Projekt2.ipynb

{
  "nbformat": 4,
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  "metadata": {
    "colab": {
      "name": "Projekt2.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "machine_shape": "hm",
      "mount_file_id": "1FyB4QKDT1fg_VCsmIDxLu-oVMRgc8eJX",
      "authorship_tag": "ABX9TyPoGrz4Bkzc9kI1IhUXJsFI",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Meddebma/pyradiomics/blob/master/Projekt2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "udLmhGGd-b8L"
      },
      "source": [
        "**Setup environment**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YPSd10aH-eKd"
      },
      "source": [
        "%pip install -q \"monai-weekly[itk, pillow]\""
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HbOGv71w-zFk"
      },
      "source": [
        "**Setup imports**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8JIt-TFM-3ZL",
        "outputId": "2a7901c0-f0fe-4fe2-8428-45318fcbcec8"
      },
      "source": [
        "# Copyright 2020 MONAI Consortium\n",
        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#     http://www.apache.org/licenses/LICENSE-2.0\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License.\n",
        "\n",
        "import logging\n",
        "import os\n",
        "import sys\n",
        "import tempfile\n",
        "import shutil\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import torch\n",
        "from torch.utils.tensorboard import SummaryWriter\n",
        "import numpy as np\n",
        "\n",
        "import monai\n",
        "from monai.networks.nets import DenseNet121, Classifier\n",
        "from monai.apps import download_and_extract\n",
        "from monai.config import print_config\n",
        "from monai.utils import first\n",
        "from monai.data import CacheDataset, DataLoader, ImageDataset, ITKReader, PILReader, Dataset\n",
        "from torch.utils.data import DataLoader \n",
        "from monai.transforms import Transform\n",
        "from monai.transforms import (\n",
        "    AddChannel,\n",
        "    Compose,\n",
        "    LoadImage,\n",
        "    RandRotate90,\n",
        "    Resize,\n",
        "    Orientation,\n",
        "    ScaleIntensity,\n",
        "    ToTensor,\n",
        "    Randomizable,\n",
        "    LoadImaged,\n",
        "    ToTensord,\n",
        ")\n",
        "import numpy as np\n",
        "import statsmodels.formula.api as smf\n",
        "import statsmodels.api as sm\n",
        "import pandas as pd\n",
        "import seaborn as sns \n",
        "from glob import glob\n",
        "import logging\n",
        "from abc import ABC, abstractmethod\n",
        "from typing import Any, Callable, Dict, Generator, Hashable, Iterable, List, Optional, Tuple\n",
        "\n",
        "import numpy as np\n",
        "import torch\n",
        "\n",
        "import numpy as np\n",
        "from monai.transforms import Transform, AddChannel, AsChannelFirst\n",
        "\n",
        "\n",
        "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
        "print_config()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "MONAI version: 0.6.dev2121\n",
            "Numpy version: 1.19.5\n",
            "Pytorch version: 1.8.1+cu101\n",
            "MONAI flags: HAS_EXT = False, USE_COMPILED = False\n",
            "MONAI rev id: ffba47d55439eb803b94d12ea75f460875c5af95\n",
            "\n",
            "Optional dependencies:\n",
            "Pytorch Ignite version: NOT INSTALLED or UNKNOWN VERSION.\n",
            "Nibabel version: 3.0.2\n",
            "scikit-image version: 0.16.2\n",
            "Pillow version: 7.1.2\n",
            "Tensorboard version: 2.5.0\n",
            "gdown version: 3.6.4\n",
            "TorchVision version: 0.9.1+cu101\n",
            "ITK version: 5.1.2\n",
            "tqdm version: 4.41.1\n",
            "lmdb version: 0.99\n",
            "psutil version: 5.4.8\n",
            "\n",
            "For details about installing the optional dependencies, please visit:\n",
            "    https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BvUL9l6t_tDM"
      },
      "source": [
        "**Data Direction**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mHeS8Rt6_zWD"
      },
      "source": [
        "data_dir= \"/content/drive/My Drive/Task09_Spleen\""
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O1S2v-B-AITM"
      },
      "source": [
        "labels_all = pd.read_excel (r'/content/drive/MyDrive/Task09_Spleen/labels.xlsx')\n"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GyjyR96rS0Bl"
      },
      "source": [
        "images = sorted(glob(os.path.join(data_dir, \"Projekt2t\", \"*.nii.gz\")))\n",
        "#labels= labels_all[\"label\"]\n",
        "labels= np.array([1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1], dtype=np.int64)\n",
        "\n"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xWuuYwtGuUiq",
        "outputId": "2c262742-e214-40f4-b411-2201839fda8a"
      },
      "source": [
        "labels"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ul6o7jMXc-Wm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "97aead9c-6083-4900-ef7b-b648e3e0c9a4"
      },
      "source": [
        "class ChannelFirstOrAdd(Transform):\n",
        "    def __init__(self):\n",
        "        self.channel_first = AsChannelFirst()\n",
        "        self.add_channel = AddChannel()\n",
        "\n",
        "    def __call__(self, im):\n",
        "        ndims = im.ndim\n",
        "        if ndims == 4:\n",
        "            return self.channel_first(im)\n",
        "        elif ndims == 3:\n",
        "            return self.add_channel(im)\n",
        "        else:\n",
        "            raise RuntimeError\n",
        "\n",
        "t = ChannelFirstOrAdd()\n",
        "already_has_channel = np.zeros((3, 4, 5, 2))\n",
        "print(t(already_has_channel).shape)\n",
        "\n",
        "needs_channel_added = np.zeros((3, 4, 5))\n",
        "print(t(needs_channel_added).shape)\n",
        "\n",
        "train_transforms = Compose([ScaleIntensity(), ChannelFirstOrAdd(), Resize((96, 96, 96),mode=\"nearest\"), ToTensor()])\n",
        "val_transforms = Compose([ScaleIntensity(), ChannelFirstOrAdd(), Resize((96, 96, 96),mode=\"nearest\"), ToTensor()])\n",
        "\n",
        "    # Define image dataset, data loader\n",
        "check_ds = ImageDataset(image_files=images, labels=labels, transform=train_transforms)\n",
        "check_loader = DataLoader(check_ds, batch_size=2, num_workers=2)\n",
        "im, label = monai.utils.misc.first(check_loader)\n",
        "print(type(im), im.shape, label)\n",
        "print(len(im.shape))\n"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(2, 3, 4, 5)\n",
            "(1, 3, 4, 5)\n",
            "<class 'torch.Tensor'> torch.Size([2, 1, 96, 96, 96]) tensor([1, 1])\n",
            "5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "no-1iFtFgcOf"
      },
      "source": [
        "train_ds = ImageDataset(image_files=images[:12], labels=labels[:12], transform=train_transforms)\n",
        "train_loader = DataLoader(train_ds, batch_size=2, shuffle=True, num_workers=2)\n",
        "\n",
        "# create a validation data loader\n",
        "val_ds = ImageDataset(image_files=images[-12:], labels=labels[-12:], transform=val_transforms)\n",
        "val_loader = DataLoader(val_ds, batch_size=2, num_workers=2)\n",
        "\n",
        "# Create DenseNet121, CrossEntropyLoss and Adam optimizer\n",
        "device = torch.device(\"cuda\")\n",
        "model = monai.networks.nets.DenseNet121(spatial_dims=3, in_channels=1, out_channels=2).to(device)\n",
        "loss_function = torch.nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.Adam(model.parameters(), 1e-5)"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 746
        },
        "id": "Fl3O9A11Zmpm",
        "outputId": "6d5db5d3-a38c-4c99-fcd1-acd6f1cf561d"
      },
      "source": [
        "# start a typical PyTorch training\n",
        "val_interval = 2\n",
        "best_metric = -1\n",
        "best_metric_epoch = -1\n",
        "epoch_loss_values = []\n",
        "metric_values = []\n",
        "writer = SummaryWriter()\n",
        "max_epochs = 5\n",
        "for epoch in range(max_epochs):\n",
        "    print(\"-\" * 10)\n",
        "    print(f\"epoch {epoch + 1}/{max_epochs}\")\n",
        "    model.train()\n",
        "    epoch_loss = 0\n",
        "    step = 0\n",
        "    for batch_data in train_loader:\n",
        "        step += 1\n",
        "        inputs, labels = batch_data[0].to(device), batch_data[1].to(device)\n",
        "        optimizer.zero_grad()\n",
        "        outputs = model(inputs)\n",
        "        loss = loss_function(outputs, labels)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        epoch_loss += loss.item()\n",
        "        epoch_len = len(train_ds) // train_loader.batch_size\n",
        "        print(f\"{step}/{epoch_len}, train_loss: {loss.item():.4f}\")\n",
        "        writer.add_scalar(\"train_loss\", loss.item(), epoch_len * epoch + step)\n",
        "    epoch_loss /= step\n",
        "    epoch_loss_values.append(epoch_loss)\n",
        "    print(f\"epoch {epoch + 1} average loss: {epoch_loss:.4f}\")\n",
        "\n",
        "    if (epoch + 1) % val_interval == 0:\n",
        "        model.eval()\n",
        "        with torch.no_grad():\n",
        "            num_correct = 0.0\n",
        "            metric_count = 0\n",
        "            for val_data in val_loader:\n",
        "                val_images, val_labels = val_data[0].to(\n",
        "                    device), val_data[1].to(device)\n",
        "                val_outputs = model(val_images)\n",
        "                value = torch.eq(val_outputs.argmax(dim=1), val_labels)\n",
        "                metric_count += len(value)\n",
        "                num_correct += value.sum().item()\n",
        "            metric = num_correct / metric_count\n",
        "            metric_values.append(metric)\n",
        "            if metric > best_metric:\n",
        "                best_metric = metric\n",
        "                best_metric_epoch = epoch + 1\n",
        "                torch.save(model.state_dict(),\n",
        "                           \"best_metric_model_classification3d_array.pth\")\n",
        "                print(\"saved new best metric model\")\n",
        "            print(\n",
        "                \"current epoch: {} current accuracy: {:.4f} \"\n",
        "                \"best accuracy: {:.4f} at epoch {}\".format(\n",
        "                    epoch + 1, metric, best_metric, best_metric_epoch\n",
        "                )\n",
        "            )\n",
        "            writer.add_scalar(\"val_accuracy\", metric, epoch + 1)\n",
        "print(\n",
        "    f\"train completed, best_metric: {best_metric:.4f} \"\n",
        "    f\"at epoch: {best_metric_epoch}\")\n",
        "writer.close()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "----------\n",
            "epoch 1/5\n",
            "1/6, train_loss: 0.7042\n",
            "2/6, train_loss: 0.5178\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Modifying image pixdim from [0.78125 0.78125 5.      1.     ] to [  0.78125     0.78125     5.        394.4946134]\n",
            "Modifying image pixdim from [0.728516 0.728516 5.       1.      ] to [  0.72851598   0.72851598   5.         358.76446171]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "3/6, train_loss: 0.6970\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "RuntimeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-19-5cc6e1054bee>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     13\u001b[0m     \u001b[0mepoch_loss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m     \u001b[0mstep\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m     \u001b[0;32mfor\u001b[0m \u001b[0mbatch_data\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtrain_loader\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m         \u001b[0mstep\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m         \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbatch_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    515\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sampler_iter\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    516\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 517\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    518\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_yielded\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    519\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_kind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0m_DatasetKind\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIterable\u001b[0m \u001b[0;32mand\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_next_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1197\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1198\u001b[0m                 \u001b[0;32mdel\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_task_info\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1199\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_process_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1200\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1201\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_try_put_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_process_data\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m   1223\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_try_put_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1224\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mExceptionWrapper\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1225\u001b[0;31m             \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreraise\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1226\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1227\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/_utils.py\u001b[0m in \u001b[0;36mreraise\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    427\u001b[0m             \u001b[0;31m# have message field\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    428\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexc_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 429\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexc_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    430\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    431\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mRuntimeError\u001b[0m: Caught RuntimeError in DataLoader worker process 1.\nOriginal Traceback (most recent call last):\n  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/_utils/worker.py\", line 202, in _worker_loop\n    data = fetcher.fetch(index)\n  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/_utils/fetch.py\", line 47, in fetch\n    return self.collate_fn(data)\n  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/_utils/collate.py\", line 83, in default_collate\n    return [default_collate(samples) for samples in transposed]\n  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/_utils/collate.py\", line 83, in <listcomp>\n    return [default_collate(samples) for samples in transposed]\n  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/_utils/collate.py\", line 55, in default_collate\n    return torch.stack(batch, 0, out=out)\nRuntimeError: stack expects each tensor to be equal size, but got [1, 96, 96, 96] at entry 0 and [2, 96, 96, 96] at entry 1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nm0LXsvlgp_N"
      },
      "source": [
        "test_ds = ImageDataset(\n",
        "    image_files=images, labels=labels, transform=val_transforms)\n",
        "test_loader = DataLoader(val_ds, batch_size=1, num_workers=2)\n",
        "itera = iter(test_loader)\n",
        "\n",
        "\n",
        "def get_next_im():\n",
        "    test_data = next(itera)\n",
        "    return test_data[0].to(device), test_data[1].unsqueeze(0).to(device)\n",
        "\n",
        "\n",
        "def plot_occlusion_heatmap(im, heatmap):\n",
        "    plt.subplots(1, 2)\n",
        "    plt.subplot(1, 2, 1)\n",
        "    plt.imshow(np.squeeze(im.cpu()))\n",
        "    plt.colorbar()\n",
        "    plt.subplot(1, 2, 2)\n",
        "    plt.imshow(heatmap)\n",
        "    plt.colorbar()\n",
        "    plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 824
        },
        "id": "vfFYizJOg1TG",
        "outputId": "e9254d9a-982b-4c34-9fca-16f7382f3481"
      },
      "source": [
        "# Get a random image and its corresponding label\n",
        "img, label = get_next_im()\n",
        "\n",
        "# Get the occlusion sensitivity map\n",
        "occ_sens = monai.visualize.OcclusionSensitivity(\n",
        "    nn_module=model, mask_size=12, n_batch=10, stride=12)\n",
        "# Only get a single slice to save time.\n",
        "# For the other dimensions (channel, width, height), use\n",
        "# -1 to use 0 and img.shape[x]-1 for min and max, respectively\n",
        "depth_slice = img.shape[2] // 2\n",
        "occ_sens_b_box = [-1, -1, depth_slice, depth_slice, -1, -1, -1, -1]\n",
        "\n",
        "occ_result, _ = occ_sens(x=img, b_box=occ_sens_b_box)\n",
        "occ_result = occ_result[..., label.item()]\n",
        "\n",
        "fig, axes = plt.subplots(1, 2, figsize=(25, 15), facecolor='white')\n",
        "\n",
        "for i, im in enumerate([img[:, :, depth_slice, ...], occ_result]):\n",
        "    cmap = 'gray' if i == 0 else 'jet'\n",
        "    ax = axes[i]\n",
        "    im_show = ax.imshow(np.squeeze(im[0][0].detach().cpu()), cmap=cmap)\n",
        "    ax.axis('off')\n",
        "    fig.colorbar(im_show, ax=ax)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Computing occlusion sensitivity: 100%|██████████| 64/64 [00:00<00:00, 77.12it/s]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Ws4Dpcyt77BSyz2rlEeSnYcOZ7PCXYTeMH6UYAAAAAAAGRikGAKBXSjEAAMAYGfNSDGcSb+wCAAAAAAyMiV0AAAAAgIFZshRDFoy0ffv2dNksMGnPnj2Ntizg5YIL8lLy1113XdWypRCmN954o9H2wgsvNNqyIJl16+qrVGfBSKWQtNpfNz1+/HjaXgpAe68slKu0btdfga3tUzZGuoaXlfadjYnsOLMArFLgUOaVV15ptP3gBz9Il82CzrKwrueeey5dPxvPWeDS/v37G21dw/BKsvNfu36pT1l7bVtEfu2zPmWBcKUAq7m5uUZbm2OvDYorjecsaO0Xf/EXG20///M/32jLnuMREW+99VajLQtpO//889P1s2dMdk2y/R89ejTd5lNPPdVoy0IHS7KQvOxZnvWzdN9lz4MsGPSyyy5L19+7d2+jLQuEK42RUhjje/33JNWf+d73vpcuWxviuVJGo5FSDLDCptblnzMztUFnmSGFn2WhPVk4USmwKGtvEwQ0FGlQWtKWBUhFRIw2NL83rckCvJofU97RzOGOyIZzx1O/NQlhOqv5NTIiIs5JhsSRZLn/VdjXB5OvwmuynNMPVLaV2i9K2vJ85bw96eeRDc3vN1lIWkQefpaNkyz8LCIPFcvasjE6XwgkKwUHnm6lZ0HWPh3dAo7z/axcqFmtruFvmfTcFXIclyVUbTJ57k3WzZUUZUO0NGzPxEKr4/dj8IzkjV0AAAAAgIExsQsAAAAAMDAmdgEAAAAABuZMrMIBAKwiauwCAMAYGYUauytkyYndLKzp4YcfTpfNvpRloS9Z4M78fF4g/N/+7d8abVlATSl87ciRZtn6l19+udGWhdu0CS/bsmVLo+2KK65I18/6moWv/eM//mO6fhZutH59s8B8Fk506aWXptvcvHlzoy0LEsquR0R+TT/84Q832jZtaqYdlAKDasdTFsgWkQdjZSFOWTDToUOH0m1mAVrZGCtNUGRj54knnqhePwvmqg0qaxOcVxuG10abbdYu22bsZG3ZuSvtuzYobLkC4bKwqyeffLLRlj03Suep63XesGFDo+2aa65ptGXP7FKgW3b8WVDZOefkoRzZdcq0OfZsnGT3/T//8z9X9ykLZCvdy7X3eLaf7GdDRH2YHzC+skC0lQw6S9dfhvCzNvLws7q2pdqby5VClP7nx5+Fl7WRhU0t1f5eWQBWFmoVEXF0Q/Nn0IZzk591pfC05tem7hMH2TfiJFhpUyFsaWPza0ccS/q5sRRUVhuKlq1fCk/LgtKaX1nL4WlJSN0bm5rfm16P5nfTLCQtIg9Fy9pKQWfZOMvCz46ny9WP8a6Batm9XAoKy8K+uu//9D8L6/d9+gPRStud6PjcS1dfjkC1QpBkRPJ9pDYobUjhaawI3+4AAAAAAAbGnD4A0CulGAAAYIwoxbBivLELAAAAADAwJnYBAAAAAAZGKQYAoFdKMQAAwBgZRSlRjtOs9cRuKU08S9nO0ryzdPfDhw+n2/zxj5sRo9/73vcabaUk9Nrk86zvpdTwrP3tt5uxp1nqeUTE2rXNNNEXX3yx0Xbs2LF0/TfffLPRln0hXlxsJkc+++yz6TYnJ5vDoNT/THZN/+Ef/qFqP1dccUW6za1btzba9u/f32j7/ve/n67/xhtvNNqy8VCbOB+Rn+fac19aNhsPbRLrs2XbHGeb489kx1R735X2na3f5jxnsvNUem5ksv1n/S9NTtUeU6lP2b6ybdaOh5LsnJbGY/Ysyu777373u9X7r/05Ujqm7H7K2tata0bebty4sbpPmenpPPG21F67n6z/x48fb7QdPXq00ZZdo1L7H//xH5+ih8A4mVrXTGIvmZg4/SnnQ0ltz5PY69fv0s/lstji699iEhtf2zZfSII/Or2+0bZ+Q/Pn0poLCp3qMnRKh978WBCxobItItYmfV37VrLghYX9X5S0faBy/dI2a5fN9h0Rb13Y/Fzy2sR5jbYjcU5VW0TE0Tir0XYsaTsazTFSap+P5nfWbDwuJG2lZVdS/oxpDvLleJa0eZYtx/r5NpfjZ0Pzc/MSHWjKng+l1Seb/Z+fzM/TTyeT+ZbJ5uf+9LlV6lNzWopVQikGAAAAAICBUYoBAOiVUgwAADBGRhFn4C+ujCVv7AIAAAAADIyJXQAAAACAgVmyFENtOE9Jm3CgTO2+ugYW1S4XkQeFvfLKK422UmBQbZBQFpgTEbFhQ7Nq/8UXX9xoO//88xttWd8jIs45p1ng/rzzmsXxs/CziDzU7Jlnnmm0vfVWM0Xg4MGD6TazEKc2IVC1IUxdx3NtIFpp/7X76ap07mrv8TZ96rp+rdJ47BLoVupn7bOs63GWAuG69r92m9k5XY5rX7rvsj61ue9PnGjGrmbPvSx87PXXX6/eT6ZNP2ufJW3Wb3PuzrSyB6PR6IzrE4y7qYkWYTKVzsRwn67bzIJ8SuE+tUFrbc7Tchx/pk2AVBZClbVlQVkREVNJkNGRc5ttGxcKUerZKck+ErYJHMpy3rKgtHML62dBb9ktVgqEywLMsmWz8LNNhW1WLpuFpEVEvD7d/C74enQLTzuWhJ9lgWjHk0C0iDwo7Xhy8fJAtW6hgW3kz438Xs6WnY76cMt8m0MJNeumfpulUPjKn4OF4TCxobn/+ePNfU0WwtOOJ+0nsmXXJR14O59vOCMLrXYbOlTyxi4AAAAAwMCY2AUAAAAAGJgz8WVtAGAVUYoBAADGyCiUYlgh3tgFAAAAABiYJd/Y3bZtW/WG3n777UbbsWPHGm1tAmYy2fptAtlq918K95mfbxYzzwKPuoYwtQn1Onz4cKMtC3Rr80ZUFkKUtUXUn/9165qJBV0Dg9qc5+zaZ4FmpQCrWm2CobqGxNXup815ahM+Vruv7Jx0DXYqnecux9SmT22OqUuQY2lf2dgt3aO121wO2X5K+87as/CzUhBhm7FfK3u+tblHa8dZ6bnT5XnU5lkErC59B3itVChY18CfLMSodO66HlPXvmbaBEZlslC0bJvzaYBVHgCVBWhNTiTnbtORdP1zJpufddZkX0WytlJ4Wm1Q2k8K62dBZ9lwKIWnZQFoGyuXK4WnJft6Y1MzcOm1iWYgWkQelPZ6NMO5awPVSu1ZUFo2Rt5Zti4oLR+3+ffVbNkuy5WUng/Z82Sx8llyJgaadX8O1q+fXdPp2kC0iJiovKalPmVjb2K6eU3mJ7N0xoiJJCjt+GRz/cWF5jM3DVmLyIPWohC0xljxxi4AAAAAwMCosQsA9EqNXQAAGCNq7K4Yb+wCAAAAAAyMiV0AAAAAgIFRigEA6JVSDAAAMEZGEVGf7U0HS07s3nHHHY220pevLDm8Nsm9lHCerZ+lvrdJ/s762SZ1vDYhvU3ifHZOS+e59px2TbzPtlnqU+3xl65z7f7bJN5n609ONod7m3T62vPc5jjbXKfavmZjtKR27JfOc+1kTNb3rC2i+zmt1ebc194Pa9fmqaPZsm2uXbZ+1v82z5I2Yz9T+8xvM2HXdXKv9pq2OfbsOLN+lsZztt2sLXs+dWWyFCiZivnTvs2uCe1drVTCeykhPTv+NuuvlCxJPmvrus3jSWJ8RMRErE/aknNS6NLCBUcbbedMvN1oW7shWfncfJuxMWm7IGl7q7B+1n68cpsReb+ytmT9twvHdGTD2Y221+O85nJxTr5+0l67/tE4K93m0eTazyfj5HhMp+vnyzbb5pP1S2N8MZkO6Xo/ZM+ixeJzI3tGNJ8lXZ9vy/Hc6brN5Vg/GyPT6c0YsVB5ncvXrnmdsrE3OZGvv5B8x5iYTMbOQvJ8ncx/3i4uZN8n8u+njBelGAAAAAAABkYpBgCgV94uBgCAMTKK6PmXVFYNb+wCAAAAAAyMiV0AAAAAgIFZshTDH/7hHzbaSgExmWzZNiFIbfZ1upX23SacqMv6bQLh2oSK1W6zNuwpIu9rbYhS6TzXhlWVzlObfdWsezrUnuc2YVeZNgFWK9WnTNcx2mbfXa9p1/WX41m2HH3KtlkKdKsNwuwanNd1PHYJ+IuIWFhIwnBaBBTWHn/Xn41dg0k/97nPVe//dBuNRkoxwApbjnCelbRSAWR9B8L1rTZYKguwKl2jbJxlAVylAKs0LOvcY422qXObgUnr32qGrEVETG9qtq3JAtFK4WnZZrOhUwpvS9rfTsLfskC0I9Fse6e9eU6PJeFlbcLTaoPSStvMls3CprIArIg87Ko2KK0UlNU1KC0b59k2S8+SiWTZ6Y7hln2HmuXb7PYszZ4b2TXNRk7pGmfnPpM9B0vr1/Yzoj5oLQtZm5rOx8jiYravLB1yBa3uH6Mrxhu7AAAAAAADY2IXAAAAAGBglizFAACw3JRiAACAMTIKpRhWiDd2AQAAAAAGZsk3dmsDrJZqf6+uIUJZ6EubgJjaPq3kcbYJu+rSp67rtzmmNkFAtboG92Xhb1k/JyfrX2RfyVCu2mW7hii10TUMsIvSMS3HeK59RixXoNtyhK/V3qOl0MRM13DH2n11DRpr88ztGgS5UoGbmeW474Dx0DWcp6uVCj/rKgvCaRMKlgUGtVl/pbQJlsrbmp+dSwFYtfsvrT+VjN0sFCxb7siGZqBaRMTkhua5n7qguf70Yr7+1Nsn0vb3OrphXdqe9f9oZdBZFkjWZptt1s/3322bWRheKayqdjzWhqy12eZy6fIs7B6SdvoDzbpqc0x5cF2z7Xjh2meyn43ZNiPycZaN3VJIW23QWm2IZUTEYouAZ8aLb30AAAAAAAOjxi4A0Cs1dgEAYIyMIqLuFxroyBu7AAAAAAADY2IXAAAAAGBglGIAAHqlFAMAAIyRUcRAslIHb8mJ3YllSNXrmhCeKfVzpb4oLkdCepv1M21S32sT5ksJ66W+vleWeF9aN9tX7X4i8mOdnGwO9+zYS+cp23+bPnXdZu2+sv63uZe73jdtxl6X9btepzb9rL1Hl2vsdBlnpfu76znJZMu22WbWp6z/bZ5Ftf1vc9+t5HOj6zMmU/vMB8bXVBzvuwuDkKWul1Lbs/auqfFdU+szpTT1Wllqe5b6Xkqiz9afj6lkm/XnOW9rnrvS9aje5kR+PSY31F3n0jk5Gmc12uaTZWuXe2fZ9Y22Y8n63ftUt5/SvrJrX5KNncVkOiMb46Vxn7Vn+2mjNHbr1z/99333Z1G/63cxXfh5VzueSmrHWWnsLcd47jp2GS6lGAAAAAAABkYpBgCgV0oxAADAGBlFLMML6CS8sQsAAAAAMDAmdgEAAAAABmbJUgxZwEqbIJfagJblCItqo014WZdtlrbbJvCntq9ZUFlJ7bG2Cdzpek5r+98mZG45rmmbsVd77RcW8t9XqF2/Nuypja7jObuepQCsbF9d+9/1HqndZpv7tuu+lkN2TtqEr2Wy8ZwFGZYsR3hbm/CzTNfgvExp/dJ9Urv+EIxGo0H3H4aoa5DOalEbylVatstyp0PXoLTa9Y8nAVjlMVYXlnUmhjV1DbUqBZ1l5y9bNgsaK4Wf1S5bCi+rXba276Vla8OiSmqX7X4vdKtguTzjcXmeJV222/Ue6fqzqU0gWhb6GDGfrN8t/Kyka/habZ96pxTDivDGLgAAAADAwJjYBQAAAAAYmG6/UwAA0JFSDAAAMEZGEXGi706sDt7YBQAAAAAYmNZv7LZ5q2alQl9K4T7Z/pcjAKvNcrX7ahOW1DUorDYkr03IXdfgvSFrc+3bhIrVbnM53nxrE3RWG2B1OvZf26dMmwCs2v1MTOQF62vDulYynLJWaf3aULLsnLQJZOsaBtj13He9x2r3Vdp/l+tc2mbX4ECf5csWAAAQ3UlEQVRg+KaSgBiashChNuE+bUKIVjJU7b1KgT+LSZ9qw4FKAVr1fTr9AVpdg4WWIxgpIg8qy5atDR+LyIPOsm2Wzkl2/fI+1S1X6lMbKxUMNY5BaV1DzTJdg85W6plX6mfteFqu8LXaPnV97rA6KMUAAPRKKQYAABgjo4ge/5/lqqIUAwAAAADAwJjYBQAAAAAYGBO7AAAdPfzww3HllVfGzMxM7Ny5s7jc3/3d38WaNWtibm7uZNuf/dmfxczMTFx55ZXxyCOPrER3AQCAMbBkjd3a8LGSrjXzakNnSoFFmdqAmeXoe2lfbdav1SYwKTt/yxEO1Ebt2FvJQLba/ZeCvrIQpGzZNkFltVYyzK827KrNcba5R7uMneXqU+091ua5UXtNliuorMv92DUcso2uz9za69z1HlmOGrNdfw6tpKHX2F1cXIw777wzHn300di6dWvccMMNMTs7G1ddddW7ljty5Ej8+Z//eXzkIx852fajH/0oHnjggXj66afjhRdeiBtvvDGeeeaZVp9toK0+g7qGJAscKp272nCiriFGXa9dFs7TJtKqNhSsFOJTGw5UWr9LgNdy9SkLBWsT9FW7rzbhZ236n6//P99/OYxvZX6uDen5NhXHT/s2z8RQs+7hb3XrtxljefhZts36vne977Nn8aAD0UYRy5DbR8IbuwAAHTz55JMxMzMTl19+eUxNTcVtt90Wu3fvbiz3R3/0R/GlL30p1q1bd7Jt9+7dcdttt8X09HRcdtllMTMzE08++eRKdh8AABgoE7sAAEs4fPhwbN++/eSf+++//13//vzzz8ell1568u9bt26N559//l3LfP/734+DBw/GLbfc0npdAACAzJKlGAAAltuZXoph8+bN76qJ29ZPf/rT+MIXvhB/9Vd/dfo6BQAAZyqlGFaMiV0AgA4uueSSOHjw4Mm/P/fcc3HJJZec/PuRI0fihz/8Yfzqr/5qRES89NJLMTs7Gw899NAp1wUAAChRigEAoIMbbrgh9u7dG/v374/5+fl44IEHYnZ29uS/n3vuufHyyy/HgQMH4sCBA/HRj340Hnroodi+fXvMzs7GAw88EMePH4/9+/fH3r1745d+6Zd6PBoAAGAolnxjN0tDL6Vp1yast/l1y64J67X97/oroF0T0ruu31XXfdX2P2vLrlFp2TbnqfaYsm2WxkOWUN51PNe2tdlmVyt5j9Yu2/UeXY5nUZuxUxrntWr31fezrMt+lmp/r1Kf3ve+5v+r7LrN2mVL++l6/ro8y7o+H1fKaDQ640sxnOqcTU5Oxn333Rc333xzLC4uxmc+85m4+uqr45577jk5eVty9dVXx2/91m/FVVddFZOTk/G1r30t/XkDp9P0MiSxZ9okhJ+JsnT5Ujp7m2XPNKXU9ez6ZUny2frHYzrd5nyS+54tmy1X6tN85frL1afjSXuWeL8c6fZdt7kc92h27G1MtPjd7ey+y85J13uxzfpd+1+/n5U7puVYP99mt9/bz9Yvjcfa/re5x/InRPftns51V9QoIk703YnVQSkGAICOPvGJT8QnPvGJd7X9yZ/8Sbrs448//q6/f/nLX44vf/nLy9U1AABgTCnFAAAAAAAwMN7YBQB6daaXYgAAAFoaRkWiwfPGLgAAAADAwCz5xu6RI0dWqh8AAMAKeCQ+1XcXAAA4DZRiAAB6pRQDAACMkVFELPTdidVBKQYAAAAAgIExsQsAAAAAMDAmdgEAAAAABkaNXQCgV2rsAgDAGFFjd8V4YxcAAAAAYGBM7AIAAAAADIxSDABAr5RiAACAMTKKiBN9d2J18MYuAAAAAMDAmNgFAAAAABgYpRgAgN6MRiOlGAAAYJyMImKx706cwpi86jomhwEAAAAAsHqY2AUAAAAAGBilGACAXinFAAAAY2ah7w6cwlTfHTg9vLELAAAAADAwJnYBAAAAAAZGKQYAoFdKMQAAwBgZhVIMK8QbuwAAAAAAA2NiFwAAAABgYEzsAgAAAAAMjBq7AECv1NgFAIAxMoqIE313YnXwxi4AAAAAwMCY2AUAAAAAGBilGACAXinFAAAAY2QUEYt9d2J18MYuAAAAAMDAmNgFAAAAABgYpRgAgN6MRiOlGAAAYJyMImKh706sDt7YBQAAAAAYGBO7AAAAAAADoxQDANArpRgAAGDMKMWwIryxCwAAAAAwMCZ2AQAAAAAGRikGAKBXSjEAAMAYGUXEib47sTp4YxcAAAAAYGBM7AIAAAAADIxSDABAr5RiAACAMTKKiMW+O7E6eGMXAAAAAGBgTOwCAAAAAAyMiV0AAAAAgIFRYxcA6JUauwAAMEZGEbHQdydWB2/sAgAAAAAMjIldAAAAAICBUYoBAOjNaDRSigEAAMaJUgwrxhu7AAAAAAADY2IXAAAAAGBglGIAAHqlFAMAAIyRUUSc6LsTq4M3dgEAAAAABsbELgAAAADAwCjFAAD0SikGAAAYM4t9d2B18MYuAAAAAMDAmNgFAAAAABgYpRgAgF4pxQAAAGPGR/wV4Y1dAAAAAICBMbELAAAAADAwJnYBAAAAAAZGjV0AoFdq7AIAALTnjV0AAAAAgAqvvvpq3HTTTbFt27a46aab4rXXXkuX+/jHPx7nnXdefPKTn0z//fOf/3ycffbZJ//+1a9+Na666qq45pprYseOHfHss8+esi8mdgEAAAAAKuzcuTN27NgRe/fujR07dsTOnTvT5e6666745je/mf7b3NxcY0L4uuuui7m5ufiXf/mX+PSnPx1f/OIXT9kXE7sAQG9Go9EZ/wcAAOBndu/eHbfffntERNx+++3xrW99K11ux44dcc455zTaFxcX46677oqvfOUr72r/tV/7tVi/fn1ERHz0ox+N55577pR9UWMXAAAAAFg1Dh8+HNu3bz/5989+9rPx2c9+tmrdQ4cOxZYtWyIi4uKLL45Dhw612vd9990Xs7OzJ7eR+cY3vhG/8Ru/ccptmdgFAAAAAFaNzZs3x9zcXPHfb7zxxnjppZca7ffee++7/r5mzZpYs2ZN9X5feOGFePDBB+Pxxx8vLvPXf/3XMTc3F9/5zndOuT0TuwBAr5Q7AAAAziR79uwp/ttFF10UL774YmzZsiVefPHFuPDCC6u3+9RTT8W+fftiZmYmIiKOHj0aMzMzsW/fvpP7vffee+M73/lOTE9Pn3J7auwCAAAAAFSYnZ2NXbt2RUTErl274lOf+lT1urfccku89NJLceDAgThw4ECsX7/+5KTuU089Fb/3e78XDz30UPVksYldAAAAAIAKd999dzz66KOxbdu22LNnT9x9990RETE3Nxd33HHHyeU+9rGPxa233hqPPfZYbN26NR555JElt3vXXXfFm2++Gbfeemtce+21MTs7e8q+rBn5/UcAoCdTU1OtfnWpDxdffPGS9bcAAID/b82a6yPiu313Y0nXX/+/x+Izvjd2AQAAAAAGxsQuAAAAAMDATPbdAQBgdVMVCgAAxskoIhb67sSq4I1dAAAAAICBMbELAAAAADAwSjEAAL1SigEAAMbJKCJO9N2JVcEbuwAAAAAAA2NiFwAAAABgYEzsAgAAAAAMjBq7AEBvRqORGrsAADBWRhGx0HcnVgVv7AIAAAAADIyJXQAAAACAgVGKAQDolVIMAAAwTkYRcaLvTqwK3tgFAAAAABgYE7sAAAAAAAOjFAMA0CulGAAAYJwoxbBSvLELAAAAADAwJnYBAAAAAAZGKQYAoFdKMQAAwLhZ6LsDq4I3dgEAAAAABsbELgAAAADAwCjFAAD0SikGAAAYJ6OIONF3J1YFb+wCAAAAAAyMiV0AAAAAgIExsQsAAAAAMDBq7AIAvRmNRmrsAgDAWBlFxELfnVgVvLELAAAAADAwJnYBAAAAAAZGKQYAoFdKMQAAwDgZRcSJvjuxKnhjFwAAAABgYEzsAgAAAAAMjFIMAECvlGIAAIBxMoqIhb47sSp4YxcAAAAAYGBM7AIAAAAADIxSDABAr5RiAACAcTKKiBN9d2JV8MYuAAAAAMDAmNgFAOjo4YcfjiuvvDJmZmZi586djX//+te/Hh/+8Ifj2muvjV/+5V+OH/3oRxERceDAgTjrrLPi2muvjWuvvTZ+//d/f6W7DgAADJRSDABAr4ZeimFxcTHuvPPOePTRR2Pr1q1xww03xOzsbFx11VUnl/md3/mdk5O2Dz30UHzhC1+Ihx9+OCIirrjiivjBD37QS98BAOD0G0XEQt+dWBW8sQsA0MGTTz4ZMzMzcfnll8fU1FTcdtttsXv37ncts3HjxpP//dZbb8WaNWtWupsAAMCYMbELALCEw4cPx/bt20/+uf/++9/1788//3xceumlJ/++devWeP755xvb+drXvhZXXHFFfPGLX4y/+Iu/ONm+f//+uO666+JXfuVX4oknnli+AwEAAMaKUgwAAEvYvHlzzM3Ndd7OnXfeGXfeeWf8zd/8Tfzpn/5p7Nq1K7Zs2RL/9V//FZs2bYp/+qd/it/8zd+Mp59++l1v+AIAAGRM7AIAvRmNRoOvsXvJJZfEwYMHT/79ueeei0suuaS4/G233Raf+9znIiJieno6pqenIyLi+uuvjyuuuCKeeeaZ2L59+/J2GgAAls0oIk703YlVQSkGAIAObrjhhti7d2/s378/5ufn44EHHojZ2dl3LbN3796T//3tb387tm3bFhHvlHlYXFyMiIj//M//jL1798bll1++cp0HAAAGyxu7AAAdTE5Oxn333Rc333xzLC4uxmc+85m4+uqr45577ont27fH7Oxs3HfffbFnz55Yu3ZtnH/++bFr166IiPj7v//7uOeee2Lt2rXxvve9L77+9a/HBRdc0PMRAQAAQ7BmNPTffwQABmtiYiI2bNjQdzeW9KEPfei01NgFAIDVYM2aD0XE/+m7G0u6/vq7x+IzvlIMAAAAAAADY2IXAAAAAGBg1NgFAHqlKhQAAIyTUUSc6LsTq4I3dgEAAAAABsbELgAAAADAwCjFAAD0SikGAAAYNwt9d2BV8MYuAAAAAMDAmNgFAAAAABgYpRgAgF4pxQAAAONkFBEn+u7EquCNXQAAAACAgTGxCwAAAAAwMEoxAAC9GY1GSjEAAMBYUYphpXhjFwAAAABgYEzsAgAAAAAMjIldAAAAAICBUWMXAOiVGrsAADBORhGx0HcnVgVv7AIAAAAADIyJXQAAAACAgVGKAQDolVIMAAAwTkYRcaLvTqwK3tgFAAAAABgYE7sAAAAAAAOjFAMA0CulGAAAYJyMImKh706sCt7YBQAAAAAYGBO7AAAAAAADoxQDANArpRgAAGCcjCLiRN+dWBW8sQsAAAAAMDAmdgEAAAAABkYpBgCgN6PRSCkGAAAYK6OIWOi7E6uCN3YBAAAAAAbGxC4AAAAAwMCY2AUAAAAAGBg1dgGAXqmxCwAA42QUESf67sSq4I1dAAAAAICBMbELAAAAADAwSjEAAL1SigEAAMbJKCIW+u7EquCNXQAAAACAgTGxCwAAAAAwMEoxAAC9UooBAADGySgiTvTdiVXBG7sAAAAAAANjYhcAAAAAYGCUYgAAeqUUAwAAjJNRRCz03YlVwRu7AAAAAAADY2IXAAAAAGBglGIAAHpz8803x8svv9x3N5b0/ve/v+8uAADAYNx88zXx8sv/t+9uLGlcPuOvGSlsBwAAAAAwKEoxAAAAAAAMjIldAAAAAICBMbELAAAAADAwJnYBAAAAAAbGxC4AAAAAwMD8P/YUECwpHhs8AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1800x1080 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}