building-a-model-to-classify-and-predict-defects(backup).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Building a Model to Classify and Predict defects in Steel**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem Statement\n",
    "### The objective of this competition is to predict the location and type of defects found in steel manufacturing using the images provided. The images are named with a unique ImageId, and our task is to segment each image and classify the defects in the test set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Description:\n",
    "### Each Image may have no defects, a defect of a single class, or defects of multiple classes (ClassId = [1, 2, 3, 4])."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Objective:\n",
    "### Given a image, our task is to classify the defect and locate the segmentation of the defect. For each image you must segment the defects if it belongs to each of the class (ClassId = [1, 2, 3, 4])."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Class Labels and mask Information:\n",
    "### There are two tasks associated with this problem\n",
    "1. classification of image into 4 defected classes (ClassId = [1, 2, 3, 4]).\n",
    "1. predict the location of defects found(segmentation)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NOTE: The segment for each defect class will be encoded into a single row, even if there are several non-contiguous defect locations on an image,you can read more about it  https://www.kaggle.com/c/severstal-steel-defect-detection/overview/evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Various Strategies and Solution Pipeline "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pipeline using segmentation models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### First and most efficient strategy is using a segmentation model,segmentation models used because they partitions an image into regions and can classify objects in a image\n",
    "https://www.analyticsvidhya.com/blog/2019/04/introduction-image-segmentation-techniques-python/\n",
    "![](https://miro.medium.com/max/1879/1*TqgNL1Z11KtfG8P3o4q75Q.png)\n",
    "\n",
    "### This is the straightforward pipeline,Where all the input data is trained using a strong training model(most probably pre-trained).the output layer of the model is CNN(4),where for each input image we get four masks(images) with segmentation of that class,i.e class([1,2,3,4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import json\n",
    "\n",
    "import cv2\n",
    "import keras\n",
    "from keras import backend as K\n",
    "from keras.models import Model\n",
    "from keras.layers import Input\n",
    "from keras.layers.convolutional import Conv2D, Conv2DTranspose\n",
    "from keras.layers.pooling import MaxPooling2D\n",
    "from keras.layers.merge import concatenate\n",
    "from keras.losses import binary_crossentropy\n",
    "from keras.callbacks import Callback, ModelCheckpoint\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from tqdm import tqdm\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(50272, 5)\n"
     ]
    },
    {
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       "      <th></th>\n",
       "      <th>ImageId_ClassId</th>\n",
       "      <th>EncodedPixels</th>\n",
       "      <th>ImageId</th>\n",
       "      <th>ClassId</th>\n",
       "      <th>hasMask</th>\n",
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      "text/plain": [
       "   ImageId_ClassId                                      EncodedPixels  \\\n",
       "0  0002cc93b.jpg_1  29102 12 29346 24 29602 24 29858 24 30114 24 3...   \n",
       "1  0002cc93b.jpg_2                                                NaN   \n",
       "2  0002cc93b.jpg_3                                                NaN   \n",
       "3  0002cc93b.jpg_4                                                NaN   \n",
       "4  00031f466.jpg_1                                                NaN   \n",
       "\n",
       "         ImageId ClassId  hasMask  \n",
       "0  0002cc93b.jpg       1     True  \n",
       "1  0002cc93b.jpg       2    False  \n",
       "2  0002cc93b.jpg       3    False  \n",
       "3  0002cc93b.jpg       4    False  \n",
       "4  00031f466.jpg       1    False  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = pd.read_csv('../input/severstal-steel-defect-detection/train.csv')\n",
    "train_df['ImageId'] = train_df['ImageId_ClassId'].apply(lambda x: x.split('_')[0])\n",
    "train_df['ClassId'] = train_df['ImageId_ClassId'].apply(lambda x: x.split('_')[1])\n",
    "train_df['hasMask'] = ~ train_df['EncodedPixels'].isna()\n",
    "\n",
    "print(train_df.shape)\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(12568, 2)\n"
     ]
    },
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>ImageId</th>\n",
       "      <th>hasMask</th>\n",
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       "  </thead>\n",
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       "      <td>0.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>12568 rows × 2 columns</p>\n",
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      ],
      "text/plain": [
       "             ImageId  hasMask\n",
       "10803  db4867ee8.jpg      3.0\n",
       "11776  ef24da2ba.jpg      3.0\n",
       "6284   7f30b9c64.jpg      2.0\n",
       "9421   bf0c81db6.jpg      2.0\n",
       "9615   c314f43f3.jpg      2.0\n",
       "...              ...      ...\n",
       "5616   714a7eec8.jpg      0.0\n",
       "2311   2ec4f0fa4.jpg      0.0\n",
       "2312   2ec8d4b22.jpg      0.0\n",
       "2314   2edaf414b.jpg      0.0\n",
       "2239   2d3c47b87.jpg      0.0\n",
       "\n",
       "[12568 rows x 2 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask_count_df = train_df.groupby('ImageId').agg(np.sum).reset_index()\n",
    "mask_count_df.sort_values('hasMask', ascending=False, inplace=True)\n",
    "print(mask_count_df.shape)\n",
    "mask_count_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [],
   "source": [
    "sub_df = pd.read_csv('../input/severstal-steel-defect-detection/sample_submission.csv')\n",
    "sub_df['ImageId'] = sub_df['ImageId_ClassId'].apply(lambda x: x.split('_')[0])\n",
    "test_imgs = pd.DataFrame(sub_df['ImageId'].unique(), columns=['ImageId'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Some Needed Utility Functions\n",
    "Source: https://www.kaggle.com/paulorzp/rle-functions-run-lenght-encode-decode"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mask encoding and decoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mask2rle(img):\n",
    "    '''\n",
    "    img: numpy array, 1 - mask, 0 - background\n",
    "    Returns run length as string formated\n",
    "    '''\n",
    "    pixels= img.T.flatten()\n",
    "    pixels = np.concatenate([[0], pixels, [0]])\n",
    "    runs = np.where(pixels[1:] != pixels[:-1])[0] + 1\n",
    "    runs[1::2] -= runs[::2]\n",
    "    return ' '.join(str(x) for x in runs)\n",
    "\n",
    "def rle2mask(mask_rle, shape=(256,1600)):\n",
    "    '''\n",
    "    mask_rle: run-length as string formated (start length)\n",
    "    shape: (width,height) of array to return \n",
    "    Returns numpy array, 1 - mask, 0 - background\n",
    "\n",
    "    '''\n",
    "    s = mask_rle.split()\n",
    "    starts, lengths = [np.asarray(x, dtype=int) for x in (s[0:][::2], s[1:][::2])]\n",
    "    starts -= 1\n",
    "    ends = starts + lengths\n",
    "    img = np.zeros(shape[0]*shape[1], dtype=np.uint8)\n",
    "    for lo, hi in zip(starts, ends):\n",
    "        img[lo:hi] = 1\n",
    "    return img.reshape(shape).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_masks(rles, input_shape):\n",
    "    depth = len(rles)\n",
    "    height, width = input_shape\n",
    "    masks = np.zeros((height, width, depth))\n",
    "    \n",
    "    for i, rle in enumerate(rles):\n",
    "        if type(rle) is str:\n",
    "            masks[:, :, i] = rle2mask(rle, (width, height))\n",
    "    \n",
    "    return masks\n",
    "\n",
    "def build_rles(masks):\n",
    "    width, height, depth = masks.shape\n",
    "    \n",
    "    rles = [mask2rle(masks[:, :, i])\n",
    "            for i in range(depth)]\n",
    "    \n",
    "    return rles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loss function\n",
    "Source for bce_dice_loss: https://lars76.github.io/neural-networks/object-detection/losses-for-segmentation/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dice_coef(y_true, y_pred, smooth=1):\n",
    "    y_true_f = K.flatten(y_true)\n",
    "    y_pred_f = K.flatten(y_pred)\n",
    "    intersection = K.sum(y_true_f * y_pred_f)\n",
    "    return (2. * intersection + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth)\n",
    "\n",
    "def dice_loss(y_true, y_pred):\n",
    "    smooth = 1.\n",
    "    y_true_f = K.flatten(y_true)\n",
    "    y_pred_f = K.flatten(y_pred)\n",
    "    intersection = y_true_f * y_pred_f\n",
    "    score = (2. * K.sum(intersection) + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth)\n",
    "    return 1. - score\n",
    "\n",
    "def bce_dice_loss(y_true, y_pred):\n",
    "    return binary_crossentropy(y_true, y_pred) + dice_loss(y_true, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Sample Test\n",
    "\n",
    "sample_filename = 'db4867ee8.jpg'\n",
    "sample_image_df = train_df[train_df['ImageId'] == sample_filename]\n",
    "sample_path = f\"../input/severstal-steel-defect-detection/train_images/{sample_image_df['ImageId'].iloc[0]}\"\n",
    "sample_img = cv2.imread(sample_path)\n",
    "sample_rles = sample_image_df['EncodedPixels'].values\n",
    "sample_masks = build_masks(sample_rles, input_shape=(256, 1600))\n",
    "\n",
    "fig, axs = plt.subplots(5, figsize=(12, 12))\n",
    "axs[0].imshow(sample_img)\n",
    "axs[0].axis('off')\n",
    "\n",
    "for i in range(4):\n",
    "    axs[i+1].imshow(sample_masks[:, :, i])\n",
    "    axs[i+1].axis('off')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Generator and Data Augmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DataGenerator(keras.utils.Sequence):\n",
    "    'Generates data for Keras'\n",
    "    def __init__(self, list_IDs, df, target_df=None, mode='fit',\n",
    "                 base_path='../input/severstal-steel-defect-detection/train_images',\n",
    "                 batch_size=32, dim=(256, 1600), n_channels=1,\n",
    "                 n_classes=4, random_state=2019, shuffle=True):\n",
    "        self.dim = dim\n",
    "        self.batch_size = batch_size\n",
    "        self.df = df\n",
    "        self.mode = mode\n",
    "        self.base_path = base_path\n",
    "        self.target_df = target_df\n",
    "        self.list_IDs = list_IDs\n",
    "        self.n_channels = n_channels\n",
    "        self.n_classes = n_classes\n",
    "        self.shuffle = shuffle\n",
    "        self.random_state = random_state\n",
    "        \n",
    "        self.on_epoch_end()\n",
    "        \n",
    "    def __len__(self):\n",
    "        'Denotes the number of batches per epoch'\n",
    "        return int(np.floor(len(self.list_IDs) / self.batch_size))\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        'Generate one batch of data'\n",
    "        # Generate indexes of the batch\n",
    "        indexes = self.indexes[index*self.batch_size:(index+1)*self.batch_size]\n",
    "\n",
    "        # Find list of IDs\n",
    "        list_IDs_batch = [self.list_IDs[k] for k in indexes]\n",
    "        \n",
    "        X = self.__generate_X(list_IDs_batch)\n",
    "        \n",
    "        if self.mode == 'fit':\n",
    "            y = self.__generate_y(list_IDs_batch)\n",
    "            return X, y\n",
    "        \n",
    "        elif self.mode == 'predict':\n",
    "            return X\n",
    "\n",
    "        else:\n",
    "            raise AttributeError('The mode parameter should be set to \"fit\" or \"predict\".')\n",
    "        \n",
    "    def on_epoch_end(self):\n",
    "        'Updates indexes after each epoch'\n",
    "        self.indexes = np.arange(len(self.list_IDs))\n",
    "        if self.shuffle == True:\n",
    "            np.random.seed(self.random_state)\n",
    "            np.random.shuffle(self.indexes)\n",
    "            \n",
    "            \n",
    "    def __generate_X(self, list_IDs_batch):\n",
    "        'Generates data containing batch_size samples'\n",
    "        # Initialization\n",
    "        X = np.empty((self.batch_size, *self.dim, self.n_channels))\n",
    "        \n",
    "        # Generate data\n",
    "        for i, ID in enumerate(list_IDs_batch):\n",
    "            im_name = self.df['ImageId'].iloc[ID]\n",
    "            img_path = f\"{self.base_path}/{im_name}\"\n",
    "            img = self.__load_grayscale(img_path)\n",
    "            \n",
    "            # Store samples\n",
    "            X[i,] = img\n",
    "\n",
    "        return X\n",
    "    \n",
    "    def __generate_y(self, list_IDs_batch):\n",
    "        y = np.empty((self.batch_size, *self.dim, self.n_classes), dtype=int)\n",
    "        \n",
    "        for i, ID in enumerate(list_IDs_batch):\n",
    "            im_name = self.df['ImageId'].iloc[ID]\n",
    "            image_df = self.target_df[self.target_df['ImageId'] == im_name]\n",
    "            \n",
    "            rles = image_df['EncodedPixels'].values\n",
    "            masks = build_masks(rles, input_shape=self.dim)\n",
    "            \n",
    "            y[i, ] = masks\n",
    "\n",
    "        return y\n",
    "    def __load_grayscale(self, img_path):\n",
    "        img = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE)\n",
    "        img = img.astype(np.float32) / 255.\n",
    "        img = np.expand_dims(img, axis=-1)\n",
    "\n",
    "        return img\n",
    "    \n",
    "    def __load_rgb(self, img_path):\n",
    "        img = cv2.imread(img_path)\n",
    "        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
    "        img = img.astype(np.float32) / 255.\n",
    "\n",
    "        return img\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "BATCH_SIZE = 16\n",
    "\n",
    "train_idx, val_idx = train_test_split(\n",
    "    mask_count_df.index, random_state=2019, test_size=0.15\n",
    ")\n",
    "\n",
    "train_generator = DataGenerator(\n",
    "    train_idx, \n",
    "    df=mask_count_df,\n",
    "    target_df=train_df,\n",
    "    batch_size=BATCH_SIZE, \n",
    "    n_classes=4\n",
    ")\n",
    "\n",
    "val_generator = DataGenerator(\n",
    "    val_idx, \n",
    "    df=mask_count_df,\n",
    "    target_df=train_df,\n",
    "    batch_size=BATCH_SIZE, \n",
    "    n_classes=4\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Architecture\n",
    "## The following model is directly taken from this awesome kernel: https://www.kaggle.com/jesperdramsch/intro-chest-xray-dicom-viz-u-nets-full-data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_model(input_shape):\n",
    "    inputs = Input(input_shape)\n",
    "\n",
    "    c1 = Conv2D(8, (3, 3), activation='elu', padding='same') (inputs)\n",
    "    c1 = Conv2D(8, (3, 3), activation='elu', padding='same') (c1)\n",
    "    p1 = MaxPooling2D((2, 2)) (c1)\n",
    "\n",
    "    c2 = Conv2D(16, (3, 3), activation='elu', padding='same') (p1)\n",
    "    c2 = Conv2D(16, (3, 3), activation='elu', padding='same') (c2)\n",
    "    p2 = MaxPooling2D((2, 2)) (c2)\n",
    "\n",
    "    c3 = Conv2D(32, (3, 3), activation='elu', padding='same') (p2)\n",
    "    c3 = Conv2D(32, (3, 3), activation='elu', padding='same') (c3)\n",
    "    p3 = MaxPooling2D((2, 2)) (c3)\n",
    "\n",
    "    c4 = Conv2D(64, (3, 3), activation='elu', padding='same') (p3)\n",
    "    c4 = Conv2D(64, (3, 3), activation='elu', padding='same') (c4)\n",
    "    p4 = MaxPooling2D(pool_size=(2, 2)) (c4)\n",
    "\n",
    "    c5 = Conv2D(64, (3, 3), activation='elu', padding='same') (p4)\n",
    "    c5 = Conv2D(64, (3, 3), activation='elu', padding='same') (c5)\n",
    "    p5 = MaxPooling2D(pool_size=(2, 2)) (c5)\n",
    "\n",
    "    c55 = Conv2D(128, (3, 3), activation='elu', padding='same') (p5)\n",
    "    c55 = Conv2D(128, (3, 3), activation='elu', padding='same') (c55)\n",
    "\n",
    "    u6 = Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same') (c55)\n",
    "    u6 = concatenate([u6, c5])\n",
    "    c6 = Conv2D(64, (3, 3), activation='elu', padding='same') (u6)\n",
    "    c6 = Conv2D(64, (3, 3), activation='elu', padding='same') (c6)\n",
    "\n",
    "    u71 = Conv2DTranspose(32, (2, 2), strides=(2, 2), padding='same') (c6)\n",
    "    u71 = concatenate([u71, c4])\n",
    "    c71 = Conv2D(32, (3, 3), activation='elu', padding='same') (u71)\n",
    "    c61 = Conv2D(32, (3, 3), activation='elu', padding='same') (c71)\n",
    "\n",
    "    u7 = Conv2DTranspose(32, (2, 2), strides=(2, 2), padding='same') (c61)\n",
    "    u7 = concatenate([u7, c3])\n",
    "    c7 = Conv2D(32, (3, 3), activation='elu', padding='same') (u7)\n",
    "    c7 = Conv2D(32, (3, 3), activation='elu', padding='same') (c7)\n",
    "\n",
    "    u8 = Conv2DTranspose(16, (2, 2), strides=(2, 2), padding='same') (c7)\n",
    "    u8 = concatenate([u8, c2])\n",
    "    c8 = Conv2D(16, (3, 3), activation='elu', padding='same') (u8)\n",
    "    c8 = Conv2D(16, (3, 3), activation='elu', padding='same') (c8)\n",
    "    \n",
    "    u9 = Conv2DTranspose(8, (2, 2), strides=(2, 2), padding='same') (c8)\n",
    "    u9 = concatenate([u9, c1], axis=3)\n",
    "    c9 = Conv2D(8, (3, 3), activation='elu', padding='same') (u9)\n",
    "    c9 = Conv2D(8, (3, 3), activation='elu', padding='same') (c9)\n",
    "\n",
    "    outputs = Conv2D(4, (1, 1), activation='sigmoid') (c9)\n",
    "\n",
    "    model = Model(inputs=[inputs], outputs=[outputs])\n",
    "    model.compile(optimizer='adam', loss=bce_dice_loss, metrics=[dice_coef])\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            (None, 256, 1600, 1) 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_1 (Conv2D)               (None, 256, 1600, 8) 80          input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_2 (Conv2D)               (None, 256, 1600, 8) 584         conv2d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2D)  (None, 128, 800, 8)  0           conv2d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_3 (Conv2D)               (None, 128, 800, 16) 1168        max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_4 (Conv2D)               (None, 128, 800, 16) 2320        conv2d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2D)  (None, 64, 400, 16)  0           conv2d_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_5 (Conv2D)               (None, 64, 400, 32)  4640        max_pooling2d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_6 (Conv2D)               (None, 64, 400, 32)  9248        conv2d_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2D)  (None, 32, 200, 32)  0           conv2d_6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_7 (Conv2D)               (None, 32, 200, 64)  18496       max_pooling2d_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_8 (Conv2D)               (None, 32, 200, 64)  36928       conv2d_7[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2D)  (None, 16, 100, 64)  0           conv2d_8[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_9 (Conv2D)               (None, 16, 100, 64)  36928       max_pooling2d_4[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_10 (Conv2D)              (None, 16, 100, 64)  36928       conv2d_9[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_5 (MaxPooling2D)  (None, 8, 50, 64)    0           conv2d_10[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_11 (Conv2D)              (None, 8, 50, 128)   73856       max_pooling2d_5[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_12 (Conv2D)              (None, 8, 50, 128)   147584      conv2d_11[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_1 (Conv2DTrans (None, 16, 100, 64)  32832       conv2d_12[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)     (None, 16, 100, 128) 0           conv2d_transpose_1[0][0]         \n",
      "                                                                 conv2d_10[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_13 (Conv2D)              (None, 16, 100, 64)  73792       concatenate_1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_14 (Conv2D)              (None, 16, 100, 64)  36928       conv2d_13[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_2 (Conv2DTrans (None, 32, 200, 32)  8224        conv2d_14[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_2 (Concatenate)     (None, 32, 200, 96)  0           conv2d_transpose_2[0][0]         \n",
      "                                                                 conv2d_8[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_15 (Conv2D)              (None, 32, 200, 32)  27680       concatenate_2[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_16 (Conv2D)              (None, 32, 200, 32)  9248        conv2d_15[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_3 (Conv2DTrans (None, 64, 400, 32)  4128        conv2d_16[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_3 (Concatenate)     (None, 64, 400, 64)  0           conv2d_transpose_3[0][0]         \n",
      "                                                                 conv2d_6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_17 (Conv2D)              (None, 64, 400, 32)  18464       concatenate_3[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_18 (Conv2D)              (None, 64, 400, 32)  9248        conv2d_17[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_4 (Conv2DTrans (None, 128, 800, 16) 2064        conv2d_18[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_4 (Concatenate)     (None, 128, 800, 32) 0           conv2d_transpose_4[0][0]         \n",
      "                                                                 conv2d_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_19 (Conv2D)              (None, 128, 800, 16) 4624        concatenate_4[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_20 (Conv2D)              (None, 128, 800, 16) 2320        conv2d_19[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_5 (Conv2DTrans (None, 256, 1600, 8) 520         conv2d_20[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_5 (Concatenate)     (None, 256, 1600, 16 0           conv2d_transpose_5[0][0]         \n",
      "                                                                 conv2d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_21 (Conv2D)              (None, 256, 1600, 8) 1160        concatenate_5[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_22 (Conv2D)              (None, 256, 1600, 8) 584         conv2d_21[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_23 (Conv2D)              (None, 256, 1600, 4) 36          conv2d_22[0][0]                  \n",
      "==================================================================================================\n",
      "Total params: 600,612\n",
      "Trainable params: 600,612\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = build_model((256, 1600, 1))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "667/667 [==============================] - 409s 614ms/step - loss: 1.1290 - dice_coef: 0.0471 - val_loss: 0.9579 - val_dice_coef: 0.0829\n",
      "Epoch 2/10\n",
      "667/667 [==============================] - 394s 590ms/step - loss: 0.9510 - dice_coef: 0.1055 - val_loss: 0.7169 - val_dice_coef: 0.1272\n",
      "Epoch 3/10\n",
      "667/667 [==============================] - 390s 584ms/step - loss: 0.8618 - dice_coef: 0.1931 - val_loss: 0.5605 - val_dice_coef: 0.2201\n",
      "Epoch 4/10\n",
      "667/667 [==============================] - 390s 585ms/step - loss: 0.7783 - dice_coef: 0.2748 - val_loss: 0.5361 - val_dice_coef: 0.3074\n",
      "Epoch 5/10\n",
      "667/667 [==============================] - 387s 580ms/step - loss: 0.7114 - dice_coef: 0.3393 - val_loss: 0.5266 - val_dice_coef: 0.3454\n",
      "Epoch 6/10\n",
      "667/667 [==============================] - 386s 579ms/step - loss: 0.6652 - dice_coef: 0.3828 - val_loss: 0.4004 - val_dice_coef: 0.3996\n",
      "Epoch 7/10\n",
      "667/667 [==============================] - 382s 573ms/step - loss: 0.6225 - dice_coef: 0.4239 - val_loss: 0.4013 - val_dice_coef: 0.4348\n",
      "Epoch 8/10\n",
      "667/667 [==============================] - 386s 578ms/step - loss: 0.5933 - dice_coef: 0.4511 - val_loss: 0.3825 - val_dice_coef: 0.4558\n",
      "Epoch 9/10\n",
      "667/667 [==============================] - 383s 575ms/step - loss: 0.5644 - dice_coef: 0.4787 - val_loss: 0.4745 - val_dice_coef: 0.4500\n",
      "Epoch 10/10\n",
      "667/667 [==============================] - 387s 581ms/step - loss: 0.5547 - dice_coef: 0.4878 - val_loss: 0.4384 - val_dice_coef: 0.4830\n"
     ]
    }
   ],
   "source": [
    "checkpoint = ModelCheckpoint(\n",
    "    'model.h5', \n",
    "    monitor='val_dice_coef', \n",
    "    verbose=0, \n",
    "    save_best_only=True, \n",
    "    save_weights_only=False,\n",
    "    mode='auto'\n",
    ")\n",
    "\n",
    "history = model.fit_generator(\n",
    "    train_generator,\n",
    "    validation_data=val_generator,\n",
    "    callbacks=[checkpoint],\n",
    "    use_multiprocessing=False,\n",
    "    workers=1,\n",
    "    epochs=10\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'args' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-14-dfdba0f94152>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"model\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'args' is not defined"
     ]
    }
   ],
   "source": [
    "model.save(args[\"model\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7dfc2fc278>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''with open('history.json', 'w') as f:\n",
    "    json.dump(history.history, f)'''\n",
    "\n",
    "history_df = pd.DataFrame(history.history)\n",
    "history_df[['loss', 'val_loss']].plot()\n",
    "history_df[['dice_coef', 'val_dice_coef']].plot()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}