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examples/advanced/2_calculate_validation_loss.py

@@ -14,7 +14,10 @@ from pathlib import Path
 import torch
 from huggingface_hub import snapshot_download
 
-from lerobot.common.datasets.lerobot_dataset import LeRobotDataset, LeRobotDatasetMetadata
+from lerobot.common.datasets.lerobot_dataset import (
+    LeRobotDataset,
+    LeRobotDatasetMetadata,
+)
 from lerobot.common.policies.diffusion.modeling_diffusion import DiffusionPolicy
 
 device = torch.device("cuda")
@@ -37,7 +40,24 @@ delta_timestamps = {
     # Load the previous action (-0.1), the next action to be executed (0.0),
     # and 14 future actions with a 0.1 seconds spacing. All these actions will be
     # used to calculate the loss.
-    "action": [-0.1, 0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3, 1.4],
+    "action": [
+        -0.1,
+        0.0,
+        0.1,
+        0.2,
+        0.3,
+        0.4,
+        0.5,
+        0.6,
+        0.7,
+        0.8,
+        0.9,
+        1.0,
+        1.1,
+        1.2,
+        1.3,
+        1.4,
+    ],
 }
 
 # Load the last 10% of episodes of the dataset as a validation set.
@@ -53,8 +73,12 @@ print(f"Number of episodes in full dataset: {total_episodes}")
 print(f"Number of episodes in training dataset (90% subset): {len(train_episodes)}")
 print(f"Number of episodes in validation dataset (10% subset): {len(val_episodes)}")
 # - Load train an val datasets
-train_dataset = LeRobotDataset("lerobot/pusht", episodes=train_episodes, delta_timestamps=delta_timestamps)
-val_dataset = LeRobotDataset("lerobot/pusht", episodes=val_episodes, delta_timestamps=delta_timestamps)
+train_dataset = LeRobotDataset(
+    "lerobot/pusht", episodes=train_episodes, delta_timestamps=delta_timestamps
+)
+val_dataset = LeRobotDataset(
+    "lerobot/pusht", episodes=val_episodes, delta_timestamps=delta_timestamps
+)
 print(f"Number of frames in training dataset (90% subset): {len(train_dataset)}")
 print(f"Number of frames in validation dataset (10% subset): {len(val_dataset)}")