Nuclie Segmentation - ICNet.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Nuclie Semantic Segmentation - ICNet using Tensorflow 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "e61ef2d8-f315-4f7f-b07e-1de0f4e8441a",
    "_uuid": "1677fddbb95f7545b6540e9201f3339a0fdbfc5d",
    "colab_type": "text",
    "id": "uj4xKkmE0mRZ"
   },
   "source": [
    "# Intro\n",
    "- Dataset used is from Kaggle's Data Science Bowl 2018 - Nuclei Segmentation\n",
    "- The architecture used is [ICNet](https://arxiv.org/abs/1704.08545), which is very common for image segmentation problems such as this.\n",
    "- This notebook has been implemented to run on Google Colab, and it downloads data directly from Kaggle using Kaggle API Token"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 524
    },
    "colab_type": "code",
    "id": "maMYw3GF4O_B",
    "outputId": "bec8195d-7783-442d-aa3b-f6e53112b236"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting tensorflow-gpu==2.0.0-alpha0\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/1a/66/32cffad095253219d53f6b6c2a436637bbe45ac4e7be0244557210dc3918/tensorflow_gpu-2.0.0a0-cp36-cp36m-manylinux1_x86_64.whl (332.1MB)\n",
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      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/13/82/f16063b4eed210dc2ab057930ac1da4fbe1e91b7b051a6c8370b401e6ae7/tf_estimator_nightly-1.14.0.dev2019030115-py2.py3-none-any.whl (411kB)\n",
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      "Collecting google-pasta>=0.1.2 (from tensorflow-gpu==2.0.0-alpha0)\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/64/bb/f1bbc131d6294baa6085a222d29abadd012696b73dcbf8cf1bf56b9f082a/google_pasta-0.1.5-py3-none-any.whl (51kB)\n",
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      "Collecting tb-nightly<1.14.0a20190302,>=1.14.0a20190301 (from tensorflow-gpu==2.0.0-alpha0)\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/a9/51/aa1d756644bf4624c03844115e4ac4058eff77acd786b26315f051a4b195/tb_nightly-1.14.0a20190301-py3-none-any.whl (3.0MB)\n",
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      "Installing collected packages: tf-estimator-nightly, google-pasta, tb-nightly, tensorflow-gpu\n",
      "Successfully installed google-pasta-0.1.5 tb-nightly-1.14.0a20190301 tensorflow-gpu-2.0.0a0 tf-estimator-nightly-1.14.0.dev2019030115\n"
     ]
    }
   ],
   "source": [
    "!pip install tensorflow-gpu==2.0.0-alpha0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "_cell_guid": "c332549b-8d23-4bb5-8497-e7a8eb8b21d2",
    "_uuid": "5c38504af3a84bee68c66d3cde74443c58df422f",
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "aMhG0SPQFMLp"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "import random\n",
    "import warnings\n",
    "import zipfile\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import re\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from tqdm import tqdm\n",
    "from itertools import chain\n",
    "from skimage.io import imread, imshow, imread_collection, concatenate_images\n",
    "from skimage.transform import resize\n",
    "from skimage.morphology import label\n",
    "\n",
    "from tensorflow.keras.models import Model, load_model\n",
    "from tensorflow.keras.layers import Input, Dropout, Lambda, BatchNormalization, ZeroPadding2D, Conv2D, Conv2DTranspose\n",
    "from tensorflow.keras.layers import concatenate, MaxPooling2D, Add, Activation, AveragePooling2D, UpSampling2D\n",
    "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n",
    "from tensorflow.keras import backend as K\n",
    "\n",
    "import tensorflow as tf\n",
    "\n",
    "# Set some parameters\n",
    "IMG_WIDTH = 256\n",
    "IMG_HEIGHT = 256\n",
    "IMG_CHANNELS = 3\n",
    "#TRAIN_PATH = '../input/stage1_train/'\n",
    "#TEST_PATH = '../input/stage1_test/'\n",
    "\n",
    "warnings.filterwarnings('ignore', category=UserWarning, module='skimage')\n",
    "seed = 42\n",
    "random.seed = seed\n",
    "np.random.seed = seed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "rHZvQWN84Ecd",
    "outputId": "7770ce10-01e5-40ab-8498-e8a7d8d835ea"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.0.0-alpha0\n"
     ]
    }
   ],
   "source": [
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 101
    },
    "colab_type": "code",
    "id": "7EWjWggA4EWk",
    "outputId": "8fa2d57e-d3d5-4574-fd95-2c44a09180de"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Is there a GPU available: \n",
      "True\n",
      "Is the Tensor on GPU #0:  \n",
      "True\n",
      "Device name: /job:localhost/replica:0/task:0/device:GPU:0\n"
     ]
    }
   ],
   "source": [
    "x = tf.random.uniform([3, 3])\n",
    "\n",
    "print(\"Is there a GPU available: \"),\n",
    "print(tf.test.is_gpu_available())\n",
    "\n",
    "print(\"Is the Tensor on GPU #0:  \"),\n",
    "print(x.device.endswith('GPU:0'))\n",
    "\n",
    "print(\"Device name: {}\".format((x.device)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "TSyUgzcX4L4I",
    "outputId": "cdf349ff-ffed-4971-9c7b-668622c6ac63"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "print(tf.executing_eagerly())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "QOrtjUBO1NaH"
   },
   "source": [
    "## Importing data from Kaggle to Google Colab\n",
    "- Follow steps from the following link: [Link](https://www.kaggle.com/general/51898)\n",
    "- Importing dataset from following competition: [Link](https://www.kaggle.com/c/data-science-bowl-2018/data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "2Eh48_9D7uKE"
   },
   "outputs": [],
   "source": [
    "setup_kaggle_api = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 41,
     "resources": {
      "http://localhost:8080/nbextensions/google.colab/files.js": {
       "data": 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",
       "headers": [
        [
         "content-type",
         "application/javascript"
        ]
       ],
       "ok": true,
       "status": 200,
       "status_text": ""
      }
     }
    },
    "colab_type": "code",
    "id": "IYeu6x--1McV",
    "outputId": "eb8fd465-9107-4ed9-94a5-c63eb33afb7b"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "     <input type=\"file\" id=\"files-aca9de00-7ad5-499a-9373-f5f06381fdd0\" name=\"files[]\" multiple disabled />\n",
       "     <output id=\"result-aca9de00-7ad5-499a-9373-f5f06381fdd0\">\n",
       "      Upload widget is only available when the cell has been executed in the\n",
       "      current browser session. Please rerun this cell to enable.\n",
       "      </output>\n",
       "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "if setup_kaggle_api == 1:\n",
    "  from google.colab import files\n",
    "\n",
    "  #Upload Kaggle API json\n",
    "  files.upload()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 151
    },
    "colab_type": "code",
    "id": "hcWp_rpe16Td",
    "outputId": "434272b9-fe99-4588-ceb4-eff24a6467c3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kaggle.json\n",
      "\u001b[K    100% |████████████████████████████████| 81kB 6.8MB/s \n",
      "\u001b[K    100% |████████████████████████████████| 5.3MB 5.8MB/s \n",
      "\u001b[K    100% |████████████████████████████████| 92kB 23.8MB/s \n",
      "\u001b[K    100% |████████████████████████████████| 51kB 20.1MB/s \n",
      "\u001b[?25h  Building wheel for kaggle-cli (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25h  Building wheel for cliff (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25h  Building wheel for pyperclip (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25h"
     ]
    }
   ],
   "source": [
    "if setup_kaggle_api == 1:\n",
    "  !mkdir -p ~/.kaggle\n",
    "  !cp kaggle.json ~/.kaggle/\n",
    "  !chmod 600 ~/.kaggle/kaggle.json\n",
    "  !ls ~/.kaggle\n",
    "  !pip install -q kaggle\n",
    "  !pip install -q kaggle-cli"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 440
    },
    "colab_type": "code",
    "id": "sSq1p7ue1_ap",
    "outputId": "fc929576-3bb2-4ea2-d191-a75a26b29271"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total 4\n",
      "-rw------- 1 root root 66 Apr  9 14:40 kaggle.json\n",
      "ref                                                          title                                                size  lastUpdated          downloadCount  \n",
      "-----------------------------------------------------------  --------------------------------------------------  -----  -------------------  -------------  \n",
      "ronitf/heart-disease-uci                                     Heart Disease UCI                                     3KB  2018-06-25 11:33:56          25711  \n",
      "karangadiya/fifa19                                           FIFA 19 complete player dataset                       2MB  2018-12-21 03:52:59          20607  \n",
      "mohansacharya/graduate-admissions                            Graduate Admissions                                   9KB  2018-12-28 10:07:14          17421  \n",
      "iarunava/cell-images-for-detecting-malaria                   Malaria Cell Images Dataset                         337MB  2018-12-05 05:40:21           4100  \n",
      "russellyates88/suicide-rates-overview-1985-to-2016           Suicide Rates Overview 1985 to 2016                 396KB  2018-12-01 19:18:25          19135  \n",
      "bigquery/crypto-ethereum-classic                             Ethereum Classic Blockchain                          70GB  2019-03-20 23:21:25              0  \n",
      "lava18/google-play-store-apps                                Google Play Store Apps                                2MB  2019-02-03 13:55:47          47848  \n",
      "safegraph/census-block-group-american-community-survey-data  Census Block Group American Community Survey Data     2GB  2018-12-22 00:29:56            673  \n",
      "fivethirtyeight/fivethirtyeight-comic-characters-dataset     FiveThirtyEight Comic Characters Dataset            577KB  2019-03-26 15:01:15           2204  \n",
      "vjchoudhary7/customer-segmentation-tutorial-in-python        Mall Customer Segmentation Data                       2KB  2018-08-11 07:23:02           6414  \n",
      "jessicali9530/stanford-dogs-dataset                          Stanford Dogs Dataset                               735MB  2019-02-13 05:45:25           2297  \n",
      "jessicali9530/celeba-dataset                                 CelebFaces Attributes (CelebA) Dataset                1GB  2018-06-01 20:08:48           6153  \n",
      "jutrera/stanford-car-dataset-by-classes-folder               Stanford Car Dataset by classes folder                2GB  2018-07-02 07:35:45           2670  \n",
      "noriuk/us-education-datasets-unification-project             U.S. Education Datasets: Unification Project         85MB  2019-03-02 18:41:52           3013  \n",
      "pavansanagapati/urban-sound-classification                   Urban Sound Classification                            6GB  2018-06-16 13:44:36           2160  \n",
      "rmisra/news-headlines-dataset-for-sarcasm-detection          News Headlines Dataset For Sarcasm Detection          2MB  2018-06-09 22:14:56           2408  \n",
      "cityofLA/los-angeles-parking-citations                       Los Angeles Parking Citations                       257MB  2019-04-08 22:19:48           2910  \n",
      "safegraph/visit-patterns-by-census-block-group               Consumer & Visitor Insights For Neighborhoods        66MB  2018-12-19 21:31:50           1241  \n",
      "anokas/kuzushiji                                             Kuzushiji-MNIST                                     318MB  2018-12-17 01:19:31            761  \n",
      "xvivancos/barcelona-data-sets                                Barcelona data sets                                   1MB  2018-12-13 14:16:53           3475  \n"
     ]
    }
   ],
   "source": [
    "#Verify\n",
    "!ls -l ~/.kaggle\n",
    "\n",
    "!kaggle datasets list\n",
    "\n",
    "#!cat ~/.kaggle/kaggle.json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 386
    },
    "colab_type": "code",
    "id": "6f7H_H1G3SXa",
    "outputId": "83da7b3a-1a19-4440-9ec8-26cb06d9a16c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ref                                                deadline             category            reward  teamCount  userHasEntered  \n",
      "-------------------------------------------------  -------------------  ---------------  ---------  ---------  --------------  \n",
      "digit-recognizer                                   2030-01-01 00:00:00  Getting Started  Knowledge       2674           False  \n",
      "titanic                                            2030-01-01 00:00:00  Getting Started  Knowledge      10991            True  \n",
      "house-prices-advanced-regression-techniques        2030-01-01 00:00:00  Getting Started  Knowledge       4437           False  \n",
      "imagenet-object-localization-challenge             2029-12-31 07:00:00  Research         Knowledge         36           False  \n",
      "competitive-data-science-predict-future-sales      2019-12-31 23:59:00  Playground           Kudos       2705           False  \n",
      "two-sigma-financial-news                           2019-07-15 23:59:00  Featured          $100,000       2927           False  \n",
      "aerial-cactus-identification                       2019-07-08 23:59:00  Playground       Knowledge        276           False  \n",
      "jigsaw-unintended-bias-in-toxicity-classification  2019-06-26 23:59:00  Featured           $65,000        560           False  \n",
      "inaturalist-2019-fgvc6                             2019-06-10 23:59:00  Research             Kudos         31           False  \n",
      "freesound-audio-tagging-2019                       2019-06-10 11:59:00  Research            $5,000        109           False  \n",
      "iwildcam-2019-fgvc6                                2019-06-07 23:59:00  Playground           Kudos         70           False  \n",
      "imet-2019-fgvc6                                    2019-06-04 23:59:00  Research             Kudos        159           False  \n",
      "LANL-Earthquake-Prediction                         2019-06-03 23:59:00  Research           $50,000       2394           False  \n",
      "landmark-recognition-2019                          2019-06-03 23:59:00  Research           $25,000         12           False  \n",
      "landmark-retrieval-2019                            2019-06-03 23:59:00  Research           $25,000         15           False  \n",
      "tmdb-box-office-prediction                         2019-05-30 23:59:00  Playground       Knowledge        633           False  \n",
      "dont-overfit-ii                                    2019-05-07 23:59:00  Playground            Swag       1542           False  \n",
      "ciphertext-challenge-ii                            2019-04-25 23:59:00  Playground            Swag         31           False  \n",
      "data-science-for-good-careervillage                2019-04-23 23:59:00  Analytics          $15,000          0           False  \n",
      "gendered-pronoun-resolution                        2019-04-22 23:59:00  Research           $25,000        667           False  \n"
     ]
    }
   ],
   "source": [
    "!kaggle competitions list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 67
    },
    "colab_type": "code",
    "id": "QK2ssZXO3VX-",
    "outputId": "4acea6a2-151e-4c12-f967-c6595a3a2b51"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ref                     deadline             category    reward  teamCount  userHasEntered  \n",
      "----------------------  -------------------  --------  --------  ---------  --------------  \n",
      "data-science-bowl-2018  2018-04-16 23:59:00  Featured  $100,000       3634            True  \n"
     ]
    }
   ],
   "source": [
    "!kaggle competitions list -s data-science-bowl-2018"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 487
    },
    "colab_type": "code",
    "id": "xSe6ULYh5GSE",
    "outputId": "d178ad46-8a05-4f6b-92ff-b19a264dbce6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading stage1_sample_submission.csv.zip to competitions\n",
      "\r",
      "  0% 0.00/2.62k [00:00<?, ?B/s]\n",
      "100% 2.62k/2.62k [00:00<00:00, 2.44MB/s]\n",
      "Downloading stage1_train_labels.csv.zip to competitions\n",
      "  0% 0.00/2.67M [00:00<?, ?B/s]\n",
      "100% 2.67M/2.67M [00:00<00:00, 73.7MB/s]\n",
      "Downloading stage1_test.zip to competitions\n",
      " 55% 5.00M/9.10M [00:00<00:00, 16.5MB/s]\n",
      "100% 9.10M/9.10M [00:00<00:00, 26.0MB/s]\n",
      "Downloading stage1_train.zip to competitions\n",
      " 97% 77.0M/79.1M [00:01<00:00, 37.8MB/s]\n",
      "100% 79.1M/79.1M [00:01<00:00, 60.6MB/s]\n",
      "Downloading stage2_sample_submission_final.csv.zip to competitions\n",
      "  0% 0.00/112k [00:00<?, ?B/s]\n",
      "100% 112k/112k [00:00<00:00, 118MB/s]\n",
      "Downloading stage2_test_final.zip to competitions\n",
      " 99% 274M/276M [00:05<00:00, 56.9MB/s]\n",
      "100% 276M/276M [00:05<00:00, 55.3MB/s]\n",
      "Downloading stage1_solution.csv.zip to competitions\n",
      "  0% 0.00/386k [00:00<?, ?B/s]\n",
      "100% 386k/386k [00:00<00:00, 118MB/s]\n",
      "Extracting files from stage1_train.zip to /content/competitions/stage1_train/\n",
      "Extracting files from stage1_test.zip to /content/competitions/stage1_test/\n",
      "Extracting files from stage1_sample_submission.csv.zip to /content/competitions/\n",
      "Extracting files from stage1_solution.csv.zip to /content/competitions/\n",
      "Extracting files from stage1_train_labels.csv.zip to /content/competitions/\n",
      "Extracting files from stage2_test_final.zip to /content/competitions/stage2_test_final/\n",
      "Extracting files from stage2_sample_submission_final.csv.zip to /content/competitions/\n"
     ]
    }
   ],
   "source": [
    "if setup_kaggle_api == 1:\n",
    "  !mkdir -p ~/competitions/\n",
    "  !kaggle competitions download -c data-science-bowl-2018 -p competitions/\n",
    "  path = '/content/competitions/'\n",
    "  for file in os.listdir('/content/competitions/'):\n",
    "  \n",
    "    #Extract images to corresponding folders\n",
    "    if '.zip' in file and 'csv' not in file:\n",
    "\n",
    "      file_folder = re.sub('\\.zip$', '/', file)\n",
    "      print(\"Extracting files from {} to {}\".format(file,path + file_folder))\n",
    "      \n",
    "      zip_ref = zipfile.ZipFile(path + file, 'r')\n",
    "      zip_ref.extractall(path + file_folder)\n",
    "      zip_ref.close()\n",
    "    \n",
    "    #Extract CSVs to path\n",
    "    if '.zip' in file and 'csv' in file:\n",
    "      \n",
    "      file_folder = re.sub('\\.zip$', '/', file)\n",
    "      print(\"Extracting files from {} to {}\".format(file,path))\n",
    "      \n",
    "      zip_ref = zipfile.ZipFile(path + file, 'r')\n",
    "      zip_ref.extractall(path)\n",
    "      zip_ref.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "59c4a25d-645f-4b74-9c53-145ac78cc481",
    "_uuid": "875af74f980236825de3a650825b46e25632422c",
    "colab_type": "text",
    "id": "vfy5kFhf0mRk"
   },
   "source": [
    "# Get the data\n",
    "Let's first import all the images and associated masks. I downsample both the training and test images to keep things light and manageable, but we need to keep a record of the original sizes of the test images to upsample our predicted masks and create correct run-length encodings later on. There are definitely better ways to handle this, but it works fine for now!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "n6uq0mx291w0"
   },
   "outputs": [],
   "source": [
    "TRAIN_PATH = '/content/competitions/stage1_train/'\n",
    "TEST_PATH = '/content/competitions/stage1_test/'\n",
    "\n",
    "dir_path = ''\n",
    "\n",
    "\n",
    "# Get train and test IDs\n",
    "train_ids = next(os.walk(TRAIN_PATH))[1]\n",
    "test_ids = next(os.walk(TEST_PATH))[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "F1a7mQPb0mRl",
    "outputId": "6a16818a-76d1-4f16-a80d-a31109a23e7c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Getting and resizing train images and masks ... \n"
     ]
    }
   ],
   "source": [
    "# Get and resize train images and masks\n",
    "X_train = np.zeros((len(train_ids), IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS), dtype=np.uint8)\n",
    "Y_train = np.zeros((len(train_ids), IMG_HEIGHT, IMG_WIDTH, 1), dtype=np.bool)\n",
    "print('Getting and resizing train images and masks ... ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "_cell_guid": "ca0cc34b-c26f-41ee-88d7-975aebdb634e",
    "_uuid": "9e389ba8bdb5b6fc03b231b6a6c84a8bde634053",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 84
    },
    "colab_type": "code",
    "id": "fePyIWU30mRo",
    "outputId": "756fd7bf-c1be-421c-aae5-678f952bda06"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 670/670 [03:05<00:00,  3.61it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Getting and resizing test images ... \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "100%|██████████| 65/65 [00:01<00:00, 41.72it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "sys.stdout.flush()\n",
    "for n, id_ in tqdm(enumerate(train_ids), total=len(train_ids)):\n",
    "    \n",
    "    #Read image files iteratively\n",
    "    path = TRAIN_PATH + id_\n",
    "    img = imread(dir_path + path + '/images/' + id_ + '.png')[:,:,:IMG_CHANNELS]\n",
    "    img = resize(img, (IMG_HEIGHT, IMG_WIDTH), mode='constant', preserve_range=True)\n",
    "    \n",
    "    #Append image to numpy array for train dataset\n",
    "    X_train[n] = img\n",
    "    \n",
    "    #Read corresponding mask files iteratively\n",
    "    mask = np.zeros((IMG_HEIGHT, IMG_WIDTH, 1), dtype=np.bool)\n",
    "    \n",
    "    #Looping through masks\n",
    "    for mask_file in next(os.walk(path + '/masks/'))[2]:\n",
    "        \n",
    "        #Read individual masks\n",
    "        mask_ = imread(dir_path + path + '/masks/' + mask_file)\n",
    "        \n",
    "        #Expand individual mask dimensions\n",
    "        mask_ = np.expand_dims(resize(mask_, (IMG_HEIGHT, IMG_WIDTH), mode='constant', \n",
    "                                      preserve_range=True), axis=-1)\n",
    "        \n",
    "        #Overlay individual masks to create a final mask for corresponding image\n",
    "        mask = np.maximum(mask, mask_)\n",
    "    \n",
    "    #Append mask to numpy array for train dataset\n",
    "    Y_train[n] = mask\n",
    "\n",
    "# Get and resize test images\n",
    "X_test = np.zeros((len(test_ids), IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS), dtype=np.uint8)\n",
    "sizes_test = []\n",
    "print('Getting and resizing test images ... ')\n",
    "sys.stdout.flush()\n",
    "for n, id_ in tqdm(enumerate(test_ids), total=len(test_ids)):\n",
    "    path = TEST_PATH + id_\n",
    "    \n",
    "    #Read images iteratively\n",
    "    img = imread(dir_path + path + '/images/' + id_ + '.png')[:,:,:IMG_CHANNELS]\n",
    "    \n",
    "    #Get test size\n",
    "    sizes_test.append([img.shape[0], img.shape[1]])\n",
    "    \n",
    "    #Resize image to match training data\n",
    "    img = resize(img, (IMG_HEIGHT, IMG_WIDTH), mode='constant', preserve_range=True)\n",
    "    \n",
    "    #Append image to numpy array for test dataset\n",
    "    X_test[n] = img\n",
    "\n",
    "print('Done!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "c0523b03-1fc5-4505-a1b8-eb35ee617c8a",
    "_uuid": "d4f8327802a1ec6139ce0585953986272ba62ce1",
    "colab_type": "text",
    "id": "6SJI_ogG0mRs"
   },
   "source": [
    "Let's see if things look all right by drawing some random images and their associated masks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "_cell_guid": "88829b53-50ce-45d9-9540-77dd7384ad4c",
    "_uuid": "283af26f0860b7069bdfd133c746e5d20971542c",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 889
    },
    "colab_type": "code",
    "id": "JY_FAGeA0mRs",
    "outputId": "2a6ed97e-df31-4e39-ad39-7379d5b72412"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/skimage/io/_plugins/matplotlib_plugin.py:51: 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",
      "  out_of_range_float = (np.issubdtype(image.dtype, np.float) and\n",
      "/usr/local/lib/python3.6/dist-packages/matplotlib/axes/_base.py:1334: MatplotlibDeprecationWarning: \n",
      "box-forced\n",
      "  \"2.2\", \"box-forced\", obj_type=\"keyword argument\")\n"
     ]
    },
    {
     "data": {
      "image/png": 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H8hjNtX/yySfxwgsvhA9/f38/ihv0+/1wXrPZxMHBwVzdIC1xrf1Sq5HCiPv7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\nKASBQCAQnIUoBIFAIBAAAP4frL5xSb4OdgQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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rpVjaEGAkaPFw59xFfl1Q/fbNQx9Zl+WgQEBALNx6VpisnWwqyzcZiiVLjchOVBkBACQR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\noAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check if training data looks all right\n",
    "ix = random.randint(0, len(train_ids))\n",
    "imshow(X_train[ix])\n",
    "plt.show()\n",
    "imshow(np.squeeze(Y_train[ix]))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "c3b9f148-1dba-4b6a-981b-6cdbf394fc3c",
    "_uuid": "986488a4c5223576be370e224426a30431911eb2",
    "colab_type": "text",
    "id": "QoWSRbA50mR1"
   },
   "source": [
    "# Build and train our neural network\n",
    "ICNet model based on [ICNet for Real-Time Semantic Segmentation on High-Resolution Images](https://arxiv.org/abs/1704.08545) used in [this repo](https://github.com/aitorzip/Keras-ICNet)\n",
    "\n",
    "![](https://hszhao.github.io/projects/icnet/figures/icnet.png)\n",
    "\n",
    "[Image Credits](https://hszhao.github.io/projects/icnet/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "_cell_guid": "c1dbc57c-b497-4ccb-b077-2053203ab7ed",
    "_uuid": "0aa97d66c29f45dfac9b0f45fcf74ba0e778ba5d",
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "glfrQpPJ0mR2"
   },
   "outputs": [],
   "source": [
    "def build_bn(width, height, n_classes, weights_path=None, train=False):\n",
    "    inp = Input(shape=(height, width, 3))\n",
    "    x = Lambda(lambda x: (x - 127.5)/255.0)(inp)\n",
    "\n",
    "    # (1/2)\n",
    "    y = Lambda(lambda x: tf.compat.v1.image.resize_bilinear(x, size=(int(x.shape[1])//2, int(x.shape[2])//2)), name='data_sub2')(x)\n",
    "    y = Conv2D(32, 3, strides=2, padding='same', activation='relu', name='conv1_1_3x3_s2')(y)\n",
    "    y = BatchNormalization(name='conv1_1_3x3_s2_bn')(y)\n",
    "    y = Conv2D(32, 3, padding='same', activation='relu', name='conv1_2_3x3')(y)\n",
    "    y = BatchNormalization(name='conv1_2_3x3_s2_bn')(y)\n",
    "    y = Conv2D(64, 3, padding='same', activation='relu', name='conv1_3_3x3')(y)\n",
    "    y = BatchNormalization(name='conv1_3_3x3_bn')(y)\n",
    "    y_ = MaxPooling2D(pool_size=3, strides=2, name='pool1_3x3_s2')(y)\n",
    "    \n",
    "    y = Conv2D(128, 1, name='conv2_1_1x1_proj')(y_)\n",
    "    y = BatchNormalization(name='conv2_1_1x1_proj_bn')(y)\n",
    "    y_ = Conv2D(32, 1, activation='relu', name='conv2_1_1x1_reduce')(y_)\n",
    "    y_ = BatchNormalization(name='conv2_1_1x1_reduce_bn')(y_)\n",
    "    y_ = ZeroPadding2D(name='padding1')(y_)\n",
    "    y_ = Conv2D(32, 3, activation='relu', name='conv2_1_3x3')(y_)\n",
    "    y_ = BatchNormalization(name='conv2_1_3x3_bn')(y_)\n",
    "    y_ = Conv2D(128, 1, name='conv2_1_1x1_increase')(y_)\n",
    "    y_ = BatchNormalization(name='conv2_1_1x1_increase_bn')(y_)\n",
    "    y = Add(name='conv2_1')([y,y_])\n",
    "    y_ = Activation('relu', name='conv2_1/relu')(y)\n",
    "\n",
    "    y = Conv2D(32, 1, activation='relu', name='conv2_2_1x1_reduce')(y_)\n",
    "    y = BatchNormalization(name='conv2_2_1x1_reduce_bn')(y)\n",
    "    y = ZeroPadding2D(name='padding2')(y)\n",
    "    y = Conv2D(32, 3, activation='relu', name='conv2_2_3x3')(y)\n",
    "    y = BatchNormalization(name='conv2_2_3x3_bn')(y)\n",
    "    y = Conv2D(128, 1, name='conv2_2_1x1_increase')(y)\n",
    "    y = BatchNormalization(name='conv2_2_1x1_increase_bn')(y)\n",
    "    y = Add(name='conv2_2')([y,y_])\n",
    "    y_ = Activation('relu', name='conv2_2/relu')(y)\n",
    "\n",
    "    y = Conv2D(32, 1, activation='relu', name='conv2_3_1x1_reduce')(y_)\n",
    "    y = BatchNormalization(name='conv2_3_1x1_reduce_bn')(y)\n",
    "    y = ZeroPadding2D(name='padding3')(y)\n",
    "    y = Conv2D(32, 3, activation='relu', name='conv2_3_3x3')(y)\n",
    "    y = BatchNormalization(name='conv2_3_3x3_bn')(y)\n",
    "    y = Conv2D(128, 1, name='conv2_3_1x1_increase')(y)\n",
    "    y = BatchNormalization(name='conv2_3_1x1_increase_bn')(y)\n",
    "    y = Add(name='conv2_3')([y,y_])\n",
    "    y_ = Activation('relu', name='conv2_3/relu')(y)\n",
    "\n",
    "    y = Conv2D(256, 1, strides=2, name='conv3_1_1x1_proj')(y_)\n",
    "    y = BatchNormalization(name='conv3_1_1x1_proj_bn')(y)\n",
    "    y_ = Conv2D(64, 1, strides=2, activation='relu', name='conv3_1_1x1_reduce')(y_)\n",
    "    y_ = BatchNormalization(name='conv3_1_1x1_reduce_bn')(y_) \n",
    "    y_ = ZeroPadding2D(name='padding4')(y_)\n",
    "    y_ = Conv2D(64, 3, activation='relu', name='conv3_1_3x3')(y_)\n",
    "    y_ = BatchNormalization(name='conv3_1_3x3_bn')(y_)\n",
    "    y_ = Conv2D(256, 1, name='conv3_1_1x1_increase')(y_)\n",
    "    y_ = BatchNormalization(name='conv3_1_1x1_increase_bn')(y_)\n",
    "    y = Add(name='conv3_1')([y,y_])\n",
    "    z = Activation('relu', name='conv3_1/relu')(y)\n",
    "\n",
    "    # (1/4)\n",
    "    y_ = Lambda(lambda x: tf.compat.v1.image.resize_bilinear(x, size=(int(x.shape[1])//2, int(x.shape[2])//2)), name='conv3_1_sub4')(z)\n",
    "    y = Conv2D(64, 1, activation='relu', name='conv3_2_1x1_reduce')(y_)\n",
    "    y = BatchNormalization(name='conv3_2_1x1_reduce_bn')(y)\n",
    "    y = ZeroPadding2D(name='padding5')(y)\n",
    "    y = Conv2D(64, 3, activation='relu', name='conv3_2_3x3')(y)\n",
    "    y = BatchNormalization(name='conv3_2_3x3_bn')(y)\n",
    "    y = Conv2D(256, 1, name='conv3_2_1x1_increase')(y)\n",
    "    y = BatchNormalization(name='conv3_2_1x1_increase_bn')(y)\n",
    "    y = Add(name='conv3_2')([y,y_])\n",
    "    y_ = Activation('relu', name='conv3_2/relu')(y)\n",
    "\n",
    "    y = Conv2D(64, 1, activation='relu', name='conv3_3_1x1_reduce')(y_)\n",
    "    y = BatchNormalization(name='conv3_3_1x1_reduce_bn')(y)\n",
    "    y = ZeroPadding2D(name='padding6')(y)\n",
    "    y = Conv2D(64, 3, activation='relu', name='conv3_3_3x3')(y)\n",
    "    y = BatchNormalization(name='conv3_3_3x3_bn')(y)\n",
    "    y = Conv2D(256, 1, name='conv3_3_1x1_increase')(y)\n",
    "    y = BatchNormalization(name='conv3_3_1x1_increase_bn')(y)\n",
    "    y = Add(name='conv3_3')([y,y_])\n",
    "    y_ = Activation('relu', name='conv3_3/relu')(y)\n",
    "\n",
    "    y = Conv2D(64, 1, activation='relu', name='conv3_4_1x1_reduce')(y_)\n",
    "    y = BatchNormalization(name='conv3_4_1x1_reduce_bn')(y)\n",
    "    y = ZeroPadding2D(name='padding7')(y)\n",
    "    y = Conv2D(64, 3, activation='relu', name='conv3_4_3x3')(y)\n",
    "    y = BatchNormalization(name='conv3_4_3x3_bn')(y)\n",
    "    y = Conv2D(256, 1, name='conv3_4_1x1_increase')(y)\n",
    "    y = BatchNormalization(name='conv3_4_1x1_increase_bn')(y)\n",
    "    y = Add(name='conv3_4')([y,y_])\n",
    "    y_ = Activation('relu', name='conv3_4/relu')(y)\n",
    "\n",
    "    y = Conv2D(512, 1, name='conv4_1_1x1_proj')(y_)\n",
    "    y = BatchNormalization(name='conv4_1_1x1_proj_bn')(y)\n",
    "    y_ = Conv2D(128, 1, activation='relu', name='conv4_1_1x1_reduce')(y_)\n",
    "    y_ = BatchNormalization(name='conv4_1_1x1_reduce_bn')(y_)\n",
    "    y_ = ZeroPadding2D(padding=2, name='padding8')(y_)\n",
    "    y_ = Conv2D(128, 3, dilation_rate=2, activation='relu', name='conv4_1_3x3')(y_)\n",
    "    y_ = BatchNormalization(name='conv4_1_3x3_bn')(y_)\n",
    "    y_ = Conv2D(512, 1, name='conv4_1_1x1_increase')(y_)\n",
    "    y_ = BatchNormalization(name='conv4_1_1x1_increase_bn')(y_)\n",
    "    y = Add(name='conv4_1')([y,y_])\n",
    "    y_ = Activation('relu', name='conv4_1/relu')(y)\n",
    "\n",
    "    y = Conv2D(128, 1, activation='relu', name='conv4_2_1x1_reduce')(y_)\n",
    "    y = BatchNormalization(name='conv4_2_1x1_reduce_bn')(y)\n",
    "    y = ZeroPadding2D(padding=2, name='padding9')(y)\n",
    "    y = Conv2D(128, 3, dilation_rate=2, activation='relu', name='conv4_2_3x3')(y)\n",
    "    y = BatchNormalization(name='conv4_2_3x3_bn')(y)\n",
    "    y = Conv2D(512, 1, name='conv4_2_1x1_increase')(y)\n",
    "    y = BatchNormalization(name='conv4_2_1x1_increase_bn')(y)\n",
    "    y = Add(name='conv4_2')([y,y_])\n",
    "    y_ = Activation('relu', name='conv4_2/relu')(y)\n",
    "\n",
    "    y = Conv2D(128, 1, activation='relu', name='conv4_3_1x1_reduce')(y_)\n",
    "    y = BatchNormalization(name='conv4_3_1x1_reduce_bn')(y)\n",
    "    y = ZeroPadding2D(padding=2, name='padding10')(y)\n",
    "    y = Conv2D(128, 3, dilation_rate=2, activation='relu', name='conv4_3_3x3')(y)\n",
    "    y = BatchNormalization(name='conv4_3_3x3_bn')(y)\n",
    "    y = Conv2D(512, 1, name='conv4_3_1x1_increase')(y)\n",
    "    y = BatchNormalization(name='conv4_3_1x1_increase_bn')(y)\n",
    "    y = Add(name='conv4_3')([y,y_])\n",
    "    y_ = Activation('relu', name='conv4_3/relu')(y)\n",
    "\n",
    "    y = Conv2D(128, 1, activation='relu', name='conv4_4_1x1_reduce')(y_)\n",
    "    y = BatchNormalization(name='conv4_4_1x1_reduce_bn')(y)\n",
    "    y = ZeroPadding2D(padding=2, name='padding11')(y)\n",
    "    y = Conv2D(128, 3, dilation_rate=2, activation='relu', name='conv4_4_3x3')(y)\n",
    "    y = BatchNormalization(name='conv4_4_3x3_bn')(y)\n",
    "    y = Conv2D(512, 1, name='conv4_4_1x1_increase')(y)\n",
    "    y = BatchNormalization(name='conv4_4_1x1_increase_bn')(y)\n",
    "    y = Add(name='conv4_4')([y,y_])\n",
    "    y_ = Activation('relu', name='conv4_4/relu')(y)\n",
    "\n",
    "    y = Conv2D(128, 1, activation='relu', name='conv4_5_1x1_reduce')(y_)\n",
    "    y = BatchNormalization(name='conv4_5_1x1_reduce_bn')(y)\n",
    "    y = ZeroPadding2D(padding=2, name='padding12')(y)\n",
    "    y = Conv2D(128, 3, dilation_rate=2, activation='relu', name='conv4_5_3x3')(y)\n",
    "    y = BatchNormalization(name='conv4_5_3x3_bn')(y)\n",
    "    y = Conv2D(512, 1, name='conv4_5_1x1_increase')(y)\n",
    "    y = BatchNormalization(name='conv4_5_1x1_increase_bn')(y)\n",
    "    y = Add(name='conv4_5')([y,y_])\n",
    "    y_ = Activation('relu', name='conv4_5/relu')(y)\n",
    "\n",
    "    y = Conv2D(128, 1, activation='relu', name='conv4_6_1x1_reduce')(y_)\n",
    "    y = BatchNormalization(name='conv4_6_1x1_reduce_bn')(y)\n",
    "    y = ZeroPadding2D(padding=2, name='padding13')(y)\n",
    "    y = Conv2D(128, 3, dilation_rate=2, activation='relu', name='conv4_6_3x3')(y)\n",
    "    y = BatchNormalization(name='conv4_6_3x3_bn')(y)\n",
    "    y = Conv2D(512, 1, name='conv4_6_1x1_increase')(y)\n",
    "    y = BatchNormalization(name='conv4_6_1x1_increase_bn')(y)\n",
    "    y = Add(name='conv4_6')([y,y_])\n",
    "    y = Activation('relu', name='conv4_6/relu')(y)\n",
    "\n",
    "    y_ = Conv2D(1024, 1, name='conv5_1_1x1_proj')(y)\n",
    "    y_ = BatchNormalization(name='conv5_1_1x1_proj_bn')(y_)\n",
    "    y = Conv2D(256, 1, activation='relu', name='conv5_1_1x1_reduce')(y)\n",
    "    y = BatchNormalization(name='conv5_1_1x1_reduce_bn')(y)\n",
    "    y = ZeroPadding2D(padding=4, name='padding14')(y)\n",
    "    y = Conv2D(256, 3, dilation_rate=4, activation='relu', name='conv5_1_3x3')(y)\n",
    "    y = BatchNormalization(name='conv5_1_3x3_bn')(y)\n",
    "    y = Conv2D(1024, 1, name='conv5_1_1x1_increase')(y)\n",
    "    y = BatchNormalization(name='conv5_1_1x1_increase_bn')(y)\n",
    "    y = Add(name='conv5_1')([y,y_])\n",
    "    y_ = Activation('relu', name='conv5_1/relu')(y)\n",
    "\n",
    "    y = Conv2D(256, 1, activation='relu', name='conv5_2_1x1_reduce')(y_)\n",
    "    y = BatchNormalization(name='conv5_2_1x1_reduce_bn')(y)\n",
    "    y = ZeroPadding2D(padding=4, name='padding15')(y)\n",
    "    y = Conv2D(256, 3, dilation_rate=4, activation='relu', name='conv5_2_3x3')(y)\n",
    "    y = BatchNormalization(name='conv5_2_3x3_bn')(y)\n",
    "    y = Conv2D(1024, 1, name='conv5_2_1x1_increase')(y)\n",
    "    y = BatchNormalization(name='conv5_2_1x1_increase_bn')(y)\n",
    "    y = Add(name='conv5_2')([y,y_])\n",
    "    y_ = Activation('relu', name='conv5_2/relu')(y)\n",
    "\n",
    "    y = Conv2D(256, 1, activation='relu', name='conv5_3_1x1_reduce')(y_)\n",
    "    y = BatchNormalization(name='conv5_3_1x1_reduce_bn')(y)\n",
    "    y = ZeroPadding2D(padding=4, name='padding16')(y)\n",
    "    y = Conv2D(256, 3, dilation_rate=4, activation='relu', name='conv5_3_3x3')(y)\n",
    "    y = BatchNormalization(name='conv5_3_3x3_bn')(y)\n",
    "    y = Conv2D(1024, 1, name='conv5_3_1x1_increase')(y)\n",
    "    y = BatchNormalization(name='conv5_3_1x1_increase_bn')(y)\n",
    "    y = Add(name='conv5_3')([y,y_])\n",
    "    y = Activation('relu', name='conv5_3/relu')(y)\n",
    "\n",
    "    h, w = y.shape[1:3].as_list()\n",
    "    pool1 = AveragePooling2D(pool_size=(h,w), strides=(h,w), name='conv5_3_pool1')(y)\n",
    "    pool1 = Lambda(lambda x: tf.compat.v1.image.resize_bilinear(x, size=(h,w)), name='conv5_3_pool1_interp')(pool1)\n",
    "    pool2 = AveragePooling2D(pool_size=(h/2,w/2), strides=(h//2,w//2), name='conv5_3_pool2')(y)\n",
    "    pool2 = Lambda(lambda x: tf.compat.v1.image.resize_bilinear(x, size=(h,w)), name='conv5_3_pool2_interp')(pool2)\n",
    "    pool3 = AveragePooling2D(pool_size=(h/3,w/3), strides=(h//3,w//3), name='conv5_3_pool3')(y)\n",
    "    pool3 = Lambda(lambda x: tf.compat.v1.image.resize_bilinear(x, size=(h,w)), name='conv5_3_pool3_interp')(pool3)\n",
    "    pool6 = AveragePooling2D(pool_size=(h/4,w/4), strides=(h//4,w//4), name='conv5_3_pool6')(y)\n",
    "    pool6 = Lambda(lambda x: tf.compat.v1.image.resize_bilinear(x, size=(h,w)), name='conv5_3_pool6_interp')(pool6)\n",
    "\n",
    "    y = Add(name='conv5_3_sum')([y, pool1, pool2, pool3, pool6])\n",
    "    y = Conv2D(256, 1, activation='relu', name='conv5_4_k1')(y)\n",
    "    y = BatchNormalization(name='conv5_4_k1_bn')(y)\n",
    "    aux_1 = Lambda(lambda x: tf.compat.v1.image.resize_bilinear(x, size=(int(x.shape[1])*2, int(x.shape[2])*2)), name='conv5_4_interp')(y)\n",
    "    y = ZeroPadding2D(padding=2, name='padding17')(aux_1)\n",
    "    y = Conv2D(128, 3, dilation_rate=2, name='conv_sub4')(y)\n",
    "    y = BatchNormalization(name='conv_sub4_bn')(y)\n",
    "    y_ = Conv2D(128, 1, name='conv3_1_sub2_proj')(z)\n",
    "    y_ = BatchNormalization(name='conv3_1_sub2_proj_bn')(y_)\n",
    "    y = Add(name='sub24_sum')([y,y_])\n",
    "    y = Activation('relu', name='sub24_sum/relu')(y)\n",
    "\n",
    "    aux_2 = Lambda(lambda x: tf.compat.v1.image.resize_bilinear(x, size=(int(x.shape[1])*2, int(x.shape[2])*2)), name='sub24_sum_interp')(y)\n",
    "    y = ZeroPadding2D(padding=2, name='padding18')(aux_2)\n",
    "    y_ = Conv2D(128, 3, dilation_rate=2, name='conv_sub2')(y)\n",
    "    y_ = BatchNormalization(name='conv_sub2_bn')(y_)\n",
    "\n",
    "    # (1)\n",
    "    y = Conv2D(32, 3, strides=2, padding='same', activation='relu', name='conv1_sub1')(x)\n",
    "    y = BatchNormalization(name='conv1_sub1_bn')(y)\n",
    "    y = Conv2D(32, 3, strides=2, padding='same', activation='relu', name='conv2_sub1')(y)\n",
    "    y = BatchNormalization(name='conv2_sub1_bn')(y)\n",
    "    y = Conv2D(64, 3, strides=2, padding='same', activation='relu', name='conv3_sub1')(y)\n",
    "    y = BatchNormalization(name='conv3_sub1_bn')(y)\n",
    "    y = Conv2D(128, 1, name='conv3_sub1_proj')(y)\n",
    "    y = BatchNormalization(name='conv3_sub1_proj_bn')(y)\n",
    "\n",
    "    y = Add(name='sub12_sum')([y,y_])\n",
    "    y = Activation('relu', name='sub12_sum/relu')(y)\n",
    "    y = Lambda(lambda x: tf.compat.v1.image.resize_bilinear(x, size=(int(x.shape[1])*2, int(x.shape[2])*2)), name='sub12_sum_interp')(y)\n",
    "    y = UpSampling2D(size=(4,4))(y)\n",
    "    out = Conv2D(n_classes, 1, activation='sigmoid', name='conv6_cls')(y)\n",
    "\n",
    "    if train:\n",
    "        aux_1 = Conv2D(n_classes, 1, activation='sigmoid', name='sub4_out')(aux_1)\n",
    "        aux_2 = Conv2D(n_classes, 1, activation='sigmoid', name='sub24_out')(aux_2)\n",
    "        model = Model(inputs=inp, outputs=[out, aux_2, aux_1])\n",
    "    else:\n",
    "        model = Model(inputs=inp, outputs=out)\n",
    "        \n",
    "    if weights_path is not None:\n",
    "        model.load_weights(weights_path, by_name=True)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 7207
    },
    "colab_type": "code",
    "id": "5qnFrZRWEREr",
    "outputId": "246cdb71-a8b8-4599-ce5e-992baa1c7622"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            [(None, 256, 256, 3) 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "lambda (Lambda)                 (None, 256, 256, 3)  0           input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "data_sub2 (Lambda)              (None, 128, 128, 3)  0           lambda[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv1_1_3x3_s2 (Conv2D)         (None, 64, 64, 32)   896         data_sub2[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1_1_3x3_s2_bn (BatchNormali (None, 64, 64, 32)   128         conv1_1_3x3_s2[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv1_2_3x3 (Conv2D)            (None, 64, 64, 32)   9248        conv1_1_3x3_s2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv1_2_3x3_s2_bn (BatchNormali (None, 64, 64, 32)   128         conv1_2_3x3[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "conv1_3_3x3 (Conv2D)            (None, 64, 64, 64)   18496       conv1_2_3x3_s2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv1_3_3x3_bn (BatchNormalizat (None, 64, 64, 64)   256         conv1_3_3x3[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "pool1_3x3_s2 (MaxPooling2D)     (None, 31, 31, 64)   0           conv1_3_3x3_bn[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2_1_1x1_reduce (Conv2D)     (None, 31, 31, 32)   2080        pool1_3x3_s2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2_1_1x1_reduce_bn (BatchNor (None, 31, 31, 32)   128         conv2_1_1x1_reduce[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "padding1 (ZeroPadding2D)        (None, 33, 33, 32)   0           conv2_1_1x1_reduce_bn[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2_1_3x3 (Conv2D)            (None, 31, 31, 32)   9248        padding1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2_1_3x3_bn (BatchNormalizat (None, 31, 31, 32)   128         conv2_1_3x3[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "conv2_1_1x1_proj (Conv2D)       (None, 31, 31, 128)  8320        pool1_3x3_s2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2_1_1x1_increase (Conv2D)   (None, 31, 31, 128)  4224        conv2_1_3x3_bn[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2_1_1x1_proj_bn (BatchNorma (None, 31, 31, 128)  512         conv2_1_1x1_proj[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv2_1_1x1_increase_bn (BatchN (None, 31, 31, 128)  512         conv2_1_1x1_increase[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv2_1 (Add)                   (None, 31, 31, 128)  0           conv2_1_1x1_proj_bn[0][0]        \n",
      "                                                                 conv2_1_1x1_increase_bn[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2_1/relu (Activation)       (None, 31, 31, 128)  0           conv2_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv2_2_1x1_reduce (Conv2D)     (None, 31, 31, 32)   4128        conv2_1/relu[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2_2_1x1_reduce_bn (BatchNor (None, 31, 31, 32)   128         conv2_2_1x1_reduce[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "padding2 (ZeroPadding2D)        (None, 33, 33, 32)   0           conv2_2_1x1_reduce_bn[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2_2_3x3 (Conv2D)            (None, 31, 31, 32)   9248        padding2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2_2_3x3_bn (BatchNormalizat (None, 31, 31, 32)   128         conv2_2_3x3[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "conv2_2_1x1_increase (Conv2D)   (None, 31, 31, 128)  4224        conv2_2_3x3_bn[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2_2_1x1_increase_bn (BatchN (None, 31, 31, 128)  512         conv2_2_1x1_increase[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv2_2 (Add)                   (None, 31, 31, 128)  0           conv2_2_1x1_increase_bn[0][0]    \n",
      "                                                                 conv2_1/relu[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2_2/relu (Activation)       (None, 31, 31, 128)  0           conv2_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv2_3_1x1_reduce (Conv2D)     (None, 31, 31, 32)   4128        conv2_2/relu[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2_3_1x1_reduce_bn (BatchNor (None, 31, 31, 32)   128         conv2_3_1x1_reduce[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "padding3 (ZeroPadding2D)        (None, 33, 33, 32)   0           conv2_3_1x1_reduce_bn[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2_3_3x3 (Conv2D)            (None, 31, 31, 32)   9248        padding3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2_3_3x3_bn (BatchNormalizat (None, 31, 31, 32)   128         conv2_3_3x3[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "conv2_3_1x1_increase (Conv2D)   (None, 31, 31, 128)  4224        conv2_3_3x3_bn[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2_3_1x1_increase_bn (BatchN (None, 31, 31, 128)  512         conv2_3_1x1_increase[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv2_3 (Add)                   (None, 31, 31, 128)  0           conv2_3_1x1_increase_bn[0][0]    \n",
      "                                                                 conv2_2/relu[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2_3/relu (Activation)       (None, 31, 31, 128)  0           conv2_3[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv3_1_1x1_reduce (Conv2D)     (None, 16, 16, 64)   8256        conv2_3/relu[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv3_1_1x1_reduce_bn (BatchNor (None, 16, 16, 64)   256         conv3_1_1x1_reduce[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "padding4 (ZeroPadding2D)        (None, 18, 18, 64)   0           conv3_1_1x1_reduce_bn[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv3_1_3x3 (Conv2D)            (None, 16, 16, 64)   36928       padding4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv3_1_3x3_bn (BatchNormalizat (None, 16, 16, 64)   256         conv3_1_3x3[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "conv3_1_1x1_proj (Conv2D)       (None, 16, 16, 256)  33024       conv2_3/relu[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv3_1_1x1_increase (Conv2D)   (None, 16, 16, 256)  16640       conv3_1_3x3_bn[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv3_1_1x1_proj_bn (BatchNorma (None, 16, 16, 256)  1024        conv3_1_1x1_proj[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv3_1_1x1_increase_bn (BatchN (None, 16, 16, 256)  1024        conv3_1_1x1_increase[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv3_1 (Add)                   (None, 16, 16, 256)  0           conv3_1_1x1_proj_bn[0][0]        \n",
      "                                                                 conv3_1_1x1_increase_bn[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv3_1/relu (Activation)       (None, 16, 16, 256)  0           conv3_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv3_1_sub4 (Lambda)           (None, 8, 8, 256)    0           conv3_1/relu[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv3_2_1x1_reduce (Conv2D)     (None, 8, 8, 64)     16448       conv3_1_sub4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv3_2_1x1_reduce_bn (BatchNor (None, 8, 8, 64)     256         conv3_2_1x1_reduce[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "padding5 (ZeroPadding2D)        (None, 10, 10, 64)   0           conv3_2_1x1_reduce_bn[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv3_2_3x3 (Conv2D)            (None, 8, 8, 64)     36928       padding5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv3_2_3x3_bn (BatchNormalizat (None, 8, 8, 64)     256         conv3_2_3x3[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "conv3_2_1x1_increase (Conv2D)   (None, 8, 8, 256)    16640       conv3_2_3x3_bn[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv3_2_1x1_increase_bn (BatchN (None, 8, 8, 256)    1024        conv3_2_1x1_increase[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv3_2 (Add)                   (None, 8, 8, 256)    0           conv3_2_1x1_increase_bn[0][0]    \n",
      "                                                                 conv3_1_sub4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv3_2/relu (Activation)       (None, 8, 8, 256)    0           conv3_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv3_3_1x1_reduce (Conv2D)     (None, 8, 8, 64)     16448       conv3_2/relu[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv3_3_1x1_reduce_bn (BatchNor (None, 8, 8, 64)     256         conv3_3_1x1_reduce[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "padding6 (ZeroPadding2D)        (None, 10, 10, 64)   0           conv3_3_1x1_reduce_bn[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv3_3_3x3 (Conv2D)            (None, 8, 8, 64)     36928       padding6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv3_3_3x3_bn (BatchNormalizat (None, 8, 8, 64)     256         conv3_3_3x3[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "conv3_3_1x1_increase (Conv2D)   (None, 8, 8, 256)    16640       conv3_3_3x3_bn[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv3_3_1x1_increase_bn (BatchN (None, 8, 8, 256)    1024        conv3_3_1x1_increase[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv3_3 (Add)                   (None, 8, 8, 256)    0           conv3_3_1x1_increase_bn[0][0]    \n",
      "                                                                 conv3_2/relu[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv3_3/relu (Activation)       (None, 8, 8, 256)    0           conv3_3[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv3_4_1x1_reduce (Conv2D)     (None, 8, 8, 64)     16448       conv3_3/relu[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv3_4_1x1_reduce_bn (BatchNor (None, 8, 8, 64)     256         conv3_4_1x1_reduce[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "padding7 (ZeroPadding2D)        (None, 10, 10, 64)   0           conv3_4_1x1_reduce_bn[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv3_4_3x3 (Conv2D)            (None, 8, 8, 64)     36928       padding7[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv3_4_3x3_bn (BatchNormalizat (None, 8, 8, 64)     256         conv3_4_3x3[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "conv3_4_1x1_increase (Conv2D)   (None, 8, 8, 256)    16640       conv3_4_3x3_bn[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv3_4_1x1_increase_bn (BatchN (None, 8, 8, 256)    1024        conv3_4_1x1_increase[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv3_4 (Add)                   (None, 8, 8, 256)    0           conv3_4_1x1_increase_bn[0][0]    \n",
      "                                                                 conv3_3/relu[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv3_4/relu (Activation)       (None, 8, 8, 256)    0           conv3_4[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv4_1_1x1_reduce (Conv2D)     (None, 8, 8, 128)    32896       conv3_4/relu[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv4_1_1x1_reduce_bn (BatchNor (None, 8, 8, 128)    512         conv4_1_1x1_reduce[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "padding8 (ZeroPadding2D)        (None, 12, 12, 128)  0           conv4_1_1x1_reduce_bn[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv4_1_3x3 (Conv2D)            (None, 8, 8, 128)    147584      padding8[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv4_1_3x3_bn (BatchNormalizat (None, 8, 8, 128)    512         conv4_1_3x3[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "conv4_1_1x1_proj (Conv2D)       (None, 8, 8, 512)    131584      conv3_4/relu[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv4_1_1x1_increase (Conv2D)   (None, 8, 8, 512)    66048       conv4_1_3x3_bn[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv4_1_1x1_proj_bn (BatchNorma (None, 8, 8, 512)    2048        conv4_1_1x1_proj[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_1_1x1_increase_bn (BatchN (None, 8, 8, 512)    2048        conv4_1_1x1_increase[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_1 (Add)                   (None, 8, 8, 512)    0           conv4_1_1x1_proj_bn[0][0]        \n",
      "                                                                 conv4_1_1x1_increase_bn[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv4_1/relu (Activation)       (None, 8, 8, 512)    0           conv4_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv4_2_1x1_reduce (Conv2D)     (None, 8, 8, 128)    65664       conv4_1/relu[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv4_2_1x1_reduce_bn (BatchNor (None, 8, 8, 128)    512         conv4_2_1x1_reduce[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "padding9 (ZeroPadding2D)        (None, 12, 12, 128)  0           conv4_2_1x1_reduce_bn[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv4_2_3x3 (Conv2D)            (None, 8, 8, 128)    147584      padding9[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv4_2_3x3_bn (BatchNormalizat (None, 8, 8, 128)    512         conv4_2_3x3[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "conv4_2_1x1_increase (Conv2D)   (None, 8, 8, 512)    66048       conv4_2_3x3_bn[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv4_2_1x1_increase_bn (BatchN (None, 8, 8, 512)    2048        conv4_2_1x1_increase[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_2 (Add)                   (None, 8, 8, 512)    0           conv4_2_1x1_increase_bn[0][0]    \n",
      "                                                                 conv4_1/relu[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv4_2/relu (Activation)       (None, 8, 8, 512)    0           conv4_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv4_3_1x1_reduce (Conv2D)     (None, 8, 8, 128)    65664       conv4_2/relu[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv4_3_1x1_reduce_bn (BatchNor (None, 8, 8, 128)    512         conv4_3_1x1_reduce[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "padding10 (ZeroPadding2D)       (None, 12, 12, 128)  0           conv4_3_1x1_reduce_bn[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv4_3_3x3 (Conv2D)            (None, 8, 8, 128)    147584      padding10[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv4_3_3x3_bn (BatchNormalizat (None, 8, 8, 128)    512         conv4_3_3x3[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "conv4_3_1x1_increase (Conv2D)   (None, 8, 8, 512)    66048       conv4_3_3x3_bn[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv4_3_1x1_increase_bn (BatchN (None, 8, 8, 512)    2048        conv4_3_1x1_increase[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_3 (Add)                   (None, 8, 8, 512)    0           conv4_3_1x1_increase_bn[0][0]    \n",
      "                                                                 conv4_2/relu[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv4_3/relu (Activation)       (None, 8, 8, 512)    0           conv4_3[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv4_4_1x1_reduce (Conv2D)     (None, 8, 8, 128)    65664       conv4_3/relu[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv4_4_1x1_reduce_bn (BatchNor (None, 8, 8, 128)    512         conv4_4_1x1_reduce[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "padding11 (ZeroPadding2D)       (None, 12, 12, 128)  0           conv4_4_1x1_reduce_bn[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv4_4_3x3 (Conv2D)            (None, 8, 8, 128)    147584      padding11[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv4_4_3x3_bn (BatchNormalizat (None, 8, 8, 128)    512         conv4_4_3x3[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "conv4_4_1x1_increase (Conv2D)   (None, 8, 8, 512)    66048       conv4_4_3x3_bn[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv4_4_1x1_increase_bn (BatchN (None, 8, 8, 512)    2048        conv4_4_1x1_increase[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_4 (Add)                   (None, 8, 8, 512)    0           conv4_4_1x1_increase_bn[0][0]    \n",
      "                                                                 conv4_3/relu[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv4_4/relu (Activation)       (None, 8, 8, 512)    0           conv4_4[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv4_5_1x1_reduce (Conv2D)     (None, 8, 8, 128)    65664       conv4_4/relu[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv4_5_1x1_reduce_bn (BatchNor (None, 8, 8, 128)    512         conv4_5_1x1_reduce[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "padding12 (ZeroPadding2D)       (None, 12, 12, 128)  0           conv4_5_1x1_reduce_bn[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv4_5_3x3 (Conv2D)            (None, 8, 8, 128)    147584      padding12[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv4_5_3x3_bn (BatchNormalizat (None, 8, 8, 128)    512         conv4_5_3x3[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "conv4_5_1x1_increase (Conv2D)   (None, 8, 8, 512)    66048       conv4_5_3x3_bn[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv4_5_1x1_increase_bn (BatchN (None, 8, 8, 512)    2048        conv4_5_1x1_increase[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_5 (Add)                   (None, 8, 8, 512)    0           conv4_5_1x1_increase_bn[0][0]    \n",
      "                                                                 conv4_4/relu[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv4_5/relu (Activation)       (None, 8, 8, 512)    0           conv4_5[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv4_6_1x1_reduce (Conv2D)     (None, 8, 8, 128)    65664       conv4_5/relu[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv4_6_1x1_reduce_bn (BatchNor (None, 8, 8, 128)    512         conv4_6_1x1_reduce[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "padding13 (ZeroPadding2D)       (None, 12, 12, 128)  0           conv4_6_1x1_reduce_bn[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv4_6_3x3 (Conv2D)            (None, 8, 8, 128)    147584      padding13[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv4_6_3x3_bn (BatchNormalizat (None, 8, 8, 128)    512         conv4_6_3x3[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "conv4_6_1x1_increase (Conv2D)   (None, 8, 8, 512)    66048       conv4_6_3x3_bn[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv4_6_1x1_increase_bn (BatchN (None, 8, 8, 512)    2048        conv4_6_1x1_increase[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_6 (Add)                   (None, 8, 8, 512)    0           conv4_6_1x1_increase_bn[0][0]    \n",
      "                                                                 conv4_5/relu[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv4_6/relu (Activation)       (None, 8, 8, 512)    0           conv4_6[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv5_1_1x1_reduce (Conv2D)     (None, 8, 8, 256)    131328      conv4_6/relu[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv5_1_1x1_reduce_bn (BatchNor (None, 8, 8, 256)    1024        conv5_1_1x1_reduce[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "padding14 (ZeroPadding2D)       (None, 16, 16, 256)  0           conv5_1_1x1_reduce_bn[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv5_1_3x3 (Conv2D)            (None, 8, 8, 256)    590080      padding14[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv5_1_3x3_bn (BatchNormalizat (None, 8, 8, 256)    1024        conv5_1_3x3[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "conv5_1_1x1_increase (Conv2D)   (None, 8, 8, 1024)   263168      conv5_1_3x3_bn[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv5_1_1x1_proj (Conv2D)       (None, 8, 8, 1024)   525312      conv4_6/relu[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv5_1_1x1_increase_bn (BatchN (None, 8, 8, 1024)   4096        conv5_1_1x1_increase[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_1_1x1_proj_bn (BatchNorma (None, 8, 8, 1024)   4096        conv5_1_1x1_proj[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv5_1 (Add)                   (None, 8, 8, 1024)   0           conv5_1_1x1_increase_bn[0][0]    \n",
      "                                                                 conv5_1_1x1_proj_bn[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_1/relu (Activation)       (None, 8, 8, 1024)   0           conv5_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv5_2_1x1_reduce (Conv2D)     (None, 8, 8, 256)    262400      conv5_1/relu[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv5_2_1x1_reduce_bn (BatchNor (None, 8, 8, 256)    1024        conv5_2_1x1_reduce[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "padding15 (ZeroPadding2D)       (None, 16, 16, 256)  0           conv5_2_1x1_reduce_bn[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv5_2_3x3 (Conv2D)            (None, 8, 8, 256)    590080      padding15[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv5_2_3x3_bn (BatchNormalizat (None, 8, 8, 256)    1024        conv5_2_3x3[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "conv5_2_1x1_increase (Conv2D)   (None, 8, 8, 1024)   263168      conv5_2_3x3_bn[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv5_2_1x1_increase_bn (BatchN (None, 8, 8, 1024)   4096        conv5_2_1x1_increase[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_2 (Add)                   (None, 8, 8, 1024)   0           conv5_2_1x1_increase_bn[0][0]    \n",
      "                                                                 conv5_1/relu[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv5_2/relu (Activation)       (None, 8, 8, 1024)   0           conv5_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv5_3_1x1_reduce (Conv2D)     (None, 8, 8, 256)    262400      conv5_2/relu[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv5_3_1x1_reduce_bn (BatchNor (None, 8, 8, 256)    1024        conv5_3_1x1_reduce[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "padding16 (ZeroPadding2D)       (None, 16, 16, 256)  0           conv5_3_1x1_reduce_bn[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv5_3_3x3 (Conv2D)            (None, 8, 8, 256)    590080      padding16[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv5_3_3x3_bn (BatchNormalizat (None, 8, 8, 256)    1024        conv5_3_3x3[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "conv5_3_1x1_increase (Conv2D)   (None, 8, 8, 1024)   263168      conv5_3_3x3_bn[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv5_3_1x1_increase_bn (BatchN (None, 8, 8, 1024)   4096        conv5_3_1x1_increase[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_3 (Add)                   (None, 8, 8, 1024)   0           conv5_3_1x1_increase_bn[0][0]    \n",
      "                                                                 conv5_2/relu[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv5_3/relu (Activation)       (None, 8, 8, 1024)   0           conv5_3[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv5_3_pool1 (AveragePooling2D (None, 1, 1, 1024)   0           conv5_3/relu[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv5_3_pool2 (AveragePooling2D (None, 2, 2, 1024)   0           conv5_3/relu[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv5_3_pool3 (AveragePooling2D (None, 4, 4, 1024)   0           conv5_3/relu[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv5_3_pool6 (AveragePooling2D (None, 4, 4, 1024)   0           conv5_3/relu[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv5_3_pool1_interp (Lambda)   (None, 8, 8, 1024)   0           conv5_3_pool1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv5_3_pool2_interp (Lambda)   (None, 8, 8, 1024)   0           conv5_3_pool2[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv5_3_pool3_interp (Lambda)   (None, 8, 8, 1024)   0           conv5_3_pool3[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv5_3_pool6_interp (Lambda)   (None, 8, 8, 1024)   0           conv5_3_pool6[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv5_3_sum (Add)               (None, 8, 8, 1024)   0           conv5_3/relu[0][0]               \n",
      "                                                                 conv5_3_pool1_interp[0][0]       \n",
      "                                                                 conv5_3_pool2_interp[0][0]       \n",
      "                                                                 conv5_3_pool3_interp[0][0]       \n",
      "                                                                 conv5_3_pool6_interp[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_4_k1 (Conv2D)             (None, 8, 8, 256)    262400      conv5_3_sum[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "conv5_4_k1_bn (BatchNormalizati (None, 8, 8, 256)    1024        conv5_4_k1[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv5_4_interp (Lambda)         (None, 16, 16, 256)  0           conv5_4_k1_bn[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "padding17 (ZeroPadding2D)       (None, 20, 20, 256)  0           conv5_4_interp[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv1_sub1 (Conv2D)             (None, 128, 128, 32) 896         lambda[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv_sub4 (Conv2D)              (None, 16, 16, 128)  295040      padding17[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv3_1_sub2_proj (Conv2D)      (None, 16, 16, 128)  32896       conv3_1/relu[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv1_sub1_bn (BatchNormalizati (None, 128, 128, 32) 128         conv1_sub1[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv_sub4_bn (BatchNormalizatio (None, 16, 16, 128)  512         conv_sub4[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv3_1_sub2_proj_bn (BatchNorm (None, 16, 16, 128)  512         conv3_1_sub2_proj[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_sub1 (Conv2D)             (None, 64, 64, 32)   9248        conv1_sub1_bn[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "sub24_sum (Add)                 (None, 16, 16, 128)  0           conv_sub4_bn[0][0]               \n",
      "                                                                 conv3_1_sub2_proj_bn[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv2_sub1_bn (BatchNormalizati (None, 64, 64, 32)   128         conv2_sub1[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "sub24_sum/relu (Activation)     (None, 16, 16, 128)  0           sub24_sum[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv3_sub1 (Conv2D)             (None, 32, 32, 64)   18496       conv2_sub1_bn[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "sub24_sum_interp (Lambda)       (None, 32, 32, 128)  0           sub24_sum/relu[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv3_sub1_bn (BatchNormalizati (None, 32, 32, 64)   256         conv3_sub1[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "padding18 (ZeroPadding2D)       (None, 36, 36, 128)  0           sub24_sum_interp[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv3_sub1_proj (Conv2D)        (None, 32, 32, 128)  8320        conv3_sub1_bn[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv_sub2 (Conv2D)              (None, 32, 32, 128)  147584      padding18[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv3_sub1_proj_bn (BatchNormal (None, 32, 32, 128)  512         conv3_sub1_proj[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv_sub2_bn (BatchNormalizatio (None, 32, 32, 128)  512         conv_sub2[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "sub12_sum (Add)                 (None, 32, 32, 128)  0           conv3_sub1_proj_bn[0][0]         \n",
      "                                                                 conv_sub2_bn[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "sub12_sum/relu (Activation)     (None, 32, 32, 128)  0           sub12_sum[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "sub12_sum_interp (Lambda)       (None, 64, 64, 128)  0           sub12_sum/relu[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "up_sampling2d (UpSampling2D)    (None, 256, 256, 128 0           sub12_sum_interp[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv6_cls (Conv2D)              (None, 256, 256, 1)  129         up_sampling2d[0][0]              \n",
      "==================================================================================================\n",
      "Total params: 6,740,481\n",
      "Trainable params: 6,711,937\n",
      "Non-trainable params: 28,544\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = build_bn(width = 256, height = 256, n_classes = 1, train=False)\n",
    "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "_cell_guid": "9415b1c4-aa69-41b9-a1e3-d6053dbd4f64",
    "_uuid": "c060db22daa2abf12b28240cd81bbcbf1ce1bf87",
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "X-siHOod0mR8"
   },
   "outputs": [],
   "source": [
    "# Fit model\n",
    "earlystopper = EarlyStopping(patience=50, verbose=1)\n",
    "checkpointer = ModelCheckpoint('model-ICNet-initial.h5', verbose=1, save_best_only=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 10802
    },
    "colab_type": "code",
    "id": "O22KojAUDzkM",
    "outputId": "7a1d2d3e-9c26-4b0d-de43-6a4b68a242f0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 536 samples, validate on 134 samples\n",
      "Epoch 1/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.4047 - accuracy: 0.8297\n",
      "Epoch 00001: val_loss improved from inf to 0.77815, saving model to model-ICNet-initial.h5\n",
      "536/536 [==============================] - 29s 53ms/sample - loss: 0.4005 - accuracy: 0.8315 - val_loss: 0.7781 - val_accuracy: 0.3600\n",
      "Epoch 2/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.1772 - accuracy: 0.9299\n",
      "Epoch 00002: val_loss improved from 0.77815 to 0.52909, saving model to model-ICNet-initial.h5\n",
      "536/536 [==============================] - 12s 23ms/sample - loss: 0.1778 - accuracy: 0.9296 - val_loss: 0.5291 - val_accuracy: 0.7385\n",
      "Epoch 3/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.1465 - accuracy: 0.9415\n",
      "Epoch 00003: val_loss improved from 0.52909 to 0.51606, saving model to model-ICNet-initial.h5\n",
      "536/536 [==============================] - 12s 23ms/sample - loss: 0.1460 - accuracy: 0.9417 - val_loss: 0.5161 - val_accuracy: 0.7125\n",
      "Epoch 4/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.1387 - accuracy: 0.9431\n",
      "Epoch 00004: val_loss improved from 0.51606 to 0.44712, saving model to model-ICNet-initial.h5\n",
      "536/536 [==============================] - 12s 23ms/sample - loss: 0.1387 - accuracy: 0.9430 - val_loss: 0.4471 - val_accuracy: 0.7635\n",
      "Epoch 5/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.1343 - accuracy: 0.9443\n",
      "Epoch 00005: val_loss improved from 0.44712 to 0.43299, saving model to model-ICNet-initial.h5\n",
      "536/536 [==============================] - 12s 23ms/sample - loss: 0.1339 - accuracy: 0.9445 - val_loss: 0.4330 - val_accuracy: 0.7626\n",
      "Epoch 6/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.1333 - accuracy: 0.9453\n",
      "Epoch 00006: val_loss did not improve from 0.43299\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.1331 - accuracy: 0.9454 - val_loss: 0.5282 - val_accuracy: 0.8309\n",
      "Epoch 7/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.1272 - accuracy: 0.9474\n",
      "Epoch 00007: val_loss did not improve from 0.43299\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.1274 - accuracy: 0.9473 - val_loss: 0.6161 - val_accuracy: 0.8273\n",
      "Epoch 8/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.1232 - accuracy: 0.9484\n",
      "Epoch 00008: val_loss did not improve from 0.43299\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.1228 - accuracy: 0.9486 - val_loss: 0.7901 - val_accuracy: 0.8273\n",
      "Epoch 9/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.1287 - accuracy: 0.9466\n",
      "Epoch 00009: val_loss did not improve from 0.43299\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.1288 - accuracy: 0.9465 - val_loss: 0.8036 - val_accuracy: 0.8273\n",
      "Epoch 10/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.1307 - accuracy: 0.9457\n",
      "Epoch 00010: val_loss did not improve from 0.43299\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.1302 - accuracy: 0.9459 - val_loss: 0.7110 - val_accuracy: 0.8274\n",
      "Epoch 11/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.1205 - accuracy: 0.9494\n",
      "Epoch 00011: val_loss did not improve from 0.43299\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.1200 - accuracy: 0.9496 - val_loss: 0.5853 - val_accuracy: 0.8323\n",
      "Epoch 12/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.1166 - accuracy: 0.9502\n",
      "Epoch 00012: val_loss did not improve from 0.43299\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.1162 - accuracy: 0.9504 - val_loss: 0.4828 - val_accuracy: 0.8500\n",
      "Epoch 13/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.1175 - accuracy: 0.9501\n",
      "Epoch 00013: val_loss improved from 0.43299 to 0.42705, saving model to model-ICNet-initial.h5\n",
      "536/536 [==============================] - 12s 23ms/sample - loss: 0.1173 - accuracy: 0.9502 - val_loss: 0.4271 - val_accuracy: 0.8592\n",
      "Epoch 14/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.1156 - accuracy: 0.9511\n",
      "Epoch 00014: val_loss improved from 0.42705 to 0.33373, saving model to model-ICNet-initial.h5\n",
      "536/536 [==============================] - 12s 23ms/sample - loss: 0.1164 - accuracy: 0.9508 - val_loss: 0.3337 - val_accuracy: 0.8794\n",
      "Epoch 15/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.1128 - accuracy: 0.9519\n",
      "Epoch 00015: val_loss improved from 0.33373 to 0.27019, saving model to model-ICNet-initial.h5\n",
      "536/536 [==============================] - 12s 23ms/sample - loss: 0.1128 - accuracy: 0.9519 - val_loss: 0.2702 - val_accuracy: 0.8953\n",
      "Epoch 16/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.1094 - accuracy: 0.9531\n",
      "Epoch 00016: val_loss improved from 0.27019 to 0.19654, saving model to model-ICNet-initial.h5\n",
      "536/536 [==============================] - 12s 23ms/sample - loss: 0.1100 - accuracy: 0.9529 - val_loss: 0.1965 - val_accuracy: 0.9204\n",
      "Epoch 17/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.1109 - accuracy: 0.9524\n",
      "Epoch 00017: val_loss did not improve from 0.19654\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.1110 - accuracy: 0.9524 - val_loss: 0.2382 - val_accuracy: 0.9088\n",
      "Epoch 18/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.1124 - accuracy: 0.9521\n",
      "Epoch 00018: val_loss improved from 0.19654 to 0.16101, saving model to model-ICNet-initial.h5\n",
      "536/536 [==============================] - 12s 23ms/sample - loss: 0.1124 - accuracy: 0.9521 - val_loss: 0.1610 - val_accuracy: 0.9303\n",
      "Epoch 19/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.1107 - accuracy: 0.9526\n",
      "Epoch 00019: val_loss did not improve from 0.16101\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.1102 - accuracy: 0.9528 - val_loss: 0.1854 - val_accuracy: 0.9313\n",
      "Epoch 20/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.1086 - accuracy: 0.9532\n",
      "Epoch 00020: val_loss did not improve from 0.16101\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.1089 - accuracy: 0.9531 - val_loss: 0.3358 - val_accuracy: 0.8728\n",
      "Epoch 21/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.1098 - accuracy: 0.9525\n",
      "Epoch 00021: val_loss did not improve from 0.16101\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.1097 - accuracy: 0.9526 - val_loss: 0.1648 - val_accuracy: 0.9343\n",
      "Epoch 22/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.1111 - accuracy: 0.9524\n",
      "Epoch 00022: val_loss did not improve from 0.16101\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.1116 - accuracy: 0.9523 - val_loss: 0.2169 - val_accuracy: 0.9210\n",
      "Epoch 23/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.1062 - accuracy: 0.9544\n",
      "Epoch 00023: val_loss did not improve from 0.16101\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.1068 - accuracy: 0.9541 - val_loss: 0.5619 - val_accuracy: 0.7077\n",
      "Epoch 24/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.1085 - accuracy: 0.9534\n",
      "Epoch 00024: val_loss did not improve from 0.16101\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.1090 - accuracy: 0.9531 - val_loss: 0.1747 - val_accuracy: 0.9307\n",
      "Epoch 25/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.1055 - accuracy: 0.9543\n",
      "Epoch 00025: val_loss did not improve from 0.16101\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.1059 - accuracy: 0.9541 - val_loss: 2.1934 - val_accuracy: 0.3661\n",
      "Epoch 26/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.1070 - accuracy: 0.9538\n",
      "Epoch 00026: val_loss improved from 0.16101 to 0.15962, saving model to model-ICNet-initial.h5\n",
      "536/536 [==============================] - 12s 23ms/sample - loss: 0.1063 - accuracy: 0.9542 - val_loss: 0.1596 - val_accuracy: 0.9343\n",
      "Epoch 27/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.1086 - accuracy: 0.9533\n",
      "Epoch 00027: val_loss did not improve from 0.15962\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.1108 - accuracy: 0.9525 - val_loss: 2.1185 - val_accuracy: 0.3896\n",
      "Epoch 28/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.1199 - accuracy: 0.9495\n",
      "Epoch 00028: val_loss did not improve from 0.15962\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.1194 - accuracy: 0.9497 - val_loss: 0.5770 - val_accuracy: 0.8099\n",
      "Epoch 29/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.1145 - accuracy: 0.9515\n",
      "Epoch 00029: val_loss did not improve from 0.15962\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.1142 - accuracy: 0.9516 - val_loss: 0.1856 - val_accuracy: 0.9298\n",
      "Epoch 30/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.1072 - accuracy: 0.9535\n",
      "Epoch 00030: val_loss did not improve from 0.15962\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.1077 - accuracy: 0.9534 - val_loss: 0.1944 - val_accuracy: 0.9251\n",
      "Epoch 31/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.1052 - accuracy: 0.9542\n",
      "Epoch 00031: val_loss did not improve from 0.15962\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.1057 - accuracy: 0.9540 - val_loss: 0.5588 - val_accuracy: 0.7367\n",
      "Epoch 32/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.1045 - accuracy: 0.9546\n",
      "Epoch 00032: val_loss improved from 0.15962 to 0.15852, saving model to model-ICNet-initial.h5\n",
      "536/536 [==============================] - 12s 23ms/sample - loss: 0.1051 - accuracy: 0.9543 - val_loss: 0.1585 - val_accuracy: 0.9351\n",
      "Epoch 33/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.1039 - accuracy: 0.9550\n",
      "Epoch 00033: val_loss did not improve from 0.15852\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.1039 - accuracy: 0.9550 - val_loss: 1.1753 - val_accuracy: 0.4966\n",
      "Epoch 34/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.1018 - accuracy: 0.9557\n",
      "Epoch 00034: val_loss did not improve from 0.15852\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.1019 - accuracy: 0.9556 - val_loss: 0.7197 - val_accuracy: 0.6408\n",
      "Epoch 35/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0998 - accuracy: 0.9565\n",
      "Epoch 00035: val_loss improved from 0.15852 to 0.15409, saving model to model-ICNet-initial.h5\n",
      "536/536 [==============================] - 12s 23ms/sample - loss: 0.1014 - accuracy: 0.9558 - val_loss: 0.1541 - val_accuracy: 0.9371\n",
      "Epoch 36/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.1029 - accuracy: 0.9552\n",
      "Epoch 00036: val_loss did not improve from 0.15409\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.1020 - accuracy: 0.9556 - val_loss: 0.1660 - val_accuracy: 0.9362\n",
      "Epoch 37/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.1019 - accuracy: 0.9557\n",
      "Epoch 00037: val_loss improved from 0.15409 to 0.13813, saving model to model-ICNet-initial.h5\n",
      "536/536 [==============================] - 12s 23ms/sample - loss: 0.1023 - accuracy: 0.9555 - val_loss: 0.1381 - val_accuracy: 0.9414\n",
      "Epoch 38/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.1039 - accuracy: 0.9548\n",
      "Epoch 00038: val_loss did not improve from 0.13813\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.1035 - accuracy: 0.9550 - val_loss: 0.1557 - val_accuracy: 0.9363\n",
      "Epoch 39/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.1040 - accuracy: 0.9549\n",
      "Epoch 00039: val_loss did not improve from 0.13813\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.1040 - accuracy: 0.9549 - val_loss: 0.1886 - val_accuracy: 0.9260\n",
      "Epoch 40/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.1052 - accuracy: 0.9547\n",
      "Epoch 00040: val_loss did not improve from 0.13813\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.1058 - accuracy: 0.9544 - val_loss: 0.2288 - val_accuracy: 0.9230\n",
      "Epoch 41/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.1052 - accuracy: 0.9545\n",
      "Epoch 00041: val_loss did not improve from 0.13813\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.1050 - accuracy: 0.9546 - val_loss: 0.1568 - val_accuracy: 0.9338\n",
      "Epoch 42/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.1037 - accuracy: 0.9549\n",
      "Epoch 00042: val_loss did not improve from 0.13813\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.1038 - accuracy: 0.9548 - val_loss: 0.1762 - val_accuracy: 0.9293\n",
      "Epoch 43/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0998 - accuracy: 0.9565\n",
      "Epoch 00043: val_loss did not improve from 0.13813\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0999 - accuracy: 0.9564 - val_loss: 0.1459 - val_accuracy: 0.9390\n",
      "Epoch 44/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0997 - accuracy: 0.9564\n",
      "Epoch 00044: val_loss did not improve from 0.13813\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0994 - accuracy: 0.9566 - val_loss: 0.1425 - val_accuracy: 0.9398\n",
      "Epoch 45/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0995 - accuracy: 0.9565\n",
      "Epoch 00045: val_loss did not improve from 0.13813\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0987 - accuracy: 0.9568 - val_loss: 0.1418 - val_accuracy: 0.9408\n",
      "Epoch 46/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0980 - accuracy: 0.9571\n",
      "Epoch 00046: val_loss did not improve from 0.13813\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0981 - accuracy: 0.9571 - val_loss: 0.1578 - val_accuracy: 0.9351\n",
      "Epoch 47/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0994 - accuracy: 0.9566\n",
      "Epoch 00047: val_loss did not improve from 0.13813\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0994 - accuracy: 0.9566 - val_loss: 0.1446 - val_accuracy: 0.9388\n",
      "Epoch 48/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0998 - accuracy: 0.9563\n",
      "Epoch 00048: val_loss did not improve from 0.13813\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.1004 - accuracy: 0.9560 - val_loss: 0.1811 - val_accuracy: 0.9309\n",
      "Epoch 49/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0973 - accuracy: 0.9574\n",
      "Epoch 00049: val_loss did not improve from 0.13813\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0990 - accuracy: 0.9567 - val_loss: 0.5109 - val_accuracy: 0.7712\n",
      "Epoch 50/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.1005 - accuracy: 0.9561\n",
      "Epoch 00050: val_loss did not improve from 0.13813\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0999 - accuracy: 0.9564 - val_loss: 0.2281 - val_accuracy: 0.9177\n",
      "Epoch 51/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0976 - accuracy: 0.9573\n",
      "Epoch 00051: val_loss did not improve from 0.13813\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0984 - accuracy: 0.9569 - val_loss: 0.5759 - val_accuracy: 0.7154\n",
      "Epoch 52/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0974 - accuracy: 0.9573\n",
      "Epoch 00052: val_loss did not improve from 0.13813\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0975 - accuracy: 0.9573 - val_loss: 0.1479 - val_accuracy: 0.9394\n",
      "Epoch 53/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0982 - accuracy: 0.9569\n",
      "Epoch 00053: val_loss did not improve from 0.13813\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0978 - accuracy: 0.9571 - val_loss: 0.1444 - val_accuracy: 0.9392\n",
      "Epoch 54/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0961 - accuracy: 0.9579\n",
      "Epoch 00054: val_loss did not improve from 0.13813\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0966 - accuracy: 0.9577 - val_loss: 0.1427 - val_accuracy: 0.9405\n",
      "Epoch 55/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0959 - accuracy: 0.9580\n",
      "Epoch 00055: val_loss did not improve from 0.13813\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0968 - accuracy: 0.9576 - val_loss: 0.1427 - val_accuracy: 0.9405\n",
      "Epoch 56/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0975 - accuracy: 0.9575\n",
      "Epoch 00056: val_loss did not improve from 0.13813\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0980 - accuracy: 0.9572 - val_loss: 0.1462 - val_accuracy: 0.9397\n",
      "Epoch 57/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0979 - accuracy: 0.9571\n",
      "Epoch 00057: val_loss did not improve from 0.13813\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0975 - accuracy: 0.9573 - val_loss: 0.1572 - val_accuracy: 0.9335\n",
      "Epoch 58/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0957 - accuracy: 0.9580\n",
      "Epoch 00058: val_loss did not improve from 0.13813\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0960 - accuracy: 0.9579 - val_loss: 0.1479 - val_accuracy: 0.9391\n",
      "Epoch 59/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0961 - accuracy: 0.9578\n",
      "Epoch 00059: val_loss did not improve from 0.13813\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0959 - accuracy: 0.9579 - val_loss: 0.1546 - val_accuracy: 0.9373\n",
      "Epoch 60/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0963 - accuracy: 0.9578\n",
      "Epoch 00060: val_loss did not improve from 0.13813\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0963 - accuracy: 0.9578 - val_loss: 0.1390 - val_accuracy: 0.9403\n",
      "Epoch 61/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0974 - accuracy: 0.9574\n",
      "Epoch 00061: val_loss did not improve from 0.13813\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0971 - accuracy: 0.9575 - val_loss: 0.1519 - val_accuracy: 0.9352\n",
      "Epoch 62/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0946 - accuracy: 0.9585\n",
      "Epoch 00062: val_loss improved from 0.13813 to 0.13580, saving model to model-ICNet-initial.h5\n",
      "536/536 [==============================] - 12s 23ms/sample - loss: 0.0949 - accuracy: 0.9584 - val_loss: 0.1358 - val_accuracy: 0.9417\n",
      "Epoch 63/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0941 - accuracy: 0.9586\n",
      "Epoch 00063: val_loss did not improve from 0.13580\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0947 - accuracy: 0.9583 - val_loss: 0.1359 - val_accuracy: 0.9420\n",
      "Epoch 64/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0955 - accuracy: 0.9579\n",
      "Epoch 00064: val_loss did not improve from 0.13580\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0952 - accuracy: 0.9581 - val_loss: 0.1559 - val_accuracy: 0.9351\n",
      "Epoch 65/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0963 - accuracy: 0.9579\n",
      "Epoch 00065: val_loss did not improve from 0.13580\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0965 - accuracy: 0.9578 - val_loss: 0.1463 - val_accuracy: 0.9368\n",
      "Epoch 66/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0960 - accuracy: 0.9578\n",
      "Epoch 00066: val_loss did not improve from 0.13580\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0957 - accuracy: 0.9579 - val_loss: 0.1395 - val_accuracy: 0.9411\n",
      "Epoch 67/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0970 - accuracy: 0.9574\n",
      "Epoch 00067: val_loss did not improve from 0.13580\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0967 - accuracy: 0.9576 - val_loss: 0.1442 - val_accuracy: 0.9383\n",
      "Epoch 68/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0998 - accuracy: 0.9565\n",
      "Epoch 00068: val_loss did not improve from 0.13580\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0999 - accuracy: 0.9564 - val_loss: 0.1626 - val_accuracy: 0.9318\n",
      "Epoch 69/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0970 - accuracy: 0.9574\n",
      "Epoch 00069: val_loss did not improve from 0.13580\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0980 - accuracy: 0.9570 - val_loss: 0.1604 - val_accuracy: 0.9332\n",
      "Epoch 70/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0984 - accuracy: 0.9568\n",
      "Epoch 00070: val_loss did not improve from 0.13580\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0982 - accuracy: 0.9570 - val_loss: 0.1383 - val_accuracy: 0.9408\n",
      "Epoch 71/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0960 - accuracy: 0.9579\n",
      "Epoch 00071: val_loss did not improve from 0.13580\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0955 - accuracy: 0.9582 - val_loss: 0.1561 - val_accuracy: 0.9379\n",
      "Epoch 72/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0948 - accuracy: 0.9584\n",
      "Epoch 00072: val_loss did not improve from 0.13580\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0944 - accuracy: 0.9586 - val_loss: 0.1488 - val_accuracy: 0.9387\n",
      "Epoch 73/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0953 - accuracy: 0.9581\n",
      "Epoch 00073: val_loss did not improve from 0.13580\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0951 - accuracy: 0.9582 - val_loss: 0.6703 - val_accuracy: 0.7036\n",
      "Epoch 74/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0945 - accuracy: 0.9586\n",
      "Epoch 00074: val_loss did not improve from 0.13580\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0944 - accuracy: 0.9586 - val_loss: 0.1552 - val_accuracy: 0.9364\n",
      "Epoch 75/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0930 - accuracy: 0.9591\n",
      "Epoch 00075: val_loss did not improve from 0.13580\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0932 - accuracy: 0.9590 - val_loss: 0.2094 - val_accuracy: 0.9222\n",
      "Epoch 76/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0933 - accuracy: 0.9590\n",
      "Epoch 00076: val_loss did not improve from 0.13580\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0933 - accuracy: 0.9590 - val_loss: 0.1381 - val_accuracy: 0.9413\n",
      "Epoch 77/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0915 - accuracy: 0.9598\n",
      "Epoch 00077: val_loss did not improve from 0.13580\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0922 - accuracy: 0.9595 - val_loss: 0.1416 - val_accuracy: 0.9402\n",
      "Epoch 78/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0916 - accuracy: 0.9597\n",
      "Epoch 00078: val_loss did not improve from 0.13580\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0912 - accuracy: 0.9599 - val_loss: 0.1493 - val_accuracy: 0.9385\n",
      "Epoch 79/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0908 - accuracy: 0.9600\n",
      "Epoch 00079: val_loss did not improve from 0.13580\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0911 - accuracy: 0.9598 - val_loss: 0.1443 - val_accuracy: 0.9407\n",
      "Epoch 80/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0934 - accuracy: 0.9590\n",
      "Epoch 00080: val_loss did not improve from 0.13580\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0932 - accuracy: 0.9591 - val_loss: 0.1403 - val_accuracy: 0.9404\n",
      "Epoch 81/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0936 - accuracy: 0.9590\n",
      "Epoch 00081: val_loss did not improve from 0.13580\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0935 - accuracy: 0.9590 - val_loss: 0.1516 - val_accuracy: 0.9396\n",
      "Epoch 82/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0917 - accuracy: 0.9596\n",
      "Epoch 00082: val_loss did not improve from 0.13580\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0920 - accuracy: 0.9595 - val_loss: 0.1471 - val_accuracy: 0.9396\n",
      "Epoch 83/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0932 - accuracy: 0.9592\n",
      "Epoch 00083: val_loss did not improve from 0.13580\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0935 - accuracy: 0.9590 - val_loss: 0.1521 - val_accuracy: 0.9374\n",
      "Epoch 84/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0906 - accuracy: 0.9601\n",
      "Epoch 00084: val_loss did not improve from 0.13580\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0910 - accuracy: 0.9599 - val_loss: 0.2177 - val_accuracy: 0.9255\n",
      "Epoch 85/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0947 - accuracy: 0.9585\n",
      "Epoch 00085: val_loss did not improve from 0.13580\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0944 - accuracy: 0.9586 - val_loss: 0.1669 - val_accuracy: 0.9346\n",
      "Epoch 86/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0921 - accuracy: 0.9596\n",
      "Epoch 00086: val_loss did not improve from 0.13580\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0921 - accuracy: 0.9596 - val_loss: 0.1399 - val_accuracy: 0.9405\n",
      "Epoch 87/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0920 - accuracy: 0.9596\n",
      "Epoch 00087: val_loss did not improve from 0.13580\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0914 - accuracy: 0.9598 - val_loss: 0.1379 - val_accuracy: 0.9421\n",
      "Epoch 88/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0911 - accuracy: 0.9598\n",
      "Epoch 00088: val_loss did not improve from 0.13580\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0909 - accuracy: 0.9599 - val_loss: 0.1445 - val_accuracy: 0.9403\n",
      "Epoch 89/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0901 - accuracy: 0.9603\n",
      "Epoch 00089: val_loss improved from 0.13580 to 0.13429, saving model to model-ICNet-initial.h5\n",
      "536/536 [==============================] - 12s 23ms/sample - loss: 0.0900 - accuracy: 0.9604 - val_loss: 0.1343 - val_accuracy: 0.9432\n",
      "Epoch 90/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0900 - accuracy: 0.9604\n",
      "Epoch 00090: val_loss did not improve from 0.13429\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0899 - accuracy: 0.9604 - val_loss: 0.1387 - val_accuracy: 0.9423\n",
      "Epoch 91/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0889 - accuracy: 0.9609\n",
      "Epoch 00091: val_loss did not improve from 0.13429\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0895 - accuracy: 0.9606 - val_loss: 0.1449 - val_accuracy: 0.9406\n",
      "Epoch 92/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0893 - accuracy: 0.9607\n",
      "Epoch 00092: val_loss did not improve from 0.13429\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0890 - accuracy: 0.9608 - val_loss: 0.1369 - val_accuracy: 0.9425\n",
      "Epoch 93/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0898 - accuracy: 0.9604\n",
      "Epoch 00093: val_loss did not improve from 0.13429\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0895 - accuracy: 0.9606 - val_loss: 0.1395 - val_accuracy: 0.9420\n",
      "Epoch 94/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0885 - accuracy: 0.9609\n",
      "Epoch 00094: val_loss did not improve from 0.13429\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0885 - accuracy: 0.9609 - val_loss: 0.1533 - val_accuracy: 0.9374\n",
      "Epoch 95/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0905 - accuracy: 0.9601\n",
      "Epoch 00095: val_loss did not improve from 0.13429\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0900 - accuracy: 0.9604 - val_loss: 0.1402 - val_accuracy: 0.9416\n",
      "Epoch 96/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0896 - accuracy: 0.9605\n",
      "Epoch 00096: val_loss did not improve from 0.13429\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0896 - accuracy: 0.9605 - val_loss: 0.1444 - val_accuracy: 0.9415\n",
      "Epoch 97/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0889 - accuracy: 0.9609\n",
      "Epoch 00097: val_loss did not improve from 0.13429\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0892 - accuracy: 0.9607 - val_loss: 0.1429 - val_accuracy: 0.9404\n",
      "Epoch 98/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0886 - accuracy: 0.9609\n",
      "Epoch 00098: val_loss did not improve from 0.13429\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0884 - accuracy: 0.9610 - val_loss: 0.1393 - val_accuracy: 0.9419\n",
      "Epoch 99/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0904 - accuracy: 0.9604\n",
      "Epoch 00099: val_loss did not improve from 0.13429\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0920 - accuracy: 0.9596 - val_loss: 0.4536 - val_accuracy: 0.7850\n",
      "Epoch 100/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0919 - accuracy: 0.9597\n",
      "Epoch 00100: val_loss did not improve from 0.13429\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0915 - accuracy: 0.9598 - val_loss: 0.1560 - val_accuracy: 0.9334\n",
      "Epoch 101/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0915 - accuracy: 0.9598\n",
      "Epoch 00101: val_loss did not improve from 0.13429\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0911 - accuracy: 0.9600 - val_loss: 0.1443 - val_accuracy: 0.9387\n",
      "Epoch 102/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0895 - accuracy: 0.9604\n",
      "Epoch 00102: val_loss did not improve from 0.13429\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0892 - accuracy: 0.9605 - val_loss: 0.1460 - val_accuracy: 0.9377\n",
      "Epoch 103/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0891 - accuracy: 0.9607\n",
      "Epoch 00103: val_loss did not improve from 0.13429\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0892 - accuracy: 0.9606 - val_loss: 0.1355 - val_accuracy: 0.9427\n",
      "Epoch 104/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0892 - accuracy: 0.9607\n",
      "Epoch 00104: val_loss did not improve from 0.13429\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0888 - accuracy: 0.9609 - val_loss: 0.1375 - val_accuracy: 0.9425\n",
      "Epoch 105/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0946 - accuracy: 0.9587\n",
      "Epoch 00105: val_loss did not improve from 0.13429\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0944 - accuracy: 0.9588 - val_loss: 0.1655 - val_accuracy: 0.9313\n",
      "Epoch 106/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0959 - accuracy: 0.9580\n",
      "Epoch 00106: val_loss did not improve from 0.13429\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0963 - accuracy: 0.9578 - val_loss: 0.1848 - val_accuracy: 0.9270\n",
      "Epoch 107/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0917 - accuracy: 0.9597\n",
      "Epoch 00107: val_loss did not improve from 0.13429\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0913 - accuracy: 0.9599 - val_loss: 0.1418 - val_accuracy: 0.9407\n",
      "Epoch 108/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0895 - accuracy: 0.9605\n",
      "Epoch 00108: val_loss did not improve from 0.13429\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0896 - accuracy: 0.9605 - val_loss: 0.1478 - val_accuracy: 0.9392\n",
      "Epoch 109/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0891 - accuracy: 0.9607\n",
      "Epoch 00109: val_loss did not improve from 0.13429\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0894 - accuracy: 0.9605 - val_loss: 0.2082 - val_accuracy: 0.9233\n",
      "Epoch 110/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0889 - accuracy: 0.9608\n",
      "Epoch 00110: val_loss improved from 0.13429 to 0.13366, saving model to model-ICNet-initial.h5\n",
      "536/536 [==============================] - 12s 23ms/sample - loss: 0.0889 - accuracy: 0.9608 - val_loss: 0.1337 - val_accuracy: 0.9432\n",
      "Epoch 111/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0877 - accuracy: 0.9613\n",
      "Epoch 00111: val_loss did not improve from 0.13366\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0874 - accuracy: 0.9615 - val_loss: 0.1342 - val_accuracy: 0.9436\n",
      "Epoch 112/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0875 - accuracy: 0.9614\n",
      "Epoch 00112: val_loss did not improve from 0.13366\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0873 - accuracy: 0.9615 - val_loss: 0.1359 - val_accuracy: 0.9417\n",
      "Epoch 113/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0898 - accuracy: 0.9606\n",
      "Epoch 00113: val_loss did not improve from 0.13366\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0895 - accuracy: 0.9607 - val_loss: 0.1473 - val_accuracy: 0.9394\n",
      "Epoch 114/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0927 - accuracy: 0.9593\n",
      "Epoch 00114: val_loss did not improve from 0.13366\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0927 - accuracy: 0.9593 - val_loss: 0.1731 - val_accuracy: 0.9358\n",
      "Epoch 115/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0913 - accuracy: 0.9598\n",
      "Epoch 00115: val_loss did not improve from 0.13366\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0906 - accuracy: 0.9602 - val_loss: 0.3313 - val_accuracy: 0.8427\n",
      "Epoch 116/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0884 - accuracy: 0.9610\n",
      "Epoch 00116: val_loss did not improve from 0.13366\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0885 - accuracy: 0.9610 - val_loss: 0.1811 - val_accuracy: 0.9287\n",
      "Epoch 117/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0874 - accuracy: 0.9613\n",
      "Epoch 00117: val_loss did not improve from 0.13366\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0875 - accuracy: 0.9613 - val_loss: 0.1353 - val_accuracy: 0.9432\n",
      "Epoch 118/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0876 - accuracy: 0.9614\n",
      "Epoch 00118: val_loss did not improve from 0.13366\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0877 - accuracy: 0.9613 - val_loss: 0.1406 - val_accuracy: 0.9416\n",
      "Epoch 119/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0872 - accuracy: 0.9615\n",
      "Epoch 00119: val_loss did not improve from 0.13366\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0870 - accuracy: 0.9616 - val_loss: 0.1361 - val_accuracy: 0.9434\n",
      "Epoch 120/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0866 - accuracy: 0.9617\n",
      "Epoch 00120: val_loss did not improve from 0.13366\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0863 - accuracy: 0.9619 - val_loss: 0.1399 - val_accuracy: 0.9420\n",
      "Epoch 121/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0867 - accuracy: 0.9616\n",
      "Epoch 00121: val_loss did not improve from 0.13366\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0863 - accuracy: 0.9618 - val_loss: 0.1405 - val_accuracy: 0.9415\n",
      "Epoch 122/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0853 - accuracy: 0.9622\n",
      "Epoch 00122: val_loss did not improve from 0.13366\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0850 - accuracy: 0.9623 - val_loss: 0.1383 - val_accuracy: 0.9430\n",
      "Epoch 123/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0846 - accuracy: 0.9625\n",
      "Epoch 00123: val_loss did not improve from 0.13366\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0849 - accuracy: 0.9624 - val_loss: 0.1366 - val_accuracy: 0.9434\n",
      "Epoch 124/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0848 - accuracy: 0.9625\n",
      "Epoch 00124: val_loss did not improve from 0.13366\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0848 - accuracy: 0.9625 - val_loss: 0.1367 - val_accuracy: 0.9430\n",
      "Epoch 125/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0840 - accuracy: 0.9628\n",
      "Epoch 00125: val_loss did not improve from 0.13366\n",
      "536/536 [==============================] - 12s 21ms/sample - loss: 0.0848 - accuracy: 0.9625 - val_loss: 0.1438 - val_accuracy: 0.9410\n",
      "Epoch 126/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0881 - accuracy: 0.9612\n",
      "Epoch 00126: val_loss did not improve from 0.13366\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0881 - accuracy: 0.9612 - val_loss: 0.1455 - val_accuracy: 0.9408\n",
      "Epoch 127/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0869 - accuracy: 0.9616\n",
      "Epoch 00127: val_loss did not improve from 0.13366\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0871 - accuracy: 0.9615 - val_loss: 0.1399 - val_accuracy: 0.9427\n",
      "Epoch 128/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0866 - accuracy: 0.9617\n",
      "Epoch 00128: val_loss did not improve from 0.13366\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0864 - accuracy: 0.9618 - val_loss: 0.1351 - val_accuracy: 0.9437\n",
      "Epoch 129/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0860 - accuracy: 0.9619\n",
      "Epoch 00129: val_loss did not improve from 0.13366\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0859 - accuracy: 0.9620 - val_loss: 0.1887 - val_accuracy: 0.9263\n",
      "Epoch 130/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0867 - accuracy: 0.9616\n",
      "Epoch 00130: val_loss did not improve from 0.13366\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0870 - accuracy: 0.9615 - val_loss: 0.1483 - val_accuracy: 0.9394\n",
      "Epoch 131/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0851 - accuracy: 0.9624\n",
      "Epoch 00131: val_loss did not improve from 0.13366\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0853 - accuracy: 0.9623 - val_loss: 0.1388 - val_accuracy: 0.9431\n",
      "Epoch 132/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0840 - accuracy: 0.9628\n",
      "Epoch 00132: val_loss did not improve from 0.13366\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0841 - accuracy: 0.9627 - val_loss: 0.1387 - val_accuracy: 0.9429\n",
      "Epoch 133/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0840 - accuracy: 0.9628\n",
      "Epoch 00133: val_loss did not improve from 0.13366\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0843 - accuracy: 0.9626 - val_loss: 0.1412 - val_accuracy: 0.9420\n",
      "Epoch 134/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0839 - accuracy: 0.9627\n",
      "Epoch 00134: val_loss did not improve from 0.13366\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0840 - accuracy: 0.9627 - val_loss: 0.1337 - val_accuracy: 0.9439\n",
      "Epoch 135/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0833 - accuracy: 0.9630\n",
      "Epoch 00135: val_loss did not improve from 0.13366\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0836 - accuracy: 0.9629 - val_loss: 0.1340 - val_accuracy: 0.9439\n",
      "Epoch 136/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0839 - accuracy: 0.9627\n",
      "Epoch 00136: val_loss did not improve from 0.13366\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0836 - accuracy: 0.9629 - val_loss: 0.1486 - val_accuracy: 0.9399\n",
      "Epoch 137/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0836 - accuracy: 0.9628\n",
      "Epoch 00137: val_loss did not improve from 0.13366\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0837 - accuracy: 0.9627 - val_loss: 0.1406 - val_accuracy: 0.9422\n",
      "Epoch 138/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0840 - accuracy: 0.9626\n",
      "Epoch 00138: val_loss did not improve from 0.13366\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0835 - accuracy: 0.9629 - val_loss: 0.1646 - val_accuracy: 0.9362\n",
      "Epoch 139/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0836 - accuracy: 0.9629\n",
      "Epoch 00139: val_loss did not improve from 0.13366\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0836 - accuracy: 0.9629 - val_loss: 0.1403 - val_accuracy: 0.9428\n",
      "Epoch 140/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0835 - accuracy: 0.9628\n",
      "Epoch 00140: val_loss did not improve from 0.13366\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0832 - accuracy: 0.9630 - val_loss: 0.1371 - val_accuracy: 0.9439\n",
      "Epoch 141/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0831 - accuracy: 0.9631\n",
      "Epoch 00141: val_loss did not improve from 0.13366\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0826 - accuracy: 0.9633 - val_loss: 0.1376 - val_accuracy: 0.9440\n",
      "Epoch 142/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0819 - accuracy: 0.9636\n",
      "Epoch 00142: val_loss did not improve from 0.13366\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0822 - accuracy: 0.9635 - val_loss: 0.1406 - val_accuracy: 0.9431\n",
      "Epoch 143/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0803 - accuracy: 0.9643\n",
      "Epoch 00143: val_loss did not improve from 0.13366\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0820 - accuracy: 0.9636 - val_loss: 0.1378 - val_accuracy: 0.9437\n",
      "Epoch 144/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0818 - accuracy: 0.9636\n",
      "Epoch 00144: val_loss did not improve from 0.13366\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0822 - accuracy: 0.9634 - val_loss: 0.1353 - val_accuracy: 0.9434\n",
      "Epoch 145/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0813 - accuracy: 0.9638\n",
      "Epoch 00145: val_loss did not improve from 0.13366\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0817 - accuracy: 0.9636 - val_loss: 0.1458 - val_accuracy: 0.9410\n",
      "Epoch 146/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0821 - accuracy: 0.9634\n",
      "Epoch 00146: val_loss did not improve from 0.13366\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0820 - accuracy: 0.9635 - val_loss: 0.1416 - val_accuracy: 0.9423\n",
      "Epoch 147/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0819 - accuracy: 0.9635\n",
      "Epoch 00147: val_loss did not improve from 0.13366\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0821 - accuracy: 0.9634 - val_loss: 0.1356 - val_accuracy: 0.9438\n",
      "Epoch 148/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0825 - accuracy: 0.9632\n",
      "Epoch 00148: val_loss did not improve from 0.13366\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0822 - accuracy: 0.9633 - val_loss: 0.1356 - val_accuracy: 0.9433\n",
      "Epoch 149/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0824 - accuracy: 0.9633\n",
      "Epoch 00149: val_loss did not improve from 0.13366\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0817 - accuracy: 0.9636 - val_loss: 0.1397 - val_accuracy: 0.9424\n",
      "Epoch 150/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0815 - accuracy: 0.9637\n",
      "Epoch 00150: val_loss did not improve from 0.13366\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0816 - accuracy: 0.9636 - val_loss: 0.1444 - val_accuracy: 0.9417\n",
      "Epoch 151/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0823 - accuracy: 0.9633\n",
      "Epoch 00151: val_loss did not improve from 0.13366\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0823 - accuracy: 0.9633 - val_loss: 0.1410 - val_accuracy: 0.9418\n",
      "Epoch 152/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0815 - accuracy: 0.9636\n",
      "Epoch 00152: val_loss did not improve from 0.13366\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0816 - accuracy: 0.9636 - val_loss: 0.1736 - val_accuracy: 0.9340\n",
      "Epoch 153/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0811 - accuracy: 0.9638\n",
      "Epoch 00153: val_loss did not improve from 0.13366\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0811 - accuracy: 0.9638 - val_loss: 0.1421 - val_accuracy: 0.9420\n",
      "Epoch 154/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0800 - accuracy: 0.9643\n",
      "Epoch 00154: val_loss did not improve from 0.13366\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0804 - accuracy: 0.9641 - val_loss: 0.1376 - val_accuracy: 0.9435\n",
      "Epoch 155/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0801 - accuracy: 0.9641\n",
      "Epoch 00155: val_loss did not improve from 0.13366\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0798 - accuracy: 0.9642 - val_loss: 0.1417 - val_accuracy: 0.9425\n",
      "Epoch 156/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0802 - accuracy: 0.9641\n",
      "Epoch 00156: val_loss did not improve from 0.13366\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0801 - accuracy: 0.9641 - val_loss: 0.1385 - val_accuracy: 0.9429\n",
      "Epoch 157/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0803 - accuracy: 0.9641\n",
      "Epoch 00157: val_loss did not improve from 0.13366\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0799 - accuracy: 0.9643 - val_loss: 0.1404 - val_accuracy: 0.9429\n",
      "Epoch 158/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0795 - accuracy: 0.9643\n",
      "Epoch 00158: val_loss did not improve from 0.13366\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0797 - accuracy: 0.9643 - val_loss: 0.1425 - val_accuracy: 0.9427\n",
      "Epoch 159/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0796 - accuracy: 0.9643\n",
      "Epoch 00159: val_loss did not improve from 0.13366\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0794 - accuracy: 0.9644 - val_loss: 0.1491 - val_accuracy: 0.9399\n",
      "Epoch 160/200\n",
      "528/536 [============================>.] - ETA: 0s - loss: 0.0786 - accuracy: 0.9648\n",
      "Epoch 00160: val_loss did not improve from 0.13366\n",
      "536/536 [==============================] - 11s 21ms/sample - loss: 0.0797 - accuracy: 0.9643 - val_loss: 0.1804 - val_accuracy: 0.9279\n",
      "Epoch 00160: early stopping\n"
     ]
    }
   ],
   "source": [
    "results = model.fit(X_train, Y_train, validation_split=0.2, batch_size=16, epochs=200, \n",
    "                    callbacks=[earlystopper, checkpointer])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "tx4KBbP1JWGc"
   },
   "outputs": [],
   "source": [
    "model.save_weights(\"model-ICNet-trained.h5\", overwrite=True)\n",
    "model.save(\"model-ICNet-trained-model.h5\", overwrite=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "1f381f5b-1b71-4daa-a417-e02f4894540b",
    "_uuid": "bb15226ea617cf91ed8f43179fccb5a15809e5a0",
    "colab_type": "text",
    "id": "lFe7GOzf0mSB"
   },
   "source": [
    "\n",
    "# Make predictions\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "_cell_guid": "2daa48d5-ac98-4e18-af3f-a582baaa44f0",
    "_uuid": "f841760b4abca1a25cb750822f88268bd79bf2ce",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 67
    },
    "colab_type": "code",
    "id": "VuMbLIro0mSC",
    "outputId": "5926d3a0-e95c-4f62-a088-0d5a2f31e74c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "603/603 [==============================] - 6s 10ms/sample\n",
      "67/67 [==============================] - 1s 8ms/sample\n",
      "65/65 [==============================] - 1s 8ms/sample\n"
     ]
    }
   ],
   "source": [
    "# Predict on train, val and test\n",
    "model = load_model('model-ICNet-trained-model.h5', custom_objects={'mean_iou': mean_iou})\n",
    "preds_train = model.predict(X_train[:int(X_train.shape[0]*0.9)], verbose=1)\n",
    "preds_val = model.predict(X_train[int(X_train.shape[0]*0.9):], verbose=1)\n",
    "preds_test = model.predict(X_test, verbose=1)\n",
    "\n",
    "# Threshold predictions\n",
    "preds_train_t = (preds_train > 0.5).astype(np.uint8)\n",
    "preds_val_t = (preds_val > 0.5).astype(np.uint8)\n",
    "preds_test_t = (preds_test > 0.5).astype(np.uint8)\n",
    "\n",
    "# Create list of upsampled test masks\n",
    "preds_test_upsampled = []\n",
    "for i in range(len(preds_test)):\n",
    "    preds_test_upsampled.append(resize(np.squeeze(preds_test[i]), \n",
    "                                       (sizes_test[i][0], sizes_test[i][1]), \n",
    "                                       mode='constant', preserve_range=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "_cell_guid": "649248cd-a1fb-4da6-ade2-4bebad44bcab",
    "_uuid": "7e06242a50870e07a080064a4912b761775990fa",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1273
    },
    "colab_type": "code",
    "id": "ZEDrrg8x0mSF",
    "outputId": "24ec0dde-65c0-4b16-d608-f4e195fdaeab"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/skimage/io/_plugins/matplotlib_plugin.py:51: 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",
      "  out_of_range_float = (np.issubdtype(image.dtype, np.float) and\n",
      "/usr/local/lib/python3.6/dist-packages/matplotlib/axes/_base.py:1334: MatplotlibDeprecationWarning: \n",
      "box-forced\n",
      "  \"2.2\", \"box-forced\", obj_type=\"keyword argument\")\n"
     ]
    },
    {
     "data": {
      "image/png": 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L0feh1Y+4ttiL+iQs12qc45sZ4Ouakq5N1OWfZ22jdl9ZT654n/+QUJFGu6qRNhJU9zY4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2cGUBx2Jerl+waZVQ/URhkLpmnCuJU5sz+uqFOHWq2canFfRB4yug0hr+S+OQt8O6NnSOyl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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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rKipSUVFRX74HAIAFjhw5opaWFrlcLp08eVIVFRVyuVy+11988UXt3r1bI0aM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\n",
      "text/plain": [
       "<Figure size 576x396 with 2 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Perform a sanity check on some random training samples\n",
    "ix = random.randint(0, len(preds_train_t))\n",
    "imshow(X_train[ix])\n",
    "plt.show()\n",
    "imshow(np.squeeze(Y_train[ix]))\n",
    "plt.show()\n",
    "imshow(np.squeeze(preds_train_t[ix]))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "_cell_guid": "4f66b75c-c694-41a1-8c91-34bb6595837b",
    "_uuid": "d4ccbb559375bc2777ffb692a20adc313159f2cc",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1273
    },
    "colab_type": "code",
    "id": "KyjhkVbg0mSK",
    "outputId": "f3ee111a-38bf-481f-a506-e2497df3d6c8"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/skimage/io/_plugins/matplotlib_plugin.py:51: 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",
      "  out_of_range_float = (np.issubdtype(image.dtype, np.float) and\n",
      "/usr/local/lib/python3.6/dist-packages/matplotlib/axes/_base.py:1334: MatplotlibDeprecationWarning: \n",
      "box-forced\n",
      "  \"2.2\", \"box-forced\", obj_type=\"keyword argument\")\n"
     ]
    },
    {
     "data": {
      "image/png": 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8cK+qXC6HYPV4PEan0wlewOrqalSnL37xi2HM/Md//AfK5XLkIWjKtPJHeQrEAU2f4GrR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rnzzJzSdFISKvxJhaLEr+DsrkPefC/SkO3SjMpYJTF5iPBYWBM3d/Jslj8/U+h+lU0BzXtsNW\n4/owrp0Utu/9VHJ4J+VEZ3svZUM69KS/TcFfem/KwZylDNDXxjZ8LFL+tNRzHK6gYzilKHD96Bgz\njFXJYVd9rq5PDeqYmZkJTPzq1atYWFiIICJl8qurq+j3+8E5rJAQMBBSGvBSLpfDbw8PDzOTuzxw\nJZXjk/Kt6Fim1hBJx+249enk6yCLbqlAYOdarVZwyGqUi2cCEz8sFovREXmKw+7v74ezWYFRrZcL\nKbXAKWT0mmPWjlG7MOGzWDtFF7RHDWnOgucDHBeJ45q7C6JUhqMvOD4z9Ryt16LXvDb/uBpKzmTY\nlmLZrum6Y077wPdVbdWfq9dTGnZKu1dKCQQXWvzOLYRUf/25WZ/1d+70T80fr+laHSfg/HqqXf2N\nR5/5HvLikVmF8rwvPj86duoH4HPUR6BCuNfr4dKlS/jxj38MYFAOm/fWajU0Go0wNsybcMWBzuFC\noRBKcvd6PczOzoa2NNqIc8M1T2VT29U+8n1TAtT3HtvJstD1t3yOCyC9V9eF749xlPsQcsopp5xy\nAnBKwk6JIzK0DBh48NXsVOlXLpejbMlutxtVLqTfABjVQF0bT2kzrtGMC+3TSAZti9aG3uvRPqrp\nqjXkkVGqhbA/+lzFRx1mcJhHP48zMz1Sg2OqMJ5rqBpZkwUR6VhljelJNGyOk1oTHuXB36kJn2Uy\nu2XgGrN+TuH3KfNfv1dLMivaJwu7T1kB1ETVQtM2ssoZeD6BPkfHwOPe9T63HFLWT9a6dy1f/y+V\nSpieng7wTK1Wi45WVW233+/j/PnzUWVU9pclQIg2MB9IoZ7l5eUwdnt7eyF8tVwuY29vL8qFUCib\nWdDAoFrr2bNnQ5/oQ/Awb5Jb46nrnvmva9ehbLckUzlV7NdxVU5JpwIy4qbWCWPcOzAIMSuXy1FR\nKcdwdTAajUYUvuqbzieC5KGChGK4sDjhurl1UhyqUeakjMudzSlHuEJK7qvwshDHwUuOPSpTcYbh\njir1NzhcpuRYt9/rsALJnZIcDy8KpoxOxyLFJLOgkXGwT4qyhEfq3bOgAe8DENfpSq1Pd2ZnCY1x\nDCQr3NUTGIF0kIPDqlnCndfG1crJUrC4lpVZVavVaJ/ru+p3XAf87fLycqozdgAAHoxJREFUcjjT\noFAoYG5uLsBArHNEHlGpVKKT6vT4Wh5PSZ8ki93pPmb57q2tLaysrIwkwfqc+B7jNecluk5SYcPu\nkOY18oMsYa+8NOseUg4Z5ZRTTjnlBOAWWwg0B1noqdlsJsOuUhCDlsYtlUpB2k9MTGBvby9KLHGN\nM+V4BEYdYl6Ayk3cFJTjWo07kv179t81K7caNAEuFUXk2qybl/ytV5FVDS2l1Xq6fOrv1DNdw/Tr\nWZoRxyOlVfnncXOo3/v/2he3Hnyutd0srT7LIaj36hgUCoXgpNTDZXws/P+U5eW/1WspGmdt6DWH\nxTza5zjL0pOm2EbK8entjLPMrl69CmBgSegZ7PV6HXfffTeAAZSztbUV9sn09DTa7XYIIeUe0jlh\n6ewf/ehHqNfr4XOz2YzuPTw8DM7oxcVFNJvNEKGUgrX0nVPWkc6tQpqpNeXrXa0JH+tx8OY4uqUC\nQU8lciZZKBTCdYZ+8cUI3WjYqlZMVAHgZpubampGp6JutHQ27/U4YJIyfR5/pxiybhqFPpwh+8Tr\nQmF/fMP5AtDvdeN5KQ7vo2Ocjl873OSLk39ryK0zaIdq9HNKOI6DdlLYvW8qfXf9XYr5Zpn3On9Z\nviGFFbSNVP8d/9XnjYOeKEi8H848fGxSEIQLv3GC1pl3Vn0sH1ftI9eEKijahisQ/X4/5Bbs7u6i\n1WqFPc+8AJa9aLVaISehXq/jzJkzuHz5MoBBCevJyckocqjT6WBpaSn064UXXgAA3H///dja2grR\nTvPz8+j3++Fzv98P0NSDDz4Y1fzyvccovJTQVr7Daz6fbAOIIXG/l/vNlSp9ju/FLLqlAoEvUK1W\nAxaui3RhYQHAwLnkheOUoZbL5fB3q9WKFn5q8JxxK67mKe8+aeMSsJzBpDTS1DXXFrKYB9vxzel1\nkAqFQlQyw/uYFf56HIPRfqt/g885KT6fwsXHMSP/nHXN/SkpZuwaqguCrI0zro8+b8dZOP1+XCIk\nq52Ulk1t1IMjjtO2/f9x80OFSpm9/16d2SmLQMfRLRzfF1mh2zdv3sTNmzcBDPyCjUYj9KnVaoX6\nZ4899hiefPLJ0Kf7778fExMTAeu/fv06+v1h7bP5+fmobHehUAjWQ6vVwsLCQkAveEYDLZFOpxMS\nYskr1PepY8V3TfneeM3f3e/zoAFgFLngM1IWHa+nrOQU5T6EnHLKKaecANxiC4EaP6saTk1NRWWp\nqQ1REqtEbLfbActTjYthp54w5RIyJbX7/X5kXnmZC2pOqRAumsNMiHM8MQWFuOafZXZrH4nPq6bn\nY6Nai2sOHuJKq4e/dbhMtcQUbOLYsf6tEIgmM2nUA7Ukj35xqET7lWVFeCinwyTeRzelx1l/Plfa\nrn9OQTdulaVMd/+d+6V0DH1dcw34GvL1l7KWOFe85lFwvhbdH5aCL9znAAy1XR0LXZ+9Xg/b29tB\nA282m0GrJ3bPsHQ9sIa/Zbvf+ta3IqimVquhWCwGK6DRaKDf7wcroFKphL/5Gz08p1AohD7Nz8+H\n59y4cQMLCwuZVnXKH5JlRfrck+e5RcFr3qZaDOwL7yV8re+XRbdUIDAsjMxfy1WoH6DZbIZJ5f39\nfh8/+MEPAAAPPPBAcCqXSiUsLS2NhH5lYXO8h89UpuebrlgsRoPrqei6YflchcB0Afgm8mM+fXE5\n89HvUwLPHc98jgsZh4EcUvAFroLJ8fnjGKQLSx0HvXccpJTCxZUUgkg5iLMCBMZtFFci3Kma+q2P\nm/oXvKSJPsfhF2cELoB1HL3PKYfmOOalbWhuj+fI6Drnuva2VJHwEGIS1wz38cHBAa5cuRJ8h/V6\nPfgMDg8P0W63Q1+4D/UMdhdCFBp7e3vY398PEFKlUkG5XA71z3SfFotF1Gq16CTEVquF9fX18Jl+\ni42NDbzsZS+LAj44T6n5c6VC//d9wLFJ5VPxPlfWlFLh7vq8LDoVFgJftlwuBw1gZWUlkqbAcPNU\nKhV0Oh284hWvADBkxvwd8T5gdJNxgtSRmzWBri2y3HWWJugOaGWoLmg86cs3pG4qZ66OfTsu7oxA\nGYoybneCuYaRcnqR1M/i7+dWjG8Ix42VssZWN5uPqTKZlGNO23aGmmKkPhYuNFLYu25m/31KgKco\nS0hoP7LWAdexa5R63Rl3Vt+9/IkLZxeI+r60zrPqJBUKhZAE1mg0MD8/jyeffBIAwqE25As89wQY\naOrdbjfsr1arhaOjo6DZKw/gPWTc1WoVc3NzUZntc+fOReVZ+Jt6vT6imJVKJSwuLoY+KzPXMXYr\nExhFBrIEMv/OSiDTSCf+XveTK5SufPHePA8hp5xyyimnE9EttRBokrKQnWqOPAADGJVy1BSIk+/t\n7YVry8vLQVIDoxBESmPJ0uBSYXCqubsJ75CLXtdwVW8nBZPo85WoWXgJaLblB9qnIpRcw1HSd/Mc\nBR9X1zj1WlbkhY6njouayn6PPjelJetz3GJyuMnnRzVofa6vE7+m65Fmecr/4NCOf9YxLxaLkT+F\n9/mYp8i1dr6rY9aejc9rfA9tLzWGDjN6CC7by7Jg2+02rl+/DmAQOrq+vo4LFy4AAL72ta+hVBoe\ngqNVi+kb/Pa3vw0Aoajdq1/9agDxkZ/9/iCiSH2QpVIpRC1ubGzg7rvvDhaEEiEhL8PCkNapqamg\nfc/MzETPIYSse08/O3St+5h7yfd8yufgcC7zKnTPpXiX/p9FpyIxrdvtolKpoNfrhXOSNbyrXq+j\nUBg6bHk+MTHCXq+H8+fPAxgMhIfjqYOJk6SbOStEi/3QSfENmpWmzj44Hsxn9HrD09Ta7XY0Udzc\n7hDku7pjmG3ytw4VpMoVaJ9TDNaZvVef9ZBVFyz6vjRnU0yEC90XsMNgqb/Zfx+71Ljob/T7lNme\n9VuFA1MOdr8/9Tz9Pdv00ENdm/rclIKQWiN8hpdf9rFSaFT75MLQBcA4yI/rVrFv9rHdbuPZZ58N\n+7pYLGJvby8w216vF5WdefHFFwMT5/MuXboU9ev5558PbdPB7LXO6vV6OPkMAO644w4sLy9HDno6\nnPv9fgh15RiRh/A6+1StVlGpVEbC1pVRUxjxc5bCqD5TPlf5ku5HPkMhMM3T0nlJBX+Mo1ORh6CL\njYthc3MzaAqNRgO1Wi1I9H5/kLCi/gZOqEpRtp1iEpxgz2B25uOM0DWrLEzaC62ltDMv0a0b1LVx\n7XupVIqOEz1ubP28A3es+uLhdypYKThVky8Wi5l99PfRXIksa0G/c4GXskRS7R3HrFTwuLWRJRxT\n1pb7I7KYbVa7WYxcmbFH+/h4pBQSdXCm/Bgp5cCFozOj1LupIjDOWdrv9wOjvnjxIjqdzsje4723\n3XYbvve970V4vh+YpVGIk5OTeP3rXx/aIKPudrtYXFwMfTx37hx+8IMfhH684Q1vwOTkZBSVpmto\nbW0tU1HQCLmJiQlMTU2NRDQ6I0+tcVf63B/Gv3Wd6DXllexLlmKU2jtZlPsQcsopp5xyAnCLLQTG\nGLM868zMTPAraCls1iHRw1mazWY48Yh1R4AhDquaiptMrvEoqZSmhHarwbVkIA5d5X1eslY1CY0x\nTsENrq0raRSHm/vsk74Die/uWkkqPZ5z4haDRhc5HOU4s8NPWZEnStSYszSZlH/ENaOsfqRCjfV/\nj1BK3aN9HAdPuQWQZWX6WKTacGsjNRacW7eaUuvSie2q9ZSy8HhvKiJO21Jr8OrVqwGCaTabEYQy\nNTWFg4ODYPmvrq7i4sWLgS+USqUIFZicnMTb3vY2AAPeMTs7G/jF3NxciC7sdruRf6BSqeChhx4K\nfIL+BrVQHNbRtaUWEaFtjpNCPamxyVrn3MOasa6RUlzH7kfVPnpI6knm9ji6pQKBJlyr1Qp+AgqB\n/f396FjJTqcT6ppMTU2hWq2GGuUUAqTjYAPdhIrNKTMBsh2c6o/Qa75hnalpn3Qzc1GlauCwH1kn\nmGV9TjEWZ/J+n/fJYS49d4GCSN/Rne3KKFRI6+Idh5VqH8dh/i4Qxglab9MZd9ZzfH2lvkv5P7jx\nj/Nl8G9/95PMFTBcIykYlv3zdrMcmC5IVZkhM3Wh6wKDoaU3b94M65r7WfutjLvf72NtbQ3PPvts\nuJ+/nZycRLVaDftgdnY2gmwrlUpol6Xy1TeRglPYHw8XdwXQsX9XGF2p0DXFueFn3js5OTmSoEml\ni5+VH3iOk455at2o8HC/xjjKIaOccsopp5wA3GILgabk7u4uJiYmwkEWwLBIHTBMWOE1HqBN7cI1\nNdXIKIVVmqqZ5yYevyOlzEd3HOs1fa4SNQLtY5aJ7mY+o6pIrjW69ulWjmZd+vt4OJuH26kJq312\nrUv7TytkHPSmY+3JPCmtPmUNuGZE7UyhHe+nO+30s5P2P2UJuiWSskzcUuV9Oo467x4Km5pfvabz\n4xada5natvY31SeH3nSc3Bryfuzs7AQtXzV3WjAayFGpVELUUblcxvnz50M10UajEZ7Dg2roSJ2a\nmsLk5GRUMZmVD+bn51Eul8Oc7u7uYmlpKXquW238mwEbXi05NW7cExp4kQq0SKEVvke0D7zuFqFb\n/74nUlCVz20WhEU6FSemdbtdbG9vY2pqKoSS6gBUKhV0u91gAi4sLESMzrP0fJO5B14xTmd6KTxa\n21bz2ZmARyApNKUT7KGW3me2l4q9Zx98DNVM1RBPfXdCa1khqw53ORSgzxkHx3hb/jkFfWSFXvr7\nO1yRgsYcmkv12SGjLHOf7abeV5miz6f2UdeyR4/4mvO/FaZLRR05LOkwp0YdZa2jlLB2YZsFTfrY\nXL9+Hc8991zYxxqp1mw2MTc3F/YPy0IQuuFpaYwY3Nraivqg4dlHR4PT0lhS4plnngnCotVqYXt7\nO7Rz1113RRCzYu98BxKrEeh761joWLmPR4WI3puCIV3Qp9ZWSkhrv53Jp2AxCikvq5FFt1QgcAAp\nrQ8PDwPTPzw8DNK/2+1ib28Pd9xxB4ABfqiDlcoz8GeQnCGmGK9PjlsEroHyOamyEIpT6iZy53RK\nS3YBw9+y37zPr2c5/E7yWReza0Z6fZzmlOq//lYpJVR8Dn0Re7vHOThTv3VB5JaElltnP7MEgmtz\nKesoqx1du7zX59Sxfe2/49cu3LOCKbwtfU5KMfI9434f+v54BgGt9263G+oRTUxM4ODgYERw0jHc\n6/UwNTUVagzdvHkztFMqlcIZCMAwJJVJYcvLy6G8xNraGlZXVwP/4Pu4H04DT9xaV8WAwofvoHse\nwIgVpu+WpTgVCvEZ5exDSuD6HPA+VzpSVrbmWnA+xlHuQ8gpp5xyygnALbYQ9JjLUqmEg4OD4FdY\nXFyMpNn09HSQ+MTBU7i6R9E4UQNT825cRJISTfYsbFXbUPyf/dK+Kpav2omSalJZMI+biym/QMqi\nYfvj4BjtQwrq8H4oBKiWWcqa0Gv+PE+qSr2v9t9NdDePx81X1lhoGQD+NitqKstPkLpX50P7qPdm\nwTM+bimIwK00tW6V3FrwNjzUlM9myDejiLrdLqanp4OF0O120Wq1Alyjx+LSilatWDNsuXbX1tYA\nAD/84Q9Df1mGmvNSKBTwqle9KpS9ePOb3xzCTj3qkGOa5afyfeZavlsxDhl5hJVDQSnrwqP0vHid\nj7uGmnvYut5P0uJ2jmSMoxMJhE9+8pP4xje+gW63i4997GP44he/iKeeeipMwEc/+lG89a1vxWc/\n+1l85jOfQbFYxIc//GF86EMfGtsuJ7dWq6FQKGB/fz8KO2W52unpaVSr1VDWggOVioEnZU1+v98f\nKd/rJqya2e6oczzYHTn8XCqVwiJmH32Du5AgOaNLvVuqXf2sTESd2X7Ggd7rFUqdCbJ9bTcV+uZH\nolKQZvlE3A+QgmYcr2X/U+1owIDfo2Oa8i+ksFZe82dpWy5YlVJYsG50Z0h6loWPjTM6F4bui3Eh\n5QEFOiYuqHRd0PH7/PPPR32emJjAtWvXgrLGUi/0Iei9vMbjKGdnZ7G7uxv4CMeYsPEb3/hGfPe7\n3wUw8Avcfffd0SmKWu+M0JGOjZLPj46FC3cdZz+Fz8eI/ITt6pw4VKW/99Bsd87r7znOJAqPLOVN\n/3Z+kAqeUDpWIHzlK1/BxYsX8fjjj2Nrawsf+MAH8IY3vAG///u/H5JEgAHm/6lPfQpPPPEEJicn\n8cEPfhDvfOc7o1LUIw+XNHQyLU74xMREwA8vXLgQUteBYTx9SoP2WHtKU3c0khzH80gazQ9IaRlZ\njiI6vT23gJRi4q4puTNbf+daiuKZ/hxlbqqJONPQxevadgqDHoep+yZKMSd9dx9T/c41n5RfSMfC\nNfmUYOXm02tqOaYspxRT0PdLbboUlu9jkaVRehspB3SWNZzCrfX7lMWj7epa73a7uHbtWrjWarXC\n/fv7++h2u0FgcAzVYtf50WJw/q47OzvB8QsAS0tLeOyxxwAMaxWp0lEqDQvhqQBL5X2krLRUTL4L\nCw/S0H2pgS36rll+AGfijja02+3oubqXU/44n7MU8TlZlqLTsQLhta99LR5++GEAA4neaDSSjX7n\nO9/BQw89FCb00UcfxTe/+U28/e1vP+4ROeWUU045nQIq9LNES4Ief/xxfP3rX0epVMKNGzfQ6XSw\ntLSEP/mTP8F//dd/4bvf/S4+/vGPAwD+6q/+CufOncNHPvKRzPa2t7fHWhA55ZRTTjn94ujETuUv\nfOELeOKJJ/DpT38aTz75JObn53H//ffjb//2b/HXf/3XeNWrXhXdfxI58y//8i/4zd/8TfzjP/5j\nCE0jnTlzBq95zWsADI7Wm52dHYvhZmGyhD48fMsdQbyW6rs6j9zMVWhK8fpisRhBPupAcgetOouW\nlpZw7dq1ESxyHPaXet/UZ4dU2Ae9rvASE9nYN+0HxzwFibVaLUxNTSVjqH3cjoOizp49i0uXLkW1\nZAgT8AzuLGduinh9d3c3OD45hln4rfeLvgmdTzfhU34E7aM765UcG/ZrZ8+eDRBO1vNS0JTDJrre\nU3uL9z777LO4ceNGuFYoDEvP7+zsYGJiInxeWlrC5uZmBH3QwUmoiPuiVquh1+sF/4OejwwM5pqw\nMY/PZD+np6dxcHCA+fl5vO3/tnc2IVG9Xxz/3pwZdaxf+R4toheMDKJNLSoqekGsoMhFEQwSJBGl\nZBQpIbTLXmwRbSpxlS2CVi2kWrSJsAkpotqIuxCpTHHGO+M02v0t8jyd+8xz9ff7//V//3nPB6KZ\n69znnnvuuc/rec7ZvRt9fX1qMZquyxdzTc+A3y9/F01Tcbq7Mj/uVS9RnWCaaqRpKNPeBf68uMx8\nfUtf9Of2WFZWhuHhYXUdvT5Yvnw5vPhHbqcvX77E3bt30dnZiSVLlmDr1q2orq4GAOzZswf9/f2o\nqKhQQgDA169f1aKwIAiC8P/PrA1CMpnEjRs3cO/ePTW909TUpBJVxONxVFVVYdOmTfjw4QMSiQRs\n28bbt29VD98L6imMjY1hamoKqVRKtXbLli1Tn8mbQHcno8iJpt4QYfLQ4Yu4ujcJHePBzug472H/\n/Pkzp0dJPRjqcdBmNH3hkK4VCoVci5jcVczk2WK6J5NnAvVa+CKvvnlG34nLFxDpXzabVTqnRTh+\n7+Q1QrLatg3btpGfnw/btjE4OIjBwUEMDQ1hYmLCtbjM5ePy0rW4TPn5+SgsLERhYSEsy0JRURGK\niopcG414L4/rXNc/fSaPNX6uvkinP3e9PL5gpzsGkPymniY/l/TKNz3q1/VCdx4wnavLxu+Xy8+v\nbXpnEokEEokEkskkUqkU0uk00uk0MpkM0um0ej6Tk5PKAWTRol8JcHi5PGnMokW/841MTU0pF1Ru\nr6lUyhX9lJ59OBzG0qVLlX7Hx8dd51EPmi8we9URdP8kHz07riP+rnIb4PUSlcPrD16Wfh4/X7cl\nqk/4M+X1H/2d5PWyv9ncTHVm/XVPTw9GR0fR3NysjtXV1aG5uRmFhYWIRqNob29HQUEBLly4gJMn\nT8KyLJw9e9blMWC8+LSw0WgUtm0jk8moc0pLS13DTn06g3sJ6NNH/CXUvZH06RPuOknf9bL4VvfJ\nyUmXTzK/Dr8n2mavu3+ZZNCHlnR/3Gi5F5HunWDy1NHdKbmcemISLz3qLqoz6XlyclJNG0xMTMCy\nLNeu85GRETVlQJmmSAbdkE27i3Wd8d/qw2x+r1w3euWs69HkyWH6PtN0gl4ufxb0G34+d0GlZ8l/\nz8vi01qmaQ1ub/q0k36c75yna+jTWPQ9mUzm2Dq9A2vWrHGFlLAsC5lMRnX2CgoK1HU+ffqkKlcA\n2LBhAyzLcoW45t9JTuBXXKPi4mKXXqnTQjJTMp2SkpIcHep2oE+5cPh0E+mCT//q+zO47fJnxBsa\n/Tq8s2aSUX9Hdfl1W9c7qPy4Xq/MxKwNwrFjx4wLw0eOHMk5Vltbi9ra2tmKVPBAdhSriMIFRKNR\nzxfSq0cI5GaE4pUakPvymioY/j0UCrnCcPPrcmVTj8dre7nuQsrXMXhcJbpPbmj63C//G8mnh7Yw\n+Vjr8pt8rvUKhGd1010I+egklUqpz5FIxNVgh8Nh12amRCKh3Iupp68/N6/KWZ+jpWP83mb67tVw\n8mNcz7PJQDLz++Uut15rOvzl5uVym5qpcTD9r9uy7vZsqpC8dMEbyImJCWUHf/31lysPATXIFJp+\nfHxc9fyBX/GI3r17B+BXDmJuq2/evMHmzZtVRR6JRBCJRFyxm2ht0XEcJJNJZTckM3UystmsWleh\n4Jf8XvS6xMtlk+6bN5xetqg3LPoMhg5/5/lImPTI0dOmmtzs9Q6I6dq6/c0kHyChKwRBEIRpfA1d\nwaMe6sN7HlhMH0rz+X2C/kaRT3mrbwocx3tOPMerHqBOHx14jUzouN7i894slykcDht7Jlxer94B\nl0HfBEZy899y+PCXdEq6SCaTLr3wzYG63qgcPrrhCVC4hxJNH/FhMI0W6Bp8SsPUqzENdb3m2L16\n2Fwfeq9RH3mZytNHCPw6fGrR1OvnZZF+gNzQ5vq6AJ9S0j1a+DugB16k5+PVOzSNFvRnwMuiNZfi\n4mKEw2EVaO7Hjx/IZrPqfaUeO5UVj8fVzmvaWEY2RAHquLcXf+9pChL4vbbFQ95Ho1GX/fENY142\nw/WqR4bln7nO+f96uTysvWk6kNuyacez17PlstK5XAbTb029f73Omo1/tQ9BEARBWLjIlJEgCIIA\nQBoEQRAEYRppEARBEAQA0iAIgiAI00iDIAiCIACQBkEQBEGYxrd9CFevXsX79+9hWRYuX76sci4E\niXg8jnPnzqGqqgoAsG7dOjQ0NODSpUuYmppCeXk5bt68qXZ9LmT6+/tx5swZnDhxArFYDENDQ0Y9\n/NusfH8quj5aW1vnJEvhn4qetXHjxo2Bto/5ymIJxwfi8bhz6tQpx3EcZ2BgwDl69KgfYvjO69ev\nnaamJtex1tZWp6enx3Ecx7l165bz8OFDP0T7n2LbthOLxZy2tjbnwYMHjuOY9WDbtlNTU+MkEgkn\nnU47Bw8edEZHR/0UfV4w6aOlpcV58eJFzu+CoI/e3l6noaHBcRzHGRkZcXbt2hVo+zDpY67sw5cp\no97eXuzbtw8AsHbtWoyNjal4JkEnHo9j7969AIDdu3ejt7fXZ4nmn0gkgs7OTle4dJMeeFa+goIC\nlZVvoWHSh4mg6GPLli24ffs2gN9ZG4NsHyZ9zJbF8p/qw5cGYXh42BWkqqSkRCXgCBoDAwM4ffo0\njh8/jlevXiGdTqspotLS0kDoJRQKqeinhEkPw8PDKCkpUb9ZqHZj0gcAdHd3o76+HufPn8fIyEhg\n9JGXl6ci5z5+/Bg7d+4MtH2Y9JGXlzcn9uFrLCPCCWj0jFWrVqGxsRH79+/H58+fUV9f7xmdMsh4\n6SFI+jl8+PCcZCn8k+FZG2tqatTxoNrHfGSx9GWEYMquVl5e7ocovlJZWYkDBw7AsiysXLkSZWVl\nGBsbU0G9vnz5Etisc9FoNEcPQc7KF/QshXrWxqDbx3xlsfSlQdi+fTuePXsG4FfSjIqKCixevNgP\nUXzlyZMn6OrqAgB8+/YN379/R11dndLN8+fPsWPHDj9F9I1t27bl6OE/ycq3UJirLIV/IqasjUG2\nj/nMYulbtNOOjg709fXBsixcuXIF69ev90MMXxkfH8fFixeRSCSQzWbR2NiI6upqtLS0IJPJYMWK\nFWhvb1fhgxcqHz9+xPXr1zE4OIhQKITKykp0dHSgtbU1Rw9Pnz5FV1cXLMtCLBbDoUOH/BZ/zjHp\nIxaL4f79+64shaWlpYHQx6NHj3Dnzh2sXr1aHbt27Rra2toCaR8mfdTV1aG7u/u/tg8Jfy0IgiAA\nkJ3KgiAIwjTSIAiCIAgApEEQBEEQppEGQRAEQQAgDYIgCIIwjTQIgiAIAgBpEARBEIRppEEQBEEQ\nAAB/A/FxZ87///sJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ubk7z8/Oanp7WqlWrann6UKhlqKRRRzleDaMwVPOtQsNqURh/9/L9RbHe1bJMmYqcP39e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\n/wNTQMcAQ/aUJAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AAGNQaQGApUxc06LSAgAYg0oLw9rVHGczHHbe9fX7DoffDZAILQCwl4HTg4QWAFjKxDUt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\nAGMQWgAAYxBaAABjEFoAAGP8P1PJZQfOhLbgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x396 with 2 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Perform a sanity check on some random validation samples\n",
    "ix = random.randint(0, len(preds_val_t))\n",
    "imshow(X_train[int(X_train.shape[0]*0.9):][ix])\n",
    "plt.show()\n",
    "imshow(np.squeeze(Y_train[int(Y_train.shape[0]*0.9):][ix]))\n",
    "plt.show()\n",
    "imshow(np.squeeze(preds_val_t[ix]))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "a6690535-b2e4-49ac-98d9-7191bfabfb6f",
    "_uuid": "6a34c98de7c6ae473f676a34fe7e099b46764eca",
    "colab_type": "text",
    "id": "K6ukpm3z0mSN"
   },
   "source": [
    "# Encode and submit our results\n",
    "\n",
    "- [Link](https://www.kaggle.com/rakhlin/fast-run-length-encoding-python) has an excellent implementation of run-length encoding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "_cell_guid": "59a0af60-a7d7-41ef-a6fe-9e3c72defa07",
    "_uuid": "4f99c1bf852e82b60bd4f982ca0df293f712cdf0",
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "I59hRG6T0mSO"
   },
   "outputs": [],
   "source": [
    "# Run-length encoding stolen from https://www.kaggle.com/rakhlin/fast-run-length-encoding-python\n",
    "def rle_encoding(x):\n",
    "    dots = np.where(x.T.flatten() == 1)[0]\n",
    "    run_lengths = []\n",
    "    prev = -2\n",
    "    for b in dots:\n",
    "        if (b>prev+1): run_lengths.extend((b + 1, 0))\n",
    "        run_lengths[-1] += 1\n",
    "        prev = b\n",
    "    return run_lengths\n",
    "\n",
    "def prob_to_rles(x, cutoff=0.5):\n",
    "    lab_img = label(x > cutoff)\n",
    "    for i in range(1, lab_img.max() + 1):\n",
    "        yield rle_encoding(lab_img == i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "31133f8c-3f40-4dff-8e1d-898d56672332",
    "_uuid": "2e07f6afc4787b068ba714428145dcb3951d718f",
    "colab_type": "text",
    "id": "kSOSfEv00mSQ"
   },
   "source": [
    "- Iterate over the test IDs and generate run-length encodings for each seperate mask identified by skimage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "_cell_guid": "22fe24a1-7659-4cc9-9d23-211f38e5b99f",
    "_uuid": "089587843ed6a3955fdcb9b23a6ec3bf5d703688",
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "QK1RkxuB0mSR"
   },
   "outputs": [],
   "source": [
    "new_test_ids = []\n",
    "rles = []\n",
    "for n, id_ in enumerate(test_ids):\n",
    "    rle = list(prob_to_rles(preds_test_upsampled[n]))\n",
    "    rles.extend(rle)\n",
    "    new_test_ids.extend([id_] * len(rle))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "20b6b627-0fd6-425d-888f-da7f39efb124",
    "_uuid": "849184a40a2c9c21506d8b8eb10ad9155fa229e8",
    "colab_type": "text",
    "id": "-1NLRdV70mSU"
   },
   "source": [
    "- Create submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "_cell_guid": "1ba0ee3a-cca0-4349-83f6-09a1ac6fcb44",
    "_uuid": "ba589f56f5be1e6886bc88f5bf9e7d0a408e4048",
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "uo5N7cZ60mSV"
   },
   "outputs": [],
   "source": [
    "# Create submission DataFrame\n",
    "sub = pd.DataFrame()\n",
    "sub['ImageId'] = new_test_ids\n",
    "sub['EncodedPixels'] = pd.Series(rles).apply(lambda x: ' '.join(str(y) for y in x))\n",
    "sub.to_csv('sub-dsbowl2018-1.csv', index=False)"
   ]
  }
 ],
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   "name": "Nuclei Segmentation - ICNet .ipynb",
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