train_ViT.ipynb

{
 "cells": [
  {
   "cell_type": "code",
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   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "SXHPkkkSA30I",
    "outputId": "a9353789-4512-440e-d434-ed0f5b79ed6c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "!pip install ipykernel\n",
    "!pip install medmnist\n",
    "!pip install matplotlib\n",
    "!pip install numpy\n",
    "!pip install scikit-learn\n",
    "!pip install scikit-image\n",
    "!pip install scipy\n",
    "!pip install pandas\n",
    "!pip install tqdm\n",
    "!pip install Pillow\n",
    "!pip install fire\n",
    "!pip install torch\n",
    "!pip install torchvision"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bHIjq5d55jAb"
   },
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "-FUxJsiK5itO"
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "from torchvision import models, transforms\n",
    "\n",
    "import torch.nn.functional as F\n",
    "import medmnist\n",
    "from medmnist import INFO, Evaluator\n",
    "from torchvision.transforms import Compose, Grayscale, Resize, ToTensor, Normalize\n",
    "\n",
    "import medmnist\n",
    "from medmnist import INFO, Evaluator\n",
    "import os\n",
    "import numpy as np\n",
    "import shutil\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import itertools\n",
    "from matplotlib import pyplot\n",
    "import matplotlib.image as mpimg\n",
    "from torch.utils.data import DataLoader\n",
    "from tqdm import tqdm\n",
    "\n",
    "from sklearn.metrics import precision_score, recall_score, roc_auc_score, accuracy_score, confusion_matrix\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Swq7gaN39rVQ"
   },
   "source": [
    "## Load OrganAmnist dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "BaoTtZjW9d9s",
    "outputId": "c8b6cfc4-1323-455f-f083-f3d1bfd11dfe"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Channels: 1\n",
      "Classes: 11\n",
      "Task: multi-class\n",
      "labels: <class 'str'>\n"
     ]
    }
   ],
   "source": [
    "data_flag = 'organamnist'\n",
    "download = True\n",
    "\n",
    "info = INFO[data_flag]\n",
    "task = info['task']\n",
    "n_channels = info['n_channels']\n",
    "n_classes = len(info['label'])\n",
    "\n",
    "print(f\"Channels: {n_channels}\")\n",
    "print(f\"Classes: {n_classes}\")\n",
    "print(f\"Task: {task}\")\n",
    "print(f\"labels: {type(info['label']['0'])}\")\n",
    "\n",
    "DataClass = getattr(medmnist, info['python_class'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4qta8BZJWh9E",
    "outputId": "7fe6f65f-6a5f-4e5c-d2d0-0a3271ad9476"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "labels: {'0': 'bladder', '1': 'femur-left', '2': 'femur-right', '3': 'heart', '4': 'kidney-left', '5': 'kidney-right', '6': 'liver', '7': 'lung-left', '8': 'lung-right', '9': 'pancreas', '10': 'spleen'}\n",
      "dict_values(['bladder', 'femur-left', 'femur-right', 'heart', 'kidney-left', 'kidney-right', 'liver', 'lung-left', 'lung-right', 'pancreas', 'spleen'])\n"
     ]
    }
   ],
   "source": [
    "print(f'labels: {info[\"label\"]}')\n",
    "classes = info[\"label\"].values()\n",
    "print(classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "wfG1IxwSriLc",
    "outputId": "b4299d39-2b0d-4e2a-a3ed-ff11b3538a06"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using downloaded and verified file: /root/.medmnist/organamnist_224.npz\n",
      "Using downloaded and verified file: /root/.medmnist/organamnist_224.npz\n",
      "Using downloaded and verified file: /root/.medmnist/organamnist_224.npz\n",
      "Using downloaded and verified file: /root/.medmnist/organamnist_224.npz\n"
     ]
    }
   ],
   "source": [
    "# Config\n",
    "data_flag = 'organamnist'\n",
    "download = True\n",
    "\n",
    "BATCH_SIZE = 64\n",
    "\n",
    "info = INFO[data_flag]\n",
    "task = info['task']\n",
    "n_channels = info['n_channels']\n",
    "n_classes = len(info['label'])\n",
    "\n",
    "DataClass = getattr(medmnist, info['python_class'])\n",
    "\n",
    "# Temporary transformation to load data as tensors without normalization\n",
    "temp_transform = transforms.Compose([transforms.ToTensor()])\n",
    "size = 224\n",
    "# Load the dataset with temporary transformation\n",
    "train_dataset = DataClass(split='train', transform=temp_transform, download=download, size=size)\n",
    "train_loader = DataLoader(dataset=train_dataset, batch_size=BATCH_SIZE, shuffle=False)\n",
    "\n",
    "mean128 = 0.0\n",
    "std128 = 0.0\n",
    "for images, _ in train_loader:\n",
    "    batch_samples = images.size(0)\n",
    "    images = images\n",
    "    mean128 += images.mean([0, 2, 3]) * batch_samples\n",
    "    std128 += images.std([0, 2, 3], unbiased=False) * batch_samples\n",
    "\n",
    "mean128 /= len(train_loader.dataset)\n",
    "std128 /= len(train_loader.dataset)\n",
    "mean128 = mean128.item()\n",
    "std128 = std128.item()\n",
    "\n",
    "data_transform128 = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Lambda(lambda x: x.repeat(3, 1, 1)),\n",
    "    transforms.Normalize(mean=[mean128] * 3, std=[std128] * 3)\n",
    "])\n",
    "\n",
    "train_dataset128 = DataClass(split='train', transform=data_transform128, download=download, size=size)\n",
    "val_dataset128 = DataClass(split='val', transform=data_transform128, download=download, size=size)\n",
    "\n",
    "train_loader128 = DataLoader(dataset=train_dataset128, batch_size=BATCH_SIZE, shuffle=True)\n",
    "val_loader128 = DataLoader(dataset=val_dataset128, batch_size=2*BATCH_SIZE, shuffle=False)\n",
    "\n",
    "test_dataset128 = DataClass(split='test', transform=data_transform128, download=download, size=size)\n",
    "test_loader128 = DataLoader(dataset=test_dataset128, batch_size=2*BATCH_SIZE, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Visualize the dataset\n",
    "# plt.figure(figsize=(10,10))\n",
    "# sample_img_idx = np.random.randint(0, len(train_dataset128), 20)\n",
    "\n",
    "# for i, j in enumerate(sample_img_idx):\n",
    "#     image, label = train_dataset128[j]\n",
    "#     ax=plt.subplot(5,5,i+1)\n",
    "#     plt.imshow(image[0])\n",
    "#     plt.title(info['label'][str(label[0])])\n",
    "#     plt.axis('off')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_iRbb3At-WLr"
   },
   "source": [
    "## Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "7VdYV490_BUf"
   },
   "outputs": [],
   "source": [
    "class CustomViT(nn.Module):\n",
    "    def __init__(self, num_classes, hidden_layer_sizes=None):\n",
    "        super(CustomViT, self).__init__()\n",
    "        self.base_model = models.vit_b_16(weights='DEFAULT')\n",
    "\n",
    "        for param in self.base_model.parameters():\n",
    "            param.requires_grad = False\n",
    "\n",
    "        num_features = self.base_model.heads.head.in_features\n",
    "        if hidden_layer_sizes:\n",
    "            layers = []\n",
    "            input_size = num_features\n",
    "            for size in hidden_layer_sizes:\n",
    "                layers.append(nn.Linear(input_size, size))\n",
    "                layers.append(nn.ReLU())\n",
    "                input_size = size\n",
    "            layers.append(nn.Linear(input_size, num_classes))\n",
    "            self.base_model.heads.head = nn.Sequential(*layers)\n",
    "        else:\n",
    "            self.base_model.heads.head = nn.Linear(num_features, num_classes)\n",
    "\n",
    "        for param in self.base_model.heads.head.parameters():\n",
    "            param.requires_grad = True\n",
    "\n",
    "    def forward(self, x):\n",
    "        return self.base_model(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Vpyl0Drr-b0C"
   },
   "outputs": [],
   "source": [
    "def compute_accuracy(model, data_loader, device):\n",
    "    model.eval()\n",
    "    correct = 0\n",
    "    total = 0\n",
    "\n",
    "    with torch.no_grad():\n",
    "        for images, labels in data_loader:\n",
    "            images, labels = images.to(device), labels.to(device)\n",
    "\n",
    "            outputs = model(images)\n",
    "            _, predicted = torch.max(outputs.data, 1)\n",
    "\n",
    "            total += labels.size(0)\n",
    "            correct += (predicted == labels).sum().item()\n",
    "\n",
    "    accuracy = correct / total * 100\n",
    "    return accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "OeEqzD2HWeYz"
   },
   "outputs": [],
   "source": [
    "def save_checkpoint(epoch, model, optimizer, loss):\n",
    "  torch.save({\n",
    "      'epoch': epoch,\n",
    "      'model_state_dict': model.state_dict(),\n",
    "      'optimizer_state_dict': optimizer.state_dict(),\n",
    "      'loss': loss,\n",
    "  }, f'checkpoint_{epoch}.pth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "FBr5QJpR_Gej"
   },
   "outputs": [],
   "source": [
    "def train(model, optimizer, train_loader, val_loader, batch_size, num_epochs, patience):\n",
    "    criterion = nn.CrossEntropyLoss()\n",
    "\n",
    "    history = {\"loss\": [], \"accuracy\": [], \"val_accuracy\": []}\n",
    "    best_val_loss = float('inf')\n",
    "    patience_counter = 0\n",
    "    best_model_weights = model.state_dict()\n",
    "\n",
    "    for epoch in range(num_epochs):\n",
    "        model.train()\n",
    "        running_loss = 0.0\n",
    "\n",
    "        for images, labels in tqdm(train_loader):\n",
    "            labels = labels.squeeze(1)\n",
    "            images, labels = images.to(device), labels.to(device)\n",
    "\n",
    "            optimizer.zero_grad()\n",
    "            outputs = model(images)\n",
    "            loss = criterion(outputs, labels)\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "\n",
    "            running_loss += loss.item()\n",
    "\n",
    "        model.eval()\n",
    "        val_loss = 0.0\n",
    "        with torch.no_grad():\n",
    "            for images, labels in val_loader:\n",
    "                labels = labels.squeeze(1)\n",
    "                images, labels = images.to(device), labels.to(device)\n",
    "                outputs = model(images)\n",
    "                loss = criterion(outputs, labels)\n",
    "                val_loss += loss.item()\n",
    "\n",
    "        val_loss /= len(val_loader)\n",
    "\n",
    "        print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {running_loss/len(train_loader):.4f}, Val Loss: {val_loss:.4f}')\n",
    "        history[\"loss\"].append(running_loss / len(train_loader))\n",
    "        history[\"accuracy\"].append(compute_accuracy(model, train_loader, device))\n",
    "        history[\"val_accuracy\"].append(compute_accuracy(model, val_loader, device))\n",
    "\n",
    "        save_checkpoint(epoch, model, optimizer, val_loss)\n",
    "\n",
    "        if val_loss < best_val_loss:\n",
    "            best_val_loss = val_loss\n",
    "            best_model_weights = model.state_dict()\n",
    "            patience_counter = 0\n",
    "        else:\n",
    "            patience_counter += 1\n",
    "            if patience_counter >= patience:\n",
    "                print(\"Early stopping triggered.\")\n",
    "                break\n",
    "\n",
    "    model.load_state_dict(best_model_weights)\n",
    "    print('Training complete!')\n",
    "    return history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "2rOPFiAb-X6d"
   },
   "outputs": [],
   "source": [
    "def get_model(shape):\n",
    "    print(f\"RUNNING ON {device}\")\n",
    "    custom_vit = CustomViT(num_classes=n_classes, hidden_layer_sizes=[1024, 512, 256])\n",
    "    custom_vit.to(device)\n",
    "    return custom_vit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "5-HCZJdo_UkN"
   },
   "outputs": [],
   "source": [
    "def setup_and_train(model, train_set, valid_set, batch_size, num_epochs):\n",
    "    optimizer = torch.optim.Adam(model.parameters(), lr=0.0001)\n",
    "    return train(model, optimizer, train_set, valid_set, batch_size, num_epochs, patience=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eXdW4Spn_ea1"
   },
   "source": [
    "## Evaluation functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "3SXgWre__bEp"
   },
   "outputs": [],
   "source": [
    "def calc_error(predictions, labels):\n",
    "    \"\"\"Return the error rate and confusions.\"\"\"\n",
    "    correct = np.sum(predictions == labels)\n",
    "    print(predictions.shape)\n",
    "    total = predictions.shape[0]\n",
    "\n",
    "    error = 100.0 - (100 * float(correct) / float(total))\n",
    "\n",
    "    confusions = np.zeros([n_classes, n_classes], np.float32)\n",
    "    bundled = zip(predictions, labels)\n",
    "    for predicted, actual in bundled:\n",
    "        confusions[predicted, actual] += 1\n",
    "\n",
    "    return error, confusions\n",
    "\n",
    "def get_predictions(model, data_loader, device):\n",
    "    model.eval()\n",
    "    all_predictions = []\n",
    "    all_labels = []\n",
    "    probabilities = []\n",
    "\n",
    "    with torch.no_grad():\n",
    "        for images, labels in tqdm(data_loader):\n",
    "            labels = labels.squeeze(1)\n",
    "            images = images.to(device)\n",
    "            labels = labels.to(device)\n",
    "\n",
    "            outputs = model(images)\n",
    "            probs = F.softmax(outputs, dim=1)\n",
    "            _, predicted = torch.max(outputs.data, 1)\n",
    "\n",
    "            all_predictions.append(predicted.cpu().numpy())\n",
    "            all_labels.append(labels.cpu().numpy())\n",
    "            probabilities.append(probs.cpu().numpy())\n",
    "\n",
    "    return (\n",
    "        np.concatenate(all_predictions),\n",
    "        np.concatenate(all_labels),\n",
    "        np.vstack(probabilities)\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "2WeGMgzT_dUk"
   },
   "outputs": [],
   "source": [
    "def eval_on_test_set(model, test_loader, batch_size):\n",
    "    # Get predictions and labels\n",
    "    test_predictions, test_labels, probs = get_predictions(model, test_loader, device)\n",
    "    test_labels = np.array(test_labels)\n",
    "    test_predictions = np.array(test_predictions)\n",
    "    test_probs = np.array(probs)\n",
    "\n",
    "    # Confusion matrix\n",
    "    test_conf_mat = confusion_matrix(test_labels, test_predictions)\n",
    "\n",
    "    # Accuracy\n",
    "    accuracy = accuracy_score(test_labels, test_predictions)\n",
    "    print(f\"Accuracy: {accuracy * 100:.2f}%\")\n",
    "\n",
    "    # Recall (macro and per class)\n",
    "    recall_macro = recall_score(test_labels, test_predictions, average='macro')\n",
    "    recall_per_class = recall_score(test_labels, test_predictions, average=None)\n",
    "    print(f\"Recall (Macro): {recall_macro * 100:.2f}%\")\n",
    "\n",
    "    # Precision (macro and per class)\n",
    "    precision_macro = precision_score(test_labels, test_predictions, average='macro')\n",
    "    precision_per_class = precision_score(test_labels, test_predictions, average=None)\n",
    "    print(f\"Precision (Macro): {precision_macro * 100:.2f}%\")\n",
    "\n",
    "    auc = roc_auc_score(test_labels, test_probs, multi_class='ovr')\n",
    "    print(f\"AUC: {auc:.2f}\")\n",
    "\n",
    "    # Plot confusion matrix\n",
    "    plt.figure(figsize=(6, 6))\n",
    "    plt.xlabel('Actual')\n",
    "    plt.ylabel('Predicted')\n",
    "    plt.grid(False)\n",
    "    plt.xticks(np.arange(len(test_conf_mat)), labels=np.arange(len(test_conf_mat)))\n",
    "    plt.yticks(np.arange(len(test_conf_mat)), labels=np.arange(len(test_conf_mat)))\n",
    "    plt.imshow(test_conf_mat, cmap=plt.cm.jet, interpolation='nearest')\n",
    "\n",
    "    for i, cas in enumerate(test_conf_mat):\n",
    "        for j, count in enumerate(cas):\n",
    "            if count > 0:\n",
    "                xoff = .07 * len(str(count))\n",
    "                plt.text(j - xoff, i + .2, int(count), fontsize=9, color='white')\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "whUmmI4E_V8Q"
   },
   "outputs": [],
   "source": [
    "def plot_hist(history):\n",
    "    plt.figure(figsize=(8,8))\n",
    "\n",
    "    plt.subplot(211)\n",
    "    plt.plot(history['accuracy'])\n",
    "    plt.plot(history['val_accuracy'])\n",
    "    plt.title('model accuracy')\n",
    "    plt.ylabel('accuracy')\n",
    "    plt.xlabel('epoch')\n",
    "    plt.legend(['train', 'validation'], loc='upper left')\n",
    "\n",
    "    # History for loss\n",
    "    plt.subplot(212)\n",
    "    plt.plot(history['loss'])\n",
    "    plt.title('model loss')\n",
    "    plt.ylabel('loss')\n",
    "    plt.xlabel('epoch')\n",
    "    plt.legend(['train'], loc='upper left')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ssjOjswW_iOc"
   },
   "source": [
    "## Run the training and evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "gZTCbakH-ImM"
   },
   "outputs": [],
   "source": [
    "if torch.backends.mps.is_available():\n",
    "    device = torch.device(\"mps\")\n",
    "elif torch.cuda.is_available():\n",
    "    device = torch.device(\"cuda\")\n",
    "else:\n",
    "    device = torch.device(\"cpu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "NRK8MxHe_oM5"
   },
   "outputs": [],
   "source": [
    "num_epochs = 15\n",
    "batch_size = 256"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "s2-mHc0VHtdI",
    "outputId": "e777574f-5e7e-4fe2-9c27-fd570fc33ea4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RUNNING ON cuda\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading: \"https://download.pytorch.org/models/vit_b_16-c867db91.pth\" to /root/.cache/torch/hub/checkpoints/vit_b_16-c867db91.pth\n",
      "100%|██████████| 330M/330M [00:01<00:00, 216MB/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 224, 224])\n"
     ]
    }
   ],
   "source": [
    "x, y = train_dataset[0]\n",
    "model = get_model(x.shape)\n",
    "print(x.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "t3ROC2oo_sr-",
    "outputId": "008ab18d-2843-4b9a-cfd2-3a07940043af"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 541/541 [01:52<00:00,  4.80it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [1/15], Loss: 0.4238, Val Loss: 0.0946\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 541/541 [01:51<00:00,  4.85it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [2/15], Loss: 0.1191, Val Loss: 0.0533\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 541/541 [01:51<00:00,  4.86it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [3/15], Loss: 0.0843, Val Loss: 0.0593\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 541/541 [01:51<00:00,  4.87it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [4/15], Loss: 0.0647, Val Loss: 0.0704\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 541/541 [01:51<00:00,  4.84it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [5/15], Loss: 0.0455, Val Loss: 0.0495\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 541/541 [01:51<00:00,  4.87it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [6/15], Loss: 0.0343, Val Loss: 0.0538\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 541/541 [01:52<00:00,  4.82it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [7/15], Loss: 0.0275, Val Loss: 0.0738\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 541/541 [01:51<00:00,  4.85it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [8/15], Loss: 0.0202, Val Loss: 0.0601\n",
      "Early stopping triggered.\n",
      "Training complete!\n"
     ]
    }
   ],
   "source": [
    "history = setup_and_train(model, train_loader128, val_loader128, batch_size, num_epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "BidkDQL77n4c"
   },
   "outputs": [],
   "source": [
    "loss_train = [0.4238, 0.1191, 0.0843, 0.0647, 0.0455, 0.0343, 0.0275, 0.0202]\n",
    "loss_valid = [0.0946, 0.0533, 0.0593, 0.0704, 0.0495, 0.0538, 0.0738, 0.0601]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 382
    },
    "id": "6Uj0wu4m8Jbj",
    "outputId": "c22c68f0-3c1d-488a-e44d-1a24500bf250"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,8))\n",
    "\n",
    "plt.subplot(211)\n",
    "plt.plot(loss_train)\n",
    "plt.plot(loss_valid)\n",
    "plt.title('Learning curves')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.legend(['train', 'validation'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 717
    },
    "id": "dQcKbANW_ynn",
    "outputId": "78d6bab4-5cf3-465c-cfd4-69d3c6350bb8"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_hist(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 624
    },
    "id": "D0XC2iKccvx7",
    "outputId": "8d5ca895-a46d-4586-e564-a0727b861a14"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 51/51 [00:17<00:00,  2.89it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 98.14%\n",
      "Recall (Macro): 98.05%\n",
      "Precision (Macro): 98.26%\n",
      "AUC: 1.00\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eval_on_test_set(model, val_loader128, batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 624
    },
    "id": "KilipVTL_0dD",
    "outputId": "ea9d9c7f-31de-4541-8bd0-b1a2532704ff"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 139/139 [00:48<00:00,  2.88it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 94.57%\n",
      "Recall (Macro): 94.01%\n",
      "Precision (Macro): 94.56%\n",
      "AUC: 1.00\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eval_on_test_set(model, test_loader128, batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "GKndIM3xXRlU"
   },
   "outputs": [],
   "source": [
    "torch.save(model.state_dict(), 'fine_tuned_vit_state_dict.pth')"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "machine_shape": "hm",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "name": "python3"
  },
  "language_info": {
   "name": "python"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}