Individual_Assignment.ipynb

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            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        }
      }
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "FDfhlC4Axb9K"
      },
      "source": [
        "import time\n",
        "import os\n",
        "import cv2\n",
        "import random\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import torch\n",
        "import torch.optim as optim\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torchvision\n",
        "import torchvision.datasets as datasets\n",
        "import torchvision.transforms as transforms\n",
        "from torchvision import models\n",
        "from torch import optim\n",
        "from sklearn.metrics import *\n",
        "from PIL import Image\n",
        "import shutil\n",
        "import zipfile\n",
        "\n",
        "num_workers = 4 #change this parameter based on your system configuration\n",
        "batch_size = 32 #change this parameter based on your system configuration\n",
        "seed = 24\n",
        "random.seed(seed)\n",
        "metrics = ['Accuracy', 'Precision', 'Recall', 'F1-score']\n",
        "categories = ['COVID', 'Lung_Opacity', 'Normal', 'Viral_Pneumonia']\n",
        "num_classes = len(categories)\n",
        "splits = ['train', 'val', 'test']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eRvHCWxfyLAx",
        "outputId": "861fa66b-b1cc-4a1e-8ae1-c3e23fba27ff"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "os.chdir('/content/drive/MyDrive/SEM7/image processing/individual project')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jiv75929tVcp"
      },
      "source": [
        "For first time to unzip the dataset uploaded"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lfZQMNJmyZwV"
      },
      "source": [
        "# local_zip = 'COVID-19_Radiography_Dataset.zip'\n",
        "# zip_ref = zipfile.ZipFile(local_zip, 'r')\n",
        "# zip_ref.extractall()\n",
        "# zip_ref.close()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mCJW7dl_zCzi"
      },
      "source": [
        "DATA_PATH = 'COVID-19_Radiography_Dataset'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mvX6BceMtduL"
      },
      "source": [
        "Split original test folder randomly and equaly between test and validation (new) folders"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "enfYXxo3zrP7",
        "outputId": "c85871f6-e98c-4681-fab0-1eef3473b690"
      },
      "source": [
        "if not os.path.isdir(os.path.join(DATA_PATH, 'val')):\n",
        "    os.mkdir(os.path.join(DATA_PATH, 'val'))\n",
        "    for subdir in categories:   \n",
        "        for root, dirs, files in os.walk(os.path.join(DATA_PATH, 'test/'+subdir)):\n",
        "            size = len(files)\n",
        "            migrate_index = random.sample(range(size), int(size/2))\n",
        "            files_to_migrate = [files[i] for i in migrate_index]\n",
        "            for file in files_to_migrate:\n",
        "                file_path = os.path.join(DATA_PATH, 'test/' + subdir + '/' + file)\n",
        "                new_dir = os.path.join(DATA_PATH, 'val/' + subdir)\n",
        "                if not os.path.exists(new_dir):\n",
        "                    os.mkdir(new_dir)\n",
        "                if not os.path.exists(os.path.join(new_dir, file)):\n",
        "                    a = shutil.move(file_path, new_dir)\n",
        "else: print(\"Folder 'val' is already available and the split didn't happen\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Folder 'val' is already available and the split didn't happen\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-8sBAOY_zuJZ",
        "outputId": "de342b3b-53ff-49d8-e371-3ab4a4ecc093"
      },
      "source": [
        "def show_dataset(data_path=DATA_PATH):\n",
        "    df = pd.DataFrame(columns = ['code'] + splits, index = categories)\n",
        "    for row in categories:\n",
        "        for col in splits:\n",
        "            df.loc[row,col] = int(len(os.listdir(os.path.join(data_path, col+'/'+row))))\n",
        "    df['total'] = df.sum(axis=1).astype(int)\n",
        "    df.loc['TOTAL'] = df.sum(axis=0).astype(int)\n",
        "    df['code'] = ['0', '1', '2','3','']\n",
        "    print(df)\n",
        "    return df\n",
        "df_dataset = show_dataset()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                code train  val test  total\n",
            "COVID              0   400   50   50    500\n",
            "Lung_Opacity       1   400   50   50    500\n",
            "Normal             2   400   50   50    500\n",
            "Viral_Pneumonia    3   400   50   50    500\n",
            "TOTAL                 1600  200  200   2000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DeYadwcaz4m1",
        "outputId": "637ec2a3-7570-45e3-a2ce-972106da70aa"
      },
      "source": [
        "def load_data(data_path=DATA_PATH, num_workers=num_workers):\n",
        "    transform_dict = {\n",
        "        'model': transforms.Compose(\n",
        "                                    [transforms.Resize(299),\n",
        "                                     transforms.CenterCrop(299),\n",
        "                                     transforms.ToTensor(),\n",
        "                                     ])}\n",
        "    train_data = datasets.ImageFolder(root=data_path + '/train', transform=transform_dict['model'])\n",
        "    train_loader = torch.utils.data.DataLoader(train_data, batch_size=batch_size, shuffle=True, num_workers=num_workers)\n",
        "    val_data = datasets.ImageFolder(root=data_path + '/val', transform=transform_dict['model'])\n",
        "    val_loader = torch.utils.data.DataLoader(val_data, batch_size=batch_size, shuffle=False, num_workers=num_workers)\n",
        "    test_data = datasets.ImageFolder(root=data_path + '/test', transform=transform_dict['model'])\n",
        "    test_loader = torch.utils.data.DataLoader(test_data, batch_size=batch_size, shuffle=False, num_workers=num_workers)\n",
        "    return train_data, train_loader, val_data, val_loader, test_data, test_loader\n",
        "\n",
        "train_data, train_loader, val_data, val_loader, test_data, test_loader = load_data()\n",
        "dataset = torch.utils.data.ConcatDataset([train_data, val_data, test_data])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:481: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  cpuset_checked))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RenUhpoE02Nx",
        "outputId": "1c987545-f0ed-44e1-c0b3-27465039c3d3"
      },
      "source": [
        "len(dataset)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2000"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YvnsgW9504Ia",
        "outputId": "23cd0b8d-3a01-4027-e275-05109f049004"
      },
      "source": [
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "print(f'You are using {device}')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "You are using cuda\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9pRh2_AwtnYE"
      },
      "source": [
        "Show images from training datasets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 666
        },
        "id": "moS0dHMz0570",
        "outputId": "e07c7b15-64bb-4cc4-94bf-3efad83efa95"
      },
      "source": [
        "def show_batch_images(dataset):\n",
        "    n_images, scale = 8, 3\n",
        "    data_loader = torch.utils.data.DataLoader(dataset, batch_size=25*n_images, shuffle = True)\n",
        "    images, labels = next(iter(data_loader))\n",
        "    for i in range(len(categories)):\n",
        "        print(f'Class: {categories[i]}')\n",
        "        images_category = images[labels==i][:n_images]\n",
        "        grid = torchvision.utils.make_grid(images_category, padding=20)\n",
        "        npgrid = grid.cpu().numpy()\n",
        "        plt.figure(figsize=(40*scale/n_images, 20*scale/n_images))\n",
        "        plt.imshow(np.transpose(npgrid, (1, 2, 0)), interpolation='nearest')\n",
        "        plt.show()\n",
        "        #plt.savefig('x-ray[i].pdf')  \n",
        "        #plt.savefig(\"viz1.png\", bbox_inches='tight')\n",
        "\n",
        "show_batch_images(train_data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Class: COVID\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x540 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Class: Lung_Opacity\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x540 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Class: Normal\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x540 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Class: Viral_Pneumonia\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x540 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EMyB_IUe1YBm"
      },
      "source": [
        "def calc_metrics(predictions, actuals, row):\n",
        "    df = pd.DataFrame(columns =metrics)\n",
        "    Y_pred = np.concatenate(predictions, axis=0)\n",
        "    Y_test = np.concatenate(actuals, axis=0)\n",
        "    df.loc[row, 'Accuracy'] = accuracy_score(Y_test, Y_pred)\n",
        "    df.loc[row, 'Precision'] = precision_score(Y_test, Y_pred, average=\"macro\")\n",
        "    df.loc[row, 'Recall'] = recall_score(Y_test, Y_pred, average=\"macro\")\n",
        "    df.loc[row, 'F1-score'] = f1_score(Y_test, Y_pred, average=\"macro\")\n",
        "    return df\n",
        "\n",
        "def create_confusion_matrix(preds, y_test):\n",
        "    ylist, predlist = [], []\n",
        "    for pred in preds:\n",
        "        for item in pred:\n",
        "            predlist.append(int(item))\n",
        "    for y in y_test:\n",
        "        for item in y:\n",
        "            ylist.append(int(item))\n",
        "    data_dict = {'y_Actual':    ylist, 'y_Predicted': predlist}\n",
        "    df = pd.DataFrame(data_dict, columns=['y_Actual','y_Predicted'])\n",
        "    cm = pd.crosstab(df['y_Actual'], df['y_Predicted'], rownames=['ACTUAL'], colnames=['PREDICTED'])\n",
        "    return cm\n",
        "\n",
        "def plot_train_val_losses(df):\n",
        "    df2 = pd.melt(df, id_vars=['epoch'], value_vars=['train', 'val'], var_name='process', value_name='loss')\n",
        "    sns.lineplot(x = \"epoch\", y = \"loss\", data = df2, hue = \"process\",\n",
        "                style = \"process\", palette = \"hot\", dashes = False, \n",
        "                markers = [\"o\", \"<\"],  legend=\"brief\").set_title(\"Train and Validation Losses by Epoch\")\n",
        "    plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TTSt8Ekc1nkj"
      },
      "source": [
        "def reset_weights(m):\n",
        "  for layer in m.children():\n",
        "   if hasattr(layer, 'reset_parameters'):\n",
        "    print(f'Reset trainable parameters of layer = {layer}')\n",
        "    layer.reset_parameters()\n",
        "\n",
        "def calc_pred_actuals(model, loader):\n",
        "    predictions, actuals = [], []\n",
        "    with torch.no_grad():\n",
        "        for data, target in loader:\n",
        "            data = data.to(device)\n",
        "            target = target.to(device)\n",
        "            Y_pred_orig = model(data)\n",
        "            _, Y_pred_tag = torch.max(Y_pred_orig, dim = 1)\n",
        "            Y_pred_tag = Y_pred_tag.detach().cpu().numpy()\n",
        "            Y_pred_tag = Y_pred_tag.reshape(len(Y_pred_tag), 1).astype('int8')\n",
        "            Y_test = target.detach().cpu().numpy()\n",
        "            Y_test = Y_test.reshape(len(Y_test), 1).astype('int8')\n",
        "            predictions.append(Y_pred_tag)\n",
        "            actuals.append(Y_test)\n",
        "    return predictions, actuals\n",
        "\n",
        "def train_val_model(model):\n",
        "    t_start = time.time()\n",
        "    global best_val_model\n",
        "    global best_val_loss\n",
        "    best_val_loss = 1\n",
        "    global best_val_epoch\n",
        "    best_val_epoch = 0\n",
        "    df = pd.DataFrame(columns = ['model_name', 'epoch', 'train', 'val'])\n",
        "    print(f\"Training model {model_name} with {df_dataset.loc['TOTAL', 'train']} samples and max of {n_epochs} epochs, and validating with {df_dataset.loc['TOTAL', 'val']} samples\\n\")\n",
        "    train_size, val_size = len(train_loader), len(val_loader)\n",
        "    for epoch in range(1, n_epochs+1):\n",
        "        # Beginning of training step\n",
        "        t0 = time.time()\n",
        "        model.train()\n",
        "        train_loss, val_loss = 0.0, 0.0\n",
        "        for i, (data, target) in enumerate(train_loader):\n",
        "            target = target.to(device)\n",
        "            data = data.to(device)\n",
        "            optimizer.zero_grad()\n",
        "            outputs = model(data)\n",
        "            loss = criterion(outputs, target)\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "            train_loss += loss.detach().cpu().numpy()\n",
        "        # Beginning of evaluation step\n",
        "        model.eval()\n",
        "        for j, (data, target) in enumerate(val_loader):\n",
        "            target = target.to(device)\n",
        "            data = data.to(device)\n",
        "            outputs = model(data)\n",
        "            loss = criterion(outputs, target)\n",
        "            val_loss += loss.detach().cpu().numpy()\n",
        "        print(f\"Epoch {epoch}:\\t train loss={train_loss/train_size:.5f} \\t val loss={val_loss/val_size:.5f} \\t time={(time.time() - t0):.2f}s\")\n",
        "        df.loc[len(df)] = [model_name, epoch, train_loss/train_size, val_loss/val_size]\n",
        "        if epoch >= best_val_epoch + 15:\n",
        "            break\n",
        "        if use_scheduler: scheduler.step(val_loss/val_size)   # Optional to use scheduler for dynamic learning rate\n",
        "    #print(f\"Best model has val loss={best_val_loss:.5f} for {best_val_epoch} epochs\")\n",
        "    print(f\"Total time training and evaluating: {(time.time()-t_start):.2f}s\")\n",
        "    return model, df\n",
        "\n",
        "def train_val_model_kfold(model, train_loader_kfold):\n",
        "    t_start = time.time()\n",
        "    df = pd.DataFrame(columns = ['model_name', 'epoch', 'train'])\n",
        "    print(f\"Training model with {df_dataset.loc['TOTAL', 'train']} samples and max of {n_epochs} epochs\")\n",
        "    train_size = len(train_loader_kfold)\n",
        "    for epoch in range(1, n_epochs+1):\n",
        "        # Beginning of training step\n",
        "        t0 = time.time()\n",
        "        #model.train()\n",
        "        train_loss = 0.0\n",
        "        for i, (data, target) in enumerate(train_loader_kfold):\n",
        "            target = target.to(device)\n",
        "            data = data.to(device)\n",
        "            optimizer.zero_grad()\n",
        "            outputs = model(data)\n",
        "            loss = criterion(outputs, target)\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "            train_loss += loss.detach().cpu().numpy()\n",
        "        print(f\"Epoch {epoch}:\\t train loss={train_loss/train_size:.5f} \\t time={(time.time() - t0):.2f}s\")\n",
        "        df.loc[len(df)] = [model_name, epoch, train_loss/train_size]\n",
        "    print(f\"Total time training and evaluating: {(time.time()-t_start):.2f}s\")\n",
        "    return model, df\n",
        "\n",
        "def accuracy_model(model, loader):\n",
        "    model.eval()\n",
        "    print(f\"Testing the model with {df_dataset.loc['TOTAL', 'test']} samples \\n\")   \n",
        "    predictions, actuals = calc_pred_actuals(model, loader)\n",
        "    conf_matrix = create_confusion_matrix(predictions, actuals)\n",
        "    df_test = calc_metrics(predictions, actuals, 'Test Results').astype(float)\n",
        "    print(df_test)\n",
        "    return df_test, conf_matrix"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_T_NEyyf1y8q"
      },
      "source": [
        "def initialize_model(model_name, num_classes, use_pretrained=True):\n",
        "    if model_name == \"resnet18\":\n",
        "        model_ft = models.resnet18(pretrained=use_pretrained)\n",
        "        for param in model_ft.parameters():\n",
        "            param.requires_grad = False\n",
        "        num_ftrs = model_ft.fc.in_features\n",
        "        model_ft.fc = nn.Linear(num_ftrs, num_classes)\n",
        "    \n",
        "    elif model_name == \"resnet34\":\n",
        "        model_ft = models.resnet34(pretrained=use_pretrained)\n",
        "        for param in model_ft.parameters():\n",
        "            param.requires_grad = False\n",
        "        num_ftrs = model_ft.fc.in_features\n",
        "        model_ft.fc = nn.Linear(num_ftrs, num_classes)\n",
        "    \n",
        "    elif model_name == \"resnet50\":\n",
        "        model_ft = models.resnet50(pretrained=use_pretrained)\n",
        "        for param in model_ft.parameters():\n",
        "            param.requires_grad = False\n",
        "        num_ftrs = model_ft.fc.in_features\n",
        "        model_ft.fc = nn.Linear(num_ftrs, num_classes)\n",
        "    \n",
        "    elif model_name == \"resnet101\":\n",
        "        model_ft = models.resnet101(pretrained=use_pretrained)\n",
        "        for param in model_ft.parameters():\n",
        "            param.requires_grad = False\n",
        "        num_ftrs = model_ft.fc.in_features\n",
        "        model_ft.fc = nn.Linear(num_ftrs, num_classes)\n",
        "\n",
        "    elif model_name == \"vgg16\":\n",
        "        model_ft = models.vgg16_bn(pretrained=use_pretrained)\n",
        "        for param in model_ft.parameters():\n",
        "            param.requires_grad = False\n",
        "        num_ftrs = model_ft.classifier[6].in_features\n",
        "        model_ft.classifier[6] = nn.Linear(num_ftrs,num_classes)\n",
        "    \n",
        "    elif model_name == \"vgg19\":\n",
        "        model_ft = models.vgg19_bn(pretrained=use_pretrained)\n",
        "        for param in model_ft.parameters():\n",
        "            param.requires_grad = False\n",
        "        num_ftrs = model_ft.classifier[6].in_features\n",
        "        model_ft.classifier[6] = nn.Linear(num_ftrs,num_classes)\n",
        "    \n",
        "    elif model_name == \"EfficientNet\":\n",
        "        from efficientnet_pytorch import EfficientNet\n",
        "        model_ft = EfficientNet.from_pretrained('efficientnet-b7',num_classes=num_classes)\n",
        "\n",
        "    elif model_name == \"inception\":\n",
        "        model_ft = models.inception_v3(pretrained=use_pretrained)\n",
        "        for param in model_ft.parameters():\n",
        "            param.requires_grad = False\n",
        "            #param.aux_logits = False    \n",
        "        #num_ftrs = model_ft.fc.in_features\n",
        "        #model_ft.AuxLogits.fc = nn.Linear(num_ftrs, num_classes)\n",
        "        num_ftrs = model_ft.fc.in_features\n",
        "        model_ft.fc = nn.Linear(num_ftrs,num_classes)\n",
        "        model_ft.aux_logits = False\n",
        "    \n",
        "    else:\n",
        "        print(\"Invalid model name, exiting...\")\n",
        "        exit()\n",
        "    \n",
        "    model_ft.to(device)\n",
        "    return model_ft"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ToEiaeK0tu2U"
      },
      "source": [
        "Import and initialize models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187,
          "referenced_widgets": [
            "f625c354f1e44bf18f6b31ad13ed19e3",
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            "572ba46fa943433b8ae936ee3ad418d9",
            "d9116fed74df4b52a7a42727c6395264",
            "cec3447537c3405f955174a9a4e35622",
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            "308e10ca087c4b35aada4bac21bb9741",
            "3e6e34edb56844509e8d367a982b26d6",
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            "d6112c08509842c9ba5e004f3d76c752"
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        },
        "id": "wEg7rbh8235W",
        "outputId": "f4551ae7-9df3-481a-dd8b-9c6e7e775d53"
      },
      "source": [
        "model_list = [\"inception\",\"resnet50\",\"vgg16\"]\n",
        "all_models = []\n",
        "for model in model_list:\n",
        "  all_models.append(initialize_model(model, num_classes, use_pretrained=True))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading: \"https://download.pytorch.org/models/inception_v3_google-0cc3c7bd.pth\" to /root/.cache/torch/hub/checkpoints/inception_v3_google-0cc3c7bd.pth\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f625c354f1e44bf18f6b31ad13ed19e3",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0.00/104M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading: \"https://download.pytorch.org/models/resnet50-0676ba61.pth\" to /root/.cache/torch/hub/checkpoints/resnet50-0676ba61.pth\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7b090616b0a54c53a123eaca300e7e0a",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0.00/97.8M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading: \"https://download.pytorch.org/models/vgg16_bn-6c64b313.pth\" to /root/.cache/torch/hub/checkpoints/vgg16_bn-6c64b313.pth\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "851455ead6304987b0618e16b1d2167a",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0.00/528M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ec4Yb7Ez263R",
        "outputId": "481fd63e-38d6-475b-8f32-1df045981b3b"
      },
      "source": [
        "np.random.seed(seed)\n",
        "torch.manual_seed(seed)\n",
        "pretrained_model_list = []\n",
        "df_epochs_list = []\n",
        "for model_name, pre_model in zip(model_list,all_models):\n",
        "  n_epochs = 20\n",
        "  learning_rate = 1e-2\n",
        "  criterion = nn.CrossEntropyLoss()\n",
        "  optimizer = torch.optim.SGD(pre_model.parameters(), lr=learning_rate)\n",
        "  use_scheduler = False   # Set True if using scheduler\n",
        "  pretrained_model, df_epochs = train_val_model(pre_model)\n",
        "  pretrained_model_list.append(pretrained_model)\n",
        "  df_epochs_list.append(df_epochs)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training model inception with 1600 samples and max of 20 epochs, and validating with 200 samples\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:481: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  cpuset_checked))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1:\t train loss=1.21621 \t val loss=1.05469 \t time=19.01s\n",
            "Epoch 2:\t train loss=0.95700 \t val loss=0.83288 \t time=18.88s\n",
            "Epoch 3:\t train loss=0.84381 \t val loss=0.73891 \t time=18.88s\n",
            "Epoch 4:\t train loss=0.75029 \t val loss=0.68207 \t time=19.00s\n",
            "Epoch 5:\t train loss=0.70134 \t val loss=0.64938 \t time=19.13s\n",
            "Epoch 6:\t train loss=0.66720 \t val loss=0.60722 \t time=19.13s\n",
            "Epoch 7:\t train loss=0.64084 \t val loss=0.57058 \t time=19.22s\n",
            "Epoch 8:\t train loss=0.62775 \t val loss=0.55028 \t time=19.22s\n",
            "Epoch 9:\t train loss=0.57832 \t val loss=0.53951 \t time=19.16s\n",
            "Epoch 10:\t train loss=0.56798 \t val loss=0.51909 \t time=19.19s\n",
            "Epoch 11:\t train loss=0.58235 \t val loss=0.52105 \t time=18.94s\n",
            "Epoch 12:\t train loss=0.54513 \t val loss=0.51965 \t time=19.22s\n",
            "Epoch 13:\t train loss=0.56283 \t val loss=0.49030 \t time=19.04s\n",
            "Epoch 14:\t train loss=0.52512 \t val loss=0.47922 \t time=19.08s\n",
            "Epoch 15:\t train loss=0.51860 \t val loss=0.48478 \t time=18.99s\n",
            "Total time training and evaluating: 286.20s\n",
            "Training model resnet50 with 1600 samples and max of 20 epochs, and validating with 200 samples\n",
            "\n",
            "Epoch 1:\t train loss=1.10196 \t val loss=0.91863 \t time=26.36s\n",
            "Epoch 2:\t train loss=0.77953 \t val loss=0.64788 \t time=26.35s\n",
            "Epoch 3:\t train loss=0.62232 \t val loss=0.58128 \t time=26.30s\n",
            "Epoch 4:\t train loss=0.57838 \t val loss=0.64373 \t time=26.44s\n",
            "Epoch 5:\t train loss=0.52742 \t val loss=0.52919 \t time=26.33s\n",
            "Epoch 6:\t train loss=0.48673 \t val loss=0.53723 \t time=26.30s\n",
            "Epoch 7:\t train loss=0.47148 \t val loss=0.55920 \t time=26.48s\n",
            "Epoch 8:\t train loss=0.45234 \t val loss=0.46929 \t time=26.39s\n",
            "Epoch 9:\t train loss=0.42990 \t val loss=0.54057 \t time=26.50s\n",
            "Epoch 10:\t train loss=0.40807 \t val loss=0.45246 \t time=26.43s\n",
            "Epoch 11:\t train loss=0.39559 \t val loss=0.50645 \t time=26.34s\n",
            "Epoch 12:\t train loss=0.39706 \t val loss=0.45655 \t time=26.58s\n",
            "Epoch 13:\t train loss=0.37875 \t val loss=0.43944 \t time=26.50s\n",
            "Epoch 14:\t train loss=0.36924 \t val loss=0.42109 \t time=26.32s\n",
            "Epoch 15:\t train loss=0.35774 \t val loss=0.42202 \t time=26.35s\n",
            "Total time training and evaluating: 396.08s\n",
            "Training model vgg16 with 1600 samples and max of 20 epochs, and validating with 200 samples\n",
            "\n",
            "Epoch 1:\t train loss=0.97049 \t val loss=0.77628 \t time=51.61s\n",
            "Epoch 2:\t train loss=0.66439 \t val loss=0.60180 \t time=51.90s\n",
            "Epoch 3:\t train loss=0.56597 \t val loss=0.54785 \t time=51.73s\n",
            "Epoch 4:\t train loss=0.52116 \t val loss=0.55944 \t time=51.66s\n",
            "Epoch 5:\t train loss=0.48218 \t val loss=0.49195 \t time=51.49s\n",
            "Epoch 6:\t train loss=0.47613 \t val loss=0.47877 \t time=51.81s\n",
            "Epoch 7:\t train loss=0.47394 \t val loss=0.46751 \t time=51.80s\n",
            "Epoch 8:\t train loss=0.44823 \t val loss=0.44091 \t time=51.79s\n",
            "Epoch 9:\t train loss=0.43496 \t val loss=0.43908 \t time=51.78s\n",
            "Epoch 10:\t train loss=0.42257 \t val loss=0.43814 \t time=51.61s\n",
            "Epoch 11:\t train loss=0.40768 \t val loss=0.42089 \t time=51.75s\n",
            "Epoch 12:\t train loss=0.39885 \t val loss=0.43289 \t time=51.76s\n",
            "Epoch 13:\t train loss=0.39214 \t val loss=0.42201 \t time=51.63s\n",
            "Epoch 14:\t train loss=0.39148 \t val loss=0.39706 \t time=51.60s\n",
            "Epoch 15:\t train loss=0.38929 \t val loss=0.41296 \t time=51.72s\n",
            "Total time training and evaluating: 775.78s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 906
        },
        "id": "PFKTVn0V290u",
        "outputId": "789365df-f775-404a-a1fc-dba457f6878a"
      },
      "source": [
        "for ep,name in zip(df_epochs_list,model_list):\n",
        "  print(name)\n",
        "  plot_train_val_losses(ep)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "inception\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "resnet50\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "vgg16\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "udIl6KiX7OgG",
        "outputId": "2d669fd9-103d-444e-be93-2a1aa20f8a12"
      },
      "source": [
        "confusions = []\n",
        "for pre,name in zip(pretrained_model_list,model_list):\n",
        "  print(name)\n",
        "  test_inception_results, conf_inception_matrix_test = accuracy_model(pre, test_loader)\n",
        "  confusions.append(conf_inception_matrix_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "inception\n",
            "Testing the model with 200 samples \n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:481: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  cpuset_checked))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              Accuracy  Precision  Recall  F1-score\n",
            "Test Results      0.84   0.842854    0.84  0.839889\n",
            "resnet50\n",
            "Testing the model with 200 samples \n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:481: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  cpuset_checked))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              Accuracy  Precision  Recall  F1-score\n",
            "Test Results     0.895   0.897575   0.895  0.894333\n",
            "vgg16\n",
            "Testing the model with 200 samples \n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:481: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  cpuset_checked))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              Accuracy  Precision  Recall  F1-score\n",
            "Test Results      0.88    0.88482    0.88  0.878175\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        },
        "id": "25giTfpv7SWd",
        "outputId": "36056215-9834-4161-e0bb-81abff0b1ff6"
      },
      "source": [
        "print(model_list[0])\n",
        "sns.heatmap(confusions[0], annot=True, fmt='g', xticklabels=categories, yticklabels=categories)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "inception\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f915f7a03d0>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EZpJCwbG0oaU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        },
        "outputId": "493bbee0-0660-4207-e4e8-ce300775ca14"
      },
      "source": [
        "print(model_list[1])\n",
        "sns.heatmap(confusions[1], annot=True, fmt='g', xticklabels=categories, yticklabels=categories)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "resnet50\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f915e681d10>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        },
        "id": "UvTuIRj9Iuva",
        "outputId": "c55cb825-8a3b-43ee-d72b-fb2f0ad7d118"
      },
      "source": [
        "print(model_list[2])\n",
        "sns.heatmap(confusions[2], annot=True, fmt='g', xticklabels=categories, yticklabels=categories)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "vgg16\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f915e5da3d0>"
            ]
          },
          "metadata": {},
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BaawcXIyIymS"
      },
      "source": [
        "from sklearn.model_selection import KFold\n",
        "def validate_model_kfold(model, model_name, k_folds):\n",
        "    #model.eval()\n",
        "    print(f\"Validating the model {model_name} with {df_dataset.loc['TOTAL', 'train']} samples and {k_folds}-folds \\n\")\n",
        "    df = pd.DataFrame(columns = metrics)\n",
        "    kfold = KFold(n_splits=k_folds, shuffle=True)\n",
        "    print(len(dataset))\n",
        "    for fold, (train_ids, test_ids) in enumerate(kfold.split(dataset)):\n",
        "        train_subsampler = torch.utils.data.SubsetRandomSampler(train_ids)\n",
        "        test_subsampler = torch.utils.data.SubsetRandomSampler(test_ids)\n",
        "        train_kfold_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, num_workers=num_workers, sampler=train_subsampler)\n",
        "        test_kfold_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, num_workers=num_workers, sampler=test_subsampler)\n",
        "        #model.apply(reset_weights)\n",
        "        model, df_vgg_epochs = train_val_model_kfold(model, train_kfold_loader)\n",
        "        predictions, actuals = calc_pred_actuals(model, test_kfold_loader)\n",
        "        conf_matrix = create_confusion_matrix(predictions, actuals)\n",
        "        df_aux = calc_metrics(predictions, actuals, 'FOLD '+str(fold+1))\n",
        "        df = df.append(df_aux)      \n",
        "    df.loc['Average'] = df.mean(axis=0)\n",
        "    print(df.astype(float))\n",
        "    return df.astype(float), conf_matrix"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hSdY24fDEFJP",
        "outputId": "e427adac-904b-4583-98e3-2d8227b2e82b"
      },
      "source": [
        "validation_our_model_results, conf_matrix_our_model_val = validate_model_kfold(all_models[2], model_name = model_list[2], k_folds = 5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validating the model vgg16 with 1600 samples and 5-folds \n",
            "\n",
            "2000\n",
            "Training model with 1600 samples and max of 20 epochs\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:481: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  cpuset_checked))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1:\t train loss=0.29611 \t time=45.23s\n",
            "Epoch 2:\t train loss=0.28783 \t time=45.23s\n",
            "Epoch 3:\t train loss=0.28151 \t time=45.05s\n",
            "Epoch 4:\t train loss=0.27489 \t time=45.11s\n",
            "Epoch 5:\t train loss=0.26917 \t time=45.12s\n",
            "Epoch 6:\t train loss=0.26395 \t time=45.32s\n",
            "Epoch 7:\t train loss=0.25861 \t time=45.03s\n",
            "Epoch 8:\t train loss=0.25591 \t time=45.00s\n",
            "Epoch 9:\t train loss=0.25027 \t time=45.00s\n",
            "Epoch 10:\t train loss=0.24662 \t time=45.04s\n",
            "Epoch 11:\t train loss=0.24241 \t time=45.02s\n",
            "Epoch 12:\t train loss=0.23808 \t time=45.05s\n",
            "Epoch 13:\t train loss=0.23569 \t time=44.95s\n",
            "Epoch 14:\t train loss=0.23119 \t time=44.83s\n",
            "Epoch 15:\t train loss=0.22937 \t time=44.93s\n",
            "Epoch 16:\t train loss=0.22700 \t time=44.47s\n",
            "Epoch 17:\t train loss=0.22180 \t time=44.90s\n",
            "Epoch 18:\t train loss=0.21753 \t time=44.95s\n",
            "Epoch 19:\t train loss=0.21730 \t time=44.82s\n",
            "Epoch 20:\t train loss=0.21424 \t time=44.85s\n",
            "Total time training and evaluating: 900.04s\n",
            "Training model with 1600 samples and max of 20 epochs\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:481: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  cpuset_checked))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1:\t train loss=0.24480 \t time=45.02s\n",
            "Epoch 2:\t train loss=0.23808 \t time=45.00s\n",
            "Epoch 3:\t train loss=0.23369 \t time=45.01s\n",
            "Epoch 4:\t train loss=0.23020 \t time=44.97s\n",
            "Epoch 5:\t train loss=0.22642 \t time=44.89s\n",
            "Epoch 6:\t train loss=0.22299 \t time=44.99s\n",
            "Epoch 7:\t train loss=0.22202 \t time=45.00s\n",
            "Epoch 8:\t train loss=0.21586 \t time=44.91s\n",
            "Epoch 9:\t train loss=0.21467 \t time=44.89s\n",
            "Epoch 10:\t train loss=0.21238 \t time=45.00s\n",
            "Epoch 11:\t train loss=0.21035 \t time=44.99s\n",
            "Epoch 12:\t train loss=0.20720 \t time=44.91s\n",
            "Epoch 13:\t train loss=0.20388 \t time=44.90s\n",
            "Epoch 14:\t train loss=0.20152 \t time=44.96s\n",
            "Epoch 15:\t train loss=0.19953 \t time=44.91s\n",
            "Epoch 16:\t train loss=0.19681 \t time=44.91s\n",
            "Epoch 17:\t train loss=0.19450 \t time=44.98s\n",
            "Epoch 18:\t train loss=0.19372 \t time=44.91s\n",
            "Epoch 19:\t train loss=0.19169 \t time=44.99s\n",
            "Epoch 20:\t train loss=0.18882 \t time=44.99s\n",
            "Total time training and evaluating: 899.23s\n",
            "Training model with 1600 samples and max of 20 epochs\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:481: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  cpuset_checked))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1:\t train loss=0.19524 \t time=44.80s\n",
            "Epoch 2:\t train loss=0.19148 \t time=44.87s\n",
            "Epoch 3:\t train loss=0.18828 \t time=44.98s\n",
            "Epoch 4:\t train loss=0.18602 \t time=44.95s\n",
            "Epoch 5:\t train loss=0.18322 \t time=44.96s\n",
            "Epoch 6:\t train loss=0.18151 \t time=44.85s\n",
            "Epoch 7:\t train loss=0.17981 \t time=44.95s\n",
            "Epoch 8:\t train loss=0.17799 \t time=44.91s\n",
            "Epoch 9:\t train loss=0.17532 \t time=44.88s\n",
            "Epoch 10:\t train loss=0.17293 \t time=44.88s\n",
            "Epoch 11:\t train loss=0.17212 \t time=44.84s\n",
            "Epoch 12:\t train loss=0.16993 \t time=44.86s\n",
            "Epoch 13:\t train loss=0.16705 \t time=44.82s\n",
            "Epoch 14:\t train loss=0.16669 \t time=44.97s\n",
            "Epoch 15:\t train loss=0.16469 \t time=44.97s\n",
            "Epoch 16:\t train loss=0.16319 \t time=44.91s\n",
            "Epoch 17:\t train loss=0.16072 \t time=44.93s\n",
            "Epoch 18:\t train loss=0.16127 \t time=44.88s\n",
            "Epoch 19:\t train loss=0.15868 \t time=44.97s\n",
            "Epoch 20:\t train loss=0.15666 \t time=45.00s\n",
            "Total time training and evaluating: 898.31s\n",
            "Training model with 1600 samples and max of 20 epochs\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:481: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  cpuset_checked))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1:\t train loss=0.16958 \t time=44.97s\n",
            "Epoch 2:\t train loss=0.16674 \t time=45.05s\n",
            "Epoch 3:\t train loss=0.16615 \t time=45.03s\n",
            "Epoch 4:\t train loss=0.16350 \t time=44.94s\n",
            "Epoch 5:\t train loss=0.16111 \t time=44.93s\n",
            "Epoch 6:\t train loss=0.15931 \t time=44.86s\n",
            "Epoch 7:\t train loss=0.15827 \t time=45.06s\n",
            "Epoch 8:\t train loss=0.15618 \t time=45.12s\n",
            "Epoch 9:\t train loss=0.15496 \t time=44.92s\n",
            "Epoch 10:\t train loss=0.15444 \t time=45.00s\n",
            "Epoch 11:\t train loss=0.15202 \t time=44.96s\n",
            "Epoch 12:\t train loss=0.15065 \t time=44.85s\n",
            "Epoch 13:\t train loss=0.14970 \t time=44.86s\n",
            "Epoch 14:\t train loss=0.14810 \t time=45.08s\n",
            "Epoch 15:\t train loss=0.14691 \t time=44.88s\n",
            "Epoch 16:\t train loss=0.14547 \t time=44.99s\n",
            "Epoch 17:\t train loss=0.14439 \t time=45.02s\n",
            "Epoch 18:\t train loss=0.14260 \t time=45.07s\n",
            "Epoch 19:\t train loss=0.14222 \t time=44.96s\n",
            "Epoch 20:\t train loss=0.14104 \t time=44.99s\n",
            "Total time training and evaluating: 899.66s\n",
            "Training model with 1600 samples and max of 20 epochs\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:481: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  cpuset_checked))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1:\t train loss=0.15318 \t time=45.03s\n",
            "Epoch 2:\t train loss=0.15130 \t time=45.02s\n",
            "Epoch 3:\t train loss=0.14988 \t time=45.15s\n",
            "Epoch 4:\t train loss=0.14636 \t time=45.16s\n",
            "Epoch 5:\t train loss=0.14622 \t time=45.21s\n",
            "Epoch 6:\t train loss=0.14491 \t time=45.00s\n",
            "Epoch 7:\t train loss=0.14384 \t time=45.12s\n",
            "Epoch 8:\t train loss=0.14201 \t time=45.07s\n",
            "Epoch 9:\t train loss=0.14098 \t time=45.04s\n",
            "Epoch 10:\t train loss=0.13991 \t time=45.07s\n",
            "Epoch 11:\t train loss=0.13810 \t time=44.96s\n",
            "Epoch 12:\t train loss=0.13705 \t time=45.00s\n",
            "Epoch 13:\t train loss=0.13597 \t time=44.98s\n",
            "Epoch 14:\t train loss=0.13560 \t time=44.93s\n",
            "Epoch 15:\t train loss=0.13445 \t time=44.93s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2e17KLTDEiU2"
      },
      "source": [
        "#save model\n",
        "torch.save(all_models[2].state_dict(), 'ourmodel.pth')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XtIQysRdt-_Z"
      },
      "source": [
        "LOAD SAVED MODEL"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EqcD8ewYByqb"
      },
      "source": [
        "PATH = 'ourmodel.pth'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RgfkNBJ3JXrN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "d25a40cdbf584354ba8f8dc0332e20ec",
            "60effefe371443a6a6b05d6d237b829a",
            "b95c2d88071b4eed8bc2ba4265eac8d9",
            "ecae9d49ff504ca0a1cbbeed0f223059",
            "f7924b89b6d5437296aad7e173ce34a7",
            "8b76ec8aba574da68b107db81c957fba",
            "4a5c86484afe41768a574627a90d9212",
            "e4fd1aba665646efa2594185cbb582ff",
            "df9f77f0823540a785e8e96faa8745d1",
            "a2efe53c71784f9fa11a96ebce4d0800",
            "ca1b8ca08f6f428ca7cb304e1528cc45"
          ]
        },
        "outputId": "2cefde5d-41ea-4695-fa4e-1fb4362e9d55"
      },
      "source": [
        "# saved_model = torch.load(PATH)\n",
        "# saved_model.eval()\n",
        "\n",
        "saved_model = initialize_model(\"vgg16\", num_classes, use_pretrained=True)\n",
        "saved_model.load_state_dict(torch.load(PATH))\n",
        "saved_model.eval()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading: \"https://download.pytorch.org/models/vgg16_bn-6c64b313.pth\" to /root/.cache/torch/hub/checkpoints/vgg16_bn-6c64b313.pth\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d25a40cdbf584354ba8f8dc0332e20ec",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0.00/528M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "VGG(\n",
              "  (features): Sequential(\n",
              "    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (2): ReLU(inplace=True)\n",
              "    (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (5): ReLU(inplace=True)\n",
              "    (6): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "    (7): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (8): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (9): ReLU(inplace=True)\n",
              "    (10): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (11): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (12): ReLU(inplace=True)\n",
              "    (13): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "    (14): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (15): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (16): ReLU(inplace=True)\n",
              "    (17): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (18): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (19): ReLU(inplace=True)\n",
              "    (20): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (21): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (22): ReLU(inplace=True)\n",
              "    (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "    (24): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (25): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (26): ReLU(inplace=True)\n",
              "    (27): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (28): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (29): ReLU(inplace=True)\n",
              "    (30): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (31): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (32): ReLU(inplace=True)\n",
              "    (33): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "    (34): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (35): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (36): ReLU(inplace=True)\n",
              "    (37): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (38): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (39): ReLU(inplace=True)\n",
              "    (40): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (41): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (42): ReLU(inplace=True)\n",
              "    (43): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "  )\n",
              "  (avgpool): AdaptiveAvgPool2d(output_size=(7, 7))\n",
              "  (classifier): Sequential(\n",
              "    (0): Linear(in_features=25088, out_features=4096, bias=True)\n",
              "    (1): ReLU(inplace=True)\n",
              "    (2): Dropout(p=0.5, inplace=False)\n",
              "    (3): Linear(in_features=4096, out_features=4096, bias=True)\n",
              "    (4): ReLU(inplace=True)\n",
              "    (5): Dropout(p=0.5, inplace=False)\n",
              "    (6): Linear(in_features=4096, out_features=4, bias=True)\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331
        },
        "id": "s2JEHN2iJwtY",
        "outputId": "48def53d-2ba2-458f-a672-3edc04e349ff"
      },
      "source": [
        "from torch.autograd import Variable\n",
        "import random\n",
        "# Disable grad\n",
        "with torch.no_grad():\n",
        "  \n",
        "  # Retrieve item\n",
        "  index = random.randint(0,170)\n",
        "  # index = 150\n",
        "  item = val_data[index]\n",
        "  image = item[0]\n",
        "  true_target = item[1]\n",
        "\n",
        "  saved_model.cuda()\n",
        "\n",
        "  # Generate prediction\n",
        "  image = image.unsqueeze(0)\n",
        "  image = Variable(image)\n",
        "  image = image.to(device)\n",
        "  prediction = saved_model(image)\n",
        "\n",
        "  # Predicted class value using argmax\n",
        "  prediction = prediction.detach().cpu().numpy()\n",
        "  print(categories)\n",
        "  print(prediction[0])\n",
        "  predicted_class = np.argmax(prediction)\n",
        "\n",
        "  # Reshape image\n",
        "  image.cpu().data.numpy()\n",
        "  # image = image.reshape(28, 28, 1)\n",
        "\n",
        "  # Show result\n",
        "  plt.imshow(np.transpose(image.cpu().numpy().squeeze(), (1,2,0)))\n",
        "  plt.title(f'Prediction: {categories[predicted_class]} \\nActual target: {categories[true_target]}')\n",
        "  plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['COVID', 'Lung_Opacity', 'Normal', 'Viral_Pneumonia']\n",
            "[ 0.7959498   0.4708463   0.41410276 -2.315463  ]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nDFEIF4cE6TV"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}