fix(profiling): handle datasets without metadata in forward artifacts

src/lerobot/utils/model_profiling.py

@@ -333,7 +333,14 @@ def write_deterministic_forward_artifacts(
     # the dataloader (PR #3406). The dataset ships camera frames as uint8 for
     # faster transport, but policies like SmolVLA/xVLA run bilinear
     # interpolation on images which doesn't support Byte tensors.
-    for cam_key in dataset.meta.camera_keys:
+    camera_keys = tuple(getattr(getattr(dataset, "meta", None), "camera_keys", ()) or ())
+    if not camera_keys:
+        camera_keys = tuple(
+            key
+            for key, value in reference_batch.items()
+            if key.startswith("observation.images.") and isinstance(value, torch.Tensor)
+        )
+    for cam_key in camera_keys:
         if cam_key in reference_batch and reference_batch[cam_key].dtype == torch.uint8:
             reference_batch[cam_key] = reference_batch[cam_key].to(dtype=torch.float32) / 255.0
     reference_batch = preprocessor(reference_batch)

tests/test_model_profiling.py

@@ -270,6 +270,30 @@ def test_deterministic_forward_artifacts_preserve_policy_mode(tmp_path):
     assert payload["outputs"]["loss"]["numel"] == 1
 
 
+def test_deterministic_forward_artifacts_infers_image_keys_without_dataset_meta(tmp_path):
+    class _ImagePolicy(torch.nn.Module):
+        def forward(self, batch):
+            image = batch["observation.images.front"]
+            assert image.dtype == torch.float32
+            assert torch.all((0.0 <= image) & (image <= 1.0))
+            return image.sum(), {"image": image}
+
+    dataset = [{"observation.images.front": torch.tensor([[[0, 255]]], dtype=torch.uint8)}]
+
+    mp.write_deterministic_forward_artifacts(
+        policy=_ImagePolicy(),
+        dataset=dataset,
+        batch_size=1,
+        preprocessor=lambda b: b,
+        output_dir=tmp_path,
+        device_type="cpu",
+    )
+
+    payload = json.loads((tmp_path / "deterministic_forward.json").read_text())
+    assert payload["outputs"]["loss"]["numel"] == 1
+    assert payload["outputs"]["output_dict"]["image"]["dtype"] == "torch.float32"
+
+
 def test_training_profiler_section_records_forward_backward_optimizer(tmp_path):
     profiler = mp.TrainingProfiler(mode="summary", output_dir=tmp_path, device=torch.device("cpu"))
     profiler.start()