**refactor(run.py): Integrate Hydra for module instantiation and clea…

…n up configurations**

- **Use Hydra to instantiate  and :**
  - Replaced manual instantiation with  and .

- **Remove hard-coded dataset paths:**
  - Eliminated hard-coded  and  to rely on Hydra configurations.

- **Update imports:**
  - Removed unused imports such as , , , , , and .
  - Added necessary imports like  from ,  from , and  from .

- **Clean up Trainer instantiation:**
  - Removed the  and related directory creation logic.
  - Updated  instantiation to exclude  and ensure proper callback management.

- **Enhance documentation:**
  - Improved docstrings for better clarity and understanding of the main function's purpose.

- **Miscellaneous:**
  - Added type hints for better code readability and maintenance.

**Benefits:**
- Enhances configurability and flexibility by leveraging Hydra's powerful configuration management.
- Simplifies the  script by removing hard-coded values and unused components.
- Improves code maintainability and readability through better documentation and type hinting.

run.py

@@ -2,86 +2,50 @@
 import os
 import random
 import warnings
+from typing import List
 
+import hydra
 import lightning as L
 import numpy as np
 import torch
-import torch.nn as nn
-from lightning.pytorch.callbacks import ModelCheckpoint
-from lightning.pytorch.loggers import TensorBoardLogger
-
-from hungarian_net.lightning_datamodules.hungarian_datamodule import HungarianDataModule, HungarianDataset
-from hungarian_net.lightning_modules.hnet_gru_lightning import HNetGRULightning
-
-
- @hydra.main(
-     config_path="configs",
-     config_name="run.yaml",
-     version_base="1.3",
- )
+from lightning.pytorch.loggers import Logger
+from omegaconf import DictConfig
+from torchmetrics import MetricCollection
+
+@hydra.main(
+    config_path="configs",
+    config_name="run.yaml",
+    version_base="1.3",
+)
 def main(cfg: DictConfig):
-    """ batch_size=256,
-    nb_epochs=1000,
-    max_len=2,
-    sample_range_used=[3000, 5000, 15000],
-    filename_train="data/reference/hung_data_train",
-    filename_test="data/reference/hung_data_test", """
+    """
+    Instantiate all necessary modules, train and test the model.
 
-
+    Args:
+        cfg (DictConfig): Hydra configuration object, passed in by the @hydra.main decorator
+    """
 
-    # TODO: Réécriture/factorisation du code sur le modèle de VibraVox de Julien HAURET
     # TODO: leverager TensorBoard, Hydra, Pytorch Lightning, RayTune, Docker
-
-    # Temporarly mock the dataloader
-    filename_train = "data/20241202/train/hung_data_train_DOA2_3000-5000-15000"
-    filename_test = "data/20241202/test/hung_data_test_DOA2_3000-5000-15000"
-
-    lightning_datamodule = HungarianDataModule(
-        train_filename=filename_train,
-        test_filename=filename_test,
-        max_len=max_len,
-        batch_size=batch_size,
-        num_workers=4,
-    )
-
-    # metrics: MetricCollection = MetricCollection(
-    #    dict(hydra.utils.instantiate(cfg.metrics))
-    # )
-
-    use_cuda = torch.cuda.is_available()
-    lightning_module = HNetGRULightning(
-        metrics=None,
-        device=torch.device("cuda" if use_cuda else "cpu"),
-        max_len=max_len,
+
+    # Instantiate LightningDataModule
+    lightning_datamodule: L.LightningDataModule = hydra.utils.instantiate(
+        cfg.lightning_datamodule
     )
 
     # Instantiate LightningModule
-    lightning_module: LightningModule = hydra.utils.instantiate(
+    metrics: MetricCollection = MetricCollection(
+        dict(hydra.utils.instantiate(cfg.metrics))
+    )
+    lightning_module: L.LightningModule = hydra.utils.instantiate(
         cfg.lightning_module,
         metrics=metrics,
+        device=torch.device("cuda" if torch.cuda.is_available() else "cpu"), #mock for now #TODO: hide device, supposed to be handled by lightning
     )
 
-    # Get current date
-    current_date = datetime.datetime.now().strftime("%Y%m%d")
-
-    os.makedirs(f"checkpoints/{current_date}", exist_ok=True)
-
-    # Human-readable filename
-    dirpath = f"checkpoints/{current_date}/"
-    out_filename = f"hnet_model_DOA{max_len}_{'-'.join(map(str, sample_range_used))}"
-
-    """ checkpoint_callback = ModelCheckpoint(
-        dirpath=dirpath,
-        filename=out_filename,
-        monitor="validation_loss",
-        save_top_k=1,
-        mode="min",
-    ) """
-
     # Instantiate Trainer
-    callbacks: List[Callback] = list(hydra.utils.instantiate(cfg.callbacks).values())
+    callbacks: List[L.Callback] = list(hydra.utils.instantiate(cfg.callbacks).values())
     logger: Logger = hydra.utils.instantiate(cfg.logging.logger)
-    trainer: Trainer = hydra.utils.instantiate(
+    trainer: L.Trainer = hydra.utils.instantiate(
         cfg.trainer, callbacks=callbacks, logger=logger, _convert_="partial"
     )