Prepare data function

gns/train.py

@@ -398,6 +398,25 @@ def setup_tensorboard(cfg, metadata):
     return writer
 
 
+def prepare_data(example, device_id):
+    """Prepare data for training or validation."""
+    position = example[0][0].to(device_id)
+    particle_type = example[0][1].to(device_id)
+
+    if len(example[0]) == 4:  # if data loader includes material_property
+        material_property = example[0][2].to(device_id)
+        n_particles_per_example = example[0][3].to(device_id)
+    elif len(example[0]) == 3:
+        material_property = None
+        n_particles_per_example = example[0][2].to(device_id)
+    else:
+        raise ValueError("Unexpected number of elements in the data loader")
+
+    labels = example[1].to(device_id)
+
+    return position, particle_type, material_property, n_particles_per_example, labels
+
+
 def train(rank, cfg, world_size, device, verbose):
     """Train the model.
 
@@ -484,9 +503,7 @@ def train(rank, cfg, world_size, device, verbose):
     writer = setup_tensorboard(cfg, metadata) if verbose else None
 
     try:
-        num_epochs = max(
-            1, (cfg.training.steps + len(train_dl) - 1) // len(train_dl)
-        )  # Calculate total epochs
+        num_epochs = max(1, (cfg.training.steps + len(train_dl) - 1) // len(train_dl))
         print(f"Total epochs = {num_epochs}")
         for epoch in tqdm(range(epoch, num_epochs), desc="Training", unit="epoch"):
             if device == torch.device("cuda"):
@@ -499,16 +516,14 @@ def train(rank, cfg, world_size, device, verbose):
             with tqdm(total=len(train_dl), desc=f"Epoch {epoch}", unit="batch") as pbar:
                 for example in train_dl:
                     steps_per_epoch += 1
-                    position = example[0][0].to(device_id)
-                    particle_type = example[0][1].to(device_id)
-                    if n_features == 3:
-                        material_property = example[0][2].to(device_id)
-                        n_particles_per_example = example[0][3].to(device_id)
-                    elif n_features == 2:
-                        n_particles_per_example = example[0][2].to(device_id)
-                    else:
-                        raise NotImplementedError
-                    labels = example[1].to(device_id)
+                    # Prepare data
+                    (
+                        position,
+                        particle_type,
+                        material_property,
+                        n_particles_per_example,
+                        labels,
+                    ) = prepare_data(example, device_id)
 
                     n_particles_per_example = n_particles_per_example.to(device_id)
                     labels = labels.to(device_id)
@@ -739,16 +754,10 @@ def _get_simulator(
 
 
 def validation(simulator, example, n_features, cfg, rank, device_id):
-    position = example[0][0].to(device_id)
-    particle_type = example[0][1].to(device_id)
-    if n_features == 3:  # if dl includes material_property
-        material_property = example[0][2].to(device_id)
-        n_particles_per_example = example[0][3].to(device_id)
-    elif n_features == 2:
-        n_particles_per_example = example[0][2].to(device_id)
-    else:
-        raise NotImplementedError
-    labels = example[1].to(device_id)
+
+    position, particle_type, material_property, n_particles_per_example, labels = (
+        prepare_data(example, device_id)
+    )
 
     # Sample the noise to add to the inputs.
     sampled_noise = noise_utils.get_random_walk_noise_for_position_sequence(