update the main function

graph_dit/main.py

@@ -11,9 +11,13 @@ import utils
 from datasets import dataset
 from diffusion_model import Graph_DiT
 from metrics.molecular_metrics_train import TrainMolecularMetricsDiscrete
+from metrics.molecular_metrics_train import TrainGraphMetricsDiscrete
 from metrics.molecular_metrics_sampling import SamplingMolecularMetrics
+from metrics.molecular_metrics_sampling import SamplingGraphMetrics
+
 
 from analysis.visualization import MolecularVisualization
+from analysis.visualization import GraphVisualization
 
 warnings.filterwarnings("ignore", category=UserWarning)
 torch.set_float32_matmul_precision("medium")
@@ -79,19 +83,20 @@ def main(cfg: DictConfig):
     datamodule = dataset.DataModule(cfg)
     datamodule.prepare_data()
     dataset_infos = dataset.DataInfos(datamodule=datamodule, cfg=cfg, dataset=datamodule.dataset)
-    # train_smiles, reference_smiles = datamodule.get_train_smiles()
+    train_smiles, reference_smiles = datamodule.get_train_smiles()
-    train_graphs, reference_graphs = datamodule.get_train_graphs()
+    # train_graphs, reference_graphs = datamodule.get_train_graphs()
 
     # get input output dimensions
     dataset_infos.compute_input_output_dims(datamodule=datamodule)
     train_metrics = TrainMolecularMetricsDiscrete(dataset_infos)
+    # train_metrics = TrainGraphMetricsDiscrete(dataset_infos)
 
-    # sampling_metrics = SamplingMolecularMetrics(
+    sampling_metrics = SamplingMolecularMetrics(
-    #     dataset_infos, train_smiles, reference_smiles
+        dataset_infos, train_smiles, reference_smiles
-    # )
-    sampling_metrics = SamplingGraphMetrics(
-        dataset_infos, train_graphs, reference_graphs
     )
+    # sampling_metrics = SamplingGraphMetrics(
+    #     dataset_infos, train_graphs, reference_graphs
+    # )
     visualization_tools = MolecularVisualization(dataset_infos)
 
     model_kwargs = {
@@ -149,6 +154,54 @@ def test(cfg: DictConfig):
     train_graphs, reference_graphs = datamodule.get_train_graphs()
 
     dataset_infos.compute_input_output_dims(datamodule=datamodule)
+    train_metrics = TrainGraphMetricsDiscrete(dataset_infos)
+
+    sampling_metrics = SamplingGraphMetrics(
+        dataset_infos, train_graphs, reference_graphs
+    )
+
+    visulization_tools = GraphVisualization(dataset_infos)
+
+    model_kwargs = {
+        "dataset_infos": dataset_infos,
+        "train_metrics": train_metrics,
+        "sampling_metrics": sampling_metrics,
+        "visualization_tools": visulization_tools,
+    }
+
+    if cfg.general.test_only:
+        cfg, _ = get_resume(cfg, model_kwargs)
+        os.chdir(cfg.general.test_only.split("checkpoints")[0])
+    elif cfg.general.resume is not None:
+        cfg, _ = get_resume_adaptive(cfg, model_kwargs)
+        os.chdir(cfg.general.resume.split("checkpoints")[0])
+    model = Graph_DiT(cfg=cfg, **model_kwargs)
+    trainer = Trainer(
+        gradient_clip_val=cfg.train.clip_grad,
+        # accelerator="cpu",
+        accelerator="gpu"
+        if torch.cuda.is_available() and cfg.general.gpus > 0
+        else "cpu",
+        devices=cfg.general.gpus
+        if torch.cuda.is_available() and cfg.general.gpus > 0
+        else None,
+        max_epochs=cfg.train.n_epochs,
+        enable_checkpointing=False,
+        check_val_every_n_epoch=cfg.train.check_val_every_n_epoch,
+        val_check_interval=cfg.train.val_check_interval,
+        strategy="ddp" if cfg.general.gpus > 1 else "auto",
+        enable_progress_bar=cfg.general.enable_progress_bar,
+        callbacks=[],
+        reload_dataloaders_every_n_epochs=0,
+        logger=[],
+    )
+
+    if not cfg.general.test_only:
+        print("start testing fit method")
+        trainer.fit(model, datamodule=datamodule, ckpt_path=cfg.general.resume)
+        if cfg.general.save_model:
+            trainer.save_checkpoint(f"checkpoints/{cfg.general.name}/last.ckpt")
+        trainer.test(model, datamodule=datamodule)
 
 if __name__ == "__main__":
     test()