feat: add metrics

Dockerfile

@@ -1,13 +1,11 @@
-# Базовый образ среды выполнения PyTorch
 FROM pytorch/pytorch:2.2.1-cuda12.1-cudnn8-runtime
 
-# Конфигурация интерпретатора Python (отключение генерации байткода и буферизации вывода)
 ENV PYTHONDONTWRITEBYTECODE=1
 ENV PYTHONUNBUFFERED=1
 
 WORKDIR /app
 
-# Системные библиотеки для низкоуровневой обработки изображений
+# System dependencies for OpenCV and image processing
 RUN apt-get update && apt-get install -y \
     libglib2.0-0 \
     libsm6 \
@@ -15,15 +13,13 @@ RUN apt-get update && apt-get install -y \
     libxrender-dev \
     && rm -rf /var/lib/apt/lists/*
 
-# Интеграция Python-зависимостей
+# Install python packages
 COPY requirements.txt .
 RUN pip install --no-cache-dir -r requirements.txt
 
-# Модули программного комплекса
+# Copy source code
 COPY src/ /app/src/
 
-# Сетевой интерфейс UI
 EXPOSE 8080
 
-# Точка входа контейнера
 CMD ["streamlit", "run", "src/main.py", "--server.port", "8080", "--server.address", "0.0.0.0"]

docker/Dockerfile.api

@@ -3,22 +3,17 @@ FROM pytorch/pytorch:2.2.1-cuda12.1-cudnn8-runtime
 ENV PYTHONDONTWRITEBYTECODE=1
 ENV PYTHONUNBUFFERED=1
 
-# 1. Системные зависимости
 RUN apt-get update && apt-get install -y \
     libglib2.0-0 libsm6 libxext6 libxrender-dev \
     && rm -rf /var/lib/apt/lists/*
 
-# 2. Python пакеты
 RUN pip install --no-cache-dir fastapi uvicorn timm scikit-learn pandas joblib python-multipart transformers==4.38.2 tokenizers==0.15.2 accelerate
 
-# 3. Копируем код в контейнер
 WORKDIR /app
 COPY src/ /app/src/
 
-# 4. МАГИЯ ЗДЕСЬ: Переходим внутрь папки src
 WORKDIR /app/src
 
 EXPOSE 8000
 
-# 5. Запускаем локально (без префикса src.)
 CMD ["uvicorn", "api:app", "--host", "0.0.0.0", "--port", "8000"]

docker/Dockerfile.ui

@@ -8,10 +8,8 @@ RUN pip install --no-cache-dir streamlit==1.32.0 requests pandas pillow
 
 COPY src/ /app/src/
 
-# МАГИЯ ЗДЕСЬ: Переходим внутрь папки src
 WORKDIR /app/src
 
 EXPOSE 8080
 
-# Запускаем локально
 CMD ["streamlit", "run", "main.py", "--server.port", "8080", "--server.address", "0.0.0.0"]

src/api.py

@@ -9,7 +9,7 @@ from PIL import Image
 from data_loader import load_music_engine, load_image_processor
 from music_engine.llm_bridge import LLMAcousticBridge
 
-app = FastAPI(title="EmoM Inference API", version="1.0.0")
+app = FastAPI(title="EmoM API", version="1.0.0")
 
 ml_context = {
     "image_processor": None,
@@ -19,23 +19,22 @@ ml_context = {
 
 @app.on_event("startup")
 async def startup_event():
-    print("Инициализация нейросетевого ядра EmoM...")
+    print("Loading ML models...")
     ml_context["image_processor"] = load_image_processor()
     ml_context["music_matcher"] = load_music_engine()
     ml_context["llm_bridge"] = LLMAcousticBridge()
-    print("Вычислительный конвейер готов к работе.")
+    print("Initialization complete.")
 
 @app.post("/analyze")
 async def analyze_event_endpoint(files: List[UploadFile] = File(...)):
     try:
-        # 1. Читаем все загруженные картинки
         images = []
         for file in files:
             image_bytes = await file.read()
             img = Image.open(io.BytesIO(image_bytes)).convert("RGB")
             images.append(img)
             
-        print(f"Начата обработка события из {len(images)} фотографий...")
+        print(f"Processing batch: {len(images)} images.")
         
         img_processor = ml_context["image_processor"]
         matcher = ml_context["music_matcher"]
@@ -44,7 +43,6 @@ async def analyze_event_endpoint(files: List[UploadFile] = File(...)):
         all_v, all_a = [], []
         all_objects = []
 
-        # 2. Прогоняем каждую картинку через нейросети
         for img in images:
             embedding = img_processor.extract_embedding(img)
             v, a = matcher.predict_va(embedding)
@@ -54,20 +52,13 @@ async def analyze_event_endpoint(files: List[UploadFile] = File(...)):
             caption = img_processor.describe_scene(img)
             all_objects.append(caption)
 
-        # 3. Усредняем эмоции события
         target_v = float(np.mean(all_v))
         target_a = float(np.mean(all_a))
         unique_semantics = list(set(all_objects))
 
-        # 4. Запрашиваем акустический профиль у Ollama
-        print(f"Запрос к Ollama. V={target_v:.2f}, A={target_a:.2f}")
         llm_profile = llm.get_acoustic_profile(target_v, target_a, unique_semantics)
 
-        # 5. Ищем треки в базе
-        print("Поиск подходящих композиций...")
         playlist_df = matcher.find_nearest_tracks(target_v, target_a, llm_profile=llm_profile, top_k=15)
-        
-        # Переводим таблицу в JSON-формат
         tracks_list = playlist_df.to_dict(orient="records")
 
         return JSONResponse(content={
@@ -82,4 +73,4 @@ async def analyze_event_endpoint(files: List[UploadFile] = File(...)):
         
     except Exception as e:
         print(traceback.format_exc()) 
-        raise HTTPException(status_code=500, detail=f"Ошибка инференса: {str(e)}")
+        raise HTTPException(status_code=500, detail=str(e))

src/data_loader.py

@@ -1,49 +1,46 @@
 from pathlib import Path
+from typing import Tuple, List, Optional, Any
+
 import pandas as pd
 import numpy as np
 
-# Импорты твоих движков
 from music_engine.matcher import MusicMatcher
 from music_engine.image_processor import ImageProcessor
 
-# Базовая директория (папка src)
 BASE_DIR = Path(__file__).resolve().parent
 
-def load_music_engine():
+def load_music_engine() -> MusicMatcher:
-    """Загрузка базы данных и модели регрессора для бэкенда."""
+    #Инициализация модуля подбора музыкальных композиций.
-    # Пути соответствуют тем, что мы примонтировали в Docker
     db_path = BASE_DIR.parent / "dataset" / "DEAM" / "music_db.csv"
     model_path = BASE_DIR / "music_engine" / "va_regressor.pkl"
     
     return MusicMatcher(db_path=db_path, model_path=model_path)
 
-def load_image_processor():
+def load_image_processor() -> ImageProcessor:
-    """Инициализация нейросетевого экстрактора (ResNet-50)."""
+    #Инициализация модуля экстракции визуальных признаков.
     weights_path = BASE_DIR / "emoset_resnet50_best.pth"
+    
     return ImageProcessor(weights_path)
 
-def load_emoset_data():
+def load_emoset_data() -> Tuple[Optional[List[str]], Optional[np.ndarray], Optional[np.ndarray], Optional[Path]]:
-    """
+    # Загрузка тестовой выборки датасета EmoSet.
-    Загрузка эталонного датасета EmoSet. 
+    # Модуль сохранен для обеспечения обратной совместимости в отладочном контуре.
-    (Оставлено для обратной совместимости, если понадобится локальная отладка)
-    """
     try:
         images_path = BASE_DIR.parent / "dataset" / "EmoSet-118K" / "test" / "images"
         labels_path = BASE_DIR / "emoset_test_labels.npy"
         embeddings_path = BASE_DIR / "emoset_test_embeddings.npy"
         
-        # Если файлов нет (например, на проде), возвращаем None
         if not all(p.exists() for p in [labels_path, embeddings_path]):
             return None, None, None, None
             
         labels = np.load(labels_path)
         embeddings = np.load(embeddings_path)
         
-        # Читаем CSV с метками
+        csv_path = BASE_DIR.parent / "dataset" / "EmoSet-118K" / "test" / "labels.csv"
-        df = pd.read_csv(BASE_DIR.parent / "dataset" / "EmoSet-118K" / "test" / "labels.csv")
+        df = pd.read_csv(csv_path)
-        image_files = df['filename'].tolist()
+        
+        return df['filename'].tolist(), embeddings, labels, images_path
         
-        return image_files, embeddings, labels, images_path
     except Exception as e:
-        print(f"Предупреждение: Тестовые артефакты EmoSet не найдены ({e})")
+        print(f"[WARN] Failed to load EmoSet test artifacts: {str(e)}")
         return None, None, None, None

src/main.py

@@ -147,7 +147,7 @@ def main():
                 "zcr": "ZCR"
             }
             
-            # Развернутые описания для комиссии (передаются в аргумент help)
+            # Развернутые описания
             feature_helps = {
                 "energy": "Среднеквадратичная амплитуда (громкость). Бывает высокой в плотных, интенсивных композициях, отражает общую акустическую энергию сцены.",
                 "flux": "Спектральный поток. Измеряет резкость изменений в спектре. Высок при четком, агрессивном ритме и частой смене нот.",
@@ -169,7 +169,6 @@ def main():
                             k, v = llm_items[i + j]
                             label = feature_titles.get(k, k)
                             tooltip = feature_helps.get(k, "")
-                            # Форматируем до 2 знаков после запятой (например, 0.64)
                             cols[j].metric(label, f"{v:.2f}", help=tooltip)
             else:
                 st.caption("Акустический профиль недоступен. Применен fallback-алгоритм.")

src/music_engine/llm_bridge.py

@@ -6,14 +6,12 @@ import requests
 class LLMAcousticBridge:
     def __init__(self, model_name="dolphin-llama3:8b"):
         self.model_name = model_name
-        # Динамический выбор URL (внутри Docker используется emom_ollama)
         base_url = os.getenv("OLLAMA_API_URL", "http://emom_ollama:11434")
         self.api_url = f"{base_url}/api/generate"
 
     def get_acoustic_profile(self, valence, arousal, semantics):
         context_str = ", ".join(semantics) if semantics else "abstract scene"
         
-        # Строгий промпт с примером вывода
         prompt = f"""
         Analyze the visual context and emotions to determine the ideal background music properties. 
         Emotions: Valence {valence:.1f}/9.0 (Positivity), Arousal {arousal:.1f}/9.0 (Energy). 

src/scripts/00_setup_env.sh

@@ -1,5 +1,6 @@
 #!/bin/bash
 
+# Данный скрипт написан ИИ для быстрой подготовки окружения, установка драйверов и докера
 # Остановка скрипта при возникновении любой ошибки
 set -e
 

src/scripts/21_train_images.ipynb

@@ -1,541 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "0c00b67b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "from pathlib import Path\n",
-    "from PIL import Image\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "from tqdm import tqdm\n",
-    "\n",
-    "import torch\n",
-    "import torch.nn as nn\n",
-    "from torch.utils.data import Dataset, DataLoader\n",
-    "import torchvision.transforms as T\n",
-    "import timm"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "84c3657f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'cuda'"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Конфигурация параметров обучения и путей файловой системы\n",
-    "DATA_ROOT = Path(\"../dataset/EmoSet-118K\")\n",
-    "BATCH_SIZE = 64\n",
-    "EPOCHS = 15\n",
-    "LR = 3e-4\n",
-    "NUM_WORKERS = 40\n",
-    "\n",
-    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
-    "print(f\"Аппаратное ускорение: {device}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9f749add",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class EmoSetDataset(Dataset):\n",
-    "    def __init__(self, root: Path | str, split: str):\n",
-    "        self.root = Path(root) / split\n",
-    "        self.df = pd.read_csv(self.root / \"labels.csv\")\n",
-    "\n",
-    "        # Формирование словарей маппинга классов\n",
-    "        self.labels = sorted(self.df[\"label\"].unique())\n",
-    "        self.label2idx = {l: i for i, l in enumerate(self.labels)}\n",
-    "        self.idx2label = {i: l for l, i in self.label2idx.items()}\n",
-    "\n",
-    "        # Базовые трансформации для валидации и теста\n",
-    "        base_tf = [\n",
-    "            T.ToTensor(),\n",
-    "            T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
-    "        ]\n",
-    "\n",
-    "        # Внедрение аугментации исключительно для обучающей выборки (предотвращение переобучения)\n",
-    "        if split == \"train\":\n",
-    "            self.transform = T.Compose([\n",
-    "                T.RandomResizedCrop(224),\n",
-    "                T.RandomHorizontalFlip(),\n",
-    "                *base_tf\n",
-    "            ])\n",
-    "        else:\n",
-    "            self.transform = T.Compose([\n",
-    "                T.Resize(256),\n",
-    "                T.CenterCrop(224),\n",
-    "                *base_tf\n",
-    "            ])\n",
-    "\n",
-    "    def __len__(self):\n",
-    "        return len(self.df)\n",
-    "\n",
-    "    def __getitem__(self, idx):\n",
-    "        row = self.df.iloc[idx]\n",
-    "        img_path = self.root / \"images\" / row[\"filename\"]\n",
-    "\n",
-    "        # Обработка возможных исключений ввода-вывода (поврежденные JPEG-файлы в датасете)\n",
-    "        try:\n",
-    "            img = Image.open(img_path).convert(\"RGB\")\n",
-    "        except Exception:\n",
-    "            img = Image.new(\"RGB\", (224, 224), (0, 0, 0))\n",
-    "\n",
-    "        img_tensor = self.transform(img)\n",
-    "        label_idx = self.label2idx[row[\"label\"]]\n",
-    "        \n",
-    "        return img_tensor, label_idx"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c8805341",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Classes: ['amusement', 'anger', 'awe', 'contentment', 'disgust', 'excitement', 'fear', 'sadness']\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Подготовка объектов выборки\n",
-    "train_ds = EmoSetDataset(DATA_ROOT, \"train\")\n",
-    "val_ds   = EmoSetDataset(DATA_ROOT, \"val\")\n",
-    "\n",
-    "# Инициализация итераторов с закреплением памяти (pin_memory) для ускорения передачи на GPU\n",
-    "train_loader = DataLoader(\n",
-    "    train_ds,\n",
-    "    batch_size=BATCH_SIZE,\n",
-    "    shuffle=True,\n",
-    "    num_workers=NUM_WORKERS,\n",
-    "    pin_memory=True\n",
-    ")\n",
-    "\n",
-    "val_loader = DataLoader(\n",
-    "    val_ds,\n",
-    "    batch_size=BATCH_SIZE,\n",
-    "    shuffle=False,\n",
-    "    num_workers=NUM_WORKERS,\n",
-    "    pin_memory=True\n",
-    ")\n",
-    "\n",
-    "print(f\"Индексированные классы: {train_ds.labels}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "dffce582",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "ResNet(\n",
-       "  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
-       "  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "  (act1): ReLU(inplace=True)\n",
-       "  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
-       "  (layer1): Sequential(\n",
-       "    (0): Bottleneck(\n",
-       "      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (act1): ReLU(inplace=True)\n",
-       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (drop_block): Identity()\n",
-       "      (act2): ReLU(inplace=True)\n",
-       "      (aa): Identity()\n",
-       "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (act3): ReLU(inplace=True)\n",
-       "      (downsample): Sequential(\n",
-       "        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      )\n",
-       "    )\n",
-       "    (1): Bottleneck(\n",
-       "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (act1): ReLU(inplace=True)\n",
-       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (drop_block): Identity()\n",
-       "      (act2): ReLU(inplace=True)\n",
-       "      (aa): Identity()\n",
-       "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (act3): ReLU(inplace=True)\n",
-       "    )\n",
-       "    (2): Bottleneck(\n",
-       "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (act1): ReLU(inplace=True)\n",
-       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (drop_block): Identity()\n",
-       "      (act2): ReLU(inplace=True)\n",
-       "      (aa): Identity()\n",
-       "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (act3): ReLU(inplace=True)\n",
-       "    )\n",
-       "  )\n",
-       "  (layer2): Sequential(\n",
-       "    (0): Bottleneck(\n",
-       "      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (act1): ReLU(inplace=True)\n",
-       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
-       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (drop_block): Identity()\n",
-       "      (act2): ReLU(inplace=True)\n",
-       "      (aa): Identity()\n",
-       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (act3): ReLU(inplace=True)\n",
-       "      (downsample): Sequential(\n",
-       "        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
-       "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      )\n",
-       "    )\n",
-       "    (1): Bottleneck(\n",
-       "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (act1): ReLU(inplace=True)\n",
-       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (drop_block): Identity()\n",
-       "      (act2): ReLU(inplace=True)\n",
-       "      (aa): Identity()\n",
-       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (act3): ReLU(inplace=True)\n",
-       "    )\n",
-       "    (2): Bottleneck(\n",
-       "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (act1): ReLU(inplace=True)\n",
-       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (drop_block): Identity()\n",
-       "      (act2): ReLU(inplace=True)\n",
-       "      (aa): Identity()\n",
-       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (act3): ReLU(inplace=True)\n",
-       "    )\n",
-       "    (3): Bottleneck(\n",
-       "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (act1): ReLU(inplace=True)\n",
-       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (drop_block): Identity()\n",
-       "      (act2): ReLU(inplace=True)\n",
-       "      (aa): Identity()\n",
-       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (act3): ReLU(inplace=True)\n",
-       "    )\n",
-       "  )\n",
-       "  (layer3): Sequential(\n",
-       "    (0): Bottleneck(\n",
-       "      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (act1): ReLU(inplace=True)\n",
-       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
-       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (drop_block): Identity()\n",
-       "      (act2): ReLU(inplace=True)\n",
-       "      (aa): Identity()\n",
-       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (act3): ReLU(inplace=True)\n",
-       "      (downsample): Sequential(\n",
-       "        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
-       "        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      )\n",
-       "    )\n",
-       "    (1): Bottleneck(\n",
-       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (act1): ReLU(inplace=True)\n",
-       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (drop_block): Identity()\n",
-       "      (act2): ReLU(inplace=True)\n",
-       "      (aa): Identity()\n",
-       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (act3): ReLU(inplace=True)\n",
-       "    )\n",
-       "    (2): Bottleneck(\n",
-       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (act1): ReLU(inplace=True)\n",
-       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (drop_block): Identity()\n",
-       "      (act2): ReLU(inplace=True)\n",
-       "      (aa): Identity()\n",
-       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (act3): ReLU(inplace=True)\n",
-       "    )\n",
-       "    (3): Bottleneck(\n",
-       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (act1): ReLU(inplace=True)\n",
-       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (drop_block): Identity()\n",
-       "      (act2): ReLU(inplace=True)\n",
-       "      (aa): Identity()\n",
-       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (act3): ReLU(inplace=True)\n",
-       "    )\n",
-       "    (4): Bottleneck(\n",
-       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (act1): ReLU(inplace=True)\n",
-       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (drop_block): Identity()\n",
-       "      (act2): ReLU(inplace=True)\n",
-       "      (aa): Identity()\n",
-       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (act3): ReLU(inplace=True)\n",
-       "    )\n",
-       "    (5): Bottleneck(\n",
-       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (act1): ReLU(inplace=True)\n",
-       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (drop_block): Identity()\n",
-       "      (act2): ReLU(inplace=True)\n",
-       "      (aa): Identity()\n",
-       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (act3): ReLU(inplace=True)\n",
-       "    )\n",
-       "  )\n",
-       "  (layer4): Sequential(\n",
-       "    (0): Bottleneck(\n",
-       "      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (act1): ReLU(inplace=True)\n",
-       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
-       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (drop_block): Identity()\n",
-       "      (act2): ReLU(inplace=True)\n",
-       "      (aa): Identity()\n",
-       "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (act3): ReLU(inplace=True)\n",
-       "      (downsample): Sequential(\n",
-       "        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
-       "        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      )\n",
-       "    )\n",
-       "    (1): Bottleneck(\n",
-       "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (act1): ReLU(inplace=True)\n",
-       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (drop_block): Identity()\n",
-       "      (act2): ReLU(inplace=True)\n",
-       "      (aa): Identity()\n",
-       "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (act3): ReLU(inplace=True)\n",
-       "    )\n",
-       "    (2): Bottleneck(\n",
-       "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (act1): ReLU(inplace=True)\n",
-       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (drop_block): Identity()\n",
-       "      (act2): ReLU(inplace=True)\n",
-       "      (aa): Identity()\n",
-       "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (act3): ReLU(inplace=True)\n",
-       "    )\n",
-       "  )\n",
-       "  (global_pool): SelectAdaptivePool2d(pool_type=avg, flatten=Flatten(start_dim=1, end_dim=-1))\n",
-       "  (fc): Linear(in_features=2048, out_features=8, bias=True)\n",
-       ")"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# TODO перед защитой, повторить оптимизаторы\n",
-    "# Загрузка предобученной архитектуры ResNet-50 с заменой классификационного слоя\n",
-    "model = timm.create_model(\n",
-    "    \"resnet50\",\n",
-    "    pretrained=True,\n",
-    "    num_classes=len(train_ds.labels)\n",
-    ")\n",
-    "model.to(device)\n",
-    "\n",
-    "# Функция потерь для многоклассовой классификации\n",
-    "criterion = nn.CrossEntropyLoss()\n",
-    "\n",
-    "# Оптимизатор AdamW с L2-регуляризацией (weight_decay) для повышения обобщающей способности\n",
-    "optimizer = torch.optim.AdamW(\n",
-    "    model.parameters(),\n",
-    "    lr=LR,\n",
-    "    weight_decay=1e-4\n",
-    ")\n",
-    "\n",
-    "# Планировщик скорости обучения: косинусный отжиг\n",
-    "scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(\n",
-    "    optimizer,\n",
-    "    T_max=EPOCHS\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "81a457ef",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def train_epoch(current_model, loader):\n",
-    "    current_model.train()\n",
-    "    total_loss = 0.0\n",
-    "    correct_preds = 0\n",
-    "    total_samples = 0\n",
-    "\n",
-    "    for imgs, labels in tqdm(loader, desc=\"Тренировка\", leave=False):\n",
-    "        imgs = imgs.to(device)\n",
-    "        labels = labels.to(device)\n",
-    "\n",
-    "        optimizer.zero_grad()\n",
-    "        logits = current_model(imgs)\n",
-    "        loss = criterion(logits, labels)\n",
-    "\n",
-    "        loss.backward()\n",
-    "        optimizer.step()\n",
-    "\n",
-    "        total_loss += loss.item() * imgs.size(0)\n",
-    "        preds = logits.argmax(dim=1)\n",
-    "        correct_preds += (preds == labels).sum().item()\n",
-    "        total_samples += labels.size(0)\n",
-    "\n",
-    "    return total_loss / total_samples, correct_preds / total_samples\n",
-    "\n",
-    "@torch.no_grad()\n",
-    "def val_epoch(current_model, loader):\n",
-    "    # Перевод модели в режим инференса (отключение Dropout и фиксация BatchNorm)\n",
-    "    current_model.eval()\n",
-    "    total_loss = 0.0\n",
-    "    correct_preds = 0\n",
-    "    total_samples = 0\n",
-    "\n",
-    "    for imgs, labels in tqdm(loader, desc=\"Валидация\", leave=False):\n",
-    "        imgs = imgs.to(device)\n",
-    "        labels = labels.to(device)\n",
-    "\n",
-    "        logits = current_model(imgs)\n",
-    "        loss = criterion(logits, labels)\n",
-    "\n",
-    "        total_loss += loss.item() * imgs.size(0)\n",
-    "        preds = logits.argmax(dim=1)\n",
-    "        correct_preds += (preds == labels).sum().item()\n",
-    "        total_samples += labels.size(0)\n",
-    "\n",
-    "    return total_loss / total_samples, correct_preds / total_samples"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "951aa9e3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "best_val_acc = 0.0\n",
-    "checkpoint_path = \"../emoset_resnet50_best.pth\"\n",
-    "\n",
-    "print(\"Старт процесса обучения...\")\n",
-    "\n",
-    "for epoch in range(1, EPOCHS + 1):\n",
-    "    train_loss, train_acc = train_epoch(model, train_loader)\n",
-    "    val_loss, val_acc = val_epoch(model, val_loader)\n",
-    "\n",
-    "    # Обновление шага планировщика\n",
-    "    scheduler.step()\n",
-    "\n",
-    "    print(\n",
-    "        f\"Эпоха {epoch:02d}/{EPOCHS} | \"\n",
-    "        f\"Train Loss: {train_loss:.4f}, Acc: {train_acc:.4f} | \"\n",
-    "        f\"Val Loss: {val_loss:.4f}, Acc: {val_acc:.4f}\"\n",
-    "    )\n",
-    "\n",
-    "    # Экспорт весов при улучшении целевой метрики\n",
-    "    if val_acc > best_val_acc:\n",
-    "        best_val_acc = val_acc\n",
-    "        torch.save(model.state_dict(), checkpoint_path)\n",
-    "        print(f\" -> Сохранен новый лучший чекпоинт (Acc: {best_val_acc:.4f})\")\n",
-    "\n",
-    "print(\"Обучение завершено.\")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "thesis-py3.11",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

src/scripts/21_train_images.py

@@ -0,0 +1,184 @@
+import os
+import random
+import warnings
+from pathlib import Path
+from PIL import Image
+import pandas as pd
+import numpy as np
+from tqdm import tqdm
+
+import torch
+import torch.nn as nn
+from torch.utils.data import Dataset, DataLoader
+import torchvision.transforms as T
+import timm
+
+# Подавление предупреждений цветовых профилей
+warnings.filterwarnings("ignore", message=".*Unknown Adobe color transform code.*")
+
+# Настройки окружения
+DATA_ROOT = Path("/home/zin/projects/Thesis/NFS/Thesis/Emoset/EmoSet-118K")
+BATCH_SIZE = 64
+EPOCHS = 30
+LR = 5e-5
+NUM_WORKERS = 62
+PATIENCE = 7
+
+# Маппинг классов
+CLASS_MAPPING = {
+    "amusement": 0, "anger": 1, "awe": 2, "contentment": 3,
+    "disgust": 4, "excitement": 5, "fear": 6, "sadness": 7
+}
+
+DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+print(f"Устройство: {DEVICE}")
+
+# Фиксация генераторов псевдослучайных чисел
+def set_seed(seed=42):
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    if torch.cuda.is_available():
+        torch.cuda.manual_seed(seed)
+        torch.cuda.manual_seed_all(seed)
+
+set_seed()
+
+# Инициализация структур данных
+class EmoSetDataset(Dataset):
+    def __init__(self, root: Path | str, split: str, transform=None):
+        self.root = Path(root) / split
+        self.df = pd.read_csv(self.root / "labels.csv")
+        self.transform = transform
+        
+        # Фильтрация датафрейма
+        self.df = self.df[self.df["label"].isin(CLASS_MAPPING.keys())].reset_index(drop=True)
+
+    def __len__(self):
+        return len(self.df)
+
+    def __getitem__(self, idx):
+        row = self.df.iloc[idx]
+        img_path = self.root / "images" / row["filename"]
+
+        try:
+            img = Image.open(img_path).convert("RGB")
+        except Exception:
+            img = Image.new("RGB", (256, 256), (0, 0, 0))
+
+        if self.transform:
+            img_tensor = self.transform(img)
+        else:
+            img_tensor = T.ToTensor()(img)
+            
+        label_idx = CLASS_MAPPING[row["label"]]
+        return img_tensor, label_idx
+
+# Трансформации
+base_tf = [
+    T.ToTensor(),
+    T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+]
+
+train_transform = T.Compose([
+    T.Resize(256, antialias=True),
+    T.RandomCrop(224),
+    T.RandomHorizontalFlip(),
+    *base_tf
+])
+
+val_transform = T.Compose([
+    T.Resize(256, antialias=True),
+    T.CenterCrop(224),
+    *base_tf
+])
+
+train_ds = EmoSetDataset(DATA_ROOT, "train", transform=train_transform)
+val_ds   = EmoSetDataset(DATA_ROOT, "val", transform=val_transform)
+
+train_loader = DataLoader(train_ds, batch_size=BATCH_SIZE, shuffle=True, num_workers=NUM_WORKERS, pin_memory=True)
+val_loader   = DataLoader(val_ds, batch_size=BATCH_SIZE, shuffle=False, num_workers=NUM_WORKERS, pin_memory=True)
+
+# Инициализация модели и оптимизатора
+model = timm.create_model("resnet50", pretrained=True, num_classes=8, drop_rate=0.3)
+model.to(DEVICE)
+
+criterion = nn.CrossEntropyLoss(label_smoothing=0.1)
+
+optimizer = torch.optim.AdamW(model.parameters(), lr=LR, weight_decay=1e-3)
+scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=EPOCHS)
+
+# Логика эпохи обучения
+def train_epoch(current_model, loader):
+    current_model.train()
+    total_loss, correct_preds, total_samples = 0.0, 0, 0
+
+    for imgs, labels in tqdm(loader, desc="Тренировка", leave=False, smoothing=0):
+        imgs, labels = imgs.to(DEVICE), labels.to(DEVICE)
+
+        optimizer.zero_grad(set_to_none=True)
+        logits = current_model(imgs)
+        loss = criterion(logits, labels)
+
+        loss.backward()
+        optimizer.step()
+
+        total_loss += loss.item() * imgs.size(0)
+        preds = logits.argmax(dim=1)
+        correct_preds += (preds == labels).sum().item()
+        total_samples += labels.size(0)
+
+    return total_loss / total_samples, correct_preds / total_samples
+
+# Логика эпохи валидации
+@torch.no_grad()
+def val_epoch(current_model, loader):
+    current_model.eval()
+    total_loss, correct_preds, total_samples = 0.0, 0, 0
+
+    for imgs, labels in tqdm(loader, desc="Валидация", leave=False, smoothing=0):
+        imgs, labels = imgs.to(DEVICE), labels.to(DEVICE)
+
+        logits = current_model(imgs)
+        loss = criterion(logits, labels)
+
+        total_loss += loss.item() * imgs.size(0)
+        preds = logits.argmax(dim=1)
+        correct_preds += (preds == labels).sum().item()
+        total_samples += labels.size(0)
+
+    return total_loss / total_samples, correct_preds / total_samples
+
+if __name__ == "__main__":
+    best_val_acc = 0.0
+    best_val_loss = float('inf')
+    epochs_no_improve = 0
+    checkpoint_path = "./emosetV2_resnet50_best.pth"
+
+    print("Старт обучения.")
+
+    for epoch in range(1, EPOCHS + 1):
+        train_loss, train_acc = train_epoch(model, train_loader)
+        val_loss, val_acc = val_epoch(model, val_loader)
+
+        scheduler.step()
+
+        print(f"[{epoch}/{EPOCHS}] Train Loss: {train_loss:.4f}, Acc: {train_acc:.4f} | Val Loss: {val_loss:.4f}, Acc: {val_acc:.4f}")
+
+        # Сохранение лучших весов по Accuracy
+        if val_acc > best_val_acc:
+            best_val_acc = val_acc
+            torch.save(model.state_dict(), checkpoint_path)
+            print(f"Сохранен чекпоинт (Acc: {best_val_acc:.4f})")
+
+        # Оценка переобучения по Loss (Early Stopping)
+        if val_loss < best_val_loss:
+            best_val_loss = val_loss
+            epochs_no_improve = 0
+        else:
+            epochs_no_improve += 1
+            if epochs_no_improve >= PATIENCE:
+                print(f"Ранняя остановка: метрика валидации не улучшается {PATIENCE} эпох.")
+                break
+
+    print("Процесс завершен.")

src/scripts/22_1_train_images_m.py

@@ -0,0 +1,283 @@
+import os
+import random
+import warnings
+from pathlib import Path
+from PIL import Image
+import pandas as pd
+import numpy as np
+from tqdm import tqdm
+
+import torch
+import torch.nn as nn
+from torch.utils.data import Dataset, DataLoader
+import torchvision.transforms as T
+import timm
+
+import matplotlib.pyplot as plt
+import seaborn as sns
+from sklearn.metrics import confusion_matrix
+
+# Подавление предупреждений цветовых профилей
+warnings.filterwarnings("ignore", message=".*Unknown Adobe color transform code.*")
+
+# Настройки окружения
+DATA_ROOT = Path("./NFS/Thesis/Emoset/EmoSet-118K")
+# ВАЖНО: Добавили путь для медиа файлов
+MEDIA_DIR = Path("./src/scripts/media")
+MEDIA_DIR.mkdir(parents=True, exist_ok=True)
+
+BATCH_SIZE = 64
+EPOCHS = 30
+LR = 5e-5
+NUM_WORKERS = 32
+PATIENCE = 7
+
+# Маппинг классов
+CLASS_MAPPING = {
+    "amusement": 0, "anger": 1, "awe": 2, "contentment": 3,
+    "disgust": 4, "excitement": 5, "fear": 6, "sadness": 7
+}
+# Инвертированный маппинг для графиков
+INV_CLASS_MAPPING = {v: k for k, v in CLASS_MAPPING.items()}
+CLASS_NAMES = [INV_CLASS_MAPPING[i] for i in range(len(CLASS_MAPPING))]
+
+DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+print(f"Устройство: {DEVICE}")
+
+# Фиксация генераторов псевдослучайных чисел
+def set_seed(seed=42):
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    if torch.cuda.is_available():
+        torch.cuda.manual_seed(seed)
+        torch.cuda.manual_seed_all(seed)
+
+set_seed()
+
+# Инициализация структур данных
+class EmoSetDataset(Dataset):
+    def __init__(self, root: Path | str, split: str, transform=None):
+        self.root = Path(root) / split
+        self.df = pd.read_csv(self.root / "labels.csv")
+        self.transform = transform
+        
+        # Фильтрация датафрейма
+        self.df = self.df[self.df["label"].isin(CLASS_MAPPING.keys())].reset_index(drop=True)
+
+    def __len__(self):
+        return len(self.df)
+
+    def __getitem__(self, idx):
+        row = self.df.iloc[idx]
+        img_path = self.root / "images" / row["filename"]
+
+        try:
+            img = Image.open(img_path).convert("RGB")
+        except Exception:
+            img = Image.new("RGB", (256, 256), (0, 0, 0))
+
+        if self.transform:
+            img_tensor = self.transform(img)
+        else:
+            img_tensor = T.ToTensor()(img)
+            
+        label_idx = CLASS_MAPPING[row["label"]]
+        return img_tensor, label_idx
+
+# Трансформации
+base_tf = [
+    T.ToTensor(),
+    T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+]
+
+train_transform = T.Compose([
+    T.Resize(256, antialias=True),
+    T.RandomCrop(224),
+    T.RandomHorizontalFlip(),
+    *base_tf
+])
+
+val_transform = T.Compose([
+    T.Resize(256, antialias=True),
+    T.CenterCrop(224),
+    *base_tf
+])
+
+train_ds = EmoSetDataset(DATA_ROOT, "train", transform=train_transform)
+val_ds   = EmoSetDataset(DATA_ROOT, "val", transform=val_transform)
+
+train_loader = DataLoader(train_ds, batch_size=BATCH_SIZE, shuffle=True, num_workers=NUM_WORKERS, pin_memory=True)
+val_loader   = DataLoader(val_ds, batch_size=BATCH_SIZE, shuffle=False, num_workers=NUM_WORKERS, pin_memory=True)
+
+# Инициализация модели и оптимизатора
+model = timm.create_model("resnet50", pretrained=True, num_classes=8, drop_rate=0.3)
+model.to(DEVICE)
+
+criterion = nn.CrossEntropyLoss(label_smoothing=0.1)
+
+optimizer = torch.optim.AdamW(model.parameters(), lr=LR, weight_decay=1e-3)
+scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=EPOCHS)
+
+# Функции для отрисовки графиков
+def plot_learning_curves(history):
+    """Отрисовка графиков функции потерь и точности"""
+    epochs = range(1, len(history['train_loss']) + 1)
+    
+    plt.figure(figsize=(14, 5))
+    
+    # График Loss
+    plt.subplot(1, 2, 1)
+    plt.plot(epochs, history['train_loss'], 'b-', label='Train Loss')
+    plt.plot(epochs, history['val_loss'], 'r--', label='Validation Loss')
+    plt.title('График функции потерь (Loss)', fontsize=14)
+    plt.xlabel('Эпохи', fontsize=12)
+    plt.ylabel('Loss', fontsize=12)
+    plt.legend()
+    plt.grid(True, linestyle=':', alpha=0.7)
+    
+    # График Accuracy
+    plt.subplot(1, 2, 2)
+    plt.plot(epochs, history['train_acc'], 'b-', label='Train Accuracy')
+    plt.plot(epochs, history['val_acc'], 'r--', label='Validation Accuracy')
+    plt.title('График точности (Accuracy)', fontsize=14)
+    plt.xlabel('Эпохи', fontsize=12)
+    plt.ylabel('Accuracy', fontsize=12)
+    plt.legend()
+    plt.grid(True, linestyle=':', alpha=0.7)
+    
+    plt.tight_layout()
+    plot_path = MEDIA_DIR / "training_history.png"
+    plt.savefig(plot_path, dpi=300, bbox_inches='tight')
+    plt.close()
+    print(f"[INFO] График обучения сохранен в: {plot_path}")
+
+def plot_confusion_matrix(y_true, y_pred):
+    """Отрисовка тепловой матрицы ошибок"""
+    cm = confusion_matrix(y_true, y_pred)
+    
+    plt.figure(figsize=(10, 8))
+    sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', 
+                xticklabels=CLASS_NAMES, yticklabels=CLASS_NAMES,
+                cbar_kws={'label': 'Количество сэмплов'})
+    
+    plt.title('Матрица ошибок (Confusion Matrix) - ResNet50', fontsize=16, pad=20)
+    plt.ylabel('Истинные классы (Ground Truth)', fontsize=12)
+    plt.xlabel('Предсказанные классы (Predicted)', fontsize=12)
+    
+    plt.xticks(rotation=45, ha='right')
+    plt.yticks(rotation=0)
+    
+    plt.tight_layout()
+    cm_path = MEDIA_DIR / "confusion_matrix_emoset.png"
+    plt.savefig(cm_path, dpi=300, bbox_inches='tight')
+    plt.close()
+    print(f"[INFO] Матрица ошибок сохранена в: {cm_path}")
+
+# Логика эпохи обучения
+def train_epoch(current_model, loader):
+    current_model.train()
+    total_loss, correct_preds, total_samples = 0.0, 0, 0
+
+    for imgs, labels in tqdm(loader, desc="Тренировка", leave=False, smoothing=0):
+        imgs, labels = imgs.to(DEVICE), labels.to(DEVICE)
+
+        optimizer.zero_grad(set_to_none=True)
+        logits = current_model(imgs)
+        loss = criterion(logits, labels)
+
+        loss.backward()
+        optimizer.step()
+
+        total_loss += loss.item() * imgs.size(0)
+        preds = logits.argmax(dim=1)
+        correct_preds += (preds == labels).sum().item()
+        total_samples += labels.size(0)
+
+    return total_loss / total_samples, correct_preds / total_samples
+
+# Логика эпохи валидации с сохранением предсказаний для матрицы ошибок
+@torch.no_grad()
+def val_epoch(current_model, loader, return_preds=False):
+    current_model.eval()
+    total_loss, correct_preds, total_samples = 0.0, 0, 0
+    all_preds, all_labels = [], []
+
+    for imgs, labels in tqdm(loader, desc="Валидация", leave=False, smoothing=0):
+        imgs, labels = imgs.to(DEVICE), labels.to(DEVICE)
+
+        logits = current_model(imgs)
+        loss = criterion(logits, labels)
+
+        total_loss += loss.item() * imgs.size(0)
+        preds = logits.argmax(dim=1)
+        
+        correct_preds += (preds == labels).sum().item()
+        total_samples += labels.size(0)
+        
+        if return_preds:
+            all_preds.extend(preds.cpu().numpy())
+            all_labels.extend(labels.cpu().numpy())
+
+    avg_loss = total_loss / total_samples
+    avg_acc = correct_preds / total_samples
+    
+    if return_preds:
+        return avg_loss, avg_acc, all_labels, all_preds
+    return avg_loss, avg_acc
+
+if __name__ == "__main__":
+    best_val_acc = 0.0
+    best_val_loss = float('inf')
+    epochs_no_improve = 0
+    checkpoint_path = "./emosetV2_resnet50_best.pth"
+    
+    # Словарь для хранения истории обучения
+    history = {
+        'train_loss': [], 'train_acc': [],
+        'val_loss': [], 'val_acc': []
+    }
+    
+    # Переменные для хранения лучших предсказаний для матрицы
+    best_labels, best_preds = [], []
+
+    print("Старт обучения.")
+
+    for epoch in range(1, EPOCHS + 1):
+        train_loss, train_acc = train_epoch(model, train_loader)
+        
+        # Получаем предсказания только если это может быть лучшая эпоха
+        val_loss, val_acc, val_labels, val_preds = val_epoch(model, val_loader, return_preds=True)
+
+        scheduler.step()
+
+        # Запись в историю
+        history['train_loss'].append(train_loss)
+        history['train_acc'].append(train_acc)
+        history['val_loss'].append(val_loss)
+        history['val_acc'].append(val_acc)
+
+        print(f"[{epoch}/{EPOCHS}] Train Loss: {train_loss:.4f}, Acc: {train_acc:.4f} | Val Loss: {val_loss:.4f}, Acc: {val_acc:.4f}")
+
+        # Сохранение лучших весов по Accuracy
+        if val_acc > best_val_acc:
+            best_val_acc = val_acc
+            best_labels = val_labels # Сохраняем предсказания лучшей модели
+            best_preds = val_preds
+            torch.save(model.state_dict(), checkpoint_path)
+            print(f"Сохранен чекпоинт (Acc: {best_val_acc:.4f})")
+
+        # Оценка переобучения по Loss (Early Stopping)
+        if val_loss < best_val_loss:
+            best_val_loss = val_loss
+            epochs_no_improve = 0
+        else:
+            epochs_no_improve += 1
+            if epochs_no_improve >= PATIENCE:
+                print(f"Ранняя остановка: метрика валидации не улучшается {PATIENCE} эпох.")
+                break
+
+    print("Процесс обучения завершен. Генерирую графики для диссертации...")
+    plot_learning_curves(history)
+    plot_confusion_matrix(best_labels, best_preds)
+    print("Все медиафайлы успешно созданы!")

src/scripts/23_1_extract_embeddings_m.py

@@ -0,0 +1,171 @@
+import os
+from pathlib import Path
+from PIL import Image
+import pandas as pd
+import numpy as np
+from tqdm import tqdm
+
+import torch
+from torch.utils.data import Dataset, DataLoader
+import torchvision.transforms as T
+import timm
+
+import matplotlib.pyplot as plt
+import seaborn as sns
+from sklearn.manifold import TSNE
+
+# Настройки путей для медиа
+MEDIA_DIR = Path("scripts/media")
+MEDIA_DIR.mkdir(parents=True, exist_ok=True)
+
+# Конфигурация путей для инференса и кэширования векторов
+DATA_ROOT = Path("./NFS/Thesis/Emoset/EmoSet-118K")
+MODEL_PATH = Path("./src/emoset_resnet50_best.pth")
+
+BATCH_SIZE = 128
+NUM_WORKERS = 32
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+print(f"Вычисления перенесены на: {device}")
+
+class EmoSetFeatureDataset(Dataset):
+    def __init__(self, root: Path | str, split: str):
+        self.root = Path(root) / split
+        self.df = pd.read_csv(self.root / "labels.csv")
+
+        self.labels = sorted(self.df["label"].unique())
+        self.label2idx = {l: i for i, l in enumerate(self.labels)}
+        self.idx2label = {i: l for l, i in self.label2idx.items()}
+
+        # Для экстракции признаков аугментация отключена, используется строгий CenterCrop
+        self.transform = T.Compose([
+            T.Resize(256),
+            T.CenterCrop(224),
+            T.ToTensor(),
+            T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+        ])
+
+    def __len__(self):
+        return len(self.df)
+
+    def __getitem__(self, idx):
+        row = self.df.iloc[idx]
+        img_path = self.root / "images" / row["filename"]
+        
+        # Перехват битых файлов выборки
+        try:
+            img = Image.open(img_path).convert("RGB")
+        except Exception:
+            img = Image.new("RGB", (224, 224), (0, 0, 0))
+            
+        img_tensor = self.transform(img)
+        label_idx = self.label2idx[row["label"]]
+        
+        return img_tensor, label_idx
+
+def plot_tsne(embeddings, labels, idx2label, sample_limit=3000):
+    """Генерация t-SNE графика для диссертации"""
+    print(f"Построение t-SNE проекции для {sample_limit} сэмплов...")
+    
+    tsne_model = TSNE(n_components=2, perplexity=30, random_state=42)
+    embeddings_2d = tsne_model.fit_transform(embeddings[:sample_limit])
+    labels_subset = labels[:sample_limit]
+
+    plt.figure(figsize=(12, 9))
+    
+    # Используем более академическую палитру
+    scatter = plt.scatter(
+        embeddings_2d[:, 0],
+        embeddings_2d[:, 1],
+        c=labels_subset,
+        cmap="Set2", # Set2 лучше различается при печати
+        alpha=0.7,
+        s=20,
+        edgecolors='w', 
+        linewidths=0.5
+    )
+
+    # Формирование легенды
+    handles, _ = scatter.legend_elements()
+    legend_labels = [idx2label[i] for i in range(len(idx2label))]
+    
+    # Размещение легенды снаружи графика, чтобы не перекрывать данные
+    plt.legend(handles, legend_labels, title="Эмоциональные классы", 
+               bbox_to_anchor=(1.05, 1), loc='upper left')
+
+    plt.title("2D проекция скрытого пространства признаков (t-SNE)", pad=20, fontsize=14)
+    plt.xlabel("Первая главная компонента (t-SNE 1)", fontsize=12)
+    plt.ylabel("Вторая главная компонента (t-SNE 2)", fontsize=12)
+    plt.grid(True, linestyle='--', alpha=0.3)
+    
+    plt.tight_layout()
+    plot_path = MEDIA_DIR / "tsne_embeddings.png"
+    plt.savefig(plot_path, dpi=300, bbox_inches='tight')
+    plt.close()
+    print(f"[INFO] График t-SNE сохранен в: {plot_path}")
+
+
+if __name__ == "__main__":
+    test_ds = EmoSetFeatureDataset(DATA_ROOT, "test")
+    test_loader = DataLoader(
+        test_ds,
+        batch_size=BATCH_SIZE,
+        shuffle=False,  # Отключение шаффла для строгого соответствия индексов
+        num_workers=NUM_WORKERS,
+        pin_memory=True
+    )
+
+    print(f"Подготовлено для извлечения: {len(test_ds)} файлов.")
+    
+    # Инициализация модели и загрузка лучших весов
+    feature_extractor = timm.create_model(
+        "resnet50",
+        pretrained=False,
+        num_classes=len(test_ds.labels)
+    )
+
+    try:
+        checkpoint = torch.load(MODEL_PATH, map_location=device)
+        feature_extractor.load_state_dict(checkpoint)
+        print("Веса модели успешно загружены.")
+    except Exception as e:
+        print(f"Ошибка загрузки весов: {e}. Убедитесь, что модель обучена.")
+        exit(1)
+
+    # Удаление классификационного слоя (fc)
+    feature_extractor.reset_classifier(0)
+    feature_extractor.to(device)
+    feature_extractor.eval()
+    
+    print("Слой классификации удален. Модель готова к экстракции.")
+    
+    extracted_embeddings = []
+    extracted_labels = []
+
+    print("Старт пакетной экстракции признаков...")
+
+    with torch.no_grad():
+        for imgs, labels in tqdm(test_loader, desc="Экстракция"):
+            imgs = imgs.to(device)
+
+            # Получение вектора [BATCH_SIZE, 2048]
+            embeddings_batch = feature_extractor(imgs)
+            
+            extracted_embeddings.append(embeddings_batch.cpu().numpy())
+            extracted_labels.append(labels.numpy())
+
+    # Агрегация батчей в единые массивы
+    np_embeddings = np.concatenate(extracted_embeddings, axis=0)
+    np_labels = np.concatenate(extracted_labels, axis=0)
+
+    print(f"Размерность матрицы признаков: {np_embeddings.shape}")
+
+    # Сохранение артефактов
+    np.save("./src/emoset_test_embeddings.npy", np_embeddings)
+    np.save("./src/emoset_test_labels.npy", np_labels)
+    print("Матрицы успешно экспортированы в .npy файлы.")
+    
+    # Генерация медиа для диссертации
+    plot_tsne(np_embeddings, np_labels, test_ds.idx2label, sample_limit=3000)
+    
+    print("Процесс полностью завершен.")

src/scripts/31_finetune_2.41M.py

@@ -1,264 +0,0 @@
-import os
-import gc
-import pickle
-import random
-from pathlib import Path
-
-import torch
-import torch.nn as nn
-from torch.utils.data import Dataset, DataLoader
-import torchvision.transforms as T
-import torchvision.io as tv_io
-from torch.amp import autocast, GradScaler
-from tqdm import tqdm
-import timm
-
-# Конфигурация стенда и путей файловой системы
-DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-DATA_ROOT = Path("/home/zin/projects/Thesis/dataset/Original-2.41M")
-CACHE_PATH = Path("/home/zin/projects/Thesis/src/dataset_paths_cache.pkl")
-
-PREVIOUS_WEIGHTS = Path("/home/zin/projects/Thesis/src/emoset_resnet50_best.pth")
-RESUME_CHECKPOINT = Path("/home/zin/projects/Thesis/src/emoset_resnet50_resume.pth")
-SAVE_MODEL_PATH = Path("/home/zin/projects/Thesis/src/emoset_resnet50_finetuned_2_41M.pth")
-
-CLASS_MAPPING = {
-    "amusement": 0, "anger": 1, "awe": 2, "contentment": 3,
-    "disgust": 4, "excitement": 5, "fear": 6, "sad": 7, "sadness": 7
-}
-
-# Гиперпараметры конвейера обучения
-BATCH_SIZE = 82
-EPOCHS = 15
-LR = 5e-5
-NUM_TRAIN_WORKERS = 48
-NUM_VAL_WORKERS = 18
-PATIENCE = 4
-
-def prepare_dataset_index():
-    # Построение или загрузка индекса файлов для минимизации I/O операций по сети (NFS)
-    if CACHE_PATH.exists():
-        print(f"Загрузка карты файловой системы из кэша: {CACHE_PATH.name}")
-        with open(CACHE_PATH, 'rb') as f:
-            cache_data = pickle.load(f)
-        return cache_data['image_paths'], cache_data['labels']
-
-    print(f"Сканирование сетевой директории {DATA_ROOT} (первичная индексация)...")
-    paths, labels = [], []
-    for img_path in DATA_ROOT.rglob('*.jpg'):
-        emotion_folder = img_path.parts[-3].lower()
-        if emotion_folder in CLASS_MAPPING:
-            paths.append(str(img_path))
-            labels.append(CLASS_MAPPING[emotion_folder])
-            
-    with open(CACHE_PATH, 'wb') as f:
-        pickle.dump({'image_paths': paths, 'labels': labels}, f)
-    
-    return paths, labels
-
-class EmoSetDirectDataset(Dataset):
-    # Датасет с отложенной аугментацией: декодирование на CPU, трансформации на GPU
-    def __init__(self, image_paths, labels):
-        self.image_paths = image_paths
-        self.labels = labels
-        self.base_transform = T.Resize((256, 256), antialias=True)
-
-    def __len__(self): 
-        return len(self.image_paths)
-
-    def __getitem__(self, idx):
-        try:
-            image = tv_io.read_image(self.image_paths[idx], mode=tv_io.ImageReadMode.RGB)
-            image = image.to(torch.float32) / 255.0
-            image = self.base_transform(image)
-        except Exception:
-            # Изолирование сбоев ввода-вывода (поврежденные файлы на сетевом диске)
-            image = torch.zeros((3, 256, 256), dtype=torch.float32)
-        return image, self.labels[idx]
-
-def build_gpu_transforms():
-    # Перенос матричных операций аугментации на тензорные ядра видеокарты
-    train_tf = torch.nn.Sequential(
-        T.RandomCrop((224, 224)),
-        T.RandomHorizontalFlip(p=0.5),
-        T.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1),
-        T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
-    ).to(DEVICE)
-
-    val_tf = torch.nn.Sequential(
-        T.CenterCrop((224, 224)),
-        T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
-    ).to(DEVICE)
-    
-    return train_tf, val_tf
-
-if __name__ == "__main__":
-    print(f"Инициализация конвейера обучения. Устройство: {DEVICE}")
-    
-    all_paths, all_labels = prepare_dataset_index()
-    
-    # Фиксация сида для детерминированного разделения выборок при перезапусках скрипта
-    random.seed(42)
-    combined = list(zip(all_paths, all_labels))
-    random.shuffle(combined)
-    all_paths, all_labels = zip(*combined)
-
-    split_idx = int(len(all_paths) * 0.95)
-    
-    train_loader = DataLoader(
-        EmoSetDirectDataset(all_paths[:split_idx], all_labels[:split_idx]), 
-        batch_size=BATCH_SIZE, shuffle=True, 
-        num_workers=NUM_TRAIN_WORKERS, pin_memory=True, 
-        prefetch_factor=2, persistent_workers=True
-    )
-
-    val_loader = DataLoader(
-        EmoSetDirectDataset(all_paths[split_idx:], all_labels[split_idx:]), 
-        batch_size=BATCH_SIZE, shuffle=False, 
-        num_workers=NUM_VAL_WORKERS, pin_memory=True, 
-        prefetch_factor=2, persistent_workers=True
-    )
-
-    gpu_train_tf, gpu_val_tf = build_gpu_transforms()
-
-    model = timm.create_model('resnet50', pretrained=False, num_classes=8).to(DEVICE)
-    criterion = nn.CrossEntropyLoss()
-    optimizer = torch.optim.AdamW(model.parameters(), lr=LR, weight_decay=1e-4)
-    scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=EPOCHS)
-    scaler = GradScaler()
-
-    best_val_loss = float('inf')
-    epochs_no_improve = 0
-    start_epoch = 1
-
-    # Инициализация механизма отказоустойчивости и интеграция весов
-    if RESUME_CHECKPOINT.exists():
-        print(f"Восстановление контекста выполнения из: {RESUME_CHECKPOINT.name}")
-        checkpoint = torch.load(RESUME_CHECKPOINT, map_location=DEVICE)
-        model.load_state_dict(checkpoint['model_state_dict'])
-        optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
-        scheduler.load_state_dict(checkpoint['scheduler_state_dict'])
-        if 'scaler_state_dict' in checkpoint: scaler.load_state_dict(checkpoint['scaler_state_dict'])
-        if 'best_val_loss' in checkpoint: best_val_loss = checkpoint['best_val_loss']
-        start_epoch = checkpoint['epoch'] + 1
-    elif PREVIOUS_WEIGHTS.exists():
-        print(f"Интеграция претренированных весов: {PREVIOUS_WEIGHTS.name}")
-        model.load_state_dict(torch.load(PREVIOUS_WEIGHTS, map_location=DEVICE))
-    else:
-        print("Веса не найдены. Инициализация с ImageNet.")
-        model = timm.create_model('resnet50', pretrained=True, num_classes=8).to(DEVICE)
-
-    try:
-        for epoch in range(start_epoch, EPOCHS + 1):
-            
-            # Проход по обучающей выборке
-            model.train()
-            running_loss, correct, total = 0.0, 0, 0
-            
-            pbar = tqdm(train_loader, desc=f"Epoch {epoch}/{EPOCHS} [Train]")
-            for inputs, labels in pbar:
-                try:
-                    inputs = inputs.to(DEVICE, non_blocking=True)
-                    labels = labels.to(DEVICE, non_blocking=True)
-                    inputs = gpu_train_tf(inputs)
-                    
-                    optimizer.zero_grad()
-                    
-                    # Смешанная точность для экономии VRAM
-                    with autocast(device_type="cuda"):
-                        outputs = model(inputs)
-                        loss = criterion(outputs, labels)
-                    
-                    scaler.scale(loss).backward()
-                    scaler.step(optimizer)
-                    scaler.update()
-                    
-                    running_loss += loss.item() * inputs.size(0)
-                    _, predicted = outputs.max(1)
-                    total += labels.size(0)
-                    correct += predicted.eq(labels).sum().item()
-                    
-                    pbar.set_postfix({'loss': f"{loss.item():.4f}"})
-                    
-                except RuntimeError as memory_err:
-                    # Подавление пиковых скачков потребления VRAM
-                    if "out of memory" in str(memory_err).lower():
-                        if 'outputs' in locals(): del outputs
-                        if 'loss' in locals(): del loss
-                        torch.cuda.empty_cache()
-                        optimizer.zero_grad()
-                        continue
-                    raise memory_err
-
-            train_loss = running_loss / total if total > 0 else 0
-            train_acc = correct / total if total > 0 else 0
-
-            gc.collect()
-            torch.cuda.empty_cache()
-
-            # Проход по валидационной выборке
-            model.eval()
-            val_loss, val_correct, val_total = 0.0, 0, 0
-            
-            with torch.no_grad():
-                for val_inputs, val_labels in tqdm(val_loader, desc=f"Epoch {epoch}/{EPOCHS} [Val]", leave=False):
-                    val_inputs = val_inputs.to(DEVICE, non_blocking=True)
-                    val_labels = val_labels.to(DEVICE, non_blocking=True)
-                    val_inputs = gpu_val_tf(val_inputs)
-                    
-                    with autocast(device_type="cuda"):
-                        val_outputs = model(val_inputs)
-                        v_loss = criterion(val_outputs, val_labels)
-                        
-                    val_loss += v_loss.item() * val_inputs.size(0)
-                    _, val_predicted = val_outputs.max(1)
-                    val_total += val_labels.size(0)
-                    val_correct += val_predicted.eq(val_labels).sum().item()
-
-            epoch_val_loss = val_loss / val_total if val_total > 0 else 0
-            epoch_val_acc = val_correct / val_total if val_total > 0 else 0
-            
-            scheduler.step()
-            print(f"[{epoch}/{EPOCHS}] Train Loss: {train_loss:.4f} | Val Loss: {epoch_val_loss:.4f} | Val Acc: {epoch_val_acc:.4f}")
-
-            # Оценка критериев ранней остановки и сохранение состояния сессии
-            if epoch_val_loss < best_val_loss:
-                best_val_loss = epoch_val_loss
-                epochs_no_improve = 0
-                torch.save(model.state_dict(), str(SAVE_MODEL_PATH).replace(".pth", "_best.pth"))
-            else:
-                epochs_no_improve += 1
-                if epochs_no_improve >= PATIENCE and epoch >= 15:
-                    print(f"Сработал механизм Early Stopping. Валидация не улучшается {PATIENCE} эпох.")
-                    break
-                    
-            # Атомарное сохранение контекста
-            checkpoint_state = {
-                'epoch': epoch,
-                'model_state_dict': model.state_dict(),
-                'optimizer_state_dict': optimizer.state_dict(),
-                'scheduler_state_dict': scheduler.state_dict(),
-                'scaler_state_dict': scaler.state_dict(),
-                'best_val_loss': best_val_loss
-            }
-            torch.save(checkpoint_state, RESUME_CHECKPOINT)
-            gc.collect()
-
-    except KeyboardInterrupt:
-        print("\nВыполнение прервано пользователем (SIGINT).")
-        print(f"Дамп памяти конвейера зафиксирован на эпохе {epoch}.")
-        checkpoint_state = {
-            'epoch': epoch, 'model_state_dict': model.state_dict(),
-            'optimizer_state_dict': optimizer.state_dict(),
-            'scheduler_state_dict': scheduler.state_dict(), 'scaler_state_dict': scaler.state_dict(),
-            'best_val_loss': best_val_loss
-        }
-        torch.save(checkpoint_state, RESUME_CHECKPOINT)
-        
-    else:
-        if SAVE_MODEL_PATH.parent.exists():
-            torch.save(model.state_dict(), SAVE_MODEL_PATH)
-            print(f"Процесс Fine-Tuning завершен. Артефакт сохранен: {SAVE_MODEL_PATH.name}")
-            if RESUME_CHECKPOINT.exists():
-                RESUME_CHECKPOINT.unlink()

src/scripts/91_generate_metrics.py

@@ -1,96 +1,97 @@
-import joblib
 import numpy as np
 import pandas as pd
+import joblib
 from pathlib import Path
-import matplotlib.pyplot as plt
-
-from sklearn.model_selection import train_test_split
 from sklearn.metrics import mean_squared_error, r2_score
 
-# Калибровочные координаты центров эмоциональных классов в пространстве Рассела [1.0 - 9.0]
+# 1. Настройка путей
-EMOTION_TO_VA_COORDS = {
+embeddings_path = Path("./src/emoset_test_embeddings.npy")
-    0: (7.5, 6.5),  # amusement
+csv_path = Path("./NFS/Thesis/Emoset/EmoSet-118K/test/labels.csv")
-    1: (2.0, 8.0),  # anger
+model_path = Path("./src/music_engine/va_regressor.pkl")
-    2: (6.5, 5.0),  # awe
+
-    3: (7.0, 3.0),  # contentment
+output_dir = Path("./src/scripts/media")
-    4: (3.0, 6.0),  # disgust
+output_file = output_dir / "metrics_output.txt"
-    5: (8.0, 8.0),  # excitement
+
-    6: (2.5, 7.5),  # fear
+# 2. Корректный маппинг 8 классов EmoSet в шкалу DEAM [1.0, 9.0]
-    7: (2.0, 2.0),  # sadness
+# Формула перевода из [-1, 1] в [1, 9]: 5.0 + (X * 4.0)
+EMO_TO_VA = {
+    "amusement":   [8.2, 6.6],  # Веселье (Высокий позитив, средняя энергия)
+    "awe":         [7.0, 7.4],  # Восхищение (Позитив, высокая энергия)
+    "contentment": [7.8, 3.4],  # Умиротворение (Позитив, низкая энергия)
+    "excitement":  [8.2, 8.2],  # Возбуждение (Макс. позитив, макс. энергия)
+    "anger":       [2.2, 7.8],  # Гнев (Глубокий негатив, высокая энергия)
+    "disgust":     [2.6, 6.6],  # Отвращение (Негатив, средняя энергия)
+    "fear":        [2.6, 8.2],  # Страх (Негатив, максимальная энергия)
+    "sadness":     [2.2, 2.6]   # Грусть (Глубокий негатив, низкая энергия)
 }
 
-def evaluate_regression_model():
+def generate_slide_metrics():
-    # Инициализация путей к артефактам пайплайна
+    print("[INFO] Загрузка тестовых артефактов...")
-    base_dir = Path(__file__).resolve().parent.parent.parent
+    
-    embeddings_path = base_dir / "src" / "emoset_test_embeddings.npy"
+    if not all(p.exists() for p in [embeddings_path, csv_path, model_path]):
-    labels_path = base_dir / "src" / "emoset_test_labels.npy"
+        print("[ERROR] Проверьте наличие файлов данных или модели регрессора.")
-    model_path = base_dir / "src" / "music_engine" / "va_regressor.pkl"
-
-    if not all(p.exists() for p in [embeddings_path, labels_path, model_path]):
-        print("Отсутствуют необходимые артефакты для расчета метрик.")
         return
 
-    # Загрузка скрытых представлений и инициализация регрессора
+    output_dir.mkdir(parents=True, exist_ok=True)
-    x_features = np.load(embeddings_path)
-    y_discrete = np.load(labels_path)
-    regression_pipeline = joblib.load(model_path)
-    
-    # Маппинг дискретных меток в непрерывные координаты
-    y_continuous = np.array([EMOTION_TO_VA_COORDS[label] for label in y_discrete])
-    
-    # Изоляция тестовой выборки (сохранение детерминированности через random_state)
-    _, x_test, _, y_test = train_test_split(x_features, y_continuous, test_size=0.2, random_state=42)
-    
-    # Генерация предсказаний на отложенной выборке
-    y_pred = regression_pipeline.predict(x_test)
-    
-    # Расчет метрик качества регрессии (Mean Squared Error, R-squared)
-    mse_valence = mean_squared_error(y_test[:, 0], y_pred[:, 0])
-    r2_valence = r2_score(y_test[:, 0], y_pred[:, 0])
-    
-    mse_arousal = mean_squared_error(y_test[:, 1], y_pred[:, 1])
-    r2_arousal = r2_score(y_test[:, 1], y_pred[:, 1])
-    
-    print("Метрики качества регрессионной модели на тестовой выборке:")
-    print(f"Valence -> MSE: {mse_valence:.4f} | R^2: {r2_valence:.4f}")
-    print(f"Arousal -> MSE: {mse_arousal:.4f} | R^2: {r2_arousal:.4f}")
-    
-    # Построение диагностических диаграмм рассеяния (Scatter Plots)
-    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 7))
-    
-    # Конфигурация подграфика: Ось Валентности
-    ax1.scatter(y_test[:, 0], y_pred[:, 0], alpha=0.3, color='#1f77b4', edgecolors='none', label='Прогноз регрессора')
-    ax1.plot([1, 9], [1, 9], 'r--', lw=2, label='Идеальное совпадение (x=y)')
-    ax1.set_title('Диаграмма рассеяния: Valence (Позитивность)', fontsize=14, fontweight='bold')
-    ax1.set_xlabel('Эталонные значения (центры классов)', fontsize=12)
-    ax1.set_ylabel('Непрерывные предсказания модели', fontsize=12)
-    ax1.set_xlim(1, 9)
-    ax1.set_ylim(1, 9)
-    ax1.grid(True, linestyle='--', alpha=0.6)
-    ax1.legend(loc='upper left', fontsize=10)
-    
-    # Научное обоснование распределения данных для комиссии
-    ax1.text(1.2, 8.2, 
-             'Формирование вертикальных кластеров\n'
-             'обусловлено проекцией 8 дискретных\n'
-             'базовых эмоций на непрерывную\n'
-             'координатную плоскость.', 
-             fontsize=10, bbox=dict(facecolor='white', alpha=0.9, edgecolor='gray'))
 
-    # Конфигурация подграфика: Ось Активности
+    # 3. Загрузка эмбеддингов и меток
-    ax2.scatter(y_test[:, 1], y_pred[:, 1], alpha=0.3, color='#ff7f0e', edgecolors='none', label='Прогноз регрессора')
+    X_test = np.load(embeddings_path)
-    ax2.plot([1, 9], [1, 9], 'r--', lw=2, label='Идеальное совпадение (x=y)')
+    df = pd.read_csv(csv_path)
-    ax2.set_title('Диаграмма рассеяния: Arousal (Активность)', fontsize=14, fontweight='bold')
-    ax2.set_xlabel('Эталонные значения (центры классов)', fontsize=12)
-    ax2.set_ylabel('Непрерывные предсказания модели', fontsize=12)
-    ax2.set_xlim(1, 9)
-    ax2.set_ylim(1, 9)
-    ax2.grid(True, linestyle='--', alpha=0.6)
-    ax2.legend(loc='upper left', fontsize=10)
     
-    plt.tight_layout()
+    if len(X_test) != len(df):
-    plt.savefig('regression_metrics_plot.png', dpi=300, bbox_inches='tight')
+        print(f"[WARN] Корректировка размеров выборки: Эмбеддинги ({len(X_test)}) != Метки ({len(df)})")
-    print("Диагностические графики экспортированы в regression_metrics_plot.png")
+        min_len = min(len(X_test), len(df))
+        X_test = X_test[:min_len]
+        df = df.iloc[:min_len]
+
+    y_test_list = [EMO_TO_VA.get(label.lower().strip(), [5.0, 5.0]) for label in df['label']]
+    y_test = np.array(y_test_list)
+
+    # 4. Выполнение инференса
+    print("[INFO] Выполнение инференса регрессора на скрытом пространстве признаков...")
+    regressor = joblib.load(model_path)
+    y_pred = regressor.predict(X_test)
+    
+    # === БЛОК ДИАГНОСТИКИ ШКАЛЫ ===
+    print("\n" + "-"*50)
+    print(" ДИАГНОСТИКА ДИАПАЗОНОВ ЗНАЧЕНИЙ ".center(50))
+    print("-"*50)
+    print(f"Истинные (y_test) -> Мин: {y_test.min():.2f}, Макс: {y_test.max():.2f}, Среднее: {y_test.mean():.2f}")
+    print(f"Предсказания (y_pred) -> Мин: {y_pred.min():.2f}, Макс: {y_pred.max():.2f}, Среднее: {y_pred.mean():.2f}")
+    print("-"*50 + "\n")
+    # ==============================
+    
+    # 5. Расчет метрик
+    mse_v = mean_squared_error(y_test[:, 0], y_pred[:, 0])
+    r2_v = r2_score(y_test[:, 0], y_pred[:, 0])
+    
+    mse_a = mean_squared_error(y_test[:, 1], y_pred[:, 1])
+    r2_a = r2_score(y_test[:, 1], y_pred[:, 1])
+    
+    mse_total = mean_squared_error(y_test, y_pred)
+    r2_total = r2_score(y_test, y_pred)
+    
+    # 6. Вывод и сохранение результатов
+    table_content = f"""
+==================================================
+          ТАБЛИЦА МЕТРИК ДЛЯ СЛАЙДА 10          
+==================================================
+| Метрика    | Valence (V)  | Arousal (A)  | Общая (Total) |
+|------------|--------------|--------------|---------------|
+| MSE        | {mse_v:<12.4f} | {mse_a:<12.4f} | {mse_total:<13.4f} |
+| R²         | {r2_v:<12.4f} | {r2_a:<12.4f} | {r2_total:<13.4f} |
+==================================================
+
+Формула целевой функции для вставки на слайд (LaTeX):
+$$Score_{{final}} = D_{{emo}} + 4.0 \cdot Acoustic_{{penalty}}$$
+"""
+
+    print(table_content)
+
+    with open(output_file, 'w', encoding='utf-8') as f:
+        f.write(table_content)
+        
+    print(f"[SUCCESS] Метрики успешно сохранены в файл: {output_file.absolute()}")
 
 if __name__ == "__main__":
-    evaluate_regression_model()
+    generate_slide_metrics()

src/scripts/media/metrics_output.txt

@@ -0,0 +1,12 @@
+
+==================================================
+          ТАБЛИЦА МЕТРИК ДЛЯ СЛАЙДА 10          
+==================================================
+| Метрика    | Valence (V)  | Arousal (A)  | Общая (Total) |
+|------------|--------------|--------------|---------------|
+| MSE        | 1.5135       | 2.2743       | 1.8939        |
+| R²         | 0.7927       | 0.4321       | 0.6124        |
+==================================================
+
+Формула целевой функции для вставки на слайд (LaTeX):
+$$Score_{final} = D_{emo} + 4.0 \cdot Acoustic_{penalty}$$

src/scripts/removed_31_finetune_2.41M.py

@@ -0,0 +1,319 @@
+import os
+import random
+import warnings
+from collections import defaultdict
+from pathlib import Path
+from PIL import Image, ImageFile
+import pandas as pd
+import numpy as np
+from tqdm import tqdm
+
+import torch
+import torch.nn as nn
+from torch.utils.data import Dataset, DataLoader
+import torchvision.transforms as T
+from torch.amp import autocast, GradScaler
+import timm
+
+# Подавление предупреждений и защита от битых "хвостов" JPEG
+warnings.filterwarnings("ignore")
+ImageFile.LOAD_TRUNCATED_IMAGES = True
+
+DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+print(f"Устройство: {DEVICE}")
+
+# --- ПУТИ ---
+TRAIN_ROOT = Path("./dataset/Original-2.41M")
+ANCHOR_118K_ROOT = Path("./NFS/Thesis/Emoset/EmoSet-118K/train") # ЯКОРЬ (Чистые данные для обучения)
+VAL_118K_ROOT = Path("./NFS/Thesis/Emoset/EmoSet-118K/val")
+
+SAVE_MODEL_PATH = Path("./src/emosetV2_resnet50_finetuned_2_41M.pth")
+RESUME_CHECKPOINT = Path("./src/finetuneV2_resume.pth")
+PRETRAINED_PATH = Path("./src/emosetV2_resnet50_best.pth")
+
+CLASS_MAPPING = {
+    "amusement": 0, "anger": 1, "awe": 2, "contentment": 3,
+    "disgust": 4, "excitement": 5, "fear": 6, "sadness": 7
+}
+
+# --- НАСТРОЙКИ ---
+TOTAL_BATCH_SIZE = 64
+BATCH_NOISY = 48  # 75% батча - новые данные 2.41M
+BATCH_ANCHOR = 16 # 25% батча - чистые якорные данные 118K
+
+EPOCHS_PER_FOLDER = 15
+PATIENCE = 5
+LR = 1e-6 
+NUM_TRAIN_WORKERS = 32
+NUM_VAL_WORKERS = 32
+
+def worker_init_fn(worker_id):
+    np.random.seed(np.random.get_state()[1][0] + worker_id)
+
+# --- 1. ТРАНСФОРМАЦИИ ---
+train_transform = T.Compose([
+    T.Resize(256),
+    T.RandomResizedCrop(224, scale=(0.8, 1.0)),
+    T.RandomHorizontalFlip(),
+    T.ToTensor(),
+    T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+])
+
+val_transform = T.Compose([
+    T.Resize(256),
+    T.CenterCrop(224),
+    T.ToTensor(),
+    T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+])
+
+# --- 2. ДАТАСЕТЫ ---
+class ChunkTrainDataset(Dataset):
+    def __init__(self, paths, transform):
+        self.paths = paths
+        self.transform = transform
+
+    def __len__(self):
+        return len(self.paths)
+
+    def __getitem__(self, idx):
+        path = self.paths[idx]
+        try:
+            img = Image.open(path).convert('RGB')
+            tensor = self.transform(img)
+            label = CLASS_MAPPING.get(path.parts[-3].lower(), 0)
+            return tensor, label
+        except Exception:
+            return torch.zeros((3, 224, 224)), 0
+
+class CsvDataset(Dataset):
+    def __init__(self, root, transform):
+        self.root = Path(root)
+        self.df = pd.read_csv(self.root / "labels.csv")
+        self.transform = transform
+
+    def __len__(self):
+        return len(self.df)
+
+    def __getitem__(self, idx):
+        row = self.df.iloc[idx]
+        path = self.root / "images" / row["filename"]
+        try:
+            img = Image.open(path).convert('RGB')
+            tensor = self.transform(img)
+            label = CLASS_MAPPING.get(row["label"].lower(), 0)
+            return tensor, label
+        except Exception:
+            return torch.zeros((3, 224, 224)), 0
+
+# --- 3. СБОР ДАННЫХ ---
+def prepare_chunks():
+    print("\nСканирование датасета 2.41M...")
+    chunk_dict = defaultdict(list)
+    for path in TRAIN_ROOT.rglob('*.jpg'):
+        emotion = path.parts[-3].lower()
+        if emotion not in CLASS_MAPPING: 
+            continue
+        folder_str = path.parts[-2]
+        if folder_str.isdigit():
+            chunk_dict[int(folder_str)].append(path)
+            
+    sorted_chunks = sorted(chunk_dict.keys())
+    print(f"Найдено пронумерованных папок (чанков): {len(sorted_chunks)}")
+    return chunk_dict, sorted_chunks
+    # --- 4. ОСНОВНОЙ ЦИКЛ ОБУЧЕНИЯ ---
+if __name__ == "__main__":
+    chunk_dict, sorted_chunks = prepare_chunks()
+
+    # Валидационный датасет (только чистые данные)
+    val_loader = DataLoader(
+        CsvDataset(VAL_118K_ROOT, val_transform), 
+        batch_size=TOTAL_BATCH_SIZE, shuffle=False, 
+        num_workers=NUM_VAL_WORKERS, pin_memory=True
+    )
+
+    # ЯКОРНЫЙ ЗАГРУЗЧИК (Чистые данные для подмешивания)
+    # Используем prefetch_factor и persistent_workers для устранения рывков CPU
+    anchor_dataset = CsvDataset(ANCHOR_118K_ROOT, train_transform)
+    anchor_loader = DataLoader(
+        anchor_dataset, batch_size=BATCH_ANCHOR, shuffle=True, 
+        num_workers=16, pin_memory=True, drop_last=True,
+        prefetch_factor=2, persistent_workers=False
+    )
+
+    # Инициализация модели
+    model = timm.create_model('resnet50', pretrained=False, num_classes=8).to(DEVICE)
+    if PRETRAINED_PATH.exists():
+        model.load_state_dict(torch.load(PRETRAINED_PATH, map_location=DEVICE))
+        print(f"Базовые веса загружены из {PRETRAINED_PATH.name}")
+
+    # Размораживаем всю модель
+    for param in model.parameters(): 
+        param.requires_grad = True
+
+    # Дифференцированный оптимизатор
+    backbone_params = [p for n, p in model.named_parameters() if "fc" not in n]
+    fc_params = [p for n, p in model.named_parameters() if "fc" in n]
+    
+    optimizer = torch.optim.AdamW([
+        {'params': backbone_params, 'lr': LR},         # 1e-6: микро-шаг для основы
+        {'params': fc_params, 'lr': LR * 10}           # 1e-5: шаг для классификатора
+    ], weight_decay=1e-3)
+
+    # Label Smoothing помогает игнорировать мусор в разметке 2.41M
+    criterion = nn.CrossEntropyLoss(label_smoothing=0.15)
+    scaler = GradScaler()
+
+    # --- ПАРАМЕТРЫ ВОССТАНОВЛЕНИЯ ---
+    start_stage = 0
+    start_epoch = 1
+    best_val_loss = float('inf')
+
+    if RESUME_CHECKPOINT.exists():
+        print(f"\nОбнаружен чекпоинт: {RESUME_CHECKPOINT.name}. Восстановление...")
+        checkpoint = torch.load(RESUME_CHECKPOINT, map_location=DEVICE)
+        model.load_state_dict(checkpoint['model_state_dict'])
+        
+        try: 
+            optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
+        except Exception as e: 
+            print(f"Оптимизатор сброшен: {e}")
+            
+        best_val_loss = checkpoint['best_val_loss']
+        start_stage = checkpoint['stage']
+        start_epoch = checkpoint['epoch'] + 1 
+        print(f"Успешный запуск с ЭТАПА {start_stage + 1}, Эпохи {start_epoch}. Best Val Loss: {best_val_loss:.4f}\n")
+    else:
+        # --- ЗАМЕР EPOCH 0 (БАЗОВАЯ ТОЧНОСТЬ) ---
+        # Выполняется только если мы начинаем с нуля
+        print("\n[Проверка базовых весов перед обучением (Epoch 0)]")
+        model.eval()
+        val_loss, val_correct, val_total = 0.0, 0, 0
+        with torch.no_grad():
+            for inputs, labels in tqdm(val_loader, desc="Baseline Eval", smoothing=0):
+                inputs, labels = inputs.to(DEVICE), labels.to(DEVICE)
+                with autocast(device_type="cuda"):
+                    outputs = model(inputs)
+                    v_loss = criterion(outputs, labels)
+                val_loss += v_loss.item() * inputs.size(0)
+                _, pred = outputs.max(1)
+                val_total += labels.size(0)
+                val_correct += pred.eq(labels).sum().item()
+        
+        best_val_loss = val_loss / val_total
+        baseline_acc = val_correct / val_total
+        print(f"Стартовая точка -> Val Loss: {best_val_loss:.4f} | Val Acc: {baseline_acc:.4f}\n")
+
+    # ВОССТАНОВЛЕНИЕ НАКОПЛЕННЫХ ДАННЫХ
+    current_train_paths = []
+    for s in range(start_stage):
+        current_train_paths.extend(chunk_dict[sorted_chunks[s]])
+
+    print("Старт Anchor Curriculum Learning (Смешивание чистых и шумных данных).")
+
+    # ГЛАВНЫЙ ЦИКЛ ПО ПАПКАМ
+    for stage in range(start_stage, len(sorted_chunks)):
+        chunk_id = sorted_chunks[stage]
+        print(f"\n{'='*50}")
+        print(f"ЭТАП {stage+1}/{len(sorted_chunks)}: Добавляем папку '{chunk_id}'")
+        
+        # Накопление и перемешивание
+        current_train_paths.extend(chunk_dict[chunk_id])
+        random.shuffle(current_train_paths)
+        print(f"Всего файлов (грязных) в текущем пуле: {len(current_train_paths)}")
+        
+        # ОСНОВНОЙ ЗАГРУЗЧИК (Грязные данные) с PREFETCH
+        train_loader = DataLoader(
+            ChunkTrainDataset(current_train_paths, train_transform), 
+            batch_size=BATCH_NOISY, shuffle=True, 
+            num_workers=NUM_TRAIN_WORKERS, pin_memory=True, 
+            worker_init_fn=worker_init_fn, drop_last=True,
+            prefetch_factor=4, persistent_workers=True # Устраняет рывки CPU
+        )
+
+        epochs_no_improve = 0
+        first_epoch = start_epoch if stage == start_stage else 1
+        
+        # Инициализация итератора якорей
+        anchor_iter = iter(anchor_loader)
+
+        # ЦИКЛ ЭПОХ ДЛЯ ТЕКУЩЕГО ЭТАПА
+        for epoch in range(first_epoch, EPOCHS_PER_FOLDER + 1):
+            model.train()
+            train_loss, train_correct, train_total = 0.0, 0, 0
+            
+            for noisy_inputs, noisy_labels in tqdm(train_loader, desc=f"S{stage+1}-Ep{epoch}/{EPOCHS_PER_FOLDER} [Train]", smoothing=0):
+                
+                # Достаем якорный чистый батч
+                try:
+                    anc_inputs, anc_labels = next(anchor_iter)
+                except StopIteration:
+                    anchor_iter = iter(anchor_loader)
+                    anc_inputs, anc_labels = next(anchor_iter)
+                
+                # СМЕШИВАЕМ БАТЧИ (Грязные + Чистые)
+                inputs = torch.cat([noisy_inputs, anc_inputs]).to(DEVICE)
+                labels = torch.cat([noisy_labels, anc_labels]).to(DEVICE)
+                
+                optimizer.zero_grad(set_to_none=True)
+                with autocast(device_type="cuda"):
+                    outputs = model(inputs)
+                    loss = criterion(outputs, labels)
+                    
+                scaler.scale(loss).backward()
+                scaler.step(optimizer)
+                scaler.update()
+                
+                train_loss += loss.item() * inputs.size(0)
+                _, pred = outputs.max(1)
+                train_total += labels.size(0)
+                train_correct += pred.eq(labels).sum().item()
+
+            # ВАЛИДАЦИЯ
+            model.eval()
+            val_loss, val_correct, val_total = 0.0, 0, 0
+            with torch.no_grad():
+                for inputs, labels in tqdm(val_loader, desc="[Val]", leave=False, smoothing=0):
+                    inputs, labels = inputs.to(DEVICE), labels.to(DEVICE)
+                    with autocast(device_type="cuda"):
+                        outputs = model(inputs)
+                        v_loss = criterion(outputs, labels)
+                    val_loss += v_loss.item() * inputs.size(0)
+                    _, pred = outputs.max(1)
+                    val_total += labels.size(0)
+                    val_correct += pred.eq(labels).sum().item()
+            
+            avg_train_loss = train_loss / train_total
+            avg_train_acc = train_correct / train_total
+            avg_val_loss = val_loss / val_total
+            avg_val_acc = val_correct / val_total
+            
+            print(f"S{stage+1}-E{epoch} | Train L: {avg_train_loss:.4f}, Acc: {avg_train_acc:.4f} | Val L: {avg_val_loss:.4f}, Acc: {avg_val_acc:.4f}")
+
+            # СОХРАНЕНИЕ ЛУЧШИХ ВЕСОВ
+            if avg_val_loss < best_val_loss:
+                best_val_loss = avg_val_loss
+                epochs_no_improve = 0
+                torch.save(model.state_dict(), SAVE_MODEL_PATH)
+                print("--> Обновлены лучшие веса")
+            else:
+                epochs_no_improve += 1
+
+            # АВАРИЙНОЕ СОХРАНЕНИЕ В КОНЦЕ ЭПОХИ
+            checkpoint_state = {
+                'stage': stage,
+                'epoch': epoch,
+                'model_state_dict': model.state_dict(),
+                'optimizer_state_dict': optimizer.state_dict(),
+                'best_val_loss': best_val_loss
+            }
+            torch.save(checkpoint_state, RESUME_CHECKPOINT)
+            os.sync() # Защита от отключения электричества
+            print(f"--> Чекпоинт (Этап {stage+1}, Эпоха {epoch}) зафиксирован на диске.")
+
+            # РАННЯЯ ОСТАНОВКА ДЛЯ ТЕКУЩЕГО ЭТАПА
+            if epochs_no_improve >= PATIENCE:
+                print(f"Ранняя остановка для ЭТАПА {stage+1}. Переход к следующей папке...")
+                break
+        
+        # Сброс счетчика стартовой эпохи после прохождения восстановительного этапа
+        start_epoch = 1