chest_x_ray_analysis_using_resnet18.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Mathurshab2210/Ml_with_Mathur/blob/main/chest_x_ray_analysis_using_resnet18.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7e6aFtI4csh7"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2dDBxfDgoTmy"
      },
      "outputs": [],
      "source": [
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OhtwptpQczn2"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8nHROCJbGdm6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6862c523-b4d1-4859-c539-f9b678434954"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aQS8tSTdc2Rw"
      },
      "outputs": [],
      "source": [
        "# License: BSD\n",
        "# Author: Sasank Chilamkurthy\n",
        "\n",
        "from __future__ import print_function, division\n",
        "\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "from torch.optim import lr_scheduler\n",
        "import torch.backends.cudnn as cudnn\n",
        "import numpy as np\n",
        "import torchvision\n",
        "from torchvision import datasets, models, transforms\n",
        "import matplotlib.pyplot as plt\n",
        "import time\n",
        "import os\n",
        "import copy\n",
        "\n",
        "cudnn.benchmark = True\n",
        "plt.ion()   # interactive mode"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2PhuCWK4dCWZ"
      },
      "outputs": [],
      "source": [
        "# Data augmentation and normalization for training\n",
        "# Just normalization for validation\n",
        "data_transforms = {\n",
        "    'train': transforms.Compose([\n",
        "        transforms.RandomResizedCrop(224),\n",
        "        transforms.RandomHorizontalFlip(),\n",
        "        transforms.ToTensor(),\n",
        "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
        "    ]),\n",
        "    'val': transforms.Compose([\n",
        "        transforms.Resize(256),\n",
        "        transforms.CenterCrop(224),\n",
        "        transforms.ToTensor(),\n",
        "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
        "    ]),\n",
        "}\n",
        "\n",
        "data_dir = '/content/drive/MyDrive/chest_xray'\n",
        "image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),\n",
        "                                          data_transforms[x])\n",
        "                  for x in ['train', 'val']}\n",
        "dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=25,\n",
        "                                             shuffle=True, num_workers=4)\n",
        "              for x in ['train', 'val']}\n",
        "dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}\n",
        "class_names = image_datasets['train'].classes\n",
        "\n",
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eJZWyhTcdMD3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 124
        },
        "outputId": "bbbb5306-46d2-4da5-ed7e-20a7864f5475"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "def imshow(inp, title=None):\n",
        "    \"\"\"Imshow for Tensor.\"\"\"\n",
        "    inp = inp.numpy().transpose((1, 2, 0))\n",
        "    mean = np.array([0.485, 0.456, 0.406])\n",
        "    std = np.array([0.229, 0.224, 0.225])\n",
        "    inp = std * inp + mean\n",
        "    inp = np.clip(inp, 0, 1)\n",
        "    plt.imshow(inp)\n",
        "    if title is not None:\n",
        "        plt.title(title)\n",
        "    plt.pause(0.001)  # pause a bit so that plots are updated\n",
        "\n",
        "\n",
        "# Get a batch of training data\n",
        "inputs, classes = next(iter(dataloaders['train']))\n",
        "\n",
        "# Make a grid from batch\n",
        "out = torchvision.utils.make_grid(inputs)\n",
        "\n",
        "imshow(out, title=[class_names[x] for x in classes])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k5pDgKasiNbD"
      },
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "h2t6N0HCeNiZ"
      },
      "outputs": [],
      "source": [
        "def train_model(model, criterion, optimizer, scheduler, num_epochs=25):\n",
        "    since = time.time()\n",
        "\n",
        "    best_model_wts = copy.deepcopy(model.state_dict())\n",
        "    best_acc = 0.0\n",
        "\n",
        "    for epoch in range(num_epochs):\n",
        "        print(f'Epoch {epoch}/{num_epochs - 1}')\n",
        "        print('-' * 10)\n",
        "\n",
        "        # Each epoch has a training and validation phase\n",
        "        for phase in ['train', 'val']:\n",
        "            if phase == 'train':\n",
        "                model.train()  # Set model to training mode\n",
        "            else:\n",
        "                model.eval()   # Set model to evaluate mode\n",
        "\n",
        "            running_loss = 0.0\n",
        "            running_corrects = 0\n",
        "\n",
        "            # Iterate over data.\n",
        "            for inputs, labels in dataloaders[phase]:\n",
        "                inputs = inputs.to(device)\n",
        "                labels = labels.to(device)\n",
        "\n",
        "                # zero the parameter gradients\n",
        "                optimizer.zero_grad()\n",
        "\n",
        "                # forward\n",
        "                # track history if only in train\n",
        "                with torch.set_grad_enabled(phase == 'train'):\n",
        "                    outputs = model(inputs)\n",
        "                    _, preds = torch.max(outputs, 1)\n",
        "                    loss = criterion(outputs, labels)\n",
        "\n",
        "                    # backward + optimize only if in training phase\n",
        "                    if phase == 'train':\n",
        "                        loss.backward()\n",
        "                        optimizer.step()\n",
        "\n",
        "                # statistics\n",
        "                running_loss += loss.item() * inputs.size(0)\n",
        "                running_corrects += torch.sum(preds == labels.data)\n",
        "            if phase == 'train':\n",
        "                scheduler.step()\n",
        "\n",
        "            epoch_loss = running_loss / dataset_sizes[phase]\n",
        "            epoch_acc = running_corrects.double() / dataset_sizes[phase]\n",
        "\n",
        "            print(f'{phase} Loss: {epoch_loss:.4f} Acc: {epoch_acc:.4f}')\n",
        "\n",
        "            # deep copy the model\n",
        "            if phase == 'val' and epoch_acc > best_acc:\n",
        "                best_acc = epoch_acc\n",
        "                best_model_wts = copy.deepcopy(model.state_dict())\n",
        "\n",
        "        print()\n",
        "\n",
        "    time_elapsed = time.time() - since\n",
        "    print(f'Training complete in {time_elapsed // 60:.0f}m {time_elapsed % 60:.0f}s')\n",
        "    print(f'Best val Acc: {best_acc:4f}')\n",
        "\n",
        "    # load best model weights\n",
        "    model.load_state_dict(best_model_wts)\n",
        "    return model"
      ]
    },
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      "execution_count": null,
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        "id": "TuOo7rGyemAm"
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      "source": [
        "def visualize_model(model, num_images=6):\n",
        "    was_training = model.training\n",
        "    model.eval()\n",
        "    images_so_far = 0\n",
        "    fig = plt.figure()\n",
        "\n",
        "    with torch.no_grad():\n",
        "        for i, (inputs, labels) in enumerate(dataloaders['val']):\n",
        "            inputs = inputs.to(device)\n",
        "            labels = labels.to(device)\n",
        "\n",
        "            outputs = model(inputs)\n",
        "            _, preds = torch.max(outputs, 1)\n",
        "\n",
        "            for j in range(inputs.size()[0]):\n",
        "                images_so_far += 1\n",
        "                ax = plt.subplot(num_images//2, 2, images_so_far)\n",
        "                ax.axis('off')\n",
        "                ax.set_title(f'predicted: {class_names[preds[j]]}')\n",
        "                imshow(inputs.cpu().data[j])\n",
        "\n",
        "                if images_so_far == num_images:\n",
        "                    model.train(mode=was_training)\n",
        "                    return\n",
        "        model.train(mode=was_training)"
      ]
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          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torchvision/models/_utils.py:209: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and will be removed in 0.15, please use 'weights' instead.\n",
            "  f\"The parameter '{pretrained_param}' is deprecated since 0.13 and will be removed in 0.15, \"\n",
            "/usr/local/lib/python3.7/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and will be removed in 0.15. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.\n",
            "  warnings.warn(msg)\n",
            "Downloading: \"https://download.pytorch.org/models/resnet18-f37072fd.pth\" to /root/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth\n"
          ]
        },
        {
          "output_type": "display_data",
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            "text/plain": [
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      "source": [
        "model_ft = models.resnet18(pretrained=True)\n",
        "num_ftrs = model_ft.fc.in_features\n",
        "# Here the size of each output sample is set to 2.\n",
        "# Alternatively, it can be generalized to nn.Linear(num_ftrs, len(class_names)).\n",
        "model_ft.fc = nn.Linear(num_ftrs, 2)\n",
        "\n",
        "model_ft = model_ft.to(device)\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "\n",
        "# Observe that all parameters are being optimized\n",
        "optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)\n",
        "\n",
        "# Decay LR by a factor of 0.1 every 7 epochs\n",
        "exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=3, gamma=0.1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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        },
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        "outputId": "b8f19f86-032a-4ea6-b22b-cf7aa07ba996"
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          "text": [
            "Epoch 0/19\n",
            "----------\n",
            "train Loss: 0.2755 Acc: 0.8831\n",
            "val Loss: 0.2002 Acc: 0.9151\n",
            "\n",
            "Epoch 1/19\n",
            "----------\n",
            "train Loss: 0.1729 Acc: 0.9308\n",
            "val Loss: 0.1589 Acc: 0.9423\n",
            "\n",
            "Epoch 2/19\n",
            "----------\n",
            "train Loss: 0.1748 Acc: 0.9316\n",
            "val Loss: 0.1470 Acc: 0.9487\n",
            "\n",
            "Epoch 3/19\n",
            "----------\n",
            "train Loss: 0.1453 Acc: 0.9453\n",
            "val Loss: 0.1145 Acc: 0.9551\n",
            "\n",
            "Epoch 4/19\n",
            "----------\n",
            "train Loss: 0.1348 Acc: 0.9491\n",
            "val Loss: 0.1181 Acc: 0.9503\n",
            "\n",
            "Epoch 5/19\n",
            "----------\n",
            "train Loss: 0.1395 Acc: 0.9489\n",
            "val Loss: 0.1307 Acc: 0.9487\n",
            "\n",
            "Epoch 6/19\n",
            "----------\n",
            "train Loss: 0.1342 Acc: 0.9460\n",
            "val Loss: 0.1457 Acc: 0.9423\n",
            "\n",
            "Epoch 7/19\n",
            "----------\n",
            "train Loss: 0.1351 Acc: 0.9498\n",
            "val Loss: 0.1490 Acc: 0.9423\n",
            "\n",
            "Epoch 8/19\n",
            "----------\n",
            "train Loss: 0.1252 Acc: 0.9515\n",
            "val Loss: 0.1144 Acc: 0.9551\n",
            "\n",
            "Epoch 9/19\n",
            "----------\n",
            "train Loss: 0.1323 Acc: 0.9496\n",
            "val Loss: 0.1345 Acc: 0.9471\n",
            "\n",
            "Epoch 10/19\n",
            "----------\n",
            "train Loss: 0.1292 Acc: 0.9517\n",
            "val Loss: 0.1131 Acc: 0.9567\n",
            "\n",
            "Epoch 11/19\n",
            "----------\n",
            "train Loss: 0.1271 Acc: 0.9580\n",
            "val Loss: 0.1208 Acc: 0.9503\n",
            "\n",
            "Epoch 12/19\n",
            "----------\n",
            "train Loss: 0.1340 Acc: 0.9493\n",
            "val Loss: 0.1387 Acc: 0.9423\n",
            "\n",
            "Epoch 13/19\n",
            "----------\n",
            "train Loss: 0.1282 Acc: 0.9502\n",
            "val Loss: 0.1275 Acc: 0.9471\n",
            "\n",
            "Epoch 14/19\n",
            "----------\n",
            "train Loss: 0.1291 Acc: 0.9544\n",
            "val Loss: 0.1094 Acc: 0.9599\n",
            "\n",
            "Epoch 15/19\n",
            "----------\n",
            "train Loss: 0.1327 Acc: 0.9510\n",
            "val Loss: 0.1203 Acc: 0.9503\n",
            "\n",
            "Epoch 16/19\n",
            "----------\n",
            "train Loss: 0.1286 Acc: 0.9532\n",
            "val Loss: 0.1221 Acc: 0.9503\n",
            "\n",
            "Epoch 17/19\n",
            "----------\n",
            "train Loss: 0.1288 Acc: 0.9498\n",
            "val Loss: 0.1104 Acc: 0.9583\n",
            "\n",
            "Epoch 18/19\n",
            "----------\n",
            "train Loss: 0.1240 Acc: 0.9519\n",
            "val Loss: 0.1300 Acc: 0.9455\n",
            "\n",
            "Epoch 19/19\n",
            "----------\n",
            "train Loss: 0.1230 Acc: 0.9529\n",
            "val Loss: 0.1286 Acc: 0.9487\n",
            "\n",
            "Training complete in 394m 41s\n",
            "Best val Acc: 0.959936\n"
          ]
        }
      ],
      "source": [
        "model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler,\n",
        "                       num_epochs=20)"
      ]
    },
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          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-2-8cd91bb375fa>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mvisualize_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_images\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'visualize_model' is not defined"
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        }
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      "source": [
        "#link to dataaset\n",
        "#https://www.kaggle.com/datasets/tolgadincer/labeled-chest-xray-images"
      ]
    },
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