feat: integrate PyTorch Lightning into training pipeline

- Refactored  to use LightningModule
- Updated training and validation steps with Lightning's hooks
- Added PyTorch Lightning configurations
- need to further integrate Lightning and Hydra by leveraging Julien' HAURET's VibraVox

hungarian_net/train_hnet.py

@@ -3,17 +3,65 @@
 import random
 import time
 
+import lightning as L
 import numpy as np
 import torch
 import torch.nn as nn
 import torch.optim as optim
+from lightning.callbacks import ModelCheckpoint
+from lightning.loggers import TensorBoardLogger
 from sklearn.metrics import f1_score
 from torch.utils.data import DataLoader
+from torch.utils.tensorboard import SummaryWriter
 
 from hungarian_net.dataset import HungarianDataset
 from hungarian_net.models import HNetGRU
 
 
+class HNetGRULightning(L.LightningModule):
+    def __init__(self, max_len, sample_range_used, class_imbalance):
+        super().__init__()
+        self.model = HNetGRU(max_len=max_len)
+        self.criterion1 = nn.BCEWithLogitsLoss(reduction="sum")
+        self.criterion2 = nn.BCEWithLogitsLoss(reduction="sum")
+        self.criterion3 = nn.BCEWithLogitsLoss(reduction="sum")
+        self.criterion_wts = [1.0, 1.0, 1.0]
+        self.sample_range_used = sample_range_used
+        self.class_imbalance = class_imbalance
+
+    def forward(self, x):
+        return self.model(x)
+
+    def training_step(self, batch, batch_idx):
+        data, target = batch
+        output1, output2, output3 = self(data)
+        l1 = self.criterion1(output1, target[0])
+        l2 = self.criterion2(output2, target[1])
+        l3 = self.criterion3(output3, target[2])
+        loss = sum(w * l for w, l in zip(self.criterion_wts, [l1, l2, l3]))
+        self.log("train_loss", loss)
+        return loss
+
+    def validation_step(self, batch, batch_idx):
+        data, target = batch
+        output1, output2, output3 = self(data)
+        l1 = self.criterion1(output1, target[0])
+        l2 = self.criterion2(output2, target[1])
+        l3 = self.criterion3(output3, target[2])
+        loss = sum(w * l for w, l in zip(self.criterion_wts, [l1, l2, l3]))
+        self.log("val_loss", loss)
+        # Calculate F1 Score or other metrics here
+        return loss
+
+    def configure_optimizers(self):
+        return optim.Adam(self.parameters())
+
+
+# @hydra.main(
+#     config_path="configs",
+#     config_name="run.yaml",
+#     version_base="1.3",
+# )
 def main(
     batch_size=256,
     nb_epochs=1000,
@@ -99,6 +147,9 @@ def main(
             The trained HNetGRU model with the best validation F1 score.
     """
 
+    # TODO: Réécriture/factorisation du code sur le modèle de VibraVox de Julien HAURET
+    # TODO: leverager TensorBoard, Hydra, Pytorch Lightning, RayTune, Docker
+
     set_seed()
 
     # Check wether to run on cpu or gpu
@@ -129,158 +180,23 @@ def main(
         drop_last=True,
     )
 
-    # load Hnet model and loss functions
-    model = HNetGRU(max_len=max_len).to(device)
-    optimizer = optim.Adam(model.parameters())
-
-    criterion1 = torch.nn.BCEWithLogitsLoss(reduction="sum")
-    criterion2 = torch.nn.BCEWithLogitsLoss(reduction="sum")
-    criterion3 = torch.nn.BCEWithLogitsLoss(reduction="sum")
-    criterion_wts = [1.0, 1.0, 1.0]
-
-    # Start training
-    best_f = -1
-    best_epoch = -1
-    for epoch in range(1, nb_epochs + 1):
-        train_start = time.time()
-        # TRAINING
-        model.train()
-        train_loss, train_l1, train_l2, train_l3 = 0, 0, 0, 0
-        for batch_idx, (data, target) in enumerate(train_loader):
-            data = data.to(device).float()
-            target1 = target[0].to(device).float()
-            target2 = target[1].to(device).float()
-            target3 = target[2].to(device).float()
-
-            optimizer.zero_grad()
-
-            output1, output2, output3 = model(data)
-
-            l1 = criterion1(output1, target1)
-            l2 = criterion2(output2, target2)
-            l3 = criterion3(output3, target3)
-            loss = criterion_wts[0] * l1 + criterion_wts[1] * l2 + criterion_wts[2] * l3
-
-            loss.backward()
-            optimizer.step()
-
-            train_l1 += l1.item()
-            train_l2 += l2.item()
-            train_l3 += l3.item()
-            train_loss += loss.item()
-
-        train_l1 /= len(train_loader.dataset)
-        train_l2 /= len(train_loader.dataset)
-        train_l3 /= len(train_loader.dataset)
-        train_loss /= len(train_loader.dataset)
-        train_time = time.time() - train_start
-
-        # TESTING
-        test_start = time.time()
-        model.eval()
-        test_loss, test_l1, test_l2, test_l3 = 0, 0, 0, 0
-        test_f = 0
-        nb_test_batches = 0
-        true_positives, false_positives, false_negatives = 0, 0, 0
-        f1_score_unweighted = 0
-        with torch.no_grad():
-            for data, target in test_loader:
-                data = data.to(device).float()
-                target1 = target[0].to(device).float()
-                target2 = target[1].to(device).float()
-                target3 = target[2].to(device).float()
-
-                output1, output2, output3 = model(data)
-                l1 = criterion1(output1, target1)
-                l2 = criterion2(output2, target2)
-                l3 = criterion3(output3, target3)
-                loss = (
-                    criterion_wts[0] * l1
-                    + criterion_wts[1] * l2
-                    + criterion_wts[2] * l3
-                )
-
-                test_l1 += l1.item()
-                test_l2 += l2.item()
-                test_l3 += l3.item()
-                test_loss += loss.item()  # sum up batch loss
-
-                f_pred = (torch.sigmoid(output1).cpu().numpy() > 0.5).reshape(-1)
-                f_ref = target1.cpu().numpy().reshape(-1)
-                test_f += f1_score(
-                    f_ref,
-                    f_pred,
-                    zero_division=1,
-                    average="weighted",
-                    sample_weight=f_score_weights,
-                )
-                nb_test_batches += 1
-
-                true_positives += np.sum((f_pred == 1) & (f_ref == 1))
-                false_positives += np.sum((f_pred == 1) & (f_ref == 0))
-                false_negatives += np.sum((f_pred == 0) & (f_ref == 1))
-
-                f1_score_unweighted += (
-                    2
-                    * true_positives
-                    / (2 * true_positives + false_positives + false_negatives)
-                )
-
-        test_l1 /= len(test_loader.dataset)
-        test_l2 /= len(test_loader.dataset)
-        test_l3 /= len(test_loader.dataset)
-        test_loss /= len(test_loader.dataset)
-        test_f /= nb_test_batches
-        test_time = time.time() - test_start
-        weighted_accuracy = train_dataset.compute_weighted_accuracy(
-            true_positives, false_positives
-        )
-
-        f1_score_unweighted /= nb_test_batches
-
-        # Early stopping
-        if test_f > best_f:
-            best_f = test_f
-            best_epoch = epoch
-
-            # Get current date
-            current_date = datetime.datetime.now().strftime("%Y%m%d")
-
-            # TODO: change model filename - leverage TensorBoard
-
-            os.makedirs(f"models/{current_date}", exist_ok=True)
-
-            # Human-readable filename
-            out_filename = f"models/{current_date}/hnet_model_DOA{max_len}_{'-'.join(map(str, sample_range_used))}.pt"
-
-            torch.save(model.state_dict(), out_filename)
-
-            model_to_return = model
-        print(
-            "Epoch: {}\t time: {:0.2f}/{:0.2f}\ttrain_loss: {:.4f} ({:.4f}, {:.4f}, {:.4f})\ttest_loss: {:.4f} ({:.4f}, {:.4f}, {:.4f})\tf_scr: {:.4f}\tbest_epoch: {}\tbest_f_scr: {:.4f}\ttrue_positives: {}\tfalse_positives: {}\tweighted_accuracy: {:.4f}".format(
-                epoch,
-                train_time,
-                test_time,
-                train_loss,
-                train_l1,
-                train_l2,
-                train_l3,
-                test_loss,
-                test_l1,
-                test_l2,
-                test_l3,
-                test_f,
-                best_epoch,
-                best_f,
-                true_positives,
-                false_positives,
-                weighted_accuracy,
-            )
-        )
-        print("F1 Score (unweighted) : {:.4f}".format(f1_score_unweighted))
-    print("Best epoch : {}\nBest F1 score : {}".format(best_epoch, best_f))
-
-    return model_to_return
+    model = HNetGRULightning(
+        max_len=max_len,
+        sample_range_used=sample_range_used,
+        class_imbalance=class_imbalance,
+    )
+
+    logger = TensorBoardLogger("tb_logs", name="hnet_model")
+    checkpoint_callback = ModelCheckpoint(monitor="val_loss", save_top_k=1, mode="min")
+
+    trainer = L.Trainer(
+        max_epochs=nb_epochs,
+        logger=logger,
+        callbacks=[checkpoint_callback],
+        # gpus=1 if use_cuda else 0
+    )
+
+    trainer.fit(model, train_loader, test_loader)
 
 
 def set_seed(seed=42):

requirements.txt

@@ -4,4 +4,5 @@ scikit-learn>=1.5.2
 scipy>=1.14.1
 torch>=2.5.1
 torchaudio>=2.5.1
-tensorboard>=2.18.0
+tensorboard>=2.18.0
+lightning>=2.4.0

tests/nonregression_tests/conftest.py

@@ -1,8 +1,6 @@
 # tests/nonregression_tests/conftest.py
 
 import pytest
-from pytest_mock import mocker
-
 from hungarian_net.models import HNetGRU
 
 

tests/scenarios_tests/model/test_scenarios_train_hnet.py

@@ -1,9 +1,7 @@
 # tests/scenarios_tests/model/test_train_hnet.py
 
 import re
-
 import pytest
-
 from hungarian_net.train_hnet import main
 
 
@@ -62,6 +60,9 @@ def test_train_model_under_various_distributions(
     else:
         sample_range_used = None  # Default values
 
+    # Mock nb_epochs to be 1 regardless of the input
+    nb_epochs = 1
+
     main(
         batch_size=batch_size,
         nb_epochs=nb_epochs,