best_cnn_segmentation_connected_components_384x384_6.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": "88d3ae34-0e97-4310-b861-bece79b30196"
      },
      "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, 37.8kB/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, 86.5MB/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, 37.3MB/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, 126MB/s]\n",
            "Downloading stage_1_test_images.zip to /content\n",
            " 83% 105M/126M [00:00<00:00, 80.2MB/s] \n",
            "100% 126M/126M [00:00<00:00, 136MB/s] \n",
            "Downloading stage_1_train_images.zip to /content\n",
            "100% 3.16G/3.17G [00:26<00:00, 108MB/s]\n",
            "100% 3.17G/3.17G [00:26<00:00, 128MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "HttYLaczdKlJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "c9354673-b492-4247-b8c9-2533b349fe24"
      },
      "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": "dc937e8c-bfd5-452f-e05f-aa11c8358dff"
      },
      "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": 7,
      "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": "af11fb6f-01a1-4e71-e26b-a81acc6cc03b"
      },
      "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": 9,
      "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 = \"model8_384.h5\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "nxYsnFo3HIH8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "fb64ca7f-571f-418c-c54f-08e7eeba003f"
      },
      "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": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Updated property [core/project].\n",
            "CommandException: No URLs matched: gs://pneumonia/model8_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",
        "        # 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(384, 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",
        "    x = keras.layers.Dropout(0.2)(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": 4020
        },
        "outputId": "38d79b80-2e54-42a5-8477-5539939d2aea"
      },
      "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 0.4 * keras.losses.binary_crossentropy(y_true, y_pred) + 0.6 * 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=24, 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": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_2 (InputLayer)            (None, 384, 384, 1)  0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_28 (Conv2D)              (None, 384, 384, 24) 216         input_2[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_27 (BatchNo (None, 384, 384, 24) 96          conv2d_28[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_27 (LeakyReLU)      (None, 384, 384, 24) 0           batch_normalization_27[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_29 (Conv2D)              (None, 384, 384, 24) 5184        leaky_re_lu_27[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_28 (BatchNo (None, 384, 384, 24) 96          conv2d_29[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_28 (LeakyReLU)      (None, 384, 384, 24) 0           batch_normalization_28[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_30 (Conv2D)              (None, 384, 384, 24) 5184        leaky_re_lu_28[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_29 (BatchNo (None, 384, 384, 24) 96          conv2d_30[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_29 (LeakyReLU)      (None, 384, 384, 24) 0           batch_normalization_29[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_31 (Conv2D)              (None, 384, 384, 48) 1152        leaky_re_lu_29[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_4 (MaxPooling2D)  (None, 192, 192, 48) 0           conv2d_31[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_30 (BatchNo (None, 192, 192, 48) 192         max_pooling2d_4[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_30 (LeakyReLU)      (None, 192, 192, 48) 0           batch_normalization_30[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_32 (Conv2D)              (None, 192, 192, 48) 20736       leaky_re_lu_30[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_31 (BatchNo (None, 192, 192, 48) 192         conv2d_32[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_31 (LeakyReLU)      (None, 192, 192, 48) 0           batch_normalization_31[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_33 (Conv2D)              (None, 192, 192, 48) 20736       leaky_re_lu_31[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "add_9 (Add)                     (None, 192, 192, 48) 0           conv2d_33[0][0]                  \n",
            "                                                                 max_pooling2d_4[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_32 (BatchNo (None, 192, 192, 48) 192         add_9[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_32 (LeakyReLU)      (None, 192, 192, 48) 0           batch_normalization_32[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_34 (Conv2D)              (None, 192, 192, 48) 20736       leaky_re_lu_32[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_33 (BatchNo (None, 192, 192, 48) 192         conv2d_34[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_33 (LeakyReLU)      (None, 192, 192, 48) 0           batch_normalization_33[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_35 (Conv2D)              (None, 192, 192, 48) 20736       leaky_re_lu_33[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "add_10 (Add)                    (None, 192, 192, 48) 0           conv2d_35[0][0]                  \n",
            "                                                                 add_9[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_34 (BatchNo (None, 192, 192, 48) 192         add_10[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_34 (LeakyReLU)      (None, 192, 192, 48) 0           batch_normalization_34[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_36 (Conv2D)              (None, 192, 192, 96) 4608        leaky_re_lu_34[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_5 (MaxPooling2D)  (None, 96, 96, 96)   0           conv2d_36[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_35 (BatchNo (None, 96, 96, 96)   384         max_pooling2d_5[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_35 (LeakyReLU)      (None, 96, 96, 96)   0           batch_normalization_35[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_37 (Conv2D)              (None, 96, 96, 96)   82944       leaky_re_lu_35[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_36 (BatchNo (None, 96, 96, 96)   384         conv2d_37[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_36 (LeakyReLU)      (None, 96, 96, 96)   0           batch_normalization_36[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_38 (Conv2D)              (None, 96, 96, 96)   82944       leaky_re_lu_36[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "add_11 (Add)                    (None, 96, 96, 96)   0           conv2d_38[0][0]                  \n",
            "                                                                 max_pooling2d_5[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_37 (BatchNo (None, 96, 96, 96)   384         add_11[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_37 (LeakyReLU)      (None, 96, 96, 96)   0           batch_normalization_37[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_39 (Conv2D)              (None, 96, 96, 96)   82944       leaky_re_lu_37[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_38 (BatchNo (None, 96, 96, 96)   384         conv2d_39[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_38 (LeakyReLU)      (None, 96, 96, 96)   0           batch_normalization_38[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_40 (Conv2D)              (None, 96, 96, 96)   82944       leaky_re_lu_38[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "add_12 (Add)                    (None, 96, 96, 96)   0           conv2d_40[0][0]                  \n",
            "                                                                 add_11[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_39 (BatchNo (None, 96, 96, 96)   384         add_12[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_39 (LeakyReLU)      (None, 96, 96, 96)   0           batch_normalization_39[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_41 (Conv2D)              (None, 96, 96, 192)  18432       leaky_re_lu_39[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_6 (MaxPooling2D)  (None, 48, 48, 192)  0           conv2d_41[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_40 (BatchNo (None, 48, 48, 192)  768         max_pooling2d_6[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_40 (LeakyReLU)      (None, 48, 48, 192)  0           batch_normalization_40[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_42 (Conv2D)              (None, 48, 48, 192)  331776      leaky_re_lu_40[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_41 (BatchNo (None, 48, 48, 192)  768         conv2d_42[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_41 (LeakyReLU)      (None, 48, 48, 192)  0           batch_normalization_41[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_43 (Conv2D)              (None, 48, 48, 192)  331776      leaky_re_lu_41[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "add_13 (Add)                    (None, 48, 48, 192)  0           conv2d_43[0][0]                  \n",
            "                                                                 max_pooling2d_6[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_42 (BatchNo (None, 48, 48, 192)  768         add_13[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_42 (LeakyReLU)      (None, 48, 48, 192)  0           batch_normalization_42[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_44 (Conv2D)              (None, 48, 48, 192)  331776      leaky_re_lu_42[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_43 (BatchNo (None, 48, 48, 192)  768         conv2d_44[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_43 (LeakyReLU)      (None, 48, 48, 192)  0           batch_normalization_43[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_45 (Conv2D)              (None, 48, 48, 192)  331776      leaky_re_lu_43[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "add_14 (Add)                    (None, 48, 48, 192)  0           conv2d_45[0][0]                  \n",
            "                                                                 add_13[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_44 (BatchNo (None, 48, 48, 192)  768         add_14[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_44 (LeakyReLU)      (None, 48, 48, 192)  0           batch_normalization_44[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_46 (Conv2D)              (None, 48, 48, 384)  73728       leaky_re_lu_44[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_7 (MaxPooling2D)  (None, 24, 24, 384)  0           conv2d_46[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_45 (BatchNo (None, 24, 24, 384)  1536        max_pooling2d_7[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_45 (LeakyReLU)      (None, 24, 24, 384)  0           batch_normalization_45[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_47 (Conv2D)              (None, 24, 24, 384)  1327104     leaky_re_lu_45[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_46 (BatchNo (None, 24, 24, 384)  1536        conv2d_47[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_46 (LeakyReLU)      (None, 24, 24, 384)  0           batch_normalization_46[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_48 (Conv2D)              (None, 24, 24, 384)  1327104     leaky_re_lu_46[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "add_15 (Add)                    (None, 24, 24, 384)  0           conv2d_48[0][0]                  \n",
            "                                                                 max_pooling2d_7[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_47 (BatchNo (None, 24, 24, 384)  1536        add_15[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_47 (LeakyReLU)      (None, 24, 24, 384)  0           batch_normalization_47[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_49 (Conv2D)              (None, 24, 24, 384)  1327104     leaky_re_lu_47[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_48 (BatchNo (None, 24, 24, 384)  1536        conv2d_49[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_48 (LeakyReLU)      (None, 24, 24, 384)  0           batch_normalization_48[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_50 (Conv2D)              (None, 24, 24, 384)  1327104     leaky_re_lu_48[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "add_16 (Add)                    (None, 24, 24, 384)  0           conv2d_50[0][0]                  \n",
            "                                                                 add_15[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_49 (BatchNo (None, 24, 24, 384)  1536        add_16[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_49 (LeakyReLU)      (None, 24, 24, 384)  0           batch_normalization_49[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "dropout_2 (Dropout)             (None, 24, 24, 384)  0           leaky_re_lu_49[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_51 (Conv2D)              (None, 24, 24, 512)  1769984     dropout_2[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_50 (BatchNo (None, 24, 24, 512)  2048        conv2d_51[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_50 (LeakyReLU)      (None, 24, 24, 512)  0           batch_normalization_50[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_52 (Conv2D)              (None, 24, 24, 512)  2359808     leaky_re_lu_50[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "add_17 (Add)                    (None, 24, 24, 512)  0           conv2d_52[0][0]                  \n",
            "                                                                 conv2d_51[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_51 (BatchNo (None, 24, 24, 512)  2048        add_17[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_51 (LeakyReLU)      (None, 24, 24, 512)  0           batch_normalization_51[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_53 (Conv2D)              (None, 24, 24, 384)  196992      leaky_re_lu_51[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_52 (BatchNo (None, 24, 24, 384)  1536        conv2d_53[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_52 (LeakyReLU)      (None, 24, 24, 384)  0           batch_normalization_52[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "dropout_3 (Dropout)             (None, 24, 24, 384)  0           leaky_re_lu_52[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_54 (Conv2D)              (None, 24, 24, 256)  98560       dropout_3[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_53 (BatchNo (None, 24, 24, 256)  1024        conv2d_54[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_53 (LeakyReLU)      (None, 24, 24, 256)  0           batch_normalization_53[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_55 (Conv2D)              (None, 24, 24, 1)    257         leaky_re_lu_53[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "up_sampling2d_1 (UpSampling2D)  (None, 384, 384, 1)  0           conv2d_55[0][0]                  \n",
            "==================================================================================================\n",
            "Total params: 11,605,689\n",
            "Trainable params: 11,595,017\n",
            "Non-trainable params: 10,672\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": 613
        },
        "outputId": "424cba93-79f2-4685-ff5f-d42ffd24c51f"
      },
      "cell_type": "code",
      "source": [
        "history = model.fit_generator(train_gen, validation_data=valid_gen, callbacks=[learning_rate, checkpoint], epochs=8, shuffle=True, verbose=1)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/8\n",
            "1926/1927 [============================>.] - ETA: 1s - loss: 0.6045 - acc: 0.9524 - mean_iou: 0.5297\n",
            "Epoch 00001: saving model to model8_384.h5\n",
            "1927/1927 [==============================] - 2047s 1s/step - loss: 0.6044 - acc: 0.9524 - mean_iou: 0.5298 - val_loss: 0.5779 - val_acc: 0.9534 - val_mean_iou: 0.5979\n",
            "Epoch 2/8\n",
            "1926/1927 [============================>.] - ETA: 1s - loss: 0.5694 - acc: 0.9569 - mean_iou: 0.5822\n",
            "Epoch 00002: saving model to model8_384.h5\n",
            "1927/1927 [==============================] - 2041s 1s/step - loss: 0.5693 - acc: 0.9569 - mean_iou: 0.5823 - val_loss: 0.5584 - val_acc: 0.9520 - val_mean_iou: 0.5848\n",
            "Epoch 3/8\n",
            "1926/1927 [============================>.] - ETA: 1s - loss: 0.5482 - acc: 0.9614 - mean_iou: 0.6143\n",
            "Epoch 00003: saving model to model8_384.h5\n",
            "1927/1927 [==============================] - 2033s 1s/step - loss: 0.5483 - acc: 0.9614 - mean_iou: 0.6143 - val_loss: 0.5294 - val_acc: 0.9645 - val_mean_iou: 0.6529\n",
            "Epoch 4/8\n",
            "1926/1927 [============================>.] - ETA: 1s - loss: 0.5330 - acc: 0.9629 - mean_iou: 0.6304\n",
            "Epoch 00004: saving model to model8_384.h5\n",
            "1927/1927 [==============================] - 2036s 1s/step - loss: 0.5330 - acc: 0.9629 - mean_iou: 0.6304 - val_loss: 0.5431 - val_acc: 0.9485 - val_mean_iou: 0.5128\n",
            "Epoch 5/8\n",
            "1926/1927 [============================>.] - ETA: 1s - loss: 0.5189 - acc: 0.9647 - mean_iou: 0.6399\n",
            "Epoch 00005: saving model to model8_384.h5\n",
            "1927/1927 [==============================] - 2037s 1s/step - loss: 0.5189 - acc: 0.9647 - mean_iou: 0.6400 - val_loss: 0.5083 - val_acc: 0.9681 - val_mean_iou: 0.6970\n",
            "Epoch 6/8\n",
            "1926/1927 [============================>.] - ETA: 1s - loss: 0.5111 - acc: 0.9656 - mean_iou: 0.6432\n",
            "Epoch 00006: saving model to model8_384.h5\n",
            "1927/1927 [==============================] - 2034s 1s/step - loss: 0.5111 - acc: 0.9656 - mean_iou: 0.6432 - val_loss: 0.5171 - val_acc: 0.9690 - val_mean_iou: 0.6859\n",
            "Epoch 7/8\n",
            "1926/1927 [============================>.] - ETA: 1s - loss: 0.5071 - acc: 0.9662 - mean_iou: 0.6446\n",
            "Epoch 00007: saving model to model8_384.h5\n",
            "1927/1927 [==============================] - 2024s 1s/step - loss: 0.5071 - acc: 0.9662 - mean_iou: 0.6446 - val_loss: 0.5013 - val_acc: 0.9663 - val_mean_iou: 0.6897\n",
            "Epoch 8/8\n",
            "1926/1927 [============================>.] - ETA: 1s - loss: 0.5027 - acc: 0.9666 - mean_iou: 0.6503\n",
            "Epoch 00008: saving model to model8_384.h5\n",
            "1927/1927 [==============================] - 2027s 1s/step - loss: 0.5027 - acc: 0.9666 - mean_iou: 0.6503 - val_loss: 0.4977 - val_acc: 0.9697 - val_mean_iou: 0.6986\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "7tyG7P79Y42U",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "6fe283d4-f9fb-4295-9000-ce5c6abb8bd8"
      },
      "cell_type": "code",
      "source": [
        "history = model.fit_generator(train_gen, validation_data=valid_gen, callbacks=[learning_rate, checkpoint], epochs=12, shuffle=True, verbose=1, initial_epoch=8)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 9/12\n",
            " 932/1927 [=============>................] - ETA: 16:43 - loss: 0.4992 - acc: 0.9672 - mean_iou: 0.6523"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "y_bLpJPBcxUh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "94b25fb3-7b47-4469-c489-72dc36f4711c"
      },
      "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": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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3e/atuLi4NNkmkUiYOfMmFi2aT3BwX8LDm662NnHiJJYtW8LRo0eYMeM6NBoN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\nsIENGza0ef63v/0tjzzyCFu3bkWhUFBQUNDumDfeeIPly5e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vo9PA2Gg0YrFcKSgrKSkh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MJYeL3EkDieTp7Ff5z4I6sOt/c4DvYNIsDPh4xkCxnJFtranJypuIz1Yh07D5VxqNjGoWIbigIJ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\nlJB2d5zBDsQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f15c50b66d8>"
            ]
          },
          "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": "2c2a4f4f-ddfa-418a-ec0a-072c503ae72b"
      },
      "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": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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GhoYQiURgMpng9Xp5GHK9XkdbWxsMw4DD4UC1WkWhUEA+n+c090wmA13XkUql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sqmvXPcP9u2xV9FNbBer8scQ85RdBwHfr8fsVhMwlQ5pNpxHBkmPTU1JftO0z7XFwgEJAWfipiB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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f15ef8a7780>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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qmJGH6r777sOXvvQlADurXG/fvh3vfve7cf/99wMAHnjgAZx66qlTbo8qg+6v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\nJf4HeuuHeQxEvsIAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f15b7d73da0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Ro+cjevxbl4xIjPgvf9d+KJbySLL4/na7DQDyPvqOSKzon7JtG7VaDT6fD6FQCI7jIBAI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\nYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f15b7dd32e8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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J6vea77Xie1QfFUlavV5HtVqFbdtCitT9VEt7nU5H3uvxeMRLRdJDIhMOhxGPx13kQiUY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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f15b7de9320>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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RRxwhigLVnUajAdd1Ua1WJTCzXq+L14q+oUgkgrGxMYyOjgpp8n0f1WoV9Xod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vrJ4vG4xRiw45Jp7mHyqT4z3UfuK48f9sTxc1FS53A4HA7HemJDKFRaUmK3nnqLwoZ0+nLYRk/S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\nSUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f15b814dc18>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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cVKvViPLEdtSDpKqNGs8BmOmc3jK+psRC1SjdQzCbzaJQKNj1a5lRM66oarmG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6ujqk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\nw+FwOBwOxzzx/wDPWa1Y7hRAUgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f15b82bf160>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Bhz70IaxcuRJ/9Vd/hTvvvBPLly/HGWecMa9jUelIJpOiwujFiDnp60yl0dFR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\nTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f15b83b7128>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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XADjyxM+wTbavKhfbYEmQChuJVa1WQ6fTge/7zrC+uRKfwWAwGAy7I7arKX1b0Gg0cMQR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\nHA6Hw7FD/D/CqCjqB/7B4QAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f15c50c4b38>"
            ]
          },
          "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": "bd13b4bb-9e00-475c-b4b2-1df2d89bd29c"
      },
      "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": 25,
      "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": "83a490db-a786-4012-b008-16a289e92018"
      },
      "cell_type": "code",
      "source": [
        "!kaggle competitions submit -c rsna-pneumonia-detection-challenge -f submission.csv -m \"Colab segmentation ff v5 384x384 12 epochs\""
      ],
      "execution_count": 26,
      "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": "c53bcc7f-32ed-4e93-8af1-119730317733"
      },
      "cell_type": "code",
      "source": [
        "save_file_to_drive(\"submission.csv\", \"submission.csv\")\n",
        "save_file_to_drive(CHECKPOINT_PATH, CHECKPOINT_PATH)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "File ID: 1l4Bl9tiUAwmy2T-yHV7GSXVMZ_f0P8f0\n",
            "File ID: 1LqM-7xd6knfZJdD2NVESq9GRpiGj4448\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'id': '1LqM-7xd6knfZJdD2NVESq9GRpiGj4448'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "metadata": {
        "id": "yf06y04TVRi2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 91
        },
        "outputId": "84c5af4f-f01c-42e1-f324-c25e4e93f759"
      },
      "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": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Updated property [core/project].\n",
            "Copying file://./model8_384.h5 [Content-Type=application/octet-stream]...\n",
            "\\\n",
            "Operation completed over 1 objects/44.5 MiB.                                     \n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}