Removing excessive logging.

crystal_diffusion/models/position_diffusion_lightning_model.py

@@ -277,7 +277,6 @@ def _get_target_normalized_score(
 
     def training_step(self, batch, batch_idx):
         """Runs a prediction step for training, returning the loss."""
-        logger.info(f"  - Starting training step with batch index {batch_idx}")
         output = self._generic_step(batch, batch_idx)
         loss = output["loss"]
 
@@ -318,12 +317,10 @@ def validation_step(self, batch, batch_idx):
             return output
 
         if self.metrics_parameters.compute_energies:
-            logger.info("        * registering reference energies")
             reference_energies = batch["potential_energy"]
             self.energy_ks_metric.register_reference_samples(reference_energies.cpu())
 
         if self.metrics_parameters.compute_structure_factor:
-            logger.info("        * registering reference distances")
             basis_vectors = torch.diag_embed(batch["box"])
             cartesian_positions = get_positions_from_coordinates(
                 relative_coordinates=batch[RELATIVE_COORDINATES],
@@ -339,7 +336,6 @@ def validation_step(self, batch, batch_idx):
                 reference_distances.cpu()
             )
 
-        logger.info(f"         Done validation step with batch index {batch_idx}")
         return output
 
     def test_step(self, batch, batch_idx):