test(reencode dataset): adding missing test for reencode dataset

tests/datasets/test_dataset_tools.py

@@ -23,6 +23,7 @@ import torch
 
 pytest.importorskip("datasets", reason="datasets is required (install lerobot[dataset])")
 
+
 from lerobot.configs import VideoEncoderConfig
 from lerobot.datasets.dataset_tools import (
     add_features,
@@ -31,9 +32,12 @@ from lerobot.datasets.dataset_tools import (
     merge_datasets,
     modify_features,
     modify_tasks,
+    reencode_dataset,
     remove_feature,
     split_dataset,
 )
+from lerobot.datasets.io_utils import load_info
+from tests.datasets.test_video_encoding import _add_frames, require_h264, require_libsvtav1
 
 
 @pytest.fixture
@@ -1326,3 +1330,41 @@ def test_convert_image_to_video_dataset_subset_episodes(tmp_path):
 
         if output_dir.exists():
             shutil.rmtree(output_dir)
+
+
+# ─── reencode_dataset ─────────────────────────────────────────────────
+
+
+@require_libsvtav1
+@require_h264
+def test_reencode_dataset_multi_key_multiprocessing(
+    tmp_path, empty_lerobot_dataset_factory, features_factory
+):
+    """Re-encode a two-camera dataset with num_workers=2 and verify metadata refresh."""
+    features = features_factory(use_videos=True)
+    initial_cfg = VideoEncoderConfig(vcodec="libsvtav1", g=2, crf=30, preset=12)
+    dataset = empty_lerobot_dataset_factory(
+        root=tmp_path / "ds",
+        features=features,
+        use_videos=True,
+        camera_encoder=initial_cfg,
+    )
+
+    _add_frames(dataset, num_frames=4)
+    dataset.save_episode()
+    _add_frames(dataset, num_frames=4)
+    dataset.save_episode()
+    dataset.finalize()
+
+    assert len(dataset.meta.video_keys) == 2
+
+    target_cfg = VideoEncoderConfig(vcodec="h264", g=6, crf=23, pix_fmt="yuv420p")
+
+    result = reencode_dataset(dataset, camera_encoder=target_cfg, num_workers=2)
+
+    assert result is dataset
+
+    persisted_info = load_info(dataset.root)
+    for vk in dataset.meta.video_keys:
+        persisted_encoder = VideoEncoderConfig.from_video_info(persisted_info.features[vk].get("info", {}))
+        assert persisted_encoder == target_cfg

tests/datasets/test_video_encoding.py

@@ -348,16 +348,22 @@ def _read_feature_info(dataset: LeRobotDataset) -> dict:
     return info["features"][VIDEO_KEY]["info"]
 
 
-def _add_frames(dataset: LeRobotDataset, num_frames: int) -> None:
-    shape = dataset.meta.features[VIDEO_KEY]["shape"]
+def _add_frames(dataset: LeRobotDataset, num_frames: int, video_keys: list[str] | None = None) -> None:
+    from lerobot.utils.constants import DEFAULT_FEATURES
+
+    if video_keys is None:
+        video_keys = dataset.meta.video_keys
     for _ in range(num_frames):
-        dataset.add_frame(
-            {
-                VIDEO_KEY: np.random.randint(0, 256, shape, dtype=np.uint8),
-                "action": np.zeros(2, dtype=np.float32),
-                "task": "test",
-            }
-        )
+        frame: dict = {"task": "test"}
+        for key, ft in dataset.meta.features.items():
+            if key in DEFAULT_FEATURES:
+                continue
+            shape = ft["shape"]
+            if key in video_keys:
+                frame[key] = np.random.randint(0, 256, shape, dtype=np.uint8)
+            else:
+                frame[key] = np.zeros(shape, dtype=np.float32)
+        dataset.add_frame(frame)
 
 
 class TestGetVideoInfo: