Covid_19_classification_pytorch.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-2qdikr7YATM",
        "colab_type": "text"
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      "source": [
        "### The following notebook demonstrates classification of covid-19 chest radiographs from bacterial pneumonia, viral pneumonia and normal cases.\n",
        "### The dataset used is [COVID-19 Detection X-Ray Dataset](https://www.kaggle.com/darshan1504/covid19-detection-xray-dataset) available on Kaggle."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z5GayDjKY529",
        "colab_type": "text"
      },
      "source": [
        "### Setup kaggle api to download dataset from kaggle\n",
        "1.   Create account on kaggle / login to kaggle account.\n",
        "2.   Go to **your account**, **api** section and create a api token. \n",
        "This will download kaggle.json file to local machine.\n",
        "3.   Upload kaggle.json here.\n",
        "4. Install kaggle\n",
        "4. Copy api command from kaggle dataset and execute command\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tq0IKTqOYjtJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from google.colab import files\n",
        "files.upload()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wPCnDJJQaeaF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!mkdir -p ~/.kaggle"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gInkcx_lapa2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!cp kaggle.json ~/.kaggle/"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9Rb3dfpiatcR",
        "colab_type": "code",
        "outputId": "58c40ea6-96f8-4990-fcce-0aa94ac5f354",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "!ls ~/.kaggle"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "kaggle.json\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qfk6l7dWawwh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!chmod 600 /root/.kaggle/kaggle.json"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "268ifo4eazhx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "! pip install -q kaggle"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ewxv9ed9bQ7E",
        "colab_type": "code",
        "outputId": "6c0b0872-73c8-462b-fee9-71d14f015011",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        }
      },
      "source": [
        "!kaggle datasets download -d darshan1504/covid19-detection-xray-dataset"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading covid19-detection-xray-dataset.zip to /content\n",
            " 97% 181M/186M [00:05<00:00, 32.8MB/s]\n",
            "100% 186M/186M [00:05<00:00, 38.5MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vYmh0fE5b_5T",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "! mkdir covid19-detection-xray-dataset"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qmavGVPPcKa-",
        "colab_type": "code",
        "outputId": "7e1ea824-c340-4fc2-c5d4-d976be71c8f2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "cd /content/covid19-detection-xray-dataset/"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/covid19-detection-xray-dataset\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LdaOBPFhbYiL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!unzip /content/covid19-detection-xray-dataset.zip"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m5BJwgrRbn5o",
        "colab_type": "text"
      },
      "source": [
        "### Import required libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JSU6JCXobmtG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim \n",
        "import numpy as np\n",
        "import torchvision\n",
        "from torchvision import datasets, models, transforms\n",
        "import matplotlib.pyplot as plt\n",
        "from torch.optim import lr_scheduler\n",
        "import time\n",
        "import os\n",
        "import copy"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yOaYpNPTcrzP",
        "colab_type": "text"
      },
      "source": [
        "### Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MwpQNcDfbyeu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data_transforms = {\n",
        "    'NonAugmentedTrain': 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",
        "    'ValData': 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/covid19-detection-xray-dataset/'\n",
        "image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),\n",
        "                                          data_transforms[x])\n",
        "                  for x in ['NonAugmentedTrain', 'ValData']}\n",
        "dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=64,\n",
        "                                             shuffle=True, num_workers=4)\n",
        "              for x in ['NonAugmentedTrain', 'ValData']}\n",
        "dataset_sizes = {x: len(image_datasets[x]) for x in ['NonAugmentedTrain', 'ValData']}\n",
        "class_names = image_datasets['NonAugmentedTrain'].classes\n",
        "\n",
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "id": "0HAP6h1Id9CT",
        "colab_type": "code",
        "outputId": "97f60f93-59a9-4d57-9f24-d6c3c3fc27d1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "device"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "device(type='cuda', index=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wZZUoDZVeBZa",
        "colab_type": "code",
        "outputId": "5a5cd372-ac69-4944-feec-ffe849968309",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "dataset_sizes"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'NonAugmentedTrain': 2002, 'ValData': 988}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "746INLYSeCni",
        "colab_type": "code",
        "outputId": "9693a6f0-35c9-43bb-d210-3d1e38328f0f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "class_names"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['BacterialPneumonia', 'COVID-19', 'Normal', 'ViralPneumonia']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pfZX67qReIPH",
        "colab_type": "text"
      },
      "source": [
        "### Check a batch of images"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qeUmiRALeD6z",
        "colab_type": "code",
        "outputId": "4150cc4a-9b8b-4cbf-b045-aa709cb7989d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 91
        }
      },
      "source": [
        "def imshow(inp, title=None):\n",
        "    \"\"\"Imshow for Tensor.\"\"\"\n",
        "    inp = inp.cpu().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['NonAugmentedTrain']))\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])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mSsx9k-feQQL",
        "colab_type": "text"
      },
      "source": [
        "### Train script"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TC9HB2jOePY1",
        "colab_type": "code",
        "colab": {}
      },
      "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('Epoch {}/{}'.format(epoch, num_epochs - 1))\n",
        "        print('-' * 10)\n",
        "\n",
        "        # Each epoch has a training and validation phase\n",
        "        for phase in ['NonAugmentedTrain', 'ValData']:\n",
        "            if phase == 'NonAugmentedTrain':\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 == 'NonAugmentedTrain'):\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 == 'NonAugmentedTrain':\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 == 'NonAugmentedTrain':\n",
        "                scheduler.step()\n",
        "\n",
        "            epoch_loss = running_loss / dataset_sizes[phase]\n",
        "            epoch_acc = running_corrects.double() / dataset_sizes[phase]\n",
        "\n",
        "            print('{} Loss: {:.4f} Acc: {:.4f}'.format(\n",
        "                phase, epoch_loss, epoch_acc))\n",
        "\n",
        "            # deep copy the model\n",
        "            if phase == 'ValData' 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('Training complete in {:.0f}m {:.0f}s'.format(\n",
        "        time_elapsed // 60, time_elapsed % 60))\n",
        "    print('Best val Acc: {:4f}'.format(best_acc))\n",
        "\n",
        "    # load best model weights\n",
        "    model.load_state_dict(best_model_wts)\n",
        "    return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "grfvLM92emks",
        "colab_type": "code",
        "colab": {}
      },
      "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['ValData']):\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('predicted: {}'.format(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)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k9EliUoEegG4",
        "colab_type": "text"
      },
      "source": [
        "### Resnet18"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I0hdbg_jeer_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model_resnet18 = models.resnet18(pretrained=True)\n",
        "num_ftrs_rsn18 = model_resnet18.fc.in_features\n",
        "\n",
        "model_resnet18.fc = nn.Linear(num_ftrs_rsn18, len(class_names))\n",
        "\n",
        "model_resnet18 = model_resnet18.to(device)\n",
        "\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "\n",
        "# Observe that all parameters are being optimized\n",
        "optimizer_ft = optim.Adam(model_resnet18.parameters(), lr=0.001, betas=(0.9, 0.999), eps=1e-08, weight_decay=0, amsgrad=False)\n",
        "\n",
        "# Decay LR by a factor of 0.1 every 7 epochs\n",
        "exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RCmBG3G6gPZ9",
        "colab_type": "code",
        "outputId": "c75ed21e-c39c-4bbe-86d3-bef1033b67e7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 920
        }
      },
      "source": [
        "model_resnet18 = train_model(model_resnet18, criterion, optimizer_ft, exp_lr_scheduler,\n",
        "                       num_epochs=10)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 0/9\n",
            "----------\n",
            "NonAugmentedTrain Loss: 0.7969 Acc: 0.6733\n",
            "ValData Loss: 2.5082 Acc: 0.7126\n",
            "\n",
            "Epoch 1/9\n",
            "----------\n",
            "NonAugmentedTrain Loss: 0.5919 Acc: 0.7542\n",
            "ValData Loss: 0.7197 Acc: 0.6619\n",
            "\n",
            "Epoch 2/9\n",
            "----------\n",
            "NonAugmentedTrain Loss: 0.5647 Acc: 0.7592\n",
            "ValData Loss: 0.6912 Acc: 0.6781\n",
            "\n",
            "Epoch 3/9\n",
            "----------\n",
            "NonAugmentedTrain Loss: 0.5328 Acc: 0.7642\n",
            "ValData Loss: 0.7737 Acc: 0.7460\n",
            "\n",
            "Epoch 4/9\n",
            "----------\n",
            "NonAugmentedTrain Loss: 0.5118 Acc: 0.7772\n",
            "ValData Loss: 0.6808 Acc: 0.7409\n",
            "\n",
            "Epoch 5/9\n",
            "----------\n",
            "NonAugmentedTrain Loss: 0.5072 Acc: 0.7877\n",
            "ValData Loss: 0.9019 Acc: 0.7247\n",
            "\n",
            "Epoch 6/9\n",
            "----------\n",
            "NonAugmentedTrain Loss: 0.5049 Acc: 0.7842\n",
            "ValData Loss: 0.9056 Acc: 0.5294\n",
            "\n",
            "Epoch 7/9\n",
            "----------\n",
            "NonAugmentedTrain Loss: 0.4693 Acc: 0.8007\n",
            "ValData Loss: 0.7134 Acc: 0.6943\n",
            "\n",
            "Epoch 8/9\n",
            "----------\n",
            "NonAugmentedTrain Loss: 0.4174 Acc: 0.8267\n",
            "ValData Loss: 0.6141 Acc: 0.7095\n",
            "\n",
            "Epoch 9/9\n",
            "----------\n",
            "NonAugmentedTrain Loss: 0.3854 Acc: 0.8267\n",
            "ValData Loss: 0.6067 Acc: 0.7176\n",
            "\n",
            "Training complete in 5m 28s\n",
            "Best val Acc: 0.745951\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ORsD6WMZhfjY",
        "colab_type": "text"
      },
      "source": [
        "### Save best resnet18 model for future purposes : Best val acc 74.59 %"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xeYIqXS5gjjt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Save checkpoints in /content/covid19-detection-xray-dataset/\n",
        "torch.save(model_resnet18.state_dict(), 'model_resnet18_10epochs.pth')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z_hp53V1icnm",
        "colab_type": "text"
      },
      "source": [
        "### Experimenting with Resnet50"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oIELgpkCiDXP",
        "colab_type": "code",
        "outputId": "dda38e55-1f4f-4dcd-8f7c-c35d576deafa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84,
          "referenced_widgets": [
            "434309b2e0a348b19d74e6b4568cf281",
            "2b604776a5474e8db09f5ab23d7c5ba5",
            "391022bcd4454f54a8022ad72510582d",
            "3dc687b64f3d43ee97e89ddc68e3a410",
            "a44f8f9863004cce8ca2ff89230d3f6c",
            "f36017809bc04cb0aff80a3f5d1eb909",
            "0e71e19b0b3f41adaf0144d779897348",
            "e64c9bf9644546dc8b4f272755671dd0"
          ]
        }
      },
      "source": [
        "model_resnet50 = models.resnet50(pretrained=True)\n",
        "num_ftrs_rsn50 = model_resnet50.fc.in_features\n",
        "\n",
        "model_resnet50.fc = nn.Linear(num_ftrs_rsn50, len(class_names))\n",
        "\n",
        "model_resnet50 = model_resnet50.to(device)\n",
        "\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "\n",
        "# Observe that all parameters are being optimized\n",
        "optimizer_ft = optim.Adam(model_resnet50.parameters(), lr=0.001, betas=(0.9, 0.999), eps=1e-08, weight_decay=0, amsgrad=False)\n",
        "\n",
        "# Decay LR by a factor of 0.1 every 7 epochs\n",
        "exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading: \"https://download.pytorch.org/models/resnet50-19c8e357.pth\" to /root/.cache/torch/checkpoints/resnet50-19c8e357.pth\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "434309b2e0a348b19d74e6b4568cf281",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, max=102502400), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "elzDCDhwi0ca",
        "colab_type": "code",
        "outputId": "d98b38ee-5e16-4605-e245-d406d98e2e92",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "model_resnet50 = train_model(model_resnet50, criterion, optimizer_ft, exp_lr_scheduler,\n",
        "                       num_epochs=15)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 0/14\n",
            "----------\n",
            "NonAugmentedTrain Loss: 0.7993 Acc: 0.6813\n",
            "ValData Loss: 3.9225 Acc: 0.4545\n",
            "\n",
            "Epoch 1/14\n",
            "----------\n",
            "NonAugmentedTrain Loss: 0.6588 Acc: 0.7288\n",
            "ValData Loss: 0.7361 Acc: 0.7075\n",
            "\n",
            "Epoch 2/14\n",
            "----------\n",
            "NonAugmentedTrain Loss: 0.6135 Acc: 0.7582\n",
            "ValData Loss: 2.8941 Acc: 0.7136\n",
            "\n",
            "Epoch 3/14\n",
            "----------\n",
            "NonAugmentedTrain Loss: 0.6254 Acc: 0.7433\n",
            "ValData Loss: 0.6764 Acc: 0.7186\n",
            "\n",
            "Epoch 4/14\n",
            "----------\n",
            "NonAugmentedTrain Loss: 0.5588 Acc: 0.7672\n",
            "ValData Loss: 0.6454 Acc: 0.7014\n",
            "\n",
            "Epoch 5/14\n",
            "----------\n",
            "NonAugmentedTrain Loss: 0.5510 Acc: 0.7757\n",
            "ValData Loss: 0.8900 Acc: 0.5982\n",
            "\n",
            "Epoch 6/14\n",
            "----------\n",
            "NonAugmentedTrain Loss: 0.5613 Acc: 0.7662\n",
            "ValData Loss: 0.7030 Acc: 0.7126\n",
            "\n",
            "Epoch 7/14\n",
            "----------\n",
            "NonAugmentedTrain Loss: 0.4900 Acc: 0.7827\n",
            "ValData Loss: 0.6213 Acc: 0.6903\n",
            "\n",
            "Epoch 8/14\n",
            "----------\n",
            "NonAugmentedTrain Loss: 0.4550 Acc: 0.8167\n",
            "ValData Loss: 0.6268 Acc: 0.7126\n",
            "\n",
            "Epoch 9/14\n",
            "----------\n",
            "NonAugmentedTrain Loss: 0.4461 Acc: 0.8092\n",
            "ValData Loss: 0.6155 Acc: 0.7034\n",
            "\n",
            "Epoch 10/14\n",
            "----------\n",
            "NonAugmentedTrain Loss: 0.4431 Acc: 0.8102\n",
            "ValData Loss: 0.6006 Acc: 0.7146\n",
            "\n",
            "Epoch 11/14\n",
            "----------\n",
            "NonAugmentedTrain Loss: 0.4171 Acc: 0.8162\n",
            "ValData Loss: 0.5983 Acc: 0.7287\n",
            "\n",
            "Epoch 12/14\n",
            "----------\n",
            "NonAugmentedTrain Loss: 0.4111 Acc: 0.8212\n",
            "ValData Loss: 0.6058 Acc: 0.6953\n",
            "\n",
            "Epoch 13/14\n",
            "----------\n",
            "NonAugmentedTrain Loss: 0.4164 Acc: 0.8212\n",
            "ValData Loss: 0.6416 Acc: 0.6812\n",
            "\n",
            "Epoch 14/14\n",
            "----------\n",
            "NonAugmentedTrain Loss: 0.3991 Acc: 0.8262\n",
            "ValData Loss: 0.5679 Acc: 0.7146\n",
            "\n",
            "Training complete in 11m 52s\n",
            "Best val Acc: 0.728745\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QyqDIEpzjHo2",
        "colab_type": "text"
      },
      "source": [
        "### Save best resnet50 model for future purposes :  Best val acc 72.87 %\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h0Ek-dy4jFWI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "torch.save(model_resnet50.state_dict(), 'model_resnet50_15epochs.pth')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8S3Ch-T3k_2X",
        "colab_type": "text"
      },
      "source": [
        "### Experimenting with Efficient-Net "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jP2N6-yUlJOw",
        "colab_type": "code",
        "outputId": "d9f46cff-0da7-4361-d19c-bb745d6dec89",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        }
      },
      "source": [
        "# Download efficient-net\n",
        "! pip install efficientnet-pytorch "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting efficientnet-pytorch\n",
            "  Downloading https://files.pythonhosted.org/packages/b8/cb/0309a6e3d404862ae4bc017f89645cf150ac94c14c88ef81d215c8e52925/efficientnet_pytorch-0.6.3.tar.gz\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.6/dist-packages (from efficientnet-pytorch) (1.5.0+cu101)\n",
            "Requirement already satisfied: future in /usr/local/lib/python3.6/dist-packages (from torch->efficientnet-pytorch) (0.16.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from torch->efficientnet-pytorch) (1.18.3)\n",
            "Building wheels for collected packages: efficientnet-pytorch\n",
            "  Building wheel for efficientnet-pytorch (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for efficientnet-pytorch: filename=efficientnet_pytorch-0.6.3-cp36-none-any.whl size=12422 sha256=2a30eaae806aca2175cccd0ff3c7a849d9d19baac3e0f5f97abb5ffd5c0cdd85\n",
            "  Stored in directory: /root/.cache/pip/wheels/42/1e/a9/2a578ba9ad04e776e80bf0f70d8a7f4c29ec0718b92d8f6ccd\n",
            "Successfully built efficientnet-pytorch\n",
            "Installing collected packages: efficientnet-pytorch\n",
            "Successfully installed efficientnet-pytorch-0.6.3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4J786o9KmkLU",
        "colab_type": "code",
        "outputId": "2ef02236-367f-4914-cc0f-7dd2429beb1d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from efficientnet_pytorch import EfficientNet\n",
        "model_efficientnetb0 = EfficientNet.from_pretrained('efficientnet-b0')\n",
        "\n",
        "num_ftrs_efb0 = model_efficientnetb0._fc.in_features\n",
        "\n",
        "model_efficientnetb0._fc = nn.Linear(num_ftrs_efb0, len(class_names))\n",
        "\n",
        "model_efficientnetb0 = model_efficientnetb0.to(device)\n",
        "\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "\n",
        "# Observe that all parameters are being optimized\n",
        "optimizer_ft = optim.Adam(model_efficientnetb0.parameters(), lr=0.001, betas=(0.9, 0.999), eps=1e-08, weight_decay=0, amsgrad=False)\n",
        "\n",
        "# Decay LR by a factor of 0.1 every 7 epochs\n",
        "exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Loaded pretrained weights for efficientnet-b0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FbbOMu37nB0k",
        "colab_type": "code",
        "outputId": "ec9320fe-d9a2-4017-e0e2-e6b367e03113",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "model_efficientnetb0 = train_model(model_efficientnetb0, criterion, optimizer_ft, exp_lr_scheduler,\n",
        "                       num_epochs=15)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 0/14\n",
            "----------\n",
            "NonAugmentedTrain Loss: 0.6967 Acc: 0.6978\n",
            "ValData Loss: 2.1510 Acc: 0.2115\n",
            "\n",
            "Epoch 1/14\n",
            "----------\n",
            "NonAugmentedTrain Loss: 0.5083 Acc: 0.7952\n",
            "ValData Loss: 2.9076 Acc: 0.2520\n",
            "\n",
            "Epoch 2/14\n",
            "----------\n",
            "NonAugmentedTrain Loss: 0.4571 Acc: 0.8037\n",
            "ValData Loss: 1.7212 Acc: 0.3887\n",
            "\n",
            "Epoch 3/14\n",
            "----------\n",
            "NonAugmentedTrain Loss: 0.4332 Acc: 0.8102\n",
            "ValData Loss: 1.5312 Acc: 0.4423\n",
            "\n",
            "Epoch 4/14\n",
            "----------\n",
            "NonAugmentedTrain Loss: 0.3934 Acc: 0.8302\n",
            "ValData Loss: 0.6290 Acc: 0.7389\n",
            "\n",
            "Epoch 5/14\n",
            "----------\n",
            "NonAugmentedTrain Loss: 0.3744 Acc: 0.8312\n",
            "ValData Loss: 1.1662 Acc: 0.4575\n",
            "\n",
            "Epoch 6/14\n",
            "----------\n",
            "NonAugmentedTrain Loss: 0.3735 Acc: 0.8437\n",
            "ValData Loss: 0.9759 Acc: 0.5870\n",
            "\n",
            "Epoch 7/14\n",
            "----------\n",
            "NonAugmentedTrain Loss: 0.3439 Acc: 0.8536\n",
            "ValData Loss: 0.9022 Acc: 0.5941\n",
            "\n",
            "Epoch 8/14\n",
            "----------\n",
            "NonAugmentedTrain Loss: 0.3067 Acc: 0.8671\n",
            "ValData Loss: 0.9838 Acc: 0.5526\n",
            "\n",
            "Epoch 9/14\n",
            "----------\n",
            "NonAugmentedTrain Loss: 0.2907 Acc: 0.8776\n",
            "ValData Loss: 1.0331 Acc: 0.5385\n",
            "\n",
            "Epoch 10/14\n",
            "----------\n",
            "NonAugmentedTrain Loss: 0.2796 Acc: 0.8736\n",
            "ValData Loss: 0.9818 Acc: 0.5668\n",
            "\n",
            "Epoch 11/14\n",
            "----------\n",
            "NonAugmentedTrain Loss: 0.2870 Acc: 0.8711\n",
            "ValData Loss: 1.0489 Acc: 0.5334\n",
            "\n",
            "Epoch 12/14\n",
            "----------\n",
            "NonAugmentedTrain Loss: 0.2660 Acc: 0.8886\n",
            "ValData Loss: 0.8986 Acc: 0.6083\n",
            "\n",
            "Epoch 13/14\n",
            "----------\n",
            "NonAugmentedTrain Loss: 0.2625 Acc: 0.8901\n",
            "ValData Loss: 0.8666 Acc: 0.6397\n",
            "\n",
            "Epoch 14/14\n",
            "----------\n",
            "NonAugmentedTrain Loss: 0.2659 Acc: 0.8846\n",
            "ValData Loss: 0.8705 Acc: 0.6285\n",
            "\n",
            "Training complete in 10m 15s\n",
            "Best val Acc: 0.738866\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "erNDf2g3qu4Q",
        "colab_type": "text"
      },
      "source": [
        "### Save best efficient-netb0 model for future purposes : Best Val acc - 73.88%"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "92woNaewoOjh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "torch.save(model_efficientnetb0.state_dict(), 'model_efficientnetb0_15epochs.pth')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8R1WH4hEw30u",
        "colab_type": "text"
      },
      "source": [
        "### Visualize Resnet18"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uNSsPjSQw8mE",
        "colab_type": "code",
        "outputId": "2e23ae2f-ff09-47dc-d306-877f9494e32a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 581
        }
      },
      "source": [
        "visualize_model(model_resnet18)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D_9Cqe2xxS1i",
        "colab_type": "text"
      },
      "source": [
        "### Visualize Resnet50"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O_j8NKZ6xcRM",
        "colab_type": "code",
        "outputId": "d58f9883-225d-4522-cdf1-530bf400947f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 581
        }
      },
      "source": [
        "visualize_model(model_resnet50)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AvWf31V0xUpd",
        "colab_type": "text"
      },
      "source": [
        "### Visualize EfficientNet-b0"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R5bRAK98xYXI",
        "colab_type": "code",
        "outputId": "227e328e-8340-412a-db34-db6fcbbdf898",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 581
        }
      },
      "source": [
        "visualize_model(model_efficientnetb0)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KvRwjXVZzAW1",
        "colab_type": "text"
      },
      "source": [
        "### Script to get Predictions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ITfPZWcYyZvG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_preds(model):\n",
        "  inputs, labels = iter(dataloaders['ValData']).next()\n",
        "\n",
        "  inputs = inputs.to(device)\n",
        "  inp = torchvision.utils.make_grid(inputs)\n",
        "\n",
        "  outputs = model(inputs)\n",
        "  _, preds = torch.max(outputs, 1)\n",
        "\n",
        "  for j in range(len(inputs)):\n",
        "      print (\"Actual label\", class_names[np.array(labels)[j])\n",
        "\n",
        "      inp = inputs.data[j]\n",
        "      imshow(inp, 'predicted:' + class_names[preds[j]])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2MvVExi71sn2",
        "colab_type": "text"
      },
      "source": [
        "### Get predictions of Resnet18 on a batch of data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d6bmPP2d1yDB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "get_preds(model_resnet18)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E2ixiB-61Fg_",
        "colab_type": "text"
      },
      "source": [
        "### Check predictions of Resnet50 on a batch of data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OfiDedBl1Kef",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "get_preds(model_resnet50)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n6LdXK6w13JM",
        "colab_type": "text"
      },
      "source": [
        "### Check predictions of EfficientNet-b0 on a batch of data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E3IzJzF41-LP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "get_preds(model_efficientnetb0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OLCBjkf6poZC",
        "colab_type": "text"
      },
      "source": [
        "### Future works\n",
        "1.   Add localization\n",
        "2.   Benchmarking with other metrics (f1, auc)\n",
        "3.   Try [CovidX dataset](https://github.com/lindawangg/COVID-Net/blob/master/docs/COVIDx.md)\n",
        "4.   Propose lightweight model.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u5Eu0t2QorGE",
        "colab_type": "text"
      },
      "source": [
        "### Conclusion thoughts\n",
        "\n",
        "1.   Very less covid images, high correraltion between pneumonia images\n",
        "2.   Model overfits quickly\n",
        "3.   Dataset cleaning, alignment is required\n",
        "4.   Model is highly sensitive between pneumonia and covid images\n",
        "5.   Augmentation does not work well. Creates high senstivity between covid and pneumonia \n"
      ]
    }
  ]
}