From f04cd7359b90a9b818384a090bfa0be4613e2466 Mon Sep 17 00:00:00 2001 From: zin Date: Tue, 2 Jun 2026 17:27:05 +0000 Subject: [PATCH] ref: refactor before chekout --- src/data_loader.py | 39 +- src/music_engine/image_processor.py | 55 +- src/music_engine/llm_bridge.py | 59 +- src/music_engine/matcher.py | 68 +- src/scripts/01_download_DEAM.py | 20 + src/scripts/02_download_EmoSet.py | 56 ++ src/scripts/11_prerp_DEAM.py | 30 + src/scripts/20_bench_GPU.py | 60 ++ ...ain_images.ipynb => 21_train_images.ipynb} | 438 +++------ src/scripts/22_extract_embeddings.ipynb | 286 ++++++ src/scripts/23_aggregate_DEAM_timeline.py | 69 ++ src/scripts/24_train_regressor.py | 80 ++ src/scripts/31_finetune_2.41M.py | 264 +++++ src/scripts/aggregate_features.ipynb | 125 --- src/scripts/download emo_dataset.ipynb | 614 ------------ src/scripts/download_DEAM.ipynb | 140 --- src/scripts/download_dataset.py | 171 ---- src/scripts/extract_embeddings.ipynb | 502 ---------- src/scripts/finetune.py | 314 ------ src/scripts/finetune_embeddings.ipynb | 467 --------- src/scripts/prep_deam.ipynb | 88 -- src/scripts/prepare_dataset_to_train.ipynb | 114 --- src/scripts/preprocessing.py | 134 --- src/scripts/preprocessing_2.1M.ipynb | 919 ------------------ src/scripts/test.ipynb | 199 ---- src/scripts/train_regressor.py | 58 -- src/scripts/tsne_embeddings.png | Bin 0 -> 1892529 bytes 27 files changed, 1103 insertions(+), 4266 deletions(-) create mode 100644 src/scripts/01_download_DEAM.py create mode 100644 src/scripts/02_download_EmoSet.py create mode 100644 src/scripts/11_prerp_DEAM.py create mode 100644 src/scripts/20_bench_GPU.py rename src/scripts/{train_images.ipynb => 21_train_images.ipynb} (73%) create mode 100644 src/scripts/22_extract_embeddings.ipynb create mode 100644 src/scripts/23_aggregate_DEAM_timeline.py create mode 100644 src/scripts/24_train_regressor.py create mode 100644 src/scripts/31_finetune_2.41M.py delete mode 100644 src/scripts/aggregate_features.ipynb delete mode 100644 src/scripts/download emo_dataset.ipynb delete mode 100644 src/scripts/download_DEAM.ipynb delete mode 100644 src/scripts/download_dataset.py delete mode 100644 src/scripts/extract_embeddings.ipynb delete mode 100644 src/scripts/finetune.py delete mode 100644 src/scripts/finetune_embeddings.ipynb delete mode 100644 src/scripts/prep_deam.ipynb delete mode 100644 src/scripts/prepare_dataset_to_train.ipynb delete mode 100644 src/scripts/preprocessing.py delete mode 100644 src/scripts/preprocessing_2.1M.ipynb delete mode 100644 src/scripts/test.ipynb delete mode 100644 src/scripts/train_regressor.py create mode 100644 src/scripts/tsne_embeddings.png diff --git a/src/data_loader.py b/src/data_loader.py index da67563..caa7ae5 100644 --- a/src/data_loader.py +++ b/src/data_loader.py @@ -1,54 +1,55 @@ -import streamlit as st +import os from pathlib import Path import pandas as pd import numpy as np +import streamlit as st + 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(): - """Загрузка базы данных и модели регрессора.""" - # music_db.csv лежит в dataset/DEAM/ (на уровень выше от src) + # Инициализация базы данных и регрессора для музыкального мэтчинга 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!") + print(f"Музыкальная БД не найдена: {db_path}") return None + return MusicMatcher(db_path=db_path, model_path=model_path) @st.cache_resource def load_image_processor(): - """Загрузка ResNet-50 для извлечения признаков на лету.""" - # Файл весов лежит в той же папке src, что и этот скрипт + # Модуль обработки визуальных признаков model_path = BASE_DIR / "emoset_resnet50_best.pth" + # Обработка пути при вызове из корневой директории if not model_path.exists(): - print(f"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) @st.cache_data 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" + # Выборка данных датасета для вкладки отладки + dataset_root = BASE_DIR.parent / "dataset" / "EmoSet-118K" / "test" + + csv_path = dataset_root / "labels.csv" + img_dir = dataset_root / "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()]): + print("Тестовые файлы датасета не найдены, вкладка отладки может работать некорректно") return None, None, None, None - df = pd.read_csv(csv_path) - image_list = df['filename'].tolist() - embs = np.load(emb_path) - lbls = np.load(lbl_path) + labels_df = pd.read_csv(csv_path) - return image_list, embs, lbls, img_dir \ No newline at end of file + test_filenames = labels_df['filename'].tolist() + test_embeddings = np.load(emb_path) + test_labels = np.load(lbl_path) + + return test_filenames, test_embeddings, test_labels, img_dir \ No newline at end of file diff --git a/src/music_engine/image_processor.py b/src/music_engine/image_processor.py index 5d13dad..fb654c2 100644 --- a/src/music_engine/image_processor.py +++ b/src/music_engine/image_processor.py @@ -1,49 +1,62 @@ +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 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') - self.emo_model = 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)) + # Модель извлечения визуальных признаков + self.feature_extractor = timm.create_model('resnet50', pretrained=False, num_classes=8) - self.emo_model.fc = torch.nn.Identity() - self.emo_model.to(self.device).eval() + if Path(weights_path).exists(): + 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}") + + # Удаление слоя классификации для вывода сырого вектора эмбеддингов + self.feature_extractor.fc = torch.nn.Identity() + self.feature_extractor.to(self.device).eval() - self.emo_transform = T.Compose([ + # 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]) ]) - print("Загрузка BLIP-2...") + # Модуль семантического описания сцены + print("Инициализация BLIP-2...") self.blip_processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b") self.blip_model = Blip2ForConditionalGeneration.from_pretrained( "Salesforce/blip2-opt-2.7b", 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') - img_tensor = self.emo_transform(img_rgb).unsqueeze(0).to(self.device) - return self.emo_model(img_tensor).cpu().numpy().flatten() + # Извлечение эмбеддингов из изображения + rgb_image = image.convert('RGB') + 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.""" - img_rgb = image.convert('RGB') - - inputs = self.blip_processor(images=img_rgb, return_tensors="pt").to(self.device, torch.float16) + # Генерация текстового описания сцены + rgb_image = image.convert('RGB') + inputs = self.blip_processor(images=rgb_image, return_tensors="pt").to(self.device, torch.float16) 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 \ No newline at end of file + + scene_description = self.blip_processor.batch_decode(generated_ids, skip_special_tokens=True)[0] + + return scene_description.strip() \ No newline at end of file diff --git a/src/music_engine/llm_bridge.py b/src/music_engine/llm_bridge.py index d8ea36e..9e698ad 100644 --- a/src/music_engine/llm_bridge.py +++ b/src/music_engine/llm_bridge.py @@ -1,31 +1,31 @@ -import requests -import json import re +import json +import requests class LLMAcousticBridge: - def __init__(self, model_name="dolphin-llama3:8b"): - self.model_name = model_name + def __init__(self, target_model="dolphin-llama3:8b"): self.api_url = "http://localhost:11434/api/generate" + self.model = target_model - def _clean_json(self, text): - """Вытаскивает чистый JSON из ответа нейросети.""" + def _extract_json(self, raw_text: str): + # Проверка на ИИдиота, LLM иногда игнорирует format="json" и оборачивает ответ в маркдаун try: - match = re.search(r'\{.*\}', text, re.DOTALL) + match = re.search(r'\{.*\}', raw_text, re.DOTALL) if match: return json.loads(match.group(0)) - return json.loads(text) - except: + return json.loads(raw_text) + except json.JSONDecodeError: + # Если ИИдиот return None - def get_acoustic_profile(self, valence, arousal, scene_descriptions): - """Просит LLM сгенерировать идеальный звук под описание.""" - # Объединяем описания, если загружено несколько фото - context_str = " | ".join(scene_descriptions) if scene_descriptions else "abstract scene" + def get_acoustic_profile(self, v_score: float, a_score: float, scene_context: list) -> dict | None: + # Агрегация контекста для обработки серии снимков (события) + context_merged = " | ".join(scene_context) if scene_context else "abstract scene" - prompt = f"""You are an expert music producer and acoustic engineer. + system_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}. +Emotions: Valence {v_score:.1f}/9.0 (Positivity), Arousal {a_score:.1f}/9.0 (Energy). +Visual Context: {context_merged}. 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) @@ -39,22 +39,27 @@ Return ONLY a valid JSON object. Do not add any text or explanation. Example: {{"energy": 0.5, "flux": 0.2, "centroid": 0.4, "pitch": 0.3, "hnr": 0.8, "zcr": 0.1}}""" try: + # Отправка промпта локальной Ollama response = requests.post(self.api_url, json={ - "model": self.model_name, - "prompt": prompt, + "model": self.model, + "prompt": system_prompt, "stream": False, "format": "json" - }, timeout=30) + }, timeout=45) response.raise_for_status() - result_text = response.json().get("response", "") - profile = self._clean_json(result_text) + raw_response = response.json().get("response", "") + profile_data = self._extract_json(raw_response) - # Проверяем, что все нужные ключи есть - required_keys = ['energy', 'flux', 'centroid', 'pitch', 'hnr', 'zcr'] - if profile and all(k in profile for k in required_keys): - return profile + # Валидация структуры ответа + expected_features = {'energy', 'flux', 'centroid', 'pitch', 'hnr', 'zcr'} + + if profile_data and expected_features.issubset(profile_data.keys()): + return profile_data + + print("LLM вернула неполный или некорректный набор акустических признаков") return None - except Exception as e: - print(f"Ошибка связи с локальной LLM: {e}") + + except requests.exceptions.RequestException as req_err: + print(f"Не удалось подключиться к Ollama: {req_err}") return None \ No newline at end of file diff --git a/src/music_engine/matcher.py b/src/music_engine/matcher.py index a5917ff..22cf06b 100644 --- a/src/music_engine/matcher.py +++ b/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 - self.music_db = self.music_db.dropna(subset=['valence', 'arousal'] + self.acoustic_features) + # Удаление записей с пропущенными целевыми или акустическими признаками + 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 + + if Path(model_path).exists(): + self.regressor = joblib.load(model_path) + else: + self.regressor = None - def predict_va(self, embedding: np.ndarray): - if self.regressor: - prediction = self.regressor.predict(embedding.reshape(1, -1))[0] - return np.clip(prediction[0], 1.0, 9.0), np.clip(prediction[1], 1.0, 9.0) - return 5.0, 5.0 + 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 + + raw_prediction = self.regressor.predict(embedding.reshape(1, -1))[0] + 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): - if not self.audio_dir.exists(): return None + 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): - # 1. Эмоциональная дистанция (как и раньше) - emo_dist = np.sqrt( - 1.0 * (self.norm_db['valence'] - target_v)**2 + - 2.5 * (self.norm_db['arousal'] - target_a)**2 - ) - self.norm_db['emo_distance'] = emo_dist + def find_nearest_tracks(self, target_v: float, target_a: float, llm_profile: dict = None, top_k: int = 5) -> pd.DataFrame: + # Расчет евклидова расстояния в эмоциональном пространстве Рассела + v_dist = (self.norm_db['valence'] - target_v) ** 2 + a_dist = (self.norm_db['arousal'] - target_a) ** 2 - # 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) \ No newline at end of file diff --git a/src/scripts/01_download_DEAM.py b/src/scripts/01_download_DEAM.py new file mode 100644 index 0000000..815585d --- /dev/null +++ b/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.") \ No newline at end of file diff --git a/src/scripts/02_download_EmoSet.py b/src/scripts/02_download_EmoSet.py new file mode 100644 index 0000000..8db5cde --- /dev/null +++ b/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.") \ No newline at end of file diff --git a/src/scripts/11_prerp_DEAM.py b/src/scripts/11_prerp_DEAM.py new file mode 100644 index 0000000..c3b871d --- /dev/null +++ b/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() \ No newline at end of file diff --git a/src/scripts/20_bench_GPU.py b/src/scripts/20_bench_GPU.py new file mode 100644 index 0000000..347de47 --- /dev/null +++ b/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() \ No newline at end of file diff --git a/src/scripts/train_images.ipynb b/src/scripts/21_train_images.ipynb similarity index 73% rename from src/scripts/train_images.ipynb rename to src/scripts/21_train_images.ipynb index 4f5c4b0..83c0a4a 100644 --- a/src/scripts/train_images.ipynb +++ b/src/scripts/21_train_images.ipynb @@ -3,30 +3,22 @@ { "cell_type": "code", "execution_count": null, - "id": "9336560f", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 1, "id": "0c00b67b", "metadata": {}, "outputs": [], "source": [ + "import os\n", + "from pathlib import Path\n", + "from PIL import Image\n", + "import pandas as pd\n", + "import numpy as np\n", + "from tqdm import tqdm\n", + "\n", "import torch\n", "import torch.nn as nn\n", "from torch.utils.data import Dataset, DataLoader\n", "import torchvision.transforms as T\n", - "\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" + "import timm" ] }, { @@ -47,41 +39,53 @@ } ], "source": [ - "# === CONFIG ===\n", + "# Конфигурация параметров обучения и путей файловой системы\n", "DATA_ROOT = Path(\"../dataset/EmoSet-118K\")\n", "BATCH_SIZE = 64\n", "EPOCHS = 15\n", "LR = 3e-4\n", - "NUM_WORKERS = 24\n", + "NUM_WORKERS = 40\n", "\n", - "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", - "DEVICE\n" + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "print(f\"Аппаратное ускорение: {device}\")" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "9f749add", "metadata": {}, "outputs": [], "source": [ "class EmoSetDataset(Dataset):\n", - " def __init__(self, root, split):\n", + " def __init__(self, root: Path | str, split: str):\n", " self.root = Path(root) / split\n", " self.df = pd.read_csv(self.root / \"labels.csv\")\n", "\n", + " # Формирование словарей маппинга классов\n", " self.labels = sorted(self.df[\"label\"].unique())\n", " self.label2idx = {l: i for i, l in enumerate(self.labels)}\n", " self.idx2label = {i: l for l, i in self.label2idx.items()}\n", "\n", - " self.transform = T.Compose([\n", - " T.Resize((224, 224)),\n", + " # Базовые трансформации для валидации и теста\n", + " base_tf = [\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", + " T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n", + " ]\n", + "\n", + " # Внедрение аугментации исключительно для обучающей выборки (предотвращение переобучения)\n", + " if split == \"train\":\n", + " self.transform = T.Compose([\n", + " T.RandomResizedCrop(224),\n", + " T.RandomHorizontalFlip(),\n", + " *base_tf\n", + " ])\n", + " else:\n", + " self.transform = T.Compose([\n", + " T.Resize(256),\n", + " T.CenterCrop(224),\n", + " *base_tf\n", + " ])\n", "\n", " def __len__(self):\n", " return len(self.df)\n", @@ -90,16 +94,21 @@ " 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", + " # Обработка возможных исключений ввода-вывода (поврежденные JPEG-файлы в датасете)\n", + " try:\n", + " img = Image.open(img_path).convert(\"RGB\")\n", + " except Exception:\n", + " img = Image.new(\"RGB\", (224, 224), (0, 0, 0))\n", "\n", - " label = self.label2idx[row[\"label\"]]\n", - " return img, label\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": 4, + "execution_count": null, "id": "c8805341", "metadata": {}, "outputs": [ @@ -112,9 +121,11 @@ } ], "source": [ + "# Подготовка объектов выборки\n", "train_ds = EmoSetDataset(DATA_ROOT, \"train\")\n", "val_ds = EmoSetDataset(DATA_ROOT, \"val\")\n", "\n", + "# Инициализация итераторов с закреплением памяти (pin_memory) для ускорения передачи на GPU\n", "train_loader = DataLoader(\n", " train_ds,\n", " batch_size=BATCH_SIZE,\n", @@ -131,12 +142,12 @@ " pin_memory=True\n", ")\n", "\n", - "print(\"Classes:\", train_ds.labels)\n" + "print(f\"Индексированные классы: {train_ds.labels}\")" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "dffce582", "metadata": {}, "outputs": [ @@ -391,55 +402,51 @@ } ], "source": [ + "# TODO перед защитой, повторить оптимизаторы\n", + "# Загрузка предобученной архитектуры ResNet-50 с заменой классификационного слоя\n", "model = timm.create_model(\n", " \"resnet50\",\n", " pretrained=True,\n", " num_classes=len(train_ds.labels)\n", ")\n", + "model.to(device)\n", "\n", - "model.to(DEVICE)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "81a457ef", - "metadata": {}, - "outputs": [], - "source": [ + "# Функция потерь для многоклассовой классификации\n", "criterion = nn.CrossEntropyLoss()\n", "\n", + "# Оптимизатор AdamW с L2-регуляризацией (weight_decay) для повышения обобщающей способности\n", "optimizer = torch.optim.AdamW(\n", " model.parameters(),\n", " lr=LR,\n", " weight_decay=1e-4\n", ")\n", "\n", + "# Планировщик скорости обучения: косинусный отжиг\n", "scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(\n", " optimizer,\n", " T_max=EPOCHS\n", - ")\n" + ")" ] }, { "cell_type": "code", - "execution_count": 7, - "id": "951aa9e3", + "execution_count": null, + "id": "81a457ef", "metadata": {}, "outputs": [], "source": [ - "def train_epoch(model, loader):\n", - " model.train()\n", - " total_loss = 0\n", - " correct = 0\n", - " total = 0\n", + "def train_epoch(current_model, loader):\n", + " current_model.train()\n", + " total_loss = 0.0\n", + " correct_preds = 0\n", + " total_samples = 0\n", "\n", - " for imgs, labels in tqdm(loader, leave=False):\n", - " imgs = imgs.to(DEVICE)\n", - " labels = labels.to(DEVICE)\n", + " for imgs, labels in tqdm(loader, desc=\"Тренировка\", leave=False):\n", + " imgs = imgs.to(device)\n", + " labels = labels.to(device)\n", "\n", " optimizer.zero_grad()\n", - " logits = model(imgs)\n", + " logits = current_model(imgs)\n", " loss = criterion(logits, labels)\n", "\n", " loss.backward()\n", @@ -447,292 +454,67 @@ "\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", + " correct_preds += (preds == labels).sum().item()\n", + " total_samples += labels.size(0)\n", + "\n", + " return total_loss / total_samples, correct_preds / total_samples\n", "\n", - " 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", + "def val_epoch(current_model, loader):\n", + " # Перевод модели в режим инференса (отключение Dropout и фиксация BatchNorm)\n", + " current_model.eval()\n", + " total_loss = 0.0\n", + " correct_preds = 0\n", + " total_samples = 0\n", "\n", - " for imgs, labels in loader:\n", - " imgs = imgs.to(DEVICE)\n", - " labels = labels.to(DEVICE)\n", + " for imgs, labels in tqdm(loader, desc=\"Валидация\", leave=False):\n", + " imgs = imgs.to(device)\n", + " labels = labels.to(device)\n", "\n", - " logits = model(imgs)\n", + " logits = current_model(imgs)\n", " loss = criterion(logits, labels)\n", "\n", " total_loss += loss.item() * imgs.size(0)\n", " preds = logits.argmax(dim=1)\n", - " correct += (preds == labels).sum().item()\n", - " total += labels.size(0)\n", + " correct_preds += (preds == labels).sum().item()\n", + " total_samples += 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 best_val_acc:\n", - " best_val_acc = val_acc\n", - " torch.save(model.state_dict(), \"emoset_resnet50_best.pth\")\n" + " return total_loss / total_samples, correct_preds / total_samples" ] }, { "cell_type": "code", "execution_count": null, - "id": "7796ef11", + "id": "951aa9e3", "metadata": {}, "outputs": [], - "source": [] + "source": [ + "best_val_acc = 0.0\n", + "checkpoint_path = \"../emoset_resnet50_best.pth\"\n", + "\n", + "print(\"Старт процесса обучения...\")\n", + "\n", + "for epoch in range(1, EPOCHS + 1):\n", + " train_loss, train_acc = train_epoch(model, train_loader)\n", + " val_loss, val_acc = val_epoch(model, val_loader)\n", + "\n", + " # Обновление шага планировщика\n", + " scheduler.step()\n", + "\n", + " print(\n", + " f\"Эпоха {epoch:02d}/{EPOCHS} | \"\n", + " f\"Train Loss: {train_loss:.4f}, Acc: {train_acc:.4f} | \"\n", + " f\"Val Loss: {val_loss:.4f}, Acc: {val_acc:.4f}\"\n", + " )\n", + "\n", + " # Экспорт весов при улучшении целевой метрики\n", + " if val_acc > best_val_acc:\n", + " best_val_acc = val_acc\n", + " torch.save(model.state_dict(), checkpoint_path)\n", + " print(f\" -> Сохранен новый лучший чекпоинт (Acc: {best_val_acc:.4f})\")\n", + "\n", + "print(\"Обучение завершено.\")" + ] } ], "metadata": { diff --git a/src/scripts/22_extract_embeddings.ipynb b/src/scripts/22_extract_embeddings.ipynb new file mode 100644 index 0000000..78498e7 --- /dev/null +++ b/src/scripts/22_extract_embeddings.ipynb @@ -0,0 +1,286 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "9a9d805d", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from pathlib import Path\n", + "from PIL import Image\n", + "import pandas as pd\n", + "import numpy as np\n", + "from tqdm import tqdm\n", + "\n", + "import torch\n", + "import torch.nn as nn\n", + "from torch.utils.data import Dataset, DataLoader\n", + "import torchvision.transforms as T\n", + "import timm\n", + "\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.manifold import TSNE" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "f292b2f9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Вычисления перенесены на: cuda\n" + ] + } + ], + "source": [ + "# Конфигурация путей для инференса и кэширования векторов\n", + "DATA_ROOT = Path(\"../../NFS/Thesis/Emoset/EmoSet-118K\")\n", + "MODEL_PATH = Path(\"../emoset_resnet50_best.pth\")\n", + "\n", + "BATCH_SIZE = 128\n", + "NUM_WORKERS = 40\n", + "\n", + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "print(f\"Вычисления перенесены на: {device}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "f78ac97f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Подготовлено для извлечения: 17716 файлов.\n" + ] + } + ], + "source": [ + "class EmoSetFeatureDataset(Dataset):\n", + " def __init__(self, root: Path | str, split: str):\n", + " self.root = Path(root) / split\n", + " self.df = pd.read_csv(self.root / \"labels.csv\")\n", + "\n", + " 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", + " # Для экстракции признаков аугментация отключена, используется строгий CenterCrop\n", + " self.transform = T.Compose([\n", + " T.Resize(256),\n", + " T.CenterCrop(224),\n", + " T.ToTensor(),\n", + " T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\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", + " # Перехват битых файлов выборки\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\n", + "\n", + "test_ds = EmoSetFeatureDataset(DATA_ROOT, \"test\")\n", + "test_loader = DataLoader(\n", + " test_ds,\n", + " batch_size=BATCH_SIZE,\n", + " shuffle=False, # Отключение шаффла для строгого соответствия индексов\n", + " num_workers=NUM_WORKERS,\n", + " pin_memory=True\n", + ")\n", + "\n", + "print(f\"Подготовлено для извлечения: {len(test_ds)} файлов.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "2bc75aed", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Слой классификации удален. Модель готова к экстракции.\n" + ] + } + ], + "source": [ + "# Инициализация модели и загрузка лучших весов\n", + "feature_extractor = timm.create_model(\n", + " \"resnet50\",\n", + " pretrained=False,\n", + " num_classes=len(test_ds.labels)\n", + ")\n", + "\n", + "checkpoint = torch.load(MODEL_PATH, map_location=device)\n", + "feature_extractor.load_state_dict(checkpoint)\n", + "\n", + "# Удаление классификационного слоя (fc) для получения сырого тензора признаков (2048 измерений)\n", + "feature_extractor.reset_classifier(0)\n", + "\n", + "feature_extractor.to(device)\n", + "feature_extractor.eval()\n", + "print(\"Слой классификации удален. Модель готова к экстракции.\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "30f4dbf3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Старт пакетной экстракции признаков...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Экстракция: 100%|██████████| 139/139 [01:10<00:00, 1.99it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Размерность матрицы признаков: (17716, 2048)\n", + "Матрицы успешно экспортированы в .npy файлы.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "extracted_embeddings = []\n", + "extracted_labels = []\n", + "\n", + "print(\"Старт пакетной экстракции признаков...\")\n", + "\n", + "with torch.no_grad():\n", + " for imgs, labels in tqdm(test_loader, desc=\"Экстракция\"):\n", + " imgs = imgs.to(device)\n", + "\n", + " # Получение вектора [BATCH_SIZE, 2048]\n", + " embeddings_batch = feature_extractor(imgs)\n", + " \n", + " extracted_embeddings.append(embeddings_batch.cpu().numpy())\n", + " extracted_labels.append(labels.numpy())\n", + "\n", + "# Агрегация батчей в единые массивы\n", + "np_embeddings = np.concatenate(extracted_embeddings, axis=0)\n", + "np_labels = np.concatenate(extracted_labels, axis=0)\n", + "\n", + "print(f\"Размерность матрицы признаков: {np_embeddings.shape}\")\n", + "\n", + "# Сохранение артефактов на диск для последующего обучения регрессора\n", + "np.save(\"../emoset_test_embeddings.npy\", np_embeddings)\n", + "np.save(\"../emoset_test_labels.npy\", np_labels)\n", + "print(\"Матрицы успешно экспортированы в .npy файлы.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "39c0ac6c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Понижение размерности с 2048 до 2D для визуальной оценки кластеризации\n", + "# Ограничение в 3000 сэмплов для читаемости графика\n", + "sample_limit = 3000\n", + "tsne_model = TSNE(n_components=2, perplexity=30, random_state=42)\n", + "embeddings_2d = tsne_model.fit_transform(np_embeddings[:sample_limit])\n", + "labels_subset = np_labels[:sample_limit]\n", + "\n", + "plt.figure(figsize=(10, 8))\n", + "\n", + "# Построение графика с привязкой реальных имен эмоций к легенде\n", + "scatter = plt.scatter(\n", + " embeddings_2d[:, 0],\n", + " embeddings_2d[:, 1],\n", + " c=labels_subset,\n", + " cmap=\"tab10\",\n", + " alpha=0.7,\n", + " s=15\n", + ")\n", + "\n", + "# Формирование легенды из словаря датасета\n", + "handles, _ = scatter.legend_elements()\n", + "legend_labels = [test_ds.idx2label[i] for i in range(len(test_ds.labels))]\n", + "plt.legend(handles, legend_labels, title=\"Эмоциональные классы\")\n", + "\n", + "plt.title(\"2D проекция скрытого пространства ResNet-50 (t-SNE)\")\n", + "plt.xlabel(\"Компонента 1\")\n", + "plt.ylabel(\"Компонента 2\")\n", + "plt.tight_layout()\n", + "\n", + "# Сохранение графика для презентации\n", + "plt.savefig(\"tsne_embeddings.png\", dpi=300)\n", + "plt.show()" + ] + } + ], + "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 +} diff --git a/src/scripts/23_aggregate_DEAM_timeline.py b/src/scripts/23_aggregate_DEAM_timeline.py new file mode 100644 index 0000000..c3424d5 --- /dev/null +++ b/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() \ No newline at end of file diff --git a/src/scripts/24_train_regressor.py b/src/scripts/24_train_regressor.py new file mode 100644 index 0000000..60f6c33 --- /dev/null +++ b/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() \ No newline at end of file diff --git a/src/scripts/31_finetune_2.41M.py b/src/scripts/31_finetune_2.41M.py new file mode 100644 index 0000000..a024db7 --- /dev/null +++ b/src/scripts/31_finetune_2.41M.py @@ -0,0 +1,264 @@ +import os +import gc +import pickle +import random +from pathlib import Path + +import torch +import torch.nn as nn +from torch.utils.data import Dataset, DataLoader +import torchvision.transforms as T +import torchvision.io as tv_io +from torch.amp import autocast, GradScaler +from tqdm import tqdm +import timm + +# Конфигурация стенда и путей файловой системы +DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu") + +DATA_ROOT = Path("/home/zin/projects/Thesis/dataset/Original-2.41M") +CACHE_PATH = Path("/home/zin/projects/Thesis/src/dataset_paths_cache.pkl") + +PREVIOUS_WEIGHTS = Path("/home/zin/projects/Thesis/src/emoset_resnet50_best.pth") +RESUME_CHECKPOINT = Path("/home/zin/projects/Thesis/src/emoset_resnet50_resume.pth") +SAVE_MODEL_PATH = Path("/home/zin/projects/Thesis/src/emoset_resnet50_finetuned_2_41M.pth") + +CLASS_MAPPING = { + "amusement": 0, "anger": 1, "awe": 2, "contentment": 3, + "disgust": 4, "excitement": 5, "fear": 6, "sad": 7, "sadness": 7 +} + +# Гиперпараметры конвейера обучения +BATCH_SIZE = 82 +EPOCHS = 15 +LR = 5e-5 +NUM_TRAIN_WORKERS = 48 +NUM_VAL_WORKERS = 18 +PATIENCE = 4 + +def prepare_dataset_index(): + # Построение или загрузка индекса файлов для минимизации I/O операций по сети (NFS) + if CACHE_PATH.exists(): + print(f"Загрузка карты файловой системы из кэша: {CACHE_PATH.name}") + with open(CACHE_PATH, 'rb') as f: + cache_data = pickle.load(f) + return cache_data['image_paths'], cache_data['labels'] + + print(f"Сканирование сетевой директории {DATA_ROOT} (первичная индексация)...") + paths, labels = [], [] + for img_path in DATA_ROOT.rglob('*.jpg'): + emotion_folder = img_path.parts[-3].lower() + if emotion_folder in CLASS_MAPPING: + paths.append(str(img_path)) + labels.append(CLASS_MAPPING[emotion_folder]) + + with open(CACHE_PATH, 'wb') as f: + pickle.dump({'image_paths': paths, 'labels': labels}, f) + + return paths, labels + +class EmoSetDirectDataset(Dataset): + # Датасет с отложенной аугментацией: декодирование на CPU, трансформации на GPU + def __init__(self, image_paths, labels): + self.image_paths = image_paths + self.labels = labels + self.base_transform = T.Resize((256, 256), antialias=True) + + def __len__(self): + return len(self.image_paths) + + def __getitem__(self, idx): + try: + image = tv_io.read_image(self.image_paths[idx], mode=tv_io.ImageReadMode.RGB) + image = image.to(torch.float32) / 255.0 + image = self.base_transform(image) + except Exception: + # Изолирование сбоев ввода-вывода (поврежденные файлы на сетевом диске) + image = torch.zeros((3, 256, 256), dtype=torch.float32) + return image, self.labels[idx] + +def build_gpu_transforms(): + # Перенос матричных операций аугментации на тензорные ядра видеокарты + train_tf = torch.nn.Sequential( + T.RandomCrop((224, 224)), + T.RandomHorizontalFlip(p=0.5), + T.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1), + T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) + ).to(DEVICE) + + val_tf = torch.nn.Sequential( + T.CenterCrop((224, 224)), + T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) + ).to(DEVICE) + + return train_tf, val_tf + +if __name__ == "__main__": + print(f"Инициализация конвейера обучения. Устройство: {DEVICE}") + + all_paths, all_labels = prepare_dataset_index() + + # Фиксация сида для детерминированного разделения выборок при перезапусках скрипта + random.seed(42) + combined = list(zip(all_paths, all_labels)) + random.shuffle(combined) + all_paths, all_labels = zip(*combined) + + split_idx = int(len(all_paths) * 0.95) + + train_loader = DataLoader( + EmoSetDirectDataset(all_paths[:split_idx], all_labels[:split_idx]), + batch_size=BATCH_SIZE, shuffle=True, + num_workers=NUM_TRAIN_WORKERS, pin_memory=True, + prefetch_factor=2, persistent_workers=True + ) + + val_loader = DataLoader( + EmoSetDirectDataset(all_paths[split_idx:], all_labels[split_idx:]), + batch_size=BATCH_SIZE, shuffle=False, + num_workers=NUM_VAL_WORKERS, pin_memory=True, + prefetch_factor=2, persistent_workers=True + ) + + gpu_train_tf, gpu_val_tf = build_gpu_transforms() + + model = timm.create_model('resnet50', pretrained=False, num_classes=8).to(DEVICE) + criterion = nn.CrossEntropyLoss() + optimizer = torch.optim.AdamW(model.parameters(), lr=LR, weight_decay=1e-4) + scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=EPOCHS) + scaler = GradScaler() + + best_val_loss = float('inf') + epochs_no_improve = 0 + start_epoch = 1 + + # Инициализация механизма отказоустойчивости и интеграция весов + if RESUME_CHECKPOINT.exists(): + print(f"Восстановление контекста выполнения из: {RESUME_CHECKPOINT.name}") + checkpoint = torch.load(RESUME_CHECKPOINT, map_location=DEVICE) + model.load_state_dict(checkpoint['model_state_dict']) + optimizer.load_state_dict(checkpoint['optimizer_state_dict']) + scheduler.load_state_dict(checkpoint['scheduler_state_dict']) + if 'scaler_state_dict' in checkpoint: scaler.load_state_dict(checkpoint['scaler_state_dict']) + if 'best_val_loss' in checkpoint: best_val_loss = checkpoint['best_val_loss'] + start_epoch = checkpoint['epoch'] + 1 + elif PREVIOUS_WEIGHTS.exists(): + print(f"Интеграция претренированных весов: {PREVIOUS_WEIGHTS.name}") + model.load_state_dict(torch.load(PREVIOUS_WEIGHTS, map_location=DEVICE)) + else: + print("Веса не найдены. Инициализация с ImageNet.") + model = timm.create_model('resnet50', pretrained=True, num_classes=8).to(DEVICE) + + try: + for epoch in range(start_epoch, EPOCHS + 1): + + # Проход по обучающей выборке + model.train() + running_loss, correct, total = 0.0, 0, 0 + + pbar = tqdm(train_loader, desc=f"Epoch {epoch}/{EPOCHS} [Train]") + for inputs, labels in pbar: + try: + inputs = inputs.to(DEVICE, non_blocking=True) + labels = labels.to(DEVICE, non_blocking=True) + inputs = gpu_train_tf(inputs) + + optimizer.zero_grad() + + # Смешанная точность для экономии VRAM + with autocast(device_type="cuda"): + outputs = model(inputs) + loss = criterion(outputs, labels) + + scaler.scale(loss).backward() + scaler.step(optimizer) + scaler.update() + + running_loss += loss.item() * inputs.size(0) + _, predicted = outputs.max(1) + total += labels.size(0) + correct += predicted.eq(labels).sum().item() + + pbar.set_postfix({'loss': f"{loss.item():.4f}"}) + + except RuntimeError as memory_err: + # Подавление пиковых скачков потребления VRAM + if "out of memory" in str(memory_err).lower(): + if 'outputs' in locals(): del outputs + if 'loss' in locals(): del loss + torch.cuda.empty_cache() + optimizer.zero_grad() + continue + raise memory_err + + train_loss = running_loss / total if total > 0 else 0 + train_acc = correct / total if total > 0 else 0 + + gc.collect() + torch.cuda.empty_cache() + + # Проход по валидационной выборке + model.eval() + val_loss, val_correct, val_total = 0.0, 0, 0 + + with torch.no_grad(): + for val_inputs, val_labels in tqdm(val_loader, desc=f"Epoch {epoch}/{EPOCHS} [Val]", leave=False): + val_inputs = val_inputs.to(DEVICE, non_blocking=True) + val_labels = val_labels.to(DEVICE, non_blocking=True) + val_inputs = gpu_val_tf(val_inputs) + + with autocast(device_type="cuda"): + val_outputs = model(val_inputs) + v_loss = criterion(val_outputs, val_labels) + + val_loss += v_loss.item() * val_inputs.size(0) + _, val_predicted = val_outputs.max(1) + val_total += val_labels.size(0) + val_correct += val_predicted.eq(val_labels).sum().item() + + epoch_val_loss = val_loss / val_total if val_total > 0 else 0 + epoch_val_acc = val_correct / val_total if val_total > 0 else 0 + + scheduler.step() + print(f"[{epoch}/{EPOCHS}] Train Loss: {train_loss:.4f} | Val Loss: {epoch_val_loss:.4f} | Val Acc: {epoch_val_acc:.4f}") + + # Оценка критериев ранней остановки и сохранение состояния сессии + if epoch_val_loss < best_val_loss: + best_val_loss = epoch_val_loss + epochs_no_improve = 0 + torch.save(model.state_dict(), str(SAVE_MODEL_PATH).replace(".pth", "_best.pth")) + else: + epochs_no_improve += 1 + if epochs_no_improve >= PATIENCE and epoch >= 15: + print(f"Сработал механизм Early Stopping. Валидация не улучшается {PATIENCE} эпох.") + break + + # Атомарное сохранение контекста + checkpoint_state = { + 'epoch': epoch, + 'model_state_dict': model.state_dict(), + 'optimizer_state_dict': optimizer.state_dict(), + 'scheduler_state_dict': scheduler.state_dict(), + 'scaler_state_dict': scaler.state_dict(), + 'best_val_loss': best_val_loss + } + torch.save(checkpoint_state, RESUME_CHECKPOINT) + gc.collect() + + except KeyboardInterrupt: + print("\nВыполнение прервано пользователем (SIGINT).") + print(f"Дамп памяти конвейера зафиксирован на эпохе {epoch}.") + checkpoint_state = { + 'epoch': epoch, 'model_state_dict': model.state_dict(), + 'optimizer_state_dict': optimizer.state_dict(), + 'scheduler_state_dict': scheduler.state_dict(), 'scaler_state_dict': scaler.state_dict(), + 'best_val_loss': best_val_loss + } + torch.save(checkpoint_state, RESUME_CHECKPOINT) + + else: + if SAVE_MODEL_PATH.parent.exists(): + torch.save(model.state_dict(), SAVE_MODEL_PATH) + print(f"Процесс Fine-Tuning завершен. Артефакт сохранен: {SAVE_MODEL_PATH.name}") + if RESUME_CHECKPOINT.exists(): + RESUME_CHECKPOINT.unlink() \ No newline at end of file diff --git a/src/scripts/aggregate_features.ipynb b/src/scripts/aggregate_features.ipynb deleted file mode 100644 index fe81cd6..0000000 --- a/src/scripts/aggregate_features.ipynb +++ /dev/null @@ -1,125 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "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\"\\nГотово! 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 -} diff --git a/src/scripts/download emo_dataset.ipynb b/src/scripts/download emo_dataset.ipynb deleted file mode 100644 index 1a02323..0000000 --- a/src/scripts/download emo_dataset.ipynb +++ /dev/null @@ -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", - 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] - } - ], - "source": [ - "!pip install datasets tqdm pillow requests\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "6f0b2e2c", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "95f07577d20642b09f2cda6f0b2cca14", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Resolving data files: 0%| | 0/18 [00:00=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", - 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"\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": null, - "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(\"\\nУСПЕХ! 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 -} diff --git a/src/scripts/download_dataset.py b/src/scripts/download_dataset.py deleted file mode 100644 index 5ddb4ae..0000000 --- a/src/scripts/download_dataset.py +++ /dev/null @@ -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 - diff --git a/src/scripts/extract_embeddings.ipynb b/src/scripts/extract_embeddings.ipynb deleted file mode 100644 index 1afe03e..0000000 --- a/src/scripts/extract_embeddings.ipynb +++ /dev/null @@ -1,502 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 2, - "id": "9a9d805d", - "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", - "from pathlib import Path\n", - "from PIL import Image\n", - "from tqdm import tqdm\n", - "\n", - "import torchvision.transforms as T\n", - "import timm\n", - "import numpy as np\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "f292b2f9", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'cuda'" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "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", - "\n", - "DEVICE\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "f78ac97f", - "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 = Image.open(self.root / \"images\" / row[\"filename\"]).convert(\"RGB\")\n", - " img = self.transform(img)\n", - " label = self.label2idx[row[\"label\"]]\n", - " return img, label\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "2bc75aed", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Samples: 17716\n", - "Classes: ['amusement', 'anger', 'awe', 'contentment', 'disgust', 'excitement', 'fear', 'sadness']\n" - ] - } - ], - "source": [ - "ds = EmoSetDataset(DATA_ROOT, \"test\")\n", - "\n", - "loader = DataLoader(\n", - " ds,\n", - " batch_size=BATCH_SIZE,\n", - " shuffle=False,\n", - " num_workers=NUM_WORKERS,\n", - " pin_memory=True\n", - ")\n", - "\n", - "print(\"Samples:\", len(ds))\n", - "print(\"Classes:\", ds.labels)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "30f4dbf3", - "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): 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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): Identity()\n", - ")" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model = timm.create_model(\n", - " \"resnet50\",\n", - " pretrained=False,\n", - " num_classes=len(ds.labels)\n", - ")\n", - "\n", - "state = torch.load(MODEL_PATH, map_location=DEVICE)\n", - "model.load_state_dict(state)\n", - "\n", - "# убираем classifier\n", - "model.reset_classifier(0)\n", - "\n", - "model.to(DEVICE)\n", - "model.eval()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "39c0ac6c", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 277/277 [00:54<00:00, 5.11it/s]\n" - ] - }, - { - "data": { - "text/plain": [ - "((17716, 2048), (17716,))" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "all_embeddings = []\n", - "all_labels = []\n", - "\n", - "with torch.no_grad():\n", - " for imgs, labels in tqdm(loader):\n", - " imgs = imgs.to(DEVICE)\n", - "\n", - " emb = model(imgs) # [B, 2048]\n", - " all_embeddings.append(emb.cpu().numpy())\n", - " all_labels.append(labels.numpy())\n", - "\n", - "all_embeddings = np.concatenate(all_embeddings, axis=0)\n", - "all_labels = np.concatenate(all_labels, axis=0)\n", - "\n", - "all_embeddings.shape, all_labels.shape\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "904fc381", - "metadata": {}, - "outputs": [], - "source": [ - "np.save(\"emoset_test_embeddings.npy\", all_embeddings)\n", - "np.save(\"emoset_test_labels.npy\", all_labels)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "9eefe8ca", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from sklearn.manifold import TSNE\n", - "import matplotlib.pyplot as plt\n", - "\n", - "tsne = TSNE(n_components=2, perplexity=30, random_state=42)\n", - "emb_2d = tsne.fit_transform(all_embeddings[:3000])\n", - "\n", - "plt.figure(figsize=(8, 8))\n", - "plt.scatter(\n", - " emb_2d[:, 0],\n", - " emb_2d[:, 1],\n", - " c=all_labels[:3000],\n", - " cmap=\"tab10\",\n", - " s=5\n", - ")\n", - "plt.title(\"t-SNE of EmoSet embeddings\")\n", - "plt.show()\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 -} diff --git a/src/scripts/finetune.py b/src/scripts/finetune.py deleted file mode 100644 index 8c33e20..0000000 --- a/src/scripts/finetune.py +++ /dev/null @@ -1,314 +0,0 @@ -import os -import gc -import pickle -import random -from pathlib import Path - -import torch -import torch.nn as nn -from torch.utils.data import Dataset, DataLoader -import torchvision.transforms as T -import torchvision.io as tv_io -from torch.amp import autocast, GradScaler -from tqdm import tqdm -import timm - -# ========================================== -# 1. КОНФИГУРАЦИЯ И ПУТИ -# ========================================== -DEVICE = "cuda" if torch.cuda.is_available() else "cpu" -print(f"Используем устройство: {DEVICE}") - -# Путь к огромному датасету на NFS -DATA_ROOT = Path("/home/zin/projects/Thesis/dataset/Original-2.41M") -CACHE_PATH = Path("/home/zin/projects/Thesis/src/dataset_paths_cache.pkl") - -# Пути к моделям -PREVIOUS_WEIGHTS = Path("/home/zin/projects/Thesis/src/emoset_resnet50_best.pth") # Старые веса (118K) -RESUME_CHECKPOINT = Path("/home/zin/projects/Thesis/src/emoset_resnet50_resume.pth") # Файл для восстановления сессии -SAVE_MODEL_PATH = Path("/home/zin/projects/Thesis/src/emoset_resnet50_finetuned_2.41M.pth") # Финальный файл - -EMO_MAP = { - "amusement": 0, "anger": 1, "awe": 2, "contentment": 3, - "disgust": 4, "excitement": 5, "fear": 6, "sad": 7, "sadness": 7 -} - -# --- НАСТРОЙКИ ОБУЧЕНИЯ --- -BATCH_SIZE = 82 -EPOCHS = 15 -LR = 5e-5 # Низкий LR, так как мы делаем Fine-Tuning -NUM_TRAIN_WORKERS = 48 -NUM_VAL_WORKERS = 18 - -# Настройки защиты от переобучения -PATIENCE = 4 -best_val_loss = float('inf') -epochs_no_improve = 0 -start_epoch = 1 - -# ========================================== -# 2. ПОДГОТОВКА ДАННЫХ (БЫСТРЫЙ КЭШ) -# ========================================== -if CACHE_PATH.exists(): - print(f"Загрузка списка файлов из кэша: {CACHE_PATH}...") - with open(CACHE_PATH, 'rb') as f: - cache_data = pickle.load(f) - all_paths = cache_data['image_paths'] - all_labels = cache_data['labels'] - print(f"Готово! Моментально загружено {len(all_paths)} путей.") -else: - print(f"Сканирование NFS директории {DATA_ROOT} (Выполняется один раз)...") - all_paths, all_labels = [], [] - for img_path in DATA_ROOT.rglob('*.jpg'): - emotion_folder = img_path.parts[-3].lower() - if emotion_folder in EMO_MAP: - all_paths.append(str(img_path)) - all_labels.append(EMO_MAP[emotion_folder]) - - with open(CACHE_PATH, 'wb') as f: - pickle.dump({'image_paths': all_paths, 'labels': all_labels}, f) - print(f"Сохранено в кэш: {len(all_paths)} изображений.") - -# Разделение на Train / Validation (95% / 5%) -random.seed(42) # Фиксируем сид, чтобы при перезапусках сплит не менялся -combined = list(zip(all_paths, all_labels)) -random.shuffle(combined) -all_paths, all_labels = zip(*combined) - -split_idx = int(len(all_paths) * 0.95) -train_paths, train_labels = all_paths[:split_idx], all_labels[:split_idx] -val_paths, val_labels = all_paths[split_idx:], all_labels[split_idx:] -print(f"Трейн: {len(train_paths)} | Валидация: {len(val_paths)}") - -# ========================================== -# 3. DATASET & DATALOADER -# ========================================== -class EmoSetDirectDataset(Dataset): - def __init__(self, image_paths, labels): - self.image_paths = image_paths - self.labels = labels - # На процессоре делаем только базовый ресайз - self.base_transform = T.Resize((256, 256), antialias=True) - - def __len__(self): return len(self.image_paths) - - def __getitem__(self, idx): - try: - image = tv_io.read_image(self.image_paths[idx], mode=tv_io.ImageReadMode.RGB) - image = image.to(torch.float32) / 255.0 - image = self.base_transform(image) - except Exception: - # Отказоустойчивость для битых файлов из интернета - image = torch.zeros((3, 256, 256), dtype=torch.float32) - return image, self.labels[idx] - -# --- ИСПРАВЛЕННЫЕ ЗАГРУЗЧИКИ С PREFETCH --- -train_loader = DataLoader( - EmoSetDirectDataset(train_paths, train_labels), - batch_size=BATCH_SIZE, - shuffle=True, - num_workers=NUM_TRAIN_WORKERS, - pin_memory=True, - prefetch_factor=2, - persistent_workers=True -) - -val_loader = DataLoader( - EmoSetDirectDataset(val_paths, val_labels), - batch_size=BATCH_SIZE, - shuffle=False, - num_workers=NUM_VAL_WORKERS, - pin_memory=True, - prefetch_factor=2, - persistent_workers=True -) - -# ========================================== -# 4. АУГМЕНТАЦИИ НА GPU (СУПЕР СКОРОСТЬ) -# ========================================== -gpu_train_transforms = torch.nn.Sequential( - T.RandomCrop((224, 224)), - T.RandomHorizontalFlip(p=0.5), - T.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1), - T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) -).to(DEVICE) - -gpu_val_transforms = torch.nn.Sequential( - T.CenterCrop((224, 224)), - T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) -).to(DEVICE) - -# ========================================== -# 5. ИНИЦИАЛИЗАЦИЯ МОДЕЛИ -# ========================================== -print("\nСоздание архитектуры ResNet-50...") -model = timm.create_model('resnet50', pretrained=False, num_classes=8).to(DEVICE) - -criterion = nn.CrossEntropyLoss() -optimizer = torch.optim.AdamW(model.parameters(), lr=LR, weight_decay=1e-4) -scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=EPOCHS) -scaler = GradScaler() - -# --- ЛОГИКА БЕСШОВНОГО ВОССТАНОВЛЕНИЯ --- -if RESUME_CHECKPOINT.exists(): - print(f"ВОССТАНОВЛЕНИЕ СЕССИИ из: {RESUME_CHECKPOINT}") - checkpoint = torch.load(RESUME_CHECKPOINT, map_location=DEVICE) - model.load_state_dict(checkpoint['model_state_dict']) - optimizer.load_state_dict(checkpoint['optimizer_state_dict']) - scheduler.load_state_dict(checkpoint['scheduler_state_dict']) - if 'scaler_state_dict' in checkpoint: - scaler.load_state_dict(checkpoint['scaler_state_dict']) - if 'best_val_loss' in checkpoint: - best_val_loss = checkpoint['best_val_loss'] - start_epoch = checkpoint['epoch'] + 1 - print(f"УСПЕХ: Продолжаем обучение с эпохи {start_epoch}") -else: - print("Чекпоинт сессии не найден. Проверяем наличие базовых весов...") - if PREVIOUS_WEIGHTS.exists(): - print(f"📥 Загрузка базовых весов (от 118K): {PREVIOUS_WEIGHTS}") - model.load_state_dict(torch.load(PREVIOUS_WEIGHTS, map_location=DEVICE)) - else: - print("ВНИМАНИЕ: Базовые веса не найдены. Обучение начнется с нуля (ImageNet).") - model = timm.create_model('resnet50', pretrained=True, num_classes=8).to(DEVICE) - -# ========================================== -# 6. ГЛАВНЫЙ ЦИКЛ ОБУЧЕНИЯ -# ========================================== -if start_epoch > EPOCHS: - print(f"\nОбучение уже было завершено (цель: {EPOCHS} эпох).") -else: - print(f"\nСтарт обучения. Целевое количество эпох: {EPOCHS}") - - try: - for epoch in range(start_epoch, EPOCHS + 1): - - # --- ФАЗА 1: ТРЕНИРОВКА --- - model.train() - running_loss, correct, total = 0.0, 0, 0 - - pbar = tqdm(train_loader, desc=f"Эпоха {epoch}/{EPOCHS} [Тренировка]") - for inputs, labels in pbar: - try: - # Перенос на GPU и применение быстрых аугментаций - inputs = inputs.to(DEVICE, non_blocking=True) - labels = labels.to(DEVICE, non_blocking=True) - inputs = gpu_train_transforms(inputs) - - optimizer.zero_grad() - - # Смешанная точность (AMP) для экономии VRAM и ускорения - with autocast(device_type="cuda"): - outputs = model(inputs) - loss = criterion(outputs, labels) - - scaler.scale(loss).backward() - scaler.step(optimizer) - scaler.update() - - running_loss += loss.item() * inputs.size(0) - _, predicted = outputs.max(1) - total += labels.size(0) - correct += predicted.eq(labels).sum().item() - - pbar.set_postfix({'loss': f"{loss.item():.4f}", 'acc': f"{correct/total:.4f}"}) - - except RuntimeError as e: - # Хендлер нехватки памяти (OOM) - if "out of memory" in str(e).lower(): - print(f"\nВНИМАНИЕ: Нехватка VRAM! Очистка...") - if 'outputs' in locals(): del outputs - if 'loss' in locals(): del loss - torch.cuda.empty_cache() - optimizer.zero_grad() - continue - raise e - - train_loss = running_loss / total if total > 0 else 0 - train_acc = correct / total if total > 0 else 0 - - # Очистка мусора перед валидацией - gc.collect() - torch.cuda.empty_cache() - - # --- ФАЗА 2: ВАЛИДАЦИЯ --- - model.eval() - val_loss, val_correct, val_total = 0.0, 0, 0 - - with torch.no_grad(): - for val_inputs, val_labels in tqdm(val_loader, desc=f"Эпоха {epoch}/{EPOCHS} [Валидация]", leave=False): - val_inputs, val_labels = val_inputs.to(DEVICE), val_labels.to(DEVICE) - val_inputs = gpu_val_transforms(val_inputs) - - with autocast(device_type="cuda"): - val_outputs = model(val_inputs) - v_loss = criterion(val_outputs, val_labels) - - val_loss += v_loss.item() * val_inputs.size(0) - _, val_predicted = val_outputs.max(1) - val_total += val_labels.size(0) - val_correct += val_predicted.eq(val_labels).sum().item() - - epoch_val_loss = val_loss / val_total if val_total > 0 else 0 - epoch_val_acc = val_correct / val_total if val_total > 0 else 0 - - scheduler.step() - print(f"🏁 Эпоха {epoch} | Train Loss: {train_loss:.4f} (Acc: {train_acc:.4f}) | Val Loss: {epoch_val_loss:.4f} (Acc: {epoch_val_acc:.4f})") - - # --- ФАЗА 3: EARLY STOPPING И СОХРАНЕНИЕ --- - if epoch_val_loss < best_val_loss: - best_val_loss = epoch_val_loss - epochs_no_improve = 0 - torch.save(model.state_dict(), "../emoset_resnet50_best_2_41M.pth") - print("Новая лучшая модель найдена! Веса сохранены.") - else: - epochs_no_improve += 1 - print(f"Валидация не улучшается {epochs_no_improve}/{PATIENCE} эпох.") - if epochs_no_improve >= PATIENCE and epoch >= 15: # Даем модели минимум 15 эпох на раскачку - print("\nСРАБОТАЛА ЗАЩИТА ОТ ПЕРЕОБУЧЕНИЯ (Early Stopping)!") - break - - # Сохранение полного состояния сессии - checkpoint_state = { - 'epoch': epoch, - 'model_state_dict': model.state_dict(), - 'optimizer_state_dict': optimizer.state_dict(), - 'scheduler_state_dict': scheduler.state_dict(), - 'scaler_state_dict': scaler.state_dict(), - 'best_val_loss': best_val_loss - } - torch.save(checkpoint_state, RESUME_CHECKPOINT) - - # Сохранение весов конкретной эпохи как бэкап - torch.save(model.state_dict(), f"../emoset_resnet50_finetuned_ep{epoch}.pth") - - gc.collect() - torch.cuda.empty_cache() - - # ========================================== - # 7. ПЕРЕХВАТ РУЧНОЙ ОСТАНОВКИ (CTRL+C) - # ========================================== - except KeyboardInterrupt: - print("\n\nОБУЧЕНИЕ ПРЕРВАНО ВРУЧНУЮ (KeyboardInterrupt)!") - print(f"Экстренное сохранение состояния конвейера на эпохе {epoch}...") - - checkpoint_state = { - 'epoch': epoch, 'model_state_dict': model.state_dict(), - 'optimizer_state_dict': optimizer.state_dict(), - 'scheduler_state_dict': scheduler.state_dict(), 'scaler_state_dict': scaler.state_dict(), - 'best_val_loss': best_val_loss - } - torch.save(checkpoint_state, RESUME_CHECKPOINT) - - interrupted_weights_path = f"../emoset_resnet50_interrupted_ep{epoch}.pth" - torch.save(model.state_dict(), interrupted_weights_path) - print(f"Прогресс безопасно зафиксирован в файле {interrupted_weights_path}. Выходим.") - - # ========================================== - # 8. ФИНАЛЬНОЕ СОХРАНЕНИЕ - # ========================================== - else: - if SAVE_MODEL_PATH.parent.exists(): - torch.save(model.state_dict(), SAVE_MODEL_PATH) - print(f"\nОБУЧЕНИЕ УСПЕШНО ЗАВЕРШЕНО! Финальная модель: {SAVE_MODEL_PATH}") - if RESUME_CHECKPOINT.exists(): - RESUME_CHECKPOINT.unlink() # Удаляем resume файл за ненадобностью \ No newline at end of file diff --git a/src/scripts/finetune_embeddings.ipynb b/src/scripts/finetune_embeddings.ipynb deleted file mode 100644 index 179c048..0000000 --- a/src/scripts/finetune_embeddings.ipynb +++ /dev/null @@ -1,467 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "71ef58af", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Используем устройство: cuda\n" - ] - } - ], - "source": [ - "import os\n", - "import torch\n", - "import torch.nn as nn\n", - "from torch.utils.data import Dataset, DataLoader\n", - "import torchvision.transforms as T\n", - "import pandas as pd\n", - "from pathlib import Path\n", - "from PIL import Image\n", - "from tqdm.notebook import tqdm\n", - "import timm\n", - "\n", - "# Проверяем GPU\n", - "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", - "print(f\"Используем устройство: {DEVICE}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "f4ae931c", - "metadata": {}, - "outputs": [], - "source": [ - "# === НАСТРОЙКИ ДООБУЧЕНИЯ ===\n", - "\n", - "# Абсолютный путь к смонтированному NFS\n", - "DATA_ROOT = Path(\"/home/zin/projects/Thesis/NFS/Thesis/Emoset/Original-2.41M\")\n", - "\n", - "# Пути относительно src/scripts/\n", - "PREVIOUS_WEIGHTS = Path(\"../emoset_resnet50_best.pth\")\n", - "SAVE_MODEL_PATH = Path(\"../emoset_resnet50_finetuned_2.41M.pth\")\n", - "\n", - "# Маппинг эмоций в те же индексы (0-7), которые использовались при первоначальном обучении\n", - "EMO_MAP = {\n", - " \"amusement\": 0,\n", - " \"anger\": 1,\n", - " \"awe\": 2,\n", - " \"contentment\": 3,\n", - " \"disgust\": 4,\n", - " \"excitement\": 5,\n", - " \"fear\": 6,\n", - " \"sad\": 7, # В твоем сообщении папка называется \"sad\"\n", - " \"sadness\": 7 # На всякий случай оставляем и классическое название\n", - "}\n", - "\n", - "BATCH_SIZE = 96\n", - "EPOCHS = 15\n", - "LR = 5e-5\n", - "NUM_WORKERS = 42" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "934cfe2c", - "metadata": {}, - "outputs": [], - "source": [ - "import pickle\n", - "import os\n", - "import torchvision.io as tv_io\n", - "\n", - "# Твои трансформации (только Resize, как мы сделали ранее)\n", - "train_transforms = T.Compose([\n", - " T.Resize((256, 256), antialias=True)\n", - "])\n", - "\n", - "class EmoSetNestedDataset(Dataset):\n", - " def __init__(self, root_dir, transform=None, cache_file=\"../dataset_cache_2.41M.pkl\"):\n", - " self.root_dir = Path(root_dir)\n", - " self.transform = transform\n", - " self.image_paths = []\n", - " self.labels = []\n", - " \n", - " # === ЛОГИКА КЭШИРОВАНИЯ ===\n", - " if os.path.exists(cache_file):\n", - " print(f\"📦 Загрузка списка файлов из локального кэша: {cache_file}...\")\n", - " with open(cache_file, 'rb') as f:\n", - " cache_data = pickle.load(f)\n", - " self.image_paths = cache_data['image_paths']\n", - " self.labels = cache_data['labels']\n", - " print(f\"⚡ Готово! Моментально загружено {len(self.image_paths)} путей.\")\n", - " else:\n", - " print(f\"🔍 Сканирование NFS директории {self.root_dir}...\")\n", - " print(\"Это займет около 8-10 минут. Выполняется один раз...\")\n", - " \n", - " for img_path in self.root_dir.rglob('*.jpg'):\n", - " emotion_folder = img_path.parts[-3].lower()\n", - " if emotion_folder in EMO_MAP:\n", - " self.image_paths.append(str(img_path))\n", - " self.labels.append(EMO_MAP[emotion_folder])\n", - " \n", - " print(f\"💾 Сохранение результатов сканирования в кэш: {cache_file}...\")\n", - " with open(cache_file, 'wb') as f:\n", - " pickle.dump({'image_paths': self.image_paths, 'labels': self.labels}, f)\n", - " \n", - " print(f\"✅ Успешно найдено и закэшировано {len(self.image_paths)} изображений.\")\n", - "\n", - " def __len__(self):\n", - " return len(self.image_paths)\n", - "\n", - " def __getitem__(self, idx):\n", - " img_path = self.image_paths[idx]\n", - " label = self.labels[idx]\n", - " \n", - " try:\n", - " image = tv_io.read_image(str(img_path), mode=tv_io.ImageReadMode.RGB)\n", - " image = image.to(torch.float32) / 255.0\n", - " except Exception as e:\n", - " image = torch.zeros((3, 256, 256), dtype=torch.float32)\n", - " \n", - " if self.transform:\n", - " image = self.transform(image)\n", - " \n", - " return image, label" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "b10adc06", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "📦 Загрузка списка файлов из локального кэша: ../dataset_paths_cache.pkl...\n", - "⚡ Готово! Моментально загружено 2048377 путей.\n", - "Батчей за одну эпоху: 21338\n", - "\n", - "Создание архитектуры ResNet50...\n", - "📝 Чекпоинт прерванной сессии не найден. Проверяем базовые веса...\n", - "УСПЕХ: Найдены предыдущие веса '../emoset_resnet50_best.pth' (из EmoSet-118K). Загружаем...\n" - ] - } - ], - "source": [ - "# Путь к кэш-файлу (лучше положить его в src, рядом со скриптами, чтобы быстро читался)\n", - "CACHE_PATH = Path(\"../dataset_paths_cache.pkl\")\n", - "\n", - "# 1. Загрузка данных напрямую из папок (или из кэша!)\n", - "train_dataset = EmoSetNestedDataset(DATA_ROOT, transform=train_transforms, cache_file=CACHE_PATH)\n", - "\n", - "train_loader = DataLoader(\n", - " train_dataset, \n", - " batch_size=BATCH_SIZE, \n", - " shuffle=True, \n", - " num_workers=NUM_WORKERS,\n", - " pin_memory=True,\n", - " prefetch_factor=1,\n", - " persistent_workers=True\n", - ")\n", - "\n", - "print(f\"Батчей за одну эпоху: {len(train_loader)}\")\n", - "\n", - "# Путь к файлу автоматического восстановления (чекпоинт полной сессии)\n", - "RESUME_CHECKPOINT = Path(\"../emoset_resnet50_checkpoint_latest.pth\")\n", - "\n", - "# 2. Инициализация архитектуры модели ResNet-50\n", - "print(\"\\nСоздание архитектуры ResNet50...\")\n", - "model = timm.create_model('resnet50', pretrained=False, num_classes=8)\n", - "model = model.to(DEVICE)\n", - "\n", - "# Инициализируем компоненты оптимизации\n", - "criterion = nn.CrossEntropyLoss()\n", - "optimizer = torch.optim.AdamW(model.parameters(), lr=LR, weight_decay=1e-4)\n", - "scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=EPOCHS)\n", - "\n", - "# По умолчанию начинаем с 1-й эпохи\n", - "start_epoch = 1\n", - "\n", - "# === ЛОГИКА ВОССТАНОВЛЕНИЯ / СТАРТА ===\n", - "if RESUME_CHECKPOINT.exists():\n", - " print(f\"🔄 ОБНАРУЖЕН ДЕЙСТВУЮЩИЙ ЧЕКПОИНТ: '{RESUME_CHECKPOINT}'\")\n", - " print(\"Восстанавливаем полное состояние сессии...\")\n", - " \n", - " # Загружаем сохраненный словарь состояния\n", - " checkpoint = torch.load(RESUME_CHECKPOINT, map_location=DEVICE)\n", - " \n", - " # Восстанавливаем всё до единого\n", - " model.load_state_dict(checkpoint['model_state_dict'])\n", - " optimizer.load_state_dict(checkpoint['optimizer_state_dict'])\n", - " scheduler.load_state_dict(checkpoint['scheduler_state_dict'])\n", - " start_epoch = checkpoint['epoch'] + 1 # Начинаем со следующей эпохи\n", - " \n", - " print(f\"🚀 УСПЕХ: Сессия восстановлена! Продолжаем обучение с эпохи {start_epoch}\")\n", - "else:\n", - " print(\"📝 Чекпоинт прерванной сессии не найден. Проверяем базовые веса...\")\n", - " if PREVIOUS_WEIGHTS.exists():\n", - " print(f\"УСПЕХ: Найдены предыдущие веса '{PREVIOUS_WEIGHTS}' (из EmoSet-118K). Загружаем...\")\n", - " model.load_state_dict(torch.load(PREVIOUS_WEIGHTS, map_location=DEVICE))\n", - " else:\n", - " print(\"ВНИМАНИЕ: Базовые веса не найдены. Начинаем обучение с нуля (ImageNet pretrained).\")\n", - " model = timm.create_model('resnet50', pretrained=True, num_classes=8).to(DEVICE)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "a7480834", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "⏰ Старт обучения. Целевое количество эпох: 15\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Эпоха 1/15 [Тренировка]: 0%| | 0/21338 [00:00 \u001b[39m\u001b[32m88\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m val_inputs, val_labels \u001b[38;5;129;01min\u001b[39;00m \u001b[43mval_loader\u001b[49m:\n\u001b[32m 89\u001b[39m val_inputs, val_labels = val_inputs.to(DEVICE), val_labels.to(DEVICE)\n\u001b[32m 91\u001b[39m \u001b[38;5;66;03m# Валидация тоже идет в смешанной точности для скорости\u001b[39;00m\n", - "\u001b[31mNameError\u001b[39m: name 'val_loader' is not defined" - ] - } - ], - "source": [ - "import gc\n", - "import torch\n", - "from torch.amp import autocast, GradScaler\n", - "from tqdm import tqdm\n", - "import torchvision.transforms as T\n", - "\n", - "# --- НАСТРОЙКИ EARLY STOPPING ---\n", - "PATIENCE = 4 # Сколько эпох ждем, если валидация не улучшается\n", - "best_val_loss = float('inf')\n", - "epochs_no_improve = 0\n", - "start_epoch = 1\n", - "\n", - "# --- ПЕРЕНОСИМ ТЯЖЕЛЫЕ АУГМЕНТАЦИИ НА GPU ---\n", - "gpu_transforms = torch.nn.Sequential(\n", - " T.RandomCrop((224, 224)),\n", - " T.RandomHorizontalFlip(p=0.5),\n", - " T.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1),\n", - " T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n", - ").to(DEVICE)\n", - "# -------------------------------------------\n", - "\n", - "print(f\"⏰ Старт обучения. Целевое количество эпох: {EPOCHS}\")\n", - "scaler = GradScaler()\n", - "\n", - "try:\n", - " for epoch in range(start_epoch, EPOCHS + 1):\n", - " # ==========================================\n", - " # 1. ТРЕНИРОВКА (TRAIN)\n", - " # ==========================================\n", - " model.train()\n", - " running_loss = 0.0\n", - " correct = 0\n", - " total = 0\n", - " \n", - " pbar = tqdm(train_loader, desc=f\"Эпоха {epoch}/{EPOCHS} [Тренировка]\")\n", - " for inputs, labels in pbar:\n", - " try:\n", - " inputs, labels = inputs.to(DEVICE), labels.to(DEVICE)\n", - " \n", - " # Применяем аугментации на лету к батчу (на GPU)\n", - " inputs = gpu_transforms(inputs)\n", - " \n", - " optimizer.zero_grad()\n", - " \n", - " with autocast(device_type=\"cuda\"): # Для PyTorch >= 2.0 лучше указывать \"cuda\"\n", - " outputs = model(inputs)\n", - " loss = criterion(outputs, labels)\n", - " \n", - " scaler.scale(loss).backward()\n", - " scaler.step(optimizer)\n", - " scaler.update()\n", - " \n", - " # Статистика\n", - " running_loss += loss.item() * inputs.size(0)\n", - " _, predicted = outputs.max(1)\n", - " total += labels.size(0)\n", - " correct += predicted.eq(labels).sum().item()\n", - " \n", - " pbar.set_postfix({'loss': f\"{loss.item():.4f}\", 'acc': f\"{correct/total:.4f}\"})\n", - " \n", - " except RuntimeError as e:\n", - " # Обработчик OOM\n", - " if \"out of memory\" in str(e).lower():\n", - " print(f\"\\n⚠️ ВНИМАНИЕ: Нехватка VRAM на батче! Выполняем экстренную очистку...\")\n", - " if 'outputs' in locals(): del outputs\n", - " if 'loss' in locals(): del loss\n", - " torch.cuda.empty_cache()\n", - " optimizer.zero_grad()\n", - " print(\"♻️ Кэш очищен. Батч пропущен. Продолжаем обучение...\")\n", - " continue\n", - " else:\n", - " raise e\n", - " \n", - " epoch_loss = running_loss / total if total > 0 else 0\n", - " epoch_acc = correct / total if total > 0 else 0\n", - " \n", - " # ==========================================\n", - " # 2. ВАЛИДАЦИЯ И EARLY STOPPING\n", - " # ==========================================\n", - " model.eval()\n", - " val_loss = 0.0\n", - " val_correct = 0\n", - " val_total = 0\n", - " \n", - " # ВАЖНО: Если у тебя нет val_loader, создай его (откуси 5-10% от датасета)\n", - " # На валидации мы НЕ применяем gpu_transforms (только нормализацию)\n", - " with torch.no_grad():\n", - " for val_inputs, val_labels in val_loader:\n", - " val_inputs, val_labels = val_inputs.to(DEVICE), val_labels.to(DEVICE)\n", - " \n", - " # Валидация тоже идет в смешанной точности для скорости\n", - " with autocast(device_type=\"cuda\"):\n", - " val_outputs = model(val_inputs)\n", - " v_loss = criterion(val_outputs, val_labels)\n", - " \n", - " val_loss += v_loss.item() * val_inputs.size(0)\n", - " _, val_predicted = val_outputs.max(1)\n", - " val_total += val_labels.size(0)\n", - " val_correct += val_predicted.eq(val_labels).sum().item()\n", - " \n", - " epoch_val_loss = val_loss / val_total if val_total > 0 else 0\n", - " epoch_val_acc = val_correct / val_total if val_total > 0 else 0\n", - " \n", - " scheduler.step()\n", - " print(f\"🏁 Эпоха {epoch} завершена | Train Loss: {epoch_loss:.4f} (Acc: {epoch_acc:.4f}) | Val Loss: {epoch_val_loss:.4f} (Acc: {epoch_val_acc:.4f})\")\n", - " \n", - " # --- ЛОГИКА РАННЕЙ ОСТАНОВКИ ---\n", - " if epoch_val_loss < best_val_loss:\n", - " best_val_loss = epoch_val_loss\n", - " epochs_no_improve = 0\n", - " # Сохраняем идеальные веса, пока сеть не переобучилась\n", - " torch.save(model.state_dict(), \"../emoset_resnet50_best.pth\")\n", - " print(\"🌟 Найдена лучшая модель! Веса сохранены.\")\n", - " else:\n", - " epochs_no_improve += 1\n", - " print(f\"⚠️ Валидационный Loss не улучшается {epochs_no_improve}/{PATIENCE} эпох.\")\n", - " \n", - " # Условие: если переобучение длится долго И мы прошли хотя бы 15 эпох\n", - " if epochs_no_improve >= PATIENCE and epoch >= 15:\n", - " print(\"\\n🛑 СРАБОТАЛА ЗАЩИТА ОТ ПЕРЕОБУЧЕНИЯ (Early Stopping)!\")\n", - " print(\"Модель начала запоминать данные вместо обобщения. Обучение досрочно завершено.\")\n", - " break # Прерываем цикл эпох\n", - " \n", - " # ==========================================\n", - " # 3. РЕГУЛЯРНОЕ СОХРАНЕНИЕ ПРОГРЕССА\n", - " # ==========================================\n", - " checkpoint_state = {\n", - " 'epoch': epoch,\n", - " 'model_state_dict': model.state_dict(),\n", - " 'optimizer_state_dict': optimizer.state_dict(),\n", - " 'scheduler_state_dict': scheduler.state_dict(),\n", - " 'scaler_state_dict': scaler.state_dict(),\n", - " 'loss': epoch_loss,\n", - " 'val_loss': epoch_val_loss\n", - " }\n", - " torch.save(checkpoint_state, RESUME_CHECKPOINT)\n", - " \n", - " # Сохранение весов конкретной эпохи (на всякий случай)\n", - " epoch_weights_path = f\"../emoset_resnet50_finetuned_ep{epoch}.pth\"\n", - " torch.save(model.state_dict(), epoch_weights_path)\n", - " \n", - " gc.collect()\n", - " torch.cuda.empty_cache()\n", - "\n", - "# ==========================================\n", - "# 4. БЕЗОПАСНЫЙ ВЫХОД ПРИ РУЧНОМ ПРЕРЫВАНИИ\n", - "# ==========================================\n", - "except KeyboardInterrupt:\n", - " print(\"\\n\\n🛑 ОБУЧЕНИЕ ПРЕРВАНО ВРУЧНУЮ (KeyboardInterrupt)!\")\n", - " print(f\"💾 Экстренное сохранение состояния конвейера на эпохе {epoch}...\")\n", - " \n", - " # Сохраняем всё, чтобы потом можно было продолжить с этого же места\n", - " checkpoint_state = {\n", - " 'epoch': epoch,\n", - " 'model_state_dict': model.state_dict(),\n", - " 'optimizer_state_dict': optimizer.state_dict(),\n", - " 'scheduler_state_dict': scheduler.state_dict(),\n", - " 'scaler_state_dict': scaler.state_dict()\n", - " }\n", - " torch.save(checkpoint_state, RESUME_CHECKPOINT)\n", - " \n", - " # Сохраняем промежуточные веса на момент остановки\n", - " interrupted_weights_path = f\"../emoset_resnet50_interrupted_ep{epoch}.pth\"\n", - " torch.save(model.state_dict(), interrupted_weights_path)\n", - " \n", - " print(f\"✅ Прогресс безопасно зафиксирован в файле {interrupted_weights_path}. Выходим.\")\n", - "\n", - "# Финальное сохранение (если цикл дошел до конца сам)\n", - "else:\n", - " if SAVE_MODEL_PATH.parent.exists():\n", - " torch.save(model.state_dict(), SAVE_MODEL_PATH)\n", - " print(f\"\\n🎉 ОБУЧЕНИЕ УСПЕШНО ЗАВЕРШЕНО! Финальная модель: {SAVE_MODEL_PATH}\")\n", - " if RESUME_CHECKPOINT.exists():\n", - " RESUME_CHECKPOINT.unlink()" - ] - } - ], - "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 -} diff --git a/src/scripts/prep_deam.ipynb b/src/scripts/prep_deam.ipynb deleted file mode 100644 index 750400c..0000000 --- a/src/scripts/prep_deam.ipynb +++ /dev/null @@ -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": null, - "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 -} diff --git a/src/scripts/prepare_dataset_to_train.ipynb b/src/scripts/prepare_dataset_to_train.ipynb deleted file mode 100644 index f080751..0000000 --- a/src/scripts/prepare_dataset_to_train.ipynb +++ /dev/null @@ -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: 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 : 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 : 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 -} diff --git a/src/scripts/preprocessing.py b/src/scripts/preprocessing.py deleted file mode 100644 index f30e904..0000000 --- a/src/scripts/preprocessing.py +++ /dev/null @@ -1,134 +0,0 @@ -import os -import random -from pathlib import Path -from tqdm.notebook import tqdm -import webdataset as wds - -# --- НАСТРОЙКИ --- -# Оригинальная папка с датасетом (на NFS) -DATA_ROOT = Path("/home/zin/projects/Thesis/NFS/Thesis/Emoset/Original-2.41M") - -# Новая папка, куда мы сложим готовые .tar архивы (шарды) -# Лучше создать её рядом с оригинальным датасетом на NFS -SHARDS_DIR = Path("/home/zin/projects/Thesis/NFS/Thesis/Emoset/shards-2.41M") -SHARDS_DIR.mkdir(parents=True, exist_ok=True) - -# Маппинг классов -EMO_MAP = { - "amusement": 0, "anger": 1, "awe": 2, "contentment": 3, - "disgust": 4, "excitement": 5, "fear": 6, "sad": 7, "sadness": 7 -} - -# Размер одного архива. 10 000 картинок — идеальный баланс -MAX_SAMPLES_PER_SHARD = 10000 - -samples = [] - -print(f"🔍 Сканирование директории {DATA_ROOT}...") -# Используем os.walk, он часто работает быстрее rglob на сетевых дисках -for root, dirs, files in os.walk(DATA_ROOT): - for file in files: - if file.lower().endswith('.jpg'): - full_path = os.path.join(root, file) - # Извлекаем эмоцию (зависит от структуры папок, берем предпоследнюю папку) - # Путь: .../amusement/0/image.jpg -> root_parts[-2] будет 'amusement' - path_parts = Path(full_path).parts - emotion_folder = path_parts[-3].lower() - - if emotion_folder in EMO_MAP: - samples.append((full_path, EMO_MAP[emotion_folder])) - -print(f"✅ Найдено изображений: {len(samples)}") - -# САМЫЙ ВАЖНЫЙ ШАГ: Глобальное перемешивание перед упаковкой -print("🔀 Перемешиваем датасет...") -random.shuffle(samples) -print("✅ Перемешивание завершено!") - -import multiprocessing as mp -from concurrent.futures import ProcessPoolExecutor, as_completed -import webdataset as wds -from PIL import Image -import io -from pathlib import Path - -# ВАЖНО: Импортируем базовый tqdm, а не notebook-версию. -# Notebook-версия в мультипроцессинге вызывает зависание графического интерфейса Jupyter. -from tqdm import tqdm - -# --- ПУТИ И НАСТРОЙКИ --- -SHARDS_DIR = Path("../../dataset/EmoSet-2.41M-shards") -SHARDS_DIR.mkdir(parents=True, exist_ok=True) - -NUM_WORKERS = 50 - -# 1. Дробим наш список на чанки -chunks = [samples[i:i + MAX_SAMPLES_PER_SHARD] for i in range(0, len(samples), MAX_SAMPLES_PER_SHARD)] - -print(f"📦 Подготовлено {len(chunks)} задач (шардов).") -print(f"💾 Целевая папка (Локальный SSD): {SHARDS_DIR}") -print(f"🚀 Запуск упаковки и сжатия в {NUM_WORKERS} потоков...\n") - -# Инициализация блокировки tqdm для мультипроцессинга (чтобы бары не съезжали) -tqdm.set_lock(mp.RLock()) - -# 2. Функция, которую выполняет каждый воркер -def build_shard(args): - shard_idx, chunk = args - shard_path = SHARDS_DIR / f"emoset-{shard_idx:06d}.tar" - - success_count = 0 - error_count = 0 - - # ХИТРОСТЬ ЗДЕСЬ: position = остаток от деления + 1. - # Это гарантирует, что все 42 воркера поделят между собой 42 строчки на экране, - # и когда воркер берет новый шард, он обновляет свою старую строчку, а не создает новую. - # leave=False заставит бар исчезнуть, когда чанк докачается. - worker_pos = (shard_idx % NUM_WORKERS) + 1 - - with wds.TarWriter(str(shard_path)) as sink: - # Рисуем прогресс-бар для текущего шарда - for i, (img_path, label) in enumerate(tqdm(chunk, desc=f"Шард {shard_idx:03d}", position=worker_pos, leave=False)): - try: - # --- МАГИЯ СЖАТИЯ --- - with Image.open(img_path) as img: - img = img.convert("RGB") - img = img.resize((256, 256), Image.Resampling.BILINEAR) - - with io.BytesIO() as img_byte_arr: - img.save(img_byte_arr, format='JPEG', quality=85) - image_data = img_byte_arr.getvalue() - # -------------------- - - key = f"{shard_idx:06d}_{i:05d}" - - sink.write({ - "__key__": key, - "jpg": image_data, - "cls": label - }) - success_count += 1 - - except Exception: - error_count += 1 - continue # Игнорируем битые файлы - - return shard_idx - -# 3. Запускаем армию воркеров -with ProcessPoolExecutor(max_workers=NUM_WORKERS) as executor: - tasks = [(i, chunk) for i, chunk in enumerate(chunks)] - - # Отправляем задачи в пул - futures = [executor.submit(build_shard, task) for task in tasks] - - # ГЛАВНЫЙ прогресс-бар (position=0, всегда висит на самой первой строчке) - for future in tqdm(as_completed(futures), total=len(tasks), desc="📊 ОБЩИЙ ПРОГРЕСС", position=0, leave=True): - try: - future.result() - except Exception: - pass - -# Печатаем пару пустых строк, чтобы финальный текст не налез на бары -print("\n" * (NUM_WORKERS + 2)) -print("🎉 ПАРАЛЛЕЛЬНАЯ УПАКОВКА И СЖАТИЕ ПОЛНОСТЬЮ ЗАВЕРШЕНЫ!") \ No newline at end of file diff --git a/src/scripts/preprocessing_2.1M.ipynb b/src/scripts/preprocessing_2.1M.ipynb deleted file mode 100644 index 05e7c1e..0000000 --- a/src/scripts/preprocessing_2.1M.ipynb +++ /dev/null @@ -1,919 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 4, - "id": "e6aa65e8", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import random\n", - "from pathlib import Path\n", - "from tqdm.notebook import tqdm\n", - "import webdataset as wds\n", - "\n", - "# --- НАСТРОЙКИ ---\n", - "# Оригинальная папка с датасетом (на NFS)\n", - "DATA_ROOT = Path(\"/home/zin/projects/Thesis/NFS/Thesis/Emoset/Original-2.41M\")\n", - "\n", - "# Новая папка, куда мы сложим готовые .tar архивы (шарды)\n", - "# Лучше создать её рядом с оригинальным датасетом на NFS\n", - "SHARDS_DIR = Path(\"/home/zin/projects/Thesis/NFS/Thesis/Emoset/shards-2.41M\")\n", - "SHARDS_DIR.mkdir(parents=True, exist_ok=True)\n", - "\n", - "# Маппинг классов\n", - "EMO_MAP = {\n", - " \"amusement\": 0, \"anger\": 1, \"awe\": 2, \"contentment\": 3,\n", - " \"disgust\": 4, \"excitement\": 5, \"fear\": 6, \"sad\": 7, \"sadness\": 7\n", - "}\n", - "\n", - "# Размер одного архива. 10 000 картинок — идеальный баланс\n", - "MAX_SAMPLES_PER_SHARD = 10000" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "09d0e56c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "🔍 Сканирование директории /home/zin/projects/Thesis/NFS/Thesis/Emoset/Original-2.41M...\n" - ] - }, - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mKeyboardInterrupt\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[5]\u001b[39m\u001b[32m, line 11\u001b[39m\n\u001b[32m 8\u001b[39m full_path = os.path.join(root, file)\n\u001b[32m 9\u001b[39m \u001b[38;5;66;03m# Извлекаем эмоцию (зависит от структуры папок, берем предпоследнюю папку)\u001b[39;00m\n\u001b[32m 10\u001b[39m \u001b[38;5;66;03m# Путь: .../amusement/0/image.jpg -> root_parts[-2] будет 'amusement'\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m11\u001b[39m path_parts = \u001b[43mPath\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfull_path\u001b[49m\u001b[43m)\u001b[49m.parts\n\u001b[32m 12\u001b[39m emotion_folder = path_parts[-\u001b[32m3\u001b[39m].lower()\n\u001b[32m 14\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m emotion_folder \u001b[38;5;129;01min\u001b[39;00m EMO_MAP:\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/.pyenv/versions/3.11.7/lib/python3.11/pathlib.py:871\u001b[39m, in \u001b[36mPath.__new__\u001b[39m\u001b[34m(cls, *args, **kwargs)\u001b[39m\n\u001b[32m 869\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mcls\u001b[39m \u001b[38;5;129;01mis\u001b[39;00m Path:\n\u001b[32m 870\u001b[39m \u001b[38;5;28mcls\u001b[39m = WindowsPath \u001b[38;5;28;01mif\u001b[39;00m os.name == \u001b[33m'\u001b[39m\u001b[33mnt\u001b[39m\u001b[33m'\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m PosixPath\n\u001b[32m--> \u001b[39m\u001b[32m871\u001b[39m \u001b[38;5;28mself\u001b[39m = \u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_from_parts\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 872\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m._flavour.is_supported:\n\u001b[32m 873\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m(\u001b[33m\"\u001b[39m\u001b[33mcannot instantiate \u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[33m on your system\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 874\u001b[39m % (\u001b[38;5;28mcls\u001b[39m.\u001b[34m__name__\u001b[39m,))\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/.pyenv/versions/3.11.7/lib/python3.11/pathlib.py:509\u001b[39m, in \u001b[36mPurePath._from_parts\u001b[39m\u001b[34m(cls, args)\u001b[39m\n\u001b[32m 504\u001b[39m \u001b[38;5;129m@classmethod\u001b[39m\n\u001b[32m 505\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m_from_parts\u001b[39m(\u001b[38;5;28mcls\u001b[39m, args):\n\u001b[32m 506\u001b[39m \u001b[38;5;66;03m# We need to call _parse_args on the instance, so as to get the\u001b[39;00m\n\u001b[32m 507\u001b[39m \u001b[38;5;66;03m# right flavour.\u001b[39;00m\n\u001b[32m 508\u001b[39m \u001b[38;5;28mself\u001b[39m = \u001b[38;5;28mobject\u001b[39m.\u001b[34m__new__\u001b[39m(\u001b[38;5;28mcls\u001b[39m)\n\u001b[32m--> \u001b[39m\u001b[32m509\u001b[39m drv, root, parts = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_parse_args\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 510\u001b[39m \u001b[38;5;28mself\u001b[39m._drv = drv\n\u001b[32m 511\u001b[39m \u001b[38;5;28mself\u001b[39m._root = root\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/.pyenv/versions/3.11.7/lib/python3.11/pathlib.py:502\u001b[39m, in \u001b[36mPurePath._parse_args\u001b[39m\u001b[34m(cls, args)\u001b[39m\n\u001b[32m 497\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 498\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[32m 499\u001b[39m \u001b[33m\"\u001b[39m\u001b[33margument should be a str object or an os.PathLike \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 500\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mobject returning str, not \u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[33m\"\u001b[39m\n\u001b[32m 501\u001b[39m % \u001b[38;5;28mtype\u001b[39m(a))\n\u001b[32m--> \u001b[39m\u001b[32m502\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_flavour\u001b[49m\u001b[43m.\u001b[49m\u001b[43mparse_parts\u001b[49m\u001b[43m(\u001b[49m\u001b[43mparts\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/.pyenv/versions/3.11.7/lib/python3.11/pathlib.py:67\u001b[39m, in \u001b[36m_Flavour.parse_parts\u001b[39m\u001b[34m(self, parts)\u001b[39m\n\u001b[32m 65\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m altsep:\n\u001b[32m 66\u001b[39m part = part.replace(altsep, sep)\n\u001b[32m---> \u001b[39m\u001b[32m67\u001b[39m drv, root, rel = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43msplitroot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpart\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 68\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m sep \u001b[38;5;129;01min\u001b[39;00m rel:\n\u001b[32m 69\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mreversed\u001b[39m(rel.split(sep)):\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/.pyenv/versions/3.11.7/lib/python3.11/pathlib.py:241\u001b[39m, in \u001b[36m_PosixFlavour.splitroot\u001b[39m\u001b[34m(self, part, sep)\u001b[39m\n\u001b[32m 239\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34msplitroot\u001b[39m(\u001b[38;5;28mself\u001b[39m, part, sep=sep):\n\u001b[32m 240\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m part \u001b[38;5;129;01mand\u001b[39;00m part[\u001b[32m0\u001b[39m] == sep:\n\u001b[32m--> \u001b[39m\u001b[32m241\u001b[39m stripped_part = part.lstrip(sep)\n\u001b[32m 242\u001b[39m \u001b[38;5;66;03m# According to POSIX path resolution:\u001b[39;00m\n\u001b[32m 243\u001b[39m \u001b[38;5;66;03m# http://pubs.opengroup.org/onlinepubs/009695399/basedefs/xbd_chap04.html#tag_04_11\u001b[39;00m\n\u001b[32m 244\u001b[39m \u001b[38;5;66;03m# \"A pathname that begins with two successive slashes may be\u001b[39;00m\n\u001b[32m 245\u001b[39m \u001b[38;5;66;03m# interpreted in an implementation-defined manner, although more\u001b[39;00m\n\u001b[32m 246\u001b[39m \u001b[38;5;66;03m# than two leading slashes shall be treated as a single slash\".\u001b[39;00m\n\u001b[32m 247\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(part) - \u001b[38;5;28mlen\u001b[39m(stripped_part) == \u001b[32m2\u001b[39m:\n", - "\u001b[31mKeyboardInterrupt\u001b[39m: " - ] - } - ], - "source": [ - "samples = []\n", - "\n", - "print(f\"🔍 Сканирование директории {DATA_ROOT}...\")\n", - "# Используем os.walk, он часто работает быстрее rglob на сетевых дисках\n", - "for root, dirs, files in os.walk(DATA_ROOT):\n", - " for file in files:\n", - " if file.lower().endswith('.jpg'):\n", - " full_path = os.path.join(root, file)\n", - " # Извлекаем эмоцию (зависит от структуры папок, берем предпоследнюю папку)\n", - " # Путь: .../amusement/0/image.jpg -> root_parts[-2] будет 'amusement'\n", - " path_parts = Path(full_path).parts\n", - " emotion_folder = path_parts[-3].lower()\n", - " \n", - " if emotion_folder in EMO_MAP:\n", - " samples.append((full_path, EMO_MAP[emotion_folder]))\n", - "\n", - "print(f\"✅ Найдено изображений: {len(samples)}\")\n", - "\n", - "# САМЫЙ ВАЖНЫЙ ШАГ: Глобальное перемешивание перед упаковкой\n", - "print(\"🔀 Перемешиваем датасет...\")\n", - "random.shuffle(samples)\n", - "print(\"✅ Перемешивание завершено!\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0fe71d72", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "📦 Подготовлено 205 задач (шардов).\n", - "💾 Целевая папка: ../../dataset/EmoSet-2.41M-shards\n", - "🚀 Запуск упаковки в 42 потоков...\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Exception in thread Thread-4 (ui_thread_func):\n", - "Traceback (most recent call last):\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/threading.py\", line 1045, in _bootstrap_inner\n", - " self.run()\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/threading.py\", line 982, in run\n", - " self._target(*self._args, **self._kwargs)\n", - " File \"/tmp/ipykernel_16083/731719608.py\", line 72, in ui_thread_func\n", - " File \"/home/zin/projects/Thesis/.venv/lib/python3.11/site-packages/tqdm/notebook.py\", line 223, in __init__\n", - " super().__init__(*args, **kwargs)\n", - " File \"/home/zin/projects/Thesis/.venv/lib/python3.11/site-packages/tqdm/std.py\", line 1001, in __init__\n", - " raise (\n", - "tqdm.std.TqdmKeyError: \"Unknown argument(s): {'color': 'blue'}\"\n", - "Process ForkProcess-39:\n", - "Process ForkProcess-35:\n", - "Process ForkProcess-28:\n", - "Process ForkProcess-29:\n", - "Process ForkProcess-43:\n", - "Process ForkProcess-38:\n", - "Process ForkProcess-36:\n", - "Process ForkProcess-37:\n", - "Process ForkProcess-34:\n", - "Traceback (most recent call last):\n", - "Traceback (most recent call last):\n", - "Traceback (most recent call last):\n", - "Traceback (most recent call last):\n", - "Traceback (most recent call last):\n", - "Traceback (most recent call last):\n", - "Traceback (most recent call last):\n", - "Process ForkProcess-27:\n", - "Process ForkProcess-25:\n", - "Traceback (most recent call last):\n", - "Process ForkProcess-30:\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n", - " self.run()\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n", - " self.run()\n", - "Traceback (most recent call last):\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n", - " self.run()\n", - "Process ForkProcess-41:\n", - "Process ForkProcess-31:\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n", - " self.run()\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n", - " self.run()\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n", - " self.run()\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n", - " self.run()\n", - "Process ForkProcess-22:\n", - "Process ForkProcess-32:\n", - "Process ForkProcess-40:\n", - "Traceback (most recent call last):\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n", - " self.run()\n", - "Traceback (most recent call last):\n", - "Process ForkProcess-42:\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n", - " self._target(*self._args, **self._kwargs)\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n", - " self._target(*self._args, **self._kwargs)\n", - "Process ForkProcess-20:\n", - "Traceback (most recent call last):\n", - "Process ForkProcess-24:\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n", - " self.run()\n", - "Process ForkProcess-23:\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n", - " self._target(*self._args, **self._kwargs)\n", - "Process ForkProcess-26:\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n", - " self._target(*self._args, **self._kwargs)\n", - "Process ForkProcess-21:\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n", - " self._target(*self._args, **self._kwargs)\n", - "Traceback (most recent call last):\n", - "Process ForkProcess-16:\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n", - " self._target(*self._args, **self._kwargs)\n", - "Traceback (most recent call last):\n", - "Process ForkProcess-13:\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n", - " self._target(*self._args, **self._kwargs)\n", - "Process ForkProcess-9:\n", - "Process ForkProcess-17:\n", - "Traceback (most recent call last):\n", - "Traceback (most recent call last):\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n", - " self._target(*self._args, **self._kwargs)\n", - "Process ForkProcess-6:\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n", - " self.run()\n", - "Traceback (most recent call last):\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n", - " self.run()\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n", - " call_item = call_queue.get(block=True)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n", - " call_item = call_queue.get(block=True)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - "Process ForkProcess-8:\n", - "Traceback (most recent call last):\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n", - " self.run()\n", - "Process ForkProcess-3:\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n", - " self._target(*self._args, **self._kwargs)\n", - "Traceback (most recent call last):\n", - "Traceback (most recent call last):\n", - "Process ForkProcess-10:\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n", - " call_item = call_queue.get(block=True)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - "Traceback (most recent call last):\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n", - " call_item = call_queue.get(block=True)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - "Process ForkProcess-7:\n", - "Process ForkProcess-11:\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n", - " call_item = call_queue.get(block=True)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - "Traceback (most recent call last):\n", - "Process ForkProcess-14:\n", - "Process ForkProcess-4:\n", - "Traceback (most recent call last):\n", - "Process ForkProcess-5:\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n", - " call_item = call_queue.get(block=True)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n", - " self.run()\n", - " self.run()\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/threading.py\", line 982, in run\n", - "Process ForkProcess-2:\n", - "Traceback (most recent call last):\n", - "Process ForkProcess-12:\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n", - " self.run()\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n", - " call_item = call_queue.get(block=True)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - "Traceback (most recent call last):\n", - "Process ForkProcess-15:\n", - "Traceback (most recent call last):\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n", - " self.run()\n", - "Traceback (most recent call last):\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n", - " self.run()\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n", - " call_item = call_queue.get(block=True)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n", - " self._target(*self._args, **self._kwargs)\n", - "Traceback (most recent call last):\n", - "Process ForkProcess-18:\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n", - " self.run()\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n", - " self._target(*self._args, **self._kwargs)\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n", - " with self._rlock:\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n", - " with self._rlock:\n", - "Traceback (most recent call last):\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n", - " self._target(*self._args, **self._kwargs)\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n", - " self.run()\n" - ] - }, - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mKeyboardInterrupt\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[3]\u001b[39m\u001b[32m, line 102\u001b[39m\n\u001b[32m 101\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m ProcessPoolExecutor(max_workers=NUM_WORKERS) \u001b[38;5;28;01mas\u001b[39;00m executor:\n\u001b[32m--> \u001b[39m\u001b[32m102\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m_\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mexecutor\u001b[49m\u001b[43m.\u001b[49m\u001b[43mmap\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbuild_shard\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtasks\u001b[49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[32m 103\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mpass\u001b[39;49;00m \u001b[38;5;66;03m# Просто ждем завершения всех задач\u001b[39;00m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py:620\u001b[39m, in \u001b[36m_chain_from_iterable_of_lists\u001b[39m\u001b[34m(iterable)\u001b[39m\n\u001b[32m 615\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 616\u001b[39m \u001b[33;03mSpecialized implementation of itertools.chain.from_iterable.\u001b[39;00m\n\u001b[32m 617\u001b[39m \u001b[33;03mEach item in *iterable* should be a list. This function is\u001b[39;00m\n\u001b[32m 618\u001b[39m \u001b[33;03mcareful not to keep references to yielded objects.\u001b[39;00m\n\u001b[32m 619\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m620\u001b[39m \u001b[43m\u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43melement\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43miterable\u001b[49m\u001b[43m:\u001b[49m\n\u001b[32m 621\u001b[39m \u001b[43m \u001b[49m\u001b[43melement\u001b[49m\u001b[43m.\u001b[49m\u001b[43mreverse\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/_base.py:619\u001b[39m, in \u001b[36mExecutor.map..result_iterator\u001b[39m\u001b[34m()\u001b[39m\n\u001b[32m 618\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m timeout \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m619\u001b[39m \u001b[38;5;28;01myield\u001b[39;00m \u001b[43m_result_or_cancel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfs\u001b[49m\u001b[43m.\u001b[49m\u001b[43mpop\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 620\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/_base.py:317\u001b[39m, in \u001b[36m_result_or_cancel\u001b[39m\u001b[34m(***failed resolving arguments***)\u001b[39m\n\u001b[32m 316\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m317\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfut\u001b[49m\u001b[43m.\u001b[49m\u001b[43mresult\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 318\u001b[39m \u001b[38;5;28;01mfinally\u001b[39;00m:\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/_base.py:451\u001b[39m, in \u001b[36mFuture.result\u001b[39m\u001b[34m(self, timeout)\u001b[39m\n\u001b[32m 449\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m.__get_result()\n\u001b[32m--> \u001b[39m\u001b[32m451\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_condition\u001b[49m\u001b[43m.\u001b[49m\u001b[43mwait\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\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/threading.py:327\u001b[39m, in \u001b[36mCondition.wait\u001b[39m\u001b[34m(self, timeout)\u001b[39m\n\u001b[32m 326\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m timeout \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m327\u001b[39m \u001b[43mwaiter\u001b[49m\u001b[43m.\u001b[49m\u001b[43macquire\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 328\u001b[39m gotit = \u001b[38;5;28;01mTrue\u001b[39;00m\n", - "\u001b[31mKeyboardInterrupt\u001b[39m: ", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[31mKeyboardInterrupt\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[3]\u001b[39m\u001b[32m, line 101\u001b[39m\n\u001b[32m 99\u001b[39m \u001b[38;5;66;03m# 3. Запускаем 42 боевых ядра\u001b[39;00m\n\u001b[32m 100\u001b[39m tasks = [(i, chunk, queue) \u001b[38;5;28;01mfor\u001b[39;00m i, chunk \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(chunks)]\n\u001b[32m--> \u001b[39m\u001b[32m101\u001b[39m \u001b[43m\u001b[49m\u001b[38;5;28;43;01mwith\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mProcessPoolExecutor\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmax_workers\u001b[49m\u001b[43m=\u001b[49m\u001b[43mNUM_WORKERS\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mas\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mexecutor\u001b[49m\u001b[43m:\u001b[49m\n\u001b[32m 102\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m_\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mexecutor\u001b[49m\u001b[43m.\u001b[49m\u001b[43mmap\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbuild_shard\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtasks\u001b[49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[32m 103\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mpass\u001b[39;49;00m \u001b[38;5;66;03m# Просто ждем завершения всех задач\u001b[39;00m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/_base.py:647\u001b[39m, in \u001b[36mExecutor.__exit__\u001b[39m\u001b[34m(self, exc_type, exc_val, exc_tb)\u001b[39m\n\u001b[32m 646\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m__exit__\u001b[39m(\u001b[38;5;28mself\u001b[39m, exc_type, exc_val, exc_tb):\n\u001b[32m--> \u001b[39m\u001b[32m647\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mshutdown\u001b[49m\u001b[43m(\u001b[49m\u001b[43mwait\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[32m 648\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \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/process.py:851\u001b[39m, in \u001b[36mProcessPoolExecutor.shutdown\u001b[39m\u001b[34m(self, wait, cancel_futures)\u001b[39m\n\u001b[32m 848\u001b[39m \u001b[38;5;28mself\u001b[39m._executor_manager_thread_wakeup.wakeup()\n\u001b[32m 850\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._executor_manager_thread \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m wait:\n\u001b[32m--> \u001b[39m\u001b[32m851\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_executor_manager_thread\u001b[49m\u001b[43m.\u001b[49m\u001b[43mjoin\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 852\u001b[39m \u001b[38;5;66;03m# To reduce the risk of opening too many files, remove references to\u001b[39;00m\n\u001b[32m 853\u001b[39m \u001b[38;5;66;03m# objects that use file descriptors.\u001b[39;00m\n\u001b[32m 854\u001b[39m \u001b[38;5;28mself\u001b[39m._executor_manager_thread = \u001b[38;5;28;01mNone\u001b[39;00m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/.pyenv/versions/3.11.7/lib/python3.11/threading.py:1119\u001b[39m, in \u001b[36mThread.join\u001b[39m\u001b[34m(self, timeout)\u001b[39m\n\u001b[32m 1116\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[33m\"\u001b[39m\u001b[33mcannot join current thread\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 1118\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m timeout \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m1119\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_wait_for_tstate_lock\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1120\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 1121\u001b[39m \u001b[38;5;66;03m# the behavior of a negative timeout isn't documented, but\u001b[39;00m\n\u001b[32m 1122\u001b[39m \u001b[38;5;66;03m# historically .join(timeout=x) for x<0 has acted as if timeout=0\u001b[39;00m\n\u001b[32m 1123\u001b[39m \u001b[38;5;28mself\u001b[39m._wait_for_tstate_lock(timeout=\u001b[38;5;28mmax\u001b[39m(timeout, \u001b[32m0\u001b[39m))\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/.pyenv/versions/3.11.7/lib/python3.11/threading.py:1139\u001b[39m, in \u001b[36mThread._wait_for_tstate_lock\u001b[39m\u001b[34m(self, block, timeout)\u001b[39m\n\u001b[32m 1136\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[32m 1138\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m1139\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[43mlock\u001b[49m\u001b[43m.\u001b[49m\u001b[43macquire\u001b[49m\u001b[43m(\u001b[49m\u001b[43mblock\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[32m 1140\u001b[39m lock.release()\n\u001b[32m 1141\u001b[39m \u001b[38;5;28mself\u001b[39m._stop()\n", - "\u001b[31mKeyboardInterrupt\u001b[39m: " - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n", - " call_item = call_queue.get(block=True)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - "Traceback (most recent call last):\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n", - " self.run()\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n", - " with self._rlock:\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n", - " self.run()\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n", - " with self._rlock:\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n", - " self.run()\n", - "Traceback (most recent call last):\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n", - " with self._rlock:\n", - "Traceback (most recent call last):\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n", - " self.run()\n", - "Traceback (most recent call last):\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n", - " with self._rlock:\n", - "Traceback (most recent call last):\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n", - " self.run()\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n", - " self._target(*self._args, **self._kwargs)\n", - "Traceback (most recent call last):\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n", - " self.run()\n", - "Traceback (most recent call last):\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n", - " self._target(*self._args, **self._kwargs)\n", - "Traceback (most recent call last):\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n", - " with self._rlock:\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n", - " self.run()\n", - "Process ForkProcess-19:\n", - "Traceback (most recent call last):\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n", - " self.run()\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n", - " self._target(*self._args, **self._kwargs)\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n", - " self.run()\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n", - " with self._rlock:\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n", - " self._target(*self._args, **self._kwargs)\n", - "Traceback (most recent call last):\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n", - " call_item = call_queue.get(block=True)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n", - " self.run()\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n", - " self._target(*self._args, **self._kwargs)\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n", - " call_item = call_queue.get(block=True)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n", - " return self._semlock.__enter__()\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n", - " return self._semlock.__enter__()\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", - "Traceback (most recent call last):\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n", - " call_item = call_queue.get(block=True)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n", - " self.run()\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n", - " self._target(*self._args, **self._kwargs)\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n", - " with self._rlock:\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n", - " self._target(*self._args, **self._kwargs)\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n", - " return self._semlock.__enter__()\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n", - " self.run()\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n", - " self._target(*self._args, **self._kwargs)\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n", - " return self._semlock.__enter__()\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n", - " self._target(*self._args, **self._kwargs)\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n", - " return self._semlock.__enter__()\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n", - " self.run()\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n", - " self.run()\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n", - " self._target(*self._args, **self._kwargs)\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n", - " return self._semlock.__enter__()\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n", - " self.run()\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n", - " self.run()\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n", - " self._target(*self._args, **self._kwargs)\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n", - " call_item = call_queue.get(block=True)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n", - " self._target(*self._args, **self._kwargs)\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n", - " self.run()\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n", - " call_item = call_queue.get(block=True)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n", - " self.run()\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n", - " self.run()\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n", - " return self._semlock.__enter__()\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n", - " self._target(*self._args, **self._kwargs)\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n", - " self.run()\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n", - " self._target(*self._args, **self._kwargs)\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n", - " call_item = call_queue.get(block=True)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - "Traceback (most recent call last):\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n", - " self._target(*self._args, **self._kwargs)\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n", - " return self._semlock.__enter__()\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n", - " call_item = call_queue.get(block=True)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n", - " with self._rlock:\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n", - " self._target(*self._args, **self._kwargs)\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n", - " self.run()\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n", - " call_item = call_queue.get(block=True)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n", - " with self._rlock:\n", - "KeyboardInterrupt\n", - "KeyboardInterrupt\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n", - " with self._rlock:\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n", - " self.run()\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n", - " self._target(*self._args, **self._kwargs)\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n", - " call_item = call_queue.get(block=True)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n", - " return self._semlock.__enter__()\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n", - " call_item = call_queue.get(block=True)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - "KeyboardInterrupt\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n", - " self._target(*self._args, **self._kwargs)\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n", - " call_item = call_queue.get(block=True)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - "KeyboardInterrupt\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n", - " call_item = call_queue.get(block=True)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - "KeyboardInterrupt\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n", - " self._target(*self._args, **self._kwargs)\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n", - " self._target(*self._args, **self._kwargs)\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n", - " call_item = call_queue.get(block=True)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - "KeyboardInterrupt\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n", - " self._target(*self._args, **self._kwargs)\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n", - " call_item = call_queue.get(block=True)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n", - " self._target(*self._args, **self._kwargs)\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n", - " call_item = call_queue.get(block=True)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n", - " with self._rlock:\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n", - " self._target(*self._args, **self._kwargs)\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n", - " with self._rlock:\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n", - " self._target(*self._args, **self._kwargs)\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n", - " self._target(*self._args, **self._kwargs)\n", - "KeyboardInterrupt\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n", - " call_item = call_queue.get(block=True)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n", - " call_item = call_queue.get(block=True)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n", - " self._target(*self._args, **self._kwargs)\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n", - " with self._rlock:\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n", - " call_item = call_queue.get(block=True)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n", - " self.run()\n", - "KeyboardInterrupt\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n", - " return self._semlock.__enter__()\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n", - " with self._rlock:\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n", - " call_item = call_queue.get(block=True)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n", - " with self._rlock:\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n", - " self._target(*self._args, **self._kwargs)\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n", - " return self._semlock.__enter__()\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n", - " return self._semlock.__enter__()\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n", - " self._target(*self._args, **self._kwargs)\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n", - " call_item = call_queue.get(block=True)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n", - " with self._rlock:\n", - "KeyboardInterrupt\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n", - " with self._rlock:\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n", - " call_item = call_queue.get(block=True)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n", - " with self._rlock:\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n", - " with self._rlock:\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n", - " call_item = call_queue.get(block=True)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n", - " call_item = call_queue.get(block=True)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n", - " with self._rlock:\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n", - " call_item = call_queue.get(block=True)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n", - " call_item = call_queue.get(block=True)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n", - " return self._semlock.__enter__()\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n", - " with self._rlock:\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n", - " with self._rlock:\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n", - " return self._semlock.__enter__()\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n", - " call_item = call_queue.get(block=True)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n", - " call_item = call_queue.get(block=True)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n", - " call_item = call_queue.get(block=True)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n", - " with self._rlock:\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n", - " call_item = call_queue.get(block=True)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n", - " with self._rlock:\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n", - " with self._rlock:\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n", - " self._target(*self._args, **self._kwargs)\n", - "KeyboardInterrupt\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n", - " return self._semlock.__enter__()\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n", - " return self._semlock.__enter__()\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n", - " return self._semlock.__enter__()\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n", - " with self._rlock:\n", - "KeyboardInterrupt\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n", - " call_item = call_queue.get(block=True)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n", - " call_item = call_queue.get(block=True)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n", - " with self._rlock:\n", - "KeyboardInterrupt\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n", - " return self._semlock.__enter__()\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n", - " return self._semlock.__enter__()\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n", - " with self._rlock:\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n", - " return self._semlock.__enter__()\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n", - " return self._semlock.__enter__()\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n", - " with self._rlock:\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n", - " return self._semlock.__enter__()\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n", - " with self._rlock:\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n", - " with self._rlock:\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n", - " with self._rlock:\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n", - " return self._semlock.__enter__()\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", - "KeyboardInterrupt\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n", - " return self._semlock.__enter__()\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", - "KeyboardInterrupt\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n", - " with self._rlock:\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 103, in get\n", - " res = self._recv_bytes()\n", - " ^^^^^^^^^^^^^^^^^^\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n", - " with self._rlock:\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n", - " return self._semlock.__enter__()\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n", - " with self._rlock:\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n", - " return self._semlock.__enter__()\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n", - " return self._semlock.__enter__()\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n", - " call_item = call_queue.get(block=True)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - "KeyboardInterrupt\n", - "KeyboardInterrupt\n", - "KeyboardInterrupt\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n", - " return self._semlock.__enter__()\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n", - " with self._rlock:\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n", - " with self._rlock:\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n", - " return self._semlock.__enter__()\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", - "KeyboardInterrupt\n", - "KeyboardInterrupt\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n", - " return self._semlock.__enter__()\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", - "KeyboardInterrupt\n", - "KeyboardInterrupt\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n", - " return self._semlock.__enter__()\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", - "KeyboardInterrupt\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n", - " return self._semlock.__enter__()\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n", - " return self._semlock.__enter__()\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n", - " return self._semlock.__enter__()\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", - "KeyboardInterrupt\n", - "KeyboardInterrupt\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n", - " return self._semlock.__enter__()\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/connection.py\", line 216, in recv_bytes\n", - " buf = self._recv_bytes(maxlength)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n", - " return self._semlock.__enter__()\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", - "KeyboardInterrupt\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n", - " return self._semlock.__enter__()\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", - "KeyboardInterrupt\n", - "KeyboardInterrupt\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n", - " with self._rlock:\n", - "KeyboardInterrupt\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n", - " return self._semlock.__enter__()\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n", - " return self._semlock.__enter__()\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", - "KeyboardInterrupt\n", - "KeyboardInterrupt\n", - "KeyboardInterrupt\n", - "KeyboardInterrupt\n", - "KeyboardInterrupt\n", - "KeyboardInterrupt\n", - "KeyboardInterrupt\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/connection.py\", line 430, in _recv_bytes\n", - " buf = self._recv(4)\n", - " ^^^^^^^^^^^^^\n", - "KeyboardInterrupt\n", - "KeyboardInterrupt\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n", - " return self._semlock.__enter__()\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", - "KeyboardInterrupt\n", - "KeyboardInterrupt\n", - " File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/connection.py\", line 395, in _recv\n", - " chunk = read(handle, remaining)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^\n", - "KeyboardInterrupt\n", - "KeyboardInterrupt\n" - ] - } - ], - "source": [ - "import multiprocessing as mp\n", - "from concurrent.futures import ProcessPoolExecutor\n", - "import webdataset as wds\n", - "from PIL import Image\n", - "import io\n", - "import threading\n", - "from pathlib import Path\n", - "\n", - "# Включаем красивую версию tqdm специально для Jupyter\n", - "from tqdm.notebook import tqdm\n", - "\n", - "# --- ПУТИ И НАСТРОЙКИ ---\n", - "SHARDS_DIR = Path(\"../../dataset/EmoSet-2.41M-shards\")\n", - "SHARDS_DIR.mkdir(parents=True, exist_ok=True)\n", - "\n", - "NUM_WORKERS = 42\n", - "MAX_SAMPLES_PER_SHARD = 10000\n", - "\n", - "# Дробим список на чанки\n", - "chunks = [samples[i:i + MAX_SAMPLES_PER_SHARD] for i in range(0, len(samples), MAX_SAMPLES_PER_SHARD)]\n", - "TOTAL_FILES = len(samples)\n", - "TOTAL_SHARDS = len(chunks)\n", - "\n", - "print(f\"📦 Подготовлено {TOTAL_SHARDS} задач (шардов).\")\n", - "print(f\"💾 Целевая папка: {SHARDS_DIR}\")\n", - "print(f\"🚀 Запуск упаковки в {NUM_WORKERS} потоков...\\n\")\n", - "\n", - "# --- ФУНКЦИЯ ДЛЯ ЯДЕР ПРОЦЕССОРА ---\n", - "def build_shard(args):\n", - " shard_idx, chunk, queue = args\n", - " shard_path = SHARDS_DIR / f\"emoset-{shard_idx:06d}.tar\"\n", - " \n", - " with wds.TarWriter(str(shard_path)) as sink:\n", - " for i, (img_path, label) in enumerate(chunk):\n", - " try:\n", - " # Магия сжатия\n", - " with Image.open(img_path) as img:\n", - " img = img.convert(\"RGB\")\n", - " img = img.resize((256, 256), Image.Resampling.BILINEAR)\n", - " with io.BytesIO() as img_byte_arr:\n", - " img.save(img_byte_arr, format='JPEG', quality=85)\n", - " image_data = img_byte_arr.getvalue()\n", - " \n", - " key = f\"{shard_idx:06d}_{i:05d}\"\n", - " sink.write({\n", - " \"__key__\": key,\n", - " \"jpg\": image_data,\n", - " \"cls\": label\n", - " })\n", - " \n", - " # Чтобы не перегружать очередь, отправляем отчет каждые 50 файлов\n", - " if (i + 1) % 50 == 0:\n", - " queue.put((\"file\", 50))\n", - " \n", - " except Exception:\n", - " # Если файл битый, всё равно считаем его \"пройденным\", чтобы бар не застрял\n", - " queue.put((\"file\", 1))\n", - " continue\n", - " \n", - " # Сообщаем об остатке файлов в чанке, которые не попали в % 50\n", - " remainder = len(chunk) % 50\n", - " if remainder != 0:\n", - " queue.put((\"file\", remainder))\n", - " \n", - " # Сообщаем, что целый шард готов\n", - " queue.put((\"shard\", 1))\n", - " return shard_idx\n", - "\n", - "# --- ФУНКЦИЯ ОТРИСОВКИ ИНТЕРФЕЙСА (Фоновый поток) ---\n", - "def ui_thread_func(q, total_files, total_shards):\n", - " # Создаем две красивые независимые полоски\n", - " pbar_files = tqdm(total=total_files, desc=\"🖼️ Сжато файлов\", color=\"blue\")\n", - " pbar_shards = tqdm(total=total_shards, desc=\"📦 Готово архивов\", color=\"green\")\n", - " \n", - " while True:\n", - " msg = q.get()\n", - " if msg == \"DONE\":\n", - " break\n", - " \n", - " msg_type, count = msg\n", - " if msg_type == \"file\":\n", - " pbar_files.update(count)\n", - " elif msg_type == \"shard\":\n", - " pbar_shards.update(count)\n", - " \n", - " pbar_files.close()\n", - " pbar_shards.close()\n", - "\n", - "# === ГЛАВНЫЙ ЗАПУСК ===\n", - "if __name__ == '__main__':\n", - " # 1. Создаем диспетчер очередей\n", - " manager = mp.Manager()\n", - " queue = manager.Queue()\n", - " \n", - " # 2. Запускаем фоновый поток отрисовки\n", - " ui_thread = threading.Thread(target=ui_thread_func, args=(queue, TOTAL_FILES, TOTAL_SHARDS))\n", - " ui_thread.start()\n", - " \n", - " # 3. Запускаем 42 боевых ядра\n", - " tasks = [(i, chunk, queue) for i, chunk in enumerate(chunks)]\n", - " with ProcessPoolExecutor(max_workers=NUM_WORKERS) as executor:\n", - " for _ in executor.map(build_shard, tasks):\n", - " pass # Просто ждем завершения всех задач\n", - " \n", - " # 4. Убиваем поток отрисовки и завершаем работу\n", - " queue.put(\"DONE\")\n", - " ui_thread.join()\n", - " \n", - " print(\"\\n🎉 ПАРАЛЛЕЛЬНАЯ УПАКОВКА И СЖАТИЕ ПОЛНОСТЬЮ ЗАВЕРШЕНЫ!\")" - ] - } - ], - "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": 5 -} diff --git a/src/scripts/test.ipynb b/src/scripts/test.ipynb deleted file mode 100644 index 1215824..0000000 --- a/src/scripts/test.ipynb +++ /dev/null @@ -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 -} diff --git a/src/scripts/train_regressor.py b/src/scripts/train_regressor.py deleted file mode 100644 index 4000283..0000000 --- a/src/scripts/train_regressor.py +++ /dev/null @@ -1,58 +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 - -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) - -print("Обучение масштабатора и RidgeCV регрессора...") -model = Pipeline([ - ('scaler', StandardScaler()), - ('regressor', MultiOutputRegressor(RidgeCV(alphas=[0.1, 1.0, 10.0, 100.0, 1000.0]))) -]) - -model.fit(X_train, y_train) - -y_pred = model.predict(X_test) - -mse = mean_squared_error(y_test, y_pred) -r2 = r2_score(y_test, y_pred) - -print(f"\nУспех! Обучение завершено!") -print(f"MSE: {mse:.4f}") -print(f"R^2 Score: {r2:.4f}") - -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)") - -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}") \ No newline at end of file diff --git a/src/scripts/tsne_embeddings.png b/src/scripts/tsne_embeddings.png new file mode 100644 index 0000000000000000000000000000000000000000..7793c17a37031914d258132e8a1f048e163d9498 GIT binary patch literal 1892529 zcmeFZXIPfk)-7zzHi9A!B4-5J453Y%l z&Rc%ivPJsB?<%g*<9#lUD(#Dl(~5sE#N5&QV@lO6u>U`yJidQBUfH*P^2Dvbf4i7? zBAzys=2zNB$DaIoqQyP9U-s+q?W}jsT`p;2miSa-%vM`(92D_tHBYT$aI)8-F*ZWf 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