Fix sub-dataset length calculation (#2533)

* fix sub-dataset dataloader length
* bump libdeeplake version to 0.0.68

deeplake/enterprise/dataloader.py

@@ -217,10 +217,13 @@ def collate_fn(self):
         return get_collate_fn(self._collate, self._mode)
 
     def __len__(self):
-        round_fn = math.floor if self._drop_last else math.ceil
-        return round_fn(
-            len(self._orig_dataset) / ((self.batch_size) * self._world_size)
+        len_ds = (
+            len(self._orig_dataset[self._tensors])
+            if self._tensors is not None
+            else len(self._orig_dataset)
         )
+        round_fn = math.floor if self._drop_last else math.ceil
+        return round_fn(len_ds / ((self.batch_size) * self._world_size))
 
     def batch(self, batch_size: int, drop_last: bool = False):
         """Returns a batched :class:`DeepLakeDataLoader` object.

deeplake/enterprise/test_pytorch.py

@@ -153,6 +153,19 @@ def test_pytorch_transform(hub_cloud_ds):
             np.testing.assert_array_equal(actual_image2, expected_image2)
 
 
+@requires_libdeeplake
+def test_inequal_tensors_dataloader_length(local_auth_ds):
+    with local_auth_ds as ds:
+        ds.create_tensor("images")
+        ds.create_tensor("label")
+        ds.images.extend(([i * np.ones((i + 1, i + 1)) for i in range(16)]))
+
+    ld = local_auth_ds.dataloader().batch(1).pytorch()
+    assert len(ld) == 0
+    ld1 = local_auth_ds.dataloader().batch(2).pytorch(tensors=["images"])
+    assert len(ld1) == 8
+
+
 @requires_torch
 @requires_libdeeplake
 def test_pytorch_transform_dict(hub_cloud_ds):

setup.py

@@ -70,7 +70,7 @@ def libdeeplake_availabe():
 extras_require["all"] = [req_map[r] for r in all_extras]
 
 if libdeeplake_availabe():
-    libdeeplake = "libdeeplake==0.0.66"
+    libdeeplake = "libdeeplake==0.0.68"
     extras_require["enterprise"] = [libdeeplake, "pyjwt"]
     extras_require["all"].append(libdeeplake)