2d_classification/mednist_tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Medical Image Classification Tutorial with the MedNIST Dataset\n",
    "\n",
    "In this tutorial, we introduce an end-to-end training and evaluation example based on the MedNIST dataset.\n",
    "\n",
    "We'll go through the following steps:\n",
    "* Create a dataset for training and testing\n",
    "* Use MONAI transforms to pre-process data\n",
    "* Use the DenseNet from MONAI for classification\n",
    "* Train the model with a PyTorch program\n",
    "* Evaluate on test dataset\n",
    "\n",
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Project-MONAI/tutorials/blob/master/2d_classification/mednist_tutorial.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "!python -c \"import monai\" || pip install -q \"monai-weekly[pillow, tqdm]\"\n",
    "!python -c \"import matplotlib\" || pip install -q matplotlib\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MONAI version: 0.6.0rc1+15.gf3d436a0\n",
      "Numpy version: 1.20.3\n",
      "Pytorch version: 1.9.0a0+c3d40fd\n",
      "MONAI flags: HAS_EXT = True, USE_COMPILED = False\n",
      "MONAI rev id: f3d436a09deefcf905ece2faeec37f55ab030003\n",
      "\n",
      "Optional dependencies:\n",
      "Pytorch Ignite version: 0.4.5\n",
      "Nibabel version: 3.2.1\n",
      "scikit-image version: 0.15.0\n",
      "Pillow version: 8.2.0\n",
      "Tensorboard version: 2.5.0\n",
      "gdown version: 3.13.0\n",
      "TorchVision version: 0.10.0a0\n",
      "ITK version: 5.1.2\n",
      "tqdm version: 4.53.0\n",
      "lmdb version: 1.2.1\n",
      "psutil version: 5.8.0\n",
      "pandas version: 1.1.4\n",
      "einops version: 0.3.0\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"
     ]
    }
   ],
   "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 os\n",
    "import shutil\n",
    "import tempfile\n",
    "import matplotlib.pyplot as plt\n",
    "import PIL\n",
    "import torch\n",
    "import numpy as np\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "from monai.apps import download_and_extract\n",
    "from monai.config import print_config\n",
    "from monai.data import decollate_batch\n",
    "from monai.metrics import ROCAUCMetric\n",
    "from monai.networks.nets import DenseNet121\n",
    "from monai.transforms import (\n",
    "    Activations,\n",
    "    AddChannel,\n",
    "    AsDiscrete,\n",
    "    Compose,\n",
    "    LoadImage,\n",
    "    RandFlip,\n",
    "    RandRotate,\n",
    "    RandZoom,\n",
    "    ScaleIntensity,\n",
    "    EnsureType,\n",
    ")\n",
    "from monai.utils import set_determinism\n",
    "\n",
    "print_config()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup data directory\n",
    "\n",
    "You can specify a directory with the `MONAI_DATA_DIRECTORY` environment variable.  \n",
    "This allows you to save results and reuse downloads.  \n",
    "If not specified a temporary directory will be used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/workspace/data/medical\n"
     ]
    }
   ],
   "source": [
    "directory = os.environ.get(\"MONAI_DATA_DIRECTORY\")\n",
    "root_dir = tempfile.mkdtemp() if directory is None else directory\n",
    "print(root_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download dataset\n",
    "\n",
    "The MedNIST dataset was gathered from several sets from [TCIA](https://wiki.cancerimagingarchive.net/display/Public/Data+Usage+Policies+and+Restrictions),\n",
    "[the RSNA Bone Age Challenge](http://rsnachallenges.cloudapp.net/competitions/4),\n",
    "and [the NIH Chest X-ray dataset](https://cloud.google.com/healthcare/docs/resources/public-datasets/nih-chest).\n",
    "\n",
    "The dataset is kindly made available by [Dr. Bradley J. Erickson M.D., Ph.D.](https://www.mayo.edu/research/labs/radiology-informatics/overview) (Department of Radiology, Mayo Clinic)\n",
    "under the Creative Commons [CC BY-SA 4.0 license](https://creativecommons.org/licenses/by-sa/4.0/).\n",
    "\n",
    "If you use the MedNIST dataset, please acknowledge the source."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "resource = \"https://drive.google.com/uc?id=1QsnnkvZyJPcbRoV_ArW8SnE1OTuoVbKE\"\n",
    "md5 = \"0bc7306e7427e00ad1c5526a6677552d\"\n",
    "\n",
    "compressed_file = os.path.join(root_dir, \"MedNIST.tar.gz\")\n",
    "data_dir = os.path.join(root_dir, \"MedNIST\")\n",
    "if not os.path.exists(data_dir):\n",
    "    download_and_extract(resource, compressed_file, root_dir, md5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set deterministic training for reproducibility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "set_determinism(seed=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read image filenames from the dataset folders\n",
    "\n",
    "First of all, check the dataset files and show some statistics.  \n",
    "There are 6 folders in the dataset: Hand, AbdomenCT, CXR, ChestCT, BreastMRI, HeadCT,  \n",
    "which should be used as the labels to train our classification model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total image count: 58954\n",
      "Image dimensions: 64 x 64\n",
      "Label names: ['AbdomenCT', 'BreastMRI', 'CXR', 'ChestCT', 'Hand', 'HeadCT']\n",
      "Label counts: [10000, 8954, 10000, 10000, 10000, 10000]\n"
     ]
    }
   ],
   "source": [
    "class_names = sorted(x for x in os.listdir(data_dir)\n",
    "                     if os.path.isdir(os.path.join(data_dir, x)))\n",
    "num_class = len(class_names)\n",
    "image_files = [\n",
    "    [\n",
    "        os.path.join(data_dir, class_names[i], x)\n",
    "        for x in os.listdir(os.path.join(data_dir, class_names[i]))\n",
    "    ]\n",
    "    for i in range(num_class)\n",
    "]\n",
    "num_each = [len(image_files[i]) for i in range(num_class)]\n",
    "image_files_list = []\n",
    "image_class = []\n",
    "for i in range(num_class):\n",
    "    image_files_list.extend(image_files[i])\n",
    "    image_class.extend([i] * num_each[i])\n",
    "num_total = len(image_class)\n",
    "image_width, image_height = PIL.Image.open(image_files_list[0]).size\n",
    "\n",
    "print(f\"Total image count: {num_total}\")\n",
    "print(f\"Image dimensions: {image_width} x {image_height}\")\n",
    "print(f\"Label names: {class_names}\")\n",
    "print(f\"Label counts: {num_each}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Randomly pick images from the dataset to visualize and check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x576 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(3, 3, figsize=(8, 8))\n",
    "for i, k in enumerate(np.random.randint(num_total, size=9)):\n",
    "    im = PIL.Image.open(image_files_list[k])\n",
    "    arr = np.array(im)\n",
    "    plt.subplot(3, 3, i + 1)\n",
    "    plt.xlabel(class_names[image_class[k]])\n",
    "    plt.imshow(arr, cmap=\"gray\", vmin=0, vmax=255)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare training, validation and test data lists\n",
    "\n",
    "Randomly select 10% of the dataset as validation and 10% as test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training count: 47156, Validation count: 5913, Test count: 5885\n"
     ]
    }
   ],
   "source": [
    "val_frac = 0.1\n",
    "test_frac = 0.1\n",
    "length = len(image_files_list)\n",
    "indices = np.arange(length)\n",
    "np.random.shuffle(indices)\n",
    "\n",
    "test_split = int(test_frac * length)\n",
    "val_split = int(val_frac * length) + test_split\n",
    "test_indices = indices[:test_split]\n",
    "val_indices = indices[test_split:val_split]\n",
    "train_indices = indices[val_split:]\n",
    "\n",
    "train_x = [image_files_list[i] for i in train_indices]\n",
    "train_y = [image_class[i] for i in train_indices]\n",
    "val_x = [image_files_list[i] for i in val_indices]\n",
    "val_y = [image_class[i] for i in val_indices]\n",
    "test_x = [image_files_list[i] for i in test_indices]\n",
    "test_y = [image_class[i] for i in test_indices]\n",
    "\n",
    "print(\n",
    "    f\"Training count: {len(train_x)}, Validation count: \"\n",
    "    f\"{len(val_x)}, Test count: {len(test_x)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define MONAI transforms, Dataset and Dataloader to pre-process data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_transforms = Compose(\n",
    "    [\n",
    "        LoadImage(image_only=True),\n",
    "        AddChannel(),\n",
    "        ScaleIntensity(),\n",
    "        RandRotate(range_x=np.pi / 12, prob=0.5, keep_size=True),\n",
    "        RandFlip(spatial_axis=0, prob=0.5),\n",
    "        RandZoom(min_zoom=0.9, max_zoom=1.1, prob=0.5),\n",
    "        EnsureType(),\n",
    "    ]\n",
    ")\n",
    "\n",
    "val_transforms = Compose(\n",
    "    [LoadImage(image_only=True), AddChannel(), ScaleIntensity(), EnsureType()])\n",
    "\n",
    "y_pred_trans = Compose([EnsureType(), Activations(softmax=True)])\n",
    "y_trans = Compose([EnsureType(), AsDiscrete(to_onehot=True, n_classes=num_class)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MedNISTDataset(torch.utils.data.Dataset):\n",
    "    def __init__(self, image_files, labels, transforms):\n",
    "        self.image_files = image_files\n",
    "        self.labels = labels\n",
    "        self.transforms = transforms\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.image_files)\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        return self.transforms(self.image_files[index]), self.labels[index]\n",
    "\n",
    "\n",
    "train_ds = MedNISTDataset(train_x, train_y, train_transforms)\n",
    "train_loader = torch.utils.data.DataLoader(\n",
    "    train_ds, batch_size=300, shuffle=True, num_workers=10)\n",
    "\n",
    "val_ds = MedNISTDataset(val_x, val_y, val_transforms)\n",
    "val_loader = torch.utils.data.DataLoader(\n",
    "    val_ds, batch_size=300, num_workers=10)\n",
    "\n",
    "test_ds = MedNISTDataset(test_x, test_y, val_transforms)\n",
    "test_loader = torch.utils.data.DataLoader(\n",
    "    test_ds, batch_size=300, num_workers=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define network and optimizer\n",
    "\n",
    "1. Set learning rate for how much the model is updated per batch.\n",
    "1. Set total epoch number, as we have shuffle and random transforms, so the training data of every epoch is different.  \n",
    "And as this is just a get start tutorial, let's just train 4 epochs.  \n",
    "If train 10 epochs, the model can achieve 100% accuracy on test dataset. \n",
    "1. Use DenseNet from MONAI and move to GPU devide, this DenseNet can support both 2D and 3D classification tasks.\n",
    "1. Use Adam optimizer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = torch.device(\"cuda:0\")\n",
    "model = DenseNet121(spatial_dims=2, in_channels=1,\n",
    "                    out_channels=num_class).to(device)\n",
    "loss_function = torch.nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.Adam(model.parameters(), 1e-5)\n",
    "max_epochs = 4\n",
    "val_interval = 1\n",
    "auc_metric = ROCAUCMetric()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model training\n",
    "\n",
    "Execute a typical PyTorch training that run epoch loop and step loop, and do validation after every epoch.  \n",
    "Will save the model weights to file if got best validation accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------\n",
      "epoch 1/4\n",
      "1/157, train_loss: 1.7898\n",
      "2/157, train_loss: 1.7560\n",
      "3/157, train_loss: 1.7461\n",
      "4/157, train_loss: 1.7241\n",
      "5/157, train_loss: 1.6973\n",
      "6/157, train_loss: 1.6706\n",
      "7/157, train_loss: 1.6388\n",
      "8/157, train_loss: 1.6210\n",
      "9/157, train_loss: 1.5989\n",
      "10/157, train_loss: 1.5599\n",
      "11/157, train_loss: 1.5826\n",
      "12/157, train_loss: 1.5339\n",
      "13/157, train_loss: 1.5235\n",
      "14/157, train_loss: 1.5098\n",
      "15/157, train_loss: 1.4746\n",
      "16/157, train_loss: 1.4584\n",
      "17/157, train_loss: 1.4365\n",
      "18/157, train_loss: 1.4328\n",
      "19/157, train_loss: 1.4274\n",
      "20/157, train_loss: 1.4327\n",
      "21/157, train_loss: 1.4017\n",
      "22/157, train_loss: 1.3231\n",
      "23/157, train_loss: 1.3180\n",
      "24/157, train_loss: 1.3001\n",
      "25/157, train_loss: 1.2958\n",
      "26/157, train_loss: 1.3021\n",
      "27/157, train_loss: 1.2336\n",
      "28/157, train_loss: 1.2154\n",
      "29/157, train_loss: 1.2595\n",
      "30/157, train_loss: 1.2050\n",
      "31/157, train_loss: 1.2161\n",
      "32/157, train_loss: 1.2106\n",
      "33/157, train_loss: 1.1495\n",
      "34/157, train_loss: 1.1550\n",
      "35/157, train_loss: 1.1246\n",
      "36/157, train_loss: 1.1607\n",
      "37/157, train_loss: 1.1126\n",
      "38/157, train_loss: 1.0987\n",
      "39/157, train_loss: 1.0694\n",
      "40/157, train_loss: 1.1181\n",
      "41/157, train_loss: 1.0576\n",
      "42/157, train_loss: 1.0703\n",
      "43/157, train_loss: 1.0414\n",
      "44/157, train_loss: 1.0446\n",
      "45/157, train_loss: 1.0313\n",
      "46/157, train_loss: 0.9786\n",
      "47/157, train_loss: 0.9767\n",
      "48/157, train_loss: 0.9579\n",
      "49/157, train_loss: 0.9659\n",
      "50/157, train_loss: 1.0069\n",
      "51/157, train_loss: 0.9868\n",
      "52/157, train_loss: 0.9637\n",
      "53/157, train_loss: 0.9301\n",
      "54/157, train_loss: 0.9382\n",
      "55/157, train_loss: 0.8923\n",
      "56/157, train_loss: 0.9034\n",
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      "epoch 1 average loss: 0.7768\n",
      "saved new best metric model\n",
      "current epoch: 1 current AUC: 0.9984 current accuracy: 0.9618 best AUC: 0.9984 at epoch: 1\n",
      "----------\n",
      "epoch 2/4\n",
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    {
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     "output_type": "stream",
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      "epoch 2 average loss: 0.1612\n",
      "saved new best metric model\n",
      "current epoch: 2 current AUC: 0.9997 current accuracy: 0.9863 best AUC: 0.9997 at epoch: 2\n",
      "----------\n",
      "epoch 3/4\n",
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      "epoch 3 average loss: 0.0743\n",
      "saved new best metric model\n",
      "current epoch: 3 current AUC: 0.9999 current accuracy: 0.9924 best AUC: 0.9999 at epoch: 3\n",
      "----------\n",
      "epoch 4/4\n",
      "1/157, train_loss: 0.0563\n",
      "2/157, train_loss: 0.0645\n",
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      "110/157, train_loss: 0.0486\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "111/157, train_loss: 0.0538\n",
      "112/157, train_loss: 0.0290\n",
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      "156/157, train_loss: 0.0478\n",
      "157/157, train_loss: 0.0295\n",
      "158/157, train_loss: 0.1089\n",
      "epoch 4 average loss: 0.0462\n",
      "saved new best metric model\n",
      "current epoch: 4 current AUC: 1.0000 current accuracy: 0.9944 best AUC: 1.0000 at epoch: 4\n",
      "train completed, best_metric: 1.0000 at epoch: 4\n"
     ]
    }
   ],
   "source": [
    "best_metric = -1\n",
    "best_metric_epoch = -1\n",
    "epoch_loss_values = []\n",
    "metric_values = []\n",
    "\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",
    "        print(\n",
    "            f\"{step}/{len(train_ds) // train_loader.batch_size}, \"\n",
    "            f\"train_loss: {loss.item():.4f}\")\n",
    "        epoch_len = len(train_ds) // train_loader.batch_size\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",
    "            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 = (\n",
    "                    val_data[0].to(device),\n",
    "                    val_data[1].to(device),\n",
    "                )\n",
    "                y_pred = torch.cat([y_pred, model(val_images)], dim=0)\n",
    "                y = torch.cat([y, val_labels], dim=0)\n",
    "            y_onehot = [y_trans(i) for i in decollate_batch(y)]\n",
    "            y_pred_act = [y_pred_trans(i) for i in decollate_batch(y_pred)]\n",
    "            auc_metric(y_pred_act, y_onehot)\n",
    "            result = auc_metric.aggregate()\n",
    "            auc_metric.reset()\n",
    "            del y_pred_act, y_onehot\n",
    "            metric_values.append(result)\n",
    "            acc_value = torch.eq(y_pred.argmax(dim=1), y)\n",
    "            acc_metric = acc_value.sum().item() / len(acc_value)\n",
    "            if result > best_metric:\n",
    "                best_metric = result\n",
    "                best_metric_epoch = epoch + 1\n",
    "                torch.save(model.state_dict(), os.path.join(\n",
    "                    root_dir, \"best_metric_model.pth\"))\n",
    "                print(\"saved new best metric model\")\n",
    "            print(\n",
    "                f\"current epoch: {epoch + 1} current AUC: {result:.4f}\"\n",
    "                f\" current accuracy: {acc_metric:.4f}\"\n",
    "                f\" best AUC: {best_metric:.4f}\"\n",
    "                f\" at epoch: {best_metric_epoch}\"\n",
    "            )\n",
    "\n",
    "print(\n",
    "    f\"train completed, best_metric: {best_metric:.4f} \"\n",
    "    f\"at epoch: {best_metric_epoch}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot the loss and metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "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",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate the model on test dataset\n",
    "\n",
    "After training and validation, we already got the best model on validation test.  \n",
    "We need to evaluate the model on test dataset to check whether it's robust and not over-fitting.  \n",
    "We'll use these predictions to generate a classification report."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.load_state_dict(torch.load(\n",
    "    os.path.join(root_dir, \"best_metric_model.pth\")))\n",
    "model.eval()\n",
    "y_true = []\n",
    "y_pred = []\n",
    "with torch.no_grad():\n",
    "    for test_data in test_loader:\n",
    "        test_images, test_labels = (\n",
    "            test_data[0].to(device),\n",
    "            test_data[1].to(device),\n",
    "        )\n",
    "        pred = model(test_images).argmax(dim=1)\n",
    "        for i in range(len(pred)):\n",
    "            y_true.append(test_labels[i].item())\n",
    "            y_pred.append(pred[i].item())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note: you may need to restart the kernel to use updated packages.\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "   AbdomenCT     0.9816    0.9917    0.9867       969\n",
      "   BreastMRI     0.9968    0.9831    0.9899       944\n",
      "         CXR     0.9979    0.9928    0.9954       973\n",
      "     ChestCT     0.9938    0.9990    0.9964       959\n",
      "        Hand     0.9934    0.9934    0.9934      1055\n",
      "      HeadCT     0.9960    0.9990    0.9975       985\n",
      "\n",
      "    accuracy                         0.9932      5885\n",
      "   macro avg     0.9932    0.9932    0.9932      5885\n",
      "weighted avg     0.9932    0.9932    0.9932      5885\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(\n",
    "    y_true, y_pred, target_names=class_names, digits=4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleanup data directory\n",
    "\n",
    "Remove directory if a temporary was used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "if directory is None:\n",
    "    shutil.rmtree(root_dir)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}