best_cnn_segmentation_connected_components_384x384_ff.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "best-cnn-segmentation-connected-components-384x384.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "metadata": {
        "id": "igEd7obtcxR2",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Approach\n",
        "\n",
        "* Firstly a convolutional neural network is used to segment the image, using the bounding boxes directly as a mask. \n",
        "* Secondly connected components is used to separate multiple areas of predicted pneumonia.\n",
        "* Finally a bounding box is simply drawn around every connected component.\n",
        "\n",
        "# Network\n",
        "\n",
        "* The network consists of a number of residual blocks with convolutions and downsampling blocks with max pooling.\n",
        "* At the end of the network a single upsampling layer converts the output to the same shape as the input.\n",
        "\n",
        "As the input to the network is 256 by 256 (instead of the original 1024 by 1024) and the network downsamples a number of times without any meaningful upsampling (the final upsampling is just to match in 256 by 256 mask) the final prediction is very crude. If the network downsamples 4 times the final bounding boxes can only change with at least 16 pixels.\n",
        "\n",
        "**Edit by EAS** - Change input image size to 320x320."
      ]
    },
    {
      "metadata": {
        "id": "vlU7J4Uvc9jY",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# install dependencies not included by Colab\n",
        "# use pip3 to ensure compatibility w/ Google Deep Learning Images \n",
        "!pip install -q pydicom \n",
        "!pip install -q imgaug \n",
        "!pip install -q kaggle\n",
        "!pip install -q tqdm \n",
        "!pip install -q PyDrive"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "LHncOor-cxSS",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import os\n",
        "import csv\n",
        "import random\n",
        "import pydicom\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from skimage import measure\n",
        "from skimage.transform import resize\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "\n",
        "from matplotlib import pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "xu0hwg-MdA1l",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from google.colab import auth\n",
        "from googleapiclient.http import MediaFileUpload\n",
        "from googleapiclient.discovery import build\n",
        "\n",
        "auth.authenticate_user()\n",
        "\n",
        "drive_service = build('drive', 'v3')\n",
        "\n",
        "def save_file_to_drive(name, path):\n",
        "    file_metadata = {\n",
        "      'name': name,\n",
        "      'mimeType': 'application/octet-stream'\n",
        "     }\n",
        "\n",
        "    media = MediaFileUpload(path, \n",
        "                    mimetype='application/octet-stream',\n",
        "                    resumable=True)\n",
        "\n",
        "    created = drive_service.files().create(body=file_metadata,\n",
        "                                   media_body=media,\n",
        "                                   fields='id').execute()\n",
        "\n",
        "    print('File ID: {}'.format(created.get('id')))\n",
        "\n",
        "    return created"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "hE3jDmgudDDP",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# enter your Kaggle credentionals here\n",
        "os.environ['KAGGLE_USERNAME']=\"skooch\"\n",
        "os.environ['KAGGLE_KEY']=\"42f8a02ee92cc773d1dbe66565673ad3\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "jowYWnxzdDtf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "outputId": "4696140f-3876-45ff-c828-f2cbb7e8afd6"
      },
      "cell_type": "code",
      "source": [
        "# If you are unable to download the competition dataset, check to see if you have \n",
        "# accepted the user agreement on the competition website. \n",
        "if not os.path.exists(\"./stage_1_detailed_class_info.csv.zip\"):\n",
        "  !kaggle competitions download -c rsna-pneumonia-detection-challenge"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading GCP%20Credits%20Request%20Link%20-%20RSNA.txt to /content\n",
            "\r  0% 0.00/55.0 [00:00<?, ?B/s]\n",
            "100% 55.0/55.0 [00:00<00:00, 36.7kB/s]\n",
            "Downloading stage_1_detailed_class_info.csv.zip to /content\n",
            "  0% 0.00/561k [00:00<?, ?B/s]\n",
            "100% 561k/561k [00:00<00:00, 51.6MB/s]\n",
            "Downloading stage_1_sample_submission.csv to /content\n",
            "  0% 0.00/51.8k [00:00<?, ?B/s]\n",
            "100% 51.8k/51.8k [00:00<00:00, 38.8MB/s]\n",
            "Downloading stage_1_train_labels.csv.zip to /content\n",
            "  0% 0.00/634k [00:00<?, ?B/s]\n",
            "100% 634k/634k [00:00<00:00, 81.5MB/s]\n",
            "Downloading stage_1_test_images.zip to /content\n",
            " 90% 114M/126M [00:02<00:00, 39.0MB/s]\n",
            "100% 126M/126M [00:02<00:00, 45.9MB/s]\n",
            "Downloading stage_1_train_images.zip to /content\n",
            "100% 3.16G/3.17G [01:05<00:00, 70.9MB/s]\n",
            "100% 3.17G/3.17G [01:05<00:00, 51.7MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "HttYLaczdKlJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "f013c5f9-d810-4ba8-9dee-dc87d44be72f"
      },
      "cell_type": "code",
      "source": [
        "# unzipping takes a few minutes\n",
        "if not os.path.exists(\"./stage_1_train_labels.csv\"):\n",
        "  print(\"Unzipping...\")\n",
        "  !unzip -q -o stage_1_test_images.zip -d stage_1_test_images\n",
        "  !unzip -q -o stage_1_train_images.zip -d stage_1_train_images\n",
        "  !unzip -q -o stage_1_train_labels.csv.zip"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Unzipping...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "sSWY-U2TcxSn",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Load pneumonia locations\n",
        "\n",
        "Table contains [filename : pneumonia location] pairs per row. \n",
        "* If a filename contains multiple pneumonia, the table contains multiple rows with the same filename but different pneumonia locations. \n",
        "* If a filename contains no pneumonia it contains a single row with an empty pneumonia location.\n",
        "\n",
        "The code below loads the table and transforms it into a dictionary. \n",
        "* The dictionary uses the filename as key and a list of pneumonia locations in that filename as value. \n",
        "* If a filename is not present in the dictionary it means that it contains no pneumonia."
      ]
    },
    {
      "metadata": {
        "id": "m_0ZGQyLdQS8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "6dc7adbf-5e16-401a-e2e1-2ec235b01d19"
      },
      "cell_type": "code",
      "source": [
        "ROOT_DIR = \"./\"\n",
        "\n",
        "train_dicom_dir = os.path.join(ROOT_DIR, 'stage_1_train_images')\n",
        "test_dicom_dir = os.path.join(ROOT_DIR, 'stage_1_test_images')\n",
        "print(\"Train dir:\", train_dicom_dir)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train dir: ./stage_1_train_images\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "hopsRmICcxSs",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# empty dictionary\n",
        "pneumonia_locations = {}\n",
        "# load table\n",
        "with open(os.path.join('./stage_1_train_labels.csv'), mode='r') as infile:\n",
        "    # open reader\n",
        "    reader = csv.reader(infile)\n",
        "    # skip header\n",
        "    next(reader, None)\n",
        "    # loop through rows\n",
        "    for rows in reader:\n",
        "        # retrieve information\n",
        "        filename = rows[0]\n",
        "        location = rows[1:5]\n",
        "        pneumonia = rows[5]\n",
        "        # if row contains pneumonia add label to dictionary\n",
        "        # which contains a list of pneumonia locations per filename\n",
        "        if pneumonia == '1':\n",
        "            # convert string to float to int\n",
        "            location = [int(float(i)) for i in location]\n",
        "            # save pneumonia location in dictionary\n",
        "            if filename in pneumonia_locations:\n",
        "                pneumonia_locations[filename].append(location)\n",
        "            else:\n",
        "                pneumonia_locations[filename] = [location]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "UoB6GBhIcxS6",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Load filenames"
      ]
    },
    {
      "metadata": {
        "id": "b_iB-IftcxTE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "2a4a6a7a-8ef1-4197-d658-3c19dbb12aca"
      },
      "cell_type": "code",
      "source": [
        "random.seed(a=1)\n",
        "# load and shuffle filenames\n",
        "folder = './stage_1_train_images'\n",
        "filenames = os.listdir(folder)\n",
        "random.shuffle(filenames)\n",
        "# split into train and validation filenames\n",
        "n_valid_samples = 2560\n",
        "train_filenames = filenames[n_valid_samples:]\n",
        "valid_filenames = filenames[:n_valid_samples]\n",
        "print('n train samples', len(train_filenames))\n",
        "print('n valid samples', len(valid_filenames))\n",
        "n_train_samples = len(filenames) - n_valid_samples"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "n train samples 23124\n",
            "n valid samples 2560\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "lorV7ZmDcxTa",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        " # Data generator\n",
        "\n",
        "The dataset is too large to fit into memory, so we need to create a generator that loads data on the fly.\n",
        "\n",
        "* The generator takes in some filenames, batch_size and other parameters.\n",
        "\n",
        "* The generator outputs a random batch of numpy images and numpy masks.\n",
        "    "
      ]
    },
    {
      "metadata": {
        "id": "dFbLdFxVehhB",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "BATCH_SIZE = 12\n",
        "IMAGE_SIZE = 384\n",
        "CHECKPOINT_PATH = \"model7_384.h5\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "nxYsnFo3HIH8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "1836b52a-5fc7-4e3c-c9c9-cd8281f4b61f"
      },
      "cell_type": "code",
      "source": [
        "# upload checkpoint to GCS\n",
        "project_id = 'mammography-198911'\n",
        "bucket_name = 'pneumonia'\n",
        "\n",
        "!gcloud config set project {project_id}\n",
        "!gsutil cp gs://{bucket_name}/{CHECKPOINT_PATH} ./{CHECKPOINT_PATH}"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Updated property [core/project].\n",
            "CommandException: No URLs matched: gs://pneumonia/model7_384.h5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "TueY1bVlcxTg",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "class generator(keras.utils.Sequence):\n",
        "    \n",
        "    def __init__(self, folder, filenames, pneumonia_locations=None, batch_size=BATCH_SIZE, image_size=IMAGE_SIZE, shuffle=True, augment=False, predict=False):\n",
        "        self.folder = folder\n",
        "        self.filenames = filenames\n",
        "        self.pneumonia_locations = pneumonia_locations\n",
        "        self.batch_size = batch_size\n",
        "        self.image_size = image_size\n",
        "        self.shuffle = shuffle\n",
        "        self.augment = augment\n",
        "        self.predict = predict\n",
        "        self.on_epoch_end()\n",
        "        \n",
        "    def __load__(self, filename):\n",
        "        # load dicom file as numpy array\n",
        "        img = pydicom.dcmread(os.path.join(self.folder, filename)).pixel_array\n",
        "        # create empty mask\n",
        "        msk = np.zeros(img.shape)\n",
        "        # get filename without extension\n",
        "        filename = filename.split('.')[0]\n",
        "        # if image contains pneumonia\n",
        "        if filename in pneumonia_locations:\n",
        "            # loop through pneumonia\n",
        "            for location in pneumonia_locations[filename]:\n",
        "                # add 1's at the location of the pneumonia\n",
        "                x, y, w, h = location\n",
        "                msk[y:y+h, x:x+w] = 1\n",
        "        # if augment then horizontal flip half the time\n",
        "        if self.augment and random.random() > 0.5:\n",
        "            img = np.fliplr(img)\n",
        "            msk = np.fliplr(msk)\n",
        "            \n",
        "        # small shifts to images\n",
        "        if self.augment:\n",
        "            h_offset = np.random.randint(low=0, high=10)\n",
        "            v_offset = np.random.randint(low=0, high=10)\n",
        "            # crop the images\n",
        "            img = img[v_offset:,h_offset:]\n",
        "            msk = msk[v_offset:,h_offset:]\n",
        "            \n",
        "        # resize both image and mask\n",
        "        img = resize(img, (self.image_size, self.image_size), mode='reflect')\n",
        "        msk = resize(msk, (self.image_size, self.image_size), mode='reflect') > 0.5\n",
        "        # add trailing channel dimension\n",
        "        img = np.expand_dims(img, -1)\n",
        "        msk = np.expand_dims(msk, -1)\n",
        "        return img, msk\n",
        "    \n",
        "    def __loadpredict__(self, filename):\n",
        "        # load dicom file as numpy array\n",
        "        img = pydicom.dcmread(os.path.join(self.folder, filename)).pixel_array\n",
        "        # resize image\n",
        "        img = resize(img, (self.image_size, self.image_size), mode='reflect')\n",
        "        # add trailing channel dimension\n",
        "        img = np.expand_dims(img, -1)\n",
        "        return img\n",
        "        \n",
        "    def __getitem__(self, index):\n",
        "        # select batch\n",
        "        filenames = self.filenames[index*self.batch_size:(index+1)*self.batch_size]\n",
        "        # predict mode: return images and filenames\n",
        "        if self.predict:\n",
        "            # load files\n",
        "            imgs = [self.__loadpredict__(filename) for filename in filenames]\n",
        "            # create numpy batch\n",
        "            imgs = np.array(imgs)\n",
        "            return imgs, filenames\n",
        "        # train mode: return images and masks\n",
        "        else:\n",
        "            # load files\n",
        "            items = [self.__load__(filename) for filename in filenames]\n",
        "            # unzip images and masks\n",
        "            imgs, msks = zip(*items)\n",
        "            # create numpy batch\n",
        "            imgs = np.array(imgs)\n",
        "            msks = np.array(msks)\n",
        "            return imgs, msks\n",
        "        \n",
        "    def on_epoch_end(self):\n",
        "        if self.shuffle:\n",
        "            random.shuffle(self.filenames)\n",
        "        \n",
        "    def __len__(self):\n",
        "        if self.predict:\n",
        "            # return everything\n",
        "            return int(np.ceil(len(self.filenames) / self.batch_size))\n",
        "        else:\n",
        "            # return full batches only\n",
        "            return int(len(self.filenames) / self.batch_size)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "3_GgDL7ncxT3",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Network"
      ]
    },
    {
      "metadata": {
        "id": "bqobZQZQcxUE",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def create_downsample(channels, inputs):\n",
        "    x = keras.layers.BatchNormalization(momentum=0.9)(inputs)\n",
        "    x = keras.layers.LeakyReLU(0)(x)\n",
        "    x = keras.layers.Conv2D(channels, 1, padding='same', use_bias=False)(x)\n",
        "    x = keras.layers.MaxPool2D(2)(x)\n",
        "    return x\n",
        "\n",
        "def create_resblock(channels, inputs):\n",
        "    x = keras.layers.BatchNormalization(momentum=0.9)(inputs)\n",
        "    x = keras.layers.LeakyReLU(0)(x)\n",
        "    x = keras.layers.Conv2D(channels, 3, padding='same', use_bias=False)(x)\n",
        "    x = keras.layers.BatchNormalization(momentum=0.9)(x)\n",
        "    x = keras.layers.LeakyReLU(0)(x)\n",
        "    x = keras.layers.Conv2D(channels, 3, padding='same', use_bias=False)(x)\n",
        "    return keras.layers.add([x, inputs])\n",
        "\n",
        "def create_network(input_size, channels, n_blocks=2, depth=4):\n",
        "    # input\n",
        "    inputs = keras.Input(shape=(input_size, input_size, 1))\n",
        "    x = keras.layers.Conv2D(channels, 3, padding='same', use_bias=False)(inputs)\n",
        "    x = keras.layers.BatchNormalization(momentum=0.9)(x)\n",
        "    x = keras.layers.LeakyReLU(0)(x)\n",
        "    \n",
        "    x = keras.layers.Conv2D(channels, 3, padding='same', use_bias=False)(x)\n",
        "    x = keras.layers.BatchNormalization(momentum=0.9)(x)\n",
        "    x = keras.layers.LeakyReLU(0)(x)\n",
        "    \n",
        "    x = keras.layers.Conv2D(channels, 3, padding='same', use_bias=False)(x)\n",
        "    \n",
        "    # residual blocks\n",
        "    for d in range(depth):\n",
        "        channels = channels * 2\n",
        "        \n",
        "        if channels > 384:\n",
        "          channels = 384\n",
        "        \n",
        "        x = create_downsample(channels, x)\n",
        "        for b in range(n_blocks):\n",
        "            x = create_resblock(channels, x)\n",
        "            \n",
        "    # output\n",
        "    x = keras.layers.BatchNormalization(momentum=0.9)(x)\n",
        "    x = keras.layers.LeakyReLU(0)(x)\n",
        "    x = keras.layers.Dropout(0.20)(x)\n",
        "    \n",
        "    x_2 = keras.layers.Conv2D(512, (3,3), padding='same', dilation_rate=(2,2), activation=None)(x)\n",
        "    x = keras.layers.BatchNormalization(momentum=0.9)(x_2)\n",
        "    x = keras.layers.LeakyReLU(0)(x)\n",
        "    \n",
        "    x = keras.layers.Conv2D(512, (3,3), padding='same', dilation_rate=(2,2), activation=None)(x)\n",
        "    # residual connection\n",
        "    x = keras.layers.add([x, x_2])\n",
        "    x = keras.layers.BatchNormalization(momentum=0.9)(x)\n",
        "    x = keras.layers.LeakyReLU(0)(x)\n",
        "    \n",
        "    x = keras.layers.Conv2D(256, 1, activation=None, kernel_regularizer=keras.regularizers.l2(l=0.01))(x)\n",
        "    x = keras.layers.BatchNormalization(momentum=0.9)(x)\n",
        "    x = keras.layers.LeakyReLU(0)(x)\n",
        "    \n",
        "    x = keras.layers.Conv2D(1, 1, activation='sigmoid')(x)\n",
        "    outputs = keras.layers.UpSampling2D(2**depth)(x)\n",
        "    model = keras.Model(inputs=inputs, outputs=outputs)\n",
        "    return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "AvdSzGI4cxUL",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Train network\n"
      ]
    },
    {
      "metadata": {
        "id": "DCgIz8CwcxUO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 3871
        },
        "outputId": "c61f01db-a33a-4639-9e52-8702d9573ddd"
      },
      "cell_type": "code",
      "source": [
        "# define iou or jaccard loss function\n",
        "def iou_loss(y_true, y_pred):\n",
        "    y_true = tf.reshape(y_true, [-1])\n",
        "    y_pred = tf.reshape(y_pred, [-1])\n",
        "    intersection = tf.reduce_sum(y_true * y_pred)\n",
        "    score = (intersection + 1.) / (tf.reduce_sum(y_true) + tf.reduce_sum(y_pred) - intersection + 1.)\n",
        "    return 1 - score\n",
        "\n",
        "# combine bce loss and iou loss\n",
        "def iou_bce_loss(y_true, y_pred):\n",
        "    return 0.5 * keras.losses.binary_crossentropy(y_true, y_pred) + 0.5 * iou_loss(y_true, y_pred)\n",
        "\n",
        "# mean iou as a metric\n",
        "def mean_iou(y_true, y_pred):\n",
        "    y_pred = tf.round(y_pred)\n",
        "    intersect = tf.reduce_sum(y_true * y_pred, axis=[1, 2, 3])\n",
        "    union = tf.reduce_sum(y_true, axis=[1, 2, 3]) + tf.reduce_sum(y_pred, axis=[1, 2, 3])\n",
        "    smooth = tf.ones(tf.shape(intersect))\n",
        "    return tf.reduce_mean((intersect + smooth) / (union - intersect + smooth))\n",
        "\n",
        "# create network and compiler\n",
        "model = create_network(input_size=IMAGE_SIZE, channels=32, n_blocks=2, depth=4)\n",
        "model.compile(optimizer='adam',\n",
        "              loss=iou_bce_loss,\n",
        "              metrics=['accuracy', mean_iou])\n",
        "\n",
        "# cosine learning rate annealing\n",
        "def cosine_annealing(x):\n",
        "    lr = 0.001\n",
        "    epochs = 20\n",
        "    return lr*(np.cos(np.pi*x/epochs)+1.)/2\n",
        "learning_rate = tf.keras.callbacks.LearningRateScheduler(cosine_annealing)\n",
        "checkpoint = keras.callbacks.ModelCheckpoint(CHECKPOINT_PATH, monitor='val_loss', verbose=1, save_best_only=False, save_weights_only=True, mode='auto', period=1)\n",
        "\n",
        "# create train and validation generators\n",
        "folder = './stage_1_train_images'\n",
        "train_gen = generator(folder, train_filenames, pneumonia_locations, batch_size=BATCH_SIZE, image_size=IMAGE_SIZE, shuffle=True, augment=True, predict=False)\n",
        "valid_gen = generator(folder, valid_filenames, pneumonia_locations, batch_size=BATCH_SIZE, image_size=IMAGE_SIZE, shuffle=False, predict=False)\n",
        "\n",
        "print(model.summary())"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_1 (InputLayer)            (None, 384, 384, 1)  0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d (Conv2D)                 (None, 384, 384, 32) 288         input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization (BatchNorma (None, 384, 384, 32) 128         conv2d[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu (LeakyReLU)         (None, 384, 384, 32) 0           batch_normalization[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_1 (Conv2D)               (None, 384, 384, 32) 9216        leaky_re_lu[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_1 (BatchNor (None, 384, 384, 32) 128         conv2d_1[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_1 (LeakyReLU)       (None, 384, 384, 32) 0           batch_normalization_1[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_2 (Conv2D)               (None, 384, 384, 32) 9216        leaky_re_lu_1[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_2 (BatchNor (None, 384, 384, 32) 128         conv2d_2[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_2 (LeakyReLU)       (None, 384, 384, 32) 0           batch_normalization_2[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_3 (Conv2D)               (None, 384, 384, 64) 2048        leaky_re_lu_2[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D)    (None, 192, 192, 64) 0           conv2d_3[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_3 (BatchNor (None, 192, 192, 64) 256         max_pooling2d[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_3 (LeakyReLU)       (None, 192, 192, 64) 0           batch_normalization_3[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_4 (Conv2D)               (None, 192, 192, 64) 36864       leaky_re_lu_3[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_4 (BatchNor (None, 192, 192, 64) 256         conv2d_4[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_4 (LeakyReLU)       (None, 192, 192, 64) 0           batch_normalization_4[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_5 (Conv2D)               (None, 192, 192, 64) 36864       leaky_re_lu_4[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "add (Add)                       (None, 192, 192, 64) 0           conv2d_5[0][0]                   \n",
            "                                                                 max_pooling2d[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_5 (BatchNor (None, 192, 192, 64) 256         add[0][0]                        \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_5 (LeakyReLU)       (None, 192, 192, 64) 0           batch_normalization_5[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_6 (Conv2D)               (None, 192, 192, 64) 36864       leaky_re_lu_5[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_6 (BatchNor (None, 192, 192, 64) 256         conv2d_6[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_6 (LeakyReLU)       (None, 192, 192, 64) 0           batch_normalization_6[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_7 (Conv2D)               (None, 192, 192, 64) 36864       leaky_re_lu_6[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "add_1 (Add)                     (None, 192, 192, 64) 0           conv2d_7[0][0]                   \n",
            "                                                                 add[0][0]                        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_7 (BatchNor (None, 192, 192, 64) 256         add_1[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_7 (LeakyReLU)       (None, 192, 192, 64) 0           batch_normalization_7[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_8 (Conv2D)               (None, 192, 192, 128 8192        leaky_re_lu_7[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2D)  (None, 96, 96, 128)  0           conv2d_8[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_8 (BatchNor (None, 96, 96, 128)  512         max_pooling2d_1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_8 (LeakyReLU)       (None, 96, 96, 128)  0           batch_normalization_8[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_9 (Conv2D)               (None, 96, 96, 128)  147456      leaky_re_lu_8[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_9 (BatchNor (None, 96, 96, 128)  512         conv2d_9[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_9 (LeakyReLU)       (None, 96, 96, 128)  0           batch_normalization_9[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_10 (Conv2D)              (None, 96, 96, 128)  147456      leaky_re_lu_9[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "add_2 (Add)                     (None, 96, 96, 128)  0           conv2d_10[0][0]                  \n",
            "                                                                 max_pooling2d_1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_10 (BatchNo (None, 96, 96, 128)  512         add_2[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_10 (LeakyReLU)      (None, 96, 96, 128)  0           batch_normalization_10[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_11 (Conv2D)              (None, 96, 96, 128)  147456      leaky_re_lu_10[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_11 (BatchNo (None, 96, 96, 128)  512         conv2d_11[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_11 (LeakyReLU)      (None, 96, 96, 128)  0           batch_normalization_11[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_12 (Conv2D)              (None, 96, 96, 128)  147456      leaky_re_lu_11[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "add_3 (Add)                     (None, 96, 96, 128)  0           conv2d_12[0][0]                  \n",
            "                                                                 add_2[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_12 (BatchNo (None, 96, 96, 128)  512         add_3[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_12 (LeakyReLU)      (None, 96, 96, 128)  0           batch_normalization_12[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_13 (Conv2D)              (None, 96, 96, 256)  32768       leaky_re_lu_12[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2D)  (None, 48, 48, 256)  0           conv2d_13[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_13 (BatchNo (None, 48, 48, 256)  1024        max_pooling2d_2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_13 (LeakyReLU)      (None, 48, 48, 256)  0           batch_normalization_13[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_14 (Conv2D)              (None, 48, 48, 256)  589824      leaky_re_lu_13[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_14 (BatchNo (None, 48, 48, 256)  1024        conv2d_14[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_14 (LeakyReLU)      (None, 48, 48, 256)  0           batch_normalization_14[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_15 (Conv2D)              (None, 48, 48, 256)  589824      leaky_re_lu_14[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "add_4 (Add)                     (None, 48, 48, 256)  0           conv2d_15[0][0]                  \n",
            "                                                                 max_pooling2d_2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_15 (BatchNo (None, 48, 48, 256)  1024        add_4[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_15 (LeakyReLU)      (None, 48, 48, 256)  0           batch_normalization_15[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_16 (Conv2D)              (None, 48, 48, 256)  589824      leaky_re_lu_15[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_16 (BatchNo (None, 48, 48, 256)  1024        conv2d_16[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_16 (LeakyReLU)      (None, 48, 48, 256)  0           batch_normalization_16[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_17 (Conv2D)              (None, 48, 48, 256)  589824      leaky_re_lu_16[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "add_5 (Add)                     (None, 48, 48, 256)  0           conv2d_17[0][0]                  \n",
            "                                                                 add_4[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_17 (BatchNo (None, 48, 48, 256)  1024        add_5[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_17 (LeakyReLU)      (None, 48, 48, 256)  0           batch_normalization_17[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_18 (Conv2D)              (None, 48, 48, 384)  98304       leaky_re_lu_17[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2D)  (None, 24, 24, 384)  0           conv2d_18[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_18 (BatchNo (None, 24, 24, 384)  1536        max_pooling2d_3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_18 (LeakyReLU)      (None, 24, 24, 384)  0           batch_normalization_18[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_19 (Conv2D)              (None, 24, 24, 384)  1327104     leaky_re_lu_18[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_19 (BatchNo (None, 24, 24, 384)  1536        conv2d_19[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_19 (LeakyReLU)      (None, 24, 24, 384)  0           batch_normalization_19[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_20 (Conv2D)              (None, 24, 24, 384)  1327104     leaky_re_lu_19[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "add_6 (Add)                     (None, 24, 24, 384)  0           conv2d_20[0][0]                  \n",
            "                                                                 max_pooling2d_3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_20 (BatchNo (None, 24, 24, 384)  1536        add_6[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_20 (LeakyReLU)      (None, 24, 24, 384)  0           batch_normalization_20[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_21 (Conv2D)              (None, 24, 24, 384)  1327104     leaky_re_lu_20[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_21 (BatchNo (None, 24, 24, 384)  1536        conv2d_21[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_21 (LeakyReLU)      (None, 24, 24, 384)  0           batch_normalization_21[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_22 (Conv2D)              (None, 24, 24, 384)  1327104     leaky_re_lu_21[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "add_7 (Add)                     (None, 24, 24, 384)  0           conv2d_22[0][0]                  \n",
            "                                                                 add_6[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_22 (BatchNo (None, 24, 24, 384)  1536        add_7[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_22 (LeakyReLU)      (None, 24, 24, 384)  0           batch_normalization_22[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "dropout (Dropout)               (None, 24, 24, 384)  0           leaky_re_lu_22[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_23 (Conv2D)              (None, 24, 24, 512)  1769984     dropout[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_23 (BatchNo (None, 24, 24, 512)  2048        conv2d_23[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_23 (LeakyReLU)      (None, 24, 24, 512)  0           batch_normalization_23[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_24 (Conv2D)              (None, 24, 24, 512)  2359808     leaky_re_lu_23[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "add_8 (Add)                     (None, 24, 24, 512)  0           conv2d_24[0][0]                  \n",
            "                                                                 conv2d_23[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_24 (BatchNo (None, 24, 24, 512)  2048        add_8[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_24 (LeakyReLU)      (None, 24, 24, 512)  0           batch_normalization_24[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_25 (Conv2D)              (None, 24, 24, 256)  131328      leaky_re_lu_24[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_25 (BatchNo (None, 24, 24, 256)  1024        conv2d_25[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_25 (LeakyReLU)      (None, 24, 24, 256)  0           batch_normalization_25[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_26 (Conv2D)              (None, 24, 24, 1)    257         leaky_re_lu_25[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "up_sampling2d (UpSampling2D)    (None, 384, 384, 1)  0           conv2d_26[0][0]                  \n",
            "==================================================================================================\n",
            "Total params: 12,848,545\n",
            "Trainable params: 12,837,473\n",
            "Non-trainable params: 11,072\n",
            "__________________________________________________________________________________________________\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "oi-WApguHRc7",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model.load_weights(CHECKPOINT_PATH)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "fM5VeiuDcxUY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 389
        },
        "outputId": "d3f7a509-0fec-415a-8dfc-36f04c68d413"
      },
      "cell_type": "code",
      "source": [
        "history = model.fit_generator(train_gen, validation_data=valid_gen, callbacks=[learning_rate, checkpoint], epochs=5, shuffle=True, verbose=1)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "1926/1927 [============================>.] - ETA: 1s - loss: 0.5630 - acc: 0.9602 - mean_iou: 0.5890\n",
            "Epoch 00001: saving model to model7_384.h5\n",
            "1927/1927 [==============================] - 2433s 1s/step - loss: 0.5630 - acc: 0.9602 - mean_iou: 0.5890 - val_loss: 0.5743 - val_acc: 0.9728 - val_mean_iou: 0.7568\n",
            "Epoch 2/5\n",
            "1926/1927 [============================>.] - ETA: 1s - loss: 0.4820 - acc: 0.9618 - mean_iou: 0.6161\n",
            "Epoch 00002: saving model to model7_384.h5\n",
            "1927/1927 [==============================] - 2406s 1s/step - loss: 0.4819 - acc: 0.9618 - mean_iou: 0.6162 - val_loss: 0.4761 - val_acc: 0.9701 - val_mean_iou: 0.7058\n",
            "Epoch 3/5\n",
            "1926/1927 [============================>.] - ETA: 1s - loss: 0.4673 - acc: 0.9643 - mean_iou: 0.6409\n",
            "Epoch 00003: saving model to model7_384.h5\n",
            "1927/1927 [==============================] - 2405s 1s/step - loss: 0.4672 - acc: 0.9643 - mean_iou: 0.6410 - val_loss: 0.4612 - val_acc: 0.9589 - val_mean_iou: 0.6099\n",
            "Epoch 4/5\n",
            "1926/1927 [============================>.] - ETA: 1s - loss: 0.4571 - acc: 0.9650 - mean_iou: 0.6419\n",
            "Epoch 00004: saving model to model7_384.h5\n",
            "1927/1927 [==============================] - 2406s 1s/step - loss: 0.4571 - acc: 0.9650 - mean_iou: 0.6418 - val_loss: 0.4801 - val_acc: 0.9682 - val_mean_iou: 0.6684\n",
            "Epoch 5/5\n",
            "1926/1927 [============================>.] - ETA: 1s - loss: 0.4464 - acc: 0.9668 - mean_iou: 0.6586\n",
            "Epoch 00005: saving model to model7_384.h5\n",
            "1927/1927 [==============================] - 2403s 1s/step - loss: 0.4464 - acc: 0.9668 - mean_iou: 0.6585 - val_loss: 0.4410 - val_acc: 0.9671 - val_mean_iou: 0.6698\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "7tyG7P79Y42U",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 129
        },
        "outputId": "4be11029-b204-4f57-fbd9-e493d7d242fb"
      },
      "cell_type": "code",
      "source": [
        "# history = model.fit_generator(train_gen, validation_data=valid_gen, callbacks=[learning_rate, checkpoint], epochs=3, shuffle=True, verbose=1)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/3\n",
            "1926/1927 [============================>.] - ETA: 1s - loss: 0.4402 - acc: 0.9675 - mean_iou: 0.6664\n",
            "Epoch 00001: saving model to model7_384.h5\n",
            "1927/1927 [==============================] - 2413s 1s/step - loss: 0.4402 - acc: 0.9675 - mean_iou: 0.6665 - val_loss: 0.4458 - val_acc: 0.9718 - val_mean_iou: 0.7105\n",
            "Epoch 2/3\n",
            "1669/1927 [========================>.....] - ETA: 5:05 - loss: 0.4363 - acc: 0.9678 - mean_iou: 0.6675"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "y_bLpJPBcxUh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "outputId": "4507a184-6e33-42e1-a0eb-cb5d8e775920"
      },
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12,4))\n",
        "plt.subplot(131)\n",
        "plt.plot(history.epoch, history.history[\"loss\"], label=\"Train loss\")\n",
        "plt.plot(history.epoch, history.history[\"val_loss\"], label=\"Valid loss\")\n",
        "plt.legend()\n",
        "plt.subplot(132)\n",
        "plt.plot(history.epoch, history.history[\"acc\"], label=\"Train accuracy\")\n",
        "plt.plot(history.epoch, history.history[\"val_acc\"], label=\"Valid accuracy\")\n",
        "plt.legend()\n",
        "plt.subplot(133)\n",
        "plt.plot(history.epoch, history.history[\"mean_iou\"], label=\"Train iou\")\n",
        "plt.plot(history.epoch, history.history[\"val_mean_iou\"], label=\"Valid iou\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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9ezaVlZUAHD9+HA8PD9555x2+/PJLvvjiCyZOnMjChQslmWY2yPfP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KUWjuTfe7LFcL8oFsY8xnvFH/LXvL/x7LhVdplTabVaO79WKBRs3LiR9PR0vL29CQsL6/xefX09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\nSUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f087deb6438>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "eHcLlm637LNs",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Sample Predictions"
      ]
    },
    {
      "metadata": {
        "id": "Ha1wGqMs7HbP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1697
        },
        "outputId": "5d13d8fd-a47d-40be-db5e-400becaaa52c"
      },
      "cell_type": "code",
      "source": [
        "THRESHOLD = 0.5\n",
        "# look at some sample predictions\n",
        "samples = np.random.choice(valid_filenames, size=8, replace=False)\n",
        "\n",
        "for filename in samples:\n",
        "    # load the image\n",
        "    img = pydicom.dcmread(os.path.join(train_dicom_dir, filename)).pixel_array\n",
        "    \n",
        "    msk = np.zeros(img.shape)\n",
        "    \n",
        "    filename = filename.split('.')[0]\n",
        "    # if image contains pneumonia\n",
        "    if filename in pneumonia_locations:\n",
        "        # loop through pneumonia\n",
        "        for location in pneumonia_locations[filename]:\n",
        "            # add 1's at the location of the pneumonia\n",
        "            x, y, w, h = location\n",
        "            msk[y:y+h, x:x+w] = 1\n",
        "        \n",
        "    # resize both image and mask\n",
        "    msk = resize(msk, (IMAGE_SIZE, IMAGE_SIZE), mode='reflect') > 0.5\n",
        "    img = resize(img, (IMAGE_SIZE, IMAGE_SIZE), mode='reflect')\n",
        "    \n",
        "    # predict the image\n",
        "    yhat = model.predict(img.reshape(1, IMAGE_SIZE, IMAGE_SIZE, 1))\n",
        "    yhat = yhat > THRESHOLD\n",
        "    # add trailing channel dimension\n",
        "    msk = np.expand_dims(msk, -1)\n",
        "\n",
        "    f, ax = plt.subplots(1, 3, figsize=(10, 4))\n",
        "    ax[0].imshow(img.reshape(IMAGE_SIZE,IMAGE_SIZE))\n",
        "    ax[0].set_title(\"Image\")\n",
        "    ax[1].imshow(msk.reshape(IMAGE_SIZE,IMAGE_SIZE))\n",
        "    ax[1].set_title(\"Label\")\n",
        "    ax[2].imshow(yhat.reshape(IMAGE_SIZE,IMAGE_SIZE))\n",
        "    ax[2].set_title(\"Prediction\")\n",
        "    plt.show()\n",
        "    "
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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M5XEpidWhvCY0NI4G6GtC4/WGg74DvelNb8Itt9yCW265BZ/5zGdw44034pJL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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f089ce8ff98>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Qq9WkYw6AEDMaplmuYrhmv9+XENJmswnLshAMBqXURoIzOzsrpS+SG3YukjzW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\nRqPR17/+9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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f089b18f470>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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hUqmUEL/RaITt27dj27ZtGI1GmJiYQDQaRTAYxPbt24VcdjodMaCTiOkSmy4h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+W7mUzG3mcbS0tLlosFIMZ8l9BRiBtqnv8O1xCvFN/lWhDn0H/++xsU/O+OJ4m/CxtxTwQEbFaE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\nr3zlK0okE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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f089c9135f8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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SE3bp1Wo1ZLNZNBoNDA0NIZPJoFwuIxgMolAooK+vT5SzSCQiJUBdAmX8Qzqd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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f089c75f780>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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eeALvfe97Ua/X8f73vx+u6wrxKBaLmJmZ2aX93HTTTQtdwkuO1atX7/Y+/t//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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f089c5df7b8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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HPWSJ5Uiql4FAQDr70um0mP3L5bKon/zuuK4rx+L13ttJ6XN9JgwMDlSYZ8Lg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\n4dgk/h/dCqcS05q1RwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f089c4e6588>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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T21R11N9JPCORiOvq80c2mDplMBgMhl3BjCRUNIMXCgVPlpKqMyQ0jFdgCcjf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\ncRzHcRynSVxQOY7jOI7jNMn/AMap2E8Xg7bOAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f089c4a2a20>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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tXq+j2Wwil8vhe9/7Hi688EJRmmgen5ycFIUrFotheHhYSoY0nVPNYrmRZU0+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8C1+dzdltAkDcAxiep6nyRpLnHT/7gdf7ohpC6ZctekyZJOZefa9Ptye/05uLEMbrnseOeT+xqm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\nNpHsUVel+gAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f089c351080>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "3tAntxescxUq",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Predict test images"
      ]
    },
    {
      "metadata": {
        "id": "fnu7ebfScxUu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "a5a1e7da-4d2b-4994-95eb-847b6da6835d"
      },
      "cell_type": "code",
      "source": [
        "THRESHOLD = 0.5\n",
        "\n",
        "# load and shuffle filenames\n",
        "folder = './stage_1_test_images'\n",
        "test_filenames = os.listdir(folder)\n",
        "print('n test samples:', len(test_filenames))\n",
        "\n",
        "# create test generator with predict flag set to True\n",
        "test_gen = generator(folder, test_filenames, None, batch_size=25, image_size=IMAGE_SIZE, shuffle=False, predict=True)\n",
        "\n",
        "# create submission dictionary\n",
        "submission_dict = {}\n",
        "# loop through testset\n",
        "for imgs, filenames in test_gen:\n",
        "    # predict batch of images\n",
        "    preds = model.predict(imgs)\n",
        "    # loop through batch\n",
        "    for pred, filename in zip(preds, filenames):\n",
        "        # resize predicted mask\n",
        "        pred = resize(pred, (1024, 1024), mode='reflect')\n",
        "        \n",
        "        # threshold predicted mask\n",
        "        comp = pred[:, :, 0] > THRESHOLD\n",
        "        \n",
        "        # apply connected components\n",
        "        comp = measure.label(comp)\n",
        "        \n",
        "        # apply bounding boxes\n",
        "        predictionString = ''\n",
        "        for region in measure.regionprops(comp):\n",
        "            # retrieve x, y, height and width\n",
        "            y, x, y2, x2 = region.bbox\n",
        "            height = y2 - y\n",
        "            width = x2 - x\n",
        "            # proxy for confidence score\n",
        "            conf = np.mean(pred[y:y+height, x:x+width])\n",
        "            # add to predictionString\n",
        "            predictionString += str(conf) + ' ' + str(x) + ' ' + str(y) + ' ' + str(width) + ' ' + str(height) + ' '\n",
        "        \n",
        "        # add filename and predictionString to dictionary\n",
        "        filename = filename.split('.')[0]\n",
        "        submission_dict[filename] = predictionString\n",
        "        \n",
        "    # stop if we've got them all\n",
        "    if len(submission_dict) >= len(test_filenames):\n",
        "        break\n",
        "\n",
        "# save dictionary as csv file\n",
        "sub = pd.DataFrame.from_dict(submission_dict,orient='index')\n",
        "sub.index.names = ['patientId']\n",
        "sub.columns = ['PredictionString']\n",
        "sub.to_csv('submission.csv')"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "n test samples: 1000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "U1NEg9QCcxU4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "f2a73cc7-53b4-4418-d9c9-a8858b756f7d"
      },
      "cell_type": "code",
      "source": [
        "!kaggle competitions submit -c rsna-pneumonia-detection-challenge -f submission.csv -m \"Colab segmentation v3 384x384 5 epochs th 0.3\""
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Successfully submitted to RSNA Pneumonia Detection Challenge"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "NTQ_vDaVfvZR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "outputId": "f130c88d-9f8f-449e-c0b8-f84d0e091f5b"
      },
      "cell_type": "code",
      "source": [
        "save_file_to_drive(\"submission.csv\", \"submission.csv\")\n",
        "save_file_to_drive(CHECKPOINT_PATH, CHECKPOINT_PATH)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "File ID: 1uuhyHl5E0wDSvSyro2m0nnMyqN3Fvj4F\n",
            "File ID: 1US_q0WXu6v66sKUYzT3lypP6aDaifEHJ\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'id': '1US_q0WXu6v66sKUYzT3lypP6aDaifEHJ'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "metadata": {
        "id": "yf06y04TVRi2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 91
        },
        "outputId": "52dfcc48-8755-4950-816e-ce11a867dfc7"
      },
      "cell_type": "code",
      "source": [
        "# upload checkpoint to GCS\n",
        "project_id = 'mammography-198911'\n",
        "bucket_name = 'pneumonia'\n",
        "\n",
        "!gcloud config set project {project_id}\n",
        "!gsutil cp ./{CHECKPOINT_PATH} gs://{bucket_name}/"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Updated property [core/project].\n",
            "Copying file://./model7_384.h5 [Content-Type=application/octet-stream]...\n",
            "\\\n",
            "Operation completed over 1 objects/49.2 MiB.                                     \n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}