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install grad_cam","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-02-14T13:45:16.885116Z","iopub.execute_input":"2026-02-14T13:45:16.885416Z","iopub.status.idle":"2026-02-14T13:45:27.134373Z","shell.execute_reply.started":"2026-02-14T13:45:16.885383Z","shell.execute_reply":"2026-02-14T13:45:27.133612Z"}},"outputs":[{"name":"stdout","text":"Collecting grad_cam\n  Downloading grad-cam-1.5.5.tar.gz (7.8 MB)\n\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m77.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m:00:01\u001b[0m0:01\u001b[0m\n\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\nRequirement already satisfied: numpy in /usr/local/lib/python3.12/dist-packages (from grad_cam) (2.0.2)\nRequirement already satisfied: Pillow in /usr/local/lib/python3.12/dist-packages (from grad_cam) (11.3.0)\nRequirement already satisfied: torch>=1.7.1 in /usr/local/lib/python3.12/dist-packages (from grad_cam) (2.8.0+cu126)\nRequirement already satisfied: torchvision>=0.8.2 in /usr/local/lib/python3.12/dist-packages (from grad_cam) (0.23.0+cu126)\nCollecting ttach (from grad_cam)\n  Downloading ttach-0.0.3-py3-none-any.whl.metadata (5.2 kB)\nRequirement already satisfied: tqdm in /usr/local/lib/python3.12/dist-packages (from grad_cam) (4.67.1)\nRequirement already satisfied: opencv-python in /usr/local/lib/python3.12/dist-packages (from grad_cam) (4.12.0.88)\nRequirement already satisfied: matplotlib in /usr/local/lib/python3.12/dist-packages (from grad_cam) (3.10.0)\nRequirement already satisfied: scikit-learn in /usr/local/lib/python3.12/dist-packages (from grad_cam) (1.6.1)\nRequirement already satisfied: filelock in /usr/local/lib/python3.12/dist-packages (from torch>=1.7.1->grad_cam) (3.20.3)\nRequirement already satisfied: typing-extensions>=4.10.0 in /usr/local/lib/python3.12/dist-packages (from torch>=1.7.1->grad_cam) (4.15.0)\nRequirement already satisfied: setuptools in /usr/local/lib/python3.12/dist-packages (from torch>=1.7.1->grad_cam) (75.2.0)\nRequirement already satisfied: sympy>=1.13.3 in /usr/local/lib/python3.12/dist-packages (from torch>=1.7.1->grad_cam) (1.13.3)\nRequirement already satisfied: networkx in /usr/local/lib/python3.12/dist-packages (from torch>=1.7.1->grad_cam) (3.5)\nRequirement already satisfied: jinja2 in /usr/local/lib/python3.12/dist-packages (from torch>=1.7.1->grad_cam) (3.1.6)\nRequirement already satisfied: fsspec in /usr/local/lib/python3.12/dist-packages (from torch>=1.7.1->grad_cam) (2025.10.0)\nRequirement already satisfied: nvidia-cuda-nvrtc-cu12==12.6.77 in /usr/local/lib/python3.12/dist-packages (from torch>=1.7.1->grad_cam) (12.6.77)\nRequirement already satisfied: nvidia-cuda-runtime-cu12==12.6.77 in /usr/local/lib/python3.12/dist-packages (from torch>=1.7.1->grad_cam) (12.6.77)\nRequirement already satisfied: nvidia-cuda-cupti-cu12==12.6.80 in /usr/local/lib/python3.12/dist-packages (from torch>=1.7.1->grad_cam) (12.6.80)\nRequirement already satisfied: nvidia-cudnn-cu12==9.10.2.21 in /usr/local/lib/python3.12/dist-packages (from torch>=1.7.1->grad_cam) (9.10.2.21)\nRequirement already satisfied: nvidia-cublas-cu12==12.6.4.1 in /usr/local/lib/python3.12/dist-packages (from torch>=1.7.1->grad_cam) (12.6.4.1)\nRequirement already satisfied: nvidia-cufft-cu12==11.3.0.4 in /usr/local/lib/python3.12/dist-packages (from torch>=1.7.1->grad_cam) (11.3.0.4)\nRequirement already satisfied: nvidia-curand-cu12==10.3.7.77 in /usr/local/lib/python3.12/dist-packages (from torch>=1.7.1->grad_cam) (10.3.7.77)\nRequirement already satisfied: nvidia-cusolver-cu12==11.7.1.2 in /usr/local/lib/python3.12/dist-packages (from torch>=1.7.1->grad_cam) (11.7.1.2)\nRequirement already satisfied: nvidia-cusparse-cu12==12.5.4.2 in /usr/local/lib/python3.12/dist-packages (from torch>=1.7.1->grad_cam) (12.5.4.2)\nRequirement already satisfied: nvidia-cusparselt-cu12==0.7.1 in /usr/local/lib/python3.12/dist-packages (from torch>=1.7.1->grad_cam) (0.7.1)\nRequirement already satisfied: nvidia-nccl-cu12==2.27.3 in /usr/local/lib/python3.12/dist-packages (from torch>=1.7.1->grad_cam) (2.27.3)\nRequirement already satisfied: nvidia-nvtx-cu12==12.6.77 in /usr/local/lib/python3.12/dist-packages (from torch>=1.7.1->grad_cam) (12.6.77)\nRequirement already satisfied: nvidia-nvjitlink-cu12==12.6.85 in /usr/local/lib/python3.12/dist-packages (from torch>=1.7.1->grad_cam) (12.6.85)\nRequirement already satisfied: nvidia-cufile-cu12==1.11.1.6 in /usr/local/lib/python3.12/dist-packages (from torch>=1.7.1->grad_cam) (1.11.1.6)\nRequirement already satisfied: triton==3.4.0 in /usr/local/lib/python3.12/dist-packages (from torch>=1.7.1->grad_cam) (3.4.0)\nRequirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib->grad_cam) (1.3.3)\nRequirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.12/dist-packages (from matplotlib->grad_cam) (0.12.1)\nRequirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.12/dist-packages (from matplotlib->grad_cam) (4.60.1)\nRequirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib->grad_cam) (1.4.9)\nRequirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.12/dist-packages (from matplotlib->grad_cam) (26.0rc2)\nRequirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib->grad_cam) (3.2.5)\nRequirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.12/dist-packages (from matplotlib->grad_cam) (2.9.0.post0)\nRequirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.12/dist-packages (from scikit-learn->grad_cam) (1.15.3)\nRequirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.12/dist-packages (from scikit-learn->grad_cam) (1.5.3)\nRequirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.12/dist-packages (from scikit-learn->grad_cam) (3.6.0)\nRequirement already satisfied: six>=1.5 in /usr/local/lib/python3.12/dist-packages (from python-dateutil>=2.7->matplotlib->grad_cam) (1.17.0)\nRequirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.12/dist-packages (from sympy>=1.13.3->torch>=1.7.1->grad_cam) (1.3.0)\nRequirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.12/dist-packages (from jinja2->torch>=1.7.1->grad_cam) (3.0.3)\nDownloading ttach-0.0.3-py3-none-any.whl (9.8 kB)\nBuilding wheels for collected packages: grad_cam\n  Building wheel for grad_cam (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n  Created wheel for grad_cam: filename=grad_cam-1.5.5-py3-none-any.whl size=44285 sha256=1c0c3e09dbc516a4b6f6c83ace013086087623428d85fb459364fb53aca67d5a\n  Stored in directory: /root/.cache/pip/wheels/fb/3b/09/2afc520f3d69bc26ae6bd87416759c820a3f7d05c1a077bbf6\nSuccessfully built grad_cam\nInstalling collected packages: ttach, grad_cam\nSuccessfully installed grad_cam-1.5.5 ttach-0.0.3\nNote: you may need to restart the kernel to use updated packages.\n","output_type":"stream"}],"execution_count":1},{"cell_type":"code","source":"# ============================================================================\n# ИМПОРТЫ\n# ============================================================================\n\n# Системные\nimport os\nimport gc\nimport sys\nimport glob\nimport json\nimport random\nimport warnings\nwarnings.filterwarnings('ignore')\n\n# Работа с данными\nimport numpy as np\nimport pandas as pd\nfrom collections import Counter\nfrom sklearn.model_selection import StratifiedGroupKFold, train_test_split\nfrom sklearn.utils.class_weight import compute_class_weight\nfrom scipy import interpolate\n\n# Визуализация\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom tqdm.auto import tqdm\n\n# Computer Vision\nimport cv2\nfrom PIL import Image\nimport pydicom\n\n# PyTorch\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\nfrom torch.utils.data import Dataset, DataLoader, WeightedRandomSampler\nfrom torch.cuda.amp import autocast, GradScaler\nfrom torchvision import models, transforms\nimport timm\n\n# Аугментации\nimport albumentations as A\nfrom albumentations.pytorch import ToTensorV2\n\n# Grad-CAM\nfrom pytorch_grad_cam import GradCAM, HiResCAM, ScoreCAM, GradCAMPlusPlus\nfrom pytorch_grad_cam.utils.model_targets import ClassifierOutputTarget\nfrom pytorch_grad_cam.utils.image import show_cam_on_image\n\n# Seed для воспроизводимости\ndef set_seed(seed=42):\n    random.seed(seed)\n    np.random.seed(seed)\n    torch.manual_seed(seed)\n    torch.cuda.manual_seed_all(seed)\n    torch.backends.cudnn.deterministic = True\n    torch.backends.cudnn.benchmark = False\n\nset_seed(42)\n\n# Настройки\ndevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\nprint(f\"Device: {device}\")\nprint(f\"PyTorch version: {torch.__version__}\")\nprint(f\"CUDA available: {torch.cuda.is_available()}\")","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"vQQjcteVosNH","outputId":"3040e7a3-c172-4a3d-8438-b0ff928c00cb","execution":{"iopub.status.busy":"2026-02-14T13:45:27.136555Z","iopub.execute_input":"2026-02-14T13:45:27.136869Z","iopub.status.idle":"2026-02-14T13:45:40.007237Z","shell.execute_reply.started":"2026-02-14T13:45:27.136838Z","shell.execute_reply":"2026-02-14T13:45:40.006571Z"}},"outputs":[{"name":"stdout","text":"Device: cuda\nPyTorch version: 2.8.0+cu126\nCUDA available: True\n","output_type":"stream"}],"execution_count":2},{"cell_type":"code","source":"# ============================================================================\n# КОНФИГУРАЦИЯ\n# ============================================================================\n\nclass Config:\n    # Пути\n    DATA_ROOT = '/kaggle/input/rsna-2024-lumbar-spine-degenerative-classification'\n    TRAIN_IMAGES = os.path.join(DATA_ROOT, \"train_images\")\n    TRAIN_CSV = os.path.join(DATA_ROOT, \"train.csv\")\n    COORDS_CSV = os.path.join(DATA_ROOT, \"train_label_coordinates.csv\")\n    DESC_CSV = os.path.join(DATA_ROOT, \"train_series_descriptions.csv\")\n\n    # Модель\n    MODEL_ARCHITECTURE = 'resnet18'  # 'resnet18' или 'resnet34' efficientnet_b0\n    MODEL_NAME = 'resnet18'\n    NUM_CLASSES = 3\n    PRETRAINED = True\n    # MODEL_NAME = 'resnet18'\n    # NUM_CLASSES = 3\n    # PRETRAINED = True\n\n    \n    # Обучение\n    BATCH_SIZE = 18\n    NUM_EPOCHS = 20\n    LEARNING_RATE = 1e-4\n    WEIGHT_DECAY = 1e-4\n    EARLY_STOPPING_PATIENCE = 5\n\n    # Данные\n    IMG_SIZE = 224\n    NUM_WORKERS = 0\n    TRAIN_VAL_SPLIT = 0.2\n\n    # 2.5D параметры\n    NUM_SAGITTAL_SLICES = 15  # для sagittal\n    NUM_AXIAL_SLICES = 10      # для axial\n\n    # Аугментации\n    USE_AUGMENTATION = True\n    AUG_LEVEL = 'medium'\n\n    # Балансировка\n    USE_CLASS_WEIGHTS = True\n    USE_WEIGHTED_SAMPLING = True\n\n    # Сохранение\n    SAVE_DIR = '/kaggle/working/models'\n\ncfg = Config()\nos.makedirs(cfg.SAVE_DIR, exist_ok=True)","metadata":{"trusted":true,"id":"TxAZTh_LosNH","execution":{"iopub.status.busy":"2026-02-14T13:45:40.008003Z","iopub.execute_input":"2026-02-14T13:45:40.008467Z","iopub.status.idle":"2026-02-14T13:45:40.014307Z","shell.execute_reply.started":"2026-02-14T13:45:40.008441Z","shell.execute_reply":"2026-02-14T13:45:40.013609Z"}},"outputs":[],"execution_count":3},{"cell_type":"code","source":"!ls /kaggle/input\n","metadata":{"id":"xPJ4SPj6tK3P","trusted":true,"execution":{"iopub.status.busy":"2026-02-14T13:45:40.015057Z","iopub.execute_input":"2026-02-14T13:45:40.015334Z","iopub.status.idle":"2026-02-14T13:45:40.160867Z","shell.execute_reply.started":"2026-02-14T13:45:40.015313Z","shell.execute_reply":"2026-02-14T13:45:40.160212Z"}},"outputs":[{"name":"stdout","text":"rsna-2024-lumbar-spine-degenerative-classification\n","output_type":"stream"}],"execution_count":4},{"cell_type":"code","source":"def load_and_prepare_data():\n    \"\"\"Загрузка и подготовка всех данных\"\"\"\n\n    print(\"📥 Загрузка CSV файлов...\")\n    df_train = pd.read_csv(cfg.TRAIN_CSV)\n    df_coords = pd.read_csv(cfg.COORDS_CSV)\n    df_desc = pd.read_csv(cfg.DESC_CSV)\n\n    # Нормализация названий колонок\n    df_train.columns = [col.lower().replace('/', '_') for col in df_train.columns]\n\n    # Merge координат с описаниями\n    df_coords = df_coords.merge(\n        df_desc[['study_id', 'series_id', 'series_description']],\n        on=['study_id', 'series_id'],\n        how='left'\n    )\n\n    # Подготовка меток\n    print(\"🔄 Преобразование меток...\")\n    condition_cols = [col for col in df_train.columns if col != 'study_id']\n    df_labels = pd.melt(\n        df_train,\n        id_vars=['study_id'],\n        value_vars=condition_cols,\n        var_name='condition_level',\n        value_name='severity'\n    )\n\n    # Извлечение base_condition и level\n    def extract_base_and_level(condition_level):\n        parts = condition_level.split('_')\n        base = '_'.join(parts[:-2])\n        level = '_'.join(parts[-2:])\n        return base, level\n\n    df_labels[['base_condition', 'level_str']] = df_labels['condition_level'].apply(\n        lambda x: pd.Series(extract_base_and_level(x))\n    )\n\n    # Маппинг уровней\n    level_map = {'l1_l2': 1, 'l2_l3': 2, 'l3_l4': 3, 'l4_l5': 4, 'l5_s1': 5}\n    df_labels['level_num'] = df_labels['level_str'].map(level_map)\n    \n    # Маппинг condition → series_description\n    condition_to_series = {\n        'spinal_canal_stenosis': 'Sagittal T2/STIR',\n        'left_neural_foraminal_narrowing': 'Sagittal T1',\n        'right_neural_foraminal_narrowing': 'Sagittal T1',\n        'left_subarticular_stenosis': 'Axial T2',\n        'right_subarticular_stenosis': 'Axial T2',\n    }\n    df_labels['series_description'] = df_labels['base_condition'].map(condition_to_series)\n    # Абсолютные значения\n    counts = df_labels['base_condition'].value_counts()\n    \n    # Доли (в процентах)\n    ratios = df_labels['base_condition'].value_counts(normalize=True) * 100\n    \n    stats = pd.DataFrame({\n        'count': counts,\n        'percent': ratios.round(2)\n    })\n    \n    print(stats)\n\n    # Merge с series_id\n    df_all = df_labels.merge(\n        df_desc[['study_id', 'series_description', 'series_id']],\n        on=['study_id', 'series_description'],\n        how='left'\n    )\n\n    # === НОВОЕ: Удаляем строки без series_id (нет соответствующей серии) ===\n    print(f\"До удаления NaN в series_id: {len(df_all)} строк\")\n    df_all = df_all.dropna(subset=['series_id']).copy()\n    print(f\"После удаления NaN в series_id: {len(df_all)} строк\")\n\n    # Подготовка координат\n    print(\"📍 Обработка координат...\")\n    df_coords['condition'] = df_coords['condition'].str.lower().str.replace(' ', '_')\n    df_coords['level_str'] = df_coords['level'].str.lower().str.replace('/', '_')\n    df_coords['base_condition'] = df_coords['condition']\n    df_coords['condition_level'] = df_coords['base_condition'] + '_' + df_coords['level_str']\n\n    # Merge координат\n    df_all = df_all.merge(\n        df_coords[['study_id', 'condition_level', 'instance_number', 'x', 'y']],\n        on=['study_id', 'condition_level'],\n        how='left'\n    )\n\n    # Интерполяция instance_number\n    print(\"🔢 Интерполяция instance_number...\")\n\n    # Глобальные средние для fallback\n    avg_instance = df_coords.groupby('base_condition')['instance_number'].mean().round().astype(int)\n    avg_x = df_coords.groupby('base_condition')['x'].mean()\n    avg_y = df_coords.groupby('base_condition')['y'].mean()\n\n    def interpolate_group(group):\n        group = group.sort_values('level_num').copy()\n\n        known = group[~group['instance_number'].isna()]\n\n        if len(known) == 0:\n            bc = group['base_condition'].iloc[0]\n            fallback = int(avg_instance.get(bc, 10))\n            group['instance_number'] = fallback\n            return group\n\n        if len(known) == 1:\n            val = int(known['instance_number'].iloc[0])\n            group['instance_number'] = val\n            return group\n\n        from scipy.interpolate import interp1d\n\n        f = interp1d(\n            known['level_num'],\n            known['instance_number'],\n            kind='linear',\n            fill_value='extrapolate'\n        )\n\n        mask = group['instance_number'].isna()\n        group.loc[mask, 'instance_number'] = f(group.loc[mask, 'level_num'])\n\n        # Защита от inf/-inf и NaN\n        group['instance_number'] = group['instance_number'].replace([np.inf, -np.inf], np.nan)\n        if group['instance_number'].isna().any():\n            fallback = known['instance_number'].median() if not known.empty else 10\n            group['instance_number'] = group['instance_number'].fillna(fallback)\n\n        # Ограничиваем и приводим к обычному int\n        group['instance_number'] = group['instance_number'].clip(lower=1, upper=2000)\n        group['instance_number'] = group['instance_number'].round(0).astype(int)  # теперь точно нет NaN/inf\n\n        return group\n\n    df_all = df_all.groupby(['study_id', 'base_condition']).apply(interpolate_group).reset_index(drop=True)\n\n    # === НОВОЕ: На всякий случай удаляем оставшиеся NaN в instance_number ===\n    print(f\"До удаления NaN в instance_number: {len(df_all)} строк\")\n    df_all = df_all.dropna(subset=['instance_number']).copy()\n    print(f\"После: {len(df_all)} строк\")\n\n    # Заполнение x, y (как было)\n    df_all['x'] = df_all.groupby(['study_id', 'base_condition'])['x'].transform(lambda g: g.fillna(g.mean()))\n    df_all['y'] = df_all.groupby(['study_id', 'base_condition'])['y'].transform(lambda g: g.fillna(g.mean()))\n    df_all['x'] = df_all.apply(lambda row: avg_x.get(row['base_condition'], 256) if pd.isna(row['x']) else row['x'], axis=1)\n    df_all['y'] = df_all.apply(lambda row: avg_y.get(row['base_condition'], 256) if pd.isna(row['y']) else row['y'], axis=1)\n\n    # Приведение типов — теперь безопасно, т.к. NaN удалены\n    df_all['series_id'] = df_all['series_id'].astype('int64')\n    df_all['instance_number'] = df_all['instance_number'].astype('int64')\n\n    # Добавление путей к изображениям\n    df_all['image_path'] = (\n        cfg.TRAIN_IMAGES + '/' +\n        df_all['study_id'].astype(str) + '/' +\n        df_all['series_id'].astype(str) + '/' +\n        df_all['instance_number'].astype(str) + '.dcm'\n    )\n\n    # Проверка существования файлов\n    print(\"✅ Проверка существования файлов...\")\n    df_all = df_all[df_all['image_path'].apply(os.path.exists)]\n\n    # Удаление NaN в severity\n    df_all = df_all.dropna(subset=['severity'])\n\n    # Маппинг severity → label\n    severity_map = {'Normal/Mild': 0, 'Moderate': 1, 'Severe': 2}\n    df_all['label'] = df_all['severity'].map(severity_map)\n\n    # Добавление modality_group\n    modality_group = {\n        'Sagittal T1': 'sagittal',\n        'Sagittal T2/STIR': 'sagittal',\n        'Axial T2': 'axial'\n    }\n    df_all['modality_group'] = df_all['series_description'].map(modality_group)\n\n    print(f\"✅ Итоговый размер данных: {len(df_all)}\")\n    print(f\"📊 Распределение классов:\")\n    print(df_all['label'].value_counts().sort_index())\n\n    return df_all\n\n# Загрузка данных\ndf_all = load_and_prepare_data()","metadata":{"trusted":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"4PPwFcpoosNI","outputId":"ee5550ff-175f-4137-c126-7c1ea4dd1c7a","execution":{"iopub.status.busy":"2026-02-14T13:45:40.162112Z","iopub.execute_input":"2026-02-14T13:45:40.162365Z","iopub.status.idle":"2026-02-14T13:47:28.27963Z","shell.execute_reply.started":"2026-02-14T13:45:40.162338Z","shell.execute_reply":"2026-02-14T13:47:28.277837Z"}},"outputs":[{"name":"stdout","text":"📥 Загрузка CSV файлов...\n🔄 Преобразование меток...\n                                  count  percent\nbase_condition                                  \nspinal_canal_stenosis              9875     20.0\nleft_neural_foraminal_narrowing    9875     20.0\nright_neural_foraminal_narrowing   9875     20.0\nleft_subarticular_stenosis         9875     20.0\nright_subarticular_stenosis        9875     20.0\nДо удаления NaN в series_id: 53095 строк\nПосле удаления NaN в series_id: 53070 строк\n📍 Обработка координат...\n🔢 Интерполяция instance_number...\nДо удаления NaN в instance_number: 53070 строк\nПосле: 53070 строк\n✅ Проверка существования файлов...\n","output_type":"stream"},{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m/tmp/ipykernel_55/1509276743.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m    190\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    191\u001b[0m \u001b[0;31m# Загрузка данных\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 192\u001b[0;31m \u001b[0mdf_all\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mload_and_prepare_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;32m/tmp/ipykernel_55/1509276743.py\u001b[0m in \u001b[0;36mload_and_prepare_data\u001b[0;34m()\u001b[0m\n\u001b[1;32m    166\u001b[0m     \u001b[0;31m# Проверка существования файлов\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    167\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"✅ Проверка существования файлов...\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 168\u001b[0;31m     \u001b[0mdf_all\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf_all\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf_all\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'image_path'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexists\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    169\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    170\u001b[0m     \u001b[0;31m# Удаление NaN в severity\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/pandas/core/series.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, func, convert_dtype, args, by_row, **kwargs)\u001b[0m\n\u001b[1;32m   4922\u001b[0m             \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4923\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4924\u001b[0;31m         ).apply()\n\u001b[0m\u001b[1;32m   4925\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4926\u001b[0m     def _reindex_indexer(\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/pandas/core/apply.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1425\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1426\u001b[0m         \u001b[0;31m# self.func is Callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1427\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_standard\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1428\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1429\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0magg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/pandas/core/apply.py\u001b[0m in \u001b[0;36mapply_standard\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1505\u001b[0m         \u001b[0;31m#  Categorical (GH51645).\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1506\u001b[0m         \u001b[0maction\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"ignore\"\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mCategoricalDtype\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1507\u001b[0;31m         mapped = obj._map_values(\n\u001b[0m\u001b[1;32m   1508\u001b[0m             \u001b[0mmapper\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcurried\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mna_action\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maction\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconvert\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert_dtype\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1509\u001b[0m         )\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/pandas/core/base.py\u001b[0m in \u001b[0;36m_map_values\u001b[0;34m(self, mapper, na_action, convert)\u001b[0m\n\u001b[1;32m    919\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmapper\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mna_action\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mna_action\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    920\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 921\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0malgorithms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmapper\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mna_action\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mna_action\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconvert\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mconvert\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    922\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    923\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mfinal\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/pandas/core/algorithms.py\u001b[0m in \u001b[0;36mmap_array\u001b[0;34m(arr, mapper, na_action, convert)\u001b[0m\n\u001b[1;32m   1741\u001b[0m     \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobject\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1742\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mna_action\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1743\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap_infer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmapper\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconvert\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mconvert\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1744\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1745\u001b[0m         return lib.map_infer_mask(\n","\u001b[0;32mlib.pyx\u001b[0m in \u001b[0;36mpandas._libs.lib.map_infer\u001b[0;34m()\u001b[0m\n","\u001b[0;32m/usr/lib/python3.12/genericpath.py\u001b[0m in \u001b[0;36mexists\u001b[0;34m(path)\u001b[0m\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "],"ename":"KeyboardInterrupt","evalue":"","output_type":"error"}],"execution_count":5},{"cell_type":"code","source":"# ============================================================================\n# АУГМЕНТАЦИИ\n# ============================================================================\n\ndef get_train_transforms_soft(img_size=224):\n    \"\"\"Мягкие аугментации для обучения\"\"\"\n    return A.Compose([\n        # Геометрия (мягко)\n        A.Resize(img_size, img_size),\n        A.HorizontalFlip(p=0.5),\n        A.ShiftScaleRotate(\n            shift_limit=0.05,\n            scale_limit=0.05,\n            rotate_limit=10,\n            border_mode=0,\n            p=0.3\n        ),\n\n        # Цвет и освещение (умеренно)\n        A.RandomBrightnessContrast(\n            brightness_limit=0.1,\n            contrast_limit=0.1,\n            p=0.3\n        ),\n\n        # Лёгкий шум (редко)\n        A.GaussNoise(\n            var_limit=(5.0, 15.0),\n            p=0.1\n        ),\n\n        # Лёгкое размытие (редко)\n        A.GaussianBlur(\n            blur_limit=(3, 3),\n            p=0.05\n        ),\n\n        # Нормализация под ImageNet\n        A.Normalize(\n            mean=[0.485, 0.456, 0.406],\n            std=[0.229, 0.224, 0.225]\n        ),\n        ToTensorV2(),\n    ])\n\ndef get_train_transforms_medium(img_size=224):\n    \"\"\"Среднее аугментация для обучения\"\"\"\n\n    return A.Compose([\n        # Геометрия\n        A.Resize(img_size, img_size),\n        A.HorizontalFlip(p=0.5),\n\n        A.ShiftScaleRotate(\n            shift_limit=0.07,\n            scale_limit=0.07,\n            rotate_limit=12,\n            border_mode=0,\n            p=0.4\n        ),\n\n        # Цвет / освещение\n        A.OneOf([\n            A.RandomBrightnessContrast(\n                brightness_limit=0.15,\n                contrast_limit=0.15,\n                p=1.0\n            ),\n            A.HueSaturationValue(\n                hue_shift_limit=0,\n                sat_shift_limit=12,\n                val_shift_limit=12,\n                p=1.0\n            ),\n        ], p=0.4),\n\n        # Шум\n        A.GaussNoise(\n            var_limit=(5.0, 25.0),\n            p=0.15\n        ),\n\n        # Размытие (очень редко)\n        A.GaussianBlur(\n            blur_limit=(3, 3),\n            p=0.07\n        ),\n\n        # Нормализация\n        A.Normalize(\n            mean=[0.485, 0.456, 0.406],\n            std=[0.229, 0.224, 0.225]\n        ),\n        ToTensorV2(),\n    ])\n\n\ndef get_train_transforms_strong(img_size=224):\n    \"\"\"Сильные аугментации для обучения\"\"\"\n    return A.Compose([\n        # Геометрические\n        A.Resize(img_size, img_size),\n        A.HorizontalFlip(p=0.5),\n        A.ShiftScaleRotate(\n            shift_limit=0.1,\n            scale_limit=0.1,\n            rotate_limit=15,\n            p=0.5\n        ),\n        A.OneOf([\n            A.ElasticTransform(alpha=120, sigma=120 * 0.05, p=0.3),\n            A.GridDistortion(p=0.3),\n            A.OpticalDistortion(distort_limit=0.1, p=0.3),\n        ], p=0.3),\n\n        # Яркость/контраст\n        A.OneOf([\n            A.RandomBrightnessContrast(\n                brightness_limit=0.2,\n                contrast_limit=0.2,\n                p=0.5\n            ),\n            A.CLAHE(clip_limit=4.0, p=0.5),\n            A.HueSaturationValue(\n                hue_shift_limit=0,\n                sat_shift_limit=20,\n                val_shift_limit=20,\n                p=0.5\n            ),\n        ], p=0.5),\n\n        # Шумы\n        A.OneOf([\n            A.GaussNoise(var_limit=(10.0, 50.0), p=0.3),\n            A.ISONoise(p=0.3),\n            A.MultiplicativeNoise(p=0.3),\n        ], p=0.2),\n\n        # Размытие\n        A.OneOf([\n            A.MotionBlur(p=0.2),\n            A.MedianBlur(blur_limit=3, p=0.2),\n            A.GaussianBlur(p=0.2),\n        ], p=0.2),\n\n        # Нормализация\n        A.Normalize(\n            mean=[0.485, 0.456, 0.406],\n            std=[0.229, 0.224, 0.225]\n        ),\n        ToTensorV2(),\n    ])\n\ndef get_val_transforms(img_size=224):\n    \"\"\"Базовые трансформации для валидации\"\"\"\n    return A.Compose([\n        A.Resize(img_size, img_size),\n        A.Normalize(\n            mean=[0.485, 0.456, 0.406],\n            std=[0.229, 0.224, 0.225]\n        ),\n        ToTensorV2(),\n    ])","metadata":{"trusted":true,"id":"07lEtVGsosNI","execution":{"iopub.status.busy":"2026-02-14T13:47:28.2807Z","iopub.status.idle":"2026-02-14T13:47:28.280942Z","shell.execute_reply.started":"2026-02-14T13:47:28.280831Z","shell.execute_reply":"2026-02-14T13:47:28.280844Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# ============================================================================\n# DATASET КЛАССЫ (ИСПРАВЛЕННАЯ ВЕРСИЯ)\n# ============================================================================\n\nclass LumbarSagittalDataset(Dataset):\n    \"\"\"Dataset для Sagittal модели (2.5D) - ФИКСИРОВАННОЕ КОЛИЧЕСТВО КАНАЛОВ\"\"\"\n\n    def __init__(self, df, levels=['l1_l2','l2_l3','l3_l4','l4_l5','l5_s1'],\n                 img_size=224, transform=None):\n        self.levels = levels\n        self.num_levels = len(levels)  # ФИКСИРОВАННОЕ = 5\n        self.img_size = img_size\n        self.transform = transform\n\n        # Фильтрация только sagittal\n        df = df[df['modality_group'] == 'sagittal'].copy()\n\n        # Группировка по study_id + base_condition\n        self.groups = []\n        grouped = df.groupby(['study_id', 'base_condition'])\n\n        skipped = 0\n        for (study_id, base_condition), g in grouped:\n            # КРИТИЧНО: проверяем наличие ВСЕХ уровней\n            present_levels = set(g['level_str'].values)\n            if not all(l in present_levels for l in levels):\n                skipped += 1\n                continue\n\n            g_sorted = g.sort_values('level_num')\n\n            # КРИТИЧНО: берём РОВНО по одному изображению на каждый уровень\n            level_data = []\n            for level in levels:\n                level_row = g_sorted[g_sorted['level_str'] == level]\n                if len(level_row) > 0:\n                    level_data.append(level_row.iloc[0])\n                else:\n                    # Этого не должно случиться из-за проверки выше\n                    break\n\n            # Проверяем, что собрали все 5 уровней\n            if len(level_data) == self.num_levels:\n                self.groups.append(pd.DataFrame(level_data))\n\n        print(f\"✅ Sagittal Dataset: {len(self.groups)} samples (skipped {skipped} incomplete)\")\n\n    def __len__(self):\n        return len(self.groups)\n\n    def load_dicom(self, path):\n        \"\"\"Загрузка DICOM с нормализацией\"\"\"\n        try:\n            ds = pydicom.dcmread(path)\n            img = ds.pixel_array.astype(np.float32)\n\n            # Нормализация 0-1\n            img = (img - img.min()) / (img.max() - img.min() + 1e-8)\n            img = (img * 255).astype(np.uint8)\n\n            # 3 канала\n            img = np.stack([img]*3, axis=-1)\n            return img\n        except Exception as e:\n            # Возвращаем чёрное изображение в случае ошибки\n            return np.zeros((self.img_size, self.img_size, 3), dtype=np.uint8)\n\n    def __getitem__(self, idx):\n        g = self.groups[idx]\n        imgs = []\n\n        # КРИТИЧНО: итерируем РОВНО по num_levels (5) строкам\n        for i in range(self.num_levels):\n            row = g.iloc[i]\n            img = self.load_dicom(row['image_path'])\n\n            # Применяем аугментации\n            if self.transform:\n                augmented = self.transform(image=img)\n                img = augmented['image']  # [3, H, W]\n            else:\n                # Базовая трансформация\n                img = cv2.resize(img, (self.img_size, self.img_size))\n                img = torch.from_numpy(img.transpose(2, 0, 1)).float() / 255.0\n\n            imgs.append(img)\n\n        # ГАРАНТИЯ: всегда 5 изображений × 3 канала = 15 каналов\n        assert len(imgs) == self.num_levels, f\"Expected {self.num_levels} images, got {len(imgs)}\"\n\n        # Склеиваем по каналам: [5, 3, H, W] → [15, H, W]\n        imgs = torch.cat(imgs, dim=0)\n\n        # ПРОВЕРКА финальной размерности\n        assert imgs.shape[0] == self.num_levels * 3, f\"Expected {self.num_levels*3} channels, got {imgs.shape[0]}\"\n\n        # Метка\n        label = g['label'].iloc[0]\n        return imgs, torch.tensor(label, dtype=torch.long)\n\n\nclass LumbarAxialDataset(Dataset):\n    \"\"\"Dataset для Axial модели (2.5D) - ФИКСИРОВАННОЕ КОЛИЧЕСТВО КАНАЛОВ\"\"\"\n\n    def __init__(self, df, levels=['l1_l2','l2_l3','l3_l4','l4_l5','l5_s1'],\n                 img_size=224, transform=None):\n        self.levels = levels\n        self.num_levels = len(levels)  # ФИКСИРОВАННОЕ = 5\n        self.img_size = img_size\n        self.transform = transform\n\n        # Фильтрация только axial\n        df = df[df['modality_group'] == 'axial'].copy()\n\n        # Группировка\n        self.groups = []\n        grouped = df.groupby(['study_id', 'base_condition'])\n\n        skipped = 0\n        for (study_id, base_condition), g in grouped:\n            # КРИТИЧНО: проверяем наличие ВСЕХ уровней\n            present_levels = set(g['level_str'].values)\n            if not all(l in present_levels for l in levels):\n                skipped += 1\n                continue\n\n            g_sorted = g.sort_values('level_num')\n\n            # КРИТИЧНО: берём РОВНО по одному изображению на каждый уровень\n            level_data = []\n            for level in levels:\n                level_row = g_sorted[g_sorted['level_str'] == level]\n                if len(level_row) > 0:\n                    level_data.append(level_row.iloc[0])\n                else:\n                    break\n\n            # Проверяем, что собрали все 5 уровней\n            if len(level_data) == self.num_levels:\n                self.groups.append(pd.DataFrame(level_data))\n\n        print(f\"✅ Axial Dataset: {len(self.groups)} samples (skipped {skipped} incomplete)\")\n\n    def __len__(self):\n        return len(self.groups)\n\n    def load_dicom(self, path):\n        \"\"\"Загрузка DICOM\"\"\"\n        try:\n            ds = pydicom.dcmread(path)\n            img = ds.pixel_array.astype(np.float32)\n            img = (img - img.min()) / (img.max() - img.min() + 1e-8)\n            img = (img * 255).astype(np.uint8)\n            img = np.stack([img]*3, axis=-1)\n            return img\n        except:\n            return np.zeros((self.img_size, self.img_size, 3), dtype=np.uint8)\n\n    def __getitem__(self, idx):\n        g = self.groups[idx]\n        imgs = []\n\n        # КРИТИЧНО: итерируем РОВНО по num_levels (5) строкам\n        for i in range(self.num_levels):\n            row = g.iloc[i]\n            img = self.load_dicom(row['image_path'])\n\n            if self.transform:\n                augmented = self.transform(image=img)\n                img = augmented['image']\n            else:\n                img = cv2.resize(img, (self.img_size, self.img_size))\n                img = torch.from_numpy(img.transpose(2, 0, 1)).float() / 255.0\n\n            imgs.append(img)\n\n        # ГАРАНТИЯ: всегда 5 изображений × 3 канала = 15 каналов\n        assert len(imgs) == self.num_levels, f\"Expected {self.num_levels} images, got {len(imgs)}\"\n\n        # [5, 3, H, W] → [15, H, W]\n        imgs = torch.cat(imgs, dim=0)\n\n        # ПРОВЕРКА\n        assert imgs.shape[0] == self.num_levels * 3, f\"Expected {self.num_levels*3} channels, got {imgs.shape[0]}\"\n\n        label = g['label'].iloc[0]\n        return imgs, torch.tensor(label, dtype=torch.long)\n\n\nclass LumbarCombinedDataset(Dataset):\n    \"\"\"Dataset для комбинированной модели - ФИКСИРОВАННОЕ КОЛИЧЕСТВО КАНАЛОВ\"\"\"\n\n    def __init__(self, df, levels=['l1_l2','l2_l3','l3_l4','l4_l5','l5_s1'],\n                 img_size=224, transform=None):\n        self.levels = levels\n        self.num_levels = len(levels)  # ФИКСИРОВАННОЕ = 5\n        self.img_size = img_size\n        self.transform = transform\n\n        # Группировка по study_id\n        self.groups = []\n        skipped = 0\n\n        for study_id in df['study_id'].unique():\n            df_study = df[df['study_id'] == study_id]\n\n            # Sagittal данные\n            df_sag = df_study[df_study['modality_group'] == 'sagittal']\n            # Axial данные\n            df_ax = df_study[df_study['modality_group'] == 'axial']\n\n            # Проверяем наличие обеих модальностей\n            if len(df_sag) == 0 or len(df_ax) == 0:\n                continue\n\n            # Группируем по base_condition\n            for base_cond in df_sag['base_condition'].unique():\n                df_sag_cond = df_sag[df_sag['base_condition'] == base_cond]\n\n                # Для spinal_canal_stenosis нет axial → пропускаем\n                if base_cond == 'spinal_canal_stenosis':\n                    continue\n\n                # Маппинг neural foraminal → subarticular\n                if 'neural_foraminal' in base_cond:\n                    ax_cond = base_cond.replace('neural_foraminal_narrowing', 'subarticular_stenosis')\n                else:\n                    ax_cond = base_cond\n\n                df_ax_cond = df_ax[df_ax['base_condition'] == ax_cond]\n\n                # КРИТИЧНО: проверяем наличие ВСЕХ уровней в обеих модальностях\n                sag_levels = set(df_sag_cond['level_str'].values)\n                ax_levels = set(df_ax_cond['level_str'].values)\n\n                if not (all(l in sag_levels for l in levels) and\n                        all(l in ax_levels for l in levels)):\n                    skipped += 1\n                    continue\n\n                df_sag_sorted = df_sag_cond.sort_values('level_num')\n                df_ax_sorted = df_ax_cond.sort_values('level_num')\n\n                # КРИТИЧНО: берём РОВНО по одному изображению на каждый уровень\n                sag_data = []\n                ax_data = []\n\n                for level in levels:\n                    sag_row = df_sag_sorted[df_sag_sorted['level_str'] == level]\n                    ax_row = df_ax_sorted[df_ax_sorted['level_str'] == level]\n\n                    if len(sag_row) > 0 and len(ax_row) > 0:\n                        sag_data.append(sag_row.iloc[0])\n                        ax_data.append(ax_row.iloc[0])\n                    else:\n                        break\n\n                # Проверяем, что собрали все 5 уровней в обеих модальностях\n                if len(sag_data) == self.num_levels and len(ax_data) == self.num_levels:\n                    self.groups.append({\n                        'sagittal': pd.DataFrame(sag_data),\n                        'axial': pd.DataFrame(ax_data),\n                        'label': sag_data[0]['label']\n                    })\n\n        print(f\"✅ Combined Dataset: {len(self.groups)} samples (skipped {skipped} incomplete)\")\n\n    def __len__(self):\n        return len(self.groups)\n\n    def load_dicom(self, path):\n        try:\n            ds = pydicom.dcmread(path)\n            img = ds.pixel_array.astype(np.float32)\n            img = (img - img.min()) / (img.max() - img.min() + 1e-8)\n            img = (img * 255).astype(np.uint8)\n            img = np.stack([img]*3, axis=-1)\n            return img\n        except:\n            return np.zeros((self.img_size, self.img_size, 3), dtype=np.uint8)\n\n    def __getitem__(self, idx):\n        group = self.groups[idx]\n\n        # Sagittal images\n        imgs_sag = []\n        for i in range(self.num_levels):\n            row = group['sagittal'].iloc[i]\n            img = self.load_dicom(row['image_path'])\n\n            if self.transform:\n                img = self.transform(image=img)['image']\n            else:\n                img = cv2.resize(img, (self.img_size, self.img_size))\n                img = torch.from_numpy(img.transpose(2, 0, 1)).float() / 255.0\n\n            imgs_sag.append(img)\n\n        # Axial images\n        imgs_ax = []\n        for i in range(self.num_levels):\n            row = group['axial'].iloc[i]\n            img = self.load_dicom(row['image_path'])\n\n            if self.transform:\n                img = self.transform(image=img)['image']\n            else:\n                img = cv2.resize(img, (self.img_size, self.img_size))\n                img = torch.from_numpy(img.transpose(2, 0, 1)).float() / 255.0\n\n            imgs_ax.append(img)\n\n        # ГАРАНТИЯ: всегда 5 sag + 5 ax = 10 изображений × 3 канала = 30 каналов\n        assert len(imgs_sag) == self.num_levels, f\"Expected {self.num_levels} sag images\"\n        assert len(imgs_ax) == self.num_levels, f\"Expected {self.num_levels} ax images\"\n\n        # Конкатенация: [5*3 + 5*3, H, W] = [30, H, W]\n        imgs_sag = torch.cat(imgs_sag, dim=0)\n        imgs_ax = torch.cat(imgs_ax, dim=0)\n        imgs_combined = torch.cat([imgs_sag, imgs_ax], dim=0)\n\n        # ПРОВЕРКА финальной размерности\n        expected_channels = self.num_levels * 3 * 2  # 5 levels × 3 RGB × 2 modalities = 30\n        assert imgs_combined.shape[0] == expected_channels, \\\n            f\"Expected {expected_channels} channels, got {imgs_combined.shape[0]}\"\n\n        label = group['label']\n        return imgs_combined, torch.tensor(label, dtype=torch.long)","metadata":{"trusted":true,"id":"19e_ZamAosNI","execution":{"iopub.status.busy":"2026-02-14T13:47:28.282079Z","iopub.status.idle":"2026-02-14T13:47:28.2824Z","shell.execute_reply.started":"2026-02-14T13:47:28.282273Z","shell.execute_reply":"2026-02-14T13:47:28.282294Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# ============================================================================\n# МОДЕЛИ\n# ============================================================================\n\nclass ResNet2_5D(nn.Module):\n    \"\"\"ResNet18 с кастомным первым слоем для 2.5D\"\"\"\n\n    def __init__(self, in_channels=15, num_classes=3, pretrained=True):\n        super(ResNet2_5D, self).__init__()\n\n        # Загружаем базовый ResNet18\n        if pretrained:\n            self.model = models.resnet18(weights='IMAGENET1K_V1')\n        else:\n            self.model = models.resnet18(weights=None)\n\n        # Модифицируем первый conv слой\n        self.model.conv1 = nn.Conv2d(\n            in_channels=in_channels,\n            out_channels=64,\n            kernel_size=7,\n            stride=2,\n            padding=3,\n            bias=False\n        )\n\n        # Инициализация нового conv1\n        nn.init.kaiming_normal_(self.model.conv1.weight, mode='fan_out', nonlinearity='relu')\n\n        # Модифицируем финальный слой\n        num_ftrs = self.model.fc.in_features\n        self.model.fc = nn.Sequential(\n            nn.Dropout(0.5),\n            nn.Linear(num_ftrs, num_classes)\n        )\n\n    def forward(self, x):\n        return self.model(x)\n\nclass ResNet34_2_5D(nn.Module):\n    \"\"\"ResNet34 с кастомным первым слоем для 2.5D\"\"\"\n\n    def __init__(self, in_channels=15, num_classes=3, pretrained=True):\n        super(ResNet34_2_5D, self).__init__()\n\n        # Загружаем базовый ResNet34\n        if pretrained:\n            self.model = models.resnet34(weights='IMAGENET1K_V1')\n            print(\"✅ Загружены предобученные веса ImageNet для ResNet34\")\n        else:\n            self.model = models.resnet34(weights=None)\n            print(\"⚠️  Модель инициализирована случайными весами\")\n\n        # Модифицируем первый conv слой\n        original_conv1_weight = self.model.conv1.weight.data.clone()\n\n        self.model.conv1 = nn.Conv2d(\n            in_channels=in_channels,\n            out_channels=64,\n            kernel_size=7,\n            stride=2,\n            padding=3,\n            bias=False\n        )\n\n        # Инициализация нового conv1\n        if pretrained and in_channels >= 3:\n            # Используем веса ImageNet для первых 3 каналов\n            with torch.no_grad():\n                # Повторяем веса для дополнительных каналов\n                # Стратегия: копируем веса RGB циклически\n                for i in range(in_channels):\n                    channel_idx = i % 3  # 0,1,2,0,1,2,...\n                    self.model.conv1.weight.data[:, i:i+1, :, :] = \\\n                        original_conv1_weight[:, channel_idx:channel_idx+1, :, :] / (in_channels // 3 + 1)\n            print(f\"✅ Инициализированы {in_channels} каналов из ImageNet весов (cycling)\")\n        else:\n            # Стандартная kaiming инициализация\n            nn.init.kaiming_normal_(self.model.conv1.weight, mode='fan_out', nonlinearity='relu')\n            print(f\"✅ Инициализированы {in_channels} каналов (Kaiming)\")\n\n        # Модифицируем финальный слой\n        num_ftrs = self.model.fc.in_features\n        self.model.fc = nn.Sequential(\n            nn.Dropout(0.5),\n            nn.Linear(num_ftrs, num_classes)\n        )\n\n        # Подсчёт параметров\n        total_params = sum(p.numel() for p in self.parameters())\n        trainable_params = sum(p.numel() for p in self.parameters() if p.requires_grad)\n        print(f\"📊 Всего параметров: {total_params:,}\")\n        print(f\"📊 Обучаемых параметров: {trainable_params:,}\")\n\n    def forward(self, x):\n        return self.model(x)\n\n\n# ============================================================================\n# НОВАЯ МОДЕЛЬ: EfficientNet-B0 для 2.5D\n# ============================================================================\nclass EfficientNetB0_2_5D(nn.Module):\n    \"\"\"EfficientNet-B0 (или tf_variant) с кастомным первым слоем для 2.5D\"\"\"\n    def __init__(self, in_channels=15, num_classes=3, pretrained=True, variant='tf_efficientnet_b0_ns'):\n        super(EfficientNetB0_2_5D, self).__init__()\n        \n        # Создаём базовую модель из timm\n        self.model = timm.create_model(\n            variant,                   # 'efficientnet_b0' или 'tf_efficientnet_b0_ns' (рекомендую ns)\n            pretrained=pretrained,\n            num_classes=0,             # временно отключаем classifier, будем делать свой\n            in_chans=3                 # сначала 3 канала, потом заменим\n        )\n        \n        print(f\"✅ Загружена предобученная {variant} (pretrained={pretrained})\")\n        \n        # Сохраняем оригинальные веса первого conv (3 канала)\n        original_conv_weight = self.model.conv_stem.weight.data.clone()\n        \n        # Заменяем первый conv на нужное количество каналов\n        self.model.conv_stem = nn.Conv2d(\n            in_channels=in_channels,\n            out_channels=self.model.conv_stem.out_channels,\n            kernel_size=self.model.conv_stem.kernel_size,\n            stride=self.model.conv_stem.stride,\n            padding=self.model.conv_stem.padding,\n            bias=False\n        )\n        \n        # Инициализация нового conv_stem\n        if pretrained and in_channels >= 3:\n            with torch.no_grad():\n                # Cycling стратегия: повторяем RGB-веса\n                for i in range(in_channels):\n                    channel_idx = i % 3\n                    self.model.conv_stem.weight.data[:, i:i+1, :, :] = \\\n                        original_conv_weight[:, channel_idx:channel_idx+1, :, :] / (in_channels // 3 + 1)\n            print(f\"✅ Инициализированы {in_channels} каналов из ImageNet весов (cycling)\")\n        else:\n            nn.init.kaiming_normal_(self.model.conv_stem.weight, mode='fan_out', nonlinearity='relu')\n            print(f\"✅ Инициализированы {in_channels} каналов (Kaiming)\")\n        \n        # Добавляем классификатор\n        num_ftrs = self.model.num_features\n        self.model.classifier = nn.Sequential(\n            nn.Dropout(p=0.3),                    # стандартный dropout для B0\n            nn.Linear(num_ftrs, num_classes)\n        )\n        \n        # Stochastic depth (опционально, но рекомендуется)\n        self.model.drop_path_rate = 0.2\n        \n        # Подсчёт параметров\n        total_params = sum(p.numel() for p in self.parameters())\n        trainable_params = sum(p.numel() for p in self.parameters() if p.requires_grad)\n        print(f\"📊 Всего параметров: {total_params:,}\")\n        print(f\"📊 Обучаемых параметров: {trainable_params:,}\")\n        \n    def forward(self, x):\n        return self.model(x)\n\n# ============================================================================\n# ФАБРИКА МОДЕЛЕЙ (ДОПОЛНЕНА)\n# ============================================================================\ndef create_model(model_type='sagittal', architecture='resnet34'):\n    \"\"\"\n    Фабрика моделей с поддержкой ResNet и EfficientNet-B0\n    Args:\n        model_type: 'sagittal', 'axial', или 'combined'\n        architecture: 'resnet18', 'resnet34', 'efficientnet_b0', 'tf_efficientnet_b0_ns'\n    \"\"\"\n    if model_type == 'sagittal':\n        in_channels = 15\n        print(f\"\\n{'='*60}\")\n        print(f\"🔷 Создание Sagittal модели ({architecture.upper()})\")\n        print(f\"{'='*60}\")\n    elif model_type == 'axial':\n        in_channels = 15\n        print(f\"\\n{'='*60}\")\n        print(f\"🔶 Создание Axial модели ({architecture.upper()})\")\n        print(f\"{'='*60}\")\n    elif model_type == 'combined':\n        in_channels = 30\n        print(f\"\\n{'='*60}\")\n        print(f\"🔸 Создание Combined модели ({architecture.upper()})\")\n        print(f\"{'='*60}\")\n    else:\n        raise ValueError(f\"Unknown model_type: {model_type}\")\n    \n    # Создаём модель\n    if architecture == 'resnet34':\n        model = ResNet34_2_5D(in_channels=in_channels, num_classes=cfg.NUM_CLASSES, pretrained=cfg.PRETRAINED)\n    elif architecture == 'resnet18':\n        model = ResNet2_5D(in_channels=in_channels, num_classes=cfg.NUM_CLASSES, pretrained=cfg.PRETRAINED)\n    elif architecture in ['efficientnet_b0', 'tf_efficientnet_b0_ns']:\n        model = EfficientNetB0_2_5D(\n            in_channels=in_channels,\n            num_classes=cfg.NUM_CLASSES,\n            pretrained=cfg.PRETRAINED,\n            variant=architecture\n        )\n    else:\n        raise ValueError(f\"Unknown architecture: {architecture}\")\n    \n    print(f\"✅ Входных каналов: {in_channels}\")\n    print(f\"✅ Выходных классов: {cfg.NUM_CLASSES}\")\n    print(f\"{'='*60}\\n\")\n    \n    return model.to(device)","metadata":{"trusted":true,"id":"ph6Oh0O_osNJ","execution":{"iopub.status.busy":"2026-02-14T13:47:28.284015Z","iopub.status.idle":"2026-02-14T13:47:28.284342Z","shell.execute_reply.started":"2026-02-14T13:47:28.28419Z","shell.execute_reply":"2026-02-14T13:47:28.284229Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# ============================================================================\n# COLLATE ФУНКЦИЯ ДЛЯ ДОПОЛНИТЕЛЬНОЙ ЗАЩИТЫ\n# ============================================================================\n\ndef custom_collate_fn(batch):\n    \"\"\"\n    Кастомная collate функция с проверкой размерностей.\n    Отфильтровывает сэмплы с неправильными размерами.\n    \"\"\"\n    # Проверяем размерности\n    valid_batch = []\n    expected_shape = None\n\n    for item in batch:\n        img, label = item\n\n        # Устанавливаем ожидаемую форму по первому элементу\n        if expected_shape is None:\n            expected_shape = img.shape\n\n        # Проверяем соответствие\n        if img.shape == expected_shape:\n            valid_batch.append(item)\n        else:\n            print(f\"⚠️  Skipping sample with shape {img.shape}, expected {expected_shape}\")\n\n    # Если все сэмплы невалидны, возвращаем пустой батч\n    if len(valid_batch) == 0:\n        raise RuntimeError(\"All samples in batch have invalid shapes!\")\n\n    # Стандартная collate\n    images = torch.stack([item[0] for item in valid_batch])\n    labels = torch.tensor([item[1] for item in valid_batch], dtype=torch.long)\n\n    return images, labels","metadata":{"trusted":true,"id":"0SyDLt7LosNJ","execution":{"iopub.status.busy":"2026-02-14T13:47:28.285356Z","iopub.status.idle":"2026-02-14T13:47:28.285623Z","shell.execute_reply.started":"2026-02-14T13:47:28.285508Z","shell.execute_reply":"2026-02-14T13:47:28.285525Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# ============================================================================\n# ВСПОМОГАТЕЛЬНЫЕ ФУНКЦИИ ДЛЯ АНАЛИЗА МОДЕЛИ\n# ============================================================================\n\ndef count_parameters(model):\n    \"\"\"Подсчёт параметров модели\"\"\"\n    total_params = sum(p.numel() for p in model.parameters())\n    trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)\n\n    print(f\"\\n📊 Статистика модели:\")\n    print(f\"   Всего параметров: {total_params:,}\")\n    print(f\"   Обучаемых: {trainable_params:,}\")\n    print(f\"   Замороженных: {total_params - trainable_params:,}\")\n\n    return total_params, trainable_params\n\n\ndef get_model_size_mb(model):\n    \"\"\"Размер модели в MB\"\"\"\n    param_size = 0\n    for param in model.parameters():\n        param_size += param.nelement() * param.element_size()\n\n    buffer_size = 0\n    for buffer in model.buffers():\n        buffer_size += buffer.nelement() * buffer.element_size()\n\n    size_mb = (param_size + buffer_size) / 1024**2\n    print(f\"   Размер модели: {size_mb:.2f} MB\\n\")\n\n    return size_mb\n\n\ndef print_model_summary(model, input_shape):\n    \"\"\"Вывод структуры модели\"\"\"\n    print(f\"\\n{'='*60}\")\n    print(\"🔍 SUMMARY МОДЕЛИ\")\n    print(f\"{'='*60}\\n\")\n\n    # Параметры\n    count_parameters(model)\n\n    # Размер\n    get_model_size_mb(model)\n\n    # Тестовый forward pass\n    model.eval()\n    with torch.no_grad():\n        x = torch.randn(1, *input_shape).to(device)\n        y = model(x)\n        print(f\"📐 Входная размерность: {x.shape}\")\n        print(f\"📐 Выходная размерность: {y.shape}\")\n        print(f\"{'='*60}\\n\")","metadata":{"id":"_oe8w7yIwoZ9","trusted":true,"execution":{"iopub.status.busy":"2026-02-14T13:47:28.286757Z","iopub.status.idle":"2026-02-14T13:47:28.287016Z","shell.execute_reply.started":"2026-02-14T13:47:28.286906Z","shell.execute_reply":"2026-02-14T13:47:28.28692Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# ============================================================================\n# ВЫБОР АУГМЕНТАЦИЙ ПО КОНФИГУ\n# ============================================================================\ndef get_train_transforms(img_size=224):\n    \"\"\"Возвращает train transforms в зависимости от cfg.AUG_LEVEL\"\"\"\n    if not cfg.USE_AUGMENTATION or cfg.AUG_LEVEL == 'none':\n        print(\"🌀 Аугментации: NONE (только Resize + Normalize)\")\n        return get_val_transforms(img_size)\n    elif cfg.AUG_LEVEL == 'soft':\n        print(\"🌀 Аугментации: SOFT\")\n        return get_train_transforms_soft(img_size)\n    elif cfg.AUG_LEVEL == 'medium':\n        print(\"🌀 Аугментации: MEDIUM\")\n        return get_train_transforms_medium(img_size)\n    elif cfg.AUG_LEVEL == 'strong':\n        print(\"🌀 Аугментации: STRONG\")\n        return get_train_transforms_strong(img_size)\n    else:\n        raise ValueError(f\"Неизвестный AUG_LEVEL: {cfg.AUG_LEVEL}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-02-14T13:47:28.288259Z","iopub.status.idle":"2026-02-14T13:47:28.288523Z","shell.execute_reply.started":"2026-02-14T13:47:28.28841Z","shell.execute_reply":"2026-02-14T13:47:28.288423Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# ============================================================================\n# БАЛАНСИРОВКА КЛАССОВ\n# ============================================================================\n\ndef compute_class_weights(df):\n    \"\"\"Вычисление весов классов для loss функции\"\"\"\n    labels = df['label'].values\n    class_weights = compute_class_weight(\n        class_weight='balanced',\n        classes=np.unique(labels),\n        y=labels\n    )\n    return torch.FloatTensor(class_weights).to(device)\n\n\ndef create_weighted_sampler(df):\n    \"\"\"Создание weighted sampler для балансировки батчей\"\"\"\n    labels = df['label'].values\n    class_counts = np.bincount(labels)\n    class_weights = 1. / class_counts\n\n    # Вес каждого сэмпла\n    sample_weights = class_weights[labels]\n\n    sampler = WeightedRandomSampler(\n        weights=sample_weights,\n        num_samples=len(sample_weights),\n        replacement=True\n    )\n\n    return sampler\n\n\ndef get_dataloaders(df, model_type='sagittal'):\n    \"\"\"Создание DataLoader с балансировкой и защитой от ошибок размерности\"\"\"\n\n    # Split по study_id\n    study_ids = df['study_id'].unique()\n    train_ids, val_ids = train_test_split(\n        study_ids,\n        test_size=cfg.TRAIN_VAL_SPLIT,\n        random_state=42\n    )\n\n    df_train = df[df['study_id'].isin(train_ids)].reset_index(drop=True)\n    df_val = df[df['study_id'].isin(val_ids)].reset_index(drop=True)\n\n    print(f\"\\n📊 Распределение классов в Train:\")\n    print(df_train['label'].value_counts().sort_index())\n    print(f\"\\n📊 Распределение классов в Val:\")\n    print(df_val['label'].value_counts().sort_index())\n\n    # Аугментации\n    train_transform = get_train_transforms(cfg.IMG_SIZE)\n    val_transform = get_val_transforms(cfg.IMG_SIZE)\n    \n    print(f\"✅ Train transforms: {cfg.AUG_LEVEL.upper() if cfg.USE_AUGMENTATION else 'NONE'}\")\n    print(f\"✅ Val transforms: базовые (Resize + Normalize)\")\n\n    # Создание датасетов\n    if model_type == 'sagittal':\n        train_dataset = LumbarSagittalDataset(df_train, transform=train_transform)\n        val_dataset = LumbarSagittalDataset(df_val, transform=val_transform)\n\n    elif model_type == 'axial':\n        train_dataset = LumbarAxialDataset(df_train, transform=train_transform)\n        val_dataset = LumbarAxialDataset(df_val, transform=val_transform)\n\n    elif model_type == 'combined':\n        train_dataset = LumbarCombinedDataset(df_train, transform=train_transform)\n        val_dataset = LumbarCombinedDataset(df_val, transform=val_transform)\n\n    # ПРОВЕРКА: тестируем первый сэмпл\n    if len(train_dataset) > 0:\n        test_img, test_label = train_dataset[0]\n        print(f\"\\n✅ Sample shape check: {test_img.shape} (label: {test_label.item()})\")\n\n    # Weighted Sampler\n    if cfg.USE_WEIGHTED_SAMPLING and len(train_dataset) > 0:\n        print(f\"\\n⚖️  Создание Weighted Sampler...\")\n        train_labels = []\n        for i in tqdm(range(len(train_dataset)), desc=\"Collecting labels\"):\n            _, label = train_dataset[i]\n            train_labels.append(label.item())\n\n        class_counts = np.bincount(train_labels)\n        class_weights = 1. / class_counts\n        sample_weights = class_weights[train_labels]\n\n        sampler = WeightedRandomSampler(\n            weights=sample_weights,\n            num_samples=len(sample_weights),\n            replacement=True\n        )\n        shuffle = False\n    else:\n        sampler = None\n        shuffle = True\n\n    # DataLoaders с кастомной collate функцией\n    train_loader = DataLoader(\n        train_dataset,\n        batch_size=cfg.BATCH_SIZE,\n        sampler=sampler,\n        shuffle=shuffle if sampler is None else False,\n        num_workers=cfg.NUM_WORKERS,\n        pin_memory=True,\n        collate_fn=custom_collate_fn  # ЗАЩИТА ОТ ОШИБОК\n    )\n\n    val_loader = DataLoader(\n        val_dataset,\n        batch_size=cfg.BATCH_SIZE,\n        shuffle=False,\n        num_workers=cfg.NUM_WORKERS,\n        pin_memory=True,\n        collate_fn=custom_collate_fn  # ЗАЩИТА ОТ ОШИБОК\n    )\n\n    return train_loader, val_loader, df_train, df_val","metadata":{"trusted":true,"id":"Z7FHAWEhosNJ","execution":{"iopub.status.busy":"2026-02-14T13:47:28.289999Z","iopub.status.idle":"2026-02-14T13:47:28.290361Z","shell.execute_reply.started":"2026-02-14T13:47:28.290242Z","shell.execute_reply":"2026-02-14T13:47:28.290257Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# ============================================================================\n# ТРЕНИРОВКА\n# ============================================================================\n\nclass EarlyStopping:\n    \"\"\"Early stopping для предотвращения переобучения\"\"\"\n\n    def __init__(self, patience=5, min_delta=0.001, mode='max'):\n        self.patience = patience\n        self.min_delta = min_delta\n        self.mode = mode\n        self.counter = 0\n        self.best_score = None\n        self.early_stop = False\n\n    def __call__(self, score):\n        if self.best_score is None:\n            self.best_score = score\n        elif self.mode == 'max':\n            if score < self.best_score + self.min_delta:\n                self.counter += 1\n                if self.counter >= self.patience:\n                    self.early_stop = True\n            else:\n                self.best_score = score\n                self.counter = 0\n        else:  # min\n            if score > self.best_score - self.min_delta:\n                self.counter += 1\n                if self.counter >= self.patience:\n                    self.early_stop = True\n            else:\n                self.best_score = score\n                self.counter = 0\n\n\ndef train_one_epoch(model, loader, criterion, optimizer, scaler, device):\n    \"\"\"Одна эпоха обучения\"\"\"\n    model.train()\n    running_loss = 0.0\n    running_corrects = 0\n    total = 0\n\n    pbar = tqdm(loader, desc=\"Training\")\n    for images, labels in pbar:\n        images, labels = images.to(device), labels.to(device)\n\n        optimizer.zero_grad()\n\n        # Mixed precision training\n        with autocast():\n            outputs = model(images)\n            loss = criterion(outputs, labels)\n\n        scaler.scale(loss).backward()\n        scaler.step(optimizer)\n        scaler.update()\n\n        # Метрики\n        running_loss += loss.item() * images.size(0)\n        preds = outputs.argmax(dim=1)\n        running_corrects += (preds == labels).sum().item()\n        total += labels.size(0)\n\n        pbar.set_postfix({\n            'loss': running_loss / total,\n            'acc': running_corrects / total\n        })\n\n    epoch_loss = running_loss / total\n    epoch_acc = running_corrects / total\n\n    return epoch_loss, epoch_acc\n\n\ndef validate(model, loader, criterion, device, epoch=None, visualize_gradcam=False):\n    \"\"\"Валидация с опциональной визуализацией Grad-CAM\"\"\"\n\n    model.eval()\n\n    running_loss = 0.0\n    running_corrects = 0\n    total = 0\n\n    all_preds = []\n    all_labels = []\n\n    pbar = tqdm(loader, desc=\"Validation\")\n\n    for batch_idx, (images, labels) in enumerate(pbar):\n\n        images = images.to(device)\n        labels = labels.to(device)\n\n        # ===== Grad-CAM отдельно (с градиентами) =====\n        if visualize_gradcam and batch_idx == 0 and epoch is not None and epoch % 5 == 0:\n\n            print(f\"\\n🔍 Grad-CAM визуализация для эпохи {epoch}...\")\n\n            visualize_batch_with_gradcam(\n                model,\n                images,\n                labels,\n                predictions=None,\n                batch_idx=epoch,\n                save_dir=os.path.join(cfg.SAVE_DIR, \"gradcam_training\")\n            )\n\n        # ===== Обычная валидация (без градиентов) =====\n        with torch.no_grad():\n\n            outputs = model(images)\n            loss = criterion(outputs, labels)\n\n        running_loss += loss.item() * images.size(0)\n\n        preds = outputs.argmax(dim=1)\n\n        running_corrects += (preds == labels).sum().item()\n        total += labels.size(0)\n\n        all_preds.extend(preds.cpu().numpy())\n        all_labels.extend(labels.cpu().numpy())\n\n        pbar.set_postfix({\n            \"loss\": running_loss / total,\n            \"acc\": running_corrects / total\n        })\n\n    epoch_loss = running_loss / total\n    epoch_acc = running_corrects / total\n\n    return epoch_loss, epoch_acc, all_preds, all_labels\n\n\ndef train_model(model, train_loader, val_loader, df_train, model_name, num_epochs=20):\n    \"\"\"Полный цикл обучения\"\"\"\n\n    # Class weights\n    if cfg.USE_CLASS_WEIGHTS:\n        class_weights = compute_class_weights(df_train)\n        criterion = nn.CrossEntropyLoss(weight=class_weights)\n        print(f\"📊 Class weights: {class_weights.cpu().numpy()}\")\n    else:\n        criterion = nn.CrossEntropyLoss()\n\n    # Optimizer\n    optimizer = optim.AdamW(\n        model.parameters(),\n        lr=cfg.LEARNING_RATE,\n        weight_decay=cfg.WEIGHT_DECAY\n    )\n\n    # Scheduler\n    scheduler = optim.lr_scheduler.ReduceLROnPlateau(\n        optimizer,\n        mode='max',\n        factor=0.5,\n        patience=3\n\n    )\n\n    # Mixed precision\n    scaler = GradScaler()\n\n    # Early stopping\n    early_stopping = EarlyStopping(patience=cfg.EARLY_STOPPING_PATIENCE, mode='max')\n\n    # История\n    history = {\n        'train_loss': [],\n        'train_acc': [],\n        'val_loss': [],\n        'val_acc': []\n    }\n\n    best_val_acc = 0.0\n\n    print(f\"\\n{'='*60}\")\n    print(f\"🚀 Начало обучения модели: {model_name}\")\n    print(f\"{'='*60}\\n\")\n\n    for epoch in range(num_epochs):\n        print(f\"\\n📅 Epoch {epoch+1}/{num_epochs}\")\n        print(\"-\" * 60)\n\n        # Train\n        train_loss, train_acc = train_one_epoch(\n            model, train_loader, criterion, optimizer, scaler, device\n        )\n\n        # Validate\n        val_loss, val_acc, val_preds, val_labels = validate(\n            model, val_loader, criterion, device,\n            epoch=epoch, \n            visualize_gradcam=True\n        )\n\n        # Scheduler step\n        scheduler.step(val_acc)\n\n        # История\n        history['train_loss'].append(train_loss)\n        history['train_acc'].append(train_acc)\n        history['val_loss'].append(val_loss)\n        history['val_acc'].append(val_acc)\n\n        # Вывод\n        print(f\"\\n📈 Epoch {epoch+1} Summary:\")\n        print(f\"   Train Loss: {train_loss:.4f} | Train Acc: {train_acc:.4f}\")\n        print(f\"   Val Loss:   {val_loss:.4f} | Val Acc:   {val_acc:.4f}\")\n\n        # Сохранение лучшей модели\n        if val_acc > best_val_acc:\n            best_val_acc = val_acc\n            torch.save({\n                'epoch': epoch,\n                'model_state_dict': model.state_dict(),\n                'optimizer_state_dict': optimizer.state_dict(),\n                'val_acc': val_acc,\n                'val_loss': val_loss,\n            }, os.path.join(cfg.SAVE_DIR, f'{model_name}_best.pth'))\n            print(f\"   ✅ Saved best model! Val Acc: {val_acc:.4f}\")\n\n        # Early stopping\n        early_stopping(val_acc)\n        if early_stopping.early_stop:\n            print(f\"\\n⚠️  Early stopping triggered at epoch {epoch+1}\")\n            break\n\n    print(f\"\\n{'='*60}\")\n    print(f\"✅ Обучение завершено!\")\n    print(f\"🏆 Best Val Acc: {best_val_acc:.4f}\")\n    print(f\"{'='*60}\\n\")\n\n    return model, history","metadata":{"trusted":true,"id":"9JtJYkTsosNK","execution":{"iopub.status.busy":"2026-02-14T13:47:28.291352Z","iopub.status.idle":"2026-02-14T13:47:28.291566Z","shell.execute_reply.started":"2026-02-14T13:47:28.291463Z","shell.execute_reply":"2026-02-14T13:47:28.291475Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# ============================================================================\n# GRAD-CAM: АВТООПРЕДЕЛЕНИЕ TARGET LAYERS\n# ============================================================================\n\ndef get_target_layers_auto(model):\n    \"\"\"\n    Автоматическое определение target layers для разных архитектур\n    \n    Args:\n        model: модель (может быть обёрткой или напрямую timm/torchvision)\n    \n    Returns:\n        list: список target layers\n    \"\"\"\n    # Получаем базовую модель\n    if hasattr(model, 'model'):\n        base_model = model.model\n        print(\"🔍 Обнаружена обёртка → работаем с model.model\")\n    else:\n        base_model = model\n        print(\"🔍 Работаем напрямую с моделью\")\n    \n    # ResNet архитектура (ResNet18, ResNet34, ResNet50, etc.)\n    if hasattr(base_model, 'layer4'):\n        target_layers = [base_model.layer4[-1]]\n        print(f\"✅ Обнаружен ResNet → target_layers: layer4[-1]\")\n        return target_layers\n    \n    # EfficientNet из timm\n    elif hasattr(base_model, 'conv_head'):\n        target_layers = [base_model.conv_head]\n        print(f\"✅ Обнаружен EfficientNet (timm) → target_layers: conv_head\")\n        return target_layers\n    \n    # EfficientNet из torchvision\n    elif hasattr(base_model, 'features'):\n        # Берём последний блок features\n        if hasattr(base_model.features, '__getitem__'):\n            target_layers = [base_model.features[-1]]\n            print(f\"✅ Обнаружен EfficientNet (torchvision) → target_layers: features[-1]\")\n            return target_layers\n    \n    # EfficientNet альтернативная структура (blocks)\n    elif hasattr(base_model, 'blocks'):\n        last_block = base_model.blocks[-1]\n        if hasattr(last_block, '__getitem__'):\n            target_layers = [last_block[-1]]\n        else:\n            target_layers = [last_block]\n        print(f\"✅ Обнаружен EfficientNet (blocks) → target_layers: blocks[-1]\")\n        return target_layers\n    \n    else:\n        # Fallback: пытаемся найти последний Conv2d слой\n        print(\"⚠️  Неизвестная архитектура, ищем последний Conv2d...\")\n        conv_layers = []\n        \n        def find_conv_layers(module, prefix=''):\n            for name, layer in module.named_children():\n                full_name = f\"{prefix}.{name}\" if prefix else name\n                if isinstance(layer, nn.Conv2d):\n                    conv_layers.append((full_name, layer))\n                else:\n                    find_conv_layers(layer, full_name)\n        \n        find_conv_layers(base_model)\n        \n        if conv_layers:\n            last_conv_name, last_conv = conv_layers[-1]\n            print(f\"✅ Найден последний Conv2d: {last_conv_name}\")\n            return [last_conv]\n        else:\n            raise ValueError(\n                f\"❌ Не удалось определить target_layers для модели типа {type(base_model)}.\\n\"\n                f\"Пожалуйста, укажите target_layers вручную.\"\n            )","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-02-14T13:47:28.292524Z","iopub.status.idle":"2026-02-14T13:47:28.292775Z","shell.execute_reply.started":"2026-02-14T13:47:28.292667Z","shell.execute_reply":"2026-02-14T13:47:28.29268Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"def compare_gradcam_variants(model, input_tensor, original_image, target_category=None):\n    \"\"\"\n    Сравнение разных вариантов CAM методов\n    УНИВЕРСАЛЬНАЯ ВЕРСИЯ для всех архитектур (БЕЗ use_cuda)\n    \"\"\"\n    # Автоматическое определение target_layers\n    target_layers = get_target_layers_auto(model)\n    \n    # Разные CAM методы (БЕЗ use_cuda параметра)\n    cam_methods = {}\n    \n    try:\n        cam_methods['GradCAM'] = GradCAM(model=model, target_layers=target_layers)\n    except Exception as e:\n        print(f\"⚠️  Ошибка создания GradCAM: {e}\")\n    \n    try:\n        cam_methods['GradCAM++'] = GradCAMPlusPlus(model=model, target_layers=target_layers)\n    except Exception as e:\n        print(f\"⚠️  Ошибка создания GradCAM++: {e}\")\n    \n    try:\n        cam_methods['HiResCAM'] = HiResCAM(model=model, target_layers=target_layers)\n    except Exception as e:\n        print(f\"⚠️  Ошибка создания HiResCAM: {e}\")\n    \n    if not cam_methods:\n        print(\"❌ Не удалось создать ни один CAM метод\")\n        return\n    \n    # Подготовка изображения\n    if original_image.max() > 1.0:\n        original_image = original_image / 255.0\n    if len(original_image.shape) == 2:\n        original_image = np.stack([original_image]*3, axis=-1)\n    elif original_image.shape[0] == 3:\n        original_image = original_image.transpose(1, 2, 0)\n    \n    # Визуализация\n    fig, axes = plt.subplots(2, 3, figsize=(18, 12))\n    axes = axes.flatten()\n    \n    # Оригинал\n    axes[0].imshow(original_image)\n    axes[0].set_title('Original Image', fontsize=12)\n    axes[0].axis('off')\n    \n    # Предсказание\n    model.eval()\n    with torch.no_grad():\n        output = model(input_tensor.to(device))\n        probabilities = torch.softmax(output, dim=1)[0]\n        prediction = torch.argmax(probabilities).item()\n    \n    axes[0].text(\n        0.5, -0.1, \n        f'Prediction: {prediction}\\nProbs: {probabilities.cpu().numpy()}',\n        transform=axes[0].transAxes, \n        ha='center', \n        fontsize=10\n    )\n    \n    # Каждый CAM метод\n    for idx, (method_name, cam_extractor) in enumerate(cam_methods.items(), start=1):\n        print(f\"Генерация {method_name}...\")\n        \n        try:\n            targets = [ClassifierOutputTarget(target_category if target_category is not None else prediction)]\n            cam = cam_extractor(input_tensor=input_tensor.to(device), targets=targets)[0]\n            \n            # Resize\n            cam_resized = cv2.resize(cam, (original_image.shape[1], original_image.shape[0]))\n            \n            # Наложение\n            visualization = show_cam_on_image(original_image, cam_resized, use_rgb=True)\n            \n            axes[idx].imshow(visualization)\n            axes[idx].set_title(method_name, fontsize=12)\n            axes[idx].axis('off')\n        \n        except Exception as e:\n            print(f\"⚠️  Ошибка при генерации {method_name}: {e}\")\n            axes[idx].text(\n                0.5, 0.5, f'{method_name}\\nError',\n                ha='center', va='center',\n                transform=axes[idx].transAxes,\n                fontsize=12, color='red'\n            )\n            axes[idx].axis('off')\n    \n    # Пустые оси\n    for idx in range(len(cam_methods)+1, 6):\n        axes[idx].axis('off')\n    \n    plt.suptitle('Comparison of CAM Methods', fontsize=16, fontweight='bold')\n    plt.tight_layout()\n    plt.show()\n    \n    # Очистка\n    for cam_ext in cam_methods.values():\n        try:\n            del cam_ext\n        except:\n            pass\n    torch.cuda.empty_cache()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-02-14T13:47:28.293872Z","iopub.status.idle":"2026-02-14T13:47:28.294131Z","shell.execute_reply.started":"2026-02-14T13:47:28.294011Z","shell.execute_reply":"2026-02-14T13:47:28.294029Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# ============================================================================\n# ВИЗУАЛИЗАЦИЯ\n# ============================================================================\ndef plot_training_history(history, model_name):\n    \"\"\"\n    График обучения: Loss и Accuracy\n    model_name — например, 'Sagittal EfficientNet-B0' или 'Combined ResNet34'\n    \"\"\"\n    arch = cfg.MODEL_ARCHITECTURE.replace('_', '-').upper()\n    \n    fig, axes = plt.subplots(1, 2, figsize=(16, 6))\n    \n    # Loss\n    axes[0].plot(history['train_loss'], label='Train Loss', marker='o', linewidth=2, color='#1f77b4')\n    axes[0].plot(history['val_loss'], label='Val Loss', marker='s', linewidth=2, color='#ff7f0e')\n    axes[0].set_xlabel('Epoch', fontsize=12)\n    axes[0].set_ylabel('Loss', fontsize=12)\n    axes[0].set_title(f'{model_name} ({arch})\\nLoss по эпохам', fontsize=14, fontweight='bold')\n    axes[0].legend(fontsize=12)\n    axes[0].grid(True, alpha=0.3)\n    \n    # Accuracy\n    axes[1].plot(history['train_acc'], label='Train Acc', marker='o', linewidth=2, color='#2ca02c')\n    axes[1].plot(history['val_acc'], label='Val Acc', marker='s', linewidth=2, color='#d62728')\n    axes[1].set_xlabel('Epoch', fontsize=12)\n    axes[1].set_ylabel('Accuracy', fontsize=12)\n    axes[1].set_title(f'{model_name} ({arch})\\nAccuracy по эпохам', fontsize=14, fontweight='bold')\n    axes[1].legend(fontsize=12)\n    axes[1].grid(True, alpha=0.3)\n    \n    plt.suptitle(f'📈 История обучения: {model_name} ({arch})', fontsize=16, y=1.02)\n    plt.tight_layout()\n    \n    # Сохранение с динамическим именем\n    safe_arch = cfg.MODEL_ARCHITECTURE  # без замен, чтобы в имени файла было оригинальное\n    filename = f'{model_name.lower().replace(\" \", \"_\")}_{safe_arch}_history.png'\n    filepath = os.path.join(cfg.SAVE_DIR, filename)\n    plt.savefig(filepath, dpi=200, bbox_inches='tight')\n    print(f\"💾 График истории сохранён: {filename}\")\n    \n    plt.show()\n\n\ndef plot_confusion_matrix(y_true, y_pred, model_name):\n    \"\"\"\n    Confusion Matrix на валидационной выборке\n    y_true, y_pred — списки или массивы настоящих и предсказанных меток\n    \"\"\"\n    arch = cfg.MODEL_ARCHITECTURE.replace('_', '-').upper()\n    \n    cm = confusion_matrix(y_true, y_pred)\n    \n    plt.figure(figsize=(9, 7))\n    sns.heatmap(\n        cm, annot=True, fmt='d', cmap='Blues', linewidths=0.5,\n        xticklabels=['Normal/Mild', 'Moderate', 'Severe'],\n        yticklabels=['Normal/Mild', 'Moderate', 'Severe'],\n        cbar_kws={'shrink': 0.8}\n    )\n    \n    plt.title(f'{model_name} ({arch})\\nConfusion Matrix (Val)', fontsize=16, fontweight='bold', pad=20)\n    plt.ylabel('True Label', fontsize=14)\n    plt.xlabel('Predicted Label', fontsize=14)\n    \n    # Добавляем проценты (опционально, но красиво)\n    total = cm.sum()\n    for i in range(cm.shape[0]):\n        for j in range(cm.shape[1]):\n            plt.text(j+0.5, i+0.7, f'{cm[i,j]}\\n({cm[i,j]/total*100:.1f}%)',\n                     ha='center', va='center', fontsize=12, color='black' if cm[i,j] < cm.max()/2 else 'white')\n    \n    plt.tight_layout()\n    \n    # Сохранение\n    safe_arch = cfg.MODEL_ARCHITECTURE\n    filename = f'{model_name.lower().replace(\" \", \"_\")}_{safe_arch}_confusion.png'\n    filepath = os.path.join(cfg.SAVE_DIR, filename)\n    plt.savefig(filepath, dpi=200, bbox_inches='tight')\n    print(f\"💾 Confusion Matrix сохранён: {filename}\")\n    \n    plt.show()","metadata":{"trusted":true,"id":"l9RgzWpiosNK","execution":{"iopub.status.busy":"2026-02-14T13:47:28.295306Z","iopub.status.idle":"2026-02-14T13:47:28.295516Z","shell.execute_reply.started":"2026-02-14T13:47:28.295415Z","shell.execute_reply":"2026-02-14T13:47:28.295427Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# ============================================================================\n# CONFUSION MATRIX\n# ============================================================================\n\nfrom sklearn.metrics import confusion_matrix, classification_report\nimport seaborn as sns\n\ndef plot_confusion_matrix(y_true, y_pred, class_names, model_name, save_path=None):\n    \"\"\"\n    Построение Confusion Matrix\n    \n    Args:\n        y_true: истинные метки\n        y_pred: предсказанные метки\n        class_names: названия классов\n        model_name: название модели\n        save_path: путь для сохранения\n    \"\"\"\n    # Вычисляем confusion matrix\n    cm = confusion_matrix(y_true, y_pred)\n    \n    # Нормализованная версия (в процентах)\n    cm_normalized = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis] * 100\n    \n    # Создаём фигуру с двумя subplots\n    fig, axes = plt.subplots(1, 2, figsize=(16, 6))\n    \n    # 1. Абсолютные значения\n    sns.heatmap(\n        cm, \n        annot=True, \n        fmt='d', \n        cmap='Blues',\n        xticklabels=class_names,\n        yticklabels=class_names,\n        ax=axes[0],\n        cbar_kws={'label': 'Count'}\n    )\n    axes[0].set_title(f'{model_name} - Confusion Matrix (Counts)', fontsize=14, fontweight='bold')\n    axes[0].set_ylabel('True Label', fontsize=12)\n    axes[0].set_xlabel('Predicted Label', fontsize=12)\n    \n    # 2. Нормализованные значения (%)\n    sns.heatmap(\n        cm_normalized, \n        annot=True, \n        fmt='.1f', \n        cmap='Blues',\n        xticklabels=class_names,\n        yticklabels=class_names,\n        ax=axes[1],\n        cbar_kws={'label': 'Percentage (%)'}\n    )\n    axes[1].set_title(f'{model_name} - Confusion Matrix (Normalized)', fontsize=14, fontweight='bold')\n    axes[1].set_ylabel('True Label', fontsize=12)\n    axes[1].set_xlabel('Predicted Label', fontsize=12)\n    \n    plt.tight_layout()\n    \n    if save_path:\n        plt.savefig(save_path, dpi=150, bbox_inches='tight')\n        print(f\"✅ Confusion Matrix сохранена: {save_path}\")\n    \n    plt.show()\n    \n    # Выводим метрики\n    print(f\"\\n{'='*80}\")\n    print(f\"📊 CLASSIFICATION REPORT: {model_name}\")\n    print(f\"{'='*80}\\n\")\n    \n    report = classification_report(\n        y_true, \n        y_pred, \n        target_names=class_names,\n        digits=4\n    )\n    print(report)\n    \n    # Детальная статистика по каждому классу\n    print(f\"\\n{'='*80}\")\n    print(f\"📈 DETAILED METRICS PER CLASS\")\n    print(f\"{'='*80}\\n\")\n    \n    for i, class_name in enumerate(class_names):\n        tp = cm[i, i]  # True Positives\n        fp = cm[:, i].sum() - tp  # False Positives\n        fn = cm[i, :].sum() - tp  # False Negatives\n        tn = cm.sum() - tp - fp - fn  # True Negatives\n        \n        precision = tp / (tp + fp) if (tp + fp) > 0 else 0\n        recall = tp / (tp + fn) if (tp + fn) > 0 else 0\n        f1 = 2 * precision * recall / (precision + recall) if (precision + recall) > 0 else 0\n        \n        print(f\"{class_name}:\")\n        print(f\"  TP: {tp:>5} | FP: {fp:>5} | FN: {fn:>5} | TN: {tn:>5}\")\n        print(f\"  Precision: {precision:.4f} | Recall: {recall:.4f} | F1-Score: {f1:.4f}\")\n        print()\n\n\ndef plot_confusion_matrices_comparison(histories, save_dir=None):\n    \"\"\"\n    Сравнение confusion matrices для нескольких моделей\n    \n    Args:\n        histories: dict с результатами {'model_name': {'y_true': [...], 'y_pred': [...]}}\n        save_dir: директория для сохранения\n    \"\"\"\n    class_names = ['Normal/Mild', 'Moderate', 'Severe']\n    \n    num_models = len(histories)\n    fig, axes = plt.subplots(1, num_models, figsize=(6*num_models, 6))\n    \n    if num_models == 1:\n        axes = [axes]\n    \n    for idx, (model_name, data) in enumerate(histories.items()):\n        cm = confusion_matrix(data['y_true'], data['y_pred'])\n        cm_normalized = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis] * 100\n\n        \n        sns.heatmap(\n            cm_normalized, \n            annot=True, \n            fmt='.1f', \n            cmap='Blues',\n            xticklabels=class_names,\n            yticklabels=class_names,\n            ax=axes[idx],\n            cbar_kws={'label': '%'}\n        )\n        axes[idx].set_title(f'{model_name}', fontsize=12, fontweight='bold')\n        axes[idx].set_ylabel('True Label' if idx == 0 else '', fontsize=10)\n        axes[idx].set_xlabel('Predicted Label', fontsize=10)\n    \n    plt.suptitle('Confusion Matrix Comparison', fontsize=16, fontweight='bold', y=1.02)\n    plt.tight_layout()\n    \n    if save_dir:\n        os.makedirs(save_dir, exist_ok=True)\n        save_path = os.path.join(save_dir, 'confusion_matrix_comparison.png')\n        plt.savefig(save_path, dpi=150, bbox_inches='tight')\n        print(f\"✅ Сравнение сохранено: {save_path}\")\n    \n    plt.show()\n\n\ndef analyze_model_errors(y_true, y_pred, class_names, model_name):\n    \"\"\"\n    Анализ ошибок модели\n    \n    Args:\n        y_true: истинные метки\n        y_pred: предсказанные метки\n        class_names: названия классов\n        model_name: название модели\n    \"\"\"\n    print(f\"\\n{'='*80}\")\n    print(f\"🔍 АНАЛИЗ ОШИБОК: {model_name}\")\n    print(f\"{'='*80}\\n\")\n    \n    y_true = np.array(y_true)\n    y_pred = np.array(y_pred)\n    \n    # Общая статистика\n    total = len(y_true)\n    correct = (y_true == y_pred).sum()\n    errors = total - correct\n    \n    print(f\"Всего предсказаний: {total}\")\n    print(f\"Правильных: {correct} ({correct/total*100:.2f}%)\")\n    print(f\"Ошибок: {errors} ({errors/total*100:.2f}%)\")\n    \n    # Анализ типов ошибок\n    print(f\"\\n{'Тип ошибки':<40} {'Количество':<15} {'% от ошибок':<15}\")\n    print(\"-\" * 70)\n    \n    error_types = {}\n    for true_class in range(len(class_names)):\n        for pred_class in range(len(class_names)):\n            if true_class != pred_class:\n                mask = (y_true == true_class) & (y_pred == pred_class)\n                count = mask.sum()\n                if count > 0:\n                    error_type = f\"{class_names[true_class]} → {class_names[pred_class]}\"\n                    error_types[error_type] = count\n                    print(f\"{error_type:<40} {count:<15} {count/errors*100:>14.1f}%\")\n    \n    # Топ-3 самых частых ошибок\n    print(f\"\\n🔥 ТОП-3 САМЫХ ЧАСТЫХ ОШИБОК:\")\n    sorted_errors = sorted(error_types.items(), key=lambda x: x[1], reverse=True)\n    for i, (error_type, count) in enumerate(sorted_errors[:3], 1):\n        print(f\"  {i}. {error_type}: {count} ({count/errors*100:.1f}% от всех ошибок)\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-02-14T13:47:28.296787Z","iopub.status.idle":"2026-02-14T13:47:28.297175Z","shell.execute_reply.started":"2026-02-14T13:47:28.297004Z","shell.execute_reply":"2026-02-14T13:47:28.297027Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# ============================================================================\n# GRAD-CAM VISUALIZATION (ИСПРАВЛЕННАЯ ВЕРСИЯ)\n# ============================================================================\n\nclass GradCAMVisualizer:\n    \"\"\"Класс для визуализации Grad-CAM на медицинских изображениях\"\"\"\n    \n    def __init__(self, model, target_layers=None, use_cuda=True):\n        \"\"\"\n        Args:\n            model: обученная модель\n            target_layers: список слоёв для визуализации (если None, берём автоматически)\n            use_cuda: использовать GPU\n        \"\"\"\n        self.model = model\n        self.device = torch.device('cuda' if use_cuda and torch.cuda.is_available() else 'cpu')\n        self.model.to(self.device)\n        self.model.eval()\n        \n        # Определяем target layer автоматически\n        if target_layers is None:\n            self.target_layers = get_target_layers_auto(model)\n        else:\n            self.target_layers = target_layers\n            print(f\"✅ Grad-CAM initialized for layers: {self.target_layers}\")\n        \n        # Создаём Grad-CAM объект (БЕЗ параметра use_cuda в новых версиях)\n        try:\n            self.cam = GradCAM(\n                model=self.model,\n                target_layers=self.target_layers\n            )\n            print(f\"✅ Grad-CAM успешно инициализирован для {len(self.target_layers)} слоёв\")\n        except Exception as e:\n            print(f\"⚠️  Ошибка при инициализации GradCAM: {e}\")\n            self.cam = None\n    \n    def generate_cam(self, input_tensor, target_category=None):\n        \"\"\"\n        Генерация Grad-CAM карты\n        \n        Args:\n            input_tensor: входной тензор [1, C, H, W]\n            target_category: индекс целевого класса (если None, берём предсказанный)\n        \n        Returns:\n            cam: Grad-CAM карта [H, W]\n            prediction: предсказанный класс\n            confidence: уверенность модели\n        \"\"\"\n        if self.cam is None:\n            # Возвращаем пустую CAM если не инициализирован\n            h, w = input_tensor.shape[2], input_tensor.shape[3]\n            return np.zeros((h, w), dtype=np.float32), 0, 0.0\n        \n        input_tensor = input_tensor.to(self.device)\n        \n        # Получаем предсказание\n        with torch.no_grad():\n            output = self.model(input_tensor)\n            probabilities = torch.softmax(output, dim=1)\n            confidence, prediction = torch.max(probabilities, dim=1)\n            prediction = prediction.item()\n            confidence = confidence.item()\n        \n        # Если target не указан, используем предсказание\n        if target_category is None:\n            target_category = prediction\n        \n        # Генерируем CAM\n        try:\n            targets = [ClassifierOutputTarget(target_category)]\n            cam = self.cam(input_tensor=input_tensor, targets=targets)\n            return cam[0], prediction, confidence\n        except Exception as e:\n            print(f\"⚠️  Ошибка при генерации CAM: {e}\")\n            h, w = input_tensor.shape[2], input_tensor.shape[3]\n            return np.zeros((h, w), dtype=np.float32), prediction, confidence\n    \n    def __del__(self):\n        \"\"\"Очистка ресурсов при удалении объекта\"\"\"\n        if hasattr(self, 'cam') and self.cam is not None:\n            try:\n                # Освобождаем хуки\n                if hasattr(self.cam, 'activations_and_grads'):\n                    self.cam.activations_and_grads.release()\n            except:\n                pass\n    \n    def visualize_2_5d(self, input_tensor, original_images, target_category=None, \n                       save_path=None, class_names=None):\n        \"\"\"\n        Визуализация Grad-CAM для 2.5D входа\n        \n        Args:\n            input_tensor: [1, 15, H, W] или [1, 30, H, W]\n            original_images: список оригинальных изображений для каждого уровня\n            target_category: целевой класс\n            save_path: путь для сохранения\n            class_names: названия классов\n        \"\"\"\n        if class_names is None:\n            class_names = ['Normal/Mild', 'Moderate', 'Severe']\n        \n        # Генерируем CAM\n        cam, prediction, confidence = self.generate_cam(input_tensor, target_category)\n        \n        # Определяем количество уровней\n        num_channels = input_tensor.shape[1]\n        num_levels = num_channels // 3  # 15 → 5 или 30 → 10\n        \n        # Создаём фигуру\n        if num_levels <= 5:\n            fig, axes = plt.subplots(2, 5, figsize=(20, 8))\n            levels_to_show = range(5)\n        else:\n            fig, axes = plt.subplots(4, 5, figsize=(20, 16))\n            levels_to_show = range(10)\n        \n        axes = axes.flatten()\n        \n        # Визуализируем каждый уровень\n        for idx, level_idx in enumerate(levels_to_show):\n            if level_idx >= len(original_images):\n                axes[idx].axis('off')\n                continue\n            \n            # Оригинальное изображение\n            orig_img = original_images[level_idx]\n            \n            # Нормализация для визуализации\n            if orig_img.max() > 1.0:\n                orig_img = orig_img / 255.0\n            \n            # Убеждаемся, что изображение в формате [H, W, 3]\n            if len(orig_img.shape) == 2:\n                orig_img = np.stack([orig_img]*3, axis=-1)\n            elif orig_img.shape[0] == 3:  # [3, H, W]\n                orig_img = orig_img.transpose(1, 2, 0)\n            \n            # Resize CAM до размера изображения\n            cam_resized = cv2.resize(cam, (orig_img.shape[1], orig_img.shape[0]))\n            \n            # Наложение CAM на изображение\n            visualization = show_cam_on_image(orig_img, cam_resized, use_rgb=True)\n            \n            # Отображение\n            axes[idx].imshow(visualization)\n            \n            # Определяем название уровня\n            if num_levels == 5:\n                level_names = ['L1/L2', 'L2/L3', 'L3/L4', 'L4/L5', 'L5/S1']\n                title = f\"{level_names[level_idx]}\"\n            else:\n                if level_idx < 5:\n                    level_names = ['L1/L2', 'L2/L3', 'L3/L4', 'L4/L5', 'L5/S1']\n                    title = f\"Sag {level_names[level_idx]}\"\n                else:\n                    level_names = ['L1/L2', 'L2/L3', 'L3/L4', 'L4/L5', 'L5/S1']\n                    title = f\"Ax {level_names[level_idx-5]}\"\n            \n            axes[idx].set_title(title, fontsize=10)\n            axes[idx].axis('off')\n        \n        # Общий заголовок\n        pred_class = class_names[prediction]\n        fig.suptitle(\n            f'Grad-CAM Visualization\\nPrediction: {pred_class} (Confidence: {confidence:.2%})',\n            fontsize=16, fontweight='bold'\n        )\n        \n        plt.tight_layout()\n        \n        if save_path:\n            plt.savefig(save_path, dpi=150, bbox_inches='tight')\n            print(f\"✅ Сохранено: {save_path}\")\n        \n        plt.show()\n    \n    def visualize_single_slice(self, input_tensor, original_image, target_category=None,\n                              save_path=None, title=None, class_names=None):\n        \"\"\"\n        Визуализация Grad-CAM для одного среза\n        \"\"\"\n        if class_names is None:\n            class_names = ['Normal/Mild', 'Moderate', 'Severe']\n        \n        # Генерируем CAM\n        cam, prediction, confidence = self.generate_cam(input_tensor, target_category)\n        \n        # Подготовка изображения\n        if original_image.max() > 1.0:\n            original_image = original_image / 255.0\n        \n        if len(original_image.shape) == 2:\n            original_image = np.stack([original_image]*3, axis=-1)\n        elif original_image.shape[0] == 3:\n            original_image = original_image.transpose(1, 2, 0)\n        \n        # Resize CAM\n        cam_resized = cv2.resize(cam, (original_image.shape[1], original_image.shape[0]))\n        \n        # Создаём визуализацию\n        visualization = show_cam_on_image(original_image, cam_resized, use_rgb=True)\n        \n        # Отображение\n        fig, axes = plt.subplots(1, 3, figsize=(15, 5))\n        \n        axes[0].imshow(original_image)\n        axes[0].set_title('Original', fontsize=12)\n        axes[0].axis('off')\n        \n        axes[1].imshow(cam_resized, cmap='jet')\n        axes[1].set_title('Grad-CAM', fontsize=12)\n        axes[1].axis('off')\n        \n        axes[2].imshow(visualization)\n        pred_class = class_names[prediction]\n        axes[2].set_title(f'Overlay\\n{pred_class} ({confidence:.1%})', fontsize=12)\n        axes[2].axis('off')\n        \n        if title:\n            fig.suptitle(title, fontsize=14, fontweight='bold')\n        \n        plt.tight_layout()\n        \n        if save_path:\n            plt.savefig(save_path, dpi=150, bbox_inches='tight')\n            print(f\"✅ Сохранено: {save_path}\")\n        \n        plt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-02-14T13:47:28.298825Z","iopub.status.idle":"2026-02-14T13:47:28.2991Z","shell.execute_reply.started":"2026-02-14T13:47:28.298972Z","shell.execute_reply":"2026-02-14T13:47:28.298998Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# ============================================================================\n# GRAD-CAM ВО ВРЕМЯ ОБУЧЕНИЯ (ИСПРАВЛЕННАЯ ВЕРСИЯ)\n# ============================================================================\n\ndef visualize_batch_with_gradcam(model, images, labels, predictions=None,\n                                batch_idx=0, save_dir=None, class_names=None):\n    \"\"\"\n    Визуализация батча с Grad-CAM во время обучения\n    ИСПРАВЛЕННАЯ ВЕРСИЯ без use_cuda параметра\n    \n    Args:\n        model: текущая модель\n        images: батч изображений [B, C, H, W]\n        labels: истинные метки [B]\n        predictions: предсказанные метки [B] (если None, вычисляются автоматически)\n        batch_idx: индекс батча\n        save_dir: директория сохранения\n        class_names: названия классов\n    \"\"\"\n    if class_names is None:\n        class_names = ['Normal/Mild', 'Moderate', 'Severe']\n    \n    # Модель в eval режиме\n    model.eval()\n    \n    # Получаем предсказания если не переданы\n    if predictions is None:\n        with torch.no_grad():\n            outputs = model(images)\n            predictions = outputs.argmax(dim=1)\n    \n    # Берём первые 4 сэмпла из батча\n    num_samples = min(4, images.shape[0])\n    \n    fig, axes = plt.subplots(num_samples, 5, figsize=(20, num_samples*4))\n    if num_samples == 1:\n        axes = axes.reshape(1, -1)\n    \n    # Создаём Grad-CAM\n    visualizer = None\n    try:\n        # Используем is_cuda вместо use_cuda\n        use_gpu = images.is_cuda if hasattr(images, 'is_cuda') else torch.cuda.is_available()\n        visualizer = GradCAMVisualizer(model, use_cuda=use_gpu)\n        \n        for i in range(num_samples):\n            # Извлекаем изображение\n            img_tensor = images[i:i+1]  # [1, C, H, W]\n            num_channels = img_tensor.shape[1]\n            num_levels = min(5, num_channels // 3)\n            \n            try:\n                # Генерируем CAM\n                cam, pred, conf = visualizer.generate_cam(\n                    img_tensor, \n                    target_category=predictions[i].item()\n                )\n                \n                for level in range(num_levels):\n                    # Извлекаем 3 канала для текущего уровня\n                    level_img = img_tensor[0, level*3:(level+1)*3].detach().cpu().numpy()  # [3, H, W]\n                    level_img = level_img.transpose(1, 2, 0)  # [H, W, 3]\n                    \n                    # Денормализация\n                    mean = np.array([0.485, 0.456, 0.406])\n                    std = np.array([0.229, 0.224, 0.225])\n                    level_img = std * level_img + mean\n                    level_img = np.clip(level_img, 0, 1)\n                    \n                    # Resize CAM\n                    cam_resized = cv2.resize(cam, (level_img.shape[1], level_img.shape[0]))\n                    \n                    # Наложение\n                    visualization = show_cam_on_image(level_img, cam_resized, use_rgb=True)\n                    \n                    axes[i, level].imshow(visualization)\n                    \n                    # Заголовок только для первого ряда\n                    if i == 0:\n                        level_names = ['L1/L2', 'L2/L3', 'L3/L4', 'L4/L5', 'L5/S1']\n                        axes[i, level].set_title(level_names[level], fontsize=10)\n                    \n                    axes[i, level].axis('off')\n                \n                # Подпись строки\n                true_class = class_names[labels[i].item()]\n                pred_class = class_names[predictions[i].item()]\n                color = 'green' if labels[i] == predictions[i] else 'red'\n                \n                axes[i, 0].text(\n                    -0.1, 0.5, \n                    f'True: {true_class}\\nPred: {pred_class} ({conf:.1%})',\n                    transform=axes[i, 0].transAxes, \n                    fontsize=9,\n                    verticalalignment='center', \n                    color=color, \n                    weight='bold'\n                )\n                \n            except Exception as e:\n                print(f\"⚠️  Ошибка при генерации CAM для сэмпла {i}: {e}\")\n                # Заполняем пустыми axes\n                for level in range(5):\n                    axes[i, level].text(\n                        0.5, 0.5, 'CAM Error', \n                        ha='center', va='center',\n                        transform=axes[i, level].transAxes,\n                        fontsize=10, color='orange'\n                    )\n                    axes[i, level].axis('off')\n        \n    except Exception as e:\n        print(f\"⚠️  Критическая ошибка Grad-CAM: {e}\")\n        import traceback\n        traceback.print_exc()\n        \n        # Заполняем все axes сообщением об ошибке\n        for i in range(num_samples):\n            for level in range(5):\n                axes[i, level].text(\n                    0.5, 0.5, 'Grad-CAM\\nUnavailable', \n                    ha='center', va='center',\n                    transform=axes[i, level].transAxes,\n                    fontsize=10, color='red'\n                )\n                axes[i, level].axis('off')\n    \n    finally:\n        # Очистка ресурсов\n        if visualizer is not None:\n            try:\n                del visualizer\n            except:\n                pass\n        torch.cuda.empty_cache()\n    \n    plt.suptitle(f'Grad-CAM Visualization - Batch {batch_idx}', fontsize=14, fontweight='bold')\n    plt.tight_layout()\n    \n    if save_dir:\n        os.makedirs(save_dir, exist_ok=True)\n        save_path = os.path.join(save_dir, f'gradcam_batch_{batch_idx}.png')\n        plt.savefig(save_path, dpi=150, bbox_inches='tight')\n        print(f\"✅ Сохранено: {save_path}\")\n    \n    plt.show()\n    plt.close(fig)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-02-14T13:47:28.300441Z","iopub.status.idle":"2026-02-14T13:47:28.300675Z","shell.execute_reply.started":"2026-02-14T13:47:28.300568Z","shell.execute_reply":"2026-02-14T13:47:28.300582Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# ============================================================================\n# ПРИМЕРЫ ИСПОЛЬЗОВАНИЯ GRAD-CAM\n# ============================================================================\n\ndef demo_gradcam():\n    \"\"\"Демонстрация Grad-CAM на обученной модели\"\"\"\n    \n    print(\"\\n\" + \"=\"*80)\n    print(\"🔥 ДЕМОНСТРАЦИЯ GRAD-CAM\")\n    print(\"=\"*80 + \"\\n\")\n    \n    # Загружаем обученную модель\n    model_path = os.path.join(cfg.SAVE_DIR, 'sagittal_resnet34_best.pth')\n    \n    if not os.path.exists(model_path):\n        print(f\"⚠️  Модель не найдена: {model_path}\")\n        print(\"Сначала обучите модель, затем запустите demo_gradcam()\")\n        return\n    \n    # ⚠️ ВАЖНО: Используй ту же архитектуру, что и при обучении\n    # Если обучал на ResNet34:\n    model = create_model(model_type='sagittal', architecture='resnet34')\n    \n    # Если обучал на EfficientNet:\n    # model = create_model(model_type='sagittal', architecture='efficientnet_b0')\n    \n    # Загружаем веса\n    checkpoint = torch.load(model_path, map_location=device)\n    model.load_state_dict(checkpoint['model_state_dict'])\n    print(f\"✅ Модель загружена: {model_path}\")\n    print(f\"   Val Acc: {checkpoint['val_acc']:.4f}\")\n    \n    # Создаём датасет\n    df_sag = df_all[df_all['modality_group'] == 'sagittal'].copy()\n    study_ids = df_sag['study_id'].unique()\n    _, val_ids = train_test_split(study_ids, test_size=0.2, random_state=42)\n    df_val = df_sag[df_sag['study_id'].isin(val_ids)].reset_index(drop=True)\n    \n    val_transform = get_val_transforms(cfg.IMG_SIZE)\n    val_dataset = LumbarSagittalDataset(df_val, transform=val_transform)\n    \n    print(f\"\\n📊 Датасет: {len(val_dataset)} сэмплов\")\n    \n    # Анализируем несколько сэмплов\n    print(\"\\n🔍 Анализ с Grad-CAM...\")\n    \n    # Выбираем случайные индексы\n    np.random.seed(42)\n    indices = np.random.choice(len(val_dataset), size=min(5, len(val_dataset)), replace=False)\n    \n    analyze_predictions_with_gradcam(\n        model=model,\n        dataset=val_dataset,\n        indices=indices,\n        model_type='sagittal',\n        save_dir=os.path.join(cfg.SAVE_DIR, 'gradcam_examples'),\n        class_names=['Normal/Mild', 'Moderate', 'Severe']\n    )\n    \n    print(\"\\n✅ Демонстрация завершена!\")\n    print(f\"💾 Результаты сохранены в: {cfg.SAVE_DIR}/gradcam_examples/\")\n\n\ndef compare_cam_methods_demo():\n    \"\"\"Демонстрация сравнения разных CAM методов\"\"\"\n    \n    print(\"\\n\" + \"=\"*80)\n    print(\"🔬 СРАВНЕНИЕ CAM МЕТОДОВ\")\n    print(\"=\"*80 + \"\\n\")\n    \n    # Загружаем модель\n    model_path = os.path.join(cfg.SAVE_DIR, 'sagittal_resnet34_best.pth')\n    \n    if not os.path.exists(model_path):\n        print(f\"⚠️  Модель не найдена: {model_path}\")\n        return\n    \n    # ⚠️ ВАЖНО: Используй ту же архитектуру, что и при обучении\n    model = create_model(model_type='sagittal', architecture='resnet34')\n    checkpoint = torch.load(model_path, map_location=device)\n    model.load_state_dict(checkpoint['model_state_dict'])\n    \n    # Датасет\n    df_sag = df_all[df_all['modality_group'] == 'sagittal'].copy()\n    study_ids = df_sag['study_id'].unique()\n    _, val_ids = train_test_split(study_ids, test_size=0.2, random_state=42)\n    df_val = df_sag[df_sag['study_id'].isin(val_ids)].reset_index(drop=True)\n    \n    val_transform = get_val_transforms(cfg.IMG_SIZE)\n    val_dataset = LumbarSagittalDataset(df_val, transform=val_transform)\n    \n    # Берём один сэмпл\n    idx = 0\n    input_tensor, label = val_dataset[idx]\n    input_tensor = input_tensor.unsqueeze(0)\n    \n    # Оригинальное изображение (берём средний уровень L3/L4)\n    group = val_dataset.groups[idx]\n    row = group.iloc[2]  # L3/L4\n    original_image = val_dataset.load_dicom(row['image_path'])\n    \n    # Сравниваем методы\n    compare_gradcam_variants(\n        model=model,\n        input_tensor=input_tensor,\n        original_image=original_image,\n        target_category=None\n    )\n    \n    print(\"\\n✅ Сравнение завершено!\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-02-14T13:47:28.301791Z","iopub.status.idle":"2026-02-14T13:47:28.302117Z","shell.execute_reply.started":"2026-02-14T13:47:28.301951Z","shell.execute_reply":"2026-02-14T13:47:28.30197Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"\n# ============================================================================\n# ГЛАВНАЯ ФУНКЦИЯ (универсальная — работает с любой архитектурой из create_model)\n# ============================================================================\ndef main():\n    \"\"\"Обучение всех трёх моделей на выбранной архитектуре\"\"\"\n    arch_name = cfg.MODEL_ARCHITECTURE.replace('_', '-').upper()  # для красивого вывода\n    print(\"\\n\" + \"=\"*80)\n    print(\"🏥 RSNA 2024 Lumbar Spine Classification - Training Pipeline\")\n    print(f\"🔧 Architecture: {arch_name}\")\n    print(\"=\"*80 + \"\\n\")\n\n    # ==================== SAGITTAL MODEL ====================\n    print(\"\\n\" + \"🔵\"*40)\n    print(f\"📘 МОДЕЛЬ 1: SAGITTAL ONLY ({arch_name})\")\n    print(\"🔵\"*40 + \"\\n\")\n\n    df_sag = df_all[df_all['modality_group'] == 'sagittal'].copy()\n    train_loader_sag, val_loader_sag, df_train_sag, df_val_sag = get_dataloaders(\n        df_sag, model_type='sagittal'\n    )\n\n    model_sag = create_model(model_type='sagittal', architecture=cfg.MODEL_ARCHITECTURE)\n    print_model_summary(model_sag, input_shape=(15, 224, 224))\n\n    model_sag, history_sag = train_model(\n        model_sag,\n        train_loader_sag,\n        val_loader_sag,\n        df_train_sag,\n        model_name=f'sagittal_{cfg.MODEL_ARCHITECTURE}',\n        num_epochs=cfg.NUM_EPOCHS\n    )\n\n    plot_training_history(history_sag, f'Sagittal {arch_name}')\n    \n    del model_sag, train_loader_sag, val_loader_sag\n    gc.collect()\n    torch.cuda.empty_cache()\n\n    # ==================== AXIAL MODEL ====================\n    print(\"\\n\" + \"🟢\"*40)\n    print(f\"📗 МОДЕЛЬ 2: AXIAL ONLY ({arch_name})\")\n    print(\"🟢\"*40 + \"\\n\")\n\n    df_ax = df_all[df_all['modality_group'] == 'axial'].copy()\n    train_loader_ax, val_loader_ax, df_train_ax, df_val_ax = get_dataloaders(\n        df_ax, model_type='axial'\n    )\n\n    model_ax = create_model(model_type='axial', architecture=cfg.MODEL_ARCHITECTURE)\n    print_model_summary(model_ax, input_shape=(15, 224, 224))\n\n    model_ax, history_ax = train_model(\n        model_ax,\n        train_loader_ax,\n        val_loader_ax,\n        df_train_ax,\n        model_name=f'axial_{cfg.MODEL_ARCHITECTURE}',\n        num_epochs=cfg.NUM_EPOCHS\n    )\n\n    plot_training_history(history_ax, f'Axial {arch_name}')\n    \n    del model_ax, train_loader_ax, val_loader_ax\n    gc.collect()\n    torch.cuda.empty_cache()\n\n    # ==================== COMBINED MODEL ====================\n    print(\"\\n\" + \"🟣\"*40)\n    print(f\"📙 МОДЕЛЬ 3: SAGITTAL + AXIAL COMBINED ({arch_name})\")\n    print(\"🟣\"*40 + \"\\n\")\n\n    train_loader_comb, val_loader_comb, df_train_comb, df_val_comb = get_dataloaders(\n        df_all, model_type='combined'\n    )\n\n    model_comb = create_model(model_type='combined', architecture=cfg.MODEL_ARCHITECTURE)\n    print_model_summary(model_comb, input_shape=(30, 224, 224))\n\n    model_comb, history_comb = train_model(\n        model_comb,\n        train_loader_comb,\n        val_loader_comb,\n        df_train_comb,\n        model_name=f'combined_{cfg.MODEL_ARCHITECTURE}',\n        num_epochs=cfg.NUM_EPOCHS\n    )\n\n    plot_training_history(history_comb, f'Combined {arch_name}')\n\n    # ==================== СРАВНЕНИЕ МОДЕЛЕЙ ====================\n    print(\"\\n\" + \"=\"*80)\n    print(\"🎉 ВСЕ МОДЕЛИ ОБУЧЕНЫ!\")\n    print(\"=\"*80 + \"\\n\")\n    print(f\"📊 Итоговые результаты ({arch_name}):\")\n    print(\"-\" * 80)\n    print(f\"{'Модель':<35} {'Best Val Acc':<15} {'Best Val Loss':<15} {'Epochs':<10}\")\n    print(\"-\" * 80)\n    print(f\"{'Sagittal':<35} {max(history_sag['val_acc']):<15.4f} {min(history_sag['val_loss']):<15.4f} {len(history_sag['val_acc']):<10}\")\n    print(f\"{'Axial':<35} {max(history_ax['val_acc']):<15.4f} {min(history_ax['val_loss']):<15.4f} {len(history_ax['val_acc']):<10}\")\n    print(f\"{'Combined':<35} {max(history_comb['val_acc']):<15.4f} {min(history_comb['val_loss']):<15.4f} {len(history_comb['val_acc']):<10}\")\n    print(\"-\" * 80)\n\n    print(f\"\\n💾 Модели сохранены в: {cfg.SAVE_DIR}\")\n    print(f\" - sagittal_{cfg.MODEL_ARCHITECTURE}_best.pth\")\n    print(f\" - axial_{cfg.MODEL_ARCHITECTURE}_best.pth\")\n    print(f\" - combined_{cfg.MODEL_ARCHITECTURE}_best.pth\\n\")\n\n    # Сохранение истории обучения\n    history_all = {\n        'sagittal': history_sag,\n        'axial': history_ax,\n        'combined': history_comb\n    }\n    history_filename = f'training_history_{cfg.MODEL_ARCHITECTURE}.pth'\n    torch.save(history_all, os.path.join(cfg.SAVE_DIR, history_filename))\n    print(f\"✅ История обучения сохранена: {history_filename}\\n\")\n\n# Запуск\nif __name__ == '__main__':\n    main()\n\n    # После обучения — визуализации (Grad-CAM и сравнение методов)\n    # Эти функции должны быть адаптированы под новую архитектуру, если нужно\n    demo_gradcam()                     # убедись, что внутри используется правильная модель\n    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============================================================================\n# ЗАГРУЗКА И АНАЛИЗ ОБУЧЕННОЙ МОДЕЛИ\n# ============================================================================\n\ndef load_and_analyze_model(model_path, model_type='sagittal'):\n    \"\"\"\n    Загрузка модели и построение confusion matrix\n    \n    Args:\n        model_path: путь к .pth файлу\n        model_type: 'sagittal', 'axial', 'combined'\n    \"\"\"\n    \n    print(f\"\\n{'='*80}\")\n    print(f\"📂 ЗАГРУЗКА МОДЕЛИ: {model_path}\")\n    print(f\"{'='*80}\\n\")\n    \n    # Проверка существования\n    if not os.path.exists(model_path):\n        print(f\"❌ Модель не найдена: {model_path}\")\n        print(\"Запустите обучение командой: train_resnet34_medium_aug()\")\n        return\n    \n    # Создаём модель\n    model = create_model(model_type=model_type, architecture='resnet18')\n    \n    # Загружаем веса\n    checkpoint = torch.load(model_path, map_location=device)\n    model.load_state_dict(checkpoint['model_state_dict'])\n    model.eval()\n    \n    print(f\"✅ Модель загружена!\")\n    print(f\"   Эпоха: {checkpoint['epoch']}\")\n    print(f\"   Val Acc: {checkpoint['val_acc']:.4f}\")\n    print(f\"   Val Loss: {checkpoint['val_loss']:.4f}\")\n    \n    # Создаём датасет\n    if model_type == 'sagittal':\n        df_data = df_all[df_all['modality_group'] == 'sagittal'].copy()\n    elif model_type == 'axial':\n        df_data = df_all[df_all['modality_group'] == 'axial'].copy()\n    else:\n        df_data = df_all.copy()\n    \n    # Split\n    study_ids = df_data['study_id'].unique()\n    _, val_ids = train_test_split(study_ids, test_size=0.2, random_state=42)\n    df_val = df_data[df_data['study_id'].isin(val_ids)].reset_index(drop=True)\n    \n    # Датасет\n    val_transform = get_val_transforms(cfg.IMG_SIZE)\n    \n    if model_type == 'sagittal':\n        val_dataset = LumbarSagittalDataset(df_val, transform=val_transform)\n    elif model_type == 'axial':\n        val_dataset = LumbarAxialDataset(df_val, transform=val_transform)\n    else:\n        val_dataset = LumbarCombinedDataset(df_val, transform=val_transform)\n    \n    val_loader = DataLoader(\n        val_dataset,\n        batch_size=16,\n        shuffle=False,\n        num_workers=2,\n        pin_memory=True\n    )\n    \n    # Получаем предсказания\n    print(\"\\n📊 Генерация предсказаний...\")\n    \n    all_preds = []\n    all_labels = []\n    all_probs = []\n    \n    with torch.no_grad():\n        for images, labels in tqdm(val_loader, desc=\"Predictions\"):\n            images = images.to(device)\n            outputs = model(images)\n            probs = torch.softmax(outputs, dim=1)\n            preds = outputs.argmax(dim=1)\n            \n            all_preds.extend(preds.cpu().numpy())\n            all_labels.extend(labels.cpu().numpy())\n            all_probs.extend(probs.cpu().numpy())\n    \n    # Confusion Matrix\n    class_names = ['Normal/Mild', 'Moderate', 'Severe']\n    \n    plot_confusion_matrix(\n        y_true=all_labels,\n        y_pred=all_preds,\n        class_names=class_names,\n        model_name=f'{model_type.capitalize()} ResNet34',\n        save_path=os.path.join(cfg.SAVE_DIR, f'confusion_matrix_{model_type}_resnet34.png')\n    )\n    \n    # Анализ ошибок\n    analyze_model_errors(\n        y_true=all_labels,\n        y_pred=all_preds,\n        class_names=class_names,\n        model_name=f'{model_type.capitalize()} ResNet34'\n    )\n    \n    return model, all_labels, all_preds, all_probs\n\n\nИспользование:\nmodel, y_true, y_pred, y_probs = load_and_analyze_model(\n    model_path='/kaggle/working/models/sagittal_resnet18_best.pth',\n    model_type='sagittal'\n)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-02-14T13:47:28.30464Z","iopub.status.idle":"2026-02-14T13:47:28.304861Z","shell.execute_reply.started":"2026-02-14T13:47:28.304759Z","shell.execute_reply":"2026-02-14T13:47:28.304771Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# ==============================================================================\n# ВИЗУАЛИЗАЦИЯ РАСПРЕДЕЛЕНИЯ КЛАССОВ ПОСЛЕ ЗАГРУЗКИ ДАННЫХ\n# ==============================================================================\n# Вставь этот блок сразу после вызова df_all = load_and_prepare_data()\n\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\n# Настройки стиля для красивого вида\nsns.set_style(\"whitegrid\")\nplt.rcParams['figure.figsize'] = (14, 6)\nplt.rcParams['font.size'] = 14\n\n# Расчёт распределения\nclass_counts = df_all['label'].value_counts().sort_index()\nclass_names = ['Normal/Mild (0)', 'Moderate (1)', 'Severe (2)']\ncolors = ['#66c2a5', '#fc8d62', '#e78ac3']  # Зелёный, оранжевый, фиолетовый — приятные цвета\nexplode = (0.03, 0.05, 0.1)  # Лёгкое выделение классов\n\n# Проценты с одним знаком после запятой\npercentages = (class_counts / len(df_all) * 100).round(1)\n\nprint(\"\\n📊 Подробное распределение классов:\")\nfor i, name in enumerate(class_names):\n    print(f\"   {name}: {class_counts[i]:,} сэмплов ({percentages[i]}%)\")\n\n# ------------------ ДВА ГРАФИКА РЯДОМ: PIE + BAR ------------------\nfig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 8))\n\n# 1. Круговая диаграмма (Pie Chart)\nwedges, texts, autotexts = ax1.pie(\n    class_counts,\n    labels=class_names,\n    autopct=lambda pct: f'{pct:.1f}%\\n({int(pct/100 * len(df_all)):,} шт.)',\n    startangle=90,\n    colors=colors,\n    explode=explode,\n    shadow=True,\n    textprops={'fontsize': 15, 'fontweight': 'bold'}\n)\n\n# Красивые белые подписи внутри\nfor autotext in autotexts:\n    autotext.set_color('white')\n    autotext.set_fontweight('bold')\n\nax1.set_title('Распределение классов\\n(круговая диаграмма)', fontsize=18, pad=20)\n\n# 2. Столбчатая диаграмма (Bar Chart)\nbars = ax2.bar(class_names, class_counts, color=colors, edgecolor='black', linewidth=1.5, alpha=0.9)\n\nax2.set_ylabel('Количество сэмплов', fontsize=16)\nax2.set_title('Распределение классов\\n(столбчатая диаграмма)', fontsize=18, pad=20)\n\n# Подписи над столбцами (количество + процент)\nfor bar, count, pct in zip(bars, class_counts, percentages):\n    height = bar.get_height()\n    ax2.text(bar.get_x() + bar.get_width()/2., height + 300,\n             f'{count:,}\\n({pct}%)',\n             ha='center', va='bottom', fontsize=15, fontweight='bold', color='black')\n\n# Убираем лишние подписи на осях\nax2.tick_params(axis='x', labelsize=14)\n\nplt.suptitle('📊 Распределение классов в финальном датасете RSNA 2024 Lumbar Spine',\n             fontsize=22, y=1.02)\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"colab":{"base_uri":"https://localhost:8080/","height":485},"id":"urBZQG8EosNK","outputId":"88a9ca0c-cac2-4e36-f408-9c06216e2773","execution":{"iopub.status.busy":"2026-02-14T13:47:28.30728Z","iopub.status.idle":"2026-02-14T13:47:28.307604Z","shell.execute_reply.started":"2026-02-14T13:47:28.307443Z","shell.execute_reply":"2026-02-14T13:47:28.307461Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import pandas as pd\nimport matplotlib.pyplot as plt\n\n# ==============================================================================\n# ТАБЛИЦЫ ДЛЯ АУГМЕНТАЦИЙ И СРАВНЕНИЯ ЭКСПЕРИМЕНТОВ\n# ==============================================================================\n\n# 1. ТАБЛИЦА ОПИСАНИЯ ТЕКУЩИХ АУГМЕНТАЦИЙ\ndata_aug = {\n    'Группа': [\n        'Геометрические', 'Геометрические', 'Геометрические', 'Геометрические (OneOf)',\n        'Геометрические (OneOf)', 'Геометрические (OneOf)', 'Яркость/Контраст (OneOf)',\n        'Яркость/Контраст (OneOf)', 'Яркость/Контраст (OneOf)', 'Шум (OneOf)',\n        'Шум (OneOf)', 'Шум (OneOf)', 'Размытие (OneOf)', 'Размытие (OneOf)',\n        'Размытие (OneOf)', 'Нормализация', 'Нормализация'\n    ],\n    'Аугментация': [\n        'Resize', 'HorizontalFlip', 'ShiftScaleRotate', 'ElasticTransform',\n        'GridDistortion', 'OpticalDistortion', 'RandomBrightnessContrast',\n        'CLAHE', 'HueSaturationValue', 'GaussNoise', 'ISONoise',\n        'MultiplicativeNoise', 'MotionBlur', 'MedianBlur', 'GaussianBlur',\n        'Normalize', 'ToTensorV2'\n    ],\n    'Параметры': [\n        'height=224, width=224', '-', 'shift_limit=0.1, scale_limit=0.1, rotate_limit=15',\n        'alpha=120, sigma=120*0.05', '-', 'distort_limit=0.1',\n        'brightness_limit=0.2, contrast_limit=0.2', 'clip_limit=4.0',\n        'hue_shift_limit=0, sat_shift_limit=20, val_shift_limit=20',\n        'var_limit=(10.0, 50.0)', '-', '-', '-', 'blur_limit=3', '-',\n        'mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]', '-'\n    ],\n    'Вероятность': [\n        '1.0', '0.5', '0.5', '0.3 (внутри OneOf)', '0.3 (внутри OneOf)', '0.3 (внутри OneOf)',\n        '0.5 (внутри OneOf)', '0.5 (внутри OneOf)', '0.5 (внутри OneOf)',\n        '0.3 (внутри OneOf)', '0.3 (внутри OneOf)', '0.3 (внутри OneOf)',\n        '0.2 (внутри OneOf)', '0.2 (внутри OneOf)', '0.2 (внутри OneOf)',\n        '1.0', '1.0'\n    ],\n    'Общая вероятность группы': [\n        '-', '-', '-', '0.3', '-', '-', '0.5', '-', '-', '0.2', '-', '-', '0.2', '-', '-', '-', '-'\n    ],\n    'Описание': [\n        'Изменение размера до 224x224',\n        'Горизонтальное отражение (зеркало)',\n        'Сдвиг, масштабирование и поворот',\n        'Эластичные деформации (имитация тканей)',\n        'Искажение по сетке',\n        'Оптическое искажение (линза)',\n        'Случайные изменения яркости и контраста',\n        'Локальная адаптивная гистограммная эквализация (хорошо для MRI)',\n        'Изменение оттенка, насыщенности и значения',\n        'Гауссов шум',\n        'ISO-шум (как на камере)',\n        'Мультипликативный шум',\n        'Размытие движения',\n        'Медианное размытие',\n        'Гауссово размытие',\n        'Нормализация по ImageNet (важно для pretrained моделей)',\n        'Преобразование в torch.Tensor'\n    ]\n}\n\ndf_aug = pd.DataFrame(data_aug)\n\nprint(\"=== ТЕКУЩИЕ АУГМЕНТАЦИИ В ОБУЧЕНИИ (train_transforms) ===\")\ndisplay(df_aug.style.set_caption(\"Таблица аугментаций\").set_table_styles([{'selector': 'caption', 'props': [('font-size', '16pt')]}]))\n\n# Для валидации (коротко)\nprint(\"\\n=== АУГМЕНТАЦИИ В ВАЛИДАЦИИ (val_transforms) ===\")\nval_aug = pd.DataFrame({\n    'Аугментация': ['Resize', 'Normalize', 'ToTensorV2'],\n    'Параметры': ['224x224', 'ImageNet mean/std', '-'],\n    'Вероятность': ['1.0', '1.0', '1.0'],\n    'Описание': ['Изменение размера', 'Нормализация', 'Преобразование в Tensor']\n})\ndisplay(val_aug.style.set_caption(\"Val transforms\").set_table_styles([{'selector': 'caption', 'props': [('font-size', '14pt')]}]))\n\n# 2. ШАБЛОН ДЛЯ СРАВНЕНИЯ ЭКСПЕРИМЕНТОВ\nprint(\"\\n=== ШАБЛОН ДЛЯ СРАВНЕНИЯ ЭКСПЕРИМЕНТОВ (заполняй после запусков) ===\")\nexperiments = pd.DataFrame(columns=[\n    'Эксперимент', 'Изменения в аугментациях', 'Epochs',\n    'Лучший Val Acc', 'Лучший Val Loss', 'Train Acc (финал)', 'Комментарий'\n])\n\n# Пример заполнения (baseline — твои текущие аугментации)\nexperiments = experiments._append({\n    'Эксперимент': 'Baseline',\n    'Изменения в аугментациях': 'Все аугментации включены (как в коде)',\n    'Epochs': 13,\n    'Лучший Val Acc': 0.8471,\n    'Лучший Val Loss': 0.9207,\n    'Train Acc (финал)': 0.8249,\n    'Комментарий': 'Хорошая обобщаемость, нет переобучения'\n}, ignore_index=True)\n\n# Добавь свои эксперименты здесь (пример строки)\n# experiments = experiments._append({'Эксперимент': 'Exp1', 'Изменения в аугментациях': 'Отключены шум и размытие', ...}, ignore_index=True)\n\ndisplay(experiments.style.set_caption(\"Сравнение экспериментов с аугментациями\")\n        .set_table_styles([{'selector': 'caption', 'props': [('font-size', '16pt')]}])\n        .background_gradient(cmap='viridis', subset=['Лучший Val Acc']))","metadata":{"trusted":true,"id":"kR6426n2osNK","execution":{"iopub.status.busy":"2026-02-14T13:47:28.308805Z","iopub.status.idle":"2026-02-14T13:47:28.309131Z","shell.execute_reply.started":"2026-02-14T13:47:28.308967Z","shell.execute_reply":"2026-02-14T13:47:28.308986Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"trusted":true,"id":"tKMYBB52osNK"},"outputs":[],"execution_count":null}]}