Classification_HBI_outcome.ipynb

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\n",
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  },
  "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/Classification_HBI_outcome.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6LB-DbzkrLl6"
      },
      "source": [
        "%pip install \"monai--weekly[nibabel, skimage, pillow, tensorboard, gdown, ignite, torchvision, einops, itk, tqdm, lmdb, psutil]\"\n",
        "%pip install itkwidgets\n",
        "%pip install SimpleITK"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WHt5hRV4MYoT"
      },
      "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",
        "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",
        "import SimpleITK as sitk\n",
        "import monai\n",
        "from monai.networks.nets import DenseNet121, Classifier, EfficientNet, EfficientNetBN\n",
        "from monai.apps import download_and_extract\n",
        "from monai.config import print_config\n",
        "from monai.utils import first\n",
        "from monai.visualize import GradCAM\n",
        "from monai.metrics import ConfusionMatrixMetric, get_confusion_matrix\n",
        "from monai.data import CacheDataset, DataLoader, ImageDataset, ITKReader, PILReader, Dataset, partition_dataset_classes, partition_dataset\n",
        "from sklearn.metrics import (\n",
        "    classification_report, confusion_matrix,\n",
        "    ConfusionMatrixDisplay, \n",
        ")\n",
        "from monai.visualize import blend_images, matshow3d, plot_2d_or_3d_image\n",
        "from monai.data import decollate_batch\n",
        "from monai.metrics import ROCAUCMetric\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",
        "    Invertd,\n",
        "    Lambda,\n",
        "    Resize,\n",
        "    Orientation,\n",
        "    Orientationd,\n",
        "    EnsureChannelFirstd,\n",
        "    ScaleIntensity,\n",
        "    AsChannelFirst,\n",
        "    AsChannelLast,\n",
        "    Activations,\n",
        "    AddChannel,\n",
        "    AddChanneld,\n",
        "    ToTensord,\n",
        "    Resized,\n",
        "    AsDiscrete,\n",
        "    Spacingd,\n",
        "    EnsureChannelFirst,\n",
        "    RepeatChannel,\n",
        "    RemoveRepeatedChannel,\n",
        "    ScaleIntensityRanged,\n",
        "    SplitChannel,\n",
        "    SqueezeDim,\n",
        "    ToTensor,\n",
        "    CropForegroundd,\n",
        "    Randomizable,\n",
        "    LoadImaged,\n",
        "    ToTensord,\n",
        "    MaskIntensityd,\n",
        "    Flip,\n",
        "    MaskIntensity,\n",
        "    ScaleIntensityd,\n",
        "    EnsureType,\n",
        "    EnsureTyped,\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",
        "import itk\n",
        "import tensorboard\n",
        "from itkwidgets import view\n",
        "\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",
        "from monai.utils import first, set_determinism\n",
        "from math import ceil\n",
        "#from downloaddata import fetch_data as fdata\n",
        "import matplotlib.pyplot as plt\n",
        "!pip install -q itkwidgets\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": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BvUL9l6t_tDM"
      },
      "source": [
        "**Data Direction**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mHeS8Rt6_zWD"
      },
      "source": [
        "data_dir= \"/content/drive/My Drive/Brain_AI\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O1S2v-B-AITM"
      },
      "source": [
        "data = pd.read_excel (r'/content/drive/MyDrive/Brain_AI/data/Projekt2/labels_excel.xlsx', header=0)\n",
        "x=[]\n",
        "l= data[\"pred\"]\n",
        "mask = \"/content/drive/MyDrive/Brain_AI/data/Putamen_Mask.nii.gz\"\n",
        "l\n",
        "\n",
        "\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GyjyR96rS0Bl",
        "outputId": "44252faf-c025-4a09-e98f-197217e85655"
      },
      "source": [
        "images = sorted(glob(os.path.join(data_dir, \"data/Projekt2/\", \"*.nii.gz\")))\n",
        "#segmentations = sorted(glob(os.path.join(data_dir, \"Projekt2/segmentation/*\", \"*.nii.gz\")))\n",
        "#image1 = \"/content/drive/MyDrive/Brain_AI/data/Projekt1/A018_2_FLIRT_CT_toMNI.nii.gz\"\n",
        "#for i in images:\n",
        " # print(os.path.basename(i))\n",
        "\n",
        "labels= l\n",
        "#labels= np.array([0,0,0,0,0,0,1,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1], dtype=np.int64)\n",
        "print(len(images))\n",
        "#print(len(segmentations))\n",
        "print(len(labels))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "306\n",
            "306\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vavEE_URN7H7"
      },
      "source": [
        "x=[]\n",
        "for i in images: \n",
        "  x.append(os.path.basename(i))\n",
        "  #y.append\n",
        "\n",
        "\n",
        "df = pd.DataFrame(x) \n",
        "df[\"labels\"]= l\n",
        "df"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 393,
          "referenced_widgets": [
            "86560c06b90c4f358174b98d9b3d1f67",
            "e84d3a45093f40799f60af59d10ee360",
            "45b7e9e667f040f8bfec8a1bf4b2dd84",
            "d6e50517fe8d4d4cb175bd9ba325b5dd",
            "44562087912a42899cd5e37fc28ef7c4",
            "272588c92b7e4e80b832cbb391c1220f",
            "0b4dfa337a5b420dae4028c580428f41"
          ]
        },
        "id": "vBnbVTeKNUc0",
        "outputId": "ccce20a3-8342-4ad3-c35f-1c3f195fcbc9"
      },
      "source": [
        "#!pip install ipywidgets\n",
        "img = itk.imread(images[3])\n",
        "\n",
        "#view(img)\n",
        "print(img.shape)\n",
        "from ipywidgets import interact, interactive, IntSlider, ToggleButtons\n",
        "#print(f\"The image object has the following dimensions: height: {height}, width:{width}, depth:{depth}\")\n",
        "# Define a function to visualize the data\n",
        "for i in img:\n",
        "  def explore_3dimage(layer):\n",
        "    plt.figure(figsize=(10, 5))\n",
        "    plt.imshow(img[layer, :,  :], cmap='gray');\n",
        "    plt.title('Explore Layers of Brain CT', fontsize=20)\n",
        "    plt.axis('off')\n",
        "    return layer\n",
        "    \n",
        "# Run the ipywidgets interact() function to explore the data\n",
        "interact(explore_3dimage, layer=(1, img.shape[2] - 1));"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(181, 217, 181)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "86560c06b90c4f358174b98d9b3d1f67",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "interactive(children=(IntSlider(value=90, description='layer', max=180, min=1), Output()), _dom_classes=('widg…"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JgOMkqS2-i4q"
      },
      "source": [
        "**Check transforms**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ceFSaonDHXsp"
      },
      "source": [
        "device = torch.device(\"cuda\")\n",
        "for data in train_loader:\n",
        "  image = data[\"image\"].to(device).cpu() \n",
        " # print(os.path.basename(image))\n",
        "  #img = itk.imread(image)\n",
        "\n",
        "  def explore_3dimage(layer):\n",
        "    plt.figure(figsize=(8, 4))\n",
        "    channel = 3\n",
        "    #plt.subplot(1, 4, range(data) + 1)\n",
        "    plt.imshow(data[\"image\"][0, 0, :, :,layer], cmap='gray');\n",
        "    plt.title('Explore Layers of Brain CT', fontsize=20)\n",
        "    plt.axis('off')\n",
        "    \n",
        "    return layer\n",
        "  plt.show()    \n",
        "# Run the ipywidgets interact() function to explore the data\n",
        "  interact(explore_3dimage, layer=(0, data[\"image\"].shape[2] - 1));\n",
        "  #plt.figure(\"check\", (18, 6))\n",
        "  #plt.subplot(1, 2, 1)\n",
        "  #plt.imshow(data[\"image\"][0, 0, :, :, 55], cmap=\"gray\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y-trGi0jHGQg",
        "outputId": "04ef6b31-cd5e-42fe-88f5-990f2a5c8896"
      },
      "source": [
        "data = [{\"image\": i, \"label\": l} for i, l in zip(images, labels)]\n",
        "transforms = Compose([\n",
        "    LoadImaged(keys=[\"image\"]),\n",
        "    AddChanneld(keys=[\"image\"]),\n",
        "    #CropForegroundd(keys=[\"image\"], source_key=[\"image\"], margin=-1),\n",
        "    #Spacingd(keys=[\"image\"], pixdim=(1.5, 1.5, 2.0), mode=(\"bilinear\", \"nearest\")),\n",
        "    Orientationd(keys=[\"image\"], axcodes=\"PLI\"),\n",
        "    Resized(keys=[\"image\"], spatial_size=(96, 96, 96)),\n",
        "    ScaleIntensityRanged(keys=['image'], a_min=-50, a_max=300, b_min=0.0, b_max=1.0, clip=True),\n",
        "    #MaskIntensityd(keys=\"image\", mask_key=\"seg\"),\n",
        "    EnsureTyped(keys=[\"image\"]),\n",
        "])\n",
        "data_partition= partition_dataset_classes(data, labels.tolist(), shuffle=True, ratios=[0.8, 0.2])\n",
        "train_ds = CacheDataset(data=data_partition[1], transform=transforms)\n",
        "train_loader = DataLoader(train_ds, batch_size=2, shuffle=False, num_workers=4, pin_memory=torch.cuda.is_available())\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Loading dataset: 100%|██████████| 12/12 [00:01<00:00,  6.53it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AJJwFbO47yGV"
      },
      "source": [
        "**Loading Data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "scT_G0HLMNjH",
        "outputId": "4e156e32-f3bc-40f3-dd2f-5f6afc071968"
      },
      "source": [
        "set_determinism(seed=0)\n",
        "#data = [{\"image\": i, \"label\": l} for i, l in zip(images, labels)]\n",
        "#data = [{\"image\": i, \"label\": l} for i, l in zip(images, labels)]\n",
        "#data = [{\"image\": i, \"label\": l} for i, l in zip(images, labels)]\n",
        "train_files = [{\"image\": i, \"label\": l} for i, l in zip(images[:250], labels[:250])]\n",
        "val_files = [{\"image\": i, \"label\": l} for i, l in zip(images[-250:], labels[-250:])]\n",
        "\n",
        "transforms = Compose([\n",
        "    LoadImaged(keys=[\"image\"]),\n",
        "    AddChanneld(keys=[\"image\"]),\n",
        "    Orientationd(keys=[\"image\"], axcodes=\"PLI\"),\n",
        "    Resized(keys=[\"image\"], spatial_size=(128, 128, 128)),\n",
        "    ScaleIntensityRanged(keys=['image'], a_min=-57, a_max=164, b_min=0.0, b_max=1.0, clip=True),\n",
        "    EnsureTyped(keys=[\"image\"]),\n",
        "])\n",
        "\n",
        "\n",
        "#data_partition= partition_dataset_classes(data, labels.tolist(), shuffle=True, ratios=[0.8, 0.1, 0.1])\n",
        "\n",
        "# create a training data loader\n",
        "train_ds = CacheDataset(data=train_files, transform=transforms)\n",
        "train_loader = DataLoader(train_ds, batch_size=8, shuffle=True, num_workers=4, pin_memory=torch.cuda.is_available())\n",
        "\n",
        "# create a validation data loader\n",
        "val_ds = CacheDataset(data=val_files, transform=transforms)\n",
        "val_loader = DataLoader(val_ds, batch_size=8, shuffle=True, num_workers=4, pin_memory=torch.cuda.is_available())\n",
        "\n",
        "# create a test data loader\n",
        "#test_ds = CacheDataset(data=data_partition[2], transform=transforms)\n",
        "#test_loader = DataLoader(test_ds, batch_size=8, shuffle=True, num_workers=4, pin_memory=torch.cuda.is_available())\n",
        "\n",
        "post_pred = Compose([EnsureType(), Activations(softmax=True)])\n",
        "post_label = Compose([EnsureType(), AsDiscrete(to_onehot=True, n_classes=2)])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Loading dataset: 100%|██████████| 250/250 [00:55<00:00,  4.49it/s]\n",
            "Loading dataset: 100%|██████████| 250/250 [00:29<00:00,  8.43it/s]\n",
            "`to_onehot=True/False` is deprecated, please use `to_onehot=num_classes` instead.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "xxkzWn0Yco_B"
      },
      "source": [
        "device = torch.device(\"cuda\")\n",
        "for data in train_loader:\n",
        "  image = data[\"image\"].to(device) \n",
        "  #plt.figure(\"check\", (18, 6))\n",
        "  #plt.subplot(1, 2, 1)\n",
        "  #plt.imshow(data[\"image\"][0, 0, :, :, 55], cmap=\"gray\")\n",
        " \n",
        "  matshow3d(\n",
        "    volume=data['image'],\n",
        "    fig=None, title=\"input image\",\n",
        "    figsize=(10, 10),\n",
        "    every_n=10,\n",
        "    frame_dim=-1,\n",
        "    show=True,\n",
        "    cmap=\"gray\",\n",
        ")\n",
        "\n",
        "  #print(inputs.shape)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RtjR-k758HU7"
      },
      "source": [
        "**Training**"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from monai.networks.nets import  resnet34\n",
        "model= resnet34(spatial_dims=3, pretrained=True, num_classes=2).to(device)\n",
        "loss_function = torch.nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.Adam(model.parameters(), 1e-6)\n",
        "auc_metric = ROCAUCMetric()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 380
        },
        "id": "Q8n-zXJg6Q1v",
        "outputId": "7e922d3f-be49-4add-c7f2-561c94068ceb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NotImplementedError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNotImplementedError\u001b[0m                       Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-11-76b896df35ed>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mmonai\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnetworks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnets\u001b[0m \u001b[0;32mimport\u001b[0m  \u001b[0mresnet34\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0mresnet34\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mspatial_dims\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpretrained\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_classes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\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[0m\u001b[1;32m      3\u001b[0m \u001b[0mloss_function\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCrossEntropyLoss\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      4\u001b[0m \u001b[0moptimizer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAdam\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1e-6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mauc_metric\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mROCAUCMetric\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/monai/networks/nets/resnet.py\u001b[0m in \u001b[0;36mresnet34\u001b[0;34m(pretrained, progress, **kwargs)\u001b[0m\n\u001b[1;32m    362\u001b[0m         \u001b[0mprogress\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mbool\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mIf\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdisplays\u001b[0m \u001b[0ma\u001b[0m \u001b[0mprogress\u001b[0m \u001b[0mbar\u001b[0m \u001b[0mof\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mdownload\u001b[0m \u001b[0mto\u001b[0m \u001b[0mstderr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    363\u001b[0m     \"\"\"\n\u001b[0;32m--> 364\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_resnet\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"resnet34\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mResNetBlock\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mget_inplanes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpretrained\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprogress\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    365\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    366\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/monai/networks/nets/resnet.py\u001b[0m in \u001b[0;36m_resnet\u001b[0;34m(arch, block, layers, block_inplanes, pretrained, progress, **kwargs)\u001b[0m\n\u001b[1;32m    323\u001b[0m         \u001b[0;31m# Would like to load dict from url but need somewhere to save the state dicts.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    324\u001b[0m         raise NotImplementedError(\n\u001b[0;32m--> 325\u001b[0;31m             \u001b[0;34m\"Currently not implemented. You need to manually download weights provided by the paper's author\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    326\u001b[0m             \u001b[0;34m\" and load then to the model with `state_dict`. See https://github.com/Tencent/MedicalNet\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    327\u001b[0m         )\n",
            "\u001b[0;31mNotImplementedError\u001b[0m: Currently not implemented. You need to manually download weights provided by the paper's author and load then to the model with `state_dict`. See https://github.com/Tencent/MedicalNet"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "device = torch.device(\"cuda\")\n",
        "pretrain = torch.load(\"/content/drive/MyDrive/Brain_AI/weights/pretrain/resnet_18.pth\")\n",
        "pretrain['state_dict'] = {k.replace(\"module.\", \"\"):v for k, v in pretrain['state_dict'].items()}\n",
        "                          \n",
        "model = resnet18().to(device)\n",
        "model.load_state_dict(pretrain['state_dict'], strict=False)\n",
        "\n",
        "\n",
        "loss_function = torch.nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.Adam(model.parameters(), 1e-6)\n",
        "auc_metric = ROCAUCMetric()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        },
        "id": "OJHVqedT0ehY",
        "outputId": "4bd6ea44-98a8-4540-e30a-0eb05eb0e276"
      },
      "execution_count": null,
      "outputs": [
        {
          "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-10-145a61006a85>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresnet18\u001b[0m\u001b[0;34m(\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----> 6\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_state_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpretrain\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'state_dict'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstrict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\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      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36mload_state_dict\u001b[0;34m(self, state_dict, strict)\u001b[0m\n\u001b[1;32m   1481\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merror_msgs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1482\u001b[0m             raise RuntimeError('Error(s) in loading state_dict for {}:\\n\\t{}'.format(\n\u001b[0;32m-> 1483\u001b[0;31m                                self.__class__.__name__, \"\\n\\t\".join(error_msgs)))\n\u001b[0m\u001b[1;32m   1484\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0m_IncompatibleKeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmissing_keys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munexpected_keys\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1485\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mRuntimeError\u001b[0m: Error(s) in loading state_dict for ResNet:\n\tsize mismatch for conv1.weight: copying a param with shape torch.Size([64, 1, 7, 7, 7]) from checkpoint, the shape in current model is torch.Size([64, 3, 7, 7, 7])."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bQWJ90U_rVmr"
      },
      "source": [
        "# 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",
        "#model = torch.hub.load('pytorch/vision', 'resnet18', pretrained=True)\n",
        "#model = monai.networks.nets.ViT(in_channels=1, img_size=(96,96,96),patch_size=(16,16,16), pos_embed='conv', classification=True,spatial_dims=3).to(device)\n",
        "loss_function = torch.nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.Adam(model.parameters(), 1e-6)\n",
        "auc_metric = ROCAUCMetric()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h2vHgB-WaSgi"
      },
      "source": [
        "print(model)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1LSEwWGo2GMR"
      },
      "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",
        "acc_values = []\n",
        "writer = SummaryWriter()\n",
        "max_epochs = 200\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[\"image\"].to(device), batch_data[\"label\"].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",
        "          \n",
        "            model.eval()\n",
        "            with torch.no_grad():\n",
        "                y_pred = torch.tensor([], dtype=torch.float32, device=device)\n",
        "                y = torch.tensor([], dtype=torch.long, device=device)\n",
        "                for val_data in val_loader:\n",
        "                    val_images, val_labels = val_data[\"image\"].to(device), val_data[\"label\"].to(device)\n",
        "                    y_pred = torch.cat([y_pred, model(val_images)], dim=0)\n",
        "                    y = torch.cat([y, val_labels], dim=0)\n",
        "\n",
        "                acc_value = torch.eq(y_pred.argmax(dim=1), y)\n",
        "                acc_metric = acc_value.sum().item() / len(acc_value)\n",
        "                y_onehot = [post_label(i) for i in decollate_batch(y)]\n",
        "                y_pred_act = [post_pred(i) for i in decollate_batch(y_pred)]\n",
        "                auc_metric(y_pred_act, y_onehot)\n",
        "                auc_result = auc_metric.aggregate()\n",
        "                auc_metric.reset()\n",
        "                del y_pred_act, y_onehot\n",
        "                metric_values.append(auc_result)\n",
        "                \n",
        "\n",
        "                if acc_metric > best_metric:\n",
        "                    best_metric = acc_metric\n",
        "                    best_metric_epoch = epoch + 1\n",
        "                    \n",
        "                    torch.save(model.state_dict(),\"/content/drive/My Drive/Brain_AI/models/best_metric_model_classification3d_outcome_Dense200.pth\")\n",
        "                    print(\"saved new best metric model\")\n",
        "                print(\n",
        "                    \"current epoch: {} current accuracy: {:.4f} current AUC: {:.4f} best accuracy: {:.4f} at epoch {}\".format(\n",
        "                        epoch + 1, acc_metric, auc_result, best_metric, best_metric_epoch\n",
        "                    )\n",
        "                )\n",
        "                writer.add_scalar(\"val_accuracy\", acc_metric, epoch + 1)\n",
        "print(f\"train completed, best_metric: {best_metric:.4f} at epoch: {best_metric_epoch}\")\n",
        "writer.close()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MLNwxbkS2Nbl"
      },
      "source": [
        "plt.figure(\"train\", (12, 6))\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.title(\"Epoch Average Loss\")\n",
        "x = [i + 1 for i in range(len(epoch_loss_values))]\n",
        "y = epoch_loss_values\n",
        "plt.xlabel(\"epoch\")\n",
        "plt.plot(x, y)\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.title(\"Val AUC\")\n",
        "x = [val_interval * (i + 1) for i in range(len(metric_values))]\n",
        "y = metric_values\n",
        "plt.xlabel(\"epoch\")\n",
        "plt.plot(x, y)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZIsk3v3gKTrB"
      },
      "source": [
        "**Evaluation Metrics**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-LyP68KcHELg"
      },
      "source": [
        "from enum import Enum\n",
        "from sklearn.metrics import (\n",
        "    classification_report, confusion_matrix,\n",
        "    ConfusionMatrixDisplay\n",
        ")\n",
        "class Diagnosis(Enum):\n",
        "    Poor_outcome = 0\n",
        "    Good_outcome = 1\n",
        "    \n",
        "\n",
        "%matplotlib inline\n",
        "# Load best model\n",
        "model.load_state_dict(torch.load(\"/content/drive/My Drive/Brain_AI/models/best_metric_model_classification3d_outcome_Dense200.pth\"))\n",
        "model.to(device)\n",
        "model.eval()\n",
        "\n",
        "with torch.no_grad():\n",
        "    y_pred = torch.tensor([], dtype=torch.float32, device=device)\n",
        "    y = torch.tensor([], dtype=torch.long, device=device)\n",
        "\n",
        "    for test_data in test_loader:\n",
        "        val_images, val_labels = test_data[\"image\"].to(\n",
        "            device), test_data[\"label\"].to(device),\n",
        "\n",
        "        outputs = model(val_images)\n",
        "        y_pred = torch.cat([y_pred, outputs.argmax(dim=1)], dim=0)\n",
        "        y = torch.cat([y, val_labels], dim=0)\n",
        "\n",
        "print(classification_report(\n",
        "    y.cpu().numpy(),\n",
        "    y_pred.cpu().numpy(),\n",
        "    target_names=[d.name for d in Diagnosis]))\n",
        "\n",
        "cm = confusion_matrix (\n",
        "    y.cpu().numpy(),\n",
        "    y_pred.cpu().numpy(),\n",
        "    normalize='true',\n",
        ")\n",
        "cmm = ConfusionMatrixMetric (include_background=True, metric_name=[\"sensitivity\",\"specificity\",\"negative predictive value\"])\n",
        "\n",
        "disp = ConfusionMatrixDisplay(\n",
        "    confusion_matrix=cm,\n",
        "    display_labels=[d.name for d in Diagnosis],\n",
        ")\n",
        "disp.plot(ax=plt.subplots(1, 1, facecolor='white')[1])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nm0LXsvlgp_N"
      },
      "source": [
        "#files = [{\"img\": img} for img in images]\n",
        "#val_files  = [{\"image\": i, \"seg\": s, \"label\": l} for i, s, l in zip(images[120:], segmentations[120:], labels[120:])]\n",
        "\n",
        "post_transforms = Compose([\n",
        "        #Activationsd(keys=\"pred\", sigmoid=True),\n",
        "        #AsDiscreted(keys=\"pred\", threshold_values=True),\n",
        "        Invertd(\n",
        "            keys=\"y_pred\",  # invert the `pred` data field, also support multiple fields\n",
        "            transform=transforms,\n",
        "            #loader=val_loader,\n",
        "            orig_keys=\"files\",  # get the previously applied pre_transforms information on the `img` data field,\n",
        "                              # then invert `pred` based on this information. we can use same info\n",
        "                              # for multiple fields, also support different orig_keys for different fields\n",
        "            #meta_keys=\"pred_meta_dict\",  # key field to save inverted meta data, every item maps to `keys`\n",
        "            #orig_meta_keys=\"img_meta_dict\",  # get the meta data from `img_meta_dict` field when inverting,\n",
        "                                             # for example, may need the `affine` to invert `Spacingd` transform,\n",
        "                                             # multiple fields can use the same meta data to invert\n",
        "            #meta_key_postfix=\"meta_dict\",  # if `meta_keys=None`, use \"{keys}_{meta_key_postfix}\" as the meta key,\n",
        "                                           # if `orig_meta_keys=None`, use \"{orig_keys}_{meta_key_postfix}\",\n",
        "                                           # otherwise, no need this arg during inverting\n",
        "            nearest_interp=True,  # change to use \"nearest\" mode in interpolation when inverting\n",
        "            to_tensor=True,  # convert to PyTorch Tensor after inverting\n",
        "        ),\n",
        "        #SaveImaged(keys=\"pred\", meta_keys=\"pred_meta_dict\", output_dir=\"./out\", output_postfix=\"seg\", resample=False),\n",
        "    ])\n",
        "\n",
        "#test_ds = CacheDataset(data=data_partition[1], transform=transforms)\n",
        "\n",
        "test_loader = DataLoader(val_ds, batch_size=1, num_workers=4)\n",
        "itera = iter(test_loader)\n",
        "\n",
        "\n",
        "def get_next_im():\n",
        "    test_data = next(itera)\n",
        "    return test_data[\"image\"].to(device), test_data[\"label\"].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",
      "source": [
        "with torch.no_grad():\n",
        "    y_pred = torch.tensor([], dtype=torch.float32, device=device)\n",
        "    y = torch.tensor([], dtype=torch.long, device=device)\n",
        "\n",
        "    for test_data in test_loader:\n",
        "        val_images, val_labels = test_data[\"image\"].to(\n",
        "            device), test_data[\"label\"].to(device),\n",
        "\n",
        "        outputs = model(val_images)"
      ],
      "metadata": {
        "id": "nulf_hqfHGH6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wtn6kvE9tSZi"
      },
      "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=16)\n",
        "# Get the CAM\n",
        "cam = monai.visualize.GradCAM(\n",
        "    nn_module=model, target_layers=\"class_layers.relu\"\n",
        ")\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",
        "\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",
        "cam_result = cam(x=img, class_idx=None)\n",
        "cam_result = cam_result[..., depth_slice]\n",
        "\n",
        "\n",
        "fig, axes = plt.subplots(1, 3, figsize=(45, 15), facecolor='white')\n",
        "\n",
        "for i, im in enumerate([img[:, :, :, :, depth_slice], occ_result, cam_result]):\n",
        "    cmap = 'gray' if i == 0 else 'jet'\n",
        "    ax = axes[i]\n",
        "    if isinstance(im, torch.Tensor):\n",
        "        im = im.cpu().detach()    \n",
        "    #im_show = ax.imshow(im[0][0], cmap=cmap)\n",
        "    im_show = ax.imshow(np.squeeze(im[0][0]), cmap=cmap)\n",
        "    ax.axis('off')\n",
        "    fig.colorbar(im_show, ax=ax)\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pZ7pxMVV_SWt",
        "outputId": "6295b6cc-33fe-4173-c221-625b028b87cd"
      },
      "source": [
        "print(img.shape)\n",
        "print(occ_result.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([1, 1, 128, 128, 128])\n",
            "torch.Size([1, 1, 1, 128, 128])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yhK5F99UDgAz",
        "outputId": "a020093c-c976-456c-ec6a-beef2835b8bf"
      },
      "source": [
        "ret = blend_images(image=img, label=cam_result, alpha=0.5, cmap=\"hsv\", rescale_arrays=False)\n",
        "print(ret.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([3, 3, 128, 128, 128])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vnIXoWUcEhWq"
      },
      "source": [
        "for i in range(5, 10):\n",
        "    # plot the slice 50 - 100 of image, label and blend result\n",
        "    slice_index = 10 * i\n",
        "    plt.figure(\"blend image and label\", (12, 4))\n",
        "    plt.subplot(1, 3, 1)\n",
        "    plt.title(f\"image slice {slice_index}\")\n",
        "    plt.imshow(img[0, 0, :, :, slice_index].cpu().detach(), cmap=\"gray\")\n",
        "    plt.subplot(1, 3, 2)\n",
        "    plt.title(f\"label slice {slice_index}\")\n",
        "    plt.imshow(cam_result[0, 0, :, :, slice_index].cpu().detach())\n",
        "    plt.subplot(1, 3, 3)\n",
        "    plt.title(f\"blend slice {slice_index}\")\n",
        "    # switch the channel dim to the last dim\n",
        "    plt.imshow(torch.moveaxis(ret[0, 0, :, :, slice_index].cpu().detach(),1,1))\n",
        "    plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kXwf045wOV2N"
      },
      "source": [
        "from torch.utils.tensorboard import SummaryWriter\n",
        "from monai.visualize import plot_2d_or_3d_image\n",
        "with SummaryWriter(log_dir=\"logs\") as writer:\n",
        "    plot_2d_or_3d_image(img, step=0, writer=writer, tag=\"Input\")\n",
        "    plot_2d_or_3d_image(cam_result, step=0, writer=writer, tag=\"CAM\")\n",
        "    plot_2d_or_3d_image(occ_result, step=0, writer=writer, tag=\"OccSens\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nv3hzdFVO3Ce"
      },
      "source": [
        "%load_ext tensorboard\n",
        "%tensorboard --logdir logs\n"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}