perf: Vectorize index clamping in Dataset.expand_index_in_trial

cebra/data/base.py

@@ -154,25 +154,22 @@ def expand_index_in_trial(self, index, trial_ids, trial_borders):
         trial_ids is in size of a length of self.index and indicate the trial id of the index belong to.
         trial_borders is in size of a length of self.idnex and indicate the border of each trial.
 
-        Todo:
-            - rewrite
         """
 
         # TODO(stes) potential room for speed improvements by pre-allocating these tensors/
         # using non_blocking copy operation.
         offset = torch.arange(-self.offset.left,
                               self.offset.right,
                               device=index.device)
-        index = torch.tensor(
-            [
-                torch.clamp(
-                    i,
-                    trial_borders[trial_ids[i]] + self.offset.left,
-                    trial_borders[trial_ids[i] + 1] - self.offset.right,
-                ) for i in index
-            ],
-            device=self.device,
-        )
+
+        trial_ids = torch.as_tensor(trial_ids, device=index.device)
+        trial_borders = torch.as_tensor(trial_borders, device=index.device)
+        batch_trial_ids = trial_ids[index]
+        min_borders = trial_borders[batch_trial_ids] + self.offset.left
+        max_borders = trial_borders[batch_trial_ids + 1] - self.offset.right
+
+        index = torch.clamp(index, min=min_borders, max=max_borders)
+
         return index[:, None] + offset[None, :]
 
     @abc.abstractmethod

tests/test_loader.py

@@ -103,6 +103,38 @@ def test_offset():
         offset = cebra.data.Offset(4, -2)
 
 
+def _expand_index_in_trial_old(dataset, index, trial_ids, trial_borders):
+    """Reference implementation of Dataset.expand_index_in_trial."""
+    offset = torch.arange(-dataset.offset.left,
+                          dataset.offset.right,
+                          device=index.device)
+    index = torch.tensor(
+        [
+            torch.clamp(
+                i,
+                trial_borders[trial_ids[i]] + dataset.offset.left,
+                trial_borders[trial_ids[i] + 1] - dataset.offset.right,
+            ) for i in index
+        ],
+        device=dataset.device,
+    )
+    return index[:, None] + offset[None, :]
+
+
+def test_expand_index_in_trial_matches_previous_implementation():
+    dataset = RandomDataset(N=12)
+    dataset.offset = cebra.data.Offset(2, 2)
+    index = torch.tensor([0, 2, 5, 7, 11])
+    trial_ids = np.repeat(np.arange(3), 4)
+    trial_borders = [0, 4, 8, 12]
+
+    expected = _expand_index_in_trial_old(dataset, index, trial_ids,
+                                          trial_borders)
+    actual = dataset.expand_index_in_trial(index, trial_ids, trial_borders)
+
+    torch.testing.assert_close(actual, expected)
+
+
 def _assert_dataset_on_correct_device(loader, device):
     assert hasattr(loader, "dataset")
     assert hasattr(loader, "device")