Tumor_Segmentation.ipynb

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      "name": "Tumor Segmentation.ipynb",
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/nasim-aust/Brain-Tumor-Segmentation-using-Unet-Model-in-2D-images/blob/master/Tumor_Segmentation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-Xeh5D5wosjh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from torch.utils.data import Dataset\n",
        "import torchvision.transforms as transforms\n",
        "import torchvision.transforms.functional as TF\n",
        "\n",
        "\n",
        "from PIL import Image\n",
        "\n",
        "import os\n",
        "import random"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VCPjrLMJosjj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class TumorDataset(Dataset):\n",
        "    \"\"\" Returns a TumorDataset class object which represents our tumor dataset.\n",
        "    TumorDataset inherits from torch.utils.data.Dataset class.\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self, root_dir, transform=True, DEBUG=False):\n",
        "        \"\"\" Constructor for our TumorDataset class.\n",
        "        Parameters:\n",
        "            root_dir(str): Directory with all the images.\n",
        "            transform(bool): Flag to apply image random transformation.\n",
        "            DEBUG(bool): To switch to debug mode for image transformation.\n",
        "        Returns: None\n",
        "        \"\"\"\n",
        "        self.root_dir = root_dir\n",
        "        self.transform = {'hflip': TF.hflip,\n",
        "                          'vflip': TF.vflip,\n",
        "                          'rotate': TF.rotate}\n",
        "        self.default_transformation = transforms.Compose([\n",
        "            transforms.Grayscale(),\n",
        "            transforms.Resize((512, 512))\n",
        "        ])\n",
        "        self.DEBUG = DEBUG\n",
        "        if not transform:\n",
        "            self.transform = None\n",
        "\n",
        "    def __getitem__(self, index):\n",
        "        \"\"\" Overridden method from inheritted class to support\n",
        "        indexing of dataset such that datset[I] can be used\n",
        "        to get Ith sample.\n",
        "        Parameters:\n",
        "            index(int): Index of the dataset sample\n",
        "        Return:\n",
        "            sample(dict): Contains the index, image, mask torch.Tensor.\n",
        "                        'index': Index of the image.\n",
        "                        'image': Contains the tumor image torch.Tensor.\n",
        "                        'mask' : Contains the mask image torch.Tensor.\n",
        "        \"\"\"\n",
        "        image_name = os.path.join(self.root_dir, str(index)+'.png')\n",
        "        mask_name = os.path.join(self.root_dir, str(index)+'_mask.png')\n",
        "\n",
        "        image = Image.open(image_name)\n",
        "        mask = Image.open(mask_name)\n",
        "\n",
        "        image = self.default_transformation(image)\n",
        "        mask = self.default_transformation(mask)\n",
        "\n",
        "        # Custom transformations\n",
        "        if self.transform:\n",
        "            image, mask = self._random_transform(image, mask)\n",
        "\n",
        "        image = TF.to_tensor(image)\n",
        "        mask = TF.to_tensor(mask)\n",
        "\n",
        "        sample = {'index': int(index), 'image': image, 'mask': mask}\n",
        "        return sample\n",
        "\n",
        "    def _random_transform(self, image, mask):\n",
        "        \"\"\" Applies a set of transformation in random order.\n",
        "        Each transformation has a probability of 0.5\n",
        "        \"\"\"\n",
        "        choice_list = list(self.transform)\n",
        "        for _ in range(len(choice_list)):\n",
        "            choice_key = random.choice(choice_list)\n",
        "            if self.DEBUG:\n",
        "                print(f'Transform choose: {choice_key}')\n",
        "            action_prob = random.randint(0, 1)\n",
        "            if action_prob >= 0.5:\n",
        "                if self.DEBUG:\n",
        "                    print(f'\\tApplying transformation: {choice_key}')\n",
        "                if choice_key == 'rotate':\n",
        "                    rotation = random.randint(15, 75)\n",
        "                    if self.DEBUG:\n",
        "                        print(f'\\t\\tRotation by: {rotation}')\n",
        "                    image = self.transform[choice_key](image, rotation)\n",
        "                    mask = self.transform[choice_key](mask, rotation)\n",
        "                else:\n",
        "                    image = self.transform[choice_key](image)\n",
        "                    mask = self.transform[choice_key](mask)\n",
        "            choice_list.remove(choice_key)\n",
        "\n",
        "        return image, mask\n",
        "\n",
        "    def __len__(self):\n",
        "        \"\"\" Overridden method from inheritted class so that\n",
        "        len(self) returns the size of the dataset.\n",
        "        \"\"\"\n",
        "        error_msg = 'Part of dataset is missing!\\nNumber of tumor and mask images are not same.'\n",
        "        total_files = len(os.listdir(self.root_dir))\n",
        "\n",
        "        assert (total_files % 2 == 0), error_msg\n",
        "        return total_files//2"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F0yrxdEMosjk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#loss\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "\n",
        "\n",
        "class DiceLoss(nn.Module):\n",
        "    \"\"\"Sørensen–Dice coefficient loss to calculate\n",
        "    the mean loss over a batch of data.This loss mainly\n",
        "    calculates the similarity between two samples.\n",
        "    To know more about this loss check this link:\n",
        "    https://en.wikipedia.org/wiki/S%C3%B8rensen%E2%80%93Dice_coefficient\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self):\n",
        "        \"\"\"Simple constructor for the class.\"\"\"\n",
        "        super(DiceLoss, self).__init__()\n",
        "\n",
        "    def forward(self, predicted, target):\n",
        "        \"\"\" Method for calculation of loss from sample.\n",
        "        Parameters:\n",
        "            predicted(torch.Tensor): Predicted output of the network.\n",
        "                                    Shape - (Batch Size,Channel,Height,Width)\n",
        "            target(torch.Tensor): Actual required output for the network\n",
        "                                    Shape - (Batch Size,Channel,Height,Width)\n",
        "        Returns:\n",
        "            The mean dice Loss over the batch size.\n",
        "        \"\"\"\n",
        "        batch = predicted.size()[0]\n",
        "        batch_loss = 0\n",
        "        for index in range(batch):\n",
        "            coefficient = self._dice_coefficient(\n",
        "                predicted[index], target[index])\n",
        "            batch_loss += coefficient\n",
        "\n",
        "        batch_loss = batch_loss / batch\n",
        "\n",
        "        return 1 - batch_loss\n",
        "\n",
        "    def _dice_coefficient(self, predicted, target):\n",
        "        \"\"\"Calculates the Sørensen–Dice Coefficient for a\n",
        "        single sample.\n",
        "        Parameters:\n",
        "            predicted(torch.Tensor): Predicted single output of the network.\n",
        "                                    Shape - (Channel,Height,Width)\n",
        "            target(torch.Tensor): Actual required single output for the network\n",
        "                                    Shape - (Channel,Height,Width)\n",
        "        Returns:\n",
        "            coefficient(torch.Tensor): Dice coefficient for the input sample.\n",
        "                                        1 represents high similarity and\n",
        "                                        0 represents low similarity.\n",
        "        \"\"\"\n",
        "        smooth = 1\n",
        "        product = torch.mul(predicted, target)\n",
        "        intersection = product.sum()\n",
        "        coefficient = (2*intersection + smooth) / \\\n",
        "            (predicted.sum() + target.sum() + smooth)\n",
        "        return coefficient\n",
        "\n",
        "\n",
        "class BCEDiceLoss(nn.Module):\n",
        "    \"\"\" Combination of Binary Cross Entropy Loss and Soft Dice Loss.\n",
        "    This combined loss is used to train the network so that both\n",
        "    benefits of the loss are leveraged.\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self, device):\n",
        "        \"\"\"Simple constructor for the class.\"\"\"\n",
        "        super(BCEDiceLoss, self).__init__()\n",
        "        self.dice_loss = DiceLoss().to(device)\n",
        "\n",
        "    def forward(self, predicted, target):\n",
        "        \"\"\" Method for calculation of combined loss from sample.\"\"\"\n",
        "        return F.binary_cross_entropy(predicted, target) \\\n",
        "            + self.dice_loss(predicted, target)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W80IwOWjosjm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch\n",
        "#import bts.loss as loss\n",
        "import torch.optim as optim\n",
        "from torch.autograd import Variable\n",
        "\n",
        "from tensorboardX import SummaryWriter\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "from datetime import datetime\n",
        "from time import time"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "weMaSN4Yosjn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class BrainTumorClassifier():\n",
        "    \"\"\" Returns a BrainTumorClassifier class object which represents our \n",
        "    optimizer for our network.\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self, model, device):\n",
        "        \"\"\" Constructor for our BrainTumorClassifier class.\n",
        "        Parameters:\n",
        "            model(DynamicUNet): UNet model to be trained.\n",
        "            device(torch.device): Device currently used for all computations.\n",
        "        Returns: \n",
        "            None\n",
        "        \"\"\"\n",
        "        self.model = model\n",
        "        self.device = device\n",
        "        self.criterion = loss.BCEDiceLoss(self.device).to(device)\n",
        "        self.log_path = datetime.now().strftime(\"%I-%M-%S_%p_on_%B_%d,_%Y\")\n",
        "\n",
        "    def train(self, epochs, trainloader, mini_batch=None, learning_rate=0.001, save_best=None, plot_image=None):\n",
        "        \"\"\" Train the model using Adam Optimizer.\n",
        "        Parameters:\n",
        "            epochs(int): Number of epochs for the training session.\n",
        "            trainloader(torch.utils.data.Dataloader): Training data\n",
        "                        loader for the optimizer.\n",
        "            mini_batch(int): Used to print logs for epoch batches.\n",
        "                            If None then batch logs won't be printed.\n",
        "                            Default: None\n",
        "            learning_rate(float): Learning rate for optimizer.\n",
        "                                  Default: 0.001\n",
        "            save_best(str): Path to save the best model. At the end of \n",
        "                            the training the epoch with losest loss will\n",
        "                            be saved. If None then model won't be saved.\n",
        "                            Default: None\n",
        "            plot_image(list): Plot some samples in Tensorboard while training.\n",
        "                          Visualization of model training progress.If None\n",
        "                          then nothing will be done.\n",
        "                          Default: None\n",
        "        Returns:\n",
        "            history(dict): Contains information about training session.\n",
        "                            'train_loss': List of loss at every epoch\n",
        "        \"\"\"\n",
        "        # Tensorboard Writter\n",
        "        self.tb_writer = SummaryWriter(log_dir=f'logs/{self.log_path}')\n",
        "        # Training session history data.\n",
        "        history = {'train_loss': list()}\n",
        "        # For save best feature. Initial loss taken a very high value.\n",
        "        last_loss = 1000\n",
        "        # Optimizer used for training process. Adam Optimizer.\n",
        "        self.optimizer = optim.Adam(self.model.parameters(), lr=learning_rate)\n",
        "        # Reducing LR on plateau feature to improve training.\n",
        "        self.scheduler = optim.lr_scheduler.ReduceLROnPlateau(\n",
        "            self.optimizer, factor=0.85, patience=2, verbose=True)\n",
        "        print('Starting Training Process')\n",
        "        # Epoch Loop\n",
        "        for epoch in range(epochs):\n",
        "            start_time = time()\n",
        "            # Training a single epoch\n",
        "            epoch_loss = self._train_epoch(trainloader, mini_batch)\n",
        "            # Collecting all epoch loss values for future visualization.\n",
        "            history['train_loss'].append(epoch_loss)\n",
        "            # Logging to Tensorboard\n",
        "            self.tb_writer.add_scalar('Train Loss', epoch_loss, epoch)\n",
        "            self.tb_writer.add_scalar(\n",
        "                'Learning Rate', self.optimizer.param_groups[0]['lr'], epoch)\n",
        "            # Reduce LR On Plateau\n",
        "            self.scheduler.step(epoch_loss)\n",
        "\n",
        "            # Plotting some sample output on TensorBoard for visualization purpose.\n",
        "            if plot_image:\n",
        "                self.model.eval()\n",
        "                self._plot_image(epoch, plot_image)\n",
        "                self.model.train()\n",
        "\n",
        "            time_taken = time()-start_time\n",
        "            # Training Logs printed.\n",
        "            print(f'Epoch: {epoch+1:03d},  ', end='')\n",
        "            print(f'Loss:{epoch_loss:.7f},  ', end='')\n",
        "            print(f'Time:{time_taken:.2f}secs', end='')\n",
        "\n",
        "            # Save the best model with lowest epoch loss feature.\n",
        "            if save_best != None and last_loss > epoch_loss:\n",
        "                self.save_model(save_best)\n",
        "                last_loss = epoch_loss\n",
        "                print(f'\\tSaved at loss: {epoch_loss:.10f}')\n",
        "            else:\n",
        "                print()\n",
        "        return history\n",
        "\n",
        "    def save_model(self, path):\n",
        "        \"\"\" Saves the currently used model to the path specified.\n",
        "        Follows the best method recommended by Pytorch\n",
        "        Link: https://pytorch.org/tutorials/beginner/saving_loading_models.html#save-load-state-dict-recommended\n",
        "        Parameters:\n",
        "            path(str): The file location where the model needs to be saved.\n",
        "        Returns:\n",
        "            None\n",
        "        \"\"\"\n",
        "        torch.save(self.model.state_dict(), path)\n",
        "\n",
        "    def restore_model(self, path):\n",
        "        \"\"\" Loads the saved model and restores it to the \"model\" object.\n",
        "        Loads the model based on device used for computation.(CPU/GPU) \n",
        "        Follows the best method recommended by Pytorch\n",
        "        Link: https://pytorch.org/tutorials/beginner/saving_loading_models.html#save-load-state-dict-recommended\n",
        "        Parameters:\n",
        "            path(str): The file location where the model is saved.\n",
        "        Returns:\n",
        "            None\n",
        "        \"\"\"\n",
        "        if self.device == 'cpu':\n",
        "            self.model.load_state_dict(torch.load(path, map_location=device))\n",
        "        else:\n",
        "            self.model.load_state_dict(torch.load(path))\n",
        "            self.model.to(self.device)\n",
        "\n",
        "    def test(self, testloader, threshold=0.5):\n",
        "        \"\"\" To test the performance of model on testing dataset.\n",
        "        Parameters:\n",
        "            testloader(torch.utils.data.Dataloader): Testing data\n",
        "                        loader for the optimizer.\n",
        "            threshold(float): Threshold value after which value will be part \n",
        "                              of output.\n",
        "                              Default: 0.5\n",
        "        Returns:\n",
        "            mean_val_score(float): The mean Sørensen–Dice Coefficient for the \n",
        "                                    whole test dataset.\n",
        "        \"\"\"\n",
        "        # Putting the model to evaluation mode\n",
        "        self.model.eval()\n",
        "        # Getting test data indices for dataloading\n",
        "        test_data_indexes = testloader.sampler.indices[:]\n",
        "        # Total testing data used.\n",
        "        data_len = len(test_data_indexes)\n",
        "        # Score after testing on dataset.\n",
        "        mean_val_score = 0\n",
        "\n",
        "        # Error checking to set testloader batch size to 1.\n",
        "        batch_size = testloader.batch_size\n",
        "        if batch_size != 1:\n",
        "            raise Exception(\"Set batch size to 1 for testing purpose\")\n",
        "        # Converting to iterator to get data in loops.\n",
        "        testloader = iter(testloader)\n",
        "        # Running the loop until no more data is left to test.\n",
        "        while len(test_data_indexes) != 0:\n",
        "            # Getting a data sample.\n",
        "            data = testloader.next()\n",
        "            # Getting the data index\n",
        "            index = int(data['index'])\n",
        "            # Removing the data index from total data indices\n",
        "            # to indicate this data score has been included.\n",
        "            if index in test_data_indexes:\n",
        "                test_data_indexes.remove(index)\n",
        "            else:\n",
        "                continue\n",
        "            # Data prepared to be given as input to model.\n",
        "            image = data['image'].view((1, 1, 512, 512)).to(self.device)\n",
        "            mask = data['mask']\n",
        "\n",
        "            # Predicted output from the input sample.\n",
        "            mask_pred = self.model(image).cpu()\n",
        "            # Threshold elimination.\n",
        "            mask_pred = (mask_pred > threshold)\n",
        "            mask_pred = mask_pred.numpy()\n",
        "            \n",
        "            mask = np.resize(mask, (1, 512, 512))\n",
        "            mask_pred = np.resize(mask_pred, (1, 512, 512))\n",
        "            \n",
        "            # Calculating the dice score for original and \n",
        "            # constructed image mask.\n",
        "            mean_val_score += self._dice_coefficient(mask_pred, mask)\n",
        "\n",
        "        # Calculating the mean score for the whole test dataset.\n",
        "        mean_val_score = mean_val_score / data_len\n",
        "        # Putting the model back to training mode.\n",
        "        self.model.train()\n",
        "        return mean_val_score\n",
        "\n",
        "    def predict(self, data, threshold=0.5):\n",
        "        \"\"\" Calculate the output mask on a single input data.\n",
        "        Parameters:\n",
        "            data(dict): Contains the index, image, mask torch.Tensor.\n",
        "                        'index': Index of the image.\n",
        "                        'image': Contains the tumor image torch.Tensor.\n",
        "                        'mask' : Contains the mask image torch.Tensor.\n",
        "            threshold(float): Threshold value after which value will be part of output.\n",
        "                                Default: 0.5\n",
        "        Returns:\n",
        "            image(numpy.ndarray): 512x512 Original brain scanned image.\n",
        "            mask(numpy.ndarray): 512x512 Original mask of scanned image.\n",
        "            output(numpy.ndarray): 512x512 Generated mask of scanned image.\n",
        "            score(float): Sørensen–Dice Coefficient for mask and output.\n",
        "                            Calculates how similar are the two images.\n",
        "        \"\"\"\n",
        "        self.model.eval()\n",
        "        image = data['image'].numpy()\n",
        "        mask = data['mask'].numpy()\n",
        "\n",
        "        image_tensor = torch.Tensor(data['image'])\n",
        "        image_tensor = image_tensor.view((-1, 1, 512, 512)).to(self.device)\n",
        "        output = self.model(image_tensor).detach().cpu()\n",
        "        output = (output > threshold)\n",
        "        output = output.numpy()\n",
        "\n",
        "        image = np.resize(image, (512, 512))\n",
        "        mask = np.resize(mask, (512, 512))\n",
        "        output = np.resize(output, (512, 512))\n",
        "        score = self._dice_coefficient(output, mask)\n",
        "        return image, mask, output, score\n",
        "\n",
        "    def _train_epoch(self, trainloader, mini_batch):\n",
        "        \"\"\" Training each epoch.\n",
        "        Parameters:\n",
        "            trainloader(torch.utils.data.Dataloader): Training data\n",
        "                        loader for the optimizer.\n",
        "            mini_batch(int): Used to print logs for epoch batches.\n",
        "        Returns:\n",
        "            epoch_loss(float): Loss calculated for each epoch.\n",
        "        \"\"\"\n",
        "        epoch_loss, batch_loss, batch_iteration = 0, 0, 0\n",
        "        for batch, data in enumerate(trainloader):\n",
        "            # Keeping track how many iteration is happening.\n",
        "            batch_iteration += 1\n",
        "            # Loading data to device used.\n",
        "            image = data['image'].to(self.device)\n",
        "            mask = data['mask'].to(self.device)\n",
        "            # Clearing gradients of optimizer.\n",
        "            self.optimizer.zero_grad()\n",
        "            # Calculation predicted output using forward pass.\n",
        "            output = self.model(image)\n",
        "            # Calculating the loss value.\n",
        "            loss_value = self.criterion(output, mask)\n",
        "            # Computing the gradients.\n",
        "            loss_value.backward()\n",
        "            # Optimizing the network parameters.\n",
        "            self.optimizer.step()\n",
        "            # Updating the running training loss\n",
        "            epoch_loss += loss_value.item()\n",
        "            batch_loss += loss_value.item()\n",
        "\n",
        "            # Printing batch logs if any.\n",
        "            if mini_batch:\n",
        "                if (batch+1) % mini_batch == 0:\n",
        "                    batch_loss = batch_loss / \\\n",
        "                        (mini_batch*trainloader.batch_size)\n",
        "                    print(\n",
        "                        f'    Batch: {batch+1:02d},\\tBatch Loss: {batch_loss:.7f}')\n",
        "                    batch_loss = 0\n",
        "\n",
        "        epoch_loss = epoch_loss/(batch_iteration*trainloader.batch_size)\n",
        "        return epoch_loss\n",
        "\n",
        "    def _plot_image(self, epoch, sample):\n",
        "        \"\"\"\n",
        "        Parameters:\n",
        "            epoch(int): Running epoch number used to plot on Tensorboard\n",
        "            sample(list): Sample inputs used to visualize the progress of\n",
        "                          training over epochs.\n",
        "        Returns:\n",
        "            None\n",
        "        \"\"\"\n",
        "        inputs = list()\n",
        "        mask = list()\n",
        "\n",
        "        # Inputs seperated.\n",
        "        for data in sample:\n",
        "            inputs.append(data['image'])\n",
        "        # Inputs stacked together in a single batch\n",
        "        inputs = torch.stack(inputs).to(self.device)\n",
        "        # Outputs gained from model after passing input.\n",
        "        outputs = self.model(inputs).detach().cpu()\n",
        "        # Adding the outputs to Tensorboard for visualization.\n",
        "        for index in range(len(sample)):\n",
        "            self.tb_writer.add_image(\n",
        "                str(sample[index]['index']), outputs[index], epoch)\n",
        "        # Deleting the samples from GPU memory to save space.\n",
        "        del inputs\n",
        "\n",
        "    def _dice_coefficient(self, predicted, target):\n",
        "        \"\"\"Calculates the Sørensen–Dice Coefficient for a\n",
        "        single sample.\n",
        "        Parameters:\n",
        "            predicted(numpy.ndarray): Predicted single output of the network.\n",
        "                                    Shape - (Channel,Height,Width)\n",
        "            target(numpy.ndarray): Actual required single output for the network\n",
        "                                    Shape - (Channel,Height,Width)\n",
        "        Returns:\n",
        "            coefficient(float): Dice coefficient for the input sample.\n",
        "                                        1 represents high similarity and\n",
        "                                        0 represents low similarity.\n",
        "        \"\"\"\n",
        "        smooth = 1\n",
        "        product = np.multiply(predicted, target)\n",
        "        intersection = np.sum(product)\n",
        "        coefficient = (2*intersection + smooth) / \\\n",
        "            (np.sum(predicted) + np.sum(target) + smooth)\n",
        "        return coefficient"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TbbtInVHosjp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#model\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "from torchsummary import summary"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hUtJ31E3osjq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class DynamicUNet(nn.Module):\n",
        "    \"\"\" This is the Pytorch version of U-Net Architecture.\n",
        "    This is not the vanilla version of U-Net.\n",
        "    For more information about U-Net Architecture check the paper here.\n",
        "    Link :- https://arxiv.org/abs/1505.04597\n",
        "    This network is modified to have only 4 blocks depth because of\n",
        "    computational limitations. \n",
        "    The input and output of this network is of the same shape.\n",
        "    Input Size of Network - (1,512,512)\n",
        "    Output Size of Network - (1,512,512)\n",
        "        Shape Format :  (Channel, Width, Height)\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self, filters, input_channels=1, output_channels=1):\n",
        "        \"\"\" Constructor for UNet class.\n",
        "        Parameters:\n",
        "            filters(list): Five filter values for the network.\n",
        "            input_channels(int): Input channels for the network. Default: 1\n",
        "            output_channels(int): Output channels for the final network. Default: 1\n",
        "        \"\"\"\n",
        "        super(DynamicUNet, self).__init__()\n",
        "\n",
        "        if len(filters) != 5:\n",
        "            raise Exception(f\"Filter list size {len(filters)}, expected 5!\")\n",
        "\n",
        "        padding = 1\n",
        "        ks = 3\n",
        "        # Encoding Part of Network.\n",
        "        #   Block 1\n",
        "        self.conv1_1 = nn.Conv2d(input_channels, filters[0], kernel_size=ks, padding=padding)\n",
        "        self.conv1_2 = nn.Conv2d(filters[0], filters[0], kernel_size=ks, padding=padding)\n",
        "        self.maxpool1 = nn.MaxPool2d(2)\n",
        "        #   Block 2\n",
        "        self.conv2_1 = nn.Conv2d(filters[0], filters[1], kernel_size=ks, padding=padding)\n",
        "        self.conv2_2 = nn.Conv2d(filters[1], filters[1], kernel_size=ks, padding=padding)\n",
        "        self.maxpool2 = nn.MaxPool2d(2)\n",
        "        #   Block 3\n",
        "        self.conv3_1 = nn.Conv2d(filters[1], filters[2], kernel_size=ks, padding=padding)\n",
        "        self.conv3_2 = nn.Conv2d(filters[2], filters[2], kernel_size=ks, padding=padding)\n",
        "        self.maxpool3 = nn.MaxPool2d(2)\n",
        "        #   Block 4\n",
        "        self.conv4_1 = nn.Conv2d(filters[2], filters[3], kernel_size=ks, padding=padding)\n",
        "        self.conv4_2 = nn.Conv2d(filters[3], filters[3], kernel_size=ks, padding=padding)\n",
        "        self.maxpool4 = nn.MaxPool2d(2)\n",
        "        \n",
        "        # Bottleneck Part of Network.\n",
        "        self.conv5_1 = nn.Conv2d(filters[3], filters[4], kernel_size=ks, padding=padding)\n",
        "        self.conv5_2 = nn.Conv2d(filters[4], filters[4], kernel_size=ks, padding=padding)\n",
        "        self.conv5_t = nn.ConvTranspose2d(filters[4], filters[3], 2, stride=2)\n",
        "\n",
        "        # Decoding Part of Network.\n",
        "        #   Block 4\n",
        "        self.conv6_1 = nn.Conv2d(filters[4], filters[3], kernel_size=ks, padding=padding)\n",
        "        self.conv6_2 = nn.Conv2d(filters[3], filters[3], kernel_size=ks, padding=padding)\n",
        "        self.conv6_t = nn.ConvTranspose2d(filters[3], filters[2], 2, stride=2)\n",
        "        #   Block 3\n",
        "        self.conv7_1 = nn.Conv2d(filters[3], filters[2], kernel_size=ks, padding=padding)\n",
        "        self.conv7_2 = nn.Conv2d(filters[2], filters[2], kernel_size=ks, padding=padding)\n",
        "        self.conv7_t = nn.ConvTranspose2d(filters[2], filters[1], 2, stride=2)\n",
        "        #   Block 2\n",
        "        self.conv8_1 = nn.Conv2d(filters[2], filters[1], kernel_size=ks, padding=padding)\n",
        "        self.conv8_2 = nn.Conv2d(filters[1], filters[1], kernel_size=ks, padding=padding)\n",
        "        self.conv8_t = nn.ConvTranspose2d(filters[1], filters[0], 2, stride=2)\n",
        "        #   Block 1\n",
        "        self.conv9_1 = nn.Conv2d(filters[1], filters[0], kernel_size=ks, padding=padding)\n",
        "        self.conv9_2 = nn.Conv2d(filters[0], filters[0], kernel_size=ks, padding=padding)\n",
        "\n",
        "        # Output Part of Network.\n",
        "        self.conv10 = nn.Conv2d(filters[0], output_channels, kernel_size=ks, padding=padding)\n",
        "\n",
        "    def forward(self, x):\n",
        "        \"\"\" Method for forward propagation in the network.\n",
        "        Parameters:\n",
        "            x(torch.Tensor): Input for the network of size (1, 512, 512).\n",
        "        Returns:\n",
        "            output(torch.Tensor): Output after the forward propagation \n",
        "                                    of network on the input.\n",
        "        \"\"\"\n",
        "\n",
        "        # Encoding Part of Network.\n",
        "        #   Block 1\n",
        "        conv1 = F.relu(self.conv1_1(x))\n",
        "        conv1 = F.relu(self.conv1_2(conv1))\n",
        "        pool1 = self.maxpool1(conv1)\n",
        "        #   Block 2\n",
        "        conv2 = F.relu(self.conv2_1(pool1))\n",
        "        conv2 = F.relu(self.conv2_2(conv2))\n",
        "        pool2 = self.maxpool2(conv2)\n",
        "        #   Block 3\n",
        "        conv3 = F.relu(self.conv3_1(pool2))\n",
        "        conv3 = F.relu(self.conv3_2(conv3))\n",
        "        pool3 = self.maxpool3(conv3)\n",
        "        #   Block 4\n",
        "        conv4 = F.relu(self.conv4_1(pool3))\n",
        "        conv4 = F.relu(self.conv4_2(conv4))\n",
        "        pool4 = self.maxpool4(conv4)\n",
        "\n",
        "        # Bottleneck Part of Network.\n",
        "        conv5 = F.relu(self.conv5_1(pool4))\n",
        "        conv5 = F.relu(self.conv5_2(conv5))\n",
        "\n",
        "        # Decoding Part of Network.\n",
        "        #   Block 4\n",
        "        up6 = torch.cat((self.conv5_t(conv5), conv4), dim=1)\n",
        "        conv6 = F.relu(self.conv6_1(up6))\n",
        "        conv6 = F.relu(self.conv6_2(conv6))\n",
        "        #   Block 3\n",
        "        up7 = torch.cat((self.conv6_t(conv6), conv3), dim=1)\n",
        "        conv7 = F.relu(self.conv7_1(up7))\n",
        "        conv7 = F.relu(self.conv7_2(conv7))\n",
        "        #   Block 2\n",
        "        up8 = torch.cat((self.conv7_t(conv7), conv2), dim=1)\n",
        "        conv8 = F.relu(self.conv8_1(up8))\n",
        "        conv8 = F.relu(self.conv8_2(conv8))\n",
        "        #   Block 1\n",
        "        up9 = torch.cat((self.conv8_t(conv8), conv1), dim=1)\n",
        "        conv9 = F.relu(self.conv9_1(up9))\n",
        "        conv9 = F.relu(self.conv9_2(conv9))\n",
        "\n",
        "        # Output Part of Network.\n",
        "        output = F.sigmoid(self.conv10(conv9))\n",
        "\n",
        "        return output\n",
        "\n",
        "    def summary(self, input_size=(1, 512, 512), batch_size=-1, device='cuda'):\n",
        "        \"\"\" Get the summary of the network in a chart like form\n",
        "        with name of layer size of the inputs and parameters \n",
        "        and some extra memory details.\n",
        "        This method uses the torchsummary package.\n",
        "        For more information check the link.\n",
        "        Link :- https://github.com/sksq96/pytorch-summary\n",
        "        Parameters:\n",
        "            input_size(tuple): Size of the input for the network in\n",
        "                                 format (Channel, Width, Height).\n",
        "                                 Default: (1,512,512)\n",
        "            batch_size(int): Batch size for the network.\n",
        "                                Default: -1\n",
        "            device(str): Device on which the network is loaded.\n",
        "                            Device can be 'cuda' or 'cpu'.\n",
        "                            Default: 'cuda'\n",
        "        Returns:\n",
        "            A printed output for IPython Notebooks.\n",
        "            Table with 3 columns for Layer Name, Input Size and Parameters.\n",
        "            torchsummary.summary() method is used.\n",
        "        \"\"\"\n",
        "        return summary(self, input_size, batch_size, device)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mAPUnNS5osjr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#plot\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.gridspec as gridspec\n",
        "import numpy as np"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hhStsp3rosjt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def result(image, mask, output, title, transparency=0.38, save_path=None):\n",
        "    \"\"\" Plots a 2x3 plot with comparisons of output and original image.\n",
        "    Works best with Jupyter Notebook/Lab.\n",
        "    Parameters:\n",
        "        image(numpy.ndarray): Array containing the original image of MRI scan.\n",
        "        mask(numpy.ndarray): Array containing the original mask of tumor.\n",
        "        output(numpy.ndarray): Model constructed mask from input image.\n",
        "        title(str): Title of the plot to be used.\n",
        "        transparency(float): Transparency level of mask on images.\n",
        "                             Default: 0.38\n",
        "        save_path(str): Saves the plot to the location specified.\n",
        "                        Does nothing if None. \n",
        "                        Default: None\n",
        "    Return:\n",
        "        None\n",
        "    \"\"\"\n",
        "\n",
        "    fig, axs = plt.subplots(2, 3, sharex=True, sharey=True, figsize=(\n",
        "        20, 15), gridspec_kw={'wspace': 0.025, 'hspace': 0.010})\n",
        "    fig.suptitle(title, x=0.5, y=0.92, fontsize=20)\n",
        "\n",
        "    axs[0][0].set_title(\"Original Mask\", fontdict={'fontsize': 16})\n",
        "    axs[0][0].imshow(mask, cmap='gray')\n",
        "    axs[0][0].set_axis_off()\n",
        "\n",
        "    axs[0][1].set_title(\"Constructed Mask\", fontdict={'fontsize': 16})\n",
        "    axs[0][1].imshow(output, cmap='gray')\n",
        "    axs[0][1].set_axis_off()\n",
        "\n",
        "    mask_diff = np.abs(np.subtract(mask, output))\n",
        "    axs[0][2].set_title(\"Mask Difference\", fontdict={'fontsize': 16})\n",
        "    axs[0][2].imshow(mask_diff, cmap='gray')\n",
        "    axs[0][2].set_axis_off()\n",
        "\n",
        "    seg_output = mask*transparency\n",
        "    seg_image = np.add(image, seg_output)/2\n",
        "    axs[1][0].set_title(\"Original Segment\", fontdict={'fontsize': 16})\n",
        "    axs[1][0].imshow(seg_image, cmap='gray')\n",
        "    axs[1][0].set_axis_off()\n",
        "\n",
        "    seg_output = output*transparency\n",
        "    seg_image = np.add(image, seg_output)/2\n",
        "    axs[1][1].set_title(\"Constructed Segment\", fontdict={'fontsize': 16})\n",
        "    axs[1][1].imshow(seg_image, cmap='gray')\n",
        "    axs[1][1].set_axis_off()\n",
        "\n",
        "    axs[1][2].set_title(\"Original Image\", fontdict={'fontsize': 16})\n",
        "    axs[1][2].imshow(image, cmap='gray')\n",
        "    axs[1][2].set_axis_off()\n",
        "\n",
        "    plt.tight_layout()\n",
        "\n",
        "    if save_path:\n",
        "        plt.savefig(save_path, dpi=90, bbox_inches='tight')\n",
        "\n",
        "    plt.show()\n",
        "\n",
        "\n",
        "def loss_graph(loss_list, save_plot=None):\n",
        "    \"\"\" Plots the loss graph from the training history data.\n",
        "    Saves it if required.\n",
        "    Parameters:\n",
        "        loss_list(list): Array of loss values at each epoch of training time.\n",
        "        save_plot(str): Saves the plot to the specified location.\n",
        "                        Does nothing if None.\n",
        "                        Default: None\n",
        "    Returns:\n",
        "        None\n",
        "    \"\"\"\n",
        "    plt.figure(figsize=(20, 10))\n",
        "    plt.title('Loss Function Over Epoch')\n",
        "    plt.xlabel('Epochs')\n",
        "    plt.ylabel('Loss Value')\n",
        "    line = plt.plot(loss_list, marker='o')\n",
        "    plt.legend((line), ('Loss Value',), loc=1)\n",
        "    if save_plot:\n",
        "        plt.savefig(save_plot)\n",
        "    plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "phklyAaNosju",
        "colab_type": "code",
        "colab": {},
        "outputId": "11bbf27b-4495-415b-a1ec-28822c7b3e1d"
      },
      "source": [
        "import os\n",
        "import warnings\n",
        "import pickle\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "import torch\n",
        "from torch.utils.data import SubsetRandomSampler\n",
        "\n",
        "import numpy as np\n",
        "%matplotlib inline\n",
        "'''\n",
        "import bts.dataset as dataset\n",
        "import bts.model as model\n",
        "import bts.classifier as classifier\n",
        "import bts.plot as plot\n",
        "'''\n",
        "\n",
        "\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "\n",
        "print('Computation Details')\n",
        "print(f'\\tDevice Used: ({device})  {torch.cuda.get_device_name(torch.cuda.current_device())}\\n')\n",
        "\n",
        "print('Packages Used Versions:-')\n",
        "print(f'\\tPytorch Version: {torch.__version__}')\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Computation Details\n",
            "\tDevice Used: (cuda)  GeForce GTX 1060 6GB\n",
            "\n",
            "Packages Used Versions:-\n",
            "\tPytorch Version: 1.0.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rTRzat5Sosjw",
        "colab_type": "text"
      },
      "source": [
        "## Hyperparameters Tuning "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DpMPsb3Bosjx",
        "colab_type": "code",
        "colab": {},
        "outputId": "75a58efc-5288-4fa8-aa0f-28e68c994171"
      },
      "source": [
        "# Dataset part used for testing\n",
        "TEST_SPLIT = 0.2\n",
        "# Batch size for training. Limited by GPU memory\n",
        "BATCH_SIZE = 6\n",
        "# Dataset folder used\n",
        "DATASET_USED = 'dataset'\n",
        "# Full Dataset path\n",
        "DATASET_PATH = os.path.join('dataset',DATASET_USED)\n",
        "# Training Epochs\n",
        "EPOCHS = 100\n",
        "# Filters used in UNet Model\n",
        "FILTER_LIST = [16,32,64,128,256]\n",
        "# Flag to train the model\n",
        "TRAIN = False\n",
        "# Flag to load saved model\n",
        "LOAD_MODEL = True\n",
        "# Flag to save model trained\n",
        "SAVE_MODEL = False\n",
        "# Model name to save or load.\n",
        "MODEL_NAME = f\"UNet-{FILTER_LIST}.pt\"\n",
        "\n",
        "print(f\"Model Name :   {MODEL_NAME}\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model Name :   UNet-[16, 32, 64, 128, 256].pt\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KoSARhgrosjz",
        "colab_type": "text"
      },
      "source": [
        "## Dataset Loading"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Njpfpilgosjz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_indices(length, new=False):\n",
        "    \"\"\" Gets the Training & Testing data indices for a\n",
        "    paticular \"DATASET_USED\".Stores the indices and returns\n",
        "    them back when the same dataset is used.\n",
        "    Parameters:\n",
        "        length(int): Length of the dataset used.\n",
        "        new(bool): Discard the saved indices and get new ones.\n",
        "    Return:\n",
        "        train_indices(list): Array of indices used for training purpose.\n",
        "        test_indices(list): Array of indices used for testing purpose.\n",
        "    \"\"\"\n",
        "    # Pickle file location of the indices.\n",
        "    file_path = os.path.join('dataset',f'split_indices_{DATASET_USED}.p')\n",
        "    data = dict()\n",
        "    if os.path.isfile(file_path) and not new:\n",
        "        # File found.\n",
        "        with open(file_path,'rb') as file :\n",
        "            data = pickle.load(file)\n",
        "            return data['train_indices'], data['test_indices']\n",
        "    else:\n",
        "        # File not found or fresh copy is required.\n",
        "        indices = list(range(length))\n",
        "        np.random.shuffle(indices)\n",
        "        split = int(np.floor(TEST_SPLIT * len(tumor_dataset)))\n",
        "        train_indices , test_indices = indices[split:], indices[:split]\n",
        "        # Indices are saved with pickle.\n",
        "        data['train_indices'] = train_indices\n",
        "        data['test_indices'] = test_indices\n",
        "        with open(file_path,'wb') as file:\n",
        "            pickle.dump(data,file)\n",
        "    return train_indices, test_indices"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i0yc-Yd0osj0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "tumor_dataset = dataset.TumorDataset(DATASET_PATH)\n",
        "\n",
        "train_indices, test_indices = get_indices(len(tumor_dataset))\n",
        "train_sampler, test_sampler = SubsetRandomSampler(train_indices), SubsetRandomSampler(test_indices)\n",
        "\n",
        "trainloader = torch.utils.data.DataLoader(tumor_dataset, BATCH_SIZE, sampler=train_sampler)\n",
        "testloader = torch.utils.data.DataLoader(tumor_dataset, 1, sampler=test_sampler)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lvAq3Fqkosj2",
        "colab_type": "text"
      },
      "source": [
        "## Model Declaration"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X7EYsR04osj2",
        "colab_type": "code",
        "colab": {},
        "outputId": "d7777590-02db-4364-d28f-4c946441bbe6"
      },
      "source": [
        "unet_model = None\n",
        "unet_classifier = None\n",
        "if not LOAD_MODEL:\n",
        "    # New model is created.\n",
        "    unet_model = model.DynamicUNet(FILTER_LIST).to(device)\n",
        "    unet_classifier = classifier.BrainTumorClassifier(unet_model,device)\n",
        "else:\n",
        "    # Saved model is loaded on memory.\n",
        "    unet_model = model.DynamicUNet(FILTER_LIST)\n",
        "    unet_classifier = classifier.BrainTumorClassifier(unet_model,device)\n",
        "    unet_classifier.restore_model(os.path.join('saved_models',MODEL_NAME))\n",
        "    print('Saved model loaded')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Saved model loaded\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LYqwDgjmosj4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\"\"\"\n",
        "till = 8\n",
        "choice = test_indices[:till]\n",
        "sample = list()\n",
        "for _ in range(till):\n",
        "    sample.append(dataset[choice[_]])\n",
        "\"\"\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JBm_vIMbosj6",
        "colab_type": "text"
      },
      "source": [
        "## Model Training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "72zEIOWIosj6",
        "colab_type": "code",
        "colab": {},
        "outputId": "94ec1c89-f06d-45d5-9f3f-3a0e69a44e3e"
      },
      "source": [
        "# Training process\n",
        "if TRAIN:\n",
        "    unet_model.train()\n",
        "    path = os.path.join('saved_models',MODEL_NAME) if SAVE_MODEL else None\n",
        "    unet_train_history = unet_classifier.train(EPOCHS,trainloader,mini_batch=100,save_best=path)\n",
        "    print(f'Training Finished after {EPOCHS} epoches')\n",
        "\n",
        "# Testing process on test data.\n",
        "unet_model.eval()\n",
        "unet_score = unet_classifier.test(testloader)\n",
        "print(f'\\n\\nDice Score {unet_score}')\n",
        "# Dice Score 0.7446110107881675"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Dice Score 0.7446110107881675\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2tnP6rZFosj8",
        "colab_type": "text"
      },
      "source": [
        "## Training Results"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-M_fSQ45osj9",
        "colab_type": "text"
      },
      "source": [
        "### Training Loss Graph"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l7pezaGeosj-",
        "colab_type": "code",
        "colab": {},
        "outputId": "f41cd81d-e5f0-41a8-f2e8-ec5dcbc4bed7"
      },
      "source": [
        "save_plot = os.path.join('images',f'{MODEL_NAME}-loss_graph.png')\n",
        "plot.loss_graph(unet_train_history['train_loss'],save_plot)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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YWLxwXtpaW3YYa2ttyeKF85qUCAAAAGDw7Mk0TGzdd2nJslXp6u5Ja0vJZReeZD8mAAAAYEQwk2kYWbSgI99793n5X+c+L7UmC0+Y0exIAAAAAIOiZBqGOudMTe+WmpvXdjc7CgAAAMCgKJmGodPmTE2SLF/9SJOTAAAAAAyOkmkYap80Icc+95AsX/Nos6MAAAAADIqSaZjqnDstN655NFu21GZHAQAAANgjJdMw1TlnajZs7M2Pf7ah2VEAAAAA9kjJNEydPndakuSG1ZbMAQAAAMOfkmmYOmJaWw6bfFButPk3AAAAMAIomYapUkpOnzvVTCYAAABgRFAyDWOnzZmWru6erH+sp9lRAAAAAJ6VkmkYO33u1CTJcrOZAAAAgGFOyTSMHTdzStpaW3LjGiUTAAAAMLwpmYax1pZxWXBke26w+TcAAAAwzCmZhrnOOVNz5/rH88TTvc2OAgAAALBbSqZhrnPutGypyYp7LZkDAAAAhi8l0zC34Mj2jCs2/wYAAACGNyXTMDd5Ymvmz5iS5WvsywQAAAAMX0qmEeD0uVOz4t7u9PZtaXYUAAAAgF1SMo0Ap82dlqc29eXO9RuaHQUAAABgl5RMI8Dpc6cmiSVzAAAAwLClZBoBZh7alo72Npt/AwAAAMOWkmmE6Jw7NcvXPJJaa7OjAAAAADyDkmmE6JwzNQ88/nTWPtrT7CgAAAAAz6BkGiE6505LYl8mAAAAYHhSMo0Qzz98ciYfND432JcJAAAAGIaUTCNEy7iSF8yZmhuVTAAAAMAwpGQaQTrnTM2qBzbksac2NzsKAAAAwA6UTCPI1n2ZfnSv2UwAAADA8KJkGkFOPaI948eV3LDa5t8AAADA8KJkGkHaJrTkhI5Ds3yNmUwAAADA8KJkGmE650zNzfd1Z1PvlmZHAQAAANimoSVTKeX8UsqqUspdpZR37+L4OaWUH5VSekspr9np2JGllH8vpdxZSrmjlDK3kVlHitPnTs3TvVty27rHmh0FAAAAYJuGlUyllJYkH07y8iTHJ7molHL8Tqfdm+TiJF/YxS3+b5IltdbjkpyR5GeNyjqSnDanf/Pv5fZlAgAAAIaRRs5kOiPJXbXWe2qtm5J8Kcmrtj+h1rq61npLkh3Wfg2UUeNrrd8cOO+JWutTDcw6Yhw2+aDMnT4py1fblwkAAAAYPhpZMnUkuW+7z2sHxgbj+Um6SylXllJWlFKWDMyM2kEp5S2llOWllOUPPvjgEEQeGU6bMy03rnk0tdZmRwEAAABI0tiSqexibLCtyPgkP5/k95KcnuTo9C+r2/FmtX681tpZa+087LDD9jXniHP63Kl5+MlN+elDTzY7CgAAAECSxpZMa5Mcsd3n2UnW7cW1KwaW2vUmWZrkBUOcb8TqnDs1SSyZAwAAAIaNRpZMNyQ5tpRyVCllQpLXJ/n6Xlw7tZSydXrSeUnuaEDGEenWtY9lXEl+/59vydmXX5OlK7qaHQkAAAAY4xpWMg3MQHp7kmVJ7kzylVrr7aWUS0spFyRJKeX0UsraJK9N8rFSyu0D1/alf6ncf5RSbk3/0rtPNCrrSLJ0RVf+8Ku3ZcvAwsOu7p5ccuWtiiYAAACgqcpo2Ty6s7OzLl++vNkxGu7sy69JV3fPM8Y72tvyvXef14REAAAAwGhVSrmx1to5mHMbuVyOBli3i4Lp2cYBAAAADgQl0wgzq71tr8YBAAAADgQl0wizeOG8tLW27DDW1tqSxQvnNSkRAAAAQDK+2QHYO4sWdCRJlixbtW1vpktfdcK2cQAAAIBmMJNpBFq0oCPfe/d5+cSb+vfdmjP94CYnAgAAAMY6JdMIdtqcqUmSG1Y/0uQkAAAAwFinZBrBph08Ic877ODcuObRZkcBAAAAxjgl0wjXOWdablzzaLZsqc2OAgAAAIxhSqYRrnPu1DzWszl3P/hEs6MAAAAAY5iSaYTrnDstSXLDakvmAAAAgOZRMo1wc6dPyvSDJ2T5Gpt/AwAAAM2jZBrhSinpnDvV5t8AAABAUymZRoHOOdOy5uGn8rMNG5sdBQAAABijlEyjwGlzpyZJbrQvEwAAANAkSqZR4MRZh+ag8eOy3JI5AAAAoEmUTKPAhPHjcsoR7Vm+2ubfAAAAQHMomUaJzjlTc/u6x9Ozqa/ZUQAAAIAxSMk0Spw+d1p6t9TcdF93s6MAAAAAY5CSaZR4wZH9m39bMgcAAAA0g5JplDh0Umuef/ghNv8GAAAAmkLJNIp0zp2WH937aPq21GZHAQAAAMYYJdMo0jlnajZs7M2PH9jQ7CgAAADAGKNkGkU650xLEkvmAAAAgANOyTSKHDGtLc+dfFButPk3AAAAcIApmUaRUko6507NDavNZAIAAAAOLCXTKHPanGnp6u7J/Y9tbHYUAAAAYAxRMo0ynXOmJkmWr7FkDgAAADhwlEyjzPGzpqSttSXLLZkDAAAADiAl0yjT2jIupx7RbiYTAAAAcEApmUahzrlTc8e6x/PE073NjgIAAACMEUqmUahz7rRsqclN93Y3OwoAAAAwRiiZRqEFR7anFJt/AwAAAAeOkmkUmjKxNfMOn5wb19j8GwAAADgwlEyj1Olzp+VHax5Nb9+WZkcBAAAAxgAl0yjVOXdqntzUl5X3b2h2FAAAAGAMUDKNUqfNmZoklswBAAAAB4SSaZTqaG/LzEMnZrmSCQAAADgAlEyjVCklp82ZmuWrPWEOAAAAaDwl0yjWOWdq1j+2MV3dPc2OAgAAAIxySqZRrHPutCQxmwkAAABoOCXTKDZ/xuQcPKEly1fblwkAAABorPHNDkDjjG8Zl472tnzxh/fmc9evyaz2tixeOC+LFnQ0OxoAAAAwyiiZRrGlK7pyz0NPpndLTZJ0dffkkitvTRJFEwAAADCkLJcbxZYsW7WtYNqqZ3Nflixb1aREAAAAwGilZBrF1u3mqXK7GwcAAADYV0qmUWxWe9tejQMAAADsKyXTKLZ44by0tbbsMNbW2pLFC+c1KREAAAAwWtn4exTburn3n3zttmzY2JuZh07MH5w/36bfAAAAwJBTMo1yixZ0pG1CS9762RvzkTe+IAuOnNrsSAAAAMAoZLncGHD8zClJkpX3b2hyEgAAAGC0UjKNAR3tbTnkoPG5c/3jzY4CAAAAjFJKpjFg3LiS+TMmZ+V6M5kAAACAxlAyjRHzZ07Onfc/nlprs6MAAAAAo5CSaYw4buaUbNjYm7WP9jQ7CgAAADAKKZnGiPkzbP4NAAAANI6SaYyYP2NySonNvwEAAICGUDKNEQcfND5zpk3KyvuVTAAAAMDQUzKNIfNnTMmdnjAHAAAANICSaQw5buaUrH74yTy1qbfZUQAAAIBRRsk0hsyfOTm1Jqts/g0AAAAMMSXTGHL8zP4nzFkyBwAAAAw1JdMYMntqWw45aLzNvwEAAIAhp2QaQ0opmT9jcu5cr2QCAAAAhpaSaYw5buaUrFy/IbXWZkcBAAAARhEl0xgzf+bkbHi6N2sf7Wl2FAAAAGAUUTKNMcdt2/zbkjkAAABg6CiZxph5h09OKcnK+z1hDgAAABg6DS2ZSinnl1JWlVLuKqW8exfHzyml/KiU0ltKec0ujk8ppXSVUv6ukTnHkoMPGp850yaZyQQAAAAMqYaVTKWUliQfTvLyJMcnuaiUcvxOp92b5OIkX9jNbT6Q5DuNyjhWHTdziplMAAAAwJBq5EymM5LcVWu9p9a6KcmXkrxq+xNqratrrbck2bLzxaWU05IcnuTfG5hxTJo/Y0pWP/xkntrU2+woAAAAwCjRyJKpI8l9231eOzC2R6WUcUn+KsniPZz3llLK8lLK8gcffHCfg441x82cnFrtywQAAAAMnUaWTGUXY3WQ174tydW11vue7aRa68drrZ211s7DDjtsrwOOVVufMLdyvZIJAAAAGBrjG3jvtUmO2O7z7CTrBnntC5P8fCnlbUkOSTKhlPJErfUZm4ez92ZPbcvkg8bb/BsAAAAYMo0smW5Icmwp5agkXUlen+QNg7mw1vrGre9LKRcn6VQwDZ1SSubPnJyV9yuZAAAAgKHRsOVytdbeJG9PsizJnUm+Umu9vZRyaSnlgiQppZxeSlmb5LVJPlZKub1RedjR/BlTsnL9htQ62BWMAAAAALvXyJlMqbVeneTqncbes937G9K/jO7Z7vEPSf6hAfHGtONmTslnr1+TtY/25Ihpk5odBwAAABjhGrnxN8PYcTMnJ4l9mQAAAIAhoWQao+bNmJxSkjs9YQ4AAAAYAkqmMWrShPGZO/1gm38DAAAAQ0LJNIbNnzHZcjkAAABgSCiZxrDjZk7JmkeeypNP9zY7CgAAADDCKZnGsPkzJqfWZNUD9mUCAAAA9o+SaQw7buaUJJ4wBwAAAOw/JdMYNntqWyYfND4rPWEOAAAA2E9KpjGslJL5M23+DQAAAOw/JdMYd9zMKVl5/4bUWpsdBQAAABjBlExj3PwZU/LE071Z+2hPs6MAAAAAI5iSaYw7bubkJMkdlswBAAAA+0HJNMbNmzE5pcTm3wAAAMB+UTKNcZMmjM/c6Qfb/BsAAADYL0omctzMyVl5v5IJAAAA2HdKJjJ/xpSseeSpPPl0b7OjAAAAACOUkokcN3NKak1W3m9fJgAAAGDfKJnY9oQ5S+YAAACAfaVkIh3tbZk8cbzNvwEAAIB9pmQipZQcN2NKVq63XA4AAADYN0omkiTzZ07Oyvs3ZMuW2uwoAAAAwAikZCJJ/+bfTzzdm67unmZHAQAAAEYgJRNJkp9t2Jgk+fm/+HbOvvyaLF3R1eREAAAAwEiiZCJLV3Tl76+9e9vnru6eXHLlrYomAAAAYNCUTGTJslXZuHnLDmM9m/uyZNmqJiUCAAAARholE1m3m32YdjcOAAAAsDMlE5nV3rZX4wAAAAA7UzKRxQvnpa21ZYexttaWLF44r0mJAAAAgJFmfLMD0HyLFnQk6d+bqWtgidx7X3n8tnEAAACAPTGTiST9RdP33n1evvLWFyZJDj5I/wgAAAAMnpKJHZw2Z2oOm3xQ/vW29c2OAgAAAIwgSiZ20DKu5PwTZuTbKx/MU5t6mx0HAAAAGCGUTDzDy0+akZ7NffnOqgebHQUAAAAYIZRMPMMZc6dl+sETcvVt9zc7CgAAADBCKJl4hvEt4/KyE2bkmjsfyMbNfc2OAwAAAIwASiZ26RUnzciTm/rynz+2ZA4AAADYMyUTu3TW0dPTPqk1/2rJHAAAADAISiZ2qbVlXF52/OH51h0P5OleS+YAAACAZ6dkYrdeftLMbHi6N9+766FmRwEAAACGOSUTu3X2856TyRPH5+pbLZkDAAAAnp2Sid2aMH5cfvH4w/Pvt9+fTb1bmh0HAAAAGMaUTDyrV5w4M49v7M119zzc7CgAAADAMKZk4lm96Njn5JCDxudfb13f7CgAAADAMKZk4llNbG3JS457bpbdfn96+yyZAwAAAHZNycQevfzEmXn0qc25/p5Hmh0FAAAAGKaUTOzRufMOy6QJLbn6NkvmAAAAgF1TMrFHE1tb8uL5z82y2+5P35ba7DgAAADAMKRkYlBeceLMPPzkpvzwp5bMAQAAAM+kZGJQzp13WCa2jsu/WjIHAAAA7IKSiUE5+KDxOff5z82/3nZ/tlgyBwAAAOxEycSgvfykGXlww9O58d5Hmx0FAAAAGGaUTAzaefOfmwnjx+XqWy2ZAwAAAHakZGLQJk9szTnHHpZ/s2QOAAAA2ImSib1y+JQJWf/YxjzvD6/O2Zdfk6UrupodCQAAABgGlEwM2tIVXfnnH/WXSjVJV3dPLrnyVkUTAAAAoGRi8JYsW5WNm7fsMNazuS9Llq1qUiIAAABguFAyMWjrunv2ahwAAAAYO5RMDNqs9ra9GgcAAADGDiUTg7Z44by0tbbsMNbaUrJ44bwmJQIAAACGi/HNDsDIsWhBR5L+vZnWdfdkwvhxaW0pedkJhzc5GQAAANBsSib2yqIFHdvKphvXPJpX//338w/fX523nXtMk5MBAAAAzWS5HPvstDlTc9785+Zj37knj/VsbnYcAAAAoImUTOyX3/3F5+exns351Hd/2uwoAAAAQBMpmdgvJ3YcmpefOCOf+q8JnTG1AAAgAElEQVR78siTm5odBwAAAGgSJRP77Xd/8fl5anNfPvadu5sdBQAAAGgSJRP77djDJ2fRqR35x+tW52ePb2x2HAAAAKAJlEwMid9+6bHZ3Ffz4W/f1ewoAAAAQBMomRgSc6YfnNd1zs4Xfnhv1j76VLPjAAAAAAdYQ0umUsr5pZRVpZS7Sinv3sXxc0opPyql9JZSXrPd+KmllOtKKbeXUm4ppfxKI3MyNN5x3rEpKfnb/zCbCQAAAMaahpVMpZSWJB9O8vIkxye5qJRy/E6n3Zvk4iRf2Gn8qSRvqrWekOT8JH9TSmlvVFaGxqz2trzhzCPzTz9am58+9GSz4wAAAAAHUCNnMp2R5K5a6z211k1JvpTkVdufUGtdXWu9JcmWncZ/XGv9ycD7dUl+luSwBmZliLztxc9La0vJ33zrx82OAgAAABxAjSyZOpLct93ntQNje6WUckaSCUnu3sWxt5RSlpdSlj/44IP7HJSh89zJE3Pxzx2Vr9+8Lqvu39DsOAAAAMAB0siSqexirO7VDUqZmeSzSX691rpl5+O11o/XWjtrrZ2HHWai03Dx1nOOziETxuevv7mq2VEAAACAA6SRJdPaJEds93l2knWDvbiUMiXJVUn+uNZ6/RBno4GmHjwhZx8zPctufyBHvfuqnH35NVm6oqvZsQAAAIAGamTJdEOSY0spR5VSJiR5fZKvD+bCgfO/muT/1lqvaGBGGmDpiq5c++P+5Ys1SVd3Ty658lZFEwAAAIxiDSuZaq29Sd6eZFmSO5N8pdZ6eynl0lLKBUlSSjm9lLI2yWuTfKyUcvvA5a9Lck6Si0spNw28Tm1UVobWkmWrsnHzjqsbezb3Zckyy+cAAABgtBrfyJvXWq9OcvVOY+/Z7v0N6V9Gt/N1n0vyuUZmo3HWdffs1TgAAAAw8jVyuRxj1Kz2tl2OT2lraKcJAAAANJGSiSG3eOG8tLW27DA2riSP9fTmM9/7aZNSAQAAAI1kaglDbtGCjiT9ezOt6+7JrPa2/M5Lj80373wg7//GHUmSXz/7qGZGBAAAAIaYkomGWLSgY1vZtNUFp3bkHV/8Ud7/jTtSa/IbL1I0AQAAwGhhuRwHzITx4/J3b3hBFp5weC79lzvyqe9aOgcAAACjhZKJA6q1pb9oOv+EGfmAogkAAABGDcvlOOBaW8blb9+wIO/4wop84F/uyK1ru3PD6ke37d+0eOG8Zyy1AwAAAIY3M5loiq1F08mzp2TpTevS1d2TmqSruyeXXHlrlq7oanZEAAAAYC8omWia1pZxeeiJTc8Y79nclyXLVjUhEQAAALCvlEw01frujbscX9fdc4CTAAAAAPtDyURTzWpv26txAAAAYHhSMtFUixfOS1tryw5jB40fl8UL5zUpEQAAALAvPF2Optr6FLkly1ZlXXdPSkmeO/mgvPKUWU1OBgAAAOwNJRNNt2hBx7ayaemKrvz2l2/Kl264N288c06TkwEAAACDZbkcw8qrTp2Vs46elr/4t1V5+Imnmx0HAAAAGCQlE8NKKSUfeNWJefLp3lz+ryubHQcAAAAYJCUTw86xh0/Ob/780bnixrVZvvqRZscBAAAABkHJxLD0zpcck1mHTswfL70tvX1bmh0HAAAA2AMlE8PSpAnj855XHp+V92/IP163ptlxAAAAgD1QMjFsLTxhRs6dd1j+zzd/nAce39jsOAAAAMCzUDIxbJVS8v4LTsimvi3506vubHYcAAAA4FkomRjW5kw/OG8793n5xs3r8r27Hmp2HAAAAGA3lEwMe//zF56XOdMn5U++dlue7u1rdhwAAABgF5RMDHsTW1vyvgtOyD0PPpnT//RbOerdV+Xsy6/J0hVdzY4GAAAADBjf7AAwGI89tTnjSvL4xt4kSVd3Ty658tYkyaIFHc2MBgAAAMRMJkaIJctWZUvdcaxnc1+WLFvVnEAAAADADvZYMpVSnl9K+Y9Sym0Dn08upfxx46PB/7Ouu2evxgEAAIADazAzmT6R5JIkm5Ok1npLktc3MhTsbFZ7216NAwAAAAfWYEqmSbXWH+401tuIMLA7ixfOS1tryw5jE1rGZfHCeU1KBAAAAGxvMBt/P1RKeV6SmiSllNckWd/QVLCTrZt7L1m2Kuu6ezJuXMlzDpmQC06Z1eRkAAAAQDK4kum3knw8yfxSSleSnyb51Yamgl1YtKBjW9n0tZu68q4v3ZSv37zO0+UAAABgGNjjcrla6z211pcmOSzJ/Frri2qtqxueDJ7FK0+eleNnTslffXNVNvVuaXYcAAAAGPP2OJOplPKenT4nSWqtlzYoE+zRuHElf/Dy+Xnzp3+YL/xgTS4++6hmRwIAAIAxbTAbfz+53asvycuTzG1gJhiUc459Tl549PT87TV35Ymn7UUPAAAAzTSY5XJ/td3rz5Kcm8QmODRdKf2zmR5+clM+8Z/3NDsOAAAAjGmDmcm0s0lJjh7qILAvTj2iPa84aUY++V/35KEnnm52HAAAABiz9lgylVJuLaXcMvC6PcmqJB9sfDQYnN972bxs7N2Sv7vmrmZHAQAAgDFrjxt/J/ml7d73Jnmg1moDHIaNow87JK/rPCKf/8Ga/MbZR+XI6ZOaHQkAAADGnN3OZCqlTCulTEuyYbtXT5IpA+MwbPz2S49Ny7iSv/rmqmZHAQAAgDHp2WYy3ZikJim7OFZjXyaGkcOnTMxvnH1UPnLt3XnLOUfnhFmHNjsSAAAAjCm7nclUaz2q1nr0wP/u/FIwMey89Reel0PbWvMX/2Y2EwAAABxog3q6XCllainljFLKOVtfjQ4Ge+vQttb81oufl+/8+MF8/+6Hmh0HAAAAxpQ9bvxdSvnNJO9KMjvJTUnOSnJdkvMaGw323pteODcf/vZdefOnf5jevppZ7W1ZvHBeFi3oaHY0AAAAGNUGM5PpXUlOT7Km1vriJAuSPNjQVLCP/u22+/PUpr5s7qupSbq6e3LJlbdm6YquZkcDAACAUW0wJdPGWuvGJCmlHFRrXZlkXmNjwb5ZsmxVNvfVHcZ6NvdlyTL7NAEAAEAj7XG5XJK1pZT2JEuTfLOU8miSdY2NBftmXXfPXo0DAAAAQ2OPJVOt9ZcH3r6vlPLtJIcm+beGpoJ9NKu9LV27KJRmtbc1IQ0AAACMHbtdLldKuaqU8sZSysFbx2qt36m1fr3WuunAxIO9s3jhvLS1tuwwVpL8zkuPbU4gAAAAGCOebU+mjyf5pSSrSylfLqUsKqVMOEC5YJ8sWtCRyy48KR3tbSlJph88ITXJTx9+stnRAAAAYFQrtdZnP6GUtiQXJHl9khcmuTrJF2ut32x8vMHr7Oysy5cvb3YMhqHfu+LmfHVFV5a+7eycNPvQZscBAACAEaOUcmOttXMw5+7x6XK11p5a65cH9mZ6WZIFsScTI8if/LfjM/3gCVn8TzdnU++WZscBAACAUWmPJVMp5fBSyjtKKd9L/xPm/j3JaQ1PBkPk0Emt+fNfPikr79+Qv/v2Xc2OAwAAAKPSs238/T9KKdck+VGS5yf5/Vrr0bXWP6i13nTAEsIQeOnxh2fRqbPykW/fldvXPdbsOAAAADDqPNtMpp9LcnmSI2qt76i1fu8AZYKGeO8rT0j7pAlZfMUt2dxn2RwAAAAMpd2WTLXWX6+1/nut1f8bZ1SYevCE/OmiE3PH+sfz0WvvbnYcAAAAGFX2uCcTjCbnnzgjv3TyzHzomp9k5f2PNzsOAAAAjBpKJsac919wQqZMbM3iK25Jr2VzAAAAMCQG83S555VSDhp4f24p5Z2llPbGR4PGmH7IQXn/q07IrV2P5QUf+GaOevdVOfvya7J0RVezowEAAMCINZiZTP+cpK+UckySTyU5KskXGpoKGmxz75aMK8njG3tTk3R19+SSK29VNAEAAMA+GkzJtKXW2pvkl5P8Ta31d5LMbGwsaKy//PcfZ0vdcaxnc1+WLFvVnEAAAAAwwg2mZNpcSrkoyZuT/MvAWGvjIkHjrevu2atxAAAA4NkNpmT69SQvTPJntdafllKOSvK5xsaCxprV3rbL8ZntEw9wEgAAABgd9lgy1VrvqLW+s9b6xVLK1CSTa62XH4Bs0DCLF85LW2vLM8Znt7dly87r6AAAAIA9GszT5a4tpUwppUxLcnOSz5RS/rrx0aBxFi3oyGUXnpSO9raUJB3tbVl4/OH54epH80dLb0utiiYAAADYG+MHcc6htdbHSym/meQztdb3llJuaXQwaLRFCzqyaEHHts+11ixZtiofufbuTGgped8FJ6SU0sSEAAAAMHIMpmQaX0qZmeR1Sf6owXmgaUopWbxwXjb3bckn/uunGd8yLn/8345TNAEAAMAgDKZkujTJsiTfq7XeUEo5OslPGhsLmqOUkj98xXHZ3Ffzqe/+NBPGj8vvL5ynaAIAAIA92GPJVGu9IskV232+J8mrGxkKmqmUkve+8vhs7tuSv7/27kxoGZff+cXnNzsWAAAADGt7LJlKKbOT/G2Ss5PUJN9N8q5a69pBXHt+kg8maUnyyZ2fSldKOSfJ3yQ5Ocnra63/tN2xNyf544GPf1pr/cdB/UUwBEop+cCrTszmvi354H/8JD95YENuXvtY1nX3ZFZ7WxYvnLfDfk4AAAAw1u3x6XJJPpPk60lmJelI8o2BsWdVSmlJ8uEkL09yfJKLSinH73TavUkuTvKFna6dluS9Sc5MckaS95ZSpg4iKwyZceNKLrvw5HTOac/Vt92fru6e1CRd3T255Mpbs3RFV7MjAgAAwLAxmJLpsFrrZ2qtvQOvf0hy2CCuOyPJXbXWe2qtm5J8Kcmrtj+h1rq61npLki07XbswyTdrrY/UWh9N8s0k5w/iO2FItYwrWffYxmeM92zuy59ffWd6+3b8T3fpiq6cffk1OerdV+Xsy69RRAEAADBmDGbj74dKKb+a5IsDny9K8vAgrutIct92n9emf2bSYOzq2mesTSqlvCXJW5LkyCOPHOStYe+s735myZQkP9vwdE5837Kc1HFoTp7dnk19W/KVG+7L0739xdPWGU9JLK0DAABg1BvMTKbfSPK6JPcnWZ/kNUl+fRDX7epxXHWQuQZ1ba3147XWzlpr52GHDWZyFey9We1tuxyfOqk1F51xZPq21Hzu+jX57HVrthVMW/Vs7suSZasOREwAAABoqsE8Xe7eJBdsP1ZK+e30b9j9bNYmOWK7z7OTrBtkrrVJzt3p2msHeS0MqcUL5+WSK29Nz+a+bWNtrS157ytP2DZDaXPfljz/j/51ly3quu6eA5QUAAAAmmcwM5l25XcHcc4NSY4tpRxVSpmQ5PXp30B8MJYleVkpZerAht8vGxiDA27Rgo5cduFJ6WhvS0nS0d6Wyy48aYclcK0t43Y742l34wAAADCaDGZPpl3Z1XK2HdRae0spb09/OdSS5NO11ttLKZcmWV5r/Xop5fQkX00yNckrSynvr7WeUGt9pJTygfQXVUlyaa31kX3MCvtt0YKOPe6rtKsZTxNbx2XxwnmNjgcAAABNt68l06D2Vqq1Xp3k6p3G3rPd+xvSvxRuV9d+Osmn9zEfHHBbS6gly1ZlXXdPapIXHj3Npt8AAACMCbstmUopG7LrMqkksf4HdmH7GU+/++WbcvVt6/OzDRvz3MkTm5wMAAAAGmu3ezLVWifXWqfs4jW51rqvM6BgzHjHS47N5r6aj33nnmZHAQAAgIbb142/gT046jkH55cXdORz16/Jzx7f2Ow4AAAA0FBKJmigd5x3THq31Pz9d+5udhQAAABoKCUTNNCc6Qfn1S/oyOd/cG8eMJsJAACAUUzJBA32jvOOzZYtNR/59l3NjgIAAAANo2SCBjti2qS85rTZ+eIP78v6x3qaHQcAAAAaQskEB8BvvfiYbKk1H/m2vZkAAAAYnZRMcAAcMW1SXnf6EfnyDfdlXbfZTAAAAIw+SiY4QH7rxcekpubD9mYCAABgFFIywQHS0d6WXzn9iHxl+X1Z++hTzY4DAAAAQ0rJBAfQb734mJQUs5kAAAAYdZRMcADNPLQtrz/jiFyxfG3ue8RsJgAAAEaP8c0OAGPN2849Jp+/fk0W/s1/pmdTX2a1t2XxwnlZtKCj2dEAAABgnymZ4AC7/p6Hk1Ly1Ka+JElXd08uufLWJFE0AQAAMGJZLgcH2JJlq9K3pe4w1rO5L0uWrWpSIgAAANh/ZjLBAbauu2eX413dPfmLf1uZzrlTc9qR03LopNYsXdGVJctWZV13j2V1AAAADGtKJjjAZrW3pWsXRVNrS8nH//OefOTa/llOh08+KA89uWnbrCfL6gAAABjOLJeDA2zxwnlpa23ZYayttSVLXnNKbn3fwnzpLWdl8cJ56e7ZbFkdAAAAI4aSCQ6wRQs6ctmFJ6WjvS0lSUd7Wy678KQsWtCRtgktOevo6fmtFx+TTb1bdnn97pbbAQAAQDNZLgdNsGhBxx6XvO1uWd2s9rZGxQIAAIB9ZiYTDFO7WlaXJBecMqsJaQAAAODZKZlgmNp5Wd2sQyfmuZMn5Gs3deXxjZubHQ8AAAB2YLkcDGM7L6u76b7uXPiR7+UD37gjS157ShOTAQAAwI7MZIIR5NQj2vO2c4/JFTeuzX/c+UCz4wAAAMA2SiYYYd75kmMzf8bkvPvKW/Pok5uaHQcAAACSKJlgxJkwflz++nWnpvupTXnP129vdhwAAABIomSCEen4WVPyzvOOzTduXperblnf7DgAAACgZIKR6n+d+7ycMvvQ/PHSW/PghqebHQcAAIAxTskEI9T4lnH5q9edkic39eUPv3praq3NjgQAAMAYpmSCEeyY507O4pfNyzfveCBfXdHV7DgAAACMYWW0zH7o7Oysy5cvb3YMOOD6ttS8/uPX5Za13Zk6aUIeePzpzGpvy+KF87JoQUez4wEAADCClVJurLV2DuZcM5lghGsZV7LwhBl5urfm/sefTk3S1d2TS668NUvNbgIAAOAAUTLBKPCZ761+xljP5r4sWbbqwIcBAABgTFIywSiwrrtnr8YBAABgqCmZYBSY1d62y/FDJo7P5r4tBzgNAAAAY5GSCUaBxQvnpa21ZYexllKyYWNvXvPR6/LTh55sUjIAAADGCiUTjAKLFnTksgtPSkd7W0qSjva2/NXrTsnfvWFBVj/0ZF7xwf/K53+wJqPlaZIAAAAMP2W0/J/Ozs7Ounz58mbHgGHn/sc2ZvE/3fz/t3fn8VFX9/7H32cmk2QCgWGHjIRVwmJQJK7UpbjErZWLbcVWq15ba5dbW9u00OXe/npvL1Ts1fZqVVqttbZXW4upFRUV3AUUjeyEXSQBwpaEwCSZzJzfH5kgSb4TZpIMM0lez8fDh5kzJ9/vZ+JjHHjnnM/Rm5v3a/r4wbo4b5Aefn2byisDyvF5VVSYpxlT/MkuEwAAAACQgowx71trC2KaS8gEdH/hsNUfl+3Qfz23XqEWb3mvx625M/MJmgAAAAAArcQTMrFdDugBXC6jW6eN0oDeGa2eCwRDmr+4NAlVAQAAAAC6E0ImoAfZd7jOcbysMqDaYOgkVwMAAAAA6E4ImYAeJMfnjfrcuXOX6JcvblR5ZUCSVFxSpmnzlmrU7EWaNm+pikvKTlaZAAAAAIAuKC3ZBQA4eYoK8zRn4RoFjlu15PW4dNunRmlLxRE9/PpWLXhjm07LydaGPTWqbwhLalzpNGfhGkmidxMAAAAAwBEhE9CDNAVE8xeXOp4ut+vQUf1p+Uda8MY2tTwToKl3EyETAAAAAMAJIRPQw8yY4o8aFJ3SL0tzrpygBa9vc3y+aSsdAAAAAAAt0ZMJQCvReje11dMJAAAAANCzETIBaKWoME9ej7vV+HVT2SoHAAAAAHBGyASglRlT/Jo7M19+n1dG0tC+mRrUO12Pvb1DG/dUJ7s8AAAAAEAKMrZld98uqqCgwK5cuTLZZQDd1q5DR/W5B5cpbK3+/vXzNbx/VrJLAgAAAAAkmDHmfWttQSxzWckEICan9MvS47edrbqGsG56ZIX2Ha5LdkkAAAAAgBRCyAQgZuOGZOvRW87S3uo63fKHd1VdG0x2SQAAAACAFEHIBCAuU0f004M3nqnSPYf11T+uVG0wlOySAAAAAAApIC3ZBQDoei7OG6xffeF03fnkh/rcg+/o4NF67a6sVY7Pq6LCPM2Ywil0AAAAANDTEDIBaJdrz/Dr9dIKLSwpPzZWVhnQnIVrJImgCQAAAAB6GEImAO22YvuhVmOBYEjzF5c6hkzFJWWav7hU5ZUBVj0BAAAAQDdDyASg3corA47jZZUBzVqwTLn9szRiQC8N75+l7ftr9OBrW1UbDB+bw6onAAAAAOg+CJkAtFuOz6syh6DJ63ErGLJ6tXSf9h3eFfX721r1BAAAAADoWgiZALRbUWGe5ixco8BxJ8x5PW7NnZl/LDg6Wt+gjw8GVHjfG47XiLYaCgAAAADQtbiSXQCArmvGFL/mzsyX3+eVkeT3eZsFTJKUlZ6mvKHZ8vu8jtfI8WWepGoBAAAAAInESiYAHTJjij+m7W5Oq54kKXdAlkJhK7fLJKpEAAAAAMBJwEomACdF61VPmbp0wmAt23pQdz5ZovqGcLJLBAAAAAB0ACuZAJw0TqueHnp9q+a9sFE1dQ168EtT5U13J6k6AAAAAEBHsJIJQFLdcdEYzZ2Zr9c37dNNj6xQVSCY7JIAAAAAAO3ASiYASXfD2bnqk+nRd54q0Q0LlmvW2cP18OvbVF4ZUI7Pq6LCvJj6PgEAAAAAkoeQCUBKuHryMPXOTNNtj72r//jHOtnIeFllQHMWrpEkgiYAAAAASGFslwOQMi4aN0i+rPRjAVOTQDCk+YtLk1ITAAAAACA2CQ2ZjDFXGGNKjTFbjDGzHZ7PMMY8FXl+hTFmZGTcY4z5ozFmjTFmgzFmTiLrBJA6DtTUO46XVwZOciUAAAAAgHgkLGQyxrglPSDpSkkTJd1gjJnYYtptkg5Za8dKulfSLyPjn5eUYa3NlzRV0teaAigA3VuOz+s4biUV3vuG7l+6WTv2H5EkFZeUadq8pRo1e5GmzVuq4pKyk1gpAAAAAOB4iezJdLakLdbabZJkjHlS0rWS1h8351pJP4t8/bSk+40xRo1/n+xljEmT5JVUL6k6gbUCSBFFhXmas3CNAsHQsbHMNJeunjxMOw8e1T0vbdI9L23S8H5e7amuVTDUuLmO3k0AAAAAkFyJDJn8kj4+7vEuSedEm2OtbTDGVEkaoMbA6VpJuyVlSfqutfZgyxsYY26XdLsk5ebmdnb9AJKgKSCav7jU8XS58sqAFq3erbsXbzwWMDVp6t1EyAQAAAAAJ18iQybjMNayn2+0OWdLCknKkdRP0pvGmFeaVkUdm2jtAkkLJKmgoKDltQF0UTOm+KMGRTk+r7564Wj99/MbHJ+ndxMAAAAAJEciG3/vkjT8uMenSCqPNieyNa6vpIOSvijpRWtt0FpbIeltSQUJrBVAFxOtd9PQvpknuRIAAAAAgJTYkOk9SacaY0YZY9IlzZL0bIs5z0q6OfL15yQttdZaSTslTTeNekk6V9LGBNYKoIspKsyT1+NuNW6tVcXh2oTfn6bjAAAAANBcwkIma22DpG9JWixpg6S/WmvXGWN+boz5bGTaI5IGGGO2SLpL0uzI+AOSektaq8aw6g/W2tWJqhVA1zNjil9zZ+bL7/PKSPL7vPrGxWNUXdugWQ8v1+6qxG2bKy4p05yFa1RWGZDVJ03HCZoAAAAA9GSmceFQ11dQUGBXrlyZ7DIAJNnKHQd16x/eU98sj/7ylXOVOyCr0+8xbd5SlTn0fvL7vHp79vROvx8AAAAAJIsx5n1rbUwtjBK5XQ4ATrqCkf3156+eo5q6Bn3h4WXaUlHTadeuawjpjU37HAMmiabjAAAAAHq2RJ4uBwBJMfkUn568/Vzd+PsVmrVgmZ74yjkaP7RPTN9bXFKm+YtLVV4ZUI7Pq69fPEZej1uvbNirNzbt05H6UNTvzUhzafm2AzpnVH8Z43R4JgAAAAB0X2yXA9Btbd1Xoy/9boWqAvXqnenR/sN1yvF5VVSYpxlT/K3mF5eUafbC1aoNhls9N6RPhqaPH6JLJwzWwSP1+vd/rFMg+EnglOYyykhz6Uh9SPn+vvrqhaN11WlD9dzq3c1Cq2j3BgAAAIBUFM92OUImAN3aI29t0389t0HH/58uPc2lz53pV47Pq/KqWu2uDGh3Va1K9xyW0/8RB2VnaMWcS+RyfbI6qeWKp6LCPF1x2lD9/YNdeuTN7dq2/4h8Xo+O1DcoGPrkql6PW3Nn5hM0AQAAAOgSCJkAICJak+4m/bI8GtbXqxxfpl7ZUOE4x0jaPu/qmO8ZDlst2Vihb/75A9WHWq+KokE4AAAAgK4inpCJnkwAurVozbiNpPU/v0LedPexsWiBVI7PG9c9XS6jyyYOUdAhYGqrJgAAAADoyjhdDkC3Fi0gyvF5mwVMklRUmCevp/mY1+NWUWFep97bl+Vp1/UAAAAAIJURMgHo1uIJjmZM8WvuzHz5fV4ZNW5r60j/JKd7u4x06GhQ333qQx2uDbbrugAAAACQitguB6BbawqIYj3hbcYUf6c15Xa69/cuG6ePDwX06yWb9MHOQ/rNrCk6fbivU+4HAAAAAMlE428ASIL3dhzUnf9XoorDdfp+YZ5uv2B0s9Pr2uJ0sh2n1QEAAABIBE6XA4AuoOpoULMXrtYLa/cob0hvVQUatLe6ts3gqLikTHMWrlEgGDo25vW4O7StDwAAAACiIWQCgC7CWqsf/n21/rpyV7PxjDSX7rhotKaNHaSwtQqHrULW6jtPfqgDR+pbXcfv8+rt2dNPVtkAAAAAeoh4QiZ6MgFAEhlj9PaWA63G6xrC+vWSLfr1ki0xXae8MtDZpQEAAABAXAiZACDJ2gqI/jFw258AACAASURBVPyVc2SM5DZGbpfRHU98oP01da3medJcen7Nbl02cYg8bg4OBQAAAHDyETIBQJLl+Lwqcwia/D6vpo0d2GzsJ1dPaNWTKc1llOVx6xt//kBD+mToi2eP0A1nD9c7Ww/QIBwAAADASUPIBABJVlSY59jMu6gwr9XcppCoZXj0mdNz9OrGCj2+/CPd+8om3ffKJrmMFIq03SurDGjOwjXNrtGE0+oAAAAAdAYafwNACujMoGf7/iO65n/f1JG6UKvnstLd+uanx8rv88rfz6u1ZVW6+8WNCgTDx+ZwWh0AAACAJpwuBwA93KjZi9SR/7t31dPqWJUFAAAAdC5OlwOAHq6tPk8v33WhyisDKqus1c2Pvuv4/V3xtLrikrJm2w7b2iIIAAAAoPNxBBEAdENFhXnyetzNxpr6PGWlp2ns4GxdNG6Q/D6v4/dbSTc9skLvf3TwJFTbOeYvLm3W10qSAsGQ5i8uTVJFAAAAQM/CSiYA6IaiNQhvuaLHqel4pselyyYM0TtbD+i6B5dp2tgB+vb0U7W7qjalt6JFW33VFVdlAQAAAF0RIRMAdFMzpvhPGAK1FUYdrW/QX1bs1EOvb9P1C5bLZaRwDKfVJUu0LYJD+2YmoRoAAACg56HxNwCgTbXBkM7571dUFWho9VwqNQgvLinTXX/98FgQ1mRonww9881pGtbXeWsgAAAAgOjiafxNTyYAQJsyPW5VOwRMUmptRcvxeRW2Up/MNBk1BmDf+PQY1dSFdN1v39GWisPJLhEAAADo1tguBwA4oWhb0aykGxYs112Xj9NZI/uf/MKa6rBW817YoCF9MvTa9z8tb/onTc+vOm2YbvnDe/rcQ8v06C1n6czcfkmrEwAAAOjOWMkEADghp9PqMj0uzTgjR5sravT5h5ZFTqM7JKlx69q0eUs1avYiTZu3VMUlZQmt76X1e/XBzkp959JxzQImSTrN31d///p56uv16Iu/W65XN1YktBYAAACgp6InEwAgJsUlZY4NwgP1IT2x/CM99PpWHThSr/FDs7V9/xHVNYSPfa/X49bcmfkJaRLeEAqr8L43ZCW99J0LleZ2/v3JvsN1uvWxd7Vh92Hdfd1kXTf1lE6vBQAAAOhu4unJRMgEAOgUR+oa9Piyj3T34o1y+mhJVJPwJ9/dqdkL1+ihG6fqitOGtjn3cG1Qdzzxvt7eckCfnTxM7++sbBWaAQAAAPgEjb8BACddr4w0ff3iMY2NmhyUVQa0fNsB1R+3wqmj2+oC9SHd+8omTcn1qXDSkBPOz8706NFbztIZw/vq2dW7VVYZkI3UNmfhmoRv6wMAAAC6Mxp/AwA6VbQm4ZI0a8FyZaW7dd7oAerrTdOiNXuObatrCnokxbyi6LF3dmhvdZ1+M2uKjDExfU9GmlsVh+tajQeCIc1fXMpqJgAAAKCdWMkEAOhUTk3CG3synaYFN03VdWeeoq37arSwpLxZ3ybpk6AnFpVH6/Xb17bokvGDdc7oAXHVuLuy1nG8PEo4BgAAAODEWMkEAOhUTSuBnJqES9Llkxr7Jo2avchxZ12sQc9vX9uqmroG/eCK8XHXGG211dC+mXFfCwAAAEAjQiYAQKebMcV/wm1n0YKewX0yTnj9ssqAHntnh6478xTlDc2Ou76iwjzNWbhGgWCo2Xg4bLW7KqBhfb1xXxMAAADo6dguBwBICqdtdVLjKXXvbj/Y5vfe+/ImSdJ3LxvXrnvPmOLX3Jn58vu8Mmo8+e4bnx6jI/UhXffbd7SloqZd1wUAAAB6MmOdzpnuggoKCuzKlSuTXQYAIA7FJWXNttV9+bwReuq9j7Xz4FH97LOTdOO5I1p9z8Y91bry12/qqxeM1o+umtCp9awtq9Itf3hPoXBYj95ylqbk9uvU6wMAAABdjTHmfWttQUxzCZkAAKmkKhDUnU+W6LXSffriObn62WcmKT3tk4W3tz32nt7dcVBv/uDT8mWld/r9PzpwRDc98q72Ha7TQzdN1UXjBnX6PQAAAICugpAJANClhcJW97xUqgdf26qzRvbTZ07P0cOvb1N5ZUBW0jWTh+n+L56ZsPtXHK7VzY++p817D+uGs4dr6cZ9jk3MAQAAgO6OkAkA0C38c1W5vvtUiRrCzcczPS7Nmzk5oWFPdW1QM+5/W9v2H2k27vW4NXdmPkETAAAAeoR4QiYafwMAUtZnTs9Rv16tT5urDYY1f3FpQu/dJ9Oj2hanz0lSIBhK+L0BAACAroiQCQCQ0vYfrnMcL68MJPzeu6tqk3ZvAAAAoKshZAIApLQcnzeu8e5ybwAAAKCrIWQCAKS0osI8eT3uZmNej1tFhXlJubckfeWCUQm/NwAAANDVEDIBAFLajCl+zZ2ZL7/PKyPJ7/OetMbbLe89ODtDGW6jv63cpSN1DQm/PwAAANCVcLocAABxeLW0Qrc99p4umTBED984VS6XSXZJAAAAQMJwuhwAAAny6bzB+uk1E/Xy+r365eKNyS4HAAAASBlpyS4AAICu5pbzR2pLRY0efn2bxgzqrS8UDE92SQAAAEDSsZIJAIA4GWP0s89O0qfGDtSPn1mj5dsOJLskAAAAIOkImQAAaAeP26UHvnSmhvfP0h1PvK8d+48kuyQAAAAgqWj8DQBAB+zYf0Qzfvu20t1GbpdLe6pqlePzqqgwz/EEvOKSMs1fXKryykCb8wAAAIBUEE/jb3oyAQDQASMH9tKN5+Tq/le3HhsrqwxozsI1ktQsQCouKdOchWsUCIbanHf8fAIpAAAAdBWETAAAdNAzJeWtxgLBkH7499X6+we7FApbhcJWJTsrVR8Kt5r3s2fXaXB2hob3z1KOzyu3y8QdSAEAAADJRsgEAEAHlVcGHMfrGsI6XNugNJeR22VaBUxNKgNBffH3KyRJHreR3+fV7qpa1TW0DqTmLy5td8jEyigAAAAkEiETAAAdlOPzqswhaPL7vCr+5rRjj6fNW+o4b0ifDN37hTO08+BRfXTwqHYePKodB4463itaoHUirIwCAABAonG6HAAAHVRUmCevx91szOtxq6gwL6Z5c66coPPHDtSss3P1wyvG64Evnim/z+t4Lyvpivve0P8u2axt+2okNQZI0+Yt1ajZizRt3lIVl5RJkoKhsNbsqtKfln/ULGBq0rQyCgAAAOgMnC4HAEAniHUrWjzzWgZDmWkuXZU/VB8fCui9HYckSTl9M1VxuE4N4U8+z90uo1N8mdpTXddqy11LRtL2eVe381UDAACgu4vndDlCJgAAUlRbgdTuqoCeX7NH817YoGCo9Wd5utulm88fodOH+3TGcJ+uf3iZyiprHe/zr9NG6a7Lx6l3BrvoAQAA0BwhEwAAPcSo2Yvk9EnecoWS48ooj0tn5vq0bNtBDe2Tqf/4zCQVThoiY0ziCwcAAECXEE/IxK8sAQDowqI1Hc9p0dOpaQWU08qoD3Ye0o8WrtEdT7yvSycM1vljBuiRt3ZwCh0AAADiwkomAAC6MKcVSl6PW3Nn5scVDAVDYf3h7e2a/2KpguHmfzZoz/UAAADQPcSzkonT5QAA6MJmTPFr7sx8+X1eGUl+n7ddgZDH7dLtF45R/97prZ7jFDoAAADEgu1yAAB0cTOm+DttlVFFdZ3jeLnDlrx4xHqqHgAAALouQiYAAHBMtB5PvixPu6/ZcktfWWVAcxaukaR2B02EVgAAAKmH7XIAAOCYosI8eT3uZmMuIx06GtSvX9ms9vRynL+4tFnPKKljW/CaQquyyoCsPgmtikvK2nU9AAAAdA5CJgAAcIxTj6e7r5usmWf6de8rm3TXX1epriF0wus02VJR47gySmoMh4KhcNw13v3ixk4NrQAAANA52C4HAACacerxdN3UUzR6YC/d89Im7Tp0VA/fVKD+vVo3CW9SebRe972yWU8s/0hGUrT1TxfPf023Thup688aruxMT9RtcOGw1bs7DuofH5arvKrW8VrRwiwAAACcHKY9y95TUUFBgV25cmWyywAAoFt7bnW57vrrKg3rm6lHbj5LYwf3bvZ8MBTWX1bs1L2vbFJ1IKhZZ+dqwrBs/fei5quPvB6XvnTOCK0pq9KK7QeVnZGms0b20ztbD6i24ZPVTRlpLp0/pr827K7RnupaZaW7Za1arWRqcnHeIM25coLyhmYn5gcAAADQwxhj3rfWFsQ0l5AJAADE44Odh3T74ytV3xDWjeeOaFxdVBlQ/17pcruMKg7X6fwxA/TTayZqwrA+ktpu1L3q40r97s1tem717qj3vHTCYH32DL8unTBYL63b26yRuCRlely6fOIQvVa6TzV1Dfr81OG66/JxGtInkybhAAAAHUDIBAAAEurjg0f1+Yfe0Z7qumbjRtK/Thupn1wzUcaYuK45avYix211RtL2eVc3G4sWHB06Uq/7X92ix5ftUJrLpQtPHaDXN+9XbfCT1VFej1tzZ+YTNAEAAMQgnpApoT2ZjDFXSPq1JLek31tr57V4PkPS45KmSjog6Xpr7Y7Ic5MlPSypj6SwpLOstc5NGAAAwEk1vH+WY4hkJb24bq9++plJcV8zx+d17KuU4/O2GnPqGyVJ/Xql66fXTNTN543U3Ys3Oq6OamoSTsgEAADQuRJ2upwxxi3pAUlXSpoo6QZjzMQW026TdMhaO1bSvZJ+GfneNElPSLrDWjtJ0sWSgomqFQAAxG9PlAbc5e1swF1UmCevx91szOtxq6gwL+5r5Q7I0v1fPFPR1lK1t0YAAABEl7CQSdLZkrZYa7dZa+slPSnp2hZzrpX0x8jXT0u6xDT+WvRySauttaskyVp7wFob+3nJAAAg4ZxWGLU1fiIzpvg1d2a+/D6vjCS/z9vhbW2dXSMAAACiS+R2Ob+kj497vEvSOdHmWGsbjDFVkgZIGifJGmMWSxok6Ulr7d0tb2CMuV3S7ZKUm5vb6S8AAABEV1SY16oBd3tXHjWJtg2uvZxqlKQr84d22j0AAADQKJEhk9MK9Zb9PKPNSZP0KUlnSToqaUmk0dSSZhOtXSBpgdTY+LvDFQMAgJg1hUGpfHJbyxoH98mQx2X0yFvb1S8rXd+4eEzcDcoBAADgLJEh0y5Jw497fIqk8ihzdkX6MPWVdDAy/rq1dr8kGWOel3SmpCUCAAApo7NXHiVCyxprgyH98O+rNX9xqdaXV2v+5ycrKz2hZ6EAAAD0CInsyfSepFONMaOMMemSZkl6tsWcZyXdHPn6c5KWWmutpMWSJhtjsiLh00WS1iewVgAA0ENkety67/ozNOfK8Xp+7W5d9+AyfXzwaKffp7ikTNPmLdWo2Ys0bd5SFZeUdfo9AAAAUknCfm0X6bH0LTUGRm5Jj1pr1xljfi5ppbX2WUmPSPqTMWaLGlcwzYp87yFjzP+oMaiykp631i5KVK0AAKBnMcboaxeNUd7QbP3b/5Xo2gfe1g1nD1dxSXmnbP0rLilr1guqrDKgOQvXSFKHVn4Vl5Sl9PZEAADQs5nGhUNdX0FBgV25cmWyywAAAF3Mtn01mrVgmSoO1zcb93rc7T7dbtq8pSqrDLQa9/u8env29HbV2TK46miNAAAAsYj0yC6IZW4it8sBAACkvNGDesvtav1HokAwpPmLS+O+3gc7DzkGTFLjiqbXN+1Te37JN3/xxlan5LW3RgAAgESgyyUAAOjx9lTVOo6XVQa0p6pWQ/tmnvAaG/dU61cvbdLL6/fKZaSwQ47kMtLNj76r8UOz9bWLRuuayTnyuF1Rt8HVN4S1bNsBvbx+j8oqnWssjxJoAQAAnGxslwMAAD1etO1tkuR2GV02YYi+dG6upo0ZqGdXlTcLhG6dNlJry6r0j1Xl6p2epq9dNFqDemfoZ/9c32pr239eO0kyRgve2KpNe2uU0zdTZ4/qrxfX7VFtMHxsrsdtNCmnj7ZUHFFNXYOy0t0KW9tsTpOObMEDAAA4kXi2yxEyAQCAHi9av6PvXT5O+2rq9LeVu3TwSL0G9vKoMtCghhbLlNJc0lcuGKM7LhotX1b6sWtGa9IdDlu9tqlCD7++TSu2H3SsyWWk688arssmDtH5YwbqxbV7WtXoMtKvPne6/mXqKZ39IwEAAJAUX8jEdjkAANDjNYU/0UKhuy4bpxfW7NEPnl7dKmCSpIG9MzX7yvGtrhmtIbfLZTR9/BBNHz9Eo2YvktOv/KyV5s6cHLXGPt40VQUatP3g0fa8ZAAAgE5HyAQAAKC2Q6GMNLdmTPHru0996Pj83mrnfkmxyPF5Hbfq5fi8bdZorVXR06v1myWbNWFotq7MH9buGgAAADoDp8sBAADEyCn4aWs8FkWFefJ63M3GvB63igrz2vw+Y4x+8S+naUquT3f9dZXWl1e3uwYAAIDOQMgEAAAQo/YGQm2ZMcWvuTPz5fd5ZdTYyHvuzPyoq6qOl5Hm1sM3TlVfr0dffXylDtTUtbsOAACAjqLxNwAAQBzaauidLKt3VerzDy3T6cN9euK2c5Sexu8RAQBA5+B0OQAAgB7mHx+W6c4nP9SXzsnVL/4lP9nlAACAboLT5QAAAHqYa8/wa/3uaj38+jZNGNZHN547ItklAQCAHoaQCQAAoJv4QeF4bdpzWD8tXqv7XtmkAzX1nbalLxW3CQIAgNRCyAQAANBNuF1Gl08aotdK92l/Tb0kqawyoDkL10hSq1Ao1uCouKRMcxauUSAYOuE1YxXPvQm3AADoGgiZAAAAupH7l25Vy46bgWBIPy5eo6pAUCMGZGnEgF764KOD+knxujaDo1DYqjoQ1NwXNhybd/w15y8ubVdwFWtolYhwCwAAJA6NvwEAALqRUbMXtQqZ4pHmMhrSJ1PVgaAO1zWccP5Prp6gcUOylTc0W+9s2a8fPbO2WSDl9bj182sn6YzhPu08eFQ7Dx7VPS+V6khdqNW1PG6j0/x9le52KT3Npfe2H1RtQ7jVPL/Pq7dnT+/AqwQAALGi8TcAAEAPlePzqqwy0Grc78vUM9+cpo8OHNVHB47q+39b5fj9DWGrc0cPUB9vmvp6PeqT6dH/Lt2sQ0eDrea6jPRfizYce2wkx1VURU+vjqn2YMiqd0aa6hvCqqlrcAyYJKnc4fXFoytswesKNQIA0BIhEwAAQDdSVJjXbIuZ1LiaqKhwvAZnZ2pwdqbOGtlf9768KUoY5dWvvnB6s7H+vdIdrzl3Zr4+depAbdp7WJv31ug/nl0Xta77rj9DuQOylNs/S9fe/5bKKmsd7/2n28459njavKWONbpcRks37tX08UPa/mE46Apb8LpCjQAAOHEluwAAAAB0nhlT/Jo7M19+n1dGjcHN3Jn5rcKJosI8eT3uZmONYVReXNcc2DtD548ZqJvPHym/z+tYk9/n1Ywpfp2Z208De2eoqHB8TPd2qjHd7VL/Xh7962Mrdcsf3tXWfTUx/mSk2mBIP39uvWN/qXkvbGw1v7ikTNPmLdWo2Ys0bd5SFZeUxXyvjpi/uDRqDywAAFIZPZkAAAB6qM7ektVyBY70yYqnjpxs13LeVfnD9PiyHfr1K5sVCIZ067SR+vYlp2rJhopWc684baheK63QojV7tHTDXh2pb90LqsnYwb11/pgBOn/MQB08Uqf/fG5DTK+lM5VXBnT+vKWOzxlJ2+ddnbB7AwDgJJ6eTIRMAAAA6DQns5fQvsN1umdxqf76/sfqle5WXUNYwdAnf7Z1m8atdcGQVf9e6SqcNEQvrdurA0fqW12rT2aapuT207vbD7ZaRXS8jjYdd/r5fOb0HL1WWqG/rNipV0srFI7yx/OsdLdeuPMCjRjQq933BwAgXoRMAAAA6DFW76rUdQ++0yxgapKV7tbvvlygc0b1V5rbdcLVVvUNYX34caW+8PAyx3t1ZDWR073TXEa9MtyqCjRoUHaGri8Yrn69PLpn8aZm89wuI1krGaMZZ/j1reljNWpgLxqEAwASjtPlAAAA0GNMPsWnBoeASZIC9SFNGzvw2OOmACZaMJOe5tLZo/rLH+WUvjS30Svr9+qSCYNljImrTqdeSw1hq9pgWA/deKYumTBEHndjy9QBvTJa1Xj+mAF6+I1t+vOKj/RMyS6dmevTmrJq1UVO4aNBOAAg2VjJBAAAgC4v2kl07d3e5rTqyOM2ys5M08EjQY0fmq1vTR+rK08bpn+uKo8aWllrtaWiRm9s3q//fG69473iXR2173CdFryxVb97c7vj806vmRVPAID2YiUTAAAAepSiwjzHbXBOp+XFItqKp2smD9Ozq8r1wKtb9K2/lGhQ9jpVHg0e26pXVhnQ7IWrVbLzkGqDYb25eZ/Kq2olNW6Na3BouJQT5VS+aAZlZ+jHV0/U79/cLqdfF5dVBrTgja2aOKyvJuX00eub9jX72bDiCQCQKKxkAgAAQLdwMlfrhMJWL67dozufLHEMjiQpOzNNnxo7UBeOG6QLTh2olTsOxXz6Xiyird5yGTVrHu42ktNuwo42MQcA9Aw0/gYAAABOglGzFzmuJjKSNv/iSqVFeiw16cwgrK0m5hecOlDrd1drfXm15r6wMeo1vnvpOE0e3leT/X01oHdGp9eYiOsBAE4utssBAAAAJ0FOlAbhOT5vq4BJatye1lkBy4mamF9w6iBdcOogPb7sI+cm5i6j+5ZsUtPvnP0+rwb09mh9+eFjq7M6urWuZRDGVj0A6N5YyQQAAAC0U1uriVIlRGmrxksmDNbasmqtKavUql1VenHtHoUctv+1d2vdtHlLVFZZ22nXa8LqKAA4eVjJBAAAAJwEJ1pNlApOVON5YwbovDEDJDVu/3NSVhnQ0o17ddG4wXK7zAnvuWP/ET27qtwxYGq63iNvbdfFeYM0emAvGWNiDo5YHQUAqYuVTAAAAAAknbiZuN/n1ayzhuv6s4brna0HmoVCt184SsGQ1T9XlWvVrioZI3lcLtWHwq2ud/xJe7n9s5Tb36t3tx9qNtfrcesXMybpgnGDdehovQ7U1OvQ0XrNWbhGVYFgq2vSyBwAEoPG3wAAAADiFm1r3X9eO0lZGWn684qP9PaWAzKSTItT7Jqc5u+ja0/365rTh2nFtoNRt+pNHdFPr5VW6LXSfVq6scKxgXo8jKTt867u4FUAAC2xXQ4AAABA3E60te6q/GHavv+IPvO/b6mmrqHV9w/OztBz/3ZBzNe76byRuum8kVG36UnSz6+dpP690tU/K139e6frlkff057q1tvwcnze9r9wAECnIGQCAAAAcMyJTsAbNbCXjjgETJK073Bd3NeTop/S5/d59eXzRjYbm33l+Faro9wuo+9fPq7NewAAEq/1uaoAAAAA0IZoq4bau5qoqDBPXo+72ZjX41ZRYV6ruTOm+DV3Zr78Pq+MpF4ZboXCVgeO1Lfr3lLjNsFp85Zq1OxFmjZvqYpLytp9LQDoyVjJBAAAACAuRYV5jr2WnEKhWMR7St/xq6PCYatv/d8H+sXzG5Tj8+qq/GFx3ZvT6gCg8xAyAQAAAIhLvKFQrNdsz/e7XEb/84UzVFG9Qt956kMNys7QWSP7x/z98xeXNgvLJCkQDGn+4lJCJgCIEyETAAAAgLi1NxRKhEyPW7/7coGue/AdffXxlfr718/XmEG9Y/pep15QklQeZRwAEB09mQAAAAB0ef16peuxW89Wmsvolj+869iE/Hgb91Tr5kffjfq8y2VUXFKmUNh2dqkA0G0Za7vH/zQLCgrsypUrk10GAAAAgCRa9XGlZi1YrlOH9NaTt5+rrPTmmzf2VNXqf14u1dPv71LvjDRdnDdIL63fq9pg+NicdLfRgF7p2l1dp7GDe+u7l47TlacN1bOrymPeIlhcUtap2wkBIFmMMe9bawtimkvIBAAAAKA7WbJhr776+EqNH5qtykBQuytrNbRvpvL9ffTG5v0Kha2+fN5I/dv0sfJlpTsGQp89PUcvrN2je1/ZpC0VNRrWJ0MHjtSrPvTJ35+8HrfmzsxvFR61bCbe1lwASHWETAAAAAB6tO//bZWefn9Xq/EzhvfVb2adqdwBWTFdJxS2+ueqcn3vb6sct875vB59vzBPDaGwgiGr+lBYD72+VYdrG1rN9fu8env29PhfDAAkUTwhE42/AQAAAHQ7y7YecBzfd7g+5oBJktwuoxlT/PruUx86Pl8ZCOonxWtjuhbNxAF0d4RMAAAAALqdaIFOe4OeHJ/X8SS6IX0y9M9vfUoet0tpbiOP26Xpv3pN5ZW1reamuY0Wrd6tK04bKrfLtKsOAEhlnC4HAAAAoNvJ8XnjGj+RosI8eT3uZmNej1tzrpygwX0y1a9XurIzPcr0uPWDwvGt5nrcRn0z0/TNv3ygT9/zmv60bIcC9SEVl5Rp2rylGjV7kabNW6rikjLH+8c6DwCSiZVMAAAAALqdosI8x+bbRYV57bpeU8PuWE6Mizb3M6fn6OX1e/TQ69v003+s07wXNqquIayGSK+nssqA5ixcI0m6ZvIw1TaEVRsM6R8flunuF0tV1xBuNY9G4gBSCY2/AQAAAHRLTqfGpUIoY63VezsO6cuPrFBtJDhqj35ZHj337QvkP251VjyvOVV/PgBSC6fLAQAAAECKGzV7kaL9beyuy8bJ63Er0+PST/+xrs3r5PbP0rmj+8vjNnr6/bJjK56kxtVbc2fmtwqPikvKHFd6Oc3tCgjMgMThdDkAAAAASHHRmon7fV59+5JTjz1+6PVtjvMGZ2fojovGaPm2A1q8bq+qAsFWcwLBkOYsXKPXSitkTGOzcSPphbV7mgVMTXPnLy5tdzgTa9DT2YFQy8Csre2EyaoR6CkImQAAAAAgCWLtGxVt3o+umqAZU/z610+NUjhsNeZHzzuujAoEQ/pgZ6UkyUZmtAyYmpRVBrTvcJ0GZWccG4slcHEKemYvXK3aYEhX5g9TKGwVCls9v6Zc//38xk7tLzV/caljYPbLFzfq2jNyjoVrjneqAQAAEYFJREFUsYZR8YRWAJpjuxwAAAAAJElnrqyZNm9p1JVRb8+eHtNcSTJGmprbT4WThsplpHte2tQq4PrFjNM0dWQ/bamo0ZaKGv16yWYdrXcOrmIxKDtD7/7okmOBUCyqAkG9vH6vvv+3VVHnpKe5NKh3hgZlZ2jj7mrHHli90t26Mn+YAsGQautDemvL/mZbDps4/RyBnoCeTAAAAADQw8TTZ8l5rkv/dsmpqm8Ia/G6vdqwu7pT6vrpNRPlNpLb7dJPi9dGnZfTN1OXThyiSycM0bmjByg9zdUqXPvmp8coze3S82t26+0t+xUMWbmNUcjh77V9vWmadVau9h2u076aOr25eX/Ue/t9XmV6XPKmu7W2zPl1G0nb510d9+sHujpCJgAAAADogTrzdLmdB47qwvmvRr3X3ddN1pjBvTR2ULau+s2bMa2iiraCyuf16KxR/fXm5n2qDYbVOyNNYwf30rryagVDrf/Oeko/r67KH6ar8odp+74a/eiZtScM12Jd6RVtnsdt9M9/+5TGD+0T9WeSiugvhY4iZAIAAAAAdFiswUysq6hONK82GNLbW/brlQ179dR7Hyvs8NdVp6117ekbFU+N6W6j9DSX6hrC+vb0U3XHxWPkcbtO9ONLuu52iiCSg9PlAAAAAAAdFmtz8qbA4kRBz4nmZXrcumTCEF0yYYiefPdjx5r2H65r1btpxhT/CUOTjtZ44bhB+vd/rNWvXt6kxev36FefP0N5Q7PbvGd7dObKo2hN0TtyiiDQFlYyAQAAAACiStZ2q3gamZ9ML6zZrZ8Ur1V1bVCXTRiiD3dVandlbaf8bOLtqxXtv0vF4Vot23pAdz75oeN96C+FeLBdDgAAAADQpaXyVq8DNXW67Y/v6cOPq5qNd7S+aMHakD4Zev7bFyg703OsIXrLn43HbXT2yH7aU12nrfuOSGoMk5z+xj84O0Pv/vjSdtWInoftcgAAAACALi3W7W3JMKB3hvYdrm81HgiG9PN/rtP5YwdocHZms+eirTwKh6027KnWO1sOOAZMkrS3uk5T/+sVSVKmx6X6hnCrflXBkNU7Ww/q4rxBuv6s4Tpv9EBt3ntYPy5e22rL3KGj9Xp+zW5dlT+sAz8FoDVWMgEAAAAAEKdRsxc5rhJqkts/S1NH9NPUEf1UFQjq/qWbFQiGjz3vcRtNyumjnQcDOnikMbBKcxk1OHQ775fl0Z2XnKrDtQ06XNegBW9sc7yn0za4luHW1y4cpeIPy/XBzkp94+Ix+t7leXK7jOP1ujJO1es8rGQCAAAAACCBcnxex5VHg3pn6GsXjdbKHYf05ub9eqakzPH7gyGr1buqNGOKX9PGDNS0sQO1fNsBxy2C//GZSc0CkkWrdzveO8fnbTXm1BT9+rNz9bNn1+u3r23VuvJq/WbWFPXN8sT82lNdy+2EZZUBzVm4RpIImhIs9c9cBAAAAAAgxRQV5snrcTcb83rc+vHVE/SVC0broZum6r0fX6I3ij4d9RrWSv/zhTN03dRTNLRvpmZM8WvuzHz5fV4ZNTY5d+rxFO3eLU/9iyYjrbF31C/+5TS9s3W/PvvAW9q093BsL7wLmL94Y9RT9ZBYrGQCAAAAACBOsfSMMsYod0CW/FFWPcW68qg9947Fl84Zobwh2fr6nz/QjAfe1vVnnaKX1lWc8JqxbkU72VvWaoMhPfthucoqax2fL6sM6MHXtuqyiYM1ZlBvGWPYVtfJ6MkEAAAAAEACpfJJeZK0p6pWX3j4He082DwIc6ox1tfyt5Uf66fFa1XbED5unktzZ05u9ZrjCXqc5k4d0U9PLP9IT638WJVHg1F7W3ncRsFQ4/iIAVka2T9Ly7YdVH3o+BpT579LqoinJxMhEwAAAAAACZbqK2bOn7tE5VWtVwC5jDRqYC9lZ3qUnZmm93YcVO1xDcybpLtdGjEgS1WBoKoCQdU1tJ4jSW6X0Zm5Pg3t69XQPhnad7hOz6/ZrfrQJ9l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            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AFgajF08oskB",
        "colab_type": "text"
      },
      "source": [
        "### Visualize Test Dataset Predictions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tjUanewXoskC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "i=0"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JjUHMWGzoskD",
        "colab_type": "code",
        "colab": {},
        "outputId": "07c94d14-a15e-41c8-9b5a-91d080294344"
      },
      "source": [
        "# Run this cell repeatedly to see some results.\n",
        "image_index = test_indices[i]\n",
        "sample = tumor_dataset[image_index]\n",
        "image, mask, output, d_score = unet_classifier.predict(sample,0.65)\n",
        "title = f'Name: {image_index}.png   Dice Score: {d_score:.5f}'\n",
        "# save_path = os.path.join('images',f'{d_score:.5f}_{image_index}.png')\n",
        "plot.result(image,mask,output,title,save_path=None)\n",
        "i += 1\n",
        "if i >= len(test_indices):\n",
        "    i = 0 "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x1080 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}