Rename num_samples -> num_frames for consistency

lerobot/common/datasets/compute_stats.py

@@ -180,13 +180,13 @@ def aggregate_stats(ls_datasets) -> dict[str, torch.Tensor]:
                 "n ... -> ...",
                 stat_key,
             )
-        total_samples = sum(d.num_samples for d in ls_datasets if data_key in d.stats)
+        total_samples = sum(d.num_frames for d in ls_datasets if data_key in d.stats)
         # Compute the "sum" statistic by multiplying each mean by the number of samples in the respective
         # dataset, then divide by total_samples to get the overall "mean".
-        # NOTE: the brackets around (d.num_samples / total_samples) are needed tor minimize the risk of
+        # NOTE: the brackets around (d.num_frames / total_samples) are needed tor minimize the risk of
         # numerical overflow!
         stats[data_key]["mean"] = sum(
-            d.stats[data_key]["mean"] * (d.num_samples / total_samples)
+            d.stats[data_key]["mean"] * (d.num_frames / total_samples)
             for d in ls_datasets
             if data_key in d.stats
         )
@@ -195,12 +195,12 @@ def aggregate_stats(ls_datasets) -> dict[str, torch.Tensor]:
         # Given two sets of data where the statistics are known:
         # σ_combined = sqrt[ (n1 * (σ1^2 + d1^2) + n2 * (σ2^2 + d2^2)) / (n1 + n2) ]
         # where d1 = μ1 - μ_combined, d2 = μ2 - μ_combined
-        # NOTE: the brackets around (d.num_samples / total_samples) are needed tor minimize the risk of
+        # NOTE: the brackets around (d.num_frames / total_samples) are needed tor minimize the risk of
         # numerical overflow!
         stats[data_key]["std"] = torch.sqrt(
             sum(
                 (d.stats[data_key]["std"] ** 2 + (d.stats[data_key]["mean"] - stats[data_key]["mean"]) ** 2)
-                * (d.num_samples / total_samples)
+                * (d.num_frames / total_samples)
                 for d in ls_datasets
                 if data_key in d.stats
             )

lerobot/common/datasets/lerobot_dataset.py

@@ -357,8 +357,8 @@ class LeRobotDataset(torch.utils.data.Dataset):
         return self.info["names"]
 
     @property
-    def num_samples(self) -> int:
-        """Number of samples/frames in selected episodes."""
+    def num_frames(self) -> int:
+        """Number of frames in selected episodes."""
         return len(self.hf_dataset) if self.hf_dataset is not None else self.total_frames
 
     @property
@@ -510,7 +510,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
         return item
 
     def __len__(self):
-        return self.num_samples
+        return self.num_frames
 
     def __getitem__(self, idx) -> dict:
         item = self.hf_dataset[idx]
@@ -544,7 +544,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
             f"  Repository ID: '{self.repo_id}',\n"
             f"  Selected episodes: {self.episodes},\n"
             f"  Number of selected episodes: {self.num_episodes},\n"
-            f"  Number of selected samples: {self.num_samples},\n"
+            f"  Number of selected samples: {self.num_frames},\n"
             f"\n{json.dumps(self.info, indent=4)}\n"
         )
 
@@ -981,9 +981,9 @@ class MultiLeRobotDataset(torch.utils.data.Dataset):
         return video_frame_keys
 
     @property
-    def num_samples(self) -> int:
+    def num_frames(self) -> int:
         """Number of samples/frames."""
-        return sum(d.num_samples for d in self._datasets)
+        return sum(d.num_frames for d in self._datasets)
 
     @property
     def num_episodes(self) -> int:
@@ -1000,7 +1000,7 @@ class MultiLeRobotDataset(torch.utils.data.Dataset):
         return 1 / self.fps - 1e-4
 
     def __len__(self):
-        return self.num_samples
+        return self.num_frames
 
     def __getitem__(self, idx: int) -> dict[str, torch.Tensor]:
         if idx >= len(self):
@@ -1009,8 +1009,8 @@ class MultiLeRobotDataset(torch.utils.data.Dataset):
         start_idx = 0
         dataset_idx = 0
         for dataset in self._datasets:
-            if idx >= start_idx + dataset.num_samples:
-                start_idx += dataset.num_samples
+            if idx >= start_idx + dataset.num_frames:
+                start_idx += dataset.num_frames
                 dataset_idx += 1
                 continue
             break
@@ -1028,7 +1028,7 @@ class MultiLeRobotDataset(torch.utils.data.Dataset):
         return (
             f"{self.__class__.__name__}(\n"
             f"  Repository IDs: '{self.repo_ids}',\n"
-            f"  Number of Samples: {self.num_samples},\n"
+            f"  Number of Samples: {self.num_frames},\n"
             f"  Number of Episodes: {self.num_episodes},\n"
             f"  Type: {'video (.mp4)' if self.video else 'image (.png)'},\n"
             f"  Recorded Frames per Second: {self.fps},\n"

lerobot/common/datasets/online_buffer.py

@@ -187,7 +187,7 @@ class OnlineBuffer(torch.utils.data.Dataset):
         assert data[OnlineBuffer.INDEX_KEY][0].item() == 0
 
         # Shift the incoming indices if necessary.
-        if self.num_samples > 0:
+        if self.num_frames > 0:
             last_episode_index = self._data[OnlineBuffer.EPISODE_INDEX_KEY][next_index - 1]
             last_data_index = self._data[OnlineBuffer.INDEX_KEY][next_index - 1]
             data[OnlineBuffer.EPISODE_INDEX_KEY] += last_episode_index + 1
@@ -227,11 +227,11 @@ class OnlineBuffer(torch.utils.data.Dataset):
         )
 
     @property
-    def num_samples(self) -> int:
+    def num_frames(self) -> int:
         return np.count_nonzero(self._data[OnlineBuffer.OCCUPANCY_MASK_KEY])
 
     def __len__(self):
-        return self.num_samples
+        return self.num_frames
 
     def _item_to_tensors(self, item: dict) -> dict:
         item_ = {}