feat: add metrics

Reviewed-on: #1

.dockerignore

@@ -0,0 +1,18 @@
+bin/
+lib/
+share/
+etc/
+include/
+pyvenv.cfg
+.idea/
+.vscode/
+__pycache__/
+*.pyc
+.git/
+runs/
+dataset/
+NFS/
+*.pth
+*.pkl
+*.npy
+.env

Dockerfile

@@ -0,0 +1,25 @@
+FROM pytorch/pytorch:2.2.1-cuda12.1-cudnn8-runtime
+
+ENV PYTHONDONTWRITEBYTECODE=1
+ENV PYTHONUNBUFFERED=1
+
+WORKDIR /app
+
+# System dependencies for OpenCV and image processing
+RUN apt-get update && apt-get install -y \
+    libglib2.0-0 \
+    libsm6 \
+    libxext6 \
+    libxrender-dev \
+    && rm -rf /var/lib/apt/lists/*
+
+# Install python packages
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy source code
+COPY src/ /app/src/
+
+EXPOSE 8080
+
+CMD ["streamlit", "run", "src/main.py", "--server.port", "8080", "--server.address", "0.0.0.0"]

Makefile

@@ -0,0 +1,20 @@
+.PHONY: up down logs restart status
+
+# Сборка и запуск контейнеров в фоновом режиме
+up:
+	docker compose up --build -d
+
+# Остановка и удаление контейнеров
+down:
+	docker compose down
+
+# Просмотр логов в реальном времени
+logs:
+	docker compose logs -f
+
+# Быстрый перезапуск
+restart: down up
+
+# Проверка статуса
+status:
+	docker compose ps

docker-compose.yml

@@ -0,0 +1,63 @@
+version: '3.8'
+
+networks:
+  emom_mesh:
+    driver: bridge
+
+services:
+  emom_ui:
+    build: 
+      context: .
+      dockerfile: docker/Dockerfile.ui
+    container_name: emom_web_ui
+    restart: unless-stopped
+    ports:
+      - "8080:8080"
+    networks:
+      - emom_mesh
+    env_file:
+      - .env
+    volumes:
+      - ./src:/app/src
+      - ${DATA_DEAM_DIR}:/app/dataset/DEAM:ro
+    depends_on:
+      - emom_inference
+
+  emom_inference:
+    build:
+      context: .
+      dockerfile: docker/Dockerfile.api
+    container_name: emom_pytorch_api
+    restart: unless-stopped
+    networks:
+      - emom_mesh
+    env_file:
+      - .env
+    volumes:
+      - ${HOST_ARTIFACTS_DIR}/emoset_resnet50_best.pth:/app/src/emoset_resnet50_best.pth:ro
+      - ${HOST_ARTIFACTS_DIR}/music_engine/va_regressor.pkl:/app/src/music_engine/va_regressor.pkl:ro
+      - ${DATA_DEAM_DIR}:/app/dataset/DEAM:ro
+      - ~/.cache/huggingface:/root/.cache/huggingface
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              count: 1
+              capabilities: [gpu]
+
+  emom_ollama:
+    image: ollama/ollama:latest
+    container_name: emom_ollama_engine
+    restart: unless-stopped
+    networks:
+      - emom_mesh
+    volumes:
+      - ~/.ollama:/root/.ollama
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              count: 1
+              capabilities: [gpu]

docker/Dockerfile.api

@@ -0,0 +1,19 @@
+FROM pytorch/pytorch:2.2.1-cuda12.1-cudnn8-runtime
+
+ENV PYTHONDONTWRITEBYTECODE=1
+ENV PYTHONUNBUFFERED=1
+
+RUN apt-get update && apt-get install -y \
+    libglib2.0-0 libsm6 libxext6 libxrender-dev \
+    && rm -rf /var/lib/apt/lists/*
+
+RUN pip install --no-cache-dir fastapi uvicorn timm scikit-learn pandas joblib python-multipart transformers==4.38.2 tokenizers==0.15.2 accelerate
+
+WORKDIR /app
+COPY src/ /app/src/
+
+WORKDIR /app/src
+
+EXPOSE 8000
+
+CMD ["uvicorn", "api:app", "--host", "0.0.0.0", "--port", "8000"]

docker/Dockerfile.ui

@@ -0,0 +1,15 @@
+FROM python:3.12-slim
+
+ENV PYTHONDONTWRITEBYTECODE=1
+ENV PYTHONUNBUFFERED=1
+
+WORKDIR /app
+RUN pip install --no-cache-dir streamlit==1.32.0 requests pandas pillow
+
+COPY src/ /app/src/
+
+WORKDIR /app/src
+
+EXPOSE 8080
+
+CMD ["streamlit", "run", "main.py", "--server.port", "8080", "--server.address", "0.0.0.0"]

requirements.txt

@@ -0,0 +1,9 @@
+streamlit==1.32.0
+torch==2.2.1
+torchvision==0.17.1
+timm==0.9.16
+pandas==2.2.1
+scikit-learn==1.4.1.post1
+joblib==1.3.2
+transformers==4.38.2
+requests==2.31.0

src/api.py

@@ -0,0 +1,76 @@
+import io
+import traceback
+import numpy as np
+from typing import List
+from fastapi import FastAPI, UploadFile, File, HTTPException
+from fastapi.responses import JSONResponse
+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 API", version="1.0.0")
+
+ml_context = {
+    "image_processor": None,
+    "music_matcher": None,
+    "llm_bridge": None
+}
+
+@app.on_event("startup")
+async def startup_event():
+    print("Loading ML models...")
+    ml_context["image_processor"] = load_image_processor()
+    ml_context["music_matcher"] = load_music_engine()
+    ml_context["llm_bridge"] = LLMAcousticBridge()
+    print("Initialization complete.")
+
+@app.post("/analyze")
+async def analyze_event_endpoint(files: List[UploadFile] = File(...)):
+    try:
+        images = []
+        for file in files:
+            image_bytes = await file.read()
+            img = Image.open(io.BytesIO(image_bytes)).convert("RGB")
+            images.append(img)
+            
+        print(f"Processing batch: {len(images)} images.")
+        
+        img_processor = ml_context["image_processor"]
+        matcher = ml_context["music_matcher"]
+        llm = ml_context["llm_bridge"]
+
+        all_v, all_a = [], []
+        all_objects = []
+
+        for img in images:
+            embedding = img_processor.extract_embedding(img)
+            v, a = matcher.predict_va(embedding)
+            all_v.append(v)
+            all_a.append(a)
+            
+            caption = img_processor.describe_scene(img)
+            all_objects.append(caption)
+
+        target_v = float(np.mean(all_v))
+        target_a = float(np.mean(all_a))
+        unique_semantics = list(set(all_objects))
+
+        llm_profile = llm.get_acoustic_profile(target_v, target_a, unique_semantics)
+
+        playlist_df = matcher.find_nearest_tracks(target_v, target_a, llm_profile=llm_profile, top_k=15)
+        tracks_list = playlist_df.to_dict(orient="records")
+
+        return JSONResponse(content={
+            "status": "success",
+            "images_processed": len(images),
+            "target_v": target_v,
+            "target_a": target_a,
+            "llm_profile": llm_profile,
+            "semantics": unique_semantics,
+            "tracks": tracks_list
+        })
+        
+    except Exception as e:
+        print(traceback.format_exc()) 
+        raise HTTPException(status_code=500, detail=str(e))

src/data_loader.py

@@ -1,54 +1,46 @@
-import streamlit as st
 from pathlib import Path
+from typing import Tuple, List, Optional, Any
+
 import pandas as pd
 import numpy as np
+
 from music_engine.matcher import MusicMatcher
 from music_engine.image_processor import ImageProcessor
 
-# Определяем базовую директорию (папка src)
 BASE_DIR = Path(__file__).resolve().parent
 
-@st.cache_resource
+def load_music_engine() -> MusicMatcher:
-def load_music_engine():
+    #Инициализация модуля подбора музыкальных композиций.
-    """Загрузка базы данных и модели регрессора."""
-    # music_db.csv лежит в dataset/DEAM/ (на уровень выше от src)
     db_path = BASE_DIR.parent / "dataset" / "DEAM" / "music_db.csv"
-    # va_regressor.pkl лежит в src/music_engine/
     model_path = BASE_DIR / "music_engine" / "va_regressor.pkl"
     
-    if not db_path.exists():
-        print(f"⚠️ Файл базы {db_path} не найден!")
-        return None
     return MusicMatcher(db_path=db_path, model_path=model_path)
 
-@st.cache_resource
+def load_image_processor() -> ImageProcessor:
-def load_image_processor():
+    #Инициализация модуля экстракции визуальных признаков.
-    """Загрузка ResNet-50 для извлечения признаков на лету."""
+    weights_path = BASE_DIR / "emoset_resnet50_best.pth"
-    # Файл весов лежит в той же папке src, что и этот скрипт
-    model_path = BASE_DIR / "emoset_resnet50_best.pth"
     
-    if not model_path.exists():
+    return ImageProcessor(weights_path)
-        print(f"❌ КРИТИЧЕСКАЯ ОШИБКА: Веса не найдены по пути: {model_path}")
-        # Если не нашли в src, попробуем поискать в корне проекта на всякий случай
-        model_path = BASE_DIR.parent / "emoset_resnet50_best.pth"
 
-    return ImageProcessor(model_path=model_path)
+def load_emoset_data() -> Tuple[Optional[List[str]], Optional[np.ndarray], Optional[np.ndarray], Optional[Path]]:
+    # Загрузка тестовой выборки датасета EmoSet.
+    # Модуль сохранен для обеспечения обратной совместимости в отладочном контуре.
+    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"
         
-@st.cache_data
+        if not all(p.exists() for p in [labels_path, embeddings_path]):
-def load_emoset_data():
-    """Загрузка тестовой выборки EmoSet для первой вкладки."""
-    # Пути относительно корня проекта
-    csv_path = BASE_DIR.parent / "dataset" / "EmoSet-118K" / "test" / "labels.csv"
-    img_dir = BASE_DIR.parent / "dataset" / "EmoSet-118K" / "test" / "images"
-    emb_path = BASE_DIR / "emoset_test_embeddings.npy"
-    lbl_path = BASE_DIR / "emoset_test_labels.npy"
-
-    if not all([csv_path.exists(), emb_path.exists(), lbl_path.exists()]):
             return None, None, None, None
             
-    df = pd.read_csv(csv_path)
+        labels = np.load(labels_path)
-    image_list = df['filename'].tolist()
+        embeddings = np.load(embeddings_path)
-    embs = np.load(emb_path)
-    lbls = np.load(lbl_path)
         
-    return image_list, embs, lbls, img_dir
+        csv_path = BASE_DIR.parent / "dataset" / "EmoSet-118K" / "test" / "labels.csv"
+        df = pd.read_csv(csv_path)
+        
+        return df['filename'].tolist(), embeddings, labels, images_path
+        
+    except Exception as e:
+        print(f"[WARN] Failed to load EmoSet test artifacts: {str(e)}")
+        return None, None, None, None

src/main.py

@@ -1,43 +1,189 @@
-import streamlit as st
-import sys
 import os
-import subprocess
+import requests
+import streamlit as st
+import streamlit.components.v1 as components
+from PIL import Image
+import base64
+from io import BytesIO
 
-from data_loader import load_music_engine, load_emoset_data, load_image_processor
+st.set_page_config(page_title="EmoM Playlist Generator", layout="wide", initial_sidebar_state="collapsed")
-from tabs.tab_dataset import render_dataset_tab
-from tabs.tab_live import render_live_tab
 
-# ----------------------------
+API_URL = os.getenv("BACKEND_API_URL", "http://emom_inference:8000") + "/analyze"
-# 1️⃣ Запуск приложения
+DEAM_AUDIO_DIR = "/app/dataset/DEAM/DEAM_audio/MEMD_audio"
-# ----------------------------
-if __name__ == "__main__":
-    if "STREAMLIT_RUN" not in os.environ:
-        os.environ["STREAMLIT_RUN"] = "1"
-        cmd = [sys.executable, "-m", "streamlit", "run", __file__, "--server.port", "8080", "--server.address", "0.0.0.0"]
-        subprocess.run(cmd)
-        sys.exit()
 
-st.set_page_config(page_title="Thesis Demo", layout="wide")
+def get_thumbnail_html(images, max_display=12):
+    html_images = ""
+    for file in images[:max_display]:
+        img = Image.open(file)
+        img.thumbnail((100, 100))
+        if img.mode != "RGB": 
+            img = img.convert("RGB")
+        buffered = BytesIO()
+        img.save(buffered, format="JPEG")
+        b64_str = base64.b64encode(buffered.getvalue()).decode()
+        html_images += f'<img src="data:image/jpeg;base64,{b64_str}" style="width: 60px; height: 60px; object-fit: cover; border-radius: 8px; margin-right: 8px; margin-bottom: 8px; border: 1px solid rgba(255, 255, 255, 0.2);">'
     
-# ----------------------------
+    if len(images) > max_display:
-# 2️⃣ Инициализация движка и данных
+        html_images += f'<span style="display: inline-block; width: 60px; height: 60px; line-height: 60px; text-align: center; background: rgba(150, 150, 150, 0.2); border-radius: 8px; vertical-align: top; font-size: 14px;">+{len(images) - max_display}</span>'
-# ----------------------------
+    return f'<div style="display: flex; flex-wrap: wrap;">{html_images}</div>'
-matcher = load_music_engine()
-image_processor = load_image_processor()
-image_files, embeddings, labels_array, images_path = load_emoset_data()
 
-# ----------------------------
+def main():
-# 3️⃣ Интерфейс и Вкладки
+    if "live_state" not in st.session_state: 
-# ----------------------------
+        st.session_state.live_state = "upload"
-st.title("🖼️ Генератор саундтреков (Research Demo)")
+    if "result_data" not in st.session_state: 
+        st.session_state.result_data = None
         
-tab1, tab2 = st.tabs(["📊 Отладка (Датасет EmoSet)", "📸 Анализ событий (Свои фото)"])
+    viewport = st.query_params.get("viewport", "desktop")
 
-with tab1:
+    st.markdown("""
-    render_dataset_tab(matcher, image_files, embeddings, labels_array, images_path)
+    <style>
+    [data-testid="stFileUploadDropzone"] { min-height: 250px !important; display: flex; align-items: center; justify-content: center; border-radius: 16px; background-color: rgba(255, 75, 75, 0.03); }
+    .spinner-container { display: flex; flex-direction: column; align-items: center; justify-content: center; min-height: 40vh; margin-top: 10vh; }
+    .big-spinner { width: 120px; height: 120px; border: 10px solid rgba(255, 75, 75, 0.1); border-top: 10px solid #ff4b4b; border-radius: 50%; animation: spin 1s linear infinite; margin-bottom: 2rem; }
+    @keyframes spin { 0% { transform: rotate(0deg); } 100% { transform: rotate(360deg); } }
+    #MainMenu {visibility: hidden;} footer {visibility: hidden;}
+    </style>
+    """, unsafe_allow_html=True)
 
-with tab2:
+    if st.session_state.live_state == "upload":
-    if image_processor:
+        upload_placeholder = st.empty()
-        render_live_tab(matcher, image_processor)
+        with upload_placeholder.container():
+            st.write("Загрузите изображения для визуально-семантического анализа.")
+            if viewport == "mobile": 
+                st.markdown("<br>", unsafe_allow_html=True)
+            
+            uploaded_files = st.file_uploader(
+                "Загрузка файлов", 
+                type=['png', 'jpg', 'jpeg'], 
+                accept_multiple_files=True,
+                label_visibility="collapsed" if viewport == "mobile" else "visible"
+            )
+            
+            if uploaded_files:
+                st.markdown("<br>", unsafe_allow_html=True)
+                if st.button("Выполнить анализ", type="primary", use_container_width=True):
+                    st.session_state.uploaded_images = uploaded_files
+                    st.session_state.live_state = "processing"
+                    upload_placeholder.empty()
+                    st.rerun()
+                
+                st.markdown("<br>", unsafe_allow_html=True)
+                st.caption("Выбранные файлы:")
+                st.markdown(get_thumbnail_html(uploaded_files), unsafe_allow_html=True)
+
+    elif st.session_state.live_state == "processing":
+        components.html("<script>window.parent.scrollTo(0, 0);</script>", height=0, width=0)
+        files = st.session_state.get("uploaded_images", [])
+        st.markdown('<div class="spinner-container"><div class="big-spinner"></div><h3 style="text-align: center; font-weight: 400;">Обработка данных...</h3></div>', unsafe_allow_html=True)
+        
+        try:
+            upload_data = [('files', (f.name, f.getvalue(), f.type)) for f in files]
+            response = requests.post(API_URL, files=upload_data, timeout=300) 
+            
+            if response.status_code == 200:
+                st.session_state.result_data = response.json()
+                st.session_state.live_state = "result"
+                st.rerun()
             else:
-        st.error("Система обработки изображений недоступна (не найдены веса ResNet).")
+                st.error(f"Ошибка сервера: {response.status_code}")
+                if st.button("Назад"):
+                    st.session_state.live_state = "upload"
+                    st.rerun()
+        except Exception as e:
+            st.error(f"Ошибка соединения: {str(e)}")
+            if st.button("Назад"):
+                st.session_state.live_state = "upload"
+                st.rerun()
+                
+    elif st.session_state.live_state == "result":
+        components.html("<script>window.parent.scrollTo(0, 0);</script>", height=0, width=0)
+        data = st.session_state.result_data
+        
+        st.header(f"Сгенерированный плейлист (обработано файлов: {data['images_processed']})")
+        
+        for row in data.get("tracks", []):
+            with st.container(border=True):
+                song_id = int(row['song_id'])
+                score = row['final_score']
+                
+                audio_path = f"{DEAM_AUDIO_DIR}/{song_id}.mp3"
+                if not os.path.exists(audio_path): 
+                    audio_path = audio_path.replace('.mp3', '.wav')
+                
+                if viewport == "desktop":
+                    c1, c2 = st.columns([1, 3])
+                    with c1:
+                        st.write(f"**Track ID:** {song_id}")
+                        st.caption(f"Score: {score:.4f}")
+                    with c2:
+                        if os.path.exists(audio_path): 
+                            st.audio(audio_path)
+                        else: 
+                            st.caption("Аудиофайл не найден")
+                else:
+                    st.write(f"**Track ID:** {song_id} (Score: {score:.4f})")
+                    if os.path.exists(audio_path): 
+                        st.audio(audio_path)
+                    else: 
+                        st.caption("Аудиофайл не найден")
+
+        st.markdown("<br>", unsafe_allow_html=True)
+        
+        with st.expander("Отладочная информация (Метрики)"):
+            st.subheader("Координаты V/A")
+            c_v, c_a = st.columns(2)
+            c_v.metric("Valence", f"{data['target_v']:.2f}")
+            c_a.metric("Arousal", f"{data['target_a']:.2f}")
+            
+            st.markdown("---")
+            st.subheader("Акустические признаки (LLM)")
+            
+            feature_titles = {
+                "energy": "RMS Energy",
+                "flux": "Spectral Flux",
+                "centroid": "Spectral Centroid",
+                "pitch": "F0 (Pitch)",
+                "hnr": "HNR",
+                "zcr": "ZCR"
+            }
+            
+            # 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.",
+                "centroid": "Спектральный центроид («яркость» звука). Высокие значения указывают на преобладание высоких частот (звонкие инструменты, открытые пространства).",
+                "pitch": "Основная частота звука. Высокий pitch характерен для позитивных, легких или, напротив, напряженных мелодий.",
+                "hnr": "Отношение гармоник к шуму. Высокий HNR — чистая мелодия и вокал. Низкий HNR — присутствие дисторшна, шумов или перкуссии.",
+                "zcr": "Частота пересечения нуля. Отражает шумовую составляющую сигнала. Высок в треках с выраженными ударными (hi-hats) или атмосферным шумом."
+            }
+            
+            llm_profile = data.get("llm_profile")
+            if llm_profile and isinstance(llm_profile, dict) and len(llm_profile) > 0:
+                cols_per_row = 2 if viewport == "mobile" else 3
+                llm_items = list(llm_profile.items())
+                
+                for i in range(0, len(llm_items), cols_per_row):
+                    cols = st.columns(cols_per_row)
+                    for j in range(cols_per_row):
+                        if i + j < len(llm_items):
+                            k, v = llm_items[i + j]
+                            label = feature_titles.get(k, k)
+                            tooltip = feature_helps.get(k, "")
+                            cols[j].metric(label, f"{v:.2f}", help=tooltip)
+            else:
+                st.caption("Акустический профиль недоступен. Применен fallback-алгоритм.")
+                            
+            st.markdown("---")
+            st.write("**Извлеченные теги (BLIP-2):**")
+            st.write(", ".join([str(c).capitalize() for c in data.get("semantics", [])]))
+
+        st.markdown("<br>", unsafe_allow_html=True)
+        
+        if st.button("Новый запрос", use_container_width=True):
+            st.session_state.live_state = "upload"
+            st.session_state.result_data = None
+            st.session_state.pop("uploaded_images", None)
+            st.rerun()
+
+if __name__ == "__main__":
+    main()

src/music_engine/image_processor.py

@@ -1,56 +1,66 @@
+import numpy as np
+from pathlib import Path
+from PIL import Image
+
 import torch
 import torchvision.transforms as T
-from PIL import Image
 import timm
-from pathlib import Path
-import numpy as np
-
-# ТЕПЕРЬ BLIP-2
 from transformers import Blip2Processor, Blip2ForConditionalGeneration
 
 class ImageProcessor:
-    def __init__(self, model_path: Path | str):
+    def __init__(self, weights_path: str | Path):
         self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
         
-        # --- ПОТОК 1: ТВОЙ АВТОРСКИЙ ЭМБЕДДИНГ (Core) ---
+        # Модель извлечения визуальных признаков
-        self.emo_model = timm.create_model('resnet50', pretrained=False, num_classes=8)
+        self.feature_extractor = timm.create_model('resnet50', pretrained=False, num_classes=8)
-        if Path(model_path).exists():
-            self.emo_model.load_state_dict(torch.load(model_path, map_location=self.device))
         
-        self.emo_model.fc = torch.nn.Identity()
+        if Path(weights_path).exists():
-        self.emo_model.to(self.device).eval()
+            self.feature_extractor.load_state_dict(torch.load(weights_path, map_location=self.device))
+        else:
+            print(f"Не удалось найти веса ResNet по пути: {weights_path}")
             
-        self.emo_transform = T.Compose([
+        # Удаление слоя классификации для вывода сырого вектора эмбеддингов
+        self.feature_extractor.fc = torch.nn.Identity()
+        self.feature_extractor.to(self.device).eval()
+
+        # Трансформации для предварительной обработки изображений
+        self.preprocess_image = T.Compose([
             T.Resize((224, 224)),
             T.ToTensor(),
             T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
         ])
 
-        # --- ПОТОК 2: СЕМАНТИЧЕСКИЙ ЭКСПЕРТ BLIP-2 ---
+        # Модуль семантического описания сцены
-        print("⏳ Загрузка тяжелой артиллерии: BLIP-2...")
+        print("Инициализация BLIP-2...")
-        # Используем версию opt-2.7b — она идеально сбалансирована для V100
+        # Обход бага конфигурации Hugging Face (ручная сборка процессора)
-        self.blip_processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b")
+        from transformers import BlipImageProcessor, AutoTokenizer
+        img_proc = BlipImageProcessor.from_pretrained("Salesforce/blip2-opt-2.7b")
+        tok = AutoTokenizer.from_pretrained("Salesforce/blip2-opt-2.7b", use_fast=False)
+        self.blip_processor = Blip2Processor(image_processor=img_proc, tokenizer=tok)
         self.blip_model = Blip2ForConditionalGeneration.from_pretrained(
             "Salesforce/blip2-opt-2.7b", 
-            torch_dtype=torch.float16 # Обязательно для скорости на V100
+            torch_dtype=torch.float16 
         ).to(self.device)
-        print("✅ BLIP-2 и ResNet-50 готовы.")
 
     @torch.no_grad()
     def extract_embedding(self, image: Image.Image) -> np.ndarray:
-        img_rgb = image.convert('RGB')
+        # Извлечение эмбеддингов из изображения
-        img_tensor = self.emo_transform(img_rgb).unsqueeze(0).to(self.device)
+        rgb_image = image.convert('RGB')
-        return self.emo_model(img_tensor).cpu().numpy().flatten()
+        img_tensor = self.preprocess_image(rgb_image).unsqueeze(0).to(self.device)
+        
+        features = self.feature_extractor(img_tensor)
+        features_np = features.cpu().numpy()
+        
+        return features_np.flatten()
         
     @torch.no_grad()
     def describe_scene(self, image: Image.Image) -> str:
-        """Генерирует описание через BLIP-2."""
+        # Генерация текстового описания сцены
-        img_rgb = image.convert('RGB')
+        rgb_image = image.convert('RGB')
         
-        # Инференс BLIP-2 требует float16 для V100
+        inputs = self.blip_processor(images=rgb_image, return_tensors="pt").to(self.device, torch.float16)
-        inputs = self.blip_processor(images=img_rgb, return_tensors="pt").to(self.device, torch.float16)
-        
-        # Генерируем описание
         generated_ids = self.blip_model.generate(**inputs, max_new_tokens=40)
-        caption = self.blip_processor.batch_decode(generated_ids, skip_special_tokens=True)[0].strip()
+        
-        return caption
+        scene_description = self.blip_processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+        
+        return scene_description.strip()

src/music_engine/llm_bridge.py

@@ -1,60 +1,65 @@
-import requests
+import os
 import json
 import re
+import requests
 
 class LLMAcousticBridge:
     def __init__(self, model_name="dolphin-llama3:8b"):
         self.model_name = model_name
-        self.api_url = "http://localhost:11434/api/generate"
+        base_url = os.getenv("OLLAMA_API_URL", "http://emom_ollama:11434")
+        self.api_url = f"{base_url}/api/generate"
 
-    def _clean_json(self, text):
+    def get_acoustic_profile(self, valence, arousal, semantics):
-        """Вытаскивает чистый JSON из ответа нейросети."""
+        context_str = ", ".join(semantics) if semantics else "abstract scene"
-        try:
-            match = re.search(r'\{.*\}', text, re.DOTALL)
-            if match:
-                return json.loads(match.group(0))
-            return json.loads(text)
-        except:
-            return None
         
-    def get_acoustic_profile(self, valence, arousal, scene_descriptions):
+        prompt = f"""
-        """Просит LLM сгенерировать идеальный звук под описание."""
-        # Объединяем описания, если загружено несколько фото
-        context_str = " | ".join(scene_descriptions) if scene_descriptions else "abstract scene"
-        
-        prompt = f"""You are an expert music producer and acoustic engineer. 
         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). 
         Visual Context: {context_str}. 
-
         Map this scene to exactly 6 acoustic features. Values MUST be floats between 0.0 and 1.0.
-1. "energy": (Loudness/Density. High for massive/busy scenes, Low for calm)
-2. "flux": (Rhythmic sharpness/Beat. High for action/people/cars, Low for static nature)
-3. "centroid": (Brightness: 0=Dark/Bass/Massive, 1=Bright/Treble/Light)
-4. "pitch": (Fundamental frequency: 0=Low pitch/Huge objects, 1=High pitch/Small objects)
-5. "hnr": (Harmonics-to-Noise: 0=Noisy/Distorted textures, 1=Clear/Melodic/Smooth textures)
-6. "zcr": (Percussiveness. High for detailed noise like leaves/rain, Low for solid blocks)
         
-Return ONLY a valid JSON object. Do not add any text or explanation.
+        1. "energy": (Loudness/Density)
-Example: {{"energy": 0.5, "flux": 0.2, "centroid": 0.4, "pitch": 0.3, "hnr": 0.8, "zcr": 0.1}}"""
+        2. "flux": (Rhythmic sharpness/Beat)
+        3. "centroid": (Brightness)
+        4. "pitch": (Fundamental frequency)
+        5. "hnr": (Harmonics-to-Noise)
+        6. "zcr": (Percussiveness)
+        
+        Return ONLY a valid JSON object. No explanations, no markdown blocks.
+        Example: {{"energy": 0.8, "flux": 0.5, "centroid": 0.6, "pitch": 0.4, "hnr": 0.9, "zcr": 0.3}}
+        """
         
         try:
-            response = requests.post(self.api_url, json={
+            payload = {
                 "model": self.model_name,
                 "prompt": prompt,
                 "stream": False,
-                "format": "json"
+                "format": "json"  # Принудительный JSON-режим Ollama
-            }, timeout=30)
+            }
-            response.raise_for_status()
             
-            result_text = response.json().get("response", "")
+            print(f"Запрос акустического профиля к Ollama...")
-            profile = self._clean_json(result_text)
+            response = requests.post(self.api_url, json=payload, timeout=120)
             
-            # Проверяем, что все нужные ключи есть
+            if response.status_code == 200:
-            required_keys = ['energy', 'flux', 'centroid', 'pitch', 'hnr', 'zcr']
+                data = response.json()
-            if profile and all(k in profile for k in required_keys):
+                response_text = data.get("response", "")
+                
+                try:
+                    # 1. Попытка прямой десериализации
+                    profile = json.loads(response_text)
                     return profile
-            return None
+                except json.JSONDecodeError:
+                    # 2. Аварийное извлечение JSON из текста с помощью регулярного выражения
+                    match = re.search(r'\{.*\}', response_text, re.DOTALL)
+                    if match:
+                        return json.loads(match.group(0))
+                    
+                    print(f"Ошибка парсинга LLM ответа: {response_text}")
+                    return {}
+            else:
+                print(f"Ollama вернула ошибку HTTP: {response.status_code}")
+                return {}
+                
         except Exception as e:
-            print(f"⚠️ Ошибка связи с локальной LLM: {e}")
+            print(f"Ошибка соединения с Ollama: {str(e)}")
-            return None
+            return {}

src/music_engine/matcher.py

@@ -1,67 +1,83 @@
+import joblib
 import numpy as np
 import pandas as pd
 from pathlib import Path
-import joblib
 
 class MusicMatcher:
     def __init__(self, db_path: Path | str, model_path: Path | str):
-        # Загружаем твою новую, обогащенную базу
+        # Загрузка базы данных музыкальных произведений
         self.music_db = pd.read_csv(db_path)
         self.acoustic_features = ['energy', 'flux', 'centroid', 'pitch', 'hnr', 'zcr']
         
-        # Удаляем строки, где нет акустических фич
+        # Удаление записей с пропущенными целевыми или акустическими признаками
-        self.music_db = self.music_db.dropna(subset=['valence', 'arousal'] + self.acoustic_features)
+        target_columns = ['valence', 'arousal'] + self.acoustic_features
+        self.music_db = self.music_db.dropna(subset=target_columns)
         
-        # Нормализуем акустику от 0 до 1, чтобы сравнивать с ответом LLM
+        # Масштабирование акустических параметров к диапазону [0, 1]
         self.norm_db = self.music_db.copy()
         for feat in self.acoustic_features:
-            f_min, f_max = self.norm_db[feat].min(), self.norm_db[feat].max()
+            f_min = self.norm_db[feat].min()
+            f_max = self.norm_db[feat].max()
             if f_max > f_min:
                 self.norm_db[f"norm_{feat}"] = (self.norm_db[feat] - f_min) / (f_max - f_min)
             else:
                 self.norm_db[f"norm_{feat}"] = 0.0
 
+        # Определение путей к аудиофайлам и загрузка модели регрессии
         self.audio_dir = Path(db_path).parent / "DEAM_audio" / "MEMD_audio"
-        self.regressor = joblib.load(model_path) if Path(model_path).exists() else None
         
-    def predict_va(self, embedding: np.ndarray):
+        if Path(model_path).exists():
-        if self.regressor:
+            self.regressor = joblib.load(model_path)
-            prediction = self.regressor.predict(embedding.reshape(1, -1))[0]
+        else:
-            return np.clip(prediction[0], 1.0, 9.0), np.clip(prediction[1], 1.0, 9.0)
+            self.regressor = None
+
+    def predict_va(self, embedding: np.ndarray) -> tuple[float, float]:
+        # Прогнозирование координат Valence/Arousal по визуальному эмбеддингу
+        if not self.regressor:
             return 5.0, 5.0
             
-    def get_audio_path(self, song_id):
+        raw_prediction = self.regressor.predict(embedding.reshape(1, -1))[0]
-        if not self.audio_dir.exists(): return None
+        valence_pred = np.clip(raw_prediction[0], 1.0, 9.0)
+        arousal_pred = np.clip(raw_prediction[1], 1.0, 9.0)
+        
+        return float(valence_pred), float(arousal_pred)
+
+    def get_audio_path(self, song_id: int | float | str) -> Path | None:
+        # Поиск физического пути к аудиофайлу в зависимости от расширения
+        if not self.audio_dir.exists():
+            return None
+            
         clean_id = str(int(float(song_id)))
         for ext in ['.mp3', '.wav']:
             path = self.audio_dir / f"{clean_id}{ext}"
-            if path.exists(): return path
+            if path.exists():
+                return path
         return None
 
-    def find_nearest_tracks(self, target_v: float, target_a: float, llm_profile: dict = None, top_k: int = 5):
+    def find_nearest_tracks(self, target_v: float, target_a: float, llm_profile: dict = None, top_k: int = 5) -> pd.DataFrame:
-        # 1. Эмоциональная дистанция (как и раньше)
+        # Расчет евклидова расстояния в эмоциональном пространстве Рассела
-        emo_dist = np.sqrt(
+        v_dist = (self.norm_db['valence'] - target_v) ** 2
-            1.0 * (self.norm_db['valence'] - target_v)**2 + 
+        a_dist = (self.norm_db['arousal'] - target_a) ** 2
-            2.5 * (self.norm_db['arousal'] - target_a)**2
-        )
-        self.norm_db['emo_distance'] = emo_dist
         
-        # Если LLM не дала ответ, сортируем только по эмоциям
+        # Взвешенное расстояние с приоритетом оси активации (Arousal)
+        self.norm_db['emo_distance'] = np.sqrt(1.0 * v_dist + 2.5 * a_dist)
+        
+        # Ранжирование только по эмоциональному критерию при отсутствии профиля LLM
         if not llm_profile:
             self.norm_db['final_score'] = self.norm_db['emo_distance']
             return self.norm_db.sort_values(by='final_score').head(top_k)
 
-        # 2. Акустическая дистанция (сравниваем треки с запросом LLM)
+        # Расчет отклонений по вектору акустических параметров LLM
         acoustic_penalty = np.zeros(len(self.norm_db))
         for feat in self.acoustic_features:
             if feat in llm_profile:
                 target_val = llm_profile[feat]
                 acoustic_penalty += np.abs(self.norm_db[f"norm_{feat}"] - target_val)
 
-        # Усредняем штраф
+        # Нормирование акустической дистанции
         self.norm_db['acoustic_distance'] = acoustic_penalty / len(self.acoustic_features)
 
-        # 3. Финальный Score (Смесь Эмоций и Акустики). Коэф 4.0 делает акустику важной!
+        # Вычисление интегральной метрики соответствия (мультимодальный скоринг)
         self.norm_db['final_score'] = self.norm_db['emo_distance'] + (self.norm_db['acoustic_distance'] * 4.0)
 
         return self.norm_db.sort_values(by='final_score').head(top_k)

src/scripts/00_setup_env.sh

@@ -0,0 +1,73 @@
+#!/bin/bash
+
+# Данный скрипт написан ИИ для быстрой подготовки окружения, установка драйверов и докера
+# Остановка скрипта при возникновении любой ошибки
+set -e
+
+# Цвета для красивого вывода в консоль
+GREEN='\033[0;32m'
+YELLOW='\033[1;33m'
+RED='\033[0;31m'
+BLUE='\033[0;34m'
+NC='\033[0m'
+
+echo -e "${BLUE}[INFO]${NC} Инициализация проверки окружения для проекта EmoM..."
+
+# 1. ПРОВЕРКА DOCKER
+if ! command -v docker &> /dev/null; then
+    echo -e "${YELLOW}[SETUP]${NC} Docker не найден. Начинаем установку..."
+    # Использование официального скрипта установки Docker
+    curl -fsSL https://get.docker.com -o get-docker.sh
+    sudo sh get-docker.sh
+    rm get-docker.sh
+    
+    # Добавляем текущего пользователя в группу docker, чтобы не писать sudo docker
+    sudo usermod -aG docker $USER
+    echo -e "${GREEN}[OK]${NC} Docker успешно установлен."
+    echo -e "${YELLOW}[ВНИМАНИЕ]${NC} Для применения прав группы docker потребуется перезайти в сессию SSH после завершения скрипта."
+else
+    echo -e "${GREEN}[OK]${NC} Docker установлен ($(docker --version))."
+fi
+
+# 2. ПРОВЕРКА DOCKER COMPOSE
+if ! docker compose version &> /dev/null; then
+    echo -e "${YELLOW}[SETUP]${NC} Плагин Docker Compose не найден. Устанавливаем..."
+    sudo apt-get update && sudo apt-get install -y docker-compose-plugin
+    echo -e "${GREEN}[OK]${NC} Docker Compose успешно установлен."
+else
+    echo -e "${GREEN}[OK]${NC} Плагин Docker Compose доступен."
+fi
+
+# 3. ПРОВЕРКА NVIDIA И ПРОБРОСА GPU В DOCKER
+if command -v nvidia-smi &> /dev/null; then
+    echo -e "${GREEN}[OK]${NC} Драйверы NVIDIA обнаружены."
+    
+    # Проверяем наличие NVIDIA Container Toolkit
+    if ! dpkg -l | grep -q nvidia-container-toolkit; then
+        echo -e "${YELLOW}[SETUP]${NC} NVIDIA Container Toolkit не найден. Выполняется установка..."
+        
+        # Настройка репозиториев NVIDIA
+        curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg
+        curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \
+            sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
+            sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
+            
+        sudo apt-get update
+        sudo apt-get install -y nvidia-container-toolkit
+        
+        # Конфигурация Docker для работы с NVIDIA
+        echo -e "${YELLOW}[SETUP]${NC} Конфигурация runtime NVIDIA для Docker..."
+        sudo nvidia-ctk runtime configure --runtime=docker
+        sudo systemctl restart docker
+        
+        echo -e "${GREEN}[OK]${NC} NVIDIA Container Toolkit установлен и настроен."
+    else
+        echo -e "${GREEN}[OK]${NC} NVIDIA Container Toolkit уже установлен."
+    fi
+else
+    echo -e "${RED}[WARN]${NC} Утилита nvidia-smi не найдена! Убедитесь, что драйверы видеокарты установлены, иначе Docker будет использовать только CPU."
+fi
+
+echo -e "\n${BLUE}[INFO]${NC} ========================================="
+echo -e "${GREEN}[SUCCESS]${NC} Окружение готово к работе!"
+echo -e "Теперь вы можете запустить проект командой: ${YELLOW}make up${NC}"

src/scripts/01_download_DEAM.py

@@ -0,0 +1,20 @@
+import shutil
+from pathlib import Path
+import kagglehub
+
+dataset_dir = Path("../dataset/DEAM")
+dataset_dir.mkdir(parents=True, exist_ok=True)
+
+print("Скачивание датасета DEAM...")
+
+# kagglehub по умолчанию тянет данные в системный кэш (~/.cache)
+cache_path = kagglehub.dataset_download("imsparsh/deam-mediaeval-dataset-emotional-analysis-in-music")
+
+print(f"Загружено в кэш: {cache_path}")
+print(f"Перенос файлов в {dataset_dir} и очистка временной директории...")
+
+# Перемещаем данные
+shutil.copytree(cache_path, dataset_dir, dirs_exist_ok=True)
+shutil.rmtree(cache_path)
+
+print("Готово. Датасет DEAM загружен, кэш очищен.")

src/scripts/02_download_EmoSet.py

@@ -0,0 +1,56 @@
+import csv
+from pathlib import Path
+
+from datasets import load_dataset
+from tqdm import tqdm
+
+# Конфигурация корневой директории локального датасета
+DATASET_DIR = Path("../dataset/EmoSet-118K")
+
+def process_and_save_split(dataset_split, split_name: str, output_dir: Path):
+    # Подготовка структуры директорий для текущей выборки
+    split_dir = output_dir / split_name
+    img_dir = split_dir / "images"
+    img_dir.mkdir(parents=True, exist_ok=True)
+
+    labels_path = split_dir / "labels.csv"
+
+    print(f"Обработка выборки: {split_name}...")
+
+    # Открытие файла разметки перед циклом для минимизации I/O операций диска
+    with open(labels_path, mode="w", newline="", encoding="utf-8") as csv_file:
+        writer = csv.writer(csv_file)
+        writer.writerow(["filename", "label"])
+
+        for example in tqdm(dataset_split, desc=split_name):
+            img = example["image"]
+            emotion_label = example["emotion"]
+            img_id = example["image_id"]
+
+            file_name = f"{img_id}.jpg"
+            
+            # Принудительная конвертация в RGB для безопасного сохранения в JPEG-формате
+            if img.mode != "RGB":
+                img = img.convert("RGB")
+                
+            img.save(img_dir / file_name, format="JPEG")
+            
+            writer.writerow([file_name, emotion_label])
+
+if __name__ == "__main__":
+    DATASET_DIR.mkdir(exist_ok=True, parents=True)
+
+    # Инициализация подключения к Hugging Face Hub
+    print("Загрузка метаданных EmoSet-118K...")
+    raw_dataset = load_dataset("Woleek/EmoSet-118K")
+
+    # Итеративная выгрузка размеченных данных
+    for split_key in ["train", "val", "test"]:
+        if split_key in raw_dataset:
+            process_and_save_split(
+                dataset_split=raw_dataset[split_key],
+                split_name=split_key,
+                output_dir=DATASET_DIR
+            )
+
+    print("Экспорт датасета завершен.")

src/scripts/11_prerp_DEAM.py

@@ -0,0 +1,30 @@
+import pandas as pd
+from pathlib import Path
+
+# Конфигурация локальных путей
+SOURCE_CSV = Path("../../dataset/DEAM/DEAM_Annotations/annotations/annotations averaged per song/song_level/static_annotations_averaged_songs_1_2000.csv")
+OUTPUT_CSV = Path("../../dataset/DEAM/music_db.csv")
+
+def prepare_deam_database():
+    if not SOURCE_CSV.exists():
+        print(f"Исходный файл аннотаций не найден: {SOURCE_CSV}")
+        return
+
+    print("Обработка разметки датасета DEAM...")
+    
+    # Загрузка сырых данных с очисткой артефактов форматирования
+    raw_df = pd.read_csv(SOURCE_CSV, skipinitialspace=True)
+    
+    # Экстракция координат пространства Рассела (Valence/Arousal)
+    processed_df = raw_df[['song_id', 'valence_mean', 'arousal_mean']].copy()
+    processed_df.columns = ['song_id', 'valence', 'arousal']
+    
+    # Приведение идентификаторов к формату файловой системы (int)
+    processed_df['song_id'] = processed_df['song_id'].astype(int)
+    
+    processed_df.to_csv(OUTPUT_CSV, index=False)
+    
+    print(f"База успешно сформирована. Всего записей: {len(processed_df)}")
+
+if __name__ == "__main__":
+    prepare_deam_database()

src/scripts/20_bench_GPU.py

@@ -0,0 +1,60 @@
+import time
+import torch
+import torch.nn as nn
+import torch.optim as optim
+
+# Конфигурация параметров нагрузочного тестирования
+NUM_SAMPLES = 300_000
+DIM_IN = 4096
+DIM_OUT = 10
+BATCH_SIZE = 16_384
+NUM_STEPS = 1000
+
+def run_gpu_benchmark():
+    # Проверка доступности аппаратного ускорения
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    print(f"Инициализация стресс-теста на устройстве: {device}")
+
+    # Генерация синтетического датасета для аллокации VRAM
+    x_data = torch.randn(NUM_SAMPLES, DIM_IN, device=device, dtype=torch.float32)
+    y_data = torch.randn(NUM_SAMPLES, DIM_OUT, device=device, dtype=torch.float32)
+
+    # Архитектура тестовой полносвязной сети
+    model = nn.Sequential(
+        nn.Linear(DIM_IN, 2048),
+        nn.ReLU(),
+        nn.Linear(2048, 1024),
+        nn.ReLU(),
+        nn.Linear(1024, 512),
+        nn.ReLU(),
+        nn.Linear(512, DIM_OUT)
+    ).to(device)
+
+    loss_fn = nn.MSELoss()
+    optimizer = optim.Adam(model.parameters(), lr=1e-3)
+
+    print("Начало прогрева GPU и симуляции цикла обучения...")
+    start_time = time.time()
+
+    for step in range(NUM_STEPS):
+        # Сэмплирование случайного батча
+        idx = torch.randint(0, NUM_SAMPLES, (BATCH_SIZE,), device=device)
+        x_batch = x_data[idx]
+        y_batch = y_data[idx]
+
+        optimizer.zero_grad()
+        predictions = model(x_batch)
+        loss = loss_fn(predictions, y_batch)
+        
+        loss.backward()
+        optimizer.step()
+
+        # Логирование статуса (каждые 100 итераций для снижения I/O overhead)
+        if step % 100 == 0:
+            print(f"Итерация {step}/{NUM_STEPS} | Текущий loss: {loss.item():.4f}")
+
+    end_time = time.time()
+    print(f"Стресс-тест завершен. Общее время: {end_time - start_time:.2f} сек.")
+
+if __name__ == "__main__":
+    run_gpu_benchmark()

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/23_aggregate_DEAM_timeline.py

@@ -0,0 +1,69 @@
+import pandas as pd
+from pathlib import Path
+from tqdm import tqdm
+
+# Конфигурация путей и целевых признаков
+BASE_DIR = Path("../../dataset/DEAM")
+MUSIC_DB_PATH = BASE_DIR / "music_db.csv"
+FEATURES_DIR = BASE_DIR / "features" / "features"
+OUTPUT_PATH = BASE_DIR / "music_db_enriched.csv"
+
+# Маппинг низкоуровневых признаков экстрактора (openSMILE/GeMAPS) в дескрипторы системы
+TARGET_FEATURES = {
+    'pcm_RMSenergy_sma_amean': 'energy',
+    'pcm_fftMag_spectralFlux_sma_amean': 'flux',
+    'pcm_fftMag_spectralCentroid_sma_amean': 'centroid',
+    'F0final_sma_amean': 'pitch',
+    'logHNR_sma_amean': 'hnr',
+    'pcm_zcr_sma_amean': 'zcr',
+    'pcm_fftMag_spectralEntropy_sma_amean': 'entropy',
+    'pcm_fftMag_psySharpness_sma_amean': 'sharpness'
+}
+
+def aggregate_acoustic_features():
+    if not MUSIC_DB_PATH.exists():
+        print(f"Базовый файл аннотаций не найден: {MUSIC_DB_PATH}")
+        return
+
+    print("Загрузка эмоциональной разметки DEAM...")
+    df_main = pd.read_csv(MUSIC_DB_PATH)
+    
+    print("Агрегация фреймовых акустических признаков...")
+    aggregated_data = []
+
+    # Итерация по трекам для сбора покадровых характеристик
+    for _, row in tqdm(df_main.iterrows(), total=len(df_main), desc="Обработка аудио-векторов"):
+        song_id = int(row['song_id'])
+        feature_file = FEATURES_DIR / f"{song_id}.csv"
+        
+        if feature_file.exists():
+            try:
+                # Чтение сырых векторов (формат csv с разделителем ';')
+                df_feat = pd.read_csv(feature_file, sep=';')
+                
+                # Усреднение характеристик по временной оси (time frames)
+                mean_features = df_feat[list(TARGET_FEATURES.keys())].mean()
+                
+                # Формирование агрегированной записи
+                track_data = {'song_id': song_id}
+                for orig_col, new_col in TARGET_FEATURES.items():
+                    track_data[new_col] = mean_features[orig_col]
+                    
+                aggregated_data.append(track_data)
+                
+            except Exception as e:
+                print(f"Ошибка парсинга файла {feature_file.name}: {e}")
+
+    # Слияние акустических дескрипторов с эмоциональными координатами (Inner Join)
+    df_features = pd.DataFrame(aggregated_data)
+    df_enriched = pd.merge(df_main, df_features, on='song_id', how='inner')
+    
+    # Очистка возможных артефактов NaN после агрегации
+    df_enriched = df_enriched.dropna(subset=list(TARGET_FEATURES.values()))
+    
+    df_enriched.to_csv(OUTPUT_PATH, index=False)
+    print(f"Экспорт завершен. Сформирована обогащенная база: {OUTPUT_PATH.name}")
+    print(f"Итоговый размер выборки: {len(df_enriched)} треков.")
+
+if __name__ == "__main__":
+    aggregate_acoustic_features()

src/scripts/24_train_regressor.py

@@ -0,0 +1,80 @@
+import joblib
+import numpy as np
+import pandas as pd
+from pathlib import Path
+
+from sklearn.linear_model import RidgeCV
+from sklearn.multioutput import MultiOutputRegressor
+from sklearn.preprocessing import StandardScaler
+from sklearn.pipeline import Pipeline
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import mean_squared_error, r2_score
+
+# Проекция дискретных классов эмоций на непрерывное пространство Рассела (Valence, Arousal)
+# Значения откалиброваны в диапазоне [1.0, 9.0]
+EMOTION_TO_VA_COORDS = {
+    0: (7.5, 6.5),  # amusement 
+    1: (2.0, 8.0),  # anger 
+    2: (6.5, 5.0),  # awe 
+    3: (7.0, 3.0),  # contentment 
+    4: (3.0, 6.0),  # disgust 
+    5: (8.0, 8.0),  # excitement 
+    6: (2.5, 7.5),  # fear 
+    7: (2.0, 2.0),  # sadness 
+}
+
+def train_va_regressor():
+    # Настройка путей
+    base_dir = Path(__file__).resolve().parent.parent
+    embeddings_path = base_dir / "emoset_test_embeddings.npy"
+    labels_path = base_dir / "emoset_test_labels.npy"
+    model_output_path = base_dir / "music_engine" / "va_regressor.pkl"
+
+    if not embeddings_path.exists() or not labels_path.exists():
+        print(f"Артефакты признаков не найдены в директории: {base_dir}")
+        return
+
+    print("Загрузка вектора признаков и меток классов...")
+    x_features = np.load(embeddings_path)
+    y_discrete = np.load(labels_path)
+
+    # Трансформация целевой переменной: классы -> непрерывные координаты V/A
+    y_continuous = np.array([EMOTION_TO_VA_COORDS[label] for label in y_discrete])
+    
+    x_train, x_test, y_train, y_test = train_test_split(
+        x_features, y_continuous, test_size=0.2, random_state=42
+    )
+
+    # Построение пайплайна: Z-масштабирование и L2-регуляризованная регрессия
+    # RidgeCV автоматически подбирает оптимальный гиперпараметр alpha (силу регуляризации)
+    print("Инициализация и обучение пайплайна RidgeCV...")
+    regression_pipeline = Pipeline([
+        ('scaler', StandardScaler()),
+        ('regressor', MultiOutputRegressor(RidgeCV(alphas=[0.1, 1.0, 10.0, 100.0, 1000.0])))
+    ])
+
+    regression_pipeline.fit(x_train, y_train)
+
+    # Оценка обобщающей способности модели
+    y_pred = regression_pipeline.predict(x_test)
+    
+    mse_score = mean_squared_error(y_test, y_pred)
+    r2 = r2_score(y_test, y_pred)
+
+    print("Обучение завершено. Метрики качества на тестовой выборке:")
+    print(f" - MSE: {mse_score:.4f}")
+    print(f" - R^2: {r2:.4f}")
+
+    # Диагностика дисперсии предсказаний
+    v_min, v_max = y_pred[:, 0].min(), y_pred[:, 0].max()
+    a_min, a_max = y_pred[:, 1].min(), y_pred[:, 1].max()
+    print(f"Распределение Valence (прогноз): [{v_min:.2f}, {v_max:.2f}] (Эталон: 1.0 - 9.0)")
+    print(f"Распределение Arousal (прогноз): [{a_min:.2f}, {a_max:.2f}] (Эталон: 1.0 - 9.0)")
+
+    # Экспорт обученного пайплайна
+    model_output_path.parent.mkdir(parents=True, exist_ok=True)
+    joblib.dump(regression_pipeline, model_output_path)
+    print(f"Пайплайн сохранен: {model_output_path.name}")
+
+if __name__ == "__main__":
+    train_va_regressor()

src/scripts/90_acc_images_model.ipynb

@@ -2,30 +2,30 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "8523d028",
    "metadata": {},
    "outputs": [],
    "source": [
-    "import torch\n",
-    "import torch.nn as nn\n",
-    "from torch.utils.data import Dataset, DataLoader\n",
-    "\n",
     "import pandas as pd\n",
+    "import numpy as np\n",
     "from pathlib import Path\n",
     "from PIL import Image\n",
     "from tqdm import tqdm\n",
     "\n",
+    "import torch\n",
+    "from torch.utils.data import Dataset, DataLoader\n",
     "import torchvision.transforms as T\n",
     "import timm\n",
-    "import numpy as np\n",
     "\n",
-    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n"
+    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "e0781b02",
    "metadata": {},
    "outputs": [
@@ -41,25 +41,26 @@
     }
    ],
    "source": [
+    "# Конфигурация путей и параметров инференса\n",
     "DATA_ROOT = Path(\"../dataset/EmoSet-118K\")\n",
     "MODEL_PATH = Path(\"./emoset_resnet50_best.pth\")\n",
     "\n",
     "BATCH_SIZE = 64\n",
     "NUM_WORKERS = 4\n",
-    "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+    "DEVICE = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
-    "\n",
+    "print(f\"Аппаратное ускорение: {DEVICE}\")"
-    "DEVICE\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "79da9640",
    "metadata": {},
    "outputs": [],
    "source": [
-    "class EmoSetDataset(Dataset):\n",
+    "class EmoSetEvaluationDataset(Dataset):\n",
-    "    def __init__(self, root, split):\n",
+    "    # Датасет для строгой валидации с центрированным кропом\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",
@@ -67,13 +68,12 @@
     "        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",
+    "        # Стандартный пайплайн трансформаций для инференса ResNet\n",
     "        self.transform = T.Compose([\n",
-    "            T.Resize((224, 224)),\n",
+    "            T.Resize(256),\n",
+    "            T.CenterCrop(224),\n",
     "            T.ToTensor(),\n",
-    "            T.Normalize(\n",
+    "            T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
-    "                mean=[0.485, 0.456, 0.406],\n",
-    "                std=[0.229, 0.224, 0.225]\n",
-    "            )\n",
     "        ])\n",
     "\n",
     "    def __len__(self):\n",
@@ -81,15 +81,23 @@
     "\n",
     "    def __getitem__(self, idx):\n",
     "        row = self.df.iloc[idx]\n",
-    "        img = Image.open(self.root / \"images\" / row[\"filename\"]).convert(\"RGB\")\n",
+    "        img_path = self.root / \"images\" / row[\"filename\"]\n",
-    "        img = self.transform(img)\n",
+    "        \n",
-    "        label = self.label2idx[row[\"label\"]]\n",
+    "        # Перехват битых файлов для непрерывности оценки\n",
-    "        return img, label\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": 7,
+   "execution_count": null,
    "id": "12201756",
    "metadata": {},
    "outputs": [
@@ -103,8 +111,8 @@
     }
    ],
    "source": [
-    "test_ds = EmoSetDataset(DATA_ROOT, \"test\")\n",
+    "# Инициализация тестовой выборки\n",
-    "\n",
+    "test_ds = EmoSetEvaluationDataset(DATA_ROOT, \"test\")\n",
     "test_loader = DataLoader(\n",
     "    test_ds,\n",
     "    batch_size=BATCH_SIZE,\n",
@@ -113,13 +121,13 @@
     "    pin_memory=True\n",
     ")\n",
     "\n",
-    "print(\"Classes:\", test_ds.labels)\n",
+    "print(f\"Индексированные классы: {test_ds.labels}\")\n",
-    "print(\"Test samples:\", len(test_ds))\n"
+    "print(f\"Размер тестовой выборки: {len(test_ds)}\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "7e3dc1d5",
    "metadata": {},
    "outputs": [
@@ -374,22 +382,17 @@
     }
    ],
    "source": [
+    "# Инициализация модели в режиме классификации\n",
     "model = timm.create_model(\n",
     "    \"resnet50\",\n",
     "    pretrained=False,\n",
     "    num_classes=len(test_ds.labels)\n",
-    ")\n",
+    ")"
-    "\n",
-    "state = torch.load(MODEL_PATH, map_location=DEVICE)\n",
-    "model.load_state_dict(state)\n",
-    "\n",
-    "model.to(DEVICE)\n",
-    "model.eval()\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "b42a84f1",
    "metadata": {},
    "outputs": [
@@ -402,27 +405,16 @@
     }
    ],
    "source": [
-    "all_preds = []\n",
+    "# Загрузка весов и перевод в режим инференса\n",
-    "all_targets = []\n",
+    "checkpoint = torch.load(MODEL_PATH, map_location=DEVICE)\n",
-    "\n",
+    "model.load_state_dict(checkpoint)\n",
-    "with torch.no_grad():\n",
+    "model.to(DEVICE)\n",
-    "    for imgs, labels in tqdm(test_loader):\n",
+    "model.eval()"
-    "        imgs = imgs.to(DEVICE)\n",
-    "        labels = labels.to(DEVICE)\n",
-    "\n",
-    "        logits = model(imgs)\n",
-    "        preds = logits.argmax(dim=1)\n",
-    "\n",
-    "        all_preds.append(preds.cpu().numpy())\n",
-    "        all_targets.append(labels.cpu().numpy())\n",
-    "\n",
-    "all_preds = np.concatenate(all_preds)\n",
-    "all_targets = np.concatenate(all_targets)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "4c1f1377",
    "metadata": {},
    "outputs": [
@@ -435,13 +427,25 @@
     }
    ],
    "source": [
-    "acc = accuracy_score(all_targets, all_preds)\n",
+    "# Сбор предсказаний на тестовой выборке\n",
-    "print(f\"Test accuracy: {acc:.4f}\")\n"
+    "all_preds = []\n",
+    "all_targets = []\n",
+    "\n",
+    "print(\"Запуск инференса на тестовой выборке...\")\n",
+    "with torch.no_grad():\n",
+    "    for imgs, labels in tqdm(test_loader, desc=\"Оценка метрик\"):\n",
+    "        imgs = imgs.to(DEVICE)\n",
+    "        \n",
+    "        logits = model(imgs)\n",
+    "        preds = logits.argmax(dim=1)\n",
+    "\n",
+    "        all_preds.append(preds.cpu().numpy())\n",
+    "        all_targets.append(labels.numpy())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "6b022825",
    "metadata": {},
    "outputs": [
@@ -468,19 +472,14 @@
     }
    ],
    "source": [
-    "print(\n",
+    "# Агрегация результатов\n",
-    "    classification_report(\n",
+    "all_preds = np.concatenate(all_preds, axis=0)\n",
-    "        all_targets,\n",
+    "all_targets = np.concatenate(all_targets, axis=0)"
-    "        all_preds,\n",
-    "        target_names=test_ds.labels,\n",
-    "        digits=4\n",
-    "    )\n",
-    ")\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "2fcb69ac",
    "metadata": {},
    "outputs": [
@@ -496,20 +495,70 @@
     }
    ],
    "source": [
-    "import matplotlib.pyplot as plt\n",
+    "# Расчет интегральных метрик классификации\n",
+    "acc = accuracy_score(all_targets, all_preds)\n",
+    "print(f\"\\nОбщая точность (Accuracy): {acc:.4f}\\n\")\n",
     "\n",
+    "print(\"Детализированный отчет (Classification Report):\")\n",
+    "print(\n",
+    "    classification_report(\n",
+    "        all_targets,\n",
+    "        all_preds,\n",
+    "        target_names=test_ds.labels,\n",
+    "        digits=4\n",
+    "    )\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2084ab91",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Построение матрицы ошибок (Confusion Matrix)\n",
     "cm = confusion_matrix(all_targets, all_preds)\n",
     "\n",
-    "plt.figure(figsize=(8, 8))\n",
+    "plt.figure(figsize=(10, 8))"
-    "plt.imshow(cm)\n",
+   ]
-    "plt.colorbar()\n",
+  },
-    "plt.xticks(range(len(test_ds.labels)), test_ds.labels, rotation=45)\n",
+  {
-    "plt.yticks(range(len(test_ds.labels)), test_ds.labels)\n",
+   "cell_type": "code",
-    "plt.xlabel(\"Predicted\")\n",
+   "execution_count": null,
-    "plt.ylabel(\"True\")\n",
+   "id": "83a84e14",
-    "plt.title(\"Confusion Matrix (Test)\")\n",
+   "metadata": {},
-    "plt.tight_layout()\n",
+   "outputs": [],
-    "plt.show()\n"
+   "source": [
+    "# Использование seaborn для академичной визуализации с числами\n",
+    "sns.heatmap(\n",
+    "    cm, \n",
+    "    annot=True, \n",
+    "    fmt=\"d\", \n",
+    "    cmap=\"Blues\", \n",
+    "    xticklabels=test_ds.labels, \n",
+    "    yticklabels=test_ds.labels,\n",
+    "    cbar=False\n",
+    ")\n",
+    "\n",
+    "plt.title(\"Матрица ошибок классификации EmoSet (ResNet-50)\", pad=20)\n",
+    "plt.xlabel(\"Предсказанный класс\", labelpad=15)\n",
+    "plt.ylabel(\"Истинный класс\", labelpad=15)\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.yticks(rotation=0)\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "280d5637",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Экспорт графика\n",
+    "plt.savefig(\"../confusion_matrix_resnet50.png\", dpi=300, bbox_inches='tight')\n",
+    "plt.show()"
    ]
   }
  ],

src/scripts/91_generate_metrics.py

@@ -0,0 +1,97 @@
+import numpy as np
+import pandas as pd
+import joblib
+from pathlib import Path
+from sklearn.metrics import mean_squared_error, r2_score
+
+# 1. Настройка путей
+embeddings_path = Path("./src/emoset_test_embeddings.npy")
+csv_path = Path("./NFS/Thesis/Emoset/EmoSet-118K/test/labels.csv")
+model_path = Path("./src/music_engine/va_regressor.pkl")
+
+output_dir = Path("./src/scripts/media")
+output_file = output_dir / "metrics_output.txt"
+
+# 2. Корректный маппинг 8 классов EmoSet в шкалу DEAM [1.0, 9.0]
+# 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 generate_slide_metrics():
+    print("[INFO] Загрузка тестовых артефактов...")
+    
+    if not all(p.exists() for p in [embeddings_path, csv_path, model_path]):
+        print("[ERROR] Проверьте наличие файлов данных или модели регрессора.")
+        return
+
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    # 3. Загрузка эмбеддингов и меток
+    X_test = np.load(embeddings_path)
+    df = pd.read_csv(csv_path)
+    
+    if len(X_test) != len(df):
+        print(f"[WARN] Корректировка размеров выборки: Эмбеддинги ({len(X_test)}) != Метки ({len(df)})")
+        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__":
+    generate_slide_metrics()

src/scripts/aggregate_features.ipynb

@@ -1,125 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "0336fd0c",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "✅ База загружена. Треков: 1744\n",
-      "🔍 Собираем акустические признаки...\n",
-      "\n",
-      "🚀 ГОТОВО! Обогащенная база сохранена: ../../dataset/DEAM/music_db_enriched.csv\n",
-      "Собрано фичей для 1744 из 1744 треков.\n",
-      "   song_id  valence  arousal    energy      flux    centroid       pitch  \\\n",
-      "0        2      3.1      3.0  0.097268  0.846947  483.421751   93.884056   \n",
-      "1        3      3.5      3.3  0.126809  0.959460  173.219616   62.682589   \n",
-      "2        4      5.7      5.5  0.156699  1.333944  466.434797   92.850316   \n",
-      "3        5      4.4      5.3  0.126455  1.009927  546.152506  158.673853   \n",
-      "4        7      5.8      6.4  0.268180  1.589191  175.369162   83.823484   \n",
-      "\n",
-      "         hnr       zcr   entropy  sharpness  \n",
-      "0   3.615380  0.034270  3.299075   0.426490  \n",
-      "1  -2.600122  0.017893  2.294971   0.165583  \n",
-      "2  -0.579130  0.042936  3.258138   0.395410  \n",
-      "3   1.751148  0.043781  3.514585   0.494367  \n",
-      "4  12.006770  0.014783  2.177862   0.170058  \n"
-     ]
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "from pathlib import Path\n",
-    "from tqdm import tqdm # для красивого прогресс-бара, если не установлен - убери\n",
-    "\n",
-    "# 1. Пути к файлам\n",
-    "base_dir = Path(\"../../dataset/DEAM\") # Поправь, если запускаешь из другого места\n",
-    "music_db_path = base_dir / \"music_db.csv\"\n",
-    "features_dir = base_dir / \"features\" / \"features\"\n",
-    "output_path = base_dir / \"music_db_enriched.csv\"\n",
-    "\n",
-    "# 2. Наш \"Золотой список\" (8 признаков)\n",
-    "target_columns = {\n",
-    "    'pcm_RMSenergy_sma_amean': 'energy',\n",
-    "    'pcm_fftMag_spectralFlux_sma_amean': 'flux',\n",
-    "    'pcm_fftMag_spectralCentroid_sma_amean': 'centroid',\n",
-    "    'F0final_sma_amean': 'pitch',\n",
-    "    'logHNR_sma_amean': 'hnr',\n",
-    "    'pcm_zcr_sma_amean': 'zcr',\n",
-    "    'pcm_fftMag_spectralEntropy_sma_amean': 'entropy',\n",
-    "    'pcm_fftMag_psySharpness_sma_amean': 'sharpness'\n",
-    "}\n",
-    "\n",
-    "# 3. Загружаем текущую базу с V/A\n",
-    "if not music_db_path.exists():\n",
-    "    print(f\"❌ ОШИБКА: Не найден файл {music_db_path}\")\n",
-    "else:\n",
-    "    df_main = pd.read_csv(music_db_path)\n",
-    "    print(f\"✅ База загружена. Треков: {len(df_main)}\")\n",
-    "\n",
-    "    # Подготавливаем новые колонки\n",
-    "    for col_name in target_columns.values():\n",
-    "        df_main[col_name] = np.nan\n",
-    "\n",
-    "    # 4. Проходимся по всем трекам и ищем их акустические CSV\n",
-    "    print(\"🔍 Собираем акустические признаки...\")\n",
-    "    found_count = 0\n",
-    "    \n",
-    "    for index, row in df_main.iterrows():\n",
-    "        song_id = int(row['song_id'])\n",
-    "        feature_file = features_dir / f\"{song_id}.csv\"\n",
-    "        \n",
-    "        if feature_file.exists():\n",
-    "            try:\n",
-    "                # Читаем CSV с признаками (разделитель там обычно точка с запятой)\n",
-    "                df_feat = pd.read_csv(feature_file, sep=';')\n",
-    "                \n",
-    "                # Усредняем значения по всем фреймам (одна песня разбита на сотни строк-фреймов)\n",
-    "                mean_features = df_feat[list(target_columns.keys())].mean()\n",
-    "                \n",
-    "                # Записываем в главную базу\n",
-    "                for orig_col, new_col in target_columns.items():\n",
-    "                    df_main.at[index, new_col] = mean_features[orig_col]\n",
-    "                    \n",
-    "                found_count += 1\n",
-    "            except Exception as e:\n",
-    "                print(f\"Ошибка чтения {feature_file}: {e}\")\n",
-    "                \n",
-    "    # 5. Сохраняем результат\n",
-    "    # Удаляем треки, для которых не нашлось фичей (если такие есть)\n",
-    "    df_main = df_main.dropna(subset=list(target_columns.values()))\n",
-    "    \n",
-    "    df_main.to_csv(output_path, index=False)\n",
-    "    print(f\"\\n🚀 ГОТОВО! Обогащенная база сохранена: {output_path}\")\n",
-    "    print(f\"Собрано фичей для {found_count} из {len(df_main)} треков.\")\n",
-    "    print(df_main.head())"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python (thesis)",
-   "language": "python",
-   "name": "thesis"
-  },
-  "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/download emo_dataset.ipynb

@@ -1,614 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "09f9237a",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Requirement already satisfied: datasets in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (4.4.2)\n",
-      "Requirement already satisfied: tqdm in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (4.67.1)\n",
-      "Requirement already satisfied: pillow in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (12.1.0)\n",
-      "Requirement already satisfied: requests in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (2.32.5)\n",
-      "Requirement already satisfied: filelock in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from datasets) (3.20.3)\n",
-      "Requirement already satisfied: numpy>=1.17 in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from datasets) (2.4.1)\n",
-      "Requirement already satisfied: pyarrow>=21.0.0 in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from datasets) (22.0.0)\n",
-      "Requirement already satisfied: dill<0.4.1,>=0.3.0 in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from datasets) (0.4.0)\n",
-      "Requirement already satisfied: pandas in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from datasets) (2.3.3)\n",
-      "Requirement already satisfied: httpx<1.0.0 in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from datasets) (0.28.1)\n",
-      "Requirement already satisfied: xxhash in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from datasets) (3.6.0)\n",
-      "Requirement already satisfied: multiprocess<0.70.19 in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from datasets) (0.70.18)\n",
-      "Requirement already satisfied: fsspec<=2025.10.0,>=2023.1.0 in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from fsspec[http]<=2025.10.0,>=2023.1.0->datasets) (2025.10.0)\n",
-      "Requirement already satisfied: huggingface-hub<2.0,>=0.25.0 in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from datasets) (1.3.1)\n",
-      "Requirement already satisfied: packaging in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from datasets) (25.0)\n",
-      "Requirement already satisfied: pyyaml>=5.1 in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from datasets) (6.0.3)\n",
-      "Requirement already satisfied: aiohttp!=4.0.0a0,!=4.0.0a1 in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from fsspec[http]<=2025.10.0,>=2023.1.0->datasets) (3.13.3)\n",
-      "Requirement already satisfied: anyio in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from httpx<1.0.0->datasets) (4.12.1)\n",
-      "Requirement already satisfied: certifi in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from httpx<1.0.0->datasets) (2026.1.4)\n",
-      "Requirement already satisfied: httpcore==1.* in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from httpx<1.0.0->datasets) (1.0.9)\n",
-      "Requirement already satisfied: idna in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from httpx<1.0.0->datasets) (3.11)\n",
-      "Requirement already satisfied: h11>=0.16 in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from httpcore==1.*->httpx<1.0.0->datasets) (0.16.0)\n",
-      "Requirement already satisfied: hf-xet<2.0.0,>=1.2.0 in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from huggingface-hub<2.0,>=0.25.0->datasets) (1.2.0)\n",
-      "Requirement already satisfied: shellingham in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from huggingface-hub<2.0,>=0.25.0->datasets) (1.5.4)\n",
-      "Requirement already satisfied: typer-slim in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from huggingface-hub<2.0,>=0.25.0->datasets) (0.21.1)\n",
-      "Requirement already satisfied: typing-extensions>=4.1.0 in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from huggingface-hub<2.0,>=0.25.0->datasets) (4.15.0)\n",
-      "Requirement already satisfied: charset_normalizer<4,>=2 in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from requests) (3.4.4)\n",
-      "Requirement already satisfied: urllib3<3,>=1.21.1 in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from requests) (2.6.3)\n",
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-      "Requirement already satisfied: aiosignal>=1.4.0 in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.10.0,>=2023.1.0->datasets) (1.4.0)\n",
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-     ]
-    }
-   ],
-   "source": [
-    "!pip install datasets tqdm pillow requests\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "6f0b2e2c",
-   "metadata": {},
-   "outputs": [
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
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-       "model_id": "d59b7dea75f84b64bb8b262b43730e51",
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
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-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0c5815040f0a4a31903348a8327811a5",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Loading dataset shards:   0%|          | 0/18 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "DatasetDict({\n",
-      "    train: Dataset({\n",
-      "        features: ['image', 'label', 'image_id', 'emotion', 'brightness', 'colorfulness', 'facial_expression', 'human_action', 'scene', 'object'],\n",
-      "        num_rows: 94481\n",
-      "    })\n",
-      "    val: Dataset({\n",
-      "        features: ['image', 'label', 'image_id', 'emotion', 'brightness', 'colorfulness', 'facial_expression', 'human_action', 'scene', 'object'],\n",
-      "        num_rows: 5905\n",
-      "    })\n",
-      "    test: Dataset({\n",
-      "        features: ['image', 'label', 'image_id', 'emotion', 'brightness', 'colorfulness', 'facial_expression', 'human_action', 'scene', 'object'],\n",
-      "        num_rows: 17716\n",
-      "    })\n",
-      "})\n"
-     ]
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset\n",
-    "from pathlib import Path\n",
-    "from PIL import Image\n",
-    "import requests\n",
-    "\n",
-    "# куда сохраняем датасет\n",
-    "DATA_DIR = Path(\"../dataset/EmoSet-118K\")\n",
-    "DATA_DIR.mkdir(exist_ok=True, parents=True)\n",
-    "\n",
-    "# загружаем через Hugging Face\n",
-    "ds = load_dataset(\"Woleek/EmoSet-118K\")\n",
-    "\n",
-    "print(ds)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "052ab073",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tqdm import tqdm\n",
-    "from pathlib import Path\n",
-    "\n",
-    "def save_split(split):\n",
-    "    split_dir = DATA_DIR / split\n",
-    "    img_dir = split_dir / \"images\"\n",
-    "    img_dir.mkdir(parents=True, exist_ok=True)\n",
-    "\n",
-    "    labels_path = split_dir / \"labels.csv\"\n",
-    "\n",
-    "    # перезаписываем labels.csv\n",
-    "    with open(labels_path, \"w\") as f:\n",
-    "        f.write(\"filename,label\\n\")\n",
-    "\n",
-    "    for example in tqdm(ds[split]):\n",
-    "        img = example[\"image\"]          # уже PIL.Image\n",
-    "        label = example[\"emotion\"]\n",
-    "        image_id = example[\"image_id\"]\n",
-    "\n",
-    "        fname = f\"{image_id}.jpg\"\n",
-    "        img.save(img_dir / fname)\n",
-    "\n",
-    "        with open(labels_path, \"a\") as f:\n",
-    "            f.write(f\"{fname},{label}\\n\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "a74ceedf",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 94481/94481 [18:43<00:00, 84.10it/s]  \n",
-      "100%|██████████| 5905/5905 [01:08<00:00, 86.57it/s] \n",
-      "100%|██████████| 17716/17716 [02:57<00:00, 100.01it/s]\n"
-     ]
-    }
-   ],
-   "source": [
-    "save_split(\"train\")\n",
-    "save_split(\"val\")\n",
-    "save_split(\"test\")\n"
-   ]
-  }
- ],
- "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/download_DEAM.ipynb

@@ -1,140 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Загрузка датасета DEAM\n",
-    "\n",
-    "Этот ноутбук предназначен для автоматизации процесса скачивания и подготовки музыкального датасета **DEAM** (Database for Emotional Analysis in Music).\n",
-    "Данные будут помещены в папку `dataset/DEAM`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Collecting kagglehub\n",
-      "  Downloading kagglehub-1.0.1-py3-none-any.whl.metadata (40 kB)\n",
-      "Collecting kagglesdk<1.0,>=0.1.22 (from kagglehub)\n",
-      "  Downloading kagglesdk-0.1.23-py3-none-any.whl.metadata (13 kB)\n",
-      "Requirement already satisfied: packaging in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from kagglehub) (25.0)\n",
-      "Requirement already satisfied: pyyaml in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from kagglehub) (6.0.3)\n",
-      "Requirement already satisfied: requests in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from kagglehub) (2.32.5)\n",
-      "Requirement already satisfied: tqdm in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from kagglehub) (4.67.1)\n",
-      "Requirement already satisfied: protobuf in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from kagglesdk<1.0,>=0.1.22->kagglehub) (6.33.4)\n",
-      "Requirement already satisfied: charset_normalizer<4,>=2 in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from requests->kagglehub) (3.4.4)\n",
-      "Requirement already satisfied: idna<4,>=2.5 in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from requests->kagglehub) (3.11)\n",
-      "Requirement already satisfied: urllib3<3,>=1.21.1 in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from requests->kagglehub) (2.6.3)\n",
-      "Requirement already satisfied: certifi>=2017.4.17 in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from requests->kagglehub) (2026.1.4)\n",
-      "Downloading kagglehub-1.0.1-py3-none-any.whl (70 kB)\n",
-      "Downloading kagglesdk-0.1.23-py3-none-any.whl (217 kB)\n",
-      "Installing collected packages: kagglesdk, kagglehub\n",
-      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2/2\u001b[0m [kagglehub]\n",
-      "\u001b[1A\u001b[2KSuccessfully installed kagglehub-1.0.1 kagglesdk-0.1.23\n",
-      "\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m25.3\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m26.1.1\u001b[0m\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
-     ]
-    }
-   ],
-   "source": [
-    "!pip install kagglehub"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Скачиваем датасет DEAM...\n",
-      "Downloading to /home/zin/.cache/kagglehub/datasets/imsparsh/deam-mediaeval-dataset-emotional-analysis-in-music/1.archive...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 1.83G/1.83G [01:09<00:00, 28.2MB/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Extracting files...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Датасет скачан во временную директорию: /home/zin/.cache/kagglehub/datasets/imsparsh/deam-mediaeval-dataset-emotional-analysis-in-music/versions/1\n",
-      "Переносим файлы в ../dataset/DEAM...\n",
-      "\n",
-      "[УСПЕХ] Датасет DEAM готов к работе!\n"
-     ]
-    }
-   ],
-   "source": [
-    "import os\n",
-    "import shutil\n",
-    "import kagglehub\n",
-    "from pathlib import Path\n",
-    "\n",
-    "# 1. Настройка путей\n",
-    "DATASET_ROOT = Path(\"../dataset\")\n",
-    "DEAM_ROOT = DATASET_ROOT / \"DEAM\"\n",
-    "DEAM_ROOT.mkdir(parents=True, exist_ok=True)\n",
-    "\n",
-    "# 2. Загрузка через kagglehub\n",
-    "print(\"Скачиваем датасет DEAM...\")\n",
-    "kaggle_cache_path = kagglehub.dataset_download(\"imsparsh/deam-mediaeval-dataset-emotional-analysis-in-music\")\n",
-    "print(f\"Датасет скачан во временную директорию: {kaggle_cache_path}\")\n",
-    "\n",
-    "# 3. Перемещение файлов в проект\n",
-    "print(f\"Переносим файлы в {DEAM_ROOT}...\")\n",
-    "shutil.copytree(kaggle_cache_path, DEAM_ROOT, dirs_exist_ok=True)\n",
-    "\n",
-    "print(\"\\n[УСПЕХ] Датасет DEAM готов к работе!\")\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python (my-python-project)",
-   "language": "python",
-   "name": "my-python-project"
-  },
-  "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": 4
-}

src/scripts/download_dataset.py

@@ -1,171 +0,0 @@
-import torch
-from torch.utils.data import Dataset, DataLoader
-from torchvision import transforms
-import os
-import json
-
-from PIL import Image
-
-
-class EmoSet(Dataset):
-    ATTRIBUTES_MULTI_CLASS = [
-        'scene', 'facial_expression', 'human_action', 'brightness', 'colorfulness',
-    ]
-    ATTRIBUTES_MULTI_LABEL = [
-        'object'
-    ]
-    NUM_CLASSES = {
-        'brightness': 11,
-        'colorfulness': 11,
-        'scene': 254,
-        'object': 409,
-        'facial_expression': 6,
-        'human_action': 264,
-    }
-
-    def __init__(self,
-                 data_root,
-                 num_emotion_classes,
-                 phase,
-                 ):
-        assert num_emotion_classes in (8, 2)
-        assert phase in ('train', 'val', 'test')
-        self.transforms_dict = self.get_data_transforms()
-
-        self.info = self.get_info(data_root, num_emotion_classes)
-
-        if phase == 'train':
-            self.transform = self.transforms_dict['train']
-        elif phase == 'val':
-            self.transform = self.transforms_dict['val']
-        elif phase == 'test':
-            self.transform = self.transforms_dict['test']
-        else:
-            raise NotImplementedError
-
-        data_store = json.load(open(os.path.join(data_root, f'{phase}.json')))
-        self.data_store = [
-            [
-                self.info['emotion']['label2idx'][item[0]],
-                item[1],
-                os.path.join(data_root, item[2]),
-                os.path.join(data_root, item[3])
-            ]
-            for item in data_store
-        ]
-
-    @classmethod
-    def get_data_transforms(cls):
-        transforms_dict = {
-            'train': transforms.Compose([
-                transforms.RandomResizedCrop(224),
-                transforms.RandomHorizontalFlip(),
-                transforms.ToTensor(),
-                transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
-            ]),
-            'val': transforms.Compose([
-                transforms.Resize(224),
-                transforms.CenterCrop(224),
-                transforms.ToTensor(),
-                transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
-            ]),
-            'test': transforms.Compose([
-                transforms.Resize(224),
-                transforms.CenterCrop(224),
-                transforms.ToTensor(),
-                transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
-            ]),
-        }
-        return transforms_dict
-
-    def get_info(self, data_root, num_emotion_classes):
-        assert num_emotion_classes in (8, 2)
-        info = json.load(open(os.path.join(data_root, 'info.json')))
-        if num_emotion_classes == 8:
-            pass
-        elif num_emotion_classes == 2:
-            emotion_info = {
-                'label2idx': {
-                    'amusement': 0,
-                    'awe': 0,
-                    'contentment': 0,
-                    'excitement': 0,
-                    'anger': 1,
-                    'disgust': 1,
-                    'fear': 1,
-                    'sadness': 1,
-                },
-                'idx2label': {
-                    '0': 'positive',
-                    '1': 'negative',
-                }
-            }
-            info['emotion'] = emotion_info
-        else:
-            raise NotImplementedError
-
-        return info
-
-    def load_image_by_path(self, path):
-        image = Image.open(path).convert('RGB')
-        image = self.transform(image)
-        return image
-
-    def load_annotation_by_path(self, path):
-        json_data = json.load(open(path))
-        return json_data
-
-    def __getitem__(self, item):
-        emotion_label_idx, image_id, image_path, annotation_path = self.data_store[item]
-        image = self.load_image_by_path(image_path)
-        annotation_data = self.load_annotation_by_path(annotation_path)
-        data = {'image_id': image_id, 'image': image, 'emotion_label_idx': emotion_label_idx}
-
-        for attribute in self.ATTRIBUTES_MULTI_CLASS:
-            # if empty, set to -1, else set to label index
-            attribute_label_idx = -1
-            if attribute in annotation_data:
-                attribute_label_idx = self.info[attribute]['label2idx'][str(annotation_data[attribute])]
-            data.update({f'{attribute}_label_idx': attribute_label_idx})
-
-        for attribute in self.ATTRIBUTES_MULTI_LABEL:
-            # if empty, set to 0, else set to 1
-            assert attribute == 'object'
-            num_classes = self.NUM_CLASSES[attribute]
-            attribute_label_idx = torch.zeros(num_classes)
-            if attribute in annotation_data:
-                for label in annotation_data[attribute]:
-                    attribute_label_idx[self.info[attribute]['label2idx'][label]] = 1
-            data.update({f'{attribute}_label_idx': attribute_label_idx})
-
-        return data
-
-    def __len__(self):
-        return len(self.data_store)
-
-
-if __name__ == '__main__':
-    data_root = r'F:\common_file_system\EmoSet\EmoSet_v5_划分train-test-val'
-    num_emotion_classes = 8
-    phase = 'train'
-
-    dataset = EmoSet(
-        data_root=data_root,
-        num_emotion_classes=num_emotion_classes,
-        phase=phase,
-    )
-
-    # print(dataset.info)
-    dataloader = DataLoader(dataset, batch_size = 16, shuffle = True)
-
-    for i, data in enumerate(dataloader):
-        pass
-        # print(data['emotion_label_idx'])
-        # print(data['scene_label_idx'])
-        # print(data['facial_expression_label_idx'])
-        # print(data['human_action_label_idx'])
-        # print(data['brightness_label_idx'])
-        # print(data['colorfulness_label_idx'])
-        # print(data['object_label_idx'])
-        # break
-

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/prep_deam.ipynb

@@ -1,88 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "b92e0213",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "from pathlib import Path"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "1763c51e",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "✅ УСПЕХ! База создана: ../../dataset/DEAM/music_db.csv\n",
-      "Всего треков в базе: 1744\n",
-      "Пример данных:\n",
-      "   song_id  valence  arousal\n",
-      "0        2      3.1      3.0\n",
-      "1        3      3.5      3.3\n",
-      "2        4      5.7      5.5\n",
-      "3        5      4.4      5.3\n",
-      "4        7      5.8      6.4\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Точный путь к оригинальным аннотациям\n",
-    "source_path = Path(\"../../dataset/DEAM/DEAM_Annotations/annotations/annotations averaged per song/song_level/static_annotations_averaged_songs_1_2000.csv\")\n",
-    "# Путь, куда сохраним очищенную базу для движка\n",
-    "output_path = Path(\"../../dataset/DEAM/music_db.csv\")\n",
-    "\n",
-    "if not source_path.exists():\n",
-    "    print(f\"❌ Исходный файл не найден по пути: {source_path}\")\n",
-    "else:\n",
-    "    # skipinitialspace=True уберет лишние пробелы в названиях колонок, если они есть\n",
-    "    df = pd.read_csv(source_path, skipinitialspace=True)\n",
-    "    \n",
-    "    # Берем только нужные колонки (по твоему примеру)\n",
-    "    clean_df = df[['song_id', 'valence_mean', 'arousal_mean']].copy()\n",
-    "    \n",
-    "    # Переименовываем для простоты кода в движке\n",
-    "    clean_df.columns = ['song_id', 'valence', 'arousal']\n",
-    "    \n",
-    "    # Приводим ID к целому числу (2, 3, 4...), чтобы искать файлы '2.mp3'\n",
-    "    clean_df['song_id'] = clean_df['song_id'].astype(int)\n",
-    "    \n",
-    "    # Сохраняем финальный файл\n",
-    "    clean_df.to_csv(output_path, index=False)\n",
-    "    \n",
-    "    print(f\"✅ УСПЕХ! База создана: {output_path}\")\n",
-    "    print(f\"Всего треков в базе: {len(clean_df)}\")\n",
-    "    print(\"Пример данных:\")\n",
-    "    print(clean_df.head())"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python (thesis)",
-   "language": "python",
-   "name": "thesis"
-  },
-  "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/prepare_dataset_to_train.ipynb

@@ -1,114 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "d70d8e32",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from concurrent.futures import ProcessPoolExecutor\n",
-    "import pandas as pd\n",
-    "from pathlib import Path\n",
-    "from PIL import Image\n",
-    "import torch\n",
-    "from torchvision import transforms\n",
-    "from tqdm import tqdm"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "31b0fa82",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "DATA_ROOT = Path(\"../dataset/EmoSet-118K\")\n",
-    "TRANSFORM = transforms.Compose([\n",
-    "    transforms.Resize((224,224)),\n",
-    "    transforms.ToTensor(),\n",
-    "    transforms.Normalize([0.485,0.456,0.406],[0.229,0.224,0.225])\n",
-    "])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "1a17ecf5",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  0%|          | 0/94481 [00:00<?, ?it/s]\n"
-     ]
-    },
-    {
-     "ename": "PicklingError",
-     "evalue": "Can't pickle <class 'pandas.core.frame.Pandas'>: attribute lookup Pandas on pandas.core.frame failed",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
-      "\u001b[31m_RemoteTraceback\u001b[39m                          Traceback (most recent call last)",
-      "\u001b[31m_RemoteTraceback\u001b[39m: \n\"\"\"\nTraceback (most recent call last):\n  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 244, in _feed\n    obj = _ForkingPickler.dumps(obj)\n          ^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/reduction.py\", line 51, in dumps\n    cls(buf, protocol).dump(obj)\n_pickle.PicklingError: Can't pickle <class 'pandas.core.frame.Pandas'>: attribute lookup Pandas on pandas.core.frame failed\n\"\"\"",
-      "\nThe above exception was the direct cause of the following exception:\n",
-      "\u001b[31mPicklingError\u001b[39m                             Traceback (most recent call last)",
-      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[3]\u001b[39m\u001b[32m, line 20\u001b[39m\n\u001b[32m     18\u001b[39m     futures = [executor.submit(process_row, row, split_dir, tensor_dir) \u001b[38;5;28;01mfor\u001b[39;00m row \u001b[38;5;129;01min\u001b[39;00m df.itertuples()]\n\u001b[32m     19\u001b[39m     \u001b[38;5;28;01mfor\u001b[39;00m f \u001b[38;5;129;01min\u001b[39;00m tqdm(futures):\n\u001b[32m---> \u001b[39m\u001b[32m20\u001b[39m         results.append(\u001b[43mf\u001b[49m\u001b[43m.\u001b[49m\u001b[43mresult\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[32m     22\u001b[39m new_df = pd.DataFrame(results)\n\u001b[32m     23\u001b[39m new_df.to_csv(DATA_ROOT / split / \u001b[33m\"\u001b[39m\u001b[33mlabels_tensor.csv\u001b[39m\u001b[33m\"\u001b[39m, index=\u001b[38;5;28;01mFalse\u001b[39;00m)\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/_base.py:449\u001b[39m, in \u001b[36mFuture.result\u001b[39m\u001b[34m(self, timeout)\u001b[39m\n\u001b[32m    447\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m CancelledError()\n\u001b[32m    448\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._state == FINISHED:\n\u001b[32m--> \u001b[39m\u001b[32m449\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m__get_result\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    451\u001b[39m \u001b[38;5;28mself\u001b[39m._condition.wait(timeout)\n\u001b[32m    453\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._state \u001b[38;5;129;01min\u001b[39;00m [CANCELLED, CANCELLED_AND_NOTIFIED]:\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/_base.py:401\u001b[39m, in \u001b[36mFuture.__get_result\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m    399\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._exception:\n\u001b[32m    400\u001b[39m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m401\u001b[39m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m._exception\n\u001b[32m    402\u001b[39m     \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[32m    403\u001b[39m         \u001b[38;5;66;03m# Break a reference cycle with the exception in self._exception\u001b[39;00m\n\u001b[32m    404\u001b[39m         \u001b[38;5;28mself\u001b[39m = \u001b[38;5;28;01mNone\u001b[39;00m\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py:244\u001b[39m, in \u001b[36mQueue._feed\u001b[39m\u001b[34m(buffer, notempty, send_bytes, writelock, reader_close, writer_close, ignore_epipe, onerror, queue_sem)\u001b[39m\n\u001b[32m    241\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[32m    243\u001b[39m \u001b[38;5;66;03m# serialize the data before acquiring the lock\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m244\u001b[39m obj = \u001b[43m_ForkingPickler\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdumps\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    245\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m wacquire \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m    246\u001b[39m     send_bytes(obj)\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/reduction.py:51\u001b[39m, in \u001b[36mForkingPickler.dumps\u001b[39m\u001b[34m(cls, obj, protocol)\u001b[39m\n\u001b[32m     48\u001b[39m \u001b[38;5;129m@classmethod\u001b[39m\n\u001b[32m     49\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mdumps\u001b[39m(\u001b[38;5;28mcls\u001b[39m, obj, protocol=\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[32m     50\u001b[39m     buf = io.BytesIO()\n\u001b[32m---> \u001b[39m\u001b[32m51\u001b[39m     \u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mbuf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprotocol\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdump\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m     52\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m buf.getbuffer()\n",
-      "\u001b[31mPicklingError\u001b[39m: Can't pickle <class 'pandas.core.frame.Pandas'>: attribute lookup Pandas on pandas.core.frame failed"
-     ]
-    }
-   ],
-   "source": [
-    "def process_row(row, split_dir, tensor_dir):\n",
-    "    img_path = split_dir / row.filename\n",
-    "    img = Image.open(img_path).convert(\"RGB\")\n",
-    "    tensor = TRANSFORM(img)\n",
-    "    tensor_path = tensor_dir / f\"{row.filename}.pt\"\n",
-    "    torch.save(tensor, tensor_path)\n",
-    "    return {\"tensor_path\": str(tensor_path), \"label\": row.label}\n",
-    "\n",
-    "for split in [\"train\",\"val\",\"test\"]:\n",
-    "    split_dir = DATA_ROOT / split / \"images\"\n",
-    "    tensor_dir = DATA_ROOT / split / \"tensors\"\n",
-    "    tensor_dir.mkdir(exist_ok=True, parents=True)\n",
-    "\n",
-    "    df = pd.read_csv(DATA_ROOT / split / \"labels.csv\")\n",
-    "\n",
-    "    results = []\n",
-    "    with ProcessPoolExecutor(max_workers=12) as executor:\n",
-    "        futures = [executor.submit(process_row, row, split_dir, tensor_dir) for row in df.itertuples()]\n",
-    "        for f in tqdm(futures):\n",
-    "            results.append(f.result())\n",
-    "\n",
-    "    new_df = pd.DataFrame(results)\n",
-    "    new_df.to_csv(DATA_ROOT / split / \"labels_tensor.csv\", index=False)\n"
-   ]
-  }
- ],
- "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/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

src/scripts/test.ipynb

@@ -1,199 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "ca08df84",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Using device: cuda\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Step 0/1000, Loss: 1.0013\n",
-      "Step 10/1000, Loss: 1.0088\n",
-      "Step 20/1000, Loss: 0.9956\n",
-      "Step 30/1000, Loss: 0.9781\n",
-      "Step 40/1000, Loss: 0.9613\n",
-      "Step 50/1000, Loss: 0.9313\n",
-      "Step 60/1000, Loss: 0.8927\n",
-      "Step 70/1000, Loss: 0.8503\n",
-      "Step 80/1000, Loss: 0.7537\n",
-      "Step 90/1000, Loss: 0.6689\n",
-      "Step 100/1000, Loss: 0.6063\n",
-      "Step 110/1000, Loss: 0.5172\n",
-      "Step 120/1000, Loss: 0.4592\n",
-      "Step 130/1000, Loss: 0.4044\n",
-      "Step 140/1000, Loss: 0.3610\n",
-      "Step 150/1000, Loss: 0.3175\n",
-      "Step 160/1000, Loss: 0.2825\n",
-      "Step 170/1000, Loss: 0.2560\n",
-      "Step 180/1000, Loss: 0.2360\n",
-      "Step 190/1000, Loss: 0.2203\n",
-      "Step 200/1000, Loss: 0.1930\n",
-      "Step 210/1000, Loss: 0.1854\n",
-      "Step 220/1000, Loss: 0.1723\n",
-      "Step 230/1000, Loss: 0.1546\n",
-      "Step 240/1000, Loss: 0.1386\n",
-      "Step 250/1000, Loss: 0.1271\n",
-      "Step 260/1000, Loss: 0.1109\n",
-      "Step 270/1000, Loss: 0.1032\n",
-      "Step 280/1000, Loss: 0.0899\n",
-      "Step 290/1000, Loss: 0.0807\n",
-      "Step 300/1000, Loss: 0.0750\n",
-      "Step 310/1000, Loss: 0.0813\n",
-      "Step 320/1000, Loss: 0.0612\n",
-      "Step 330/1000, Loss: 0.0544\n",
-      "Step 340/1000, Loss: 0.0552\n",
-      "Step 350/1000, Loss: 0.0446\n",
-      "Step 360/1000, Loss: 0.0403\n",
-      "Step 370/1000, Loss: 0.0350\n",
-      "Step 380/1000, Loss: 0.0612\n",
-      "Step 390/1000, Loss: 0.0364\n",
-      "Step 400/1000, Loss: 0.0322\n",
-      "Step 410/1000, Loss: 0.0302\n",
-      "Step 420/1000, Loss: 0.0519\n",
-      "Step 430/1000, Loss: 0.0319\n",
-      "Step 440/1000, Loss: 0.0260\n",
-      "Step 450/1000, Loss: 0.0208\n",
-      "Step 460/1000, Loss: 0.0409\n",
-      "Step 470/1000, Loss: 0.0291\n",
-      "Step 480/1000, Loss: 0.0234\n",
-      "Step 490/1000, Loss: 0.0194\n",
-      "Step 500/1000, Loss: 0.0274\n",
-      "Step 510/1000, Loss: 0.0231\n",
-      "Step 520/1000, Loss: 0.0199\n",
-      "Step 530/1000, Loss: 0.0154\n",
-      "Step 540/1000, Loss: 0.0278\n",
-      "Step 550/1000, Loss: 0.0185\n",
-      "Step 560/1000, Loss: 0.0180\n",
-      "Step 570/1000, Loss: 0.0152\n",
-      "Step 580/1000, Loss: 0.0132\n",
-      "Step 590/1000, Loss: 0.0111\n",
-      "Step 600/1000, Loss: 0.0396\n",
-      "Step 610/1000, Loss: 0.0179\n",
-      "Step 620/1000, Loss: 0.0148\n",
-      "Step 630/1000, Loss: 0.0123\n",
-      "Step 640/1000, Loss: 0.0265\n",
-      "Step 650/1000, Loss: 0.0133\n",
-      "Step 660/1000, Loss: 0.0128\n",
-      "Step 670/1000, Loss: 0.0107\n",
-      "Step 680/1000, Loss: 0.0142\n",
-      "Step 690/1000, Loss: 0.0202\n",
-      "Step 700/1000, Loss: 0.0125\n",
-      "Step 710/1000, Loss: 0.0107\n",
-      "Step 720/1000, Loss: 0.0140\n",
-      "Step 730/1000, Loss: 0.0195\n",
-      "Step 740/1000, Loss: 0.0148\n",
-      "Step 750/1000, Loss: 0.0109\n",
-      "Step 760/1000, Loss: 0.0094\n",
-      "Step 770/1000, Loss: 0.0121\n",
-      "Step 780/1000, Loss: 0.0233\n",
-      "Step 790/1000, Loss: 0.0151\n",
-      "Step 800/1000, Loss: 0.0134\n",
-      "Step 810/1000, Loss: 0.0117\n",
-      "Step 820/1000, Loss: 0.0124\n",
-      "Step 830/1000, Loss: 0.0221\n",
-      "Step 840/1000, Loss: 0.0161\n",
-      "Step 850/1000, Loss: 0.0136\n",
-      "Step 860/1000, Loss: 0.0161\n",
-      "Step 870/1000, Loss: 0.0194\n",
-      "Step 880/1000, Loss: 0.0145\n",
-      "Step 890/1000, Loss: 0.0149\n",
-      "Step 900/1000, Loss: 0.0232\n",
-      "Step 910/1000, Loss: 0.0166\n",
-      "Step 920/1000, Loss: 0.0156\n",
-      "Step 930/1000, Loss: 0.0276\n",
-      "Step 940/1000, Loss: 0.0176\n",
-      "Step 950/1000, Loss: 0.0152\n",
-      "Step 960/1000, Loss: 0.0162\n",
-      "Step 970/1000, Loss: 0.0143\n",
-      "Step 980/1000, Loss: 0.0136\n",
-      "Step 990/1000, Loss: 0.0117\n",
-      "Total time: 67.25 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "import torch\n",
-    "import torch.nn as nn\n",
-    "import torch.optim as optim\n",
-    "import time\n",
-    "\n",
-    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
-    "print(\"Using device:\", device)\n",
-    "\n",
-    "\n",
-    "# Огромные параметры\n",
-    "N, D_in, H1, H2, H3, D_out = 300_000, 4096, 2048, 1024, 512, 10\n",
-    "batch_size = 16_384  # большой батч\n",
-    "steps = 1000          # много итераций для длительной нагрузки\n",
-    "\n",
-    "# Случайные данные на GPU\n",
-    "x = torch.randn(N, D_in, device=device, dtype=torch.float32)\n",
-    "y = torch.randn(N, D_out, device=device, dtype=torch.float32)\n",
-    "\n",
-    "model = nn.Sequential(\n",
-    "    nn.Linear(D_in, H1),\n",
-    "    nn.ReLU(),\n",
-    "    nn.Linear(H1, H2),\n",
-    "    nn.ReLU(),\n",
-    "    nn.Linear(H2, H3),\n",
-    "    nn.ReLU(),\n",
-    "    nn.Linear(H3, D_out)\n",
-    ").to(device)\n",
-    "\n",
-    "loss_fn = nn.MSELoss()\n",
-    "optimizer = optim.Adam(model.parameters(), lr=1e-3)\n",
-    "\n",
-    "start = time.time()\n",
-    "for t in range(steps):\n",
-    "    idx = torch.randint(0, N, (batch_size,), device=device)\n",
-    "    x_batch = x[idx]\n",
-    "    y_batch = y[idx]\n",
-    "\n",
-    "    y_pred = model(x_batch)\n",
-    "    loss = loss_fn(y_pred, y_batch)\n",
-    "\n",
-    "    optimizer.zero_grad()\n",
-    "    loss.backward()\n",
-    "    optimizer.step()\n",
-    "\n",
-    "    if t % 10 == 0:\n",
-    "        # замедляем вывод, чтобы можно было наблюдать\n",
-    "        print(f\"Step {t}/{steps}, Loss: {loss.item():.4f}\")\n",
-    "\n",
-    "end = time.time()\n",
-    "print(f\"Total time: {end-start:.2f} s\")\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": ".venv",
-   "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/train_images.ipynb

@@ -1,759 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9336560f",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "0c00b67b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "import torch.nn as nn\n",
-    "from torch.utils.data import Dataset, DataLoader\n",
-    "import torchvision.transforms as T\n",
-    "\n",
-    "import pandas as pd\n",
-    "from pathlib import Path\n",
-    "from PIL import Image\n",
-    "from tqdm import tqdm\n",
-    "\n",
-    "import timm\n",
-    "import numpy as np\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "84c3657f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'cuda'"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# === CONFIG ===\n",
-    "DATA_ROOT = Path(\"../dataset/EmoSet-118K\")\n",
-    "BATCH_SIZE = 64          # V100 спокойно тянет\n",
-    "EPOCHS = 15\n",
-    "LR = 3e-4\n",
-    "NUM_WORKERS = 24\n",
-    "\n",
-    "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
-    "DEVICE\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "9f749add",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class EmoSetDataset(Dataset):\n",
-    "    def __init__(self, root, split):\n",
-    "        self.root = Path(root) / split\n",
-    "        self.df = pd.read_csv(self.root / \"labels.csv\")\n",
-    "\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",
-    "        self.transform = T.Compose([\n",
-    "            T.Resize((224, 224)),\n",
-    "            T.ToTensor(),\n",
-    "            T.Normalize(\n",
-    "                mean=[0.485, 0.456, 0.406],\n",
-    "                std=[0.229, 0.224, 0.225]\n",
-    "            )\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",
-    "        img = Image.open(img_path).convert(\"RGB\")\n",
-    "        img = self.transform(img)\n",
-    "\n",
-    "        label = self.label2idx[row[\"label\"]]\n",
-    "        return img, label\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "c8805341",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Classes: ['amusement', 'anger', 'awe', 'contentment', 'disgust', 'excitement', 'fear', 'sadness']\n"
-     ]
-    }
-   ],
-   "source": [
-    "train_ds = EmoSetDataset(DATA_ROOT, \"train\")\n",
-    "val_ds   = EmoSetDataset(DATA_ROOT, \"val\")\n",
-    "\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(\"Classes:\", train_ds.labels)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "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": [
-    "model = timm.create_model(\n",
-    "    \"resnet50\",\n",
-    "    pretrained=True,\n",
-    "    num_classes=len(train_ds.labels)\n",
-    ")\n",
-    "\n",
-    "model.to(DEVICE)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "81a457ef",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "criterion = nn.CrossEntropyLoss()\n",
-    "\n",
-    "optimizer = torch.optim.AdamW(\n",
-    "    model.parameters(),\n",
-    "    lr=LR,\n",
-    "    weight_decay=1e-4\n",
-    ")\n",
-    "\n",
-    "scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(\n",
-    "    optimizer,\n",
-    "    T_max=EPOCHS\n",
-    ")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "951aa9e3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def train_epoch(model, loader):\n",
-    "    model.train()\n",
-    "    total_loss = 0\n",
-    "    correct = 0\n",
-    "    total = 0\n",
-    "\n",
-    "    for imgs, labels in tqdm(loader, leave=False):\n",
-    "        imgs = imgs.to(DEVICE)\n",
-    "        labels = labels.to(DEVICE)\n",
-    "\n",
-    "        optimizer.zero_grad()\n",
-    "        logits = 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 == labels).sum().item()\n",
-    "        total += labels.size(0)\n",
-    "\n",
-    "    return total_loss / total, correct / total\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "fb7e9398",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@torch.no_grad()\n",
-    "def val_epoch(model, loader):\n",
-    "    model.eval()\n",
-    "    total_loss = 0\n",
-    "    correct = 0\n",
-    "    total = 0\n",
-    "\n",
-    "    for imgs, labels in loader:\n",
-    "        imgs = imgs.to(DEVICE)\n",
-    "        labels = labels.to(DEVICE)\n",
-    "\n",
-    "        logits = 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 == labels).sum().item()\n",
-    "        total += labels.size(0)\n",
-    "\n",
-    "    return total_loss / total, correct / total\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "9e870e5d",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  0%|          | 0/1477 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "                                                    \r"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 01 | Train loss: 0.8383, acc: 0.6954 | Val loss: 0.6694, acc: 0.7563\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "                                                   \r"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 02 | Train loss: 0.5462, acc: 0.7972 | Val loss: 0.6592, acc: 0.7594\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "                                                   \r"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 03 | Train loss: 0.3654, acc: 0.8632 | Val loss: 0.7263, acc: 0.7600\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "                                                   \r"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 04 | Train loss: 0.2111, acc: 0.9230 | Val loss: 0.8572, acc: 0.7472\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "                                                    \r"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 05 | Train loss: 0.1187, acc: 0.9585 | Val loss: 1.0372, acc: 0.7453\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "                                                    \r"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 06 | Train loss: 0.0690, acc: 0.9768 | Val loss: 1.1982, acc: 0.7529\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "                                                    \r"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 07 | Train loss: 0.0466, acc: 0.9843 | Val loss: 1.3178, acc: 0.7492\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "                                                   \r"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 08 | Train loss: 0.0295, acc: 0.9905 | Val loss: 1.3926, acc: 0.7551\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "                                                   \r"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 09 | Train loss: 0.0204, acc: 0.9938 | Val loss: 1.4682, acc: 0.7497\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "                                                   \r"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 10 | Train loss: 0.0146, acc: 0.9955 | Val loss: 1.4784, acc: 0.7604\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "                                                   \r"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 11 | Train loss: 0.0087, acc: 0.9975 | Val loss: 1.5263, acc: 0.7580\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "                                                   \r"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 12 | Train loss: 0.0057, acc: 0.9987 | Val loss: 1.5689, acc: 0.7558\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "                                                    \r"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 13 | Train loss: 0.0044, acc: 0.9990 | Val loss: 1.5952, acc: 0.7566\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "                                                    \r"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 14 | Train loss: 0.0030, acc: 0.9993 | Val loss: 1.6130, acc: 0.7600\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "                                                   \r"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 15 | Train loss: 0.0025, acc: 0.9995 | Val loss: 1.5921, acc: 0.7627\n"
-     ]
-    }
-   ],
-   "source": [
-    "best_val_acc = 0.0\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",
-    "    scheduler.step()\n",
-    "\n",
-    "    print(\n",
-    "        f\"Epoch {epoch:02d} | \"\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",
-    "    if val_acc > best_val_acc:\n",
-    "        best_val_acc = val_acc\n",
-    "        torch.save(model.state_dict(), \"emoset_resnet50_best.pth\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7796ef11",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "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/train_regressor.py

@@ -1,65 +0,0 @@
-import numpy as np
-import pandas as pd
-from pathlib import Path
-from sklearn.linear_model import RidgeCV
-from sklearn.multioutput import MultiOutputRegressor
-from sklearn.preprocessing import StandardScaler
-from sklearn.pipeline import Pipeline
-from sklearn.model_selection import train_test_split
-from sklearn.metrics import mean_squared_error, r2_score
-import joblib
-
-# 1. Алфавитный маппинг EmoSet
-EMO_VA_MAP = {
-    0: (7.5, 6.5), # amusement 
-    1: (2.0, 8.0), # anger 
-    2: (6.5, 5.0), # awe 
-    3: (7.0, 3.0), # contentment 
-    4: (3.0, 6.0), # disgust 
-    5: (8.0, 8.0), # excitement 
-    6: (2.5, 7.5), # fear 
-    7: (2.0, 2.0), # sadness 
-}
-
-BASE_DIR = Path(__file__).resolve().parent.parent
-EMBEDDINGS_PATH = BASE_DIR / "emoset_test_embeddings.npy"
-LABELS_PATH = BASE_DIR / "emoset_test_labels.npy"
-
-print("Загрузка данных...")
-X = np.load(EMBEDDINGS_PATH)
-y_labels = np.load(LABELS_PATH)
-
-y_va = np.array([EMO_VA_MAP[label] for label in y_labels])
-X_train, X_test, y_train, y_test = train_test_split(X, y_va, test_size=0.2, random_state=42)
-
-# 2. НОВАЯ, ПРАВИЛЬНАЯ АРХИТЕКТУРА (Pipeline)
-print("Обучение масштабатора и RidgeCV регрессора...")
-# Pipeline гарантирует, что при предсказании в main.py новые векторы тоже будут масштабированы
-model = Pipeline([
-    ('scaler', StandardScaler()),
-    ('regressor', MultiOutputRegressor(RidgeCV(alphas=[0.1, 1.0, 10.0, 100.0, 1000.0])))
-])
-
-model.fit(X_train, y_train)
-
-# 3. Диагностика и Оценка
-y_pred = model.predict(X_test)
-
-mse = mean_squared_error(y_test, y_pred)
-r2 = r2_score(y_test, y_pred)
-
-print(f"\n[УСПЕХ] Обучение завершено!")
-print(f"MSE: {mse:.4f}")
-print(f"R^2 Score: {r2:.4f}")
-
-# === ТОТ САМЫЙ ТЕСТ НА КОЛЛАПС ===
-print("\n--- ДИАГНОСТИКА РАЗБРОСА ПРЕДСКАЗАНИЙ ---")
-print(f"Valence: от {y_pred[:, 0].min():.2f} до {y_pred[:, 0].max():.2f} (Эталон: 2.0 - 8.0)")
-print(f"Arousal: от {y_pred[:, 1].min():.2f} до {y_pred[:, 1].max():.2f} (Эталон: 2.0 - 8.0)")
-# ===============================================
-
-# 4. Сохранение (Pipeline сохраняется целиком со StandardScaler)
-output_model_path = BASE_DIR / "music_engine" / "va_regressor.pkl"
-output_model_path.parent.mkdir(parents=True, exist_ok=True)
-joblib.dump(model, output_model_path)
-print(f"\nМодель сохранена в: {output_model_path}")

src/tabs/tab_dataset.py

@@ -42,7 +42,7 @@ def render_dataset_tab(matcher, image_files, embeddings, labels_array, images_pa
                         st.session_state.ds_current_options = random.sample(range(len(image_files)), 6)
                     st.rerun()
     else:
-        st.success("✅ Анализ завершен! Ваш эмоциональный профиль готов.")
+        st.success("Анализ завершен! Ваш эмоциональный профиль готов.")
         
         all_v, all_a = [], []
         for idx in st.session_state.ds_chosen_indices:
@@ -56,7 +56,7 @@ def render_dataset_tab(matcher, image_files, embeddings, labels_array, images_pa
         col_left, col_right = st.columns([1, 2])
 
         with col_left:
-            st.header("📊 Ваш профиль")
+            st.header("Ваш профиль")
             st.metric("Позитивность (Valence)", f"{target_v:.2f}")
             st.metric("Энергия (Arousal)", f"{target_a:.2f}")
             

src/tabs/tab_live.py

@@ -1,75 +1,162 @@
 import streamlit as st
+import streamlit.components.v1 as components
 import numpy as np
 from PIL import Image
-import matplotlib.pyplot as plt
+import base64
-from music_engine.llm_bridge import LLMAcousticBridge # ИМПОРТИРУЕМ МОСТ
+from io import BytesIO
+from music_engine.llm_bridge import LLMAcousticBridge
+
+# Вспомогательная функция для крохотного предпросмотра
+def get_thumbnail_html(images, max_display=12):
+    html_images = ""
+    for file in images[:max_display]:
+        img = Image.open(file)
+        img.thumbnail((100, 100)) # Сжимаем картинку
+        if img.mode != "RGB":
+            img = img.convert("RGB")
+        
+        buffered = BytesIO()
+        img.save(buffered, format="JPEG")
+        b64_str = base64.b64encode(buffered.getvalue()).decode()
+        
+        # Строгие стили для квадратных миниатюр
+        html_images += f'<img src="data:image/jpeg;base64,{b64_str}" style="width: 60px; height: 60px; object-fit: cover; border-radius: 8px; margin-right: 8px; margin-bottom: 8px; border: 1px solid rgba(255, 255, 255, 0.2);">'
+    
+    # Индикатор оставшихся фото, если их много
+    if len(images) > max_display:
+        html_images += f'<span style="display: inline-block; width: 60px; height: 60px; line-height: 60px; text-align: center; background: rgba(150, 150, 150, 0.2); border-radius: 8px; vertical-align: top; font-size: 14px;">+{len(images) - max_display}</span>'
+
+    return f'<div style="display: flex; flex-wrap: wrap;">{html_images}</div>'
 
 def render_live_tab(matcher, image_processor):
+    if "live_state" not in st.session_state:
+        st.session_state.live_state = "upload"
+    if "result_data" not in st.session_state:
+        st.session_state.result_data = None
+        
+    viewport = st.query_params.get("viewport", "desktop")
+
+    # ==========================================
+    # CSS ИНЪЕКЦИИ
+    # ==========================================
+    st.markdown("""
+    <style>
+    [data-testid="stFileUploadDropzone"] {
+        min-height: 250px !important;
+        display: flex;
+        align-items: center;
+        justify-content: center;
+        border-radius: 16px;
+        background-color: rgba(255, 75, 75, 0.03);
+    }
+    .spinner-container {
+        display: flex; flex-direction: column; align-items: center;
+        justify-content: center; min-height: 40vh; margin-top: 10vh;
+    }
+    .big-spinner {
+        width: 120px; height: 120px; border: 10px solid rgba(255, 75, 75, 0.1);
+        border-top: 10px solid #ff4b4b; border-radius: 50%;
+        animation: spin 1s linear infinite; margin-bottom: 2rem;
+    }
+    @keyframes spin { 0% { transform: rotate(0deg); } 100% { transform: rotate(360deg); } }
+    </style>
+    """, unsafe_allow_html=True)
+
+    # ==========================================
+    # ЭКРАН 1: ЗАГРУЗКА
+    # ==========================================
+    if st.session_state.live_state == "upload":
+        
+        upload_placeholder = st.empty()
+        with upload_placeholder.container():
             st.write("Загрузите фотографии с вашего устройства. Система проанализирует эмоции и семантику кадра.")
             
+            if viewport == "mobile":
+                st.markdown("<br>", unsafe_allow_html=True)
+                
             uploaded_files = st.file_uploader(
                 "Перетащите изображения сюда", 
                 type=['png', 'jpg', 'jpeg'], 
-        accept_multiple_files=True
+                accept_multiple_files=True,
+                label_visibility="collapsed" if viewport == "mobile" else "visible"
             )
             
             if uploaded_files:
-        st.subheader("Анализ визуальных признаков:")
+                # 1. КНОПКА СРАЗУ ПОСЛЕ ЗАГРУЗКИ (Не нужно скроллить вниз)
+                st.markdown("<br>", unsafe_allow_html=True)
+                if st.button("Сгенерировать саундтрек", type="primary", use_container_width=True):
+                    st.session_state.uploaded_images = uploaded_files
+                    st.session_state.live_state = "processing"
+                    upload_placeholder.empty()
+                    st.rerun()
+                
+                # 2. МИНИАТЮРЫ ПОД КНОПКОЙ
+                st.markdown("<br>", unsafe_allow_html=True)
+                st.caption("Выбранные кадры:")
+                # Генерируем компактный блок миниатюр
+                st.markdown(get_thumbnail_html(uploaded_files), unsafe_allow_html=True)
+
+    # ==========================================
+    # ЭКРАН 2: АНАЛИЗ (СПИННЕР)
+    # ==========================================
+    elif st.session_state.live_state == "processing":
+        
+        components.html("<script>window.parent.scrollTo(0, 0);</script>", height=0, width=0)
+        
+        files = st.session_state.get("uploaded_images", [])
+        st.markdown('<div class="spinner-container"><div class="big-spinner"></div></div>', unsafe_allow_html=True)
+        status_text = st.empty()
         
-        cols = st.columns(min(len(uploaded_files), 5))
         images = []
         all_objects = []
+        all_v, all_a = [], []
+        
+        for i, file in enumerate(files):
+            status_text.markdown(f"<h3 style='text-align: center; font-weight: 400;'>Анализ кадра {i + 1} из {len(files)}...</h3>", unsafe_allow_html=True)
             
-        for i, file in enumerate(uploaded_files):
             img = Image.open(file)
             images.append(img)
-            with cols[i % 5]:
-                st.image(img, use_container_width=True)
-                with st.spinner("VLM Анализ..."):
-                    caption = image_processor.describe_scene(img)
-                    st.caption(f"👁️ *{caption.capitalize()}*")
-                    all_objects.append(caption)
             
-        if st.button("🎵 Сгенерировать саундтрек", type="primary", use_container_width=True):
-            
-            # 1. Извлекаем эмоции
-            all_v, all_a = [], []
-            for img in images:
             embedding = image_processor.extract_embedding(img)
             v, a = matcher.predict_va(embedding)
             all_v.append(v)
             all_a.append(a)
             
+            caption = image_processor.describe_scene(img)
+            all_objects.append(caption)
+            
         target_v, target_a = np.mean(all_v), np.mean(all_a)
         
-            # 2. Переводим Объекты -> Акустику через LLM
+        status_text.markdown("<h3 style='text-align: center; font-weight: 400;'>Трансляция семантики в аудиопрофиль...</h3>", unsafe_allow_html=True)
-            with st.spinner("Phi-3 генерирует акустический профиль..."):
         llm = LLMAcousticBridge()
         llm_profile = llm.get_acoustic_profile(target_v, target_a, list(set(all_objects)))
         
-            # 3. Ищем треки
+        status_text.markdown("<h3 style='text-align: center; font-weight: 400;'>Поиск идеальных композиций...</h3>", unsafe_allow_html=True)
-            with st.spinner("Поиск треков в базе DEAM..."):
+        playlist = matcher.find_nearest_tracks(target_v, target_a, llm_profile=llm_profile, top_k=15)
-                playlist = matcher.find_nearest_tracks(target_v, target_a, llm_profile=llm_profile, top_k=5)
         
-            st.success("✅ Кросс-модальный анализ завершен!")
+        st.session_state.result_data = {
+            "target_v": target_v,
+            "target_a": target_a,
+            "llm_profile": llm_profile,
+            "playlist": playlist,
+            "semantics": list(set(all_objects))
+        }
+        st.session_state.live_state = "result"
+        st.rerun()
         
-            # ВЫВОД РЕЗУЛЬТАТОВ
+    # ==========================================
-            col_left, col_right = st.columns([1, 2])
+    # ЭКРАН 3: РЕЗУЛЬТАТЫ
+    # ==========================================
+    elif st.session_state.live_state == "result":
         
-            with col_left:
+        components.html("<script>window.parent.scrollTo(0, 0);</script>", height=0, width=0)
-                st.header("📊 Профиль")
-                st.metric("Valence (Настроение)", f"{target_v:.2f}")
-                st.metric("Arousal (Энергия)", f"{target_a:.2f}")
         
-                if llm_profile:
+        data = st.session_state.result_data
-                    st.write("**Требования LLM к звуку:**")
+        st.header("Рекомендованный плейлист")
-                    for k, v in llm_profile.items():
-                        st.caption(f"- {k}: {v:.2f}")
         
-            with col_right:
+        for _, row in data["playlist"].iterrows():
-                st.header("🎵 Плейлист")
-                for _, row in playlist.iterrows():
             with st.container(border=True):
+                if viewport == "desktop":
                     c1, c2 = st.columns([1, 3])
                     with c1:
                         st.write(f"**Track:** {int(row['song_id'])}")
@@ -80,3 +167,42 @@ def render_live_tab(matcher, image_processor):
                             st.audio(str(audio_path))
                         else:
                             st.warning("Файл не найден")
+                else:
+                    st.write(f"**Track:** {int(row['song_id'])} (Score: {row['final_score']:.2f})")
+                    audio_path = matcher.get_audio_path(row['song_id'])
+                    if audio_path:
+                        st.audio(str(audio_path))
+                    else:
+                        st.warning("Файл не найден")
+
+        st.markdown("<br>", unsafe_allow_html=True)
+        
+        with st.expander("Технические параметры анализа"):
+            c_v, c_a = st.columns(2)
+            c_v.metric("Valence (Настроение)", f"{data['target_v']:.2f}")
+            c_a.metric("Arousal (Энергия)", f"{data['target_a']:.2f}")
+            
+            st.markdown("---")
+            st.write("**Акустические таргеты (LLM):**")
+            if data["llm_profile"]:
+                cols_per_row = 2 if viewport == "mobile" else 3
+                llm_items = list(data["llm_profile"].items())
+                
+                for i in range(0, len(llm_items), cols_per_row):
+                    cols = st.columns(cols_per_row)
+                    for j in range(cols_per_row):
+                        if i + j < len(llm_items):
+                            k, v = llm_items[i + j]
+                            cols[j].metric(k, f"{v:.2f}")
+                            
+            st.markdown("---")
+            st.write("**Обнаруженная семантика:**")
+            st.write(", ".join([str(c).capitalize() for c in data["semantics"]]))
+
+        st.markdown("<br>", unsafe_allow_html=True)
+        
+        if st.button("Новый анализ", use_container_width=True):
+            st.session_state.live_state = "upload"
+            st.session_state.result_data = None
+            st.session_state.pop("uploaded_images", None)
+            st.rerun()

tree.txt

@@ -0,0 +1,461 @@
+.
+├── bin
+│   ├── activate
+│   ├── activate.csh
+│   ├── activate.fish
+│   ├── activate.nu
+│   ├── activate.ps1
+│   ├── activate_this.py
+│   ├── debugpy
+│   ├── debugpy-adapter
+│   ├── f2py
+│   ├── fonttools
+│   ├── httpx
+│   ├── ipython
+│   ├── ipython3
+│   ├── isympy
+│   ├── jlpm
+│   ├── jsonpointer
+│   ├── jsonschema
+│   ├── jupyter
+│   ├── jupyter-dejavu
+│   ├── jupyter-events
+│   ├── jupyter-execute
+│   ├── jupyter-kernel
+│   ├── jupyter-kernelspec
+│   ├── jupyter-lab
+│   ├── jupyter-labextension
+│   ├── jupyter-labhub
+│   ├── jupyter-migrate
+│   ├── jupyter-nbconvert
+│   ├── jupyter-run
+│   ├── jupyter-server
+│   ├── jupyter-troubleshoot
+│   ├── jupyter-trust
+│   ├── normalizer
+│   ├── numpy-config
+│   ├── pip
+│   ├── pip3
+│   ├── pip3.12
+│   ├── proton
+│   ├── proton-viewer
+│   ├── pybabel
+│   ├── pyftmerge
+│   ├── pyftsubset
+│   ├── pygmentize
+│   ├── pyjson5
+│   ├── python -> /usr/bin/python3
+│   ├── python3 -> python
+│   ├── python3.12 -> python
+│   ├── send2trash
+│   ├── streamlit
+│   ├── streamlit.cmd
+│   ├── torchfrtrace
+│   ├── torchrun
+│   ├── tqdm
+│   ├── ttx
+│   ├── watchmedo
+│   └── wsdump
+├── CACHEDIR.TAG
+├── docker
+│   ├── Dockerfile.api
+│   └── Dockerfile.ui
+├── docker-compose.yml
+├── Dockerfile
+├── .dockerignore
+├── .env
+├── etc
+│   └── jupyter
+│       ├── jupyter_notebook_config.d
+│       │   └── jupyterlab.json
+│       ├── jupyter_server_config.d
+│       │   ├── jupyterlab.json
+│       │   ├── jupyter-lsp-jupyter-server.json
+│       │   ├── jupyter_server_terminals.json
+│       │   └── notebook_shim.json
+│       └── nbconfig
+│           └── notebook.d
+├── .gitignore
+├── .idea
+│   ├── .gitignore
+│   ├── inspectionProfiles
+│   │   └── profiles_settings.xml
+│   ├── misc.xml
+│   ├── modules.xml
+│   ├── Thesis.iml
+│   ├── vcs.xml
+│   └── workspace.xml
+├── lib
+│   └── python3.12
+│       └── site-packages
+│           ├── altair
+│           ├── altair-6.0.0.dist-info
+│           ├── anyio
+│           ├── anyio-4.12.1.dist-info
+│           ├── argon2
+│           ├── argon2_cffi-25.1.0.dist-info
+│           ├── _argon2_cffi_bindings
+│           ├── argon2_cffi_bindings-25.1.0.dist-info
+│           ├── arrow
+│           ├── arrow-1.4.0.dist-info
+│           ├── asttokens
+│           ├── asttokens-3.0.1.dist-info
+│           ├── async_lru
+│           ├── async_lru-2.0.5.dist-info
+│           ├── attr
+│           ├── attrs
+│           ├── attrs-25.4.0.dist-info
+│           ├── babel
+│           ├── babel-2.17.0.dist-info
+│           ├── beautifulsoup4-4.14.3.dist-info
+│           ├── bleach
+│           ├── bleach-6.3.0.dist-info
+│           ├── blinker
+│           ├── blinker-1.9.0.dist-info
+│           ├── bs4
+│           ├── cachetools
+│           ├── cachetools-6.2.4.dist-info
+│           ├── certifi
+│           ├── certifi-2026.1.4.dist-info
+│           ├── cffi
+│           ├── cffi-2.0.0.dist-info
+│           ├── _cffi_backend.cpython-312-x86_64-linux-gnu.so
+│           ├── charset_normalizer
+│           ├── charset_normalizer-3.4.4.dist-info
+│           ├── click
+│           ├── click-8.3.1.dist-info
+│           ├── comm
+│           ├── comm-0.2.3.dist-info
+│           ├── contourpy
+│           ├── contourpy-1.3.3.dist-info
+│           ├── cycler
+│           ├── cycler-0.12.1.dist-info
+│           ├── dateutil
+│           ├── debugpy
+│           ├── debugpy-1.8.19.dist-info
+│           ├── decorator-5.2.1.dist-info
+│           ├── decorator.py
+│           ├── defusedxml
+│           ├── defusedxml-0.7.1.dist-info
+│           ├── _distutils_hack
+│           ├── distutils-precedence.pth
+│           ├── .DS_Store
+│           ├── executing
+│           ├── executing-2.2.1.dist-info
+│           ├── fastjsonschema
+│           ├── fastjsonschema-2.21.2.dist-info
+│           ├── filelock
+│           ├── filelock-3.20.3.dist-info
+│           ├── fontTools
+│           ├── fonttools-4.61.1.dist-info
+│           ├── fqdn
+│           ├── fqdn-1.5.1.dist-info
+│           ├── fsspec
+│           ├── fsspec-2026.1.0.dist-info
+│           ├── functorch
+│           ├── git
+│           ├── gitdb
+│           ├── gitdb-4.0.12.dist-info
+│           ├── gitpython-3.1.46.dist-info
+│           ├── google
+│           ├── h11
+│           ├── h11-0.16.0.dist-info
+│           ├── httpcore
+│           ├── httpcore-1.0.9.dist-info
+│           ├── httpx
+│           ├── httpx-0.28.1.dist-info
+│           ├── idna
+│           ├── idna-3.11.dist-info
+│           ├── ipykernel
+│           ├── ipykernel-7.1.0.dist-info
+│           ├── ipykernel_launcher.py
+│           ├── IPython
+│           ├── ipython-9.9.0.dist-info
+│           ├── ipython_pygments_lexers-1.1.1.dist-info
+│           ├── ipython_pygments_lexers.py
+│           ├── isoduration
+│           ├── isoduration-20.11.0.dist-info
+│           ├── isympy.py
+│           ├── jedi
+│           ├── jedi-0.19.2.dist-info
+│           ├── jinja2
+│           ├── jinja2-3.1.6.dist-info
+│           ├── joblib
+│           ├── joblib-1.5.3.dist-info
+│           ├── json5
+│           ├── json5-0.13.0.dist-info
+│           ├── jsonpointer-3.0.0.dist-info
+│           ├── jsonpointer.py
+│           ├── jsonschema
+│           ├── jsonschema-4.26.0.dist-info
+│           ├── jsonschema_specifications
+│           ├── jsonschema_specifications-2025.9.1.dist-info
+│           ├── jupyter_client
+│           ├── jupyter_client-8.8.0.dist-info
+│           ├── jupyter_core
+│           ├── jupyter_core-5.9.1.dist-info
+│           ├── jupyter_events
+│           ├── jupyter_events-0.12.0.dist-info
+│           ├── jupyterlab
+│           ├── jupyterlab-4.5.1.dist-info
+│           ├── jupyterlab_pygments
+│           ├── jupyterlab_pygments-0.3.0.dist-info
+│           ├── jupyterlab_server
+│           ├── jupyterlab_server-2.28.0.dist-info
+│           ├── jupyter_lsp
+│           ├── jupyter_lsp-2.3.0.dist-info
+│           ├── jupyter.py
+│           ├── jupyter_server
+│           ├── jupyter_server-2.17.0.dist-info
+│           ├── jupyter_server_terminals
+│           ├── jupyter_server_terminals-0.5.3.dist-info
+│           ├── kiwisolver
+│           ├── kiwisolver-1.4.9.dist-info
+│           ├── lark
+│           ├── lark-1.3.1.dist-info
+│           ├── markupsafe
+│           ├── markupsafe-3.0.3.dist-info
+│           ├── matplotlib
+│           ├── matplotlib-3.10.8.dist-info
+│           ├── matplotlib_inline
+│           ├── matplotlib_inline-0.2.1.dist-info
+│           ├── mistune
+│           ├── mistune-3.2.0.dist-info
+│           ├── mpl_toolkits
+│           ├── mpmath
+│           ├── mpmath-1.3.0.dist-info
+│           ├── narwhals
+│           ├── narwhals-2.15.0.dist-info
+│           ├── nbclient
+│           ├── nbclient-0.10.4.dist-info
+│           ├── nbconvert
+│           ├── nbconvert-7.16.6.dist-info
+│           ├── nbformat
+│           ├── nbformat-5.10.4.dist-info
+│           ├── nest_asyncio-1.6.0.dist-info
+│           ├── nest_asyncio.py
+│           ├── networkx
+│           ├── networkx-3.6.1.dist-info
+│           ├── notebook_shim
+│           ├── notebook_shim-0.2.4.dist-info
+│           ├── numpy
+│           ├── numpy-2.4.1.dist-info
+│           ├── numpy.libs
+│           ├── nvidia
+│           ├── nvidia_cublas_cu12-12.8.4.1.dist-info
+│           ├── nvidia_cuda_cupti_cu12-12.8.90.dist-info
+│           ├── nvidia_cuda_nvrtc_cu12-12.8.93.dist-info
+│           ├── nvidia_cuda_runtime_cu12-12.8.90.dist-info
+│           ├── nvidia_cudnn_cu12-9.10.2.21.dist-info
+│           ├── nvidia_cufft_cu12-11.3.3.83.dist-info
+│           ├── nvidia_cufile_cu12-1.13.1.3.dist-info
+│           ├── nvidia_curand_cu12-10.3.9.90.dist-info
+│           ├── nvidia_cusolver_cu12-11.7.3.90.dist-info
+│           ├── nvidia_cusparse_cu12-12.5.8.93.dist-info
+│           ├── nvidia_cusparselt_cu12-0.7.1.dist-info
+│           ├── nvidia_nccl_cu12-2.27.5.dist-info
+│           ├── nvidia_nvjitlink_cu12-12.8.93.dist-info
+│           ├── nvidia_nvshmem_cu12-3.3.20.dist-info
+│           ├── nvidia_nvtx_cu12-12.8.90.dist-info
+│           ├── packaging
+│           ├── packaging-25.0.dist-info
+│           ├── pandas
+│           ├── pandas-2.3.3.dist-info
+│           ├── pandocfilters-1.5.1.dist-info
+│           ├── pandocfilters.py
+│           ├── parso
+│           ├── parso-0.8.5.dist-info
+│           ├── pexpect
+│           ├── pexpect-4.9.0.dist-info
+│           ├── PIL
+│           ├── pillow-12.1.0.dist-info
+│           ├── pillow.libs
+│           ├── pip
+│           ├── pip-25.3.dist-info
+│           ├── pkg_resources
+│           ├── platformdirs
+│           ├── platformdirs-4.5.1.dist-info
+│           ├── prometheus_client
+│           ├── prometheus_client-0.23.1.dist-info
+│           ├── prompt_toolkit
+│           ├── prompt_toolkit-3.0.52.dist-info
+│           ├── protobuf-6.33.4.dist-info
+│           ├── psutil
+│           ├── psutil-7.2.1.dist-info
+│           ├── ptyprocess
+│           ├── ptyprocess-0.7.0.dist-info
+│           ├── pure_eval
+│           ├── pure_eval-0.2.3.dist-info
+│           ├── pyarrow
+│           ├── pyarrow-22.0.0.dist-info
+│           ├── pycparser
+│           ├── pycparser-2.23.dist-info
+│           ├── pydeck
+│           ├── pydeck-0.9.1.dist-info
+│           ├── pygments
+│           ├── pygments-2.19.2.dist-info
+│           ├── pylab.py
+│           ├── pyparsing
+│           ├── pyparsing-3.3.1.dist-info
+│           ├── python_dateutil-2.9.0.post0.dist-info
+│           ├── pythonjsonlogger
+│           ├── python_json_logger-4.0.0.dist-info
+│           ├── pytz
+│           ├── pytz-2025.2.dist-info
+│           ├── pyyaml-6.0.3.dist-info
+│           ├── pyzmq-27.1.0.dist-info
+│           ├── pyzmq.libs
+│           ├── referencing
+│           ├── referencing-0.37.0.dist-info
+│           ├── requests
+│           ├── requests-2.32.5.dist-info
+│           ├── rfc3339_validator-0.1.4.dist-info
+│           ├── rfc3339_validator.py
+│           ├── rfc3986_validator-0.1.1.dist-info
+│           ├── rfc3986_validator.py
+│           ├── rfc3987_syntax
+│           ├── rfc3987_syntax-1.1.0.dist-info
+│           ├── rpds
+│           ├── rpds_py-0.30.0.dist-info
+│           ├── scikit_learn-1.8.0.dist-info
+│           ├── scikit_learn.libs
+│           ├── scipy
+│           ├── scipy-1.17.0.dist-info
+│           ├── scipy.libs
+│           ├── send2trash
+│           ├── send2trash-2.0.0.dist-info
+│           ├── setuptools
+│           ├── setuptools-80.9.0.dist-info
+│           ├── six-1.17.0.dist-info
+│           ├── six.py
+│           ├── sklearn
+│           ├── smmap
+│           ├── smmap-5.0.2.dist-info
+│           ├── soupsieve
+│           ├── soupsieve-2.8.1.dist-info
+│           ├── stack_data
+│           ├── stack_data-0.6.3.dist-info
+│           ├── streamlit
+│           ├── streamlit-1.53.0.dist-info
+│           ├── sympy
+│           ├── sympy-1.14.0.dist-info
+│           ├── tenacity
+│           ├── tenacity-9.1.2.dist-info
+│           ├── terminado
+│           ├── terminado-0.18.1.dist-info
+│           ├── threadpoolctl-3.6.0.dist-info
+│           ├── threadpoolctl.py
+│           ├── tinycss2
+│           ├── tinycss2-1.4.0.dist-info
+│           ├── toml
+│           ├── toml-0.10.2.dist-info
+│           ├── torch
+│           ├── torch-2.9.1.dist-info
+│           ├── torchaudio
+│           ├── torchaudio-2.9.1.dist-info
+│           ├── torchgen
+│           ├── torchvision
+│           ├── torchvision-0.24.1.dist-info
+│           ├── torchvision.libs
+│           ├── tornado
+│           ├── tornado-6.5.4.dist-info
+│           ├── tqdm
+│           ├── tqdm-4.67.1.dist-info
+│           ├── traitlets
+│           ├── traitlets-5.14.3.dist-info
+│           ├── triton
+│           ├── triton-3.5.1.dist-info
+│           ├── typing_extensions-4.15.0.dist-info
+│           ├── typing_extensions.py
+│           ├── tzdata
+│           ├── tzdata-2025.3.dist-info
+│           ├── uri_template
+│           ├── uri_template-1.3.0.dist-info
+│           ├── urllib3
+│           ├── urllib3-2.6.3.dist-info
+│           ├── _virtualenv.pth
+│           ├── _virtualenv.py
+│           ├── watchdog
+│           ├── watchdog-6.0.0.dist-info
+│           ├── wcwidth
+│           ├── wcwidth-0.2.14.dist-info
+│           ├── webcolors
+│           ├── webcolors-25.10.0.dist-info
+│           ├── webencodings
+│           ├── webencodings-0.5.1.dist-info
+│           ├── websocket
+│           ├── websocket_client-1.9.0.dist-info
+│           ├── _yaml
+│           ├── yaml
+│           └── zmq
+├── Makefile
+├── NFS
+├── poetry.lock
+├── pyproject.toml
+├── pyvenv.cfg
+├── README.md
+├── requirements.txt
+├── runs
+├── share
+│   ├── applications
+│   │   └── jupyterlab.desktop
+│   ├── icons
+│   │   └── hicolor
+│   │       └── scalable
+│   ├── jupyter
+│   │   ├── kernels
+│   │   │   └── python3
+│   │   ├── lab
+│   │   │   ├── schemas
+│   │   │   ├── static
+│   │   │   └── themes
+│   │   ├── labextensions
+│   │   │   └── jupyterlab_pygments
+│   │   ├── nbconvert
+│   │   │   └── templates
+│   │   └── nbextensions
+│   │       └── pydeck
+│   └── man
+│       └── man1
+│           ├── ipython.1
+│           ├── isympy.1
+│           └── ttx.1
+├── src
+│   ├── 5_epoch_emoset_resnet50_finetuned_2.41M.pth
+│   ├── api.py
+│   ├── data_loader.py
+│   ├── dataset_paths_cache.pkl
+│   ├── emoset_resnet50_best.pth
+│   ├── emoset_resnet50_finetuned_2_41M_best.pth
+│   ├── emoset_resnet50_resume.pth
+│   ├── emoset_test_embeddings.npy
+│   ├── emoset_test_labels.npy
+│   ├── main.py
+│   ├── music_engine
+│   │   ├── image_processor.py
+│   │   ├── __init__.py
+│   │   ├── llm_bridge.py
+│   │   ├── matcher.py
+│   │   └── va_regressor.pkl
+│   ├── scripts
+│   │   ├── 00_setup_env.sh
+│   │   ├── 01_download_DEAM.py
+│   │   ├── 02_download_EmoSet.py
+│   │   ├── 11_prerp_DEAM.py
+│   │   ├── 20_bench_GPU.py
+│   │   ├── 21_train_images.ipynb
+│   │   ├── 22_extract_embeddings.ipynb
+│   │   ├── 23_aggregate_DEAM_timeline.py
+│   │   ├── 24_train_regressor.py
+│   │   ├── 31_finetune_2.41M.py
+│   │   ├── 90_acc_images_model.ipynb
+│   │   └── 91_generate_metrics.py
+│   └── tabs
+│       ├── tab_dataset.py
+│       └── tab_live.py
+├── tree.txt
+└── .vscode
+    ├── launch.json
+    └── tasks.json
+
+322 directories, 137 files