{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b00dbc42",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[XPU] Cache cleared and peak memory stats reset.\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "\n",
    "def clean_device_memory():\n",
    "    if torch.xpu.is_available():\n",
    "        torch.xpu.empty_cache()\n",
    "        torch.xpu.reset_peak_memory_stats()\n",
    "        print(\"[XPU] Cache cleared and peak memory stats reset.\")\n",
    "    elif torch.cuda.is_available():\n",
    "        torch.cuda.empty_cache()\n",
    "        torch.cuda.reset_peak_memory_stats()\n",
    "        print(\"[CUDA] Cache cleared and peak memory stats reset.\")\n",
    "    else:\n",
    "        print(\"No XPU or CUDA device found.\")\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    clean_device_memory()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "598b91f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Python310\\lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--- Starting ViT Training with Larger Batch Size ---\n",
      "Using device: xpu with batch size: 16\n",
      "Torch version: 2.7.0+xpu\n",
      "Current XPU memory allocated: 0.0 MB\n",
      "Max XPU memory allocated: 0.0 MB\n",
      "\n",
      "[1/4] Loading dataset...\n",
      "[DEBUG] Found 10676 real images\n",
      "[DEBUG] Found 76182 fake images\n",
      "[DEBUG] Using 10676 samples per class (balanced)\n",
      "[DEBUG] Final dataset size: 21352 samples\n",
      "[DEBUG] Batch size: 16 | Total batches: 1335\n",
      "\n",
      "[2/4] Initializing model...\n",
      "\n",
      "[Memory] Allocated: 327.30 MB | Reserved: 382.00 MB | Max: 327.30 MB\n",
      "\n",
      "[3/4] Starting training...\n",
      "\n",
      "[Epoch 1/10]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[Memory] Allocated: 327.30 MB | Reserved: 382.00 MB | Max: 327.30 MB\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
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     "text": [
      "\n",
      "[Memory] Allocated: 1326.77 MB | Reserved: 1592.00 MB | Max: 3180.14 MB\n"
     ]
    },
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     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
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      "\n",
      "[Memory] Allocated: 1325.01 MB | Reserved: 1624.00 MB | Max: 3180.14 MB\n"
     ]
    },
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    },
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     "text": [
      "\n",
      "[Memory] Allocated: 1325.77 MB | Reserved: 1622.00 MB | Max: 3180.14 MB\n"
     ]
    },
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    },
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     "name": "stdout",
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      "\n",
      "[Memory] Allocated: 1326.49 MB | Reserved: 1612.00 MB | Max: 3180.14 MB\n"
     ]
    },
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    },
    {
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      "\n",
      "[Memory] Allocated: 1325.77 MB | Reserved: 1602.00 MB | Max: 3180.14 MB\n"
     ]
    },
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    },
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      "\n",
      "[Memory] Allocated: 1325.48 MB | Reserved: 1584.00 MB | Max: 3180.14 MB\n"
     ]
    },
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     "name": "stdout",
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      "\n",
      "[Memory] Allocated: 1326.30 MB | Reserved: 1612.00 MB | Max: 3180.14 MB\n"
     ]
    },
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     ]
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      "[Memory] Allocated: 1325.49 MB | Reserved: 1614.00 MB | Max: 3180.14 MB\n"
     ]
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      "[Memory] Allocated: 1325.71 MB | Reserved: 1592.00 MB | Max: 3180.14 MB\n"
     ]
    },
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      "\n",
      "[Memory] Allocated: 1325.77 MB | Reserved: 1602.00 MB | Max: 3180.14 MB\n"
     ]
    },
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     ]
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     ]
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     ]
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     ]
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     ]
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     ]
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     ]
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     ]
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     ]
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     ]
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Results] Loss: 923.3108 | Accuracy: 54.88%\n",
      "\n",
      "[Memory] Allocated: 1331.43 MB | Reserved: 1640.00 MB | Max: 3180.14 MB\n",
      "\n",
      "[Epoch 2/10]\n"
     ]
    },
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Results] Loss: 603.1762 | Accuracy: 77.53%\n",
      "\n",
      "[Memory] Allocated: 1331.50 MB | Reserved: 1598.00 MB | Max: 3180.14 MB\n",
      "\n",
      "[4/4] Saving model...\n",
      "Model saved to ./vit_digital_forensics_xpu.pth\n"
     ]
    }
   ],
   "source": [
    "# ViT Training with Larger Batch Size on XPU (Fixed Dataset Class)\n",
    "\n",
    "import os\n",
    "import glob\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from torchvision import transforms\n",
    "from PIL import Image\n",
    "import timm\n",
    "from tqdm import tqdm\n",
    "import random\n",
    "\n",
    "# ----------------------------\n",
    "# CONFIGURATION\n",
    "# ----------------------------\n",
    "DATASET_DIR = 'frames'\n",
    "BATCH_SIZE = 16  # Increased from 8 to 16\n",
    "EPOCHS = 10\n",
    "LEARNING_RATE = 3e-5\n",
    "NUM_CLASSES = 2\n",
    "IMG_SIZE = 224\n",
    "DEVICE = 'xpu' if torch.xpu.is_available() else 'cuda' if torch.cuda.is_available() else 'cpu'\n",
    "MODEL_SAVE_PATH = './vit_digital_forensics_xpu.pth'\n",
    "\n",
    "print(\"\\n--- Starting ViT Training with Larger Batch Size ---\")\n",
    "print(f\"Using device: {DEVICE} with batch size: {BATCH_SIZE}\")\n",
    "print(\"Torch version:\", torch.__version__)\n",
    "print(\"Current XPU memory allocated:\", torch.xpu.memory_allocated()/1024**2, \"MB\")\n",
    "print(\"Max XPU memory allocated:\", torch.xpu.max_memory_allocated()/1024**2, \"MB\")\n",
    "\n",
    "# ----------------------------\n",
    "# DATASET CLASS (FIXED IMPLEMENTATION)\n",
    "# ----------------------------\n",
    "class ForgeryImageDataset(Dataset):\n",
    "    def __init__(self, root_dir, transform=None):\n",
    "        self.samples = []\n",
    "        self.transform = transform\n",
    "        \n",
    "        # Verify dataset structure\n",
    "        real_folder = os.path.join(root_dir, 'real')\n",
    "        fake_folder = os.path.join(root_dir, 'fake')\n",
    "        print(f\"[DEBUG] Found {len(glob.glob(os.path.join(real_folder, '*.jpg')))} real images\")\n",
    "        print(f\"[DEBUG] Found {len(glob.glob(os.path.join(fake_folder, '*.jpg')))} fake images\")\n",
    "        \n",
    "        for label, name in enumerate(['real', 'fake']):\n",
    "            folder = os.path.join(root_dir, name)\n",
    "            for img_path in glob.glob(os.path.join(folder, '*.jpg')):\n",
    "                self.samples.append((img_path, label))\n",
    "\n",
    "        # Balance classes\n",
    "        real_samples = [s for s in self.samples if s[1] == 0]\n",
    "        fake_samples = [s for s in self.samples if s[1] == 1]\n",
    "        min_len = min(len(real_samples), len(fake_samples))\n",
    "        print(f\"[DEBUG] Using {min_len} samples per class (balanced)\")\n",
    "        \n",
    "        self.samples = random.sample(real_samples, min_len) + random.sample(fake_samples, min_len)\n",
    "        random.shuffle(self.samples)\n",
    "        print(f\"[DEBUG] Final dataset size: {len(self.samples)} samples\")\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.samples)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        img_path, label = self.samples[idx]\n",
    "        image = Image.open(img_path).convert('RGB')\n",
    "        if self.transform:\n",
    "            image = self.transform(image)\n",
    "        return image, label\n",
    "\n",
    "# ----------------------------\n",
    "# TRANSFORMS\n",
    "# ----------------------------\n",
    "transform = transforms.Compose([\n",
    "    transforms.Resize((IMG_SIZE, IMG_SIZE)),\n",
    "    transforms.RandomHorizontalFlip(),\n",
    "    transforms.RandomRotation(10),\n",
    "    transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2),\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize([0.5]*3, [0.5]*3)\n",
    "])\n",
    "\n",
    "# ----------------------------\n",
    "# MEMORY MONITORING FUNCTION\n",
    "# ----------------------------\n",
    "def print_memory_stats():\n",
    "    if DEVICE == 'xpu':\n",
    "        stats = {\n",
    "            'allocated': torch.xpu.memory_allocated()/1024**2,\n",
    "            'reserved': torch.xpu.memory_reserved()/1024**2,\n",
    "            'max_alloc': torch.xpu.max_memory_allocated()/1024**2\n",
    "        }\n",
    "        print(\"\\n[Memory] Allocated: {allocated:.2f} MB | Reserved: {reserved:.2f} MB | Max: {max_alloc:.2f} MB\".format(**stats))\n",
    "\n",
    "# ----------------------------\n",
    "# TRAINING LOOP\n",
    "# ----------------------------\n",
    "def train():\n",
    "    # Initialize dataset and loader\n",
    "    print(\"\\n[1/4] Loading dataset...\")\n",
    "    dataset = ForgeryImageDataset(DATASET_DIR, transform=transform)\n",
    "    loader = DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=0)\n",
    "    print(f\"[DEBUG] Batch size: {BATCH_SIZE} | Total batches: {len(loader)}\")\n",
    "    \n",
    "    # Initialize model\n",
    "    print(\"\\n[2/4] Initializing model...\")\n",
    "    model = timm.create_model('vit_base_patch16_224', pretrained=True, num_classes=NUM_CLASSES)\n",
    "    model = model.to(DEVICE)\n",
    "    criterion = nn.CrossEntropyLoss()\n",
    "    optimizer = optim.AdamW(model.parameters(), lr=LEARNING_RATE)\n",
    "    print_memory_stats()\n",
    "    \n",
    "    # Training loop\n",
    "    print(\"\\n[3/4] Starting training...\")\n",
    "    model.train()\n",
    "    for epoch in range(EPOCHS):\n",
    "        print(f\"\\n[Epoch {epoch+1}/{EPOCHS}]\")\n",
    "        total_loss, correct = 0, 0\n",
    "        \n",
    "        for batch_idx, (imgs, labels) in enumerate(tqdm(loader, desc=\"Training\")):\n",
    "            # Memory check every 50 batches\n",
    "            if batch_idx % 50 == 0:\n",
    "                print_memory_stats()\n",
    "            \n",
    "            # Move data to device\n",
    "            imgs, labels = imgs.to(DEVICE), labels.to(DEVICE)\n",
    "            \n",
    "            # Forward pass\n",
    "            optimizer.zero_grad()\n",
    "            outputs = model(imgs)\n",
    "            loss = criterion(outputs, labels)\n",
    "            \n",
    "            # Backward pass\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            \n",
    "            # Metrics\n",
    "            total_loss += loss.item()\n",
    "            correct += (torch.argmax(outputs, dim=1) == labels).sum().item()\n",
    "            \n",
    "            # Clean up\n",
    "            del outputs\n",
    "            torch.xpu.empty_cache()\n",
    "        \n",
    "        # Epoch statistics\n",
    "        epoch_acc = 100. * correct / len(dataset)\n",
    "        print(f\"[Results] Loss: {total_loss:.4f} | Accuracy: {epoch_acc:.2f}%\")\n",
    "        print_memory_stats()\n",
    "\n",
    "    # Save model\n",
    "    print(\"\\n[4/4] Saving model...\")\n",
    "    torch.save(model.state_dict(), MODEL_SAVE_PATH)\n",
    "    print(f\"Model saved to {MODEL_SAVE_PATH}\")\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7459c841",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Python310\\lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n",
      "[I 2025-05-18 12:22:12,743] A new study created in memory with name: no-name-0238caf2-2544-421d-8841-7ed656b2c736\n",
      "C:\\Users\\devcloud\\AppData\\Local\\Temp\\ipykernel_12100\\463889153.py:98: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  lr = trial.suggest_loguniform('lr', 1e-5, 1e-3)\n",
      "C:\\Users\\devcloud\\AppData\\Local\\Temp\\ipykernel_12100\\463889153.py:100: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  weight_decay = trial.suggest_loguniform('weight_decay', 1e-6, 1e-2)\n",
      "[I 2025-05-18 13:36:53,991] Trial 0 finished with value: 0.5346604215456674 and parameters: {'lr': 0.00019442949702926413, 'batch_size': 8, 'weight_decay': 0.0066764067597068905}. Best is trial 0 with value: 0.5346604215456674.\n",
      "[I 2025-05-18 14:44:15,574] Trial 1 finished with value: 0.5381733021077283 and parameters: {'lr': 0.00010325233112691653, 'batch_size': 16, 'weight_decay': 1.5499675871668855e-05}. Best is trial 1 with value: 0.5381733021077283.\n",
      "[I 2025-05-18 15:54:16,517] Trial 2 finished with value: 0.7782201405152225 and parameters: {'lr': 1.0369533463569177e-05, 'batch_size': 32, 'weight_decay': 0.009006790996778456}. Best is trial 2 with value: 0.7782201405152225.\n",
      "[I 2025-05-18 17:03:08,338] Trial 3 finished with value: 0.5091334894613583 and parameters: {'lr': 0.0003046327737509729, 'batch_size': 16, 'weight_decay': 7.688366508752892e-06}. Best is trial 2 with value: 0.7782201405152225.\n",
      "[I 2025-05-18 18:11:08,096] Trial 4 finished with value: 0.5110070257611241 and parameters: {'lr': 0.0008134874694963872, 'batch_size': 32, 'weight_decay': 0.0012103868091215206}. Best is trial 2 with value: 0.7782201405152225.\n",
      "[I 2025-05-18 19:20:58,079] Trial 5 finished with value: 0.7440281030444965 and parameters: {'lr': 1.0562531428879552e-05, 'batch_size': 32, 'weight_decay': 0.0021515636255839536}. Best is trial 2 with value: 0.7782201405152225.\n",
      "[I 2025-05-18 20:31:06,385] Trial 6 finished with value: 0.5014051522248244 and parameters: {'lr': 0.0006120287586805153, 'batch_size': 16, 'weight_decay': 0.0003485698669576425}. Best is trial 2 with value: 0.7782201405152225.\n",
      "[I 2025-05-18 21:38:58,408] Trial 7 finished with value: 0.6770491803278689 and parameters: {'lr': 3.2343860924914236e-05, 'batch_size': 16, 'weight_decay': 1.1758959449886972e-05}. Best is trial 2 with value: 0.7782201405152225.\n",
      "[I 2025-05-18 22:56:43,453] Trial 8 finished with value: 0.4960187353629977 and parameters: {'lr': 0.0009598946035424287, 'batch_size': 8, 'weight_decay': 0.007537464093474474}. Best is trial 2 with value: 0.7782201405152225.\n",
      "[I 2025-05-19 00:13:39,096] Trial 9 finished with value: 0.6517564402810304 and parameters: {'lr': 2.5743923801217405e-05, 'batch_size': 8, 'weight_decay': 1.3856930026507902e-06}. Best is trial 2 with value: 0.7782201405152225.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Best hyperparameters found:\n",
      "{'lr': 1.0369533463569177e-05, 'batch_size': 32, 'weight_decay': 0.009006790996778456}\n",
      "Accuracy: 77.82%\n",
      "Model saved at: vit_best_model_optuna.pth\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import glob\n",
    "import random\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import Dataset, DataLoader, random_split\n",
    "from torchvision import transforms\n",
    "from PIL import Image\n",
    "import timm\n",
    "from tqdm import tqdm\n",
    "import optuna\n",
    "\n",
    "# ----------------------------\n",
    "# CONFIGURATION\n",
    "# ----------------------------\n",
    "DATASET_DIR = \"frames\"\n",
    "DEVICE = 'xpu' if torch.xpu.is_available() else 'cuda' if torch.cuda.is_available() else 'cpu'\n",
    "IMG_SIZE = 224\n",
    "NUM_CLASSES = 2\n",
    "MODEL_SAVE_PATH = \"vit_best_model_optuna.pth\"\n",
    "\n",
    "# ----------------------------\n",
    "# DATASET CLASS\n",
    "# ----------------------------\n",
    "class ForgeryImageDataset(Dataset):\n",
    "    def __init__(self, root_dir, transform=None):\n",
    "        self.samples = []\n",
    "        self.transform = transform\n",
    "\n",
    "        for label, name in enumerate(['real', 'fake']):\n",
    "            folder = os.path.join(root_dir, name)\n",
    "            for img_path in glob.glob(os.path.join(folder, '*.jpg')):\n",
    "                self.samples.append((img_path, label))\n",
    "\n",
    "        # Balance classes\n",
    "        real_samples = [s for s in self.samples if s[1] == 0]\n",
    "        fake_samples = [s for s in self.samples if s[1] == 1]\n",
    "        min_len = min(len(real_samples), len(fake_samples))\n",
    "        self.samples = random.sample(real_samples, min_len) + random.sample(fake_samples, min_len)\n",
    "        random.shuffle(self.samples)\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.samples)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        img_path, label = self.samples[idx]\n",
    "        image = Image.open(img_path).convert('RGB')\n",
    "        if self.transform:\n",
    "            image = self.transform(image)\n",
    "        return image, label\n",
    "\n",
    "# ----------------------------\n",
    "# TRANSFORMS\n",
    "# ----------------------------\n",
    "transform = transforms.Compose([\n",
    "    transforms.Resize((IMG_SIZE, IMG_SIZE)),\n",
    "    transforms.RandomHorizontalFlip(),\n",
    "    transforms.ColorJitter(0.1, 0.1, 0.1),\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize([0.5]*3, [0.5]*3)\n",
    "])\n",
    "\n",
    "# ----------------------------\n",
    "# TRAINING FUNCTION\n",
    "# ----------------------------\n",
    "def train_model(model, train_loader, optimizer, criterion):\n",
    "    model.train()\n",
    "    for imgs, labels in train_loader:\n",
    "        imgs, labels = imgs.to(DEVICE), labels.to(DEVICE)\n",
    "        optimizer.zero_grad()\n",
    "        outputs = model(imgs)\n",
    "        loss = criterion(outputs, labels)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "# ----------------------------\n",
    "# VALIDATION FUNCTION\n",
    "# ----------------------------\n",
    "def evaluate_model(model, val_loader):\n",
    "    model.eval()\n",
    "    correct = 0\n",
    "    total = 0\n",
    "    with torch.no_grad():\n",
    "        for imgs, labels in val_loader:\n",
    "            imgs, labels = imgs.to(DEVICE), labels.to(DEVICE)\n",
    "            outputs = model(imgs)\n",
    "            preds = outputs.argmax(1)\n",
    "            correct += (preds == labels).sum().item()\n",
    "            total += labels.size(0)\n",
    "    return correct / total\n",
    "\n",
    "# ----------------------------\n",
    "# OPTUNA OBJECTIVE FUNCTION\n",
    "# ----------------------------\n",
    "def objective(trial):\n",
    "    # Hyperparameters to tune\n",
    "    lr = trial.suggest_loguniform('lr', 1e-5, 1e-3)\n",
    "    batch_size = trial.suggest_categorical('batch_size', [8, 16, 32])\n",
    "    weight_decay = trial.suggest_loguniform('weight_decay', 1e-6, 1e-2)\n",
    "\n",
    "    # Prepare dataset\n",
    "    dataset = ForgeryImageDataset(DATASET_DIR, transform=transform)\n",
    "    val_len = int(0.2 * len(dataset))\n",
    "    train_len = len(dataset) - val_len\n",
    "    train_ds, val_ds = random_split(dataset, [train_len, val_len])\n",
    "    train_loader = DataLoader(train_ds, batch_size=batch_size, shuffle=True)\n",
    "    val_loader = DataLoader(val_ds, batch_size=batch_size)\n",
    "\n",
    "    # Model\n",
    "    model = timm.create_model('vit_base_patch16_224', pretrained=True, num_classes=NUM_CLASSES)\n",
    "    model = model.to(DEVICE)\n",
    "\n",
    "    criterion = nn.CrossEntropyLoss()\n",
    "    optimizer = optim.AdamW(model.parameters(), lr=lr, weight_decay=weight_decay)\n",
    "\n",
    "    # Training loop (fewer epochs to save time)\n",
    "    EPOCHS = 5\n",
    "    for _ in range(EPOCHS):\n",
    "        train_model(model, train_loader, optimizer, criterion)\n",
    "\n",
    "    acc = evaluate_model(model, val_loader)\n",
    "    trial.set_user_attr(\"model\", model.state_dict())\n",
    "    return acc\n",
    "\n",
    "# ----------------------------\n",
    "# RUN OPTUNA\n",
    "# ----------------------------\n",
    "study = optuna.create_study(direction=\"maximize\")\n",
    "study.optimize(objective, n_trials=10)\n",
    "\n",
    "# ----------------------------\n",
    "# SAVE BEST MODEL\n",
    "# ----------------------------\n",
    "best_trial = study.best_trial\n",
    "best_model_state = best_trial.user_attrs[\"model\"]\n",
    "\n",
    "final_model = timm.create_model('vit_base_patch16_224', pretrained=True, num_classes=NUM_CLASSES)\n",
    "final_model.load_state_dict(best_model_state)\n",
    "torch.save(final_model.state_dict(), MODEL_SAVE_PATH)\n",
    "\n",
    "print(\"\\nBest hyperparameters found:\")\n",
    "print(best_trial.params)\n",
    "print(f\"Accuracy: {best_trial.value:.2%}\")\n",
    "print(f\"Model saved at: {MODEL_SAVE_PATH}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "764639bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Python310\\lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using device: xpu\n",
      "\n",
      "===== Training vit_base_patch16_224 =====\n",
      "Epoch 1/5 | Train Acc: 68.58% | Val Acc: 76.79%\n",
      "Epoch 2/5 | Train Acc: 81.67% | Val Acc: 80.18%\n",
      "Epoch 3/5 | Train Acc: 85.97% | Val Acc: 86.66%\n",
      "Epoch 4/5 | Train Acc: 88.99% | Val Acc: 85.64%\n",
      "Epoch 5/5 | Train Acc: 90.65% | Val Acc: 85.36%\n",
      "\n",
      "===== Training vit_base_patch32_224 =====\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Python310\\lib\\site-packages\\huggingface_hub\\file_download.py:143: UserWarning: `huggingface_hub` cache-system uses symlinks by default to efficiently store duplicated files but your machine does not support them in C:\\Users\\devcloud\\.cache\\huggingface\\hub\\models--timm--vit_base_patch32_224.augreg_in21k_ft_in1k. Caching files will still work but in a degraded version that might require more space on your disk. This warning can be disabled by setting the `HF_HUB_DISABLE_SYMLINKS_WARNING` environment variable. For more details, see https://huggingface.co/docs/huggingface_hub/how-to-cache#limitations.\n",
      "To support symlinks on Windows, you either need to activate Developer Mode or to run Python as an administrator. In order to activate developer mode, see this article: https://docs.microsoft.com/en-us/windows/apps/get-started/enable-your-device-for-development\n",
      "  warnings.warn(message)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5 | Train Acc: 61.57% | Val Acc: 71.96%\n",
      "Epoch 2/5 | Train Acc: 69.05% | Val Acc: 74.73%\n",
      "Epoch 3/5 | Train Acc: 73.89% | Val Acc: 74.59%\n",
      "Epoch 4/5 | Train Acc: 76.91% | Val Acc: 77.67%\n",
      "Epoch 5/5 | Train Acc: 79.54% | Val Acc: 79.54%\n",
      "\n",
      "===== Training vit_small_patch16_224 =====\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Python310\\lib\\site-packages\\huggingface_hub\\file_download.py:143: UserWarning: `huggingface_hub` cache-system uses symlinks by default to efficiently store duplicated files but your machine does not support them in C:\\Users\\devcloud\\.cache\\huggingface\\hub\\models--timm--vit_small_patch16_224.augreg_in21k_ft_in1k. Caching files will still work but in a degraded version that might require more space on your disk. This warning can be disabled by setting the `HF_HUB_DISABLE_SYMLINKS_WARNING` environment variable. For more details, see https://huggingface.co/docs/huggingface_hub/how-to-cache#limitations.\n",
      "To support symlinks on Windows, you either need to activate Developer Mode or to run Python as an administrator. In order to activate developer mode, see this article: https://docs.microsoft.com/en-us/windows/apps/get-started/enable-your-device-for-development\n",
      "  warnings.warn(message)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5 | Train Acc: 64.53% | Val Acc: 71.56%\n",
      "Epoch 2/5 | Train Acc: 75.12% | Val Acc: 83.55%\n",
      "Epoch 3/5 | Train Acc: 80.67% | Val Acc: 81.25%\n",
      "Epoch 4/5 | Train Acc: 83.72% | Val Acc: 85.68%\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import time\n",
    "import csv\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import DataLoader, WeightedRandomSampler\n",
    "from torchvision import transforms, datasets\n",
    "import timm\n",
    "from tqdm import tqdm\n",
    "\n",
    "# ===================== CONFIG =====================\n",
    "DATA_DIR = \"frames\"  # root that contains train/ and val/\n",
    "OUTPUT_DIR = os.path.join(DATA_DIR, \"model_outputs\")\n",
    "CSV_PATH = os.path.join(OUTPUT_DIR, \"vit_comparison_results.csv\")\n",
    "os.makedirs(OUTPUT_DIR, exist_ok=True)\n",
    "\n",
    "DEVICE = (\n",
    "    torch.device(\"xpu\") if torch.xpu.is_available()\n",
    "    else torch.device(\"cuda\") if torch.cuda.is_available()\n",
    "    else torch.device(\"cpu\")\n",
    ")\n",
    "print(\"Using device:\", DEVICE)\n",
    "\n",
    "# Hyper‑parameters fixed from Optuna best trial\n",
    "EPOCHS = 5\n",
    "BATCH_SIZE = 32\n",
    "LR = 1.0369533463569177e-05\n",
    "WEIGHT_DECAY = 0.009006790996778456\n",
    "IMG_SIZE = 224\n",
    "NUM_CLASSES = 2\n",
    "\n",
    "# Vision Transformer variants to compare (timm names)\n",
    "MODEL_NAMES = [\n",
    "    \"vit_large_patch16_224\",\n",
    "    \"deit_base_patch16_224\",\n",
    "    \"swin_tiny_patch4_window7_224\",\n",
    "]\n",
    "\n",
    "# ===================== DATA =====================\n",
    "transform_train = transforms.Compose([\n",
    "    transforms.Resize((IMG_SIZE, IMG_SIZE)),\n",
    "    transforms.RandomHorizontalFlip(),\n",
    "    transforms.RandomRotation(10),\n",
    "    transforms.ColorJitter(0.1, 0.1, 0.1),\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize([0.5]*3, [0.5]*3),\n",
    "])\n",
    "\n",
    "transform_val = transforms.Compose([\n",
    "    transforms.Resize((IMG_SIZE, IMG_SIZE)),\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize([0.5]*3, [0.5]*3),\n",
    "])\n",
    "\n",
    "train_ds = datasets.ImageFolder(os.path.join(DATA_DIR, \"train\"), transform=transform_train)\n",
    "val_ds   = datasets.ImageFolder(os.path.join(DATA_DIR, \"val\"),   transform=transform_val)\n",
    "\n",
    "# ----- Balanced Sampler for train loader -----\n",
    "class_counts = [0]*NUM_CLASSES\n",
    "for _ , label in train_ds.samples:\n",
    "    class_counts[label] += 1\n",
    "class_weights = [sum(class_counts)/c for c in class_counts]\n",
    "sample_weights = [class_weights[label] for _ , label in train_ds.samples]\n",
    "train_sampler = WeightedRandomSampler(sample_weights, num_samples=len(train_ds), replacement=True)\n",
    "\n",
    "train_loader = DataLoader(train_ds, batch_size=BATCH_SIZE, sampler=train_sampler, num_workers=0)\n",
    "val_loader   = DataLoader(val_ds,   batch_size=BATCH_SIZE, shuffle=False,   num_workers=0)\n",
    "\n",
    "# ===================== TRAIN & EVAL FUNCTIONS =====================\n",
    "\n",
    "def train_one_epoch(model, loader, criterion, optimizer):\n",
    "    model.train()\n",
    "    running_loss, running_correct, total = 0.0, 0, 0\n",
    "    for imgs, labels in loader:\n",
    "        imgs, labels = imgs.to(DEVICE), labels.to(DEVICE)\n",
    "        optimizer.zero_grad()\n",
    "        outputs = model(imgs)\n",
    "        loss = criterion(outputs, labels)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        running_loss += loss.item() * imgs.size(0)\n",
    "        running_correct += (outputs.argmax(1) == labels).sum().item()\n",
    "        total += labels.size(0)\n",
    "    return running_loss/total, running_correct/total\n",
    "\n",
    "\n",
    "def evaluate(model, loader, criterion):\n",
    "    model.eval()\n",
    "    loss_sum, correct, total = 0.0, 0, 0\n",
    "    with torch.no_grad():\n",
    "        for imgs, labels in loader:\n",
    "            imgs, labels = imgs.to(DEVICE), labels.to(DEVICE)\n",
    "            outputs = model(imgs)\n",
    "            loss = criterion(outputs, labels)\n",
    "            loss_sum += loss.item()*imgs.size(0)\n",
    "            correct += (outputs.argmax(1)==labels).sum().item()\n",
    "            total += labels.size(0)\n",
    "    return loss_sum/total, correct/total\n",
    "\n",
    "# ===================== MAIN LOOP =====================\n",
    "results = []\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "\n",
    "for model_name in MODEL_NAMES:\n",
    "    print(f\"\\n===== Training {model_name} =====\")\n",
    "    model = timm.create_model(model_name, pretrained=True, num_classes=NUM_CLASSES)\n",
    "    model.to(DEVICE)\n",
    "\n",
    "    optimizer = optim.AdamW(model.parameters(), lr=LR, weight_decay=WEIGHT_DECAY)\n",
    "\n",
    "    start_time = time.time()\n",
    "    for epoch in range(1, EPOCHS+1):\n",
    "        train_loss, train_acc = train_one_epoch(model, train_loader, criterion, optimizer)\n",
    "        val_loss, val_acc     = evaluate(model, val_loader, criterion)\n",
    "        print(f\"Epoch {epoch}/{EPOCHS} | Train Acc: {train_acc*100:.2f}% | Val Acc: {val_acc*100:.2f}%\")\n",
    "\n",
    "    elapsed = (time.time() - start_time)/60  # minutes\n",
    "    model_path = os.path.join(OUTPUT_DIR, f\"{model_name.replace('/', '_')}.pth\")\n",
    "    torch.save(model.state_dict(), model_path)\n",
    "\n",
    "    results.append({\n",
    "        'model': model_name,\n",
    "        'val_accuracy': f\"{val_acc*100:.2f}\",\n",
    "        'train_accuracy': f\"{train_acc*100:.2f}\",\n",
    "        'time_min': f\"{elapsed:.1f}\",\n",
    "        'weights_path': model_path\n",
    "    })\n",
    "\n",
    "# ===================== SAVE CSV =====================\n",
    "with open(CSV_PATH, 'w', newline='') as csvfile:\n",
    "    writer = csv.DictWriter(csvfile, fieldnames=results[0].keys())\n",
    "    writer.writeheader()\n",
    "    for row in results:\n",
    "        writer.writerow(row)\n",
    "\n",
    "print(\"\\n🏁 All models trained. Results saved to:\", CSV_PATH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cd8a5e3c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Python310\\lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using device: xpu\n",
      "\n",
      "===== Training vit_small_patch32_224 =====\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                         \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5 | Train Acc: 87.94% | Val Acc: 88.12%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                         \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/5 | Train Acc: 88.36% | Val Acc: 88.20%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                         \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3/5 | Train Acc: 89.15% | Val Acc: 88.60%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                         \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 4/5 | Train Acc: 90.41% | Val Acc: 88.95%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                         \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 5/5 | Train Acc: 91.58% | Val Acc: 88.38%\n",
      "Model saved to model_outputs\\vit_small_patch32_224.pth\n",
      "\n",
      "===== Training vit_tiny_patch16_224 =====\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                         \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5 | Train Acc: 87.85% | Val Acc: 87.91%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                         \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/5 | Train Acc: 88.47% | Val Acc: 88.59%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                         \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3/5 | Train Acc: 89.47% | Val Acc: 88.69%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                         \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 4/5 | Train Acc: 90.95% | Val Acc: 88.95%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                         \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 5/5 | Train Acc: 92.41% | Val Acc: 89.20%\n",
      "Model saved to model_outputs\\vit_tiny_patch16_224.pth\n",
      "\n",
      "===== Training vit_tiny_patch32_224 =====\n",
      "Error training vit_tiny_patch32_224: Unknown model (vit_tiny_patch32_224)\n",
      "\n",
      "===== Training vit_large_patch16_224 =====\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Python310\\lib\\site-packages\\huggingface_hub\\file_download.py:143: UserWarning: `huggingface_hub` cache-system uses symlinks by default to efficiently store duplicated files but your machine does not support them in C:\\Users\\devcloud\\.cache\\huggingface\\hub\\models--timm--vit_large_patch16_224.augreg_in21k_ft_in1k. Caching files will still work but in a degraded version that might require more space on your disk. This warning can be disabled by setting the `HF_HUB_DISABLE_SYMLINKS_WARNING` environment variable. For more details, see https://huggingface.co/docs/huggingface_hub/how-to-cache#limitations.\n",
      "To support symlinks on Windows, you either need to activate Developer Mode or to run Python as an administrator. In order to activate developer mode, see this article: https://docs.microsoft.com/en-us/windows/apps/get-started/enable-your-device-for-development\n",
      "  warnings.warn(message)\n",
      "                                                                             \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5 | Train Acc: 88.04% | Val Acc: 88.32%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                             \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/5 | Train Acc: 89.53% | Val Acc: 89.89%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                             \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3/5 | Train Acc: 91.60% | Val Acc: 90.58%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                             \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 4/5 | Train Acc: 93.46% | Val Acc: 90.49%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                             \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 5/5 | Train Acc: 94.89% | Val Acc: 91.22%\n",
      "Model saved to model_outputs\\vit_large_patch16_224.pth\n",
      "\n",
      "===== Training vit_large_patch32_224 =====\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Python310\\lib\\site-packages\\huggingface_hub\\file_download.py:143: UserWarning: `huggingface_hub` cache-system uses symlinks by default to efficiently store duplicated files but your machine does not support them in C:\\Users\\devcloud\\.cache\\huggingface\\hub\\models--timm--vit_large_patch32_224.orig_in21k. Caching files will still work but in a degraded version that might require more space on your disk. This warning can be disabled by setting the `HF_HUB_DISABLE_SYMLINKS_WARNING` environment variable. For more details, see https://huggingface.co/docs/huggingface_hub/how-to-cache#limitations.\n",
      "To support symlinks on Windows, you either need to activate Developer Mode or to run Python as an administrator. In order to activate developer mode, see this article: https://docs.microsoft.com/en-us/windows/apps/get-started/enable-your-device-for-development\n",
      "  warnings.warn(message)\n",
      "                                                                           \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5 | Train Acc: 87.97% | Val Acc: 88.16%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                           \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/5 | Train Acc: 88.31% | Val Acc: 88.42%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                           \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3/5 | Train Acc: 89.06% | Val Acc: 88.70%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                           \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 4/5 | Train Acc: 90.13% | Val Acc: 89.34%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                           \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 5/5 | Train Acc: 91.31% | Val Acc: 89.23%\n",
      "Model saved to model_outputs\\vit_large_patch32_224.pth\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import torch\n",
    "from torch import nn\n",
    "from torchvision import datasets, transforms\n",
    "from torch.utils.data import DataLoader\n",
    "from timm import create_model\n",
    "from tqdm import tqdm\n",
    "\n",
    "# Paths\n",
    "data_dir = \"frames\"\n",
    "\n",
    "# Device\n",
    "device = torch.device(\"xpu\" if torch.xpu.is_available() else \"cpu\")\n",
    "print(f\"Using device: {device}\")\n",
    "\n",
    "# Hyperparameters\n",
    "BATCH_SIZE = 32\n",
    "LR = 1.03695e-5\n",
    "WEIGHT_DECAY = 0.0090067\n",
    "EPOCHS = 5\n",
    "\n",
    "# Transform\n",
    "transform = transforms.Compose([\n",
    "    transforms.Resize((224, 224)),\n",
    "    transforms.ToTensor(),\n",
    "])\n",
    "\n",
    "# Datasets & Dataloaders\n",
    "train_dataset = datasets.ImageFolder(os.path.join(data_dir, 'train'), transform=transform)\n",
    "val_dataset = datasets.ImageFolder(os.path.join(data_dir, 'val'), transform=transform)\n",
    "\n",
    "train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True)\n",
    "val_loader = DataLoader(val_dataset, batch_size=BATCH_SIZE, shuffle=False)\n",
    "\n",
    "# Vision Transformer model variants\n",
    "vit_models = [\n",
    "    \"vit_small_patch32_224\",\n",
    "    \"vit_tiny_patch16_224\",\n",
    "    \"vit_tiny_patch32_224\",\n",
    "    \"vit_large_patch16_224\",\n",
    "    \"vit_large_patch32_224\",\n",
    "    # \"vit_huge_patch14_224\"  # Uncomment if you have enough resources\n",
    "]\n",
    "\n",
    "# Training Function\n",
    "def train_model(model_name):\n",
    "    print(f\"\\n===== Training {model_name} =====\")\n",
    "\n",
    "    model = create_model(model_name, pretrained=True, num_classes=2)\n",
    "    model.to(device)\n",
    "\n",
    "    criterion = nn.CrossEntropyLoss()\n",
    "    optimizer = torch.optim.AdamW(model.parameters(), lr=LR, weight_decay=WEIGHT_DECAY)\n",
    "\n",
    "    for epoch in range(EPOCHS):\n",
    "        model.train()\n",
    "        train_correct = 0\n",
    "        total_train = 0\n",
    "\n",
    "        for images, labels in tqdm(train_loader, desc=f\"Epoch {epoch+1}/{EPOCHS} - Training\", leave=False):\n",
    "            images, labels = images.to(device), labels.to(device)\n",
    "            optimizer.zero_grad()\n",
    "            outputs = model(images)\n",
    "            loss = criterion(outputs, labels)\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "\n",
    "            _, preds = torch.max(outputs, 1)\n",
    "            train_correct += (preds == labels).sum().item()\n",
    "            total_train += labels.size(0)\n",
    "\n",
    "        train_acc = 100 * train_correct / total_train\n",
    "\n",
    "        # Evaluation\n",
    "        model.eval()\n",
    "        val_correct = 0\n",
    "        total_val = 0\n",
    "        with torch.no_grad():\n",
    "            for images, labels in val_loader:\n",
    "                images, labels = images.to(device), labels.to(device)\n",
    "                outputs = model(images)\n",
    "                _, preds = torch.max(outputs, 1)\n",
    "                val_correct += (preds == labels).sum().item()\n",
    "                total_val += labels.size(0)\n",
    "\n",
    "        val_acc = 100 * val_correct / total_val\n",
    "        print(f\"Epoch {epoch+1}/{EPOCHS} | Train Acc: {train_acc:.2f}% | Val Acc: {val_acc:.2f}%\")\n",
    "\n",
    "    # Save model weights\n",
    "    os.makedirs(\"model_outputs\", exist_ok=True)\n",
    "    save_path = os.path.join(\"model_outputs\", f\"{model_name}.pth\")\n",
    "    torch.save(model.state_dict(), save_path)\n",
    "    print(f\"Model saved to {save_path}\")\n",
    "\n",
    "# Run training for each ViT model\n",
    "for model_name in vit_models:\n",
    "    try:\n",
    "        train_model(model_name)\n",
    "    except Exception as e:\n",
    "        print(f\"Error training {model_name}: {str(e)}\")"
   ]
  }
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