Add de novo training pytest

tests/test_denovo_baseline_training.py

@@ -0,0 +1,63 @@
+import pytest
+
+
+def denovo_baseline_training():
+    import pytorch_lightning as pl
+    from pytorch_lightning import Trainer
+
+    from massspecgym.data import MassSpecDataset, MassSpecDataModule
+    from massspecgym.data.transforms import SpecTokenizer
+    from massspecgym.models.de_novo import SmilesTransformer
+    from massspecgym.models.tokenizers import SmilesBPETokenizer
+
+    # Load dataset
+    dataset = MassSpecDataset(
+        spec_transform=SpecTokenizer(n_peaks=60),
+        mol_transform=None
+    )
+
+    # Init data module
+    data_module = MassSpecDataModule(
+        dataset=dataset,
+        batch_size=32
+    )
+
+    # Init model
+    model = SmilesTransformer(
+        input_dim=2,
+        d_model=512,
+        nhead=8,
+        num_encoder_layers=4,
+        num_decoder_layers=4,
+        dropout=0.0,
+        smiles_tokenizer=SmilesBPETokenizer(max_len=200),
+        k_predictions=1,
+        pre_norm=False,
+        max_smiles_len=100,
+        validate_only_loss=True
+    )
+
+    # Init trainer
+    project = "MassSpecGymDeNovo"
+    name = "SmilesTransformer_debug_overfitting"
+    logger = pl.loggers.WandbLogger(
+        project=project,
+        name=name,
+        tags=[],
+        log_model=False,
+        mode="disabled"
+    )
+    trainer = Trainer(
+        accelerator="cpu", max_epochs=1, logger=logger, log_every_n_steps=1, check_val_every_n_epoch=50,
+        limit_train_batches=3, limit_val_batches=3
+    )
+
+    # Train
+    trainer.fit(model, datamodule=data_module)
+
+
+def test_denovo_baseline_training():
+    try:
+        denovo_baseline_training()
+    except Exception as e:
+        pytest.fail(f"denovo_baseline_training() raised an exception: {e}")