "                                          train=True, \n",
    "                                          preprocessed_masks=y_valid_Sym)\n",
    "\n",
    "    batch_size = 32\n",
    "\n",
    "    valid_loader_Sym = torch.utils.data.DataLoader(dataset=salt_ID_dataset_valid_Sym, \n",
    "                                               batch_size=batch_size, \n",
    "                                               shuffle=False)\n",
    "\n",
    "    mask_pred_valid_Sym = []\n",
    "    model.eval()\n",
    "    for images, masks in valid_loader_Sym:\n",
    "        images = Variable(images.cuda())\n",
    "    #     images = Variable(images)\n",
    "        y_preds = model(images)\n",
    "        y_preds=F.sigmoid(y_preds)\n",
    "\n",
    "\n",
    "        for i, _ in enumerate(images):\n",
    "            y_pred = y_preds[i] \n",
    "            y_pred = y_pred.cpu().data.numpy()\n",
    "            mask_pred_valid_Sym.append(y_pred[:,:,::-1])\n",
    "\n",
    "\n",
    "\n",
    "    mask_pred_valid_Sym=np.array(mask_pred_valid_Sym) \n",
    "    print(mask_pred_valid_Sym.shape)\n",
    "\n",
    "    VAL_USE_SYM_FLAG=True\n",
    "\n",
    "\n",
    "\n",
    "    import time\n",
    "    since=time.time()\n",
    "\n",
    "    mask_pred_valid = []\n",
    "    mask_truth_valid=[]\n",
    "\n",
    "    model.eval()\n",
    "    for images, masks in val_loader:\n",
    "        images = Variable(images.cuda())\n",
    "    #     images = Variable(images)\n",
    "        y_preds = model(images)\n",
    "        y_preds=F.sigmoid(y_preds)\n",
    "\n",
    "        for i, _ in enumerate(images):\n",
    "            y_pred = y_preds[i] \n",
    "            y_pred = y_pred.cpu().data.numpy()\n",
    "            mask_pred_valid.append(y_pred)\n",
    "            mask_truth_valid.append(masks[i].numpy())\n",
    "\n",
    "\n",
    "    mask_pred_valid=np.array(mask_pred_valid)\n",
    "    mask_truth_valid=np.array(mask_truth_valid)       \n",
    "    print(mask_truth_valid.shape)\n",
    "    print(mask_truth_valid.shape)\n",
    "\n",
    "    if VAL_USE_SYM_FLAG:\n",
    "        mask_pred_valid=0.5*(mask_pred_valid+mask_pred_valid_Sym)\n",
    "\n",
    "    print(\"cost time:\",time.time()-since)\n",
    "    \n",
    "    import gc\n",
    "    del x_valid_Sym\n",
    "    del y_valid_Sym\n",
    "    del salt_ID_dataset_valid_Sym\n",
    "    del valid_loader_Sym\n",
    "    gc.collect()\n",
    "    \n",
    "    \n",
    "    # Scoring for last model\n",
    "    thresholds = np.linspace(0.3, 1, 51)\n",
    "    ious = np.array([iou_metric_batch(mask_truth_valid, np.int32(mask_pred_valid > threshold)) \n",
    "                     for threshold in tqdm_notebook(thresholds)])\n",
    "\n",
    "    # thresholds = np.linspace(0.3, 0.9,121 )\n",
    "    # ious = np.array([iou_metric_batch(mask_truth_valid, np.int32(mask_pred_valid > threshold)) \n",
    "    #                  for threshold in tqdm_notebook(thresholds)])\n",
    "\n",
    "\n",
    "    threshold_best_index = np.argmax(ious) \n",
    "    iou_best = ious[threshold_best_index]\n",
    "    threshold_best = thresholds[threshold_best_index]\n",
    "\n",
    "    plt.plot(thresholds, ious)\n",
    "    plt.plot(threshold_best, iou_best, \"xr\", label=\"Best threshold\")\n",
    "    plt.xlabel(\"Threshold\")\n",
    "    plt.ylabel(\"IoU\")\n",
    "    plt.title(\"Threshold vs IoU ({}, {},{})\".format(threshold_best, iou_best,model_name))\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b4341c36dd5741dfb987367129667a0f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f59553618d0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qIYiItG9bRxkpIYiItGv5uepUFhERICcnQn4sR01GIiISdCyryUhERCiI52rY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\nRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# # Scoring for last model\n",
    "# thresholds = np.linspace(0.3, 1, 51)\n",
    "# ious = np.array([iou_metric_batch(mask_truth_valid, np.int32(mask_pred_valid > threshold)) \n",
    "#                  for threshold in tqdm_notebook(thresholds)])\n",
    "\n",
    "# # thresholds = np.linspace(0.3, 0.9,121 )\n",
    "# # ious = np.array([iou_metric_batch(mask_truth_valid, np.int32(mask_pred_valid > threshold)) \n",
    "# #                  for threshold in tqdm_notebook(thresholds)])\n",
    "\n",
    "\n",
    "# threshold_best_index = np.argmax(ious) \n",
    "# iou_best = ious[threshold_best_index]\n",
    "# threshold_best = thresholds[threshold_best_index]\n",
    "\n",
    "# plt.plot(thresholds, ious)\n",
    "# plt.plot(threshold_best, iou_best, \"xr\", label=\"Best threshold\")\n",
    "# plt.xlabel(\"Threshold\")\n",
    "# plt.ylabel(\"IoU\")\n",
    "# plt.title(\"Threshold vs IoU ({}, {})\".format(threshold_best, iou_best))\n",
    "# plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 可以完美复现 在众多模型中根据最佳阈值选择最佳模型  \n",
    "\n",
    "* 用os.listdir()获取所有保存的模型的名字 然后写个循环把最佳模型找出来\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# 准备测试数据并测试"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#model=U_net_with_residual(16)\n",
    "model=UNetResNet(encoder_depth=34,num_classes=1,num_filters=16, dropout_2d_ration=0.8,pretrained=False)\n",
    "\n",
    "\n",
    "state = torch.load('Results/{}'.format(\"MM.pt\"))#,map_location={'cuda:0': 'cpu'})  #\n",
    "model.load_state_dict(state) #,map_location='cpu'\n",
    "\n",
    "if torch.cuda.is_available():\n",
    "    model=model.cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 指定最佳阈值\n",
    "threshold_best=0.51"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "used for converting the decoded image to rle mask\n",
    "Fast compared to previous one\n",
    "\"\"\"\n",
    "def rle_encode(im):\n",
    "    '''\n",
    "    im: numpy array, 1 - mask, 0 - background\n",
    "    Returns run length as string formated\n",
    "    '''\n",
    "    pixels = im.flatten(order = 'F')\n",
    "    pixels = np.concatenate([[0], pixels, [0]])\n",
    "    runs = np.where(pixels[1:] != pixels[:-1])[0] + 1\n",
    "    runs[1::2] -= runs[::2]\n",
    "    return ' '.join(str(x) for x in runs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1e0e0ff30cf346e3a0755c2ef0f8d080",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/mnt/ml/anaconda35/lib/python3.6/site-packages/keras_preprocessing/image.py:489: UserWarning: grayscale is deprecated. Please use color_mode = \"grayscale\"\n",
      "  warnings.warn('grayscale is deprecated. Please use '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "x_test = np.array([upsample((np.array(load_img(\"TestData/images/{}.png\".format(idx), grayscale = True))) / 255) for idx in tqdm_notebook(test_df.index)]).reshape(-1, img_size_target, img_size_target, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18000, 128, 128, 1)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(18000, 128, 128, 3)\n"
     ]
    }
   ],
   "source": [
    "mean= [0.485,0.456,0.406]\n",
    "std=  [0.229,0.224,0.225]\n",
    "\n",
    "x_test=np.concatenate((x_test-mean[2]/std[2],\n",
    "                  x_test-mean[1]/std[1],\n",
    "                  x_test-mean[0]/std[0]),axis=-1)\n",
    "\n",
    "print(x_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(18000, 3, 128, 128)\n"
     ]
    }
   ],
   "source": [
    "x_test2=np.transpose(x_test,(0,3,1,2)).astype(np.float32)\n",
    "print(x_test2.shape)\n",
    "y_test2=np.zeros_like(x_test2).astype(np.float32)\n",
    "\n",
    "salt_ID_dataset_test = saltIDDataset(x_test2, \n",
    "                                      train=True, \n",
    "                                      preprocessed_masks=y_test2)\n",
    "\n",
    "batch_size = 32\n",
    "\n",
    "test_loader = torch.utils.data.DataLoader(dataset=salt_ID_dataset_test, \n",
    "                                           batch_size=batch_size, \n",
    "                                           shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(18000, 3, 128, 128)\n"
     ]
    }
   ],
   "source": [
    "x_test_Sym=np.transpose(np.array([np.fliplr(x) for x in x_test]),(0,3,1,2)).astype(np.float32)\n",
    "print(x_test_Sym.shape)\n",
    "\n",
    "y_test_Sym=np.zeros_like(x_test_Sym).astype(np.float32)\n",
    "\n",
    "salt_ID_dataset_test_Sym = saltIDDataset(x_test_Sym, \n",
    "                                      train=True, \n",
    "                                      preprocessed_masks=y_test_Sym)\n",
    "\n",
    "batch_size = 32\n",
    "\n",
    "test_loader_Sym = torch.utils.data.DataLoader(dataset=salt_ID_dataset_test_Sym, \n",
    "                                           batch_size=batch_size, \n",
    "                                           shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/mnt/ml/anaconda35/lib/python3.6/site-packages/torch/nn/functional.py:1749: UserWarning: Default upsampling behavior when mode=bilinear is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details.\n",
      "  \"See the documentation of nn.Upsample for details.\".format(mode))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(18000, 1, 128, 128)\n"
     ]
    }
   ],
   "source": [
    "mask_pred_test_Sym = []\n",
    "model.eval()\n",
    "for images, masks in test_loader_Sym:\n",
    "    images = Variable(images.cuda())\n",
    "#     images = Variable(images)\n",
    "    y_preds = model(images)\n",
    "    y_preds=F.sigmoid(y_preds)\n",
    "    \n",
    "    \n",
    "    for i, _ in enumerate(images):\n",
    "        y_pred = y_preds[i] \n",
    "        y_pred = y_pred.cpu().data.numpy()\n",
    "        mask_pred_test_Sym.append(y_pred[:,:,::-1])\n",
    "       \n",
    "        \n",
    "        \n",
    "mask_pred_test_Sym=np.array(mask_pred_test_Sym) \n",
    "print(mask_pred_test_Sym.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/mnt/ml/anaconda35/lib/python3.6/site-packages/torch/nn/functional.py:1749: UserWarning: Default upsampling behavior when mode=bilinear is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details.\n",
      "  \"See the documentation of nn.Upsample for details.\".format(mode))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(18000, 1, 128, 128)\n",
      "cost time: 39.17193126678467\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "since=time.time()\n",
    "\n",
    "mask_pred_test = []\n",
    "model.eval()\n",
    "for images, masks in test_loader:\n",
    "    images = Variable(images.cuda())\n",
    "#     images = Variable(images)\n",
    "    y_preds = model(images)\n",
    "    y_preds=F.sigmoid(y_preds)\n",
    "    \n",
    "    for i, _ in enumerate(images):\n",
    "        y_pred = y_preds[i] \n",
    "        y_pred = y_pred.cpu().data.numpy()\n",
    "        mask_pred_test.append(y_pred)\n",
    "       \n",
    "\n",
    "        \n",
    "mask_pred_test=np.array(mask_pred_test) \n",
    "print(mask_pred_test.shape)\n",
    "mask_pred_test=0.5*(mask_pred_test+mask_pred_test_Sym)\n",
    "\n",
    "print(\"cost time:\",time.time()-since)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.51"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "threshold_best"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a90cca2e29df4167b0b87152b2c55e79",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Usedtime = 24.021095037460327 s\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "t1 = time.time()\n",
    "pred_dict = {idx: rle_encode(np.round(np.transpose(downsample(np.transpose(mask_pred_test[i],(1,2,0))),(2,0,1)) > threshold_best))\n",
    "             for i, idx in enumerate(tqdm_notebook(test_df.index.values))}\n",
    "t2 = time.time()\n",
    "\n",
    "print(f\"Usedtime = {t2-t1} s\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sub = pd.DataFrame.from_dict(pred_dict,orient='index')\n",
    "sub.index.names = ['id']\n",
    "sub.columns = ['rle_mask']\n",
    "sub.to_csv('submission_CCC.csv')"
   ]
  },
  {
   "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
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# CRF后处理(CPU模式下运行)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 安装pydensecrf   \n",
    "```shell\n",
    "apt-get update\n",
    "source /etc/profile \n",
    "conda install gxx_linux-64\n",
    "pip install pydensecrf\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pydensecrf.densecrf as dcrf\n",
    "from skimage.io import imread, imsave\n",
    "from pydensecrf.utils import unary_from_labels, create_pairwise_bilateral\n",
    "from skimage.color import gray2rgb\n",
    "from skimage.color import rgb2gray\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from tqdm import tqdm_notebook\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def rle_decode(rle_mask):\n",
    "    '''\n",
    "    rle_mask: run-length as string formated (start length)\n",
    "    shape: (height,width) of array to return \n",
    "    Returns numpy array, 1 - mask, 0 - background\n",
    "\n",
    "    '''\n",
    "    s = rle_mask.split()\n",
    "    starts, lengths = [np.asarray(x, dtype=int) for x in (s[0:][::2], s[1:][::2])]\n",
    "    starts -= 1\n",
    "    ends = starts + lengths\n",
    "    img = np.zeros(101*101, dtype=np.uint8)\n",
    "    for lo, hi in zip(starts, ends):\n",
    "        img[lo:hi] = 1\n",
    "    return img.reshape(101,101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2160x1080 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "reading and decoding the submission \n",
    "\n",
    "\"\"\"\n",
    "df = pd.read_csv('submission_CCC.csv')\n",
    "i = 0\n",
    "j = 0\n",
    "plt.figure(figsize=(30,15))\n",
    "plt.subplots_adjust(bottom=0.2, top=0.8, hspace=0.2)  #adjust this to change vertical and horiz. spacings..\n",
    "# Visualizing the predicted outputs\n",
    "while True:\n",
    "    if str(df.loc[i,'rle_mask'])!=str(np.nan):        \n",
    "        decoded_mask = rle_decode(df.loc[i,'rle_mask'])\n",
    "        plt.subplot(1,6,j+1)\n",
    "        plt.imshow(decoded_mask)\n",
    "        plt.title('ID: '+df.loc[i,'id'])\n",
    "        j = j + 1\n",
    "        if j>5:\n",
    "            break\n",
    "    i = i + 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Function to apply CRF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Original_image = Image which has to labelled\n",
    "#Mask image = Which has been labelled by some technique..\n",
    "def crf(original_image, mask_img):\n",
    "    \n",
    "    # Converting annotated image to RGB if it is Gray scale\n",
    "    if(len(mask_img.shape)<3):\n",
    "        mask_img = gray2rgb(mask_img)\n",
    "\n",
    "#     #Converting the annotations RGB color to single 32 bit integer\n",
    "    annotated_label = mask_img[:,:,0] + (mask_img[:,:,1]<<8) + (mask_img[:,:,2]<<16)\n",
    "    \n",
    "#     # Convert the 32bit integer color to 0,1, 2, ... labels.\n",
    "    colors, labels = np.unique(annotated_label, return_inverse=True)\n",
    "\n",
    "    n_labels = 2\n",
    "    \n",
    "    #Setting up the CRF model\n",
    "    d = dcrf.DenseCRF2D(original_image.shape[1], original_image.shape[0], n_labels)\n",
    "\n",
    "    # get unary potentials (neg log probability)\n",
    "    U = unary_from_labels(labels, n_labels, gt_prob=0.7, zero_unsure=False)\n",
    "    d.setUnaryEnergy(U)\n",
    "\n",
    "    # This adds the color-independent term, features are the locations only.\n",
    "    d.addPairwiseGaussian(sxy=(3, 3), compat=3, kernel=dcrf.DIAG_KERNEL,\n",
    "                      normalization=dcrf.NORMALIZE_SYMMETRIC)\n",
    "        \n",
    "    #Run Inference for 10 steps \n",
    "    Q = d.inference(10)\n",
    "\n",
    "    # Find out the most probable class for each pixel.\n",
    "    MAP = np.argmax(Q, axis=0)\n",
    "\n",
    "    return MAP.reshape((original_image.shape[0],original_image.shape[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_path = 'TestData/images/'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Visualizing the effect of applying CRF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bz7ae6C4Ye5h5XYTWswBz/3uc+trXXx4sXVGKxAzLQ2BzHaMO3GRi7JZGWyPmfVBiOGGzYc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\nbMJisfhrSb6eu/ndf2+5XH5tG3HsxWKxOJ3kd3K3A9b7SX5zuVx+f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      "text/plain": [
       "<Figure size 2160x2160 with 24 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "visualizing the effect of applying CRF\n",
    "\n",
    "\"\"\"\n",
    "np.random.seed(100)\n",
    "nImgs = 8\n",
    "i = np.random.randint(1000)\n",
    "j = 1\n",
    "plt.figure(figsize=(30,30))\n",
    "while True:\n",
    "    if str(df.loc[i,'rle_mask'])!=str(np.nan):        \n",
    "        decoded_mask = rle_decode(df.loc[i,'rle_mask'])        \n",
    "        orig_img = imread(test_path+df.loc[i,'id']+'.png')\n",
    "        #Applying CRF on FCN-16 annotated image\n",
    "        crf_output = crf(orig_img,decoded_mask)\n",
    "        plt.subplot(nImgs,4,4*j-3)\n",
    "        plt.imshow(orig_img)\n",
    "        plt.title('Original image')\n",
    "        plt.subplot(nImgs,4,4*j-2)\n",
    "        plt.imshow(np.fliplr(np.rot90(decoded_mask,k=3)))\n",
    "        plt.title('Original Mask')\n",
    "        plt.subplot(nImgs,4,4*j-1)\n",
    "        plt.imshow(np.fliplr(np.rot90(crf_output,k=3)))\n",
    "        plt.title('Mask after CRF')\n",
    "        if j == nImgs:\n",
    "            break\n",
    "        else:\n",
    "            j = j + 1\n",
    "    i = i + 1\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "used for converting the decoded image to rle mask\n",
    "\n",
    "\"\"\"\n",
    "def rle_encode(im):\n",
    "    '''\n",
    "    im: numpy array, 1 - mask, 0 - background\n",
    "    Returns run length as string formated\n",
    "    '''\n",
    "    pixels = im.flatten()\n",
    "    pixels = np.concatenate([[0], pixels, [0]])\n",
    "    runs = np.where(pixels[1:] != pixels[:-1])[0] + 1\n",
    "    runs[1::2] -= runs[::2]\n",
    "    return ' '.join(str(x) for x in runs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f3e34329790e4e3da28b251ee575afc8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Applying CRF on the predicted mask \n",
    "\n",
    "\"\"\"\n",
    "for i in tqdm_notebook(range(df.shape[0])):\n",
    "    if str(df.loc[i,'rle_mask'])!=str(np.nan):        \n",
    "        decoded_mask = rle_decode(df.loc[i,'rle_mask'])        \n",
    "        orig_img = imread(test_path+df.loc[i,'id']+'.png')        \n",
    "        crf_output = crf(orig_img,decoded_mask)\n",
    "        df.loc[i,'rle_mask'] = rle_encode(crf_output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.to_csv('CRF_submission_CCC.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FAQ:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [Dice loss 与 F1 score的关系](https://dev.to/andys0975/what-is-dice-loss-for-image-segmentation-3p85)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}