validate episode hashes in data config before training

egomimic/eval/latent_dataset.py

@@ -71,6 +71,7 @@ def build_dataset(
     task: str,
     embodiment: str,
     resolver,
+    dataset_name: str | None = None,
     hashes: list[str] | None = None,
     frames_per_episode: int | None = 128,
     stride: int | None = None,
@@ -94,6 +95,7 @@ def build_dataset(
         logger.info("[build_dataset] %s | random mode | filter=%s", embodiment, lam)
         return MultiDataset._from_resolver(
             resolver,
+            dataset_name=dataset_name,
             filters=filters,
             mode="total",
             valid_ratio=valid_ratio,
@@ -123,6 +125,7 @@ def build_dataset(
         )
         base = MultiDataset._from_resolver(
             resolver,
+            dataset_name=dataset_name,
             filters=filters,
             mode="total",
             valid_ratio=valid_ratio,

egomimic/hydra_configs/data/aria.yaml

@@ -15,8 +15,7 @@ train_datasets:
         stride: 3
     filters:
       _target_: egomimic.rldb.filters.DatasetFilter
-      filter_lambdas:
-        - "lambda row: row['episode_hash'] == '2025-09-20-17-47-54-000000'"
+      episode_hashes: ["2025-09-20-17-47-54-000000"]
     mode: total
 
 valid_datasets:

egomimic/hydra_configs/data/eva.yaml

@@ -14,8 +14,7 @@ train_datasets:
         mode: cartesian
     filters:
       _target_: egomimic.rldb.filters.DatasetFilter
-      filter_lambdas:
-        - "lambda row: row['episode_hash'] == '2025-12-26-18-07-46-296000'"
+      episode_hashes: ["2025-12-26-18-07-46-296000"]
     mode: total
 
 valid_datasets:

egomimic/hydra_configs/data/mecka.yaml

@@ -15,8 +15,7 @@ train_datasets:
         stride: 1
     filters:
       _target_: egomimic.rldb.filters.DatasetFilter
-      filter_lambdas:
-        - "lambda row: row['episode_hash'] == '69199812208123403bbdb24f'"
+      episode_hashes: ["6972164b391867c01c9d3265"]
     mode: total
 
 valid_datasets:

egomimic/hydra_configs/data/scale.yaml

@@ -15,8 +15,7 @@ train_datasets:
         stride: 1
     filters:
       _target_: egomimic.rldb.filters.DatasetFilter
-      filter_lambdas:
-        - "lambda row: row['episode_hash'] in {'2026-03-16-01-22-26-448000'}"
+      episode_hashes: ["2026-04-30-09-41-51-255837"]
     mode: total
 
 valid_datasets:

egomimic/rldb/filters.py

@@ -9,8 +9,19 @@
 
 
 class DatasetFilter:
-    def __init__(self, filter_lambdas: Sequence[str] | None = None) -> None:
+    def __init__(
+        self,
+        filter_lambdas: Sequence[str] | None = None,
+        episode_hashes: Sequence[str] | None = None,
+    ) -> None:
         self.filter_lambdas = list(filter_lambdas or [])
+        # Pinned episode hashes. Empty = no pin. Validated at resolve time by the
+        # resolvers (missing / deleted / wrong embodiment is an error there).
+        if isinstance(episode_hashes, str):  # `filters.episode_hashes=abc` override
+            episode_hashes = [episode_hashes]
+        self.episode_hashes: frozenset[str] = frozenset(
+            str(h) for h in (episode_hashes or [])
+        )
         self.filters = []
         for expr in self.filter_lambdas:
             try:
@@ -24,12 +35,17 @@ def __init__(self, filter_lambdas: Sequence[str] | None = None) -> None:
             self.filters.append(predicate)
 
     def __repr__(self) -> str:
-        return f"DatasetFilter(filter_lambdas={self.filter_lambdas!r})"
+        return (
+            f"DatasetFilter(filter_lambdas={self.filter_lambdas!r}, "
+            f"episode_hashes={sorted(self.episode_hashes)!r})"
+        )
 
     def matches(self, row: Mapping[str, Any]) -> bool:
         row = dict(row)
         if row.get("is_deleted", False):
             return False
+        if self.episode_hashes and row.get("episode_hash") not in self.episode_hashes:
+            return False
         for expr, predicate in zip(self.filter_lambdas, self.filters, strict=True):
             result = predicate(row)
             if not isinstance(result, bool):
@@ -41,14 +57,17 @@ def matches(self, row: Mapping[str, Any]) -> bool:
 
 class ScaleAnnotationDatasetFilter(DatasetFilter):
     def __init__(
-        self, project_name: str, filter_lambdas: Sequence[str] | None = None
+        self,
+        project_name: str,
+        filter_lambdas: Sequence[str] | None = None,
+        episode_hashes: Sequence[str] | None = None,
     ) -> None:
         self.project_name = project_name
         self.api_key = os.environ["SCALE_API_KEY"]
         self.tasks = get_completed_tasks(self.project_name, self.api_key)
         self.df = build_df_from_tasks(self.tasks)
         self.completed_episode_hashes = set(self.df["SEQUENCE_ID"].unique().tolist())
-        super().__init__(filter_lambdas)
+        super().__init__(filter_lambdas, episode_hashes)
 
     def matches(self, row: Mapping[str, Any]) -> bool:
         if row.get("episode_hash") not in self.completed_episode_hashes: