| import torchvision |
| import torchvision.transforms as transforms |
|
|
| import pytorch_lightning as pl |
| from pytorch_lightning.loggers import CSVLogger |
| from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint |
|
|
| from pathlib import Path |
| import yaml |
| import logging |
|
|
| from src import model |
|
|
| def setup_logging(nivel : str): |
| path_final = get_project_root() |
| Path(path_final/"logs").mkdir(exist_ok=True) |
| path_final = path_final / "logs"/ "mdl-mlops.log" |
|
|
| logging.basicConfig( |
| level = getattr(logging, nivel.upper(), logging.DEBUG), |
| format = "%(asctime)s | %(levelname)-8s | %(funcName)s.%(lineno)s | %(message)s", |
| datefmt="%Y-%m-%d %H:%M:%S", |
| handlers = [ |
| logging.StreamHandler(), |
| logging.FileHandler(path_final) |
| ] |
| ) |
|
|
| def get_project_root() -> Path: |
| return Path(__file__).resolve().parents[1] |
|
|
| def load_config(nombre : str) -> dict: |
| logger = logging.getLogger(__name__) |
| logger.info("Cargando configuración...") |
| raiz_proyecto = get_project_root() |
| fichero_leer = raiz_proyecto / "config" / nombre |
|
|
| with open(fichero_leer) as file: |
| output = yaml.safe_load(file) |
| logger.info("Carga de configuración completada") |
| return output |
|
|
| def download_mnist(): |
| transform = transforms.Compose([ |
| transforms.ToTensor() |
| ]) |
|
|
| train_dataset = torchvision.datasets.MNIST( |
| root='./data', train=True, download=True, transform=transform |
| ) |
| test_dataset = torchvision.datasets.MNIST( |
| root='./data', train=False, download=True, transform=transform |
| ) |
|
|
| def load_model_config(): |
| config = load_config("gbl_config.yaml") |
| return load_config(config["model_configuration"]) |
|
|
| def load_best_model(): |
| config = load_model_config() |
| return model.ConvCVAE.load_from_checkpoint(f"models/main/best_model-{config['model_version']}.ckpt") |
